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Modeling nonbreeding distributions of shorebirds and waterfowl in response to climate change

To identify areas on the landscape that may contribute to a robust network of conservation areas, we modeled the probabilities of occurrence of several en route migratory shorebirds and wintering waterfowl in the southern Great Plains of North America, including responses to changing climate. We pre...

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Autores principales: Reese, Gordon C., Skagen, Susan K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5330909/
https://www.ncbi.nlm.nih.gov/pubmed/28261460
http://dx.doi.org/10.1002/ece3.2755
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author Reese, Gordon C.
Skagen, Susan K.
author_facet Reese, Gordon C.
Skagen, Susan K.
author_sort Reese, Gordon C.
collection PubMed
description To identify areas on the landscape that may contribute to a robust network of conservation areas, we modeled the probabilities of occurrence of several en route migratory shorebirds and wintering waterfowl in the southern Great Plains of North America, including responses to changing climate. We predominantly used data from the eBird citizen‐science project to model probabilities of occurrence relative to land‐use patterns, spatial distribution of wetlands, and climate. We projected models to potential future climate conditions using five representative general circulation models of the Coupled Model Intercomparison Project 5 (CMIP5). We used Random Forests to model probabilities of occurrence and compared the time periods 1981–2010 (hindcast) and 2041–2070 (forecast) in “model space.” Projected changes in shorebird probabilities of occurrence varied with species‐specific general distribution pattern, migration distance, and spatial extent. Species using the western and northern portion of the study area exhibited the greatest likelihoods of decline, whereas species with more easterly occurrences, mostly long‐distance migrants, had the greatest projected increases in probability of occurrence. At an ecoregional extent, differences in probabilities of shorebird occurrence ranged from −0.015 to 0.045 when averaged across climate models, with the largest increases occurring early in migration. Spatial shifts are predicted for several shorebird species. Probabilities of occurrence of wintering Mallards and Northern Pintail are predicted to increase by 0.046 and 0.061, respectively, with northward shifts projected for both species. When incorporated into partner land management decision tools, results at ecoregional extents can be used to identify wetland complexes with the greatest potential to support birds in the nonbreeding season under a wide range of future climate scenarios.
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spelling pubmed-53309092017-03-03 Modeling nonbreeding distributions of shorebirds and waterfowl in response to climate change Reese, Gordon C. Skagen, Susan K. Ecol Evol Original Research To identify areas on the landscape that may contribute to a robust network of conservation areas, we modeled the probabilities of occurrence of several en route migratory shorebirds and wintering waterfowl in the southern Great Plains of North America, including responses to changing climate. We predominantly used data from the eBird citizen‐science project to model probabilities of occurrence relative to land‐use patterns, spatial distribution of wetlands, and climate. We projected models to potential future climate conditions using five representative general circulation models of the Coupled Model Intercomparison Project 5 (CMIP5). We used Random Forests to model probabilities of occurrence and compared the time periods 1981–2010 (hindcast) and 2041–2070 (forecast) in “model space.” Projected changes in shorebird probabilities of occurrence varied with species‐specific general distribution pattern, migration distance, and spatial extent. Species using the western and northern portion of the study area exhibited the greatest likelihoods of decline, whereas species with more easterly occurrences, mostly long‐distance migrants, had the greatest projected increases in probability of occurrence. At an ecoregional extent, differences in probabilities of shorebird occurrence ranged from −0.015 to 0.045 when averaged across climate models, with the largest increases occurring early in migration. Spatial shifts are predicted for several shorebird species. Probabilities of occurrence of wintering Mallards and Northern Pintail are predicted to increase by 0.046 and 0.061, respectively, with northward shifts projected for both species. When incorporated into partner land management decision tools, results at ecoregional extents can be used to identify wetland complexes with the greatest potential to support birds in the nonbreeding season under a wide range of future climate scenarios. John Wiley and Sons Inc. 2017-02-07 /pmc/articles/PMC5330909/ /pubmed/28261460 http://dx.doi.org/10.1002/ece3.2755 Text en © 2017 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research
Reese, Gordon C.
Skagen, Susan K.
Modeling nonbreeding distributions of shorebirds and waterfowl in response to climate change
title Modeling nonbreeding distributions of shorebirds and waterfowl in response to climate change
title_full Modeling nonbreeding distributions of shorebirds and waterfowl in response to climate change
title_fullStr Modeling nonbreeding distributions of shorebirds and waterfowl in response to climate change
title_full_unstemmed Modeling nonbreeding distributions of shorebirds and waterfowl in response to climate change
title_short Modeling nonbreeding distributions of shorebirds and waterfowl in response to climate change
title_sort modeling nonbreeding distributions of shorebirds and waterfowl in response to climate change
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5330909/
https://www.ncbi.nlm.nih.gov/pubmed/28261460
http://dx.doi.org/10.1002/ece3.2755
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