The random encounter model (REM) estimates animal densities from camera‐trap data by correcting capture rates for a set of biological variables of the animals (average group size, speed and activity level) and characteristics of camera sensors. The REM has been widely used for setups in which cameras are mounted on trees or other structures aimed parallel to the ground. Here, we modify the REM formula to accommodate an alternative field of view acquired with vertically oriented camera traps, a type of deployment used to avoid camera theft and damage. We show how the calculations can be adapted to account for a different detection zone with minor modifications. We find that the effective detection area can be close to a rectangle with dimensions influenced by the properties of the Fresnel lens of the camera's motion sensor, the body mass of different species and the height of the camera. The other REM parameters remain the same. We tested the modified REM (vREM) by applying it to wildlife data collected with vertically oriented camera traps in Bardia National Park, Nepal. We further validated that the effective detection area for the camera model used was best approximated as a rectangle shape using maximum likelihood estimation. Density estimates obtained broadly matched independent density estimates for nine species from the previous studies in Bardia with varying body sizes by four orders of magnitude. We conclude that these modifications allow the REM to be effectively used for mammal density estimation for species with a wide range of body sizes, with vertically oriented camera traps.

中文翻译：

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一种使用垂直相机陷阱进行野生动物密度估计的随机遭遇模型


随机相遇模型 （REM） 通过校正动物的一组生物变量（平均群体大小、速度和活动水平）和相机传感器特征的捕获率，从相机陷阱数据中估计动物密度。REM 已广泛用于将摄像机安装在树木或其他与地面平行的结构上。在这里，我们修改了 REM 公式以适应使用垂直方向的相机陷阱获得的替代视野，这是一种用于避免相机被盗和损坏的部署类型。我们展示了如何调整计算以考虑不同的检测区域，只需稍作修改。我们发现，有效检测区域可以接近一个矩形，其尺寸受相机运动传感器菲涅尔透镜的特性、不同物种的体重和相机的高度影响。其他 REM 参数保持不变。我们通过将修改后的 REM （vREM） 应用于尼泊尔巴尔迪亚国家公园使用垂直相机陷阱收集的野生动物数据来测试它。我们进一步验证了所使用的相机模型的有效检测区域最好使用最大似然估计近似为矩形形状。密度估计值与之前在 Bardia 的研究中获得的 9 个物种的独立密度估计值大致匹配，这些物种的体型相差四个数量级。我们得出结论，这些修改使 REM 可以有效地用于具有广泛体型的物种的哺乳动物密度估计，并带有垂直方向的相机陷阱。