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Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling

An upsurge in anthropogenic impacts has hastened the decline of the red panda (Ailurus fulgens). The red panda is a global conservation icon, but holistic conservation management has been hampered by research being restricted to certain locations and population clusters. Building a comprehensive pot...

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Autores principales: Thapa, Arjun, Wu, Ruidong, Hu, Yibo, Nie, Yonggang, Singh, Paras B., Khatiwada, Janak R., Yan, Li, Gu, Xiaodong, Wei, Fuwen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6238126/
https://www.ncbi.nlm.nih.gov/pubmed/30464826
http://dx.doi.org/10.1002/ece3.4526
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author Thapa, Arjun
Wu, Ruidong
Hu, Yibo
Nie, Yonggang
Singh, Paras B.
Khatiwada, Janak R.
Yan, Li
Gu, Xiaodong
Wei, Fuwen
author_facet Thapa, Arjun
Wu, Ruidong
Hu, Yibo
Nie, Yonggang
Singh, Paras B.
Khatiwada, Janak R.
Yan, Li
Gu, Xiaodong
Wei, Fuwen
author_sort Thapa, Arjun
collection PubMed
description An upsurge in anthropogenic impacts has hastened the decline of the red panda (Ailurus fulgens). The red panda is a global conservation icon, but holistic conservation management has been hampered by research being restricted to certain locations and population clusters. Building a comprehensive potential habitat map for the red panda is imperative to advance the conservation effort and ensure coordinated management across international boundaries. Here, we use occurrence records of both subspecies of red pandas from across their entire range to build a habitat model using the maximum entropy algorithm (MaxEnt 3.3.3k) and the least correlated bioclimatic variables. We found that the subspecies have separate climatic spaces dominated by temperature‐associated variables in the eastern geographic distribution limit and precipitation‐associated variables in the western distribution limit. Annual precipitation (BIO12) and maximum temperature in the warmest months (BIO5) were major predictors of habitat suitability for A. f. fulgens and A. f. styani, respectively. Our model predicted 134,975 km(2) of red panda habitat based on 10 percentile thresholds in China (62% of total predicted habitat), Nepal (15%), Myanmar (9%), Bhutan (9%), and India (5%). Existing protected areas (PAs) encompass 28% of red panda habitat, meaning the PA network is currently insufficient and alternative conservation mechanisms are needed to protect the habitat. Bhutan's PAs provide good coverage for the red panda habitat. Furthermore, large areas of habitat were predicted in cross‐broader areas, and transboundary conservation will be necessary.
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spelling pubmed-62381262018-11-21 Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling Thapa, Arjun Wu, Ruidong Hu, Yibo Nie, Yonggang Singh, Paras B. Khatiwada, Janak R. Yan, Li Gu, Xiaodong Wei, Fuwen Ecol Evol Original Research An upsurge in anthropogenic impacts has hastened the decline of the red panda (Ailurus fulgens). The red panda is a global conservation icon, but holistic conservation management has been hampered by research being restricted to certain locations and population clusters. Building a comprehensive potential habitat map for the red panda is imperative to advance the conservation effort and ensure coordinated management across international boundaries. Here, we use occurrence records of both subspecies of red pandas from across their entire range to build a habitat model using the maximum entropy algorithm (MaxEnt 3.3.3k) and the least correlated bioclimatic variables. We found that the subspecies have separate climatic spaces dominated by temperature‐associated variables in the eastern geographic distribution limit and precipitation‐associated variables in the western distribution limit. Annual precipitation (BIO12) and maximum temperature in the warmest months (BIO5) were major predictors of habitat suitability for A. f. fulgens and A. f. styani, respectively. Our model predicted 134,975 km(2) of red panda habitat based on 10 percentile thresholds in China (62% of total predicted habitat), Nepal (15%), Myanmar (9%), Bhutan (9%), and India (5%). Existing protected areas (PAs) encompass 28% of red panda habitat, meaning the PA network is currently insufficient and alternative conservation mechanisms are needed to protect the habitat. Bhutan's PAs provide good coverage for the red panda habitat. Furthermore, large areas of habitat were predicted in cross‐broader areas, and transboundary conservation will be necessary. John Wiley and Sons Inc. 2018-10-12 /pmc/articles/PMC6238126/ /pubmed/30464826 http://dx.doi.org/10.1002/ece3.4526 Text en © 2018 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the 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
Thapa, Arjun
Wu, Ruidong
Hu, Yibo
Nie, Yonggang
Singh, Paras B.
Khatiwada, Janak R.
Yan, Li
Gu, Xiaodong
Wei, Fuwen
Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling
title Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling
title_full Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling
title_fullStr Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling
title_full_unstemmed Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling
title_short Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling
title_sort predicting the potential distribution of the endangered red panda across its entire range using maxent modeling
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6238126/
https://www.ncbi.nlm.nih.gov/pubmed/30464826
http://dx.doi.org/10.1002/ece3.4526
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