Goldberg Joshua F, Tempa Tshering, Norbu Nawang, Hebblewhite Mark, Mills L Scott, Wangchuk Tshewang R, Lukacs Paul
Wildlife Biology Program, Department of Ecosystem and Conservation Science, College of Forestry and Conservation, University of Montana, Missoula, Montana, United States of America.
Ugyen Wangchuck Institute for Conservation and Environment, Department of Forests and Park Services, Ministry of Agriculture and Forests, Lamai Goempa, Bumthang, Bhutan.
PLoS One. 2015 Nov 4;10(11):e0140757. doi: 10.1371/journal.pone.0140757. eCollection 2015.
Many large carnivores occupy a wide geographic distribution, and face threats from habitat loss and fragmentation, poaching, prey depletion, and human wildlife-conflicts. Conservation requires robust techniques for estimating population densities and trends, but the elusive nature and low densities of many large carnivores make them difficult to detect. Spatial capture-recapture (SCR) models provide a means for handling imperfect detectability, while linking population estimates to individual movement patterns to provide more accurate estimates than standard approaches. Within this framework, we investigate the effect of different sample interval lengths on density estimates, using simulations and a common leopard (Panthera pardus) model system. We apply Bayesian SCR methods to 89 simulated datasets and camera-trapping data from 22 leopards captured 82 times during winter 2010-2011 in Royal Manas National Park, Bhutan. We show that sample interval length from daily, weekly, monthly or quarterly periods did not appreciably affect median abundance or density, but did influence precision. We observed the largest gains in precision when moving from quarterly to shorter intervals. We therefore recommend daily sampling intervals for monitoring rare or elusive species where practicable, but note that monthly or quarterly sample periods can have similar informative value. We further develop a novel application of Bayes factors to select models where multiple ecological factors are integrated into density estimation. Our simulations demonstrate that these methods can help identify the "true" explanatory mechanisms underlying the data. Using this method, we found strong evidence for sex-specific movement distributions in leopards, suggesting that sexual patterns of space-use influence density. This model estimated a density of 10.0 leopards/100 km2 (95% credibility interval: 6.25-15.93), comparable to contemporary estimates in Asia. These SCR methods provide a guide to monitor and observe the effect of management interventions on leopards and other species of conservation interest.
许多大型食肉动物分布在广阔的地理区域,面临着栖息地丧失与破碎化、偷猎、猎物减少以及人类与野生动物冲突等威胁。保护工作需要可靠的技术来估计种群密度和趋势,但许多大型食肉动物难以捉摸的特性以及低密度使其难以被发现。空间捕获-重捕(SCR)模型提供了一种处理不完全可探测性的方法,同时将种群估计与个体移动模式联系起来,以提供比标准方法更准确的估计。在此框架内,我们使用模拟和一个普通豹(豹属豹种)模型系统,研究不同采样间隔长度对密度估计的影响。我们将贝叶斯SCR方法应用于89个模拟数据集以及2010 - 2011年冬季在不丹皇家玛纳斯国家公园捕获的22只豹的82次相机捕捉数据。我们发现,每日、每周、每月或每季度的采样间隔长度对中位数丰度或密度没有显著影响,但确实会影响精度。我们观察到,从每季度间隔变为更短间隔时,精度提升最大。因此,我们建议在可行的情况下,对于监测珍稀或难以捉摸的物种采用每日采样间隔,但需注意每月或每季度的采样周期也可具有类似的信息价值。我们进一步开发了贝叶斯因子的一种新应用,用于在将多个生态因子整合到密度估计中的模型选择。我们的模拟表明,这些方法有助于识别数据背后的“真实”解释机制。使用这种方法,我们发现了豹存在性别特异性移动分布的有力证据,这表明空间利用的性别模式会影响密度。该模型估计密度为每100平方公里10.0只豹(95%可信区间:6.25 - 15.93),与亚洲目前的估计值相当。这些SCR方法为监测和观察管理干预对豹及其他具有保护意义的物种的影响提供了指导。