Applying a random encounter model to estimate lion density from camera traps in Serengeti National Park, Tanzania.
Applying a random encounter model to estimate lion density from camera traps in Serengeti National Park, Tanzania.
复制标题
采用随机遭遇模型来估计坦桑尼亚塞伦盖蒂国家公园的相机陷阱的狮子密度。
DOI：
10.1002/jwmg.902
复制
发表时间：
2015-08
期刊：
影响因子：
--
通讯作者：
Rowcliffe JM
中科院分区：
文献类型：
--
作者：
Cusack JJ;Swanson A;Coulson T;Packer C;Carbone C;Dickman AJ;Kosmala M;Lintott C;Rowcliffe JM
随机相遇模型（REM）是一种新的方法，估计动物密度从相机陷阱数据，而不需要个人识别。它从来没有被用来估计大型食肉动物物种的密度，尽管这些是世界上大多数相机陷阱研究的重点。在这种情况下，我们应用REM估计的密度雌狮（豹狮子）实施相机陷阱在坦桑尼亚塞伦盖蒂国家公园，比较估计的参考值来自骄傲普查数据。更具体地说，我们试图占偏见造成的非随机相机放置在狮子休息的网站下孤立的树木比较估计来自夜间与白天的照片，旱季和雨季之间，栖息地之间的树木覆盖量不同。总的来说，我们记录了169和163个独立的摄影事件的雌狮从7,608和12,137相机陷阱天进行了在2010年的旱季和2011年的雨季，分别。虽然所有的REM模型都认为高估了雌狮的密度，但只考虑夜间事件的模型得出的估计值与基于所有摄影事件的估计值相比偏差要小得多。我们的结论是，限制REM估计期间和栖息地，动物运动更有可能是随机的相机，可以帮助减少偏见的估计密度为女性塞伦盖蒂狮子。我们强调，准确的快速眼动估计仍然依赖于可靠的措施，动物运动的平均速度和相机检测区域的尺寸。© 2015作者。《野生动物管理杂志》（Journal of Wildlife Management），由Wiley Periodicals，Inc.出版。代表野生动物协会
The random encounter model (REM) is a novel method for estimating animal density from camera trap data without the need for individual recognition. It has never been used to estimate the density of large carnivore species, despite these being the focus of most camera trap studies worldwide. In this context, we applied the REM to estimate the density of female lions (Panthera leo) from camera traps implemented in Serengeti National Park, Tanzania, comparing estimates to reference values derived from pride census data. More specifically, we attempted to account for bias resulting from non-random camera placement at lion resting sites under isolated trees by comparing estimates derived from night versus day photographs, between dry and wet seasons, and between habitats that differ in their amount of tree cover. Overall, we recorded 169 and 163 independent photographic events of female lions from 7,608 and 12,137 camera trap days carried out in the dry season of 2010 and the wet season of 2011, respectively. Although all REM models considered over-estimated female lion density, models that considered only night-time events resulted in estimates that were much less biased relative to those based on all photographic events. We conclude that restricting REM estimation to periods and habitats in which animal movement is more likely to be random with respect to cameras can help reduce bias in estimates of density for female Serengeti lions. We highlight that accurate REM estimates will nonetheless be dependent on reliable measures of average speed of animal movement and camera detection zone dimensions. © 2015 The Authors. Journal of Wildlife Management published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.