Estimating abundance of wildlife populations can be challenging and costly, especially for species that are difficult to detect and that live at low densities, such as cougars (Puma concolor). Remote, motion‐sensitive cameras are a relatively efficient monitoring tool, but most abundance estimation techniques using remote cameras rely on some or all of the population being uniquely identifiable. Recently developed methods estimate abundance from encounter rates with remote cameras and do not require identifiable individuals. We used 2 methods, the time‐to‐event and space‐to‐event models, to estimate the density of 2 cougar populations in Idaho, USA, over 3 winters from 2016–2019. We concurrently estimated cougar density using the random encounter model (REM), an existing camera‐based method for unmarked populations, and genetic spatial capture recapture (SCR), an established method for monitoring cougar populations. In surveys for which we successfully estimated density using the SCR model, the time‐to‐event estimates were more precise and showed comparable variation between survey years. The space‐to‐event estimates were less precise than the SCR estimates and were more variable between survey years. Compared to REM, time‐to‐event was more precise and consistent, and space‐to‐event was less precise and consistent. Low sample sizes made the space‐to‐event and SCR models inconsistent from survey to survey, and non‐random camera placement may have biased both of the camera‐based estimators high. We show that camera‐based estimators can perform comparably to existing methods for estimating abundance in unmarked species that live at low densities. With the time‐ and space‐to‐event models, managers could use remote cameras to monitor populations of multiple species at broader spatial and temporal scales than existing methods allow. © 2020 The Wildlife Society.

中文翻译：

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使用相机估算未标记的低密度物种的丰度

估计野生动植物种群的数量可能是具有挑战性和昂贵的，特别是对于那些难以发现且生活在低密度的物种，例如美洲狮（美洲狮））。远程，对运动敏感的摄像机是一种相对高效的监视工具，但是大多数使用远程摄像机的丰度估算技术都依赖于可唯一标识的部分或全部人口。最近开发的方法根据与远程摄像机的相遇率来估计丰度，并且不需要可识别的个人。我们使用事件时间模型和空间事件模型这两种方法来估计美国爱达荷州2016年至2019年3个冬季中2个美洲狮种群的密度。我们同时使用随机相遇模型（REM）（一种现有的基于相机的无标记种群方法）和遗传空间捕获捕获（SCR）（一种已建立的监测美洲狮种群的方法）来估算美洲狮密度。在我们使用SCR模型成功估算出密度的调查中，事件发生时间的估计更加精确，并且在调查年份之间显示出可比的变化。事件间隔估计的准确性不如SCR估计，并且在调查年份之间变化更大。与快速眼动相比，事件发生时间更精确，更一致，而空间事件更不精确，更一致。低样本量使得每次调查的事件空间和SCR模型不一致，并且非随机的相机放置可能会使基于相机的估计量偏高。我们表明，基于相机的估算器可以与现有方法相媲美，以估算低密度生活的无标记物种的丰度。利用时空事件模型，管理人员可以使用远程摄像机以比现有方法所允许的更宽的时空尺度监视多种物种的种群。