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@article{moghairib_model-agnostic_2026,
	title = {A {Model}-{Agnostic} {Representation} of {Prairie} {Pothole} {Hydrology}: {Enhancing} {Generality} and {Implementation} {Across} {Hydrological} {Models}},
	volume = {62},
	copyright = {© 2026. The Author(s).},
	issn = {1944-7973},
	shorttitle = {A {Model}-{Agnostic} {Representation} of {Prairie} {Pothole} {Hydrology}},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2025WR043074},
	doi = {10.1029/2025WR043074},
	abstract = {Modeling streamflow in low-lying, flat, and pothole-dominated prairie or Arctic regions is challenging due to variable non-contributing areas that influence how runoff translates to streamflow. Several modeling approaches have been developed to represent these dynamics, but many (a) lump depressions and permit spill only after a fixed capacity is reached, (b) rely heavily on calibration, (c) are unsuitable for large basins, (d) do not account for non-pothole contributions, and/or (e) are not model-agnostic. Here we present HDSv2, a second-generation Hysteretic Depressional Storage (HDS) module that is open-source, model-agnostic, numerically robust, and grounded in long-established physical understanding of prairie potholes. HDSv2 represents dynamic contributing area and storage–discharge hysteresis, enabling realistic simulation of fill-and-spill behavior and cold-region processes. We couple HDSv2 with three hydrological and land-surface models of differing architectures: HYPE (Hydrological Predictions for the Environment), MESH (Modélisation Environnementale communautaire—Surface and Hydrology), and SUMMA (Structure for Unifying Multiple Modeling Alternatives), applied in the Smith Creek River Basin, Canada. Results show that HDSv2 improves numerical stability and process fidelity relative to the original HDS model, which exhibited instabilities affecting contributing-area simulation within HYPE. Across all host models, integrating HDSv2 produces more robust hydrographs than the original configurations and better reproduces observed relationships between depressional storage and contributing area. Although hydrograph improvements vary by host, additional performance metrics show consistent gains in both high and low flow conditions. These findings demonstrate that HDSv2 provides a transferable and scalable pathway for incorporating depressional-storage dynamics into diverse hydrological models and regions.},
	language = {en},
	number = {4},
	urldate = {2026-05-27},
	journal = {Water Resources Research},
	author = {Moghairib, Mohamed and Clark, Martyn P. and Pietroniro, Alain and Stadnyk, Tricia},
	year = {2026},
	note = {\_eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2025WR043074},
	keywords = {NALCMS},
	pages = {e2025WR043074},
}

Modeling streamflow in low-lying, flat, and pothole-dominated prairie or Arctic regions is challenging due to variable non-contributing areas that influence how runoff translates to streamflow. Several modeling approaches have been developed to represent these dynamics, but many (a) lump depressions and permit spill only after a fixed capacity is reached, (b) rely heavily on calibration, (c) are unsuitable for large basins, (d) do not account for non-pothole contributions, and/or (e) are not model-agnostic. Here we present HDSv2, a second-generation Hysteretic Depressional Storage (HDS) module that is open-source, model-agnostic, numerically robust, and grounded in long-established physical understanding of prairie potholes. HDSv2 represents dynamic contributing area and storage–discharge hysteresis, enabling realistic simulation of fill-and-spill behavior and cold-region processes. We couple HDSv2 with three hydrological and land-surface models of differing architectures: HYPE (Hydrological Predictions for the Environment), MESH (Modélisation Environnementale communautaire—Surface and Hydrology), and SUMMA (Structure for Unifying Multiple Modeling Alternatives), applied in the Smith Creek River Basin, Canada. Results show that HDSv2 improves numerical stability and process fidelity relative to the original HDS model, which exhibited instabilities affecting contributing-area simulation within HYPE. Across all host models, integrating HDSv2 produces more robust hydrographs than the original configurations and better reproduces observed relationships between depressional storage and contributing area. Although hydrograph improvements vary by host, additional performance metrics show consistent gains in both high and low flow conditions. These findings demonstrate that HDSv2 provides a transferable and scalable pathway for incorporating depressional-storage dynamics into diverse hydrological models and regions.

@article{kessel_canada1water_2026,
	title = {{Canada1Water}: {Hydraulic} parametrized integrated soil, bedrock and peatlands datasets},
	volume = {65},
	issn = {2352-3409},
	shorttitle = {{Canada1Water}},
	url = {https://www.sciencedirect.com/science/article/pii/S2352340926001897},
	doi = {10.1016/j.dib.2026.112636},
	abstract = {The Canada1Water (C1W) initiative is a national-scale effort to model Canada’s hydrologic cycle, integrating groundwater, surface water, and climate influences across continental Canada and shared watersheds with the United States. Soil, bedrock, and peatlands critically connect atmospheric processes, vegetation, and hydrology. Infiltration and recharge are governed by the hydraulic properties and topographic and pressure gradients, making accurate representation of surficial substrates essential in hydrological modelling. For continental-scale groundwater–surface water modelling in Canada, a 7-layer hydrostratigraphic framework was developed, with the upper two soil layers being 0–0.5 m and 0.5–1.0 m below ground surface. National coverage of mineral soils was derived from the global OpenLandMap dataset at 250 m resolution, using soil texture, bulk density, and water content at 33 and 1500 kPa from six depth intervals. These inputs were vertically averaged into two soil layers and parameterized using Rosetta v3 pedotransfer function approaches to derive residual and saturated water content, van Genuchten parameters (α and n), and saturated hydraulic conductivity. Exposed bedrock was explicitly mapped using a combination of land cover classification (NALCMS v2015 barren land and snow/ice classes) and national-scale surficial geology datasets, with depth-dependent expansion of bedrock extent in the lower soil layer. Peatlands were delineated using peatland extent products and OpenLandMap soil organic carbon content, producing a three-dimensional peat model classified into moderately decomposed, decomposed, and well-decomposed peat, each assigned depth-dependent hydraulic properties based on published literature. The final product is an 18-band raster GeoTIFF, comprising OpenLandMap soil attributes, Rosetta-derived hydraulic parameters, and specific classifications for bedrock and peatlands. The dataset provides seamless 250 m resolution coverage across all Canadian provinces and territories and extends into transboundary watersheds with the United States. By integrating mineral soils, bedrock outcrops, and peatlands into its comprehensive hydraulic parameterization, this dataset is ideally suited for supra-regional hydrological modelling.},
	urldate = {2026-05-27},
	journal = {Data in Brief},
	author = {Kessel, Eric D. and Frey, Steve K. and Lapen, David R. and Geng, Xiaoyuan and Russell, Hazen A. J.},
	month = apr,
	year = {2026},
	keywords = {NALCMS},
	pages = {112636},
}

The Canada1Water (C1W) initiative is a national-scale effort to model Canada’s hydrologic cycle, integrating groundwater, surface water, and climate influences across continental Canada and shared watersheds with the United States. Soil, bedrock, and peatlands critically connect atmospheric processes, vegetation, and hydrology. Infiltration and recharge are governed by the hydraulic properties and topographic and pressure gradients, making accurate representation of surficial substrates essential in hydrological modelling. For continental-scale groundwater–surface water modelling in Canada, a 7-layer hydrostratigraphic framework was developed, with the upper two soil layers being 0–0.5 m and 0.5–1.0 m below ground surface. National coverage of mineral soils was derived from the global OpenLandMap dataset at 250 m resolution, using soil texture, bulk density, and water content at 33 and 1500 kPa from six depth intervals. These inputs were vertically averaged into two soil layers and parameterized using Rosetta v3 pedotransfer function approaches to derive residual and saturated water content, van Genuchten parameters (α and n), and saturated hydraulic conductivity. Exposed bedrock was explicitly mapped using a combination of land cover classification (NALCMS v2015 barren land and snow/ice classes) and national-scale surficial geology datasets, with depth-dependent expansion of bedrock extent in the lower soil layer. Peatlands were delineated using peatland extent products and OpenLandMap soil organic carbon content, producing a three-dimensional peat model classified into moderately decomposed, decomposed, and well-decomposed peat, each assigned depth-dependent hydraulic properties based on published literature. The final product is an 18-band raster GeoTIFF, comprising OpenLandMap soil attributes, Rosetta-derived hydraulic parameters, and specific classifications for bedrock and peatlands. The dataset provides seamless 250 m resolution coverage across all Canadian provinces and territories and extends into transboundary watersheds with the United States. By integrating mineral soils, bedrock outcrops, and peatlands into its comprehensive hydraulic parameterization, this dataset is ideally suited for supra-regional hydrological modelling.

@article{chathuranika_climate_2026,
	title = {Climate {Change} {Impacts} on {Hydrology} in the {Upper} {James} {Watershed}, {Virginia}},
	volume = {62},
	copyright = {© 2026 The Author(s). Journal of the American Water Resources Association published by Wiley Periodicals LLC on behalf of American Water Resources Association.},
	issn = {1752-1688},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1752-1688.70099},
	doi = {10.1111/1752-1688.70099},
	abstract = {Hydrological modeling of the Upper James Watershed (UJW), Virginia, is critical for predicting water availability, flood management, agriculture, ecosystem protection, and hydropower production under increasing climate change. The Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) is applied to evaluate climate change impacts on key hydrological components within the watershed. Future climate conditions were assessed for the near (NF: 2026–2050), mid (MF: 2051–2075), and far (FF: 2076–2100) periods using three Global Climate Models (GCMs) under Shared Socioeconomic Pathways SSP 2–4.5 and SSP 5–8.5. Climate data were bias-corrected using the Linear Scaling Method (LSM) and used to drive the HEC-HMS model. Results project annual precipitation reductions of 8.19\% (SSP 2–4.5) and 14.63\% (SSP 5–8.5) for 2026–2100 relative to the 1998–2022 baseline, with corresponding annual streamflow changes of a 4.16\% increase and a 0.62\% decrease, respectively. Key hydrological components, including infiltration, evapotranspiration, interception, surface runoff, and baseflow, are projected to decline across all future periods, with reductions ranging from 10.17\% (NF) to 17.00\% (FF) under SSP 2–4.5 and from 11.74\% (NF) to 30.20\% (FF) under SSP 5–8.5. These results highlight the need for improved reservoir operations, sustainable land-use practices, and enhanced flood and drought forecasting to mitigate climate impacts and support informed decision-making.},
	language = {en},
	number = {2},
	urldate = {2026-05-22},
	journal = {JAWRA Journal of the American Water Resources Association},
	author = {Chathuranika, Imiya Mudiyanselage and Ismael, Dalya},
	year = {2026},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/1752-1688.70099},
	keywords = {NALCMS},
	pages = {e70099},
}

Hydrological modeling of the Upper James Watershed (UJW), Virginia, is critical for predicting water availability, flood management, agriculture, ecosystem protection, and hydropower production under increasing climate change. The Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) is applied to evaluate climate change impacts on key hydrological components within the watershed. Future climate conditions were assessed for the near (NF: 2026–2050), mid (MF: 2051–2075), and far (FF: 2076–2100) periods using three Global Climate Models (GCMs) under Shared Socioeconomic Pathways SSP 2–4.5 and SSP 5–8.5. Climate data were bias-corrected using the Linear Scaling Method (LSM) and used to drive the HEC-HMS model. Results project annual precipitation reductions of 8.19% (SSP 2–4.5) and 14.63% (SSP 5–8.5) for 2026–2100 relative to the 1998–2022 baseline, with corresponding annual streamflow changes of a 4.16% increase and a 0.62% decrease, respectively. Key hydrological components, including infiltration, evapotranspiration, interception, surface runoff, and baseflow, are projected to decline across all future periods, with reductions ranging from 10.17% (NF) to 17.00% (FF) under SSP 2–4.5 and from 11.74% (NF) to 30.20% (FF) under SSP 5–8.5. These results highlight the need for improved reservoir operations, sustainable land-use practices, and enhanced flood and drought forecasting to mitigate climate impacts and support informed decision-making.

@article{nowack_combining_2026,
	title = {Combining space use with diet data to investigate foraging tactics of black bears in response to the pulsed availability of migratory caribou calves},
	volume = {21},
	issn = {1932-6203},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0346054},
	doi = {10.1371/journal.pone.0346054},
	abstract = {Migratory caribou (Rangifer tarandus) is a key component of the arctic food web. Female caribou typically gather in late spring to give birth, creating a predictable, ephemeral resource pulse that can influence consumer behavior. As an omnivore, the black bear (Ursus americanus) predates on caribou neonates during calving when they are most vulnerable and occur at high densities. We studied black bears in northern Québec and Labrador interacting with the Rivière-aux-Feuilles (RAFH) and Rivière-George (RGH) migratory caribou herds. Our aim was to assess the spatial response of bears to caribou distribution during calving by investigating the potential correlation between bear movements and their relative trophic position. We expected bears with higher trophic positions to adopt behaviors favoring caribou encounters during the caribou calving period. We used GPS telemetry data from 40 bears and 319 female caribou between 2012 and 2019 to calculate movement metrics and home ranges, and to assess seasonal variation in movement patterns and shared space use between bears and caribou. We then analyzed the relationships between space use data and relative trophic position of bears obtained from stable isotope analysis of blood serum. The relative trophic position of black bears differed between study sites, with individuals in northern Québec (RAFH) exhibiting higher trophic positions than those in Labrador (RGH). Relative trophic positions varied strongly among individuals, indicating substantial individual differences in foraging strategies. Greater proportional overlap of bear ranges with caribou calving ground was associated with higher trophic positions, while higher use of caribou-preferred habitat by bears surprisingly correlated with lower relative trophic positions. The absence of a clear link between movement metrics and trophic position may reflect the inherently opportunistic foraging behavior of black bears. Additional research at the individual level is needed to gain deeper insight into black bear foraging strategies in response to pulsed resources.},
	language = {en},
	number = {4},
	urldate = {2026-05-27},
	journal = {PLOS ONE},
	author = {Nowack, Linda and Bonin, Michaël and Leclerc, Martin and Michelot, Candice and Dussault, Christian and Taillon, Joëlle and Pisapio, John and Côté, Steeve D.},
	month = apr,
	year = {2026},
	note = {Publisher: Public Library of Science},
	keywords = {NALCMS},
	pages = {e0346054},
}

Migratory caribou (Rangifer tarandus) is a key component of the arctic food web. Female caribou typically gather in late spring to give birth, creating a predictable, ephemeral resource pulse that can influence consumer behavior. As an omnivore, the black bear (Ursus americanus) predates on caribou neonates during calving when they are most vulnerable and occur at high densities. We studied black bears in northern Québec and Labrador interacting with the Rivière-aux-Feuilles (RAFH) and Rivière-George (RGH) migratory caribou herds. Our aim was to assess the spatial response of bears to caribou distribution during calving by investigating the potential correlation between bear movements and their relative trophic position. We expected bears with higher trophic positions to adopt behaviors favoring caribou encounters during the caribou calving period. We used GPS telemetry data from 40 bears and 319 female caribou between 2012 and 2019 to calculate movement metrics and home ranges, and to assess seasonal variation in movement patterns and shared space use between bears and caribou. We then analyzed the relationships between space use data and relative trophic position of bears obtained from stable isotope analysis of blood serum. The relative trophic position of black bears differed between study sites, with individuals in northern Québec (RAFH) exhibiting higher trophic positions than those in Labrador (RGH). Relative trophic positions varied strongly among individuals, indicating substantial individual differences in foraging strategies. Greater proportional overlap of bear ranges with caribou calving ground was associated with higher trophic positions, while higher use of caribou-preferred habitat by bears surprisingly correlated with lower relative trophic positions. The absence of a clear link between movement metrics and trophic position may reflect the inherently opportunistic foraging behavior of black bears. Additional research at the individual level is needed to gain deeper insight into black bear foraging strategies in response to pulsed resources.

@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{bunn_data_2026,
	title = {Data to support the modelling of integrated groundwater-surface water flow in permafrost regions, using the {Carcajou} {Watershed}, {Northwest} {Territories}, {Canada}},
	volume = {66},
	issn = {2352-3409},
	url = {https://www.sciencedirect.com/science/article/pii/S235234092600332X},
	doi = {10.1016/j.dib.2026.112802},
	abstract = {{\textgreater} 50 \% of Canada's landmass is underlain by perennially frozen ground (permafrost). These permafrost regions are changing rapidly in response to a warming climate, with Canada’s arctic warming at a rate {\textgreater} 3 times higher than global average. As permafrost distribution shifts in response to this warming, it alters the water cycle, affecting both water quantity and water quality. However, large-scale hydrological models that integrate climate change impacts often lack permafrost representation. The Carcajou Regional Model Dataset contains the inputs needed to construct a standalone groundwater model or a fully integrated groundwater – surface water model of the Carcajou watershed in the Northwest Territories, Canada, a zone of transition between continuous permafrost and discontinuous permafrost. The three-dimensional geological data was created by refining national-scale mapping products, with local-scale data. Land cover, topography, and surface water flow were generated from national scale mapping products. Climate forcing data for the period from 2010 to 2016 was generated based national scale historical products. The proper approach to representing permafrost in large-scale hydrological models is largely undefined, with no consensus on conceptualization or parameterization. This dataset allows researchers to rapidly test new approaches to permafrost representation, without the need for additional data collection. The data set includes long term surface water discharge records to assess model performance and compare representations. The data set can be further adapted to simulate projected permafrost distribution scenarios and be used to forecast the effect of permafrost change on water quantity.},
	urldate = {2026-05-21},
	journal = {Data in Brief},
	author = {Bunn, Melissa I. and Khader, Omar and Kessel, Eric and Frey, Steven K. and Erler, Andre R. and Russell, Hazen A.},
	month = jun,
	year = {2026},
	keywords = {NALCMS},
	pages = {112802},
}

\textgreater 50 % of Canada's landmass is underlain by perennially frozen ground (permafrost). These permafrost regions are changing rapidly in response to a warming climate, with Canada’s arctic warming at a rate \textgreater 3 times higher than global average. As permafrost distribution shifts in response to this warming, it alters the water cycle, affecting both water quantity and water quality. However, large-scale hydrological models that integrate climate change impacts often lack permafrost representation. The Carcajou Regional Model Dataset contains the inputs needed to construct a standalone groundwater model or a fully integrated groundwater – surface water model of the Carcajou watershed in the Northwest Territories, Canada, a zone of transition between continuous permafrost and discontinuous permafrost. The three-dimensional geological data was created by refining national-scale mapping products, with local-scale data. Land cover, topography, and surface water flow were generated from national scale mapping products. Climate forcing data for the period from 2010 to 2016 was generated based national scale historical products. The proper approach to representing permafrost in large-scale hydrological models is largely undefined, with no consensus on conceptualization or parameterization. This dataset allows researchers to rapidly test new approaches to permafrost representation, without the need for additional data collection. The data set includes long term surface water discharge records to assess model performance and compare representations. The data set can be further adapted to simulate projected permafrost distribution scenarios and be used to forecast the effect of permafrost change on water quantity.

@article{liang_derivation_2026,
	title = {Derivation and evaluation of {Landsat}-derived annual aboveground biomass maps for {Arctic} and {Boreal} {North} {America}, 1984-2022},
	volume = {341},
	issn = {0034-4257},
	url = {https://www.sciencedirect.com/science/article/pii/S0034425726002166},
	doi = {10.1016/j.rse.2026.115446},
	abstract = {Arctic and boreal regions (ABRs) are experiencing rapid warming and increasingly severe wildfires, threatening their roles as global carbon sinks. High quality time series maps of aboveground biomass (AGB) are key for characterizing and attributing spatiotemporal dynamics of carbon stocks in these regions. However, existing maps at regional to global scales often lack the spatial resolution or temporal coverage needed to capture the heterogeneous and dynamic nature of Arctic-boreal AGB change. To address these limitations, we developed annual (1984-2022) 30-m resolution AGB density maps for Alaska and Canada (11.2 x 106 km2). The maps were produced by using extensive training datasets, including 45,002 unique ground plots and 100,000 km2 of airborne lidar data, and time-series spectral features derived from the Continuous Change Detection and Classification (CCDC) algorithm fitted on Landsat Collection 2 Surface Reflectance. Using the eXtreme Gradient Boosting model, we generated annual wall-to-wall maps of AGB along with associated uncertainties. Our maps suggest a ∼ 41 Pg stock of AGB at 2022, representing a 12\% increase from 1984. Our maps achieve high accuracy and low bias on holdout testing data (R2 = 0.72, Bias= -5.03\%, RMSE\% = 62.7\%), representing an average of 16.0 percentage points increase in R2 values and 22.7 percentage points decrease in relative bias compared to six existing AGB products. We show using repeat ground measurements that these maps effectively capture AGB loss and recovery due to fire and harvest, gradual AGB changes, and both live and dead tree AGB components in boreal regions. By integrating extensive calibration data with multi-decadal satellite observations and advanced machine learning techniques, these map products provide a robust tool for advancing the understanding of carbon dynamics under global change in Arctic-boreal North America.},
	urldate = {2026-05-27},
	journal = {Remote Sensing of Environment},
	author = {Liang, Wanwan and van Lier, Olivier and Friedl, Mark A. and Randerson, James T. and Rogers, Brendan M. and Burrell, Arden and Hu, Kai-Ting and Tompalski, Piotr and Macander, Matthew J. and Yang, Daryl and Morton, Douglas C. and Bullock, Eric and Lu, Jiaming and Zhang, Yingtong and Zhu, Xiaoran and Wang, Jonathan A.},
	month = aug,
	year = {2026},
	keywords = {NALCMS, Terrestrial Ecoregions (CEC 1997)},
	pages = {115446},
}

Arctic and boreal regions (ABRs) are experiencing rapid warming and increasingly severe wildfires, threatening their roles as global carbon sinks. High quality time series maps of aboveground biomass (AGB) are key for characterizing and attributing spatiotemporal dynamics of carbon stocks in these regions. However, existing maps at regional to global scales often lack the spatial resolution or temporal coverage needed to capture the heterogeneous and dynamic nature of Arctic-boreal AGB change. To address these limitations, we developed annual (1984-2022) 30-m resolution AGB density maps for Alaska and Canada (11.2 x 106 km2). The maps were produced by using extensive training datasets, including 45,002 unique ground plots and 100,000 km2 of airborne lidar data, and time-series spectral features derived from the Continuous Change Detection and Classification (CCDC) algorithm fitted on Landsat Collection 2 Surface Reflectance. Using the eXtreme Gradient Boosting model, we generated annual wall-to-wall maps of AGB along with associated uncertainties. Our maps suggest a ∼ 41 Pg stock of AGB at 2022, representing a 12% increase from 1984. Our maps achieve high accuracy and low bias on holdout testing data (R2 = 0.72, Bias= -5.03%, RMSE% = 62.7%), representing an average of 16.0 percentage points increase in R2 values and 22.7 percentage points decrease in relative bias compared to six existing AGB products. We show using repeat ground measurements that these maps effectively capture AGB loss and recovery due to fire and harvest, gradual AGB changes, and both live and dead tree AGB components in boreal regions. By integrating extensive calibration data with multi-decadal satellite observations and advanced machine learning techniques, these map products provide a robust tool for advancing the understanding of carbon dynamics under global change in Arctic-boreal North America.

@article{sahagun_sanchez_valor_2026,
	title = {El valor económico de los servicios ecosistémicos en las Áreas {Protegidas} de {México}},
	volume = {65},
	issn = {2340-0129, 0210-5462},
	url = {https://dialnet.unirioja.es/servlet/articulo?codigo=10630162},
	abstract = {This study aimed to estimate the economic value of the ecosystem services provided by Protected Natural Areas and Areas Voluntarily Designated for Conservation in Mexico, generating baseline information to support their management and maintenance. A benefit transfer method was applied to calculate the value of ecosystem services using pre-established coefficients, and a geographic information system was used to segregate and analyze them. The results indicate that the approximate value of ecosystem services from protected areas in Mexico is 391 billion dollars, with Biosphere Reserves and Flora and Fauna Protection Areas accounting for the largest share. Regionally, the highest value is found in the Coastal Plain and Gulf of Mexico, the Yucatan Peninsula, and the Mexican Caribbean. The information helps establish reference values for the ecosystem services provided by protected areas in Mexico. It can serve as a basis for designing plans and programs to improve their management and conservation in the territory.
El objetivo del presente estudio fue estimar el valor económico de los servicios ecosistémicos provistos por las Áreas Naturales Protegidas y las Áreas Destinadas Voluntariamente para la Conservación en México para generar información de línea base que apoye su manejo y mantenimiento. Se aplicó un método de transferencia de beneficios para calcular el valor de los servicios ecosistémicos a través de coeficientes preestablecidos y se utilizó un sistema de información geográfica para su segregación y análisis. Los resultados indican que el valor aproximado de los servicios ecosistémicos de las áreas protegidas en México es de 391 mil millones de dólares siendo las Reservas de la Biosfera y las Áreas de Protección de Flora y Fauna las que concentran el monto mayor. Regionalmente el valor más alto se congrega en la Planicie Costera y Golfo de México y la Península de Yucatán y Caribe Mexicano. La información generada es de especial utilidad para establecer valores de referencia para los servicios ecosistémicos provistos por las áreas protegidas en México y puede servir de base para el diseño de planes y programas dirigidos a mejorar su manejo y conservación en el territorio.},
	language = {Spanish},
	number = {1},
	urldate = {2026-05-29},
	journal = {Cuadernos geográficos de la Universidad de Granada},
	author = {Sahagún Sánchez, Francisco Javier and Leija, Edgar G. and Espinoza Guzmán, Marco Antonio},
	year = {2026},
	note = {Publisher: Departamento de Análisis Geográfico Regional y Geografía Física
Section: Cuadernos geográficos de la Universidad de Granada},
	keywords = {NALCMS},
	pages = {198--218},
}

This study aimed to estimate the economic value of the ecosystem services provided by Protected Natural Areas and Areas Voluntarily Designated for Conservation in Mexico, generating baseline information to support their management and maintenance. A benefit transfer method was applied to calculate the value of ecosystem services using pre-established coefficients, and a geographic information system was used to segregate and analyze them. The results indicate that the approximate value of ecosystem services from protected areas in Mexico is 391 billion dollars, with Biosphere Reserves and Flora and Fauna Protection Areas accounting for the largest share. Regionally, the highest value is found in the Coastal Plain and Gulf of Mexico, the Yucatan Peninsula, and the Mexican Caribbean. The information helps establish reference values for the ecosystem services provided by protected areas in Mexico. It can serve as a basis for designing plans and programs to improve their management and conservation in the territory. El objetivo del presente estudio fue estimar el valor económico de los servicios ecosistémicos provistos por las Áreas Naturales Protegidas y las Áreas Destinadas Voluntariamente para la Conservación en México para generar información de línea base que apoye su manejo y mantenimiento. Se aplicó un método de transferencia de beneficios para calcular el valor de los servicios ecosistémicos a través de coeficientes preestablecidos y se utilizó un sistema de información geográfica para su segregación y análisis. Los resultados indican que el valor aproximado de los servicios ecosistémicos de las áreas protegidas en México es de 391 mil millones de dólares siendo las Reservas de la Biosfera y las Áreas de Protección de Flora y Fauna las que concentran el monto mayor. Regionalmente el valor más alto se congrega en la Planicie Costera y Golfo de México y la Península de Yucatán y Caribe Mexicano. La información generada es de especial utilidad para establecer valores de referencia para los servicios ecosistémicos provistos por las áreas protegidas en México y puede servir de base para el diseño de planes y programas dirigidos a mejorar su manejo y conservación en el territorio.

@phdthesis{clelland_fanning_2026,
	type = {Doctoral},
	title = {Fanning the flames: {Past} and future links between climate change and fire activity across boreal forests},
	shorttitle = {Fanning the flames},
	url = {https://etheses.durham.ac.uk/id/eprint/16487/},
	abstract = {Boreal forests are experiencing unprecedented environmental change, with increasing fire activity posing significant ecological and climatic challenges. This thesis investigates past, present and future interactions between climate and fire across the boreal biome, combining reanalysis datasets, burned area products and machine learning to examine drivers of fire occurrence and project potential changes in future burned area. Chapter 2 evaluates the reliability of three reanalysis datasets across Siberia by comparing six climate variables related to fire activity with in situ observations from meteorological stations. The datasets are mostly good representations for the studied variables but also show notable regional biases and inconsistencies, highlighting the importance of bias correction in the boreal region. Chapter 3 evaluates twelve satellite imagery-derived burned area products across Arctic-boreal North America and Russia from 2001 to 2020, identifying consistent patterns in North America but major discrepancies in southern Siberia, and highlighting the strengths, limitations and suitability of each product for various research and monitoring applications. Chapter 4 develops machine learning models to project burned area across boreal and tundra biomes from 2025 to 2100 under different climate change projections using climate model outputs. Results show strong scenario- and region-dependent increases in burned area, particularly in North America, suggesting that climate change will intensify fire regimes in many areas. The final discussion chapter combines these findings, emphasising that climate-fire relationships are non-uniform and modulated by vegetation, topography and climate. It discusses the implications of increasing fire activity for carbon accounting, ecosystem resilience and climate feedbacks, and highlights the need for adaptive fire management and further research into vegetation-fire-climate feedbacks in boreal systems. Together, the thesis provides a novel approach to understanding boreal climate-fire dynamics and underscores the urgency of addressing fire risk in a warming world.},
	language = {en},
	urldate = {2026-06-01},
	school = {Durham University},
	author = {CLELLAND, ANDREW ALISTAIR},
	year = {2026},
	keywords = {NALCMS},
}

Boreal forests are experiencing unprecedented environmental change, with increasing fire activity posing significant ecological and climatic challenges. This thesis investigates past, present and future interactions between climate and fire across the boreal biome, combining reanalysis datasets, burned area products and machine learning to examine drivers of fire occurrence and project potential changes in future burned area. Chapter 2 evaluates the reliability of three reanalysis datasets across Siberia by comparing six climate variables related to fire activity with in situ observations from meteorological stations. The datasets are mostly good representations for the studied variables but also show notable regional biases and inconsistencies, highlighting the importance of bias correction in the boreal region. Chapter 3 evaluates twelve satellite imagery-derived burned area products across Arctic-boreal North America and Russia from 2001 to 2020, identifying consistent patterns in North America but major discrepancies in southern Siberia, and highlighting the strengths, limitations and suitability of each product for various research and monitoring applications. Chapter 4 develops machine learning models to project burned area across boreal and tundra biomes from 2025 to 2100 under different climate change projections using climate model outputs. Results show strong scenario- and region-dependent increases in burned area, particularly in North America, suggesting that climate change will intensify fire regimes in many areas. The final discussion chapter combines these findings, emphasising that climate-fire relationships are non-uniform and modulated by vegetation, topography and climate. It discusses the implications of increasing fire activity for carbon accounting, ecosystem resilience and climate feedbacks, and highlights the need for adaptive fire management and further research into vegetation-fire-climate feedbacks in boreal systems. Together, the thesis provides a novel approach to understanding boreal climate-fire dynamics and underscores the urgency of addressing fire risk in a warming world.

@article{castle_global_2026,
	title = {Global forest dataset incongruence creates high uncertainties for conservation, climate, and development policy},
	volume = {9},
	issn = {2590-3330, 2590-3322},
	url = {https://www.cell.com/one-earth/abstract/S2590-3322(25)00384-7},
	doi = {10.1016/j.oneear.2025.101558},
	language = {English},
	number = {2},
	urldate = {2026-05-22},
	journal = {One Earth},
	author = {Castle, Sarah E. and Newton, Peter and Oldekop, Johan A. and Baylis, Kathy and Miller, Daniel C.},
	month = feb,
	year = {2026},
	note = {Publisher: Elsevier},
	keywords = {NALCMS},
}

@article{mohammadimanesh_hiresnet_2026,
	title = {{HiResNet}: {A} {Low}-to-{High} {Deep} {Learning} {Framework} for {Updating} {Large}-{Scale} {Land} {Cover} {Inventory}},
	volume = {64},
	issn = {1558-0644},
	shorttitle = {{HiResNet}},
	url = {https://ieeexplore.ieee.org/document/11460176},
	doi = {10.1109/TGRS.2026.3676677},
	abstract = {Large-scale land cover (LC) mapping is critical for monitoring Earth’s surface and managing environmental changes. While machine learning (ML) and deep learning (DL) methods have significantly advanced LC mapping efforts, the reliance on high-quality training labels remains a major limitation due to their time-intensive and costly collection process. To address this challenge, we propose HiResNet, a novel DL framework that combines a multiscale feature extractor (MSFE) within convolutional neural networks (CNNs), a vision transformer (ViT) architecture, and a weakly-supervised loss function, to produce a 10 m LC map using an existing low-resolution (LR) 30 m LC map. In particular, this framework reduces the dependence on high-resolution (HR) training labels while generating HR (10 m) LC maps using Sentinel-2 imagery and LR 30 m labels. The capability of the proposed method is evaluated in the province of Ontario (ON), Canada, a region characterized by a diverse landscape and different LC types, covering approximately 1.07 million km2. The results demonstrate a remarkable capability of the proposed model to enhance spatial resolution and achieve high classification accuracy, with an overall accuracy (OA) of 89\% for large-scale LC classification. Compared to conventional methods and advanced DL architectures, such as Random Forest (RF), ViT, HRNet, SegFormer, DeepLabV3, and L2HNet, the proposed method exhibits a superior performance by handling class imbalances and extracting complex features. Furthermore, uncertainty estimation using Shannon entropy and class disagreement metrics demonstrates that the highest levels of uncertainty are associated with object edges, narrow features (e.g., rivers and roads), and complex LC classes like forests.},
	urldate = {2026-05-27},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	author = {Mohammadimanesh, Fariba and Marjani, Mohammad and Mahdianpari, Masoud},
	year = {2026},
	keywords = {NALCMS},
	pages = {1--20},
}

Large-scale land cover (LC) mapping is critical for monitoring Earth’s surface and managing environmental changes. While machine learning (ML) and deep learning (DL) methods have significantly advanced LC mapping efforts, the reliance on high-quality training labels remains a major limitation due to their time-intensive and costly collection process. To address this challenge, we propose HiResNet, a novel DL framework that combines a multiscale feature extractor (MSFE) within convolutional neural networks (CNNs), a vision transformer (ViT) architecture, and a weakly-supervised loss function, to produce a 10 m LC map using an existing low-resolution (LR) 30 m LC map. In particular, this framework reduces the dependence on high-resolution (HR) training labels while generating HR (10 m) LC maps using Sentinel-2 imagery and LR 30 m labels. The capability of the proposed method is evaluated in the province of Ontario (ON), Canada, a region characterized by a diverse landscape and different LC types, covering approximately 1.07 million km2. The results demonstrate a remarkable capability of the proposed model to enhance spatial resolution and achieve high classification accuracy, with an overall accuracy (OA) of 89% for large-scale LC classification. Compared to conventional methods and advanced DL architectures, such as Random Forest (RF), ViT, HRNet, SegFormer, DeepLabV3, and L2HNet, the proposed method exhibits a superior performance by handling class imbalances and extracting complex features. Furthermore, uncertainty estimation using Shannon entropy and class disagreement metrics demonstrates that the highest levels of uncertainty are associated with object edges, narrow features (e.g., rivers and roads), and complex LC classes like forests.

@article{qi_how_2026,
	title = {How {Much} {Uncertainty} {Does} the {Tower} {Create} in {Tower}-{Based} {Albedo} {Observations}?},
	volume = {18},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/18/4/631},
	doi = {10.3390/rs18040631},
	abstract = {Towers often have complex structures and higher reflectances than the surrounding surfaces. To identify the uncertainty of the tower in tower-based albedo observations, a combined approach involving simulation and field observations was applied to three newly constructed towers in the Saihanba region. The simulation results demonstrate that tower presence influenced the albedo measurements, with the impact primarily controlled by tower reflectance rather than sensor height. When the tower-body albedo markedly differs from the background, systematic correction is necessary. Field validation indicates that for the towers in the Saihanba region, the contrast in albedo between the tower and the surrounding background is small, and both the simulated and observed albedos agree well with the ground measurements, with differences below 0.0075. By identifying when and how tower structures distort albedo observations, this study enhances confidence in ground-based validation and provides practical guidance for future tower design and material selection.},
	language = {en},
	number = {4},
	urldate = {2026-05-29},
	journal = {Remote Sensing},
	author = {Qi, Jinlin and Zhang, Yusha and Shi, Peirong and Wan, Huawei and Li, Chen and Wang, Ziyu and Mao, Chenzheng and Fang, Ruojing and Zhou, Hongmin},
	month = jan,
	year = {2026},
	note = {Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {NALCMS},
	pages = {631},
}

Towers often have complex structures and higher reflectances than the surrounding surfaces. To identify the uncertainty of the tower in tower-based albedo observations, a combined approach involving simulation and field observations was applied to three newly constructed towers in the Saihanba region. The simulation results demonstrate that tower presence influenced the albedo measurements, with the impact primarily controlled by tower reflectance rather than sensor height. When the tower-body albedo markedly differs from the background, systematic correction is necessary. Field validation indicates that for the towers in the Saihanba region, the contrast in albedo between the tower and the surrounding background is small, and both the simulated and observed albedos agree well with the ground measurements, with differences below 0.0075. By identifying when and how tower structures distort albedo observations, this study enhances confidence in ground-based validation and provides practical guidance for future tower design and material selection.

@article{rawlins_hydrological_2026,
	title = {Hydrological {Cycle} {Intensification} and {Permafrost} {Thaw} {Drive} {Increased} {Freshwater} and {Organic} {Carbon} {Inputs} to {Northern} {Alaska} {Estuaries}},
	volume = {40},
	copyright = {© 2026. The Author(s).},
	issn = {1944-9224},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2025GB008822},
	doi = {10.1029/2025GB008822},
	abstract = {Understanding how hydrological inflows and climate change influence individual estuaries across northern Alaska is limited by a paucity of measured data, necessitating the application of suitably scaled numerical process models. This study uses an updated model to quantify freshwater discharge and dissolved organic carbon (DOC) export from the North Slope of Alaska (NSA) to coastal waters of the Beaufort Sea and examines climate-linked temporal changes. The model was applied at 1 {\textbackslash}textkm{\textbackslash} resolution across the 166,483 \{\vphantom{\}}{\textbackslash}textkm{\textasciicircum}2{\textbackslash} NSA domain over 1980–2023. Watershed inputs to coastal waters were estimated by routing along river networks to 1,039 outlets at the land-sea boundary. Key simulation parameters were evaluated to demonstrate model efficacy, then spatial and temporal patterns were quantified. Exports to specific lagoons, bays, and sounds demonstrate how landscape composition, terrestrial drainage basin size, and estuary area modulate watershed influence on coastal ecosystems across the region. Freshwater discharge and DOC export increased over the greater than four decades, associated with changes in precipitation and permafrost thaw. Surface and supra-permafrost subsurface fluxes also increased, with pronounced rises in proportional contributions via subsurface flow during summer and autumn. These changes have the potential to substantially impact salinity and trophic conditions along Alaska's Beaufort Sea coast. Our study highlights the value of model-data syntheses that resolve regional-to-local scale fluxes where observational measurements are sparse, and provides novel quantitative export metrics that will be useful to researchers, resource managers, and other stakeholders with interests in the climate, hydrology, and biogeochemistry of coastal northern Alaska.},
	language = {en},
	number = {4},
	urldate = {2026-05-29},
	journal = {Global Biogeochemical Cycles},
	author = {Rawlins, Michael A. and Connolly, Craig T. and McClelland, James W.},
	year = {2026},
	note = {\_eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2025GB008822},
	keywords = {NALCMS},
	pages = {e2025GB008822},
}

Understanding how hydrological inflows and climate change influence individual estuaries across northern Alaska is limited by a paucity of measured data, necessitating the application of suitably scaled numerical process models. This study uses an updated model to quantify freshwater discharge and dissolved organic carbon (DOC) export from the North Slope of Alaska (NSA) to coastal waters of the Beaufort Sea and examines climate-linked temporal changes. The model was applied at 1 \textkm\ resolution across the 166,483 \p̌hantom\\textkm\textasciicircum2\ NSA domain over 1980–2023. Watershed inputs to coastal waters were estimated by routing along river networks to 1,039 outlets at the land-sea boundary. Key simulation parameters were evaluated to demonstrate model efficacy, then spatial and temporal patterns were quantified. Exports to specific lagoons, bays, and sounds demonstrate how landscape composition, terrestrial drainage basin size, and estuary area modulate watershed influence on coastal ecosystems across the region. Freshwater discharge and DOC export increased over the greater than four decades, associated with changes in precipitation and permafrost thaw. Surface and supra-permafrost subsurface fluxes also increased, with pronounced rises in proportional contributions via subsurface flow during summer and autumn. These changes have the potential to substantially impact salinity and trophic conditions along Alaska's Beaufort Sea coast. Our study highlights the value of model-data syntheses that resolve regional-to-local scale fluxes where observational measurements are sparse, and provides novel quantitative export metrics that will be useful to researchers, resource managers, and other stakeholders with interests in the climate, hydrology, and biogeochemistry of coastal northern Alaska.

@article{rincon_hydrological_2026,
	title = {Hydrological and thermal regime shifts in a sub-{Arctic} river under climate change: implications of potential changes in thermal habitat for {Arctic} char and {Atlantic} salmon},
	volume = {17},
	issn = {2040-2244},
	shorttitle = {Hydrological and thermal regime shifts in a sub-{Arctic} river under climate change},
	url = {https://doi.org/10.2166/wcc.2026.417},
	doi = {10.2166/wcc.2026.417},
	abstract = {Graphical Abstract. Graph showing the work flow of the study. Legend: Graphical AbstractClimate change-driven effects are significant socio-environmental concerns in the Arctic and sub-Arctic regions. This study assesses the impact of climate change on the hydrological and thermal regimes of the Aux-Mélèzes River, an important sub-Arctic watershed in northeastern Canada. Using the process-based CEQUEAU model, we projected future (2040–2100) thermal regime shifts, validating against historical data (1980–2020) with strong performance metrics for summer flows and water temperature. Historically, the Aux-Mélèzes River has provided suitable summer habitat for Arctic char and Atlantic salmon, but projections indicate statistically significant increases in mean summer water temperature across all climate scenarios. Temperature duration curve analysis reveals a shift towards higher temperatures, with the probability of exceeding the 23 °C thermal stress threshold for key fish species increasing significantly by the century’s end. Furthermore, maximum summer water temperature projections highlight tributaries as critical warming hotspots, posing risks to fish habitat. The onset of positive water temperature is expected to decline by 45–55 days, significantly changing seasonal thermal timing. These hydrological and temperature changes represent a socio-ecological threat, emphasizing the need for adaptive management and enhanced predictive capabilities in these rapidly changing environments.},
	number = {4},
	urldate = {2026-05-29},
	journal = {Journal of Water and Climate Change},
	author = {Rincón, Eisinhower and St-Hilaire, André and Bergeron, Normand and Dugdale, Stephen},
	month = apr,
	year = {2026},
	keywords = {NALCMS},
	pages = {1032--1055},
}

Graphical Abstract. Graph showing the work flow of the study. Legend: Graphical AbstractClimate change-driven effects are significant socio-environmental concerns in the Arctic and sub-Arctic regions. This study assesses the impact of climate change on the hydrological and thermal regimes of the Aux-Mélèzes River, an important sub-Arctic watershed in northeastern Canada. Using the process-based CEQUEAU model, we projected future (2040–2100) thermal regime shifts, validating against historical data (1980–2020) with strong performance metrics for summer flows and water temperature. Historically, the Aux-Mélèzes River has provided suitable summer habitat for Arctic char and Atlantic salmon, but projections indicate statistically significant increases in mean summer water temperature across all climate scenarios. Temperature duration curve analysis reveals a shift towards higher temperatures, with the probability of exceeding the 23 °C thermal stress threshold for key fish species increasing significantly by the century’s end. Furthermore, maximum summer water temperature projections highlight tributaries as critical warming hotspots, posing risks to fish habitat. The onset of positive water temperature is expected to decline by 45–55 days, significantly changing seasonal thermal timing. These hydrological and temperature changes represent a socio-ecological threat, emphasizing the need for adaptive management and enhanced predictive capabilities in these rapidly changing environments.

@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{ayala-borda_life_2026,
	title = {Life on the {Edge}: {Ecosystem} {Features} of {Lakes} {Across} a {Mountain}–{Prairie} {Elevation} {Gradient} in {Western} {Canada}},
	volume = {19},
	copyright = {© 2026 The Author(s). Ecohydrology published by John Wiley \& Sons Ltd.},
	issn = {1936-0592},
	shorttitle = {Life on the {Edge}},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/eco.70148},
	doi = {10.1002/eco.70148},
	abstract = {Ecological transition zones such as mountain elevation gradients can influence the function and productivity of lakes through changes in climatic characteristics and catchment composition (i.e., land use/land cover). However, surveys of limnological features along such ecological transition zones show variable patterns, making it difficult to extrapolate past results to regions such as the eastern slopes of the Canadian Rocky Mountains. To test the effect of elevation and gradients of environmental variability across such an ecological transition zone, we sampled 11 lakes spanning a 600 m elevation gradient between the prairies and Montane Cordillera of southwest Alberta. We analysed the catchment composition, water chemistry, lake metabolic rates (based on oxygen isotopic analysis), dissolved organic matter (DOM) composition, and zooplankton community composition and diet based on fatty acid analysis. Changes in catchment composition and water temperature along the elevation gradient were not clearly linked to DOM content or composition. Most lakes were autotrophic in summer. ERrates of gross primary production (GPP) and ecosystem respiration (ER) shifted modestly when scaled with elevation, and increased with DOM content and water temperature. Net ecosystem production (NEP = GPP − ER) decreased slightly at higher elevation and peaked at intermediate DOM levels. Zooplankton polyunsaturated fatty acid content was also unrelated to elevation but linked to lake trophic state. Overall, climatic and landscape shifts with elevation transmit inconsistent effects to the limnology of these regional lakes, possibly due to the region's overall low precipitation regime and lakes' weak hydrological connectivity with surrounding terrestrial habitats.},
	language = {en},
	number = {1},
	urldate = {2026-05-21},
	journal = {Ecohydrology},
	author = {Ayala-Borda, Paola and Lapierre, Jean-François and Rautio, Milla and Johnston, Sarah Ellen and Bogard, Matthew J.},
	year = {2026},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/eco.70148},
	keywords = {NALCMS},
	pages = {e70148},
}

Ecological transition zones such as mountain elevation gradients can influence the function and productivity of lakes through changes in climatic characteristics and catchment composition (i.e., land use/land cover). However, surveys of limnological features along such ecological transition zones show variable patterns, making it difficult to extrapolate past results to regions such as the eastern slopes of the Canadian Rocky Mountains. To test the effect of elevation and gradients of environmental variability across such an ecological transition zone, we sampled 11 lakes spanning a 600 m elevation gradient between the prairies and Montane Cordillera of southwest Alberta. We analysed the catchment composition, water chemistry, lake metabolic rates (based on oxygen isotopic analysis), dissolved organic matter (DOM) composition, and zooplankton community composition and diet based on fatty acid analysis. Changes in catchment composition and water temperature along the elevation gradient were not clearly linked to DOM content or composition. Most lakes were autotrophic in summer. ERrates of gross primary production (GPP) and ecosystem respiration (ER) shifted modestly when scaled with elevation, and increased with DOM content and water temperature. Net ecosystem production (NEP = GPP − ER) decreased slightly at higher elevation and peaked at intermediate DOM levels. Zooplankton polyunsaturated fatty acid content was also unrelated to elevation but linked to lake trophic state. Overall, climatic and landscape shifts with elevation transmit inconsistent effects to the limnology of these regional lakes, possibly due to the region's overall low precipitation regime and lakes' weak hydrological connectivity with surrounding terrestrial habitats.

@article{gupta_machine_2026,
	title = {Machine {Learning}-{Based} {Reconstructions} of {Historical} {Daily} and {Monthly} {Runoff} for the {Laurentian} {Great} {Lakes}},
	volume = {13},
	copyright = {2026 The Author(s)},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-026-07000-0},
	doi = {10.1038/s41597-026-07000-0},
	abstract = {High-quality regional streamflow datasets are necessary to support local water resources planning and management in the Great Lakes basin. However, the region’s observed streamflow records are limited by the availability of surface water gauges, which go in and out of service over time. In this paper, we present a continuous reconstruction of daily streamflow from 1951–2013 at 656 gauged locations throughout the Great Lakes basin. This dataset is created using a novel regional Long Short-Term Memory (LSTM) model that integrates local climate data, physical catchment characteristics, and runoff observations from nearby gauged sites. We also use this model to estimate monthly runoff into Lakes Superior, Michigan-Huron, St. Clair, Erie, and Ontario. The daily reconstruction product will equip water managers with information to support local water resources analyses, while the monthly runoff product can help improve historical water balance estimation across the Great Lakes and provide critical context for lake level shifts under a changing climate.},
	language = {en},
	number = {1},
	urldate = {2026-05-27},
	journal = {Scientific Data},
	author = {Gupta, Rohini S. and Wi, Sungwook and Steinschneider, Scott},
	month = mar,
	year = {2026},
	note = {Publisher: Nature Publishing Group},
	keywords = {NALCMS},
	pages = {624},
}

High-quality regional streamflow datasets are necessary to support local water resources planning and management in the Great Lakes basin. However, the region’s observed streamflow records are limited by the availability of surface water gauges, which go in and out of service over time. In this paper, we present a continuous reconstruction of daily streamflow from 1951–2013 at 656 gauged locations throughout the Great Lakes basin. This dataset is created using a novel regional Long Short-Term Memory (LSTM) model that integrates local climate data, physical catchment characteristics, and runoff observations from nearby gauged sites. We also use this model to estimate monthly runoff into Lakes Superior, Michigan-Huron, St. Clair, Erie, and Ontario. The daily reconstruction product will equip water managers with information to support local water resources analyses, while the monthly runoff product can help improve historical water balance estimation across the Great Lakes and provide critical context for lake level shifts under a changing climate.

@article{bonney_monitoring_2026,
	title = {Monitoring snow cover dynamics at 30-m resolution in higher latitude regions using {Harmonized} {Landsat} {Sentinel}-2},
	volume = {233},
	issn = {0924-2716},
	url = {https://www.sciencedirect.com/science/article/pii/S0924271626000328},
	doi = {10.1016/j.isprsjprs.2026.01.032},
	abstract = {Snow is an essential climate variable that is important for hydrology, climate, soil temperature and permafrost, vegetation, animal habitat, and socioeconomics. Wide-area snow cover dynamics (SCD), including the start and end of snow cover, are generally monitored by satellites with coarse spatial resolutions (250–1000 m) and high temporal (daily) resolutions. Higher spatial resolution (HSR) monitoring (10–30 m) has been limited to small areas because of computational constraints and infrequent cloud-free observations. Here, we develop a new method to map wide-area HSR SCD (snow start date, end date, length, periods, status) by leveraging the recently released Harmonized Landsat Sentinel-2 (HLS) v2.0, which has a 2–3-day revisit at 30-m resolution. The method is built around SpatialTemporal Asset Catalogs (STACs) and open-source Python tools. We utilize tiled datacubes, snow classification, and a model involving implausibility checking, cleaning, and finding peaks in data with gaps due to orbit frequencies and clouds. We demonstrate SCD mapping and validation across Canada’s Hudson Bay Lowlands (HBL) and an area in northern Alaska for each snow-year from 2018 to 2019 to 2023–2024 and multi-year composites (2018–2024). We also provide timing uncertainties and a quality metric for all pixels. Performance is best for snow end date, having strong relationships with both visually interpreted SCD from primarily very high-resolution imagery and measured local-scale snow depth. The combination of lower cloud cover and lower solar zenith angles during melt periods leads to lower uncertainties for snow end date compared to start date and length. Performance is better for all metrics at higher latitudes (e.g., northern Alaska), where satellite observations are more frequent due to increased orbit overlap. Although we have only completed validation for the HBL, Canada-wide products using this methodology are available publicly as STACs on the CCMEO Data Cube and will continue to be updated. Addition validation across Canada and methodology improvements are ongoing.},
	urldate = {2026-05-21},
	journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
	author = {Bonney, Mitchell T. and Zhang, Yu},
	month = mar,
	year = {2026},
	keywords = {NALCMS},
	pages = {360--382},
}

Snow is an essential climate variable that is important for hydrology, climate, soil temperature and permafrost, vegetation, animal habitat, and socioeconomics. Wide-area snow cover dynamics (SCD), including the start and end of snow cover, are generally monitored by satellites with coarse spatial resolutions (250–1000 m) and high temporal (daily) resolutions. Higher spatial resolution (HSR) monitoring (10–30 m) has been limited to small areas because of computational constraints and infrequent cloud-free observations. Here, we develop a new method to map wide-area HSR SCD (snow start date, end date, length, periods, status) by leveraging the recently released Harmonized Landsat Sentinel-2 (HLS) v2.0, which has a 2–3-day revisit at 30-m resolution. The method is built around SpatialTemporal Asset Catalogs (STACs) and open-source Python tools. We utilize tiled datacubes, snow classification, and a model involving implausibility checking, cleaning, and finding peaks in data with gaps due to orbit frequencies and clouds. We demonstrate SCD mapping and validation across Canada’s Hudson Bay Lowlands (HBL) and an area in northern Alaska for each snow-year from 2018 to 2019 to 2023–2024 and multi-year composites (2018–2024). We also provide timing uncertainties and a quality metric for all pixels. Performance is best for snow end date, having strong relationships with both visually interpreted SCD from primarily very high-resolution imagery and measured local-scale snow depth. The combination of lower cloud cover and lower solar zenith angles during melt periods leads to lower uncertainties for snow end date compared to start date and length. Performance is better for all metrics at higher latitudes (e.g., northern Alaska), where satellite observations are more frequent due to increased orbit overlap. Although we have only completed validation for the HBL, Canada-wide products using this methodology are available publicly as STACs on the CCMEO Data Cube and will continue to be updated. Addition validation across Canada and methodology improvements are ongoing.

@article{taghipourjavi_demand_2026,
	title = {On demand machine learning-driven surface freeze-thaw retrieval across {Canadian} agricultural regions using {Sentinel}-1 {SAR} data},
	volume = {6},
	issn = {2673-6187},
	url = {https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2025.1728399/full},
	doi = {10.3389/frsen.2025.1728399},
	abstract = {This study explores the prediction of freeze-thaw (FT) states across agricultural fields in four Canadian provinces-Alberta, Manitoba, Saskatchewan, and Québec-using Random Forest classification and regression models. Soil temperature data at a 5 cm depth were gathered from 174 agricultural weather stations from 2016 to 2023. Sentinel-1 SAR VH radar backscatter indicators were processed using Google Earth Engine (GEE). Two modeling approaches were evaluated: a classification model trained on in situ data, where soil states were rigidly classified as either frozen or thawed, and a regression model trained against in situ soil freezing probabilities. Additionally, other site-specific ancillary variables such as latitude, altitude, crop type, and soil type were tested as potential predictors. The regression model using the Exponential Freeze-Thaw Algorithm (EFTA) derived from VH radar backscatter (VHEFTA) demonstrated strong discrimination between frozen and thawed states, and emerged as the most influential factor, accounting for over 90\% of the model’s predictive ability. Models using VHEFTA alone achieved up to 81.4\% accuracy for classifying FT state, with only a slight improvement to 82.1\% when combined with other predictors. Spatial and temporal validation showed stable accuracy (0.79–0.83) and F1-scores (0.75–0.88) across regions and years. Evaluation of model sensitivity to seasonal and temperature variability revealed that although uncertainties were not fully eliminated during transitional periods for both binary and probability-based FT models, binary-based models consistently showed lower error rates and stronger performance. The final FT model was implemented within an interactive web-based tool that generates on-demand FT maps for user-supplied regions of interest across Canadian agricultural and open-land areas.},
	language = {English},
	urldate = {2026-05-29},
	journal = {Frontiers in Remote Sensing},
	author = {Taghipourjavi, Shahabeddin and Kinnard, Christophe and Roy, Alexandre},
	month = jan,
	year = {2026},
	note = {Publisher: Frontiers},
	keywords = {NALCMS},
}

This study explores the prediction of freeze-thaw (FT) states across agricultural fields in four Canadian provinces-Alberta, Manitoba, Saskatchewan, and Québec-using Random Forest classification and regression models. Soil temperature data at a 5 cm depth were gathered from 174 agricultural weather stations from 2016 to 2023. Sentinel-1 SAR VH radar backscatter indicators were processed using Google Earth Engine (GEE). Two modeling approaches were evaluated: a classification model trained on in situ data, where soil states were rigidly classified as either frozen or thawed, and a regression model trained against in situ soil freezing probabilities. Additionally, other site-specific ancillary variables such as latitude, altitude, crop type, and soil type were tested as potential predictors. The regression model using the Exponential Freeze-Thaw Algorithm (EFTA) derived from VH radar backscatter (VHEFTA) demonstrated strong discrimination between frozen and thawed states, and emerged as the most influential factor, accounting for over 90% of the model’s predictive ability. Models using VHEFTA alone achieved up to 81.4% accuracy for classifying FT state, with only a slight improvement to 82.1% when combined with other predictors. Spatial and temporal validation showed stable accuracy (0.79–0.83) and F1-scores (0.75–0.88) across regions and years. Evaluation of model sensitivity to seasonal and temperature variability revealed that although uncertainties were not fully eliminated during transitional periods for both binary and probability-based FT models, binary-based models consistently showed lower error rates and stronger performance. The final FT model was implemented within an interactive web-based tool that generates on-demand FT maps for user-supplied regions of interest across Canadian agricultural and open-land areas.

@article{palm_roads_2026,
	title = {Roads, {Soil}, {Snow}, and {Topography} {Influence} {Genetic} {Connectivity}: {A} {Machine} {Learning} {Approach} for a {Peripheral} {American} {Badger} {Population}},
	volume = {16},
	copyright = {© 2026 The Author(s). Ecology and Evolution published by British Ecological Society and John Wiley \& Sons Ltd.},
	issn = {2045-7758},
	shorttitle = {Roads, {Soil}, {Snow}, and {Topography} {Influence} {Genetic} {Connectivity}},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.73467},
	doi = {10.1002/ece3.73467},
	abstract = {Effective management and conservation of peripheral populations require an understanding of the landscape conditions inhibiting dispersal and spatially explicit predictions of connectivity. Here, we modeled landscape resistance and genetic connectivity for the western population of an American badger subspecies (Taxidea taxus jeffersonii) across 170,000 km2 in southern British Columbia, Canada, using 116 genetic samples genotyped at 14 microsatellite loci. We used gradient boosting machine models in a corridor-based approach to predict genetic distances between pairs of individual badgers as a function of landscape variable data. Spatial genetic autocorrelation tests and our top model predicted that genetic similarities of T. t. jeffersonii were present up to 110 km. Gene diversity was lowest in the Cariboo region in the northwest portion of the study area and highest in the Okanagan region in the southeast. Our analyses suggest that the genetic connectivity of T. t. jeffersonii was impeded by colluvial soil parent material, geographic distance, steep slopes, and major roads, but was facilitated by organic and fluvial soil parent materials, and areas with relatively little snow cover during winter. Our predictive maps of landscape resistance and connectivity can help guide management actions such as habitat protection and underpass placement on major roads to promote genetic connectivity.},
	language = {en},
	number = {4},
	urldate = {2026-05-28},
	journal = {Ecology and Evolution},
	author = {Palm, Eric C. and Landguth, Erin L. and Lamy, Karina and Gorrell, Jamieson C. and Weir, Richard D. and Richardson, Emma L. and Forbes, Krystyn J. and Davis, Helen and Burgar, Joanna M.},
	year = {2026},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.73467},
	keywords = {NALCMS},
	pages = {e73467},
}

Effective management and conservation of peripheral populations require an understanding of the landscape conditions inhibiting dispersal and spatially explicit predictions of connectivity. Here, we modeled landscape resistance and genetic connectivity for the western population of an American badger subspecies (Taxidea taxus jeffersonii) across 170,000 km2 in southern British Columbia, Canada, using 116 genetic samples genotyped at 14 microsatellite loci. We used gradient boosting machine models in a corridor-based approach to predict genetic distances between pairs of individual badgers as a function of landscape variable data. Spatial genetic autocorrelation tests and our top model predicted that genetic similarities of T. t. jeffersonii were present up to 110 km. Gene diversity was lowest in the Cariboo region in the northwest portion of the study area and highest in the Okanagan region in the southeast. Our analyses suggest that the genetic connectivity of T. t. jeffersonii was impeded by colluvial soil parent material, geographic distance, steep slopes, and major roads, but was facilitated by organic and fluvial soil parent materials, and areas with relatively little snow cover during winter. Our predictive maps of landscape resistance and connectivity can help guide management actions such as habitat protection and underpass placement on major roads to promote genetic connectivity.

@article{newcomb_snow_2026,
	title = {Snow simulations predict future changes in rain-on-snow events across the upper {Gallatin} {River} watershed, a {Greater} {Yellowstone} {Ecosystem} headwater system},
	volume = {64},
	issn = {2214-5818},
	url = {https://www.sciencedirect.com/science/article/pii/S2214581826001515},
	doi = {10.1016/j.ejrh.2026.103253},
	abstract = {Study region
The upper Gallatin River watershed, an alpine headwater system in the Greater Yellowstone Ecosystem, in Wyoming and Montana.
Study focus
As global and regional air temperatures rise, mountain headwaters across the Greater Yellowstone Ecosystem (GYE) are projected to receive more precipitation falling as rain. While the hydrologic effects of this snow-to-rain transition depends on a variety of factors, it can lead to an increased occurrence of rain-on-snow (RoS) events. To investigate these changes, we used high-resolution (30 m) SnowModel simulations of the upper Gallatin River watershed. Simulations were run for 2001–2013 using two scenarios: (1) historical meteorology as control and (2) pseudo-global-warming (PGW) where control air temperature and precipitation conditions were perturbed to represent mean end-of-century conditions under a high-emissions scenario.
New hydrological insights for the region
SnowModel outputs show that changes in PGW precipitation and snow accumulation varied with elevation. Warmer air temperatures at low elevations ({\textless} 2500 m) led to less snow accumulation and less precipitation falling as snow. Colder baseline air temperatures for elevations above 2500 meters (m) resulted in minor reductions in winter snowfall fraction. For PGW simulations, spring (April-June) months experienced higher rainfall, and elevations above 2500 m experienced more RoS events. Snowpacks between 2500–3100 m generated more snowmelt during RoS events, which was reflected in the watershed average. More high-intensity melt events can affect aquatic habitat, water quality, and the accuracy of streamflow forecasts across the region.},
	urldate = {2026-05-27},
	journal = {Journal of Hydrology: Regional Studies},
	author = {Newcomb, Sarah K. and Barnhart, Theodore B. and Heldmyer, Aaron J. and Storb, Meryl B.},
	month = apr,
	year = {2026},
	keywords = {NALCMS},
	pages = {103253},
}

Study region The upper Gallatin River watershed, an alpine headwater system in the Greater Yellowstone Ecosystem, in Wyoming and Montana. Study focus As global and regional air temperatures rise, mountain headwaters across the Greater Yellowstone Ecosystem (GYE) are projected to receive more precipitation falling as rain. While the hydrologic effects of this snow-to-rain transition depends on a variety of factors, it can lead to an increased occurrence of rain-on-snow (RoS) events. To investigate these changes, we used high-resolution (30 m) SnowModel simulations of the upper Gallatin River watershed. Simulations were run for 2001–2013 using two scenarios: (1) historical meteorology as control and (2) pseudo-global-warming (PGW) where control air temperature and precipitation conditions were perturbed to represent mean end-of-century conditions under a high-emissions scenario. New hydrological insights for the region SnowModel outputs show that changes in PGW precipitation and snow accumulation varied with elevation. Warmer air temperatures at low elevations (\textless 2500 m) led to less snow accumulation and less precipitation falling as snow. Colder baseline air temperatures for elevations above 2500 meters (m) resulted in minor reductions in winter snowfall fraction. For PGW simulations, spring (April-June) months experienced higher rainfall, and elevations above 2500 m experienced more RoS events. Snowpacks between 2500–3100 m generated more snowmelt during RoS events, which was reflected in the watershed average. More high-intensity melt events can affect aquatic habitat, water quality, and the accuracy of streamflow forecasts across the region.

@article{cebulski_snowfall_2026,
	title = {Snowfall {Interception} and {Ablation} in {Needleleaf} {Canopies}: {Observations} and {Model} {Development}},
	shorttitle = {Snowfall {Interception} and {Ablation} in {Needleleaf} {Canopies}},
	url = {https://hdl.handle.net/10388/18176},
	abstract = {Snow falls on forests covering 23\% of the global landmass. Here, interception by forest canopies plays a crucial role in the terrestrial water cycle and land-atmosphere energy exchanges. Global changes in climate and vegetation cover have the potential to alter how snowfall is intercepted and subsequently partitioned between sublimation to the atmosphere or unloading, melt, and drip to the ground; however, the understanding required to investigate potential changes to water resources and energy exchanges is currently limited. In cold continental climates, up to half of annual snowfall may be lost to the atmosphere through sublimation of snow intercepted in needleleaf canopies, and in warmer climates, higher melt rates cause a large fraction of intercepted snow to reach the ground as liquid. Snow interception processes are strongly dependent on both meteorology and canopy density, leading to differing process emergence in existing theories developed in distinctive climates, seasons, and forest types. Consequently, simulations of the mass and energy balance of intercepted snow have demonstrated variable accuracy across different climates and canopy structures, introducing uncertainty into predictions by hydrological and land-surface models. Moreover, limited observations of snowfall interception and ablation in needleleaf forests have limited evaluation of existing theories across a wide range of climatic conditions and forest types, thereby hindering diagnosis of process uncertainty in existing models. The aim of this research was to evaluate the suitability of existing theories of snow interception and intercepted snow ablation in needleleaf canopies and link improved process understanding to model development and validation across a broad range of canopy structures and meteorological conditions. Expanding measurements of snow interception and canopy snow ablation processes across broader range of environmental conditions, while also better isolating individual processes, could help clarify the applicability of existing theories and guide improvements to enhance model accuracy across diverse sites. New observations of initial snow interception and throughfall, collected across a range of canopy densities and meteorological conditions when ablation processes were minimal, revealed new relationships for predicting initial snow interception and throughfall. Observations of canopy density and sub-canopy throughfall using Uncrewed Aerial Vehicle-borne Light Detection and Ranging (UAV-lidar), snow surveys, and weighed subcanopy snow buckets showed a strong linear relationship between initial interception efficiency (initial snow interception divided by snowfall) and the snow-leaf contact area. This metric accounts for the potential increase in contact area with increasing snowfall trajectory zenith angle, which is defined as the departure in degrees from a vertical plane. As a result, the snow-leaf contact area was found to be highly sensitive to wind speed in sparse canopies, increasing by a factor of 2 with an increase in wind speed from 0 to 1 m s{\textasciicircum}\{-1\}. Contrary to existing theories, no relationships were found between initial interception efficiency and canopy snow load or air temperature. These observations reveal that existing interception efficiency calculations fail in sparse canopies. A new parameterisation was developed that calculates initial snow interception and throughfall as a function of snowfall and snow-leaf contact area, which improved the simulation of throughfall across a range of canopy densities and meteorological conditions. This approach is consistent with several rainfall interception studies that also separate initial rainfall interception, evaporation, and drip processes, and compute interception as a function of canopy cover. However, it extends these existing approaches from those suited to rainfall, by accounting for changes in the snow-leaf contact area associated with the snowfall trajectory zenith angle; a necessary addition given the lower terminal fall velocity of snowflakes compared to raindrops. An evaluation of existing theories representing ablation of snow intercepted in needleleaf canopies was conducted using observations collected by a weighed hanging tree, weighed subcanopy snow buckets, tipping bucket rain gauges, weighing precipitation gauges, and eddy correlations stations, collected across a wide range of meteorological conditions, and with more direct measurements of unloading and drip than those used in previous studies. A new model that incorporates intercepted snow unloading as a function of canopy snow load, wind shear stress, and melt—coupled with a new canopy snow energy and mass balance to calculate sublimation and melt—improved simulation of intercepted snow ablation relative to previous approaches. While existing models have identified canopy snow load, wind, and melt as key predictors of unloading, they performed less well due to incomplete representation of both cold/dry-snow and melt-driven unloading processes. This resulted either from not including the wind-driven unloading process or from approximating intercepted snowmelt and associated unloading as a function of air- or ice-bulb temperature. The new relationships provide a stronger physical basis by representing wind-driven unloading through shear stress and melt-induced unloading as a function of energy balance-based intercepted snowmelt. Moreover, the ratio of snow unloading to meltwater generation during intercepted snowmelt was found to be a function of snow load, in contrast to previous work that used a constant ratio. These new canopy snow mass and energy parameterisations show potential to improve calculation of the partitioning of snowfall by interception processes in needleleaf forests within hydrological and land-surface models. An uncalibrated validation of the new canopy snow mass and energy balance parameterisation was conducted using observations from four distinctive needleleaf forests characterised by differing tree species, canopy structures, and climatic conditions. At sites where observations of canopy intercepted snow load were available, the new model reduced errors in calculating canopy snow load and the duration of snow interception. Additionally, the new model reduced errors in simulating subcanopy snow water equivalent (SWE) compared to an existing model across all four sites. The largest improvements in prediction were observed at the warm, humid maritime site and were attributed to better representation of intercepted snow melt and the melt-induced unloading process. Improved process representation also enabled clearer process-level diagnosis of the influence of snow interception on snow accumulation in differing environments. At two cold, low-wind, and lower-snowfall sites, roughly half of the annual snowfall was sublimated to the atmosphere from intercepted snow. A cold, wind-exposed site with higher annual snowfall had higher unloading rates, which reduced the relative amount of intercepted snow sublimation. In contrast, at a temperate-maritime site with high snowfall, nearly half of annual snowfall melted within the canopy and, combined with drip from this snowmelt, and melt-induced unloading, partitioned the most snowfall towards the ground of all four sites, despite the high initial interception efficiency. This research reveals variability in snowfall interception and intercepted snow ablation processes across diverse canopy densities and meteorological regimes, and shows that this variability has not been fully represented in existing models. New parameterisations of canopy snow mass and energy balance, addressing these limitations, were introduced to more explicitly represent the full range of snow interception processes. This new model provided more accurate simulations of intercepted snow load and subcanopy SWE across a broad range of climatic conditions and forest types and also improved process diagnosis, though further validation is required across a diverse range of sites.},
	language = {en},
	urldate = {2026-06-01},
	author = {Cebulski, Alex C.},
	year = {2026},
	keywords = {NALCMS},
}

Snow falls on forests covering 23% of the global landmass. Here, interception by forest canopies plays a crucial role in the terrestrial water cycle and land-atmosphere energy exchanges. Global changes in climate and vegetation cover have the potential to alter how snowfall is intercepted and subsequently partitioned between sublimation to the atmosphere or unloading, melt, and drip to the ground; however, the understanding required to investigate potential changes to water resources and energy exchanges is currently limited. In cold continental climates, up to half of annual snowfall may be lost to the atmosphere through sublimation of snow intercepted in needleleaf canopies, and in warmer climates, higher melt rates cause a large fraction of intercepted snow to reach the ground as liquid. Snow interception processes are strongly dependent on both meteorology and canopy density, leading to differing process emergence in existing theories developed in distinctive climates, seasons, and forest types. Consequently, simulations of the mass and energy balance of intercepted snow have demonstrated variable accuracy across different climates and canopy structures, introducing uncertainty into predictions by hydrological and land-surface models. Moreover, limited observations of snowfall interception and ablation in needleleaf forests have limited evaluation of existing theories across a wide range of climatic conditions and forest types, thereby hindering diagnosis of process uncertainty in existing models. The aim of this research was to evaluate the suitability of existing theories of snow interception and intercepted snow ablation in needleleaf canopies and link improved process understanding to model development and validation across a broad range of canopy structures and meteorological conditions. Expanding measurements of snow interception and canopy snow ablation processes across broader range of environmental conditions, while also better isolating individual processes, could help clarify the applicability of existing theories and guide improvements to enhance model accuracy across diverse sites. New observations of initial snow interception and throughfall, collected across a range of canopy densities and meteorological conditions when ablation processes were minimal, revealed new relationships for predicting initial snow interception and throughfall. Observations of canopy density and sub-canopy throughfall using Uncrewed Aerial Vehicle-borne Light Detection and Ranging (UAV-lidar), snow surveys, and weighed subcanopy snow buckets showed a strong linear relationship between initial interception efficiency (initial snow interception divided by snowfall) and the snow-leaf contact area. This metric accounts for the potential increase in contact area with increasing snowfall trajectory zenith angle, which is defined as the departure in degrees from a vertical plane. As a result, the snow-leaf contact area was found to be highly sensitive to wind speed in sparse canopies, increasing by a factor of 2 with an increase in wind speed from 0 to 1 m s\textasciicircum\-1\. Contrary to existing theories, no relationships were found between initial interception efficiency and canopy snow load or air temperature. These observations reveal that existing interception efficiency calculations fail in sparse canopies. A new parameterisation was developed that calculates initial snow interception and throughfall as a function of snowfall and snow-leaf contact area, which improved the simulation of throughfall across a range of canopy densities and meteorological conditions. This approach is consistent with several rainfall interception studies that also separate initial rainfall interception, evaporation, and drip processes, and compute interception as a function of canopy cover. However, it extends these existing approaches from those suited to rainfall, by accounting for changes in the snow-leaf contact area associated with the snowfall trajectory zenith angle; a necessary addition given the lower terminal fall velocity of snowflakes compared to raindrops. An evaluation of existing theories representing ablation of snow intercepted in needleleaf canopies was conducted using observations collected by a weighed hanging tree, weighed subcanopy snow buckets, tipping bucket rain gauges, weighing precipitation gauges, and eddy correlations stations, collected across a wide range of meteorological conditions, and with more direct measurements of unloading and drip than those used in previous studies. A new model that incorporates intercepted snow unloading as a function of canopy snow load, wind shear stress, and melt—coupled with a new canopy snow energy and mass balance to calculate sublimation and melt—improved simulation of intercepted snow ablation relative to previous approaches. While existing models have identified canopy snow load, wind, and melt as key predictors of unloading, they performed less well due to incomplete representation of both cold/dry-snow and melt-driven unloading processes. This resulted either from not including the wind-driven unloading process or from approximating intercepted snowmelt and associated unloading as a function of air- or ice-bulb temperature. The new relationships provide a stronger physical basis by representing wind-driven unloading through shear stress and melt-induced unloading as a function of energy balance-based intercepted snowmelt. Moreover, the ratio of snow unloading to meltwater generation during intercepted snowmelt was found to be a function of snow load, in contrast to previous work that used a constant ratio. These new canopy snow mass and energy parameterisations show potential to improve calculation of the partitioning of snowfall by interception processes in needleleaf forests within hydrological and land-surface models. An uncalibrated validation of the new canopy snow mass and energy balance parameterisation was conducted using observations from four distinctive needleleaf forests characterised by differing tree species, canopy structures, and climatic conditions. At sites where observations of canopy intercepted snow load were available, the new model reduced errors in calculating canopy snow load and the duration of snow interception. Additionally, the new model reduced errors in simulating subcanopy snow water equivalent (SWE) compared to an existing model across all four sites. The largest improvements in prediction were observed at the warm, humid maritime site and were attributed to better representation of intercepted snow melt and the melt-induced unloading process. Improved process representation also enabled clearer process-level diagnosis of the influence of snow interception on snow accumulation in differing environments. At two cold, low-wind, and lower-snowfall sites, roughly half of the annual snowfall was sublimated to the atmosphere from intercepted snow. A cold, wind-exposed site with higher annual snowfall had higher unloading rates, which reduced the relative amount of intercepted snow sublimation. In contrast, at a temperate-maritime site with high snowfall, nearly half of annual snowfall melted within the canopy and, combined with drip from this snowmelt, and melt-induced unloading, partitioned the most snowfall towards the ground of all four sites, despite the high initial interception efficiency. This research reveals variability in snowfall interception and intercepted snow ablation processes across diverse canopy densities and meteorological regimes, and shows that this variability has not been fully represented in existing models. New parameterisations of canopy snow mass and energy balance, addressing these limitations, were introduced to more explicitly represent the full range of snow interception processes. This new model provided more accurate simulations of intercepted snow load and subcanopy SWE across a broad range of climatic conditions and forest types and also improved process diagnosis, though further validation is required across a diverse range of sites.

@article{shrestha_hydroclimate_2026,
	title = {The {Hydroclimate} of the {Ottawa} {River} {Basin} {Under} {Climate} {Change}},
	volume = {62},
	issn = {1752-1688},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1752-1688.70082},
	doi = {10.1111/1752-1688.70082},
	abstract = {The freshwater resource of the Ottawa River basin (ORB) is vital for ecological services and economic activities in the region. With climate change impacts becoming more evident, sustainable management is imperative. As a first step, it is important to assess impacts on hydro-climatology. A coupled hydrological model (WATFLOOD-RAVEN) was set up, calibrated, and validated, with all principal reservoirs implemented using the DZTR principle. The coupled model was forced with high-resolution, bias-corrected future climate projections. Ensemble results show a warmer and wetter future. Under RCP 4.5, which recent studies suggest as the more likely pathway, mean annual precipitation and temperature may increase as much as 8.6\% and 3.2°C, respectively. Seasonal and monthly changes are more sporadic. Snowpack is expected to decrease substantially, while actual evapotranspiration increases moderately. Annual streamflow may rise by 7\%–10\%, but spring freshet streamflow is projected to shift earlier (1.1 days/decade) with a lower (12\%) peak. Other indicators, such as time to center of mass and spring pulse onset, also point to earlier shifts. Results further indicate more frequent extreme high (Q10) and low (Q90) flow conditions. Substantial increases in winter reservoir inflows are likely to raise reservoir depths and reduce capacity to store the spring freshet. Better quantification of climate change impacts will aid in developing coordinated responses to climate-related challenges.},
	language = {en},
	number = {1},
	urldate = {2026-05-29},
	journal = {JAWRA Journal of the American Water Resources Association},
	author = {Shrestha, Narayan K. and Seglenieks, Frank},
	year = {2026},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/1752-1688.70082},
	keywords = {NALCMS},
	pages = {e70082},
}

The freshwater resource of the Ottawa River basin (ORB) is vital for ecological services and economic activities in the region. With climate change impacts becoming more evident, sustainable management is imperative. As a first step, it is important to assess impacts on hydro-climatology. A coupled hydrological model (WATFLOOD-RAVEN) was set up, calibrated, and validated, with all principal reservoirs implemented using the DZTR principle. The coupled model was forced with high-resolution, bias-corrected future climate projections. Ensemble results show a warmer and wetter future. Under RCP 4.5, which recent studies suggest as the more likely pathway, mean annual precipitation and temperature may increase as much as 8.6% and 3.2°C, respectively. Seasonal and monthly changes are more sporadic. Snowpack is expected to decrease substantially, while actual evapotranspiration increases moderately. Annual streamflow may rise by 7%–10%, but spring freshet streamflow is projected to shift earlier (1.1 days/decade) with a lower (12%) peak. Other indicators, such as time to center of mass and spring pulse onset, also point to earlier shifts. Results further indicate more frequent extreme high (Q10) and low (Q90) flow conditions. Substantial increases in winter reservoir inflows are likely to raise reservoir depths and reduce capacity to store the spring freshet. Better quantification of climate change impacts will aid in developing coordinated responses to climate-related challenges.

@article{haghiri_present_2026,
	title = {The present and future of {North} {American} rainwater harvesting},
	volume = {71},
	issn = {0262-6667},
	url = {https://doi.org/10.1080/02626667.2026.2633302},
	doi = {10.1080/02626667.2026.2633302},
	abstract = {North America faces increasing water scarcity driven by climate change and population growth. This study applies the analytical hierarchy process (AHP) integrated with GIS to assess rainwater harvesting (RWH) suitability across the continent for 2020 and 2100 under the CMIP6 SSP5–8.5 scenario. Eight biophysical factors – topography, slope, soil texture, soil moisture, precipitation, NDVI, drainage density, and land cover – were analyzed using satellite and national datasets at 1/24° resolution. Expert-based AHP pairwise comparisons were used to derive criterion weights, which were then integrated through GIS-based weighted overlay analysis. Results indicate that moderately and highly suitable areas dominate, expanding from 10.55 to 10.91 million km2 (45\%) and 7.30 to 7.61 million km2 (32\%), respectively, between 2020 and 2100. Very highly suitable zones remain below 5\%. These findings reveal a subtle but positive continental trend in RWH feasibility, offering a robust geospatial foundation for climate-resilient water management and investment planning across North America.},
	number = {7},
	urldate = {2026-05-27},
	journal = {Hydrological Sciences Journal},
	author = {Haghiri, Mohammad and Meysami, Sahar and Asef, Mohammad Reza},
	month = may,
	year = {2026},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/02626667.2026.2633302},
	keywords = {NALCMS},
	pages = {1399--1414},
}

North America faces increasing water scarcity driven by climate change and population growth. This study applies the analytical hierarchy process (AHP) integrated with GIS to assess rainwater harvesting (RWH) suitability across the continent for 2020 and 2100 under the CMIP6 SSP5–8.5 scenario. Eight biophysical factors – topography, slope, soil texture, soil moisture, precipitation, NDVI, drainage density, and land cover – were analyzed using satellite and national datasets at 1/24° resolution. Expert-based AHP pairwise comparisons were used to derive criterion weights, which were then integrated through GIS-based weighted overlay analysis. Results indicate that moderately and highly suitable areas dominate, expanding from 10.55 to 10.91 million km2 (45%) and 7.30 to 7.61 million km2 (32%), respectively, between 2020 and 2100. Very highly suitable zones remain below 5%. These findings reveal a subtle but positive continental trend in RWH feasibility, offering a robust geospatial foundation for climate-resilient water management and investment planning across North America.

@article{wang_wetter_2026,
	title = {Wetter conditions amplify simulated deep drainage differences between grasslands and forests in the {Post} {Oak} {Savanna}},
	volume = {64},
	issn = {2214-5818},
	url = {https://www.sciencedirect.com/science/article/pii/S2214581826001084},
	doi = {10.1016/j.ejrh.2026.103210},
	abstract = {Study region
The Post Oak Savanna (POS) ecoregion of Texas, U.S.
Study focus
Woody plant encroachment in the POS has been linked to reduced groundwater recharge, but it remains unclear whether the removal of these woody plants may increase groundwater recharge rates. To evaluate differences in recharge under contrasting vegetation covers, we simulated deep drainage at 18 paired grassland–forest sites using Hydrus-1D. The simulations were analyzed to assess spatiotemporal variability in deep drainage between paired sites, with these differences serving as a proxy for potential recharge changes due to woody plant removal. Additionally, simulations were used to identify the influence of precipitation and soil texture on the magnitude of deep drainage differences between grasslands and forests.
New hydrological insights for the region
Deep drainage was consistently higher in grasslands, which also showed greater variability, while forests had more uniformly limited deep drainage. The differences in deep drainage between forested and grassland sites were larger in wetter subregions and years, with precipitation playing a stronger role than sand content. Higher precipitation amplified deep drainage increases, and soils with lower sand content promoted stronger responses of deep drainage increases than sandier soils. These findings indicate strong promise for enhancing potential recharge through woody plant removal in the POS, but this potential is limited in the southern subregion where precipitation is low. This highlights the importance of regional differences when prioritizing restoration efforts to support groundwater sustainability.},
	urldate = {2026-05-29},
	journal = {Journal of Hydrology: Regional Studies},
	author = {Wang, Mingxiu and Wyatt, Briana M. and Wilcox, Bradford P.},
	month = apr,
	year = {2026},
	keywords = {NALCMS},
	pages = {103210},
}

Study region The Post Oak Savanna (POS) ecoregion of Texas, U.S. Study focus Woody plant encroachment in the POS has been linked to reduced groundwater recharge, but it remains unclear whether the removal of these woody plants may increase groundwater recharge rates. To evaluate differences in recharge under contrasting vegetation covers, we simulated deep drainage at 18 paired grassland–forest sites using Hydrus-1D. The simulations were analyzed to assess spatiotemporal variability in deep drainage between paired sites, with these differences serving as a proxy for potential recharge changes due to woody plant removal. Additionally, simulations were used to identify the influence of precipitation and soil texture on the magnitude of deep drainage differences between grasslands and forests. New hydrological insights for the region Deep drainage was consistently higher in grasslands, which also showed greater variability, while forests had more uniformly limited deep drainage. The differences in deep drainage between forested and grassland sites were larger in wetter subregions and years, with precipitation playing a stronger role than sand content. Higher precipitation amplified deep drainage increases, and soils with lower sand content promoted stronger responses of deep drainage increases than sandier soils. These findings indicate strong promise for enhancing potential recharge through woody plant removal in the POS, but this potential is limited in the southern subregion where precipitation is low. This highlights the importance of regional differences when prioritizing restoration efforts to support groundwater sustainability.

@article{wehr_white-tailed_2026,
	title = {White-{Tailed} {Deer} {Baiting} {Altered} {Black} {Bear} {Site} {Use} but {Not} {Movements} or {Range} {Size}},
	volume = {16},
	copyright = {© 2026 The Author(s). Ecology and Evolution published by British Ecological Society and John Wiley \& Sons Ltd.},
	issn = {2045-7758},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.73015},
	doi = {10.1002/ece3.73015},
	abstract = {Intentional anthropogenic food subsidies (i.e., baiting and supplemental feeding) can have profound individual-level effects on wildlife. We assessed the influence of bait sites targeting white-tailed deer (Odocoileus virginianus) on nontarget American black bears (Ursus americanus) in Michigan's Upper Peninsula, USA. We used black bear GPS collar data collected during July–August 2013–2019 to assess differences in recursive site use metrics (i.e., number, duration, and time between visits) and movement metrics (i.e., range size, step length, and turning angle) using a before–after control-impact design. We compared site use at 116 baited and 81 unbaited control sites before (pretreatment) and after (treatment) bait applications. We compared movements between 32 GPS-monitored bear-years (i.e., individual bears monitored during July–August of each calendar year) where bait was within the bear's home range and eight bear-years where bait applications occurred outside individuals' home ranges. Bears exhibited 3 times more visits and 11 times longer durations of use at active bait sites during the treatment period than unbaited and pretreatment controls. Active bait site visits were also more nocturnal during the treatment period than visits to unbaited and pretreatment control sites. Movement metrics did not, however, differ between bears with and without bait in their home ranges regardless of treatment period. Our results support the known adaptability of black bears to anthropogenic food subsidies but indicate use of this resource did not influence larger-scale movement patterns. Our work furthers understanding of black bear behavior by explicitly identifying greater use of baited versus unbaited sites without alteration of movement metrics.},
	language = {en},
	number = {2},
	urldate = {2026-05-29},
	journal = {Ecology and Evolution},
	author = {Wehr, Nathaniel H. and Fowler, Nicholas L. and Kautz, Todd M. and Petroelje, Tyler R. and Beyer Jr, Dean E. and Belant, Jerrold L.},
	year = {2026},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.73015},
	keywords = {NALCMS},
	pages = {e73015},
}

Intentional anthropogenic food subsidies (i.e., baiting and supplemental feeding) can have profound individual-level effects on wildlife. We assessed the influence of bait sites targeting white-tailed deer (Odocoileus virginianus) on nontarget American black bears (Ursus americanus) in Michigan's Upper Peninsula, USA. We used black bear GPS collar data collected during July–August 2013–2019 to assess differences in recursive site use metrics (i.e., number, duration, and time between visits) and movement metrics (i.e., range size, step length, and turning angle) using a before–after control-impact design. We compared site use at 116 baited and 81 unbaited control sites before (pretreatment) and after (treatment) bait applications. We compared movements between 32 GPS-monitored bear-years (i.e., individual bears monitored during July–August of each calendar year) where bait was within the bear's home range and eight bear-years where bait applications occurred outside individuals' home ranges. Bears exhibited 3 times more visits and 11 times longer durations of use at active bait sites during the treatment period than unbaited and pretreatment controls. Active bait site visits were also more nocturnal during the treatment period than visits to unbaited and pretreatment control sites. Movement metrics did not, however, differ between bears with and without bait in their home ranges regardless of treatment period. Our results support the known adaptability of black bears to anthropogenic food subsidies but indicate use of this resource did not influence larger-scale movement patterns. Our work furthers understanding of black bear behavior by explicitly identifying greater use of baited versus unbaited sites without alteration of movement metrics.

@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.

@phdthesis{rootes_eastern_2025,
	type = {Doctor of {Philosophy}},
	title = {Eastern {Spruce} {Budworm} {In} {The} {Great} {Lakes} {Region}: {Forest} {Regeneration}, {Natural} {Enemies}, {And} {Dispersal}},
	shorttitle = {Eastern {Spruce} {Budworm} {In} {The} {Great} {Lakes} {Region}},
	url = {https://hdl.handle.net/11299/279274},
	abstract = {The eastern spruce budworm (Choristoneura fumiferana Clemens), (Lepidoptera: Tortricidae), is native to northeastern North America and feeds primarily on balsam fir (Abies balsamea (L.) Mill) and white spruce (Picea glauca (Moench) Voss). Outbreaking populations have been mapped annually in Minnesota since 1954. This dissertation examines forest regeneration in areas of northeastern Minnesota and on Isle Royale 15 years following outbreaks of spruce budworm, the natural enemy communities in both locations, and examines the potential connectedness of regional populations in Minnesota and Wisconsin by looking for evidence of dispersal over a flight season using adult trap catches and meteorological modeling. On Isle Royale, trees suffered higher mortality and negative alterations to their growth compared to similar forests in Minnesota. Those results suggest a positive correlation between moose density and adverse effects on forest regeneration in areas recovering from an outbreak of spruce budworm and support the use of wolves as predators of moose on the island to restore ecological function. Late instar larvae and pupae of spruce budworm were sampled from current outbreaks in these same locations in 2023 and 2024. Though the diversity of the parasitoid community was similar between the two locations, Isle Royale demonstrated higher rates of parasitism (18.8\% to 33.7\%) than Minnesota (10.3\% to 17.1\%), suggesting that natural enemies in both locations are capable of assisting with mediation of outbreaking spruce budworm populations. Fluctuations in populations of spruce budworm are likely related not only to natural enemy populations but dispersal of adult spruce budworms. To assess dispersal, I evaluated ten areas in northeastern Minnesota and northern Wisconsin in summer 2024 using automatic trapping methods and meteorological modeling. I found that sub-populations of spruce budworm were highly synchronized and that trap captures were predicted by captures in neighboring sites, precipitation, temperature, wind speed, and landscape composition (i.e., percent of a putative trajectory over forest or open water). Outbreaks of spruce budworm are multi-faceted, impacted by factors such as natural enemies and regional-wide dispersal, and can result in cascading effects on forest regeneration and composition.},
	language = {en},
	urldate = {2026-06-01},
	school = {University of Minnesota},
	author = {Rootes, Jessica Michelle},
	month = dec,
	year = {2025},
	keywords = {NALCMS},
}

The eastern spruce budworm (Choristoneura fumiferana Clemens), (Lepidoptera: Tortricidae), is native to northeastern North America and feeds primarily on balsam fir (Abies balsamea (L.) Mill) and white spruce (Picea glauca (Moench) Voss). Outbreaking populations have been mapped annually in Minnesota since 1954. This dissertation examines forest regeneration in areas of northeastern Minnesota and on Isle Royale 15 years following outbreaks of spruce budworm, the natural enemy communities in both locations, and examines the potential connectedness of regional populations in Minnesota and Wisconsin by looking for evidence of dispersal over a flight season using adult trap catches and meteorological modeling. On Isle Royale, trees suffered higher mortality and negative alterations to their growth compared to similar forests in Minnesota. Those results suggest a positive correlation between moose density and adverse effects on forest regeneration in areas recovering from an outbreak of spruce budworm and support the use of wolves as predators of moose on the island to restore ecological function. Late instar larvae and pupae of spruce budworm were sampled from current outbreaks in these same locations in 2023 and 2024. Though the diversity of the parasitoid community was similar between the two locations, Isle Royale demonstrated higher rates of parasitism (18.8% to 33.7%) than Minnesota (10.3% to 17.1%), suggesting that natural enemies in both locations are capable of assisting with mediation of outbreaking spruce budworm populations. Fluctuations in populations of spruce budworm are likely related not only to natural enemy populations but dispersal of adult spruce budworms. To assess dispersal, I evaluated ten areas in northeastern Minnesota and northern Wisconsin in summer 2024 using automatic trapping methods and meteorological modeling. I found that sub-populations of spruce budworm were highly synchronized and that trap captures were predicted by captures in neighboring sites, precipitation, temperature, wind speed, and landscape composition (i.e., percent of a putative trajectory over forest or open water). Outbreaks of spruce budworm are multi-faceted, impacted by factors such as natural enemies and regional-wide dispersal, and can result in cascading effects on forest regeneration and composition.

@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{lohman_exploring_2025,
	address = {Reno},
	type = {Master of {Science}},
	title = {Exploring regionalization techniques: {Simulation} and application in ecology},
	url = {https://scholarwolf.unr.edu/items/408d3d04-2da1-43cc-997e-d12f534ea820},
	language = {English},
	urldate = {2026-06-01},
	school = {University of Nevada - Reno},
	author = {Lohman, Madeleine},
	month = dec,
	year = {2025},
	keywords = {NALCMS},
}

@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.

@techreport{government_of_canada_jasper_2025,
	type = {Information {Report}},
	title = {Jasper wildfire complex 2024 : fire behaviour documentation, reconstruction, and analysis.: {Fo133}-1/{433E}-{PDF} - {Government} of {Canada} {Publications} - {Canada}.ca},
	shorttitle = {Jasper wildfire complex 2024},
	url = {https://publications.gc.ca/site/eng/9.953712/publication.html},
	abstract = {Publication information / bibliographic Record.},
	language = {eng},
	number = {NOR-X-433},
	urldate = {2026-05-29},
	institution = {Natural Resources Canada, Canadian Forest Service},
	author = {Government of Canada, Public Services {and} Procurement Canada},
	year = {2025},
	note = {Last Modified: 2013-04-03},
	keywords = {NALCMS},
}

Publication information / bibliographic Record.

@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.

@phdthesis{demichele_multi-criteria_2025,
	address = {Anchorage, Alaska, United States},
	type = {Master of {Science}},
	title = {Multi-{Criteria} {Analysis} for {Navigating} {Wildfire} {Risk} in {Alaska}'s {Matanuska}-{Susitna} {Borough}},
	url = {https://www.proquest.com/openview/41673b80a6f8fcff933266c8f9389ccf/1?pq-origsite=gscholar&cbl=18750&diss=y},
	abstract = {Explore millions of resources from scholarly journals, books, newspapers, videos and more, on the ProQuest Platform.},
	language = {English},
	urldate = {2026-06-09},
	school = {University of Alaska Anchorage},
	author = {DeMichele, Kathleen},
	month = jan,
	year = {2025},
	keywords = {NALCMS},
}

Explore millions of resources from scholarly journals, books, newspapers, videos and more, on the ProQuest Platform.

@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.

@phdthesis{hovland_patterns_2025,
	title = {Patterns and {Consequences} of {Monarch} {Wintering} {Behavior} in the {Southeastern} {US}},
	url = {https://openscholar.uga.edu/record/27798},
	abstract = {Eastern North American monarchs (Danaus plexippus) employ a diversity of overwintering behaviors, including long distance migration to central Mexico, but also winter-breeding and non-breeding overwintering in the southeastern United States. Winter-breeding alters infectious disease dynamics, increasing the prevalence of the monarch parasite Ophryocystis elektroscirrha (O.E.) in winter-breeding populations. I modeled interactions between returning spring migrants and resident winter-breeding monarchs in a simulated habitat patch and found that fewer new migrants were recruited from the patch into the migration when residents were present, and that migrants eclosing in habitat patches shared with residents were more likely to be parasitized than in habitat used only by migrants. Additionally, I fit logistic regression models to associate the breeding status of winter monarchs in the Southeast with habitat characteristics. My results concur with the idea that winter-breeding monarchs use human-modified habitat and suggest non-breeding monarchs may winter in wetlands in warm regions},
	language = {eng},
	urldate = {2026-06-01},
	author = {Hovland, Charlotte Pastor},
	collaborator = {Altizer, Sonia and Hall, Richard and Lunn, Tamika},
	month = dec,
	year = {2025},
	keywords = {NALCMS},
}

Eastern North American monarchs (Danaus plexippus) employ a diversity of overwintering behaviors, including long distance migration to central Mexico, but also winter-breeding and non-breeding overwintering in the southeastern United States. Winter-breeding alters infectious disease dynamics, increasing the prevalence of the monarch parasite Ophryocystis elektroscirrha (O.E.) in winter-breeding populations. I modeled interactions between returning spring migrants and resident winter-breeding monarchs in a simulated habitat patch and found that fewer new migrants were recruited from the patch into the migration when residents were present, and that migrants eclosing in habitat patches shared with residents were more likely to be parasitized than in habitat used only by migrants. Additionally, I fit logistic regression models to associate the breeding status of winter monarchs in the Southeast with habitat characteristics. My results concur with the idea that winter-breeding monarchs use human-modified habitat and suggest non-breeding monarchs may winter in wetlands in warm regions

@phdthesis{perbet_perturbations_2025,
	address = {Quebec City, QC},
	title = {Perturbations et régénérations des forêts canadiennes par télédétection et apprentissage profond},
	url = {https://hdl.handle.net/20.500.11794/178085},
	abstract = {La forêt canadienne joue un rôle crucial en fournissant des biens et services écologiques et économiques, en régulant le climat, et en agissant comme un puits de carbone. Cependant, les perturbations forestières peuvent transformer la forêt en source de carbone. Les changements climatiques ont intensifié la fréquence et l'intensité des perturbations, et potentiellement modifié la capacité de régénération des forêts. Dans ce contexte, la surveillance précise et continue de la dynamique forestière devient stratégique pour les objectifs de carboneutralité du Canada. Plusieurs produits cartographiques développées à partir d'images satellitaires, disponibles depuis quelques années, permettent de suivre les perturbations et l'évolution de la structure des forêts. Cependant, les modèles traditionnellement utilisés rencontrent des difficultés à distinguer les perturbations mineures et les changements progressifs. De plus, les approches utilisées pour observer la croissance post-perturbation à grande échelle, sous-estiment la durée réelle de restauration. L'objectif principal de cette thèse est d'évaluer les méthodes d'apprentissage profond pour améliorer et mettre à jour les cartographies temporelles de la forêt boréale canadienne, tant en ce qui concerne les perturbations que la régénération forestière. Les deux premiers chapitres portent sur l'évaluation de l'apport des méthodes d'apprentissage profond pour la classification des perturbations forestières. Ces nouvelles approches ont d'abord été appliquées à la forêt boréale du Québec dans le premier chapitre, puis étendues à l'ensemble du territoire canadien dans le second. Le troisième chapitre vise quant à lui à évaluer une méthode d'apprentissage profond pour le suivi de la régénération de la biomasse forestière. Les résultats ont montré que l'utilisation d'un réseau de neurones convolutifs unidimensionnels pour classifier des séquences spectro-temporelles courtes améliore la classification des perturbations partielles et progressives. Les cartes de perturbations annuelles au Canada entre 1985 et 2024 présentent de nouvelles classes encore jamais cartographiées à cette échelle spatio-temporelle. En particulier, le modèle de classification à correctement identifié les défoliations moyennes et sévères et résultantes d'épidémies d'insectes forestiers. Dans le troisième chapitre, une approche combinant la série temporelle Landsat, les données de Lidar aérien et un réseau de neurones convolutifs de type Unet a permis de cartographier la biomasse aérienne. Ces cartes annuelles de biomasse ont montré leur intérêt pour observer la dynamique forestière après les perturbations sévères et identifier les zones dont la croissance était problématique. En développant ces nouveaux jeux de données à partir de modèle d'apprentissage profond, cette thèse a permis d'améliorer et de mettre à jour les connaissances sur la dynamique forestière au Canada.},
	language = {French \& English},
	urldate = {2026-06-01},
	school = {Université Laval},
	author = {Perbet, Pauline},
	year = {2025},
	keywords = {NALCMS},
}

La forêt canadienne joue un rôle crucial en fournissant des biens et services écologiques et économiques, en régulant le climat, et en agissant comme un puits de carbone. Cependant, les perturbations forestières peuvent transformer la forêt en source de carbone. Les changements climatiques ont intensifié la fréquence et l'intensité des perturbations, et potentiellement modifié la capacité de régénération des forêts. Dans ce contexte, la surveillance précise et continue de la dynamique forestière devient stratégique pour les objectifs de carboneutralité du Canada. Plusieurs produits cartographiques développées à partir d'images satellitaires, disponibles depuis quelques années, permettent de suivre les perturbations et l'évolution de la structure des forêts. Cependant, les modèles traditionnellement utilisés rencontrent des difficultés à distinguer les perturbations mineures et les changements progressifs. De plus, les approches utilisées pour observer la croissance post-perturbation à grande échelle, sous-estiment la durée réelle de restauration. L'objectif principal de cette thèse est d'évaluer les méthodes d'apprentissage profond pour améliorer et mettre à jour les cartographies temporelles de la forêt boréale canadienne, tant en ce qui concerne les perturbations que la régénération forestière. Les deux premiers chapitres portent sur l'évaluation de l'apport des méthodes d'apprentissage profond pour la classification des perturbations forestières. Ces nouvelles approches ont d'abord été appliquées à la forêt boréale du Québec dans le premier chapitre, puis étendues à l'ensemble du territoire canadien dans le second. Le troisième chapitre vise quant à lui à évaluer une méthode d'apprentissage profond pour le suivi de la régénération de la biomasse forestière. Les résultats ont montré que l'utilisation d'un réseau de neurones convolutifs unidimensionnels pour classifier des séquences spectro-temporelles courtes améliore la classification des perturbations partielles et progressives. Les cartes de perturbations annuelles au Canada entre 1985 et 2024 présentent de nouvelles classes encore jamais cartographiées à cette échelle spatio-temporelle. En particulier, le modèle de classification à correctement identifié les défoliations moyennes et sévères et résultantes d'épidémies d'insectes forestiers. Dans le troisième chapitre, une approche combinant la série temporelle Landsat, les données de Lidar aérien et un réseau de neurones convolutifs de type Unet a permis de cartographier la biomasse aérienne. Ces cartes annuelles de biomasse ont montré leur intérêt pour observer la dynamique forestière après les perturbations sévères et identifier les zones dont la croissance était problématique. En développant ces nouveaux jeux de données à partir de modèle d'apprentissage profond, cette thèse a permis d'améliorer et de mettre à jour les connaissances sur la dynamique forestière au Canada.

@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{pilat_predicting_2025,
	title = {Predicting {Suitable} {Habitat} for {Skwenkwínem} ({Claytonia} lanceolata), a {Culturally} {Significant} {Plant}, {Using} a {Reproducible} {Species} {Distribution} {Model}},
	volume = {15},
	copyright = {© 2025 His Majesty the King in Right of Canada and The Author(s). Ecology and Evolution published by British Ecological Society and John Wiley \& Sons Ltd. Reproduced with the permission of the Minister of Agriculture and Agri-Food Canada.},
	issn = {2045-7758},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.72615},
	doi = {10.1002/ece3.72615},
	abstract = {Colonialism and a changing climate have threatened culturally significant food plants and the well-being of those who rely on them. An example is skwenkwínem (western spring beauty, Claytonia lanceolata: Pursh), which has declined in quality and accessibility for the Secwépemc People of Skeetchestn Indian Band. This study aims to provide predictions and visual tools to inform Skeetchestn's conservation efforts for their skwenkwínem patches, from a computationally reproducible species distribution model. Using a set of predictors informed by qualitative interviews with Skeetchestn community members (Informed model), and the 19 bioclimatic variables from WorldClim (WorldClim model), we predicted suitable habitat for skwenkwínem over its known range. For both our total study area and Skeetchestn Territory, we predicted a decrease in suitable habitat from the present to 2081–2100, based on the WorldClim CMIP6 climate change scenario SSP 5–8.5 (worst case). These predictions use Skeetchestn's knowledge of skwenkwínem and commonly used bioclimatic predictors to support Skeetchestn's goals of food sovereignty.},
	language = {en},
	number = {12},
	urldate = {2026-05-28},
	journal = {Ecology and Evolution},
	author = {Pilat, Hannah E. and Ensing, David J. and Pither, Jason},
	year = {2025},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.72615},
	keywords = {NALCMS},
	pages = {e72615},
}

Colonialism and a changing climate have threatened culturally significant food plants and the well-being of those who rely on them. An example is skwenkwínem (western spring beauty, Claytonia lanceolata: Pursh), which has declined in quality and accessibility for the Secwépemc People of Skeetchestn Indian Band. This study aims to provide predictions and visual tools to inform Skeetchestn's conservation efforts for their skwenkwínem patches, from a computationally reproducible species distribution model. Using a set of predictors informed by qualitative interviews with Skeetchestn community members (Informed model), and the 19 bioclimatic variables from WorldClim (WorldClim model), we predicted suitable habitat for skwenkwínem over its known range. For both our total study area and Skeetchestn Territory, we predicted a decrease in suitable habitat from the present to 2081–2100, based on the WorldClim CMIP6 climate change scenario SSP 5–8.5 (worst case). These predictions use Skeetchestn's knowledge of skwenkwínem and commonly used bioclimatic predictors to support Skeetchestn's goals of food sovereignty.

@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{babadi_ataabadi_reconstructing_2025,
	title = {Reconstructing {Historical} {Landsat} {Time} {Series} {Using} a {Transformer}-{Based} {Deep} {Learning} {Approach}: {A} {Case} {Study} in the {Canadian} {Prairies} {Region}},
	volume = {51},
	issn = {0703-8992},
	shorttitle = {Reconstructing {Historical} {Landsat} {Time} {Series} {Using} a {Transformer}-{Based} {Deep} {Learning} {Approach}},
	url = {https://doi.org/10.1080/07038992.2025.2603738},
	doi = {10.1080/07038992.2025.2603738},
	abstract = {The Landsat program is crucial for monitoring environmental changes, benefiting from its long record of moderate-spatial-resolution imagery, well-established calibration, and commitment to providing open-access data. However, the sparse and irregular observation intervals in Landsat time series pose challenges for applications requiring temporally consistent data. This study evaluates the performance of two deep learning models, CFC-mmRNN and a Transformer-based network, for reconstructing Landsat time series under varying data availability conditions. Accuracy is analyzed across spectral bands, seasons, and data densities to assess their effectiveness in handling irregular temporal gaps. The results indicate that the Transformer model outperforms CfC-mmRNN. While both models achieve similar accuracy in high-density cases, CfC-mmRNN’s performance declines sharper as data density decreases. In contrast, the Transformer model maintains the temporal structure of the reconstructed time series even at very low densities, with about two observations per year. These findings suggest that CfC-mmRNN remains effective for time series applications when sufficient observations are available, and it is directly applicable to forecasting tasks. The Transformer model, however, offers a more robust solution for reconstructing sparse Landsat time series, particularly in data-scarce conditions. This study underscores the importance of selecting an appropriate deep learning method to enhance Landsat time series reconstruction.},
	number = {1},
	urldate = {2026-05-21},
	journal = {Canadian Journal of Remote Sensing},
	author = {Babadi Ataabadi, Masoud and Pouliot, Darren and Chen, Dongmei and Oluwadare, Temitope Seun},
	month = dec,
	year = {2025},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/07038992.2025.2603738},
	keywords = {NALCMS},
	pages = {2603738},
}

The Landsat program is crucial for monitoring environmental changes, benefiting from its long record of moderate-spatial-resolution imagery, well-established calibration, and commitment to providing open-access data. However, the sparse and irregular observation intervals in Landsat time series pose challenges for applications requiring temporally consistent data. This study evaluates the performance of two deep learning models, CFC-mmRNN and a Transformer-based network, for reconstructing Landsat time series under varying data availability conditions. Accuracy is analyzed across spectral bands, seasons, and data densities to assess their effectiveness in handling irregular temporal gaps. The results indicate that the Transformer model outperforms CfC-mmRNN. While both models achieve similar accuracy in high-density cases, CfC-mmRNN’s performance declines sharper as data density decreases. In contrast, the Transformer model maintains the temporal structure of the reconstructed time series even at very low densities, with about two observations per year. These findings suggest that CfC-mmRNN remains effective for time series applications when sufficient observations are available, and it is directly applicable to forecasting tasks. The Transformer model, however, offers a more robust solution for reconstructing sparse Landsat time series, particularly in data-scarce conditions. This study underscores the importance of selecting an appropriate deep learning method to enhance Landsat time series reconstruction.

@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{lewis_space_2025,
	address = {Missoula, MT},
	title = {Space {Use}, {Habitat} {Selection}, and {Flight} {Behavior} of {Eagles} {Across} {Northwestern} {North} {America}},
	url = {https://scholarworks.umt.edu/etd/12583/},
	abstract = {Explore millions of resources from scholarly journals, books, newspapers, videos and more, on the ProQuest Platform.},
	language = {en},
	urldate = {2026-01-21},
	school = {University of Montana},
	author = {Lewis, Stephen Bernard},
	month = dec,
	year = {2025},
	keywords = {Landfills, NALCMS},
}

Explore millions of resources from scholarly journals, books, newspapers, videos and more, on the ProQuest Platform.

@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.

@incollection{beck_sage-grouse_2023,
	title = {Sage-{Grouse}},
	isbn = {978-3-031-34037-6},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-34037-6_10},
	abstract = {In this chapter, we summarize the ecology and conservation issues affecting greater (Centrocercus urophasianus) and Gunnison (C. minimus) sage-grouse, iconic and obligate species of rangelands in the sagebrush (Artemisia spp.) biome in western North America. Greater...},
	language = {en},
	urldate = {2026-05-20},
	booktitle = {Rangeland {Wildlife} {Ecology} and {Conservation}},
	publisher = {Springer, Cham},
	author = {Beck, Jeffrey L. and Christiansen, Thomas J. and Davies, Kirk W. and Dinkins, Jonathan B. and Monroe, Adrian P. and Naugle, David E. and Schroeder, Michael A.},
	year = {2023},
	doi = {10.1007/978-3-031-34037-6_10},
	keywords = {NALCMS},
	pages = {295--338},
}

In this chapter, we summarize the ecology and conservation issues affecting greater (Centrocercus urophasianus) and Gunnison (C. minimus) sage-grouse, iconic and obligate species of rangelands in the sagebrush (Artemisia spp.) biome in western North America. Greater...

@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 unwrappin