Camera trapping is widely used in ecological studies to estimate animal density, although these studies are largely restricted to animals that can be identified to the individual level. The random encounter model, developed by Rowcliffe et al. (J Anal Ecol 45(4):1228–1236, 2008), estimates animal density from camera-trap data without the need to identify animals. Although the REM can provide reliable density estimates, it lacks the potential to account for the multiple sources of variance in the modelling process. The density estimator in REM is a ratio, and since the variance of a ratio estimator is intractable, we examine and compare the finite sample performance of many approaches for obtaining confidence intervals via simulation studies. We also propose an integrated random encounter model as a parametric alternative, which is flexible and can incorporate covariates and random effects. A data example from Whipsnade Wild Animal Park, Bedfordshire, south England, is used to demonstrate the application of these methods. Supplementary materials accompanying this paper appear on-line.

中文翻译：

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使用随机遭遇建模的动物密度估计的统计发展

相机诱捕广泛用于生态学研究以估计动物密度，尽管这些研究主要限于可以识别到个体水平的动物。由 Rowcliffe 等人开发的随机遭遇模型。(J Anal Ecol 45(4):1228–1236, 2008)，无需识别动物即可根据相机陷阱数据估计动物密度。尽管 REM 可以提供可靠的密度估计，但它缺乏考虑建模过程中多种方差来源的潜力。REM 中的密度估计量是一个比率，并且由于比率估计量的方差难以处理，我们检查并比较了许多通过模拟研究获得置信区间的方法的有限样本性能。我们还提出了一个集成的随机遭遇模型作为参数替代方案，这是灵活的，可以结合协变量和随机效应。来自英格兰南部贝德福德郡 Whipsnade 野生动物公园的数据示例用于演示这些方法的应用。本文随附的补充材料已在线发布。