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@article{hanberry_considering_2026,
	title = {Considering tree species of the future: {Tree} species in {Mexico} predicted to have suitable current climate in the {United} {States} and {Canada}},
	volume = {15},
	issn = {2197-5620},
	shorttitle = {Considering tree species of the future},
	url = {https://www.sciencedirect.com/science/article/pii/S2197562025001289},
	doi = {10.1016/j.fecs.2025.100419},
	abstract = {Suitable climate for tree species currently located in Mexico may shift to the United States and Canada under future warming, resulting in the potential for tree species shifts, with new species gained in the USA and Canada and species losses in Mexico. To isolate dynamics, I modeled distributions of 184 commonly recorded wide-ranging or transboundary tree species of North America, including presence in Mexico, and 258 tree species primarily abundant in Mexico, including 51 endemic species, under current climate and predicted to three future climates (projected warming of 4.3–8.8 °C during 2071–2100 in North America). Secondarily, I identified patterns between species distributions and outcomes. Model accuracies were 0.98 for withheld samples, with coldest temperature as the most important variable. Species with distributions that were larger in area and lower in elevation (likely related to the modeling algorithm being able to locate more analogous climates) and higher in latitude (due to greater area in the northern North American continent) were more probable to expand distributions under warming temperatures. Predicted losses in suitable future climate conditions occurred for 36 of 184 wide-ranging species and 103 of 258 Mexican species. For wide-ranging species, losses occurred in Mexico and extended along the USA Gulf Coast, with gains in the western USA and Canada. For Mexican species, losses occurred south of Mexico, with gains in northern Mexico, the southeastern USA, and along the Pacific Coast from the USA to Canada. In Mexico, tree species overall continued to have suitable future climate conditions. In the USA, 38 of 184 wide-ranging species and 246 of 258 Mexican species may be gained in the future, and generally, predictions were for suitable climate conditions both now and in the future. In Canada, suitable future climate conditions for 35 of 184 wide-ranging species were predicted, of which 10 species were predicted to have suitable current climate conditions, and suitable future climate conditions for 80 of 258 Mexican species were predicted, of which 21 species were predicted to have suitable current climate conditions. Many tree species present in Mexico were predicted to already have a suitable current climate in the USA and Canada, which suggests a lag in ecosystem transition that may be addressed by current management to support biodiversity.},
	urldate = {2026-01-21},
	journal = {Forest Ecosystems},
	author = {Hanberry, Brice B.},
	month = apr,
	year = {2026},
	keywords = {NALCMS},
	pages = {100419},
}

Suitable climate for tree species currently located in Mexico may shift to the United States and Canada under future warming, resulting in the potential for tree species shifts, with new species gained in the USA and Canada and species losses in Mexico. To isolate dynamics, I modeled distributions of 184 commonly recorded wide-ranging or transboundary tree species of North America, including presence in Mexico, and 258 tree species primarily abundant in Mexico, including 51 endemic species, under current climate and predicted to three future climates (projected warming of 4.3–8.8 °C during 2071–2100 in North America). Secondarily, I identified patterns between species distributions and outcomes. Model accuracies were 0.98 for withheld samples, with coldest temperature as the most important variable. Species with distributions that were larger in area and lower in elevation (likely related to the modeling algorithm being able to locate more analogous climates) and higher in latitude (due to greater area in the northern North American continent) were more probable to expand distributions under warming temperatures. Predicted losses in suitable future climate conditions occurred for 36 of 184 wide-ranging species and 103 of 258 Mexican species. For wide-ranging species, losses occurred in Mexico and extended along the USA Gulf Coast, with gains in the western USA and Canada. For Mexican species, losses occurred south of Mexico, with gains in northern Mexico, the southeastern USA, and along the Pacific Coast from the USA to Canada. In Mexico, tree species overall continued to have suitable future climate conditions. In the USA, 38 of 184 wide-ranging species and 246 of 258 Mexican species may be gained in the future, and generally, predictions were for suitable climate conditions both now and in the future. In Canada, suitable future climate conditions for 35 of 184 wide-ranging species were predicted, of which 10 species were predicted to have suitable current climate conditions, and suitable future climate conditions for 80 of 258 Mexican species were predicted, of which 21 species were predicted to have suitable current climate conditions. Many tree species present in Mexico were predicted to already have a suitable current climate in the USA and Canada, which suggests a lag in ecosystem transition that may be addressed by current management to support biodiversity.

@article{retamoza-gonzalez_identifying_2026,
	title = {Identifying priority restoration areas by mapping land-use change drivers},
	volume = {22},
	issn = {2667-0100},
	url = {https://www.sciencedirect.com/science/article/pii/S266701002500318X},
	doi = {10.1016/j.envc.2025.101399},
	abstract = {Land-use and land-cover (LULC) changes are key drivers of vegetation cover loss. Northwestern Mexico hosts the only Mediterranean-climate region in the country, a socio-ecosystem that, due to its distinct socioeconomic and ecological dynamics, simultaneously undergoes processes of anthropization and vegetation recovery, driven by agricultural expansion and land abandonment. In order to identify areas with high recovery and conservation potential within this socio-ecosystem, we evaluated land-cover losses, gains, and rates of change between 2015 and 2020. Using LISA spatial autocorrelation analysis, we identified clusters of anthropization and vegetation recovery, as well as priority areas for conservation actions. Overall, LULC change within the Mexican Mediterranean socio-ecosystem reached 4 \%, with coastal shrub being the land-cover type that experienced the greatest loss (64,443 ha), primarily converted to agricultural land, which expanded by 66,203 ha. Anthropization and recovery were the dominant processes in this region. High anthropization clusters were concentrated in mountainous regions and peri-urban areas along the coastal belt, likely associated with agricultural and livestock expansion, whereas recovery was concentrated in the southern portion of the study area, within large agricultural zones, possibly linked to field abandonment due to saline intrusion. Through spatial correlation analysis of change drivers, we identified five zones within the Mexican Mediterranean: Tijuana Coastal Shrubland, Ensenada Coastal Shrubland, Central Coastal Shrubland, Camalú–San Quintín Coastal Rosetophyllous Corridor, and the San Pedro Mártir Boundary Zone, where conservation and restoration efforts should be prioritized through the design and implementation of public policies regulating agricultural expansion at the expense of coastal scrub and other native vegetation types.},
	urldate = {2026-01-21},
	journal = {Environmental Challenges},
	author = {Retamoza-González, Enrique Alfonso and Eaton-González, B. Ricardo and Leyva-Aguilera, Juana Claudia and Reyes-Orta, Marisa and Arias-Rojo, Hector Manuel},
	month = mar,
	year = {2026},
	keywords = {NALCMS, Terrestrial Ecoregions},
	pages = {101399},
}

Land-use and land-cover (LULC) changes are key drivers of vegetation cover loss. Northwestern Mexico hosts the only Mediterranean-climate region in the country, a socio-ecosystem that, due to its distinct socioeconomic and ecological dynamics, simultaneously undergoes processes of anthropization and vegetation recovery, driven by agricultural expansion and land abandonment. In order to identify areas with high recovery and conservation potential within this socio-ecosystem, we evaluated land-cover losses, gains, and rates of change between 2015 and 2020. Using LISA spatial autocorrelation analysis, we identified clusters of anthropization and vegetation recovery, as well as priority areas for conservation actions. Overall, LULC change within the Mexican Mediterranean socio-ecosystem reached 4 %, with coastal shrub being the land-cover type that experienced the greatest loss (64,443 ha), primarily converted to agricultural land, which expanded by 66,203 ha. Anthropization and recovery were the dominant processes in this region. High anthropization clusters were concentrated in mountainous regions and peri-urban areas along the coastal belt, likely associated with agricultural and livestock expansion, whereas recovery was concentrated in the southern portion of the study area, within large agricultural zones, possibly linked to field abandonment due to saline intrusion. Through spatial correlation analysis of change drivers, we identified five zones within the Mexican Mediterranean: Tijuana Coastal Shrubland, Ensenada Coastal Shrubland, Central Coastal Shrubland, Camalú–San Quintín Coastal Rosetophyllous Corridor, and the San Pedro Mártir Boundary Zone, where conservation and restoration efforts should be prioritized through the design and implementation of public policies regulating agricultural expansion at the expense of coastal scrub and other native vegetation types.

@article{mitchell_additional_2025,
	title = {Additional common milkweed would help {Canada} meet its share of the trinational eastern migratory monarch butterfly recovery target},
	volume = {10},
	url = {https://www.facetsjournal.com/doi/10.1139/facets-2024-0063},
	doi = {10.1139/facets-2024-0063},
	abstract = {The eastern migratory monarch butterfly (Danaus plexippus) population has declined by ∼84\% between 1993 and 2024. Population recovery in the Midwestern United States is limited by the availability of the monarch's main host plant for egg laying—common milkweed (Asclepias syriaca). The extent to which common milkweed availability is limiting in other breeding regions is unknown. Our objective was to determine whether Canada has enough common milkweed to support its share of the trinational eastern migratory monarch population recovery target, given ∼29 stems of common milkweed are needed to contribute one adult monarch into the fall migratory population. To meet this objective, we estimated the number of common milkweed stems in Canada using published common milkweed availability estimates by land cover type. We also estimated the size of the Canadian monarch population if the recovery target was achieved using published estimates of wintering monarch density in Mexico, fall migration survival rates, and the relative proportion of monarchs entering fall migration from Canada. We estimate that Canada currently has 484 million common milkweed stems (range: 111 million–1 billion stems) and increasing this amount by 1.61 times (i.e., by ∼295 million stems), or equivalently, by 61\%, would support the recovery target.},
	urldate = {2025-09-02},
	journal = {FACETS},
	author = {Mitchell, Greg W. and Kirby, Patrick and Duffe, Jason and Fahrig, Lenore and Girard, Judith and Johnston, Mark K. and Larrivée, Maxim and Martin, Amanda E. and Momeni-Dehaghi, Iman and Pasher, Jon and Rezek, Elizabeth and Shapiro, Elisabeth D. and Thogmartin, Wayne E. and Pouliot, Darren},
	month = jan,
	year = {2025},
	note = {Publisher: Canadian Science Publishing},
	keywords = {NALCMS},
	pages = {1--14},
}

The eastern migratory monarch butterfly (Danaus plexippus) population has declined by ∼84% between 1993 and 2024. Population recovery in the Midwestern United States is limited by the availability of the monarch's main host plant for egg laying—common milkweed (Asclepias syriaca). The extent to which common milkweed availability is limiting in other breeding regions is unknown. Our objective was to determine whether Canada has enough common milkweed to support its share of the trinational eastern migratory monarch population recovery target, given ∼29 stems of common milkweed are needed to contribute one adult monarch into the fall migratory population. To meet this objective, we estimated the number of common milkweed stems in Canada using published common milkweed availability estimates by land cover type. We also estimated the size of the Canadian monarch population if the recovery target was achieved using published estimates of wintering monarch density in Mexico, fall migration survival rates, and the relative proportion of monarchs entering fall migration from Canada. We estimate that Canada currently has 484 million common milkweed stems (range: 111 million–1 billion stems) and increasing this amount by 1.61 times (i.e., by ∼295 million stems), or equivalently, by 61%, would support the recovery target.

@mastersthesis{talbot_amelioration_2025,
	title = {Amélioration de la modélisation des variables hydrologiques et évaluation de l’impact des changements climatiques au {Québec}},
	url = {https://espace.etsmtl.ca/id/eprint/3628/},
	abstract = {Ce mémoire vise à améliorer la modélisation des variables hydrologiques et à évaluer les impacts des changements climatiques sur les bassins versants du Québec, en mettant l'accent sur l'application du modèle hydrologique WaSiM et les projections climatiques issues de CMIP6. La recherche se concentre sur trois aspects clés : l'optimisation de la configuration du modèle hydrologique, la comparaison entre les méthodes de modélisation conventionnelle et asynchrone, et l'évaluation des impacts des changements climatiques sur les variables hydrologiques.

Dans le premier volet de l’étude, trois configurations du modèle WaSiM sont comparées. La configuration intégrant la recharge de la nappe phréatique dans la calibration des paramètres du modèle, bien que moins performante en termes de reproduction des débits observés, offre une précision supérieure dans la modélisation des variables hydrologiques internes, soulignant l’importance d’une calibration multi-variable pour limiter le phénomène d’équifinalité lors de l’optimisation du modèle et améliorer la représentation des variables hydrologiques.

Le deuxième volet de l’étude compare les performances des méthodes de modélisation conventionnelle et asynchrone. L'approche asynchrone, qui élimine la dépendance aux données météorologiques historiques, présente des avantages potentiels dans les régions où les données d'observation sont limitées ou peu fiables. Toutefois, elle montre des lacunes dans la représentation temporelle des événements hydrologiques, en particulier la fonte des neiges. Cette méthode souffre d’une incohérence dans la synchronisation des processus saisonniers. Cela souligne la nécessité d’intégrer une forme de synchronicité dans la méthode asynchrone, afin d'améliorer la fiabilité des projections hydrologiques.

Le troisième volet porte sur l’évaluation des impacts des changements climatiques dans 34 bassins versants du sud du Québec. Les résultats montrent une augmentation des précipitations totales et des températures, avec une transition significative des précipitations neigeuses à la pluie, une réduction de l'accumulation de neige, et un décalage des crues printanières vers des périodes plus précoces. Les projections indiquent également une augmentation de la recharge des nappes phréatiques en hiver et au printemps, tandis que l'évapotranspiration s’intensifie durant les mois plus chauds. Les résultats de cette étude fournissent des informations cruciales pour la gestion des ressources en eau dans un contexte de changements climatiques.

Ce mémoire met en évidence l’importance d’une calibration multi-variable et l’utilisation des dernières projections climatiques pour améliorer la modélisation hydrologique.},
	language = {fr},
	urldate = {2025-07-31},
	school = {École de technologie supérieure},
	author = {Talbot, Frédéric},
	month = mar,
	year = {2025},
	keywords = {NALCMS},
}

Ce mémoire vise à améliorer la modélisation des variables hydrologiques et à évaluer les impacts des changements climatiques sur les bassins versants du Québec, en mettant l'accent sur l'application du modèle hydrologique WaSiM et les projections climatiques issues de CMIP6. La recherche se concentre sur trois aspects clés : l'optimisation de la configuration du modèle hydrologique, la comparaison entre les méthodes de modélisation conventionnelle et asynchrone, et l'évaluation des impacts des changements climatiques sur les variables hydrologiques. Dans le premier volet de l’étude, trois configurations du modèle WaSiM sont comparées. La configuration intégrant la recharge de la nappe phréatique dans la calibration des paramètres du modèle, bien que moins performante en termes de reproduction des débits observés, offre une précision supérieure dans la modélisation des variables hydrologiques internes, soulignant l’importance d’une calibration multi-variable pour limiter le phénomène d’équifinalité lors de l’optimisation du modèle et améliorer la représentation des variables hydrologiques. Le deuxième volet de l’étude compare les performances des méthodes de modélisation conventionnelle et asynchrone. L'approche asynchrone, qui élimine la dépendance aux données météorologiques historiques, présente des avantages potentiels dans les régions où les données d'observation sont limitées ou peu fiables. Toutefois, elle montre des lacunes dans la représentation temporelle des événements hydrologiques, en particulier la fonte des neiges. Cette méthode souffre d’une incohérence dans la synchronisation des processus saisonniers. Cela souligne la nécessité d’intégrer une forme de synchronicité dans la méthode asynchrone, afin d'améliorer la fiabilité des projections hydrologiques. Le troisième volet porte sur l’évaluation des impacts des changements climatiques dans 34 bassins versants du sud du Québec. Les résultats montrent une augmentation des précipitations totales et des températures, avec une transition significative des précipitations neigeuses à la pluie, une réduction de l'accumulation de neige, et un décalage des crues printanières vers des périodes plus précoces. Les projections indiquent également une augmentation de la recharge des nappes phréatiques en hiver et au printemps, tandis que l'évapotranspiration s’intensifie durant les mois plus chauds. Les résultats de cette étude fournissent des informations cruciales pour la gestion des ressources en eau dans un contexte de changements climatiques. Ce mémoire met en évidence l’importance d’une calibration multi-variable et l’utilisation des dernières projections climatiques pour améliorer la modélisation hydrologique.

@article{aryal_assessing_2025,
	title = {Assessing {Climate} and {Watershed} {Controls} on {Rain}-on-{Snow} {Runoff} {Using} {XGBoost}-{SHAP} {Explainable} {AI} ({XAI})},
	volume = {15},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2076-3263},
	url = {https://www.mdpi.com/2076-3263/15/12/467},
	doi = {10.3390/geosciences15120467},
	abstract = {Rain-on-snow (ROS) events significantly impact hydrological processes in snowy regions, yet their seasonal drivers remain poorly understood, particularly in low-elevation and low-gradient catchments. This study uses an XGBoost-SHAP explainable artificial intelligence (XAI) model to analyze meteorological and watershed controls on ROS runoff in the Laurentian Great Lakes region. We used daily discharge, precipitation, temperature, and snow depth data from 2000 to 2023, available from HYSETS, to identify ROS runoff. The XGBoost model’s performance for predicting ROS runoff was higher in winter (R2 = 0.65, Nash–Sutcliffe = 0.59) than in spring (R2 = 0.56, Nash–Sutcliffe = 0.49), indicating greater predictability in colder months. The results reveal that rainfall and temperature dominated ROS runoff generation, jointly explaining more than 60\% of total model importance, while snow depth accounted for 8–12\% depending on season. Winter runoff is predominantly governed by climatic factors—rainfall, air temperature, and their interactions—with soil permeability and slope orientation playing secondary roles. In contrast, spring runoff shows increased sensitivity to land cover characteristics, particularly agricultural and shrub cover, as vegetation-driven processes become more influential. Snow depth effects shift from predominantly negative in winter, where snow acts as storage, to positive contributions in spring at shallow to moderate depths. ROS runoff responded positively to air temperatures exceeding approximately 2.5 °C in both winter and spring. Land cover influences on ROS runoff differ by vegetation type and season. Agricultural areas consistently increase runoff in both seasons, likely due to limited infiltration, whereas shrub-dominated regions exhibit stronger runoff enhancement in spring. The seasonal shift in dominant controls underscores the importance of accounting for land–climate interactions in predicting ROS runoff under future climate scenarios. These insights are essential for improving flood forecasting, managing water resources, and developing adaptive strategies.},
	language = {en},
	number = {12},
	urldate = {2026-01-21},
	journal = {Geosciences},
	author = {Aryal, Yog},
	month = dec,
	year = {2025},
	note = {Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {NALCMS},
	pages = {467},
}

Rain-on-snow (ROS) events significantly impact hydrological processes in snowy regions, yet their seasonal drivers remain poorly understood, particularly in low-elevation and low-gradient catchments. This study uses an XGBoost-SHAP explainable artificial intelligence (XAI) model to analyze meteorological and watershed controls on ROS runoff in the Laurentian Great Lakes region. We used daily discharge, precipitation, temperature, and snow depth data from 2000 to 2023, available from HYSETS, to identify ROS runoff. The XGBoost model’s performance for predicting ROS runoff was higher in winter (R2 = 0.65, Nash–Sutcliffe = 0.59) than in spring (R2 = 0.56, Nash–Sutcliffe = 0.49), indicating greater predictability in colder months. The results reveal that rainfall and temperature dominated ROS runoff generation, jointly explaining more than 60% of total model importance, while snow depth accounted for 8–12% depending on season. Winter runoff is predominantly governed by climatic factors—rainfall, air temperature, and their interactions—with soil permeability and slope orientation playing secondary roles. In contrast, spring runoff shows increased sensitivity to land cover characteristics, particularly agricultural and shrub cover, as vegetation-driven processes become more influential. Snow depth effects shift from predominantly negative in winter, where snow acts as storage, to positive contributions in spring at shallow to moderate depths. ROS runoff responded positively to air temperatures exceeding approximately 2.5 °C in both winter and spring. Land cover influences on ROS runoff differ by vegetation type and season. Agricultural areas consistently increase runoff in both seasons, likely due to limited infiltration, whereas shrub-dominated regions exhibit stronger runoff enhancement in spring. The seasonal shift in dominant controls underscores the importance of accounting for land–climate interactions in predicting ROS runoff under future climate scenarios. These insights are essential for improving flood forecasting, managing water resources, and developing adaptive strategies.

@article{aryal_assessing_2025,
	title = {Assessing antecedent climatic and hydrological conditions and anthropogenic impacts to drive catastrophic flooding in the northeastern {United} {States}},
	volume = {6},
	issn = {2673-6187},
	url = {https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2025.1639845/full},
	doi = {10.3389/frsen.2025.1639845},
	abstract = {The northeastern US has been experiencing catastrophic flooding in recent years. Flooding in the region is occurring more frequently and with higher intensity, causing substantial economic losses. The flooding may be caused by changes in climatic and hydrological drivers such as extreme precipitation, rapid snowmelt, and saturated soil moisture conditions, along with impacts from human-driven interventions like changes in land cover and urban imperviousness. This study analyzes the abovementioned variables to indicate flood risk and understand whether they contribute to flooding in the Northeastern US region. This study utilized various remote sensing satellite products for analyzing variables to fulfil the study’s objectives, wherever possible, such as Terra Moderate Resolution Imaging Spectroradiometer (MODIS) maximum snow cover extent, Soil Moisture Active Passive (SMAP) soil moisture, Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) precipitation, North American Land Change Monitoring System (NALCMS) land cover, and National Land Cover Database (NLCD) urban imperviousness. The region experienced wetter antecedent soil moisture conditions ({\textgreater}0.5 m3/m3) during spring due to rapid snowmelt (seasonal decline of ∼97\%) in all the periods considered in the study. Moreover, the summer precipitation fed excess water (∼50–60 mm more rainfall than the decadal average) into wetter ground conditions, overwhelming the region’s overall hydrology and water balance and causing significant flooding. In addition, ∼1,838 sq. km. of croplands and ∼1,363 sq. km. of forests transitioned into built-up areas in a decade, increasing impervious surface and further exacerbating flooding risk in the region.},
	language = {English},
	urldate = {2026-01-21},
	journal = {Frontiers in Remote Sensing},
	author = {Aryal, Aashutosh and Lakshmi, Venkataraman},
	month = sep,
	year = {2025},
	note = {Publisher: Frontiers},
	keywords = {NALCMS},
}

The northeastern US has been experiencing catastrophic flooding in recent years. Flooding in the region is occurring more frequently and with higher intensity, causing substantial economic losses. The flooding may be caused by changes in climatic and hydrological drivers such as extreme precipitation, rapid snowmelt, and saturated soil moisture conditions, along with impacts from human-driven interventions like changes in land cover and urban imperviousness. This study analyzes the abovementioned variables to indicate flood risk and understand whether they contribute to flooding in the Northeastern US region. This study utilized various remote sensing satellite products for analyzing variables to fulfil the study’s objectives, wherever possible, such as Terra Moderate Resolution Imaging Spectroradiometer (MODIS) maximum snow cover extent, Soil Moisture Active Passive (SMAP) soil moisture, Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) precipitation, North American Land Change Monitoring System (NALCMS) land cover, and National Land Cover Database (NLCD) urban imperviousness. The region experienced wetter antecedent soil moisture conditions (\textgreater0.5 m3/m3) during spring due to rapid snowmelt (seasonal decline of ∼97%) in all the periods considered in the study. Moreover, the summer precipitation fed excess water (∼50–60 mm more rainfall than the decadal average) into wetter ground conditions, overwhelming the region’s overall hydrology and water balance and causing significant flooding. In addition, ∼1,838 sq. km. of croplands and ∼1,363 sq. km. of forests transitioned into built-up areas in a decade, increasing impervious surface and further exacerbating flooding risk in the region.

@article{shafeian_assessing_2025,
	title = {Assessing spatial distribution and quantification of native trees in {Saskatchewan}’s prairie landscape using remote sensing techniques},
	volume = {58},
	issn = {NULL},
	url = {https://doi.org/10.1080/22797254.2024.2438638},
	doi = {10.1080/22797254.2024.2438638},
	abstract = {The importance of trees in non-forest landscapes has been the focus of only a few studies. However, these trees provide many important ecosystem services. In this study, we mapped and quantified these trees using Sentinel-2 (S2) and very high-resolution (VHR) Google satellite imagery without any field campaigns. We performed a Random Forest (RF) classification to map the spatial distribution of native trees in different scenarios. The optimal model showed an overall accuracy and kappa of 0.99 and 0.98, respectively. We mapped 40,500 km2 of tree cover, including native tree cover (approximately 29,565 km2 ≈10.5\%), excluding plantations, regional and provincial parks, and water bodies in the Canadian prairie region of Saskatchewan. According to our results, the highest numbers of native trees were found in the eastern and northwestern parts of the study area – cluster “BLK\_1” and the “Black” soil zone, with total cover of 5,388 and 13,233 km2, respectively. The lowest numbers of native trees were found in the southwest side of the study area – cluster “BRN\_6” and the “Brown” soil zone, with total cover of 2.38 and 979.5 km2, respectively. This research is important as detecting and quantifying native trees is an integral part of studies on carbon sequestration, economics, and effective management strategies.},
	number = {1},
	urldate = {2025-05-08},
	journal = {European Journal of Remote Sensing},
	author = {Shafeian, Elham and , Bryan J., Mood and , Kenneth W., Belcher and and Laroque, Colin P.},
	month = dec,
	year = {2025},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/22797254.2024.2438638},
	keywords = {NALCMS},
	pages = {2438638},
}

The importance of trees in non-forest landscapes has been the focus of only a few studies. However, these trees provide many important ecosystem services. In this study, we mapped and quantified these trees using Sentinel-2 (S2) and very high-resolution (VHR) Google satellite imagery without any field campaigns. We performed a Random Forest (RF) classification to map the spatial distribution of native trees in different scenarios. The optimal model showed an overall accuracy and kappa of 0.99 and 0.98, respectively. We mapped 40,500 km2 of tree cover, including native tree cover (approximately 29,565 km2 ≈10.5%), excluding plantations, regional and provincial parks, and water bodies in the Canadian prairie region of Saskatchewan. According to our results, the highest numbers of native trees were found in the eastern and northwestern parts of the study area – cluster “BLK_1” and the “Black” soil zone, with total cover of 5,388 and 13,233 km2, respectively. The lowest numbers of native trees were found in the southwest side of the study area – cluster “BRN_6” and the “Brown” soil zone, with total cover of 2.38 and 979.5 km2, respectively. This research is important as detecting and quantifying native trees is an integral part of studies on carbon sequestration, economics, and effective management strategies.

@article{kovacek_bcub_2025,
	title = {{BCUB} – a large-sample ungauged basin attribute dataset for {British} {Columbia}, {Canada}},
	volume = {17},
	issn = {1866-3508},
	url = {https://essd.copernicus.org/articles/17/259/2025/},
	doi = {10.5194/essd-17-259-2025},
	abstract = {The British Columbia Ungauged Basin (BCUB) dataset is an open-source, extensible dataset of attributes describing terrain, soil, land cover, and climate indices of over 1.2 million ungauged catchments in British Columbia, Canada, including trans-boundary regions. The attributes included in the dataset are similar to those found in the large-sample hydrology literature for their association with hydrological processes. The BCUB database is intended to support water resources research and practice, namely monitoring network analysis studies, or hydrological modelling where basin characterization is used for model calibration. The dataset and the complete workflow to collect and process input data, to derive stream networks, and to delineate sub-basins and extract attributes, are available under a Creative Commons BY 4.0 license. The DOI link for the BCUB dataset is https://doi.org/10.5683/SP3/JNKZVT (Kovacek and Weijs, 2023).},
	language = {English},
	number = {1},
	urldate = {2025-05-08},
	journal = {Earth System Science Data},
	author = {Kovacek, Daniel and Weijs, Steven},
	month = jan,
	year = {2025},
	note = {Publisher: Copernicus GmbH},
	keywords = {NALCMS},
	pages = {259--275},
}

The British Columbia Ungauged Basin (BCUB) dataset is an open-source, extensible dataset of attributes describing terrain, soil, land cover, and climate indices of over 1.2 million ungauged catchments in British Columbia, Canada, including trans-boundary regions. The attributes included in the dataset are similar to those found in the large-sample hydrology literature for their association with hydrological processes. The BCUB database is intended to support water resources research and practice, namely monitoring network analysis studies, or hydrological modelling where basin characterization is used for model calibration. The dataset and the complete workflow to collect and process input data, to derive stream networks, and to delineate sub-basins and extract attributes, are available under a Creative Commons BY 4.0 license. The DOI link for the BCUB dataset is https://doi.org/10.5683/SP3/JNKZVT (Kovacek and Weijs, 2023).

@article{hanberry_clarifying_2025,
	title = {Clarifying {Influences} of {Sampling} {Bias} ({Concentration}) and {Locational} {Errors} ({Uncertainties}) on {Precision} or {Generality} of {Species} {Distribution} {Models}},
	volume = {14},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2073-445X},
	url = {https://www.mdpi.com/2073-445X/14/8/1620},
	doi = {10.3390/land14081620},
	abstract = {Locational errors and sampling bias may produce unrepresentative species distribution models. To decompose the influence of errors, I modeled species distributions of 31 mammal species from georeferenced records and random samples from range maps, with potential sources of errors added or removed, using the random forests algorithm. Errors included the addition of (1) cities, (2) administrative centers, (3) records flagged as potential errors (e.g., outliers), and (4) urban records to range map samples; the removal of (5) flagged records and (6) urban records from georeferenced records; and the addition of (7) random points and (8) clustered points to georeferenced records. I also examined separation between thinned and unthinned (i.e., locally concentrated) records and ocean and land areas. Errors generally did not perturb species distributions, particularly if errors were located within species ranges. The greatest departure relative to unaltered models (mean niche overlap values of 0.96 out of 1) was due to the addition of administrative centers at a 13\% error rate. Because locational errors overall do not occur in modern georeferenced records, outliers may provide important samples from undersampled areas. Delineating land from ocean coordinates may require a land layer at the highest available resolution and buffered to match the distance of locational uncertainty for georeferenced records. Predicted areas for species distributions increased along the spectrum of models from concentrated georeferenced records, thinned records, and random samples from range maps. Species distributions modeled with all georeferenced records will have the greatest sampling concentration (to differentiate from bias, because predictive modeling is not hypothesis testing), resulting in model locational precision, whereas species distribution models from random samples of range maps will have locational generality (rather than errors). The risk of removing samples of suitable conditions is the generation of unrepresentative models whereas the benefit of sample removal is slightly more generalized models, but which also may represent overpredictions.},
	language = {en},
	number = {8},
	urldate = {2026-01-21},
	journal = {Land},
	author = {Hanberry, Brice B.},
	month = aug,
	year = {2025},
	note = {Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {NALCMS},
	pages = {1620},
}

Locational errors and sampling bias may produce unrepresentative species distribution models. To decompose the influence of errors, I modeled species distributions of 31 mammal species from georeferenced records and random samples from range maps, with potential sources of errors added or removed, using the random forests algorithm. Errors included the addition of (1) cities, (2) administrative centers, (3) records flagged as potential errors (e.g., outliers), and (4) urban records to range map samples; the removal of (5) flagged records and (6) urban records from georeferenced records; and the addition of (7) random points and (8) clustered points to georeferenced records. I also examined separation between thinned and unthinned (i.e., locally concentrated) records and ocean and land areas. Errors generally did not perturb species distributions, particularly if errors were located within species ranges. The greatest departure relative to unaltered models (mean niche overlap values of 0.96 out of 1) was due to the addition of administrative centers at a 13% error rate. Because locational errors overall do not occur in modern georeferenced records, outliers may provide important samples from undersampled areas. Delineating land from ocean coordinates may require a land layer at the highest available resolution and buffered to match the distance of locational uncertainty for georeferenced records. Predicted areas for species distributions increased along the spectrum of models from concentrated georeferenced records, thinned records, and random samples from range maps. Species distributions modeled with all georeferenced records will have the greatest sampling concentration (to differentiate from bias, because predictive modeling is not hypothesis testing), resulting in model locational precision, whereas species distribution models from random samples of range maps will have locational generality (rather than errors). The risk of removing samples of suitable conditions is the generation of unrepresentative models whereas the benefit of sample removal is slightly more generalized models, but which also may represent overpredictions.

@article{xu_ecoregion-based_2025,
	title = {Ecoregion-{Based} {Landslide} {Susceptibility} {Mapping}: {A} {Spatially} {Partitioned} {Modeling} {Strategy} for {Oregon}, {USA}},
	volume = {15},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2076-3417},
	shorttitle = {Ecoregion-{Based} {Landslide} {Susceptibility} {Mapping}},
	url = {https://www.mdpi.com/2076-3417/15/20/11242},
	doi = {10.3390/app152011242},
	abstract = {Conventional non-partitioned Landslide Susceptibility Mapping (LSM), which neglects geospatial heterogeneity, often has limitations in accurately capturing local risk patterns. To address this challenge, this study investigated the effectiveness of localized modeling in the environmentally diverse state of Oregon, USA, by comparing ecoregion-based local models with the non-partitioned model. We partitioned Oregon into seven distinct units using the U.S. Environmental Protection Agency (EPA) Level III Ecoregions and developed one global and seven local models with the eXtreme Gradient Boosting (XGBoost) algorithm. A comprehensive evaluation framework, including the Area Under the Curve (AUC), Landslide Density (LD), and the Total Deviation Index (TDI), was used to compare the models. The results demonstrated the clear superiority of the partitioned strategy. Moreover, different ecoregions were found to have distinct dominant landslide conditioning factors, revealing strong spatial non-stationarity. Although all models generated high AUC values ({\textgreater}0.93), LD analysis showed that the local models were significantly more efficient at identifying high-risk zones. This advantage was particularly pronounced in critical, landslide-prone western areas; for instance, in the Willamette–Georgia–Puget Lowland, the local model’s LD value in the ‘very high’ susceptibility class was over 3.5 times that of the global model. High TDI values (some {\textgreater}35\%) further confirmed fundamental spatial discrepancies between the risk maps obtained by the two strategies. This research substantiated that, in geographically complex terrains, partitioned modeling is an effective approach for more accurate and reliable LSM, providing a scientific basis for developing targeted regional disaster mitigation policies.},
	language = {en},
	number = {20},
	urldate = {2026-01-21},
	journal = {Applied Sciences},
	author = {Xu, Zhixiang and Zuo, Peng and Zhao, Wen and Zhou, Zeyu and Shao, Xiangyu and Yu, Junpo and Yu, Haize and Wang, Weijie and Gan, Junwei and Duan, Jinshun and Jin, Jiming},
	month = jan,
	year = {2025},
	note = {Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {NALCMS},
	pages = {11242},
}

Conventional non-partitioned Landslide Susceptibility Mapping (LSM), which neglects geospatial heterogeneity, often has limitations in accurately capturing local risk patterns. To address this challenge, this study investigated the effectiveness of localized modeling in the environmentally diverse state of Oregon, USA, by comparing ecoregion-based local models with the non-partitioned model. We partitioned Oregon into seven distinct units using the U.S. Environmental Protection Agency (EPA) Level III Ecoregions and developed one global and seven local models with the eXtreme Gradient Boosting (XGBoost) algorithm. A comprehensive evaluation framework, including the Area Under the Curve (AUC), Landslide Density (LD), and the Total Deviation Index (TDI), was used to compare the models. The results demonstrated the clear superiority of the partitioned strategy. Moreover, different ecoregions were found to have distinct dominant landslide conditioning factors, revealing strong spatial non-stationarity. Although all models generated high AUC values (\textgreater0.93), LD analysis showed that the local models were significantly more efficient at identifying high-risk zones. This advantage was particularly pronounced in critical, landslide-prone western areas; for instance, in the Willamette–Georgia–Puget Lowland, the local model’s LD value in the ‘very high’ susceptibility class was over 3.5 times that of the global model. High TDI values (some \textgreater35%) further confirmed fundamental spatial discrepancies between the risk maps obtained by the two strategies. This research substantiated that, in geographically complex terrains, partitioned modeling is an effective approach for more accurate and reliable LSM, providing a scientific basis for developing targeted regional disaster mitigation policies.

@article{talbot_enhancing_2025,
	title = {Enhancing physically based and distributed hydrological model calibration through internal state variable constraints},
	volume = {29},
	issn = {1027-5606},
	url = {https://hess.copernicus.org/articles/29/6549/2025/},
	doi = {10.5194/hess-29-6549-2025},
	abstract = {Accurately representing hydrological processes remains a major challenge in hydrological modeling. Recent studies have demonstrated the benefits of multi-variable calibration, which integrates additional hydrological variables such as evapotranspiration and soil moisture alongside streamflow to improve model realism. However, groundwater recharge as a calibration variable remains relatively underexplored.

 This study evaluates how incorporating groundwater recharge into the calibration of the Water Balance Simulation Model (WaSiM) affects hydrological variables representation. Three configurations were tested: Baseline (BL) with streamflow-only calibration, Physical Groundwater Model (GW) with physically-based groundwater flow, and Physical Groundwater with Recharge Calibration (GW-RC), which further constrains groundwater recharge during calibration. The models were calibrated and applied to 34 catchments in Southern Québec. Their performance was evaluated using the Kling-Gupta Efficiency (KGE) for streamflow and spatial estimates of groundwater recharge derived from a previous research project conducted in the same region.

 Results indicate that while calibrating on streamflow alone produces high KGE values (median KGE = 0.83 for GW and 0.82 for BL), but it comes at the cost of misrepresenting subsurface hydrological processes. Adding groundwater recharge constraints (GW-RC) reduce streamflow performance, with a median KGE of 0.77 for GW-RC, but improves hydrological variable representation, especially in seasonal runoff patterns, where it better captures the balance between surface runoff and interflow during snowmelt. Additionally, GW-RC showed the smallest differences with the groundwater recharge estimates.

 These findings illustrate the consequence of equifinality in streamflow-based calibration, where multiple parameter sets can yield similar streamflow outputs while misrepresenting internal hydrological processes. Incorporating groundwater recharge constraints improves the representation of internal hydrological processes while maintaining strong streamflow simulation performance, which could ultimately enhance reliability of climate change adaptation and water resource management strategies.},
	language = {English},
	number = {22},
	urldate = {2026-01-21},
	journal = {Hydrology and Earth System Sciences},
	author = {Talbot, Frédéric and Sylvain, Jean-Daniel and Drolet, Guillaume and Poulin, Annie and Arsenault, Richard},
	month = nov,
	year = {2025},
	note = {Publisher: Copernicus GmbH},
	keywords = {NALCMS},
	pages = {6549--6576},
}

Accurately representing hydrological processes remains a major challenge in hydrological modeling. Recent studies have demonstrated the benefits of multi-variable calibration, which integrates additional hydrological variables such as evapotranspiration and soil moisture alongside streamflow to improve model realism. However, groundwater recharge as a calibration variable remains relatively underexplored. This study evaluates how incorporating groundwater recharge into the calibration of the Water Balance Simulation Model (WaSiM) affects hydrological variables representation. Three configurations were tested: Baseline (BL) with streamflow-only calibration, Physical Groundwater Model (GW) with physically-based groundwater flow, and Physical Groundwater with Recharge Calibration (GW-RC), which further constrains groundwater recharge during calibration. The models were calibrated and applied to 34 catchments in Southern Québec. Their performance was evaluated using the Kling-Gupta Efficiency (KGE) for streamflow and spatial estimates of groundwater recharge derived from a previous research project conducted in the same region. Results indicate that while calibrating on streamflow alone produces high KGE values (median KGE = 0.83 for GW and 0.82 for BL), but it comes at the cost of misrepresenting subsurface hydrological processes. Adding groundwater recharge constraints (GW-RC) reduce streamflow performance, with a median KGE of 0.77 for GW-RC, but improves hydrological variable representation, especially in seasonal runoff patterns, where it better captures the balance between surface runoff and interflow during snowmelt. Additionally, GW-RC showed the smallest differences with the groundwater recharge estimates. These findings illustrate the consequence of equifinality in streamflow-based calibration, where multiple parameter sets can yield similar streamflow outputs while misrepresenting internal hydrological processes. Incorporating groundwater recharge constraints improves the representation of internal hydrological processes while maintaining strong streamflow simulation performance, which could ultimately enhance reliability of climate change adaptation and water resource management strategies.

@article{abud-russell_environmental_2025,
	title = {Environmental evidence of surface manifestations of regional groundwater flows in the lower {Colorado} {River} {Basin}: the case {Mexicali} {Valley}, {Mexico}},
	volume = {31},
	issn = {2352-801X},
	shorttitle = {Environmental evidence of surface manifestations of regional groundwater flows in the lower {Colorado} {River} {Basin}},
	url = {https://www.sciencedirect.com/science/article/pii/S2352801X25001079},
	doi = {10.1016/j.gsd.2025.101510},
	abstract = {Transboundary water in the Lower Colorado River Basin, shared between Mexico and the United States, demands robust, collaborative governance frameworks to address drought and increasing water demands. Although groundwater resources have become increasingly strategic for meeting water demands in both countries, they rarely receive formal consideration in official studies. Through geospatial analysis, this research delineates for the US portion of the Lower Colorado River Basin, the spatial distribution of recharge and discharge zones within the interconnected groundwater and surface water systems to characterize the regional components of groundwater flow. Employing a methodology based on the Tóthian framework of gravity-driven groundwater flow systems, the study emphasizes the interconnection of groundwater with perennial surface water, soil taxonomy, land use and land-forms as indirect evidence of its surface manifestation throughout this basin. The results are represented in discharge and recharge zones related to regional groundwater flow. Results highlight the need for binational data homologation and integration process to strengthen the available groundwater assessment tools for sustainable management in the U.S.-Mexico border.},
	urldate = {2026-01-21},
	journal = {Groundwater for Sustainable Development},
	author = {Abud-Russell, Yussef Ricardo and Hatch-Kuri, Gonzalo and Lara, María del Carmen Carmona and Aguilar-Castillo, Tatiana},
	month = nov,
	year = {2025},
	keywords = {NALCMS},
	pages = {101510},
}

Transboundary water in the Lower Colorado River Basin, shared between Mexico and the United States, demands robust, collaborative governance frameworks to address drought and increasing water demands. Although groundwater resources have become increasingly strategic for meeting water demands in both countries, they rarely receive formal consideration in official studies. Through geospatial analysis, this research delineates for the US portion of the Lower Colorado River Basin, the spatial distribution of recharge and discharge zones within the interconnected groundwater and surface water systems to characterize the regional components of groundwater flow. Employing a methodology based on the Tóthian framework of gravity-driven groundwater flow systems, the study emphasizes the interconnection of groundwater with perennial surface water, soil taxonomy, land use and land-forms as indirect evidence of its surface manifestation throughout this basin. The results are represented in discharge and recharge zones related to regional groundwater flow. Results highlight the need for binational data homologation and integration process to strengthen the available groundwater assessment tools for sustainable management in the U.S.-Mexico border.

@article{cathcart_estimates_2025,
	title = {Estimates of critical loads and exceedances of acidity and nutrient nitrogen for mineral soils in {Canada} for 2014–2016 average annual sulfur and nitrogen atmospheric deposition},
	volume = {22},
	issn = {1726-4170},
	url = {https://bg.copernicus.org/articles/22/535/2025/},
	doi = {10.5194/bg-22-535-2025},
	abstract = {The steady-state simple mass balance model was applied to natural and semi-natural terrestrial ecosystems across Canada to produce nation-wide critical loads of acidity (maximum sulfur, CLmaxS; maximum nitrogen, CLmaxN; minimum nitrogen, CLminN) and nutrient nitrogen (CLnutN) at 250 m resolution. Parameterisation of the model for Canadian ecosystems was considered with attention to the selection of the chemical criterion for damage at a site-specific resolution, with comparison between protection levels of 5 \% and 20 \% growth reduction (approximating commonly chosen base-cation-to-aluminum ratios of 1 and 10, respectively). Other parameters explored include modelled base cation deposition and site-specific nutrient and base cation uptake estimates based on North American tree chemistry data and tree species and biomass maps. Critical loads of acidity were estimated to be low (e.g., below 500 eq. ha−1 yr−1) for much of the country, particularly above 60° N latitude, where base cation weathering rates are low due to cold annual average temperature. Exceedances were mapped relative to annual sulfur and nitrogen deposition averaged over 2014–2016. Results show that under a conservative estimate (5 \% protection level), 10 \% of Canada's protected and conserved areas in the study area experienced exceedance of some level of the soil critical load of acidity, while 70 \% experienced exceedance of the soil critical load of nutrient nitrogen.},
	language = {English},
	number = {2},
	urldate = {2025-07-31},
	journal = {Biogeosciences},
	author = {Cathcart, Hazel and Aherne, Julian and Moran, Michael D. and Savic-Jovcic, Verica and Makar, Paul A. and Cole, Amanda},
	month = jan,
	year = {2025},
	note = {Publisher: Copernicus GmbH},
	keywords = {NALCMS},
	pages = {535--554},
}

The steady-state simple mass balance model was applied to natural and semi-natural terrestrial ecosystems across Canada to produce nation-wide critical loads of acidity (maximum sulfur, CLmaxS; maximum nitrogen, CLmaxN; minimum nitrogen, CLminN) and nutrient nitrogen (CLnutN) at 250 m resolution. Parameterisation of the model for Canadian ecosystems was considered with attention to the selection of the chemical criterion for damage at a site-specific resolution, with comparison between protection levels of 5 % and 20 % growth reduction (approximating commonly chosen base-cation-to-aluminum ratios of 1 and 10, respectively). Other parameters explored include modelled base cation deposition and site-specific nutrient and base cation uptake estimates based on North American tree chemistry data and tree species and biomass maps. Critical loads of acidity were estimated to be low (e.g., below 500 eq. ha−1 yr−1) for much of the country, particularly above 60° N latitude, where base cation weathering rates are low due to cold annual average temperature. Exceedances were mapped relative to annual sulfur and nitrogen deposition averaged over 2014–2016. Results show that under a conservative estimate (5 % protection level), 10 % of Canada's protected and conserved areas in the study area experienced exceedance of some level of the soil critical load of acidity, while 70 % experienced exceedance of the soil critical load of nutrient nitrogen.

@techreport{olson_estimating_2025,
	title = {Estimating flood discharges at selected annual exceedance probabilities for unregulated, rural streams in {Vermont}, 2023},
	url = {https://pubs.usgs.gov/publication/sir20255088},
	abstract = {This report provides estimates of flood discharge at selected annual exceedance probabilities (AEPs) for streamgages in and adjacent to Vermont and equations for estimating flood discharges at AEPs of 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent (recurrence intervals of 2-, 5-, 10-, 25-, 50-, 100-, and 500-years, respectively) for ungaged, unregulated, rural streams in Vermont with drainage areas between 0.47 and 851 square miles. The equations were developed using generalized least-squares regression and flood-frequency and drainage-basin characteristics from 156 streamgages. Flood-frequency analyses were completed using data through the 2023 water year. The drainage-basin characteristics used as explanatory variables in the regression equations are drainage area, percentage of wetland area, and basin-wide mean of the average annual precipitation. The average standard errors of prediction used to estimate flood discharges at the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent AEP with these equations are 34.9, 37.1, 38.2, 41.6,...},
	language = {en},
	number = {2025-5088},
	urldate = {2026-01-21},
	institution = {U.S. Geological Survey},
	author = {Olson, Scott A.},
	year = {2025},
	doi = {10.3133/sir20255088},
	note = {ISSN: 2328-0328
Publication Title: Scientific Investigations Report},
	keywords = {NALCMS},
}

This report provides estimates of flood discharge at selected annual exceedance probabilities (AEPs) for streamgages in and adjacent to Vermont and equations for estimating flood discharges at AEPs of 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent (recurrence intervals of 2-, 5-, 10-, 25-, 50-, 100-, and 500-years, respectively) for ungaged, unregulated, rural streams in Vermont with drainage areas between 0.47 and 851 square miles. The equations were developed using generalized least-squares regression and flood-frequency and drainage-basin characteristics from 156 streamgages. Flood-frequency analyses were completed using data through the 2023 water year. The drainage-basin characteristics used as explanatory variables in the regression equations are drainage area, percentage of wetland area, and basin-wide mean of the average annual precipitation. The average standard errors of prediction used to estimate flood discharges at the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent AEP with these equations are 34.9, 37.1, 38.2, 41.6,...

@phdthesis{collinge_evaluating_2025,
	title = {Evaluating {Drivers} of {Spatial} {Distribution} for the {Endangered} {Eastern} {Red} {Bat} ({Lasiurus} borealis) {Under} {Climate} {Change}},
	url = {https://hdl.handle.net/1807/142688},
	abstract = {Understanding how climatic conditions and biotic interactions impact species distributions is essential for effective biodiversity conservation. The eastern red bat (Lasiurus borealis), a migratory species recently designated endangered in Canada, is particularly threatened by the rapid development of wind energy. Protection of this species is contingent on information about where it roosts, forages, and migrates within its range, which is currently lacking. Here, I investigated changes in the bat’s migration phenology over the past century and modelled its contemporary and future distribution based on a range of climatic, anthropogenic and biotic variables. I found that bats appear to migrate earlier in spring and later in autumn, that temperature and forest type are key to predicting their distribution, and that their seasonal range is projected to shift northward, but also contract, under climate change. These results highlight the importance of including spatial and temporal considerations in conservation planning.},
	language = {en},
	urldate = {2025-07-31},
	author = {Collinge, Germain},
	month = mar,
	year = {2025},
	keywords = {NALCMS},
}

Understanding how climatic conditions and biotic interactions impact species distributions is essential for effective biodiversity conservation. The eastern red bat (Lasiurus borealis), a migratory species recently designated endangered in Canada, is particularly threatened by the rapid development of wind energy. Protection of this species is contingent on information about where it roosts, forages, and migrates within its range, which is currently lacking. Here, I investigated changes in the bat’s migration phenology over the past century and modelled its contemporary and future distribution based on a range of climatic, anthropogenic and biotic variables. I found that bats appear to migrate earlier in spring and later in autumn, that temperature and forest type are key to predicting their distribution, and that their seasonal range is projected to shift northward, but also contract, under climate change. These results highlight the importance of including spatial and temporal considerations in conservation planning.

@phdthesis{diprima_evaluating_2025,
	title = {Evaluating the {Causes} of {Land} {Subsidence} on the {U}.{S}. {Mid}-{Atlantic} {Coast} {Measured} with {InSAR}},
	url = {https://scholars.unh.edu/honors/883},
	author = {DiPrima, Jordan},
	month = jan,
	year = {2025},
	keywords = {NALCMS},
}

@article{villaruel_evaluating_2025,
	title = {Evaluating time-lagged relationships between groundwater storage and river discharge using {GRACE}-based data: insights from the {Potomac} {Basin}},
	volume = {7},
	issn = {2515-7620},
	shorttitle = {Evaluating time-lagged relationships between groundwater storage and river discharge using {GRACE}-based data},
	url = {https://dx.doi.org/10.1088/2515-7620/ade36f},
	doi = {10.1088/2515-7620/ade36f},
	abstract = {This study evaluates the utility of a recently available GRACE-based groundwater drought index (GDI) in supporting regional water supply management, with application to the Potomac River Basin, in the U.S. Middle Atlantic region. As the primary drinking water source for the Washington Metropolitan Area (WMA), effective management of the Potomac River’s resources is critical, especially in the context of climate change, with the expected increase in severity and frequency of extreme events. Our analysis integrates 22 years of data, including GRACE-based groundwater storage (GWS) index estimates, river discharge (Q) measurements, and meteorological records, to investigate trends and predictive relationships between past GWS, as determined by the GRACE-based drought index, and streamflow. Seasonal Mann-Kendall trend analyses consistently identified severe declining trends in groundwater storage (GWS), as well as moderate declines in minimum streamflow and well water levels over the past 22 years. Granger Causality (GC) tests revealed significant time lags of 49 weeks to 22 months at weekly and monthly scales, respectively depending on a region’s hydrogeomorphic characteristics. Vector Autoregressive (VAR) Models and Forecast Error Variance Decomposition (FEVD) highlighted the variable contributions of precipitation and temperature to the GWS-Q relationship, revealing a strong autoregressive component of Q, but also reveal that GWS plays an important role, and this role increases with time. These findings underscore the interconnectedness of groundwater and surface water systems and the importance of integrated predictive models to enhance water management strategies. Incorporating GRACE-based seasonal groundwater forecasts into drought preparedness tools could bolster efforts to mitigate regional climate change impacts and improve the resilience of water resources in the Potomac River Basin. While practical use of native GRACE data has been challenging for local, small-scale applications, this study demonstrates the utility of the GRACE-based GDI in forecasting low flows and informing regional water resource management decisions during droughts.},
	language = {en},
	number = {7},
	urldate = {2025-07-31},
	journal = {Environmental Research Communications},
	author = {Villaruel, AJ and Seck, Alimatou and Schultz, Cherie},
	month = jul,
	year = {2025},
	note = {Publisher: IOP Publishing},
	keywords = {NALCMS},
	pages = {075003},
}

This study evaluates the utility of a recently available GRACE-based groundwater drought index (GDI) in supporting regional water supply management, with application to the Potomac River Basin, in the U.S. Middle Atlantic region. As the primary drinking water source for the Washington Metropolitan Area (WMA), effective management of the Potomac River’s resources is critical, especially in the context of climate change, with the expected increase in severity and frequency of extreme events. Our analysis integrates 22 years of data, including GRACE-based groundwater storage (GWS) index estimates, river discharge (Q) measurements, and meteorological records, to investigate trends and predictive relationships between past GWS, as determined by the GRACE-based drought index, and streamflow. Seasonal Mann-Kendall trend analyses consistently identified severe declining trends in groundwater storage (GWS), as well as moderate declines in minimum streamflow and well water levels over the past 22 years. Granger Causality (GC) tests revealed significant time lags of 49 weeks to 22 months at weekly and monthly scales, respectively depending on a region’s hydrogeomorphic characteristics. Vector Autoregressive (VAR) Models and Forecast Error Variance Decomposition (FEVD) highlighted the variable contributions of precipitation and temperature to the GWS-Q relationship, revealing a strong autoregressive component of Q, but also reveal that GWS plays an important role, and this role increases with time. These findings underscore the interconnectedness of groundwater and surface water systems and the importance of integrated predictive models to enhance water management strategies. Incorporating GRACE-based seasonal groundwater forecasts into drought preparedness tools could bolster efforts to mitigate regional climate change impacts and improve the resilience of water resources in the Potomac River Basin. While practical use of native GRACE data has been challenging for local, small-scale applications, this study demonstrates the utility of the GRACE-based GDI in forecasting low flows and informing regional water resource management decisions during droughts.

@phdthesis{bradford_finding_2025,
	title = {Finding {Space} for the {Island} {Marble} {Butterfly}: {Modeling} the {Habitat} of a {Highly} {Endangered} {Endemic} {Butterfly} in the {Salish} {Sea} {Ecoregion}},
	shorttitle = {Finding {Space} for the {Island} {Marble} {Butterfly}},
	url = {https://cedar.wwu.edu/wwuet/1429},
	school = {Western Washington University},
	author = {Bradford, Emily},
	month = jan,
	year = {2025},
	keywords = {NALCMS},
}

@article{bellamy_forcing_2025,
	title = {Forcing {Mechanisms} of {Strong} {Surface} {Winds} in a {Dust} {Storm}–{Prone}, {High}-{Latitude} {Proglacial} {Valley}},
	url = {https://journals.ametsoc.org/view/journals/apme/64/1/JAMC-D-24-0044.1.xml},
	doi = {10.1175/JAMC-D-24-0044.1},
	abstract = {Proglacial valleys are important historical and contemporary sources of mineral dust emissions. These dust sources are situated within a complex mountainous terrain, which induce a series of superimposed meteorological phenomena. This paper assesses the forcing mechanisms of high wind speed (HWS) events in the dust storm–prone A’ą¨y Chu (Slims River) valley in southwest Yukon, Canada (60.96°N, 138.60°W). The period of 7 July 2021–30 September 2022 (≈15 months) is examined using surface meteorological stations distributed within and near the valley. A hierarchical indexing approach is applied, supported by high-resolution Weather Research and Forecasting (WRF) simulations, to identify dominant meteorological forcing mechanisms of strong wind events. Half (50.0\%) of HWS events in the valley occur under high above-valley wind speeds. Little evidence was found that off-glacier winds drive HWS, despite frequent attribution of these winds to local dust emissions in the literature. A summertime valley wind system is likely responsible for persistent nocturnal HWS near the valley delta (61.00°N, 138.52°W), contributing to the 28\% of summer HWS under distinctly calm aloft conditions. Efforts to constrain HWS characteristics from ambient conditions were not successful, partly confounded by adjacent valley wind systems. Ascertaining the configuration of phenomena responsible for high surface winds in these locations is key to yielding the predictive capacity of dust emissions from these rapidly deglaciating landscapes. Significance Statement Mineral dust emissions from high-latitude ({\textgreater}50°N) regions are poorly represented in current modeling approaches. Many dust sources are situated in a mountainous terrain, which heavily influences local wind systems. We investigated the drivers of strong surface winds in the A’ą¨y Chu valley (Canada) with a combination of surface observations and meteorological modeling. The relative importance of different meteorological systems is identified, indicating key phenomena to resolve in future efforts to simulate mineral dust emissions from this region and quantify contributions to the global dust budget.},
	language = {en},
	urldate = {2025-07-31},
	author = {Bellamy, Daniel and Nadeau, Daniel F. and King, James},
	month = jan,
	year = {2025},
	note = {Section: Journal of Applied Meteorology and Climatology},
	keywords = {NALCMS},
}

Proglacial valleys are important historical and contemporary sources of mineral dust emissions. These dust sources are situated within a complex mountainous terrain, which induce a series of superimposed meteorological phenomena. This paper assesses the forcing mechanisms of high wind speed (HWS) events in the dust storm–prone A’ą¨y Chu (Slims River) valley in southwest Yukon, Canada (60.96°N, 138.60°W). The period of 7 July 2021–30 September 2022 (≈15 months) is examined using surface meteorological stations distributed within and near the valley. A hierarchical indexing approach is applied, supported by high-resolution Weather Research and Forecasting (WRF) simulations, to identify dominant meteorological forcing mechanisms of strong wind events. Half (50.0%) of HWS events in the valley occur under high above-valley wind speeds. Little evidence was found that off-glacier winds drive HWS, despite frequent attribution of these winds to local dust emissions in the literature. A summertime valley wind system is likely responsible for persistent nocturnal HWS near the valley delta (61.00°N, 138.52°W), contributing to the 28% of summer HWS under distinctly calm aloft conditions. Efforts to constrain HWS characteristics from ambient conditions were not successful, partly confounded by adjacent valley wind systems. Ascertaining the configuration of phenomena responsible for high surface winds in these locations is key to yielding the predictive capacity of dust emissions from these rapidly deglaciating landscapes. Significance Statement Mineral dust emissions from high-latitude (\textgreater50°N) regions are poorly represented in current modeling approaches. Many dust sources are situated in a mountainous terrain, which heavily influences local wind systems. We investigated the drivers of strong surface winds in the A’ą¨y Chu valley (Canada) with a combination of surface observations and meteorological modeling. The relative importance of different meteorological systems is identified, indicating key phenomena to resolve in future efforts to simulate mineral dust emissions from this region and quantify contributions to the global dust budget.

@techreport{bourgoin_global_2025,
	title = {Global {Forest} {Maps} for the {Year} 2020 to {Support} the {EU} {Regulation} on {Deforestation}-free {Supply} {Chains}},
	url = {https://publications.jrc.ec.europa.eu/repository/handle/JRC141702},
	abstract = {This document presents the second version of the Global map of Forest Cover for the year 2020 at 10 m spatial resolution (GFC 2020, version 2 dated 7 December 2024). This map is based on the combination of several global land cover / land use data sets and provides a spatially explicit representation of forest presence and absence for the year 2020. Additionally, this document presents a preliminary version of the Global map of Forest Types for the year 2020 (GFT 2020, version v0 dated 7 December 2024). The report presents the approaches that were developed to produce these two new maps and an analysis of the results in comparison with other international sources of information. These maps are designed to support the risk assessment in the implementation of the EU Regulation on deforestation-free supply chains (EUDR, Regulation (EU) 2023/1115), but their use is not mandatory, not exclusive and not legally binding. The primary access to this map is via the EU Observatory on Deforestation and Forest Degradation.},
	language = {en},
	urldate = {2025-05-08},
	author = {Bourgoin, Clement and Verhegghen, Astrid and Carboni, Silvia and Degreve, Lucas and Ameztoy, ARAMENDI Iban and Ceccherini, Guido and Colditz, Rene and Achard, Frederic},
	year = {2025},
	doi = {10.2760/1975879},
	note = {ISBN: 9789268262603
ISSN: 1831-9424},
	keywords = {NALCMS},
}

This document presents the second version of the Global map of Forest Cover for the year 2020 at 10 m spatial resolution (GFC 2020, version 2 dated 7 December 2024). This map is based on the combination of several global land cover / land use data sets and provides a spatially explicit representation of forest presence and absence for the year 2020. Additionally, this document presents a preliminary version of the Global map of Forest Types for the year 2020 (GFT 2020, version v0 dated 7 December 2024). The report presents the approaches that were developed to produce these two new maps and an analysis of the results in comparison with other international sources of information. These maps are designed to support the risk assessment in the implementation of the EU Regulation on deforestation-free supply chains (EUDR, Regulation (EU) 2023/1115), but their use is not mandatory, not exclusive and not legally binding. The primary access to this map is via the EU Observatory on Deforestation and Forest Degradation.

@article{saenz-ceja_habitat_2025,
	title = {Habitat fragmentation and low patch connectivity threaten the genus {Abies} {Mill}. ({Pinaceae}) in southern {North} {America}},
	volume = {25},
	issn = {1436-378X},
	url = {https://doi.org/10.1007/s10113-025-02419-4},
	doi = {10.1007/s10113-025-02419-4},
	abstract = {Fir forests (Abies, Pinaceae) are patchily distributed in the montane ecoregions of the United States Southwest, Mexico, and northern Guatemala. We assessed habitat fragmentation for nine Abies species native to southern North America. We estimated the number of patches (NumP), mean patch size (MPS), median patch size (MedPS), mean shape index (MSI), patch density (PD), total edge (TE), mean patch edge (MPE), class area (CA), total landscape area (TLA), and percentage of class area within an ecoregion (ZLAND) based on Abies species distribution maps. Then, we calculated NumP, MPS, and ZLAND for land covers in each ecoregion between 2010 and 2020. We estimated the Integral Index of Connectivity of patches covered by Abies species according to two dispersal scenarios (0–500 m and 0–1000 m) and classified them as low, medium, or high connectivity. We also assessed the extent to which protected areas are intersected with patch connectivity levels. Most Abies species occupied small patches with low patch densities in restricted areas of each ecoregion. NumP increased significantly in conifer forests (76\%) and croplands (72\%), MPS decreased in mixed forests (39\%), conifer forests (37.4\%), and croplands (34.8\%), whereas ZLAND increased in urban areas (2.7\%). Most patches exhibited low connectivity in both dispersal scenarios; only two species hosted large medium and high connectivity patches. Moreover, at least 30\% of the distribution of two species was protected, and most of the protected extent was intersected with low connectivity patches. Our findings suggest habitat fragmentation and low patch connectivity could threaten Abies species long term.},
	language = {en},
	number = {3},
	urldate = {2025-07-31},
	journal = {Regional Environmental Change},
	author = {Sáenz-Ceja, Jesús E. and Mendoza, Manuel E.},
	month = jun,
	year = {2025},
	keywords = {NALCMS, Protected Areas, Railroads, Terrestrial Ecoregions},
	pages = {84},
}

Fir forests (Abies, Pinaceae) are patchily distributed in the montane ecoregions of the United States Southwest, Mexico, and northern Guatemala. We assessed habitat fragmentation for nine Abies species native to southern North America. We estimated the number of patches (NumP), mean patch size (MPS), median patch size (MedPS), mean shape index (MSI), patch density (PD), total edge (TE), mean patch edge (MPE), class area (CA), total landscape area (TLA), and percentage of class area within an ecoregion (ZLAND) based on Abies species distribution maps. Then, we calculated NumP, MPS, and ZLAND for land covers in each ecoregion between 2010 and 2020. We estimated the Integral Index of Connectivity of patches covered by Abies species according to two dispersal scenarios (0–500 m and 0–1000 m) and classified them as low, medium, or high connectivity. We also assessed the extent to which protected areas are intersected with patch connectivity levels. Most Abies species occupied small patches with low patch densities in restricted areas of each ecoregion. NumP increased significantly in conifer forests (76%) and croplands (72%), MPS decreased in mixed forests (39%), conifer forests (37.4%), and croplands (34.8%), whereas ZLAND increased in urban areas (2.7%). Most patches exhibited low connectivity in both dispersal scenarios; only two species hosted large medium and high connectivity patches. Moreover, at least 30% of the distribution of two species was protected, and most of the protected extent was intersected with low connectivity patches. Our findings suggest habitat fragmentation and low patch connectivity could threaten Abies species long term.

@article{murphy_jaguar_2025,
	title = {Jaguar density estimation in {Mexico}: {The} conservation importance of considering home range orientation in spatial capture–recapture},
	volume = {7},
	copyright = {© 2025 The Author(s). Conservation Science and Practice published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.},
	issn = {2578-4854},
	shorttitle = {Jaguar density estimation in {Mexico}},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/csp2.13301},
	doi = {10.1111/csp2.13301},
	abstract = {Accurate estimation of population parameters for imperiled wildlife is crucial for effective conservation decision-making. Population density is commonly used for monitoring imperiled species across space and time, and spatial capture–recapture (SCR) models can produce unbiased density estimates. However, many imperiled species are restricted to fragmented remnant habitats in landscapes severely modified by humans, which can alter animal space use in ways that violate typical SCR model assumptions, possibly cryptically biasing density estimates and misinforming conservation actions. Using data from a two-year camera-trapping survey in the Central Pacific Coast region, Mexico, we demonstrate the potential importance to endangered jaguar (Panthera onca) conservation of considering non-circular home ranges when estimating population density with SCR. Strong evidence existed that jaguars had elliptical home ranges wherein movements primarily occurred along linearly arranged coastal habitats that the camera array aligned with. Accounting for this movement with the SCR anisotropic detection function transformation, density estimates were 30\%–32\% higher than estimates from standard SCR models that assumed circular home ranges. Given much of suitable jaguar habitat in Mexico is fragmented and linearly oriented along coastlines and mountain ranges, accommodating irregular space use in SCR may be critical for obtaining reliable density estimates to inform effective jaguar conservation.},
	language = {en},
	number = {2},
	urldate = {2025-05-08},
	journal = {Conservation Science and Practice},
	author = {Murphy, Sean M. and Luja, Victor H.},
	year = {2025},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/csp2.13301},
	keywords = {NALCMS},
	pages = {e13301},
}

Accurate estimation of population parameters for imperiled wildlife is crucial for effective conservation decision-making. Population density is commonly used for monitoring imperiled species across space and time, and spatial capture–recapture (SCR) models can produce unbiased density estimates. However, many imperiled species are restricted to fragmented remnant habitats in landscapes severely modified by humans, which can alter animal space use in ways that violate typical SCR model assumptions, possibly cryptically biasing density estimates and misinforming conservation actions. Using data from a two-year camera-trapping survey in the Central Pacific Coast region, Mexico, we demonstrate the potential importance to endangered jaguar (Panthera onca) conservation of considering non-circular home ranges when estimating population density with SCR. Strong evidence existed that jaguars had elliptical home ranges wherein movements primarily occurred along linearly arranged coastal habitats that the camera array aligned with. Accounting for this movement with the SCR anisotropic detection function transformation, density estimates were 30%–32% higher than estimates from standard SCR models that assumed circular home ranges. Given much of suitable jaguar habitat in Mexico is fragmented and linearly oriented along coastlines and mountain ranges, accommodating irregular space use in SCR may be critical for obtaining reliable density estimates to inform effective jaguar conservation.

@article{mayor_landscape_2025,
	title = {Landscape diversity promotes landscape functioning in {North} {America}},
	volume = {6},
	copyright = {2025 The Author(s)},
	issn = {2662-4435},
	url = {https://www.nature.com/articles/s43247-025-02000-1},
	doi = {10.1038/s43247-025-02000-1},
	abstract = {Biodiversity–ecosystem functioning experiments have established generally positive species richness-productivity relationships in plots of single ecosystem types, typically grassland or forest. However, it remains unclear whether these findings apply in real-world landscapes that resemble a heterogeneous mosaic of different ecosystem and plant types that interact through biotic and abiotic processes. Here, we show that landscape-level diversity, measured as number of land-cover types (different ecosystems) per 250×250 m, is positively related to landscape-wide remotely-sensed primary production across all of North America, covering 16 of 18 ecoregions of Earth. At higher landscape diversity, productivity was temporally more stable, and 20-year greening trends were accelerated. These effects occurred independent of local species diversity, suggesting emergent mechanisms at hitherto neglected levels of biological organization. Specifically, mechanisms related to interactions among land-cover types unfold at the scale of entire landscapes, similar to, but not necessarily resulting from, interactions between species within single ecosystems.},
	language = {en},
	number = {1},
	urldate = {2025-01-28},
	journal = {Communications Earth \& Environment},
	author = {Mayor, Sarah and Altermatt, Florian and Crowther, Thomas W. and Hordijk, Iris and Landauer, Simon and Oehri, Jacqueline and Chacko, Merin Reji and Schaepman, Michael E. and Schmid, Bernhard and Niklaus, Pascal A.},
	month = jan,
	year = {2025},
	note = {Publisher: Nature Publishing Group},
	keywords = {NALCMS},
	pages = {1--9},
}

Biodiversity–ecosystem functioning experiments have established generally positive species richness-productivity relationships in plots of single ecosystem types, typically grassland or forest. However, it remains unclear whether these findings apply in real-world landscapes that resemble a heterogeneous mosaic of different ecosystem and plant types that interact through biotic and abiotic processes. Here, we show that landscape-level diversity, measured as number of land-cover types (different ecosystems) per 250×250 m, is positively related to landscape-wide remotely-sensed primary production across all of North America, covering 16 of 18 ecoregions of Earth. At higher landscape diversity, productivity was temporally more stable, and 20-year greening trends were accelerated. These effects occurred independent of local species diversity, suggesting emergent mechanisms at hitherto neglected levels of biological organization. Specifically, mechanisms related to interactions among land-cover types unfold at the scale of entire landscapes, similar to, but not necessarily resulting from, interactions between species within single ecosystems.

@article{mohammadimanesh_large_2025,
	title = {Large {Scale} {Land} {Cover} {Mapping} in {Ontario}, {Canada}, {Using} a {Deep} {Learning} {Framework}},
	issn = {2151-1535},
	url = {https://ieeexplore.ieee.org/abstract/document/10982192},
	doi = {10.1109/JSTARS.2025.3566611},
	abstract = {This study addresses the challenge of large-scale land cover mapping using advanced deep learning models. While state-of-the-art deep learning methods have demonstrated promising results in various remote sensing applications, their efficiency for large-scale semantic segmentation tasks remains underexplored. A key limitation is their reliance on extensive training datasets. To address this issue, we propose a two-stage classification approach that integrates Random Forest (RF) for initial land cover mapping and MobileUNetR, a lightweight hybrid convolution-transformer model, for a refined classification. Leveraging Sentinel-1 and Sentinel-2 data, the land cover map of Ontario at spatial resolution of 10 meter, aligned with the North American Land Change Monitoring System (NALCMS) Level I legend, encompassing 11 classes, is generated. The findings of this study reveal that MobileUNeTR surpasses widely used models like UNet and PSPNet in terms of both accuracy and efficiency, underscoring its suitability for large-scale land cover mapping. In particular, an overall accuracy of 85\% and a Kappa coefficient of 0.83 are achieved with MobileUNetR, which has only about one-fourth to one-fifth the number of parameters compared to other deep learning models examined in this study. As the only deep learning model examined in this study combining convolutional and transformer blocks, MobileUNetR demonstrates the superiority of hybrid architectures for large-scale semantic segmentation. This is due to its capability in capturing both local and global features, which are essential for semantic segmentation of heterogeneous land cover classes with varying sizes and spectral signatures.},
	urldate = {2025-05-08},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	author = {Mohammadimanesh, Fariba},
	year = {2025},
	keywords = {NALCMS},
	pages = {1--15},
}

This study addresses the challenge of large-scale land cover mapping using advanced deep learning models. While state-of-the-art deep learning methods have demonstrated promising results in various remote sensing applications, their efficiency for large-scale semantic segmentation tasks remains underexplored. A key limitation is their reliance on extensive training datasets. To address this issue, we propose a two-stage classification approach that integrates Random Forest (RF) for initial land cover mapping and MobileUNetR, a lightweight hybrid convolution-transformer model, for a refined classification. Leveraging Sentinel-1 and Sentinel-2 data, the land cover map of Ontario at spatial resolution of 10 meter, aligned with the North American Land Change Monitoring System (NALCMS) Level I legend, encompassing 11 classes, is generated. The findings of this study reveal that MobileUNeTR surpasses widely used models like UNet and PSPNet in terms of both accuracy and efficiency, underscoring its suitability for large-scale land cover mapping. In particular, an overall accuracy of 85% and a Kappa coefficient of 0.83 are achieved with MobileUNetR, which has only about one-fourth to one-fifth the number of parameters compared to other deep learning models examined in this study. As the only deep learning model examined in this study combining convolutional and transformer blocks, MobileUNetR demonstrates the superiority of hybrid architectures for large-scale semantic segmentation. This is due to its capability in capturing both local and global features, which are essential for semantic segmentation of heterogeneous land cover classes with varying sizes and spectral signatures.

@article{wegscheider_modeling_2025,
	title = {Modeling nature-based restoration potential across aquatic–terrestrial boundaries},
	volume = {n/a},
	copyright = {© 2025 His Majesty the King in Right of Canada and The Author(s). Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology. Reproduced with the permission of the Minister of Environment and Climate Change Canada.},
	issn = {1523-1739},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cobi.70046},
	doi = {10.1111/cobi.70046},
	abstract = {Today, few watersheds remain untouched by global change processes arising from climate warming, impoundments, channelization, water extraction, pollution, and urbanization. The need for restoration has resulted in a myriad of interventions, generally performed at small scales, which have limited measurable impact in restoring biodiversity and ecosystem functions. We propose bringing nature-based restoration (also referred to as rewilding) principles to rivers and their watersheds to allow freshwater ecosystems to heal themselves and present a case study example for the Wolastoq, a transboundary watershed on North America's east coast. We aimed to identify key areas for the provision of the ecosystem function secondary productivity in the watershed and explored how the existing network of protected lands contributes to its conservation. We first developed species distribution models for 94 aquatic insects and 5 aerial insectivores and then considered human footprint and existing protected areas when employing spatial prioritization to meet 2 area-based targets (17\% and 30\% [i.e., Aichi Biodiversity Target 11 and Canada's 30×30, respectively]) for conservation or restoration of freshwater secondary production. Current conservation protection in the watershed was predicted to be insufficient to protect either ecosystem function providers or receivers of secondary production. By considering integrated conservation strategies, restoration and conservation actions can be better allocated throughout habitat patches to ensure sustained provision of ecosystem functions across the watershed. Nature-based restoration and conservation can help inform Canada's area-based targets, providing a framework for incorporating ecosystem functions into conservation planning and offering practical insights for policy and restoration efforts aimed at safeguarding biodiversity.},
	language = {en},
	number = {n/a},
	urldate = {2025-05-08},
	journal = {Conservation Biology},
	author = {Wegscheider, B. and Rideout, N. K. and Monk, W. A. and Gray, M. A. and Steeves, R. and Baird, D. J.},
	year = {2025},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/cobi.70046},
	keywords = {NALCMS},
	pages = {e70046},
}

Today, few watersheds remain untouched by global change processes arising from climate warming, impoundments, channelization, water extraction, pollution, and urbanization. The need for restoration has resulted in a myriad of interventions, generally performed at small scales, which have limited measurable impact in restoring biodiversity and ecosystem functions. We propose bringing nature-based restoration (also referred to as rewilding) principles to rivers and their watersheds to allow freshwater ecosystems to heal themselves and present a case study example for the Wolastoq, a transboundary watershed on North America's east coast. We aimed to identify key areas for the provision of the ecosystem function secondary productivity in the watershed and explored how the existing network of protected lands contributes to its conservation. We first developed species distribution models for 94 aquatic insects and 5 aerial insectivores and then considered human footprint and existing protected areas when employing spatial prioritization to meet 2 area-based targets (17% and 30% [i.e., Aichi Biodiversity Target 11 and Canada's 30×30, respectively]) for conservation or restoration of freshwater secondary production. Current conservation protection in the watershed was predicted to be insufficient to protect either ecosystem function providers or receivers of secondary production. By considering integrated conservation strategies, restoration and conservation actions can be better allocated throughout habitat patches to ensure sustained provision of ecosystem functions across the watershed. Nature-based restoration and conservation can help inform Canada's area-based targets, providing a framework for incorporating ecosystem functions into conservation planning and offering practical insights for policy and restoration efforts aimed at safeguarding biodiversity.

@article{huff_national_2025,
	title = {National {Exposed} {Sediment} {Search} and {Inventory} ({NESSI}): {Utilizing} {Satellite} {Imagery} and {Machine} {Learning} to {Identify} {Dredged} {Sediment} {Placement} {Site} {Recovery}},
	volume = {17},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2072-4292},
	shorttitle = {National {Exposed} {Sediment} {Search} and {Inventory} ({NESSI})},
	url = {https://www.mdpi.com/2072-4292/17/2/186},
	doi = {10.3390/rs17020186},
	abstract = {Anthropogenic activity leads to changes in sediment dynamics, creating imbalances in sediment distributions across the landscape. These imbalances can be variable within a littoral system, with adjacent areas experiencing sediment starvation and excess sediment. Historically, sediments were viewed as an inconvenient biproduct destined for disposal; however, beneficial use of dredge material (BUDM) is a practice that has grown as a preferred methodology for utilizing sediment as a resource to help alleviate the sediment imbalances within a system. BUDM enables organizations to adopt a more innovative and sustainable sediment management approach that also provides ecological, economic, and social co-benefits. Although location data are available on BUDM sites, especially in the US, there is limited understanding on how these sites evolve within the larger landscape, which is necessary for quantifying the co-benefits. To move towards BUDM more broadly, new tools need to be developed to allow researchers and managers to understand the effects and benefits of this practice. The National Exposed Sediment Search and Inventory (NESSI) was built to show the capability of using machine learning techniques to identify dredged sediments. A combination of satellite imagery data obtained and processed using Google Earth Engine and machine learning algorithms were applied at known dredged material placement sites to develop a time series of dredged material placement events and subsequent site recovery. These disturbance-to-recovery time series are then used in a landscape analysis application to better understand site evolution within the context of the surrounding areas.},
	language = {en},
	number = {2},
	urldate = {2025-01-28},
	journal = {Remote Sensing},
	author = {Huff, Thomas P. and Russ, Emily R. and Swannack, Todd M.},
	month = jan,
	year = {2025},
	note = {Number: 2
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {NALCMS},
	pages = {186},
}

Anthropogenic activity leads to changes in sediment dynamics, creating imbalances in sediment distributions across the landscape. These imbalances can be variable within a littoral system, with adjacent areas experiencing sediment starvation and excess sediment. Historically, sediments were viewed as an inconvenient biproduct destined for disposal; however, beneficial use of dredge material (BUDM) is a practice that has grown as a preferred methodology for utilizing sediment as a resource to help alleviate the sediment imbalances within a system. BUDM enables organizations to adopt a more innovative and sustainable sediment management approach that also provides ecological, economic, and social co-benefits. Although location data are available on BUDM sites, especially in the US, there is limited understanding on how these sites evolve within the larger landscape, which is necessary for quantifying the co-benefits. To move towards BUDM more broadly, new tools need to be developed to allow researchers and managers to understand the effects and benefits of this practice. The National Exposed Sediment Search and Inventory (NESSI) was built to show the capability of using machine learning techniques to identify dredged sediments. A combination of satellite imagery data obtained and processed using Google Earth Engine and machine learning algorithms were applied at known dredged material placement sites to develop a time series of dredged material placement events and subsequent site recovery. These disturbance-to-recovery time series are then used in a landscape analysis application to better understand site evolution within the context of the surrounding areas.

@article{wanner_novel_2025,
	title = {Novel environmental variables help explain winter weather effects on activity and habitat selection of greater sage-grouse along the border of {Colorado} and {Wyoming}, {USA}},
	volume = {69},
	issn = {1432-1254},
	url = {https://doi.org/10.1007/s00484-024-02827-x},
	doi = {10.1007/s00484-024-02827-x},
	abstract = {For non-hibernating species within temperate climates, survival during severe winter weather often depends on individuals’ behavioral response and available refugia. Identifying refugia habitat that sustains populations during adverse winter conditions can be difficult and complex. This study provides an example of how modeled, biologically relevant snow and weather information can help identify important relationships between habitat selection and dynamic winter landscapes using greater sage-grouse (Centrocercus urophasianus, hereafter “sage-grouse”) as a model species. We evaluated whether sage-grouse responded to weather conditions in two ways: through (1) positive selection for refugia habitat to minimize adverse weather exposure, or (2) lowered activity level to minimize thermoregulation and locomotion expense. Our results suggested that sage-grouse respond to winter weather conditions by seeking refugia rather than changing daily activity levels. During periods of lower wind chill temperatures and greater wind speeds, sage-grouse selected areas with sheltered aspects and greater sagebrush (Artemisia spp.) cover. Broadly, sage-grouse selected winter home ranges in sagebrush shrublands characterized by higher wind chill temperatures, greater wind speeds, and greater blizzarding conditions. However, within these home ranges, sage-grouse specifically selected habitats with greater above-snow sagebrush cover, lower wind speeds, and lower blizzarding conditions. Our study underscores the importance of examining habitat selection at narrower temporal scales than entire seasons and demonstrates the value of incorporating targeted weather variables that wholistically synthesize winter conditions. This research allows identification of refugia habitat that sustain populations during winter disproportionate to their spatial extent or frequency of use, facilitating more targeted management and conservation efforts.},
	language = {en},
	number = {2},
	urldate = {2025-05-08},
	journal = {International Journal of Biometeorology},
	author = {Wanner, Caitlyn P. and Pratt, Aaron C. and Reinking, Adele K. and Liston, Glen E. and Beck, Jeffrey L.},
	month = feb,
	year = {2025},
	keywords = {NALCMS},
	pages = {469--485},
}

For non-hibernating species within temperate climates, survival during severe winter weather often depends on individuals’ behavioral response and available refugia. Identifying refugia habitat that sustains populations during adverse winter conditions can be difficult and complex. This study provides an example of how modeled, biologically relevant snow and weather information can help identify important relationships between habitat selection and dynamic winter landscapes using greater sage-grouse (Centrocercus urophasianus, hereafter “sage-grouse”) as a model species. We evaluated whether sage-grouse responded to weather conditions in two ways: through (1) positive selection for refugia habitat to minimize adverse weather exposure, or (2) lowered activity level to minimize thermoregulation and locomotion expense. Our results suggested that sage-grouse respond to winter weather conditions by seeking refugia rather than changing daily activity levels. During periods of lower wind chill temperatures and greater wind speeds, sage-grouse selected areas with sheltered aspects and greater sagebrush (Artemisia spp.) cover. Broadly, sage-grouse selected winter home ranges in sagebrush shrublands characterized by higher wind chill temperatures, greater wind speeds, and greater blizzarding conditions. However, within these home ranges, sage-grouse specifically selected habitats with greater above-snow sagebrush cover, lower wind speeds, and lower blizzarding conditions. Our study underscores the importance of examining habitat selection at narrower temporal scales than entire seasons and demonstrates the value of incorporating targeted weather variables that wholistically synthesize winter conditions. This research allows identification of refugia habitat that sustain populations during winter disproportionate to their spatial extent or frequency of use, facilitating more targeted management and conservation efforts.

@article{brown_predicting_2025,
	title = {Predicting {Rapid}, {Climate}-{Driven} {Shifts} in {North} {American} {Habitat} {Suitability} for the {Purple} {Pitcher} {Plant} ({Sarracenia} purpurea {L}.)},
	volume = {14},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2223-7747},
	url = {https://www.mdpi.com/2223-7747/14/21/3337},
	doi = {10.3390/plants14213337},
	abstract = {Climate change is shifting where suitable habitats occur for many species across the planet. Sarracenia purpurea L., the most widely distributed pitcher plant species in North America, already faces significant threats from land use change. While S. purpurea is well studied at physiological and local scales, threat assessments for this species at biogeographic scales are absent. Here, we remedy this by using Habitat Suitability Models to predict current suitable habitats and estimate climate-based shifts in the suitable habitat for S. purpurea in the near (2040) and long term (2100). The models predicted large areas of habitat loss in the southeastern United States and the western portion of the Great Lakes region by 2040. While the models also predict significant gains in suitable habitats north of the current S. purpurea range, the limited dispersal ability of this species precludes the possibility of natural migration to newly suitable habitats. Our results suggest that the degradation of considerable portions of current suitable habitats is already occurring and will continue in the future. Particularly threatened are the southern subspecies (e.g., Sarracenia purpurea subsp. venosa) of S. purpurea. We therefore urge land managers to make conservation efforts targeting threatened subspecies and encourage further the biogeographic investigation of less widely distributed congenerics of S. purpurea.},
	language = {en},
	number = {21},
	urldate = {2026-01-21},
	journal = {Plants},
	author = {Brown, Christian H. and Frick, Benjamin L. and Mohan, Jacqueline E.},
	month = jan,
	year = {2025},
	note = {Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {NALCMS},
	pages = {3337},
}

Climate change is shifting where suitable habitats occur for many species across the planet. Sarracenia purpurea L., the most widely distributed pitcher plant species in North America, already faces significant threats from land use change. While S. purpurea is well studied at physiological and local scales, threat assessments for this species at biogeographic scales are absent. Here, we remedy this by using Habitat Suitability Models to predict current suitable habitats and estimate climate-based shifts in the suitable habitat for S. purpurea in the near (2040) and long term (2100). The models predicted large areas of habitat loss in the southeastern United States and the western portion of the Great Lakes region by 2040. While the models also predict significant gains in suitable habitats north of the current S. purpurea range, the limited dispersal ability of this species precludes the possibility of natural migration to newly suitable habitats. Our results suggest that the degradation of considerable portions of current suitable habitats is already occurring and will continue in the future. Particularly threatened are the southern subspecies (e.g., Sarracenia purpurea subsp. venosa) of S. purpurea. We therefore urge land managers to make conservation efforts targeting threatened subspecies and encourage further the biogeographic investigation of less widely distributed congenerics of S. purpurea.

@article{wilson_predicting_2025,
	title = {Predicting carbon storage in {North} {American} maritime boreal forests under combined disturbances},
	volume = {40},
	issn = {1572-9761},
	url = {https://doi.org/10.1007/s10980-025-02145-6},
	doi = {10.1007/s10980-025-02145-6},
	abstract = {Understanding how forest disturbances, such as fire and herbivory, affect carbon storage across the landscape can help inform forest management and disturbance mitigation. However, this is made difficult by uncertainties in carbon predictions and limited records of disturbance histories.},
	language = {en},
	number = {7},
	urldate = {2025-07-31},
	journal = {Landscape Ecology},
	author = {Wilson, Emmerson R. and Leroux, Shawn J. and Moran, Rachael and Whitaker, Darroch and Wiersma, Yolanda},
	month = jul,
	year = {2025},
	keywords = {NALCMS},
	pages = {131},
}

Understanding how forest disturbances, such as fire and herbivory, affect carbon storage across the landscape can help inform forest management and disturbance mitigation. However, this is made difficult by uncertainties in carbon predictions and limited records of disturbance histories.

@article{schmidt_quantifying_2025,
	title = {Quantifying {Carbon} {Pools} and {Fluxes} in {Southern} {Ontario} {Temperate} {Swamps}},
	volume = {32},
	url = {https://www.mires-and-peat.net/article/151276},
	doi = {10.19189/001c.151276},
	abstract = {Swamps are treed wetlands, which account for the highest areal extent of wetland types in Southern Ontario due to the difficulties of conversion for development and agriculture. However, little is known about the environmental factors and processes that influence carbon (C) dynamics or the resulting C stocks and fluxes therein. We studied 12 swamps across Southern Ontario for 12 consecutive months to measure the exchange of carbon dioxide (CO{\textasciitilde}2{\textasciitilde}) and methane (CH{\textasciitilde}4{\textasciitilde}) between soil and atmosphere and estimate current C stocks in soil and biomass. We found mean growing season soil respiration of CO{\textasciitilde}2{\textasciitilde} and CH{\textasciitilde}4{\textasciitilde} across the swamps ranged from 11 g m{\textasciicircum}-2{\textasciicircum} d{\textasciicircum}-1{\textasciicircum} to 49 g m{\textasciicircum}-2{\textasciicircum} d{\textasciicircum}-1{\textasciicircum} and -3 mg m{\textasciicircum}-2{\textasciicircum} d{\textasciicircum}-1{\textasciicircum} to 44 mg m{\textasciicircum}-2{\textasciicircum} d{\textasciicircum}-1{\textasciicircum}, respectively, with a water table threshold at about 20 cm where fluxes changed substantially. Biomass stored 7.92–37.0 kg m{\textasciicircum}-2{\textasciicircum} of C across the sites, and soil stored 11.6–35.5 kg m{\textasciicircum}-2{\textasciicircum}. Over the study year, the swamps functioned as C sinks and followed similar patterns of relationships to environmental conditions as bogs and fens, with little difference between hydrogeomorphic settings or canopy types.},
	language = {en},
	urldate = {2026-01-21},
	journal = {Mires and Peat},
	author = {Schmidt, Megan A. and Santia, Veronica and Price, Samantha and Khumbani, Harry A. W. and Strack, Maria},
	month = nov,
	year = {2025},
	note = {Publisher: International Mire Conservation Group and International Peatland Society},
	keywords = {Climate Zones, NALCMS, North American Forests, Population Density},
}

Swamps are treed wetlands, which account for the highest areal extent of wetland types in Southern Ontario due to the difficulties of conversion for development and agriculture. However, little is known about the environmental factors and processes that influence carbon (C) dynamics or the resulting C stocks and fluxes therein. We studied 12 swamps across Southern Ontario for 12 consecutive months to measure the exchange of carbon dioxide (CO~2~) and methane (CH~4~) between soil and atmosphere and estimate current C stocks in soil and biomass. We found mean growing season soil respiration of CO~2~ and CH~4~ across the swamps ranged from 11 g m\textasciicircum-2\textasciicircum d\textasciicircum-1\textasciicircum to 49 g m\textasciicircum-2\textasciicircum d\textasciicircum-1\textasciicircum and -3 mg m\textasciicircum-2\textasciicircum d\textasciicircum-1\textasciicircum to 44 mg m\textasciicircum-2\textasciicircum d\textasciicircum-1\textasciicircum, respectively, with a water table threshold at about 20 cm where fluxes changed substantially. Biomass stored 7.92–37.0 kg m\textasciicircum-2\textasciicircum of C across the sites, and soil stored 11.6–35.5 kg m\textasciicircum-2\textasciicircum. Over the study year, the swamps functioned as C sinks and followed similar patterns of relationships to environmental conditions as bogs and fens, with little difference between hydrogeomorphic settings or canopy types.

@article{matz_rdaforest_2025,
	title = {{RDAforest}: {Identifying} {Environmental} {Drivers} of {Polygenic} {Adaptation}},
	volume = {25},
	copyright = {© 2025 The Author(s). Molecular Ecology Resources published by John Wiley \& Sons Ltd.},
	issn = {1755-0998},
	shorttitle = {{RDAforest}},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.70002},
	doi = {10.1111/1755-0998.70002},
	abstract = {Identifying environmental gradients driving genetic adaptation is one of the major goals of ecological genomics. We present RDAforest, a methodology that leverages the predominantly polygenic nature of adaptation and harnesses the versatility of random forest regression to solve this problem. Instead of computing individual SNP-environment associations, RDAforest seeks to explain the overall genetic covariance structure based on multiple environmental predictors. By relying on random forest instead of linear regression, this method can detect non-linear and non-monotonous dependencies as well as all possible interactions between predictors. It also incorporates a novel procedure to select the best predictor out of several correlated ones, and uses jackknifing to model uncertainty of genetic structure determination. Lastly, our methodology incorporates delineation and plotting of “adaptive neighbourhoods”—areas on the landscape that are predicted to harbour differentially adapted individuals. Such maps can be used as a guide for planning conservation and ecological restoration efforts. We demonstrate the use of RDAforest in two simulated scenarios and one real dataset (North American grey wolves).},
	language = {en},
	number = {8},
	urldate = {2026-01-21},
	journal = {Molecular Ecology Resources},
	author = {Matz, Mikhail V. and Black, Kristina L.},
	year = {2025},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/1755-0998.70002},
	keywords = {NALCMS},
	pages = {e70002},
}

Identifying environmental gradients driving genetic adaptation is one of the major goals of ecological genomics. We present RDAforest, a methodology that leverages the predominantly polygenic nature of adaptation and harnesses the versatility of random forest regression to solve this problem. Instead of computing individual SNP-environment associations, RDAforest seeks to explain the overall genetic covariance structure based on multiple environmental predictors. By relying on random forest instead of linear regression, this method can detect non-linear and non-monotonous dependencies as well as all possible interactions between predictors. It also incorporates a novel procedure to select the best predictor out of several correlated ones, and uses jackknifing to model uncertainty of genetic structure determination. Lastly, our methodology incorporates delineation and plotting of “adaptive neighbourhoods”—areas on the landscape that are predicted to harbour differentially adapted individuals. Such maps can be used as a guide for planning conservation and ecological restoration efforts. We demonstrate the use of RDAforest in two simulated scenarios and one real dataset (North American grey wolves).

@article{li_reconstructing_2025,
	title = {Reconstructing {Evapotranspiration} in {British} {Columbia} {Since} 1850 {Using} {Publicly} {Available} {Tree}-{Ring} {Plots} and {Climate} {Data}},
	volume = {17},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/17/5/930},
	doi = {10.3390/rs17050930},
	abstract = {Evapotranspiration (ET) rates will be affected by climate change and increasing frequency of extreme heat events. To understand how forests may respond to probable future climate conditions, it may be helpful to look at the past relationship between climate and ET. This can be accomplished using satellite imagery since the 1980s, but prior to that, a different approach is required. Using a global ET dataset (1982 to 2010) with 1 km resolution, climate station information from 1850 to 2010, and 54 tree-ring plots from the International Tree-Ring Data Bank (ITRDB) database, ET reconstructions were developed for each vegetated pixel with point-by-point regressions in British Columbia. ET was estimated for the province of British Columbia in Canada from 1850 to 1981, using random forest, support vector machine, and convolutional neural network regressions. ET satellite images from 1982 to 2010 formed our dataset to train models for each vegetated pixel. The random forest regression outperformed the other approaches with lower errors and better robustness (adjusted R2 value = 0.69; root mean square error = 10.72 mm/month). Modeled findings indicate that ET rates are generally increasing in British Columbia (ET = 0.0064 × Year + 52.339), but there were regional effects on local ET, as only the Humid Temperate ecodomain had strong correlations of ET with mean summer temperature (r = 0.257, p {\textless} 0.01) and mean summer precipitation (r = −0.208, p {\textless} 0.05). These historical estimates provide an opportunity to observe spatiotemporal variation in ET across British Columbia and elsewhere.},
	language = {en},
	number = {5},
	urldate = {2025-07-31},
	journal = {Remote Sensing},
	author = {Li, Hang and Rex, John},
	month = jan,
	year = {2025},
	note = {Number: 5
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {NALCMS},
	pages = {930},
}

Evapotranspiration (ET) rates will be affected by climate change and increasing frequency of extreme heat events. To understand how forests may respond to probable future climate conditions, it may be helpful to look at the past relationship between climate and ET. This can be accomplished using satellite imagery since the 1980s, but prior to that, a different approach is required. Using a global ET dataset (1982 to 2010) with 1 km resolution, climate station information from 1850 to 2010, and 54 tree-ring plots from the International Tree-Ring Data Bank (ITRDB) database, ET reconstructions were developed for each vegetated pixel with point-by-point regressions in British Columbia. ET was estimated for the province of British Columbia in Canada from 1850 to 1981, using random forest, support vector machine, and convolutional neural network regressions. ET satellite images from 1982 to 2010 formed our dataset to train models for each vegetated pixel. The random forest regression outperformed the other approaches with lower errors and better robustness (adjusted R2 value = 0.69; root mean square error = 10.72 mm/month). Modeled findings indicate that ET rates are generally increasing in British Columbia (ET = 0.0064 × Year + 52.339), but there were regional effects on local ET, as only the Humid Temperate ecodomain had strong correlations of ET with mean summer temperature (r = 0.257, p \textless 0.01) and mean summer precipitation (r = −0.208, p \textless 0.05). These historical estimates provide an opportunity to observe spatiotemporal variation in ET across British Columbia and elsewhere.

@article{drever_restoring_2025,
	title = {Restoring forest cover at diverse sites across {Canada} can balance synergies and trade-offs},
	issn = {2590-3322},
	url = {https://www.sciencedirect.com/science/article/pii/S259033222500003X},
	doi = {10.1016/j.oneear.2025.101177},
	abstract = {Swift action to restore forests is critical for mitigating climate change and preserving biodiversity. Canada has an ambitious program to plant two billion trees to help exceed the country’s emissions targets while restoring forest habitat and providing social and economic benefits. We conducted a systematic analysis of where new tree cover can maximally achieve these benefits while minimizing implementation costs. Accounting for critiques of global restoration mapping that include the overestimation of mitigation potential and inadequate biodiversity and social safeguards, we find that 19.1 Mha are available, which is much more than the approximately 1.2 Mha needed to plant two billion trees. Optimization scenarios for 1.2 Mha revealed synergies and trade-offs. Scenarios prioritizing low costs, accessibility, and high growth are concentrated in temperate and coastal areas, overlapping partly with biodiversity scenarios, but with trade-offs of higher costs. A diverse portfolio of regionally restored sites, each tailored for specific attributes, is most likely to deliver multiple benefits at the pace demanded by the current crises.},
	urldate = {2025-01-20},
	journal = {One Earth},
	author = {Drever, C. Ronnie and Long, Alison M. and Cook-Patton, Susan C. and Celanowicz, Elizabeth and Fargione, Joe and Fisher, Kathryn and Hounsell, Steve and Kurz, Werner A. and Mitchell, Matthew and Robinson, Nathaniel and Pither, Richard and Schuster, Richard and Deziel, Val and Xu, Zach},
	month = jan,
	year = {2025},
	keywords = {NALCMS},
	pages = {101177},
}

Swift action to restore forests is critical for mitigating climate change and preserving biodiversity. Canada has an ambitious program to plant two billion trees to help exceed the country’s emissions targets while restoring forest habitat and providing social and economic benefits. We conducted a systematic analysis of where new tree cover can maximally achieve these benefits while minimizing implementation costs. Accounting for critiques of global restoration mapping that include the overestimation of mitigation potential and inadequate biodiversity and social safeguards, we find that 19.1 Mha are available, which is much more than the approximately 1.2 Mha needed to plant two billion trees. Optimization scenarios for 1.2 Mha revealed synergies and trade-offs. Scenarios prioritizing low costs, accessibility, and high growth are concentrated in temperate and coastal areas, overlapping partly with biodiversity scenarios, but with trade-offs of higher costs. A diverse portfolio of regionally restored sites, each tailored for specific attributes, is most likely to deliver multiple benefits at the pace demanded by the current crises.

@article{maavara_river_2025,
	title = {River metabolism in the contiguous {United} {States}: {A} {West} of extremes},
	volume = {390},
	shorttitle = {River metabolism in the contiguous {United} {States}},
	url = {https://www.science.org/doi/abs/10.1126/science.adu9843},
	doi = {10.1126/science.adu9843},
	abstract = {River metabolism is among the most uncertain fluxes in the global carbon cycle. We present estimates for gross primary productivity (GPP) and ecosystem respiration (ER) for more than 175,000 rivers across the contiguous United States (CONUS), including metabolic responses to extreme hydrological conditions. Our model predicts an annual GPP in CONUS rivers of 10.1 teragrams of carbon per year and an ER of 18.7 teragrams of carbon per year, which implies that net ecosystem productivity (NEP; where NEP = GPP – ER) is a small contributor to river carbon dioxide emissions. More than 70\% of river metabolism occurs in the West, where regions of both extreme heterotrophy and autotrophy exist. Autotrophy is prominent across the West and is sensitive to drought, particularly in understudied biomes such as arid desert shrublands, which may indicate that global riverine uptake of carbon dioxide is higher than hypothesized.},
	number = {6773},
	urldate = {2026-01-21},
	journal = {Science},
	author = {Maavara, Taylor and Yuan, Zimin and Johnson, Andrew M. and Zhang, Shuang and Aho, Kelly S. and Brinkerhoff, Craig B. and Logozzo, Laura A. and Raymond, Peter},
	month = nov,
	year = {2025},
	note = {Publisher: American Association for the Advancement of Science},
	keywords = {NALCMS},
	pages = {622--627},
}

River metabolism is among the most uncertain fluxes in the global carbon cycle. We present estimates for gross primary productivity (GPP) and ecosystem respiration (ER) for more than 175,000 rivers across the contiguous United States (CONUS), including metabolic responses to extreme hydrological conditions. Our model predicts an annual GPP in CONUS rivers of 10.1 teragrams of carbon per year and an ER of 18.7 teragrams of carbon per year, which implies that net ecosystem productivity (NEP; where NEP = GPP – ER) is a small contributor to river carbon dioxide emissions. More than 70% of river metabolism occurs in the West, where regions of both extreme heterotrophy and autotrophy exist. Autotrophy is prominent across the West and is sensitive to drought, particularly in understudied biomes such as arid desert shrublands, which may indicate that global riverine uptake of carbon dioxide is higher than hypothesized.

@phdthesis{wang_spatiotemporal_2025,
	title = {Spatiotemporal {Dynamics} and {Climate}-{Driven} {Risk} of {Compound} {Flood} {Events} in the {Great} {Lakes} {Basin}},
	url = {https://hdl.handle.net/20.500.14721/38586},
	abstract = {Floods are among the most frequent and economically significant natural hazards affecting communities worldwide. Understanding their mechanisms, spatiotemporal patterns and projected changes is crucial to develop effective strategies for risk mitigation. This study analyzes three types of climate-driven flood events in the Great Lakes basin, driven by Rain-on-Snow (ROS), Saturation Excess flooding (SEF), and Successive Rainfall (SR), in the historical period (1985-2014) and the future period (2071-2100). Assessments are based on WRF-Hydro simulations forced by RDRS observations and eight downscaled and bias-adjusted General Circulation Models (GCMs) under SSP5-8.5 emission scenario. Based on the model outputs, the characteristics of extreme events including intensity, frequency, and seasonality, are evaluated and their contributions to the heavy runoff are investigated. Furthermore, flood risk is characterized by integrating the intensity and frequency of events with exposure and vulnerability for the Great Lakes basin. Overall, snow-related events are projected to decrease, while rain-related events are expected to increase. Single rainfall extreme events are projected to dominate future surface runoff generation, while single snowmelt events are more sensitive to climate change in the northern sub-basin. The contribution of ROS is less pronounced and is expected to decrease, whereas the SEF events show a higher contribution with an increasing impact in the future period. In addition, flood temporal and spatial correlation analyses show that future floods are likely to occur earlier, with increasing spring floods across the region. The spatial correlation between floods in the Lake Erie and Lake Michigan Basins is stronger and projected to increase, suggesting greater influence from large-scale atmospheric drivers. The seasonal shifts underscore the need for adaptive, season-specific flood management policies in the Great Lakes region. These results emphasize that the study of compound extreme events should move beyond individual event characteristics and place greater emphasis on hydroclimatic interactions and their associated impacts.},
	language = {en},
	urldate = {2026-01-21},
	school = {The University of Western Ontario},
	author = {Wang, Yuxuan},
	month = aug,
	year = {2025},
	keywords = {NALCMS},
}

Floods are among the most frequent and economically significant natural hazards affecting communities worldwide. Understanding their mechanisms, spatiotemporal patterns and projected changes is crucial to develop effective strategies for risk mitigation. This study analyzes three types of climate-driven flood events in the Great Lakes basin, driven by Rain-on-Snow (ROS), Saturation Excess flooding (SEF), and Successive Rainfall (SR), in the historical period (1985-2014) and the future period (2071-2100). Assessments are based on WRF-Hydro simulations forced by RDRS observations and eight downscaled and bias-adjusted General Circulation Models (GCMs) under SSP5-8.5 emission scenario. Based on the model outputs, the characteristics of extreme events including intensity, frequency, and seasonality, are evaluated and their contributions to the heavy runoff are investigated. Furthermore, flood risk is characterized by integrating the intensity and frequency of events with exposure and vulnerability for the Great Lakes basin. Overall, snow-related events are projected to decrease, while rain-related events are expected to increase. Single rainfall extreme events are projected to dominate future surface runoff generation, while single snowmelt events are more sensitive to climate change in the northern sub-basin. The contribution of ROS is less pronounced and is expected to decrease, whereas the SEF events show a higher contribution with an increasing impact in the future period. In addition, flood temporal and spatial correlation analyses show that future floods are likely to occur earlier, with increasing spring floods across the region. The spatial correlation between floods in the Lake Erie and Lake Michigan Basins is stronger and projected to increase, suggesting greater influence from large-scale atmospheric drivers. The seasonal shifts underscore the need for adaptive, season-specific flood management policies in the Great Lakes region. These results emphasize that the study of compound extreme events should move beyond individual event characteristics and place greater emphasis on hydroclimatic interactions and their associated impacts.

@article{slabon_synthetic_2025,
	title = {Synthetic {Aperture} {Radar} as a {Tool} for {Tornado} {Classification} in {Forested} {Areas}},
	volume = {51},
	issn = {0703-8992},
	url = {https://doi.org/10.1080/07038992.2025.2579315},
	doi = {10.1080/07038992.2025.2579315},
	abstract = {Canada’s tornado climatology has historically been challenging to accurately assess. Many Canadian tornadoes are currently rated on the Enhanced Fujita scale using treefall as a damage indicator, though this approach may occasionally underestimate tornado intensity. In this work, we propose that the use of synthetic aperture radar (SAR) images of tornado tracks may lead to more accurate tornado ratings in tornadoes that have at least 25\% of their track consisting of forested areas. We create a replicable methodology by which SAR data can be used to classify tornadoes by generating difference images using Sentinel-1 C-Band radar data acquired before and after the tornado event. We apply this methodology to a large number of recent Canadian tornadoes. The results showed that only higher intensity tornadoes (EF3+) were detectable in the SAR change detection images. Critically, the results revealed possible misclassifications, where tornadoes originally assigned a rating of EF2 based on treefall damage indicators were visible in the SAR imagery. Our study provides a foundation for future work, providing an improved methodology for classifying tornadoes in remote forested areas.},
	number = {1},
	urldate = {2026-01-21},
	journal = {Canadian Journal of Remote Sensing},
	author = {Slabon, Sophia G. and Neish, Catherine D. and Miller, Connell S.},
	month = dec,
	year = {2025},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/07038992.2025.2579315},
	keywords = {NALCMS},
	pages = {2579315},
}

Canada’s tornado climatology has historically been challenging to accurately assess. Many Canadian tornadoes are currently rated on the Enhanced Fujita scale using treefall as a damage indicator, though this approach may occasionally underestimate tornado intensity. In this work, we propose that the use of synthetic aperture radar (SAR) images of tornado tracks may lead to more accurate tornado ratings in tornadoes that have at least 25% of their track consisting of forested areas. We create a replicable methodology by which SAR data can be used to classify tornadoes by generating difference images using Sentinel-1 C-Band radar data acquired before and after the tornado event. We apply this methodology to a large number of recent Canadian tornadoes. The results showed that only higher intensity tornadoes (EF3+) were detectable in the SAR change detection images. Critically, the results revealed possible misclassifications, where tornadoes originally assigned a rating of EF2 based on treefall damage indicators were visible in the SAR imagery. Our study provides a foundation for future work, providing an improved methodology for classifying tornadoes in remote forested areas.

@article{regimbal_acadian_2025,
	title = {The {Acadian} {Flycatcher} is a habitat specialist, and it shows},
	volume = {20},
	copyright = {© 2025 by the author(s)},
	issn = {1712-6568},
	url = {https://ace-eco.org/vol20/iss1/art25/},
	doi = {10.5751/ACE-02892-200125},
	abstract = {Declines in North American bird populations are being driven by a suite of threats, and it can be difficult to disentangle drivers of decline for any single species, especially those with large ranges or that experience different threats in different parts of their range. Community science platforms offer new and rapidly expanding datasets to observe species across space and time. Here, we use community science databases to analyze the habitat characteristics of the Acadian Flycatcher (Empidonax virescens), a Neotropical migrant songbird that is threatened across portions of its range. The Acadian Flycatcher is often described as a habitat specialist, though often only where it is considered at risk. We use Acadian Flycatcher observations sourced from one of the biggest community science platforms, eBird, and assess habitat land cover and co-occurrence of species associated with habitat quality for each of these observations. We use publicly available land use data to assess habitat cover and assess co-occurring species using observations sourced from multiple community science platforms. Our results show the Acadian Flycatcher is largely observed in high-quality landscapes of preferred habitat (31\% deciduous forest and 11\% wetland cover) and without invasive vegetation more than bird observation sites where the Acadian Flycatcher was not detected (25\% more likely to be near preferred trees, 77\% less likely to be near invasive vegetation, p {\textless} 0.001). Our results also show an overrepresentation of urban land cover, potentially highlighting a bias in some community science data due to observer behavior. Overall, our results support land management strategies that maintain patches of native land cover and manage invasive species and highlight how community science databases can provide important information about species’ presence over space and time.},
	language = {en},
	number = {1},
	urldate = {2025-07-31},
	journal = {Avian Conservation and Ecology},
	author = {Regimbal, Nicole L. and Riskin, Shelby H.},
	month = jun,
	year = {2025},
	note = {Publisher: The Resilience Alliance},
	keywords = {NALCMS},
}

Declines in North American bird populations are being driven by a suite of threats, and it can be difficult to disentangle drivers of decline for any single species, especially those with large ranges or that experience different threats in different parts of their range. Community science platforms offer new and rapidly expanding datasets to observe species across space and time. Here, we use community science databases to analyze the habitat characteristics of the Acadian Flycatcher (Empidonax virescens), a Neotropical migrant songbird that is threatened across portions of its range. The Acadian Flycatcher is often described as a habitat specialist, though often only where it is considered at risk. We use Acadian Flycatcher observations sourced from one of the biggest community science platforms, eBird, and assess habitat land cover and co-occurrence of species associated with habitat quality for each of these observations. We use publicly available land use data to assess habitat cover and assess co-occurring species using observations sourced from multiple community science platforms. Our results show the Acadian Flycatcher is largely observed in high-quality landscapes of preferred habitat (31% deciduous forest and 11% wetland cover) and without invasive vegetation more than bird observation sites where the Acadian Flycatcher was not detected (25% more likely to be near preferred trees, 77% less likely to be near invasive vegetation, p \textless 0.001). Our results also show an overrepresentation of urban land cover, potentially highlighting a bias in some community science data due to observer behavior. Overall, our results support land management strategies that maintain patches of native land cover and manage invasive species and highlight how community science databases can provide important information about species’ presence over space and time.

@article{fendereski_tracking_2025,
	title = {Tracking changes in wetlandscape properties of the {Lake} {Winnipeg} {Watershed} using {Landsat} inundation products (1984–2020)},
	volume = {136},
	issn = {1569-8432},
	url = {https://www.sciencedirect.com/science/article/pii/S1569843225000238},
	doi = {10.1016/j.jag.2025.104376},
	abstract = {Wetlandscapes—hydrologically connected networks of wetlands—vary over time, causing changes in their provision of hydrological, biogeochemical, and ecological functions to landscapes. Here, we developed a method for mapping wetlands and extracting wetlandscape properties from Landsat-derived inundation data and applied this method to the Lake Winnipeg Watershed (LWW). We first mapped the annual (1984–2020) time series of inundated areas using a fusion of two Landsat-derived inundation products, Global Surface Water Extent (GSWE) and Dynamic Surface Water Extent (DSWE), finding that this fusion reduced omission errors from 17 \% for GSWE and 18 \% for DSWE to 8 \% overall. We then used the inundated area maps to identify the topological structure of the wetlandscape, i.e., networks composed of nodes (representing wetlands) and their links (representing hydrological connectivity among wetlands). The time series of the wetlandscape properties (number, size, and connectivity of wetlands) showed coherence with a concurrent increase in precipitation over the watershed. The LWW is transitioning to a more extensive wetland area consisting of a greater number of larger wetlands with increased connections among them (p {\textless} 0.1). With Landsat-derived inundation products widely available globally, we suggest using the method developed here to analyze changes in wetlandscape properties in other regions worldwide.},
	urldate = {2025-05-08},
	journal = {International Journal of Applied Earth Observation and Geoinformation},
	author = {Fendereski, Forough and Ma, Shizhou and Mohammady, Sassan and Spence, Christopher and Trick, Charles G. and Creed, Irena F.},
	month = feb,
	year = {2025},
	keywords = {NALCMS},
	pages = {104376},
}

Wetlandscapes—hydrologically connected networks of wetlands—vary over time, causing changes in their provision of hydrological, biogeochemical, and ecological functions to landscapes. Here, we developed a method for mapping wetlands and extracting wetlandscape properties from Landsat-derived inundation data and applied this method to the Lake Winnipeg Watershed (LWW). We first mapped the annual (1984–2020) time series of inundated areas using a fusion of two Landsat-derived inundation products, Global Surface Water Extent (GSWE) and Dynamic Surface Water Extent (DSWE), finding that this fusion reduced omission errors from 17 % for GSWE and 18 % for DSWE to 8 % overall. We then used the inundated area maps to identify the topological structure of the wetlandscape, i.e., networks composed of nodes (representing wetlands) and their links (representing hydrological connectivity among wetlands). The time series of the wetlandscape properties (number, size, and connectivity of wetlands) showed coherence with a concurrent increase in precipitation over the watershed. The LWW is transitioning to a more extensive wetland area consisting of a greater number of larger wetlands with increased connections among them (p \textless 0.1). With Landsat-derived inundation products widely available globally, we suggest using the method developed here to analyze changes in wetlandscape properties in other regions worldwide.

@article{mariotti_transforming_2025,
	title = {Transforming abandoned mines into solar farms: a pathway to renewable energy development and sustainable land use},
	volume = {2},
	issn = {2753-3751},
	shorttitle = {Transforming abandoned mines into solar farms},
	url = {https://dx.doi.org/10.1088/2753-3751/adb6a3},
	doi = {10.1088/2753-3751/adb6a3},
	abstract = {The demand for renewable energy is becoming increasingly apparent, but a significant challenge lies in the limited availability of land. To limit environmental impacts associated with new development in previously undisturbed lands, this study investigates the potential to convert abandoned mines in Florida and Pennsylvania into solar farms, aligning with federal and state-level clean energy goals and climate objectives. Thirteen abandoned mines in Florida and five in Pennsylvania were identified as suitable sites for solar farm development. These solar installations have the potential to generate up to 22\% of Florida’s and 0.017\% of Pennsylvania’s annual electricity needs. Employing various criteria such as acreage, slope, aspect, proximity to infrastructure, and insolation, the research assesses the feasibility of this conversion using advanced modeling techniques. The study was conducted in two phases: first, an in-depth spatial suitability assessment utilizing GIS tools (ArcGIS Pro) to evaluate potential mine sites based on specified criteria. Second, solar power potential and carbon emission reduction are estimated based on the use of two different solar panel setups with different installed capacity and of which one is static and the other one solar-tracking. This study underscores the importance of interdisciplinary approaches in addressing environmental and energy challenges by advancing our understanding of sustainable land use practices and contributing to the collective pursuit of clean energy objectives.},
	language = {en},
	number = {1},
	urldate = {2025-07-31},
	journal = {Environmental Research: Energy},
	author = {Mariotti, Erin and Engström, Johanna},
	month = mar,
	year = {2025},
	note = {Publisher: IOP Publishing},
	keywords = {NALCMS},
	pages = {015013},
}

The demand for renewable energy is becoming increasingly apparent, but a significant challenge lies in the limited availability of land. To limit environmental impacts associated with new development in previously undisturbed lands, this study investigates the potential to convert abandoned mines in Florida and Pennsylvania into solar farms, aligning with federal and state-level clean energy goals and climate objectives. Thirteen abandoned mines in Florida and five in Pennsylvania were identified as suitable sites for solar farm development. These solar installations have the potential to generate up to 22% of Florida’s and 0.017% of Pennsylvania’s annual electricity needs. Employing various criteria such as acreage, slope, aspect, proximity to infrastructure, and insolation, the research assesses the feasibility of this conversion using advanced modeling techniques. The study was conducted in two phases: first, an in-depth spatial suitability assessment utilizing GIS tools (ArcGIS Pro) to evaluate potential mine sites based on specified criteria. Second, solar power potential and carbon emission reduction are estimated based on the use of two different solar panel setups with different installed capacity and of which one is static and the other one solar-tracking. This study underscores the importance of interdisciplinary approaches in addressing environmental and energy challenges by advancing our understanding of sustainable land use practices and contributing to the collective pursuit of clean energy objectives.

@article{boulerice_utilisation_2025,
	title = {Utilisation de l'{ADN} environnemental en milieu lotique pour informer la théorie des métacommunautés},
	url = {http://hdl.handle.net/11143/22610},
	abstract = {L’ADN environnemental (ADNe) est devenu un outil incontournable pour l’étude rapide et non invasive de la biodiversité.Cependant,son application aux systèmes de rivière dans le cadre de la théorie des métacommunautés reste largement inexplorée, les recherches s’étant principalement concentrées sur les milieux lentiques. L’un des principaux défis liés à l’utilisation des données d’ADNe dans les systèmes de rivière réside dans le transport des molécules d’ADN loin de leur site d’excrétion, ce qui peut entraîner des fausses détections. Dans ce mémoire, nous proposons une approche novatrice combinant la théorie des métacommunautés et la modélisation de l’ADN environnemental pour analyser les données d’ADNe des poissons dans les réseaux de rivière. Notre étude porte sur deux bassins versants situés dans la province de Québec, au Canada : les réseaux hydrographiques des rivières Châteauguay et Saint-François. En utilisant le partitionnement de la variation, nous avons identifié les variables qui structurent les métacommunautés de poissons à l’échelle des espèces et des sites. Nos résultats montrent que l’environnement structuré spatialement est le principal moteur des métacommunautés de poissons dans les deux réseaux étudiés, en accord avec le concept de continuum des rivières. De plus, nos analyses capturent la structure des communautés à la fois aux échelles locale et régionale : les variables environnementales non structurées spatialement reflètent les dynamiques à une échelle plus fine, tandis que les variables spatiales mettent en évidence les dynamiques à une échelle plus large. Nous avons également relié nos résultats aux caractéristiques écologiques des espèces, démontrant ainsi que le modèle reflète fidèlement les facteurs environnementaux et biologiques influençant leur distribution. L’analyse des sites révèle toutefois des différences entre les deux bassins versants. Les sites en amont du réseau de la rivière Châteauguay présentent une plus grande diversité écologique, tandis que le réseau de la rivière Saint-François se caractérise par une homogénéité plus marquée. Ces tendances suggèrent que les mécanismes de maintien de la biodiversité varient entre les deux systèmes, confirmant par ailleurs l’efficacité du modèle pour corriger les biais liés à l’ADNe. En conclusion, nos résultats soulignent l’importance d’adopter une perspective basée sur la théorie des métacommunautés pour interpréter les données d’ADN environnemental. Cette approche enrichit la compréhension des systèmes de rivière et met en lumière le potentiel de l’ADNe pour guider les stratégies de conservation et de gestion des habitats d’eau douce.},
	language = {fre},
	urldate = {2025-07-31},
	author = {Boulerice, Laurie},
	month = apr,
	year = {2025},
	note = {Publisher: Université de Sherbrooke},
	keywords = {NALCMS},
}

L’ADN environnemental (ADNe) est devenu un outil incontournable pour l’étude rapide et non invasive de la biodiversité.Cependant,son application aux systèmes de rivière dans le cadre de la théorie des métacommunautés reste largement inexplorée, les recherches s’étant principalement concentrées sur les milieux lentiques. L’un des principaux défis liés à l’utilisation des données d’ADNe dans les systèmes de rivière réside dans le transport des molécules d’ADN loin de leur site d’excrétion, ce qui peut entraîner des fausses détections. Dans ce mémoire, nous proposons une approche novatrice combinant la théorie des métacommunautés et la modélisation de l’ADN environnemental pour analyser les données d’ADNe des poissons dans les réseaux de rivière. Notre étude porte sur deux bassins versants situés dans la province de Québec, au Canada : les réseaux hydrographiques des rivières Châteauguay et Saint-François. En utilisant le partitionnement de la variation, nous avons identifié les variables qui structurent les métacommunautés de poissons à l’échelle des espèces et des sites. Nos résultats montrent que l’environnement structuré spatialement est le principal moteur des métacommunautés de poissons dans les deux réseaux étudiés, en accord avec le concept de continuum des rivières. De plus, nos analyses capturent la structure des communautés à la fois aux échelles locale et régionale : les variables environnementales non structurées spatialement reflètent les dynamiques à une échelle plus fine, tandis que les variables spatiales mettent en évidence les dynamiques à une échelle plus large. Nous avons également relié nos résultats aux caractéristiques écologiques des espèces, démontrant ainsi que le modèle reflète fidèlement les facteurs environnementaux et biologiques influençant leur distribution. L’analyse des sites révèle toutefois des différences entre les deux bassins versants. Les sites en amont du réseau de la rivière Châteauguay présentent une plus grande diversité écologique, tandis que le réseau de la rivière Saint-François se caractérise par une homogénéité plus marquée. Ces tendances suggèrent que les mécanismes de maintien de la biodiversité varient entre les deux systèmes, confirmant par ailleurs l’efficacité du modèle pour corriger les biais liés à l’ADNe. En conclusion, nos résultats soulignent l’importance d’adopter une perspective basée sur la théorie des métacommunautés pour interpréter les données d’ADN environnemental. Cette approche enrichit la compréhension des systèmes de rivière et met en lumière le potentiel de l’ADNe pour guider les stratégies de conservation et de gestion des habitats d’eau douce.

@article{sullender_warm_2025,
	title = {Warm winters, hot moose: temperature drives activity and habitat trade-offs across a cold-adapted species’ range},
	volume = {20},
	issn = {1748-9326},
	shorttitle = {Warm winters, hot moose},
	url = {https://doi.org/10.1088/1748-9326/ae263a},
	doi = {10.1088/1748-9326/ae263a},
	abstract = {Moose (Alces alces) are a cold-adapted species that may be vulnerable to overheating at relatively low temperatures in winter. Moose have two main strategies for thermal regulation: shifting activity patterns and selecting habitat that provides thermal refuge. In this study, we compared how moose use these two strategies in response to winter temperature across their latitudinal range. First, we used hidden Markov models to delineate encamped and traveling movement states for five populations of global positioning system-collared moose in relation to time of day, temperature, and snow depth. Next, we used step-selection functions to determine influential covariates of encamped locations. As air temperatures and snow depths increased, moose from all populations were more likely to remain in an encamped, relatively stationary state. All moose became less diurnal and more nocturnal at high temperatures, although the magnitude of changes in activity varied by population. Encamped northern moose selected shrubby habitat that presents foraging opportunities, whereas encamped southern moose selected for coniferous forest that provides poor forage but offers shade in southern regions. The only moose population to select for lower temperatures also experienced the warmest winter on record during our study period, which may explain this population’s low overall activity rates. Our results indicate that moose along their southern range extent are responding to elevated mid-winter temperatures by initially altering activity patterns and subsequently selecting for potential thermal refugia at the expense of foraging habitat, while northern moose were unlikely to shift habitat selection based on temperature unless faced with an anomalously warm winter. As climate change is implicated in range contraction and population declines, our findings suggest that high winter temperatures may be causing moose to not only reduce overall activity but also to forgo preferred foraging habitat in favor of prioritizing thermal refuge, thus forcing a trade-off between nutrition and thermoregulation.},
	language = {en},
	number = {12},
	urldate = {2026-01-21},
	journal = {Environmental Research Letters},
	author = {Sullender, Benjamin K and Cunningham, Calum X and Reinking, Adele K and Liston, Glen E and Levine, Rebecca L and Verzuh, Tana L and Boelman, Natalie T and Boutin, Stan and Suitor, Mike and Monteith, Kevin L and Hebblewhite, Mark and Brinkman, Todd J and Prugh, Laura R},
	month = dec,
	year = {2025},
	note = {Publisher: IOP Publishing},
	keywords = {NALCMS},
	pages = {124064},
}

Moose (Alces alces) are a cold-adapted species that may be vulnerable to overheating at relatively low temperatures in winter. Moose have two main strategies for thermal regulation: shifting activity patterns and selecting habitat that provides thermal refuge. In this study, we compared how moose use these two strategies in response to winter temperature across their latitudinal range. First, we used hidden Markov models to delineate encamped and traveling movement states for five populations of global positioning system-collared moose in relation to time of day, temperature, and snow depth. Next, we used step-selection functions to determine influential covariates of encamped locations. As air temperatures and snow depths increased, moose from all populations were more likely to remain in an encamped, relatively stationary state. All moose became less diurnal and more nocturnal at high temperatures, although the magnitude of changes in activity varied by population. Encamped northern moose selected shrubby habitat that presents foraging opportunities, whereas encamped southern moose selected for coniferous forest that provides poor forage but offers shade in southern regions. The only moose population to select for lower temperatures also experienced the warmest winter on record during our study period, which may explain this population’s low overall activity rates. Our results indicate that moose along their southern range extent are responding to elevated mid-winter temperatures by initially altering activity patterns and subsequently selecting for potential thermal refugia at the expense of foraging habitat, while northern moose were unlikely to shift habitat selection based on temperature unless faced with an anomalously warm winter. As climate change is implicated in range contraction and population declines, our findings suggest that high winter temperatures may be causing moose to not only reduce overall activity but also to forgo preferred foraging habitat in favor of prioritizing thermal refuge, thus forcing a trade-off between nutrition and thermoregulation.

@article{qiu_wildfire_2025,
	title = {Wildfire smoke exposure and mortality burden in the {USA} under climate change},
	volume = {647},
	copyright = {2025 The Author(s), under exclusive licence to Springer Nature Limited},
	issn = {1476-4687},
	url = {https://www.nature.com/articles/s41586-025-09611-w},
	doi = {10.1038/s41586-025-09611-w},
	abstract = {Wildfire activity has increased in the USA and is projected to accelerate under future climate change1–3. However, our understanding of the impacts of climate change on wildfire activity, smoke and health outcomes remains highly uncertain because of the difficulty of modelling the causal chain from climate to wildfire to air pollution and health. Here we quantify the mortality burden in the USA due to wildfire smoke fine particulate matter (PM2.5) under climate change. We construct an ensemble of statistical and machine learning models that link climate to wildfire smoke PM2.5 and empirically estimate smoke PM2.5–mortality relationships using data on all recorded deaths in the USA. We project that smoke PM2.5 could result in 71,420 excess deaths (95\% confidence interval: 34,930–98,430) per year by 2050 under a high-warming scenario (shared socioeconomic pathway scenario 3-7.0, SSP3-7.0)—a 73\% increase relative to the estimated 2011–2020 average annual excess deaths from smoke. Cumulative excess deaths from smoke PM2.5 could reach 1.9 million between 2026 and 2055. We find evidence for mortality impacts of smoke PM2.5 that last up to 3 years after exposure. When monetized, climate-driven smoke deaths result in economic damages that exceed existing estimates of climate-driven damages from all other causes combined in the USA4,5. Our research suggests that the health impacts of climate-driven wildfire smoke could be among the most important and costly consequences of a warming climate in the USA.},
	language = {en},
	number = {8091},
	urldate = {2026-01-21},
	journal = {Nature},
	author = {Qiu, Minghao and Li, Jessica and Gould, Carlos F. and Jing, Renzhi and Kelp, Makoto and Childs, Marissa L. and Wen, Jeff and Xie, Yuanyu and Lin, Meiyun and Kiang, Mathew V. and Heft-Neal, Sam and Diffenbaugh, Noah S. and Burke, Marshall},
	month = nov,
	year = {2025},
	note = {Publisher: Nature Publishing Group},
	keywords = {NALCMS},
	pages = {935--943},
}

Wildfire activity has increased in the USA and is projected to accelerate under future climate change1–3. However, our understanding of the impacts of climate change on wildfire activity, smoke and health outcomes remains highly uncertain because of the difficulty of modelling the causal chain from climate to wildfire to air pollution and health. Here we quantify the mortality burden in the USA due to wildfire smoke fine particulate matter (PM2.5) under climate change. We construct an ensemble of statistical and machine learning models that link climate to wildfire smoke PM2.5 and empirically estimate smoke PM2.5–mortality relationships using data on all recorded deaths in the USA. We project that smoke PM2.5 could result in 71,420 excess deaths (95% confidence interval: 34,930–98,430) per year by 2050 under a high-warming scenario (shared socioeconomic pathway scenario 3-7.0, SSP3-7.0)—a 73% increase relative to the estimated 2011–2020 average annual excess deaths from smoke. Cumulative excess deaths from smoke PM2.5 could reach 1.9 million between 2026 and 2055. We find evidence for mortality impacts of smoke PM2.5 that last up to 3 years after exposure. When monetized, climate-driven smoke deaths result in economic damages that exceed existing estimates of climate-driven damages from all other causes combined in the USA4,5. Our research suggests that the health impacts of climate-driven wildfire smoke could be among the most important and costly consequences of a warming climate in the USA.

@article{bourget_data_2024,
	title = {A data science approach to climate change risk assessment applied to pluvial flood occurrences for the {United} {States} and {Canada}},
	issn = {0515-0361, 1783-1350},
	url = {https://www.cambridge.org/core/journals/astin-bulletin-journal-of-the-iaa/article/data-science-approach-to-climate-change-risk-assessment-applied-to-pluvial-flood-occurrences-for-the-united-states-and-canada/E28763E2E9F9AFC003CF6806534E47F7},
	doi = {10.1017/asb.2024.19},
	abstract = {There is mounting pressure on (re)insurers to quantify the impacts of climate change, notably on the frequency and severity of claims due to weather events such as flooding. This is however a very challenging task for (re)insurers as it requires modeling at the scale of a portfolio and at a high enough spatial resolution to incorporate local climate change effects. In this paper, we introduce a data science approach to climate change risk assessment of pluvial flooding for insurance portfolios over Canada and the United States (US). The underlying flood occurrence model quantifies the financial impacts of short-term (12–48 h) precipitation dynamics over the present (2010–2030) and future climate (2040–2060) by leveraging statistical/machine learning and regional climate models. The flood occurrence model is designed for applications that do not require street-level precision as is often the case for scenario and trend analyses. It is applied at the full scale of Canada and the US over 10–25 km grids. Our analyses show that climate change and urbanization will typically increase losses over Canada and the US, while impacts are strongly heterogeneous from one state or province to another, or even within a territory. Portfolio applications highlight the importance for a (re)insurer to differentiate between future changes in hazard and exposure, as the latter may magnify or attenuate the impacts of climate change on losses.},
	language = {en},
	urldate = {2024-08-23},
	journal = {ASTIN Bulletin: The Journal of the IAA},
	author = {Bourget, Mathilde and Boudreault, Mathieu and Carozza, David A. and Boudreault, Jérémie and Raymond, Sébastien},
	month = may,
	year = {2024},
	keywords = {NALCMS},
	pages = {1--23},
}

There is mounting pressure on (re)insurers to quantify the impacts of climate change, notably on the frequency and severity of claims due to weather events such as flooding. This is however a very challenging task for (re)insurers as it requires modeling at the scale of a portfolio and at a high enough spatial resolution to incorporate local climate change effects. In this paper, we introduce a data science approach to climate change risk assessment of pluvial flooding for insurance portfolios over Canada and the United States (US). The underlying flood occurrence model quantifies the financial impacts of short-term (12–48 h) precipitation dynamics over the present (2010–2030) and future climate (2040–2060) by leveraging statistical/machine learning and regional climate models. The flood occurrence model is designed for applications that do not require street-level precision as is often the case for scenario and trend analyses. It is applied at the full scale of Canada and the US over 10–25 km grids. Our analyses show that climate change and urbanization will typically increase losses over Canada and the US, while impacts are strongly heterogeneous from one state or province to another, or even within a territory. Portfolio applications highlight the importance for a (re)insurer to differentiate between future changes in hazard and exposure, as the latter may magnify or attenuate the impacts of climate change on losses.

@article{petrakis_ensemble_2024,
	title = {An {Ensemble} {Mean} {Method} for {Remote} {Sensing} of {Actual} {Evapotranspiration} to {Estimate} {Water} {Budget} {Response} across a {Restoration} {Landscape}},
	volume = {16},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/16/12/2122},
	doi = {10.3390/rs16122122},
	abstract = {Estimates of actual evapotranspiration (ETa) are valuable for effective monitoring and management of water resources. In areas that lack ground-based monitoring networks, remote sensing allows for accurate and consistent estimates of ETa across a broad scale—though each algorithm has limitations (i.e., ground-based validation, temporal consistency, spatial resolution). We developed an ensemble mean ETa (EMET) product to incorporate advancements and reduce uncertainty among algorithms (e.g., energy-balance, optical-only), which we use to estimate vegetative water use in response to restoration practices being implemented on the ground using management interventions (i.e., fencing pastures, erosion control structures) on a private ranch in Baja California Sur, Mexico. This paper describes the development of a monthly EMET product, the assessment of changes using EMET over time and across multiple land use/land cover types, and the evaluation of differences in vegetation and water distribution between watersheds treated by restoration and their controls. We found that in the absence of a ground-based monitoring network, the EMET product is more robust than using a single ETa data product and can augment the efficacy of ETa-based studies. We then found increased ETa within the restored watershed when compared to the control sites, which we attribute to increased plant water availability.},
	language = {en},
	number = {12},
	urldate = {2024-06-28},
	journal = {Remote Sensing},
	author = {Petrakis, Roy E. and Norman, Laura M. and Villarreal, Miguel L. and Senay, Gabriel B. and Friedrichs, MacKenzie O. and Cassassuce, Florance and Gomis, Florent and Nagler, Pamela L.},
	month = jan,
	year = {2024},
	note = {Number: 12
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {NALCMS},
	pages = {2122},
}

Estimates of actual evapotranspiration (ETa) are valuable for effective monitoring and management of water resources. In areas that lack ground-based monitoring networks, remote sensing allows for accurate and consistent estimates of ETa across a broad scale—though each algorithm has limitations (i.e., ground-based validation, temporal consistency, spatial resolution). We developed an ensemble mean ETa (EMET) product to incorporate advancements and reduce uncertainty among algorithms (e.g., energy-balance, optical-only), which we use to estimate vegetative water use in response to restoration practices being implemented on the ground using management interventions (i.e., fencing pastures, erosion control structures) on a private ranch in Baja California Sur, Mexico. This paper describes the development of a monthly EMET product, the assessment of changes using EMET over time and across multiple land use/land cover types, and the evaluation of differences in vegetation and water distribution between watersheds treated by restoration and their controls. We found that in the absence of a ground-based monitoring network, the EMET product is more robust than using a single ETa data product and can augment the efficacy of ETa-based studies. We then found increased ETa within the restored watershed when compared to the control sites, which we attribute to increased plant water availability.

@article{clelland_annual_2024,
	title = {Annual and {Seasonal} {Patterns} of {Burned} {Area} {Products} in {Arctic}-{Boreal} {North} {America} and {Russia} for 2001–2020},
	volume = {16},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/16/17/3306},
	doi = {10.3390/rs16173306},
	abstract = {Boreal and Arctic regions have warmed up to four times quicker than the rest of the planet since the 1970s. As a result, boreal and tundra ecosystems are experiencing more frequent and higher intensity extreme weather events and disturbances, such as wildfires. Yet limitations in ground and satellite data across the Arctic and boreal regions have challenged efforts to track these disturbances at regional scales. In order to effectively monitor the progression and extent of wildfires in the Arctic-boreal zone, it is essential to determine whether burned area (BA) products are accurate representations of BA. Here, we use 12 different datasets together with MODIS active fire data to determine the total yearly BA and seasonal patterns of fires in Arctic-boreal North America and Russia for the years 2001–2020. We found relatively little variability between the datasets in North America, both in terms of total BA and seasonality, with an average BA of 2.55 ± 1.24 (standard deviation) Mha/year for our analysis period, the majority (ca. 41\%) of which occurs in July. In contrast, in Russia, there are large disparities between the products—GFED5 produces over four times more BA than GFED4s in southern Siberia. These disparities occur due to the different methodologies used; dNBR (differenced Normalized Burn Ratio) of short-term composites from Landsat images used alongside hotspot data was the most consistently successful in representing BA. We stress caution using GABAM in these regions, especially for the years 2001–2013, as Landsat-7 ETM+ scan lines are mistaken as burnt patches, increasing errors of commission. On the other hand, we highlight using regional products where possible, such as ABoVE-FED or ABBA in North America, and the Talucci et al. fire perimeter product in Russia, due to their detection of smaller fires which are often missed by global products.},
	language = {en},
	number = {17},
	urldate = {2025-01-27},
	journal = {Remote Sensing},
	author = {Clelland, Andrew A. and Marshall, Gareth J. and Baxter, Robert and Potter, Stefano and Talucci, Anna C. and Rady, Joshua M. and Genet, Hélène and Rogers, Brendan M. and Natali, Susan M.},
	month = jan,
	year = {2024},
	note = {Number: 17
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {NALCMS},
	pages = {3306},
}

Boreal and Arctic regions have warmed up to four times quicker than the rest of the planet since the 1970s. As a result, boreal and tundra ecosystems are experiencing more frequent and higher intensity extreme weather events and disturbances, such as wildfires. Yet limitations in ground and satellite data across the Arctic and boreal regions have challenged efforts to track these disturbances at regional scales. In order to effectively monitor the progression and extent of wildfires in the Arctic-boreal zone, it is essential to determine whether burned area (BA) products are accurate representations of BA. Here, we use 12 different datasets together with MODIS active fire data to determine the total yearly BA and seasonal patterns of fires in Arctic-boreal North America and Russia for the years 2001–2020. We found relatively little variability between the datasets in North America, both in terms of total BA and seasonality, with an average BA of 2.55 ± 1.24 (standard deviation) Mha/year for our analysis period, the majority (ca. 41%) of which occurs in July. In contrast, in Russia, there are large disparities between the products—GFED5 produces over four times more BA than GFED4s in southern Siberia. These disparities occur due to the different methodologies used; dNBR (differenced Normalized Burn Ratio) of short-term composites from Landsat images used alongside hotspot data was the most consistently successful in representing BA. We stress caution using GABAM in these regions, especially for the years 2001–2013, as Landsat-7 ETM+ scan lines are mistaken as burnt patches, increasing errors of commission. On the other hand, we highlight using regional products where possible, such as ABoVE-FED or ABBA in North America, and the Talucci et al. fire perimeter product in Russia, due to their detection of smaller fires which are often missed by global products.

@article{k_c_assessing_2024,
	title = {Assessing {Land}-{Cover} {Change} {Trends}, {Patterns}, and {Transitions} in {Coalfield} {Counties} of {Eastern} {Kentucky}, {USA}},
	volume = {13},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2073-445X},
	url = {https://www.mdpi.com/2073-445X/13/9/1541},
	doi = {10.3390/land13091541},
	abstract = {Surface coal mining and reclamation have greatly reshaped eastern Kentucky’s landscape affecting its socioeconomic, environmental and climatic aspects. This study examined the land-cover changes, trends and patterns in Floyd, Knott, Letcher, Magoffin, Martin, Perry, and Pike counties from 2004 to 2019. Using a random forest classifier, land cover was categorized into seven major classes, i.e., water, barren land, developed land, forest, shrubland, herbaceous, and planted/cultivated, majorly based on Landsat images. The Kappa accuracy ranged from 75 to 89\%. The results showed a notable increase in forest area from 5052 sq km to 5305 sq km accompanied by a substantial decrease in barren land from 179 sq km to 91 sq km from 2004 to 2019. These findings demonstrated that reclamation activities positively impacted the forest expansion and reduced the barren land of the study area. Key land-cover transitions included barren land to shrubland/herbaceous, forest to shrubland, and shrubland to forest, indicating vegetation growth from 2004 to 2019. An autocorrelation analysis indicated similar land-cover types clustered together, showing effective forest restoration efforts. As surface coal mining and reclamation significantly influenced the landscapes of the coalfield counties in eastern Kentucky, this study provides a holistic perspective for understanding the repercussions of these transformations, including their effects on humans, society, and environmental health.},
	language = {en},
	number = {9},
	urldate = {2025-01-27},
	journal = {Land},
	author = {K C, Suraj and Gyawali, Buddhi R. and Lucas, Shawn and Antonious, George F. and Chiluwal, Anuj and Zourarakis, Demetrio},
	month = sep,
	year = {2024},
	note = {Number: 9
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {NALCMS},
	pages = {1541},
}

Surface coal mining and reclamation have greatly reshaped eastern Kentucky’s landscape affecting its socioeconomic, environmental and climatic aspects. This study examined the land-cover changes, trends and patterns in Floyd, Knott, Letcher, Magoffin, Martin, Perry, and Pike counties from 2004 to 2019. Using a random forest classifier, land cover was categorized into seven major classes, i.e., water, barren land, developed land, forest, shrubland, herbaceous, and planted/cultivated, majorly based on Landsat images. The Kappa accuracy ranged from 75 to 89%. The results showed a notable increase in forest area from 5052 sq km to 5305 sq km accompanied by a substantial decrease in barren land from 179 sq km to 91 sq km from 2004 to 2019. These findings demonstrated that reclamation activities positively impacted the forest expansion and reduced the barren land of the study area. Key land-cover transitions included barren land to shrubland/herbaceous, forest to shrubland, and shrubland to forest, indicating vegetation growth from 2004 to 2019. An autocorrelation analysis indicated similar land-cover types clustered together, showing effective forest restoration efforts. As surface coal mining and reclamation significantly influenced the landscapes of the coalfield counties in eastern Kentucky, this study provides a holistic perspective for understanding the repercussions of these transformations, including their effects on humans, society, and environmental health.

@article{ogawa_bayesian_2024,
	title = {Bayesian integrated species distribution models for hierarchical resource selection by a soaring bird},
	volume = {82},
	issn = {1574-9541},
	url = {https://www.sciencedirect.com/science/article/pii/S1574954124003297},
	doi = {10.1016/j.ecoinf.2024.102787},
	abstract = {Migratory birds exhibit seasonal geographic range (hereafter, range) dynamics during the annual cycle. Few studies have examined how migratory birds select their habitats for range occupancy at the species level and space use at the individual level simultaneously. We hypothesized that environmental variables directly related to fitness components would affect the range occupancy probabilities of migrants, whereas environment variables related to movements and flights would affect the space use intensities of migrants. We built Bayesian integrated species distribution models (ISDMs) to evaluate the effects of climate conditions, wind conditions, and landcover compositions on the seasonal range dynamics of American white pelicans Pelecanus erythrorhynchos (hereafter, pelican) during summer and winter. The ISDMs estimated the summer range occupancy probabilities of pelicans with Breeding Bird Survey data, winter range occupancy probabilities with Christmas Bird Count data, and summer and winter space-use intensity rates with eBird data jointly. We evaluated the predictive performance of ISDMs using independent datasets of pelican GPS locations. Integrated species distribution models outperformed the occupancy-only models in the predictive performance of occupancy probabilities. Climate conditions had opposite effects on the range occupancy probabilities between the breeding and non-breeding grounds, whereas landcovers had relatively consistent effects on range occupancy probabilities between the seasons.},
	urldate = {2025-01-27},
	journal = {Ecological Informatics},
	author = {Ogawa, Ryo and Wang, Guiming and Burger, L. Wes and Strickland, Bronson K. and Davis, J. Brian and Cunningham, Fred L.},
	month = sep,
	year = {2024},
	keywords = {NALCMS},
	pages = {102787},
}

Migratory birds exhibit seasonal geographic range (hereafter, range) dynamics during the annual cycle. Few studies have examined how migratory birds select their habitats for range occupancy at the species level and space use at the individual level simultaneously. We hypothesized that environmental variables directly related to fitness components would affect the range occupancy probabilities of migrants, whereas environment variables related to movements and flights would affect the space use intensities of migrants. We built Bayesian integrated species distribution models (ISDMs) to evaluate the effects of climate conditions, wind conditions, and landcover compositions on the seasonal range dynamics of American white pelicans Pelecanus erythrorhynchos (hereafter, pelican) during summer and winter. The ISDMs estimated the summer range occupancy probabilities of pelicans with Breeding Bird Survey data, winter range occupancy probabilities with Christmas Bird Count data, and summer and winter space-use intensity rates with eBird data jointly. We evaluated the predictive performance of ISDMs using independent datasets of pelican GPS locations. Integrated species distribution models outperformed the occupancy-only models in the predictive performance of occupancy probabilities. Climate conditions had opposite effects on the range occupancy probabilities between the breeding and non-breeding grounds, whereas landcovers had relatively consistent effects on range occupancy probabilities between the seasons.

@article{singh_bayesian_2024,
	title = {Bayesian physical–statistical retrieval of snow water equivalent and snow depth from {X}- and {Ku}-band synthetic aperture radar – demonstration using airborne {SnowSAr} in {SnowEx}'17},
	volume = {18},
	issn = {1994-0416},
	url = {https://tc.copernicus.org/articles/18/747/2024/},
	doi = {10.5194/tc-18-747-2024},
	abstract = {A physical–statistical framework to estimate snow water equivalent (SWE) and snow depth from synthetic aperture radar (SAR) measurements is presented and applied to four SnowSAR flight-line data sets collected during the SnowEx'2017 field campaign in Grand Mesa, Colorado, USA. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, snow density and snow depth, and physical model parameters. Prior distributions are derived from multilayer snow hydrology predictions driven by downscaled numerical weather prediction (NWP) forecasts. To reduce the signal-to-noise ratio, SnowSAR measurements at 1 m resolution were upscaled by simple averaging to 30 and 90 m resolution. To reduce the number of physical parameters, the multilayer snowpack is transformed for Bayesian inference into an equivalent one- or two-layer snowpack with the same snow mass and volume backscatter. Successful retrievals meeting NASEM (2018) science requirements are defined by absolute convergence backscatter errors ≤1.2 dB and local SnowSAR incidence angles between 30 and 45∘ for X- and Ku-band VV-pol backscatter measurements and were achieved for 75 \% to 87 \% of all grassland pixels with SWE up to 0.7 m and snow depth up to 2 m. SWE retrievals compare well with snow pit observations, showing strong skill in deep snow with average absolute SWE residuals of 5 \%–7 \% (15 \%–18 \%) for the two-layer (one-layer) retrieval algorithm. Furthermore, the spatial distributions of snow depth retrievals vis-à-vis lidar estimates have Bhattacharya coefficients above 94 \% (90 \%) for homogeneous grassland pixels at 30 m (90 m resolution), and values up to 76 \% in mixed forest and grassland areas, indicating that the retrievals closely capture snowpack spatial variability. Because NWP forecasts are available everywhere, the proposed approach could be applied to SWE and snow depth retrievals from a dedicated global snow mission.},
	language = {English},
	number = {2},
	urldate = {2024-03-13},
	journal = {The Cryosphere},
	author = {Singh, Siddharth and Durand, Michael and Kim, Edward and Barros, Ana P.},
	month = feb,
	year = {2024},
	note = {Publisher: Copernicus GmbH},
	keywords = {NALCMS},
	pages = {747--773},
}

A physical–statistical framework to estimate snow water equivalent (SWE) and snow depth from synthetic aperture radar (SAR) measurements is presented and applied to four SnowSAR flight-line data sets collected during the SnowEx'2017 field campaign in Grand Mesa, Colorado, USA. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, snow density and snow depth, and physical model parameters. Prior distributions are derived from multilayer snow hydrology predictions driven by downscaled numerical weather prediction (NWP) forecasts. To reduce the signal-to-noise ratio, SnowSAR measurements at 1 m resolution were upscaled by simple averaging to 30 and 90 m resolution. To reduce the number of physical parameters, the multilayer snowpack is transformed for Bayesian inference into an equivalent one- or two-layer snowpack with the same snow mass and volume backscatter. Successful retrievals meeting NASEM (2018) science requirements are defined by absolute convergence backscatter errors ≤1.2 dB and local SnowSAR incidence angles between 30 and 45∘ for X- and Ku-band VV-pol backscatter measurements and were achieved for 75 % to 87 % of all grassland pixels with SWE up to 0.7 m and snow depth up to 2 m. SWE retrievals compare well with snow pit observations, showing strong skill in deep snow with average absolute SWE residuals of 5 %–7 % (15 %–18 %) for the two-layer (one-layer) retrieval algorithm. Furthermore, the spatial distributions of snow depth retrievals vis-à-vis lidar estimates have Bhattacharya coefficients above 94 % (90 %) for homogeneous grassland pixels at 30 m (90 m resolution), and values up to 76 % in mixed forest and grassland areas, indicating that the retrievals closely capture snowpack spatial variability. Because NWP forecasts are available everywhere, the proposed approach could be applied to SWE and snow depth retrievals from a dedicated global snow mission.

@incollection{tecuapetla-gomez_caracterizacion_2024,
	title = {Caracterización de series de tiempo de {NDVI} en bosques mexicanos vía análisis funcional de datos aplicado a imágenes de satélite},
	isbn = {978-607-27-2346-7},
	url = {https://www.selper.org.mx/wp-content/uploads/2024/07/14.-Aplicaciones-de-la-Geomatica-AC-1.pdf},
	language = {Español},
	booktitle = {Aplicaciones geomáticas en el estudio de la sustentabilidad},
	publisher = {Universidad Autónoma de Nuevo León},
	author = {Tecuapetla-Gómez, Inder Rafael and Galicia Gómez, Berenice Fanny and Rosales Marticorena, Luis Francisco},
	year = {2024},
	keywords = {NALCMS},
}

@phdthesis{hasan_creating_2024,
	title = {Creating an urban heat vulnerability index ({HVI}) in the face of climate change employing geospatial technology in {Halifax}, {Canada}},
	url = {http://library2.smu.ca/xmlui/handle/01/31900},
	abstract = {Heat waves are one of the most common weather events happening in recent decades,
posing threats to public health especially in urban built-up environments. This study employs geospatial techniques to evaluate urban heat vulnerability in the city of Halifax,
Nova Scotia, Canada. The Heat Vulnerability Index (HVI) was developed through the
utilization of the Geographic Information System (GIS), integrating exposure, sensitivity,
and adaptive capacity measures generated using Remote Sensing (GIS) and socioeconomic datasets for four years covering: 2006, 2011, 2016, 2021. The process applies an Equal Weight Approach (EWA) to assign equal importance to the 16 normalized variables considered in creating the comprehensive HVI. The overarching goal of this study was to assess heat vulnerability at a local level by offering a detailed analysis of these 16 proposed indicators in an urban setting. The results revealed that the HVI
attained its peak in the year 2021, exhibiting a variable trajectory in its scores, with all
years demonstrating a significant high-risk zone encompassing the regional center.
Findings may enable multiple stakeholders to understand spatial variability of
temperature anomalies at local level and may identify vulnerable populations at risks.},
	language = {en},
	urldate = {2024-08-12},
	author = {Hasan, Md Mehedi},
	month = mar,
	year = {2024},
	note = {Accepted: 2024-04-25T11:57:44Z
Publisher: Halifax, N.S. : Saint Mary’s University},
	keywords = {NALCMS},
}

Heat waves are one of the most common weather events happening in recent decades, posing threats to public health especially in urban built-up environments. This study employs geospatial techniques to evaluate urban heat vulnerability in the city of Halifax, Nova Scotia, Canada. The Heat Vulnerability Index (HVI) was developed through the utilization of the Geographic Information System (GIS), integrating exposure, sensitivity, and adaptive capacity measures generated using Remote Sensing (GIS) and socioeconomic datasets for four years covering: 2006, 2011, 2016, 2021. The process applies an Equal Weight Approach (EWA) to assign equal importance to the 16 normalized variables considered in creating the comprehensive HVI. The overarching goal of this study was to assess heat vulnerability at a local level by offering a detailed analysis of these 16 proposed indicators in an urban setting. The results revealed that the HVI attained its peak in the year 2021, exhibiting a variable trajectory in its scores, with all years demonstrating a significant high-risk zone encompassing the regional center. Findings may enable multiple stakeholders to understand spatial variability of temperature anomalies at local level and may identify vulnerable populations at risks.

@phdthesis{kalashnikov_dry_2024,
	type = {Doctor of {Philosophy} ({PhD})},
	title = {Dry {Lightning} in the {Western} {United} {States}: {Meteorological} {Conditions}, {Wildfire} {Ignition}, {Air} {Quality} {Impacts}, and {Future} {Projections}},
	copyright = {Creative Commons Attribution 4.0 International, Open},
	shorttitle = {{DRY} {LIGHTNING} {IN} {THE} {WESTERN} {UNITED} {STATES}},
	url = {https://rex.libraries.wsu.edu/esploro/outputs/doctoral/99901120940501842},
	abstract = {Cloud-to-ground lightning occurring with little to no rainfall – typically referred to as “dry lightning” – is a major source of wildfire ignition in the western United States (WUS) during summer months. Although lightning-caused wildfires occur naturally and are generally ecologically beneficial, changing climatic conditions are increasing the risk of large and severe wildfires. Motivated by these impacts, my dissertation aims to advance our understanding of dry lightning in the WUS and its associated meteorological conditions, wildfire ignitions, air quality impacts, and future projections. In the first chapter, I provide an overview of the motivations for undertaking this dissertation. In the second chapter, I use gridded air pollutant and population data to examine compound air pollution episodes in the WUS. This study demonstrates an increase in the co-occurrence of two harmful air pollutants (fine particulate matter and ground-level ozone) during the WUS wildfire season in the past two decades, and increasing population exposure to these co-occurrences of 25 million person-days per year largely driven by increasing wildfire activity. I show that the largest population exposure to co-occurring air pollution was associated with the major outbreak of dry lightning that ignited hundreds of wildfires in California during August, 2020. To better understand dry lightning in this fire-prone region, in the third chapter I examine the meteorological and geographical factors associated with dry lightning in central and northern California. I apply k-means clustering to atmospheric reanalysis data to identify four types of meteorological patterns associated with the largest dry lightning outbreaks over this region, and quantify the spatial patterns of enhanced dry lightning risk associated with each pattern. In the fourth chapter, I use radar-derived rainfall data and gridded climatological variables to investigate the precipitation amounts and biophysical factors associated with lightning-caused wildfire ignitions across the WUS. Critically, my results refine the widely-used \<2.5mm precipitation amount to define dry lightning by demonstrating that substantial regional variation exists in ignition-relevant precipitation amounts depending on local topography, vegetation, and climate. In the fifth chapter, I use Convolutional Neural Networks (CNNs) to predict cloud-to-ground lightning in the WUS at the grid cell level using a suite of reanalysis-derived meteorological variables as predictors. The CNNs are skillful at predicting lightning (domain-median AUC = 0.8) and realistically capture the year-to-year variation of lightning activity across the WUS (domain-median interannual correlation = 0.87). The CNN-based predictive models developed in this study can be applied to output from global climate models, thus enabling the ability to project future lightning and lightning-caused wildfires. In the final chapter, I summarize my findings from the four studies that comprise my dissertation. The outcomes of my research can be useful to forecasters and fire managers to anticipate possible wildfire ignitions in the present climate, and can be used to inform planning, management, and policy decisions around future lightning-caused wildfires in the WUS.},
	language = {en},
	urldate = {2024-08-23},
	school = {Washington State University},
	author = {Kalashnikov, Dmitri A.},
	collaborator = {Singh, Deepti and Moffett, Kevan B and Walden, Von P and Loikith, Paul C},
	year = {2024},
	note = {Medium: application/pdf
Publisher: Washington State University},
	keywords = {NALCMS},
}

Cloud-to-ground lightning occurring with little to no rainfall – typically referred to as “dry lightning” – is a major source of wildfire ignition in the western United States (WUS) during summer months. Although lightning-caused wildfires occur naturally and are generally ecologically beneficial, changing climatic conditions are increasing the risk of large and severe wildfires. Motivated by these impacts, my dissertation aims to advance our understanding of dry lightning in the WUS and its associated meteorological conditions, wildfire ignitions, air quality impacts, and future projections. In the first chapter, I provide an overview of the motivations for undertaking this dissertation. In the second chapter, I use gridded air pollutant and population data to examine compound air pollution episodes in the WUS. This study demonstrates an increase in the co-occurrence of two harmful air pollutants (fine particulate matter and ground-level ozone) during the WUS wildfire season in the past two decades, and increasing population exposure to these co-occurrences of 25 million person-days per year largely driven by increasing wildfire activity. I show that the largest population exposure to co-occurring air pollution was associated with the major outbreak of dry lightning that ignited hundreds of wildfires in California during August, 2020. To better understand dry lightning in this fire-prone region, in the third chapter I examine the meteorological and geographical factors associated with dry lightning in central and northern California. I apply k-means clustering to atmospheric reanalysis data to identify four types of meteorological patterns associated with the largest dry lightning outbreaks over this region, and quantify the spatial patterns of enhanced dry lightning risk associated with each pattern. In the fourth chapter, I use radar-derived rainfall data and gridded climatological variables to investigate the precipitation amounts and biophysical factors associated with lightning-caused wildfire ignitions across the WUS. Critically, my results refine the widely-used <2.5mm precipitation amount to define dry lightning by demonstrating that substantial regional variation exists in ignition-relevant precipitation amounts depending on local topography, vegetation, and climate. In the fifth chapter, I use Convolutional Neural Networks (CNNs) to predict cloud-to-ground lightning in the WUS at the grid cell level using a suite of reanalysis-derived meteorological variables as predictors. The CNNs are skillful at predicting lightning (domain-median AUC = 0.8) and realistically capture the year-to-year variation of lightning activity across the WUS (domain-median interannual correlation = 0.87). The CNN-based predictive models developed in this study can be applied to output from global climate models, thus enabling the ability to project future lightning and lightning-caused wildfires. In the final chapter, I summarize my findings from the four studies that comprise my dissertation. The outcomes of my research can be useful to forecasters and fire managers to anticipate possible wildfire ignitions in the present climate, and can be used to inform planning, management, and policy decisions around future lightning-caused wildfires in the WUS.

@article{yu_enhancing_2024,
	title = {Enhancing long short-term memory ({LSTM})-based streamflow prediction with a spatially distributed approach},
	volume = {28},
	issn = {1027-5606},
	url = {https://hess.copernicus.org/articles/28/2107/2024/},
	doi = {10.5194/hess-28-2107-2024},
	abstract = {Deep learning (DL) algorithms have previously demonstrated their effectiveness in streamflow prediction. However, in hydrological time series modelling, the performance of existing DL methods is often bound by limited spatial information, as these data-driven models are typically trained with lumped (spatially aggregated) input data. In this study, we propose a hybrid approach, namely the Spatially Recursive (SR) model, that integrates a lumped long short-term memory (LSTM) network seamlessly with a physics-based hydrological routing simulation for enhanced streamflow prediction. The lumped LSTM was trained on the basin-averaged meteorological and hydrological variables derived from 141 gauged basins located in the Great Lakes region of North America. The SR model involves applying the trained LSTM at the subbasin scale for local streamflow predictions which are then translated to the basin outlet by the hydrological routing model. We evaluated the efficacy of the SR model with respect to predicting streamflow at 224 gauged stations across the Great Lakes region and compared its performance to that of the standalone lumped LSTM model. The results indicate that the SR model achieved performance levels on par with the lumped LSTM in basins used for training the LSTM. Additionally, the SR model was able to predict streamflow more accurately on large basins (e.g., drainage area greater than 2000 km2), underscoring the substantial information loss associated with basin-wise feature aggregation. Furthermore, the SR model outperformed the lumped LSTM when applied to basins that were not part of the LSTM training (i.e., pseudo-ungauged basins). The implication of this study is that the lumped LSTM predictions, especially in large basins and ungauged basins, can be reliably improved by considering spatial heterogeneity at finer resolution via the SR model.},
	language = {English},
	number = {9},
	urldate = {2024-06-03},
	journal = {Hydrology and Earth System Sciences},
	author = {Yu, Qiutong and Tolson, Bryan A. and Shen, Hongren and Han, Ming and Mai, Juliane and Lin, Jimmy},
	month = may,
	year = {2024},
	note = {Publisher: Copernicus GmbH},
	keywords = {NALCMS},
	pages = {2107--2122},
}

Deep learning (DL) algorithms have previously demonstrated their effectiveness in streamflow prediction. However, in hydrological time series modelling, the performance of existing DL methods is often bound by limited spatial information, as these data-driven models are typically trained with lumped (spatially aggregated) input data. In this study, we propose a hybrid approach, namely the Spatially Recursive (SR) model, that integrates a lumped long short-term memory (LSTM) network seamlessly with a physics-based hydrological routing simulation for enhanced streamflow prediction. The lumped LSTM was trained on the basin-averaged meteorological and hydrological variables derived from 141 gauged basins located in the Great Lakes region of North America. The SR model involves applying the trained LSTM at the subbasin scale for local streamflow predictions which are then translated to the basin outlet by the hydrological routing model. We evaluated the efficacy of the SR model with respect to predicting streamflow at 224 gauged stations across the Great Lakes region and compared its performance to that of the standalone lumped LSTM model. The results indicate that the SR model achieved performance levels on par with the lumped LSTM in basins used for training the LSTM. Additionally, the SR model was able to predict streamflow more accurately on large basins (e.g., drainage area greater than 2000 km2), underscoring the substantial information loss associated with basin-wise feature aggregation. Furthermore, the SR model outperformed the lumped LSTM when applied to basins that were not part of the LSTM training (i.e., pseudo-ungauged basins). The implication of this study is that the lumped LSTM predictions, especially in large basins and ungauged basins, can be reliably improved by considering spatial heterogeneity at finer resolution via the SR model.

@article{engel_estimating_2024,
	title = {Estimating {Stage}-{Frequency} {Curves} for {Engineering} {Design} in {Small} {Ungauged} {Arctic} {Watersheds}},
	volume = {16},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2073-4441},
	url = {https://www.mdpi.com/2073-4441/16/10/1321},
	doi = {10.3390/w16101321},
	abstract = {The design of hydraulic structures in the Arctic is complicated by shallow relief, which cause unique runoff processes that promote snow-damming and refreeze of runoff. We discuss the challenges encountered in modeling snowmelt runoff into two coastal freshwater lagoons in Utqiaġvik, Alaska. Stage-frequency curves with quantified uncertainty were required to design two new discharge gates that would allow snowmelt runoff flows through a proposed coastal revetment. To estimate runoff hydrographs arriving at the lagoons, we modeled snowpack accumulation and ablation using SnowModel which in turn was used to force a physically-based hydraulic runoff model (HEC-RAS). Our results demonstrate the successful development of stage-frequency curves by incorporating a Monte Carlo simulation approach that quantifies the variability in runoff timing and volume. Our process highlights the complexities of Arctic hydrology by incorporating significant delays in runoff onset due to localized snow accumulation and melting processes. This methodology not only addresses the uncertainty in snow-damming and refreeze processes which affect the arrival time of snowmelt inflow peaks, but is also adaptable for application in other challenging environments where secondary runoff processes are predominant.},
	language = {en},
	number = {10},
	urldate = {2024-06-03},
	journal = {Water},
	author = {Engel, Chandler and Wagner, Anna and Giovando, Jeremy and Ho, David and Morriss, Blaine and Deeb, Elias},
	month = jan,
	year = {2024},
	note = {Number: 10
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {NALCMS},
	pages = {1321},
}

The design of hydraulic structures in the Arctic is complicated by shallow relief, which cause unique runoff processes that promote snow-damming and refreeze of runoff. We discuss the challenges encountered in modeling snowmelt runoff into two coastal freshwater lagoons in Utqiaġvik, Alaska. Stage-frequency curves with quantified uncertainty were required to design two new discharge gates that would allow snowmelt runoff flows through a proposed coastal revetment. To estimate runoff hydrographs arriving at the lagoons, we modeled snowpack accumulation and ablation using SnowModel which in turn was used to force a physically-based hydraulic runoff model (HEC-RAS). Our results demonstrate the successful development of stage-frequency curves by incorporating a Monte Carlo simulation approach that quantifies the variability in runoff timing and volume. Our process highlights the complexities of Arctic hydrology by incorporating significant delays in runoff onset due to localized snow accumulation and melting processes. This methodology not only addresses the uncertainty in snow-damming and refreeze processes which affect the arrival time of snowmelt inflow peaks, but is also adaptable for application in other challenging environments where secondary runoff processes are predominant.

@article{braden_estimating_2024,
	title = {Estimating forest extent across {Mexico}},
	volume = {19},
	issn = {1748-9326},
	url = {https://dx.doi.org/10.1088/1748-9326/ad193e},
	doi = {10.1088/1748-9326/ad193e},
	abstract = {Information on forest extent and tree cover is required to evaluate the status of natural resources, conservation practices, and environmental policies. The challenge is that different forest definitions, remote sensing-based (RSB) products, and data availability can lead to discrepancies in reporting total forest area. Consequently, errors in forest extent can be propagated into forest biomass and carbon estimates. Here, we present a simple approach to compare forest extent estimates from seven regional and global land or tree cover RSB products at 30 m resolution across Mexico. We found substantial differences in forest extent estimates for Mexico, ranging from 387 607 km2 to 675 239 km2. These differences were dependent on the RSB product and forest definition used. Next, we compared these RSB products with two independent forest inventory datasets at national (n = 26 220 plots) and local scales (n = 754 plots). The greatest accuracy among RSB products and forest inventory data was within the tropical moist forest (range 82\%–95\%), and the smallest was within the subtropical desert (range {\textless}10\%–80\%) and subtropical steppe ecological zones (range {\textless}10\%–60\%). We developed a forest extent agreement map by combining seven RSB products and identifying a consensus in their estimates. We found a forest area of 288 749 km2 with high forest extent agreement, and 340 661 km2 with medium forest extent agreement. The high-to-medium forest extent agreement of 629 410 km2 is comparable to the official national estimate of 656 920 km2. We found a high forest extent agreement across the Yucatan Peninsula and mountain areas in the Sierra Madre Oriental and Sierra Madre Occidental. The tropical dry forest and subtropical mountain system represent the two ecological zones with the highest areas of disagreement among RSB products. These findings show discrepancies in forest extent estimates across ecological zones in Mexico, where additional ground data and research are needed. Dataset available at https://doi.org/10.3334/ORNLDAAC/2320.},
	language = {en},
	number = {1},
	urldate = {2024-01-31},
	journal = {Environmental Research Letters},
	author = {Braden, Dustin and Mondal, Pinki and Park, Taejin and Rosa, José Armando Alanís de la and Leal, Metzli Ileana Aldrete and Lara, Rubi Angélica Cuenca and Saucedo, Rafael Mayorga and Paz, Fernando and Salas-Aguilar, Victor Manuel and Soriano-Luna, María de Los Ángeles and Vargas, Rodrigo},
	month = jan,
	year = {2024},
	note = {Publisher: IOP Publishing},
	keywords = {NALCMS},
	pages = {014083},
}

Information on forest extent and tree cover is required to evaluate the status of natural resources, conservation practices, and environmental policies. The challenge is that different forest definitions, remote sensing-based (RSB) products, and data availability can lead to discrepancies in reporting total forest area. Consequently, errors in forest extent can be propagated into forest biomass and carbon estimates. Here, we present a simple approach to compare forest extent estimates from seven regional and global land or tree cover RSB products at 30 m resolution across Mexico. We found substantial differences in forest extent estimates for Mexico, ranging from 387 607 km2 to 675 239 km2. These differences were dependent on the RSB product and forest definition used. Next, we compared these RSB products with two independent forest inventory datasets at national (n = 26 220 plots) and local scales (n = 754 plots). The greatest accuracy among RSB products and forest inventory data was within the tropical moist forest (range 82%–95%), and the smallest was within the subtropical desert (range \textless10%–80%) and subtropical steppe ecological zones (range \textless10%–60%). We developed a forest extent agreement map by combining seven RSB products and identifying a consensus in their estimates. We found a forest area of 288 749 km2 with high forest extent agreement, and 340 661 km2 with medium forest extent agreement. The high-to-medium forest extent agreement of 629 410 km2 is comparable to the official national estimate of 656 920 km2. We found a high forest extent agreement across the Yucatan Peninsula and mountain areas in the Sierra Madre Oriental and Sierra Madre Occidental. The tropical dry forest and subtropical mountain system represent the two ecological zones with the highest areas of disagreement among RSB products. These findings show discrepancies in forest extent estimates across ecological zones in Mexico, where additional ground data and research are needed. Dataset available at https://doi.org/10.3334/ORNLDAAC/2320.

@article{xin_estimation_2024,
	title = {Estimation of {Freshwater} {Discharge} from the {Gulf} of {Alaska} {Drainage} {Basins}},
	volume = {16},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2073-4441},
	url = {https://www.mdpi.com/2073-4441/16/18/2690},
	doi = {10.3390/w16182690},
	abstract = {The freshwater discharge from catchments along the Gulf of Alaska, termed Alaska discharge, is characterized by significant quantity and variability. Owing to subarctic climate and mountainous topography, the Alaska discharge variations may deliver possible impacts beyond the local hydrology. While short-term and local discharge estimation has been frequently realized, a longer time span and a discussion on cascading impacts remain unexplored in this area. In this study, the Alaska discharge during 1982–2022 is estimated using the Soil and Water Assessment Tool (SWAT). The adequate balance between the model complexity and the functional efficiency of SWAT suits the objective well, and discharge simulation is successfully conducted after customization in melting calculations and careful calibrations. During 1982−2022, the Alaska discharge is estimated to be 14,396 ± 819 m3⋅s−1⋅yr−1, with meltwater contributing approximately 53\%. Regarding variation in the Alaska discharge, the interannual change is found to be negatively correlated with sea surface salinity anomalies in the Alaska Stream, while the decadal change positively correlates with the North Pacific Gyre Oscillation, with reasonable time lags in both cases. These new findings provide insights into the relationship between local hydrology and regional climate in this area. More importantly, we provide rare evidence that variation in freshwater discharge may affect properties beyond the local hydrology.},
	language = {en},
	number = {18},
	urldate = {2025-01-27},
	journal = {Water},
	author = {Xin, Peng and Shi, Muqing and Mitsudera, Humio and Shiraiwa, Takayuki},
	month = jan,
	year = {2024},
	note = {Number: 18
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {NALCMS},
	pages = {2690},
}

The freshwater discharge from catchments along the Gulf of Alaska, termed Alaska discharge, is characterized by significant quantity and variability. Owing to subarctic climate and mountainous topography, the Alaska discharge variations may deliver possible impacts beyond the local hydrology. While short-term and local discharge estimation has been frequently realized, a longer time span and a discussion on cascading impacts remain unexplored in this area. In this study, the Alaska discharge during 1982–2022 is estimated using the Soil and Water Assessment Tool (SWAT). The adequate balance between the model complexity and the functional efficiency of SWAT suits the objective well, and discharge simulation is successfully conducted after customization in melting calculations and careful calibrations. During 1982−2022, the Alaska discharge is estimated to be 14,396 ± 819 m3⋅s−1⋅yr−1, with meltwater contributing approximately 53%. Regarding variation in the Alaska discharge, the interannual change is found to be negatively correlated with sea surface salinity anomalies in the Alaska Stream, while the decadal change positively correlates with the North Pacific Gyre Oscillation, with reasonable time lags in both cases. These new findings provide insights into the relationship between local hydrology and regional climate in this area. More importantly, we provide rare evidence that variation in freshwater discharge may affect properties beyond the local hydrology.

@article{barnhart_evaluating_2024,
	title = {Evaluating {Distributed} {Snow} {Model} {Resolution} and {Meteorology} {Parameterizations} {Against} {Streamflow} {Observations}: {Finer} {Is} {Not} {Always} {Better}},
	volume = {60},
	copyright = {Published 2024. This article is a U.S. Government work and is in the public domain in the USA.},
	issn = {1944-7973},
	shorttitle = {Evaluating {Distributed} {Snow} {Model} {Resolution} and {Meteorology} {Parameterizations} {Against} {Streamflow} {Observations}},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2023WR035982},
	doi = {10.1029/2023WR035982},
	abstract = {Estimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub-meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management-relevant aspects of the hydrograph using non-parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer-resolution simulations are useful.},
	language = {en},
	number = {7},
	urldate = {2025-01-27},
	journal = {Water Resources Research},
	author = {Barnhart, Theodore B. and Putman, Annie L. and Heldmyer, Aaron J. and Rey, David M. and Hammond, John C. and Driscoll, Jessica M. and Sexstone, Graham A.},
	year = {2024},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2023WR035982},
	keywords = {NALCMS},
	pages = {e2023WR035982},
}

Estimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub-meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management-relevant aspects of the hydrograph using non-parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer-resolution simulations are useful.

@article{esperon-rodriguez_evaluating_2024,
	title = {Evaluating the impact of protected areas in lowering extinction risks in a biodiversity hotspot},
	volume = {297},
	issn = {0006-3207},
	url = {https://www.sciencedirect.com/science/article/pii/S0006320724002908},
	doi = {10.1016/j.biocon.2024.110728},
	abstract = {The tropics harbour the highest species number on Earth, making them critical hotspots of biodiversity. However, there is an increased extinction risk towards the tropics, aka the tropical biodiversity crisis. Today, biodiversity loss is considered the main threat humanity faces. Yet, key locations may act as refugia (areas that ensure the persistence of biodiversity under environmental changes). Here, we used a set of five environmental variables (elevation, maximum temperature of the warmest month, temperature annual range, annual precipitation, and the human footprint index) to quantify the main drivers of extinction risk inside and outside protected areas for 7609 species of terrestrial animals and vascular plants distributed across Mexico. We used ordinal logistic regressions to assess drivers of extinction risk, as defined by the IUCN Red List, and evaluate the impact of the existing network of protected areas across Mexico to lower this risk. Outside protected areas, extinction risk was the highest for mammals and the lowest for reptiles. Protected areas had a positive impact by lowering the extinction risk of birds compared to outside these areas, while the opposite was observed for mammals, reptiles, vascular plants, amphibians, and arthropods. Extinction risk was greater in regions with high annual precipitation, high maximum temperature of the warmest month, low temperature annual range, as well as in areas with high elevations and human pressures on the environement. The network of protected areas in Mexico is crucial for the conservation of biodiversity; however, this network requires revision that accounts for climate change scenarios to better protect regions facing the greatest risks.},
	urldate = {2025-01-27},
	journal = {Biological Conservation},
	author = {Esperon-Rodriguez, Manuel and Correa-Metrio, Alex and Beaumont, Linda J. and Baumgartner, John B. and Lenoir, Jonathan},
	month = sep,
	year = {2024},
	keywords = {NALCMS},
	pages = {110728},
}

The tropics harbour the highest species number on Earth, making them critical hotspots of biodiversity. However, there is an increased extinction risk towards the tropics, aka the tropical biodiversity crisis. Today, biodiversity loss is considered the main threat humanity faces. Yet, key locations may act as refugia (areas that ensure the persistence of biodiversity under environmental changes). Here, we used a set of five environmental variables (elevation, maximum temperature of the warmest month, temperature annual range, annual precipitation, and the human footprint index) to quantify the main drivers of extinction risk inside and outside protected areas for 7609 species of terrestrial animals and vascular plants distributed across Mexico. We used ordinal logistic regressions to assess drivers of extinction risk, as defined by the IUCN Red List, and evaluate the impact of the existing network of protected areas across Mexico to lower this risk. Outside protected areas, extinction risk was the highest for mammals and the lowest for reptiles. Protected areas had a positive impact by lowering the extinction risk of birds compared to outside these areas, while the opposite was observed for mammals, reptiles, vascular plants, amphibians, and arthropods. Extinction risk was greater in regions with high annual precipitation, high maximum temperature of the warmest month, low temperature annual range, as well as in areas with high elevations and human pressures on the environement. The network of protected areas in Mexico is crucial for the conservation of biodiversity; however, this network requires revision that accounts for climate change scenarios to better protect regions facing the greatest risks.

@phdthesis{panos_evaluation_2024,
	type = {Master of {Science}},
	title = {Evaluation of avian use of agricultural cover crops during the winter, migration stopover, and the breeding season in {Tennessee}},
	url = {https://trace.tennessee.edu/utk_gradthes/11344},
	school = {University of Tennessee - Knoxville},
	author = {Panos, Brittany},
	month = may,
	year = {2024},
	keywords = {NALCMS},
}

@article{ariano_examining_2024,
	title = {Examining contaminant transport hotspots and their predictability across contrasted watersheds},
	volume = {196},
	issn = {1573-2959},
	url = {https://doi.org/10.1007/s10661-024-13053-8},
	doi = {10.1007/s10661-024-13053-8},
	abstract = {Hydrobiogeochemical processes governing water quantity and quality are highly variable in space and time. Focusing on thirty river locations in Québec, Canada, three water quality hotness indices were used to classify watersheds as contaminant transport hotspots. Concentration and load data for suspended solids (SS), total nitrogen (TN), and total phosphorous (TP) were used to identify transport hotspots, and results were compared across hotness indices with different data requirements. The role of hydroclimatic and physiographic characteristics on the occurrence and temporal persistence of transport hotspots was examined. Results show that the identification of transport hotspots was dependent on both the type of data and the hotness index used. Relationships between temporal and spatial predictors, however, were generally consistent. Annual transport hotspot occurrence was found to be related to temporal characteristics such as the number of dry days, potential evapotranspiration, and snow water equivalent, while hotspot temporal persistence was correlated to landcover characteristics. Stark differences in the identification of SS, TN, and TP transport hotspots were attributed to differences in mobilization processes and provided insights into dominant water and nutrient flowpaths in the studied watersheds. This study highlighted the importance of comparing contaminant dynamics across watersheds even when high-frequency water quality data or discharge data are not available. Characterizing hotspot occurrence and persistence, among hotness indices and water quality parameters, could be useful for watershed managers when identifying problematic watersheds, exploring legacy effects, and establishing a prioritization framework for areas that would benefit from enhanced routine monitoring or targeted mitigation strategies.},
	language = {en},
	number = {10},
	urldate = {2025-01-17},
	journal = {Environmental Monitoring and Assessment},
	author = {Ariano, Sarah S. and Bain, Jamie and Ali, Geneviève},
	month = sep,
	year = {2024},
	keywords = {NALCMS},
	pages = {885},
}

Hydrobiogeochemical processes governing water quantity and quality are highly variable in space and time. Focusing on thirty river locations in Québec, Canada, three water quality hotness indices were used to classify watersheds as contaminant transport hotspots. Concentration and load data for suspended solids (SS), total nitrogen (TN), and total phosphorous (TP) were used to identify transport hotspots, and results were compared across hotness indices with different data requirements. The role of hydroclimatic and physiographic characteristics on the occurrence and temporal persistence of transport hotspots was examined. Results show that the identification of transport hotspots was dependent on both the type of data and the hotness index used. Relationships between temporal and spatial predictors, however, were generally consistent. Annual transport hotspot occurrence was found to be related to temporal characteristics such as the number of dry days, potential evapotranspiration, and snow water equivalent, while hotspot temporal persistence was correlated to landcover characteristics. Stark differences in the identification of SS, TN, and TP transport hotspots were attributed to differences in mobilization processes and provided insights into dominant water and nutrient flowpaths in the studied watersheds. This study highlighted the importance of comparing contaminant dynamics across watersheds even when high-frequency water quality data or discharge data are not available. Characterizing hotspot occurrence and persistence, among hotness indices and water quality parameters, could be useful for watershed managers when identifying problematic watersheds, exploring legacy effects, and establishing a prioritization framework for areas that would benefit from enhanced routine monitoring or targeted mitigation strategies.

@article{pflug_extending_2024,
	title = {Extending the utility of space-borne snow water equivalent observations over vegetated areas with data assimilation},
	volume = {28},
	issn = {1027-5606},
	url = {https://hess.copernicus.org/articles/28/631/2024/},
	doi = {10.5194/hess-28-631-2024},
	abstract = {Snow is a vital component of the earth system, yet no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use a fraternal twin observing system simulation experiment, specifically investigating how much snow simulated using widely used models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24∘×37∘ domain in the western USA and Canada, simulating snow at 250 m resolution and hourly time steps in water year 2019. We perform two data assimilation experiments, including (1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals and (2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that, relative to a nature run, or assumed true simulation of snow evolution, assimilating synthetic SWE observations improved average SWE biases at maximum snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 \%, to within 1 \%. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at the time of maximum snow volume were 111 mm and average SWE biases were on the order of 150 \%. Here the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 \%) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River region, melt-season SWE biases were improved from 63 \% to within 1 \%, and the Nash–Sutcliffe efficiency of runoff improved from −2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability.},
	language = {English},
	number = {3},
	urldate = {2024-03-13},
	journal = {Hydrology and Earth System Sciences},
	author = {Pflug, Justin M. and Wrzesien, Melissa L. and Kumar, Sujay V. and Cho, Eunsang and Arsenault, Kristi R. and Houser, Paul R. and Vuyovich, Carrie M.},
	month = feb,
	year = {2024},
	note = {Publisher: Copernicus GmbH},
	keywords = {NALCMS},
	pages = {631--648},
}

Snow is a vital component of the earth system, yet no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use a fraternal twin observing system simulation experiment, specifically investigating how much snow simulated using widely used models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24∘×37∘ domain in the western USA and Canada, simulating snow at 250 m resolution and hourly time steps in water year 2019. We perform two data assimilation experiments, including (1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals and (2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that, relative to a nature run, or assumed true simulation of snow evolution, assimilating synthetic SWE observations improved average SWE biases at maximum snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 %, to within 1 %. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at the time of maximum snow volume were 111 mm and average SWE biases were on the order of 150 %. Here the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 %) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River region, melt-season SWE biases were improved from 63 % to within 1 %, and the Nash–Sutcliffe efficiency of runoff improved from −2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability.

@phdthesis{moore_factors_2024,
	title = {Factors influencing the diversity and abundance of grassland birds in the {Waterton} {Foothills} {Parkland}, {Alberta}, {Canada}},
	url = {http://hdl.handle.net/1993/38628},
	abstract = {Grassland birds are among the most imperilled taxa in North America, with declines being primarily associated with the widespread conversion of grasslands for agriculture. However, alteration of existing vegetation communities and the landscape context in which habitats occur can be important factors affecting the use of remaining grassland habitats by birds. The Waterton Foothills Parkland region in southern Alberta, Canada is characterized by complex topography consisting of native grassland-forest-wetland mosaics. In this thesis, I used naturally occurring landscape heterogeneity and management-relevant habitat metrics to evaluate multi-scale ecological responses of grassland birds. I evaluated how the presence, abundance, and diversity of grassland birds responded to their habitat using Autonomous Recording Units, in-field vegetation surveys, and spatial landscape assessments. I found that the grassland bird community mostly responded to grassland fragmentation and topography within 400 to 800 m extents. Fragmentation increased the total diversity of grassland birds because spillover of facultative grassland birds at edges was greater than decreases in the diversity of a limited pool of obligate species in more fragmented landscapes. Sites with more rugged terrain was autocorrelated with native grasslands, suggesting that landscape-scale responses of grassland birds to terrain ruggedness were associated with the distribution of different grassland types. Local measures of grassland type represented by the percent composition of non-native species and rangeland health were key determinants of the grassland bird community. Healthy, native grasslands supported higher abundances of Vesper and Clay-coloured Sparrows and unhealthy, modified grasslands supported more Savannah Sparrows and Western Meadowlarks. This research supports that sections and quarter-sections of land are informative units for examining the abundance and diversity of grassland birds in the Waterton Foothills Parkland based on responses of the grassland community to grassland fragmentation, topography, non-native vegetation invasion, and range health.},
	language = {eng},
	urldate = {2024-11-20},
	author = {Moore, Zachary},
	month = aug,
	year = {2024},
	keywords = {NALCMS},
}

Grassland birds are among the most imperilled taxa in North America, with declines being primarily associated with the widespread conversion of grasslands for agriculture. However, alteration of existing vegetation communities and the landscape context in which habitats occur can be important factors affecting the use of remaining grassland habitats by birds. The Waterton Foothills Parkland region in southern Alberta, Canada is characterized by complex topography consisting of native grassland-forest-wetland mosaics. In this thesis, I used naturally occurring landscape heterogeneity and management-relevant habitat metrics to evaluate multi-scale ecological responses of grassland birds. I evaluated how the presence, abundance, and diversity of grassland birds responded to their habitat using Autonomous Recording Units, in-field vegetation surveys, and spatial landscape assessments. I found that the grassland bird community mostly responded to grassland fragmentation and topography within 400 to 800 m extents. Fragmentation increased the total diversity of grassland birds because spillover of facultative grassland birds at edges was greater than decreases in the diversity of a limited pool of obligate species in more fragmented landscapes. Sites with more rugged terrain was autocorrelated with native grasslands, suggesting that landscape-scale responses of grassland birds to terrain ruggedness were associated with the distribution of different grassland types. Local measures of grassland type represented by the percent composition of non-native species and rangeland health were key determinants of the grassland bird community. Healthy, native grasslands supported higher abundances of Vesper and Clay-coloured Sparrows and unhealthy, modified grasslands supported more Savannah Sparrows and Western Meadowlarks. This research supports that sections and quarter-sections of land are informative units for examining the abundance and diversity of grassland birds in the Waterton Foothills Parkland based on responses of the grassland community to grassland fragmentation, topography, non-native vegetation invasion, and range health.

@article{duarte_flood_2024,
	title = {Flood prevention benefits provided by {Canadian} natural ecosystems},
	volume = {70},
	issn = {2212-0416},
	url = {https://www.sciencedirect.com/science/article/pii/S2212041624000779},
	doi = {10.1016/j.ecoser.2024.101670},
	abstract = {The escalating impacts of climate change have heightened concerns about the frequency and severity of natural disasters, particularly extreme flooding events. Future projections underscore the necessity for innovative flood prevention strategies, including broad-scale nature-based solutions. Here, we present the first comprehensive assessment of the flood prevention benefits provided by Canadian natural ecosystems and identify key areas crucial for human well-being. Using spatially explicit modeling, we (1) evaluated the potential runoff retention by natural ecosystems and (2) identified downstream urban and agricultural areas critically dependent on these natural benefits, particularly those in floodplains and close proximity to upstream natural ecosystems. The natural ecosystems within the top 5 \% of sub-basins, representing regions with a high priority for conservation practices aimed at flood prevention, play a crucial role in safeguarding approximately 54 \% (∼6,000 km2) of the total built-up area and 74 \% (∼16,900 km2) of the total cropland situated within floodplains. Additionally, they are positioned upstream of floodplain-based urban zones belonging to 358 population centers, directly benefiting 3.7 million people (∼10 \% of the Canadian population) and indirectly benefiting almost 20.1 million people (∼56 \% of the Canadian population). Moreover, among Canada’s 5.2 million km2 of flood-preventing natural ecosystems, we identified a small fraction (10 \%) whose loss or degradation would result in a significant ({\textgreater}50 \%) increase in runoff. Several of these crucial ecosystems are situated in less populated northern regions, where local governments might want to incentivize conservation initiatives to support flood prevention. Our research underscores the imperative to integrate nature-based solutions into national strategies that consider the results of spatial planning analyses. Establishing other effective area-based conservation measures in the priority regions highlighted in this study can contribute towards reaching current ambitious environmental goals and provide critical flood prevention benefits. Additionally, our methods are transferable to other regions worldwide, leveraging globally available datasets and ensuring computational feasibility.},
	urldate = {2025-01-27},
	journal = {Ecosystem Services},
	author = {Duarte, Gabriela T. and Schuster, Richard and Edwards, Marc and Dallaire, Camille O. and Vári, Ágnes and Mitchell, Matthew G. E.},
	month = dec,
	year = {2024},
	keywords = {NALCMS},
	pages = {101670},
}

The escalating impacts of climate change have heightened concerns about the frequency and severity of natural disasters, particularly extreme flooding events. Future projections underscore the necessity for innovative flood prevention strategies, including broad-scale nature-based solutions. Here, we present the first comprehensive assessment of the flood prevention benefits provided by Canadian natural ecosystems and identify key areas crucial for human well-being. Using spatially explicit modeling, we (1) evaluated the potential runoff retention by natural ecosystems and (2) identified downstream urban and agricultural areas critically dependent on these natural benefits, particularly those in floodplains and close proximity to upstream natural ecosystems. The natural ecosystems within the top 5 % of sub-basins, representing regions with a high priority for conservation practices aimed at flood prevention, play a crucial role in safeguarding approximately 54 % (∼6,000 km2) of the total built-up area and 74 % (∼16,900 km2) of the total cropland situated within floodplains. Additionally, they are positioned upstream of floodplain-based urban zones belonging to 358 population centers, directly benefiting 3.7 million people (∼10 % of the Canadian population) and indirectly benefiting almost 20.1 million people (∼56 % of the Canadian population). Moreover, among Canada’s 5.2 million km2 of flood-preventing natural ecosystems, we identified a small fraction (10 %) whose loss or degradation would result in a significant (\textgreater50 %) increase in runoff. Several of these crucial ecosystems are situated in less populated northern regions, where local governments might want to incentivize conservation initiatives to support flood prevention. Our research underscores the imperative to integrate nature-based solutions into national strategies that consider the results of spatial planning analyses. Establishing other effective area-based conservation measures in the priority regions highlighted in this study can contribute towards reaching current ambitious environmental goals and provide critical flood prevention benefits. Additionally, our methods are transferable to other regions worldwide, leveraging globally available datasets and ensuring computational feasibility.

@article{sanderfoot_hazardous_2024,
	title = {Hazardous wildfire smoke events can alter dawn soundscapes in dry forests of central and eastern {Washington}, {United} {States}},
	volume = {54},
	issn = {2351-9894},
	url = {https://www.sciencedirect.com/science/article/pii/S2351989424002488},
	doi = {10.1016/j.gecco.2024.e03044},
	abstract = {As global wildfire activity increases, wildlife are facing greater exposure to hazardous smoke pollution – with unknown consequences for biodiversity. Research on the effects of smoke on wild animals is extremely limited, in part due to the inherent logistical challenges of observing how animals respond to smoke in real time. Passive acoustic monitoring may be a powerful tool to safely and effectively monitor biodiversity before, during, and after major smoke events. In this study, we used data collected from a large-scale network of bioacoustic recorders at 92 sites in central and eastern Washington state during August–September, 2019–2020 to investigate the effect of wildfire smoke on dawn soundscapes and, by extension, acoustically active wildlife. We used acoustic indices to document and characterize changes in soundscapes related to smoke exposure, including the Acoustic Complexity Index (ACI), Bioacoustic Index (BI), and Normalized Difference Soundscape Index (NDSI). Higher values of these indices likely indicate higher levels of biodiversity in our study area. We hypothesized that wildfire smoke would reduce bird vocalizations, leading to declines in ACI, BI, and NDSI at dawn, when birds are most active. We used linear and quantile regression models to test for an effect of daily exposure to fine particulate matter (PM2.5), a marker of wildfire smoke, on the mean daily values and the upper 90th percentile of each index at dawn. We also conducted a before-during-after analysis of a particularly hazardous smoke event that impacted our study area on September 12–14, 2020. We did not observe linear effects of daily PM2.5 on average or peak daily values of acoustic indices; however, we did observe a significant reduction in ACI and BI during the three-day smoke event in 2020 and in the two weeks following this air pollution episode. Our results indicate that, on average, ACI and BI were reduced by 2.7 \% and 15.9 \% during and 1.5 \% and 11.0 \% afterward, respectively. These findings add further evidence that wildfire smoke alters soundscapes, likely due to changes in the presence, abundance, or behavior of acoustically active animals. Furthermore, our study demonstrates that wildfire smoke may have delayed and/or cumulative effects on acoustically active wildlife. Our study highlights the potential for passive acoustic monitoring to document wildlife responses to smoke pollution and identify potentially relevant exposure periods.},
	urldate = {2025-01-27},
	journal = {Global Ecology and Conservation},
	author = {Sanderfoot, Olivia V. and Tingley, Morgan W. and Bassing, Sarah B. and Vaughan, Joseph K. and June, Nicole A. and Gardner, Beth},
	month = oct,
	year = {2024},
	keywords = {NALCMS},
	pages = {e03044},
}

As global wildfire activity increases, wildlife are facing greater exposure to hazardous smoke pollution – with unknown consequences for biodiversity. Research on the effects of smoke on wild animals is extremely limited, in part due to the inherent logistical challenges of observing how animals respond to smoke in real time. Passive acoustic monitoring may be a powerful tool to safely and effectively monitor biodiversity before, during, and after major smoke events. In this study, we used data collected from a large-scale network of bioacoustic recorders at 92 sites in central and eastern Washington state during August–September, 2019–2020 to investigate the effect of wildfire smoke on dawn soundscapes and, by extension, acoustically active wildlife. We used acoustic indices to document and characterize changes in soundscapes related to smoke exposure, including the Acoustic Complexity Index (ACI), Bioacoustic Index (BI), and Normalized Difference Soundscape Index (NDSI). Higher values of these indices likely indicate higher levels of biodiversity in our study area. We hypothesized that wildfire smoke would reduce bird vocalizations, leading to declines in ACI, BI, and NDSI at dawn, when birds are most active. We used linear and quantile regression models to test for an effect of daily exposure to fine particulate matter (PM2.5), a marker of wildfire smoke, on the mean daily values and the upper 90th percentile of each index at dawn. We also conducted a before-during-after analysis of a particularly hazardous smoke event that impacted our study area on September 12–14, 2020. We did not observe linear effects of daily PM2.5 on average or peak daily values of acoustic indices; however, we did observe a significant reduction in ACI and BI during the three-day smoke event in 2020 and in the two weeks following this air pollution episode. Our results indicate that, on average, ACI and BI were reduced by 2.7 % and 15.9 % during and 1.5 % and 11.0 % afterward, respectively. These findings add further evidence that wildfire smoke alters soundscapes, likely due to changes in the presence, abundance, or behavior of acoustically active animals. Furthermore, our study demonstrates that wildfire smoke may have delayed and/or cumulative effects on acoustically active wildlife. Our study highlights the potential for passive acoustic monitoring to document wildlife responses to smoke pollution and identify potentially relevant exposure periods.

@article{shao_invasion_2024,
	title = {Invasion dynamics of the {European} {Collared}-{Dove} in {North} {America} are explained by combined effects of habitat and climate},
	volume = {126},
	issn = {0010-5422},
	url = {https://doi.org/10.1093/ornithapp/duad052},
	doi = {10.1093/ornithapp/duad052},
	abstract = {Global biodiversity is increasingly threatened by the spread of invasive species. Understanding the mechanisms influencing the initial colonization and persistence of invaders is therefore needed if conservation actions are to prevent new invasions or strive to slow their spread. The Eurasian Collared-Dove (Streptopelia decaocto, EUCO) is one of the most successful avian invasive species in North America; however, to our knowledge, no study has simultaneously examined the role that climate-matching, human activity, directional propagation, and local density have in this invasion process. Our research expands upon a cellular-automata-based hierarchical model developed to assess directional invasion dynamics to further quantify the impacts of climate, elevation, and land cover type on the spread of EUCO in North America. Our results suggest that EUCO’s dispersal patterns can largely be explained by the effects of habitat, climate, and environmental conditions at different stages of the invasion process rather than some innate preferred north-westerly spread. Specifically, EUCO initially colonized warm and wet grassland habitats and tended to persist in urban areas. We also found that while EUCO were more likely to spread to the northeast of existing habitats, directional preference did not drive persistence and recolonization events. These findings highlight the importance of incorporating both neighborhood effects and environmental factors in the modelling of range-expanding species, adding to the toolset available to researchers to model invasive species spread. Further, our research demonstrates that historical records of invasive species occurrences can provide the data resources needed to disentangle the characteristics driving species invasion and enable predictions that are of critical importance to resource managers.• The Eurasian Collared-Dove is an invasive species in North America. Starting from the Bahamas in 1974, it has colonized much of the U.S. and Canada.• The Eurasian Collared-Dove tended to spread to the northwest, and previous studies suggested that this was due to an innate preference for this direction. However, these studies did not consider that this might also be explained by habitat selection.• We studied the Eurasian Collared-Dove’s range expansion using data from the Project Feeder Watch citizen science study. Our findings suggest that they prefer grasslands and thrive in areas with higher temperatures, more precipitation, and higher elevations, with little regard for direction.• These results add to our understanding of the invasion process of the Eurasian Collared-Dove in North America, and broadly highlight the importance of historical records to disentangle the characteristics driving species invasion that are important to resource managers. The new methods can also be applied to understand the spread of other invasive species.},
	number = {1},
	urldate = {2024-03-13},
	journal = {Ornithological Applications},
	author = {Shao, Yiran and Ethier, Danielle M and Bonner, Simon J},
	month = feb,
	year = {2024},
	keywords = {NALCMS},
	pages = {duad052},
}

Global biodiversity is increasingly threatened by the spread of invasive species. Understanding the mechanisms influencing the initial colonization and persistence of invaders is therefore needed if conservation actions are to prevent new invasions or strive to slow their spread. The Eurasian Collared-Dove (Streptopelia decaocto, EUCO) is one of the most successful avian invasive species in North America; however, to our knowledge, no study has simultaneously examined the role that climate-matching, human activity, directional propagation, and local density have in this invasion process. Our research expands upon a cellular-automata-based hierarchical model developed to assess directional invasion dynamics to further quantify the impacts of climate, elevation, and land cover type on the spread of EUCO in North America. Our results suggest that EUCO’s dispersal patterns can largely be explained by the effects of habitat, climate, and environmental conditions at different stages of the invasion process rather than some innate preferred north-westerly spread. Specifically, EUCO initially colonized warm and wet grassland habitats and tended to persist in urban areas. We also found that while EUCO were more likely to spread to the northeast of existing habitats, directional preference did not drive persistence and recolonization events. These findings highlight the importance of incorporating both neighborhood effects and environmental factors in the modelling of range-expanding species, adding to the toolset available to researchers to model invasive species spread. Further, our research demonstrates that historical records of invasive species occurrences can provide the data resources needed to disentangle the characteristics driving species invasion and enable predictions that are of critical importance to resource managers.• The Eurasian Collared-Dove is an invasive species in North America. Starting from the Bahamas in 1974, it has colonized much of the U.S. and Canada.• The Eurasian Collared-Dove tended to spread to the northwest, and previous studies suggested that this was due to an innate preference for this direction. However, these studies did not consider that this might also be explained by habitat selection.• We studied the Eurasian Collared-Dove’s range expansion using data from the Project Feeder Watch citizen science study. Our findings suggest that they prefer grasslands and thrive in areas with higher temperatures, more precipitation, and higher elevations, with little regard for direction.• These results add to our understanding of the invasion process of the Eurasian Collared-Dove in North America, and broadly highlight the importance of historical records to disentangle the characteristics driving species invasion that are important to resource managers. The new methods can also be applied to understand the spread of other invasive species.

@phdthesis{krebs_large-scale_2024,
	title = {Large-{Scale} {GIS}-{Modeling} of {Dog}-{Travois} {Transport} {Suitability} of {Landscapes} in {Western} {North} {America}},
	copyright = {http://rightsstatements.org/vocab/InC/1.0/},
	url = {https://repository.arizona.edu/handle/10150/675317},
	abstract = {This thesis analyzes actual and potential long-distance use of the dog-pulled travois in western North America by developing a Geographic Information Systems (GIS) suitability model. The travois, consisting of a wooden A-frame sled originally pulled by dogs and, later, horses, was widely used across the North American Great Plains to facilitate the transport of supplies and trade goods. However, the absence of archaeological evidence makes it difficult to evaluate imperfect ethnographic data and assess how widespread travois use was, or could have been, in ancient times. Historic and experimental data indicate several shortcomings to travois transport based on the terrain it is being used on and the mass and physiology of the dogs used to pull it. Archaeological, historical, and experimental accounts of travois performance are reviewed to model the topographical and ecological limitations of travois-assisted transport. Limitations include, but are not restricted to, the slope (terrain) over which travois can be hauled, the temperature at which the draft dogs become unproductive and overheat, and the effectiveness of travel over specific types of vegetation. GIS modeling is used to assess the large-scale suitability of terrain for travois travel based on these projected limiting factors, and to calculate least-cost paths between select locations on the Great Plains and Intermountain West. Finally, the models produced by these analyses are compared with existing research on travois use and long-distance exchange in the western US to assess concordance with current evidence, elucidate gaps in ethnographic data, and generate predications for regions of possible dog-facilitated travois use. Beyond the enhancement of the limited available ethnographic accounts, this exploratory thesis provides guidance for future investigations of domestic dog use; especially as a template for detailed site-level analyses of travois and dog use on the local landscape, identifying prospective areas for survey and excavation of further archaeological evidence, and refining the understanding of trade interactions and human-dog relationships within and beyond the Great Plains.},
	language = {en},
	urldate = {2024-11-20},
	author = {Krebs, Talon},
	year = {2024},
	note = {Accepted: 2024-09-22T06:01:32Z
Publisher: The University of Arizona.},
	keywords = {NALCMS},
}

This thesis analyzes actual and potential long-distance use of the dog-pulled travois in western North America by developing a Geographic Information Systems (GIS) suitability model. The travois, consisting of a wooden A-frame sled originally pulled by dogs and, later, horses, was widely used across the North American Great Plains to facilitate the transport of supplies and trade goods. However, the absence of archaeological evidence makes it difficult to evaluate imperfect ethnographic data and assess how widespread travois use was, or could have been, in ancient times. Historic and experimental data indicate several shortcomings to travois transport based on the terrain it is being used on and the mass and physiology of the dogs used to pull it. Archaeological, historical, and experimental accounts of travois performance are reviewed to model the topographical and ecological limitations of travois-assisted transport. Limitations include, but are not restricted to, the slope (terrain) over which travois can be hauled, the temperature at which the draft dogs become unproductive and overheat, and the effectiveness of travel over specific types of vegetation. GIS modeling is used to assess the large-scale suitability of terrain for travois travel based on these projected limiting factors, and to calculate least-cost paths between select locations on the Great Plains and Intermountain West. Finally, the models produced by these analyses are compared with existing research on travois use and long-distance exchange in the western US to assess concordance with current evidence, elucidate gaps in ethnographic data, and generate predications for regions of possible dog-facilitated travois use. Beyond the enhancement of the limited available ethnographic accounts, this exploratory thesis provides guidance for future investigations of domestic dog use; especially as a template for detailed site-level analyses of travois and dog use on the local landscape, identifying prospective areas for survey and excavation of further archaeological evidence, and refining the understanding of trade interactions and human-dog relationships within and beyond the Great Plains.

@misc{manitoba_natural_resources_and_northern_development_forestry_and_peatlands_branch_manitobas_2024,
	title = {Manitoba's {Five} {Year} {Report} on the {Status} of {Forestry}},
	url = {https://storymaps.arcgis.com/stories/217b32789dab4446a84e91b6447e5fc6},
	abstract = {April 2016 - March 2021},
	language = {en},
	urldate = {2024-09-13},
	journal = {ArcGIS StoryMaps},
	author = {{Manitoba Natural Resources and Northern Development; Forestry and Peatlands Branch}},
	month = jul,
	year = {2024},
	keywords = {NALCMS},
}

April 2016 - March 2021

@article{shanley_mapping_2024,
	title = {Mapping forest-based natural climate solutions},
	volume = {5},
	copyright = {2024 The Author(s)},
	issn = {2662-4435},
	url = {https://www.nature.com/articles/s43247-024-01678-z},
	doi = {10.1038/s43247-024-01678-z},
	abstract = {Natural climate solutions are critical actions of ecosystem stewardship to mitigate climate change. However, prioritizing locations and possible actions is challenging. We demonstrate a generalizable approach for identifying potential opportunities for natural climate solutions by creating a spatial hierarchy of land management restrictions. Global forest carbon stocks and flux models were then used to explore forest-based natural climate solutions in the high-carbon density coastal temperate rainforests of western North America. Our results show 13 million hectares are available for action, an area that holds 4,900 ± 640 megatonnes of carbon dioxide equivalent and represents 45\% of regional and 0.5\% of global aboveground forest carbon stocks. Based on historical trends, a 10\% reduction in average annual forest carbon loss through improved forest management and conservation could reduce forest carbon emissions by 9.1 megatonnes of carbon dioxide equivalent per year, corresponding to 5.2\% of the 2030 land-based climate commitments made by the United States and Canada. Large-scale implementation of natural climate solutions will require collaborative planning with forest-dependent communities, industry, governments, and Indigenous peoples.},
	language = {en},
	number = {1},
	urldate = {2024-09-13},
	journal = {Communications Earth \& Environment},
	author = {Shanley, Colin S. and Graves, Rose A. and Drever, C. Ronnie and Schindel, Michael and Robertson, James C. and Case, Michael J. and Biswas, Tanushree},
	month = sep,
	year = {2024},
	note = {Publisher: Nature Publishing Group},
	keywords = {NALCMS},
	pages = {1--12},
}

Natural climate solutions are critical actions of ecosystem stewardship to mitigate climate change. However, prioritizing locations and possible actions is challenging. We demonstrate a generalizable approach for identifying potential opportunities for natural climate solutions by creating a spatial hierarchy of land management restrictions. Global forest carbon stocks and flux models were then used to explore forest-based natural climate solutions in the high-carbon density coastal temperate rainforests of western North America. Our results show 13 million hectares are available for action, an area that holds 4,900 ± 640 megatonnes of carbon dioxide equivalent and represents 45% of regional and 0.5% of global aboveground forest carbon stocks. Based on historical trends, a 10% reduction in average annual forest carbon loss through improved forest management and conservation could reduce forest carbon emissions by 9.1 megatonnes of carbon dioxide equivalent per year, corresponding to 5.2% of the 2030 land-based climate commitments made by the United States and Canada. Large-scale implementation of natural climate solutions will require collaborative planning with forest-dependent communities, industry, governments, and Indigenous peoples.

@article{wang_mapping_2024,
	title = {Mapping the distance between fire hazard and disaster for communities in {Canadian} forests},
	volume = {30},
	copyright = {Global Change Biology© 2024 His Majesty the King in Right of Canada and The Authors. Global Change Biology published by John Wiley \& Sons Ltd.  Reproduced with the permission of the Minister of Natural Resources Canada.},
	issn = {1365-2486},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.17221},
	doi = {10.1111/gcb.17221},
	abstract = {Communities interspersed throughout the Canadian wildland are threatened by fires that have become bigger and more frequent in some parts of the country in recent decades. Identifying the fireshed (source area) and pathways from which wildland fire may ignite and spread from the landscape to a community is crucial for risk-reduction strategy and planning. We used outputs from a fire simulation model, including fire polygons and rate of spread, to map firesheds, fire pathways and corridors and spread distances for 1980 communities in the forested areas of Canada. We found fireshed sizes are larger in the north, where the mean distances between ecumene and fireshed perimeters were greater than 10 km. The Rayleigh Z test indicated that simulated fires around a large proportion of communities show significant directional trends, and these trends are stronger in the Boreal Plains and Shields than in the Rocky Mountain area. The average distance from which fire, when spreading at the maximum simulated rate, could reach the community perimeter was approximately 5, 12 and 18 km in 1, 2 and 3 days, respectively. The average daily spread distances increased latitudinally, from south to north. Spread distances were the shortest in the Pacific Maritime, Atlantic Maritime and Boreal Plains Ecozones, implying lower rates of spread compared to the rest of the country. The fire corridors generated from random ignitions and from ignitions predicted from local fire history differ, indicating that factors other than fuel (e.g. fire weather, ignition pattern) play a significant role in determining the direction that fires burn into a community.},
	language = {en},
	number = {3},
	urldate = {2024-03-13},
	journal = {Global Change Biology},
	author = {Wang, Xianli and Swystun, Tom and McFayden, Colin B. and Erni, Sandy and Oliver, Jacqueline and Taylor, Stephen W. and Flannigan, Mike D.},
	year = {2024},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.17221},
	keywords = {NALCMS},
	pages = {e17221},
}

Communities interspersed throughout the Canadian wildland are threatened by fires that have become bigger and more frequent in some parts of the country in recent decades. Identifying the fireshed (source area) and pathways from which wildland fire may ignite and spread from the landscape to a community is crucial for risk-reduction strategy and planning. We used outputs from a fire simulation model, including fire polygons and rate of spread, to map firesheds, fire pathways and corridors and spread distances for 1980 communities in the forested areas of Canada. We found fireshed sizes are larger in the north, where the mean distances between ecumene and fireshed perimeters were greater than 10 km. The Rayleigh Z test indicated that simulated fires around a large proportion of communities show significant directional trends, and these trends are stronger in the Boreal Plains and Shields than in the Rocky Mountain area. The average distance from which fire, when spreading at the maximum simulated rate, could reach the community perimeter was approximately 5, 12 and 18 km in 1, 2 and 3 days, respectively. The average daily spread distances increased latitudinally, from south to north. Spread distances were the shortest in the Pacific Maritime, Atlantic Maritime and Boreal Plains Ecozones, implying lower rates of spread compared to the rest of the country. The fire corridors generated from random ignitions and from ignitions predicted from local fire history differ, indicating that factors other than fuel (e.g. fire weather, ignition pattern) play a significant role in determining the direction that fires burn into a community.

@article{browning_measuring_2024,
	title = {Measuring the 3-30-300 rule to help cities meet nature access thresholds},
	volume = {907},
	issn = {0048-9697},
	url = {https://www.sciencedirect.com/science/article/pii/S0048969723063660},
	doi = {10.1016/j.scitotenv.2023.167739},
	abstract = {The 3-30-300 rule offers benchmarks for cities to promote equitable nature access. It dictates that individuals should see three trees from their dwelling, have 30 \% tree canopy in their neighborhood, and live within 300 m of a high-quality green space. Implementing this demands thorough measurement, monitoring, and evaluation methods, yet little guidance is currently available to pursue these actions. To overcome this gap, we employed an expert-based consensus approach to review the available ways to measure 3-30-300 as well as each measure's strengths and weaknesses. We described seven relevant data and processes: vegetation indices, street level analyses, tree inventories, questionnaires, window view analyses, land cover maps, and green space maps. Based on the reviewed strengths and weaknesses of each measure, we presented a suitability matrix to link recommended measures with each component of the rule. These recommendations included surveys and window-view analyses for the ‘3 component’, high-resolution land cover maps for the ‘30 component’, and green space maps with network analyses for the ‘300 component’. These methods, responsive to local situations and resources, not only implement the 3-30-300 rule but foster broader dialogue on local desires and requirements. Consequently, these techniques can guide strategic investments in urban greening for health, equity, biodiversity, and climate adaptation.},
	urldate = {2023-11-24},
	journal = {Science of The Total Environment},
	author = {Browning, M. H. E. M. and Locke, D. H. and Konijnendijk, C. and Labib, S. M. and Rigolon, A. and Yeager, R. and Bardhan, M. and Berland, A. and Dadvand, P. and Helbich, M. and Li, F. and Li, H. and James, P. and Klompmaker, J. and Reuben, A. and Roman, L. A. and Tsai, W. -L. and Patwary, M. and O'Neil-Dunne, J. and Ossola, A. and Wang, R. and Yang, B. and Yi, L. and Zhang, J. and Nieuwenhuijsen, M.},
	month = jan,
	year = {2024},
	keywords = {NALCMS},
	pages = {167739},
}

The 3-30-300 rule offers benchmarks for cities to promote equitable nature access. It dictates that individuals should see three trees from their dwelling, have 30 % tree canopy in their neighborhood, and live within 300 m of a high-quality green space. Implementing this demands thorough measurement, monitoring, and evaluation methods, yet little guidance is currently available to pursue these actions. To overcome this gap, we employed an expert-based consensus approach to review the available ways to measure 3-30-300 as well as each measure's strengths and weaknesses. We described seven relevant data and processes: vegetation indices, street level analyses, tree inventories, questionnaires, window view analyses, land cover maps, and green space maps. Based on the reviewed strengths and weaknesses of each measure, we presented a suitability matrix to link recommended measures with each component of the rule. These recommendations included surveys and window-view analyses for the ‘3 component’, high-resolution land cover maps for the ‘30 component’, and green space maps with network analyses for the ‘300 component’. These methods, responsive to local situations and resources, not only implement the 3-30-300 rule but foster broader dialogue on local desires and requirements. Consequently, these techniques can guide strategic investments in urban greening for health, equity, biodiversity, and climate adaptation.

@techreport{qiu_mortality_2024,
	type = {Working {Paper}},
	title = {Mortality {Burden} {From} {Wildfire} {Smoke} {Under} {Climate} {Change}},
	url = {https://www.nber.org/papers/w32307},
	abstract = {Wildfire activity has increased in the US and is projected to accelerate under future climate change. However, our understanding of the impacts of climate change on wildfire smoke and health remains highly uncertain. We quantify the past and future mortality burden in the US due to wildfire smoke fine particulate matter (PM2.5). We construct an ensemble of statistical and machine learning models that link variation in climate to wildfire smoke PM2.5, and empirically estimate smoke PM2.5-mortality relationships using georeferenced data on all recorded deaths in the US from 2006 to 2019. We project that climate-driven increases in future smoke PM2.5 could result in 27,800 excess deaths per year by 2050 under a high warming scenario, a 76\% increase relative to estimated 2011-2020 averages. Cumulative excess deaths from wildfire smoke PM2.5 could exceed 700,000 between 2025-2055. When monetized, climate-induced smoke deaths result in annual damages of \$244 billion by mid-century, comparable to the estimated sum of all other damages in the US in prior analyses. Our research suggests that the health cost of climate-driven wildfire smoke could be among the most important and costly consequences of a warming climate in the US.},
	number = {32307},
	urldate = {2024-06-03},
	institution = {National Bureau of Economic Research},
	author = {Qiu, Minghao and Li, Jessica and Gould, Carlos F. and Jing, Renzhi and Kelp, Makoto and Childs, Marissa and Kiang, Mathew and Heft-Neal, Sam and Diffenbaugh, Noah and Burke, Marshall},
	month = apr,
	year = {2024},
	doi = {10.3386/w32307},
	keywords = {NALCMS},
}

Wildfire activity has increased in the US and is projected to accelerate under future climate change. However, our understanding of the impacts of climate change on wildfire smoke and health remains highly uncertain. We quantify the past and future mortality burden in the US due to wildfire smoke fine particulate matter (PM2.5). We construct an ensemble of statistical and machine learning models that link variation in climate to wildfire smoke PM2.5, and empirically estimate smoke PM2.5-mortality relationships using georeferenced data on all recorded deaths in the US from 2006 to 2019. We project that climate-driven increases in future smoke PM2.5 could result in 27,800 excess deaths per year by 2050 under a high warming scenario, a 76% increase relative to estimated 2011-2020 averages. Cumulative excess deaths from wildfire smoke PM2.5 could exceed 700,000 between 2025-2055. When monetized, climate-induced smoke deaths result in annual damages of $244 billion by mid-century, comparable to the estimated sum of all other damages in the US in prior analyses. Our research suggests that the health cost of climate-driven wildfire smoke could be among the most important and costly consequences of a warming climate in the US.

@phdthesis{lee_multi-scale_2024,
	title = {Multi-scale {Patterns} of {Breeding} {Habitat} {Selection} in {Sandhill} {Cranes} {Across} {Canada}’s {Eastern} {Boreal} {Forest}},
	url = {http://hdl.handle.net/10012/20665},
	abstract = {The boreal forest of Canada serves as a critical breeding ground for numerous waterbird species, including sandhill cranes (Antigone canadensis). As sandhill cranes continue to expand their range in the boreal forest, it becomes increasingly important to identify habitat characteristics essential for breeding sandhill cranes for effective conservation and management. Limited research exists regarding the habitat dynamics of breeding sandhill cranes across this vast and remote landscape, necessitating research to understand spatial drivers of territory selection and habitat use in the boreal forest. Using high-resolution satellite telemetry data, we quantified the effects of land cover and land use on breeding habitat selection of sandhill cranes in the boreal forest of Ontario and Quebec, Canada across different scales: the landscape level (i.e., second order selection, which considers the overall landscape within which territories are established) and within the breeding range (i.e., third order selection, which focuses on specific habitat features selected within these territories). At the second order, or landscape level, sandhill cranes established breeding territories containing greater proportions of cropland, recently disturbed areas (e.g., forest cutblocks and burned areas), and wetlands. Sandhill cranes also selected territories with lower proportions of forest, open habitat, and water. At third order, or within their breeding ranges, sandhill cranes selected cropland, wetlands, recently disturbed areas, open habitat, and water, while avoiding forests and urban areas. Our findings suggest that current levels of anthropogenic disturbance do not negatively affect sandhill crane habitat selection, and that wetlands continue to play a crucial role in breeding habitat selection in the boreal forest. However, further research is required to explore the detailed impacts of forestry operations and the selection of recently disturbed areas on breeding behaviour and nest success in sandhill cranes. Our findings highlight the importance of using multi-scale approaches in habitat selection analyses that consider both broad ecological scales and the specific habitat requirements of individuals at the local scale. By comparing habitat use across both landscape and local scales, we demonstrate how sandhill cranes adapt their breeding habitat selection based on the availability and quality of different habitat types, allowing for robust inferences on the mechanisms that drive patterns of habitat selection both within their breeding territories and across the broader landscape. Collectively, this research contributes to the growing body of literature on breeding habitat selection of sandhill cranes, addressing important questions concerning patterns of habitat selection in response to a gradient of land cover and land uses classes in the boreal forest. Findings from this research can be applied to land management practices and assist managers when making inferences about sandhill crane habitat use in the boreal forest. Overall, this empirical approach can also be applied to a variety of species across diverse landscapes to assess how ecological processes differentiate across spatial scales and can support large-scale conservation efforts that ultimately benefits sandhill crane populations and biodiversity conservation in the boreal forest and beyond.},
	language = {en},
	urldate = {2024-08-12},
	author = {Lee, Kiaunna},
	month = jun,
	year = {2024},
	note = {Publisher: University of Waterloo},
	keywords = {NALCMS},
}

The boreal forest of Canada serves as a critical breeding ground for numerous waterbird species, including sandhill cranes (Antigone canadensis). As sandhill cranes continue to expand their range in the boreal forest, it becomes increasingly important to identify habitat characteristics essential for breeding sandhill cranes for effective conservation and management. Limited research exists regarding the habitat dynamics of breeding sandhill cranes across this vast and remote landscape, necessitating research to understand spatial drivers of territory selection and habitat use in the boreal forest. Using high-resolution satellite telemetry data, we quantified the effects of land cover and land use on breeding habitat selection of sandhill cranes in the boreal forest of Ontario and Quebec, Canada across different scales: the landscape level (i.e., second order selection, which considers the overall landscape within which territories are established) and within the breeding range (i.e., third order selection, which focuses on specific habitat features selected within these territories). At the second order, or landscape level, sandhill cranes established breeding territories containing greater proportions of cropland, recently disturbed areas (e.g., forest cutblocks and burned areas), and wetlands. Sandhill cranes also selected territories with lower proportions of forest, open habitat, and water. At third order, or within their breeding ranges, sandhill cranes selected cropland, wetlands, recently disturbed areas, open habitat, and water, while avoiding forests and urban areas. Our findings suggest that current levels of anthropogenic disturbance do not negatively affect sandhill crane habitat selection, and that wetlands continue to play a crucial role in breeding habitat selection in the boreal forest. However, further research is required to explore the detailed impacts of forestry operations and the selection of recently disturbed areas on breeding behaviour and nest success in sandhill cranes. Our findings highlight the importance of using multi-scale approaches in habitat selection analyses that consider both broad ecological scales and the specific habitat requirements of individuals at the local scale. By comparing habitat use across both landscape and local scales, we demonstrate how sandhill cranes adapt their breeding habitat selection based on the availability and quality of different habitat types, allowing for robust inferences on the mechanisms that drive patterns of habitat selection both within their breeding territories and across the broader landscape. Collectively, this research contributes to the growing body of literature on breeding habitat selection of sandhill cranes, addressing important questions concerning patterns of habitat selection in response to a gradient of land cover and land uses classes in the boreal forest. Findings from this research can be applied to land management practices and assist managers when making inferences about sandhill crane habitat use in the boreal forest. Overall, this empirical approach can also be applied to a variety of species across diverse landscapes to assess how ecological processes differentiate across spatial scales and can support large-scale conservation efforts that ultimately benefits sandhill crane populations and biodiversity conservation in the boreal forest and beyond.

@article{prevey_non-native_2024,
	title = {Non-native plant invasion after fire in western {USA} varies by functional type and with climate},
	issn = {1573-1464},
	url = {https://doi.org/10.1007/s10530-023-03235-9},
	doi = {10.1007/s10530-023-03235-9},
	abstract = {Invasions by non-native plant species after fire can negatively affect important ecosystem services and lead to invasion-fire cycles that further degrade ecosystems. The relationship between fire and plant invasion is complex, and the risk of invasion varies greatly between functional types and across geographic scales. Here, we examined patterns and predictors of non-native plant invasion following fire across the western United States. We specifically analyzed how the abundance of non-native plants after fire was related to fire characteristics and environmental conditions, such as climate, soil, and topography, in 26,729 vegetation plots from government networks and individual studies. Non-native plant cover was higher in plots measured after wildfires compared to prescribed burns or unburned plots. The post-fire cover of non-native species varied by plant functional type, and only the cover of short-lived (i.e., annual and biennial) forbs and short-lived C3 grasses was significantly higher in burned plots compared to unburned plots. Cool-season short-lived grasses composed most of the non-native post-fire vegetation, with cheatgrass (Bromus tectorum) being the most recorded species in the dataset. Climate variables were the most influential predictors of the cover of non-native short-lived grasses and forbs after fires, with invasion being more common in areas with drier summers and a higher proportion of yearly precipitation falling in October through March. Models using future projected climate for mid (2041–2070) and end (2071–2100) of century showed a potential for increasing post-fire invasion risk at higher elevations and latitudes. These findings highlight priorities for mitigation, monitoring, and restoration efforts to reduce post-fire plant invasion risk across the western United States.},
	language = {en},
	urldate = {2024-03-11},
	journal = {Biological Invasions},
	author = {Prevéy, Janet S. and Jarnevich, Catherine S. and Pearse, Ian S. and Munson, Seth M. and Stevens, Jens T. and Barrett, Kevin J. and Coop, Jonathan D. and Day, Michelle A. and Firmage, David and Fornwalt, Paula J. and Haynes, Katharine M. and Johnston, James D. and Kerns, Becky K. and Krawchuk, Meg A. and Miller, Becky A. and Nietupski, Ty C. and Roque, Jacquilyn and Springer, Judith D. and Stevens-Rumann, Camille S. and Stoddard, Michael T. and Tortorelli, Claire M.},
	month = feb,
	year = {2024},
	keywords = {NALCMS},
}

Invasions by non-native plant species after fire can negatively affect important ecosystem services and lead to invasion-fire cycles that further degrade ecosystems. The relationship between fire and plant invasion is complex, and the risk of invasion varies greatly between functional types and across geographic scales. Here, we examined patterns and predictors of non-native plant invasion following fire across the western United States. We specifically analyzed how the abundance of non-native plants after fire was related to fire characteristics and environmental conditions, such as climate, soil, and topography, in 26,729 vegetation plots from government networks and individual studies. Non-native plant cover was higher in plots measured after wildfires compared to prescribed burns or unburned plots. The post-fire cover of non-native species varied by plant functional type, and only the cover of short-lived (i.e., annual and biennial) forbs and short-lived C3 grasses was significantly higher in burned plots compared to unburned plots. Cool-season short-lived grasses composed most of the non-native post-fire vegetation, with cheatgrass (Bromus tectorum) being the most recorded species in the dataset. Climate variables were the most influential predictors of the cover of non-native short-lived grasses and forbs after fires, with invasion being more common in areas with drier summers and a higher proportion of yearly precipitation falling in October through March. Models using future projected climate for mid (2041–2070) and end (2071–2100) of century showed a potential for increasing post-fire invasion risk at higher elevations and latitudes. These findings highlight priorities for mitigation, monitoring, and restoration efforts to reduce post-fire plant invasion risk across the western United States.

@article{wi_need_2024,
	title = {On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration},
	volume = {28},
	issn = {1027-5606},
	shorttitle = {On the need for physical constraints in deep learning rainfall–runoff projections under climate change},
	url = {https://hess.copernicus.org/articles/28/479/2024/},
	doi = {10.5194/hess-28-479-2024},
	abstract = {Deep learning (DL) rainfall–runoff models outperform conceptual, process-based models in a range of applications. However, it remains unclear whether DL models can produce physically plausible projections of streamflow under climate change. We investigate this question through a sensitivity analysis of modeled responses to increases in temperature and potential evapotranspiration (PET), with other meteorological variables left unchanged. Previous research has shown that temperature-based PET methods overestimate evaporative water loss under warming compared with energy budget-based PET methods. We therefore assume that reliable streamflow responses to warming should exhibit less evaporative water loss when forced with smaller, energy-budget-based PET compared with temperature-based PET. We conduct this assessment using three conceptual, process-based rainfall–runoff models and three DL models, trained and tested across 212 watersheds in the Great Lakes basin. The DL models include a Long Short-Term Memory network (LSTM), a mass-conserving LSTM (MC-LSTM), and a novel variant of the MC-LSTM that also respects the relationship between PET and evaporative water loss (MC-LSTM-PET). After validating models against historical streamflow and actual evapotranspiration, we force all models with scenarios of warming, historical precipitation, and both temperature-based (Hamon) and energy-budget-based (Priestley–Taylor) PET, and compare their responses in long-term mean daily flow, low flows, high flows, and seasonal streamflow timing. We also explore similar responses using a national LSTM fit to 531 watersheds across the United States to assess how the inclusion of a larger and more diverse set of basins influences signals of hydrological response under warming. The main results of this study are as follows:

   The three Great Lakes DL models substantially outperform all process-based models in streamflow estimation. The MC-LSTM-PET also matches the best process-based models and outperforms the MC-LSTM in estimating actual evapotranspiration.

   All process-based models show a downward shift in long-term mean daily flows under warming, but median shifts are considerably larger under temperature-based PET (−17 \% to −25 \%) than energy-budget-based PET (−6 \% to −9 \%). The MC-LSTM-PET model exhibits similar differences in water loss across the different PET forcings. Conversely, the LSTM exhibits unrealistically large water losses under warming using Priestley–Taylor PET (−20 \%), while the MC-LSTM is relatively insensitive to the PET method.

   DL models exhibit smaller changes in high flows and seasonal timing of flows as compared with the process-based models, while DL estimates of low flows are within the range estimated by the process-based models.

   Like the Great Lakes LSTM, the national LSTM also shows unrealistically large water losses under warming (−25 \%), but it is more stable when many inputs are changed under warming and better aligns with process-based model responses for seasonal timing of flows.

   Ultimately, the results of this sensitivity analysis suggest that physical considerations regarding model architecture and input variables may be necessary to promote the physical realism of deep-learning-based hydrological projections under climate change.},
	language = {English},
	number = {3},
	urldate = {2024-03-11},
	journal = {Hydrology and Earth System Sciences},
	author = {Wi, Sungwook and Steinschneider, Scott},
	month = feb,
	year = {2024},
	note = {Publisher: Copernicus GmbH},
	keywords = {NALCMS},
	pages = {479--503},
}

Deep learning (DL) rainfall–runoff models outperform conceptual, process-based models in a range of applications. However, it remains unclear whether DL models can produce physically plausible projections of streamflow under climate change. We investigate this question through a sensitivity analysis of modeled responses to increases in temperature and potential evapotranspiration (PET), with other meteorological variables left unchanged. Previous research has shown that temperature-based PET methods overestimate evaporative water loss under warming compared with energy budget-based PET methods. We therefore assume that reliable streamflow responses to warming should exhibit less evaporative water loss when forced with smaller, energy-budget-based PET compared with temperature-based PET. We conduct this assessment using three conceptual, process-based rainfall–runoff models and three DL models, trained and tested across 212 watersheds in the Great Lakes basin. The DL models include a Long Short-Term Memory network (LSTM), a mass-conserving LSTM (MC-LSTM), and a novel variant of the MC-LSTM that also respects the relationship between PET and evaporative water loss (MC-LSTM-PET). After validating models against historical streamflow and actual evapotranspiration, we force all models with scenarios of warming, historical precipitation, and both temperature-based (Hamon) and energy-budget-based (Priestley–Taylor) PET, and compare their responses in long-term mean daily flow, low flows, high flows, and seasonal streamflow timing. We also explore similar responses using a national LSTM fit to 531 watersheds across the United States to assess how the inclusion of a larger and more diverse set of basins influences signals of hydrological response under warming. The main results of this study are as follows: The three Great Lakes DL models substantially outperform all process-based models in streamflow estimation. The MC-LSTM-PET also matches the best process-based models and outperforms the MC-LSTM in estimating actual evapotranspiration. All process-based models show a downward shift in long-term mean daily flows under warming, but median shifts are considerably larger under temperature-based PET (−17 % to −25 %) than energy-budget-based PET (−6 % to −9 %). The MC-LSTM-PET model exhibits similar differences in water loss across the different PET forcings. Conversely, the LSTM exhibits unrealistically large water losses under warming using Priestley–Taylor PET (−20 %), while the MC-LSTM is relatively insensitive to the PET method. DL models exhibit smaller changes in high flows and seasonal timing of flows as compared with the process-based models, while DL estimates of low flows are within the range estimated by the process-based models. Like the Great Lakes LSTM, the national LSTM also shows unrealistically large water losses under warming (−25 %), but it is more stable when many inputs are changed under warming and better aligns with process-based model responses for seasonal timing of flows. Ultimately, the results of this sensitivity analysis suggest that physical considerations regarding model architecture and input variables may be necessary to promote the physical realism of deep-learning-based hydrological projections under climate change.

@article{richter_opportunities_2024,
	title = {Opportunities for {Restoring} {Environmental} {Flows} in the {Rio} {Grande}–{Rio} {Bravo} {Basin} {Spanning} the {US}–{Mexico} {Border}},
	volume = {150},
	copyright = {This work is made available under the terms of the Creative Commons Attribution 4.0 International license, https://creativecommons.org/licenses/by/4.0/.},
	url = {https://ascelibrary.org/doi/10.1061/JWRMD5.WRENG-6278},
	doi = {10.1061/JWRMD5.WRENG-6278},
	abstract = {AbstractThe Rio Grande–Rio Bravo’s flow regime has been highly altered for more than 130\ years,
yet the river ecosystem still supports important biodiversity including numerous endangered
species. More than 80\% of water consumed in the basin goes to ...},
	language = {EN},
	number = {2},
	urldate = {2024-01-10},
	journal = {Journal of Water Resources Planning and Management},
	author = {Richter, Brian D. and Prunes, Enrique and Liu, Ning and Caldwell, Peter and Wei, Dongyang and Davis, Kyle Frankel and Sandoval-Solis, Samuel and Herrera, Gabriela Rendon and Rodriguez, Ramon Saiz and Ao, Yufei and Lamsal, Gambhir and Amaya, Maria and Shahbol, Natalie and Marston, Landon},
	month = feb,
	year = {2024},
	note = {Publisher: American Society of Civil Engineers},
	keywords = {NALCMS},
	pages = {04023079},
}

AbstractThe Rio Grande–Rio Bravo’s flow regime has been highly altered for more than 130 years, yet the river ecosystem still supports important biodiversity including numerous endangered species. More than 80% of water consumed in the basin goes to ...

@article{agboola_optimizing_2024,
	title = {Optimizing landslide susceptibility mapping using machine learning and geospatial techniques},
	volume = {81},
	issn = {1574-9541},
	url = {https://www.sciencedirect.com/science/article/pii/S1574954124001250},
	doi = {10.1016/j.ecoinf.2024.102583},
	abstract = {Landslides present a substantial risk to human lives, the environment, and infrastructure. Consequently, it is crucial to highlight the regions prone to future landslides by examining the correlation between past landslides and various geo-environmental factors. This study aims to investigate the optimal data selection and machine learning model, or ensemble technique, for evaluating the vulnerability of areas to landslides and determining the most accurate approach. To attain our objectives, we considered two different scenarios for selecting landslide-free random points (a slope threshold and a buffer-based approach) and performed a comparative analysis of five machine learning models for landslide susceptibility mapping, namely: Support Vector Machine (SVM), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The study area for this research is an area in Polk County in Western North Carolina that has experienced fatal landslides, leading to casualties and significant damage to infrastructure, properties, and road networks. The model construction process involves the utilization of a dataset comprising 1215 historical landslide occurrences and 1215 non-landslide points. We integrated a total of fourteen geospatial data layers, consisting of topographic variables, soil data, geological data, and land cover attributes. We use various metrics to assess the models' performance, including accuracy, F1-score, Kappa score, and AUC-ROC. In addition, we used the seeded-cell area index (SCAI) to evaluate map consistency. The ensemble of the five models using Weighted Average produces outstanding results, with an AUC-ROC of 99.4\% for the slope threshold scenario and 91.8\% for the buffer-based scenario. Our findings emphasize the significant impact of non-landslide random sampling on model performance in landslide susceptibility mapping. Furthermore, by optimally identifying landslide-prone regions and hotspots that need urgent risk management and land use planning, our study demonstrates the effectiveness of machine learning models in analyzing landslide susceptibility and providing valuable insights for informed decision-making and disaster risk reduction initiatives.},
	urldate = {2024-04-24},
	journal = {Ecological Informatics},
	author = {Agboola, Gazali and Beni, Leila Hashemi and Elbayoumi, Tamer and Thompson, Gary},
	month = jul,
	year = {2024},
	keywords = {NALCMS},
	pages = {102583},
}

Landslides present a substantial risk to human lives, the environment, and infrastructure. Consequently, it is crucial to highlight the regions prone to future landslides by examining the correlation between past landslides and various geo-environmental factors. This study aims to investigate the optimal data selection and machine learning model, or ensemble technique, for evaluating the vulnerability of areas to landslides and determining the most accurate approach. To attain our objectives, we considered two different scenarios for selecting landslide-free random points (a slope threshold and a buffer-based approach) and performed a comparative analysis of five machine learning models for landslide susceptibility mapping, namely: Support Vector Machine (SVM), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The study area for this research is an area in Polk County in Western North Carolina that has experienced fatal landslides, leading to casualties and significant damage to infrastructure, properties, and road networks. The model construction process involves the utilization of a dataset comprising 1215 historical landslide occurrences and 1215 non-landslide points. We integrated a total of fourteen geospatial data layers, consisting of topographic variables, soil data, geological data, and land cover attributes. We use various metrics to assess the models' performance, including accuracy, F1-score, Kappa score, and AUC-ROC. In addition, we used the seeded-cell area index (SCAI) to evaluate map consistency. The ensemble of the five models using Weighted Average produces outstanding results, with an AUC-ROC of 99.4% for the slope threshold scenario and 91.8% for the buffer-based scenario. Our findings emphasize the significant impact of non-landslide random sampling on model performance in landslide susceptibility mapping. Furthermore, by optimally identifying landslide-prone regions and hotspots that need urgent risk management and land use planning, our study demonstrates the effectiveness of machine learning models in analyzing landslide susceptibility and providing valuable insights for informed decision-making and disaster risk reduction initiatives.

@article{mower_parallel_2024,
	title = {Parallel {SnowModel} (v1.0): a parallel implementation of a distributed snow-evolution modeling system ({SnowModel})},
	volume = {17},
	issn = {1991-959X},
	shorttitle = {Parallel {SnowModel} (v1.0)},
	url = {https://gmd.copernicus.org/articles/17/4135/2024/},
	doi = {10.5194/gmd-17-4135-2024},
	abstract = {SnowModel, a spatially distributed snow-evolution modeling system, was parallelized using Coarray Fortran for high-performance computing architectures to allow high-resolution (1 m to hundreds of meters) simulations over large regional- to continental-scale domains. In the parallel algorithm, the model domain was split into smaller rectangular sub-domains that are distributed over multiple processor cores using one-dimensional decomposition. All the memory allocations from the original code were reduced to the size of the local sub-domains, allowing each core to perform fewer computations and requiring less memory for each process. Most of the subroutines in SnowModel were simple to parallelize; however, there were certain physical processes, including blowing snow redistribution and components within the solar radiation and wind models, that required non-trivial parallelization using halo-exchange patterns. To validate the parallel algorithm and assess parallel scaling characteristics, high-resolution (100 m grid) simulations were performed over several western United States domains and over the contiguous United States (CONUS) for a year. The CONUS scaling experiment had approximately 70 \% parallel efficiency; runtime decreased by a factor of 1.9 running on 1800 cores relative to 648 cores (the minimum number of cores that could be used to run such a large domain because of memory and time limitations). CONUS 100 m simulations were performed for 21 years (2000–2021) using 46 238 and 28 260 grid cells in the x and y dimensions, respectively. Each year was simulated using 1800 cores and took approximately 5 h to run.},
	language = {English},
	number = {10},
	urldate = {2024-06-03},
	journal = {Geoscientific Model Development},
	author = {Mower, Ross and Gutmann, Ethan D. and Liston, Glen E. and Lundquist, Jessica and Rasmussen, Soren},
	month = may,
	year = {2024},
	note = {Publisher: Copernicus GmbH},
	keywords = {NALCMS},
	pages = {4135--4154},
}

SnowModel, a spatially distributed snow-evolution modeling system, was parallelized using Coarray Fortran for high-performance computing architectures to allow high-resolution (1 m to hundreds of meters) simulations over large regional- to continental-scale domains. In the parallel algorithm, the model domain was split into smaller rectangular sub-domains that are distributed over multiple processor cores using one-dimensional decomposition. All the memory allocations from the original code were reduced to the size of the local sub-domains, allowing each core to perform fewer computations and requiring less memory for each process. Most of the subroutines in SnowModel were simple to parallelize; however, there were certain physical processes, including blowing snow redistribution and components within the solar radiation and wind models, that required non-trivial parallelization using halo-exchange patterns. To validate the parallel algorithm and assess parallel scaling characteristics, high-resolution (100 m grid) simulations were performed over several western United States domains and over the contiguous United States (CONUS) for a year. The CONUS scaling experiment had approximately 70 % parallel efficiency; runtime decreased by a factor of 1.9 running on 1800 cores relative to 648 cores (the minimum number of cores that could be used to run such a large domain because of memory and time limitations). CONUS 100 m simulations were performed for 21 years (2000–2021) using 46 238 and 28 260 grid cells in the x and y dimensions, respectively. Each year was simulated using 1800 cores and took approximately 5 h to run.

@techreport{avila-garcia_d_planes_2024,
	title = {Planes de {Acción} para el {Manejo} {Integral} de {Cuencas} - {Guía} {Metodológica} ({FMCN})},
	url = {https://agua.org.mx/biblioteca/planes-de-accion-para-el-manejo-integral-de-cuencas-guia-metodologica-fmcn/},
	abstract = {El Instituto Nacional de Ecología y Cambio Climático (INECC) y el Fondo Mexicano para la Conservación de la Naturaleza (FMCN) colaboran con aliados regionales en la implementación del proyecto CONECTA “Conectando la salud de las cuencas con la producción ganadera y agroforestal sostenible” (2021-2026), que es financiado por el Fondo para el Medio Ambiente Mundial (GEF por sus siglas en inglés) a través del Banco Mundial (BM). El objetivo de CONECTA es mejorar el manejo integrado del paisaje y promover prácticas productivas climáticamente inteligentes.},
	urldate = {2024-03-11},
	institution = {Instituto Nacional de Ecología y Cambio Climático (INECC)},
	author = {{Ávila-García, D.} and {Hernández, E.} and {Fernández-Montes de Oca, A.} and {Cicchini, F.} and {Alvarado, J.} and {López S.}},
	month = feb,
	year = {2024},
	keywords = {NALCMS},
	pages = {124},
}

El Instituto Nacional de Ecología y Cambio Climático (INECC) y el Fondo Mexicano para la Conservación de la Naturaleza (FMCN) colaboran con aliados regionales en la implementación del proyecto CONECTA “Conectando la salud de las cuencas con la producción ganadera y agroforestal sostenible” (2021-2026), que es financiado por el Fondo para el Medio Ambiente Mundial (GEF por sus siglas en inglés) a través del Banco Mundial (BM). El objetivo de CONECTA es mejorar el manejo integrado del paisaje y promover prácticas productivas climáticamente inteligentes.

@article{huang_potential_2024,
	title = {Potential for spatial coexistence of a transboundary migratory species and wind energy development},
	volume = {14},
	copyright = {2024 This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-024-66490-3},
	doi = {10.1038/s41598-024-66490-3},
	abstract = {Global expansion in wind energy development is a notable achievement of the international community’s effort to reduce carbon emissions during energy production. However, the increasing number of wind turbines have unintended consequences for migratory birds and bats. Wind turbine curtailment and other mitigation strategies can reduce fatalities, but improved spatial and temporal data are needed to identify the most effective way for wind energy development and volant migratory species to coexist. Mexican free-tailed bats (Tadarida brasiliensis mexicana) account for a large proportion of known bat fatalities at wind facilities in the southwestern US. We examined the geographic concordance between existing wind energy generation facilities, areas of high wind potential amenable for future deployment of wind facilities, and seasonally suitable habitat for these bats. We used ecological niche modeling to determine species distribution during each of 4 seasons. We used a multi-criteria GIS-based approach to produce a wind turbine siting suitability map. We identified seasonal locations with highest and lowest potential for the species’ probability of occurrence, providing a potential explanation for the higher observed fatalities during fall migration. Thirty percent of 33,606 wind turbines within the southwestern US occurred in highly suitable areas for Mexican free-tailed bats, primarily in west Texas. There is also broad spatial overlap between areas of high wind potential and areas of suitable habitat for Mexican free-tailed bats. Because of this high degree of overlap, our results indicate that post-construction strategies, such as curtailing the timing of operations and deterrents, would be more effective for bat conservation than strategic siting of new wind energy installations.},
	language = {en},
	number = {1},
	urldate = {2024-09-16},
	journal = {Scientific Reports},
	author = {Huang, Ta-Ken and Feng, Xiao and Derbridge, Jonathan J. and Libby, Kaitlin and Diffendorfer, Jay E. and Thogmartin, Wayne E. and McCracken, Gary and Medellin, Rodrigo and López-Hoffman, Laura},
	month = jul,
	year = {2024},
	note = {Publisher: Nature Publishing Group},
	keywords = {NALCMS},
	pages = {17050},
}

Global expansion in wind energy development is a notable achievement of the international community’s effort to reduce carbon emissions during energy production. However, the increasing number of wind turbines have unintended consequences for migratory birds and bats. Wind turbine curtailment and other mitigation strategies can reduce fatalities, but improved spatial and temporal data are needed to identify the most effective way for wind energy development and volant migratory species to coexist. Mexican free-tailed bats (Tadarida brasiliensis mexicana) account for a large proportion of known bat fatalities at wind facilities in the southwestern US. We examined the geographic concordance between existing wind energy generation facilities, areas of high wind potential amenable for future deployment of wind facilities, and seasonally suitable habitat for these bats. We used ecological niche modeling to determine species distribution during each of 4 seasons. We used a multi-criteria GIS-based approach to produce a wind turbine siting suitability map. We identified seasonal locations with highest and lowest potential for the species’ probability of occurrence, providing a potential explanation for the higher observed fatalities during fall migration. Thirty percent of 33,606 wind turbines within the southwestern US occurred in highly suitable areas for Mexican free-tailed bats, primarily in west Texas. There is also broad spatial overlap between areas of high wind potential and areas of suitable habitat for Mexican free-tailed bats. Because of this high degree of overlap, our results indicate that post-construction strategies, such as curtailing the timing of operations and deterrents, would be more effective for bat conservation than strategic siting of new wind energy installations.

@article{kramer_potential_2024,
	title = {Potential landscape connectivity for invasive wild pigs ({Sus} scrofa) across the northern prairies of {North} {America}},
	volume = {26},
	issn = {1573-1464},
	url = {https://doi.org/10.1007/s10530-024-03326-1},
	doi = {10.1007/s10530-024-03326-1},
	abstract = {Understanding landscape scale connectivity is an essential component in the management of invasive species since connectivity facilitates their invasion potential. Invasive wild pigs (Sus scrofa) are among the most prolific invaders on the planet, causing billions of dollars in agricultural and environmental damage annually. Newly introduced to Canada in the 1980s, we examined wild pig invasion potential across the northern prairies from western Canada into the currently wild pig-free northern U.S. states. We used GPS collar data collected in the Canadian prairies to quantify resource selection and incorporated results into an electric circuit theory framework to evaluate potential regional landscape connectivity. While available landcover types in this region were dominated by crops and grasslands, wild pigs were predominately located in deciduous forest, crops, and wetlands. Resource selection modelling indicated wild pigs selected deciduous forest and wetlands over other landcover types. These selection tendencies resulted in areas at greater risk of occupation in an intermixture dominated by crops interspersed with waterbodies and deciduous forest fragments, which facilitated movement. Given the pervasiveness of this intermixture across the northern prairies, there was a high potential for invasive wild pigs to move throughout much of the region with areas in southern Saskatchewan and Manitoba, northeastern Montana, North and South Dakota, and western portions of Minnesota being particularly vulnerable. Our work highlights a need for monitoring and science-based response strategies for likely southward spread of this invasive species to prevent or reduce potential crop damage, risks to native species, and disease transmission to humans, pets, livestock, and wildlife.},
	language = {en},
	number = {8},
	urldate = {2024-08-12},
	journal = {Biological Invasions},
	author = {Kramer, Corey J. and Boudreau, Melanie R. and Powers, Ryan and VerCauteren, Kurt C. and Miller, Ryan S. and Brook, Ryan K.},
	month = aug,
	year = {2024},
	keywords = {NALCMS, Terrestrial Ecoregions},
	pages = {2525--2538},
}

Understanding landscape scale connectivity is an essential component in the management of invasive species since connectivity facilitates their invasion potential. Invasive wild pigs (Sus scrofa) are among the most prolific invaders on the planet, causing billions of dollars in agricultural and environmental damage annually. Newly introduced to Canada in the 1980s, we examined wild pig invasion potential across the northern prairies from western Canada into the currently wild pig-free northern U.S. states. We used GPS collar data collected in the Canadian prairies to quantify resource selection and incorporated results into an electric circuit theory framework to evaluate potential regional landscape connectivity. While available landcover types in this region were dominated by crops and grasslands, wild pigs were predominately located in deciduous forest, crops, and wetlands. Resource selection modelling indicated wild pigs selected deciduous forest and wetlands over other landcover types. These selection tendencies resulted in areas at greater risk of occupation in an intermixture dominated by crops interspersed with waterbodies and deciduous forest fragments, which facilitated movement. Given the pervasiveness of this intermixture across the northern prairies, there was a high potential for invasive wild pigs to move throughout much of the region with areas in southern Saskatchewan and Manitoba, northeastern Montana, North and South Dakota, and western portions of Minnesota being particularly vulnerable. Our work highlights a need for monitoring and science-based response strategies for likely southward spread of this invasive species to prevent or reduce potential crop damage, risks to native species, and disease transmission to humans, pets, livestock, and wildlife.

@inproceedings{boschee_predicting_2024,
	title = {Predicting {Flood} {Damages} using {Machine} {Learning} and {National} {Flood} {Insurance} {Program} {Data}},
	url = {https://ams.confex.com/ams/104ANNUAL/mediafile/Handout/Paper440366/AMSPoster_Boschee.pdf},
	publisher = {AMS},
	author = {Boschee, Azara and Corringham, Tom and Hu, Weiming},
	year = {2024},
	keywords = {NALCMS},
}

@article{seydi_predictive_2024,
	title = {Predictive {Understanding} of {Links} {Between} {Vegetation} and {Soil} {Burn} {Severities} {Using} {Physics}-{Informed} {Machine} {Learning}},
	volume = {12},
	copyright = {© 2024. The Author(s).},
	issn = {2328-4277},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2024EF004873},
	doi = {10.1029/2024EF004873},
	abstract = {Burn severity is fundamental to post-fire impact assessment and emergency response. Vegetation Burn Severity (VBS) can be derived from satellite observations. However, Soil Burn Severity (SBS) assessment—critical for mitigating hydrologic and geologic hazards—requires costly and laborious field recalibration of VBS maps. Here, we develop a physics-informed Machine Learning model capable of accurately estimating SBS while revealing the intricate relationships between soil and vegetation burn severities. Our SBS classification model uses VBS, as well as climatological, meteorological, ecological, geological, and topographical wildfire covariates. This model demonstrated an overall accuracy of 89\% for out-of-sample test data. The model exhibited scalability with additional data, and was able to extract universal functional relationships between vegetation and soil burn severities across the western US. VBS had the largest control on SBS, followed by weather (e.g., wind, fire danger, temperature), climate (e.g., annual precipitation), topography (e.g., elevation), and soil characteristics (e.g., soil organic carbon content). The relative control of processes on SBS changes across regions. Our model revealed nuanced relationships between VBS and SBS; for example, a similar VBS with lower wind speeds—that is, higher fire residence time—translates to a higher SBS. This transferrable model develops reliable and timely SBS maps using satellite and publicly accessible data, providing science-based insights for managers and diverse stakeholders.},
	language = {en},
	number = {8},
	urldate = {2025-01-27},
	journal = {Earth's Future},
	author = {Seydi, Seyd Teymoor and Abatzoglou, John T. and AghaKouchak, Amir and Pourmohamad, Yavar and Mishra, Ashok and Sadegh, Mojtaba},
	year = {2024},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2024EF004873},
	keywords = {NALCMS},
	pages = {e2024EF004873},
}

Burn severity is fundamental to post-fire impact assessment and emergency response. Vegetation Burn Severity (VBS) can be derived from satellite observations. However, Soil Burn Severity (SBS) assessment—critical for mitigating hydrologic and geologic hazards—requires costly and laborious field recalibration of VBS maps. Here, we develop a physics-informed Machine Learning model capable of accurately estimating SBS while revealing the intricate relationships between soil and vegetation burn severities. Our SBS classification model uses VBS, as well as climatological, meteorological, ecological, geological, and topographical wildfire covariates. This model demonstrated an overall accuracy of 89% for out-of-sample test data. The model exhibited scalability with additional data, and was able to extract universal functional relationships between vegetation and soil burn severities across the western US. VBS had the largest control on SBS, followed by weather (e.g., wind, fire danger, temperature), climate (e.g., annual precipitation), topography (e.g., elevation), and soil characteristics (e.g., soil organic carbon content). The relative control of processes on SBS changes across regions. Our model revealed nuanced relationships between VBS and SBS; for example, a similar VBS with lower wind speeds—that is, higher fire residence time—translates to a higher SBS. This transferrable model develops reliable and timely SBS maps using satellite and publicly accessible data, providing science-based insights for managers and diverse stakeholders.

@misc{braden_satellite-derived_2024,
	title = {Satellite-{Derived} {Forest} {Extent} {Likelihood} {Map} for {Mexico}},
	url = {https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2320},
	doi = {10.3334/ORNLDAAC/2320},
	abstract = {This dataset provides a comparison of forest extent agreement from seven remote sensing-based products across Mexico. These satellite-derived products include European Space Agency 2020 Land Cover Map for Mexico (ESA), Globeland30 2020 (Globeland30), Commission for Environmental Cooperation 2015 Land Cover Map (CEC), Impact Observatory 2020 Land Cover Map (IO), NAIP Trained Mean Percent Cover Map (NEX-TC), Global Land Analysis and Discovery Global 2010 Tree Cover (Hansen-TC), and Global Forest Cover Change Tree Cover 30 m Global (GFCC-TC). All products included data at 10-30 m resolution and represented the state of forest or tree cover from 2010 to 2020. These seven products were chosen based on: a) feedback from end-users in Mexico; b) availability and FAIR (findable, accessible, interoperable, and replicable) data principles; and c) products representing different methodological approaches from global to regional scales. The combined agreement map documents forest cover for each satellite-derived product at 30-m resolution across Mexico. The data are in cloud optimized GeoTIFF format and cover the period 2010-2020. A shapefile is included that outlines Mexico mainland areas.},
	language = {en},
	urldate = {2024-04-24},
	author = {Braden, D. and Mondal, P. and Park, T. and De la rosa, J.A.A. and Leal, M.I.A. and Lara, R.A.C. and Saucedo, R.M. and Salas-Aguilar, V.M. and Soriano-Luna, M.A. and Vargas, R.},
	collaborator = {{ORNL DAAC}},
	year = {2024},
	note = {Artwork Size: 0 MB
Medium: COG, ESRI Shapefile
Publisher: [object Object]},
	keywords = {NALCMS},
}

This dataset provides a comparison of forest extent agreement from seven remote sensing-based products across Mexico. These satellite-derived products include European Space Agency 2020 Land Cover Map for Mexico (ESA), Globeland30 2020 (Globeland30), Commission for Environmental Cooperation 2015 Land Cover Map (CEC), Impact Observatory 2020 Land Cover Map (IO), NAIP Trained Mean Percent Cover Map (NEX-TC), Global Land Analysis and Discovery Global 2010 Tree Cover (Hansen-TC), and Global Forest Cover Change Tree Cover 30 m Global (GFCC-TC). All products included data at 10-30 m resolution and represented the state of forest or tree cover from 2010 to 2020. These seven products were chosen based on: a) feedback from end-users in Mexico; b) availability and FAIR (findable, accessible, interoperable, and replicable) data principles; and c) products representing different methodological approaches from global to regional scales. The combined agreement map documents forest cover for each satellite-derived product at 30-m resolution across Mexico. The data are in cloud optimized GeoTIFF format and cover the period 2010-2020. A shapefile is included that outlines Mexico mainland areas.

@article{bowser_self-reported_2024,
	title = {Self-reported tick exposure as an indicator of {Lyme} disease risk in an endemic region of {Quebec}, {Canada}},
	volume = {15},
	issn = {1877-959X},
	url = {https://www.sciencedirect.com/science/article/pii/S1877959X23001528},
	doi = {10.1016/j.ttbdis.2023.102271},
	abstract = {Background
Lyme disease (LD) and other tick-borne diseases are emerging across Canada. Spatial and temporal LD risk is typically estimated using acarological surveillance and reported human cases, the former not considering human behavior leading to tick exposure and the latter occurring after infection.
Objectives
The primary objective was to explore, at the census subdivision level (CSD), the associations of self-reported tick exposure, alternative risk indicators (predicted tick density, eTick submissions, public health risk level), and ecological variables (Ixodes scapularis habitat suitability index and cumulative degree days {\textgreater} 0 °C) with incidence proportion of LD. A secondary objective was to explore which of these predictor variables were associated with self-reported tick exposure at the CSD level.
Methods
Self-reported tick exposure was measured in a cross-sectional populational health survey conducted in 2018, among 10,790 respondents living in 116 CSDs of the Estrie region, Quebec, Canada. The number of reported LD cases per CSD in 2018 was obtained from the public health department. Generalized linear mixed-effets models accounting for spatial autocorrelation were built to fulfill the objectives.
Results
Self-reported tick exposure ranged from 0.0 \% to 61.5 \% (median 8.9 \%) and reported LD incidence rates ranged from 0 to 324 cases per 100,000 person-years, per CSD. A positive association was found between self-reported tick exposure and LD incidence proportion (ß = 0.08, CI = 0.04,0.11, p {\textless} 0.0001). The best-fit model included public health risk level (AIC: 144.2), followed by predicted tick density, ecological variables, self-reported tick exposure and eTick submissions (AIC: 158.4, 158.4, 160.4 and 170.1 respectively). Predicted tick density was the only significant predictor of self-reported tick exposure (ß = 0.83, CI = 0.16,1.50, p = 0.02).
Discussion
This proof-of-concept study explores self-reported tick exposure as a potential indicator of LD risk using populational survey data. This approach may offer a low-cost and simple tool for evaluating LD risk and deserves further evaluation.},
	number = {1},
	urldate = {2024-01-10},
	journal = {Ticks and Tick-borne Diseases},
	author = {Bowser, Natasha and Bouchard, Catherine and Sautié Castellanos, Miguel and Baron, Geneviève and Carabin, Hélène and Chuard, Pierre and Leighton, Patrick and Milord, François and Richard, Lucie and Savage, Jade and Tardy, Olivia and Aenishaenslin, Cécile},
	month = jan,
	year = {2024},
	keywords = {NALCMS},
	pages = {102271},
}

Background Lyme disease (LD) and other tick-borne diseases are emerging across Canada. Spatial and temporal LD risk is typically estimated using acarological surveillance and reported human cases, the former not considering human behavior leading to tick exposure and the latter occurring after infection. Objectives The primary objective was to explore, at the census subdivision level (CSD), the associations of self-reported tick exposure, alternative risk indicators (predicted tick density, eTick submissions, public health risk level), and ecological variables (Ixodes scapularis habitat suitability index and cumulative degree days \textgreater 0 °C) with incidence proportion of LD. A secondary objective was to explore which of these predictor variables were associated with self-reported tick exposure at the CSD level. Methods Self-reported tick exposure was measured in a cross-sectional populational health survey conducted in 2018, among 10,790 respondents living in 116 CSDs of the Estrie region, Quebec, Canada. The number of reported LD cases per CSD in 2018 was obtained from the public health department. Generalized linear mixed-effets models accounting for spatial autocorrelation were built to fulfill the objectives. Results Self-reported tick exposure ranged from 0.0 % to 61.5 % (median 8.9 %) and reported LD incidence rates ranged from 0 to 324 cases per 100,000 person-years, per CSD. A positive association was found between self-reported tick exposure and LD incidence proportion (ß = 0.08, CI = 0.04,0.11, p \textless 0.0001). The best-fit model included public health risk level (AIC: 144.2), followed by predicted tick density, ecological variables, self-reported tick exposure and eTick submissions (AIC: 158.4, 158.4, 160.4 and 170.1 respectively). Predicted tick density was the only significant predictor of self-reported tick exposure (ß = 0.83, CI = 0.16,1.50, p = 0.02). Discussion This proof-of-concept study explores self-reported tick exposure as a potential indicator of LD risk using populational survey data. This approach may offer a low-cost and simple tool for evaluating LD risk and deserves further evaluation.

@article{roy_sharp-tailed_2024,
	title = {Sharp-tailed {Grouse} increase site use after prescribed fire but not mechanical treatments during the fall},
	volume = {19},
	copyright = {© 2024 by the author(s)},
	issn = {1712-6568},
	url = {https://ace-eco.org/vol19/iss1/art1/},
	doi = {10.5751/ACE-02574-190101},
	abstract = {In the Great Lakes Region, Sharp-tailed Grouse (Tympanuchus phasianellus) use open habitats of grass and brush that require frequent management. Wildlife managers expressed concern that Sharp-tailed Grouse were not responding to management throughout the year, so we examined responses to prescribed fire and mechanical treatment (mowing or shearing) conducted during the fall. We surveyed Sharp-tailed Grouse use and vegetation at 15 mechanical treatments, 10 prescribed burns, and 25 control sites in a before-after-control-impact-paired design. We surveyed Sharp-tailed Grouse use before management, and one week, one month, one year, and three years after management by conducting fecal pellet surveys along transects at each site. Sharp-tailed Grouse responses, as indicated by differences between fecal pellet counts at treatments and paired controls during each survey, increased following prescribed fire, but did not change after mechanical treatments. However, increased Sharp-tailed Grouse use following prescribed fire was temporary, thus management should be conducted at least once every three years at each site. Changes in vegetation metrics at managed sites were also temporary and most metrics returned to pre-treatment levels after one year, although shrub height at sites that received mechanical treatments and the forb response following prescribed fire persisted for {\textgreater} 3 years. We suggest that fall prescribed fire is more effective at increasing Sharp-tailed Grouse use of sites than fall mechanical treatment, which could be due to differences in vegetation responses, site size, landscape context, or cues produced by fire that attract Sharp-tailed Grouse. However, mechanical treatments maintain Sharp-tailed Grouse habitat, and without management, unchecked woody encroachment reduces habitat. Targeting mowing and shearing at sites known to be used by Sharp-tailed Grouse may prioritize management activities to sites that will have the most impact. Prescribed fire and mechanical treatments produced different Sharp-tailed Grouse and vegetation responses in the fall and should be used to address different management objectives.},
	language = {en},
	number = {1},
	urldate = {2024-03-13},
	journal = {Avian Conservation and Ecology},
	author = {Roy, Charlotte L. and Giudice, John and Shartell, Lindsey M.},
	month = jan,
	year = {2024},
	note = {Publisher: The Resilience Alliance},
	keywords = {NALCMS},
}

In the Great Lakes Region, Sharp-tailed Grouse (Tympanuchus phasianellus) use open habitats of grass and brush that require frequent management. Wildlife managers expressed concern that Sharp-tailed Grouse were not responding to management throughout the year, so we examined responses to prescribed fire and mechanical treatment (mowing or shearing) conducted during the fall. We surveyed Sharp-tailed Grouse use and vegetation at 15 mechanical treatments, 10 prescribed burns, and 25 control sites in a before-after-control-impact-paired design. We surveyed Sharp-tailed Grouse use before management, and one week, one month, one year, and three years after management by conducting fecal pellet surveys along transects at each site. Sharp-tailed Grouse responses, as indicated by differences between fecal pellet counts at treatments and paired controls during each survey, increased following prescribed fire, but did not change after mechanical treatments. However, increased Sharp-tailed Grouse use following prescribed fire was temporary, thus management should be conducted at least once every three years at each site. Changes in vegetation metrics at managed sites were also temporary and most metrics returned to pre-treatment levels after one year, although shrub height at sites that received mechanical treatments and the forb response following prescribed fire persisted for \textgreater 3 years. We suggest that fall prescribed fire is more effective at increasing Sharp-tailed Grouse use of sites than fall mechanical treatment, which could be due to differences in vegetation responses, site size, landscape context, or cues produced by fire that attract Sharp-tailed Grouse. However, mechanical treatments maintain Sharp-tailed Grouse habitat, and without management, unchecked woody encroachment reduces habitat. Targeting mowing and shearing at sites known to be used by Sharp-tailed Grouse may prioritize management activities to sites that will have the most impact. Prescribed fire and mechanical treatments produced different Sharp-tailed Grouse and vegetation responses in the fall and should be used to address different management objectives.

@article{felfelani_simulation_2024,
	title = {Simulation of groundwater-flow dynamics in the {U}.{S}. {Northern} {High} {Plains} driven by multi-model estimates of surficial aquifer recharge},
	issn = {0022-1694},
	url = {https://www.sciencedirect.com/science/article/pii/S0022169424000970},
	doi = {10.1016/j.jhydrol.2024.130703},
	abstract = {There is growing interest in incorporating higher-resolution groundwater modeling within the framework of large-scale land surface models (LSMs), including processes such as three-dimensional flow, variable soil saturation, and surface water/groundwater interactions. Conversely, complex groundwater models (e.g., the U.S. Geological Survey Groundwater-Flow Model, MODFLOW) often use simpler representations of land surface dynamics (e.g., surface vegetation, evapotranspiration, recharge) and may benefit from higher process fidelity and temporal resolutions in these inputs. This study investigates the potential of improving groundwater representation in LSMs and land surface dynamics in MODFLOW through forcing MODFLOW with recharge from LSMs. Groundwater simulations build on an existing and well-calibrated MODFLOW model of the U.S. Northern High Plains aquifer, a hydrologically complex basin under the dual impacts of conversion of native vegetation to intense irrigated agricultural fields and climate change. Simulated groundwater recharge from four different land models are used to drive MODFLOW groundwater simulations. Results show relatively large discrepancies between recharge estimates among simulations. Forcing MODFLOW using recharge simulated by some of the LSMs in place of a simple water balance model marginally improves MODFLOW groundwater simulation. Further, our results support the efficacy of coupling LSMs to a sophisticated groundwater model such as MODFLOW. The coupling results in notable improvements in matching the historical groundwater levels through reduction of the skewness coefficient in percentage bias histogram (from 1.50 and 1.41 in original LSMs to 0.44 and 0.27, respectively, when MODFLOW is forced by groundwater recharge from LSMs) and reduction of bias. This modeling effort seeks to identify the best compromise between comprehensive land surface processes from global LSMs and advanced representation of groundwater from regional models.},
	urldate = {2024-01-31},
	journal = {Journal of Hydrology},
	author = {Felfelani, Farshid and Hughes, Joseph and Chen, Fei and Dugger, Aubrey and Schneider, Tim and Gochis, David and Traylor, Jonathan and Essaid, Hedeff},
	month = jan,
	year = {2024},
	keywords = {NALCMS},
	pages = {130703},
}

There is growing interest in incorporating higher-resolution groundwater modeling within the framework of large-scale land surface models (LSMs), including processes such as three-dimensional flow, variable soil saturation, and surface water/groundwater interactions. Conversely, complex groundwater models (e.g., the U.S. Geological Survey Groundwater-Flow Model, MODFLOW) often use simpler representations of land surface dynamics (e.g., surface vegetation, evapotranspiration, recharge) and may benefit from higher process fidelity and temporal resolutions in these inputs. This study investigates the potential of improving groundwater representation in LSMs and land surface dynamics in MODFLOW through forcing MODFLOW with recharge from LSMs. Groundwater simulations build on an existing and well-calibrated MODFLOW model of the U.S. Northern High Plains aquifer, a hydrologically complex basin under the dual impacts of conversion of native vegetation to intense irrigated agricultural fields and climate change. Simulated groundwater recharge from four different land models are used to drive MODFLOW groundwater simulations. Results show relatively large discrepancies between recharge estimates among simulations. Forcing MODFLOW using recharge simulated by some of the LSMs in place of a simple water balance model marginally improves MODFLOW groundwater simulation. Further, our results support the efficacy of coupling LSMs to a sophisticated groundwater model such as MODFLOW. The coupling results in notable improvements in matching the historical groundwater levels through reduction of the skewness coefficient in percentage bias histogram (from 1.50 and 1.41 in original LSMs to 0.44 and 0.27, respectively, when MODFLOW is forced by groundwater recharge from LSMs) and reduction of bias. This modeling effort seeks to identify the best compromise between comprehensive land surface processes from global LSMs and advanced representation of groundwater from regional models.

@article{marsh_snowdrift-permitting_2024,
	title = {Snowdrift-{Permitting} {Simulations} of {Seasonal} {Snowpack} {Processes} {Over} {Large} {Mountain} {Extents}},
	volume = {60},
	copyright = {© 2024. The Author(s).},
	issn = {1944-7973},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2023WR036948},
	doi = {10.1029/2023WR036948},
	abstract = {The melt of seasonal snowpack in mountain regions provides downstream river basins with a critical supply of freshwater. Snowdrift-permitting models have been proposed as a way to accurately simulate snowpack heterogeneity that stems from differences in energy inputs, over winter redistribution, sublimation, melt, and variations in precipitation. However, these spatial scales can be computationally intractable for large extents. In this work, the multiscale Canadian Hydrological Model (CHM) was applied to simulate snowpacks at snowdrift-permitting scales (≈50 m) across the Canadian Cordillera and adjacent regions (1.37 million km2) forced by downscaled atmospheric data. The use of a multiscale land surface representation resulted in a reduction of computational elements of 98\% while preserving land-surface heterogeneity. CHM includes complex terrain windflow and radiative transfer calculations, lapses temperature, humidity, and precipitation with elevation, redistributes snow by avalanching, wind transport and forest canopy interception and calculates the energetics of canopy and surface snowpacks. Model outputs were compared to a set of multiscale observations including snow-covered area (SCA) from Sentinel and Landsat imagery, snow depth from uncrewed aerial system lidar, and point surface observations of depth and density. Including snow redistribution and sublimation processes improved the summer SCA r2 from 0.7 to 0.9. At larger scales, inclusion of snow redistribution processes delayed full snowpack ablation by an average of 33 days, demonstrating process emergence with scale. These simulations show how multiscale modeling can improve snowpack predictions to support prediction of water supply, droughts, and floods.},
	language = {en},
	number = {8},
	urldate = {2025-01-27},
	journal = {Water Resources Research},
	author = {Marsh, Christopher B. and Lv, Zhibang and Vionnet, Vincent and Harder, Phillip and Spiteri, Raymond J. and Pomeroy, John W.},
	year = {2024},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2023WR036948},
	keywords = {NALCMS},
	pages = {e2023WR036948},
}

The melt of seasonal snowpack in mountain regions provides downstream river basins with a critical supply of freshwater. Snowdrift-permitting models have been proposed as a way to accurately simulate snowpack heterogeneity that stems from differences in energy inputs, over winter redistribution, sublimation, melt, and variations in precipitation. However, these spatial scales can be computationally intractable for large extents. In this work, the multiscale Canadian Hydrological Model (CHM) was applied to simulate snowpacks at snowdrift-permitting scales (≈50 m) across the Canadian Cordillera and adjacent regions (1.37 million km2) forced by downscaled atmospheric data. The use of a multiscale land surface representation resulted in a reduction of computational elements of 98% while preserving land-surface heterogeneity. CHM includes complex terrain windflow and radiative transfer calculations, lapses temperature, humidity, and precipitation with elevation, redistributes snow by avalanching, wind transport and forest canopy interception and calculates the energetics of canopy and surface snowpacks. Model outputs were compared to a set of multiscale observations including snow-covered area (SCA) from Sentinel and Landsat imagery, snow depth from uncrewed aerial system lidar, and point surface observations of depth and density. Including snow redistribution and sublimation processes improved the summer SCA r2 from 0.7 to 0.9. At larger scales, inclusion of snow redistribution processes delayed full snowpack ablation by an average of 33 days, demonstrating process emergence with scale. These simulations show how multiscale modeling can improve snowpack predictions to support prediction of water supply, droughts, and floods.

@article{schmidt_spatial_2024,
	title = {Spatial distribution of wildfire threat in the far north: exposure assessment in boreal communities},
	issn = {1573-0840},
	shorttitle = {Spatial distribution of wildfire threat in the far north},
	url = {https://doi.org/10.1007/s11069-023-06365-4},
	doi = {10.1007/s11069-023-06365-4},
	abstract = {Increased wildfire activity has raised concerns among communities about how to assess and prepare for this threat. There is a need for wildfire hazard assessment approaches that capture local variability to inform decisions, produce results understood by the public, and are updatable in a timely manner. We modified an existing approach to assess decadal wildfire hazards based primarily on ember dispersal and wildfire proximity, referencing landscape changes from 1984 through 2014. Our modifications created a categorical flammability hazard scheme, rather than dichotomous, and integrated wildfire exposure results across spatial scales. We used remote sensed land cover from four historical decadal points to create flammability hazard and wildfire exposure maps for three arctic communities (Anchorage and Fairbanks, Alaska and Whitehorse, Yukon). Within the Fairbanks study area, we compared 2014 flammability hazard, wildfire exposure, and FlamMap burn probabilities among burned (2014–2023) and unburned areas. Unlike burn probabilities, there were significantly higher in exposure values among burned and unburned locations (Wilcoxon; p {\textless} 0.001) and exposure rose as flammability hazard classes increased (Kruskal–Wallis; p {\textless} 0.001). Very high flammability hazard class supported 75\% of burned areas and burns tended to occur in areas with 60\% exposure or greater. Areas with high exposure values are more prone to burn and thus desirable for mitigation actions. By working with wildfire practitioners and communities, we created a tool that rapidly assesses wildfire hazards and is easily modified to help identify and prioritize mitigation activities.},
	language = {en},
	urldate = {2024-03-13},
	journal = {Natural Hazards},
	author = {Schmidt, Jennifer I. and Ziel, Robert H. and Calef, Monika P. and Varvak, Anna},
	month = jan,
	year = {2024},
	keywords = {NALCMS},
}

Increased wildfire activity has raised concerns among communities about how to assess and prepare for this threat. There is a need for wildfire hazard assessment approaches that capture local variability to inform decisions, produce results understood by the public, and are updatable in a timely manner. We modified an existing approach to assess decadal wildfire hazards based primarily on ember dispersal and wildfire proximity, referencing landscape changes from 1984 through 2014. Our modifications created a categorical flammability hazard scheme, rather than dichotomous, and integrated wildfire exposure results across spatial scales. We used remote sensed land cover from four historical decadal points to create flammability hazard and wildfire exposure maps for three arctic communities (Anchorage and Fairbanks, Alaska and Whitehorse, Yukon). Within the Fairbanks study area, we compared 2014 flammability hazard, wildfire exposure, and FlamMap burn probabilities among burned (2014–2023) and unburned areas. Unlike burn probabilities, there were significantly higher in exposure values among burned and unburned locations (Wilcoxon; p \textless 0.001) and exposure rose as flammability hazard classes increased (Kruskal–Wallis; p \textless 0.001). Very high flammability hazard class supported 75% of burned areas and burns tended to occur in areas with 60% exposure or greater. Areas with high exposure values are more prone to burn and thus desirable for mitigation actions. By working with wildfire practitioners and communities, we created a tool that rapidly assesses wildfire hazards and is easily modified to help identify and prioritize mitigation activities.

@article{ali_spatiotemporal_2024,
	title = {Spatiotemporal variability of runoff events in response to rainfall, snowmelt, and rain-on-snow in the {Lake} {Erie} {Basin}},
	volume = {53},
	issn = {2214-5818},
	url = {https://www.sciencedirect.com/science/article/pii/S2214581824001228},
	doi = {10.1016/j.ejrh.2024.101774},
	abstract = {Study Region
The Lake Erie Basin spanning Canada and the United States.
Study Focus
Countless studies have performed event-based hyetograph-hydrograph analyses. However, most studies focused on experimental forested catchments and rainfall-runoff analyses, thereby neglecting larger, mid-latitude, heterogenous watersheds where snowmelt and rain-on-snow events can generate significant runoff. The goal of the present study was, therefore, to examine runoff generation in contrasted watersheds, by: (1) quantifying the spatiotemporal variability of event-specific runoff response across the Lake Erie Basin; 2) assessing the difference in runoff response characteristics based on precipitation type; and 3) identifying the influence of event precipitation and watershed characteristics on runoff response. Daily gridded climate and gauged streamflow data spanning 2000–2019 were used for 99 watersheds, resulting in the analysis of 28,123 rainfall, snowmelt, rain-on-snow and mixed events and their associated runoff responses.
New Hydrological Insights for the Region
Rainfall events were rarely identified as the triggers of the largest runoff responses. Conversely, snowmelt and rain-on-snow events led to high-magnitude and flashy runoff responses for 70 out of 99 watersheds. Watershed characteristics pertaining to topography, soil, and land use were good predictors of the temporal variability of runoff response metrics. Given the link between runoff-driven nutrient mobilization and recurrent algal blooms in Lake Erie, this study offers a comprehensive assessment of the variability of runoff responses and triggering precipitation events across the large transboundary basin.},
	urldate = {2024-08-12},
	journal = {Journal of Hydrology: Regional Studies},
	author = {Ali, Geneviève and Siebert, Krystal and Mizero, Steven Mugisha},
	month = jun,
	year = {2024},
	keywords = {NALCMS},
	pages = {101774},
}

Study Region The Lake Erie Basin spanning Canada and the United States. Study Focus Countless studies have performed event-based hyetograph-hydrograph analyses. However, most studies focused on experimental forested catchments and rainfall-runoff analyses, thereby neglecting larger, mid-latitude, heterogenous watersheds where snowmelt and rain-on-snow events can generate significant runoff. The goal of the present study was, therefore, to examine runoff generation in contrasted watersheds, by: (1) quantifying the spatiotemporal variability of event-specific runoff response across the Lake Erie Basin; 2) assessing the difference in runoff response characteristics based on precipitation type; and 3) identifying the influence of event precipitation and watershed characteristics on runoff response. Daily gridded climate and gauged streamflow data spanning 2000–2019 were used for 99 watersheds, resulting in the analysis of 28,123 rainfall, snowmelt, rain-on-snow and mixed events and their associated runoff responses. New Hydrological Insights for the Region Rainfall events were rarely identified as the triggers of the largest runoff responses. Conversely, snowmelt and rain-on-snow events led to high-magnitude and flashy runoff responses for 70 out of 99 watersheds. Watershed characteristics pertaining to topography, soil, and land use were good predictors of the temporal variability of runoff response metrics. Given the link between runoff-driven nutrient mobilization and recurrent algal blooms in Lake Erie, this study offers a comprehensive assessment of the variability of runoff responses and triggering precipitation events across the large transboundary basin.

@article{hanson_systematic_2024,
	title = {Systematic conservation prioritization with the prioritizr {R} package},
	volume = {n/a},
	copyright = {© 2024 The Author(s). Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.},
	issn = {1523-1739},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cobi.14376},
	doi = {10.1111/cobi.14376},
	abstract = {Plans for expanding protected area systems (prioritizations) need to fulfill conservation objectives. They also need to account for other factors, such as economic feasibility and anthropogenic land-use requirements. Although prioritizations are often generated with decision support tools, most tools have limitations that hinder their use for decision-making. We outlined how the prioritizr R package (https://prioritizr.net) can be used for systematic conservation prioritization. This decision support tool provides a flexible interface to build conservation planning problems. It can leverage a variety of commercial (e.g., Gurobi) and open-source (e.g., CBC and SYMPHONY) exact algorithm solvers to identify optimal solutions in a short period. It is also compatible with a variety of spatially explicit (e.g., ESRI Shapefile, GeoTIFF) and nonspatial tabular (e.g., Microsoft Excel Spreadsheet) data formats. Additionally, it provides functionality for evaluating prioritizations, such as assessing the relative importance of different places selected by a prioritization. To showcase the prioritizr R package, we applied it to a case study based in Washington state (United States) for which we developed a prioritization to improve protected area coverage of native avifauna. We accounted for land acquisition costs, existing protected areas, places that might not be suitable for protected area establishment, and spatial fragmentation. We also conducted a benchmark analysis to examine the performance of different solvers. The prioritization identified 12,400 km2 of priority areas for increasing the percentage of species’ distributions covered by protected areas. Although open source and commercial solvers were able to quickly solve large-scale conservation planning problems, commercial solvers were required for complex, large-scale problems.. The prioritizr R package is available on the Comprehensive R Archive Network (CRAN). In addition to reserve selection, it can inform habitat restoration, connectivity enhancement, and ecosystem service provisioning. It has been used in numerous conservation planning exercises to inform best practices and aid real-world decision-making.},
	language = {en},
	number = {n/a},
	urldate = {2025-01-27},
	journal = {Conservation Biology},
	author = {Hanson, Jeffrey O. and Schuster, Richard and Strimas-Mackey, Matthew and Morrell, Nina and Edwards, Brandon P. M. and Arcese, Peter and Bennett, Joseph R. and Possingham, Hugh P.},
	month = sep,
	year = {2024},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/cobi.14376},
	keywords = {NALCMS},
	pages = {e14376},
}

Plans for expanding protected area systems (prioritizations) need to fulfill conservation objectives. They also need to account for other factors, such as economic feasibility and anthropogenic land-use requirements. Although prioritizations are often generated with decision support tools, most tools have limitations that hinder their use for decision-making. We outlined how the prioritizr R package (https://prioritizr.net) can be used for systematic conservation prioritization. This decision support tool provides a flexible interface to build conservation planning problems. It can leverage a variety of commercial (e.g., Gurobi) and open-source (e.g., CBC and SYMPHONY) exact algorithm solvers to identify optimal solutions in a short period. It is also compatible with a variety of spatially explicit (e.g., ESRI Shapefile, GeoTIFF) and nonspatial tabular (e.g., Microsoft Excel Spreadsheet) data formats. Additionally, it provides functionality for evaluating prioritizations, such as assessing the relative importance of different places selected by a prioritization. To showcase the prioritizr R package, we applied it to a case study based in Washington state (United States) for which we developed a prioritization to improve protected area coverage of native avifauna. We accounted for land acquisition costs, existing protected areas, places that might not be suitable for protected area establishment, and spatial fragmentation. We also conducted a benchmark analysis to examine the performance of different solvers. The prioritization identified 12,400 km2 of priority areas for increasing the percentage of species’ distributions covered by protected areas. Although open source and commercial solvers were able to quickly solve large-scale conservation planning problems, commercial solvers were required for complex, large-scale problems.. The prioritizr R package is available on the Comprehensive R Archive Network (CRAN). In addition to reserve selection, it can inform habitat restoration, connectivity enhancement, and ecosystem service provisioning. It has been used in numerous conservation planning exercises to inform best practices and aid real-world decision-making.

@article{saenz-ceja_temporal_2024,
	title = {Temporal and spatial patterns of fire regime disruption in conifer forests of western {North} {America}},
	volume = {0},
	issn = {0272-3646},
	url = {https://doi.org/10.1080/02723646.2024.2331292},
	doi = {10.1080/02723646.2024.2331292},
	abstract = {Temporal and spatial patterns of fire regime disruption were reconstructed in conifer forests of western North America from information on pre-disruption and disrupted mean fire intervals (MFIs) of 498 dendrochronology-based fire chronologies. We identified the conifer forest types most affected by MFI shift and the influence of land category designation on MFI change. We also mapped the years of the MFI shift, the last fire recorded, and disrupted/pre-disruption MFI ratios. Fire cessation and longer MFIs predominated in most fire chronologies and conifer forest types. MFI was significantly higher in most conifer forest types, with most differences in dry conifer forests. MFI shift occurred mainly before the designation of protected, federal, social-property, and private areas. MFI shift began in 1829 in the United States of America Southwest, a region subjected to prolonged fire exclusion and with the highest disrupted/pre-disruption MFI ratios. Fire regime disruption moved gradually into the Pacific Northwest and the Sierra Nevada until reaching the northern conifer forests of Canada and Alaska. In contrast, one-third of fire chronologies in Mexican conifer forests retained pre-disruption MFIs. Our findings allowed us to identify areas with MFIs outside of their natural variability, with prolonged fire exclusion, or with intact fire regimes.},
	number = {0},
	urldate = {2024-04-24},
	journal = {Physical Geography},
	author = {Sáenz-Ceja, Jesús E. and Mendoza, Manuel E.},
	year = {2024},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/02723646.2024.2331292},
	keywords = {NALCMS, Protected Areas},
	pages = {1--33},
}

Temporal and spatial patterns of fire regime disruption were reconstructed in conifer forests of western North America from information on pre-disruption and disrupted mean fire intervals (MFIs) of 498 dendrochronology-based fire chronologies. We identified the conifer forest types most affected by MFI shift and the influence of land category designation on MFI change. We also mapped the years of the MFI shift, the last fire recorded, and disrupted/pre-disruption MFI ratios. Fire cessation and longer MFIs predominated in most fire chronologies and conifer forest types. MFI was significantly higher in most conifer forest types, with most differences in dry conifer forests. MFI shift occurred mainly before the designation of protected, federal, social-property, and private areas. MFI shift began in 1829 in the United States of America Southwest, a region subjected to prolonged fire exclusion and with the highest disrupted/pre-disruption MFI ratios. Fire regime disruption moved gradually into the Pacific Northwest and the Sierra Nevada until reaching the northern conifer forests of Canada and Alaska. In contrast, one-third of fire chronologies in Mexican conifer forests retained pre-disruption MFIs. Our findings allowed us to identify areas with MFIs outside of their natural variability, with prolonged fire exclusion, or with intact fire regimes.

@article{fernandez-figueroa_green_2024,
	title = {The green convergence: {United} {States} lakes are collectively moving toward a eutrophic state},
	volume = {139},
	issn = {1568-9883},
	shorttitle = {The green convergence},
	url = {https://www.sciencedirect.com/science/article/pii/S1568988324001549},
	doi = {10.1016/j.hal.2024.102721},
	abstract = {Nutrient enrichment and climate change promote algal blooms, leading to many lakes being characterized as eutrophic (i.e., green) worldwide. We examined recent eutrophication trends of freshwater lakes at a national scale by collating 32 years (1990–2021) of growing season (July-September) in situ chlorophyll-a, nutrient, transparency, and climate data for 1,082 lakes across 32 freshwater ecoregions in the United States. Based on chlorophyll-a, 78.2 \% (427/546) of lakes initially exhibited eutrophic conditions and have remained eutrophic. Moreover, non-eutrophic lakes converged toward a eutrophic state, with oligotrophic (i.e., clear) or mesotrophic (i.e., moderately clear) lakes becoming greener, and hypereutrophic (i.e., very green) becoming less green. Optimized Hot Spot Analysis suggests lakes in the Appalachian Piedmont and Apalachicola freshwater ecoregions eutrophied more rapidly than other locations. Results suggest nutrient management targeting eutrophic lakes has hindered further degradation, but poor preventative management of clear lakes has led to their eutrophication.},
	urldate = {2025-01-27},
	journal = {Harmful Algae},
	author = {Fernandez-Figueroa, Edna G. and Rogers, Stephanie R. and Waters, Matthew N. and Wilson, Alan E.},
	month = nov,
	year = {2024},
	keywords = {NALCMS},
	pages = {102721},
}

Nutrient enrichment and climate change promote algal blooms, leading to many lakes being characterized as eutrophic (i.e., green) worldwide. We examined recent eutrophication trends of freshwater lakes at a national scale by collating 32 years (1990–2021) of growing season (July-September) in situ chlorophyll-a, nutrient, transparency, and climate data for 1,082 lakes across 32 freshwater ecoregions in the United States. Based on chlorophyll-a, 78.2 % (427/546) of lakes initially exhibited eutrophic conditions and have remained eutrophic. Moreover, non-eutrophic lakes converged toward a eutrophic state, with oligotrophic (i.e., clear) or mesotrophic (i.e., moderately clear) lakes becoming greener, and hypereutrophic (i.e., very green) becoming less green. Optimized Hot Spot Analysis suggests lakes in the Appalachian Piedmont and Apalachicola freshwater ecoregions eutrophied more rapidly than other locations. Results suggest nutrient management targeting eutrophic lakes has hindered further degradation, but poor preventative management of clear lakes has led to their eutrophication.

@article{hethcoat_unrecorded_2024,
	title = {Unrecorded {Tundra} {Fires} in {Canada}, 1986–2022},
	volume = {16},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/16/2/230},
	doi = {10.3390/rs16020230},
	abstract = {Climate-driven changes in fire regimes are expected across the pan-Arctic region. Trends in arctic fires are thought to be generally increasing; however, fire mapping across the region is far from comprehensive or systematic. We developed a new detection workflow and built a dataset of unrecorded tundra fires in Canada using Landsat data. We built a reference dataset of spectral indices from previously mapped fires in northern Canada to train a Random Forest model for detecting new fires between 1986 and 2022. In addition, we used time series information for each pixel to reduce false positives and narrow the large search space down to a finite set of regions that had experienced changes. We found 209 previously undetected fires in the Arctic and sub-Arctic regions, increasing the mapped burned area by approximately 30\%. The median fire size was small, with roughly 3/4 of the fires being {\textless}100 ha in size. The majority of newly detected fires (69\%) did not have satellite-derived hotspots associated with them. The dataset presented here is commission error-free and can be viewed as a reference dataset for future analyses. Moreover, future improvements and updates will leverage these data to improve the detection workflow outlined here, particularly for small and low-severity fires. These data can facilitate broader analyses that examine trends and environmental drivers of fire across the Arctic region. Such analyses could begin to untangle the mechanisms driving heterogeneous fire responses to climate observed across regions of the Circumpolar North.},
	language = {en},
	number = {2},
	urldate = {2024-03-20},
	journal = {Remote Sensing},
	author = {Hethcoat, Matthew G. and Jain, Piyush and Parisien, Marc-André and Skakun, Rob and Rogic, Luka and Whitman, Ellen},
	month = jan,
	year = {2024},
	note = {Number: 2
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {NALCMS},
	pages = {230},
}

Climate-driven changes in fire regimes are expected across the pan-Arctic region. Trends in arctic fires are thought to be generally increasing; however, fire mapping across the region is far from comprehensive or systematic. We developed a new detection workflow and built a dataset of unrecorded tundra fires in Canada using Landsat data. We built a reference dataset of spectral indices from previously mapped fires in northern Canada to train a Random Forest model for detecting new fires between 1986 and 2022. In addition, we used time series information for each pixel to reduce false positives and narrow the large search space down to a finite set of regions that had experienced changes. We found 209 previously undetected fires in the Arctic and sub-Arctic regions, increasing the mapped burned area by approximately 30%. The median fire size was small, with roughly 3/4 of the fires being \textless100 ha in size. The majority of newly detected fires (69%) did not have satellite-derived hotspots associated with them. The dataset presented here is commission error-free and can be viewed as a reference dataset for future analyses. Moreover, future improvements and updates will leverage these data to improve the detection workflow outlined here, particularly for small and low-severity fires. These data can facilitate broader analyses that examine trends and environmental drivers of fire across the Arctic region. Such analyses could begin to untangle the mechanisms driving heterogeneous fire responses to climate observed across regions of the Circumpolar North.

@article{udell_using_2024,
	title = {Using mobile acoustic monitoring and false-positive {N}-mixture models to estimate bat abundance and population trends},
	volume = {94},
	copyright = {Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Ecological Monographs published by Wiley Periodicals LLC on behalf of The Ecological Society of America.},
	issn = {1557-7015},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ecm.1617},
	doi = {10.1002/ecm.1617},
	abstract = {Estimating the abundance of unmarked animal populations from acoustic data is challenging due to the inability to identify individuals and the need to adjust for observation biases including detectability (false negatives), species misclassification (false positives), and sampling exposure. Acoustic surveys conducted along mobile transects were designed to avoid counting individuals more than once, where raw counts are commonly treated as an index of abundance. More recently, false-positive abundance models have been developed to estimate abundance while accounting for imperfect detection and misclassification. We adapted these methods to model summertime abundance and trends of three species of bats at multiple spatial scales using acoustic recordings collected along mobile transects by partners of the North American Bat Monitoring Program (NABat) from 2012 to 2020. This multiscale modeling spanned individual transect routes, larger NABat grid cells (10 km × 10 km), and across the entire extent of modeled species ranges. We estimated relationships between species abundances and a suite of abiotic and biotic predictors (landcover types, climatological variables, physiographic diversity, building density, and the impacts of white-nose syndrome [WNS]) and found varying levels of support between species. We present clear evidence of substantial declines in populations of tricolored bats (Perimyotis subflavus) and little brown bats (Myotis lucifugus), declines that corresponded in space and time with the progression of WNS, a devastating disease of hibernating bats. In contrast, our analysis revealed that similar population-wide declines probably have not occurred in big brown bats (Eptesicus fuscus), a species known to be less affected by WNS. This study provides the first abundance-based species distribution predictions and population trends for bats in their summer ranges in North America. These models will probably be applicable to assessing wildlife populations in other monitoring programs where acoustic data are used or where false-negative and false-positive detections are present. Finally, our abundance framework (as a spatial point pattern process) can serve as a foundation from which more sophisticated integrated species distribution models that incorporate additional streams of monitoring data (e.g., stationary acoustics, captures) can be developed for North American bats.},
	language = {en},
	number = {4},
	urldate = {2025-01-27},
	journal = {Ecological Monographs},
	author = {Udell, Bradley J. and Straw, Bethany Rose and Loeb, Susan C. and Irvine, Kathryn M. and Thogmartin, Wayne E. and Lausen, Cori L. and Reichard, Jonathan D. and Coleman, Jeremy T. H. and Cryan, Paul M. and Frick, Winifred F. and Reichert, Brian E.},
	year = {2024},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ecm.1617},
	keywords = {NALCMS},
	pages = {e1617},
}

Estimating the abundance of unmarked animal populations from acoustic data is challenging due to the inability to identify individuals and the need to adjust for observation biases including detectability (false negatives), species misclassification (false positives), and sampling exposure. Acoustic surveys conducted along mobile transects were designed to avoid counting individuals more than once, where raw counts are commonly treated as an index of abundance. More recently, false-positive abundance models have been developed to estimate abundance while accounting for imperfect detection and misclassification. We adapted these methods to model summertime abundance and trends of three species of bats at multiple spatial scales using acoustic recordings collected along mobile transects by partners of the North American Bat Monitoring Program (NABat) from 2012 to 2020. This multiscale modeling spanned individual transect routes, larger NABat grid cells (10 km × 10 km), and across the entire extent of modeled species ranges. We estimated relationships between species abundances and a suite of abiotic and biotic predictors (landcover types, climatological variables, physiographic diversity, building density, and the impacts of white-nose syndrome [WNS]) and found varying levels of support between species. We present clear evidence of substantial declines in populations of tricolored bats (Perimyotis subflavus) and little brown bats (Myotis lucifugus), declines that corresponded in space and time with the progression of WNS, a devastating disease of hibernating bats. In contrast, our analysis revealed that similar population-wide declines probably have not occurred in big brown bats (Eptesicus fuscus), a species known to be less affected by WNS. This study provides the first abundance-based species distribution predictions and population trends for bats in their summer ranges in North America. These models will probably be applicable to assessing wildlife populations in other monitoring programs where acoustic data are used or where false-negative and false-positive detections are present. Finally, our abundance framework (as a spatial point pattern process) can serve as a foundation from which more sophisticated integrated species distribution models that incorporate additional streams of monitoring data (e.g., stationary acoustics, captures) can be developed for North American bats.

@article{li_validation_2024,
	title = {Validation of the vertical canopy cover profile products derived from {GEDI} over selected forest sites},
	volume = {10},
	issn = {2666-0172},
	url = {https://www.sciencedirect.com/science/article/pii/S2666017224000427},
	doi = {10.1016/j.srs.2024.100158},
	abstract = {Canopy cover (CC) quantifies the proportion of canopy materials projected vertically onto the ground surface. CC is a crucial canopy structural variable and is commonly used in many ecological and climatic models. The vertical CC profile product is currently available from the Global Ecosystem Dynamics Investigation (GEDI). However, detailed information about the accuracy and uncertainty of the GEDI vertical CC profile product remains limited. The objective of this study is to validate the GEDI CC product over selected forest sites using reference values derived from digital hemispherical photography (DHP), airborne laser scanning (ALS) point clouds, and simulated waveforms. The accuracy of CC was quantified and analyzed regarding GEDI observation conditions, waveform processing, and estimation methods. The results show that the GEDI total CC correlates well with those estimated from DHP, ALS, and simulated waveform data (r2 = 0.65, 0.71, and 0.71, respectively) but is systematically underestimated (bias = −0.05, −0.11, and −0.07, respectively) based on reference data. Compared with the ALS-estimated CC, needleleaf forest shows the highest correlation for vertical CC (r2 ≥ 0.65) and shrubland shows the lowest bias for total CC (bias = −0.13). The mean absolute error (MAE) of the GEDI CC decreases from 0.15 to 0.09 as the estimation height increases from ground to 35 m. The GEDI total CCs derived from the waveform interpretation algorithms A2 and A6 display the highest r2 (≥ 0.6) and smallest RMSE (≤ 0.23) compared to those of the other algorithms. The CC accuracy increases with beam sensitivity and decreases with increasing canopy cover. The GEDI CC was improved at moderate CC values using a canopy-to-ground backscattering coefficient ratio (ρv/ρg) determined with the regression method. The partial difference between GEDI CC and ALS CC is attributed to definitional discrepancies. Further improvement of the CC algorithm can be made by using vegetation-specific waveform processing algorithms and realistic ρv/ρg values.},
	urldate = {2025-01-17},
	journal = {Science of Remote Sensing},
	author = {Li, Yu and Fang, Hongliang and Wang, Yao and Li, Sijia and Ma, Tian and Wu, Yunjia and Tang, Hao},
	month = dec,
	year = {2024},
	keywords = {NALCMS},
	pages = {100158},
}

Canopy cover (CC) quantifies the proportion of canopy materials projected vertically onto the ground surface. CC is a crucial canopy structural variable and is commonly used in many ecological and climatic models. The vertical CC profile product is currently available from the Global Ecosystem Dynamics Investigation (GEDI). However, detailed information about the accuracy and uncertainty of the GEDI vertical CC profile product remains limited. The objective of this study is to validate the GEDI CC product over selected forest sites using reference values derived from digital hemispherical photography (DHP), airborne laser scanning (ALS) point clouds, and simulated waveforms. The accuracy of CC was quantified and analyzed regarding GEDI observation conditions, waveform processing, and estimation methods. The results show that the GEDI total CC correlates well with those estimated from DHP, ALS, and simulated waveform data (r2 = 0.65, 0.71, and 0.71, respectively) but is systematically underestimated (bias = −0.05, −0.11, and −0.07, respectively) based on reference data. Compared with the ALS-estimated CC, needleleaf forest shows the highest correlation for vertical CC (r2 ≥ 0.65) and shrubland shows the lowest bias for total CC (bias = −0.13). The mean absolute error (MAE) of the GEDI CC decreases from 0.15 to 0.09 as the estimation height increases from ground to 35 m. The GEDI total CCs derived from the waveform interpretation algorithms A2 and A6 display the highest r2 (≥ 0.6) and smallest RMSE (≤ 0.23) compared to those of the other algorithms. The CC accuracy increases with beam sensitivity and decreases with increasing canopy cover. The GEDI CC was improved at moderate CC values using a canopy-to-ground backscattering coefficient ratio (ρv/ρg) determined with the regression method. The partial difference between GEDI CC and ALS CC is attributed to definitional discrepancies. Further improvement of the CC algorithm can be made by using vegetation-specific waveform processing algorithms and realistic ρv/ρg values.

@article{iverson_winter_2024,
	title = {Winter {GPS} tagging reveals home ranges during the breeding season for a boreal-nesting migrant songbird, the {Golden}-crowned {Sparrow}},
	volume = {19},
	issn = {1932-6203},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0305369},
	doi = {10.1371/journal.pone.0305369},
	abstract = {Determining space use for species is fundamental to understanding their ecology, and tracking animals can reveal insights into their spatial ecology on home ranges and territories. Recent technological advances have led to GPS-tracking devices light enough for birds as small as {\textasciitilde}30 g, creating novel opportunities to remotely monitor fine-scale movements and space use for these smaller species. We tested whether miniaturized GPS tags can allow us to understand space use of migratory birds away from their capture sites and sought to understand both pre-breeding space use as well as territory and habitat use on the breeding grounds. We used GPS tags to characterize home ranges on the breeding grounds for a migratory songbird with limited available breeding information, the Golden-crowned Sparrow (Zonotrichia atricapilla). Using GPS points from 23 individuals across 26 tags (three birds tagged twice), we found home ranges in Alaska and British Columbia were on average 44.1 ha (95\% kernel density estimate). In addition, estimates of territory sizes based on field observations (mean 2.1 ha, 95\% minimum convex polygon [MCP]) were three times smaller than 95\% MCPs created using GPS tags (mean 6.5 ha). Home ranges included a variety of land cover classes, with shrubland particularly dominant (64–100\% of home range cover for all but one bird). Three birds tracked twice returned to the same breeding area each year, supporting high breeding site fidelity for this species. We found reverse spring migration for five birds that flew up to 154 km past breeding destinations before returning. GPS-tracking technology allowed for critical ecological insights into this migratory species that breeds in very remote locations.},
	language = {en},
	number = {6},
	urldate = {2024-08-12},
	journal = {PLOS ONE},
	author = {Iverson, Autumn R. and Humple, Diana L. and Cormier, Renée L. and Hahn, Thomas P. and Block, Theadora A. and Shizuka, Daizaburo and Lyon, Bruce E. and Chaine, Alexis S. and Hudson, Emily J. and Hull, Elisha M.},
	month = jun,
	year = {2024},
	note = {Publisher: Public Library of Science},
	keywords = {NALCMS},
	pages = {e0305369},
}

Determining space use for species is fundamental to understanding their ecology, and tracking animals can reveal insights into their spatial ecology on home ranges and territories. Recent technological advances have led to GPS-tracking devices light enough for birds as small as ~30 g, creating novel opportunities to remotely monitor fine-scale movements and space use for these smaller species. We tested whether miniaturized GPS tags can allow us to understand space use of migratory birds away from their capture sites and sought to understand both pre-breeding space use as well as territory and habitat use on the breeding grounds. We used GPS tags to characterize home ranges on the breeding grounds for a migratory songbird with limited available breeding information, the Golden-crowned Sparrow (Zonotrichia atricapilla). Using GPS points from 23 individuals across 26 tags (three birds tagged twice), we found home ranges in Alaska and British Columbia were on average 44.1 ha (95% kernel density estimate). In addition, estimates of territory sizes based on field observations (mean 2.1 ha, 95% minimum convex polygon [MCP]) were three times smaller than 95% MCPs created using GPS tags (mean 6.5 ha). Home ranges included a variety of land cover classes, with shrubland particularly dominant (64–100% of home range cover for all but one bird). Three birds tracked twice returned to the same breeding area each year, supporting high breeding site fidelity for this species. We found reverse spring migration for five birds that flew up to 154 km past breeding destinations before returning. GPS-tracking technology allowed for critical ecological insights into this migratory species that breeds in very remote locations.

@article{yeardley_comparison_2023,
	title = {A {Comparison} of {Two} {Macroinvertebrate} {Multi}-{Plate} {Sampling} {Methods} to {Inform} {Great} {Lakes} {Monitoring} and {Remediation} {Efforts}},
	volume = {14},
	copyright = {http://creativecommons.org/licenses/by/4.0/},
	url = {https://www.scirp.org/journal/paperinformation.aspx?paperid=129647},
	doi = {10.4236/jep.2023.1412052},
	abstract = {Hester-Dendy (HD) multi-plate samplers have been widely used by state and federal government agencies for bioassessment of water quality through use of macroinvertebrate community data. To help guide remediation and restoration efforts at the Niagara River Great Lakes Area of Concern site, a multi-agency study was conducted in 2014 to assess the contribution of seven major urban tributaries on the US side of the river toward the impairment of the Niagara River. As part of this study, macroinvertebrate communities were sampled using two co-located versions of HD samplers: one version used by the New York State Department of Environmental Conservation (NYSDEC) and another by the US Environmental Protection Agency Office of Research and Development. Samplers were deployed in tributaries in highly developed watersheds with high percent impervious surface. The two sampling methods varied in terms of number and size of plates, between-plate spacing, and deployment method. Comparison of the similarity/grouping of communities with multivariate ordination techniques, Nonmetric Multidimensional Scaling and Multi-Response Permutation Procedure, showed that both methods were able to detect differences in communities at stations, despite some grouping by month and method. The indices and metrics derived from the two HD methods were found to give comparable but not identical assessments of water quality. Despite their differences, the methods were robust with respect to water quality categories derived from indices used nationally (HBI) and by NY state (BAP). For the common richness metrics, total taxa and EPT richness, there was no statistical difference between means from 3 samplings. Some metrics, especially percent tolerant collector-gatherer individuals, did show significant differences at certain stations. Indicator Species Analysis showed some taxa associated with each method. The observed community differences were thought mostly due to the difference in sampler deployment position.},
	language = {en},
	number = {12},
	urldate = {2024-03-11},
	journal = {Journal of Environmental Protection},
	author = {Yeardley, Roger and Duffy, Brian and Kimbrough, Kimani and Lazorchak, Jim and Mills, Marc A. and Johnson, Ed},
	month = dec,
	year = {2023},
	note = {Number: 12
Publisher: Scientific Research Publishing},
	keywords = {NALCMS},
	pages = {933--953},
}

Hester-Dendy (HD) multi-plate samplers have been widely used by state and federal government agencies for bioassessment of water quality through use of macroinvertebrate community data. To help guide remediation and restoration efforts at the Niagara River Great Lakes Area of Concern site, a multi-agency study was conducted in 2014 to assess the contribution of seven major urban tributaries on the US side of the river toward the impairment of the Niagara River. As part of this study, macroinvertebrate communities were sampled using two co-located versions of HD samplers: one version used by the New York State Department of Environmental Conservation (NYSDEC) and another by the US Environmental Protection Agency Office of Research and Development. Samplers were deployed in tributaries in highly developed watersheds with high percent impervious surface. The two sampling methods varied in terms of number and size of plates, between-plate spacing, and deployment method. Comparison of the similarity/grouping of communities with multivariate ordination techniques, Nonmetric Multidimensional Scaling and Multi-Response Permutation Procedure, showed that both methods were able to detect differences in communities at stations, despite some grouping by month and method. The indices and metrics derived from the two HD methods were found to give comparable but not identical assessments of water quality. Despite their differences, the methods were robust with respect to water quality categories derived from indices used nationally (HBI) and by NY state (BAP). For the common richness metrics, total taxa and EPT richness, there was no statistical difference between means from 3 samplings. Some metrics, especially percent tolerant collector-gatherer individuals, did show significant differences at certain stations. Indicator Species Analysis showed some taxa associated with each method. The observed community differences were thought mostly due to the difference in sampler deployment position.

@techreport{dupont_social-ecological_2023,
	type = {preprint},
	title = {A social-ecological geography of southern {Canadian} {Lakes}},
	url = {http://biorxiv.org/lookup/doi/10.1101/2023.03.09.531893},
	abstract = {Abstract
          Anthropogenic pressures, including urban and agricultural expansion, can negatively influence a lake’s capacity to provide aquatic ecosystem services (ES). However, identifying lakes most at risk of losing their ES requires integrating information on lake ecological state, global change threats, and ES demand. Here, we provide a social-ecological framework that combines these features within a regional context based on an ecological evaluation of the state of 659 lakes across Canada. From deviation of impacted lakes to reference ones, we identified much higher concentrations of total nitrogen and chloride as the main indicators of altered lake ecological state in all regions identified. Lake ecological state was mapped using an additive colour model along with regional scores of threat levels and recreational ES demand. Population density and agriculture were linked to high lake vulnerability. Lakes in Southern Ontario were most concerning, being highly altered, under threat, and heavily used. Lakes near urban centers along coasts were altered and used, but less threatened, whereas those in the Prairies were altered and threatened, but less used. Our novel framework provides the first social-ecological geography of Canadian lakes, and, is a promising tool to assess lake state and vulnerability at scales relevant for management.
          
            Plan language summary
            Plain language title: Assessing overall lake health across Canada to identify sites for restoration and conservation
            Canadians love to swim, fish, and navigate in and on the countless lakes across the country. But Canadian lakes are under a considerable amount of pressure from human activities in their watershed. The expansion of cities, intensive farming, wetland loss, and industrial development all results in the transfer of pollutants to aquatic habitats, threatening the health of lakes and the ecosystem services they provide. Where are lakes being used across Canada? What condition are they in and is their use under threat from different pressures? To answer these questions, we combined information from many different sources, including a national scale lake assessment, through the NSERC Strategic Network Cluster Lake Pulse to create the first social-ecological geography of southern Canadian lakes. Regionally specific baseline conditions were established from lakes considered healthy due to limited human activities in their watershed. When lakes with impacted watershed were compared to healthy ones within their specific region, two early warning signals of human pressure, pollution from nitrogen found in fertilizers and sewage, and chloride found in road salt, determined whether a lake was altered. We combined these two health indicators, with information on future potential lake threats and use by the population for recreational purposes. Using a colour-coded mapping technique, we were able to identify regions where lakes were altered, threatened, and used. These regions occurred primarily around dense urban areas, of southern Ontario and Quebec, and major cities on the east and west coast. Lakes were altered and threatened, but seemingly less used in the Prairie Provinces. The novel approach is very adaptable, easy to understand, and can be used at more regional levels for management to determine priority sites for conservation and restoration, as well as in science communication to describe overall lake health.},
	language = {en},
	urldate = {2023-06-02},
	institution = {Ecology},
	author = {Dupont, Andréanne and Botrel, Morgan and St-Gelais, Nicolas Fortin and Poisot, Timothée and Maranger, Roxane},
	month = mar,
	year = {2023},
	doi = {10.1101/2023.03.09.531893},
	keywords = {NALCMS},
}

Abstract Anthropogenic pressures, including urban and agricultural expansion, can negatively influence a lake’s capacity to provide aquatic ecosystem services (ES). However, identifying lakes most at risk of losing their ES requires integrating information on lake ecological state, global change threats, and ES demand. Here, we provide a social-ecological framework that combines these features within a regional context based on an ecological evaluation of the state of 659 lakes across Canada. From deviation of impacted lakes to reference ones, we identified much higher concentrations of total nitrogen and chloride as the main indicators of altered lake ecological state in all regions identified. Lake ecological state was mapped using an additive colour model along with regional scores of threat levels and recreational ES demand. Population density and agriculture were linked to high lake vulnerability. Lakes in Southern Ontario were most concerning, being highly altered, under threat, and heavily used. Lakes near urban centers along coasts were altered and used, but less threatened, whereas those in the Prairies were altered and threatened, but less used. Our novel framework provides the first social-ecological geography of Canadian lakes, and, is a promising tool to assess lake state and vulnerability at scales relevant for management. Plan language summary Plain language title: Assessing overall lake health across Canada to identify sites for restoration and conservation Canadians love to swim, fish, and navigate in and on the countless lakes across the country. But Canadian lakes are under a considerable amount of pressure from human activities in their watershed. The expansion of cities, intensive farming, wetland loss, and industrial development all results in the transfer of pollutants to aquatic habitats, threatening the health of lakes and the ecosystem services they provide. Where are lakes being used across Canada? What condition are they in and is their use under threat from different pressures? To answer these questions, we combined information from many different sources, including a national scale lake assessment, through the NSERC Strategic Network Cluster Lake Pulse to create the first social-ecological geography of southern Canadian lakes. Regionally specific baseline conditions were established from lakes considered healthy due to limited human activities in their watershed. When lakes with impacted watershed were compared to healthy ones within their specific region, two early warning signals of human pressure, pollution from nitrogen found in fertilizers and sewage, and chloride found in road salt, determined whether a lake was altered. We combined these two health indicators, with information on future potential lake threats and use by the population for recreational purposes. Using a colour-coded mapping technique, we were able to identify regions where lakes were altered, threatened, and used. These regions occurred primarily around dense urban areas, of southern Ontario and Quebec, and major cities on the east and west coast. Lakes were altered and threatened, but seemingly less used in the Prairie Provinces. The novel approach is very adaptable, easy to understand, and can be used at more regional levels for management to determine priority sites for conservation and restoration, as well as in science communication to describe overall lake health.

@incollection{pilliod_amphibians_2023,
	address = {Cham},
	title = {Amphibians and {Reptiles}},
	isbn = {978-3-031-34037-6},
	url = {https://doi.org/10.1007/978-3-031-34037-6_25},
	abstract = {Amphibians and reptiles are a diverse group of ectothermic vertebrates that occupy a variety of habitats in rangelands of North America, from wetlands to the driest deserts. These two classes of vertebrates are often referred to as herpetofauna and are studied under the field of herpetology. In U.S. rangelands, there are approximately 66 species of frogs and toads, 58 salamanders, 98 lizards, 111 snakes, and 27 turtles and tortoises. Herpetofauna tend to be poorly studied compared with other vertebrates, which creates a challenge for biologists and landowners who are trying to manage rangeland activities for this diverse group of animals and their habitats. Degradation of habitats from human land use and alteration of natural processes, like wildfire, are primary threats to herpetofauna populations. Disease, non-native predators, collection for the pet trade, and persecution are also conservation concerns for some species. Properly managed livestock grazing is generally compatible with herpetofauna conservation, and private and public rangelands provide crucial habitat for many species. Climate change also poses a threat to herpetofauna, but we have an incomplete understanding of the potential effects on species. Dispersal and adaptation could provide some capacity for species to persist on rangelands as climates, disturbance regimes, and habitats change. However, inadequate information and considerable uncertainty will make climate mitigation planning difficult for the foreseeable future. Planning for and mitigating effects of climate change, and interactions with other stressors, is an urgent area for research. Maintaining large, heterogeneous land areas as rangelands will certainly be an important part of the conservation strategy for herpetofauna in North America.},
	language = {en},
	urldate = {2023-10-04},
	booktitle = {Rangeland {Wildlife} {Ecology} and {Conservation}},
	publisher = {Springer International Publishing},
	author = {Pilliod, David S. and Esque, Todd C.},
	editor = {McNew, Lance B. and Dahlgren, David K. and Beck, Jeffrey L.},
	year = {2023},
	doi = {10.1007/978-3-031-34037-6_25},
	keywords = {NALCMS},
	pages = {861--895},
}

Amphibians and reptiles are a diverse group of ectothermic vertebrates that occupy a variety of habitats in rangelands of North America, from wetlands to the driest deserts. These two classes of vertebrates are often referred to as herpetofauna and are studied under the field of herpetology. In U.S. rangelands, there are approximately 66 species of frogs and toads, 58 salamanders, 98 lizards, 111 snakes, and 27 turtles and tortoises. Herpetofauna tend to be poorly studied compared with other vertebrates, which creates a challenge for biologists and landowners who are trying to manage rangeland activities for this diverse group of animals and their habitats. Degradation of habitats from human land use and alteration of natural processes, like wildfire, are primary threats to herpetofauna populations. Disease, non-native predators, collection for the pet trade, and persecution are also conservation concerns for some species. Properly managed livestock grazing is generally compatible with herpetofauna conservation, and private and public rangelands provide crucial habitat for many species. Climate change also poses a threat to herpetofauna, but we have an incomplete understanding of the potential effects on species. Dispersal and adaptation could provide some capacity for species to persist on rangelands as climates, disturbance regimes, and habitats change. However, inadequate information and considerable uncertainty will make climate mitigation planning difficult for the foreseeable future. Planning for and mitigating effects of climate change, and interactions with other stressors, is an urgent area for research. Maintaining large, heterogeneous land areas as rangelands will certainly be an important part of the conservation strategy for herpetofauna in North America.

@phdthesis{haight_anthropogenic_2023,
	title = {Anthropogenic {Environments} {Shape} {Wildlife} {Communities} and {Human}-{Wildlife} {Coexistence} {Across} {Urbanizing} {Landscapes}},
	url = {https://keep.lib.asu.edu/items/189248},
	abstract = {With a growing majority of humans living within cities and towns, urbanization is one of the most persistent drivers of change in global land use and challenges to sustainability and biodiversity conservation. The development of cities and towns can substantially shape local and regional environments in which wildlife communities persist. Although urbanization can negatively affect wildlife communities – through processes such as habitat fragmentation and non-native species introduction – cities can also provide resources to wildlife, such as through food, water, and space, creating potential opportunities for conservation. However, managing wildlife communities persisting in urbanizing landscapes requires better understanding of how urbanized landscapes influence the ability of wildlife to coexist with one another and with people at local and regional scales. In this dissertation, I addressed these research needs by evaluating the environmental and human factors driving dynamic wildlife community distributions and people’s attitudes towards wildlife. In my first two chapters,I used wildlife camera data collected from across the Phoenix Metropolitan Area, AZ to examine seasonal patterns of wildlife space use, species richness, and interspecific interactions across levels of urbanization with varying landscape characteristics, including plant productivity and spatial land use heterogeneity. Here I found that urbanization was a primary driver of wildlife community characteristics within the region, but that seasonal resource availability and landscape heterogeneity could have mediating influences that require further exploration. In my third chapter, I partnered with wildlife researchers across North America to examine how relationships between urbanization and community composition vary among cities with distinct social-ecological characteristics, finding that effects of local urbanization were more negative in warmer, less vegetated, and more urbanized cities. In my fourth and final chapter, I explored the potential for human-wildlife coexistence by examining how various ideological, environmental, and sociodemographic factors influenced Phoenix area residents’ level of comfort living near different wildlife groups. Although I found that residents’ attitudes were primarily shaped by their relatively static wildlife values, comfort living near wildlife also depended on the characteristics of the neighboring environment, of the residents, and of the wildlife involved, indicating the potential for facilitating conditions for human-wildlife coexistence. Altogether, the findings of this dissertation suggest that the management of wildlife and their interactions with people within cities would benefit from more proactive and holistic consideration of the interacting environmental, wildlife, and human characteristics that influence the persistence of biodiversity within an increasingly urbanized world.},
	language = {en},
	urldate = {2024-01-09},
	school = {Arizona State University},
	author = {Haight, Jeffrey Douglas},
	year = {2023},
	keywords = {NALCMS},
}

With a growing majority of humans living within cities and towns, urbanization is one of the most persistent drivers of change in global land use and challenges to sustainability and biodiversity conservation. The development of cities and towns can substantially shape local and regional environments in which wildlife communities persist. Although urbanization can negatively affect wildlife communities – through processes such as habitat fragmentation and non-native species introduction – cities can also provide resources to wildlife, such as through food, water, and space, creating potential opportunities for conservation. However, managing wildlife communities persisting in urbanizing landscapes requires better understanding of how urbanized landscapes influence the ability of wildlife to coexist with one another and with people at local and regional scales. In this dissertation, I addressed these research needs by evaluating the environmental and human factors driving dynamic wildlife community distributions and people’s attitudes towards wildlife. In my first two chapters,I used wildlife camera data collected from across the Phoenix Metropolitan Area, AZ to examine seasonal patterns of wildlife space use, species richness, and interspecific interactions across levels of urbanization with varying landscape characteristics, including plant productivity and spatial land use heterogeneity. Here I found that urbanization was a primary driver of wildlife community characteristics within the region, but that seasonal resource availability and landscape heterogeneity could have mediating influences that require further exploration. In my third chapter, I partnered with wildlife researchers across North America to examine how relationships between urbanization and community composition vary among cities with distinct social-ecological characteristics, finding that effects of local urbanization were more negative in warmer, less vegetated, and more urbanized cities. In my fourth and final chapter, I explored the potential for human-wildlife coexistence by examining how various ideological, environmental, and sociodemographic factors influenced Phoenix area residents’ level of comfort living near different wildlife groups. Although I found that residents’ attitudes were primarily shaped by their relatively static wildlife values, comfort living near wildlife also depended on the characteristics of the neighboring environment, of the residents, and of the wildlife involved, indicating the potential for facilitating conditions for human-wildlife coexistence. Altogether, the findings of this dissertation suggest that the management of wildlife and their interactions with people within cities would benefit from more proactive and holistic consideration of the interacting environmental, wildlife, and human characteristics that influence the persistence of biodiversity within an increasingly urbanized world.

@article{elliott_application_2023,
	title = {Application of habitat association models across regions: {Useful} explanatory power retained in wetland bird case study},
	volume = {14},
	issn = {2150-8925, 2150-8925},
	shorttitle = {Application of habitat association models across regions},
	url = {https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecs2.4499},
	doi = {10.1002/ecs2.4499},
	language = {en},
	number = {5},
	urldate = {2023-06-02},
	journal = {Ecosphere},
	author = {Elliott, Lisa H. and Bracey, Annie M. and Niemi, Gerald J. and Johnson, Douglas H. and Gehring, Thomas M. and Gnass Giese, Erin E. and Fiorino, Giuseppe E. and Howe, Robert W. and Lawrence, Gregory J. and Norment, Christopher J. and Tozer, Douglas C. and Igl, Lawrence D.},
	month = may,
	year = {2023},
	keywords = {NALCMS},
	pages = {e4499},
}

@misc{flower_arcgis_2023,
	title = {{ArcGIS} {StoryMaps} - {Salish} {Sea} {Atlas}, {Land} {Cover} and {Land} {Use}},
	url = {https://storymaps.arcgis.com/stories/43e8f7863cc548638f4545dd39b7bf69},
	abstract = {Land Cover and Land Use in the Salish Sea Bioregion. Part of the Salish Sea Atlas.},
	language = {en},
	urldate = {2023-11-27},
	journal = {ArcGIS StoryMaps - Salish Sea Atlas},
	author = {Flower, Aquila},
	month = feb,
	year = {2023},
	keywords = {NALCMS},
}

Land Cover and Land Use in the Salish Sea Bioregion. Part of the Salish Sea Atlas.

@article{mei_can_2023,
	title = {Can {Hydrological} {Models} {Benefit} {From} {Using} {Global} {Soil} {Moisture}, {Evapotranspiration}, and {Runoff} {Products} as {Calibration} {Targets}?},
	volume = {59},
	copyright = {© 2023. The Authors.},
	issn = {1944-7973},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2022WR032064},
	doi = {10.1029/2022WR032064},
	abstract = {Hydrological models are usually calibrated to in-situ streamflow observations with reasonably long and uninterrupted records. This is challenging for poorly gage or ungaged basins where such information is not available. Even for gaged basins, the single-objective calibration to gaged streamflow cannot guarantee reliable forecasts because, as has been documented elsewhere, the inverse problem is mathematically ill-posed. Therefore, the inclusion of other observations, and the reproduction of other hydrological variables beyond streamflow, become critical components of accurate hydrological forecasting. In this study, six single- and multi-objective model calibration schemes based on different combinations of gaged streamflow, global-scale gridded soil moisture, actual evapotranspiration (ET), and runoff products are used for the calibration of a process-based hydrological model for 20 catchments located within the Lake Michigan watershed, of the Laurentian Great Lakes. Results show that the addition of gridded soil moisture to gaged streamflow in model calibration improves the ET simulation performance for most of the catchments, leading to the overall best-performing models. The monthly streamflow simulation performance for the experiments using gridded runoff products to inform the model is outperformed by those using the gaged streamflow, but the discrepancy is mitigated with increasing catchment scale. A new visualization method that effectively synthesizes model performance for the simulations of streamflow, soil moisture, and ET was also proposed. Based on the method, it is revealed that the streamflow simulation performance is relatively weak for baseflow-dominated catchments; overall, the 20 catchment models simulate streamflow and ET better than soil moisture.},
	language = {en},
	number = {2},
	urldate = {2023-06-23},
	journal = {Water Resources Research},
	author = {Mei, Yiwen and Mai, Juliane and Do, Hong Xuan and Gronewold, Andrew and Reeves, Howard and Eberts, Sandra and Niswonger, Richard and Regan, R. Steven and Hunt, Randall J.},
	year = {2023},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022WR032064},
	keywords = {NALCMS},
	pages = {e2022WR032064},
}

Hydrological models are usually calibrated to in-situ streamflow observations with reasonably long and uninterrupted records. This is challenging for poorly gage or ungaged basins where such information is not available. Even for gaged basins, the single-objective calibration to gaged streamflow cannot guarantee reliable forecasts because, as has been documented elsewhere, the inverse problem is mathematically ill-posed. Therefore, the inclusion of other observations, and the reproduction of other hydrological variables beyond streamflow, become critical components of accurate hydrological forecasting. In this study, six single- and multi-objective model calibration schemes based on different combinations of gaged streamflow, global-scale gridded soil moisture, actual evapotranspiration (ET), and runoff products are used for the calibration of a process-based hydrological model for 20 catchments located within the Lake Michigan watershed, of the Laurentian Great Lakes. Results show that the addition of gridded soil moisture to gaged streamflow in model calibration improves the ET simulation performance for most of the catchments, leading to the overall best-performing models. The monthly streamflow simulation performance for the experiments using gridded runoff products to inform the model is outperformed by those using the gaged streamflow, but the discrepancy is mitigated with increasing catchment scale. A new visualization method that effectively synthesizes model performance for the simulations of streamflow, soil moisture, and ET was also proposed. Based on the method, it is revealed that the streamflow simulation performance is relatively weak for baseflow-dominated catchments; overall, the 20 catchment models simulate streamflow and ET better than soil moisture.

@article{rincon_combining_2023,
	title = {Combining {Landsat} {TIR}-imagery data and {ERA5} reanalysis information with different calibration strategies to improve simulations of streamflow and river temperature in the {Canadian} {Subarctic}},
	volume = {37},
	copyright = {© 2023 The Authors. Hydrological Processes published by John Wiley \& Sons Ltd.},
	issn = {1099-1085},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hyp.15008},
	doi = {10.1002/hyp.15008},
	abstract = {Arctic and Subarctic environments are among the most vulnerable regions to climate change. Increases in liquid precipitation and changes in snowmelt onset are cited as the main drivers of change in streamflow and water temperature patterns in some of the largest rivers of the Canadian Arctic. However, in spite of this evidence, there is still a lack of research on water temperature, particularly in the eastern Canadian Arctic. In this paper, we use the CEQUEAU hydrological-water temperature model to derive consistent long-term daily flow and stream temperature time series in Aux Mélèzes River, a non-regulated basin (41 297 km2) in the eastern Canadian subarctic. The model was forced using reanalysis data from the fifth-generation ECMWF atmospheric reanalyses (ERA5) from 1979 to 2020. We used water temperature derived from thermal infrared (TIR) images as reference data to calibrate CEQUEAU's water temperature model, with calibration performed using single-site, multi-site, and upscaling factors approaches. Our results indicate that the CEQUEAU model can simulate streamflow patterns in the river and shows excellent spatiotemporal performance with Kling-Gupta Efficiency (KGE) metric {\textgreater}0.8. Using the best-performing flow simulation as one of the inputs allowed us to produce synthetic daily water temperature time series throughout the basin, with the multi-site calibration approach being the most accurate with root mean square errors (RMSE) {\textless}2.0°C. The validation of the water temperature simulations with a three-year in situ data logger dataset yielded an RMSE = 1.38°C for the summer temperatures, highlighting the robustness of the calibrated parameters and the chosen calibration strategy. This research demonstrates the reliability of TIR imagery and ERA5 as sources of model calibration data in data-sparse environments and underlines the CEQUEAU model as an assessment tool, opening the door to its use to assess climate change impact on the arctic regions of Canada.},
	language = {en},
	number = {10},
	urldate = {2023-11-24},
	journal = {Hydrological Processes},
	author = {Rincón, Eisinhower and St-hilaire, André and Bergeron, Normand E. and Dugdale, Stephen J.},
	year = {2023},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.15008},
	keywords = {NALCMS},
	pages = {e15008},
}

Arctic and Subarctic environments are among the most vulnerable regions to climate change. Increases in liquid precipitation and changes in snowmelt onset are cited as the main drivers of change in streamflow and water temperature patterns in some of the largest rivers of the Canadian Arctic. However, in spite of this evidence, there is still a lack of research on water temperature, particularly in the eastern Canadian Arctic. In this paper, we use the CEQUEAU hydrological-water temperature model to derive consistent long-term daily flow and stream temperature time series in Aux Mélèzes River, a non-regulated basin (41 297 km2) in the eastern Canadian subarctic. The model was forced using reanalysis data from the fifth-generation ECMWF atmospheric reanalyses (ERA5) from 1979 to 2020. We used water temperature derived from thermal infrared (TIR) images as reference data to calibrate CEQUEAU's water temperature model, with calibration performed using single-site, multi-site, and upscaling factors approaches. Our results indicate that the CEQUEAU model can simulate streamflow patterns in the river and shows excellent spatiotemporal performance with Kling-Gupta Efficiency (KGE) metric \textgreater0.8. Using the best-performing flow simulation as one of the inputs allowed us to produce synthetic daily water temperature time series throughout the basin, with the multi-site calibration approach being the most accurate with root mean square errors (RMSE) \textless2.0°C. The validation of the water temperature simulations with a three-year in situ data logger dataset yielded an RMSE = 1.38°C for the summer temperatures, highlighting the robustness of the calibrated parameters and the chosen calibration strategy. This research demonstrates the reliability of TIR imagery and ERA5 as sources of model calibration data in data-sparse environments and underlines the CEQUEAU model as an assessment tool, opening the door to its use to assess climate change impact on the arctic regions of Canada.

@article{arsenault_continuous_2023,
	title = {Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models},
	volume = {27},
	issn = {1607-7938},
	url = {https://hess.copernicus.org/articles/27/139/2023/},
	doi = {10.5194/hess-27-139-2023},
	abstract = {Abstract. This study investigates the ability of long short-term memory (LSTM) neural networks to perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological model-dependent regionalization methods are applied to 148 catchments in northeast North America and compared to an LSTM model that uses the exact same available data as the hydrological models. While conceptual model-based methods attempt to derive parameterizations at ungauged sites from other similar or nearby catchments, the LSTM model uses all available data in the region to maximize the information content and increase its robustness. Furthermore, by design, the LSTM does not require explicit definition of hydrological processes and derives its own structure from the provided data. The LSTM networks were able to clearly outperform the hydrological models in a leave-one-out cross-validation regionalization setting on most catchments in the study area, with the LSTM model outperforming the hydrological models in 93 \% to 97 \% of catchments depending on the hydrological model. Furthermore, for up to 78 \% of the catchments, the LSTM model was able to predict streamflow more accurately on pseudo-ungauged catchments than hydrological models calibrated on the target data, showing that the LSTM model's structure was better suited to convert the meteorological data and geophysical descriptors into streamflow than the hydrological models even calibrated to those sites in these cases. Furthermore, the LSTM model robustness was tested by varying its hyperparameters, and still outperformed hydrological models in regionalization in almost all cases. Overall, LSTM networks have the potential to change the regionalization research landscape by providing clear improvement pathways over traditional methods in the field of streamflow prediction in ungauged catchments.},
	number = {1},
	journal = {Hydrology and Earth System Sciences},
	author = {Arsenault, Richard and Martel, Jean-Luc and Brunet, Frédéric and Brissette, François and Mai, Juliane},
	month = jan,
	year = {2023},
	keywords = {NALCMS},
	pages = {139--157},
}

Abstract. This study investigates the ability of long short-term memory (LSTM) neural networks to perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological model-dependent regionalization methods are applied to 148 catchments in northeast North America and compared to an LSTM model that uses the exact same available data as the hydrological models. While conceptual model-based methods attempt to derive parameterizations at ungauged sites from other similar or nearby catchments, the LSTM model uses all available data in the region to maximize the information content and increase its robustness. Furthermore, by design, the LSTM does not require explicit definition of hydrological processes and derives its own structure from the provided data. The LSTM networks were able to clearly outperform the hydrological models in a leave-one-out cross-validation regionalization setting on most catchments in the study area, with the LSTM model outperforming the hydrological models in 93 % to 97 % of catchments depending on the hydrological model. Furthermore, for up to 78 % of the catchments, the LSTM model was able to predict streamflow more accurately on pseudo-ungauged catchments than hydrological models calibrated on the target data, showing that the LSTM model's structure was better suited to convert the meteorological data and geophysical descriptors into streamflow than the hydrological models even calibrated to those sites in these cases. Furthermore, the LSTM model robustness was tested by varying its hyperparameters, and still outperformed hydrological models in regionalization in almost all cases. Overall, LSTM networks have the potential to change the regionalization research landscape by providing clear improvement pathways over traditional methods in the field of streamflow prediction in ungauged catchments.

@article{palm_corridor-based_2023,
	title = {Corridor-based approach with spatial cross-validation reveals scale-dependent effects of geographic distance, human footprint and canopy cover on grizzly bear genetic connectivity},
	volume = {32},
	copyright = {© 2023 His Majesty the King in Right of Canada. Molecular Ecology © 2023 John Wiley \& Sons Ltd. Reproduced with the permission of the Minister of Environment and Climate Change Canada. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.},
	issn = {1365-294X},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/mec.17098},
	doi = {10.1111/mec.17098},
	abstract = {Understanding how human infrastructure and other landscape attributes affect genetic differentiation in animals is an important step for identifying and maintaining dispersal corridors for these species. We built upon recent advances in the field of landscape genetics by using an individual-based and multiscale approach to predict landscape-level genetic connectivity for grizzly bears (Ursus arctos) across 100,000 km2 in Canada's southern Rocky Mountains. We used a genetic dataset with 1156 unique individuals genotyped at nine microsatellite loci to identify landscape characteristics that influence grizzly bear gene flow at multiple spatial scales and map predicted genetic connectivity through a matrix of rugged terrain, large protected areas, highways and a growing human footprint. Our corridor-based modelling approach used a machine learning algorithm that objectively parameterized landscape resistance, incorporated spatial cross validation and variable selection and explicitly accounted for isolation by distance. This approach avoided overfitting, discarded variables that did not improve model performance across withheld test datasets and spatial predictive capacity compared to random cross-validation. We found that across all spatial scales, geographic distance explained more variation in genetic differentiation in grizzly bears than landscape variables. Human footprint inhibited connectivity across all spatial scales, while open canopies inhibited connectivity at the broadest spatial scale. Our results highlight the negative effect of human footprint on genetic connectivity, provide strong evidence for using spatial cross-validation in landscape genetics analyses and show that multiscale analyses provide additional information on how landscape variables affect genetic differentiation.},
	language = {en},
	number = {19},
	urldate = {2024-01-31},
	journal = {Molecular Ecology},
	author = {Palm, Eric C. and Landguth, Erin L. and Holden, Zachary A. and Day, Casey C. and Lamb, Clayton T. and Frame, Paul F. and Morehouse, Andrea T. and Mowat, Garth and Proctor, Michael F. and Sawaya, Michael A. and Stenhouse, Gordon and Whittington, Jesse and Zeller, Katherine A.},
	year = {2023},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/mec.17098},
	keywords = {NALCMS},
	pages = {5211--5227},
}

Understanding how human infrastructure and other landscape attributes affect genetic differentiation in animals is an important step for identifying and maintaining dispersal corridors for these species. We built upon recent advances in the field of landscape genetics by using an individual-based and multiscale approach to predict landscape-level genetic connectivity for grizzly bears (Ursus arctos) across 100,000 km2 in Canada's southern Rocky Mountains. We used a genetic dataset with 1156 unique individuals genotyped at nine microsatellite loci to identify landscape characteristics that influence grizzly bear gene flow at multiple spatial scales and map predicted genetic connectivity through a matrix of rugged terrain, large protected areas, highways and a growing human footprint. Our corridor-based modelling approach used a machine learning algorithm that objectively parameterized landscape resistance, incorporated spatial cross validation and variable selection and explicitly accounted for isolation by distance. This approach avoided overfitting, discarded variables that did not improve model performance across withheld test datasets and spatial predictive capacity compared to random cross-validation. We found that across all spatial scales, geographic distance explained more variation in genetic differentiation in grizzly bears than landscape variables. Human footprint inhibited connectivity across all spatial scales, while open canopies inhibited connectivity at the broadest spatial scale. Our results highlight the negative effect of human footprint on genetic connectivity, provide strong evidence for using spatial cross-validation in landscape genetics analyses and show that multiscale analyses provide additional information on how landscape variables affect genetic differentiation.

@techreport{gebhardt_creation_2023,
	address = {Germany},
	title = {Creation of a harmonized land cover map as an example for the entire region of the {Geneva} {Air} {Pollution} {Convention}},
	url = {https://www.umweltbundesamt.de/en/publikationen/creation-of-a-harmonized-land-cover-map-as-an},
	abstract = {A new map product for calculating the critical loads for eutrophication and acidification of terrestrial ecosystems and for modelling air quality was created in this project. The spatial extent of the map covers a large number of countries in Europe, the Caucasus and Central Asia. The results of this project are map products representing the land cover in 217 EUNIS classes and 13 RCG classes. In addition to the individual, national application of the data set, a broader use of the data is planned within the framework of the Convention on Long-Range Transboundary Air Pollution.},
	language = {en},
	urldate = {2024-09-18},
	institution = {German Environment Agency},
	author = {Gebhardt, Steffen},
	year = {2023},
	keywords = {NALCMS},
	pages = {122},
}

A new map product for calculating the critical loads for eutrophication and acidification of terrestrial ecosystems and for modelling air quality was created in this project. The spatial extent of the map covers a large number of countries in Europe, the Caucasus and Central Asia. The results of this project are map products representing the land cover in 217 EUNIS classes and 13 RCG classes. In addition to the individual, national application of the data set, a broader use of the data is planned within the framework of the Convention on Long-Range Transboundary Air Pollution.

@article{curasi_evaluating_2023,
	title = {Evaluating the {Performance} of the {Canadian} {Land} {Surface} {Scheme} {Including} {Biogeochemical} {Cycles} ({CLASSIC}) {Tailored} to the {Pan}-{Canadian} {Domain}},
	volume = {15},
	copyright = {© 2023 His Majesty the King in Right of Canada and The Authors. Reproduced with the permission of the Minister of Environment and Climate Change.},
	issn = {1942-2466},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2022MS003480},
	doi = {10.1029/2022MS003480},
	abstract = {Canada's boreal forests and tundra ecosystems are responding to unprecedented climate change with implications for the global carbon (C) cycle and global climate. However, our ability to model the response of Canada's terrestrial ecosystems to climate change is limited and there has been no comprehensive, process-based assessment of Canada's terrestrial C cycle. We tailor the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) to Canada and evaluate its C cycling performance against independent reference data. We utilize skill scores to assess model performance against reference data alongside benchmark scores that quantify the level of agreement between the reference data sets to aid in interpretation. Our results demonstrate CLASSIC's sensitivity to prescribed vegetation cover. They also show that the addition of five region-specific Plant functional types (PFTs) improves CLASSIC's skill at simulating the Canadian C cycle. CLASSIC performs well when tailored to Canada, falls within the range of the reference data sets, and meets or exceeds the benchmark scores for most C cycling processes. New region-specific land cover products, well-informed PFT parameterizations, and more detailed reference data sets will facilitate improvements to the representation of the terrestrial C cycle in regional and global land surface models. Incorporating a parameterization for boreal disturbance processes and explicitly representing peatlands and permafrost soils will improve CLASSIC's future performance in Canada and other boreal regions. This is an important step toward a comprehensive process-based assessment of Canada's terrestrial C cycle and evaluating Canada's net C balance under climate change.},
	language = {en},
	number = {4},
	urldate = {2023-06-23},
	journal = {Journal of Advances in Modeling Earth Systems},
	author = {Curasi, Salvatore R. and Melton, Joe R. and Humphreys, Elyn R. and Wang, Libo and Seiler, Christian and Cannon, Alex J. and Chan, Ed and Qu, Bo},
	year = {2023},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022MS003480},
	keywords = {NALCMS},
	pages = {e2022MS003480},
}

Canada's boreal forests and tundra ecosystems are responding to unprecedented climate change with implications for the global carbon (C) cycle and global climate. However, our ability to model the response of Canada's terrestrial ecosystems to climate change is limited and there has been no comprehensive, process-based assessment of Canada's terrestrial C cycle. We tailor the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) to Canada and evaluate its C cycling performance against independent reference data. We utilize skill scores to assess model performance against reference data alongside benchmark scores that quantify the level of agreement between the reference data sets to aid in interpretation. Our results demonstrate CLASSIC's sensitivity to prescribed vegetation cover. They also show that the addition of five region-specific Plant functional types (PFTs) improves CLASSIC's skill at simulating the Canadian C cycle. CLASSIC performs well when tailored to Canada, falls within the range of the reference data sets, and meets or exceeds the benchmark scores for most C cycling processes. New region-specific land cover products, well-informed PFT parameterizations, and more detailed reference data sets will facilitate improvements to the representation of the terrestrial C cycle in regional and global land surface models. Incorporating a parameterization for boreal disturbance processes and explicitly representing peatlands and permafrost soils will improve CLASSIC's future performance in Canada and other boreal regions. This is an important step toward a comprehensive process-based assessment of Canada's terrestrial C cycle and evaluating Canada's net C balance under climate change.

@article{shu_evolution_2023,
	title = {Evolution of the local climate in {Montreal} and {Ottawa} before, during and after a heatwave and the effects on urban heat islands},
	issn = {00489697},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723031182},
	doi = {10.1016/j.scitotenv.2023.164497},
	language = {en},
	urldate = {2023-06-02},
	journal = {Science of The Total Environment},
	author = {Shu, Chang and Gaur, Abhishek and Wang, Liangzhu and Lacasse, Michael A.},
	month = may,
	year = {2023},
	keywords = {NALCMS},
	pages = {164497},
}

@article{tsenov_exploration_2023,
	title = {Exploration of convolutional neural network architectures for large region map automation},
	volume = {17},
	issn = {1931-3195, 1931-3195},
	url = {https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-17/issue-1/018505/Exploration-of-convolutional-neural-network-architectures-for-large-region-map/10.1117/1.JRS.17.018505.full},
	doi = {10.1117/1.JRS.17.018505},
	abstract = {Deep learning semantic segmentation algorithms have provided improved frameworks for the automated production of land use and land cover (LULC) maps, which significantly increases the frequency of map generation as well as consistency of production quality. In this research, a total of 28 different model variations were examined to improve the accuracy of LULC maps. The experiments were carried out using Landsat 5/7 or Landsat 8 satellite images with the North American land change monitoring system (NALCMS) labels. The performance of various convolutional neural networks and extension combinations were assessed, where Visual Geometry Group Network with an output stride of 4, and modified U-Net architecture, provided the best results. Additional expanded analysis of the generated LULC maps was also provided. Using a deep neural network, this work achieved 92.4\% accuracy for 13 LULC classes within southern Manitoba representing a 15.8\% improvement over published results for the NALCMS. Based on the large regions of interest, higher radiometric resolution of Landsat 8 data resulted in better overall accuracies (88.04\%) compare to Landsat 5/7 (80.66\%) for 16 LULC classes. This represents an 11.44\% and 4.06\% increase in overall accuracy compared to previously published NALCMS results, including larger land area and higher number of LULC classes incorporated into the models compared to other published LULC map automation methods.},
	number = {1},
	urldate = {2023-06-27},
	journal = {Journal of Applied Remote Sensing},
	author = {Tsenov, Rostyslav-Mykola and Henry, Christopher J. and Storie, Joni L. and Storie, Christopher D. and Murray, Brent and Sokolov, Mikhail},
	month = feb,
	year = {2023},
	note = {Publisher: SPIE},
	keywords = {NALCMS},
	pages = {018505},
}

Deep learning semantic segmentation algorithms have provided improved frameworks for the automated production of land use and land cover (LULC) maps, which significantly increases the frequency of map generation as well as consistency of production quality. In this research, a total of 28 different model variations were examined to improve the accuracy of LULC maps. The experiments were carried out using Landsat 5/7 or Landsat 8 satellite images with the North American land change monitoring system (NALCMS) labels. The performance of various convolutional neural networks and extension combinations were assessed, where Visual Geometry Group Network with an output stride of 4, and modified U-Net architecture, provided the best results. Additional expanded analysis of the generated LULC maps was also provided. Using a deep neural network, this work achieved 92.4% accuracy for 13 LULC classes within southern Manitoba representing a 15.8% improvement over published results for the NALCMS. Based on the large regions of interest, higher radiometric resolution of Landsat 8 data resulted in better overall accuracies (88.04%) compare to Landsat 5/7 (80.66%) for 16 LULC classes. This represents an 11.44% and 4.06% increase in overall accuracy compared to previously published NALCMS results, including larger land area and higher number of LULC classes incorporated into the models compared to other published LULC map automation methods.

@article{gregory_exploring_2023,
	title = {Exploring within-ecodistrict lake organic matter variability and identifying possible environmental contaminant biomarkers using sedimentomics},
	volume = {871},
	issn = {0048-9697},
	url = {https://www.sciencedirect.com/science/article/pii/S004896972300596X},
	doi = {10.1016/j.scitotenv.2023.161981},
	abstract = {Sedimentomics methods offer insight into the physiological parameters that influence freshwater sediment organic matter (sedOM). To date, most sedimentomics studies characterized variations across large spatial and environmental gradients; here we examine whether sedimentomics methods capture subtle sedOM variations within a relatively homogeneous study area in southwestern Nova Scotia, Canada. Additionally, we explore the lake sedimentome for candidate biomarkers related to ongoing carnivorous animal farming in the region. Sediment cores were recovered from seven lakes across a trophic (oligo- to eu- trophic) and anthropogenic land use gradient (carnivorous animal farming in catchment, downstream of farming, no farming nearby). Subsamples that dated prior to 1910 (pre-carnivorous animal farming) and later than 2010 (during carnivorous animal farming) were analyzed using UHPLC-HRMS in both negative (ESI−) and positive (ESI+) electrospray ionization modes. Cluster analysis (k-means) showed replicate samples from a given lake clustered distinctly from one another in both ESI modes, indicating sedOM captured subtle variations between lake systems. PCA combined with multiple linear regression indicated carnivorous animal farming and OM source explained most of the observed variation in lake sedOM. Principal component analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) of ESI− and ESI+ data sets identified 103 unique candidate biomarkers. Ten strong candidate biomarkers were identified using graphical methods; more research is required for biomarker verification and molecular characterization. Our results indicate sedimentomics could be used in environmentally homogeneous areas, offering insight into the controls of sedOM cycling. Additionally, we identified prospective biomarkers related to carnivorous animal farming that could be used to understand relative contributions of farming to ongoing eutrophication issues in southwestern Nova Scotia.},
	language = {en},
	urldate = {2023-06-23},
	journal = {Science of The Total Environment},
	author = {Gregory, B. R. B. and Bell, M. A. and Sproule, A. and Shields, S. W. and Overy, D. P. and Blais, J. M.},
	month = may,
	year = {2023},
	keywords = {NALCMS},
	pages = {161981},
}

Sedimentomics methods offer insight into the physiological parameters that influence freshwater sediment organic matter (sedOM). To date, most sedimentomics studies characterized variations across large spatial and environmental gradients; here we examine whether sedimentomics methods capture subtle sedOM variations within a relatively homogeneous study area in southwestern Nova Scotia, Canada. Additionally, we explore the lake sedimentome for candidate biomarkers related to ongoing carnivorous animal farming in the region. Sediment cores were recovered from seven lakes across a trophic (oligo- to eu- trophic) and anthropogenic land use gradient (carnivorous animal farming in catchment, downstream of farming, no farming nearby). Subsamples that dated prior to 1910 (pre-carnivorous animal farming) and later than 2010 (during carnivorous animal farming) were analyzed using UHPLC-HRMS in both negative (ESI−) and positive (ESI+) electrospray ionization modes. Cluster analysis (k-means) showed replicate samples from a given lake clustered distinctly from one another in both ESI modes, indicating sedOM captured subtle variations between lake systems. PCA combined with multiple linear regression indicated carnivorous animal farming and OM source explained most of the observed variation in lake sedOM. Principal component analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) of ESI− and ESI+ data sets identified 103 unique candidate biomarkers. Ten strong candidate biomarkers were identified using graphical methods; more research is required for biomarker verification and molecular characterization. Our results indicate sedimentomics could be used in environmentally homogeneous areas, offering insight into the controls of sedOM cycling. Additionally, we identified prospective biomarkers related to carnivorous animal farming that could be used to understand relative contributions of farming to ongoing eutrophication issues in southwestern Nova Scotia.

@article{isted_ferruginous_2023,
	title = {Ferruginous {Hawk} movements respond predictably to intra‐annual variation but unexpectedly to anthropogenic habitats},
	issn = {0019-1019, 1474-919X},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/ibi.13200},
	doi = {10.1111/ibi.13200},
	language = {en},
	urldate = {2023-06-02},
	journal = {Ibis},
	author = {Isted, Georgia H. and Thomas, Robert J. and Warner, Kevin S. and Stuber, Matthew J. and Ellsworth, Ethan and Katzner, Todd E.},
	month = apr,
	year = {2023},
	keywords = {NALCMS},
	pages = {ibi.13200},
}

@article{mckellar_first-ever_2023,
	title = {First-ever satellite tracking of {Black} {Terns} ({Chlidonias} niger): {Insights} into home range and habitat selection},
	volume = {13},
	copyright = {© 2023 The Authors. Ecology and Evolution published by John Wiley \& Sons Ltd.},
	issn = {2045-7758},
	shorttitle = {First-ever satellite tracking of {Black} {Terns} ({Chlidonias} niger)},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.10716},
	doi = {10.1002/ece3.10716},
	abstract = {Understanding animal movement across the annual cycle is critical for developing appropriate conservation plans, but the large size and high cost of tracking devices can limit the spatial and temporal resolution at which movement data can be collected, especially for small avian species. Furthermore, for species with low breeding site fidelity, the ability to obtain tracking data from small, archival tags is hindered by low recapture rates. We deployed satellite tracking devices on four adult Black Terns (Chlidonias niger), a declining waterbird with low site fidelity, to examine space use and selection of resources within individual breeding home ranges. We also provide a preliminary assessment of habitat use during fall stopover. We found that home ranges were extensive (mean 283.7 km2) and distances travelled from the nest substantially larger (up to 35 km) than previously thought ( 2.5 km). Terns showed selection for wetlands and open water on the breeding grounds, but also showed selection for developed areas. This may reflect humans selecting similar landscape features for recreation and development as terns, and suggests that terns can tolerate the light degree of development (e.g. cottages, boat launches, etc.) within our study area. Despite a small sample size, this is the first study to track individual Black Terns at a high resolution with implications for conservation and wetland management practices relevant to the spatial scales at which habitat is used by the species.},
	language = {en},
	number = {11},
	urldate = {2024-01-10},
	journal = {Ecology and Evolution},
	author = {McKellar, Ann E. and Clements, Sarah J.},
	year = {2023},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.10716},
	keywords = {NALCMS},
	pages = {e10716},
}

Understanding animal movement across the annual cycle is critical for developing appropriate conservation plans, but the large size and high cost of tracking devices can limit the spatial and temporal resolution at which movement data can be collected, especially for small avian species. Furthermore, for species with low breeding site fidelity, the ability to obtain tracking data from small, archival tags is hindered by low recapture rates. We deployed satellite tracking devices on four adult Black Terns (Chlidonias niger), a declining waterbird with low site fidelity, to examine space use and selection of resources within individual breeding home ranges. We also provide a preliminary assessment of habitat use during fall stopover. We found that home ranges were extensive (mean 283.7 km2) and distances travelled from the nest substantially larger (up to 35 km) than previously thought ( 2.5 km). Terns showed selection for wetlands and open water on the breeding grounds, but also showed selection for developed areas. This may reflect humans selecting similar landscape features for recreation and development as terns, and suggests that terns can tolerate the light degree of development (e.g. cottages, boat launches, etc.) within our study area. Despite a small sample size, this is the first study to track individual Black Terns at a high resolution with implications for conservation and wetland management practices relevant to the spatial scales at which habitat is used by the species.

@techreport{schnorbus_future_2023,
	title = {Future {Design} {Flood} {Values} in the {Fraser} and {Peace} {River} {Basins} {Using} the {CanESM2}-{LE}},
	url = {https://www2.gov.bc.ca/assets/gov/driving-and-transportation/environment/climate-action/moti_streamflow_design_values.pdf},
	language = {en},
	institution = {Pacific Climate Impacts Consortium},
	author = {Schnorbus, Markus A. and Alaya, Mohamed Ali Ben},
	month = mar,
	year = {2023},
	keywords = {NALCMS},
	pages = {38},
}

@article{van_den_bosch_habitat_2023,
	title = {Habitat selection of resident and non-resident gray wolves: implications for habitat connectivity},
	volume = {13},
	copyright = {2023 The Author(s)},
	issn = {2045-2322},
	shorttitle = {Habitat selection of resident and non-resident gray wolves},
	url = {https://www.nature.com/articles/s41598-023-47815-0},
	doi = {10.1038/s41598-023-47815-0},
	abstract = {Habitat selection studies facilitate assessing and predicting species distributions and habitat connectivity, but habitat selection can vary temporally and among individuals, which is often ignored. We used GPS telemetry data from 96 Gray wolves (Canis lupus) in the western Great Lakes region of the USA to assess differences in habitat selection while wolves exhibited resident (territorial) or non-resident (dispersing or floating) movements and discuss implications for habitat connectivity. We used a step-selection function (SSF) to assess habitat selection by wolves exhibiting resident or non-resident movements, and modeled circuit connectivity throughout the western Great Lakes region. Wolves selected for natural land cover and against areas with high road densities, with no differences in selection among wolves when resident, dispersing, or floating. Similar habitat selection between resident and non-resident wolves may be due to similarity in environmental conditions, when non-resident movements occur largely within established wolf range rather than near the periphery or beyond the species range. Alternatively, non-resident wolves may travel through occupied territories because higher food availability or lower human disturbance outweighs risks posed by conspecifics. Finally, an absence of differences in habitat selection between resident and non-resident wolf movements may be due to other unknown reasons. We recommend considering context-dependency when evaluating differences in movements and habitat use between resident and non-resident individuals. Our results also provide independent validation of a previous species distribution model and connectivity analysis suggesting most potential wolf habitat in the western Great Lakes region is occupied, with limited connectivity to unoccupied habitat.},
	language = {en},
	number = {1},
	urldate = {2024-01-10},
	journal = {Scientific Reports},
	author = {van den Bosch, M. and Kellner, K. F. and Gantchoff, M. G. and Patterson, B. R. and Barber-Meyer, S. M. and Beyer, D. E. and Erb, J. D. and Isaac, E. J. and MacFarland, D. M. and Moore, S. A. and Norton, D. C. and Petroelje, T. R. and Price Tack, J. L. and Roell, B. J. and Schrage, M. and Belant, J. L.},
	month = nov,
	year = {2023},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {NALCMS},
	pages = {20415},
}

Habitat selection studies facilitate assessing and predicting species distributions and habitat connectivity, but habitat selection can vary temporally and among individuals, which is often ignored. We used GPS telemetry data from 96 Gray wolves (Canis lupus) in the western Great Lakes region of the USA to assess differences in habitat selection while wolves exhibited resident (territorial) or non-resident (dispersing or floating) movements and discuss implications for habitat connectivity. We used a step-selection function (SSF) to assess habitat selection by wolves exhibiting resident or non-resident movements, and modeled circuit connectivity throughout the western Great Lakes region. Wolves selected for natural land cover and against areas with high road densities, with no differences in selection among wolves when resident, dispersing, or floating. Similar habitat selection between resident and non-resident wolves may be due to similarity in environmental conditions, when non-resident movements occur largely within established wolf range rather than near the periphery or beyond the species range. Alternatively, non-resident wolves may travel through occupied territories because higher food availability or lower human disturbance outweighs risks posed by conspecifics. Finally, an absence of differences in habitat selection between resident and non-resident wolf movements may be due to other unknown reasons. We recommend considering context-dependency when evaluating differences in movements and habitat use between resident and non-resident individuals. Our results also provide independent validation of a previous species distribution model and connectivity analysis suggesting most potential wolf habitat in the western Great Lakes region is occupied, with limited connectivity to unoccupied habitat.

@article{hammond_high_2023,
	title = {High {Resolution} {SnowModel} {Simulations} {Reveal} {Future} {Elevation}-{Dependent} {Snow} {Loss} and {Earlier}, {Flashier} {Surface} {Water} {Input} for the {Upper} {Colorado} {River} {Basin}},
	volume = {11},
	copyright = {© 2023 The Authors. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.},
	issn = {2328-4277},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2022EF003092},
	doi = {10.1029/2022EF003092},
	abstract = {Continued climate warming is reducing seasonal snowpacks in the western United States, where {\textgreater}50\% of historical water supplies were snowmelt-derived. In the Upper Colorado River Basin, declining snow water equivalent (SWE) and altered surface water input (SWI, rainfall and snowmelt available to enter the soil) timing and magnitude affect streamflow generation and water availability. To adapt effectively to future conditions, we need to understand current spatiotemporal distributions of SWE and SWI and how they may change in future decades. We developed 100-m SnowModel simulations for water years 2001–2013 and two scenarios: control (CTL) and pseudo-global-warming (PGW). The PGW fraction of precipitation falling as snow was lower relative to CTL, except for November–April at high elevations. PGW peak SWE was lower for low (−45\%) and mid elevations (−14\%), while the date of peak SWE was uniformly earlier in the year for all elevations (17–23 days). Currently unmonitored high elevation snow represented a greater fraction of total PGW SWE. PGW peak daily SWI was higher for all elevations (30\%–42\%), while the dates of SWI peaks and centroids were earlier in the year for all elevations under PGW. PGW displayed elevated winter SWI, lower summer SWI, and changes in spring SWI timing were elevation-dependent. Although PGW peak SWI was elevated and earlier compared to CTL, SWI was more evenly distributed throughout the year for PGW. These simulated shifts in the timing and magnitude of SWE and SWI have broad implications for water management in dry, snow-dominated regions.},
	language = {en},
	number = {2},
	urldate = {2023-06-23},
	journal = {Earth's Future},
	author = {Hammond, John C. and Sexstone, Graham A. and Putman, Annie L. and Barnhart, Theodore B. and Rey, David M. and Driscoll, Jessica M. and Liston, Glen E. and Rasmussen, Kristen L. and McGrath, Daniel and Fassnacht, Steven R. and Kampf, Stephanie K.},
	year = {2023},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022EF003092},
	keywords = {NALCMS},
	pages = {e2022EF003092},
}

Continued climate warming is reducing seasonal snowpacks in the western United States, where \textgreater50% of historical water supplies were snowmelt-derived. In the Upper Colorado River Basin, declining snow water equivalent (SWE) and altered surface water input (SWI, rainfall and snowmelt available to enter the soil) timing and magnitude affect streamflow generation and water availability. To adapt effectively to future conditions, we need to understand current spatiotemporal distributions of SWE and SWI and how they may change in future decades. We developed 100-m SnowModel simulations for water years 2001–2013 and two scenarios: control (CTL) and pseudo-global-warming (PGW). The PGW fraction of precipitation falling as snow was lower relative to CTL, except for November–April at high elevations. PGW peak SWE was lower for low (−45%) and mid elevations (−14%), while the date of peak SWE was uniformly earlier in the year for all elevations (17–23 days). Currently unmonitored high elevation snow represented a greater fraction of total PGW SWE. PGW peak daily SWI was higher for all elevations (30%–42%), while the dates of SWI peaks and centroids were earlier in the year for all elevations under PGW. PGW displayed elevated winter SWI, lower summer SWI, and changes in spring SWI timing were elevation-dependent. Although PGW peak SWI was elevated and earlier compared to CTL, SWI was more evenly distributed throughout the year for PGW. These simulated shifts in the timing and magnitude of SWE and SWI have broad implications for water management in dry, snow-dominated regions.

@article{visscher_human_2023,
	title = {Human impact on deer use is greater than predators and competitors in a multiuse recreation area},
	volume = {197},
	issn = {00033472},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0003347223000039},
	doi = {10.1016/j.anbehav.2023.01.003},
	language = {en},
	urldate = {2023-06-02},
	journal = {Animal Behaviour},
	author = {Visscher, Darcy R. and Walker, Philip D. and Flowers, Mitchell and Kemna, Colborne and Pattison, Jesse and Kushnerick, Brandon},
	month = mar,
	year = {2023},
	keywords = {NALCMS},
	pages = {61--69},
}

@article{ahmed_implementing_2023,
	title = {Implementing a parsimonious variable contributing area algorithm for the prairie pothole region in the {HYPE} modelling framework},
	volume = {167},
	issn = {1364-8152},
	url = {https://www.sciencedirect.com/science/article/pii/S136481522300155X},
	doi = {10.1016/j.envsoft.2023.105769},
	abstract = {The North American prairie region is known for its poorly defined drainage system with numerous surface depressions that lead to variable contributing areas for streamflow generation. Current approaches of representing surface depressions are either simplistic or computationally demanding. In this study, a variable contributing area algorithm is implemented in the HYdrological Predictions for the Environment (HYPE) model and evaluated in the Canadian prairies. HYPE's local lake module is replaced with a Hysteretic Depressional Storage (HDS) algorithm to estimate the variable contributing fractions of subbasins. The modified model shows significant improvements in simulating the streamflows of two prairie basins in Saskatchewan, Canada. The modified model can replicate the hysteretic relationships between the water volume and contributing area of the basins. With the inclusion of the HDS algorithm in HYPE, the global HYPE modelling community can now simulate an important hydrological phenomenon, previously unavailable in the model.},
	language = {en},
	urldate = {2023-08-11},
	journal = {Environmental Modelling \& Software},
	author = {Ahmed, Mohamed Ismaiel and Shook, Kevin and Pietroniro, Alain and Stadnyk, Tricia and Pomeroy, John W. and Pers, Charlotta and Gustafsson, David},
	month = sep,
	year = {2023},
	keywords = {NALCMS},
	pages = {105769},
}

The North American prairie region is known for its poorly defined drainage system with numerous surface depressions that lead to variable contributing areas for streamflow generation. Current approaches of representing surface depressions are either simplistic or computationally demanding. In this study, a variable contributing area algorithm is implemented in the HYdrological Predictions for the Environment (HYPE) model and evaluated in the Canadian prairies. HYPE's local lake module is replaced with a Hysteretic Depressional Storage (HDS) algorithm to estimate the variable contributing fractions of subbasins. The modified model shows significant improvements in simulating the streamflows of two prairie basins in Saskatchewan, Canada. The modified model can replicate the hysteretic relationships between the water volume and contributing area of the basins. With the inclusion of the HDS algorithm in HYPE, the global HYPE modelling community can now simulate an important hydrological phenomenon, previously unavailable in the model.

@article{rodriguez_gonzalez_integration_2023,
	title = {Integration of the {Livelihood} and {Ecosystem} {Services} {Frameworks}—{A} {Case} {Study} on {Urban} {Agriculture} in {Mexico} {City}},
	volume = {27},
	url = {https://journals.ametsoc.org/view/journals/eint/27/1/EI-D-22-0010.1.xml},
	doi = {10.1175/EI-D-22-0010.1},
	abstract = {Abstract Strategies that demonstrate renewed potential to enhance both social and ecological systems are crucial in today’s era of rapid urbanization. However, the approaches used to understand the impacts of such strategies sometimes favor social over environmental theory, or the opposite, but do not always consider both equally. Our study addresses this disconnect by exploring the role of urban agriculture (UA) as an alleviation and land management strategy in Mexico City (MC), Mexico. Our integrated design combined the ecosystem services framework, which was primarily used to assess material and nonmaterial benefits MC residents obtain from UA spaces and its associated vegetation, and the livelihoods framework, which was used to evaluate the relationship between UA and societal impacts. We used a mixed-method approach to quantify the amount of food produced, assess crop diversity, assess six distinct ecological processes linked to UA, identify cultural benefits, and conduct an evaluation of contributions to livelihood capitals. Our study documented the role of UA in supporting ecological processes, connecting humans to nature, and providing a supplemental source of income. However, a multitude of unintended outcomes are identified, such as trade-offs between different ecological processes, constraints in promoting formal education beyond agroecological knowledge, and an inability to fully elevate families out of poverty. Our integrated approach demonstrated how the ecosystem services and livelihoods frameworks can be used simultaneously to provide thorough assessments of socioecological systems, identifying outcomes that could go unnoticed without an interdisciplinary lens.},
	language = {EN},
	number = {1},
	urldate = {2023-06-27},
	journal = {Earth Interactions},
	author = {Rodríguez González, Mayra I. and Scott, Christian Kelly and Marquina, Tatiana and Mewa, Demeke B. and Polo, Jorge García and Peng, Binbin},
	month = may,
	year = {2023},
	note = {Publisher: American Meteorological Society
Section: Earth Interactions},
	keywords = {NALCMS},
}

Abstract Strategies that demonstrate renewed potential to enhance both social and ecological systems are crucial in today’s era of rapid urbanization. However, the approaches used to understand the impacts of such strategies sometimes favor social over environmental theory, or the opposite, but do not always consider both equally. Our study addresses this disconnect by exploring the role of urban agriculture (UA) as an alleviation and land management strategy in Mexico City (MC), Mexico. Our integrated design combined the ecosystem services framework, which was primarily used to assess material and nonmaterial benefits MC residents obtain from UA spaces and its associated vegetation, and the livelihoods framework, which was used to evaluate the relationship between UA and societal impacts. We used a mixed-method approach to quantify the amount of food produced, assess crop diversity, assess six distinct ecological processes linked to UA, identify cultural benefits, and conduct an evaluation of contributions to livelihood capitals. Our study documented the role of UA in supporting ecological processes, connecting humans to nature, and providing a supplemental source of income. However, a multitude of unintended outcomes are identified, such as trade-offs between different ecological processes, constraints in promoting formal education beyond agroecological knowledge, and an inability to fully elevate families out of poverty. Our integrated approach demonstrated how the ecosystem services and livelihoods frameworks can be used simultaneously to provide thorough assessments of socioecological systems, identifying outcomes that could go unnoticed without an interdisciplinary lens.

@article{iverson_land_2023,
	title = {Land cover and {NDVI} are important predictors in habitat selection along migration for the {Golden}-crowned {Sparrow}, a temperate-zone migrating songbird},
	volume = {11},
	issn = {2051-3933},
	url = {https://movementecologyjournal.biomedcentral.com/articles/10.1186/s40462-022-00353-2},
	doi = {10.1186/s40462-022-00353-2},
	abstract = {Background: Migrating passerines in North America have shown sharp declines. Understanding habitat selection and threats along migration paths are critical research needs, but details about migrations have been limited due to the difficulty of tracking small birds. Recent technological advances of tiny GPS-tags provide new opportunities to delineate fine-scale movements in small passerines during a life stage that has previously been inherently difficult to study. Methods: We investigated habitat selection along migration routes for a temperate-zone migratory passerine, the Golden-crowned Sparrow (Zonotrichia atricapilla), given GPS tags on California wintering grounds. We used a resource selection function combined with conditional logistic regression to compare matched sets of known stopover locations and available but unused locations to determine how land cover class, vegetation greenness and climate variables influence habitat selection during migration. We also provide general migration descriptions for this understudied species including migration distance, duration, and elevation, and repeated use of stopover areas. Results: We acquired 22 tracks across 19 individuals, with a total of 541 valid spring and fall migration locations. Birds traveled to breeding grounds in Alaska and British Columbia along coastal routes, selecting for shrubland and higher vegetation greenness in both migration seasons as well as grasslands during fall migration. However, model interactions showed they selected sites with lower levels of greenness when in forest (both seasons) and shrubland (fall only), which may reflect their preference for more open habitats or represent a trade-off in selection between habitat type and productivity. Birds also selected for locations with higher daily maximum temperature during spring migration. Routes during spring migration were lower in elevation on average, shorter in duration, and had fewer long stopovers than in fall migration. For two birds, we found repeated use of the same stopover areas in spring and fall migration. Conclusions: Using miniaturized GPS, this study provides new insight into habitat selection along migration routes for a common temperate-zone migrating songbird, contributing to a better understanding of full annual cycle models, and informing conservation efforts. Golden-crowned Sparrows selected for specific habitats along migration routes, and we found previously unknown behaviors such as repeated use of the same stopover areas by individuals across different migratory seasons.},
	number = {1},
	journal = {Movement Ecology},
	author = {Iverson, Autumn R. and Humple, Diana L. and Cormier, Renée L. and Hull, Josh},
	month = jan,
	year = {2023},
	note = {Publisher: BioMed Central Ltd},
	keywords = {NALCMS},
	pages = {2},
}

Background: Migrating passerines in North America have shown sharp declines. Understanding habitat selection and threats along migration paths are critical research needs, but details about migrations have been limited due to the difficulty of tracking small birds. Recent technological advances of tiny GPS-tags provide new opportunities to delineate fine-scale movements in small passerines during a life stage that has previously been inherently difficult to study. Methods: We investigated habitat selection along migration routes for a temperate-zone migratory passerine, the Golden-crowned Sparrow (Zonotrichia atricapilla), given GPS tags on California wintering grounds. We used a resource selection function combined with conditional logistic regression to compare matched sets of known stopover locations and available but unused locations to determine how land cover class, vegetation greenness and climate variables influence habitat selection during migration. We also provide general migration descriptions for this understudied species including migration distance, duration, and elevation, and repeated use of stopover areas. Results: We acquired 22 tracks across 19 individuals, with a total of 541 valid spring and fall migration locations. Birds traveled to breeding grounds in Alaska and British Columbia along coastal routes, selecting for shrubland and higher vegetation greenness in both migration seasons as well as grasslands during fall migration. However, model interactions showed they selected sites with lower levels of greenness when in forest (both seasons) and shrubland (fall only), which may reflect their preference for more open habitats or represent a trade-off in selection between habitat type and productivity. Birds also selected for locations with higher daily maximum temperature during spring migration. Routes during spring migration were lower in elevation on average, shorter in duration, and had fewer long stopovers than in fall migration. For two birds, we found repeated use of the same stopover areas in spring and fall migration. Conclusions: Using miniaturized GPS, this study provides new insight into habitat selection along migration routes for a common temperate-zone migrating songbird, contributing to a better understanding of full annual cycle models, and informing conservation efforts. Golden-crowned Sparrows selected for specific habitats along migration routes, and we found previously unknown behaviors such as repeated use of the same stopover areas by individuals across different migratory seasons.

@article{bansal_large_2023,
	title = {Large increases in methane emissions expected from {North} {America}’s largest wetland complex},
	volume = {9},
	issn = {2375-2548},
	url = {https://www.science.org/doi/10.1126/sciadv.ade1112},
	doi = {10.1126/sciadv.ade1112},
	abstract = {Natural methane (CH
              4
              ) emissions from aquatic ecosystems may rise because of human-induced climate warming, although the magnitude of increase is highly uncertain. Using an exceptionally large CH
              4
              flux dataset ({\textasciitilde}19,000 chamber measurements) and remotely sensed information, we modeled plot- and landscape-scale wetland CH
              4
              emissions from the Prairie Pothole Region (PPR), North America’s largest wetland complex. Plot-scale CH
              4
              emissions were driven by hydrology, temperature, vegetation, and wetland size. Historically, landscape-scale PPR wetland CH
              4
              emissions were largely dependent on total wetland extent. However, regardless of future wetland extent, PPR CH
              4
              emissions are predicted to increase by two- or threefold by 2100 under moderate or severe warming scenarios, respectively. Our findings suggest that international efforts to decrease atmospheric CH
              4
              concentrations should jointly account for anthropogenic and natural emissions to maintain climate mitigation targets to the end of the century.
            
          , 
            
              Future increases in wetland CH
              4
              emissions due to climate warming may offset anthropogenic CH
              4
              -reduction actions.},
	language = {en},
	number = {9},
	urldate = {2023-06-02},
	journal = {Science Advances},
	author = {Bansal, Sheel and Post Van Der Burg, Max and Fern, Rachel R. and Jones, John W. and Lo, Rachel and McKenna, Owen P. and Tangen, Brian A. and Zhang, Zhen and Gleason, Robert A.},
	month = mar,
	year = {2023},
	keywords = {NALCMS},
	pages = {eade1112},
}

Natural methane (CH 4 ) emissions from aquatic ecosystems may rise because of human-induced climate warming, although the magnitude of increase is highly uncertain. Using an exceptionally large CH 4 flux dataset (~19,000 chamber measurements) and remotely sensed information, we modeled plot- and landscape-scale wetland CH 4 emissions from the Prairie Pothole Region (PPR), North America’s largest wetland complex. Plot-scale CH 4 emissions were driven by hydrology, temperature, vegetation, and wetland size. Historically, landscape-scale PPR wetland CH 4 emissions were largely dependent on total wetland extent. However, regardless of future wetland extent, PPR CH 4 emissions are predicted to increase by two- or threefold by 2100 under moderate or severe warming scenarios, respectively. Our findings suggest that international efforts to decrease atmospheric CH 4 concentrations should jointly account for anthropogenic and natural emissions to maintain climate mitigation targets to the end of the century. , Future increases in wetland CH 4 emissions due to climate warming may offset anthropogenic CH 4 -reduction actions.

@article{ahmed_learning_2023,
	title = {Learning from hydrological models’ challenges: {A} case study from the {Nelson} basin model intercomparison project},
	volume = {623},
	issn = {0022-1694},
	shorttitle = {Learning from hydrological models’ challenges},
	url = {https://www.sciencedirect.com/science/article/pii/S002216942300762X},
	doi = {10.1016/j.jhydrol.2023.129820},
	abstract = {Intercomparison studies play an important, but limited role in understanding the usefulness and limitations of currently available hydrological models. Comparison studies are often limited to well-behaved hydrological regimes, where rainfall-runoff processes dominate the hydrological response. These efforts have not covered western Canada due to the difficulty in simulating that region’s complex cold region hydrology with varying spatiotemporal contributing areas. This intercomparison study is the first of a series of studies under the intercomparison project of the international and interprovincial transboundary Nelson-Churchill River Basin (NCRB) in North America (Nelson-MIP), which encompasses different ecozones with major areas of the non-contributing Prairie potholes, forests, glaciers, mountains, and permafrost. The performance of eight hydrological and land surface models is compared at different unregulated watersheds within the NCRB. This is done to assess the models’ streamflow performance and overall fidelity without and with calibration, to capture the underlying physics of the region and to better understand why models struggle to accurately simulate its hydrology. Results show that some of the participating models have difficulties in simulating streamflow and/or internal hydrological variables (e.g., evapotranspiration) over Prairie watersheds but most models performed well elsewhere. This stems from model structural deficiencies, despite the various models being well calibrated to observed streamflow. Some model structural changes are identified for the participating models for future improvement. The outcomes of this study offer guidance for practitioners for the accurate prediction of NCRB streamflow, and for increasing confidence in future projections of water resources supply and management.},
	language = {en},
	urldate = {2023-08-11},
	journal = {Journal of Hydrology},
	author = {Ahmed, Mohamed Ismaiel and Stadnyk, Tricia and Pietroniro, Alain and Awoye, Hervé and Bajracharya, Ajay and Mai, Juliane and Tolson, Bryan A. and Shen, Hongren and Craig, James R. and Gervais, Mark and Sagan, Kevin and Wruth, Shane and Koenig, Kristina and Lilhare, Rajtantra and Déry, Stephen J. and Pokorny, Scott and Venema, Hank and Muhammad, Ameer and Taheri, Mahkameh},
	month = aug,
	year = {2023},
	keywords = {NALCMS},
	pages = {129820},
}

Intercomparison studies play an important, but limited role in understanding the usefulness and limitations of currently available hydrological models. Comparison studies are often limited to well-behaved hydrological regimes, where rainfall-runoff processes dominate the hydrological response. These efforts have not covered western Canada due to the difficulty in simulating that region’s complex cold region hydrology with varying spatiotemporal contributing areas. This intercomparison study is the first of a series of studies under the intercomparison project of the international and interprovincial transboundary Nelson-Churchill River Basin (NCRB) in North America (Nelson-MIP), which encompasses different ecozones with major areas of the non-contributing Prairie potholes, forests, glaciers, mountains, and permafrost. The performance of eight hydrological and land surface models is compared at different unregulated watersheds within the NCRB. This is done to assess the models’ streamflow performance and overall fidelity without and with calibration, to capture the underlying physics of the region and to better understand why models struggle to accurately simulate its hydrology. Results show that some of the participating models have difficulties in simulating streamflow and/or internal hydrological variables (e.g., evapotranspiration) over Prairie watersheds but most models performed well elsewhere. This stems from model structural deficiencies, despite the various models being well calibrated to observed streamflow. Some model structural changes are identified for the participating models for future improvement. The outcomes of this study offer guidance for practitioners for the accurate prediction of NCRB streamflow, and for increasing confidence in future projections of water resources supply and management.

@article{kalashnikov_lightning-ignited_2023,
	title = {Lightning-{Ignited} {Wildfires} in the {Western} {United} {States}: {Ignition} {Precipitation} and {Associated} {Environmental} {Conditions}},
	volume = {50},
	copyright = {© 2023. The Authors. Geophysical Research Letters published by Wiley Periodicals LLC on behalf of American Geophysical Union.},
	issn = {1944-8007},
	shorttitle = {Lightning-{Ignited} {Wildfires} in the {Western} {United} {States}},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2023GL103785},
	doi = {10.1029/2023GL103785},
	abstract = {Cloud-to-ground lightning with minimal rainfall (“dry” lightning) is a major wildfire ignition source in the western United States (WUS). Although dry lightning is commonly defined as occurring with {\textless}2.5 mm of daily-accumulated precipitation, a rigorous quantification of precipitation amounts concurrent with lightning-ignited wildfires (LIWs) is lacking. We combine wildfire, lightning and precipitation data sets to quantify these ignition precipitation amounts across ecoprovinces of the WUS. The median precipitation for all LIWs is 2.8 mm but varies with vegetation and fire characteristics. “Holdover” fires not detected until 2–5 days following ignition occur with significantly higher precipitation (5.1 mm) compared to fires detected promptly after ignition (2.5 mm), and with cooler and wetter environmental conditions. Further, there is substantial variation in precipitation associated with promptly-detected (1.7–4.6 mm) and holdover (3.0–7.7 mm) fires across ecoprovinces. Consequently, the widely-used 2.5 mm threshold does not fully capture lightning ignition risk and incorporating ecoprovince-specific precipitation amounts would better inform WUS wildfire prediction and management.},
	language = {en},
	number = {16},
	urldate = {2023-08-30},
	journal = {Geophysical Research Letters},
	author = {Kalashnikov, Dmitri A. and Abatzoglou, John T. and Loikith, Paul C. and Nauslar, Nicholas J. and Bekris, Yianna and Singh, Deepti},
	year = {2023},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2023GL103785},
	keywords = {NALCMS},
	pages = {e2023GL103785},
}

Cloud-to-ground lightning with minimal rainfall (“dry” lightning) is a major wildfire ignition source in the western United States (WUS). Although dry lightning is commonly defined as occurring with \textless2.5 mm of daily-accumulated precipitation, a rigorous quantification of precipitation amounts concurrent with lightning-ignited wildfires (LIWs) is lacking. We combine wildfire, lightning and precipitation data sets to quantify these ignition precipitation amounts across ecoprovinces of the WUS. The median precipitation for all LIWs is 2.8 mm but varies with vegetation and fire characteristics. “Holdover” fires not detected until 2–5 days following ignition occur with significantly higher precipitation (5.1 mm) compared to fires detected promptly after ignition (2.5 mm), and with cooler and wetter environmental conditions. Further, there is substantial variation in precipitation associated with promptly-detected (1.7–4.6 mm) and holdover (3.0–7.7 mm) fires across ecoprovinces. Consequently, the widely-used 2.5 mm threshold does not fully capture lightning ignition risk and incorporating ecoprovince-specific precipitation amounts would better inform WUS wildfire prediction and management.

@phdthesis{mclellan_linking_2023,
	address = {Peterborough, Ontario, Canada},
	type = {Master of {Science}},
	title = {Linking large scale monitoring and spatially explicit capture–recapture models to identify factors shaping large carnivore densities: case study of the {American} black bear in {Ontario}, {Canada}},
	url = {https://digitalcollections.trentu.ca/objects/etd-1057},
	school = {Trent University},
	author = {McLellan, Brynn},
	month = jan,
	year = {2023},
	keywords = {NALCMS},
}

@article{du_mapping_2023,
	title = {Mapping {Annual} {Global} {Forest} {Gain} {From} 1983 to 2021 {With} {Landsat} {Imagery}},
	volume = {16},
	issn = {1939-1404, 2151-1535},
	url = {https://ieeexplore.ieee.org/document/10103610/},
	doi = {10.1109/JSTARS.2023.3267796},
	urldate = {2023-06-02},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	author = {Du, Zhenrong and Yu, Le and Yang, Jianyu and Coomes, David and Kanniah, Kasturi and Fu, Haohuan and Gong, Peng},
	year = {2023},
	keywords = {NALCMS},
	pages = {4195--4204},
}

@article{wang_mapping_2023,
	title = {Mapping of {ESA}'s {Climate} {Change} {Initiative} land cover data to plant functional types for use in the {CLASSIC} land model},
	volume = {20},
	issn = {1726-4170},
	url = {https://bg.copernicus.org/articles/20/2265/2023/},
	doi = {10.5194/bg-20-2265-2023},
	abstract = {Plant functional types (PFTs) are used to represent vegetation distribution in land surface models (LSMs). Previous studies have shown large differences in the geographical distribution of PFTs currently used in various LSMs, which may arise from the differences in the underlying land cover products but also the methods used to map or reclassify land cover data to the PFTs that a given LSM represents. There are large uncertainties associated with existing PFT mapping methods since they are largely based on expert judgement and therefore are subjective. In this study, we propose a new approach to inform the mapping or the cross-walking process using analyses from sub-pixel fractional error matrices, which allows for a quantitative assessment of the fractional composition of the land cover categories in a dataset. We use the Climate Change Initiative (CCI) land cover product produced by the European Space Agency (ESA). Previous work has shown that compared to fine-resolution maps over Canada, the ESA-CCI product provides an improved land cover distribution compared to that from the GLC2000 dataset currently used in the CLASSIC (Canadian Land Surface Scheme Including Biogeochemical Cycles) model. A tree cover fraction dataset and a fine-resolution land cover map over Canada are used to compute the sub-pixel fractional composition of the land cover classes in ESA-CCI, which is then used to create a cross-walking table for mapping the ESA-CCI land cover categories to nine PFTs represented in the CLASSIC model. There are large differences between the new PFT distributions and those currently used in the model. Offline simulations performed with the CLASSIC model using the ESA-CCI-based PFTs show improved winter albedo compared to that based on the GLC2000 dataset. This emphasizes the importance of accurate representation of vegetation distribution for realistic simulation of surface albedo in LSMs. Results in this study suggest that the sub-pixel fractional composition analyses are an effective way to reduce uncertainties in the PFT mapping process and therefore, to some extent, objectify the otherwise subjective process.},
	language = {English},
	number = {12},
	urldate = {2023-08-11},
	journal = {Biogeosciences},
	author = {Wang, Libo and Arora, Vivek K. and Bartlett, Paul and Chan, Ed and Curasi, Salvatore R.},
	month = jun,
	year = {2023},
	note = {Publisher: Copernicus GmbH},
	keywords = {NALCMS},
	pages = {2265--2282},
}

Plant functional types (PFTs) are used to represent vegetation distribution in land surface models (LSMs). Previous studies have shown large differences in the geographical distribution of PFTs currently used in various LSMs, which may arise from the differences in the underlying land cover products but also the methods used to map or reclassify land cover data to the PFTs that a given LSM represents. There are large uncertainties associated with existing PFT mapping methods since they are largely based on expert judgement and therefore are subjective. In this study, we propose a new approach to inform the mapping or the cross-walking process using analyses from sub-pixel fractional error matrices, which allows for a quantitative assessment of the fractional composition of the land cover categories in a dataset. We use the Climate Change Initiative (CCI) land cover product produced by the European Space Agency (ESA). Previous work has shown that compared to fine-resolution maps over Canada, the ESA-CCI product provides an improved land cover distribution compared to that from the GLC2000 dataset currently used in the CLASSIC (Canadian Land Surface Scheme Including Biogeochemical Cycles) model. A tree cover fraction dataset and a fine-resolution land cover map over Canada are used to compute the sub-pixel fractional composition of the land cover classes in ESA-CCI, which is then used to create a cross-walking table for mapping the ESA-CCI land cover categories to nine PFTs represented in the CLASSIC model. There are large differences between the new PFT distributions and those currently used in the model. Offline simulations performed with the CLASSIC model using the ESA-CCI-based PFTs show improved winter albedo compared to that based on the GLC2000 dataset. This emphasizes the importance of accurate representation of vegetation distribution for realistic simulation of surface albedo in LSMs. Results in this study suggest that the sub-pixel fractional composition analyses are an effective way to reduce uncertainties in the PFT mapping process and therefore, to some extent, objectify the otherwise subjective process.

@article{pouw_mapping_2023,
	title = {Mapping snow depth on {Canadian} sub-arctic lakes using ground-penetrating radar},
	volume = {17},
	issn = {1994-0416},
	url = {https://tc.copernicus.org/articles/17/2367/2023/},
	doi = {10.5194/tc-17-2367-2023},
	abstract = {Ice thickness across lake ice is mainly influenced by the presence of snow and its distribution, which affects the rate of lake ice growth. The distribution of snow depth over lake ice varies due to wind redistribution and snowpack metamorphism, affecting the variability of lake ice thickness. Accurate and consistent snow depth data on lake ice are sparse and challenging to obtain. However, high spatial resolution lake snow depth observations are necessary for the next generation of thermodynamic lake ice models to improve the understanding of how the varying distribution of snow depth influences lake ice formation and growth. This study was conducted using ground-penetrating radar (GPR) acquisitions with ∼9 cm sampling resolution along transects totalling ∼44 km to map snow depth over four Canadian sub-arctic freshwater lakes. The lake snow depth derived from GPR two-way travel time (TWT) resulted in an average relative error of under 10 \% when compared to 2430 in situ snow depth observations for the early and late winter season. The snow depth derived from GPR TWTs for the early winter season was estimated with a root mean square error (RMSE) of 1.6 cm and a mean bias error of 0.01 cm, while the accuracy for the late winter season on a deeper snowpack was estimated with a RMSE of 2.9 cm and a mean bias error of 0.4 cm. The GPR-derived snow depths were interpolated to create 1 m spatial resolution snow depth maps. The findings showed improved lake snow depth retrieval accuracy and introduced a fast and efficient method to obtain high spatial resolution snow depth information. The results suggest that GPR acquisitions can be used to derive lake snow depth, providing a viable alternative to manual snow depth monitoring methods. The findings can lead to an improved understanding of snow and lake ice interactions, which is essential for northern communities' safety and wellbeing and the scientific modelling community.},
	language = {English},
	number = {6},
	urldate = {2023-06-16},
	journal = {The Cryosphere},
	author = {Pouw, Alicia F. and Kheyrollah Pour, Homa and MacLean, Alex},
	month = jun,
	year = {2023},
	note = {Publisher: Copernicus GmbH},
	keywords = {NALCMS},
	pages = {2367--2385},
}

Ice thickness across lake ice is mainly influenced by the presence of snow and its distribution, which affects the rate of lake ice growth. The distribution of snow depth over lake ice varies due to wind redistribution and snowpack metamorphism, affecting the variability of lake ice thickness. Accurate and consistent snow depth data on lake ice are sparse and challenging to obtain. However, high spatial resolution lake snow depth observations are necessary for the next generation of thermodynamic lake ice models to improve the understanding of how the varying distribution of snow depth influences lake ice formation and growth. This study was conducted using ground-penetrating radar (GPR) acquisitions with ∼9 cm sampling resolution along transects totalling ∼44 km to map snow depth over four Canadian sub-arctic freshwater lakes. The lake snow depth derived from GPR two-way travel time (TWT) resulted in an average relative error of under 10 % when compared to 2430 in situ snow depth observations for the early and late winter season. The snow depth derived from GPR TWTs for the early winter season was estimated with a root mean square error (RMSE) of 1.6 cm and a mean bias error of 0.01 cm, while the accuracy for the late winter season on a deeper snowpack was estimated with a RMSE of 2.9 cm and a mean bias error of 0.4 cm. The GPR-derived snow depths were interpolated to create 1 m spatial resolution snow depth maps. The findings showed improved lake snow depth retrieval accuracy and introduced a fast and efficient method to obtain high spatial resolution snow depth information. The results suggest that GPR acquisitions can be used to derive lake snow depth, providing a viable alternative to manual snow depth monitoring methods. The findings can lead to an improved understanding of snow and lake ice interactions, which is essential for northern communities' safety and wellbeing and the scientific modelling community.

@article{tardy_mechanistic_2023,
	title = {Mechanistic movement models to predict geographic range expansions of ticks and tick-borne pathogens: {Case} studies with {Ixodes} scapularis and {Amblyomma} americanum in eastern {North} {America}},
	volume = {14},
	issn = {1877959X},
	shorttitle = {Mechanistic movement models to predict geographic range expansions of ticks and tick-borne pathogens},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1877959X23000432},
	doi = {10.1016/j.ttbdis.2023.102161},
	language = {en},
	number = {4},
	urldate = {2023-06-02},
	journal = {Ticks and Tick-borne Diseases},
	author = {Tardy, Olivia and Acheson, Emily Sohanna and Bouchard, Catherine and Chamberland, Éric and Fortin, André and Ogden, Nicholas H. and Leighton, Patrick A.},
	month = jul,
	year = {2023},
	keywords = {NALCMS},
	pages = {102161},
}

@phdthesis{falkenstein_modeling_2023,
	type = {Master of {Science} ({MS})},
	title = {Modeling of {Surficial} and {Groundwater} {Hydrology} in {Southeastern} {Georgia} {Including} the {Okefenokee}},
	url = {https://esploro.libs.uga.edu/esploro/outputs/graduate/MODELING-OF-SURFICIAL-AND-GROUNDWATER-HYDROLOGY/9949574514802959},
	urldate = {2023-10-05},
	school = {University of Georgia},
	author = {Falkenstein, Lauryn},
	month = aug,
	year = {2023},
	keywords = {NALCMS},
}

@techreport{dillon_pacific_2023,
	title = {Pacific coast temperate forest regional timber product flow analysis},
	url = {https://www.fs.usda.gov/research/treesearch/66788},
	language = {en},
	urldate = {2024-01-10},
	author = {Dillon, Thale and Morgan, Todd A.},
	year = {2023},
	keywords = {NALCMS},
}

@article{edwards_point_2023,
	title = {Point count offsets for estimating population sizes of north {American} landbirds},
	issn = {0019-1019},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/ibi.13169},
	doi = {10.1111/ibi.13169},
	journal = {Ibis International Journal of Avian Science},
	author = {Edwards, Brandon P. M. and Smith, Adam C. and Docherty, Teegan D. S. and Gahbauer, Marcel A. and Gillespie, Caitlyn R. and Grinde, Alexis R. and Harmer, Taylor and Iles, David T. and Matsuoka, Steven M. and Michel, Nicole L. and Murray, Andrew and Niemi, Gerald J. and Pasher, Jon and Pavlacky, David C. and Robinson, Barry G. and Ryder, Thomas B. and Sólymos, Péter and Stralberg, Diana and Zlonis, Edmund J.},
	month = jan,
	year = {2023},
	keywords = {NALCMS},
}

@article{walker_predation_2023,
	title = {Predation risk drives aposematic signal conformity},
	volume = {77},
	issn = {0014-3820},
	url = {https://doi.org/10.1093/evolut/qpad162},
	doi = {10.1093/evolut/qpad162},
	abstract = {Contrary to expectations regarding efficient predator education mediated by lack of ambiguity and enhanced prey recognition, aposematic signals often show considerable intraspecific variability. For example, some striped skunks (Mephitis mephitis) are almost entirely white, others have black-and-white stripes of equivalent thicknesses, yet others are mostly black. We tested the ecological correlates of this variation in patterning using 749 museum skins collected across North America. Skunks had longer white–black borders and more bilaterally symmetrical stripes in areas with a greater number of potential predator species, and this effect was more marked for mammalian than avian predators, the latter of which may be less deterred by noxious defenses. Skunks from locations with greater predator diversity were less variable in the extent of whiteness on their dorsa and less variable in the length of their white–black borders, suggesting strong selection from predators leads to greater conformity in stripe patterns, even at the same location, but weak selection from predators leads to relaxed selection on pattern conformity. Skunks exhibited greater areas of black pelage in areas of greater humidity conforming to Gloger’s rule. Our results indicate that relaxed predation pressure is key to warning signal variation in this iconic species, whereas stronger pressure leads to signal conformity and stronger signals.},
	number = {11},
	urldate = {2024-01-10},
	journal = {Evolution},
	author = {Walker, Hannah and Caro, Tim and Bell, Donovan and Ferguson, Adam and Stankowich, Theodore},
	month = nov,
	year = {2023},
	keywords = {NALCMS},
	pages = {2492--2503},
}

Contrary to expectations regarding efficient predator education mediated by lack of ambiguity and enhanced prey recognition, aposematic signals often show considerable intraspecific variability. For example, some striped skunks (Mephitis mephitis) are almost entirely white, others have black-and-white stripes of equivalent thicknesses, yet others are mostly black. We tested the ecological correlates of this variation in patterning using 749 museum skins collected across North America. Skunks had longer white–black borders and more bilaterally symmetrical stripes in areas with a greater number of potential predator species, and this effect was more marked for mammalian than avian predators, the latter of which may be less deterred by noxious defenses. Skunks from locations with greater predator diversity were less variable in the extent of whiteness on their dorsa and less variable in the length of their white–black borders, suggesting strong selection from predators leads to greater conformity in stripe patterns, even at the same location, but weak selection from predators leads to relaxed selection on pattern conformity. Skunks exhibited greater areas of black pelage in areas of greater humidity conforming to Gloger’s rule. Our results indicate that relaxed predation pressure is key to warning signal variation in this iconic species, whereas stronger pressure leads to signal conformity and stronger signals.

@article{calef_predicting_2023,
	title = {Predicting the {Unpredictable}: {Predicting} {Landcover} in {Boreal} {Alaska} and the {Yukon} {Including} {Succession} and {Wildfire} {Potential}},
	volume = {14},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {1999-4907},
	shorttitle = {Predicting the {Unpredictable}},
	url = {https://www.mdpi.com/1999-4907/14/8/1577},
	doi = {10.3390/f14081577},
	abstract = {The boreal forest of northwestern North America covers an extensive area, contains vast amounts of carbon in its vegetation and soil, and is characterized by extensive wildfires. Catastrophic crown fires in these forests are fueled predominantly by only two evergreen needle-leaf tree species, black spruce (Picea mariana (Mill.) B.S.P.) and lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.). Identifying where these flammable species grow through time in the landscape is critical for understanding wildfire risk, damages, and human exposure. Because medium resolution landcover data that include species detail are lacking, we developed a compound modeling approach that enabled us to refine the available evergreen forest category into highly flammable species and less flammable species. We then expanded our refined landcover at decadal time steps from 1984 to 2014. With the aid of an existing burn model, FlamMap, and simple succession rules, we were able to predict future landcover at decadal steps until 2054. Our resulting land covers provide important information to communities in our study area on current and future wildfire risk and vegetation changes and could be developed in a similar fashion for other areas.},
	language = {en},
	number = {8},
	urldate = {2023-08-11},
	journal = {Forests},
	author = {Calef, Monika P. and Schmidt, Jennifer I. and Varvak, Anna and Ziel, Robert},
	month = aug,
	year = {2023},
	note = {Number: 8
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {NALCMS},
	pages = {1577},
}

The boreal forest of northwestern North America covers an extensive area, contains vast amounts of carbon in its vegetation and soil, and is characterized by extensive wildfires. Catastrophic crown fires in these forests are fueled predominantly by only two evergreen needle-leaf tree species, black spruce (Picea mariana (Mill.) B.S.P.) and lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.). Identifying where these flammable species grow through time in the landscape is critical for understanding wildfire risk, damages, and human exposure. Because medium resolution landcover data that include species detail are lacking, we developed a compound modeling approach that enabled us to refine the available evergreen forest category into highly flammable species and less flammable species. We then expanded our refined landcover at decadal time steps from 1984 to 2014. With the aid of an existing burn model, FlamMap, and simple succession rules, we were able to predict future landcover at decadal steps until 2054. Our resulting land covers provide important information to communities in our study area on current and future wildfire risk and vegetation changes and could be developed in a similar fashion for other areas.

@article{currie_prioritizing_2023,
	title = {Prioritizing ecological restoration of converted lands in {Canada} by spatially integrating organic carbon storage and biodiversity benefits},
	issn = {2578-4854, 2578-4854},
	url = {https://conbio.onlinelibrary.wiley.com/doi/10.1111/csp2.12924},
	doi = {10.1111/csp2.12924},
	language = {en},
	urldate = {2023-06-02},
	journal = {Conservation Science and Practice},
	author = {Currie, Jessica and Merritt, Will and Liang, Chris and Sothe, Camile and Beatty, Craig R. and Shackelford, Nancy and Hirsh‐Pearson, Kristen and Gonsamo, Alemu and Snider, James},
	month = apr,
	year = {2023},
	keywords = {NALCMS},
	pages = {e12924},
}

@phdthesis{braden_quantifying_2023,
	title = {Quantifying uncertainty of forest extent estimates in {Mexico} by comparing satellite-derived land and tree cover products},
	url = {https://udspace.udel.edu/handle/19716/32760},
	abstract = {Information about forest extent and tree cover is needed to evaluate the status of natural resources, conservation practices and environmental policies. The challenge is that different forest definitions, remote sensing products, and data availability can lead to discrepancies in reporting forest area and ultimately forest carbon estimates. Here, I compared forest extent estimates from 7 regional and global land or tree cover products at 30 m resolution across mainland Mexico. Comparison results presented significant uncertainty in forest extent estimates for Mexico, ranging from 387,607 km2 to 675,239 km2 depending on which satellite-derived product and forest definition is utilized. Next, I compared these satellite-derived products with two independent forest inventory datasets at the national scale (n=21,167) and at the local scale (n=486). The highest agreement between satellite-derived products and forest inventory data is within the tropical moist forest, and the least agreement is within the subtropical steppe ecozones. I further developed a hybrid uncertainty product by combining the 7 forest extents to calculate forest likelihood. I identified a forest area of 340,661 km2 that has low agreement among satellite-derived products. The tropical dry forest and subtropical mountain system represented the two ecozones with the greatest amount of disagreement among satellite-derived products. These findings identify uncertainty surrounding forest extent estimates across ecozones in Mexico where additional ground data and research is needed.},
	language = {en},
	urldate = {2023-06-02},
	school = {University of Delaware},
	author = {Braden, Dustin S.},
	year = {2023},
	keywords = {NALCMS},
}

Information about forest extent and tree cover is needed to evaluate the status of natural resources, conservation practices and environmental policies. The challenge is that different forest definitions, remote sensing products, and data availability can lead to discrepancies in reporting forest area and ultimately forest carbon estimates. Here, I compared forest extent estimates from 7 regional and global land or tree cover products at 30 m resolution across mainland Mexico. Comparison results presented significant uncertainty in forest extent estimates for Mexico, ranging from 387,607 km2 to 675,239 km2 depending on which satellite-derived product and forest definition is utilized. Next, I compared these satellite-derived products with two independent forest inventory datasets at the national scale (n=21,167) and at the local scale (n=486). The highest agreement between satellite-derived products and forest inventory data is within the tropical moist forest, and the least agreement is within the subtropical steppe ecozones. I further developed a hybrid uncertainty product by combining the 7 forest extents to calculate forest likelihood. I identified a forest area of 340,661 km2 that has low agreement among satellite-derived products. The tropical dry forest and subtropical mountain system represented the two ecozones with the greatest amount of disagreement among satellite-derived products. These findings identify uncertainty surrounding forest extent estimates across ecozones in Mexico where additional ground data and research is needed.

@phdthesis{johnston_representativeness_2023,
	type = {thesis},
	title = {Representativeness of {Protected} {Areas} in the {Algonquin} to {Adirondacks} {Region}: {A} {Gap} {Analysis}},
	shorttitle = {Representativeness of {Protected} {Areas} in the {Algonquin} to {Adirondacks} {Region}},
	url = {https://qspace.library.queensu.ca/handle/1974/31671},
	abstract = {Protected area networks play an important role in the conservation of biodiversity. A useful approach for determining their suitability is assessing how represented certain ecosystem types are in these networks, in comparison to the region as a whole. Here, a gap analysis was performed to analyze representation of ecosystem types within protected areas of the Algonquin to Adirondacks region. ArcGIS Pro software was used to create a map of ecosystem types across the region based on land cover and topography. The composition of ecosystems contained within protected areas was then compared with the composition of the region as a whole. The results show certain ecosystem types as overrepresented in protected areas, while others are underrepresented, and that this varies by ecoregion. These findings have implications for how new protected areas should be established moving forward, with protected areas potentially needing to be modified to account for representation. The next step in this analysis would be the addition of current and future climate data, as climate change will very likely affect the composition of ecosystem types in protected areas.},
	language = {en},
	urldate = {2023-06-02},
	school = {Queen's University},
	author = {Johnston, Grace},
	month = may,
	year = {2023},
	note = {Accepted: 2023-05-26T13:37:14Z},
	keywords = {NALCMS, Terrestrial Ecoregions},
}

Protected area networks play an important role in the conservation of biodiversity. A useful approach for determining their suitability is assessing how represented certain ecosystem types are in these networks, in comparison to the region as a whole. Here, a gap analysis was performed to analyze representation of ecosystem types within protected areas of the Algonquin to Adirondacks region. ArcGIS Pro software was used to create a map of ecosystem types across the region based on land cover and topography. The composition of ecosystems contained within protected areas was then compared with the composition of the region as a whole. The results show certain ecosystem types as overrepresented in protected areas, while others are underrepresented, and that this varies by ecoregion. These findings have implications for how new protected areas should be established moving forward, with protected areas potentially needing to be modified to account for representation. The next step in this analysis would be the addition of current and future climate data, as climate change will very likely affect the composition of ecosystem types in protected areas.

@article{parada_san_2023,
	title = {San {Marcos}, {Guerrero} {Y} {Su} {Desafio} {Turistico} {Para} {Una} {Construcción} {Socioecológica} {Del} {Paisaje}},
	copyright = {Derechos de autor 2024 Papeles de Geografía},
	issn = {1989-4627},
	url = {https://revistas.um.es/geografia/article/view/572111},
	doi = {10.6018/geografia.572111},
	abstract = {The socio-ecological consequences of the appropriation and dispossession processes originated by tourism megaprojects have been documented, as well as the beneficial changes and the way to reduce their negative impacts. In the coastal municipality of San Marcos, Guerrero, Mexico, a project of this nature will be developed, so the objective of the work was to analyze the possible effects that it may have on the socioecosystem. The "agency" and "social structure" were established, based on semi-structured interviews, field observations and land use cartography. The current scenario shows an agency defined by multiple labor identities, the cattle ranch among them. The social structure exhibits 3 driving forces, the agrarian policy supported by the "sistema de cargos", the economic-cultural ones mainly due to the use of grasslands, and the natural forces, represented by labor activities in the not "transformed" ecosystems, threatened by land use changes and pollution. Social risks conjecture a differential impact between urban and rural residents, and ecological risks, depending on whether they are ecosystems such as beaches, jungle or aquatic environments. The standardization of new practices is necessary together with a socio-institutional perspective and the bottom-up organization to counteract the impacts of the project.},
	language = {es},
	number = {69},
	urldate = {2024-06-13},
	journal = {Papeles de Geografía},
	author = {Parada, Jaime Matus and Aragón, Ivan Ernesto Roldán and Carpio-Cortes, Leticia and Maldonado, Marco Antonio},
	year = {2023},
	note = {Number: 69},
	keywords = {NALCMS},
}

The socio-ecological consequences of the appropriation and dispossession processes originated by tourism megaprojects have been documented, as well as the beneficial changes and the way to reduce their negative impacts. In the coastal municipality of San Marcos, Guerrero, Mexico, a project of this nature will be developed, so the objective of the work was to analyze the possible effects that it may have on the socioecosystem. The "agency" and "social structure" were established, based on semi-structured interviews, field observations and land use cartography. The current scenario shows an agency defined by multiple labor identities, the cattle ranch among them. The social structure exhibits 3 driving forces, the agrarian policy supported by the "sistema de cargos", the economic-cultural ones mainly due to the use of grasslands, and the natural forces, represented by labor activities in the not "transformed" ecosystems, threatened by land use changes and pollution. Social risks conjecture a differential impact between urban and rural residents, and ecological risks, depending on whether they are ecosystems such as beaches, jungle or aquatic environments. The standardization of new practices is necessary together with a socio-institutional perspective and the bottom-up organization to counteract the impacts of the project.

@incollection{norman_satellite_2023,
	title = {Satellite {Observations} of {Forest} {Disturbances} for the {Conterminous} {United} {States} for the 2021 {Growing} {Season}},
	volume = {General Technical Report SRS-273},
	url = {https://www.fs.usda.gov/research/treesearch/66983},
	booktitle = {Forest {Health} {Monitoring}: {National} {Status}, {Trends}, and {Analysis} 2022},
	publisher = {U.S. Department of Agriculture},
	author = {Norman, Steven P. and Christie, William M.},
	editor = {Potter, Kevin M. and Conkling, Barbara L.},
	year = {2023},
	note = {Section: 4},
	keywords = {NALCMS},
	pages = {83--90},
}

@article{zulian_seasonal_2023,
	title = {Seasonal variation in drivers of bird-window collisions on the west coast of {British} {Columbia}, {Canada}},
	volume = {18},
	copyright = {© 2023 by the author(s)},
	issn = {1712-6568},
	url = {https://ace-eco.org/vol18/iss2/art15/},
	doi = {10.5751/ACE-02482-180215},
	abstract = {We examined the effects of façade-level building and vegetation features on bird-window collision risk, and how these effects varied across seasons at a Pacific coastal campus with mild winters, abundant evergreen vegetation, and seasonally varied bird communities. We searched for bird carcasses at 57 façades of 8 buildings at the University of British Columbia (UBC) over 155 days between January 2015 and March 2017 (total: 8835 façade surveys). Collision monitoring occurred across five equal sampling periods that represented stages of the annual cycle of the bird community, including the fall and spring migratory periods, the breeding season, and the long overwintering period. For each season, we compared logistic regression models predicting the odds of a collision from different sets of façade and vegetation characteristics expected to influence collisions: façade area, area of glass, porous surface cover (ground and shrub vegetation, soil, leaf litter), tree cover, and the number of building stories reflecting vegetation. Consistent with other studies, area of glass had a positive influence on collision probability in all seasons; however, the effect was strongest during the fall migratory period, when daily collision mortality rates peaked at UBC. The number of stories reflecting vegetation also increased collision probability, but only in the fall, indicating that the vertical extent of vegetation and reflective glass may affect collision risk differently as bird communities change across seasons. Façade area increased collision probability only in the winter (a long and lethal period for bird collisions at UBC), reflecting different risk factors associated with the species most vulnerable to collisions in this season. Our results highlight the need to measure building and vegetation effects across the longest and most lethal stages of the annual cycle of birds, both to predict the impact of proposed buildings and to prioritize mitigation strategies that will result in the greatest conservation benefits.},
	language = {en},
	number = {2},
	urldate = {2024-01-10},
	journal = {Avian Conservation and Ecology},
	author = {Zulian, Viviane and Norris, Andrea R. and Cockle, Kristina L. and Porter, Alison N. and Do, Lauryn G. and Groot, Krista L. De},
	month = nov,
	year = {2023},
	note = {Publisher: The Resilience Alliance},
	keywords = {NALCMS},
}

We examined the effects of façade-level building and vegetation features on bird-window collision risk, and how these effects varied across seasons at a Pacific coastal campus with mild winters, abundant evergreen vegetation, and seasonally varied bird communities. We searched for bird carcasses at 57 façades of 8 buildings at the University of British Columbia (UBC) over 155 days between January 2015 and March 2017 (total: 8835 façade surveys). Collision monitoring occurred across five equal sampling periods that represented stages of the annual cycle of the bird community, including the fall and spring migratory periods, the breeding season, and the long overwintering period. For each season, we compared logistic regression models predicting the odds of a collision from different sets of façade and vegetation characteristics expected to influence collisions: façade area, area of glass, porous surface cover (ground and shrub vegetation, soil, leaf litter), tree cover, and the number of building stories reflecting vegetation. Consistent with other studies, area of glass had a positive influence on collision probability in all seasons; however, the effect was strongest during the fall migratory period, when daily collision mortality rates peaked at UBC. The number of stories reflecting vegetation also increased collision probability, but only in the fall, indicating that the vertical extent of vegetation and reflective glass may affect collision risk differently as bird communities change across seasons. Façade area increased collision probability only in the winter (a long and lethal period for bird collisions at UBC), reflecting different risk factors associated with the species most vulnerable to collisions in this season. Our results highlight the need to measure building and vegetation effects across the longest and most lethal stages of the annual cycle of birds, both to predict the impact of proposed buildings and to prioritize mitigation strategies that will result in the greatest conservation benefits.

@article{kershaw_seasonally_2023,
	title = {Seasonally distinct runoff–recharge partitioning in an alpine tundra catchment},
	volume = {34},
	issn = {1045-6740, 1099-1530},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/ppp.2174},
	doi = {10.1002/ppp.2174},
	language = {en},
	number = {1},
	urldate = {2023-06-01},
	journal = {Permafrost and Periglacial Processes},
	author = {Kershaw, Geoffrey G. L. and English, Michael C. and Wolfe, Brent B.},
	month = jan,
	year = {2023},
	keywords = {NALCMS},
	pages = {94--107},
}

@article{chen_shifting_2023,
	title = {Shifting taxonomic and functional community composition of rivers under land use change},
	volume = {104},
	copyright = {© 2023 The Ecological Society of America.},
	issn = {1939-9170},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ecy.4155},
	doi = {10.1002/ecy.4155},
	abstract = {Land use intensification has led to conspicuous changes in plant and animal communities across the world. Shifts in trait-based functional composition have recently been hypothesized to manifest at lower levels of environmental change when compared to species-based taxonomic composition; however, little is known about the commonalities in these responses across taxonomic groups and geographic regions. We investigated this hypothesis by testing for taxonomic and geographic similarities in the composition of riverine fish and insect communities across gradients of land use in major hydrological regions of the conterminous United States. We analyzed an extensive data set representing 556 species and 33 functional trait modalities from 8023 fish communities and 1434 taxa and 50 trait modalities from 5197 aquatic insect communities. Our results demonstrate abrupt threshold changes in both taxonomic and functional community composition due to land use conversion. Functional composition consistently demonstrated lower land use threshold responses compared to taxonomic composition for both fish (urban p = 0.069; agriculture p = 0.029) and insect (urban p = 0.095; agriculture p = 0.043) communities according to gradient forest models. We found significantly lower thresholds for urban versus agricultural land use for fishes (taxonomic and functional p {\textless} 0.001) and insects (taxonomic p = 0.001; functional p = 0.033). We further revealed that threshold responses in functional composition were more geographically consistent than for taxonomic composition to both urban and agricultural land use change. Traits contributing the most to overall functional composition change differed along urban and agricultural land gradients and conformed to predicted ecological mechanisms underpinning community change. This study points to reliable early-warning thresholds that accurately forecast compositional shifts in riverine communities to land use conversion, and highlight the importance of considering trait-based indicators of community change to inform large-scale land use management strategies and policies.},
	language = {en},
	number = {11},
	urldate = {2024-01-10},
	journal = {Ecology},
	author = {Chen, Kai and Midway, Stephen R. and Peoples, Brandon K. and Wang, Beixin and Olden, Julian D.},
	year = {2023},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ecy.4155},
	keywords = {NALCMS},
	pages = {e4155},
}

Land use intensification has led to conspicuous changes in plant and animal communities across the world. Shifts in trait-based functional composition have recently been hypothesized to manifest at lower levels of environmental change when compared to species-based taxonomic composition; however, little is known about the commonalities in these responses across taxonomic groups and geographic regions. We investigated this hypothesis by testing for taxonomic and geographic similarities in the composition of riverine fish and insect communities across gradients of land use in major hydrological regions of the conterminous United States. We analyzed an extensive data set representing 556 species and 33 functional trait modalities from 8023 fish communities and 1434 taxa and 50 trait modalities from 5197 aquatic insect communities. Our results demonstrate abrupt threshold changes in both taxonomic and functional community composition due to land use conversion. Functional composition consistently demonstrated lower land use threshold responses compared to taxonomic composition for both fish (urban p = 0.069; agriculture p = 0.029) and insect (urban p = 0.095; agriculture p = 0.043) communities according to gradient forest models. We found significantly lower thresholds for urban versus agricultural land use for fishes (taxonomic and functional p \textless 0.001) and insects (taxonomic p = 0.001; functional p = 0.033). We further revealed that threshold responses in functional composition were more geographically consistent than for taxonomic composition to both urban and agricultural land use change. Traits contributing the most to overall functional composition change differed along urban and agricultural land gradients and conformed to predicted ecological mechanisms underpinning community change. This study points to reliable early-warning thresholds that accurately forecast compositional shifts in riverine communities to land use conversion, and highlight the importance of considering trait-based indicators of community change to inform large-scale land use management strategies and policies.

@article{greaves_simulating_2023,
	title = {Simulating future climate change impacts on snow- and ice-related driving hazards in {Arctic}-boreal regions},
	volume = {18},
	issn = {1748-9326},
	url = {https://iopscience.iop.org/article/10.1088/1748-9326/acb5b1},
	doi = {10.1088/1748-9326/acb5b1},
	abstract = {Abstract
            
              As Arctic and boreal regions rapidly warm, the frequency and seasonal timing of hazardous driving conditions on all-season Arctic-boreal roads are likely to change. Because these roads link remote Arctic areas to the rest of the North American road system, climate change may substantially affect safety and quality of life for northern residents and commercial enterprises. To gain insight into future hazardous driving conditions, we built Random Forest models that predict the occurrence of hazardous driving conditions by linking snow, ice, and weather simulated by a spatially explicit modeling system (SnowModel) to archived road condition reports from two highly trafficked all-season northern roads: the Dalton Highway (Alaska, USA) and Dempster Highway (Yukon, Canada). We applied these models to downscaled future climate trajectories for the study period of 2006–2100. We estimated future trends in the frequency and timing of icy, wet-icy, and snowy road surfaces, blowing and drifting snow, and high winds. We found that as the climate warms, and the portion of the year when snow and ice occur becomes shorter, overall frequency of snow storms and ice- and snow-related driving hazards decreased. For example, the mean number of days per year when roads are covered in snow or ice decreased by 51 d (−21\%) on the Dalton Highway between the 2006–2020 and 2081–2100 time periods. However, the intensity of storms was predicted to increase, resulting in higher mean annual storm wind speeds (Dalton +0.56 m s
              −1
              [+17\%]) and snowfall totals (Dalton +0.3 cm [+36\%]). Our models also predicted increasing frequency of wet-icy driving conditions during November, December, January, and February, when daylength is short and hazardous conditions may be more difficult to perceive. Our findings may help road managers and drivers adapt their expectations and behaviors to minimize accident risk on Arctic-boreal roads in the future.},
	number = {2},
	urldate = {2023-06-02},
	journal = {Environmental Research Letters},
	author = {Greaves, Heather E and Boelman, Natalie T and Brinkman, Todd J and Liston, Glen E and Prugh, Laura R and Reinking, Adele K},
	month = feb,
	year = {2023},
	keywords = {NALCMS},
	pages = {025006},
}

Abstract As Arctic and boreal regions rapidly warm, the frequency and seasonal timing of hazardous driving conditions on all-season Arctic-boreal roads are likely to change. Because these roads link remote Arctic areas to the rest of the North American road system, climate change may substantially affect safety and quality of life for northern residents and commercial enterprises. To gain insight into future hazardous driving conditions, we built Random Forest models that predict the occurrence of hazardous driving conditions by linking snow, ice, and weather simulated by a spatially explicit modeling system (SnowModel) to archived road condition reports from two highly trafficked all-season northern roads: the Dalton Highway (Alaska, USA) and Dempster Highway (Yukon, Canada). We applied these models to downscaled future climate trajectories for the study period of 2006–2100. We estimated future trends in the frequency and timing of icy, wet-icy, and snowy road surfaces, blowing and drifting snow, and high winds. We found that as the climate warms, and the portion of the year when snow and ice occur becomes shorter, overall frequency of snow storms and ice- and snow-related driving hazards decreased. For example, the mean number of days per year when roads are covered in snow or ice decreased by 51 d (−21%) on the Dalton Highway between the 2006–2020 and 2081–2100 time periods. However, the intensity of storms was predicted to increase, resulting in higher mean annual storm wind speeds (Dalton +0.56 m s −1 [+17%]) and snowfall totals (Dalton +0.3 cm [+36%]). Our models also predicted increasing frequency of wet-icy driving conditions during November, December, January, and February, when daylength is short and hazardous conditions may be more difficult to perceive. Our findings may help road managers and drivers adapt their expectations and behaviors to minimize accident risk on Arctic-boreal roads in the future.

@article{hoppinen_snow_2023,
	title = {Snow {Water} {Equivalent} {Retrieval} {Over} {Idaho}, {Part} {B}: {Using} {L}-band {UAVSAR} {Repeat}-{Pass} {Interferometry}},
	shorttitle = {Snow {Water} {Equivalent} {Retrieval} {Over} {Idaho}, {Part} {B}},
	url = {https://tc.copernicus.org/preprints/tc-2023-127/},
	doi = {10.5194/tc-2023-127},
	abstract = {{\textless}p{\textgreater}{\textless}strong class="journal-contentHeaderColor"{\textgreater}Abstract.{\textless}/strong{\textgreater} This study evaluates using interferometry on low frequency synthetic aperture radar (SAR) images to monitor snow water equivalent (SWE) over seasonal and synoptic scales. We retrieved SWE changes from nine pairs of SAR images, mean 8 days temporal baseline, captured by an L-band aerial platform, NASA's UAVSAR, over central Idaho as part of the NASA SnowEx 2020 and 2021 campaigns. The retrieved SWE changes were compared against coincident in situ measurements (SNOTEL and snow pits from the SnowEx field campaign) and to 100 m gridded SnowModel modeled SWE changes. The comparison of in situ to retrieved shows a strong Pearson correlation (R = 0.80) and low RMSE (0.1 m, n = 64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R = 0.52, n = 57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R = 0.60, RMSD = 0.023 m, n = 3.2e6) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R = 0.72, RMSD = 0.013 m, n = 6.5e4). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy.{\textless}/p{\textgreater}},
	language = {English},
	urldate = {2024-01-31},
	journal = {The Cryosphere Discussions},
	author = {Hoppinen, Zachary Marshall and Oveisgharan, Shadi and Marshall, Hans-Peter and Mower, Ross and Elder, Kelly and Vuyovich, Carrie},
	month = aug,
	year = {2023},
	note = {Publisher: Copernicus GmbH},
	keywords = {NALCMS},
	pages = {1--24},
}

\textlessp\textgreater\textlessstrong class="journal-contentHeaderColor"\textgreaterAbstract.\textless/strong\textgreater This study evaluates using interferometry on low frequency synthetic aperture radar (SAR) images to monitor snow water equivalent (SWE) over seasonal and synoptic scales. We retrieved SWE changes from nine pairs of SAR images, mean 8 days temporal baseline, captured by an L-band aerial platform, NASA's UAVSAR, over central Idaho as part of the NASA SnowEx 2020 and 2021 campaigns. The retrieved SWE changes were compared against coincident in situ measurements (SNOTEL and snow pits from the SnowEx field campaign) and to 100 m gridded SnowModel modeled SWE changes. The comparison of in situ to retrieved shows a strong Pearson correlation (R = 0.80) and low RMSE (0.1 m, n = 64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R = 0.52, n = 57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R = 0.60, RMSD = 0.023 m, n = 3.2e6) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R = 0.72, RMSD = 0.013 m, n = 6.5e4). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy.\textless/p\textgreater

@phdthesis{walker_summer_2023,
	title = {Summer spatial ecology of woodland caribou across northern {Ontario}},
	url = {https://era.library.ualberta.ca/items/51d3c670-2df6-414f-9195-aa28166689e6},
	abstract = {Caribou (Rangifer tarandus) numbers continue to decline across their circumpolar range with boreal woodland caribou (R. t. caribou;...},
	language = {en},
	urldate = {2024-01-09},
	author = {Walker, Philip},
	year = {2023},
	doi = {10.7939/r3-2r8s-cq77},
	keywords = {NALCMS},
}

Caribou (Rangifer tarandus) numbers continue to decline across their circumpolar range with boreal woodland caribou (R. t. caribou;...

@article{mulrooney_value_2023,
	title = {The value of natural capital in {Canada}’s national parks and national marine conservation areas},
	issn = {24112119, 0960233X},
	url = {https://parksjournal.com/wp-content/uploads/2023/12/10.2305-EKNN8645-Mulrooney.pdf},
	doi = {10.2305/EKNN8645},
	abstract = {Growing attention is being given to protected areas and the ability of their natural capital assets to provide a varied and long-term stream of benefits to individuals and society in general. These areas are often heralded for ensuring natural capital assets persist, but value is often limited to the economic impact of visitor expenditures and the associated effects on regional and national economies. Few studies have attempted to quantify the economic value of natural capital assets in protected areas, especially in Canada. This study uses a benefit transfer approach to produce an initial estimate of the potential economic value of ecosystem services and natural capital associated with the terrestrial and marine environments in Canada’s federal system of national parks and national marine conservation areas. The results suggest that the economic value of these assets ranges between CA\$ 156 billion and CA\$ 588 billion annually.},
	number = {29.2},
	urldate = {2024-05-15},
	journal = {PARKS},
	author = {Mulrooney, Dan and Jones, Brenda},
	month = nov,
	year = {2023},
	keywords = {NALCMS},
	pages = {41--51},
}

Growing attention is being given to protected areas and the ability of their natural capital assets to provide a varied and long-term stream of benefits to individuals and society in general. These areas are often heralded for ensuring natural capital assets persist, but value is often limited to the economic impact of visitor expenditures and the associated effects on regional and national economies. Few studies have attempted to quantify the economic value of natural capital assets in protected areas, especially in Canada. This study uses a benefit transfer approach to produce an initial estimate of the potential economic value of ecosystem services and natural capital associated with the terrestrial and marine environments in Canada’s federal system of national parks and national marine conservation areas. The results suggest that the economic value of these assets ranges between CA$ 156 billion and CA$ 588 billion annually.

@article{larabi_towards_2023,
	title = {Towards reducing the high cost of parameter sensitivity analysis in hydrologic modeling: a regional parameter sensitivity analysis approach},
	volume = {27},
	issn = {1027-5606},
	shorttitle = {Towards reducing the high cost of parameter sensitivity analysis in hydrologic modeling},
	url = {https://hess.copernicus.org/articles/27/3241/2023/},
	doi = {10.5194/hess-27-3241-2023},
	abstract = {Land surface models have many parameters that have a spatially variable impact on model outputs. In applying these models, sensitivity analysis (SA) is sometimes performed as an initial step to select calibration parameters. As these models are applied to large domains, performing sensitivity analysis across the domain is computationally prohibitive. Here, using a Variable Infiltration Capacity model (VIC) deployment to a large domain as an example, we show that watershed classification based on climatic attributes and vegetation land cover helps to identify the spatial pattern of parameter sensitivity within the domain at a reduced cost. We evaluate the sensitivity of 44 VIC model parameters with regard to streamflow, evapotranspiration and snow water equivalent over 25 basins with a median size of 5078 km2. Basins are clustered based on their climatic and land cover attributes. Performance in transferring parameter sensitivity between basins of the same cluster is evaluated by the F1 score. Results show that two donor basins per cluster are sufficient to correctly identify sensitive parameters in a target basin, with F1 scores ranging between 0.66 (evapotranspiration) and 1 (snow water equivalent). While climatic attributes are sufficient to identify sensitive parameters for streamflow and evapotranspiration, including the vegetation class significantly improves skill in identifying sensitive parameters for the snow water equivalent. This work reveals that there is opportunity to leverage climate and land cover attributes to greatly increase the efficiency of parameter sensitivity analysis and facilitate more rapid deployment of land surface models over large spatial domains.},
	language = {English},
	number = {17},
	urldate = {2023-10-04},
	journal = {Hydrology and Earth System Sciences},
	author = {Larabi, Samah and Mai, Juliane and Schnorbus, Markus and Tolson, Bryan A. and Zwiers, Francis},
	month = sep,
	year = {2023},
	note = {Publisher: Copernicus GmbH},
	keywords = {NALCMS},
	pages = {3241--3263},
}

Land surface models have many parameters that have a spatially variable impact on model outputs. In applying these models, sensitivity analysis (SA) is sometimes performed as an initial step to select calibration parameters. As these models are applied to large domains, performing sensitivity analysis across the domain is computationally prohibitive. Here, using a Variable Infiltration Capacity model (VIC) deployment to a large domain as an example, we show that watershed classification based on climatic attributes and vegetation land cover helps to identify the spatial pattern of parameter sensitivity within the domain at a reduced cost. We evaluate the sensitivity of 44 VIC model parameters with regard to streamflow, evapotranspiration and snow water equivalent over 25 basins with a median size of 5078 km2. Basins are clustered based on their climatic and land cover attributes. Performance in transferring parameter sensitivity between basins of the same cluster is evaluated by the F1 score. Results show that two donor basins per cluster are sufficient to correctly identify sensitive parameters in a target basin, with F1 scores ranging between 0.66 (evapotranspiration) and 1 (snow water equivalent). While climatic attributes are sufficient to identify sensitive parameters for streamflow and evapotranspiration, including the vegetation class significantly improves skill in identifying sensitive parameters for the snow water equivalent. This work reveals that there is opportunity to leverage climate and land cover attributes to greatly increase the efficiency of parameter sensitivity analysis and facilitate more rapid deployment of land surface models over large spatial domains.

@phdthesis{malesis_urban_2023,
	type = {Thesis},
	title = {Urban {Landscape} {Complexity} as a {Driver} of {Urban} {Evolution} in {White} {Clover} ({Trifolium} repens)},
	copyright = {none},
	url = {https://digital.lib.washington.edu:443/researchworks/handle/1773/50932},
	abstract = {Cities are influencing evolution on contemporary timescales, but the mechanisms driving evolutionary change are not well understood. Previous studies on urban eco-evolutionary dynamics generally assume that urban structures predictably evolve from a dense core to less intensive peripheries, discounting their spatial complexities. Cities are mosaics of patches, each governed by a unique set of parameters that interact to create a unique set of ecological conditions and stressors distributed unevenly across the landscape. Using an urban-rural transects approach, prior studies identified variance in Hydrogen Cyanide (HCN) production in Trifolium repens (white clover) contingent on the distance from city centers. This investigation introduces a refined approach via the urban complexity framework, incorporating measures of landscape heterogeneity, connectivity, and historical contingency to explicate variability in HCN production across 20 North American cities. A model selection approach was employed to comparatively evaluate the explanatory power of these predictors to distance from city center along an urban-rural transect in multi-city and single city models. The results reveal that multivariate models incorporating urban complexity variables, notably the connectivity of cropland, demonstrated higher adjusted R2 values than univariate distance-based models in multi-city contexts.  Although this finding does not significantly augment the predictive efficacy of single city models, it underscores the shared explanatory contribution of these variables. In conclusion, this investigation posits urban complexity as a critical determinant of urban eco-evolutionary dynamics, warranting further exploration to effectively inform the practice of urban planning.},
	language = {en\_US},
	urldate = {2024-01-09},
	author = {Malesis, Anna},
	year = {2023},
	note = {Accepted: 2023-09-27T17:22:19Z},
	keywords = {NALCMS, Terrestrial Ecoregions (CEC 1997)},
}

Cities are influencing evolution on contemporary timescales, but the mechanisms driving evolutionary change are not well understood. Previous studies on urban eco-evolutionary dynamics generally assume that urban structures predictably evolve from a dense core to less intensive peripheries, discounting their spatial complexities. Cities are mosaics of patches, each governed by a unique set of parameters that interact to create a unique set of ecological conditions and stressors distributed unevenly across the landscape. Using an urban-rural transects approach, prior studies identified variance in Hydrogen Cyanide (HCN) production in Trifolium repens (white clover) contingent on the distance from city centers. This investigation introduces a refined approach via the urban complexity framework, incorporating measures of landscape heterogeneity, connectivity, and historical contingency to explicate variability in HCN production across 20 North American cities. A model selection approach was employed to comparatively evaluate the explanatory power of these predictors to distance from city center along an urban-rural transect in multi-city and single city models. The results reveal that multivariate models incorporating urban complexity variables, notably the connectivity of cropland, demonstrated higher adjusted R2 values than univariate distance-based models in multi-city contexts. Although this finding does not significantly augment the predictive efficacy of single city models, it underscores the shared explanatory contribution of these variables. In conclusion, this investigation posits urban complexity as a critical determinant of urban eco-evolutionary dynamics, warranting further exploration to effectively inform the practice of urban planning.

@phdthesis{wagner_using_2023,
	type = {Master of {Science}, {Civil} and {Environmental} {Engineering}},
	title = {Using {Unpiloted} {Aerial} {Vehicle} {Structure} from {Motion} and {SnowModel} to {Map} {Spatial} {Distribution} of {Wind} {Deposited} {Snow} in {Mount} {Washington}, {NH} {Avalanche} {Terrain}},
	url = {https://unh.primo.exlibrisgroup.com/discovery/fulldisplay?context=PC&vid=01USNH_UNH:MAIN&search_scope=MyInst_and_CI&tab=Everything&docid=cdi_proquest_journals_2825660613},
	abstract = {East of the Rocky Mountains, United States avalanche terrain is almost exclusive to Mount Washington, New Hampshire. Mount Washington’s east-aspect glacial cirques are subject to frequent wind slab avalanche problems due to high winds and ample snowfall in fetch areas above the cirques. Quantification of these slabs’ location, extent, and depth is an integral part of avalanche forecasting and risk assessment. This research used SnowModel, a spatially distributed snow-evolution model, to simulate wind slab depth maps using Mount Washington Observatory meteorologic station data on a 1 m grid. SnowModel’s SnowTran-3D, a snow redistribution by wind algorithm, is tested for one of the first times in the Eastern United States. Snowpack seasonal evolution and accumulation event-based model performance is calibrated and validated using 15 snow depth maps collected throughout the winter of 2021-2022. Snow depth maps were constructed via Structure from Motion (SfM) analysis photogrammetry. SfM maps were derived from optical imagery collected using an Unpiloted Aerial Vehicle (UAV) and were able to quantify wind slab depth with a 5 cm spatial resolution. Limited ground validation showed UAV SfM values are accurate with a 30 cm RMSE on the 2/01/2022 sample date. Total snow depth and snow depth change map time series of each study location consistently show wind-transported snow accumulation and erosion patterns on Mount Washington. SnowModel can capture Mount Washington’s widespread snow redistribution trends but fails to quantify the magnitude and distribution of wind slabs as the UAV SfM can. SnowModel-derived snow depth was compared to Landsat 8’s Normalized Difference Snow Index (NDSI) and shows a significant signal in snow depth increase when NDSI exceeds 0.4. This study provides the first of its kind approach for capturing Mount Washington’s winter snowpack evolution using UAV SfM and a physically based snow evolution model.},
	language = {eng},
	urldate = {2023-08-11},
	school = {University of New Hampshire},
	author = {Wagner, Cameron},
	year = {2023},
	note = {Book Title: Using Unpiloted Aerial Vehicle Structure from Motion and SnowModel to Map Spatial Distribution of Wind Deposited Snow in Mount Washington, NH Avalanche Terrain
ISBN: 9798379712600},
	keywords = {NALCMS},
}

East of the Rocky Mountains, United States avalanche terrain is almost exclusive to Mount Washington, New Hampshire. Mount Washington’s east-aspect glacial cirques are subject to frequent wind slab avalanche problems due to high winds and ample snowfall in fetch areas above the cirques. Quantification of these slabs’ location, extent, and depth is an integral part of avalanche forecasting and risk assessment. This research used SnowModel, a spatially distributed snow-evolution model, to simulate wind slab depth maps using Mount Washington Observatory meteorologic station data on a 1 m grid. SnowModel’s SnowTran-3D, a snow redistribution by wind algorithm, is tested for one of the first times in the Eastern United States. Snowpack seasonal evolution and accumulation event-based model performance is calibrated and validated using 15 snow depth maps collected throughout the winter of 2021-2022. Snow depth maps were constructed via Structure from Motion (SfM) analysis photogrammetry. SfM maps were derived from optical imagery collected using an Unpiloted Aerial Vehicle (UAV) and were able to quantify wind slab depth with a 5 cm spatial resolution. Limited ground validation showed UAV SfM values are accurate with a 30 cm RMSE on the 2/01/2022 sample date. Total snow depth and snow depth change map time series of each study location consistently show wind-transported snow accumulation and erosion patterns on Mount Washington. SnowModel can capture Mount Washington’s widespread snow redistribution trends but fails to quantify the magnitude and distribution of wind slabs as the UAV SfM can. SnowModel-derived snow depth was compared to Landsat 8’s Normalized Difference Snow Index (NDSI) and shows a significant signal in snow depth increase when NDSI exceeds 0.4. This study provides the first of its kind approach for capturing Mount Washington’s winter snowpack evolution using UAV SfM and a physically based snow evolution model.

@phdthesis{pouw_utilizing_2023,
	title = {Utilizing {Ground}-{Penetrating} {Radar} to {Estimate} the {Spatial} {Distribution} of {Snow} {Depth} over {Lake} {Ice} in {Canada}’s {Sub}-{Arctic}},
	url = {https://scholars.wlu.ca/etd/2510},
	author = {Pouw, Alicia},
	month = jan,
	year = {2023},
	keywords = {NALCMS, Terrestrial Ecoregions},
}

@article{fernandes_validation_2023,
	title = {Validation of {Simplified} {Level} 2 {Prototype} {Processor} {Sentinel}-2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over {North} {American} forests},
	volume = {293},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425723001517},
	doi = {10.1016/j.rse.2023.113600},
	language = {en},
	urldate = {2023-06-02},
	journal = {Remote Sensing of Environment},
	author = {Fernandes, Richard and Brown, Luke and Canisius, Francis and Dash, Jadu and He, Liming and Hong, Gang and Huang, Lucy and Le, Nhu Quynh and MacDougall, Camryn and Meier, Courtney and Darko, Patrick Osei and Shah, Hemit and Spafford, Lynsay and Sun, Lixin},
	month = aug,
	year = {2023},
	keywords = {NALCMS},
	pages = {113600},
}

@techreport{caldwell_wassi_2023,
	title = {{WaSSI} {Ecosystem} {Services} {Model}, ver. 1.2, {User} {Guide}},
	url = {https://www.fs.usda.gov/research/srs/understory/wassi-ecosystem-services-model-user-guide},
	language = {en},
	urldate = {2023-06-09},
	institution = {U.S. Department of Agriculture, Forest Service},
	author = {Caldwell, P. and Sun, G. and McNulty, S. and Myers, J.M. and Cohen, E. and Herring, R. and Martinez, E.},
	month = feb,
	year = {2023},
	keywords = {NALCMS},
}

@article{hanson_comparison_2022,
	title = {A comparison of approaches for including connectivity in systematic conservation planning},
	volume = {59},
	issn = {0021-8901, 1365-2664},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/1365-2664.14251},
	doi = {10.1111/1365-2664.14251},
	language = {en},
	number = {10},
	urldate = {2023-06-01},
	journal = {Journal of Applied Ecology},
	author = {Hanson, Jeffrey O. and Vincent, Jaimie and Schuster, Richard and Fahrig, Lenore and Brennan, Angela and Martin, Amanda E. and Hughes, Josie S. and Pither, Richard and Bennett, Joseph R.},
	month = oct,
	year = {2022},
	keywords = {NALCMS},
	pages = {2507--2519},
}

@article{neupane_novel_2022,
	title = {A novel model to accurately predict continental-scale timing of forest green-up},
	volume = {108},
	issn = {15698432},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0303243422000733},
	doi = {10.1016/j.jag.2022.102747},
	abstract = {The yearly cycles in vegetation greenness are among the most important drivers of ecosystem processes. Predictive models for the timing of vegetation greenup and senescence are crucial for understanding how biological communities respond to global change. Greenup timing is closely tied to climate and also tracks yearly variability in temperature, and the strength of this relationship varies spatio-temporally. Local studies have been useful in understanding underlying mechanisms but they are insufficient in explaining larger scale variabilities. Large-scale studies using remotely-sensed data have the potential to harness regional dynamics, even if underlying mechanisms remain unknown, Yet predictive power using these approaches is low. Here, we predict vegetation phenology across Eastern North America via a novel class of Bayesian regression model. Our modeling framework provides continental-level peak greenup time predictions with high accuracy using satellite observations from the MODerate resolution Imaging Spectroradiometer (MODIS). In addition to taking into account temporal structure at individual sites, our models make use of information from the entire study extent regardless of their spatial proximity. Models were built from 2000 to 2016 and showed high prediction accuracy (R2 {\textgreater} 95\%). Out-of-sample predictions for the years 2017 and 2018 showed accuracy within days of the predicted peaks, even though yearly greenup timing can vary by up to 30 days across the study region. Performance was remarkably high across deciduous and mixed forest types. Our method is generalizable to temperate forests across the globe and provides a basis for backcasting and forecasting forest greenup for any time periods where daily temperatures, whether directly measured or modeled, are available.},
	journal = {International Journal of Applied Earth Observation and Geoinformation},
	author = {Neupane, N. and Peruzzi, M. and Arab, A. and Mayor, S.J. and Withey, J.C. and Ries, L. and Finley, A.O.},
	month = apr,
	year = {2022},
	note = {Publisher: Elsevier B.V.},
	keywords = {NALCMS},
	pages = {102747},
}

The yearly cycles in vegetation greenness are among the most important drivers of ecosystem processes. Predictive models for the timing of vegetation greenup and senescence are crucial for understanding how biological communities respond to global change. Greenup timing is closely tied to climate and also tracks yearly variability in temperature, and the strength of this relationship varies spatio-temporally. Local studies have been useful in understanding underlying mechanisms but they are insufficient in explaining larger scale variabilities. Large-scale studies using remotely-sensed data have the potential to harness regional dynamics, even if underlying mechanisms remain unknown, Yet predictive power using these approaches is low. Here, we predict vegetation phenology across Eastern North America via a novel class of Bayesian regression model. Our modeling framework provides continental-level peak greenup time predictions with high accuracy using satellite observations from the MODerate resolution Imaging Spectroradiometer (MODIS). In addition to taking into account temporal structure at individual sites, our models make use of information from the entire study extent regardless of their spatial proximity. Models were built from 2000 to 2016 and showed high prediction accuracy (R2 \textgreater 95%). Out-of-sample predictions for the years 2017 and 2018 showed accuracy within days of the predicted peaks, even though yearly greenup timing can vary by up to 30 days across the study region. Performance was remarkably high across deciduous and mixed forest types. Our method is generalizable to temperate forests across the globe and provides a basis for backcasting and forecasting forest greenup for any time periods where daily temperatures, whether directly measured or modeled, are available.

@article{eppler_adapting_2022,
	title = {Adapting {InSAR} {Phase} {Linking} for {Seasonally} {Snow}-{Covered} {Terrain}},
	volume = {60},
	issn = {15580644},
	url = {https://ieeexplore.ieee.org/document/9807376},
	doi = {10.1109/TGRS.2022.3186522},
	abstract = {Interferometric synthetic aperture radar (InSAR) time series analysis of natural terrain allows for characterization of long-term geophysical trends over extended areas and, in the case of distributed scatterers (DSs), is significantly enhanced by methods that exploit the full complex-valued scattering statistics. Phase-linking (PL) estimators impose a phase-closure constraint in order to estimate the temporal wrapped-phase history of a DS directly from its complex backscatter sample coherence matrix. Some PL methods, such as the SqueeSAR and maximum-likelihood-estimator of Interferometric phase (EMI) estimators, rely on knowledge of the coherence magnitude matrix. The true coherence magnitude is a priori unknown and must therefore be estimated from the data. Bias in these estimated coherence magnitudes reduces PL performance when the true coherence magnitude is low. Many areas of the Earth are seasonally snow-covered and, for natural terrain, this leads to severe cross-season decorrelation. This poses a significant challenge for PL estimators due to bias of the near-zero cross-season coherence magnitude estimates. We introduce a clustering approach to mitigate the PL estimator bias problem that exploits the fact that in natural terrain, many DSs decorrelate similarly. This allows for averaging over large numbers of same-behaving DS, which provides robust debiasing of the coherence magnitudes used during PL. We apply our method to a RADARSAT-2 spotlight-mode InSAR dataset over a site in the western Canadian Arctic and demonstrate significant reductions in a posteriori phase variance when compared to existing PL methods.},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	author = {Eppler, Jayson and Rabus, Bernhard T.},
	year = {2022},
	note = {Publisher: IEEE},
	keywords = {NALCMS},
}

Interferometric synthetic aperture radar (InSAR) time series analysis of natural terrain allows for characterization of long-term geophysical trends over extended areas and, in the case of distributed scatterers (DSs), is significantly enhanced by methods that exploit the full complex-valued scattering statistics. Phase-linking (PL) estimators impose a phase-closure constraint in order to estimate the temporal wrapped-phase history of a DS directly from its complex backscatter sample coherence matrix. Some PL methods, such as the SqueeSAR and maximum-likelihood-estimator of Interferometric phase (EMI) estimators, rely on knowledge of the coherence magnitude matrix. The true coherence magnitude is a priori unknown and must therefore be estimated from the data. Bias in these estimated coherence magnitudes reduces PL performance when the true coherence magnitude is low. Many areas of the Earth are seasonally snow-covered and, for natural terrain, this leads to severe cross-season decorrelation. This poses a significant challenge for PL estimators due to bias of the near-zero cross-season coherence magnitude estimates. We introduce a clustering approach to mitigate the PL estimator bias problem that exploits the fact that in natural terrain, many DSs decorrelate similarly. This allows for averaging over large numbers of same-behaving DS, which provides robust debiasing of the coherence magnitudes used during PL. We apply our method to a RADARSAT-2 spotlight-mode InSAR dataset over a site in the western Canadian Arctic and demonstrate significant reductions in a posteriori phase variance when compared to existing PL methods.

@article{shu_added_2022,
	title = {Added value of convection permitting climate modelling in urban overheating assessments},
	volume = {207},
	issn = {03601323},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S036013232100812X},
	doi = {10.1016/j.buildenv.2021.108415},
	language = {en},
	urldate = {2023-06-15},
	journal = {Building and Environment},
	author = {Shu, Chang and Gaur, Abhishek and Wang, Liangzhu (Leon) and Bartko, Michal and Laouadi, Abdelaziz and Ji, Lili and Lacasse, Michael},
	month = jan,
	year = {2022},
	keywords = {NALCMS},
	pages = {108415},
}

@phdthesis{kimes_assessing_2022,
	title = {Assessing the {Influence} of {Landscape} {Characteristics} on {Bat} {Fatalities} at {South} {Texas} {Wind} {Energy} {Facilities}},
	url = {https://digital.library.txstate.edu/handle/10877/16360},
	abstract = {Although wind energy is a viable renewable energy source, strikes by wind 
turbine blades unintentionally cause bat fatalities. Previous research has suggested siting 
of wind energy facilities, and turbine placement within facilities, influence the number of 
bat fatalities; however, there is a knowledge gap regarding the reasons for the variability. 
This study occurred in Texas, the leading producer of wind energy and home to the 
greatest diversity and largest colonies of bats in the United States. The objective of my 
thesis was to assess the influence of landscape characteristics surrounding wind energy 
facilities and around specific turbines on the number of bat fatalities. I systematically 
searched 200 wind turbines and collected 1,067 bat carcasses at Hidalgo and Los Vientos 
Wind Energy Facilities from 2017–2018; I found Tadarida brasiliensis (n = 577), 
Lasiurus intermedius (n = 203), L. ega (n = 69), Nycticeius humeralis (n = 51), L. 
xanthinus (n = 30), L. cinereus (n = 18), L. blossevilli (n = 2) Myotis velifer (n = 2), 
Nyctinomops macrotis (n = 1), Perimyotis subflavus (n = 1), and unknown spp. (n = 113). 
I used Fragstats and ArcGIS Pro to acquire landscape metrics at the two facilities and 
among the 100 wind turbines at each facility at multiple scales (100 m, 500 m, 1 km, 5 
km, and 25 km). Landscape characteristics included landcover types such as barren, 
crops, herbaceous, developed, shrub/scrub, hay/pasture, forest, wetlands, and open water, 
proximity to water sources, elevation and degree of slope. Using generalized linear 
models, zero-inflated and negative binomial models, and AIC model selection, results 
indicate that landscape characteristics at the broadest scale (5 km and 25 km) examined were most strongly associated with estimated bat fatality rates. I suggest wind farms 
should be constructed in areas that consist of uniform and connected habitat throughout 
the facility, without the presence of fragmented water sources. Managers should also 
implement mitigation efforts and pre/post-construction assessments to potentially reduce 
bat fatalities.},
	language = {en},
	urldate = {2023-06-01},
	author = {Kimes, Houston},
	month = dec,
	year = {2022},
	keywords = {NALCMS},
}

Although wind energy is a viable renewable energy source, strikes by wind turbine blades unintentionally cause bat fatalities. Previous research has suggested siting of wind energy facilities, and turbine placement within facilities, influence the number of bat fatalities; however, there is a knowledge gap regarding the reasons for the variability. This study occurred in Texas, the leading producer of wind energy and home to the greatest diversity and largest colonies of bats in the United States. The objective of my thesis was to assess the influence of landscape characteristics surrounding wind energy facilities and around specific turbines on the number of bat fatalities. I systematically searched 200 wind turbines and collected 1,067 bat carcasses at Hidalgo and Los Vientos Wind Energy Facilities from 2017–2018; I found Tadarida brasiliensis (n = 577), Lasiurus intermedius (n = 203), L. ega (n = 69), Nycticeius humeralis (n = 51), L. xanthinus (n = 30), L. cinereus (n = 18), L. blossevilli (n = 2) Myotis velifer (n = 2), Nyctinomops macrotis (n = 1), Perimyotis subflavus (n = 1), and unknown spp. (n = 113). I used Fragstats and ArcGIS Pro to acquire landscape metrics at the two facilities and among the 100 wind turbines at each facility at multiple scales (100 m, 500 m, 1 km, 5 km, and 25 km). Landscape characteristics included landcover types such as barren, crops, herbaceous, developed, shrub/scrub, hay/pasture, forest, wetlands, and open water, proximity to water sources, elevation and degree of slope. Using generalized linear models, zero-inflated and negative binomial models, and AIC model selection, results indicate that landscape characteristics at the broadest scale (5 km and 25 km) examined were most strongly associated with estimated bat fatality rates. I suggest wind farms should be constructed in areas that consist of uniform and connected habitat throughout the facility, without the presence of fragmented water sources. Managers should also implement mitigation efforts and pre/post-construction assessments to potentially reduce bat fatalities.

@article{lalla_central-place_2022,
	title = {Central-place foraging poses variable constraints year-round in a neotropical migrant},
	volume = {10},
	issn = {2051-3933},
	url = {https://movementecologyjournal.biomedcentral.com/articles/10.1186/s40462-022-00337-2},
	doi = {10.1186/s40462-022-00337-2},
	abstract = {Background: “Central-place foragers” are constrained in their habitat selection and foraging range by the frequency with which they need to return to a central place. For example, chick-rearing songbirds that must feed their offspring hourly might be expected to have smaller foraging ranges compared to non-breeding songbirds that return nightly to a roost. Methods: We used GPS units to compare the foraging behaviour of an aerial insectivorous bird, the purple martin (Progne subis), during the breeding season in three regions across North America, as well as the non-breeding season in South America. Specifically, we tested foraging range size and habitat selection. Results: Foraging range did not vary among regions during breeding (14.0 ± 39.2 km2) and was larger during the nonbreeding period (8840 ± 8150 km2). Purple martins strongly preferred aquatic habitats to other available habitats year-round and in the Amazon commuted from night roosts in low productivity sediment-poor water, where risk of predation was probably low, to daytime foraging sites in productive sediment-rich water sites. Conclusions: We provide the first estimates for foraging range size in purple martins and demonstrate foraging preference for aquatic habitats throughout two stages of the annual cycle. Understanding foraging constraints and habitat of aerial insectivores may help plan conservation actions throughout their annual cycle. Future research should quantify foraging behaviour during the post-breeding period and during migration.},
	number = {1},
	journal = {Movement Ecology},
	author = {Lalla, Kristen M. and Fraser, Kevin C. and Frei, Barbara and Fischer, Jason D. and Siegrist, Joe and Ray, James D. and Cohn-Haft, Mario and Elliott, Kyle H.},
	month = sep,
	year = {2022},
	note = {Publisher: BioMed Central Ltd},
	keywords = {NALCMS},
	pages = {39},
}

Background: “Central-place foragers” are constrained in their habitat selection and foraging range by the frequency with which they need to return to a central place. For example, chick-rearing songbirds that must feed their offspring hourly might be expected to have smaller foraging ranges compared to non-breeding songbirds that return nightly to a roost. Methods: We used GPS units to compare the foraging behaviour of an aerial insectivorous bird, the purple martin (Progne subis), during the breeding season in three regions across North America, as well as the non-breeding season in South America. Specifically, we tested foraging range size and habitat selection. Results: Foraging range did not vary among regions during breeding (14.0 ± 39.2 km2) and was larger during the nonbreeding period (8840 ± 8150 km2). Purple martins strongly preferred aquatic habitats to other available habitats year-round and in the Amazon commuted from night roosts in low productivity sediment-poor water, where risk of predation was probably low, to daytime foraging sites in productive sediment-rich water sites. Conclusions: We provide the first estimates for foraging range size in purple martins and demonstrate foraging preference for aquatic habitats throughout two stages of the annual cycle. Understanding foraging constraints and habitat of aerial insectivores may help plan conservation actions throughout their annual cycle. Future research should quantify foraging behaviour during the post-breeding period and during migration.

@phdthesis{rogers_complementarity_2022,
	type = {Thesis},
	title = {Complementarity of {Solar} {Induced} {Chlorophyll} {Fluorescence} and the {Photochemical} {Reflectance} {Index} for {Remote} {Estimation} of {Terrestrial} {Gross} {Primary} {Productivity}},
	url = {https://tspace.library.utoronto.ca/handle/1807/123193},
	abstract = {Terrestrial vegetation helps mitigate the accumulation of CO2 in the atmosphere through photosynthesis, which locks CO2 into vast stores of vegetative material. However, the rate of this flux is itself sensitive to global change and may fluctuate in ways that are difficult to predict. Monitoring gross primary productivity (GPP) continuously across both space and time is thus critical to understanding the risks of continued climate change. This thesis evaluates satellite remote sensing measures to improve assessment of carbon uptake and explores the complementarity and confounding factors of two physiologically related spectral indices: solar induced chlorophyll fluorescence (SIF), and the photochemical reflectance index (PRI).An evaluation of the impact of land cover and latitude on SIF phenology across the Province of Ontario using a GIS approach shows higher SIF magnitudes in more densely vegetated land cover types, early start of season in urban environments, and delayed start of season in croplands. Exploiting the 2017 North American Solar Eclipse as a natural shading experiment over a mixed forest canopy at proximal scale indicates that changes in PRI  can result from multiple scattering of light through a forest canopy. As SIF and PRI are highly sensitive to canopy structure, I devise and test methods to measure leaf area index from understory light sensors. I report a near 20-year record of canopy structure for a mixed forest site. Finally, I report the results of a comprehensive field campaign to characterize the causes of variability in SIF, PRI and GPP across diurnal, seasonal and interannual time periods. Variability in PRI and SIF yield are associated with changes in canopy chlorophyll content and canopy structure. The findings support emerging evidence that structure and radiation dominate SIF variation and highlight limitations of PRI in tracking light stress over long time series. 
The findings advance our ability to assess vegetation productivity from space and indicate factors that may confound our interpretation of remotely sensed spectral indices.},
	language = {en},
	urldate = {2023-06-01},
	author = {Rogers, Cheryl Anne},
	month = jun,
	year = {2022},
	note = {Accepted: 2022-06-29T15:17:13Z},
	keywords = {NALCMS},
}

Terrestrial vegetation helps mitigate the accumulation of CO2 in the atmosphere through photosynthesis, which locks CO2 into vast stores of vegetative material. However, the rate of this flux is itself sensitive to global change and may fluctuate in ways that are difficult to predict. Monitoring gross primary productivity (GPP) continuously across both space and time is thus critical to understanding the risks of continued climate change. This thesis evaluates satellite remote sensing measures to improve assessment of carbon uptake and explores the complementarity and confounding factors of two physiologically related spectral indices: solar induced chlorophyll fluorescence (SIF), and the photochemical reflectance index (PRI).An evaluation of the impact of land cover and latitude on SIF phenology across the Province of Ontario using a GIS approach shows higher SIF magnitudes in more densely vegetated land cover types, early start of season in urban environments, and delayed start of season in croplands. Exploiting the 2017 North American Solar Eclipse as a natural shading experiment over a mixed forest canopy at proximal scale indicates that changes in PRI can result from multiple scattering of light through a forest canopy. As SIF and PRI are highly sensitive to canopy structure, I devise and test methods to measure leaf area index from understory light sensors. I report a near 20-year record of canopy structure for a mixed forest site. Finally, I report the results of a comprehensive field campaign to characterize the causes of variability in SIF, PRI and GPP across diurnal, seasonal and interannual time periods. Variability in PRI and SIF yield are associated with changes in canopy chlorophyll content and canopy structure. The findings support emerging evidence that structure and radiation dominate SIF variation and highlight limitations of PRI in tracking light stress over long time series. The findings advance our ability to assess vegetation productivity from space and indicate factors that may confound our interpretation of remotely sensed spectral indices.

@article{jenckes_concentrationdischarge_2022,
	title = {Concentration‐{Discharge} {Patterns} {Across} the {Gulf} of {Alaska} {Reveal} {Geomorphological} and {Glacierization} {Controls} on {Stream} {Water} {Solute} {Generation} and {Export}},
	volume = {49},
	issn = {0094-8276, 1944-8007},
	url = {https://onlinelibrary.wiley.com/doi/10.1029/2021GL095152},
	doi = {10.1029/2021GL095152},
	language = {en},
	number = {1},
	urldate = {2023-06-15},
	journal = {Geophysical Research Letters},
	author = {Jenckes, Jordan and Ibarra, Daniel E. and Munk, Lee Ann},
	month = jan,
	year = {2022},
	keywords = {NALCMS},
}

@article{stevens-rumann_considering_2022,
	title = {Considering regeneration failure in the context of changing climate and disturbance regimes in western {North} {America}},
	volume = {52},
	issn = {0045-5067, 1208-6037},
	url = {https://cdnsciencepub.com/doi/10.1139/cjfr-2022-0054},
	doi = {10.1139/cjfr-2022-0054},
	abstract = {Wildfire-mediated changes to forests have prompted numerous studies on post-fire forest recovery of coniferous forests. Given climate change, a growing body of work demonstrates that conifer regeneration in temperate and boreal forests is declining, a phenomenon often termed “regeneration failure.” However, the definition and parameters are numerous and variable. Characterization of drought also varies greatly, thus hindering the ability to compare results among areas. This review discusses new perspectives on conifer regeneration failure and places these studies into the context of drought and fire activity. We focus this review on three forest types where conifer regeneration failure is well documented: western boreal forests, cold mixed-conifer forests, and dry pine forests. To place the challenges to conifer tree regeneration in the context of regional climate trends, we present a novel regional analysis that summarizes drought conditions prior, during, and following the year of a large wildfire. We demonstrate the need to assess failure in the context of specific forest dynamics and welldefined metrics. For example, tree establishment may historically occur over longer periods, and current and future climate may exacerbate this and not promote pre-fire forest structure and composition. Many forests are undergoing rapid change and the type, magnitude, and causes of changes need to be compared among areas. As such, we should be cautious of quantifying “regeneration failure” and drought without providing spatial and temporal context.},
	language = {en},
	number = {10},
	urldate = {2023-06-01},
	journal = {Canadian Journal of Forest Research},
	author = {Stevens-Rumann, Camille S. and Prichard, Susan J. and Whitman, Ellen and Parisien, Marc-André and Meddens, Arjan J.H.},
	month = oct,
	year = {2022},
	keywords = {Lakes \& Rivers, NALCMS, Terrestrial Ecoregions},
	pages = {1281--1302},
}

Wildfire-mediated changes to forests have prompted numerous studies on post-fire forest recovery of coniferous forests. Given climate change, a growing body of work demonstrates that conifer regeneration in temperate and boreal forests is declining, a phenomenon often termed “regeneration failure.” However, the definition and parameters are numerous and variable. Characterization of drought also varies greatly, thus hindering the ability to compare results among areas. This review discusses new perspectives on conifer regeneration failure and places these studies into the context of drought and fire activity. We focus this review on three forest types where conifer regeneration failure is well documented: western boreal forests, cold mixed-conifer forests, and dry pine forests. To place the challenges to conifer tree regeneration in the context of regional climate trends, we present a novel regional analysis that summarizes drought conditions prior, during, and following the year of a large wildfire. We demonstrate the need to assess failure in the context of specific forest dynamics and welldefined metrics. For example, tree establishment may historically occur over longer periods, and current and future climate may exacerbate this and not promote pre-fire forest structure and composition. Many forests are undergoing rapid change and the type, magnitude, and causes of changes need to be compared among areas. As such, we should be cautious of quantifying “regeneration failure” and drought without providing spatial and temporal context.

@article{cervantes-huerta_controlling_2022,
	title = {Controlling human activities as confounding variable in road studies},
	volume = {96},
	issn = {01959255},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0195925522001184},
	doi = {10.1016/j.eiar.2022.106852},
	language = {en},
	urldate = {2023-06-01},
	journal = {Environmental Impact Assessment Review},
	author = {Cervantes-Huerta, R. and Equihua, M. and Colino-Rabanal, V.J. and González-Romero, A. and Duran-Antonio, J. and González-Gallina, A.},
	month = sep,
	year = {2022},
	keywords = {NALCMS},
	pages = {106852},
}

@article{yu_controls_2022,
	title = {Controls of contemporary (2001–2018) springtime waterflow dynamics in a {Large}, snowmelt-dominated basin in northeastern {North} {America}},
	volume = {14},
	issn = {25899155},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2589915521000456},
	doi = {10.1016/j.hydroa.2021.100117},
	language = {en},
	urldate = {2023-06-15},
	journal = {Journal of Hydrology X},
	author = {Yu, Xindi and Bourque, Charles P.-A.},
	month = jan,
	year = {2022},
	keywords = {NALCMS},
	pages = {100117},
}

@article{schmidt_determinants_2022,
	title = {Determinants of genetic diversity and species richness of {North} {American} amphibians},
	volume = {49},
	issn = {0305-0270, 1365-2699},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/jbi.14480},
	doi = {10.1111/jbi.14480},
	language = {en},
	number = {11},
	urldate = {2023-06-01},
	journal = {Journal of Biogeography},
	author = {Schmidt, Chloé and Munshi‐South, Jason and Dray, Stéphane and Garroway, Colin J.},
	month = nov,
	year = {2022},
	keywords = {NALCMS},
	pages = {2005--2015},
}

@article{rodriguez-sanchez_diversity_2022,
	title = {Diversity of insects associated with the fruits of four tree species of {Lauraceae} from {Los} {Tuxtlas} region, {Mexico}: an annotated and illustrated taxonomic list},
	volume = {93},
	issn = {2007-8706},
	url = {https://revista.ib.unam.mx/index.php/bio/article/view/4178},
	doi = {10.22201/ib.20078706e.2022.93.4178},
	abstract = {Besides recruitment and dispersal, fruits are key resources for the maintenance of insect communities. This study is focused on the insects inhabiting the fruits of 4 wild Lauraceae species. Although the trees of this family are important elements of tropical forests, their interaction with insects, especially in association with fruits, remains poorly studied in wild tree species. Our study aims to characterize the diversity of insects associated with fruits of Damburneya ambigens, Damburneya gentlei, Damburneya salicifolia, and Nectandra turbacensis, in the rainforest of Los Tuxtlas, Veracruz. We present an illustrated taxonomic list of species, annotated with a comprehensive review of the insects' natural history and their interactions with Lauraceae species. We reared 54 insect species from approximately 6,500 fruits, some of which represent potential new species and records for Mexico. Insect species diversity was high and differed between Lauraceae species. The reared insects comprise a wide variety of distributional ranges, feeding types, and habitats. This research provides novel information about the interactions among insects and fruits of Lauraceae and the complexity of their trophic networks in tropical rainforests. Furthermore, it evidences the importance of wild fruits as resources for insect communities.},
	journal = {Revista Mexicana de Biodiversidad},
	author = {Rodríguez-Sánchez, Edna and Giraldo-Kalil, Laura J. and Núñez-Farfán, Juan},
	month = nov,
	year = {2022},
	note = {Publisher: Universidad Nacional Autonoma de Mexico},
	keywords = {NALCMS},
	pages = {e934178},
}

Besides recruitment and dispersal, fruits are key resources for the maintenance of insect communities. This study is focused on the insects inhabiting the fruits of 4 wild Lauraceae species. Although the trees of this family are important elements of tropical forests, their interaction with insects, especially in association with fruits, remains poorly studied in wild tree species. Our study aims to characterize the diversity of insects associated with fruits of Damburneya ambigens, Damburneya gentlei, Damburneya salicifolia, and Nectandra turbacensis, in the rainforest of Los Tuxtlas, Veracruz. We present an illustrated taxonomic list of species, annotated with a comprehensive review of the insects' natural history and their interactions with Lauraceae species. We reared 54 insect species from approximately 6,500 fruits, some of which represent potential new species and records for Mexico. Insect species diversity was high and differed between Lauraceae species. The reared insects comprise a wide variety of distributional ranges, feeding types, and habitats. This research provides novel information about the interactions among insects and fruits of Lauraceae and the complexity of their trophic networks in tropical rainforests. Furthermore, it evidences the importance of wild fruits as resources for insect communities.

@article{gaboriau_drivers_2022,
	title = {Drivers of extreme wildfire years in the 1965–2019 fire regime of the {T}{\textbackslash}lı̨chǫ {First} {Nation} territory, {Canada}},
	volume = {29},
	issn = {1195-6860},
	url = {https://www.tandfonline.com/doi/full/10.1080/11956860.2022.2070342},
	doi = {10.1080/11956860.2022.2070342},
	abstract = {Exceptionally large areas burned in 2014 in central Northwest Territories (Canada), leading members of the T{\textbackslash}lchǫ First Nation to characterize this year as 'extreme'. Top-down climatic and bottom-up environmental drivers of fire behavior and areas burned in the boreal forest are relatively well understood, but not the drivers of extreme wildfire years (EWY). We investigated the temporal and spatial distributions of fire regime components (fire occurrence, size, cause, fire season length) on the T{\textbackslash}lchǫ territory from 1965 to 2019. We used BioSIM and data from weather stations to interpolate mean weather conditions, fuel moisture content and fire-weather indices for each fire season, and we described the environmental characteristics of burned areas. We identified and characterized EWY, i.e., years exceeding the 80th percentile of annual area burned for the study period. Temperature and fuel moisture were the main drivers of areas burned. Nine EWY occurred from 1965 to 2019, including 2014. Compared to non-EWY, EWY had significantly higher mean temperature ({\textgreater}14.7°C) and exceeded threshold values of Drought Code ({\textgreater}514), Initial Spread Index ({\textgreater}7), and Fire Weather Index ({\textgreater}19). Our results will help limit the effects of EWY on human safety, health and Indigenous livelihoods and lifestyles.},
	number = {3},
	journal = {Écoscience},
	author = {Gaboriau, Dorian M. and Asselin, Hugo and Ali, Adam A. and Hély, Christelle and Girardin, Martin P.},
	month = jul,
	year = {2022},
	note = {Publisher: Codicille},
	keywords = {NALCMS},
	pages = {249--265},
}

Exceptionally large areas burned in 2014 in central Northwest Territories (Canada), leading members of the T\lchǫ First Nation to characterize this year as 'extreme'. Top-down climatic and bottom-up environmental drivers of fire behavior and areas burned in the boreal forest are relatively well understood, but not the drivers of extreme wildfire years (EWY). We investigated the temporal and spatial distributions of fire regime components (fire occurrence, size, cause, fire season length) on the T\lchǫ territory from 1965 to 2019. We used BioSIM and data from weather stations to interpolate mean weather conditions, fuel moisture content and fire-weather indices for each fire season, and we described the environmental characteristics of burned areas. We identified and characterized EWY, i.e., years exceeding the 80th percentile of annual area burned for the study period. Temperature and fuel moisture were the main drivers of areas burned. Nine EWY occurred from 1965 to 2019, including 2014. Compared to non-EWY, EWY had significantly higher mean temperature (\textgreater14.7°C) and exceeded threshold values of Drought Code (\textgreater514), Initial Spread Index (\textgreater7), and Fire Weather Index (\textgreater19). Our results will help limit the effects of EWY on human safety, health and Indigenous livelihoods and lifestyles.

@article{olthof_dynamic_2022,
	title = {Dynamic surface water maps of {Canada} from 1984 to 2019 {Landsat} satellite imagery},
	volume = {279},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425722002358},
	doi = {10.1016/j.rse.2022.113121},
	language = {en},
	urldate = {2023-06-01},
	journal = {Remote Sensing of Environment},
	author = {Olthof, Ian and Rainville, Thomas},
	month = sep,
	year = {2022},
	keywords = {NALCMS},
	pages = {113121},
}

@article{gentine_environmental_2022,
	title = {Environmental predictors of phytoplankton chlorophyll-a in {Great} {Lakes} coastal wetlands},
	volume = {48},
	issn = {03801330},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0380133022001058},
	doi = {10.1016/j.jglr.2022.04.015},
	language = {en},
	number = {4},
	urldate = {2023-06-15},
	journal = {Journal of Great Lakes Research},
	author = {Gentine, Joseph A. and Conard, Whitney M. and O'Reilly, Katherine E. and Cooper, Matthew J. and Fiorino, Giuseppe E. and Harrison, Anna M. and Hein, Marina and Moerke, Ashley H. and Ruetz, Carl R. and Uzarski, Donald G. and Lamberti, Gary A.},
	month = aug,
	year = {2022},
	keywords = {NALCMS},
	pages = {927--934},
}

@article{silvan-cardenas_estimating_2022,
	title = {Estimating {Time} of {Urbanization} with {Moderate}-{Resolution} {Sensors}},
	volume = {75},
	issn = {0033-0124, 1467-9272},
	url = {https://www.tandfonline.com/doi/full/10.1080/00330124.2022.2125882},
	doi = {10.1080/00330124.2022.2125882},
	language = {en},
	number = {3},
	urldate = {2023-06-01},
	journal = {The Professional Geographer},
	author = {Silván-Cárdenas, José Luis and Tapia-McClung, Rodrigo and Valdiviezo-Navarro, Juan Carlos and Salazar-Garibay, Adán},
	month = oct,
	year = {2022},
	keywords = {NALCMS},
	pages = {463--480},
}

@article{zhao_evaluating_2022,
	title = {Evaluating fine-scale phenology from {PlanetScope} satellites with ground observations across temperate forests in eastern {North} {America}},
	volume = {283},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425722004163},
	doi = {10.1016/j.rse.2022.113310},
	language = {en},
	urldate = {2023-06-01},
	journal = {Remote Sensing of Environment},
	author = {Zhao, Yingyi and Lee, Calvin K.F. and Wang, Zhihui and Wang, Jing and Gu, Yating and Xie, Jing and Law, Ying Ki and Song, Guangqin and Bonebrake, Timothy C. and Yang, Xi and Nelson, Bruce W. and Wu, Jin},
	month = dec,
	year = {2022},
	keywords = {NALCMS},
	pages = {113310},
}

@article{mccabe_factors_2022,
	title = {Factors associated with returns of snowy owls to airports following translocation},
	volume = {86},
	issn = {19372817},
	url = {https://wildlife.onlinelibrary.wiley.com/doi/abs/10.1002/jwmg.22230},
	doi = {10.1002/jwmg.22230},
	abstract = {Human-dominated environments often include ecological traps for wildlife, such as airports that may be perceived as suitable habitat by grassland birds but reduce fitness because of collisions with aircraft. Birds of prey are often attracted to airports where collisions with aircraft (i.e., bird strikes) are usually fatal for the birds and are a significant threat to flight safety. The snowy owl (Bubo scandiacus) is known for its nomadism, exhibiting unpredictable and highly variable movements during the nonbreeding season, including being a common visitor to airports, which often have high small-mammal populations and mimic flat, open habitats used naturally by owls. Since 2009, the Federal Aviation Administration reported an average of 22 snowy owl deaths annually due to aircraft collisions throughout 55 North American airports. To aid in active management of owls at airports, we assessed relocation data of 42 telemetry-tracked snowy owls from 2000–2020 in the United States and Canada. Owls that returned to the airport after relocation (33\%) frequently crisscrossed and perched near runways where they were at risk of strikes. Adult females and immature males were more likely to return than the other sex and age classes, and returns were less likely to occur as the distance between the release site and the airport increased. Owls relocated in open habitats with a greater proportion of wetland and cropland (including grasslands and pasture) land cover classes were also less likely to return. We conclude that inclusion of multiple factors to limit return rates of relocated snowy owls from airport facilities can unspring the ecological trap presented by airports to these owls.},
	number = {5},
	journal = {Journal of Wildlife Management},
	author = {McCabe, Rebecca A. and Wiebe, Karen L. and Therrien, Jean François and Gauthier, Gilles and Smith, Norman E. and Weidensaul, C. Scott and Brinker, David F. and Allard, Maxime and Skelling, Marilou G. and Molina, Pierre and Fuller, Mark R. and Bates, Kirk K. and Elliott, Kyle H.},
	year = {2022},
	keywords = {NALCMS},
	pages = {1--15},
}

Human-dominated environments often include ecological traps for wildlife, such as airports that may be perceived as suitable habitat by grassland birds but reduce fitness because of collisions with aircraft. Birds of prey are often attracted to airports where collisions with aircraft (i.e., bird strikes) are usually fatal for the birds and are a significant threat to flight safety. The snowy owl (Bubo scandiacus) is known for its nomadism, exhibiting unpredictable and highly variable movements during the nonbreeding season, including being a common visitor to airports, which often have high small-mammal populations and mimic flat, open habitats used naturally by owls. Since 2009, the Federal Aviation Administration reported an average of 22 snowy owl deaths annually due to aircraft collisions throughout 55 North American airports. To aid in active management of owls at airports, we assessed relocation data of 42 telemetry-tracked snowy owls from 2000–2020 in the United States and Canada. Owls that returned to the airport after relocation (33%) frequently crisscrossed and perched near runways where they were at risk of strikes. Adult females and immature males were more likely to return than the other sex and age classes, and returns were less likely to occur as the distance between the release site and the airport increased. Owls relocated in open habitats with a greater proportion of wetland and cropland (including grasslands and pasture) land cover classes were also less likely to return. We conclude that inclusion of multiple factors to limit return rates of relocated snowy owls from airport facilities can unspring the ecological trap presented by airports to these owls.

@techreport{perry_forest_2022,
	address = {Washington, D.C.},
	title = {Forest {Atlas} of the {United} {States}},
	url = {https://www.fs.usda.gov/treesearch/pubs/64468},
	language = {en},
	number = {FS-1172},
	urldate = {2023-06-01},
	institution = {U.S. Department of Agriculture, Forest Service},
	author = {Perry, Charles H. and Finco, Mark V. and Wilson, Barry T.},
	month = jun,
	year = {2022},
	doi = {10.2737/FS-1172},
	keywords = {NALCMS},
	pages = {FS--1172},
}

@article{hanberry_20000_2022,
	title = {From 20,000 {Years} {Ago} to {Near} {Present} {Climate} {Classification} of {North} {America}},
	volume = {8},
	issn = {2055-298X},
	url = {http://www.openquaternary.com/articles/10.5334/oq.116/},
	doi = {10.5334/oq.116},
	abstract = {Climate classification allows an efficient encapsulation of climate data into climate units. For North America and most of Central America during 20, 14, 13, 11, 10, 7, 5, and 1 thousand years ago (ka) and recent years, I applied a Köppen-Trewartha classification system, but with dry classes subsumed under primary thermal classes to preserve information. The boreal and polar classes decreased from a combined 70\% of area during 20 ka until reaching 42\% of area at 7 ka, after which the area remained relatively stable. Conversely, the subtropical and temperate classes increased from 25\% of area until reaching 53\% of area at 7 ka, with slight increase of the tropical class. The combined dry subclasses increased from 7.5\% to 15\% of area, primarily in the subtropical and temperate classes, displaying unique trends over time. Based on ordination, the classes since 5 ka are similar; the 1950 interval is most similar to 1 and 5 ka and the intervals of 1600 and 1800 are most similar. The climate classes and transitions generally corresponded with major vegetation distributions. Visually, political boundaries appeared to parallel climate classes, which might indicate the influence of long-standing ecological differences on human land use and settlement. A future research need is identifying the influence of climate on directing settlement and political boundary establishment.},
	language = {en},
	urldate = {2023-06-01},
	journal = {Open Quaternary},
	author = {Hanberry, Brice B.},
	month = aug,
	year = {2022},
	keywords = {NALCMS},
	pages = {11},
}

Climate classification allows an efficient encapsulation of climate data into climate units. For North America and most of Central America during 20, 14, 13, 11, 10, 7, 5, and 1 thousand years ago (ka) and recent years, I applied a Köppen-Trewartha classification system, but with dry classes subsumed under primary thermal classes to preserve information. The boreal and polar classes decreased from a combined 70% of area during 20 ka until reaching 42% of area at 7 ka, after which the area remained relatively stable. Conversely, the subtropical and temperate classes increased from 25% of area until reaching 53% of area at 7 ka, with slight increase of the tropical class. The combined dry subclasses increased from 7.5% to 15% of area, primarily in the subtropical and temperate classes, displaying unique trends over time. Based on ordination, the classes since 5 ka are similar; the 1950 interval is most similar to 1 and 5 ka and the intervals of 1600 and 1800 are most similar. The climate classes and transitions generally corresponded with major vegetation distributions. Visually, political boundaries appeared to parallel climate classes, which might indicate the influence of long-standing ecological differences on human land use and settlement. A future research need is identifying the influence of climate on directing settlement and political boundary establishment.

@article{covarrubias_functional_2022,
	title = {Functional connectivity of an endemic tree frog in a highly threatened tropical dry forest in {Mexico}},
	volume = {29},
	issn = {1195-6860, 2376-7626},
	url = {https://www.tandfonline.com/doi/full/10.1080/11956860.2021.1921935},
	doi = {10.1080/11956860.2021.1921935},
	language = {en},
	number = {1},
	urldate = {2023-06-02},
	journal = {Écoscience},
	author = {Covarrubias, Sara and Gutiérrez-Rodríguez, Carla and Rojas-Soto, Octavio and Hernández-Guzmán, Rafael and González, Clementina},
	month = jan,
	year = {2022},
	keywords = {NALCMS},
	pages = {69--85},
}

@incollection{garcia-alvarez_general_2022,
	address = {Cham},
	title = {General {Land} {Use} {Cover} {Datasets} for {America} and {Asia}},
	isbn = {978-3-030-90997-0 978-3-030-90998-7},
	url = {https://link.springer.com/10.1007/978-3-030-90998-7_18},
	abstract = {Abstract
            In this chapter we review some examples of general Land Use Cover (LUC) mapping at a supra-national level in America and Asia. These datasets provide a general overview of the land uses and covers in specific American or Asian regions, without focusing on any particular land use or cover. For Asia, we have only identified one dataset mapping the Himalayan region, whereas for America five different datasets were identified. Only three of these are reviewed here, as the other two (SERENA, South America 30 m) are not available for download. The most ambitious project of all those reviewed is NALCMS, which coordinates the production of a LUC map for the whole of North America (Canada, Mexico, USA) at detailed scales (30–250 m) and using the same classification legend. It is the only dataset of all those reviewed that provides a time series of LUC maps (2005, 2010 and 2015). The Himalaya Regional Land Cover database is a vector-based map that provides information on LUC changes over the period 1970/80–2007 at a scale of 1:350,000. The other two American datasets—LBA-ECO LC-08 (1 km, 1987/91) and MERISAM2009 (300 m, 2008/10)—are raster-based and only available for one date, therefore making change detection impossible.},
	language = {en},
	urldate = {2023-06-01},
	booktitle = {Land {Use} {Cover} {Datasets} and {Validation} {Tools}},
	publisher = {Springer International Publishing},
	author = {García-Álvarez, David and Lara Hinojosa, Javier},
	editor = {García-Álvarez, David and Camacho Olmedo, María Teresa and Paegelow, Martin and Mas, Jean François},
	year = {2022},
	doi = {10.1007/978-3-030-90998-7_18},
	keywords = {NALCMS},
	pages = {361--372},
}

Abstract In this chapter we review some examples of general Land Use Cover (LUC) mapping at a supra-national level in America and Asia. These datasets provide a general overview of the land uses and covers in specific American or Asian regions, without focusing on any particular land use or cover. For Asia, we have only identified one dataset mapping the Himalayan region, whereas for America five different datasets were identified. Only three of these are reviewed here, as the other two (SERENA, South America 30 m) are not available for download. The most ambitious project of all those reviewed is NALCMS, which coordinates the production of a LUC map for the whole of North America (Canada, Mexico, USA) at detailed scales (30–250 m) and using the same classification legend. It is the only dataset of all those reviewed that provides a time series of LUC maps (2005, 2010 and 2015). The Himalaya Regional Land Cover database is a vector-based map that provides information on LUC changes over the period 1970/80–2007 at a scale of 1:350,000. The other two American datasets—LBA-ECO LC-08 (1 km, 1987/91) and MERISAM2009 (300 m, 2008/10)—are raster-based and only available for one date, therefore making change detection impossible.

@article{schmidt_genetic_2022,
	title = {Genetic and species‐level biodiversity patterns are linked by demography and ecological opportunity},
	volume = {76},
	issn = {0014-3820, 1558-5646},
	url = {https://academic.oup.com/evolut/article/76/1/86/6728931},
	doi = {10.1111/evo.14407},
	language = {en},
	number = {1},
	urldate = {2023-06-15},
	journal = {Evolution},
	author = {Schmidt, Chloé and Dray, Stéphane and Garroway, Colin J.},
	month = jan,
	year = {2022},
	keywords = {NALCMS},
	pages = {86--100},
}

@article{rodriguezbarrera_grassland_2022,
	title = {Grassland type and seasonal effects have a bigger influence on plant functional and taxonomical diversity than prairie dog disturbances in semiarid grasslands},
	volume = {12},
	issn = {2045-7758},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/ece3.9040},
	doi = {10.1002/ece3.9040},
	abstract = {Prairie dogs (Cynomys sp.) are considered keystone species and ecosystem engineers for their grazing and burrowing activities (summarized here as disturbances). As climate changes and its variability increases, the mechanisms underlying organisms' interactions with their habitat will likely shift. Understanding the mediating role of prairie dog disturbance on vegetation structure, and its interaction with environmental conditions through time, will increase knowledge on the risks and vulnerability of grasslands. Here, we compared how plant taxonomical diversity, functional diversity metrics, and community-weighted trait means (CWM) respond to prairie dog C. mexicanus disturbance across grassland types and seasons (dry and wet) in a priority conservation semiarid grassland of Northeast Mexico. Our findings suggest that functional metrics and CWM analyses responded to interactions between prairie dog disturbance, grassland type and season, whilst species diversity and cover measures were less sensitive to the role of prairie dog disturbance. We found weak evidence that prairie dog disturbance has a negative effect on vegetation structure, except for minimal effects on C4 and graminoid cover, but which depended mainly on season. Grassland type and season explained most of the effects on plant functional and taxonomic diversity as well as CWM traits. Furthermore, we found that leaf area as well as forb and annual cover increased during the wet season, independent of prairie dog disturbance. Our results provide evidence that grassland type and season have a stronger effect than prairie dog disturbance on the vegetation of this short-grass, water-restricted grassland ecosystem. We argue that focusing solely on disturbance and grazing effects is misleading, and attention is needed on the relationships between vegetation and environmental conditions which will be critical to understand semiarid grassland dynamics under future climate change conditions in the region.},
	number = {7},
	journal = {Ecology and Evolution},
	author = {Rodriguez‐Barrera, Maria Gabriela and Kühn, Ingolf and Estrada‐Castillón, Eduardo and Cord, Anna F.},
	month = jul,
	year = {2022},
	note = {Publisher: John Wiley and Sons Ltd},
	keywords = {Grasslands, NALCMS},
}

Prairie dogs (Cynomys sp.) are considered keystone species and ecosystem engineers for their grazing and burrowing activities (summarized here as disturbances). As climate changes and its variability increases, the mechanisms underlying organisms' interactions with their habitat will likely shift. Understanding the mediating role of prairie dog disturbance on vegetation structure, and its interaction with environmental conditions through time, will increase knowledge on the risks and vulnerability of grasslands. Here, we compared how plant taxonomical diversity, functional diversity metrics, and community-weighted trait means (CWM) respond to prairie dog C. mexicanus disturbance across grassland types and seasons (dry and wet) in a priority conservation semiarid grassland of Northeast Mexico. Our findings suggest that functional metrics and CWM analyses responded to interactions between prairie dog disturbance, grassland type and season, whilst species diversity and cover measures were less sensitive to the role of prairie dog disturbance. We found weak evidence that prairie dog disturbance has a negative effect on vegetation structure, except for minimal effects on C4 and graminoid cover, but which depended mainly on season. Grassland type and season explained most of the effects on plant functional and taxonomic diversity as well as CWM traits. Furthermore, we found that leaf area as well as forb and annual cover increased during the wet season, independent of prairie dog disturbance. Our results provide evidence that grassland type and season have a stronger effect than prairie dog disturbance on the vegetation of this short-grass, water-restricted grassland ecosystem. We argue that focusing solely on disturbance and grazing effects is misleading, and attention is needed on the relationships between vegetation and environmental conditions which will be critical to understand semiarid grassland dynamics under future climate change conditions in the region.

@mastersthesis{melichar_high-resolution_2022,
	title = {High-{Resolution} {Vegetation} {Mapping} in the {Sonoran} and {Mojave} {Deserts} using {Random} {Forest} {Classification} of {Multi}-{Temporal} {Landsat} 8 {Data} and {Phenology} {Metrics}},
	url = {https://repository.arizona.edu/handle/10150/666145},
	school = {University of Arizona},
	author = {Melichar, Madeline},
	year = {2022},
	keywords = {NALCMS},
}

@techreport{government_of_canada_human_2022,
	title = {Human {Activity} and the {Environment} 2021: {Accounting} for ecosystem change in {Canada}},
	url = {https://www150.statcan.gc.ca/n1/pub/16-201-x/16-201-x2021001-eng.htm},
	abstract = {The report provides some of the latest statistics on the extent and condition of Canada's ecosystems, as well as estimates of the supply and use of selected ecosystem services. This has been accomplished through the use of ecosystem accounting, which involves the structured compilation of information on ecosystems such as forests, agro-ecosystems, wetlands, and marine and coastal areas, their condition or quality and the ecosystem services they provide, following the requirements of a coherent statistical framework.},
	language = {eng},
	urldate = {2024-09-19},
	author = {Government of Canada, Statistics Canada},
	month = jan,
	year = {2022},
	note = {Last Modified: 2022-01-25},
	keywords = {NALCMS},
}

The report provides some of the latest statistics on the extent and condition of Canada's ecosystems, as well as estimates of the supply and use of selected ecosystem services. This has been accomplished through the use of ecosystem accounting, which involves the structured compilation of information on ecosystems such as forests, agro-ecosystems, wetlands, and marine and coastal areas, their condition or quality and the ecosystem services they provide, following the requirements of a coherent statistical framework.

@article{cunningham_human_2022,
	title = {Human and animal movements combine with snow to increase moose-vehicle collisions in winter},
	volume = {17},
	issn = {1748-9326},
	url = {https://dx.doi.org/10.1088/1748-9326/aca8bf},
	doi = {10.1088/1748-9326/aca8bf},
	abstract = {Wildlife-vehicle collisions imperil humans, wildlife, and property. Collisions with moose (Alces alces) are especially consequential and there are indications they may increase during severe winters. We tested hypotheses regarding the influence of moose movements and vehicular traffic patterns on collision risk. We first modeled daily snow depth and accumulation across 5.6 million km2 of the North American Arctic-Boreal region. Next, we analyzed the movements and road use of 113 GPS-collared moose in response to snow depth. Finally, we examined the influence of these snow properties on vehicular traffic and 7680 moose-vehicle collisions. As winter progressed and the snowpack deepened in each study area, GPS-collared moose migrated to lower elevations, leading them into areas with shallower snow but higher road densities. This elevational migration corresponded with a higher probability of road-use by moose (by up to ten-fold) in winter than in summer. Corresponding to these patterns, moose-vehicle collisions were 2.4–5.7 times more frequent from December through February (compared to early summer). Collision risk was highest when and where snow depth was less than 120 cm, indicating that migration into areas with shallower snow increased collision risk in those areas. Most (82\%) moose-vehicle collisions occurred after dark. This pattern was strongest during winter, when nighttime traffic volumes were eight times higher than summer due to longer nights. Overall, our findings suggest that concurrent seasonal changes in human and wildlife behavior increase the frequency of moose-vehicle collisions during winter. Snow depth influences collisions primarily through its impacts on moose movement, while strong seasonal changes in daylight hours cause an increase in nighttime traffic that further contributes to risk. This information may help predict times and places where risk of moose-vehicle collisions are highest and to develop seasonally dynamic mitigation strategies.},
	language = {en},
	number = {12},
	urldate = {2023-06-23},
	journal = {Environmental Research Letters},
	author = {Cunningham, Calum X. and Liston, Glen E. and Reinking, Adele K. and Boelman, Natalie T. and Brinkman, Todd J. and Joly, Kyle and Hebblewhite, Mark and Boutin, Stan and Czetwertynski, Sophie and Sielecki, Leonard E. and Prugh, Laura R.},
	month = dec,
	year = {2022},
	note = {Publisher: IOP Publishing},
	keywords = {NALCMS},
	pages = {125007},
}

Wildlife-vehicle collisions imperil humans, wildlife, and property. Collisions with moose (Alces alces) are especially consequential and there are indications they may increase during severe winters. We tested hypotheses regarding the influence of moose movements and vehicular traffic patterns on collision risk. We first modeled daily snow depth and accumulation across 5.6 million km2 of the North American Arctic-Boreal region. Next, we analyzed the movements and road use of 113 GPS-collared moose in response to snow depth. Finally, we examined the influence of these snow properties on vehicular traffic and 7680 moose-vehicle collisions. As winter progressed and the snowpack deepened in each study area, GPS-collared moose migrated to lower elevations, leading them into areas with shallower snow but higher road densities. This elevational migration corresponded with a higher probability of road-use by moose (by up to ten-fold) in winter than in summer. Corresponding to these patterns, moose-vehicle collisions were 2.4–5.7 times more frequent from December through February (compared to early summer). Collision risk was highest when and where snow depth was less than 120 cm, indicating that migration into areas with shallower snow increased collision risk in those areas. Most (82%) moose-vehicle collisions occurred after dark. This pattern was strongest during winter, when nighttime traffic volumes were eight times higher than summer due to longer nights. Overall, our findings suggest that concurrent seasonal changes in human and wildlife behavior increase the frequency of moose-vehicle collisions during winter. Snow depth influences collisions primarily through its impacts on moose movement, while strong seasonal changes in daylight hours cause an increase in nighttime traffic that further contributes to risk. This information may help predict times and places where risk of moose-vehicle collisions are highest and to develop seasonally dynamic mitigation strategies.

@misc{mai_hydrohub_2022,
	title = {{HydroHub} - {Sensitivity} analysis over {North} {America}},
	copyright = {Helmholtz Centre for Environmental Research (UFZ)},
	url = {https://www.hydrohub.org/sa_introduction.html},
	urldate = {2023-06-14},
	author = {Mai, Julie},
	year = {2022},
	keywords = {NALCMS},
}

@article{sari_impact_2022,
	title = {Impact of land-use land-cover datasets and urban parameterization on weather simulation over the {Jakarta} {Metropolitan} {Area}},
	volume = {1039},
	issn = {1755-1307},
	url = {https://iopscience.iop.org/article/10.1088/1755-1315/1039/1/012036},
	doi = {10.1088/1755-1315/1039/1/012036},
	abstract = {Human-caused changes in land-use and land-cover (LULC) are most visible in metropolitan areas, when the majority of the land has been converted to urban land or built up. This study presents a modeling approach for simulating the spatiotemporal distribution of urban microclimate with the Weather Research and Forecasting (WRF) model using four urban parameterization schemes, namely a bulk, urban canopy model (UCM), building effect parameterization (BEP), and building energy model (BEM). The WRF model is set-up at 1 km spatial resolutions over the Jakarta Metropolitan Area to study the model's sensitivity to the usage of alternative LULC datasets, the default MODIS and its modification 2017. The results show that the UCM and BEM schemes appear to be reliable in mapping urban weather conditions for all meteorological parameters examined. Given that the LULC categories in urban areas remained unchanged, changing the LULC in the model did not result in a large difference in error. The LULC dataset, on the other hand, can be used as information related to suburban areas that continue to grow in concurrently with urbanization. LULC updates can provide insight into how much temperature rise is occurring in urban areas and how it affects climate change.},
	number = {1},
	journal = {IOP Conference Series: Earth and Environmental Science},
	author = {Sari, D. L. and June, T. and {Perdinan} and Hidayat, R. and Hanggoro, W. and Arifin, H. S.},
	month = sep,
	year = {2022},
	note = {Publisher: Institute of Physics},
	keywords = {NALCMS},
	pages = {012036},
}

Human-caused changes in land-use and land-cover (LULC) are most visible in metropolitan areas, when the majority of the land has been converted to urban land or built up. This study presents a modeling approach for simulating the spatiotemporal distribution of urban microclimate with the Weather Research and Forecasting (WRF) model using four urban parameterization schemes, namely a bulk, urban canopy model (UCM), building effect parameterization (BEP), and building energy model (BEM). The WRF model is set-up at 1 km spatial resolutions over the Jakarta Metropolitan Area to study the model's sensitivity to the usage of alternative LULC datasets, the default MODIS and its modification 2017. The results show that the UCM and BEM schemes appear to be reliable in mapping urban weather conditions for all meteorological parameters examined. Given that the LULC categories in urban areas remained unchanged, changing the LULC in the model did not result in a large difference in error. The LULC dataset, on the other hand, can be used as information related to suburban areas that continue to grow in concurrently with urbanization. LULC updates can provide insight into how much temperature rise is occurring in urban areas and how it affects climate change.

@article{diller_influences_2022,
	title = {Influences of seasonality and habitat quality on {Great} {Lakes} coastal wetland fish community composition and diets},
	volume = {30},
	issn = {0923-4861},
	url = {https://doi.org/10.1007/s11273-022-09862-8},
	doi = {10.1007/s11273-022-09862-8},
	abstract = {Great Lakes coastal wetlands (GLCW) have been severely degraded by anthropogenic activity over the last several decades despite their critical role in fish production. Many Great Lakes fish species use coastal wetland habitats for spawning, feeding, shelter, and nurseries throughout the year. The goal of our study was to compare GLCW fish community composition in the spring, summer, and fall months and investigate how water quality relates to fish diversity, the presence of functional groups, and juvenile fish diets. We summarized fish data collected from GLCW across the basin and used the coastal wetland monitoring program's water quality-land use indicator to quantify water quality. Basin-wide, we found taxonomic and functional group differences in community composition among three sampling seasons, as well as across the range of water quality. Water quality was positively associated with the abundance of small cyprinids and the relative abundance of some habitat and reproductive specialists. Seasonal differences were also observed for many of these functional groups, with more temperature- and pollution-sensitive fishes captured in the spring and more nest-spawning fishes captured in the summer and fall. In our diet study, we found that age-0 fish primarily consumed zooplankton in the fall, whereas age-1 fish primarily consumed macroinvertebrates in the spring. Moreover, wetland quality was positively associated with trichopteran prey abundance. We concluded that taxonomic and functional composition of fish communities in GLCW vary markedly with respect to water quality and season. Thus, a full understanding of communities across a gradient of quality requires multi-season sampling.},
	number = {3},
	journal = {Wetlands Ecology and Management},
	author = {Diller, Sara N. and Harrison, Anna M. and Kowalski, Kurt P. and Brady, Valerie J. and Ciborowski, Jan J. H. and Cooper, Matthew J. and Dumke, Joshua D. and Gathman, Joseph P. and Ruetz, Carl R. and Uzarski, Donald G. and Wilcox, Douglas A. and Schaeffer, Jeffrey S.},
	month = jun,
	year = {2022},
	note = {ISBN: 0123456789
Publisher: Springer Netherlands},
	keywords = {NALCMS},
	pages = {439--460},
}

Great Lakes coastal wetlands (GLCW) have been severely degraded by anthropogenic activity over the last several decades despite their critical role in fish production. Many Great Lakes fish species use coastal wetland habitats for spawning, feeding, shelter, and nurseries throughout the year. The goal of our study was to compare GLCW fish community composition in the spring, summer, and fall months and investigate how water quality relates to fish diversity, the presence of functional groups, and juvenile fish diets. We summarized fish data collected from GLCW across the basin and used the coastal wetland monitoring program's water quality-land use indicator to quantify water quality. Basin-wide, we found taxonomic and functional group differences in community composition among three sampling seasons, as well as across the range of water quality. Water quality was positively associated with the abundance of small cyprinids and the relative abundance of some habitat and reproductive specialists. Seasonal differences were also observed for many of these functional groups, with more temperature- and pollution-sensitive fishes captured in the spring and more nest-spawning fishes captured in the summer and fall. In our diet study, we found that age-0 fish primarily consumed zooplankton in the fall, whereas age-1 fish primarily consumed macroinvertebrates in the spring. Moreover, wetland quality was positively associated with trichopteran prey abundance. We concluded that taxonomic and functional composition of fish communities in GLCW vary markedly with respect to water quality and season. Thus, a full understanding of communities across a gradient of quality requires multi-season sampling.

@article{poessel_interpreting_2022,
	title = {Interpreting long‐distance movements of non‐migratory golden eagles: {Prospecting} and nomadism?},
	volume = {13},
	issn = {2150-8925},
	url = {https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.4072},
	doi = {10.1002/ecs2.4072},
	abstract = {Movements by animals can serve different functions and occur over a variety of spatial and temporal scales. Routine movement types, such as residency (localized movements) and migration, have been well studied. However, nonroutine movement types, such as dispersal, prospecting, and nomadism, are less well understood. Documenting these rarely detected events requires tracking large numbers of individuals across all age classes. We studied {\textgreater}500 golden eagles (Aquila chrysaetos) tracked by telemetry over a 10-year period in western North America, of which 160 engaged in nonroutine, long-distance ({\textgreater}300 km) movements. We identified spatial and temporal correlates of those movements at both small and large scales, and we quantified movement timing and direction. We further tested which age and sex classes of eagles were more likely to engage in these movements. Our analysis of 88,093 daily tracks suggested that distances traveled by eagles were responsive to the updraft potential of the spatial and temporal landscape they encountered. Tracks covered longer distances at locations and times of higher updraft potential, and older birds traveled farther than younger birds. By contrast, after decomposing daily tracks into 563 nonroutine, long-distance movements measured at a multiday scale, only the duration of travel was responsive to environmental conditions encountered by eagles. Multiday trips that were longer were those initiated in open and warm landscapes and those that ended in mountainous regions. Finally, long-distance movements were more frequently made in seasons other than winter, in north–south directions, and by young birds. We documented clear correlates of nonroutine, long-distance movements by golden eagles at small, local scales but found little evidence of such correlates at larger, regional scales. Most long-distance movements we documented fit patterns associated with traditional definitions of prospecting and nomadism but not migration. Our study is the first to describe these movement types by golden eagles, and as such provides a foundation for subsequent study into the movement ecology of other species.},
	number = {6},
	journal = {Ecosphere},
	author = {Poessel, Sharon A. and Woodbridge, Brian and Smith, Brian W. and Murphy, Robert K. and Bedrosian, Bryan E. and Bell, Douglas A. and Bittner, David and Bloom, Peter H. and Crandall, Ross H. and Domenech, Robert and Fisher, Robert N. and Haggerty, Patricia K. and Slater, Steven J. and Tracey, Jeff A. and Watson, James W. and Katzner, Todd E.},
	month = jun,
	year = {2022},
	note = {Publisher: John Wiley and Sons Inc},
	keywords = {NALCMS},
}

Movements by animals can serve different functions and occur over a variety of spatial and temporal scales. Routine movement types, such as residency (localized movements) and migration, have been well studied. However, nonroutine movement types, such as dispersal, prospecting, and nomadism, are less well understood. Documenting these rarely detected events requires tracking large numbers of individuals across all age classes. We studied \textgreater500 golden eagles (Aquila chrysaetos) tracked by telemetry over a 10-year period in western North America, of which 160 engaged in nonroutine, long-distance (\textgreater300 km) movements. We identified spatial and temporal correlates of those movements at both small and large scales, and we quantified movement timing and direction. We further tested which age and sex classes of eagles were more likely to engage in these movements. Our analysis of 88,093 daily tracks suggested that distances traveled by eagles were responsive to the updraft potential of the spatial and temporal landscape they encountered. Tracks covered longer distances at locations and times of higher updraft potential, and older birds traveled farther than younger birds. By contrast, after decomposing daily tracks into 563 nonroutine, long-distance movements measured at a multiday scale, only the duration of travel was responsive to environmental conditions encountered by eagles. Multiday trips that were longer were those initiated in open and warm landscapes and those that ended in mountainous regions. Finally, long-distance movements were more frequently made in seasons other than winter, in north–south directions, and by young birds. We documented clear correlates of nonroutine, long-distance movements by golden eagles at small, local scales but found little evidence of such correlates at larger, regional scales. Most long-distance movements we documented fit patterns associated with traditional definitions of prospecting and nomadism but not migration. Our study is the first to describe these movement types by golden eagles, and as such provides a foundation for subsequent study into the movement ecology of other species.

@article{lemieux_investigating_2022,
	title = {Investigating the sources and dynamics of naturally occurring radioactive material ({NORM}) in the {Red} {Deer} {River}, {Alberta}, {Canada}},
	volume = {194},
	issn = {15732959},
	url = {https://doi.org/10.1007/s10661-022-09800-4},
	doi = {10.1007/s10661-022-09800-4},
	abstract = {Distinguishing between natural and anthropogenic controls on the proportions of naturally occurring radioactive materials (NORMs) in the environment is important for water resource management. In this study, the dynamics of uranium (U) and thorium (Th), two of the most prominent NORM elements, were investigated in the Red Deer River basin using monitoring data collected from 2015 to 2018. More than twofold increases in median proportions of total U (from 0.73 to 1.53 µg/L) and Th (from 0.008 to 0.104 µg/L) were observed for sites located downstream of the Steveville badlands, an area of highly erodible bedrock that a \${\textbackslash}sim\$ 300 km section of the river flows through. Input is highly variable, coinciding mainly with increases in total suspended solids during intense rainstorms in the late summer. In-depth examination of monitoring data through factor analysis, multiple linear regression, mass balance calculations, and land use analysis highlights the importance of erosion and subsequent particle transport along river banks in the badlands area on the distribution of total U and Th, while also revealing that groundwater-surface water interaction affects proportions of dissolved U throughout the river. No significant influence from industry or land use on U and Th export was found, and proportions of U and Th in water and suspended sediment are within the natural ranges expected for surface waters and sediments/soils. Methodology employed in this study provides a basic framework for analysis of environmental monitoring datasets, which can be employed in the absence of radiochemical data to study the fate, transport, and sources of NORMs.},
	number = {3},
	journal = {Environmental Monitoring and Assessment},
	author = {Lemieux, Alexander and Kromrey, Natalie and Brinkmann, Lars},
	year = {2022},
	pmid = {35178617},
	note = {ISBN: 0123456789
Publisher: Springer International Publishing},
	keywords = {NALCMS},
}

Distinguishing between natural and anthropogenic controls on the proportions of naturally occurring radioactive materials (NORMs) in the environment is important for water resource management. In this study, the dynamics of uranium (U) and thorium (Th), two of the most prominent NORM elements, were investigated in the Red Deer River basin using monitoring data collected from 2015 to 2018. More than twofold increases in median proportions of total U (from 0.73 to 1.53 µg/L) and Th (from 0.008 to 0.104 µg/L) were observed for sites located downstream of the Steveville badlands, an area of highly erodible bedrock that a ${\}sim$ 300 km section of the river flows through. Input is highly variable, coinciding mainly with increases in total suspended solids during intense rainstorms in the late summer. In-depth examination of monitoring data through factor analysis, multiple linear regression, mass balance calculations, and land use analysis highlights the importance of erosion and subsequent particle transport along river banks in the badlands area on the distribution of total U and Th, while also revealing that groundwater-surface water interaction affects proportions of dissolved U throughout the river. No significant influence from industry or land use on U and Th export was found, and proportions of U and Th in water and suspended sediment are within the natural ranges expected for surface waters and sediments/soils. Methodology employed in this study provides a basic framework for analysis of environmental monitoring datasets, which can be employed in the absence of radiochemical data to study the fate, transport, and sources of NORMs.

@incollection{garcia-alvarez_land_2022,
	address = {Cham},
	title = {Land {Use} {Cover} {Datasets}: {A} {Review}},
	isbn = {978-3-030-90997-0 978-3-030-90998-7},
	shorttitle = {Land {Use} {Cover} {Datasets}},
	url = {https://link.springer.com/10.1007/978-3-030-90998-7_4},
	abstract = {Abstract
            This chapter presents a review of Land Use Cover (LUC) datasets at global and supranational scales. To this end, we differentiate between LUC maps (Sect. 3) and reference LUC datasets (Sect. 4). The former map how different land uses or covers are distributed across the Earth’s surface. The latter provides a sample of LUC data for specific points on Earth and are normally used in LUC mapping and modelling calibration and validation exercises. We also include a brief presentation of the main producers of LUC datasets (Sect. 2). The LUC maps reviewed here are classified according to different criteria. First, we differentiate between general LUC maps (Sect. 3.2), which provide information about all land uses and covers on Earth, and thematic LUC maps (Sect. 3.3), which focus on the mapping of a specific land use or cover. Second, we classify general and thematic LUC maps according to their extent, distinguishing between global and supra-national LUC maps. The general maps are classified according to the continent for which they provide information, either fully or partially, while the thematic maps are classified according to the type of land use or cover they focus on. Most of the datasets reviewed in this chapter are characterized in detail in Part IV of this book, to which this chapter acts as an introduction. This chapter includes a series of tables with all the datasets, indicating those for which a detailed description is provided in Part IV.},
	language = {en},
	urldate = {2023-06-01},
	booktitle = {Land {Use} {Cover} {Datasets} and {Validation} {Tools}},
	publisher = {Springer International Publishing},
	author = {García-Álvarez, David and Nanu, Sabina Florina},
	editor = {García-Álvarez, David and Camacho Olmedo, María Teresa and Paegelow, Martin and Mas, Jean François},
	year = {2022},
	doi = {10.1007/978-3-030-90998-7_4},
	keywords = {NALCMS},
	pages = {47--66},
}

Abstract This chapter presents a review of Land Use Cover (LUC) datasets at global and supranational scales. To this end, we differentiate between LUC maps (Sect. 3) and reference LUC datasets (Sect. 4). The former map how different land uses or covers are distributed across the Earth’s surface. The latter provides a sample of LUC data for specific points on Earth and are normally used in LUC mapping and modelling calibration and validation exercises. We also include a brief presentation of the main producers of LUC datasets (Sect. 2). The LUC maps reviewed here are classified according to different criteria. First, we differentiate between general LUC maps (Sect. 3.2), which provide information about all land uses and covers on Earth, and thematic LUC maps (Sect. 3.3), which focus on the mapping of a specific land use or cover. Second, we classify general and thematic LUC maps according to their extent, distinguishing between global and supra-national LUC maps. The general maps are classified according to the continent for which they provide information, either fully or partially, while the thematic maps are classified according to the type of land use or cover they focus on. Most of the datasets reviewed in this chapter are characterized in detail in Part IV of this book, to which this chapter acts as an introduction. This chapter includes a series of tables with all the datasets, indicating those for which a detailed description is provided in Part IV.

@article{rogers_land_2022,
	title = {Land cover and latitude affect vegetation phenology determined from solar induced fluorescence across {Ontario}, {Canada}},
	volume = {114},
	issn = {15698432},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1569843222002242},
	doi = {10.1016/j.jag.2022.103036},
	abstract = {Land use decisions directly affect the terrestrial carbon balance by changing the quantity and type of vegetation present. However, such consequences are difficult for decisionmakers to account for in environmental impact assessments. Solar induced chlorophyll fluorescence (SIF) measured from satellite is strongly related to gross primary productivity of terrestrial vegetation and presents a new source of information from which to assess environmental impacts of land use decisions. We assess the usefulness of SIF measurements in accounting for changes in vegetation primary productivity due to land use change. We use a weighted double logistic regression based on the land cover mixture within SIF measurements from the TROPOspheric Monitoring Instrument (TROPOMI) to separate the phenology of the SIF signal by land cover for the region of Ontario, Canada. We use SIF integrated annually to determine, in relative units, the consequences of land use change on photosynthetic carbon uptake for the study region. Finally, we map GPP at a spatial resolution of 30 m across the province of Ontario using previously reported relationships between SIF and GPP. We find that SIF tracks expected biogeographical patterns of productivity: urban areas exhibit an earlier start of the growing season, lower SIF magnitude and later end of season than natural land cover classes, whereas croplands exhibit a later start of season. Patterns of phenology and SIF magnitude show land cover transitions from Broadleaf Deciduous Forests to croplands or urban environments to have the highest impacts on carbon uptake. Satellite-based SIF measurements are useful in exploring the geographic variation of vegetation productivity and can support inclusion of carbon accounting in environmental assessment at regional scale.},
	journal = {International Journal of Applied Earth Observations and Geoinformation},
	author = {Rogers, Cheryl A. and Chen, Jing M.},
	month = nov,
	year = {2022},
	note = {Publisher: Elsevier B.V.},
	keywords = {NALCMS},
	pages = {103036},
}

Land use decisions directly affect the terrestrial carbon balance by changing the quantity and type of vegetation present. However, such consequences are difficult for decisionmakers to account for in environmental impact assessments. Solar induced chlorophyll fluorescence (SIF) measured from satellite is strongly related to gross primary productivity of terrestrial vegetation and presents a new source of information from which to assess environmental impacts of land use decisions. We assess the usefulness of SIF measurements in accounting for changes in vegetation primary productivity due to land use change. We use a weighted double logistic regression based on the land cover mixture within SIF measurements from the TROPOspheric Monitoring Instrument (TROPOMI) to separate the phenology of the SIF signal by land cover for the region of Ontario, Canada. We use SIF integrated annually to determine, in relative units, the consequences of land use change on photosynthetic carbon uptake for the study region. Finally, we map GPP at a spatial resolution of 30 m across the province of Ontario using previously reported relationships between SIF and GPP. We find that SIF tracks expected biogeographical patterns of productivity: urban areas exhibit an earlier start of the growing season, lower SIF magnitude and later end of season than natural land cover classes, whereas croplands exhibit a later start of season. Patterns of phenology and SIF magnitude show land cover transitions from Broadleaf Deciduous Forests to croplands or urban environments to have the highest impacts on carbon uptake. Satellite-based SIF measurements are useful in exploring the geographic variation of vegetation productivity and can support inclusion of carbon accounting in environmental assessment at regional scale.

@article{currey_large_2022,
	title = {Large contribution of woody plant expansion to recent vegetative greening of the {Northern} {Great} {Plains}},
	volume = {49},
	issn = {0305-0270, 1365-2699},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/jbi.14391},
	doi = {10.1111/jbi.14391},
	language = {en},
	number = {8},
	urldate = {2023-06-01},
	journal = {Journal of Biogeography},
	author = {Currey, Bryce and McWethy, David B. and Fox, Nicholas R. and Brookshire, E. N. Jack},
	month = aug,
	year = {2022},
	keywords = {NALCMS},
	pages = {1443--1454},
}

@inproceedings{ikegawa_levee_2022,
	title = {Levee protected area detection for improved flood risk assessment in global hydrology models},
	url = {https://www.climatechange.ai/papers/neurips2022/11},
	abstract = {Climate Change AI - NeurIPS 2022 Accepted Work},
	language = {en-US},
	urldate = {2023-06-23},
	booktitle = {Climate {Change} {AI}},
	publisher = {Climate Change AI},
	author = {Ikegawa, Masato and Hascoet, Tristan E. M. and Pellet, Victor and Zhou, Xudong and Takiguchi, Tetsuya and Yamazaki, Dai},
	month = dec,
	year = {2022},
	keywords = {NALCMS},
}

Climate Change AI - NeurIPS 2022 Accepted Work

@article{zaghloul_long_2022,
	title = {Long {Term} {Trend} {Analysis} of {River} {Flow} and {Climate} in {Northern} {Canada}},
	volume = {9},
	issn = {2306-5338},
	url = {https://www.mdpi.com/2306-5338/9/11/197},
	doi = {10.3390/hydrology9110197},
	abstract = {Changes in water resources within basins can significantly impact ecosystems, agriculture, and biodiversity, among others. Basins in northern Canada have a cold climate, and the recent changes in climate can have a profound impact on water resources in these basins. Therefore, it is crucial to study long term trends in water flow as well as their influential factors, such as temperature and precipitation. This study focused on analyzing long term trends in water flow across the Athabasca River Basin (ARB) and Peace River Basin (PRB). Long term trends in temperature and precipitation within these basins were also studied. Water flow data from 18 hydrometric stations provided by Water Survey of Canada were analyzed using the Mann-Kendall test and Sen's slope. In addition, hybrid climate data provided by Alberta Environment and Parks at approximately 10 km spatial resolution were analyzed for the ARB and its surrounding regions during 1950–2019. Trend analysis was performed on the water flow data on monthly, seasonal, and annual scales, and the results were cross-checked with trends in temperature and precipitation and land use and land cover data. The overall temperature across the basins has been increasing since 1950, while precipitation showed an insignificant decrease during this period. Winter water flow in the upper ARB has been slowly and steadily increasing since 1956 because of the rising temperatures and the subsequent slow melting of snowpacks/glaciers. The warm season flows in the middle and lower subregions declined up to 1981, then started to show an increasing trend. The middle and lower ARB exhibited a rapid increase in warm-season water flow since 2015. A similar trend change was also observed in the PRB. The gradual increase in water flow observed in the recent decades may continue by the mid-century, which is beneficial for agriculture, forestry, fishery, and industry. However, climate and land cover changes may alter the trend of water flow in the future; therefore, it is important to have a proper management plan for water usage in the next decades.},
	number = {11},
	journal = {Hydrology},
	author = {Zaghloul, Mohamed Sherif and Ghaderpour, Ebrahim and Dastour, Hatef and Farjad, Babak and Gupta, Anil and Eum, Hyung and Achari, Gopal and Hassan, Quazi K.},
	month = nov,
	year = {2022},
	note = {Publisher: MDPI AG},
	keywords = {NALCMS},
	pages = {197},
}

Changes in water resources within basins can significantly impact ecosystems, agriculture, and biodiversity, among others. Basins in northern Canada have a cold climate, and the recent changes in climate can have a profound impact on water resources in these basins. Therefore, it is crucial to study long term trends in water flow as well as their influential factors, such as temperature and precipitation. This study focused on analyzing long term trends in water flow across the Athabasca River Basin (ARB) and Peace River Basin (PRB). Long term trends in temperature and precipitation within these basins were also studied. Water flow data from 18 hydrometric stations provided by Water Survey of Canada were analyzed using the Mann-Kendall test and Sen's slope. In addition, hybrid climate data provided by Alberta Environment and Parks at approximately 10 km spatial resolution were analyzed for the ARB and its surrounding regions during 1950–2019. Trend analysis was performed on the water flow data on monthly, seasonal, and annual scales, and the results were cross-checked with trends in temperature and precipitation and land use and land cover data. The overall temperature across the basins has been increasing since 1950, while precipitation showed an insignificant decrease during this period. Winter water flow in the upper ARB has been slowly and steadily increasing since 1956 because of the rising temperatures and the subsequent slow melting of snowpacks/glaciers. The warm season flows in the middle and lower subregions declined up to 1981, then started to show an increasing trend. The middle and lower ARB exhibited a rapid increase in warm-season water flow since 2015. A similar trend change was also observed in the PRB. The gradual increase in water flow observed in the recent decades may continue by the mid-century, which is beneficial for agriculture, forestry, fishery, and industry. However, climate and land cover changes may alter the trend of water flow in the future; therefore, it is important to have a proper management plan for water usage in the next decades.

@article{yu_machinelearning_2022,
	title = {Machine‐{Learning} {Estimation} of {Snow} {Depth} in 2021 {Texas} {Statewide} {Winter} {Storm} {Using} {SAR} {Imagery}},
	volume = {49},
	issn = {0094-8276, 1944-8007},
	url = {https://onlinelibrary.wiley.com/doi/10.1029/2022GL099119},
	doi = {10.1029/2022GL099119},
	language = {en},
	number = {17},
	urldate = {2023-06-01},
	journal = {Geophysical Research Letters},
	author = {Yu, Xiao and Hu, Xie and Wang, Guoquan and Wang, Kaicun and Chen, Xuelong},
	month = sep,
	year = {2022},
	keywords = {NALCMS},
}

@article{wang_mapping_2022,
	title = {Mapping of {ESA}-{CCI} land cover data to plant functional types for use in the {CLASSIC} land model},
	volume = {923},
	url = {https://doi.org/10.5194/egusphere-2022-923},
	doi = {https://doi.org/10.5194/egusphere-2022-923},
	abstract = {1 Plant functional types (PFTs) are used to represent vegetation distribution in land surface models 2 (LSMs). Large differences are found in the geographical distribution of PFTs currently used in 3 various LSMs. These differences arise from the differences in the underlying land cover products 4 but also the methods used to map or reclassify land cover data to the PFTs that a given LSM 5 represents. There are large uncertainties associated with existing PFT mapping methods since 6 they are largely based on expert judgment and therefore are subjective. In this study, we propose 7 a new approach to inform the mapping or the cross-walking process using analyses from sub-8 pixel fractional error matrices, which allows for a quantitative assessment of the fractional 9 composition of the land cover categories in a dataset. We use the Climate Change Initiative 10 (CCI) land cover product produced by the European Space Agency (ESA). A previous study has 11 shown that compared to fine-resolution maps over Canada, the ESA-CCI product provides an 12 improved land cover distribution compared to that from the GLC2000 dataset currently used in 13 the CLASSIC (Canadian Land Surface Scheme Including Biogeochemical Cycles) model. A tree 14 cover fraction dataset and a fine-resolution land cover map over Canada are used to compute the 15 sub-pixel fractional composition of the land cover classes in ESA-CCI, which is then used to 16 create a cross-walking table for mapping the ESA-CCI land cover categories to nine PFTs 17 represented in the CLASSIC model. There are large differences between the new PFTs and those 18 currently used in the model. Offline simulations performed with the CLASSIC model using the 19 ESA-CCI based PFTs show improved winter albedo compared to that based on the GLC2000 20 dataset. This emphasizes the importance of accurate representation of vegetation distribution for 21},
	journal = {EGUsphere},
	author = {Wang, Libo and Arora, Vivek K and Bartlett, Paul and Chan, Ed and Curasi, Salvatore R},
	year = {2022},
	keywords = {NALCMS},
	pages = {43},
}

1 Plant functional types (PFTs) are used to represent vegetation distribution in land surface models 2 (LSMs). Large differences are found in the geographical distribution of PFTs currently used in 3 various LSMs. These differences arise from the differences in the underlying land cover products 4 but also the methods used to map or reclassify land cover data to the PFTs that a given LSM 5 represents. There are large uncertainties associated with existing PFT mapping methods since 6 they are largely based on expert judgment and therefore are subjective. In this study, we propose 7 a new approach to inform the mapping or the cross-walking process using analyses from sub-8 pixel fractional error matrices, which allows for a quantitative assessment of the fractional 9 composition of the land cover categories in a dataset. We use the Climate Change Initiative 10 (CCI) land cover product produced by the European Space Agency (ESA). A previous study has 11 shown that compared to fine-resolution maps over Canada, the ESA-CCI product provides an 12 improved land cover distribution compared to that from the GLC2000 dataset currently used in 13 the CLASSIC (Canadian Land Surface Scheme Including Biogeochemical Cycles) model. A tree 14 cover fraction dataset and a fine-resolution land cover map over Canada are used to compute the 15 sub-pixel fractional composition of the land cover classes in ESA-CCI, which is then used to 16 create a cross-walking table for mapping the ESA-CCI land cover categories to nine PFTs 17 represented in the CLASSIC model. There are large differences between the new PFTs and those 18 currently used in the model. Offline simulations performed with the CLASSIC model using the 19 ESA-CCI based PFTs show improved winter albedo compared to that based on the GLC2000 20 dataset. This emphasizes the importance of accurate representation of vegetation distribution for 21

@article{mancuso_migration_2022,
	title = {Migration and non-breeding ecology of the {Yellow}-breasted {Chat} {Icteria} virens},
	volume = {163},
	issn = {2193-7192, 2193-7206},
	url = {https://link.springer.com/10.1007/s10336-021-01931-8},
	doi = {10.1007/s10336-021-01931-8},
	abstract = {Abstract
            
              Detailed information spanning the full annual cycle is lacking for most songbird populations. We examined breeding, migration, and non-breeding sites for the Yellow-breasted Chat (
              Icteria virens,
              chat). We deployed archival GPS tags and light-level geolocators on breeding chats in British Columbia and light-level geolocators in California from 2013 to 2017 to determine migration routes and non-breeding sites. We examined whether chats overwintered in protected areas and characterized the percent of land cover within 1 km. We used a combination of genetics and stable hydrogen isotopes from feathers collected on non-breeding chats in Nayarit, Mexico (2017–2019) and migrating chats in Chiapas, Mexico (2018) and Veracruz, Mexico (2014–2015) to determine subspecies and infer breeding location. Endangered chats in British Columbia followed the Pacific Flyway and spent the non-breeding period in Sinaloa and Nayarit, Mexico. Two out of five chats spent the non-breeding period in protected areas, and the most common landcover type used was tropical or subtropical broadleaf deciduous forest. We found no mixing of eastern and western chats in our Mexico sites, suggesting strong migratory connectivity at the subspecies level. Western chats likely originating from multiple breeding latitudes spent the non-breeding period in Nayarit. Eastern Yellow-breasted Chats likely breeding across various latitudes migrated through Veracruz and Chiapas. Our results provide precise migration routes and non-breeding locations, and describe habitat cover types for chats, notably an endangered population in British Columbia, which may be valuable for habitat protection and conservation efforts.
            
          , 
            Zusammenfassung
            
              Zug- und Überwinterungsökologie beim Flötenstärling
              
                Icteria virens
              
            
            
              Für die meisten Singvogelpopulationen fehlen detaillierte Informationen über Zug und Überwinterung. Hier vergleichen wir die Zug- und Überwinterungslokalitäten von zwei Brutpopulationen des Flötenstärlings
              Icteria virens
              im südlichen British Columbia, Kanada beziehungsweise in Nordkalifornien, USA. Zwischen 2013–2017 setzen wir in British Columbia Helldunkelgeolokatoren und Satellitensender, in Kalifornien Helldunkelgeolokatoren ein. Mittels einer Kombination aus der Analyse stabiler Wasserstoffisotope mit genetischen Daten aus Federn zogen wir Rückschlüsse auf die Brutorte ziehender Flötenstärlinge in Veracruz und Chiapas, Mexiko sowie überwinternder Flötenstärlinge in Nayarit, Mexiko (2014–2019). Die stark gefährdeten Flötenstärlinge aus British Columbia folgten dem Pazifischen Flyway und überwinterten in Sinaloa und Nayarit, Mexiko. Im Westen auf verschiedenen Breitengraden brütende Flötenstärlinge überwinterten in Nayarit, die im Osten auf verschiedenen Breitengraden brütenden Flötenstärlinge zogen dagegen durch Veracruz und Chiapas. Unsere Ergebnisse liefern genaue Informationen zu Zug und Überwinterung, welche wertvoll für den Erhalt der stark gefährdeten Flötenstärlinge in British Columbia sind.},
	language = {en},
	number = {1},
	urldate = {2023-06-15},
	journal = {Journal of Ornithology},
	author = {Mancuso, Kristen A. and Hodges, Karen E. and Alexander, John D. and Grosselet, Manuel and Bezener, A. Michael and Morales, Luis and Martinez, Sarahy C. and Castellanos-Labarcena, Jessica and Russello, Michael A. and Rockwell, Sarah M. and Bieber, Matthias E. and Bishop, Christine A.},
	month = jan,
	year = {2022},
	keywords = {NALCMS},
	pages = {37--50},
}

Abstract Detailed information spanning the full annual cycle is lacking for most songbird populations. We examined breeding, migration, and non-breeding sites for the Yellow-breasted Chat ( Icteria virens, chat). We deployed archival GPS tags and light-level geolocators on breeding chats in British Columbia and light-level geolocators in California from 2013 to 2017 to determine migration routes and non-breeding sites. We examined whether chats overwintered in protected areas and characterized the percent of land cover within 1 km. We used a combination of genetics and stable hydrogen isotopes from feathers collected on non-breeding chats in Nayarit, Mexico (2017–2019) and migrating chats in Chiapas, Mexico (2018) and Veracruz, Mexico (2014–2015) to determine subspecies and infer breeding location. Endangered chats in British Columbia followed the Pacific Flyway and spent the non-breeding period in Sinaloa and Nayarit, Mexico. Two out of five chats spent the non-breeding period in protected areas, and the most common landcover type used was tropical or subtropical broadleaf deciduous forest. We found no mixing of eastern and western chats in our Mexico sites, suggesting strong migratory connectivity at the subspecies level. Western chats likely originating from multiple breeding latitudes spent the non-breeding period in Nayarit. Eastern Yellow-breasted Chats likely breeding across various latitudes migrated through Veracruz and Chiapas. Our results provide precise migration routes and non-breeding locations, and describe habitat cover types for chats, notably an endangered population in British Columbia, which may be valuable for habitat protection and conservation efforts. , Zusammenfassung Zug- und Überwinterungsökologie beim Flötenstärling Icteria virens Für die meisten Singvogelpopulationen fehlen detaillierte Informationen über Zug und Überwinterung. Hier vergleichen wir die Zug- und Überwinterungslokalitäten von zwei Brutpopulationen des Flötenstärlings Icteria virens im südlichen British Columbia, Kanada beziehungsweise in Nordkalifornien, USA. Zwischen 2013–2017 setzen wir in British Columbia Helldunkelgeolokatoren und Satellitensender, in Kalifornien Helldunkelgeolokatoren ein. Mittels einer Kombination aus der Analyse stabiler Wasserstoffisotope mit genetischen Daten aus Federn zogen wir Rückschlüsse auf die Brutorte ziehender Flötenstärlinge in Veracruz und Chiapas, Mexiko sowie überwinternder Flötenstärlinge in Nayarit, Mexiko (2014–2019). Die stark gefährdeten Flötenstärlinge aus British Columbia folgten dem Pazifischen Flyway und überwinterten in Sinaloa und Nayarit, Mexiko. Im Westen auf verschiedenen Breitengraden brütende Flötenstärlinge überwinterten in Nayarit, die im Osten auf verschiedenen Breitengraden brütenden Flötenstärlinge zogen dagegen durch Veracruz und Chiapas. Unsere Ergebnisse liefern genaue Informationen zu Zug und Überwinterung, welche wertvoll für den Erhalt der stark gefährdeten Flötenstärlinge in British Columbia sind.

@techreport{odonoghue_moose_2022,
	title = {Moose survey: {Beaver} {River} {Watershed}, early winter 2019, {SR}-22-08},
	url = {https://open.yukon.ca/sites/default/files/SR-22-08-moose-survey-beaver-river-2019.pdf},
	author = {O’Donoghue, M. and Czetwertynski, S.},
	year = {2022},
	keywords = {NALCMS},
	pages = {29},
}

@article{johnston_multidecadal_2022,
	title = {Multidecadal {Trends} in {Organic} {Carbon} {Flux} {Through} a {Grassland} {River} {Network} {Shaped} by {Human} {Controls} and {Climatic} {Cycles}},
	volume = {49},
	issn = {0094-8276, 1944-8007},
	url = {https://onlinelibrary.wiley.com/doi/10.1029/2021GL096885},
	doi = {10.1029/2021GL096885},
	language = {en},
	number = {4},
	urldate = {2023-06-15},
	journal = {Geophysical Research Letters},
	author = {Johnston, Sarah Ellen and Gunawardana, Panditha V. S. L. and Rood, Stewart B. and Bogard, Matthew J.},
	month = feb,
	year = {2022},
	keywords = {NALCMS},
}

@article{zylstra_multiseason_2022,
	title = {Multi‐season climate projections forecast declines in migratory monarch butterflies},
	volume = {28},
	issn = {1354-1013, 1365-2486},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.16349},
	doi = {10.1111/gcb.16349},
	language = {en},
	number = {21},
	urldate = {2023-06-01},
	journal = {Global Change Biology},
	author = {Zylstra, Erin R. and Neupane, Naresh and Zipkin, Elise F.},
	month = nov,
	year = {2022},
	keywords = {NALCMS},
	pages = {6135--6151},
}

@article{erratt_paleolimnological_2022,
	title = {Paleolimnological evidence reveals climate-related preeminence of cyanobacteria in a temperate meromictic lake},
	volume = {79},
	issn = {0706-652X},
	url = {https://cdnsciencepub.com/doi/10.1139/cjfas-2021-0095},
	doi = {10.1139/cjfas-2021-0095},
	abstract = {Meromictic lakes provide a physically stable environment in which proxies for potentially harmful cyanobacteria are exceptionally well-preserved in the sediments. In Sunfish Lake, a meromictic lake that has recently become the focus of citizen concern due to the apparent rise in cyanobacteria blooms, we used a multi-proxy paleolimnological approach pairing novel spectral (i.e., VNIRS) and molecular (i.e., qPCR) assessment tools to explore long-term cyanobacteria trends. We hypothesized that climate change over the past 50 years altered the Sunfish Lake environment to favour cyanobacteria dominance, resulting in an increased incidence of bloom events. Spectral and genetic results aligned to reveal an unprecedented abundance of cyanobacteria in modern times and coincided with warmer and wetter climatic conditions in the region. Our findings offer evidence for climate-driven shifts in cyanobacteria abundance and suggest that a shift towards warmer and wetter conditions supports the rise of cyanobacteria in lakes.},
	number = {4},
	journal = {Canadian Journal of Fisheries and Aquatic Sciences},
	author = {Erratt, Kevin and Creed, Irena F. and Favot, Elizabeth J. and Todoran, Irina and Tai, Vera and Smol, John P. and Trick, Charles G.},
	month = apr,
	year = {2022},
	note = {Publisher: Canadian Science Publishing},
	keywords = {NALCMS},
	pages = {558--565},
}

Meromictic lakes provide a physically stable environment in which proxies for potentially harmful cyanobacteria are exceptionally well-preserved in the sediments. In Sunfish Lake, a meromictic lake that has recently become the focus of citizen concern due to the apparent rise in cyanobacteria blooms, we used a multi-proxy paleolimnological approach pairing novel spectral (i.e., VNIRS) and molecular (i.e., qPCR) assessment tools to explore long-term cyanobacteria trends. We hypothesized that climate change over the past 50 years altered the Sunfish Lake environment to favour cyanobacteria dominance, resulting in an increased incidence of bloom events. Spectral and genetic results aligned to reveal an unprecedented abundance of cyanobacteria in modern times and coincided with warmer and wetter climatic conditions in the region. Our findings offer evidence for climate-driven shifts in cyanobacteria abundance and suggest that a shift towards warmer and wetter conditions supports the rise of cyanobacteria in lakes.

@article{elshamy_physically_2022,
	title = {Physically based cold regions river flood prediction in data‐sparse regions: {The} {Yukon} {River} {Basin} flow forecasting system},
	issn = {1753-318X, 1753-318X},
	shorttitle = {Physically based cold regions river flood prediction in data‐sparse regions},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/jfr3.12835},
	doi = {10.1111/jfr3.12835},
	language = {en},
	urldate = {2023-06-01},
	journal = {Journal of Flood Risk Management},
	author = {Elshamy, Mohamed and Loukili, Youssef and Pomeroy, John W. and Pietroniro, Alain and Richard, Dominique and Princz, Daniel},
	month = jul,
	year = {2022},
	keywords = {NALCMS},
}

@article{zhu_remote_2022,
	title = {Remote sensing of land change: {A} multifaceted perspective},
	volume = {282},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425722003728},
	doi = {10.1016/j.rse.2022.113266},
	abstract = {The discipline of land change science has been evolving rapidly in the past decades. Remote sensing played a major role in one of the essential components of land change science, which includes observation, monitoring, and characterization of land change. In this paper, we proposed a new framework of the multifaceted view of land change through the lens of remote sensing and recommended five facets of land change including change location, time, target, metric, and agent. We also evaluated the impacts of spatial, spectral, temporal, angular, and data-integration domains of the remotely sensed data on observing, monitoring, and characterization of different facets of land change, as well as discussed some of the current land change products. We recommend clarifying the specific land change facet being studied in remote sensing of land change, reporting multiple or all facets of land change in remote sensing products, shifting the focus from land cover change to specific change metric and agent, integrating social science data and multi-sensor datasets for a deeper and fuller understanding of land change, and recognizing limitations and weaknesses of remote sensing in land change studies.},
	journal = {Remote Sensing of Environment},
	author = {Zhu, Zhe and Qiu, Shi and Ye, Su},
	month = dec,
	year = {2022},
	note = {Publisher: Elsevier Inc.},
	keywords = {NALCMS},
	pages = {113266},
}

The discipline of land change science has been evolving rapidly in the past decades. Remote sensing played a major role in one of the essential components of land change science, which includes observation, monitoring, and characterization of land change. In this paper, we proposed a new framework of the multifaceted view of land change through the lens of remote sensing and recommended five facets of land change including change location, time, target, metric, and agent. We also evaluated the impacts of spatial, spectral, temporal, angular, and data-integration domains of the remotely sensed data on observing, monitoring, and characterization of different facets of land change, as well as discussed some of the current land change products. We recommend clarifying the specific land change facet being studied in remote sensing of land change, reporting multiple or all facets of land change in remote sensing products, shifting the focus from land cover change to specific change metric and agent, integrating social science data and multi-sensor datasets for a deeper and fuller understanding of land change, and recognizing limitations and weaknesses of remote sensing in land change studies.

@article{cunningham_seasonal_2022,
	title = {Seasonal {Habitat} {Selection} by {American} {White} {Pelicans}},
	volume = {14},
	issn = {14242818},
	url = {https://digitalcommons.unl.edu/icwdm_usdanwrc/2606/},
	doi = {10.3390/d14100821},
	abstract = {Resource utilization strategies of avian migrants are a major concern for conservation and management. Understanding seasonal habitat selection by migratory birds helps us explain the ongoing continental declines of migratory bird populations. Our objective was to compare the second-order and third-order habitat selection by the American White Pelican (Pelecanus erythrorhynchos; hereafter pelican) between the breeding and non-breeding grounds. We tested the Lack hypothesis that habitat selection by migratory birds is stronger on the breeding grounds than on the non-breeding grounds. We used random-effect Dirichlet-multinomial models to estimate the second-order habitat selection between the seasons with the GPS locations of 32 tracked pelicans. We used Gaussian Markov random field models to estimate the third-order habitat selection by pelicans at the breeding and non-breeding grounds, accounting for spatial autocorrelation. Pelicans strongly selected waterbodies and wetlands at both non-breeding and breeding grounds, tracking their foraging habitats between the seasons at the home range level. However, pelicans exhibited seasonal differences in the strength of the third-order selection of wetlands and waterbodies with foraging habitat selection being stronger at the breeding grounds than at the non-breeding grounds, supporting the Lack hypothesis.},
	number = {10},
	journal = {Diversity},
	author = {Cunningham, Frederick L. and Wang, Guiming and King, D. Tommy},
	year = {2022},
	keywords = {NALCMS},
}

Resource utilization strategies of avian migrants are a major concern for conservation and management. Understanding seasonal habitat selection by migratory birds helps us explain the ongoing continental declines of migratory bird populations. Our objective was to compare the second-order and third-order habitat selection by the American White Pelican (Pelecanus erythrorhynchos; hereafter pelican) between the breeding and non-breeding grounds. We tested the Lack hypothesis that habitat selection by migratory birds is stronger on the breeding grounds than on the non-breeding grounds. We used random-effect Dirichlet-multinomial models to estimate the second-order habitat selection between the seasons with the GPS locations of 32 tracked pelicans. We used Gaussian Markov random field models to estimate the third-order habitat selection by pelicans at the breeding and non-breeding grounds, accounting for spatial autocorrelation. Pelicans strongly selected waterbodies and wetlands at both non-breeding and breeding grounds, tracking their foraging habitats between the seasons at the home range level. However, pelicans exhibited seasonal differences in the strength of the third-order selection of wetlands and waterbodies with foraging habitat selection being stronger at the breeding grounds than at the non-breeding grounds, supporting the Lack hypothesis.

@article{sokolov_semantically-consistent_2022,
	title = {Semantically-consistent {Landsat} 8 image to {Sentinel}-2 image translation for alpine areas},
	url = {http://arxiv.org/abs/2212.12056},
	abstract = {The availability of frequent and cost-free satellite images is in growing demand in the research world. Such satellite constellations as Landsat 8 and Sentinel-2 provide a massive amount of valuable data daily. However, the discrepancy in the sensors' characteristics of these satellites makes it senseless to use a segmentation model trained on either dataset and applied to another, which is why domain adaptation techniques have recently become an active research area in remote sensing. In this paper, an experiment of domain adaptation through style-transferring is conducted using the HRSemI2I model to narrow the sensor discrepancy between Landsat 8 and Sentinel-2. This paper's main contribution is analyzing the expediency of that approach by comparing the results of segmentation using domain-adapted images with those without adaptation. The HRSemI2I model, adjusted to work with 6-band imagery, shows significant intersection-over-union performance improvement for both mean and per class metrics. A second contribution is providing different schemes of generalization between two label schemes - NALCMS 2015 and CORINE. The first scheme is standardization through higher-level land cover classes, and the second is through harmonization validation in the field.},
	author = {Sokolov, M. and Storie, J. L. and Henry, C. J. and Storie, C. D. and Cameron, J. and Ødegård, R. S. and Zubinaite, V. and Stikbakke, S.},
	month = dec,
	year = {2022},
	note = {\_eprint: 2212.12056},
	keywords = {NALCMS},
}

The availability of frequent and cost-free satellite images is in growing demand in the research world. Such satellite constellations as Landsat 8 and Sentinel-2 provide a massive amount of valuable data daily. However, the discrepancy in the sensors' characteristics of these satellites makes it senseless to use a segmentation model trained on either dataset and applied to another, which is why domain adaptation techniques have recently become an active research area in remote sensing. In this paper, an experiment of domain adaptation through style-transferring is conducted using the HRSemI2I model to narrow the sensor discrepancy between Landsat 8 and Sentinel-2. This paper's main contribution is analyzing the expediency of that approach by comparing the results of segmentation using domain-adapted images with those without adaptation. The HRSemI2I model, adjusted to work with 6-band imagery, shows significant intersection-over-union performance improvement for both mean and per class metrics. A second contribution is providing different schemes of generalization between two label schemes - NALCMS 2015 and CORINE. The first scheme is standardization through higher-level land cover classes, and the second is through harmonization validation in the field.

@article{eppler_snow_2022,
	title = {Snow water equivalent change mapping from slope-correlated synthetic aperture radar interferometry ({InSAR}) phase variations},
	volume = {16},
	issn = {1994-0424},
	url = {https://tc.copernicus.org/articles/16/1497/2022/},
	doi = {10.5194/tc-16-1497-2022},
	abstract = {Abstract. Area-based measurements of snow water equivalent (SWE) are important for understanding earth system processes such as glacier mass balance, winter
hydrological storage in drainage basins, and ground thermal regimes. Remote sensing techniques are ideally suited for wide-scale area-based mapping
with the most commonly used technique to measure SWE being passive microwave, which is limited to coarse spatial resolutions of 25 km or
greater and to areas without significant topographic variation. Passive microwave also has a negative bias for large SWE. Another method is
repeat-pass synthetic aperture radar interferometry (InSAR) that allows measurement of SWE change at much higher spatial resolution. However, it
has not been widely adopted because (1) the phase unwrapping problem has not been robustly addressed, especially for interferograms with poor
coherence, and (2) SWE change maps scaled directly from repeat-pass interferograms are not an absolute measurement but contain unknown offsets for
each contiguous coherent area. We develop and test a novel method for repeat-pass InSAR-based dry-snow SWE estimation that exploits the sensitivity
of the dry-snow refraction-induced InSAR phase to topographic variations. The method robustly estimates absolute SWE change at spatial resolutions
of {\textless} 1 km without the need for phase unwrapping. We derive a quantitative signal model for this new SWE change estimator and identify
the relevant sources of bias. The method is demonstrated using both simulated SWE distributions and a 9-year RADARSAT-2 (C-band,
5.405 GHz) spotlight-mode dataset near Inuvik, Northwest Territories (NWT), Canada. SWE results are compared to in situ snow survey measurements and estimates from
ERA5 reanalysis. Our method performs well in high-relief areas, thus providing complementary coverage to passive-microwave-based SWE
estimation. Further, our method has the advantage of requiring only a single wavelength band and thus can utilize existing spaceborne synthetic
aperture radar systems.},
	language = {en},
	number = {4},
	urldate = {2023-06-15},
	journal = {The Cryosphere},
	author = {Eppler, Jayson and Rabus, Bernhard and Morse, Peter},
	month = apr,
	year = {2022},
	keywords = {NALCMS},
	pages = {1497--1521},
}

Abstract. Area-based measurements of snow water equivalent (SWE) are important for understanding earth system processes such as glacier mass balance, winter hydrological storage in drainage basins, and ground thermal regimes. Remote sensing techniques are ideally suited for wide-scale area-based mapping with the most commonly used technique to measure SWE being passive microwave, which is limited to coarse spatial resolutions of 25 km or greater and to areas without significant topographic variation. Passive microwave also has a negative bias for large SWE. Another method is repeat-pass synthetic aperture radar interferometry (InSAR) that allows measurement of SWE change at much higher spatial resolution. However, it has not been widely adopted because (1) the phase unwrapping problem has not been robustly addressed, especially for interferograms with poor coherence, and (2) SWE change maps scaled directly from repeat-pass interferograms are not an absolute measurement but contain unknown offsets for each contiguous coherent area. We develop and test a novel method for repeat-pass InSAR-based dry-snow SWE estimation that exploits the sensitivity of the dry-snow refraction-induced InSAR phase to topographic variations. The method robustly estimates absolute SWE change at spatial resolutions of \textless 1 km without the need for phase unwrapping. We derive a quantitative signal model for this new SWE change estimator and identify the relevant sources of bias. The method is demonstrated using both simulated SWE distributions and a 9-year RADARSAT-2 (C-band, 5.405 GHz) spotlight-mode dataset near Inuvik, Northwest Territories (NWT), Canada. SWE results are compared to in situ snow survey measurements and estimates from ERA5 reanalysis. Our method performs well in high-relief areas, thus providing complementary coverage to passive-microwave-based SWE estimation. Further, our method has the advantage of requiring only a single wavelength band and thus can utilize existing spaceborne synthetic aperture radar systems.

@phdthesis{peraza_arcila_spatial_2022,
	address = {Ottawa, Ontario},
	type = {Master of {Science}},
	title = {Spatial {Patterns} of {Mercury} {Accumulation} in {Wolverine} ({Gulo} gulo) {Across} the {Western} {Canadian} {Arctic}: {Landscape}, {Climate} and {Dietary} {Factors}},
	shorttitle = {Spatial {Patterns} of {Mercury} {Accumulation} in {Wolverine} ({Gulo} gulo) {Across} the {Western} {Canadian} {Arctic}},
	url = {https://repository.library.carleton.ca/concern/etds/rv042v04n},
	language = {en},
	urldate = {2023-06-01},
	school = {Carleton University},
	author = {Peraza Arcila, Maria},
	year = {2022},
	doi = {10.22215/etd/2022-15084},
	keywords = {NALCMS, Terrestrial Ecoregions, Terrestrial Ecoregions (CEC 1997)},
}

@article{heckford_spatially_2022,
	title = {Spatially explicit correlates of plant functional traits inform landscape patterns of resource quality},
	volume = {37},
	issn = {0921-2973, 1572-9761},
	url = {https://link.springer.com/10.1007/s10980-021-01334-3},
	doi = {10.1007/s10980-021-01334-3},
	language = {en},
	number = {1},
	urldate = {2023-06-15},
	journal = {Landscape Ecology},
	author = {Heckford, Travis R. and Leroux, Shawn J. and Vander Wal, Eric and Rizzuto, Matteo and Balluffi-Fry, Juliana and Richmond, Isabella C. and Wiersma, Yolanda F.},
	month = jan,
	year = {2022},
	keywords = {NALCMS},
	pages = {59--80},
}

@article{mai_great_2022,
	title = {The {Great} {Lakes} {Runoff} {Intercomparison} {Project} {Phase} 4: the {Great} {Lakes} ({GRIP}-{GL})},
	volume = {26},
	issn = {1607-7938},
	url = {https://hess.copernicus.org/articles/26/3537/2022/},
	doi = {10.5194/hess-26-3537-2022},
	number = {13},
	journal = {Hydrology and Earth System Sciences},
	author = {Mai, Juliane and Shen, Hongren and Tolson, Bryan A. and Gaborit, Étienne and Arsenault, Richard and Craig, James R. and Fortin, Vincent and Fry, Lauren M. and Gauch, Martin and Klotz, Daniel and Kratzert, Frederik and O'Brien, Nicole and Princz, Daniel G. and Rasiya Koya, Sinan and Roy, Tirthankar and Seglenieks, Frank and Shrestha, Narayan K. and Temgoua, André G. T. and Vionnet, Vincent and Waddell, Jonathan W.},
	month = jul,
	year = {2022},
	note = {Publisher: Copernicus GmbH},
	keywords = {NALCMS},
	pages = {3537--3572},
}

@article{shrestha_impacts_2022,
	title = {The {Impacts} of {Climate} {Change} on {Land} {Hydroclimatology} of the {Laurentian} {Great} {Lakes} {Basin}},
	volume = {4},
	issn = {2624-9375},
	url = {https://www.frontiersin.org/articles/10.3389/frwa.2022.801134/full},
	doi = {10.3389/frwa.2022.801134},
	abstract = {The freshwater resources of the Laurentian Great Lakes basin contribute significantly to the environment and economy of the region. With the impacts of climate change becoming more evident, sustainable management of the freshwater resources of the Laurentian Great Lakes basin is important. This study uses 36 simulations from 6 regional climate models to quantify trends and changes in land-area precipitation and temperature in two future periods (mid-century, 2035–2064 and end-century, 2065–2094) with reference to a baseline period (1951–2005) for two emission scenarios (RCP4.5 and RCP 8.5). Climatic forcings from these 36 simulations are used as input to a calibrated and validated hydrological model to assess changes in land snowpack and actual evapotranspiration, and runoff to lake. Ensemble results show wetter (7 to 15\% increase in annual precipitation) and warmer (2.4–5.0°C increase in annual mean temperature) future conditions on GL land areas. Seasonal and monthly changes in precipitation and mean temperature are more sporadic, for instance although precipitation is projected to increase overall, in some scenarios, summer precipitation is expected to decrease. Projected increases in highest one-day precipitation and decreases in number of wet days indicate possible increases in extreme precipitation in future. Minimum temperature is expected to increase in a higher rate than maximum temperature. Ensemble results from the hydrological model show projected decrease in snowpack (29–58\%). Similarly, actual evapotranspiration is projected to increase, especially during summer months (up to 0.4 mm/day). Annually, runoff is expected to increase (up to 48\% in Superior, 40\% in Michigan-Huron, 25\% Erie and 28\% in Ontario). Seasonal and monthly changes in runoff are more sporadic (e.g., projected decrease up to 17\% in Erie subdomain in October). Such contrasting patterns of changes in land hydroclimatology of the GL basin will pose challenges to sustainable management of the water resources of the basin in future.},
	number = {July},
	journal = {Frontiers in Water},
	author = {Shrestha, Narayan K. and Seglenieks, Frank and Temgoua, André G. T. and Dehghan, Armin},
	month = jul,
	year = {2022},
	keywords = {NALCMS},
	pages = {1--22},
}

The freshwater resources of the Laurentian Great Lakes basin contribute significantly to the environment and economy of the region. With the impacts of climate change becoming more evident, sustainable management of the freshwater resources of the Laurentian Great Lakes basin is important. This study uses 36 simulations from 6 regional climate models to quantify trends and changes in land-area precipitation and temperature in two future periods (mid-century, 2035–2064 and end-century, 2065–2094) with reference to a baseline period (1951–2005) for two emission scenarios (RCP4.5 and RCP 8.5). Climatic forcings from these 36 simulations are used as input to a calibrated and validated hydrological model to assess changes in land snowpack and actual evapotranspiration, and runoff to lake. Ensemble results show wetter (7 to 15% increase in annual precipitation) and warmer (2.4–5.0°C increase in annual mean temperature) future conditions on GL land areas. Seasonal and monthly changes in precipitation and mean temperature are more sporadic, for instance although precipitation is projected to increase overall, in some scenarios, summer precipitation is expected to decrease. Projected increases in highest one-day precipitation and decreases in number of wet days indicate possible increases in extreme precipitation in future. Minimum temperature is expected to increase in a higher rate than maximum temperature. Ensemble results from the hydrological model show projected decrease in snowpack (29–58%). Similarly, actual evapotranspiration is projected to increase, especially during summer months (up to 0.4 mm/day). Annually, runoff is expected to increase (up to 48% in Superior, 40% in Michigan-Huron, 25% Erie and 28% in Ontario). Seasonal and monthly changes in runoff are more sporadic (e.g., projected decrease up to 17% in Erie subdomain in October). Such contrasting patterns of changes in land hydroclimatology of the GL basin will pose challenges to sustainable management of the water resources of the basin in future.

@article{margolis_north_2022,
	title = {The {North} {American} tree‐ring fire‐scar network},
	volume = {13},
	issn = {2150-8925, 2150-8925},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/ecs2.4159},
	doi = {10.1002/ecs2.4159},
	language = {en},
	number = {7},
	urldate = {2023-06-01},
	journal = {Ecosphere},
	author = {Margolis, Ellis Q. and Guiterman, Christopher H. and Chavardès, Raphaël D. and Coop, Jonathan D. and Copes‐Gerbitz, Kelsey and Dawe, Denyse A. and Falk, Donald A. and Johnston, James D. and Larson, Evan and Li, Hang and Marschall, Joseph M. and Naficy, Cameron E. and Naito, Adam T. and Parisien, Marc‐André and Parks, Sean A. and Portier, Jeanne and Poulos, Helen M. and Robertson, Kevin M. and Speer, James H. and Stambaugh, Michael and Swetnam, Thomas W. and Tepley, Alan J. and Thapa, Ichchha and Allen, Craig D. and Bergeron, Yves and Daniels, Lori D. and Fulé, Peter Z. and Gervais, David and Girardin, Martin P. and Harley, Grant L. and Harvey, Jill E. and Hoffman, Kira M. and Huffman, Jean M. and Hurteau, Matthew D. and Johnson, Lane B. and Lafon, Charles W. and Lopez, Manuel K. and Maxwell, R. Stockton and Meunier, Jed and North, Malcolm and Rother, Monica T. and Schmidt, Micah R. and Sherriff, Rosemary L. and Stachowiak, Lauren A. and Taylor, Alan and Taylor, Erana J. and Trouet, Valerie and Villarreal, Miguel L. and Yocom, Larissa L. and Arabas, Karen B. and Arizpe, Alexis H. and Arseneault, Dominique and Tarancón, Alicia Azpeleta and Baisan, Christopher and Bigio, Erica and Biondi, Franco and Cahalan, Gabriel D. and Caprio, Anthony and Cerano‐Paredes, Julián and Collins, Brandon M. and Dey, Daniel C. and Drobyshev, Igor and Farris, Calvin and Fenwick, M. Adele and Flatley, William and Floyd, M. Lisa and Gedalof, Ze'ev and Holz, Andres and Howard, Lauren F. and Huffman, David W. and Iniguez, Jose and Kipfmueller, Kurt F. and Kitchen, Stanley G. and Lombardo, Keith and McKenzie, Donald and Merschel, Andrew G. and Metlen, Kerry L. and Minor, Jesse and O'Connor, Christopher D. and Platt, Laura and Platt, William J. and Saladyga, Thomas and Stan, Amanda B. and Stephens, Scott and Sutheimer, Colleen and Touchan, Ramzi and Weisberg, Peter J.},
	month = jul,
	year = {2022},
	keywords = {NALCMS},
}

@phdthesis{wooldridge_genomic_2022,
	type = {Doctoral {Dissertation}},
	title = {The genomic basis of repeated adaptation in deer mice},
	url = {https://dash.harvard.edu/handle/1/37372007},
	school = {Harvard University},
	author = {Wooldridge, Tyler Brock},
	year = {2022},
	keywords = {NALCMS},
}

@article{oliva_occurrence_2022,
	title = {The occurrence of potentially pathogenic fungi and protists in {Canadian} lakes predicted using geomatics, in situ and satellite-derived variables: {Towards} a tele-epidemiological approach},
	volume = {209},
	issn = {00431354},
	shorttitle = {The occurrence of potentially pathogenic fungi and protists in {Canadian} lakes predicted using geomatics, in situ and satellite-derived variables},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0043135421011295},
	doi = {10.1016/j.watres.2021.117935},
	language = {en},
	urldate = {2023-06-01},
	journal = {Water Research},
	author = {Oliva, Anaïs and Garner, Rebecca E. and Walsh, David and Huot, Yannick},
	month = feb,
	year = {2022},
	keywords = {NALCMS},
	pages = {117935},
}

@article{mai_sensitivity_2022,
	title = {The sensitivity of simulated streamflow to individual hydrologic processes across {North} {America}},
	volume = {13},
	copyright = {2022 The Author(s)},
	issn = {2041-1723},
	url = {https://www.nature.com/articles/s41467-022-28010-7},
	doi = {10.1038/s41467-022-28010-7},
	abstract = {Streamflow sensitivity to different hydrologic processes varies in both space and time. This sensitivity is traditionally evaluated for the parameters specific to a given hydrologic model simulating streamflow. In this study, we apply a novel analysis over more than 3000 basins across North America considering a blended hydrologic model structure, which includes not only parametric, but also structural uncertainties. This enables seamless quantification of model process sensitivities and parameter sensitivities across a continuous set of models. It also leads to high-level conclusions about the importance of water cycle components on streamflow predictions, such as quickflow being the most sensitive process for streamflow simulations across the North American continent. The results of the 3000 basins are used to derive an approximation of sensitivities based on physiographic and climatologic data without the need to perform expensive sensitivity analyses. Detailed spatio-temporal inputs and results are shared through an interactive website.},
	language = {en},
	number = {1},
	urldate = {2023-06-14},
	journal = {Nature Communications},
	author = {Mai, Juliane and Craig, James R. and Tolson, Bryan A. and Arsenault, Richard},
	month = jan,
	year = {2022},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {NALCMS},
	pages = {455},
}

Streamflow sensitivity to different hydrologic processes varies in both space and time. This sensitivity is traditionally evaluated for the parameters specific to a given hydrologic model simulating streamflow. In this study, we apply a novel analysis over more than 3000 basins across North America considering a blended hydrologic model structure, which includes not only parametric, but also structural uncertainties. This enables seamless quantification of model process sensitivities and parameter sensitivities across a continuous set of models. It also leads to high-level conclusions about the importance of water cycle components on streamflow predictions, such as quickflow being the most sensitive process for streamflow simulations across the North American continent. The results of the 3000 basins are used to derive an approximation of sensitivities based on physiographic and climatologic data without the need to perform expensive sensitivity analyses. Detailed spatio-temporal inputs and results are shared through an interactive website.

@mastersthesis{knolla_three_2022,
	title = {Three {Method} {Tsunami} {Vulnerability} {Analysis} of the {United} {States} {East} {Coast}},
	url = {https://scholars.fhsu.edu/theses/3189/},
	school = {Fort Hays State University},
	author = {Knolla, Joshua},
	year = {2022},
	keywords = {NALCMS},
}

@techreport{blaine_towards_2022,
	title = {Towards a {Northern} {Landscapes} {Sensitivity} {Atlas}: {Cartographic} {Approaches} to {Net} {Environmental} {Benefit} {Analysis} ({NEBA})},
	url = {https://www.nwt-esrf.org/sites/nesrf/files/2022-08/NAIT%2C%20Towards%20a%20Northern%20Landscapes%20Sensitivity%20Atlas%2C%20Cartographic%20Approaches%20to%20Net%20Environmental%20Benefit%20Analysis%20%28NEBA%29%20Proof%20of%20Concept%20Report.pdf},
	language = {en},
	author = {Blaine, Dave},
	year = {2022},
	keywords = {NALCMS},
}

@article{congram_tracking_2022,
	title = {Tracking the prevalence of a fungal pathogen, {Batrachochytrium} dendrobatidis (chytrid fungus), using environmental {DNA}},
	volume = {4},
	issn = {2637-4943},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/edn3.283},
	doi = {10.1002/edn3.283},
	abstract = {Chytridiomycosis, a primary disease driving widespread and unprecedented amphibian declines, is caused by the fungal pathogen Batrachochytrium dendrobatidis (Bd). Tracking Bd through space and time requires monitoring protocols that efficiently and reliably assess pathogen prevalence and intensity, which in turn requires an understanding of environment–pathogen dynamics. Environmental DNA (eDNA) was used to track Bd prevalence and intensity in 95 waterbodies in southern Ontario, Canada, and assess zoospore counts relative to biotic, abiotic, and geographic factors. Bd was also monitored on a semi-weekly basis in 10 waterbodies to better understand patterns of temporal variability. Bd showed variable prevalence, with 47\% and 29\% of waterbodies having zoospores detected in May and July, respectively. Patterns of prevalence were markedly variable both within and across waterbodies, indicating high spatio-temporal heterogeneity. Bd prevalence was not related to environmental factors, geographic variables, or amphibian species richness, but intensity was negatively related to estimated canopy cover. In intensively sampled waterbodies, Bd counts were highly variable through time, with some sites switching from detection to non-detection (and vice versa) across 2-week intervals. We conclude that eDNA can be a useful tool for monitoring Bd zoospores in wetlands but emphasize the need for additional research into environmental and methodological factors affecting zoospore detection and abundance before this method should be widely adopted.},
	number = {3},
	journal = {Environmental DNA},
	author = {Congram, Megan and Torres Vilaça, Sibelle and Wilson, Chris C. and Kyle, Chris J. and Lesbarrères, David and Wikston, Madison J. H. and Beaty, Lynne and Murray, Dennis L.},
	month = may,
	year = {2022},
	note = {Publisher: John Wiley and Sons Inc},
	keywords = {NALCMS},
	pages = {687--699},
}

Chytridiomycosis, a primary disease driving widespread and unprecedented amphibian declines, is caused by the fungal pathogen Batrachochytrium dendrobatidis (Bd). Tracking Bd through space and time requires monitoring protocols that efficiently and reliably assess pathogen prevalence and intensity, which in turn requires an understanding of environment–pathogen dynamics. Environmental DNA (eDNA) was used to track Bd prevalence and intensity in 95 waterbodies in southern Ontario, Canada, and assess zoospore counts relative to biotic, abiotic, and geographic factors. Bd was also monitored on a semi-weekly basis in 10 waterbodies to better understand patterns of temporal variability. Bd showed variable prevalence, with 47% and 29% of waterbodies having zoospores detected in May and July, respectively. Patterns of prevalence were markedly variable both within and across waterbodies, indicating high spatio-temporal heterogeneity. Bd prevalence was not related to environmental factors, geographic variables, or amphibian species richness, but intensity was negatively related to estimated canopy cover. In intensively sampled waterbodies, Bd counts were highly variable through time, with some sites switching from detection to non-detection (and vice versa) across 2-week intervals. We conclude that eDNA can be a useful tool for monitoring Bd zoospores in wetlands but emphasize the need for additional research into environmental and methodological factors affecting zoospore detection and abundance before this method should be widely adopted.

@mastersthesis{johnson_tuktu_2022,
	title = {Tuktu {Past}, {Present}, and {Future}: {State} of {Torngat} {Mountains} {Caribou} and their {Forage} in a {Changing} {Environment}},
	shorttitle = {Tuktu {Past}, {Present}, and {Future}},
	url = {https://uwspace.uwaterloo.ca/handle/10012/18311},
	abstract = {Caribou (Rangifer tarandus) populations are in decline across Canada, making this charismatic species a major conservation concern. For Inuit of Northern Labrador and Quebec, caribou are a cultural keystone species with nutritional, cultural, and spiritual value. Inuit have long been aware that the Torngat Mountains Caribou (TMC) population is distinct from the overlapping George River Caribou (GRC) herd, but this distinction has only recently been recognized by federal and provincial governments. Therefore, limited TMC specific data are available. The objective of this thesis is to summarize existing information on the TMC, identify knowledge gaps, and contribute to the growing body of research on the TMC. One threat facing the TMC is climate change. Arctic warming has resulted in shrub expansion in Eastern Canada’s tundra which, in turn, has negatively impacted lichens, an important caribou food source. This study investigates changes to caribou forage availability due to ambient and experimental warming at two tundra sites located within the range of the TMC in Nunatsiavut, Labrador. The main questions we address are: 1) What proportion of total vegetation is suitable caribou forage and how has this changed with time and experimental warming? 2) Which forage species are most impacted by recent climate change? To answer these questions, we analyzed vegetation data collected over a 14-year period within the TMC’s range. Permanent, control and warming plots were established at Nakvak Brook and Torr Bay in 2007 and 2009 respectively and re-sampled every 3-6 years. From these vegetation data, we identified species of high, medium, and low caribou forage quality based on published literature. We then modelled the observed changes in forage availability. Results of this study found that caribou are more likely to be forage limited in the winter than during the summer. Consistent with shrub expansion, we found that birch, and ericaceous shrub species increased with time at Torr Bay. Conversely, we found that willow species declined in abundance at Nakvak Brook. We did not find that lichen species were significantly affected by time or warming at either of our sites. Our research provides valuable insight into recent changes in caribou forage availability for the TMC. This knowledge will help to inform appropriate conservation and management measures so that the TMC can continue to persist and contribute to the social-ecological resilience of northern communities.},
	language = {en},
	urldate = {2023-06-01},
	school = {University of Waterloo},
	author = {Johnson, Alexandra},
	month = may,
	year = {2022},
	note = {Accepted: 2022-05-20T17:27:57Z},
	keywords = {NALCMS},
}

Caribou (Rangifer tarandus) populations are in decline across Canada, making this charismatic species a major conservation concern. For Inuit of Northern Labrador and Quebec, caribou are a cultural keystone species with nutritional, cultural, and spiritual value. Inuit have long been aware that the Torngat Mountains Caribou (TMC) population is distinct from the overlapping George River Caribou (GRC) herd, but this distinction has only recently been recognized by federal and provincial governments. Therefore, limited TMC specific data are available. The objective of this thesis is to summarize existing information on the TMC, identify knowledge gaps, and contribute to the growing body of research on the TMC. One threat facing the TMC is climate change. Arctic warming has resulted in shrub expansion in Eastern Canada’s tundra which, in turn, has negatively impacted lichens, an important caribou food source. This study investigates changes to caribou forage availability due to ambient and experimental warming at two tundra sites located within the range of the TMC in Nunatsiavut, Labrador. The main questions we address are: 1) What proportion of total vegetation is suitable caribou forage and how has this changed with time and experimental warming? 2) Which forage species are most impacted by recent climate change? To answer these questions, we analyzed vegetation data collected over a 14-year period within the TMC’s range. Permanent, control and warming plots were established at Nakvak Brook and Torr Bay in 2007 and 2009 respectively and re-sampled every 3-6 years. From these vegetation data, we identified species of high, medium, and low caribou forage quality based on published literature. We then modelled the observed changes in forage availability. Results of this study found that caribou are more likely to be forage limited in the winter than during the summer. Consistent with shrub expansion, we found that birch, and ericaceous shrub species increased with time at Torr Bay. Conversely, we found that willow species declined in abundance at Nakvak Brook. We did not find that lichen species were significantly affected by time or warming at either of our sites. Our research provides valuable insight into recent changes in caribou forage availability for the TMC. This knowledge will help to inform appropriate conservation and management measures so that the TMC can continue to persist and contribute to the social-ecological resilience of northern communities.

@phdthesis{di_cecco_understanding_2022,
	title = {Understanding biodiversity responses to global change: {Populations}, communities, and species distributions},
	copyright = {In Copyright - Educational Use Permitted},
	shorttitle = {Understanding biodiversity responses to global change},
	url = {https://cdr.lib.unc.edu/concern/dissertations/td96kc271},
	abstract = {Human influence on global ecosystems is pervasive. To mitigate the effects of climate change and land use change, there is a need for developing a predictive understanding of how global biodiversity has been impacted. Identifying ecological traits of species associated with species that are vulnerable to, tolerant of, or benefitting from anthropogenic change can help predict ecological communities of the future. In this dissertation, I investigated the ecological impacts of global change at three levels: populations, communities, and range distributions.Population responses to anthropogenic change may be context dependent: climate change effects may be exacerbated by simultaneous land use changes, or intraspecific population response to climate change may depend on whether the population is in a warmer or colder portion of the species’ range. To address these questions, I modeled how forest fragmentation and climate change predict changes in population trends of 67 forest breeding bird species throughout the United States and Canada. Secondly, I determined whether ecological traits such as migratory strategy, habitat specialization, and thermal niche width can predict the susceptibility of species to the impacts of forest fragmentation and climate change. As a result of ongoing anthropogenic change, ecological communities have reshuffled. Understanding how communities are changing requires consideration of compositional shifts in species identity and abundance and how they are related to global change. I examined the compositional change in bird communities, comparing the relative contribution of land use and climate change variables from local to regional scales over the past 25 years in the United States and Canada. Additionally, I measured how species traits may explain turnover in response to climate and land use change.	Impacts from local climate and land use change on populations and communities ultimately scale up to impact species range distributions. In response, species may undergo shifts in population size, sites occupied within their range, and shifts in range extent. Niche breadth on various axes may influence the direction and magnitude of these responses. Using annual survey data on breeding birds over forty years, I characterized the relative importance of niche breadth in explaining changes in species range responses.},
	language = {en},
	urldate = {2023-06-01},
	school = {The University of North Carolina at Chapel Hill University Libraries},
	author = {Di Cecco, Grace J.},
	year = {2022},
	doi = {10.17615/51CN-7Q10},
	keywords = {NALCMS},
}

Human influence on global ecosystems is pervasive. To mitigate the effects of climate change and land use change, there is a need for developing a predictive understanding of how global biodiversity has been impacted. Identifying ecological traits of species associated with species that are vulnerable to, tolerant of, or benefitting from anthropogenic change can help predict ecological communities of the future. In this dissertation, I investigated the ecological impacts of global change at three levels: populations, communities, and range distributions.Population responses to anthropogenic change may be context dependent: climate change effects may be exacerbated by simultaneous land use changes, or intraspecific population response to climate change may depend on whether the population is in a warmer or colder portion of the species’ range. To address these questions, I modeled how forest fragmentation and climate change predict changes in population trends of 67 forest breeding bird species throughout the United States and Canada. Secondly, I determined whether ecological traits such as migratory strategy, habitat specialization, and thermal niche width can predict the susceptibility of species to the impacts of forest fragmentation and climate change. As a result of ongoing anthropogenic change, ecological communities have reshuffled. Understanding how communities are changing requires consideration of compositional shifts in species identity and abundance and how they are related to global change. I examined the compositional change in bird communities, comparing the relative contribution of land use and climate change variables from local to regional scales over the past 25 years in the United States and Canada. Additionally, I measured how species traits may explain turnover in response to climate and land use change. Impacts from local climate and land use change on populations and communities ultimately scale up to impact species range distributions. In response, species may undergo shifts in population size, sites occupied within their range, and shifts in range extent. Niche breadth on various axes may influence the direction and magnitude of these responses. Using annual survey data on breeding birds over forty years, I characterized the relative importance of niche breadth in explaining changes in species range responses.

@article{horton_using_2022,
	title = {Using snow depth observations to provide insight into the quality of snowpack simulations for regional-scale avalanche forecasting},
	volume = {16},
	issn = {1994-0424},
	url = {https://tc.copernicus.org/articles/16/3393/2022/},
	doi = {10.5194/tc-16-3393-2022},
	abstract = {Abstract. The combination of numerical weather prediction and snowpack models has
potential to provide valuable information about snow avalanche
conditions in remote areas. However, the output of snowpack models is
sensitive to precipitation inputs, which can be difficult to verify in
mountainous regions. To examine how existing observation networks can
help interpret the accuracy of snowpack models, we compared snow depths
predicted by a weather–snowpack model chain with data from automated
weather stations and manual observations. Data from the 2020–2021 winter
were compiled for 21 avalanche forecast regions across western Canada
covering a range of climates and observation networks. To perform
regional-scale comparisons, SNOWPACK model simulations were run at
select grid points from the High-Resolution Deterministic
Prediction System (HRDPS) numerical weather prediction model to
represent conditions at treeline elevations, and observed snow depths
were upscaled to the same locations. Snow depths in the Coast Mountain
range were systematically overpredicted by the model, while snow depths
in many parts of the interior Rocky Mountain range were underpredicted.
These discrepancies had a greater impact on simulated snowpack
conditions in the interior ranges, where faceting was more sensitive to
snow depth. To put the comparisons in context, the quality of the
upscaled observations was assessed by checking whether snow depth
changes during stormy periods were consistent with the forecast
avalanche hazard. While some regions had high-quality observations,
other regions were poorly represented by available observations,
suggesting in some situations modelled snow depths could be more
reliable than observations. The analysis provides insights into the
potential for validating weather and snowpack models with readily
available observations, as well as for how avalanche forecasters can better
interpret the accuracy of snowpack simulations.},
	language = {en},
	number = {8},
	urldate = {2023-06-01},
	journal = {The Cryosphere},
	author = {Horton, Simon and Haegeli, Pascal},
	month = aug,
	year = {2022},
	keywords = {NALCMS},
	pages = {3393--3411},
}

Abstract. The combination of numerical weather prediction and snowpack models has potential to provide valuable information about snow avalanche conditions in remote areas. However, the output of snowpack models is sensitive to precipitation inputs, which can be difficult to verify in mountainous regions. To examine how existing observation networks can help interpret the accuracy of snowpack models, we compared snow depths predicted by a weather–snowpack model chain with data from automated weather stations and manual observations. Data from the 2020–2021 winter were compiled for 21 avalanche forecast regions across western Canada covering a range of climates and observation networks. To perform regional-scale comparisons, SNOWPACK model simulations were run at select grid points from the High-Resolution Deterministic Prediction System (HRDPS) numerical weather prediction model to represent conditions at treeline elevations, and observed snow depths were upscaled to the same locations. Snow depths in the Coast Mountain range were systematically overpredicted by the model, while snow depths in many parts of the interior Rocky Mountain range were underpredicted. These discrepancies had a greater impact on simulated snowpack conditions in the interior ranges, where faceting was more sensitive to snow depth. To put the comparisons in context, the quality of the upscaled observations was assessed by checking whether snow depth changes during stormy periods were consistent with the forecast avalanche hazard. While some regions had high-quality observations, other regions were poorly represented by available observations, suggesting in some situations modelled snow depths could be more reliable than observations. The analysis provides insights into the potential for validating weather and snowpack models with readily available observations, as well as for how avalanche forecasters can better interpret the accuracy of snowpack simulations.

@article{mohammadpour_vegetation_2022,
	title = {Vegetation {Mapping} with {Random} {Forest} {Using} {Sentinel} 2 and {GLCM} {Texture} {Feature}—{A} {Case} {Study} for {Lousã} {Region}, {Portugal}},
	volume = {14},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/14/18/4585},
	doi = {10.3390/rs14184585},
	abstract = {Vegetation mapping requires accurate information to allow its use in applications such as sustainable forest management against the effects of climate change and the threat of wildfires. Remote sensing provides a powerful resource of fundamental data at different spatial resolutions and spectral regions, making it an essential tool for vegetation mapping and biomass management. Due to the ever-increasing availability of free data and software, satellites have been predominantly used to map, analyze, and monitor natural resources for conservation purposes. This study aimed to map vegetation from Sentinel-2 (S2) data in a complex and mixed vegetation cover of the Lousã district in Portugal. We used ten multispectral bands with a spatial resolution of 10 m, and four vegetation indices, including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Enhanced Vegetation Index (EVI), and Soil Adjusted Vegetation Index (SAVI). After applying principal component analysis (PCA) on the 10 S2A bands, four texture features, including mean (ME), homogeneity (HO), correlation (CO), and entropy (EN), were derived for the first three principal components. Textures were obtained using the Gray-Level Co-Occurrence Matrix (GLCM). As a result, 26 independent variables were extracted from S2. After defining the land use classes using an object-based approach, the Random Forest (RF) classifier was applied. The map accuracy was evaluated by the confusion matrix, using the metrics of overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and kappa coefficient (Kappa). The described classification methodology showed a high OA of 90.5\% and kappa of 89\% for vegetation mapping. Using GLCM texture features and vegetation indices increased the accuracy by up to 2\%; however, classification using GLCM texture features and spectral bands achieved the highest OA (92\%), indicating the texture features′ capability in detecting the variability of forest species at stand level. The ME and CO showed the highest contribution to the classification accuracy among the GLCM textures. GNDVI outperformed other vegetation indices in variable importance. Moreover, using only S2A spectral bands, especially bands 11, 12, and 2, showed a high potential to classify the map with an OA of 88\%. This study showed that adding at least one GLCM texture feature and at least one vegetation index into the S2A spectral bands may effectively increase the accuracy metrics and tree species discrimination.},
	number = {18},
	journal = {Remote Sensing},
	author = {Mohammadpour, Pegah and Viegas, Domingos Xavier and Viegas, Carlos},
	month = sep,
	year = {2022},
	note = {Publisher: MDPI},
	keywords = {NALCMS},
	pages = {4585},
}

Vegetation mapping requires accurate information to allow its use in applications such as sustainable forest management against the effects of climate change and the threat of wildfires. Remote sensing provides a powerful resource of fundamental data at different spatial resolutions and spectral regions, making it an essential tool for vegetation mapping and biomass management. Due to the ever-increasing availability of free data and software, satellites have been predominantly used to map, analyze, and monitor natural resources for conservation purposes. This study aimed to map vegetation from Sentinel-2 (S2) data in a complex and mixed vegetation cover of the Lousã district in Portugal. We used ten multispectral bands with a spatial resolution of 10 m, and four vegetation indices, including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Enhanced Vegetation Index (EVI), and Soil Adjusted Vegetation Index (SAVI). After applying principal component analysis (PCA) on the 10 S2A bands, four texture features, including mean (ME), homogeneity (HO), correlation (CO), and entropy (EN), were derived for the first three principal components. Textures were obtained using the Gray-Level Co-Occurrence Matrix (GLCM). As a result, 26 independent variables were extracted from S2. After defining the land use classes using an object-based approach, the Random Forest (RF) classifier was applied. The map accuracy was evaluated by the confusion matrix, using the metrics of overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and kappa coefficient (Kappa). The described classification methodology showed a high OA of 90.5% and kappa of 89% for vegetation mapping. Using GLCM texture features and vegetation indices increased the accuracy by up to 2%; however, classification using GLCM texture features and spectral bands achieved the highest OA (92%), indicating the texture features′ capability in detecting the variability of forest species at stand level. The ME and CO showed the highest contribution to the classification accuracy among the GLCM textures. GNDVI outperformed other vegetation indices in variable importance. Moreover, using only S2A spectral bands, especially bands 11, 12, and 2, showed a high potential to classify the map with an OA of 88%. This study showed that adding at least one GLCM texture feature and at least one vegetation index into the S2A spectral bands may effectively increase the accuracy metrics and tree species discrimination.

@article{vonbank_wetland_2022,
	title = {Wetland use by {Greater} {White}-fronted {Geese} and spatial overlap with waterfowl conservation priority areas in {Mexico}},
	volume = {17},
	issn = {1712-6568},
	url = {http://www.ace-eco.org/vol17/iss2/art20/},
	doi = {10.5751/ACE-02204-170220},
	abstract = {Greater White-fronted Geese (Anser albifrons frontalis) use both agricultural and wetland habitats throughout winter with changes in use exhibited temporally and in relation to environmental and landscape factors. Currently, an unknown proportion of Greater White-fronted Geese winter in Mexico, largely along the Gulf Coast and in the Central Highlands, where information regarding wintering ecology is largely unknown. Because conservation efforts for waterfowl typically focus on wetland habitats, understanding factors influencing wetland use is imperative to developing informed conservation strategies. During winters, 2016– 2018, we used remote sensing to measure characteristics of 91 wetlands used by GPS-tagged Greater White-fronted Geese, and modeled how wetland and landscape variables influenced wetland use and selection. Our top model of wetland use indicated that larger wetlands and wetlands that were closer to other used wetlands were related to increased use. There was an interaction between wetland type and distance to agriculture, indicating that Greater White-fronted Geese exhibited increased use of emergent herbaceous/grass and woody wetland types that were in closer proximity to agriculture. Our wetland-selection model indicated that woody and emergent wetlands that were larger in size were selected at greater rates than available wetlands on the landscape. Additionally, we conducted a spatial comparison of used wetlands in this study with wetlands previously identified as important for waterfowl conservation in Mexico in the literature. Of 91 wetlands used by geese, only 7.7\% fell within wetland complexes identified as priority for waterfowl conservation or specific wetlands important to Greater White-fronted Geese by previous research, and all were within the Laguna Madre de Mexico and Rio Grande Delta regions in Tamaulipas. Wetlands in Mexico are being degraded at a rapid rate, and information such as this is important for future management and conservation-planning efforts throughout Mexico for wetland-dependent species such as the Greater White-fronted Goose.},
	number = {2},
	journal = {Avian Conservation and Ecology},
	author = {VonBank, Jay A. and Vasquez, Joshua P. and Loghry, Jason P. and Kraai, Kevin J. and Cao, Lei and Ballard, Bart M.},
	month = oct,
	year = {2022},
	note = {Publisher: Resilience Alliance},
	keywords = {NALCMS},
	pages = {art20},
}

Greater White-fronted Geese (Anser albifrons frontalis) use both agricultural and wetland habitats throughout winter with changes in use exhibited temporally and in relation to environmental and landscape factors. Currently, an unknown proportion of Greater White-fronted Geese winter in Mexico, largely along the Gulf Coast and in the Central Highlands, where information regarding wintering ecology is largely unknown. Because conservation efforts for waterfowl typically focus on wetland habitats, understanding factors influencing wetland use is imperative to developing informed conservation strategies. During winters, 2016– 2018, we used remote sensing to measure characteristics of 91 wetlands used by GPS-tagged Greater White-fronted Geese, and modeled how wetland and landscape variables influenced wetland use and selection. Our top model of wetland use indicated that larger wetlands and wetlands that were closer to other used wetlands were related to increased use. There was an interaction between wetland type and distance to agriculture, indicating that Greater White-fronted Geese exhibited increased use of emergent herbaceous/grass and woody wetland types that were in closer proximity to agriculture. Our wetland-selection model indicated that woody and emergent wetlands that were larger in size were selected at greater rates than available wetlands on the landscape. Additionally, we conducted a spatial comparison of used wetlands in this study with wetlands previously identified as important for waterfowl conservation in Mexico in the literature. Of 91 wetlands used by geese, only 7.7% fell within wetland complexes identified as priority for waterfowl conservation or specific wetlands important to Greater White-fronted Geese by previous research, and all were within the Laguna Madre de Mexico and Rio Grande Delta regions in Tamaulipas. Wetlands in Mexico are being degraded at a rapid rate, and information such as this is important for future management and conservation-planning efforts throughout Mexico for wetland-dependent species such as the Greater White-fronted Goose.

@inproceedings{ikegawa_where_2022,
	address = {Kuala Lumpur, Malaysia},
	title = {Where {Do} {Humans} {Build} {Levees}? {A} {Case} {Study} on the {Contiguous} {United} {States}},
	isbn = {978-1-6654-2792-0},
	shorttitle = {Where {Do} {Humans} {Build} {Levees}?},
	url = {https://ieeexplore.ieee.org/document/9884228/},
	doi = {10.1109/IGARSS46834.2022.9884228},
	urldate = {2023-06-09},
	booktitle = {{IGARSS} 2022 - 2022 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},
	publisher = {IEEE},
	author = {Ikegawa, M. and Hascoet, T. and Pellet, V. and Watanabe, M. and Zhou, X. and Tanaka, Y. and Takiguchi, T. and Yamazaki, D.},
	month = jul,
	year = {2022},
	keywords = {NALCMS},
	pages = {2765--2768},
}

@article{tepley_wildfire_2022,
	title = {Wildfire evacuation patterns and syndromes across {Canada}'s forested regions},
	volume = {13},
	issn = {2150-8925},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/ecs2.4255},
	doi = {10.1002/ecs2.4255},
	abstract = {Human exposure to wildfire is increasing in many regions globally—a trend likely to continue as climate change drives increases in wildfire activity and human populations continue to expand into fire-prone landscapes. In Canada, this trend is reflected by a steady increase in the annual number of wildfire evacuations since the 1980s. Evacuations can be costly and cause severe stress, even when homes remain undamaged. Because many factors driving community vulnerability are likely correlated, classifying at-risk communities into groups whose members share common drivers of wildfire vulnerability will be helpful in identifying the key wildfire evacuation “syndromes” that are repeated in different parts of the landscape. Understanding these syndromes will aid in anticipating and mitigating the effects of future fire exposure. Here, we classify the populated places across Canada's forested regions into 19 groups using variables describing their potential vulnerability to wildfire, including the surrounding land cover, land use and infrastructure, and the local fire regime. Then, we evaluate the utility of these groups by comparing actual wildfire exposure among the groups using a unique dataset of 1043 wildfire evacuations from 1980 to 2019. We identified three main evacuation syndromes that represent 79\% of all evacuations and are distinct in their geographic distribution, the characteristics of the fires that drove the evacuations, the communities exposed, and the likely mode of evacuation. In remote areas dominated by conifer forest, evacuations were driven primarily by lightning-ignited fires in the summer. Exposed communities typically lacked access to the road network, making it important to plan for evacuation by air. In less remote mixedwood forest areas, evacuations were driven largely by human-ignited fires in the spring, and most communities had access to major roads. In interior British Columbia, evacuations were mainly in the summer and driven by both lightning- and human-ignited fires. These areas experienced the greatest increase in evacuation frequency over the last two decades, reflecting the local trend of increasing wildfire activity. These differences highlight how the major risk factors vary spatially across the forested regions and temporally over the fire season—knowledge that will facilitate more effective planning for future fire seasons.},
	number = {10},
	journal = {Ecosphere},
	author = {Tepley, Alan J. and Parisien, Marc‐André and Wang, Xianli and Oliver, Jacqueline A. and Flannigan, Mike D.},
	month = oct,
	year = {2022},
	note = {Publisher: John Wiley and Sons Inc},
	keywords = {NALCMS},
}

Human exposure to wildfire is increasing in many regions globally—a trend likely to continue as climate change drives increases in wildfire activity and human populations continue to expand into fire-prone landscapes. In Canada, this trend is reflected by a steady increase in the annual number of wildfire evacuations since the 1980s. Evacuations can be costly and cause severe stress, even when homes remain undamaged. Because many factors driving community vulnerability are likely correlated, classifying at-risk communities into groups whose members share common drivers of wildfire vulnerability will be helpful in identifying the key wildfire evacuation “syndromes” that are repeated in different parts of the landscape. Understanding these syndromes will aid in anticipating and mitigating the effects of future fire exposure. Here, we classify the populated places across Canada's forested regions into 19 groups using variables describing their potential vulnerability to wildfire, including the surrounding land cover, land use and infrastructure, and the local fire regime. Then, we evaluate the utility of these groups by comparing actual wildfire exposure among the groups using a unique dataset of 1043 wildfire evacuations from 1980 to 2019. We identified three main evacuation syndromes that represent 79% of all evacuations and are distinct in their geographic distribution, the characteristics of the fires that drove the evacuations, the communities exposed, and the likely mode of evacuation. In remote areas dominated by conifer forest, evacuations were driven primarily by lightning-ignited fires in the summer. Exposed communities typically lacked access to the road network, making it important to plan for evacuation by air. In less remote mixedwood forest areas, evacuations were driven largely by human-ignited fires in the spring, and most communities had access to major roads. In interior British Columbia, evacuations were mainly in the summer and driven by both lightning- and human-ignited fires. These areas experienced the greatest increase in evacuation frequency over the last two decades, reflecting the local trend of increasing wildfire activity. These differences highlight how the major risk factors vary spatially across the forested regions and temporally over the fire season—knowledge that will facilitate more effective planning for future fire seasons.

@article{young_changing_2021,
	title = {A {Changing} {Hydrological} {Regime}: {Trends} in {Magnitude} and {Timing} of {Glacier} {Ice} {Melt} and {Glacier} {Runoff} in a {High} {Latitude} {Coastal} {Watershed}},
	volume = {57},
	issn = {0043-1397},
	url = {https://onlinelibrary.wiley.com/doi/10.1029/2020WR027404},
	doi = {10.1029/2020WR027404},
	abstract = {With a unique biogeophysical signature relative to other freshwater sources, meltwater from glaciers plays a crucial role in the hydrological and ecological regime of high latitude coastal areas. Today, as glaciers worldwide exhibit persistent negative mass balance, glacier runoff is changing in both magnitude and timing, with potential downstream impacts on infrastructure, ecosystems, and ecosystem resources. However, runoff trends may be difficult to detect in coastal systems with large precipitation variability. Here, we use the coupled energy balance and water routing model SnowModel-HydroFlow to examine changes in timing and magnitude of runoff from the western Juneau Icefield in Southeast Alaska between 1980 and 2016. We find that under sustained glacier mass loss (−0.57 ± 0.12 m w. e. a−1), several hydrological variables related to runoff show increasing trends. This includes annual and spring glacier ice melt volumes (+10\% and +16\% decade−1) which, because of higher proportions of precipitation, translate to smaller increases in glacier runoff (+3\% and +7\% decade−1) and total watershed runoff (+1.4\% and +3\% decade−1). These results suggest that the western Juneau Icefield watersheds are still in an increasing glacier runoff period prior to reaching “peak water.” In terms of timing, we find that maximum glacier ice melt is occurring earlier (2.5 days decade−1), indicating a change in the source and quality of freshwater being delivered downstream in the early summer. Our findings highlight that even in maritime climates with large precipitation variability, high latitude coastal watersheds are experiencing hydrological regime change driven by ongoing glacier mass loss.},
	number = {7},
	journal = {Water Resources Research},
	author = {Young, Joanna C. and Pettit, Erin and Arendt, Anthony and Hood, Eran and Liston, Glen E. and Beamer, Jordan},
	month = jul,
	year = {2021},
	keywords = {NALCMS},
	pages = {1--30},
}

With a unique biogeophysical signature relative to other freshwater sources, meltwater from glaciers plays a crucial role in the hydrological and ecological regime of high latitude coastal areas. Today, as glaciers worldwide exhibit persistent negative mass balance, glacier runoff is changing in both magnitude and timing, with potential downstream impacts on infrastructure, ecosystems, and ecosystem resources. However, runoff trends may be difficult to detect in coastal systems with large precipitation variability. Here, we use the coupled energy balance and water routing model SnowModel-HydroFlow to examine changes in timing and magnitude of runoff from the western Juneau Icefield in Southeast Alaska between 1980 and 2016. We find that under sustained glacier mass loss (−0.57 ± 0.12 m w. e. a−1), several hydrological variables related to runoff show increasing trends. This includes annual and spring glacier ice melt volumes (+10% and +16% decade−1) which, because of higher proportions of precipitation, translate to smaller increases in glacier runoff (+3% and +7% decade−1) and total watershed runoff (+1.4% and +3% decade−1). These results suggest that the western Juneau Icefield watersheds are still in an increasing glacier runoff period prior to reaching “peak water.” In terms of timing, we find that maximum glacier ice melt is occurring earlier (2.5 days decade−1), indicating a change in the source and quality of freshwater being delivered downstream in the early summer. Our findings highlight that even in maritime climates with large precipitation variability, high latitude coastal watersheds are experiencing hydrological regime change driven by ongoing glacier mass loss.

@inproceedings{melichar_machine_2021,
	address = {University of Arizona, Tuscon, Arizona, United States},
	title = {A {Machine} {Learning} and {Data} {Fusion} {Approach} for {Classifying} {Landsat} {OLI} {Spectral} and {Vegetation} {Dynamic} {Data} in {Support} of {Habitat} {Mapping} in the {Santa} {Ritas}},
	url = {https://www.tucson.ars.ag.gov/rise/2021/Posters/NaglerPoster.pdf},
	language = {en},
	author = {Melichar, Madeline and Barreto-Muñoz, Armando and Didan, Kamel and Duberstein, Jennie and Thomas, Kathryn and Nagler, Pamela},
	month = nov,
	year = {2021},
	keywords = {NALCMS},
}

@article{nedd_synthesis_2021,
	title = {A {Synthesis} of {Land} {Use}/{Land} {Cover} {Studies}: {Definitions}, {Classification} {Systems}, {Meta}-{Studies}, {Challenges} and {Knowledge} {Gaps} on a {Global} {Landscape}},
	volume = {10},
	issn = {2073-445X},
	shorttitle = {A {Synthesis} of {Land} {Use}/{Land} {Cover} {Studies}},
	url = {https://www.mdpi.com/2073-445X/10/9/994},
	doi = {10.3390/land10090994},
	abstract = {Land is a natural resource that humans have utilized for life and various activities. Land use/land cover change (LULCC) has been of great concern to many countries over the years. Some of the main reasons behind LULCC are rapid population growth, migration, and the conversion of rural to urban areas. LULC has a considerable impact on the land-atmosphere/climate interactions. Over the past two decades, numerous studies conducted in LULC have investigated various areas of the field of LULC. However, the assemblage of information is missing for some aspects. Therefore, to provide coherent guidance, a literature review to scrutinize and evaluate many studies in particular topical areas is employed. This research study collected approximately four hundred research articles and investigated five (5) areas of interest, including (1) LULC definitions; (2) classification systems used to classify LULC globally; (3) direct and indirect changes of meta-studies associated with LULC; (4) challenges associated with LULC; and (5) LULC knowledge gaps. The synthesis revealed that LULC definitions carried vital terms, and classification systems for LULC are at the national, regional, and global scales. Most meta-studies for LULC were in the categories of direct and indirect land changes. Additionally, the analysis showed significant areas of LULC challenges were data consistency and quality. The knowledge gaps highlighted a fall in the categories of ecosystem services, forestry, and data/image modeling in LULC. Core findings exhibit common patterns, discrepancies, and relationships from the multiple studies. While literature review as a tool showed similarities among various research studies, our results recommend researchers endeavor to perform further synthesis in the field of LULC to promote our overall understanding, since research investigations will continue in LULC.},
	language = {en},
	number = {9},
	urldate = {2023-06-15},
	journal = {Land},
	author = {Nedd, Ryan and Light, Katie and Owens, Marcia and James, Neil and Johnson, Elijah and Anandhi, Aavudai},
	month = sep,
	year = {2021},
	keywords = {NALCMS},
	pages = {994},
}

Land is a natural resource that humans have utilized for life and various activities. Land use/land cover change (LULCC) has been of great concern to many countries over the years. Some of the main reasons behind LULCC are rapid population growth, migration, and the conversion of rural to urban areas. LULC has a considerable impact on the land-atmosphere/climate interactions. Over the past two decades, numerous studies conducted in LULC have investigated various areas of the field of LULC. However, the assemblage of information is missing for some aspects. Therefore, to provide coherent guidance, a literature review to scrutinize and evaluate many studies in particular topical areas is employed. This research study collected approximately four hundred research articles and investigated five (5) areas of interest, including (1) LULC definitions; (2) classification systems used to classify LULC globally; (3) direct and indirect changes of meta-studies associated with LULC; (4) challenges associated with LULC; and (5) LULC knowledge gaps. The synthesis revealed that LULC definitions carried vital terms, and classification systems for LULC are at the national, regional, and global scales. Most meta-studies for LULC were in the categories of direct and indirect land changes. Additionally, the analysis showed significant areas of LULC challenges were data consistency and quality. The knowledge gaps highlighted a fall in the categories of ecosystem services, forestry, and data/image modeling in LULC. Core findings exhibit common patterns, discrepancies, and relationships from the multiple studies. While literature review as a tool showed similarities among various research studies, our results recommend researchers endeavor to perform further synthesis in the field of LULC to promote our overall understanding, since research investigations will continue in LULC.

@article{stefanuk_accumulated_2021,
	title = {Accumulated {Heating} and {Chilling} {Are} {Important} {Drivers} of {Forest} {Phenology} and {Productivity} in the {Algonquin}-to-{Adirondacks} {Conservation} {Corridor} of {Eastern} {North} {America}},
	volume = {12},
	issn = {1999-4907},
	url = {https://www.mdpi.com/1999-4907/12/3/282},
	doi = {10.3390/f12030282},
	abstract = {Research Highlights: Forest phenology and productivity were responsive to seasonal heating and chilling accumulation, but responses differed across the temperature range. Background and Objectives: Temperate forests have responded to recent climate change worldwide, but the pattern and magnitude of response have varied, necessitating additional studies at higher spatial and temporal resolutions. We investigated climatic drivers of inter-annual variation in forest phenology and productivity across the Algonquin-to-Adirondacks (A2A) conservation corridor of eastern North America. Methods: We used remotely sensed indices from the AVHRR sensor series and a suite of gridded climate data from the Daymet database spanning from 1989–2014. We used random forest regression to characterize forest–climate relationships between forest growth indices and climatological variables. Results: A large portion of the annual variation in phenology and productivity was explained by climate (pR2 {\textgreater} 80\%), with variation largely driven by accumulated heating and chilling degree days. Only very minor relationships with precipitation-related variables were evident. Conclusions: Our results indicate that anthropogenic climate change in the A2A has not yet reached the point of triggering widespread changes in forest phenology and productivity, but the sensitivity of forest growth to inter-annual variation in seasonal temperature accumulation suggests that more temperate forest area will be affected by climate change as warming continues.},
	language = {en},
	number = {3},
	urldate = {2023-06-15},
	journal = {Forests},
	author = {Stefanuk, Michael A. and Danby, Ryan K.},
	month = mar,
	year = {2021},
	keywords = {NALCMS},
	pages = {282},
}

Research Highlights: Forest phenology and productivity were responsive to seasonal heating and chilling accumulation, but responses differed across the temperature range. Background and Objectives: Temperate forests have responded to recent climate change worldwide, but the pattern and magnitude of response have varied, necessitating additional studies at higher spatial and temporal resolutions. We investigated climatic drivers of inter-annual variation in forest phenology and productivity across the Algonquin-to-Adirondacks (A2A) conservation corridor of eastern North America. Methods: We used remotely sensed indices from the AVHRR sensor series and a suite of gridded climate data from the Daymet database spanning from 1989–2014. We used random forest regression to characterize forest–climate relationships between forest growth indices and climatological variables. Results: A large portion of the annual variation in phenology and productivity was explained by climate (pR2 \textgreater 80%), with variation largely driven by accumulated heating and chilling degree days. Only very minor relationships with precipitation-related variables were evident. Conclusions: Our results indicate that anthropogenic climate change in the A2A has not yet reached the point of triggering widespread changes in forest phenology and productivity, but the sensitivity of forest growth to inter-annual variation in seasonal temperature accumulation suggests that more temperate forest area will be affected by climate change as warming continues.

@phdthesis{congram_assessing_2021,
	address = {Ontario, Canada},
	type = {Master of {Science}},
	title = {Assessing the environmental correlates of a lethal amphibian pathogen},
	url = {https://digitalcollections.trentu.ca/objects/etd-970},
	school = {Trent University},
	author = {Congram, M},
	month = sep,
	year = {2021},
	keywords = {NALCMS},
}

@article{pouliot_assessment_2021,
	title = {Assessment of {Landsat} {Based} {Deep}-{Learning} {Membership} {Analysis} for {Development} of from–to {Change} {Time} {Series} in the {Prairie} {Region} of {Canada} from 1984 to 2018},
	volume = {13},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/13/4/634},
	doi = {10.3390/rs13040634},
	abstract = {The prairie region of Canada is a dynamically changing landscape in relation to past and present anthropogenic activities and recent climate change. Improving our understanding of the rate, timing, and distribution of landscape change is needed to determine the impact on wildlife populations and biodiversity, ultimately leading to better-informed management regarding requirements for habitat amount and its connectedness. In this research, we assessed the viability of an approach to detect from–to class changes designed to be scalable to the prairie region with the capacity for local refinement. It employed a deep-learning convolutional neural network to model general land covers and examined class memberships to identify land-cover conversions. For this implementation, eight land-cover categories were derived from the Agriculture and Agri-Food Canada Annual Space-Based Crop Inventory. Change was assessed in three study areas that contained different mixes of grassland, pasture, and forest cover. Results showed that the deep-learning method produced the highest accuracy across all classes relative to an implementation of random forest that included some first-order texture measures. Overall accuracy was 4\% greater with the deep-learning classifier and class accuracies were more balanced. Evaluation of change accuracy suggested good performance for many conversions such as grassland to crop, forest to crop, water to dryland covers, and most bare/developed-related changes. Changes involving pasture with grassland or cropland were more difficult to detect due to spectral confusion among classes. Similarly, conversion to forests in some cases was poorly detected due to gradual and subtle change characteristics combined with confusion between forest, shrub, and croplands. The proposed framework involved several processing steps that can be explored to enhance the thematic content and accuracy for large regional implementation. Evaluation for understanding connectivity in natural land covers and related declines in species at risk is planned for future research.},
	number = {4},
	journal = {Remote Sensing},
	author = {Pouliot, Darren and Alavi, Niloofar and Wilson, Scott and Duffe, Jason and Pasher, Jon and Davidson, Andrew and Daneshfar, Bahram and Lindsay, Emily},
	month = feb,
	year = {2021},
	keywords = {NALCMS},
	pages = {634},
}

The prairie region of Canada is a dynamically changing landscape in relation to past and present anthropogenic activities and recent climate change. Improving our understanding of the rate, timing, and distribution of landscape change is needed to determine the impact on wildlife populations and biodiversity, ultimately leading to better-informed management regarding requirements for habitat amount and its connectedness. In this research, we assessed the viability of an approach to detect from–to class changes designed to be scalable to the prairie region with the capacity for local refinement. It employed a deep-learning convolutional neural network to model general land covers and examined class memberships to identify land-cover conversions. For this implementation, eight land-cover categories were derived from the Agriculture and Agri-Food Canada Annual Space-Based Crop Inventory. Change was assessed in three study areas that contained different mixes of grassland, pasture, and forest cover. Results showed that the deep-learning method produced the highest accuracy across all classes relative to an implementation of random forest that included some first-order texture measures. Overall accuracy was 4% greater with the deep-learning classifier and class accuracies were more balanced. Evaluation of change accuracy suggested good performance for many conversions such as grassland to crop, forest to crop, water to dryland covers, and most bare/developed-related changes. Changes involving pasture with grassland or cropland were more difficult to detect due to spectral confusion among classes. Similarly, conversion to forests in some cases was poorly detected due to gradual and subtle change characteristics combined with confusion between forest, shrub, and croplands. The proposed framework involved several processing steps that can be explored to enhance the thematic content and accuracy for large regional implementation. Evaluation for understanding connectivity in natural land covers and related declines in species at risk is planned for future research.

@techreport{bgc_engineering_inc_cariboo_2021,
	title = {Cariboo {Regional} {District} {Flood} {Hazard} {Assessment}},
	url = {https://www.fraserbasin.bc.ca/_Library/TR_Flood/CRD_Flood_Hazard_Assessment_2021_Final_Web.pdf},
	institution = {Frasier Basin Council},
	author = {{BGC Engineering Inc.}},
	month = jun,
	year = {2021},
	keywords = {NALCMS},
	pages = {120},
}

@article{zylstra_changes_2021,
	title = {Changes in climate drive recent monarch butterfly dynamics},
	volume = {5},
	issn = {2397-334X},
	url = {https://www.nature.com/articles/s41559-021-01504-1},
	doi = {10.1038/s41559-021-01504-1},
	language = {en},
	number = {10},
	urldate = {2023-06-15},
	journal = {Nature Ecology \& Evolution},
	author = {Zylstra, Erin R. and Ries, Leslie and Neupane, Naresh and Saunders, Sarah P. and Ramírez, M. Isabel and Rendón-Salinas, Eduardo and Oberhauser, Karen S. and Farr, Matthew T. and Zipkin, Elise F.},
	month = jul,
	year = {2021},
	keywords = {NALCMS},
	pages = {1441--1452},
}

@article{yao_contrasting_2021,
	title = {Contrasting stream water temperature responses to global change in the {Mid}-{Atlantic} {Region} of the {United} {States}: {A} process-based modeling study},
	volume = {601},
	issn = {00221694},
	shorttitle = {Contrasting stream water temperature responses to global change in the {Mid}-{Atlantic} {Region} of the {United} {States}},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169421006818},
	doi = {10.1016/j.jhydrol.2021.126633},
	language = {en},
	urldate = {2023-06-15},
	journal = {Journal of Hydrology},
	author = {Yao, Yuanzhi and Tian, Hanqin and Kalin, Latif and Pan, Shufen and Friedrichs, Marjorie A.M. and Wang, Jing and Li, Ya},
	month = oct,
	year = {2021},
	keywords = {NALCMS},
	pages = {126633},
}

@incollection{heung_digital_2021,
	title = {Digital {Soil} {Mapping}},
	url = {https://openpress.usask.ca/soilscience/chapter/digital-soil-mapping/},
	language = {en},
	urldate = {2023-06-08},
	booktitle = {Digging into {Canadian} {Soils}},
	publisher = {Canadian Society of Soil Science},
	author = {Heung, Brandon and Saurette, Daniel and Bulmer, Chuck E.},
	month = aug,
	year = {2021},
	keywords = {NALCMS},
}

@article{gosal_drivers_2021,
	title = {Do drivers of nature visitation vary spatially? {The} importance of context for understanding visitation of nature areas in {Europe} and {North} {America}},
	volume = {776},
	issn = {00489697},
	url = {https://doi.org/10.1016/j.scitotenv.2021.145190},
	doi = {10.1016/j.scitotenv.2021.145190},
	abstract = {Nature visitation is important, both culturally and economically. Given the contribution of nature recreation to multiple societal goals, comprehending determinants of nature visitation is essential to understand the drivers associated with the popularity of nature areas, for example, to inform land-use planning or site management strategies to maximise benefits. Understanding the factors related to nature, tourism and recreation can support the management of nature areas and thereby, also conservation efforts and biodiversity protection. This study applied a Multiscale Geographically Weighted Regression (MGWR) to quantify the spatially varying influence of different factors associated with nature visitation in Europe and North America. Results indicated that some explanatory variables were stationary for all sites (age 15 to 65, population density (within 25 km), GDP, area, built-up areas, plateaus, and mountains). In contrast, others exhibited significant spatial non-stationarity (locally variable): needle-leaf trees (conifers), trails, travel time, roads, and Red List birds and amphibians. Needle-leaf trees and travel time were found to be negatively significant in Europe. Roads were found to have a significant positive effect in North America. Trails and Red List bird species were found to have a positive effect in both North America and North Europe, with a greater effect in Europe. Red List amphibians was the only spatially variable predictor to have both a positive and negative impact, with selected sites in North America and northern Europe being positive, whereas Iceland and central and southern Europe were negative. The scale of the response-predictor relationship (bandwidth) of these locally variable predictors was smallest for Red List amphibians at 1033 km, with all other spatially variable predictors between 9558 and 12,285 km. The study demonstrates the contribution that MGWR, a spatially explicit model, can make to support a deeper understanding of processes associated with nature visitation in different geographic contexts.},
	journal = {Science of The Total Environment},
	author = {Gosal, Arjan S. and Giannichi, Marta Lisli and Beckmann, Michael and Comber, Alexis and Massenberg, Julian R. and Palliwoda, Julia and Roddis, Philippa and Schägner, Jan Philipp and Wilson, Jamie and Ziv, Guy},
	month = jul,
	year = {2021},
	pmid = {33639459},
	note = {Publisher: The Authors},
	keywords = {NALCMS},
	pages = {145190},
}

Nature visitation is important, both culturally and economically. Given the contribution of nature recreation to multiple societal goals, comprehending determinants of nature visitation is essential to understand the drivers associated with the popularity of nature areas, for example, to inform land-use planning or site management strategies to maximise benefits. Understanding the factors related to nature, tourism and recreation can support the management of nature areas and thereby, also conservation efforts and biodiversity protection. This study applied a Multiscale Geographically Weighted Regression (MGWR) to quantify the spatially varying influence of different factors associated with nature visitation in Europe and North America. Results indicated that some explanatory variables were stationary for all sites (age 15 to 65, population density (within 25 km), GDP, area, built-up areas, plateaus, and mountains). In contrast, others exhibited significant spatial non-stationarity (locally variable): needle-leaf trees (conifers), trails, travel time, roads, and Red List birds and amphibians. Needle-leaf trees and travel time were found to be negatively significant in Europe. Roads were found to have a significant positive effect in North America. Trails and Red List bird species were found to have a positive effect in both North America and North Europe, with a greater effect in Europe. Red List amphibians was the only spatially variable predictor to have both a positive and negative impact, with selected sites in North America and northern Europe being positive, whereas Iceland and central and southern Europe were negative. The scale of the response-predictor relationship (bandwidth) of these locally variable predictors was smallest for Red List amphibians at 1033 km, with all other spatially variable predictors between 9558 and 12,285 km. The study demonstrates the contribution that MGWR, a spatially explicit model, can make to support a deeper understanding of processes associated with nature visitation in different geographic contexts.

@article{graham_phylogeny_2021,
	title = {Do phylogeny and habitat influence admixture among four {North} {American} chickadee (family: {Paridae}) species?},
	volume = {52},
	issn = {0908-8857, 1600-048X},
	shorttitle = {Do phylogeny and habitat influence admixture among four {North} {American} chickadee (family},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/jav.02695},
	doi = {10.1111/jav.02695},
	language = {en},
	number = {5},
	urldate = {2023-06-15},
	journal = {Journal of Avian Biology},
	author = {Graham, Brendan A. and Gazeley, Ian and Otter, Ken A. and Burg, Theresa},
	month = may,
	year = {2021},
	keywords = {NALCMS},
	pages = {jav.02695},
}

@phdthesis{garcia_downscaling_2021,
	type = {Master of {Science}},
	title = {Downscaling of {GOES}-16's land surface temperature product using epitomes},
	url = {https://scholarworks.utep.edu/open_etd/3260},
	school = {Electrical Engineering, University of Texas - El Paso},
	author = {Garcia, R.},
	year = {2021},
	keywords = {NALCMS},
}

@article{hafen_environmental_2021,
	title = {Environmental {Conditions} {Associated} with {Occurrences} of the {Threatened} {Yaqui} {Catfish} in the {Yaqui} {River} {Basin}, {Mexico}},
	volume = {41},
	issn = {0275-5947},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/nafm.10653},
	doi = {10.1002/nafm.10653},
	abstract = {The Yaqui Catfish Ictalurus pricei is an understudied species, with limited information available on its ecology, distribution, and local habitat use. Native to the southwestern United States and northwestern Mexico, Yaqui Catfish populations are declining, which has prompted listing of the species as threatened in the United States and as a species of concern in Mexico. Water overallocation, habitat degradation, invasive species introductions, and hybridization with nonnative Channel Catfish I. punctatus have caused the populations in Mexico to decline. The United States population collapsed after years of low recruitment. To better focus conservation efforts as well as define habitat associated with Yaqui Catfish occurrences, we assessed the distribution in the Yaqui River basin of Mexico by using historical data at a landscape scale. Yaqui Catfish were historically found across the watershed among a diversity of environments but were most frequently associated with small, intermittent streams. Basin land cover was dominated by forest, shrubland, and grassland, and Yaqui Catfish generally occurred in stream segments at similar proportions. However, a small number of Yaqui Catfish occurrences were associated with urban and cropland land cover types in proportions greater than the availability of those categories on the landscape. With the species facing declines in the region, this work will help to inform future conservation efforts aimed at securing the Yaqui Catfish, protecting suitable habitat, and better defining its current status in Mexico.},
	number = {S1},
	journal = {North American Journal of Fisheries Management},
	author = {Hafen, Thomas and Taylor, Andrew T. and Hendrickson, Dean A. and Stewart, David R. and Long, James M.},
	month = oct,
	year = {2021},
	keywords = {NALCMS},
	pages = {S54--S63},
}

The Yaqui Catfish Ictalurus pricei is an understudied species, with limited information available on its ecology, distribution, and local habitat use. Native to the southwestern United States and northwestern Mexico, Yaqui Catfish populations are declining, which has prompted listing of the species as threatened in the United States and as a species of concern in Mexico. Water overallocation, habitat degradation, invasive species introductions, and hybridization with nonnative Channel Catfish I. punctatus have caused the populations in Mexico to decline. The United States population collapsed after years of low recruitment. To better focus conservation efforts as well as define habitat associated with Yaqui Catfish occurrences, we assessed the distribution in the Yaqui River basin of Mexico by using historical data at a landscape scale. Yaqui Catfish were historically found across the watershed among a diversity of environments but were most frequently associated with small, intermittent streams. Basin land cover was dominated by forest, shrubland, and grassland, and Yaqui Catfish generally occurred in stream segments at similar proportions. However, a small number of Yaqui Catfish occurrences were associated with urban and cropland land cover types in proportions greater than the availability of those categories on the landscape. With the species facing declines in the region, this work will help to inform future conservation efforts aimed at securing the Yaqui Catfish, protecting suitable habitat, and better defining its current status in Mexico.

@techreport{schoeneberg_exploring_2021,
	title = {Exploring the {Strength} and {Limitations} of {PCIC}'s {CMIP5} {Hydrologic} {Scenarios}},
	url = {https://pacificclimate.org/news-and-events/news/2021/new-report-exploring-strength-and-limitations-pcic’s-cmip5-hydrologic-scenarios},
	institution = {Pacific Climate Impacts Consortium},
	author = {Schoeneberg, Arelia T. and Schnorbus, Markus A.},
	year = {2021},
	keywords = {NALCMS},
	pages = {42},
}

@article{tsuruta_exploring_2021,
	title = {Exploring the operational impacts of climate change and glacier loss in the upper {Columbia} {River} {Basin}, {Canada}},
	volume = {35},
	issn = {0885-6087, 1099-1085},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.14253},
	doi = {10.1002/hyp.14253},
	language = {en},
	number = {7},
	urldate = {2023-06-15},
	journal = {Hydrological Processes},
	author = {Tsuruta, Kai and Schnorbus, Markus A.},
	month = jul,
	year = {2021},
	keywords = {NALCMS},
}

@techreport{schoeneberg_future_2021,
	title = {Future {Design} {Flood} {Values} in the {Upper} {Fraser} {River} {Basin} {Using} the {CanESM2}-{LE} {\textbar} {Pacific} {Climate} {Impacts} {Consortium}},
	url = {https://pacificclimate.org/resources/publications/future-design-flood-values-upper-fraser-river-basin-using-canesm2-le},
	urldate = {2025-01-28},
	institution = {Pacific Climate Impacts Consortium},
	author = {Schoeneberg, Arelia T. and Sun, Qiaohong and Schnorbus, Markus A.},
	month = mar,
	year = {2021},
	keywords = {NALCMS},
}

@techreport{sims_good_2021,
	address = {Bonn, Germany},
	title = {Good {Practice} {Guidance}. {SDG} {Indicator} 15.3.1, {Proportion} of {Land} {That} {Is} {Degraded} {Over} {Total} {Land} {Area}. {Version} 2.0},
	url = {https://www.unccd.int/resources/manuals-and-guides/good-practice-guidance-sdg-indicator-1531-proportion-land-degraded},
	institution = {United Nations Convention to Combat Desertification},
	author = {Sims, N.C. and Newnham, G.J. and England, J.R. and {Guerschmanm J.} and Cox, S.J.D. and Roxburgh, S.H. and Viscarra Rossel, R.A. and Fritz, S. and Wheeler, I.},
	year = {2021},
	keywords = {NALCMS},
}

@article{mai_great_2021,
	title = {Great {Lakes} {Runoff} {Intercomparison} {Project} {Phase} 3: {Lake} {Erie} ({GRIP}-{E})},
	volume = {26},
	issn = {1084-0699, 1943-5584},
	shorttitle = {Great {Lakes} {Runoff} {Intercomparison} {Project} {Phase} 3},
	url = {https://ascelibrary.org/doi/10.1061/%28ASCE%29HE.1943-5584.0002097},
	doi = {10.1061/(ASCE)HE.1943-5584.0002097},
	language = {en},
	number = {9},
	urldate = {2023-06-15},
	journal = {Journal of Hydrologic Engineering},
	author = {Mai, Juliane and Tolson, Bryan A. and Shen, Hongren and Gaborit, Étienne and Fortin, Vincent and Gasset, Nicolas and Awoye, Hervé and Stadnyk, Tricia A. and Fry, Lauren M. and Bradley, Emily A. and Seglenieks, Frank and Temgoua, André G. T. and Princz, Daniel G. and Gharari, Shervan and Haghnegahdar, Amin and Elshamy, Mohamed E. and Razavi, Saman and Gauch, Martin and Lin, Jimmy and Ni, Xiaojing and Yuan, Yongping and McLeod, Meghan and Basu, Nandita B. and Kumar, Rohini and Rakovec, Oldrich and Samaniego, Luis and Attinger, Sabine and Shrestha, Narayan K. and Daggupati, Prasad and Roy, Tirthankar and Wi, Sungwook and Hunter, Tim and Craig, James R. and Pietroniro, Alain},
	month = sep,
	year = {2021},
	keywords = {NALCMS},
	pages = {05021020},
}

@article{mitchell_identifying_2021,
	title = {Identifying key ecosystem service providing areas to inform national-scale conservation planning},
	volume = {16},
	issn = {1748-9326},
	url = {https://iopscience.iop.org/article/10.1088/1748-9326/abc121},
	doi = {10.1088/1748-9326/abc121},
	abstract = {Abstract
            
              Effectively conserving ecosystem services in order to maintain human wellbeing is a global need that requires an understanding of where ecosystem services are produced by ecosystems and where people benefit from these services. However, approaches to effectively identify key locations that have the capacity to supply ecosystem services and actually contribute to meeting human demand for those services are lacking at broad spatial scales. We developed new methods that integrate measures of the capacity of ecosystems to provide services with indicators of human demand and ability to access these services. We then identified important areas for three ecosystem services currently central to protected area management in Canada—carbon storage, freshwater, and nature-based recreation—and evaluated how these hotspots align with Canada’s current protected areas and resource development tenures. We find that locations of ecosystem service capacity overlap only weakly (27–36\%) with actual service providing areas (incorporating human access and demand). Overlapping hotspots of provision for multiple ecosystem services are also extremely limited across Canada; only 1.2\% (∼56 000 km
              2
              ) of the total ecosystem service hotspot area in Canada consists of overlap between all three ecosystem services. Canada’s current protected area network also targets service capacity to a greater degree than provision. Finally, one-half to two-thirds of current ecosystem service hotspots (54–66\%) overlap with current and planned resource extraction activities. Our analysis demonstrates how to identify areas where conservation and ecosystem service management actions should be focused to more effectively target ecosystem services to ensure that critical areas for ecosystem services that directly benefit people are conserved. Further development of these methods at national scales to assess ecosystem service capacity and demand and integrate this with conventional biodiversity and conservation planning information will help ensure that both biodiversity and ecosystem services are effectively safeguarded.},
	number = {1},
	urldate = {2023-06-15},
	journal = {Environmental Research Letters},
	author = {Mitchell, Matthew G E and Schuster, Richard and Jacob, Aerin L and Hanna, Dalal E L and Dallaire, Camille Ouellet and Raudsepp-Hearne, Ciara and Bennett, Elena M and Lehner, Bernhard and Chan, Kai M A},
	month = jan,
	year = {2021},
	keywords = {NALCMS},
	pages = {014038},
}

Abstract Effectively conserving ecosystem services in order to maintain human wellbeing is a global need that requires an understanding of where ecosystem services are produced by ecosystems and where people benefit from these services. However, approaches to effectively identify key locations that have the capacity to supply ecosystem services and actually contribute to meeting human demand for those services are lacking at broad spatial scales. We developed new methods that integrate measures of the capacity of ecosystems to provide services with indicators of human demand and ability to access these services. We then identified important areas for three ecosystem services currently central to protected area management in Canada—carbon storage, freshwater, and nature-based recreation—and evaluated how these hotspots align with Canada’s current protected areas and resource development tenures. We find that locations of ecosystem service capacity overlap only weakly (27–36%) with actual service providing areas (incorporating human access and demand). Overlapping hotspots of provision for multiple ecosystem services are also extremely limited across Canada; only 1.2% (∼56 000 km 2 ) of the total ecosystem service hotspot area in Canada consists of overlap between all three ecosystem services. Canada’s current protected area network also targets service capacity to a greater degree than provision. Finally, one-half to two-thirds of current ecosystem service hotspots (54–66%) overlap with current and planned resource extraction activities. Our analysis demonstrates how to identify areas where conservation and ecosystem service management actions should be focused to more effectively target ecosystem services to ensure that critical areas for ecosystem services that directly benefit people are conserved. Further development of these methods at national scales to assess ecosystem service capacity and demand and integrate this with conventional biodiversity and conservation planning information will help ensure that both biodiversity and ecosystem services are effectively safeguarded.

@article{sapena_identifying_2021,
	title = {Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: {Simulation} and analysis},
	volume = {35},
	issn = {1365-8816, 1362-3087},
	shorttitle = {Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics},
	url = {https://www.tandfonline.com/doi/full/10.1080/13658816.2020.1817463},
	doi = {10.1080/13658816.2020.1817463},
	language = {en},
	number = {2},
	urldate = {2023-06-15},
	journal = {International Journal of Geographical Information Science},
	author = {Sapena, Marta and Ruiz, Luis A.},
	month = feb,
	year = {2021},
	keywords = {NALCMS},
	pages = {375--396},
}

@techreport{fournier_illinois_2021,
	title = {Illinois {Waterfowl} {Surveys} and {Investigations} {W}-43-{R}-68 {Annual} {Report} {FY2021}},
	copyright = {This document is a product of the Illinois Natural History Survey, and has been selected and made available by the Illinois Natural History Survey and the University Library, University of Illinois at Urbana-Champaign. It is intended solely for noncommercial research and educational use, and proper attribution is requested.},
	url = {https://hdl.handle.net/2142/111628},
	abstract = {We will aerially identify and enumerate ducks, geese, swans, and select other waterbirds at selected sites in the Illinois and central Mississippi river valleys of Illinois during autumn and early winter and summarize and distribute these data to conservation partners and the general public. Specifically, we will:
• Identify and enumerate waterfowl and select other waterbirds along the Illinois and central Mississippi rivers of Illinois during autumn (≥40 sites) and spring migration (≥40 sites) using light aircraft,
• Compute annual use-days and peak abundances for observed species and compare with long-term averages,
• Provide general inference regarding the distribution of waterfowl in space and time relative to habitat conditions, and
• Summarize and distribute these data to agency personnel, research collaborators, the scientific community, and the general public through popular articles, oral presentations, technical reports, peer-reviewed publications, and other means.},
	urldate = {2023-06-16},
	author = {Fournier, Auriel M. V. and Yetter, Aaron P. and Gilbert, Andrew D. and Osborn, Joshua M. and Kross, Chelsea S. and Askren, Ryan J. and Beach, Cheyenne R. and Hagy, Heath M. and Ward, Michael P.},
	month = sep,
	year = {2021},
	note = {Publisher: Illinois Natural History Survey},
	keywords = {NALCMS},
}

We will aerially identify and enumerate ducks, geese, swans, and select other waterbirds at selected sites in the Illinois and central Mississippi river valleys of Illinois during autumn and early winter and summarize and distribute these data to conservation partners and the general public. Specifically, we will: • Identify and enumerate waterfowl and select other waterbirds along the Illinois and central Mississippi rivers of Illinois during autumn (≥40 sites) and spring migration (≥40 sites) using light aircraft, • Compute annual use-days and peak abundances for observed species and compare with long-term averages, • Provide general inference regarding the distribution of waterfowl in space and time relative to habitat conditions, and • Summarize and distribute these data to agency personnel, research collaborators, the scientific community, and the general public through popular articles, oral presentations, technical reports, peer-reviewed publications, and other means.

@article{rastandeh_incorporating_2021,
	title = {Incorporating social values and wildlife habitats for biodiversity conservation modeling in landscapes of the {Great} {Plains}},
	volume = {36},
	issn = {0921-2973, 1572-9761},
	url = {http://link.springer.com/10.1007/s10980-020-01190-7},
	doi = {10.1007/s10980-020-01190-7},
	language = {en},
	number = {4},
	urldate = {2023-06-15},
	journal = {Landscape Ecology},
	author = {Rastandeh, Amin and Jarchow, Meghann and Carnes, Morgan},
	month = apr,
	year = {2021},
	keywords = {NALCMS},
	pages = {1137--1160},
}

@phdthesis{radwan_land_2021,
	type = {Doctoral {Dissertation}},
	title = {Land cover change from {National} to global scales: {A} spatiotemporal assessment of trajectories, transitions and drivers},
	url = {https://eprints.lancs.ac.uk/id/eprint/164522/},
	school = {Lancaster University},
	author = {Radwan, Taher Mohamed Ali},
	year = {2021},
	keywords = {NALCMS},
}

@article{touati_landscape_2021,
	title = {Landscape {Freeze}/{Thaw} {Mapping} from {Active} and {Passive} {Microwave} {Earth} {Observations} over the {Tursujuq} {National} {Park}, {Quebec}, {Canada}},
	volume = {28},
	issn = {1195-6860},
	url = {https://www.tandfonline.com/doi/full/10.1080/11956860.2021.1969790},
	doi = {10.1080/11956860.2021.1969790},
	number = {3-4},
	journal = {Écoscience},
	author = {Touati, Cheima and Ratsimbazafy, Tahiana and Poulin, Jimmy and Bernier, Monique and Homayouni, Saeid and Ludwig, Ralf},
	month = oct,
	year = {2021},
	note = {Publisher: Taylor \& Francis},
	keywords = {NALCMS},
	pages = {421--433},
}

@article{mccabe_landscape_2021,
	title = {Landscape cover type, not social dominance, is associated with the winter movement patterns of {Snowy} {Owls} in temperate areas},
	volume = {138},
	issn = {0004-8038},
	url = {https://academic.oup.com/auk/article/doi/10.1093/ornithology/ukaa082/6159444},
	doi = {10.1093/ornithology/ukaa082},
	abstract = {Migrating animals occur along a continuum from species that spend the nonbreeding season at a fixed location to species that are nomadic during the nonbreeding season, essentially continuously moving. Such variation is likely driven by the economics of territoriality or heterogeneity in the environment. The Snowy Owl (Bubo scandiacus) is known for its complex seasonal movements, and thus an excellent model to test these ideas, as many individuals travel unpredictably along irregular routes during both the breeding and nonbreeding seasons. Two possible explanations for this large variation in the propensity to move are (1) dominance hierarchies in which dominant individuals (adult females in this case) monopolize some key, consistent resources, and move less than subdominants; and (2) habitat heterogeneity in which individuals foraging in rich and less heterogenic environments are less mobile. We analyzed fine-scale telemetry data (global positioning system [GPS]/global system for mobile communication [GSM]) from 50 Snowy Owls tagged in eastern and central North America from 2013 to 2019, comparing space use during the winter period according to sex and age, and to land cover attributes. We used variograms to classify individuals as nomadic (58\%) or range-resident (42\%), and found that nomadic owls had ten times larger wintering areas than range-resident owls. The frequency of nomadism was similar in socially-dominant adult females, immatures, and males. However, nomadism increased from west to east, and north to south, and was positively associated with the use of water and negatively associated with croplands. We conclude that many individual Snowy Owls in Eastern North America are nomadic during the nonbreeding season and that movement patterns during this time are driven primarily by extrinsic factors, specifically heterogeneity in habitat and prey availability, as opposed to intrinsic factors associated with spacing behavior, such as age and sex.},
	number = {2},
	journal = {Ornithology},
	author = {McCabe, Rebecca A. and Therrien, Jean-françois and Wiebe, Karen L. and Gauthier, Gilles and Brinker, David and Weidensaul, Scott and Elliott, Kyle},
	month = may,
	year = {2021},
	keywords = {NALCMS},
	pages = {1--12},
}

Migrating animals occur along a continuum from species that spend the nonbreeding season at a fixed location to species that are nomadic during the nonbreeding season, essentially continuously moving. Such variation is likely driven by the economics of territoriality or heterogeneity in the environment. The Snowy Owl (Bubo scandiacus) is known for its complex seasonal movements, and thus an excellent model to test these ideas, as many individuals travel unpredictably along irregular routes during both the breeding and nonbreeding seasons. Two possible explanations for this large variation in the propensity to move are (1) dominance hierarchies in which dominant individuals (adult females in this case) monopolize some key, consistent resources, and move less than subdominants; and (2) habitat heterogeneity in which individuals foraging in rich and less heterogenic environments are less mobile. We analyzed fine-scale telemetry data (global positioning system [GPS]/global system for mobile communication [GSM]) from 50 Snowy Owls tagged in eastern and central North America from 2013 to 2019, comparing space use during the winter period according to sex and age, and to land cover attributes. We used variograms to classify individuals as nomadic (58%) or range-resident (42%), and found that nomadic owls had ten times larger wintering areas than range-resident owls. The frequency of nomadism was similar in socially-dominant adult females, immatures, and males. However, nomadism increased from west to east, and north to south, and was positively associated with the use of water and negatively associated with croplands. We conclude that many individual Snowy Owls in Eastern North America are nomadic during the nonbreeding season and that movement patterns during this time are driven primarily by extrinsic factors, specifically heterogeneity in habitat and prey availability, as opposed to intrinsic factors associated with spacing behavior, such as age and sex.

@phdthesis{cabrera-cruz_nocturnal_2021,
	type = {Doctoral {Dissertation}},
	title = {Nocturnal light pollution and land bird migration},
	url = {https://udspace.udel.edu/items/eecbb3aa-4512-44f0-aaea-3c4006da3b33},
	school = {University of Delaware},
	author = {Cabrera-Cruz, Sergio},
	year = {2021},
	keywords = {NALCMS},
}

@article{carrera-hernandez_not_2021,
	title = {Not all {DEMs} are equal: {An} evaluation of six globally available 30 m resolution {DEMs} with geodetic benchmarks and {LiDAR} in {Mexico}},
	volume = {261},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425721001929},
	doi = {10.1016/j.rse.2021.112474},
	abstract = {This work assesses the vertical accuracy of eight Digital Surface Models (DSMs) currently available for Mexico (LiDAR, ALOS AW3D30 V2 and V3, ASTER GDEM V2 and V3, SRTM, NASADEM and Mexico's Continuous Elevation Model (CEM)). The AW3D30, ASTER GDEM, SRTM and NASADEM DSMs cover nearly the entire globe and can be downloaded at no cost, while the LiDAR and CEM DSMs are distributed by Mexico's Institute of Geography and Statistics (INEGI). The accuracy of these DSMs is assessed by considering: 1) benchmarks as reference data at the national level, and 2) LiDAR DSM as reference data on six different zones with variability in slope, vegetation cover and elevation. Using geodetic benchmarks as reference elevation on those areas covered by LiDAR (ALiDAR=370,200 km2, nbench=24,175), it was found that LiDAR has the best vertical accuracy of all DSMs considered (MAELiDAR = 1.96), which is why it was used as reference elevation to develop seven DEMs of Difference (DoDs) with the remainder DSMs. Using ncells = 350 × 106 for the aforementioned comparisons, it was found that the vertical accuracy of AW3D30 V2 and V3 is similar (MAE=2.5 m), followed by NASADEM, SRTM, CEM, ASTER GDEM3 and ASTER GDEM 2, with MAE values of 3.1, 3.8, 4.6, 6.0 and 7.2 m respectively. The previously mentioned values vary according to slope and slope orientation (i.e. aspect): for flat areas (slope≤5∘), the NASADEM exhibits the lowest MAE (with MAE values of 1.6 for slope≤1∘ and MAE = 2.0 m when 1∘{\textless}slope≤5∘), whereas MAEAW3D30V3=1.9 and 2.2 m for the previously mentioned slopes. With the use of radial boxplots developed on slope groups of 5∘, it was found that both MAE and bias are increasingly affected by aspect as slope increases on all the DSMs. In the case of both AW3D30 DSMs, on flat terrain a difference of only 0.1 m in bias (i.e. median of differences with respect to LiDAR) is found between SE and NW slopes; however, this difference increases according to slope: 0.6 m for 5∘{\textless}slope≤10∘, 1.2 m for 10∘{\textless}slope≤15∘, and 1.9 m for 15∘{\textless}slope≤20∘. Through the analyses undertaken, it is shown that slope—and not vegetation cover—is the factor that has the largest impact on the error of DSMs, and that the effect of aspect on error increases as terrain steepens. This work shows that all DSMs present errors and that an adequate accuracy assessment of DSMs needs to consider the spatial distribution of GCPs, Difference of DSMs (DoDs) and derivatives of DSMs (i.e., slope and aspect) as the use of DoDs provide information on DSM errors (i.e. interpolation artefacts) that can not be assessed through the use of geodetic benchmarks and because DSM errors depend on both slope and aspect.},
	journal = {Remote Sensing of Environment},
	author = {Carrera-Hernández, J.J.},
	month = aug,
	year = {2021},
	keywords = {NALCMS},
	pages = {112474},
}

This work assesses the vertical accuracy of eight Digital Surface Models (DSMs) currently available for Mexico (LiDAR, ALOS AW3D30 V2 and V3, ASTER GDEM V2 and V3, SRTM, NASADEM and Mexico's Continuous Elevation Model (CEM)). The AW3D30, ASTER GDEM, SRTM and NASADEM DSMs cover nearly the entire globe and can be downloaded at no cost, while the LiDAR and CEM DSMs are distributed by Mexico's Institute of Geography and Statistics (INEGI). The accuracy of these DSMs is assessed by considering: 1) benchmarks as reference data at the national level, and 2) LiDAR DSM as reference data on six different zones with variability in slope, vegetation cover and elevation. Using geodetic benchmarks as reference elevation on those areas covered by LiDAR (ALiDAR=370,200 km2, nbench=24,175), it was found that LiDAR has the best vertical accuracy of all DSMs considered (MAELiDAR = 1.96), which is why it was used as reference elevation to develop seven DEMs of Difference (DoDs) with the remainder DSMs. Using ncells = 350 × 106 for the aforementioned comparisons, it was found that the vertical accuracy of AW3D30 V2 and V3 is similar (MAE=2.5 m), followed by NASADEM, SRTM, CEM, ASTER GDEM3 and ASTER GDEM 2, with MAE values of 3.1, 3.8, 4.6, 6.0 and 7.2 m respectively. The previously mentioned values vary according to slope and slope orientation (i.e. aspect): for flat areas (slope≤5∘), the NASADEM exhibits the lowest MAE (with MAE values of 1.6 for slope≤1∘ and MAE = 2.0 m when 1∘\textlessslope≤5∘), whereas MAEAW3D30V3=1.9 and 2.2 m for the previously mentioned slopes. With the use of radial boxplots developed on slope groups of 5∘, it was found that both MAE and bias are increasingly affected by aspect as slope increases on all the DSMs. In the case of both AW3D30 DSMs, on flat terrain a difference of only 0.1 m in bias (i.e. median of differences with respect to LiDAR) is found between SE and NW slopes; however, this difference increases according to slope: 0.6 m for 5∘\textlessslope≤10∘, 1.2 m for 10∘\textlessslope≤15∘, and 1.9 m for 15∘\textlessslope≤20∘. Through the analyses undertaken, it is shown that slope—and not vegetation cover—is the factor that has the largest impact on the error of DSMs, and that the effect of aspect on error increases as terrain steepens. This work shows that all DSMs present errors and that an adequate accuracy assessment of DSMs needs to consider the spatial distribution of GCPs, Difference of DSMs (DoDs) and derivatives of DSMs (i.e., slope and aspect) as the use of DoDs provide information on DSM errors (i.e. interpolation artefacts) that can not be assessed through the use of geodetic benchmarks and because DSM errors depend on both slope and aspect.

@misc{danielson_overview_2021,
	title = {Overview of the 2015 {North} {American} {Land} {Use}/{Land} {Cover} {Change} {Product} at 30m {Resolution}: {Methods} and {Results} – {Conferences}},
	shorttitle = {Overview of the 2015 {North} {American} {Land} {Use}/{Land} {Cover} {Change} {Product} at 30m {Resolution}},
	url = {https://conferences.asprs.org/class/1304/},
	language = {en-US},
	urldate = {2023-06-09},
	author = {Danielson, P. and Homer, C. and Dewitz, J. and Latifovic, R. and McFarlane-Winchester, M. and Ressl, R. A. and Jurado, D.},
	year = {2021},
	note = {Presenters: \_:n2954},
	keywords = {NALCMS},
}

@phdthesis{tokuyama_parasites_2021,
	type = {Master of {Science}},
	title = {Parasites and the city: {Characterizing} the influence of urbanization on gastrointestinal parasite communities in {Los} {Angeles} area coyotes ({Canis} latrans)},
	url = {https://escholarship.org/uc/item/4rn3s2rt},
	school = {Biology, University of California - Los Angeles},
	author = {Tokuyama, A.F.N.},
	year = {2021},
	keywords = {NALCMS, Political Boundaries},
}

@article{brandell_patterns_2021,
	title = {Patterns and processes of pathogen exposure in gray wolves across {North} {America}},
	volume = {11},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-021-81192-w},
	doi = {10.1038/s41598-021-81192-w},
	abstract = {Abstract
            
              The presence of many pathogens varies in a predictable manner with latitude, with infections decreasing from the equator towards the poles. We investigated the geographic trends of pathogens infecting a widely distributed carnivore: the gray wolf (
              Canis lupus
              ). Specifically, we investigated which variables best explain and predict geographic trends in seroprevalence across North American wolf populations and the implications of the underlying mechanisms. We compiled a large serological dataset of nearly 2000 wolves from 17 study areas, spanning 80° longitude and 50° latitude. Generalized linear mixed models were constructed to predict the probability of seropositivity of four important pathogens: canine adenovirus, herpesvirus, parvovirus, and distemper virus—and two parasites:
              Neospora caninum
              and
              Toxoplasma gondii
              . Canine adenovirus and herpesvirus were the most widely distributed pathogens, whereas
              N. caninum
              was relatively uncommon. Canine parvovirus and distemper had high annual variation, with western populations experiencing more frequent outbreaks than eastern populations. Seroprevalence of all infections increased as wolves aged, and denser wolf populations had a greater risk of exposure. Probability of exposure was positively correlated with human density, suggesting that dogs and synanthropic animals may be important pathogen reservoirs. Pathogen exposure did not appear to follow a latitudinal gradient, with the exception of
              N. caninum
              . Instead, clustered study areas were more similar: wolves from the Great Lakes region had lower odds of exposure to the viruses, but higher odds of exposure to
              N. caninum
              and
              T. gondii
              ; the opposite was true for wolves from the central Rocky Mountains. Overall, mechanistic predictors were more informative of seroprevalence trends than latitude and longitude. Individual host characteristics as well as inherent features of ecosystems determined pathogen exposure risk on a large scale. This work emphasizes the importance of biogeographic wildlife surveillance, and we expound upon avenues of future research of cross-species transmission, spillover, and spatial variation in pathogen infection.},
	language = {en},
	number = {1},
	urldate = {2023-06-15},
	journal = {Scientific Reports},
	author = {Brandell, Ellen E. and Cross, Paul C. and Craft, Meggan E. and Smith, Douglas W. and Dubovi, Edward J. and Gilbertson, Marie L. J. and Wheeldon, Tyler and Stephenson, John A. and Barber-Meyer, Shannon and Borg, Bridget L. and Sorum, Mathew and Stahler, Daniel R. and Kelly, Allicia and Anderson, Morgan and Cluff, H. Dean and MacNulty, Daniel R. and Watts, Dominique E. and Roffler, Gretchen H. and Schwantje, Helen and Hebblewhite, Mark and Beckmen, Kimberlee and Fenton, Heather and Hudson, Peter J.},
	month = feb,
	year = {2021},
	keywords = {NALCMS},
	pages = {3722},
}

Abstract The presence of many pathogens varies in a predictable manner with latitude, with infections decreasing from the equator towards the poles. We investigated the geographic trends of pathogens infecting a widely distributed carnivore: the gray wolf ( Canis lupus ). Specifically, we investigated which variables best explain and predict geographic trends in seroprevalence across North American wolf populations and the implications of the underlying mechanisms. We compiled a large serological dataset of nearly 2000 wolves from 17 study areas, spanning 80° longitude and 50° latitude. Generalized linear mixed models were constructed to predict the probability of seropositivity of four important pathogens: canine adenovirus, herpesvirus, parvovirus, and distemper virus—and two parasites: Neospora caninum and Toxoplasma gondii . Canine adenovirus and herpesvirus were the most widely distributed pathogens, whereas N. caninum was relatively uncommon. Canine parvovirus and distemper had high annual variation, with western populations experiencing more frequent outbreaks than eastern populations. Seroprevalence of all infections increased as wolves aged, and denser wolf populations had a greater risk of exposure. Probability of exposure was positively correlated with human density, suggesting that dogs and synanthropic animals may be important pathogen reservoirs. Pathogen exposure did not appear to follow a latitudinal gradient, with the exception of N. caninum . Instead, clustered study areas were more similar: wolves from the Great Lakes region had lower odds of exposure to the viruses, but higher odds of exposure to N. caninum and T. gondii ; the opposite was true for wolves from the central Rocky Mountains. Overall, mechanistic predictors were more informative of seroprevalence trends than latitude and longitude. Individual host characteristics as well as inherent features of ecosystems determined pathogen exposure risk on a large scale. This work emphasizes the importance of biogeographic wildlife surveillance, and we expound upon avenues of future research of cross-species transmission, spillover, and spatial variation in pathogen infection.

@article{liu_performance_2021,
	title = {Performance evaluation of {GEDI} and {ICESat}-2 laser altimeter data for terrain and canopy height retrievals},
	volume = {264},
	issn = {00344257},
	url = {https://doi.org/10.1016/j.rse.2021.112571},
	doi = {10.1016/j.rse.2021.112571},
	abstract = {With the advent of the next generation of space-based laser altimeters, ICESat-2 and GEDI, we are entering an exciting era of active remote sensing of forests that offers unprecedented opportunities for the observation of forest structure. Consistent comparisons of the accuracy of terrain and canopy height retrievals for these two missions are essential for continued improvement and further application. Because the time interval between the spaceborne products and validation data may introduce additional errors, we validate the newly released GEDI L2A product (version 2) and the ICESat-2 ATL08 product (version 4) using high-resolution, locally calibrated airborne lidar products acquired in the same year (2019) as the reference datasets. In addition, our study area contains 40 sites located in the U.S. mainland, Alaska, and Hawaii that encompass a variety of eco-climatic zones and vegetation cover types; thus, it avoids the uncertainties associated with small sample sizes and restricted spatial coverage. The results show that ICESat-2 and GEDI yield reasonable estimates of terrain height, with root mean squared errors (RMSEs) of 2.24 and 4.03 m for mid and low latitudes, respectively, and 0.98 m for high latitudes (ICESat-2 only). ICESat-2 outperforms GEDI across the board for terrain height retrieval, although they both have better accuracy than existing SRTM and GMTED DEM products. Analyses of the error factors suggest that steep slopes ({\textgreater}30°) present the greatest challenge for both GEDI and ICESat-2; in addition, tall ({\textgreater}20 m) and dense canopies ({\textgreater}90\%) forest ecosystems also reduce the accuracy of the terrain height estimates. When ICESat-2 and GEDI data are used for canopy height retrieval, the use of only strong/power beam data acquired at night is recommended, as the overall RMSEs decrease from 7.21 and 5.02 m to 3.93 and 3.56 m, respectively, compared to using all data regardless of daytime and beam strength. GEDI outperforms ICESat-2 across the board for canopy height retrieval, as ICESat-2 has a larger potential bias for almost all forest types and cover conditions. ICESat-2 tends to overestimate the canopy height of dwarf shrublands and underestimate the canopy height of forest, and there is a gradual downward shift in the distribution of residuals with increasing canopy height. Overall, ICESat-2 with photon counting technology and GEDI with full waveform technology each represent the state of the art in spaceborne laser altimeters for terrain and canopy height retrieval. Combined, these two missions can take advantage of the unique strengths of each instrument.},
	journal = {Remote Sensing of Environment},
	author = {Liu, Aobo and Cheng, Xiao and Chen, Zhuoqi},
	month = oct,
	year = {2021},
	note = {Publisher: Elsevier Inc.},
	keywords = {NALCMS},
	pages = {112571},
}

With the advent of the next generation of space-based laser altimeters, ICESat-2 and GEDI, we are entering an exciting era of active remote sensing of forests that offers unprecedented opportunities for the observation of forest structure. Consistent comparisons of the accuracy of terrain and canopy height retrievals for these two missions are essential for continued improvement and further application. Because the time interval between the spaceborne products and validation data may introduce additional errors, we validate the newly released GEDI L2A product (version 2) and the ICESat-2 ATL08 product (version 4) using high-resolution, locally calibrated airborne lidar products acquired in the same year (2019) as the reference datasets. In addition, our study area contains 40 sites located in the U.S. mainland, Alaska, and Hawaii that encompass a variety of eco-climatic zones and vegetation cover types; thus, it avoids the uncertainties associated with small sample sizes and restricted spatial coverage. The results show that ICESat-2 and GEDI yield reasonable estimates of terrain height, with root mean squared errors (RMSEs) of 2.24 and 4.03 m for mid and low latitudes, respectively, and 0.98 m for high latitudes (ICESat-2 only). ICESat-2 outperforms GEDI across the board for terrain height retrieval, although they both have better accuracy than existing SRTM and GMTED DEM products. Analyses of the error factors suggest that steep slopes (\textgreater30°) present the greatest challenge for both GEDI and ICESat-2; in addition, tall (\textgreater20 m) and dense canopies (\textgreater90%) forest ecosystems also reduce the accuracy of the terrain height estimates. When ICESat-2 and GEDI data are used for canopy height retrieval, the use of only strong/power beam data acquired at night is recommended, as the overall RMSEs decrease from 7.21 and 5.02 m to 3.93 and 3.56 m, respectively, compared to using all data regardless of daytime and beam strength. GEDI outperforms ICESat-2 across the board for canopy height retrieval, as ICESat-2 has a larger potential bias for almost all forest types and cover conditions. ICESat-2 tends to overestimate the canopy height of dwarf shrublands and underestimate the canopy height of forest, and there is a gradual downward shift in the distribution of residuals with increasing canopy height. Overall, ICESat-2 with photon counting technology and GEDI with full waveform technology each represent the state of the art in spaceborne laser altimeters for terrain and canopy height retrieval. Combined, these two missions can take advantage of the unique strengths of each instrument.

@article{newell_pig_2021,
	title = {Pig pigmentation: testing {Gloger}’s rule},
	volume = {102},
	issn = {0022-2372, 1545-1542},
	shorttitle = {Pig pigmentation},
	url = {https://academic.oup.com/jmammal/article/102/6/1525/6381348},
	doi = {10.1093/jmammal/gyab090},
	abstract = {Abstract
            Comparative studies indicate that several mammalian clades obey Gloger’s rule in that they exhibit darker coloration in humid warm climates, although the mechanisms responsible for this association still are poorly understood. We surveyed external appearances of a single species, the feral pig (Sus scrofa), shot at 48 hunting lodges across North America and matched these to potential abiotic drivers, namely: relative humidity, temperature, precipitation, and ultraviolet (UV) radiation, and to biotic factors of habitat shade and predation pressure. We found that darker animals occupy locations of greater precipitation and warmer temperatures, as expected from Gloger’s rule. The recent range expansion of S. scrofa implies selection for pelage coloration has occurred very rapidly. Separating pelage coloration into eumelanin- and phaeomelanin-based pigmentation, we found more pronounced eumelanin-based pelage in areas of higher rainfall and temperatures and UV radiation, whereas pelage phaeomelanin is related to cool dry climates with lower UV radiation. This implies that humidity or UV protection but not crypsis are the mechanisms underlying Gloger’s rule in this species and the factors driving eumelanin and phaeomelanin expression in mammalian pelage are different, reinforcing new interpretations of this venerable rule.},
	language = {en},
	number = {6},
	urldate = {2023-06-15},
	journal = {Journal of Mammalogy},
	author = {Newell, Caroline and Walker, Hannah and Caro, Tim},
	editor = {Ge, Deyan},
	month = dec,
	year = {2021},
	keywords = {NALCMS},
	pages = {1525--1535},
}

Abstract Comparative studies indicate that several mammalian clades obey Gloger’s rule in that they exhibit darker coloration in humid warm climates, although the mechanisms responsible for this association still are poorly understood. We surveyed external appearances of a single species, the feral pig (Sus scrofa), shot at 48 hunting lodges across North America and matched these to potential abiotic drivers, namely: relative humidity, temperature, precipitation, and ultraviolet (UV) radiation, and to biotic factors of habitat shade and predation pressure. We found that darker animals occupy locations of greater precipitation and warmer temperatures, as expected from Gloger’s rule. The recent range expansion of S. scrofa implies selection for pelage coloration has occurred very rapidly. Separating pelage coloration into eumelanin- and phaeomelanin-based pigmentation, we found more pronounced eumelanin-based pelage in areas of higher rainfall and temperatures and UV radiation, whereas pelage phaeomelanin is related to cool dry climates with lower UV radiation. This implies that humidity or UV protection but not crypsis are the mechanisms underlying Gloger’s rule in this species and the factors driving eumelanin and phaeomelanin expression in mammalian pelage are different, reinforcing new interpretations of this venerable rule.

@article{gaur_potential_2021,
	title = {Potential future changes in wildfire weather and behavior around 11 {Canadian} cities},
	volume = {35},
	issn = {22120955},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2212095520300912},
	doi = {10.1016/j.uclim.2020.100735},
	language = {en},
	urldate = {2023-06-15},
	journal = {Urban Climate},
	author = {Gaur, Abhishek and Bénichou, Noureddine and Armstrong, Marianne and Hill, Fiona},
	month = jan,
	year = {2021},
	keywords = {NALCMS},
	pages = {100735},
}

@article{pollock_predicting_2021,
	title = {Predicting high-risk areas for {African} swine fever spread at the wild-domestic pig interface in {Ontario}},
	volume = {191},
	issn = {01675877},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0167587721000854},
	doi = {10.1016/j.prevetmed.2021.105341},
	language = {en},
	urldate = {2023-06-15},
	journal = {Preventive Veterinary Medicine},
	author = {Pollock, Lisa A. and Newton, Erica J. and Koen, Erin L.},
	month = jun,
	year = {2021},
	keywords = {NALCMS},
	pages = {105341},
}

@article{jain_predicting_2021,
	title = {Predicting the {Location} of {Maple} {Habitat} {Under} {Warming} {Scenarios} in {Two} {Regions} at the {Northern} {Range} in {Canada}},
	volume = {67},
	issn = {0015-749X, 1938-3738},
	url = {https://academic.oup.com/forestscience/article/67/4/446/6270781},
	doi = {10.1093/forsci/fxab012},
	abstract = {Abstract
            Acer saccharum and Acer rubrum, two main maple species in North America, will be affected by ongoing global warming. Several studies predict a southern limit contraction but limited northward expansion of maple distribution for the future. We aimed to understand the main drivers of current maple distribution to quantify potential changes to maple habitat in this context. We identified the local conditions of maple stands and assessed the potential changes under warming scenarios. We selected two regions in Quebec, Canada, at the northern distribution of the species and applied probability distribution function and principal component analysis to identify the factors explaining the occurrence of maple compared with other forest species, croplands, and grasslands. Maple habitat was estimated under warming scenarios up to 2°C. We observed a lower density of maple stands in the north (7.76\%), compared to the south (33.01\%). Warming increased maple stands by 3.54 and 1.45\% in the northernmost and southernmost regions, respectively, with the highest increases being estimated at the initial warming (+1.0°C). We conclude that maple of northern regions can benefit from local warming if precipitation does not become limiting. These changes could increase the potential for the regional maple industry and forest management.},
	language = {en},
	number = {4},
	urldate = {2023-06-15},
	journal = {Forest Science},
	author = {Jain, Pratiksha and Khare, Siddhartha and Sylvain, Jean-Daniel and Raymond, Patricia and Rossi, Sergio},
	month = aug,
	year = {2021},
	keywords = {NALCMS},
	pages = {446--456},
}

Abstract Acer saccharum and Acer rubrum, two main maple species in North America, will be affected by ongoing global warming. Several studies predict a southern limit contraction but limited northward expansion of maple distribution for the future. We aimed to understand the main drivers of current maple distribution to quantify potential changes to maple habitat in this context. We identified the local conditions of maple stands and assessed the potential changes under warming scenarios. We selected two regions in Quebec, Canada, at the northern distribution of the species and applied probability distribution function and principal component analysis to identify the factors explaining the occurrence of maple compared with other forest species, croplands, and grasslands. Maple habitat was estimated under warming scenarios up to 2°C. We observed a lower density of maple stands in the north (7.76%), compared to the south (33.01%). Warming increased maple stands by 3.54 and 1.45% in the northernmost and southernmost regions, respectively, with the highest increases being estimated at the initial warming (+1.0°C). We conclude that maple of northern regions can benefit from local warming if precipitation does not become limiting. These changes could increase the potential for the regional maple industry and forest management.

@article{pedersen_quantifying_2021,
	title = {Quantifying effects of snow depth on caribou winter range selection and movement in {Arctic} {Alaska}},
	volume = {9},
	issn = {2051-3933},
	url = {https://movementecologyjournal.biomedcentral.com/articles/10.1186/s40462-021-00276-4},
	doi = {10.1186/s40462-021-00276-4},
	abstract = {Background: Caribou and reindeer across the Arctic spend more than two thirds of their lives moving in snow. Yet snow-specific mechanisms driving their winter ecology and potentially influencing herd health and movement patterns are not well known. Integrative research coupling snow and wildlife sciences using observations, models, and wildlife tracking technologies can help fill this knowledge void. Methods: Here, we quantified the effects of snow depth on caribou winter range selection and movement. We used location data of Central Arctic Herd (CAH) caribou in Arctic Alaska collected from 2014 to 2020 and spatially distributed and temporally evolving snow depth data produced by SnowModel. These landscape-scale (90 m), daily snow depth data reproduced the observed spatial snow-depth variability across typical areal extents occupied by a wintering caribou during a 24-h period. Results: We found that fall snow depths encountered by the herd north of the Brooks Range exerted a strong influence on selection of two distinct winter range locations. In winters with relatively shallow fall snow depth (2016/17, 2018/19, and 2019/20), the majority of the CAH wintered on the tundra north of the Brooks Range mountains. In contrast, during the winters with relatively deep fall snow depth (2014/15, 2015/16, and 2017/18), the majority of the CAH caribou wintered in the mountainous boreal forest south of the Brooks Range. Long-term (19 winters; 2001–2020) monitoring of CAH caribou winter distributions confirmed this relationship. Additionally, snow depth affected movement and selection differently within these two habitats: in the mountainous boreal forest, caribou avoided areas with deeper snow, but when on the tundra, snow depth did not trigger significant deep-snow avoidance. In both wintering habitats, CAH caribou selected areas with higher lichen abundance, and they moved significantly slower when encountering deeper snow. Conclusions: In general, our findings indicate that regional-scale selection of winter range is influenced by snow depth at or prior to fall migration. During winter, daily decision-making within the winter range is driven largely by snow depth. This integrative approach of coupling snow and wildlife observations with snow-evolution and caribou-movement modeling to quantify the multi-facetted effects of snow on wildlife ecology is applicable to caribou and reindeer herds throughout the Arctic.},
	number = {1},
	journal = {Movement Ecology},
	author = {Pedersen, Stine Højlund and Bentzen, Torsten W. and Reinking, Adele K. and Liston, Glen E. and Elder, Kelly and Lenart, Elizabeth A. and Prichard, Alexander K. and Welker, Jeffrey M.},
	month = dec,
	year = {2021},
	note = {Publisher: BioMed Central Ltd},
	keywords = {NALCMS},
	pages = {48},
}

Background: Caribou and reindeer across the Arctic spend more than two thirds of their lives moving in snow. Yet snow-specific mechanisms driving their winter ecology and potentially influencing herd health and movement patterns are not well known. Integrative research coupling snow and wildlife sciences using observations, models, and wildlife tracking technologies can help fill this knowledge void. Methods: Here, we quantified the effects of snow depth on caribou winter range selection and movement. We used location data of Central Arctic Herd (CAH) caribou in Arctic Alaska collected from 2014 to 2020 and spatially distributed and temporally evolving snow depth data produced by SnowModel. These landscape-scale (90 m), daily snow depth data reproduced the observed spatial snow-depth variability across typical areal extents occupied by a wintering caribou during a 24-h period. Results: We found that fall snow depths encountered by the herd north of the Brooks Range exerted a strong influence on selection of two distinct winter range locations. In winters with relatively shallow fall snow depth (2016/17, 2018/19, and 2019/20), the majority of the CAH wintered on the tundra north of the Brooks Range mountains. In contrast, during the winters with relatively deep fall snow depth (2014/15, 2015/16, and 2017/18), the majority of the CAH caribou wintered in the mountainous boreal forest south of the Brooks Range. Long-term (19 winters; 2001–2020) monitoring of CAH caribou winter distributions confirmed this relationship. Additionally, snow depth affected movement and selection differently within these two habitats: in the mountainous boreal forest, caribou avoided areas with deeper snow, but when on the tundra, snow depth did not trigger significant deep-snow avoidance. In both wintering habitats, CAH caribou selected areas with higher lichen abundance, and they moved significantly slower when encountering deeper snow. Conclusions: In general, our findings indicate that regional-scale selection of winter range is influenced by snow depth at or prior to fall migration. During winter, daily decision-making within the winter range is driven largely by snow depth. This integrative approach of coupling snow and wildlife observations with snow-evolution and caribou-movement modeling to quantify the multi-facetted effects of snow on wildlife ecology is applicable to caribou and reindeer herds throughout the Arctic.

@article{crossley_recent_2021,
	title = {Recent climate change is creating hotspots of butterfly increase and decline across {North} {America}},
	volume = {27},
	issn = {1354-1013},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.15582},
	doi = {10.1111/gcb.15582},
	abstract = {Some insect populations are experiencing dramatic declines, endangering the crucial ecosystem services they provide. Yet, other populations appear robust, highlighting the need to better define patterns and underlying drivers of recent change in insect numbers. We examined abundance and biodiversity trends for North American butterflies using a unique citizen-science dataset that has recorded observations of over 8 million butterflies across 456 species, 503 sites, nine ecoregions, and 26 years. Butterflies are a biodiverse group of pollinators, herbivores, and prey, making them useful bellwethers of environmental change. We found great heterogeneity in butterfly species' abundance trends, aggregating near zero, but with a tendency toward decline. There was strong spatial clustering, however, into regions of increase, decrease, or relative stasis. Recent precipitation and temperature appeared to largely drive these patterns, with butterflies generally declining at increasingly dry and hot sites but increasing at relatively wet or cool sites. In contrast, landscape and butterfly trait predictors had little influence, though abundance trends were slightly more positive around urban areas. Consistent with varying responses by different species, no overall directional change in butterfly species richness or evenness was detected. Overall, a mosaic of butterfly decay and rebound hotspots appeared to largely reflect geographic variability in climate drivers. Ongoing controversy about insect declines might dissipate with a shift in focus to the causes of heterogeneous responses among taxa and sites, with climate change emerging as a key suspect when pollinator communities are broadly impacted.},
	number = {12},
	journal = {Global Change Biology},
	author = {Crossley, Michael S. and Smith, Olivia M. and Berry, Lauren L. and Phillips‐Cosio, Robert and Glassberg, Jeffrey and Holman, Kaylen M. and Holmquest, Jacquelin G. and Meier, Amanda R. and Varriano, Sofia A. and McClung, Maureen R. and Moran, Matthew D. and Snyder, William E.},
	month = jun,
	year = {2021},
	pmid = {33749964},
	keywords = {NALCMS},
	pages = {2702--2714},
}

Some insect populations are experiencing dramatic declines, endangering the crucial ecosystem services they provide. Yet, other populations appear robust, highlighting the need to better define patterns and underlying drivers of recent change in insect numbers. We examined abundance and biodiversity trends for North American butterflies using a unique citizen-science dataset that has recorded observations of over 8 million butterflies across 456 species, 503 sites, nine ecoregions, and 26 years. Butterflies are a biodiverse group of pollinators, herbivores, and prey, making them useful bellwethers of environmental change. We found great heterogeneity in butterfly species' abundance trends, aggregating near zero, but with a tendency toward decline. There was strong spatial clustering, however, into regions of increase, decrease, or relative stasis. Recent precipitation and temperature appeared to largely drive these patterns, with butterflies generally declining at increasingly dry and hot sites but increasing at relatively wet or cool sites. In contrast, landscape and butterfly trait predictors had little influence, though abundance trends were slightly more positive around urban areas. Consistent with varying responses by different species, no overall directional change in butterfly species richness or evenness was detected. Overall, a mosaic of butterfly decay and rebound hotspots appeared to largely reflect geographic variability in climate drivers. Ongoing controversy about insect declines might dissipate with a shift in focus to the causes of heterogeneous responses among taxa and sites, with climate change emerging as a key suspect when pollinator communities are broadly impacted.

@article{schrimpf_reduced_2021,
	title = {Reduced human activity during {COVID}-19 alters avian land use across {North} {America}},
	volume = {7},
	issn = {2375-2548},
	url = {https://www.science.org/doi/10.1126/sciadv.abf5073},
	doi = {10.1126/sciadv.abf5073},
	abstract = {Bird counts increased in locations with reduced human traffic resulting from the COVID-19 pandemic.
          , 
            The COVID-19 pandemic resulted in extraordinary declines in human mobility, which, in turn, may affect wildlife. Using records of more than 4.3 million birds observed by volunteers from March to May 2017–2020 across Canada and the United States, we found that counts of 66 (80\%) of 82 focal bird species changed in pandemic-altered areas, usually increasing in comparison to prepandemic abundances in urban habitat, near major roads and airports, and in counties where lockdowns were more pronounced or occurred at the same time as peak bird migration. Our results indicate that human activity affects many of North America’s birds and suggest that we could make urban spaces more attractive to birds by reducing traffic and mitigating the disturbance from human transportation after we emerge from the pandemic.},
	language = {en},
	number = {39},
	urldate = {2023-06-15},
	journal = {Science Advances},
	author = {Schrimpf, Michael B. and Des Brisay, Paulson G. and Johnston, Alison and Smith, Adam C. and Sánchez-Jasso, Jessica and Robinson, Barry G. and Warrington, Miyako H. and Mahony, Nancy A. and Horn, Andrew G. and Strimas-Mackey, Matthew and Fahrig, Lenore and Koper, Nicola},
	month = sep,
	year = {2021},
	keywords = {NALCMS},
	pages = {eabf5073},
}

Bird counts increased in locations with reduced human traffic resulting from the COVID-19 pandemic. , The COVID-19 pandemic resulted in extraordinary declines in human mobility, which, in turn, may affect wildlife. Using records of more than 4.3 million birds observed by volunteers from March to May 2017–2020 across Canada and the United States, we found that counts of 66 (80%) of 82 focal bird species changed in pandemic-altered areas, usually increasing in comparison to prepandemic abundances in urban habitat, near major roads and airports, and in counties where lockdowns were more pronounced or occurred at the same time as peak bird migration. Our results indicate that human activity affects many of North America’s birds and suggest that we could make urban spaces more attractive to birds by reducing traffic and mitigating the disturbance from human transportation after we emerge from the pandemic.

@phdthesis{tango_reproducibility_2021,
	address = {Middlebury},
	type = {{BA} {Thesis}},
	title = {Reproducibility of a {Geographical} {Study} on the {Effects} of {Wind} {Turbines} on {Bat} {Fatalities} in the {Northeast} {United} {States}},
	url = {https://middlebury.figshare.com/articles/thesis/Reproducibility_of_a_Geographical_Study_on_the_Effects_of_Wind_Turbines_on_Bat_Fatalities_in_the_Northeast_United_States/21565893},
	abstract = {With the expansion of wind energy as an alternative to fossil fuel use, it is important that we have the tools necessary to analyze ecological impacts. While geographic information systems (GIS) have many tools for this analysis, the lack of reproducibility in the field will lead to both decreased accuracy of results and slow down knowledge gain during a time in which research speed could prevent extinctions. In this integrative geography and biology thesis, I study the reproducibility of a geographical analysis to better understand barriers to its reproduction. The study, conducted by consulting group DNV GL for the Wind Wildlife Research Fund, analyzes the relationship between tree-roosting bat fatalities and landscape features at onshore wind farms in the northeast United States (Peters et al. 2020). I improve its future reproducibility by analyzing the ways in which both the bat fatality studies used to provide data for the Peters et al. (2020) study and the study itself could have been reported better for clarity and transparency with regards to methods, data, and sources of uncertainty. I also improve its future reproducibility by creating models and R scripts that reproduce the methods of this study to the fullest degree reasonably possible. The models and R scripts can be applied at various spatial scales to calculate landscape metrics (i.e., connectivity, percent area of land cover type, forest core area, etc.) and determine which are most correlated with hoary bat, eastern red bat, and silver-haired bat fatalities in a particular region. Methods, models, and R scripts are publicly available and could be applied to other species and land cover type contexts. 3},
	school = {Middlebury College},
	author = {Tango, Maddie},
	year = {2021},
	keywords = {NALCMS},
}

With the expansion of wind energy as an alternative to fossil fuel use, it is important that we have the tools necessary to analyze ecological impacts. While geographic information systems (GIS) have many tools for this analysis, the lack of reproducibility in the field will lead to both decreased accuracy of results and slow down knowledge gain during a time in which research speed could prevent extinctions. In this integrative geography and biology thesis, I study the reproducibility of a geographical analysis to better understand barriers to its reproduction. The study, conducted by consulting group DNV GL for the Wind Wildlife Research Fund, analyzes the relationship between tree-roosting bat fatalities and landscape features at onshore wind farms in the northeast United States (Peters et al. 2020). I improve its future reproducibility by analyzing the ways in which both the bat fatality studies used to provide data for the Peters et al. (2020) study and the study itself could have been reported better for clarity and transparency with regards to methods, data, and sources of uncertainty. I also improve its future reproducibility by creating models and R scripts that reproduce the methods of this study to the fullest degree reasonably possible. The models and R scripts can be applied at various spatial scales to calculate landscape metrics (i.e., connectivity, percent area of land cover type, forest core area, etc.) and determine which are most correlated with hoary bat, eastern red bat, and silver-haired bat fatalities in a particular region. Methods, models, and R scripts are publicly available and could be applied to other species and land cover type contexts. 3

@article{mccabe_resource_2021,
	title = {Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distributions},
	volume = {44},
	issn = {0906-7590, 1600-0587},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/ecog.06058},
	doi = {10.1111/ecog.06058},
	language = {en},
	number = {12},
	urldate = {2023-06-15},
	journal = {Ecography},
	author = {McCabe, Jennifer D. and Clare, John D. and Miller, Tricia A. and Katzner, Todd E. and Cooper, Jeff and Somershoe, Scott and Hanni, David and Kelly, Christine A. and Sargent, Robert and Soehren, Eric C. and Threadgill, Carrie and Maddox, Mercedes and Stober, Jonathan and Martell, Mark and Salo, Thomas and Berry, Andrew and Lanzone, Michael J. and Braham, Melissa A. and McClure, Christopher J. W.},
	month = dec,
	year = {2021},
	keywords = {NALCMS},
	pages = {1756--1768},
}

@article{yao_riverine_2021,
	title = {Riverine {Carbon} {Cycling} {Over} the {Past} {Century} in the {Mid}‐{Atlantic} {Region} of the {United} {States}},
	volume = {126},
	issn = {2169-8953, 2169-8961},
	url = {https://onlinelibrary.wiley.com/doi/10.1029/2020JG005968},
	doi = {10.1029/2020JG005968},
	language = {en},
	number = {5},
	urldate = {2023-06-15},
	journal = {Journal of Geophysical Research: Biogeosciences},
	author = {Yao, Yuanzhi and Tian, Hanqin and Pan, Shufen and Najjar, Raymond G. and Friedrichs, Marjorie A. M. and Bian, Zihao and Li, Hong‐Yi and Hofmann, Eileen E.},
	month = may,
	year = {2021},
	keywords = {NALCMS},
}

@phdthesis{gaboriau_regimes_2021,
	type = {Doctorat sur {Mesure} en Écologie {Forestière}},
	title = {Régimes des feux holocène, contemporain et futur aux {Territoires} du {Nord}-{Ouest}, {Canada}},
	url = {https://tel.archives-ouvertes.fr/tel-03346459/},
	school = {Sciences Agricoles, Université du Québec à Abitibi - Témiscamingue},
	author = {Gaboriau, D.},
	year = {2021},
	keywords = {NALCMS},
}

@misc{flower_salish_2021,
	title = {Salish {Sea} {Atlas} – {An} open access digital {Atlas} of the {Salish} {Sea} {Bioregion}.},
	copyright = {College of the Environment, Western Washington University},
	url = {https://wp.wwu.edu/salishseaatlas/},
	urldate = {2023-06-14},
	author = {Flower, Aquila},
	year = {2021},
	keywords = {NALCMS},
}

@misc{kathryn_hansen_shrimp_2021,
	type = {Text.{Article}},
	title = {Shrimp {Farms} {Spread} in {Sinaloa}},
	url = {https://earthobservatory.nasa.gov/images/148128/shrimp-farms-spread-in-sinaloa?src=eoa-iotd},
	abstract = {The coastal state is one of the largest shrimp farming regions in Mexico.},
	language = {en},
	urldate = {2024-09-11},
	journal = {NASA Earth Observatory Image of the Day},
	author = {{Kathryn Hansen}},
	month = apr,
	year = {2021},
	note = {Publisher: NASA Earth Observatory},
	keywords = {NALCMS},
}

The coastal state is one of the largest shrimp farming regions in Mexico.

@article{eppler_snow_2021,
	title = {Snow {Water} {Equivalent} {Change} {Mapping} from {Slope} {Correlated} {InSAR} {Phase} {Variations}},
	url = {https://tc.copernicus.org/preprints/tc-2021-359/tc-2021-359.pdf},
	doi = {https://doi.org/10.5194/tc-2021-359},
	journal = {The Cryosphere Discuss [preprint]},
	author = {Eppler, Jayson and Rabus, Bernhard and Morse, Peter},
	year = {2021},
	keywords = {NALCMS},
}

@incollection{heung_soils_2021,
	title = {Soils of the {Atlantic} {Provinces}},
	url = {https://openpress.usask.ca/soilscience/chapter/soils-of-the-atlantic-provinces/},
	language = {en},
	urldate = {2023-06-08},
	booktitle = {Digging into {Canadian} {Soils}},
	publisher = {Canadian Society of Soil Science},
	author = {Heung, Brandon and Keys, Kevin and Burton, David L. and Lynch, Derek H.},
	month = aug,
	year = {2021},
	keywords = {NALCMS},
}

@techreport{bansal_spatially_2021,
	type = {other},
	title = {Spatially explicit methane emissions from the largest wetland complex in {North} {America}: {Past}, present and future},
	shorttitle = {Spatially explicit methane emissions from the largest wetland complex in {North} {America}},
	url = {https://meetingorganizer.copernicus.org/EGU21/EGU21-3186.html},
	abstract = {\<p\>Almost half of all biogenically-produced methane is emitted from small lakes and wetlands. The Prairie Pothole Region (PPR) is the largest wetland complex in North America (10\<sup\>th\</sup\> largest in the world), and contains 5\&\#8211;8 million wetlands and lakes, which can potentially influence continental and global methane budgets. However, there is considerable uncertainty of past, current and future emissions of methane from PPR wetlands due to a lack of landscape-scale models based on PPR-specific data. We used a bottom-up approach to develop a spatially explicit, temporally dynamic model of wetland and lake methane emissions from the PPR. Using a dataset of \>20,000 static-chamber flux measurements, we first developed a chamber model to understand functional relationships between methane fluxes and covariates, and then upscaled to the landscape using GIS and remotely sensed proxies for each covariate. Covariates in the chamber model included water-filled pore space (WFPS), hydroperiod, soil temperature, wetland size, land cover, and normalized difference vegetation index (NDVI). Proxies for upscaling included the Dynamic Surface Water Extent (for WFPS, hydroperiod, and area) and NDVI based on Landsat imagery, ClimateNA (for soil temperature), and the North American Land Change Monitoring System (for land cover). Methane emissions increased nonlinearly with increasing WFPS, soil temperature and NDVI, and was greater in wetlands surrounded by grasslands compared to cropland due to low organic carbon substrates in sediment of cropped wetlands. Methane flux had a hump-shaped relationship with area, with the highest emissions in mid-sized wetlands (1-4 ha) that had relatively long hydroperiods and high vegetation cover, whereas methane flux from water bodies \>10 ha was negligible due to their relatively high sulfate concentrations. Despite the potential for high total emission from the PPR as would indicate from global models, total emissions were relatively low ({\textasciitilde}5 and 100 Gg methane) per year during historic dry (1991) and current wet years (2011), respectively, with wetland extent is the primary driver of regional emissions. Future warmer temperature scenarios (under RCP 8.5) indicate that annual methane emissions from the PPR could double.\</p\>},
	urldate = {2023-06-09},
	institution = {pico},
	author = {Bansal, Sheel and Post Van Der Burg, Max and Lo, Rachel and McKenna, Owen},
	month = mar,
	year = {2021},
	doi = {10.5194/egusphere-egu21-3186},
	keywords = {NALCMS},
}

<p>Almost half of all biogenically-produced methane is emitted from small lakes and wetlands. The Prairie Pothole Region (PPR) is the largest wetland complex in North America (10^th largest in the world), and contains 5&#8211;8 million wetlands and lakes, which can potentially influence continental and global methane budgets. However, there is considerable uncertainty of past, current and future emissions of methane from PPR wetlands due to a lack of landscape-scale models based on PPR-specific data. We used a bottom-up approach to develop a spatially explicit, temporally dynamic model of wetland and lake methane emissions from the PPR. Using a dataset of >20,000 static-chamber flux measurements, we first developed a chamber model to understand functional relationships between methane fluxes and covariates, and then upscaled to the landscape using GIS and remotely sensed proxies for each covariate. Covariates in the chamber model included water-filled pore space (WFPS), hydroperiod, soil temperature, wetland size, land cover, and normalized difference vegetation index (NDVI). Proxies for upscaling included the Dynamic Surface Water Extent (for WFPS, hydroperiod, and area) and NDVI based on Landsat imagery, ClimateNA (for soil temperature), and the North American Land Change Monitoring System (for land cover). Methane emissions increased nonlinearly with increasing WFPS, soil temperature and NDVI, and was greater in wetlands surrounded by grasslands compared to cropland due to low organic carbon substrates in sediment of cropped wetlands. Methane flux had a hump-shaped relationship with area, with the highest emissions in mid-sized wetlands (1-4 ha) that had relatively long hydroperiods and high vegetation cover, whereas methane flux from water bodies >10 ha was negligible due to their relatively high sulfate concentrations. Despite the potential for high total emission from the PPR as would indicate from global models, total emissions were relatively low (~5 and 100 Gg methane) per year during historic dry (1991) and current wet years (2011), respectively, with wetland extent is the primary driver of regional emissions. Future warmer temperature scenarios (under RCP 8.5) indicate that annual methane emissions from the PPR could double.</p>

@article{chen_spatiotemporal_2021,
	title = {Spatiotemporal analysis of land use pattern and stream water quality in southern {Alberta}, {Canada}},
	volume = {242},
	issn = {01697722},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0169772221000917},
	doi = {10.1016/j.jconhyd.2021.103852},
	language = {en},
	urldate = {2023-06-15},
	journal = {Journal of Contaminant Hydrology},
	author = {Chen, Zhikun and An, Chunjiang and Tan, Qian and Tian, Xuelin and Li, Gongchen and Zhou, Yang},
	month = oct,
	year = {2021},
	keywords = {NALCMS},
	pages = {103852},
}

@article{daniels_strategic_2021,
	title = {Strategic {Radiology} {Outreach} {Planning} for {Underserved} {Populations} {Using} {Geographic} {Information} {Systems}},
	volume = {18},
	issn = {15461440},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1546144020311078},
	doi = {10.1016/j.jacr.2020.08.022},
	language = {en},
	number = {4},
	urldate = {2023-06-15},
	journal = {Journal of the American College of Radiology},
	author = {Daniels, Marcus J. and Game, Alina and Mollura, Daniel J. and England, Ryan W.},
	month = apr,
	year = {2021},
	keywords = {NALCMS},
	pages = {537--544},
}

@article{hager_tail_2021,
	title = {Tail {Length} {Evolution} in {Deer} {Mice}: {Linking} {Morphology}, {Behavior}, and {Function}},
	volume = {61},
	issn = {1540-7063},
	url = {https://academic.oup.com/icb/article/61/2/385/6237492},
	doi = {10.1093/icb/icab030},
	abstract = {Determining how variation in morphology affects animal performance (and ultimately fitness) is key to understanding the complete process of evolutionary adaptation. Long tails have evolved many times in arboreal and semi-arboreal rodents; in deer mice, long tails have evolved repeatedly in populations occupying forested habitat even within a single species (Peromyscus maniculatus). Here, we use a combination of functional modeling, laboratory studies, and museum records to test hypotheses about the function of tail-length variation in deer mice. First, we use computational models, informed by museum records documenting natural variation in tail length, to test whether differences in tail morphology between forest and prairie subspecies can influence performance in behavioral contexts relevant for tail use. We find that the deer- mouse tail plays little role in statically adjusting center of mass or in correcting body pitch and yaw, but rather it can affect body roll during arboreal locomotion. In this context, we find that even intraspecific tail-length variation could result in substantial differences in how much body rotation results from equivalent tail motions (i.e., tail effectiveness), but the relationship between commonly-used metrics of tail-length variation and effectiveness is non-linear. We further test whether caudal vertebra length, number, and shape are associated with differences in how much the tail can bend to curve around narrow substrates (i.e., tail curvature) and find that, as predicted, the shape of the caudal vertebrae is associated with intervertebral bending angle across taxa. However, although forest and prairie mice typically differ in both the length and number of caudal vertebrae, we do not find evidence that this pattern is the result of a functional trade-off related to tail curvature. Together, these results highlight how even simple models can both generate and exclude hypotheses about the functional consequences of trait variation for organismal-level performance.},
	number = {2},
	journal = {Integrative and Comparative Biology},
	author = {Hager, Emily R. and Hoekstra, Hopi E.},
	month = sep,
	year = {2021},
	pmid = {33871633},
	note = {Publisher: Oxford University Press},
	keywords = {NALCMS},
	pages = {385--397},
}

Determining how variation in morphology affects animal performance (and ultimately fitness) is key to understanding the complete process of evolutionary adaptation. Long tails have evolved many times in arboreal and semi-arboreal rodents; in deer mice, long tails have evolved repeatedly in populations occupying forested habitat even within a single species (Peromyscus maniculatus). Here, we use a combination of functional modeling, laboratory studies, and museum records to test hypotheses about the function of tail-length variation in deer mice. First, we use computational models, informed by museum records documenting natural variation in tail length, to test whether differences in tail morphology between forest and prairie subspecies can influence performance in behavioral contexts relevant for tail use. We find that the deer- mouse tail plays little role in statically adjusting center of mass or in correcting body pitch and yaw, but rather it can affect body roll during arboreal locomotion. In this context, we find that even intraspecific tail-length variation could result in substantial differences in how much body rotation results from equivalent tail motions (i.e., tail effectiveness), but the relationship between commonly-used metrics of tail-length variation and effectiveness is non-linear. We further test whether caudal vertebra length, number, and shape are associated with differences in how much the tail can bend to curve around narrow substrates (i.e., tail curvature) and find that, as predicted, the shape of the caudal vertebrae is associated with intervertebral bending angle across taxa. However, although forest and prairie mice typically differ in both the length and number of caudal vertebrae, we do not find evidence that this pattern is the result of a functional trade-off related to tail curvature. Together, these results highlight how even simple models can both generate and exclude hypotheses about the functional consequences of trait variation for organismal-level performance.

@techreport{bgc_engineering_inc_thompson-nicola_2021,
	title = {Thompson-{Nicola} {Regional} {District} {Flood} {Hazard} {Assessment}},
	url = {https://www.fraserbasin.bc.ca/_Library/TR_Flood/TNRD_Flood_Hazard_Assessment_2021_Final_Web.pdf},
	institution = {Frasier Basin Council},
	author = {{BGC Engineering Inc.}},
	month = jun,
	year = {2021},
	keywords = {NALCMS},
	pages = {115},
}

@phdthesis{kramer_understanding_2021,
	type = {Master of {Science}},
	title = {Understanding resource selection, resource use, and landscape connectivity for invasive wild pigs ({Sus} scrofa) in the prairies: {Implications} for management},
	url = {https://hdl.handle.net/10388/13350},
	school = {Animal and Poultry Science, University of Saskatchewan},
	author = {Kramer, C.J.},
	year = {2021},
	keywords = {NALCMS},
}

@article{kirk_using_2021,
	title = {Using {Ecological} {Niche} {Models} for {Population} and {Range} {Estimates} of a {Threatened} {Snake} {Species} ({Crotalus} oreganus) in {Canada}},
	volume = {13},
	issn = {1424-2818},
	url = {https://www.mdpi.com/1424-2818/13/10/467},
	doi = {10.3390/d13100467},
	abstract = {Modelling the distribution and abundance of species at risk is extremely important for their conservation and management. We used ecological niche models (ENMs) to predict the occurrence of western rattlesnakes (Crotalus oreganus) in British Columbia (BC), Canada. We applied this to existing population estimates to support a threshold of occurrence for management and conservation. We also identified predictors influencing rattlesnake distribution and abundance in this region. Using a Geographic Information Systems platform, we incorporated ENMs, capture–mark–recapture (CMR) and radio-telemetry results, province-wide observations, Landsat imagery and provincial databases for agricultural land use to produce quantitative, spatially explicit, population estimates across BC. Using available western rattlesnake habitat estimated at 183.9 km2 and averaging estimates calculated from densities in three study populations, we generated a mean adult population size of 9722 (±SD 3009; 0.8 relative index of occurrence [RIO] threshold). Only a small area (21.6 km2) of suitable land cover was located within protected areas, potentially protecting an estimated 1144 (±354) adults. Most suitable land cover was within 500 m of roads (170.6 km2), representing potential habitat being used by an estimated 9017 (±2791) adults. At the threshold RIO value chosen (0.8), only a very small area of farmland provided suitable land cover. Our results highlight the possibility of high mortality rates for western rattlesnakes near roads and the fact that protected areas do not provide sufficient coverage to conserve the population. Given that this species has relatively low mobility and high site fidelity to home ranges, our population estimate for BC provides a useful reference for the northern part of the species’ range. It also fulfills a need to estimate population size within political jurisdictions where conservation management decisions are made, as well as presenting a method that can be applied to other parts of the range, including the southern United States. Our study provides an important benchmark for future monitoring of western rattlesnakes in BC using a repeatable and transparent approach. Similar applications can be extrapolated and applied for other threatened species to identify and quantify population distributions and threats, further supporting conservation prioritization tools to be used to maximize the effectiveness of conservation strategies under financial constraints.},
	language = {en},
	number = {10},
	urldate = {2023-06-15},
	journal = {Diversity},
	author = {Kirk, David Anthony and Karimi, Sahebeh and Maida, Jared R. and Harvey, Jessica A. and Larsen, Karl W. and Bishop, Christine A.},
	month = sep,
	year = {2021},
	keywords = {NALCMS},
	pages = {467},
}

Modelling the distribution and abundance of species at risk is extremely important for their conservation and management. We used ecological niche models (ENMs) to predict the occurrence of western rattlesnakes (Crotalus oreganus) in British Columbia (BC), Canada. We applied this to existing population estimates to support a threshold of occurrence for management and conservation. We also identified predictors influencing rattlesnake distribution and abundance in this region. Using a Geographic Information Systems platform, we incorporated ENMs, capture–mark–recapture (CMR) and radio-telemetry results, province-wide observations, Landsat imagery and provincial databases for agricultural land use to produce quantitative, spatially explicit, population estimates across BC. Using available western rattlesnake habitat estimated at 183.9 km2 and averaging estimates calculated from densities in three study populations, we generated a mean adult population size of 9722 (±SD 3009; 0.8 relative index of occurrence [RIO] threshold). Only a small area (21.6 km2) of suitable land cover was located within protected areas, potentially protecting an estimated 1144 (±354) adults. Most suitable land cover was within 500 m of roads (170.6 km2), representing potential habitat being used by an estimated 9017 (±2791) adults. At the threshold RIO value chosen (0.8), only a very small area of farmland provided suitable land cover. Our results highlight the possibility of high mortality rates for western rattlesnakes near roads and the fact that protected areas do not provide sufficient coverage to conserve the population. Given that this species has relatively low mobility and high site fidelity to home ranges, our population estimate for BC provides a useful reference for the northern part of the species’ range. It also fulfills a need to estimate population size within political jurisdictions where conservation management decisions are made, as well as presenting a method that can be applied to other parts of the range, including the southern United States. Our study provides an important benchmark for future monitoring of western rattlesnakes in BC using a repeatable and transparent approach. Similar applications can be extrapolated and applied for other threatened species to identify and quantify population distributions and threats, further supporting conservation prioritization tools to be used to maximize the effectiveness of conservation strategies under financial constraints.

@article{chen_vegetation_2021,
	title = {Vegetation grows more luxuriantly in {Arctic} permafrost drained lake basins},
	volume = {27},
	issn = {1354-1013},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.15853},
	doi = {10.1111/gcb.15853},
	abstract = {As Arctic warming, permafrost thawing, and thermokarst development intensify, increasing evidence suggests that the frequency and magnitude of thermokarst lake drainage events are increasing. Presently, we lack a quantitative understanding of vegetation dynamics in drained lake basins, which is necessary to assess the extent to which plant growth in thawing ecosystems will offset the carbon released from permafrost. In this study, continuous satellite observations were used to detect thermokarst lake drainage events in northern Alaska over the past 20 years, and an advanced temporal segmentation and change detection algorithm allowed us to determine the year of drainage for each lake. Quantitative analysis showed that the greenness (normalized difference vegetation index [NDVI]) of tundra vegetation growing on wet and nutrient-rich lake sediments increased approximately 10 times faster than that of the peripheral vegetation. It takes approximately 5 years (4–6 years for the 25\%–75\% range) for the drainage lake area to reach the greenness level of the peripheral vegetation. Eventually, the NDVI values of the drained lake basins were 0.15 (or 25\%) higher than those of the surrounding areas. In addition, we found less lush vegetation in the floodplain drained lake basins, possibly due to water logging. We further explored the key environmental drivers affecting vegetation dynamics in and around the drained lake basins. The results showed that our multivariate regression model well simulated the growth dynamics of the drainage lake ecosystem ((Formula presented.), p {\textless}.001) and peripheral vegetation ((Formula presented.), p {\textless}.001). Among climate variables, moisture variables were more influential than temperature variables, indicating that vegetation growth in this area is susceptible to water stress. Our study provides valuable information for better modeling of vegetation dynamics in thermokarst lake areas and provides new insights into Arctic greening and carbon balance studies as thermokarst lake drainage intensifies.},
	number = {22},
	journal = {Global Change Biology},
	author = {Chen, Yating and Liu, Aobo and Cheng, Xiao},
	month = nov,
	year = {2021},
	note = {Publisher: John Wiley and Sons Inc},
	keywords = {NALCMS},
	pages = {5865--5876},
}

As Arctic warming, permafrost thawing, and thermokarst development intensify, increasing evidence suggests that the frequency and magnitude of thermokarst lake drainage events are increasing. Presently, we lack a quantitative understanding of vegetation dynamics in drained lake basins, which is necessary to assess the extent to which plant growth in thawing ecosystems will offset the carbon released from permafrost. In this study, continuous satellite observations were used to detect thermokarst lake drainage events in northern Alaska over the past 20 years, and an advanced temporal segmentation and change detection algorithm allowed us to determine the year of drainage for each lake. Quantitative analysis showed that the greenness (normalized difference vegetation index [NDVI]) of tundra vegetation growing on wet and nutrient-rich lake sediments increased approximately 10 times faster than that of the peripheral vegetation. It takes approximately 5 years (4–6 years for the 25%–75% range) for the drainage lake area to reach the greenness level of the peripheral vegetation. Eventually, the NDVI values of the drained lake basins were 0.15 (or 25%) higher than those of the surrounding areas. In addition, we found less lush vegetation in the floodplain drained lake basins, possibly due to water logging. We further explored the key environmental drivers affecting vegetation dynamics in and around the drained lake basins. The results showed that our multivariate regression model well simulated the growth dynamics of the drainage lake ecosystem ((Formula presented.), p \textless.001) and peripheral vegetation ((Formula presented.), p \textless.001). Among climate variables, moisture variables were more influential than temperature variables, indicating that vegetation growth in this area is susceptible to water stress. Our study provides valuable information for better modeling of vegetation dynamics in thermokarst lake areas and provides new insights into Arctic greening and carbon balance studies as thermokarst lake drainage intensifies.

@article{kelsey_winter_2021,
	title = {Winter snow and spring temperature have differential effects on vegetation phenology and productivity across {Arctic} plant communities},
	volume = {27},
	issn = {1354-1013, 1365-2486},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.15505},
	doi = {10.1111/gcb.15505},
	language = {en},
	number = {8},
	urldate = {2023-06-15},
	journal = {Global Change Biology},
	author = {Kelsey, Katharine C. and Pedersen, Stine Højlund and Leffler, A. Joshua and Sexton, Joseph O. and Feng, Min and Welker, Jeffrey M.},
	month = apr,
	year = {2021},
	keywords = {NALCMS},
	pages = {1572--1586},
}

@article{harrison_basin-wide_2020,
	title = {A {Basin}-{Wide} {Survey} of {Coastal} {Wetlands} of the {Laurentian} {Great} {Lakes}: {Development} and {Comparison} of {Water} {Quality} {Indices}},
	volume = {40},
	issn = {0277-5212},
	url = {http://link.springer.com/10.1007/s13157-019-01198-z},
	doi = {10.1007/s13157-019-01198-z},
	abstract = {Coastal wetlands of the Laurentian Great Lakes are vital habitats for biota of ecological and economic importance. These habitats are susceptible to water quality impairments driven by runoff from the landscape due to their location along the shoreline. Monitoring of the overall status of biotic and abiotic conditions of coastal wetlands within the Great Lakes has been ongoing for over a decade. Here, we utilize measurements of aquatic physicochemical and land cover variables from 877 vegetation zones in 511 coastal wetland sites spanning the US and Canadian shorelines of the entire Great Lakes basin. Our objective is to develop water quality indices based on physicochemical measures (Chem-Rank), land use/land cover (LULC-Rank), and their combined effects (Sum-Rank and Simplified Sum-Rank), for both vegetation zones and wetland sites. We found that water quality differed among wetland vegetation types and among Great Lakes regions, corroborating previous findings that human land use alters coastal wetland water quality. Future monitoring can use these straightforward, easy-to-calculate indices to assess the abiotic condition of aquatic habitats. Our data support the need for management efforts focused on reducing nutrient and pollution loads that stem from human activities, particularly in the developed southern portions of the Great Lakes basin.},
	number = {3},
	journal = {Wetlands},
	author = {Harrison, Anna M. and Reisinger, Alexander J. and Cooper, Matthew J. and Brady, Valerie J. and Ciborowski, Jan J. H. and O'Reilly, Katherine E. and Ruetz, Carl R. and Wilcox, Douglas A. and Uzarski, Donald G.},
	month = jun,
	year = {2020},
	note = {ISBN: 1315701901198
Publisher: Wetlands},
	keywords = {NALCMS},
	pages = {465--477},
}

Coastal wetlands of the Laurentian Great Lakes are vital habitats for biota of ecological and economic importance. These habitats are susceptible to water quality impairments driven by runoff from the landscape due to their location along the shoreline. Monitoring of the overall status of biotic and abiotic conditions of coastal wetlands within the Great Lakes has been ongoing for over a decade. Here, we utilize measurements of aquatic physicochemical and land cover variables from 877 vegetation zones in 511 coastal wetland sites spanning the US and Canadian shorelines of the entire Great Lakes basin. Our objective is to develop water quality indices based on physicochemical measures (Chem-Rank), land use/land cover (LULC-Rank), and their combined effects (Sum-Rank and Simplified Sum-Rank), for both vegetation zones and wetland sites. We found that water quality differed among wetland vegetation types and among Great Lakes regions, corroborating previous findings that human land use alters coastal wetland water quality. Future monitoring can use these straightforward, easy-to-calculate indices to assess the abiotic condition of aquatic habitats. Our data support the need for management efforts focused on reducing nutrient and pollution loads that stem from human activities, particularly in the developed southern portions of the Great Lakes basin.

@article{arsenault_comprehensive_2020,
	title = {A comprehensive, multisource database for hydrometeorological modeling of 14,425 {North} {American} watersheds},
	volume = {7},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-020-00583-2},
	doi = {10.1038/s41597-020-00583-2},
	abstract = {The Hydrometeorological Sandbox - École de technologie supérieure (HYSETS) is a rich, comprehensive and large-scale database for hydrological modelling covering 14425 watersheds in North America. The database includes data covering the period 1950–2018 depending on the type and source of data. The data include a wide array of hydrometeorological data required to perform hydrological and climate change impact studies: (1) watershed properties including boundaries, area, elevation slope, land use and other physiographic information; (2) hydrometric gauging station discharge time-series; (3) precipitation, maximum and minimum daily air temperature time-series from weather station records and from (4) the SCDNA infilled gauge meteorological dataset; (5) the NRCan and Livneh gridded interpolated products' meteorological data; (6) ERA5 and ERA5-Land reanalysis data; and (7) the SNODAS and ERA5-Land snow water equivalent estimates. All data have been processed and averaged at the watershed scale, and provides a solid basis for hydrological modelling, climate change impact studies, model calibration assessment, regionalization method evaluation and essentially any study requiring access to large amounts of spatiotemporally varied hydrometeorological data.},
	number = {1},
	journal = {Scientific Data},
	author = {Arsenault, Richard and Brissette, François and Martel, Jean-Luc and Troin, Magali and Lévesque, Guillaume and Davidson-Chaput, Jonathan and Gonzalez, Mariana Castañeda and Ameli, Ali and Poulin, Annie},
	month = jul,
	year = {2020},
	pmid = {32686687},
	note = {ISBN: 4159702000583},
	keywords = {NALCMS},
	pages = {243},
}

The Hydrometeorological Sandbox - École de technologie supérieure (HYSETS) is a rich, comprehensive and large-scale database for hydrological modelling covering 14425 watersheds in North America. The database includes data covering the period 1950–2018 depending on the type and source of data. The data include a wide array of hydrometeorological data required to perform hydrological and climate change impact studies: (1) watershed properties including boundaries, area, elevation slope, land use and other physiographic information; (2) hydrometric gauging station discharge time-series; (3) precipitation, maximum and minimum daily air temperature time-series from weather station records and from (4) the SCDNA infilled gauge meteorological dataset; (5) the NRCan and Livneh gridded interpolated products' meteorological data; (6) ERA5 and ERA5-Land reanalysis data; and (7) the SNODAS and ERA5-Land snow water equivalent estimates. All data have been processed and averaged at the watershed scale, and provides a solid basis for hydrological modelling, climate change impact studies, model calibration assessment, regionalization method evaluation and essentially any study requiring access to large amounts of spatiotemporally varied hydrometeorological data.

@article{van_wilgenburg_cost_2020,
	title = {A cost efficient spatially balanced hierarchical sampling design for monitoring boreal birds incorporating access costs and habitat stratification},
	volume = {15},
	issn = {1932-6203},
	url = {https://dx.plos.org/10.1371/journal.pone.0234494},
	doi = {10.1371/journal.pone.0234494},
	language = {en},
	number = {6},
	urldate = {2023-06-15},
	journal = {PLOS ONE},
	author = {Van Wilgenburg, Steven L. and Mahon, C. Lisa and Campbell, Greg and McLeod, Logan and Campbell, Margaret and Evans, Dean and Easton, Wendy and Francis, Charles M. and Haché, Samuel and Machtans, Craig S. and Mader, Caitlin and Pankratz, Rhiannon F. and Russell, Rich and Smith, Adam C. and Thomas, Peter and Toms, Judith D. and Tremblay, Junior A.},
	editor = {Koch, Frank H.},
	month = jun,
	year = {2020},
	keywords = {NALCMS},
	pages = {e0234494},
}

@article{lu_cultivated_2020,
	title = {A cultivated planet in 2010 – {Part} 1: {The} global synergy cropland map},
	volume = {12},
	issn = {1866-3516},
	shorttitle = {A cultivated planet in 2010 – {Part} 1},
	url = {https://essd.copernicus.org/articles/12/1913/2020/},
	doi = {10.5194/essd-12-1913-2020},
	abstract = {Abstract. Information on global cropland distribution and
agricultural production is critical for the world's agricultural monitoring
and food security. We present datasets of cropland extent and agricultural
production in a two-paper series of a cultivated planet in 2010. In the
first part, we propose a new Self-adapting Statistics Allocation Model
(SASAM) to develop the global map of cropland distribution. SASAM is based
on the fusion of multiple existing cropland maps and multilevel statistics
of the cropland area, which is independent of training samples. First,
cropland area statistics are used to rank the input cropland maps, and then
a scoring table is built to indicate the agreement among the input datasets.
Secondly, statistics are allocated adaptively to the pixels with higher
agreement scores until the cumulative cropland area is close to the
statistics. The multilevel allocation results are then integrated to obtain
the extent of cropland. We applied SASAM to produce a global cropland
synergy map with a 500 m spatial resolution for circa 2010. The accuracy
assessments show that the synergy map has higher accuracy than the input
datasets and better consistency with the cropland statistics. The synergy
cropland map is available via an open-data repository (https://doi.org/10.7910/DVN/ZWSFAA; Lu et al., 2020). This new cropland map
has been used as an essential input to the Spatial Production Allocation
Model (SPAM) for producing the global dataset of agricultural production
for circa 2010, which is described in the second part of the two-paper series.},
	language = {en},
	number = {3},
	urldate = {2023-06-15},
	journal = {Earth System Science Data},
	author = {Lu, Miao and Wu, Wenbin and You, Liangzhi and See, Linda and Fritz, Steffen and Yu, Qiangyi and Wei, Yanbing and Chen, Di and Yang, Peng and Xue, Bing},
	month = aug,
	year = {2020},
	keywords = {NALCMS},
	pages = {1913--1928},
}

Abstract. Information on global cropland distribution and agricultural production is critical for the world's agricultural monitoring and food security. We present datasets of cropland extent and agricultural production in a two-paper series of a cultivated planet in 2010. In the first part, we propose a new Self-adapting Statistics Allocation Model (SASAM) to develop the global map of cropland distribution. SASAM is based on the fusion of multiple existing cropland maps and multilevel statistics of the cropland area, which is independent of training samples. First, cropland area statistics are used to rank the input cropland maps, and then a scoring table is built to indicate the agreement among the input datasets. Secondly, statistics are allocated adaptively to the pixels with higher agreement scores until the cumulative cropland area is close to the statistics. The multilevel allocation results are then integrated to obtain the extent of cropland. We applied SASAM to produce a global cropland synergy map with a 500 m spatial resolution for circa 2010. The accuracy assessments show that the synergy map has higher accuracy than the input datasets and better consistency with the cropland statistics. The synergy cropland map is available via an open-data repository (https://doi.org/10.7910/DVN/ZWSFAA; Lu et al., 2020). This new cropland map has been used as an essential input to the Spatial Production Allocation Model (SPAM) for producing the global dataset of agricultural production for circa 2010, which is described in the second part of the two-paper series.

@article{plassin_socio-environmental_2020,
	title = {A socio-environmental geodatabase for integrative research in the transboundary {Rio} {Grande}/{Río} {Bravo} basin},
	volume = {7},
	issn = {2052-4463},
	url = {http://www.nature.com/articles/s41597-020-0410-1},
	doi = {10.1038/s41597-020-0410-1},
	abstract = {Integrative research on water resources requires a wide range of socio-environmental datasets to better understand human-water interactions and inform decision-making. However, in transboundary watersheds, integrating cross-disciplinary and multinational datasets is a daunting task due to the disparity of data sources and the inconsistencies in data format, content, resolution, and language. This paper introduces a socio-environmental geodatabase that transcends political and disciplinary boundaries in the Rio Grande/Río Bravo basin (RGB). The geodatabase aggregates 145 GIS data layers on five main themes: (i) Water \& Land Governance, (ii) Hydrology, (iii) Water Use \& Hydraulic Infrastructures, (iv) Socio-Economics, and (v) Biophysical Environment. Datasets were primarily collected from public open-access data sources, processed with ArcGIS, and documented through the FGCD metadata standard. By synthesizing a broad array of datasets and mapping public and private water governance, we expect to advance interdisciplinary research in the RGB, provide a replicable approach to dataset compilation for transboundary watersheds, and ultimately foster transboundary collaboration for sustainable resource management.},
	number = {1},
	journal = {Scientific Data},
	author = {Plassin, Sophie and Koch, Jennifer and Paladino, Stephanie and Friedman, Jack R. and Spencer, Kyndra and Vaché, Kellie B.},
	month = mar,
	year = {2020},
	pmid = {32144267},
	keywords = {NALCMS},
	pages = {80},
}

Integrative research on water resources requires a wide range of socio-environmental datasets to better understand human-water interactions and inform decision-making. However, in transboundary watersheds, integrating cross-disciplinary and multinational datasets is a daunting task due to the disparity of data sources and the inconsistencies in data format, content, resolution, and language. This paper introduces a socio-environmental geodatabase that transcends political and disciplinary boundaries in the Rio Grande/Río Bravo basin (RGB). The geodatabase aggregates 145 GIS data layers on five main themes: (i) Water & Land Governance, (ii) Hydrology, (iii) Water Use & Hydraulic Infrastructures, (iv) Socio-Economics, and (v) Biophysical Environment. Datasets were primarily collected from public open-access data sources, processed with ArcGIS, and documented through the FGCD metadata standard. By synthesizing a broad array of datasets and mapping public and private water governance, we expect to advance interdisciplinary research in the RGB, provide a replicable approach to dataset compilation for transboundary watersheds, and ultimately foster transboundary collaboration for sustainable resource management.

@phdthesis{young_alaskas_2020,
	type = {Doctor of {Philosophy}},
	title = {Alaska's shrinking glaciers: {Integrated} glaciological research for hydrological, ecological, and environmental education applications},
	url = {http://hdl.handle.net/11122/11302},
	school = {Geosciences, University of Alaska - Fairbanks},
	author = {Young, J.},
	year = {2020},
	keywords = {NALCMS},
}

@article{mallory_apparent_2020,
	title = {Apparent {Collapse} of the {Peary} {Caribou} (\textit{{Rangifer} tarandus pearyi}) {Population} on {Axel} {Heiberg} {Island}, {Nunavut}, {Canada}},
	volume = {73},
	issn = {1923-1245, 0004-0843},
	url = {https://journalhosting.ucalgary.ca/index.php/arctic/article/view/71605},
	doi = {10.14430/arctic71605},
	abstract = {In spring 2019, we conducted a comprehensive abundance and distribution survey for Peary caribou (Rangifer tarandus pearyi) and muskox (Ovibos moschatus) on Axel Heiberg Island, Nunavut, Canada. Although much of Axel Heiberg Island is rugged and extensively glaciated, areas east of the Princess Margaret mountain range have high productivity given the latitude and have supported relatively large numbers of Peary caribou and muskoxen. This region of the island has been previously identified as a potential High Arctic refugium. The last island-wide survey, in 2007, estimated 4237 muskoxen (95\% confidence interval [CI] [3371:5325]) and 2291 Peary caribou (95\% CI [1636:3208]); based on our 2019 results, it appears that muskox numbers have been stable on Axel Heiberg Island since then. Using distance sampling and density surface models, we estimated 3772 muskoxen (95\% CI [3001:4742]) on Axel Heiberg Island during our 2019 survey. In contrast, Peary caribou, which is listed as an endangered species under the Canadian Species at Risk Act, appear to have declined dramatically from the 2007 estimate. During the 2019 survey, we observed only six Peary caribou and could not generate an island-wide estimate. Abrupt declines in numbers are characteristic of the species and are usually related to poor winter conditions such as dense snowpack or extreme weather events that result in widespread ground-fast icing. However, the limited monitoring information available at the northern extent of Peary caribou range presents major challenges to our understanding of the mechanisms leading to this near total absence of approximately 20\% of range-wide Peary caribou numbers.},
	number = {4},
	urldate = {2023-06-15},
	journal = {ARCTIC},
	author = {Mallory, Conor D. and Fredlund, Matthew and Campbell, Mitch W.},
	month = dec,
	year = {2020},
	keywords = {NALCMS},
	pages = {499--508},
}

In spring 2019, we conducted a comprehensive abundance and distribution survey for Peary caribou (Rangifer tarandus pearyi) and muskox (Ovibos moschatus) on Axel Heiberg Island, Nunavut, Canada. Although much of Axel Heiberg Island is rugged and extensively glaciated, areas east of the Princess Margaret mountain range have high productivity given the latitude and have supported relatively large numbers of Peary caribou and muskoxen. This region of the island has been previously identified as a potential High Arctic refugium. The last island-wide survey, in 2007, estimated 4237 muskoxen (95% confidence interval [CI] [3371:5325]) and 2291 Peary caribou (95% CI [1636:3208]); based on our 2019 results, it appears that muskox numbers have been stable on Axel Heiberg Island since then. Using distance sampling and density surface models, we estimated 3772 muskoxen (95% CI [3001:4742]) on Axel Heiberg Island during our 2019 survey. In contrast, Peary caribou, which is listed as an endangered species under the Canadian Species at Risk Act, appear to have declined dramatically from the 2007 estimate. During the 2019 survey, we observed only six Peary caribou and could not generate an island-wide estimate. Abrupt declines in numbers are characteristic of the species and are usually related to poor winter conditions such as dense snowpack or extreme weather events that result in widespread ground-fast icing. However, the limited monitoring information available at the northern extent of Peary caribou range presents major challenges to our understanding of the mechanisms leading to this near total absence of approximately 20% of range-wide Peary caribou numbers.

@article{cabrera-cruz_artificial_2020,
	title = {Artificial {Light} at {Night} is {Related} to {Broad}-{Scale} {Stopover} {Distributions} of {Nocturnally} {Migrating} {Landbirds} along the {Yucatan} {Peninsula}, {Mexico}},
	volume = {12},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/12/3/395},
	doi = {10.3390/rs12030395},
	abstract = {The distributions of birds during migratory stopovers are influenced by a hierarchy of factors. For example, in temperate regions, migrants are concentrated near areas of bright artificial light at night (ALAN) and also the coastlines of large water bodies at broad spatial scales. However, less is known about what drives broad-scale stopover distributions in the tropics. We quantified seasonal densities of nocturnally migrating landbirds during spring and fall of 2011–2015, using two weather radars on the Yucatan peninsula, Mexico (Sabancuy and Cancun). We tested the influence of environmental predictors in explaining broad-scale bird stopover densities. We predicted higher densities in areas (1) closer to the coast in the fall and farther away in spring and (2) closer to bright ALAN and with lower ALAN intensity in both seasons. We found that birds were more concentrated near the coastline in the fall and away from it in spring around Cancun but not Sabancuy. Counter to our expectations, we detected increased bird densities with increased distance from lights in spring around Sabancuy, and in both seasons around Cancun, suggesting avoidance of bright areas during those seasons. This is the first evidence of broad-scale bird avoidance of bright areas during stopover.},
	number = {3},
	journal = {Remote Sensing},
	author = {Cabrera-Cruz, Sergio A. and Cohen, Emily B. and Smolinsky, Jaclyn A. and Buler, Jeffrey J.},
	month = jan,
	year = {2020},
	note = {Publisher: MDPI AG},
	keywords = {NALCMS},
	pages = {395},
}

The distributions of birds during migratory stopovers are influenced by a hierarchy of factors. For example, in temperate regions, migrants are concentrated near areas of bright artificial light at night (ALAN) and also the coastlines of large water bodies at broad spatial scales. However, less is known about what drives broad-scale stopover distributions in the tropics. We quantified seasonal densities of nocturnally migrating landbirds during spring and fall of 2011–2015, using two weather radars on the Yucatan peninsula, Mexico (Sabancuy and Cancun). We tested the influence of environmental predictors in explaining broad-scale bird stopover densities. We predicted higher densities in areas (1) closer to the coast in the fall and farther away in spring and (2) closer to bright ALAN and with lower ALAN intensity in both seasons. We found that birds were more concentrated near the coastline in the fall and away from it in spring around Cancun but not Sabancuy. Counter to our expectations, we detected increased bird densities with increased distance from lights in spring around Sabancuy, and in both seasons around Cancun, suggesting avoidance of bright areas during those seasons. This is the first evidence of broad-scale bird avoidance of bright areas during stopover.

@article{lopez-espinoza_assessing_2020,
	title = {Assessing the {Impact} of {Land} {Use} and {Land} {Cover} {Data} {Representation} on {Weather} {Forecast} {Quality}: {A} {Case} {Study} in {Central} {Mexico}},
	volume = {11},
	issn = {2073-4433},
	url = {https://www.mdpi.com/2073-4433/11/11/1242},
	doi = {10.3390/atmos11111242},
	abstract = {In atmospheric modeling, an accurate representation of land cover is required because such information impacts water and energy budgets and, consequently, the performance of models in simulating regional climate. This study analyzes the impact of the land cover data on an operational weather forecasting system using the Weather Research and Forecasting (WRF) model for central Mexico, with the aim of improving the quality of the operative forecast. Two experiments were conducted using different land cover datasets: a United States Geological Survey (USGS) map and an updated North American Land Change Monitoring System (NALCMS) map. The experiments were conducted as a daily 120 h forecast for each day of January, April, July, and September of 2012, and the near-surface temperature, wind speed, and hourly precipitation were analyzed. Both experiments were compared with observations from meteorological stations. The statistical analysis of this study showed that wind speed and near-surface temperature prediction may be further improved with the updated and more accurate NALCMS dataset, particularly in the forecast covering 48 to 72 h. The Root Mean Square Error (RMSE) of the average wind speed reached a maximum reduction of up to 1.2 m s−1, whereas for the near-surface temperature there was a reduction of up to 0.6 °C. The RMSE of the average hourly precipitation was very similar between both experiments, however the location of precipitation was modified.},
	number = {11},
	journal = {Atmosphere},
	author = {López-Espinoza, Erika Danaé and Zavala-Hidalgo, Jorge and Mahmood, Rezaul and Gómez-Ramos, Octavio},
	month = nov,
	year = {2020},
	keywords = {NALCMS},
	pages = {1242},
}

In atmospheric modeling, an accurate representation of land cover is required because such information impacts water and energy budgets and, consequently, the performance of models in simulating regional climate. This study analyzes the impact of the land cover data on an operational weather forecasting system using the Weather Research and Forecasting (WRF) model for central Mexico, with the aim of improving the quality of the operative forecast. Two experiments were conducted using different land cover datasets: a United States Geological Survey (USGS) map and an updated North American Land Change Monitoring System (NALCMS) map. The experiments were conducted as a daily 120 h forecast for each day of January, April, July, and September of 2012, and the near-surface temperature, wind speed, and hourly precipitation were analyzed. Both experiments were compared with observations from meteorological stations. The statistical analysis of this study showed that wind speed and near-surface temperature prediction may be further improved with the updated and more accurate NALCMS dataset, particularly in the forecast covering 48 to 72 h. The Root Mean Square Error (RMSE) of the average wind speed reached a maximum reduction of up to 1.2 m s−1, whereas for the near-surface temperature there was a reduction of up to 0.6 °C. The RMSE of the average hourly precipitation was very similar between both experiments, however the location of precipitation was modified.

@phdthesis{tisserant_biodiversite_2020,
	type = {Philosophiæ {Doctor}},
	title = {Biodiversité et génie végétal: {Réponse} taxonomique et fonctionnelle de la flore vasculaire riveraine à la stabilisation de berge},
	url = {http://hdl.handle.net/20.500.11794/40081},
	school = {Biologie Végétale, Université Laval},
	author = {Tisserant, M.},
	year = {2020},
	keywords = {NALCMS},
}

@article{jenkins_biotic_2020,
	title = {Biotic interactions govern the distribution of coexisting ungulates in the {Arctic} {Archipelago} – {A} case for conservation planning},
	volume = {24},
	issn = {23519894},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2351989420307800},
	doi = {10.1016/j.gecco.2020.e01239},
	language = {en},
	urldate = {2023-06-15},
	journal = {Global Ecology and Conservation},
	author = {Jenkins, Deborah A. and Lecomte, Nicolas and Andrews, Geoffrey and Yannic, Glenn and Schaefer, James A.},
	month = dec,
	year = {2020},
	keywords = {NALCMS},
	pages = {e01239},
}

@article{yang_characterizing_2020,
	title = {Characterizing spatiotemporal patterns of crop phenology across {North} {America} during 2000–2016 using satellite imagery and agricultural survey data},
	volume = {170},
	issn = {09242716},
	url = {https://doi.org/10.1016/j.isprsjprs.2020.10.005},
	doi = {10.1016/j.isprsjprs.2020.10.005},
	abstract = {Crop phenology represents an integrative indicator of climate change and plays a vital role in terrestrial carbon dynamics and sustainable agricultural development. However, spatiotemporal variations of crop phenology remain unclear at large scales. This knowledge gap has hindered our ability to realistically quantify the biogeochemical dynamics in agroecosystems, predict future climate, and make informed decisions for climate change mitigation and adaptation. In this study, we improved an EVI-curve-based approach and used it to detect spatiotemporal patterns in cropping intensity and five major phenological stages over North America during 2000–2016 using vegetation index in combination with agricultural survey data and other ancillary maps. Our predicted crop phenological stages showed strong linear relationships with the survey-based datasets, with R2, RMSEs, and MAEs in the ranges of 0.35 –0.99, three to ten days, and two to eight days, respectively. During the study period, the planting dates were advanced by 0.60 days/year (p {\textless} 0.01), and harvesting dates were delayed by 0.78 days/year (p {\textless} 0.01) over North America. A minimum temperature increase by 1 °C caused a 4.26-day planting advance (r = −0.50, p {\textless} 0. 01) or a 0.66-day harvest delay (r = 0.10, p {\textless} 0.01). While, a higher maximum temperature resulted in a planting advance by 4.48 days/°C (r = −0.62, p {\textless} 0.01) or a harvest advance by 2.22 days/°C (r = −0.40, p {\textless} 0.01). Our analysis illustrated evident spatiotemporal variations in crop phenology in response to climate change and management practices. The derived crop phenological datasets and cropping intensity maps can be used in regional climate assessments and in developing adaptation strategies.},
	journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
	author = {Yang, Yanjun and Ren, Wei and Tao, Bo and Ji, Lei and Liang, Liang and Ruane, Alex C. and Fisher, Joshua B. and Liu, Jiangui and Sama, Michael and Li, Zhe and Tian, Qingjiu},
	month = dec,
	year = {2020},
	note = {Publisher: Elsevier B.V.},
	keywords = {NALCMS},
	pages = {156--173},
}

Crop phenology represents an integrative indicator of climate change and plays a vital role in terrestrial carbon dynamics and sustainable agricultural development. However, spatiotemporal variations of crop phenology remain unclear at large scales. This knowledge gap has hindered our ability to realistically quantify the biogeochemical dynamics in agroecosystems, predict future climate, and make informed decisions for climate change mitigation and adaptation. In this study, we improved an EVI-curve-based approach and used it to detect spatiotemporal patterns in cropping intensity and five major phenological stages over North America during 2000–2016 using vegetation index in combination with agricultural survey data and other ancillary maps. Our predicted crop phenological stages showed strong linear relationships with the survey-based datasets, with R2, RMSEs, and MAEs in the ranges of 0.35 –0.99, three to ten days, and two to eight days, respectively. During the study period, the planting dates were advanced by 0.60 days/year (p \textless 0.01), and harvesting dates were delayed by 0.78 days/year (p \textless 0.01) over North America. A minimum temperature increase by 1 °C caused a 4.26-day planting advance (r = −0.50, p \textless 0. 01) or a 0.66-day harvest delay (r = 0.10, p \textless 0.01). While, a higher maximum temperature resulted in a planting advance by 4.48 days/°C (r = −0.62, p \textless 0.01) or a harvest advance by 2.22 days/°C (r = −0.40, p \textless 0.01). Our analysis illustrated evident spatiotemporal variations in crop phenology in response to climate change and management practices. The derived crop phenological datasets and cropping intensity maps can be used in regional climate assessments and in developing adaptation strategies.

@article{potter_climate_2020,
	title = {Climate change decreases the cooling effect from postfire albedo in boreal {North} {America}},
	volume = {26},
	issn = {1354-1013, 1365-2486},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.14888},
	doi = {10.1111/gcb.14888},
	language = {en},
	number = {3},
	urldate = {2023-06-15},
	journal = {Global Change Biology},
	author = {Potter, Stefano and Solvik, Kylen and Erb, Angela and Goetz, Scott J. and Johnstone, Jill F. and Mack, Michelle C. and Randerson, James T. and Román, Miguel O. and Schaaf, Crystal L. and Turetsky, Merritt R. and Veraverbeke, Sander and Walker, Xanthe J. and Wang, Zhuosen and Massey, Richard and Rogers, Brendan M.},
	month = mar,
	year = {2020},
	keywords = {NALCMS},
	pages = {1592--1607},
}

@phdthesis{stehelin_climate_2020,
	type = {Doctor of {Philosophy}},
	title = {Climate change implications for distribution, phenology and conservation of {Olive}-sided {Flycatchers} ({Contopus} cooperi) and {Western} {Wood}-{Pewees} ({Csordidulus}) in northwestern {North} {America}},
	school = {Renewable Resources, University of Alberta},
	author = {Stehelin, T.},
	year = {2020},
	doi = {10.7939/r3-mm4n-1t87},
	keywords = {NALCMS},
}

@article{prasad_combining_2020,
	title = {Combining {US} and {Canadian} forest inventories to assess habitat suitability and migration potential of 25 tree species under climate change},
	volume = {26},
	issn = {1366-9516, 1472-4642},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/ddi.13078},
	doi = {10.1111/ddi.13078},
	language = {en},
	number = {9},
	urldate = {2023-06-15},
	journal = {Diversity and Distributions},
	author = {Prasad, Anantha and Pedlar, John and Peters, Matt and McKenney, Dan and Iverson, Louis and Matthews, Steve and Adams, Bryce},
	month = sep,
	year = {2020},
	keywords = {NALCMS},
	pages = {1142--1159},
}

@article{bolton_continental-scale_2020,
	title = {Continental-scale land surface phenology from harmonized {Landsat} 8 and {Sentinel}-2 imagery},
	volume = {240},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425720300547},
	doi = {10.1016/j.rse.2020.111685},
	journal = {Remote Sensing of Environment},
	author = {Bolton, Douglas K. and Gray, Josh M. and Melaas, Eli K. and Moon, Minkyu and Eklundh, Lars and Friedl, Mark A.},
	month = apr,
	year = {2020},
	note = {Publisher: Elsevier},
	keywords = {NALCMS},
	pages = {111685},
}

@phdthesis{guevara_country_2020,
	type = {Doctor of {Philosophy}},
	title = {Country to global prediction of soil organic carbon and soil moisture using digital soil mapping},
	url = {https://udspace.udel.edu/handle/19716/28171},
	school = {Plant and Soil Sciences, University of Delaware},
	author = {Guevara, M.},
	year = {2020},
	keywords = {NALCMS},
}

@phdthesis{mathias_designing_2020,
	type = {Master of {Landscape} {Architecture}},
	title = {Designing against habitat loss: {Facilitating} movement of the {Louisiana} black bear},
	url = {http://hdl.handle.net/2142/108039},
	school = {Landscape Architecture, University of Illinois},
	author = {Mathias, L.},
	year = {2020},
	keywords = {Lakes \& Rivers, Major Roads, NALCMS, Protected Areas, Railroads},
}

@article{castiglia_detection_2020,
	title = {Detection of cryptic diversity in lizards ({Squamata}) from two {Biosphere} {Reserves} in {Mesoamerica}},
	volume = {14},
	issn = {1993-078X},
	url = {https://compcytogen.pensoft.net/article/57765/},
	doi = {10.3897/CompCytogen.v14.i4.57765},
	abstract = {A combined approach based on karyology and DNA taxonomy allowed us to characterize the taxonomic peculiarities in 10 Mesoamerican lizard species, belonging to six genera and five families, inhabiting two Biosphere Reserve in Chiapas, Mexico: La Sepultura Biosphere Reserve, and Montes Azules Biosphere. The karyotypes of four species, Phyllodactylus sp. 3 ( P. tuberculosus species group) (2n = 38), Holcosus festivus (Lichtenstein et von Martens, 1856) (2n = 50), Anolis lemurinus Cope, 1861 (2n = 40), and A. uniformis Cope, 1885 (2n = 29–30) are described for the first time, the last one showing a particular X 1 X 1 X 2 X 2 /X 1 X 2 Y condition. In Aspidoscelis deppii (Wiegmann, 1834) (2n = 50) and Anolis capito Peters, 1863 (2n = 42), we found a different karyotype from the ones previously reported for these species. Moreover, in A. capito , the cytogenetic observation is concurrent with a considerable genetic divergence (9\%) at the studied mtDNA marker (MT-ND2), which is indicative of a putative new cryptic species. The skink Scincella cherriei (Cope, 1893), showed high values of genetic divergence (5.2\% at 16S gene) between the specimens from Montes Azules and those from Costa Rica and Nicaragua, comparable to the values typical of sister species in skinks. A lower level of genetic divergence, compatible with an intraspecific phylogeographic structure, has been identified in Lepidophyma flavimaculatum Duméril, 1851. These new data identify taxa that urgently require more in-depth taxonomic studies especially in these areas where habitat alteration is proceeding at an alarming rate.},
	number = {4},
	journal = {Comparative Cytogenetics},
	author = {Castiglia, Riccardo and Flores-Villela, Oscar Alberto and Bezerra, Alexandra M. R. and Gornung, Ekaterina and Annesi, Flavia and Muñoz-Alonso, Luis Antonio and Solano, Emanuela},
	month = dec,
	year = {2020},
	note = {Publisher: Pensoft Publishers},
	keywords = {NALCMS},
	pages = {613--638},
}

A combined approach based on karyology and DNA taxonomy allowed us to characterize the taxonomic peculiarities in 10 Mesoamerican lizard species, belonging to six genera and five families, inhabiting two Biosphere Reserve in Chiapas, Mexico: La Sepultura Biosphere Reserve, and Montes Azules Biosphere. The karyotypes of four species, Phyllodactylus sp. 3 ( P. tuberculosus species group) (2n = 38), Holcosus festivus (Lichtenstein et von Martens, 1856) (2n = 50), Anolis lemurinus Cope, 1861 (2n = 40), and A. uniformis Cope, 1885 (2n = 29–30) are described for the first time, the last one showing a particular X 1 X 1 X 2 X 2 /X 1 X 2 Y condition. In Aspidoscelis deppii (Wiegmann, 1834) (2n = 50) and Anolis capito Peters, 1863 (2n = 42), we found a different karyotype from the ones previously reported for these species. Moreover, in A. capito , the cytogenetic observation is concurrent with a considerable genetic divergence (9%) at the studied mtDNA marker (MT-ND2), which is indicative of a putative new cryptic species. The skink Scincella cherriei (Cope, 1893), showed high values of genetic divergence (5.2% at 16S gene) between the specimens from Montes Azules and those from Costa Rica and Nicaragua, comparable to the values typical of sister species in skinks. A lower level of genetic divergence, compatible with an intraspecific phylogeographic structure, has been identified in Lepidophyma flavimaculatum Duméril, 1851. These new data identify taxa that urgently require more in-depth taxonomic studies especially in these areas where habitat alteration is proceeding at an alarming rate.

@phdthesis{garvey_examination_2020,
	type = {Master of {Science}},
	title = {Examination of parasite assemblages in killifish of the genus {Fundulus} across the {Atlantic} coast of the {United} {States} and {Canada}},
	url = {https://nsuworks.nova.edu/occ_stuetd/532},
	school = {Natural Sciences and Oceanography, Marine Biology, Nova Southeastern University},
	author = {Garvey, D.},
	year = {2020},
	keywords = {NALCMS},
}

@phdthesis{hewitt_factors_2020,
	type = {Degree {Project}},
	title = {Factors controlling mercury concentration in rivers in the {Mackenzie} {River} {Basin}, {Northwestern} {Canada}},
	url = {http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414036},
	school = {Earth Sciences, Uppsala University},
	author = {Hewitt, J.},
	year = {2020},
	keywords = {NALCMS},
}

@article{gharari_flexible_2020,
	title = {Flexible vector-based spatial configurations in land models},
	volume = {24},
	issn = {1607-7938},
	url = {https://hess.copernicus.org/articles/24/5953/2020/},
	doi = {10.5194/hess-24-5953-2020},
	number = {12},
	journal = {Hydrology and Earth System Sciences},
	author = {Gharari, Shervan and Clark, Martyn P. and Mizukami, Naoki and Knoben, Wouter J. M. and Wong, Jefferson S. and Pietroniro, Alain},
	month = dec,
	year = {2020},
	keywords = {NALCMS},
	pages = {5953--5971},
}

@article{eccles_geospatial_2020,
	title = {Geospatial analysis of the patterns of chemical exposures among biota in the {Canadian} {Oil} {Sands} {Region}},
	volume = {15},
	issn = {1932-6203},
	url = {https://dx.plos.org/10.1371/journal.pone.0239086},
	doi = {10.1371/journal.pone.0239086},
	abstract = {Understanding the patterns of chemical exposure among biota across a landscape is challenging due to the spatial heterogeneity and complexity of the sources, pathways, and fate of the different chemicals. While spatially-driven relationships between contaminant sources and biota body burdens of a single chemical are commonly modelled, there has been little effort on modelling chemical mixtures across multiple wildlife species in the Canadian Oil Sands region. In this study, we used spatial principal components analysis (sPCA) to assess spatial patterns of the body burdens of 22 metals and Potentially Toxic Elements (PTEs) in 492 individual wildlife, including fur-bearing mammals, colonial waterbirds, and amphibians collected from the Canadian Oil Sands region in Canada. Spatial analysis and mapping both indicate that some of the complex exposures in the studied biota are distributed randomly across a landscape, which suggests background or non-point source exposures. In contrast, the pattern of exposure for seven metals and PTEs, including mercury, vanadium, lead, rubidium, lithium, strontium, and barium, exhibited a clustered pattern to the east of the open-pit mining area and in regions downstream of oil sands development which indicates point-source input. This analysis demonstrated useful methods for integrating monitoring datasets and identifying sources and potential drivers of exposure to chemical mixtures in biota across a landscape. These results can be used to support an adaptive monitoring program by identifying regions needing additional monitoring, health impact assessments, and possible intervention strategies.},
	number = {9},
	journal = {PLOS ONE},
	author = {Eccles, Kristin M. and Pauli, Bruce and Chan, Hing Man},
	editor = {Aherne, Julian},
	month = sep,
	year = {2020},
	pmid = {32997667},
	note = {Publisher: Public Library of Science},
	keywords = {NALCMS},
	pages = {e0239086},
}

Understanding the patterns of chemical exposure among biota across a landscape is challenging due to the spatial heterogeneity and complexity of the sources, pathways, and fate of the different chemicals. While spatially-driven relationships between contaminant sources and biota body burdens of a single chemical are commonly modelled, there has been little effort on modelling chemical mixtures across multiple wildlife species in the Canadian Oil Sands region. In this study, we used spatial principal components analysis (sPCA) to assess spatial patterns of the body burdens of 22 metals and Potentially Toxic Elements (PTEs) in 492 individual wildlife, including fur-bearing mammals, colonial waterbirds, and amphibians collected from the Canadian Oil Sands region in Canada. Spatial analysis and mapping both indicate that some of the complex exposures in the studied biota are distributed randomly across a landscape, which suggests background or non-point source exposures. In contrast, the pattern of exposure for seven metals and PTEs, including mercury, vanadium, lead, rubidium, lithium, strontium, and barium, exhibited a clustered pattern to the east of the open-pit mining area and in regions downstream of oil sands development which indicates point-source input. This analysis demonstrated useful methods for integrating monitoring datasets and identifying sources and potential drivers of exposure to chemical mixtures in biota across a landscape. These results can be used to support an adaptive monitoring program by identifying regions needing additional monitoring, health impact assessments, and possible intervention strategies.

@phdthesis{gondor_grassland_2020,
	type = {Master of {Natural} {Resources}},
	title = {Grassland restoration and climate change: {Altar} {Valley}, {Arizona} case study},
	url = {https://ir.library.oregonstate.edu/concern/graduate_projects/8049gc513},
	school = {Forest Ecosystems and Society, Oregon State University},
	author = {Gondor, A.},
	year = {2020},
	keywords = {NALCMS},
}

@techreport{valenti_great_2020,
	title = {Great {Lakes} {Water} {Resources} {II} {A} {Google} {Earth} {Engine} {Tool} to {Automate} {Wetland} {Mapping} {Using} {Optical} and {Radar} {Satellite} {Sensors} in the {Great} {Lakes} {Basin} for {Wetland} {Management} and {Monitoring}},
	url = {https://ntrs.nasa.gov/citations/20205000953},
	abstract = {The Great Lakes Basin is one of the world's largest freshwater ecosystems. The Basin harbors over 200,000 acres of wetlands. These wetlands provide a variety of environmental, ecological, and recreational functions to over 30 million people in the region. Some of these functions include improving water quality, mitigating flood impacts, providing wildlife habitat, and housing recreational activities. However, due to anthropogenic activities, habitat conversion and degradation threaten to disrupt or destroy remaining wetland ecosystems. Maps of wetland distribution based on ground surveys are costly and labor-intensive, prohibiting timely evaluations of wetland loss and gain. The Great Lakes Water Resources II team at the NASA Jet Propulsion Laboratory developed the Wetlands Extent Tool 2.0 (WET 2.0) in Google Earth Engine to automate mapping of wetland distribution in the Great Lakes Basin. The team partnered with the US Fish and Wildlife Service (USFWS), Environmental Protection Agency (EPA), Minnesota Department of Natural Resources (MDNR), the National Oceanic and Atmospheric Administration (NOAA), and Ducks Unlimited (DU). WET 2.0 incorporates Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C- SAR), and Sentinel-2 Multispectral Instrument (MSI) satellite data. WET 2.0 is trained to classify anywhere in the Great Lakes Basin. Utilizing a Random Forest classifier, WET 2.0 is capable of automatically mapping wetland extent in the entire Great Lakes Basin, achieving a mean overall accuracy of 80.12\% when tested in Michigan. Findings and maps produced in WET 2.0 will enable our partners to identify areas of ecosystem degradation and wetland destruction in order to enact environmental practices and policy initiatives to maintain environmental and economic health in the area.},
	institution = {NASA DEVELOP National Program California - Jet Propulsion Laboratory},
	author = {Valenti, Vanessa and Carcelen, Erica and Lange, Katie and Russo, Nicholas},
	year = {2020},
	keywords = {NALCMS},
	pages = {28},
}

The Great Lakes Basin is one of the world's largest freshwater ecosystems. The Basin harbors over 200,000 acres of wetlands. These wetlands provide a variety of environmental, ecological, and recreational functions to over 30 million people in the region. Some of these functions include improving water quality, mitigating flood impacts, providing wildlife habitat, and housing recreational activities. However, due to anthropogenic activities, habitat conversion and degradation threaten to disrupt or destroy remaining wetland ecosystems. Maps of wetland distribution based on ground surveys are costly and labor-intensive, prohibiting timely evaluations of wetland loss and gain. The Great Lakes Water Resources II team at the NASA Jet Propulsion Laboratory developed the Wetlands Extent Tool 2.0 (WET 2.0) in Google Earth Engine to automate mapping of wetland distribution in the Great Lakes Basin. The team partnered with the US Fish and Wildlife Service (USFWS), Environmental Protection Agency (EPA), Minnesota Department of Natural Resources (MDNR), the National Oceanic and Atmospheric Administration (NOAA), and Ducks Unlimited (DU). WET 2.0 incorporates Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C- SAR), and Sentinel-2 Multispectral Instrument (MSI) satellite data. WET 2.0 is trained to classify anywhere in the Great Lakes Basin. Utilizing a Random Forest classifier, WET 2.0 is capable of automatically mapping wetland extent in the entire Great Lakes Basin, achieving a mean overall accuracy of 80.12% when tested in Michigan. Findings and maps produced in WET 2.0 will enable our partners to identify areas of ecosystem degradation and wetland destruction in order to enact environmental practices and policy initiatives to maintain environmental and economic health in the area.

@article{specht_habitat_2020,
	title = {Habitat selection and nest survival in two {Great} {Plains} shorebirds},
	volume = {15},
	issn = {1712-6568},
	url = {http://www.ace-eco.org/vol15/iss1/art3/},
	doi = {10.5751/ACE-01487-150103},
	language = {en},
	number = {1},
	urldate = {2023-06-15},
	journal = {Avian Conservation and Ecology},
	author = {Specht, Hannah and St-Louis, Véronique and Gratto-Trevor, Cheri L. and Koper, Nicola and Skaggs, Cassandra G. and Ronningen, Tait and Arnold, Todd W.},
	year = {2020},
	keywords = {NALCMS},
	pages = {art3},
}

@article{rodriguez-prado_hydrological_2020,
	title = {Hydrological modeling for mining water availability in a {Mexican} watershed},
	volume = {11},
	issn = {1923-6026, 2689-8403},
	url = {https://www.tandfonline.com/doi/full/10.1080/19236026.2020.1730138},
	doi = {10.1080/19236026.2020.1730138},
	language = {en},
	number = {1},
	urldate = {2023-06-15},
	journal = {CIM Journal},
	author = {Rodriguez-Prado, A.},
	month = jan,
	year = {2020},
	keywords = {NALCMS},
	pages = {1--8},
}

@phdthesis{eaman_implications_2020,
	type = {Honors designation in {Environmental} {Studies}},
	title = {Implications for the branched tetraether membrane lipid temperature proxy in {Arctic} paleoclimate reconstruction-{Evidence} over the {Holocene} from {Baffin} {Island} lacustrine sediment},
	url = {https://scholar.colorado.edu/concern/undergraduate_honors_theses/7w62f925w},
	school = {Environmental Studies, University of Colorado},
	author = {Eaman, K.A.},
	year = {2020},
	keywords = {NALCMS},
}

@article{zhou_kalman_2020,
	title = {Kalman filter method for generating time-series synthetic {Landsat} images and their uncertainty from {Landsat} and {MODIS} observations},
	volume = {239},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425719306480},
	doi = {10.1016/j.rse.2019.111628},
	language = {en},
	urldate = {2023-06-15},
	journal = {Remote Sensing of Environment},
	author = {Zhou, Fuqun and Zhong, Detang},
	month = mar,
	year = {2020},
	keywords = {NALCMS},
	pages = {111628},
}

@misc{landfire_landfire_2020,
	title = {{LANDFIRE} 2020 {Update}},
	url = {https://www.landfire.gov/data/lf2020},
	urldate = {2024-09-18},
	author = {{LANDFIRE}},
	year = {2020},
	keywords = {NALCMS},
}

@techreport{peters_landscape_2020,
	address = {Washington, DC},
	title = {Landscape {Factors} {Associated} with {Fatalities} of {Migratory} {Tree}-{Roosting} {Bats} at {Wind} {Energy} {Facilities}: {An} {Initial} {Assessment}},
	url = {https://rewi.org/resources/wwrf-landscape-factors-bats-2019/},
	institution = {Wind Wildlife Research Fund},
	author = {Peters, Kimberly and Evans, Ian and Traiger, Elizabeth and Collins, Jon and Mathews, Cristen and Klehr, Amanda},
	year = {2020},
	keywords = {NALCMS},
	pages = {64},
}