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.

中文翻译：

---

应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中的狮子密度。

随机遭遇模型（REM）是一种新颖的方法，可从相机陷阱数据估算动物密度，而无需个体识别。尽管这些是全世界大多数相机陷阱研究的重点，但从未被用来估计大型食肉动物的密度。在这种情况下，我们应用REM来估计雌狮的密度（Panthera leo）拍摄自坦桑尼亚塞伦盖蒂国家公园的摄影机陷阱，将估算值与根据自有人口普查数据得出的参考值进行了比较。更具体地说，我们试图通过比较夜间和白天的照片，干旱季节和潮湿季节之间以及树木覆盖量不同的生境之间的估计值，来解释由于偏僻的照相机在孤立的树木下的狮子休息地点非随机放置相机而造成的偏差。 。总体而言，我们分别记录了在2010年的旱季和2011年的雨季分别进行的7,608和12,137个相机捕获日的雌狮的169和163次独立摄影事件。尽管所有的REM模型都认为雌狮密度被高估了，但是仅考虑夜间事件的模型相对于基于所有摄影事件的模型，其估计偏差要小得多。我们得出的结论是，将REM估计值限制在动物可能相对于照相机而言更可能随机运动的时期和栖息地中，可以帮助减少雌性塞伦盖蒂狮子的密度估计值中的偏差。我们着重指出，准确的REM估算将取决于动物运动的平均速度和相机检测区域尺寸的可靠度量。©2015作者。由Wiley Periodicals，Inc.代表野生动物协会出版的《野生动物管理杂志》。