基于红外相机的不可个体识别动物种群密度估算方法

李珍珍, 杜梦甜, 朱原辛, 王大伟, 李治霖, 王天明

A practical guide for estimating the density of unmarked populations using camera traps

Zhenzhen Li, Mengtian Du, Yuanxin Zhu, Dawei Wang, Zhilin Li, Tianming Wang

表1 随机相遇模型(REM)、随机相遇与停留时间(REST)模型、相机前停留时间(TIFC)模型以及红外相机距离取样法(CTDS)的模型假设和数据需求

Table 1 Model assumptions and data requirements of random encounter model (REM), random encounter and staying time (REST) model, time in front of the camera (TIFC) model, camera trap distance sampling (CTDS)

REM REST TIFC CTDS 模型假设 Model assumptions 相机随机放置 Random placement of cameras √ √ √ √ 动物运动独立于相机(动物运动不受相机影响) Animal movement is independent of camera √ √ √ √ 种群封闭 Closed population √ √ √ √ 探测区域内的动物能被完美探测到 Animals in the detection area can be perfectly detected √ √ √ √ 观测到的动物停留时间的分布与动物实际运动的分布很好地吻合 The observed distribution of staying time in the focal area must represent a good fit for the distribution that animal movements actually follow √ 观测到的动物停留时间符合一定的参数分布 The observed staying time must follow a given parametric distribution √ 距离测量是精确的 Distances measured accurately √ 动物距离的分布可用已知函数拟合 Distribution of animal distance can be fitted by known functions √ 视野面积 Area of viewshed √ √ √ 数据需求 Data requirements 相机工作时间 Working hours of cameras √ √ √ √ 动物数量 Number of the animals √ √ √ √ 动物运动速度 Animal movement speed √ 动物停留时间 Animal staying time in front of the camera √ √ 动物距相机距离 Distance between animal and camera √ 模型产出 Model output 是否可以基于协变量外推 Covariate-driven prediction of density beyond the sampling frame √ √