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Identifying priority landscapes for conservation of snow leopards in Pakistan

Pakistan’s total estimated snow leopard habitat is about 80,000 km(2) of which about half is considered prime habitat. However, this preliminary demarcation was not always in close agreement with the actual distribution—the discrepancy may be huge at the local and regional level. Recent technologica...

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Autores principales: Hameed, Shoaib, Din, Jaffar ud, Ali, Hussain, Kabir, Muhammad, Younas, Muhammad, ur Rehman, Ejaz, Bari, Fathul, Hao, Wang, Bischof, Richard, Nawaz, Muhammad Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7644022/
https://www.ncbi.nlm.nih.gov/pubmed/33151925
http://dx.doi.org/10.1371/journal.pone.0228832
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author Hameed, Shoaib
Din, Jaffar ud
Ali, Hussain
Kabir, Muhammad
Younas, Muhammad
ur Rehman, Ejaz
Bari, Fathul
Hao, Wang
Bischof, Richard
Nawaz, Muhammad Ali
author_facet Hameed, Shoaib
Din, Jaffar ud
Ali, Hussain
Kabir, Muhammad
Younas, Muhammad
ur Rehman, Ejaz
Bari, Fathul
Hao, Wang
Bischof, Richard
Nawaz, Muhammad Ali
author_sort Hameed, Shoaib
collection PubMed
description Pakistan’s total estimated snow leopard habitat is about 80,000 km(2) of which about half is considered prime habitat. However, this preliminary demarcation was not always in close agreement with the actual distribution—the discrepancy may be huge at the local and regional level. Recent technological developments like camera trapping and molecular genetics allow for collecting reliable presence records that could be used to construct realistic species distribution based on empirical data and advanced mathematical approaches like MaxEnt. The current study followed this approach to construct an accurate distribution of the species in Pakistan. Moreover, movement corridors, among different landscapes, were also identified through circuit theory. The probability of habitat suitability, generated from 98 presence points and 11 environmental variables, scored the snow leopard’s assumed range in Pakistan, from 0 to 0.97. A large portion of the known range represented low-quality habitat, including areas in lower Chitral, Swat, Astore, and Kashmir. Conversely, Khunjerab, Misgar, Chapursan, Qurumber, Broghil, and Central Karakoram represented high-quality habitats. Variables with higher contributions in the MaxEnt model were precipitation during the driest month (34%), annual mean temperature (19.5%), mean diurnal range of temperature (9.8%), annual precipitation (9.4%), and river density (9.2). The model was validated through receiver operating characteristic (ROC) plots and defined thresholds. The average test AUC in Maxent for the replicate runs was 0.933 while the value of AUC by ROC curve calculated at 0.15 threshold was 1.00. These validation tests suggested a good model fit and strong predictive power. The connectivity analysis revealed that the population in the Hindukush landscape appears to be more connected with the population in Afghanistan as compared to other populations in Pakistan. Similarly, the Pamir-Karakoram population is better connected with China and Tajikistan, while the Himalayan population was connected with the population in India. Based on our findings we propose three model landscapes to be considered under the Global Snow Leopard Ecosystem Protection Program (GSLEP) agenda as regional priority areas, to safeguard the future of the snow leopard in Pakistan and the region. These landscapes fall within mountain ranges of the Himalaya, Hindu Kush and Karakoram-Pamir, respectively. We also identified gaps in the existing protected areas network and suggest new protected areas in Chitral and Gilgit-Baltistan to protect critical habitats of snow leopard in Pakistan.
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spelling pubmed-76440222020-11-16 Identifying priority landscapes for conservation of snow leopards in Pakistan Hameed, Shoaib Din, Jaffar ud Ali, Hussain Kabir, Muhammad Younas, Muhammad ur Rehman, Ejaz Bari, Fathul Hao, Wang Bischof, Richard Nawaz, Muhammad Ali PLoS One Research Article Pakistan’s total estimated snow leopard habitat is about 80,000 km(2) of which about half is considered prime habitat. However, this preliminary demarcation was not always in close agreement with the actual distribution—the discrepancy may be huge at the local and regional level. Recent technological developments like camera trapping and molecular genetics allow for collecting reliable presence records that could be used to construct realistic species distribution based on empirical data and advanced mathematical approaches like MaxEnt. The current study followed this approach to construct an accurate distribution of the species in Pakistan. Moreover, movement corridors, among different landscapes, were also identified through circuit theory. The probability of habitat suitability, generated from 98 presence points and 11 environmental variables, scored the snow leopard’s assumed range in Pakistan, from 0 to 0.97. A large portion of the known range represented low-quality habitat, including areas in lower Chitral, Swat, Astore, and Kashmir. Conversely, Khunjerab, Misgar, Chapursan, Qurumber, Broghil, and Central Karakoram represented high-quality habitats. Variables with higher contributions in the MaxEnt model were precipitation during the driest month (34%), annual mean temperature (19.5%), mean diurnal range of temperature (9.8%), annual precipitation (9.4%), and river density (9.2). The model was validated through receiver operating characteristic (ROC) plots and defined thresholds. The average test AUC in Maxent for the replicate runs was 0.933 while the value of AUC by ROC curve calculated at 0.15 threshold was 1.00. These validation tests suggested a good model fit and strong predictive power. The connectivity analysis revealed that the population in the Hindukush landscape appears to be more connected with the population in Afghanistan as compared to other populations in Pakistan. Similarly, the Pamir-Karakoram population is better connected with China and Tajikistan, while the Himalayan population was connected with the population in India. Based on our findings we propose three model landscapes to be considered under the Global Snow Leopard Ecosystem Protection Program (GSLEP) agenda as regional priority areas, to safeguard the future of the snow leopard in Pakistan and the region. These landscapes fall within mountain ranges of the Himalaya, Hindu Kush and Karakoram-Pamir, respectively. We also identified gaps in the existing protected areas network and suggest new protected areas in Chitral and Gilgit-Baltistan to protect critical habitats of snow leopard in Pakistan. Public Library of Science 2020-11-05 /pmc/articles/PMC7644022/ /pubmed/33151925 http://dx.doi.org/10.1371/journal.pone.0228832 Text en © 2020 Hameed et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Hameed, Shoaib
Din, Jaffar ud
Ali, Hussain
Kabir, Muhammad
Younas, Muhammad
ur Rehman, Ejaz
Bari, Fathul
Hao, Wang
Bischof, Richard
Nawaz, Muhammad Ali
Identifying priority landscapes for conservation of snow leopards in Pakistan
title Identifying priority landscapes for conservation of snow leopards in Pakistan
title_full Identifying priority landscapes for conservation of snow leopards in Pakistan
title_fullStr Identifying priority landscapes for conservation of snow leopards in Pakistan
title_full_unstemmed Identifying priority landscapes for conservation of snow leopards in Pakistan
title_short Identifying priority landscapes for conservation of snow leopards in Pakistan
title_sort identifying priority landscapes for conservation of snow leopards in pakistan
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7644022/
https://www.ncbi.nlm.nih.gov/pubmed/33151925
http://dx.doi.org/10.1371/journal.pone.0228832
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