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Estimating the density of a small population of leopards ( Panthera pardus ) in central Iran using multi-session photographic‐sampling data
Mammalian Biology ( IF 1.6 ) Pub Date : 2021-01-08 , DOI: 10.1007/s42991-020-00096-w
Mohammad S. Farhadinia , Pouyan Behnoud , Kaveh Hobeali , Seyed Jalal Mousavi , Fatemeh Hosseini-Zavarei , Navid Gholikhani , Hasan Akbari , Alexander Braczkowski , Morteza Eslami , Peyman Moghadas , David W. Macdonald

West Asian drylands host a number of threatened large carnivores, including the leopard (Panthera pardus) which is limited generally to areas with low primary productivity. While conservation efforts have focused on these areas for several decades, reliable population density estimates are missing for many of them. Spatially explicit capture–recapture (SECR) methodology is a widely accepted population density estimation tool to monitor populations of large carnivores and it incorporates animal movement in the statistical estimation process. We employed multi-session maximum-likelihood SECR modeling to estimate the density of a small population of leopard in a mountainous environment surrounded by deserts in central Iran. During 6724 camera trap nights, we detected 8 and 5 independent leopards in 2012 and 2016 sessions, respectively. The top-performing model produced density estimates of 1.6 (95% CI = 0.9–2.9) and 1.0 (95% CI = 0.6–1.6) independent leopards/100 km2 in 2012 and 2016, respectively. Both sex and season had substantial effects on spatial scale (σ), with larger movements recorded for males, and during winter. The estimates from our density estimation exercise represent some of the lowest densities across the leopard global range and strengthen the notion that arid habitats support low densities of the species. These small populations are vulnerable to demographic stochasticity, and monitoring temporal changes in their population density and composition is a critical tool in assisting conservation managers to better understand their population performance.



中文翻译:

使用多阶段摄影采样数据估算伊朗中部一小群豹(Panthera pardus)的密度

西亚旱地收容了许多濒临灭绝的大型食肉动物,包括豹(Panthera pardus)通常仅限于初级生产力较低的地区。尽管数十年来,保护工作一直集中在这些地区,但许多地区缺少可靠的人口密度估算。空间显式捕获-捕获(SECR)方法是一种广泛接受的人口密度估计工具,用于监视大型食肉动物的种群,并将动物运动纳入了统计估计过程。我们采用了多阶段最大似然SECR模型来估计伊朗中部沙漠环绕的山区环境中一小群豹子的密度。在6724个相机陷阱之夜期间,我们分别在2012年和2016年检测到8和5个独立的豹子。绩效最高的模型得出的密度估计值为1.6(95%CI = 0.9-2.9)和1.0(95%CI = 0.6-1)。2在2012年和2016年,分别。性别和季节都对空间尺度(σ)产生重大影响,其中男性和冬季运动幅度更大。我们的密度估算工作得出的估算值代表了整个豹子全球范围内的最低密度,并强化了干旱栖息地支持该物种低密度的观念。这些小种群很容易受到人口统计随机性的影响,因此监测其种群密度和组成的时间变化是协助保护管理人员更好地了解其种群表现的关键工具。

更新日期:2021-01-08
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