New York Cooperative Fish and Wildlife Research Unit
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By Cooperative Research Units
February 19, 2026
The New York Cooperative Fish and Wildlife Research Unit (est. 1963) is a partnership among the New York State Department of Environmental Conservation, Cornell University, the U.S. Geological Survey, the U.S. Fish and Wildlife Service, and the Wildlife Management Institute.
Moose survival and habitat‐associated risk of endoparasites Moose survival and habitat‐associated risk of endoparasites
Parasite-induced morbidity and mortality can alter the trajectories of incidental host populations. Yet, parasites rarely act in isolation and may be one of a multitude of biotic and abiotic stressors that collectively shape mortality risk in vertebrate populations. We quantified sources of mortality in a low-density population of moose (Alces alces) in New York State and investigated...
Authors
Jennifer A. Grauer, Jacqueline L. Frair, Krysten L. Schuler, Manigandan Lejeune, David W. Kramer, Angela K. Fuller
Potential interactions between birds and floating photovoltaic solar energy: Spatially informed species vulnerabilities, techno-ecological risks, and sustainability trade-offs Potential interactions between birds and floating photovoltaic solar energy: Spatially informed species vulnerabilities, techno-ecological risks, and sustainability trade-offs
Floating photovoltaics (floating solar panels; FPV) can reduce the negative impacts of solar energy development in terrestrial environments, but their effects on freshwater ecosystems remain poorly understood. We examined potential FPV interactions with avian biodiversity, using previously modeled technical potential of FPV in the northeastern United States. We developed a vulnerability...
Authors
Allison D. Binley, Adam Gallaher, Amanda D. Rodewald, Steven Mark Grodsky
DeepFaune New England: A species classification model for trail camera images in northeastern North America DeepFaune New England: A species classification model for trail camera images in northeastern North America
The DeepFaune New England model classifies wildlife species in trail camera images, identifying 24 taxa from northeastern North America with high (97%) accuracy. The model was adapted from the DeepFaune model for identifying European wildlife, demonstrating the practicality of transfer learning across continents. The majority of training data is openly licensed, and the model itself is...
Authors
Laurence A. Clarfeld, Katherine D. Gieder, Angela K. Fuller, Zhongqi Miao, Alexej P.K. Sirén, Shevenell M. Webb, Toni Lyn Morelli, Jillian R. Kilborn, Catherine B. Callahan, Leighlan S. Prout, Rachel Cliché, Riley K. Patry, Christopher Bernier, Susan Staats, Therese M. Donovan
Spatially explicit power analyses to inform occupancy‐based multi‐species wildlife monitoring programmes Spatially explicit power analyses to inform occupancy‐based multi‐species wildlife monitoring programmes
1. Current and accurate information on wildlife populations is integral to successful biodiversity management and conservation globally. Nevertheless, many monitoring programs fail in their attempts to accurately monitor populations of interest due to interlinked issues including insufficient sample sizes, inappropriate duration, lack of reproducibility, and lack of clearly stated...
Authors
Joshua P. Twining, Angela K. Fuller
Environmental controls of suppressed fall crop productivity in an agrivoltaic solar array Environmental controls of suppressed fall crop productivity in an agrivoltaic solar array
Globally, agrivoltaics (AV) research has revealed how microclimates created by photovoltaic (PV) panels can be leveraged to promote reciprocal benefits for agricultural land use and PV energy generation. Yet, in regions of the United States where emissions reduction laws are likely to lead to greater PV development on croplands, empirical evaluation of such co-location remain under...
Authors
Matthew A. Sturchio, Dana F. Russell, Jasmine Schmidt, Caroline Marschner, Antonio DiTomasso, Jinwook Kim, Steven Mark Grodsky
The New York Cooperative Fish and Wildlife Research Unit (est. 1963) is a partnership among the New York State Department of Environmental Conservation, Cornell University, the U.S. Geological Survey, the U.S. Fish and Wildlife Service, and the Wildlife Management Institute.
Moose survival and habitat‐associated risk of endoparasites Moose survival and habitat‐associated risk of endoparasites
Parasite-induced morbidity and mortality can alter the trajectories of incidental host populations. Yet, parasites rarely act in isolation and may be one of a multitude of biotic and abiotic stressors that collectively shape mortality risk in vertebrate populations. We quantified sources of mortality in a low-density population of moose (Alces alces) in New York State and investigated...
Authors
Jennifer A. Grauer, Jacqueline L. Frair, Krysten L. Schuler, Manigandan Lejeune, David W. Kramer, Angela K. Fuller
Potential interactions between birds and floating photovoltaic solar energy: Spatially informed species vulnerabilities, techno-ecological risks, and sustainability trade-offs Potential interactions between birds and floating photovoltaic solar energy: Spatially informed species vulnerabilities, techno-ecological risks, and sustainability trade-offs
Floating photovoltaics (floating solar panels; FPV) can reduce the negative impacts of solar energy development in terrestrial environments, but their effects on freshwater ecosystems remain poorly understood. We examined potential FPV interactions with avian biodiversity, using previously modeled technical potential of FPV in the northeastern United States. We developed a vulnerability...
Authors
Allison D. Binley, Adam Gallaher, Amanda D. Rodewald, Steven Mark Grodsky
DeepFaune New England: A species classification model for trail camera images in northeastern North America DeepFaune New England: A species classification model for trail camera images in northeastern North America
The DeepFaune New England model classifies wildlife species in trail camera images, identifying 24 taxa from northeastern North America with high (97%) accuracy. The model was adapted from the DeepFaune model for identifying European wildlife, demonstrating the practicality of transfer learning across continents. The majority of training data is openly licensed, and the model itself is...
Authors
Laurence A. Clarfeld, Katherine D. Gieder, Angela K. Fuller, Zhongqi Miao, Alexej P.K. Sirén, Shevenell M. Webb, Toni Lyn Morelli, Jillian R. Kilborn, Catherine B. Callahan, Leighlan S. Prout, Rachel Cliché, Riley K. Patry, Christopher Bernier, Susan Staats, Therese M. Donovan
Spatially explicit power analyses to inform occupancy‐based multi‐species wildlife monitoring programmes Spatially explicit power analyses to inform occupancy‐based multi‐species wildlife monitoring programmes
1. Current and accurate information on wildlife populations is integral to successful biodiversity management and conservation globally. Nevertheless, many monitoring programs fail in their attempts to accurately monitor populations of interest due to interlinked issues including insufficient sample sizes, inappropriate duration, lack of reproducibility, and lack of clearly stated...
Authors
Joshua P. Twining, Angela K. Fuller
Environmental controls of suppressed fall crop productivity in an agrivoltaic solar array Environmental controls of suppressed fall crop productivity in an agrivoltaic solar array
Globally, agrivoltaics (AV) research has revealed how microclimates created by photovoltaic (PV) panels can be leveraged to promote reciprocal benefits for agricultural land use and PV energy generation. Yet, in regions of the United States where emissions reduction laws are likely to lead to greater PV development on croplands, empirical evaluation of such co-location remain under...
Authors
Matthew A. Sturchio, Dana F. Russell, Jasmine Schmidt, Caroline Marschner, Antonio DiTomasso, Jinwook Kim, Steven Mark Grodsky