Ecology and management programs designed to track population trends over time increasingly are using passive monitoring methods to estimate terrestrial mammal densities. Researchers use motion‐sensing cameras in mammal studies because they are cost‐effective and advances in statistical methods incorporate motion‐sensing camera data to estimate mammal densities. Density estimation involving unmarked individuals, however, remains challenging and empirical tests of statistical models are relatively rare. We tested the random encounter and staying time model (REST), a new means of estimating the density of an unmarked population, using human volunteers and simulated camera surveys. The REST method produced unbiased estimates of density, regardless of changes in human abundance, movement rates, home range sizes, or simulated camera effort. These advances in statistical methods when applied to motion‐sensing camera data provide innovative avenues of large‐mammal monitoring that have the potential to be applied to a broad spectrum of conservation and management studies, provided assumptions for the REST method are rigorously tested and met. © 2020 The Wildlife Society.

中文翻译：

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人类志愿者的随机遭遇和停留时间模型测试

旨在随着时间推移跟踪种群趋势的生态和管理计划越来越多地使用被动监测方法来估计陆生哺乳动物的密度。在哺乳动物研究中，研究人员使用运动感应相机是因为它们具有成本效益，并且统计方法的进步还结合了运动感应相机数据来估计哺乳动物的密度。然而，涉及未标记个体的密度估计仍然具有挑战性，并且统计模型的经验检验相对较少。我们使用人类志愿者和模拟相机调查，测试了随机遭遇和停留时间模型（REST），这是一种估算未标记人群密度的新方法。REST方法可得出密度的无偏估计，而无需考虑人的丰度，运动速率，家庭范围大小或模拟的相机工作量的变化。将统计方法应用于运动传感相机数据时，这些进步为大哺乳动物监测提供了创新途径，如果严格测试并满足REST方法的假设，则有可能应用于广泛的保护和管理研究。©2020野生动物协会。