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印度跨喜马拉雅地区拉达克雪豹分布与种群的综合评估:基于证据的保护方法标准化

Comprehensive assessment of snow leopard distribution and population in the Indian Trans-Himalaya, Ladakh: Standardizing methods for evidence-based conservation.

作者信息

Raina Pankaj, Mungi Ninad Avinash, Kumar Ujjwal, Rathi Aman Deep, Khan Niazul H, Patel Dimpi A, Bhasin Anchal, Bisht Shikha, Hiby Lex, Pandav Bivash, Sultan Mohd Sajid, Takpa Jigmet Jigmet, Jhala Yadvendradev V

机构信息

Department of Wildlife Protection, Leh, Union Territory of Ladakh, India.

Wildlife Institute of India, Dehradun, India.

出版信息

PLoS One. 2025 May 7;20(5):e0322136. doi: 10.1371/journal.pone.0322136. eCollection 2025.

DOI:10.1371/journal.pone.0322136
PMID:40333686
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12057866/
Abstract

Effective conservation of threatened species depends on accurate scientific assessment of their occurrence and population status. This information is often lacking or has poor scientific reliability for low-density carnivores, such as snow leopards (Panthera uncia) that inhabit remote and challenging habitats. We address prevalent sampling and study design limitations and evaluate the population and distribution of snow leopards and their prey using a double sampling approach across the Trans-Himalayan Ladakh (~59,000 km2), India. We used spatial data on the sign occurrence of snow leopards, collected by replicate sign surveys of 6,149 km to model occupancy and potential distribution. Regions representing varying occupancy were used to stratify density-estimation using spatially explicit capture-recapture by sampling 956 camera trap locations with an effort of 97,313 trap nights. Camera traps captured 26,130 images of 126 unique snow leopards identified by a pattern recognition program using their distinctive forehead pelage patterns. Low-elevation grassy and resource-rich regions, with moderate climatic conditions and complex terrain had higher presence of herbivores and consequently higher occupancy of snow leopards. Density of snow leopards was estimated at ~ 1 per 100 km2 with a large movement parameter (σ) of 4.09 (SE 0.15) km and detection at home-range centre (g0) of 0.003 (SE 0.0003). Snow leopard density reached up to 3.18 per 100 km2 and was driven by the distribution of their wild and domestic prey in suitable habitats. The snow leopards in Ladakh occupied 47,572 km2, holding globally highest extensive densities of snow leopards in Hemis National Park (2.073 ± 0.278 per 100 km²), Kargil (1.257 ± 0.480 per 100 km²), and Leh (1.029 ± 0.434 per 100 km²), and making one of the world's largest contiguous populations of 477 (CI 380-598) snow leopards. This population holds global significance as an important source of snow leopards, predominantly (61%) occurring in multi-use areas and closely linked with wild and domestic herbivores. We offer a robust and comprehensive method for large-scale population estimation of snow leopards, applicable globally. The co-occurrence of humans and wildlife across the landscape underscores the need for inclusive and evidence-based conservation planning, especially considering the impending large-scale infrastructural development and escalating global climatic changes.

摘要

有效保护濒危物种依赖于对其出现情况和种群状况进行准确的科学评估。对于低密度食肉动物,如栖息在偏远且条件恶劣栖息地的雪豹(Panthera uncia),此类信息往往缺失或科学可靠性较差。我们解决了普遍存在的采样和研究设计限制问题,并在印度跨喜马拉雅地区的拉达克(面积约59,000平方公里)采用双重采样方法评估了雪豹及其猎物的种群数量和分布情况。我们利用通过对6,149公里进行重复痕迹调查收集到的雪豹痕迹出现的空间数据,来模拟占有率和潜在分布。利用代表不同占有率的区域,通过对956个相机陷阱位置进行采样(共97,313个陷阱夜的工作量),采用空间明确的捕获 - 重捕法对密度进行分层估计。相机陷阱捕获了26,130张126只独特雪豹的图像,这些雪豹通过模式识别程序利用其独特的额头毛发图案得以识别。低海拔的草地和资源丰富的地区,气候条件适中且地形复杂,食草动物的存在较多,因此雪豹的占有率也较高。估计雪豹的密度约为每100平方公里1只,其大移动参数(σ)为4.09(标准误差0.15)公里,在活动范围中心的检测概率(g0)为0.003(标准误差0.0003)。雪豹密度最高可达每100平方公里3.18只,这是由其野生和家养猎物在适宜栖息地的分布所驱动的。拉达克的雪豹占据了47,572平方公里的区域,在列城国家公园(每100平方公里2.073±0.278只)、卡吉尔(每100平方公里1.257±0.480只)和列城(每100平方公里1.029±0.434只)拥有全球最高的雪豹广泛分布密度,形成了一个由477只(置信区间380 - 598)雪豹组成的世界上最大的连续种群之一。这一种群作为雪豹的重要来源具有全球意义,主要(61%)出现在多用途区域,并且与野生和家养食草动物紧密相连。我们提供了一种强大而全面的雪豹大规模种群估计方法,可在全球范围内应用。人类与野生动物在整个区域的共存凸显了进行包容性和基于证据的保护规划的必要性,特别是考虑到即将到来的大规模基础设施建设和不断升级的全球气候变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7b/12057866/d01cc59c17b9/pone.0322136.g005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7b/12057866/d01cc59c17b9/pone.0322136.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7b/12057866/236cb30621d0/pone.0322136.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7b/12057866/3cda3f70fcc2/pone.0322136.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e7b/12057866/7716e08715fc/pone.0322136.g003.jpg
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