Camera traps are commonly used to monitor animal populations, but statistical estimators of density from camera‐trap data for species that cannot be individually identified are still in development, and few models take space use into account. We present a model to estimate the density of unmarked populations, which considers species’ space use. The model assumes that animal movements depend on space use probability. Using a hypothetical animal population, we carried out a set of computer simulations on individual movements and camera‐trap monitoring to estimate population density with sampling data. Our results demonstrated that the model performed well, and highlighted the importance of adding space use information into density estimation approaches. We also tested the effect of multiple variables (i.e., day range, no. of camera traps, time span of monitoring duration) on density estimates. Increasing the number of camera traps and monitoring duration may reduce the variance of density estimate. Precision of density estimates increased when the species’ day range increased. Subject to accurate measurement of model parameters, this method provides a potential approach to estimate unmarked population density using camera‐trap data. © 2020 The Wildlife Society.

中文翻译：

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异质空间中利用相机陷阱估算未标记种群的密度

照相机诱捕器通常用于监视动物种群，但是仍无法开发从照相机诱捕器数据中无法单独识别的物种的密度统计估计值，并且很少有模型考虑空间使用。我们提出一个模型来估计未标记种群的密度，该模型考虑了物种的空间利用。该模型假设动物运动取决于空间利用概率。我们使用一个假设的动物种群，对个体运动和摄像头监视进行了一组计算机模拟，以利用采样数据估算种群密度。我们的结果表明该模型运行良好，并强调了将空间使用信息添加到密度估计方法中的重要性。我们还测试了多个变量（例如，日期范围，相机陷阱的数量，监视持续时间的时间跨度）上的密度估算值。增加相机陷阱的数量和监视持续时间可以减少密度估计的方差。当物种的日范围增加时，密度估计的精度也会增加。在对模型参数进行精确测量的基础上，该方法提供了一种潜在的方法，可以使用相机陷阱数据估算未标记的人口密度。©2020野生动物协会。