Predicting 21st‐century polar bear habitat distribution from global climate models
Projections of polar bear ( Ursus maritimus ) sea ice habitat distribution in the polar basin during the 21st century were developed to understand the consequences of anticipated sea ice reductions on polar bear populations. We used location data from satellite‐collared polar bears and environmental...
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crwiley:10.1890/07-2089.1 2024-06-23T07:50:47+00:00 Predicting 21st‐century polar bear habitat distribution from global climate models Durner, George M. Douglas, David C. Nielson, Ryan M. Amstrup, Steven C. McDonald, Trent L. Stirling, Ian Mauritzen, Mette Born, Erik W. Wiig, Øystein DeWeaver, Eric Serreze, Mark C. Belikov, Stanislav E. Holland, Marika M. Maslanik, James Aars, Jon Bailey, David A. Derocher, Andrew E. 2009 http://dx.doi.org/10.1890/07-2089.1 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1890%2F07-2089.1 https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/07-2089.1 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Ecological Monographs volume 79, issue 1, page 25-58 ISSN 0012-9615 1557-7015 journal-article 2009 crwiley https://doi.org/10.1890/07-2089.1 2024-06-04T06:39:09Z Projections of polar bear ( Ursus maritimus ) sea ice habitat distribution in the polar basin during the 21st century were developed to understand the consequences of anticipated sea ice reductions on polar bear populations. We used location data from satellite‐collared polar bears and environmental data (e.g., bathymetry, distance to coastlines, and sea ice) collected from 1985 to 1995 to build resource selection functions (RSFs). RSFs described habitats that polar bears preferred in summer, autumn, winter, and spring. When applied to independent data from 1996 to 2006, the RSFs consistently identified habitats most frequently used by polar bears. We applied the RSFs to monthly maps of 21st‐century sea ice concentration projected by 10 general circulation models (GCMs) used in the Intergovernmental Panel of Climate Change Fourth Assessment Report, under the A1B greenhouse gas forcing scenario. Despite variation in their projections, all GCMs indicated habitat losses in the polar basin during the 21st century. Losses in the highest‐valued RSF habitat (optimal habitat) were greatest in the southern seas of the polar basin, especially the Chukchi and Barents seas, and least along the Arctic Ocean shores of Banks Island to northern Greenland. Mean loss of optimal polar bear habitat was greatest during summer; from an observed 1.0 million km 2 in 1985–1995 (baseline) to a projected multi‐model mean of 0.32 million km 2 in 2090–2099 (−68% change). Projected winter losses of polar bear habitat were less: from 1.7 million km 2 in 1985–1995 to 1.4 million km 2 in 2090–2099 (−17% change). Habitat losses based on GCM multi‐model means may be conservative; simulated rates of habitat loss during 1985–2006 from many GCMs were less than the actual observed rates of loss. Although a reduction in the total amount of optimal habitat will likely reduce polar bear populations, exact relationships between habitat losses and population demographics remain unknown. Density and energetic effects may become important as polar bears ... Article in Journal/Newspaper Arctic Arctic Ocean Banks Island Chukchi Climate change Greenland Sea ice Ursus maritimus Wiley Online Library Arctic Arctic Ocean Greenland Ecological Monographs 79 1 25 58 |
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Open Polar |
collection |
Wiley Online Library |
op_collection_id |
crwiley |
language |
English |
description |
Projections of polar bear ( Ursus maritimus ) sea ice habitat distribution in the polar basin during the 21st century were developed to understand the consequences of anticipated sea ice reductions on polar bear populations. We used location data from satellite‐collared polar bears and environmental data (e.g., bathymetry, distance to coastlines, and sea ice) collected from 1985 to 1995 to build resource selection functions (RSFs). RSFs described habitats that polar bears preferred in summer, autumn, winter, and spring. When applied to independent data from 1996 to 2006, the RSFs consistently identified habitats most frequently used by polar bears. We applied the RSFs to monthly maps of 21st‐century sea ice concentration projected by 10 general circulation models (GCMs) used in the Intergovernmental Panel of Climate Change Fourth Assessment Report, under the A1B greenhouse gas forcing scenario. Despite variation in their projections, all GCMs indicated habitat losses in the polar basin during the 21st century. Losses in the highest‐valued RSF habitat (optimal habitat) were greatest in the southern seas of the polar basin, especially the Chukchi and Barents seas, and least along the Arctic Ocean shores of Banks Island to northern Greenland. Mean loss of optimal polar bear habitat was greatest during summer; from an observed 1.0 million km 2 in 1985–1995 (baseline) to a projected multi‐model mean of 0.32 million km 2 in 2090–2099 (−68% change). Projected winter losses of polar bear habitat were less: from 1.7 million km 2 in 1985–1995 to 1.4 million km 2 in 2090–2099 (−17% change). Habitat losses based on GCM multi‐model means may be conservative; simulated rates of habitat loss during 1985–2006 from many GCMs were less than the actual observed rates of loss. Although a reduction in the total amount of optimal habitat will likely reduce polar bear populations, exact relationships between habitat losses and population demographics remain unknown. Density and energetic effects may become important as polar bears ... |
format |
Article in Journal/Newspaper |
author |
Durner, George M. Douglas, David C. Nielson, Ryan M. Amstrup, Steven C. McDonald, Trent L. Stirling, Ian Mauritzen, Mette Born, Erik W. Wiig, Øystein DeWeaver, Eric Serreze, Mark C. Belikov, Stanislav E. Holland, Marika M. Maslanik, James Aars, Jon Bailey, David A. Derocher, Andrew E. |
spellingShingle |
Durner, George M. Douglas, David C. Nielson, Ryan M. Amstrup, Steven C. McDonald, Trent L. Stirling, Ian Mauritzen, Mette Born, Erik W. Wiig, Øystein DeWeaver, Eric Serreze, Mark C. Belikov, Stanislav E. Holland, Marika M. Maslanik, James Aars, Jon Bailey, David A. Derocher, Andrew E. Predicting 21st‐century polar bear habitat distribution from global climate models |
author_facet |
Durner, George M. Douglas, David C. Nielson, Ryan M. Amstrup, Steven C. McDonald, Trent L. Stirling, Ian Mauritzen, Mette Born, Erik W. Wiig, Øystein DeWeaver, Eric Serreze, Mark C. Belikov, Stanislav E. Holland, Marika M. Maslanik, James Aars, Jon Bailey, David A. Derocher, Andrew E. |
author_sort |
Durner, George M. |
title |
Predicting 21st‐century polar bear habitat distribution from global climate models |
title_short |
Predicting 21st‐century polar bear habitat distribution from global climate models |
title_full |
Predicting 21st‐century polar bear habitat distribution from global climate models |
title_fullStr |
Predicting 21st‐century polar bear habitat distribution from global climate models |
title_full_unstemmed |
Predicting 21st‐century polar bear habitat distribution from global climate models |
title_sort |
predicting 21st‐century polar bear habitat distribution from global climate models |
publisher |
Wiley |
publishDate |
2009 |
url |
http://dx.doi.org/10.1890/07-2089.1 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1890%2F07-2089.1 https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/07-2089.1 |
geographic |
Arctic Arctic Ocean Greenland |
geographic_facet |
Arctic Arctic Ocean Greenland |
genre |
Arctic Arctic Ocean Banks Island Chukchi Climate change Greenland Sea ice Ursus maritimus |
genre_facet |
Arctic Arctic Ocean Banks Island Chukchi Climate change Greenland Sea ice Ursus maritimus |
op_source |
Ecological Monographs volume 79, issue 1, page 25-58 ISSN 0012-9615 1557-7015 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1890/07-2089.1 |
container_title |
Ecological Monographs |
container_volume |
79 |
container_issue |
1 |
container_start_page |
25 |
op_container_end_page |
58 |
_version_ |
1802641694270685184 |