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

doi:10.17520/biods.2022422

Abstract

Abstract:

Background & Aim: Estimating population density is essential for wildlife management and conservation, but it is challenging to achieve. Camera trapping is a pervasive method used in mammal surveys and a cost-effective way to overcome this challenge, for which several methods have been described to estimate population density when individuals are indiscernible (i.e. unmarked populations). However, there are few examples of their use in China. We aim to provide a practical guide for conducting camera trap surveys to estimate the density of mammals applying the random encounter model (REM), random encounter and staying time (REST) model, time in front of the camera (TIFC) model and the camera trap distance sampling (CTDS).

Review Results: First, we provide a brief explanation about the structure and assumptions of the REM, REST, TIFC and CTDS models. Next, we describe essential steps in planning a field survey: determination of objectives, design of camera placement, and the layout of the camera station. We then develop detail-oriented instruction for conducting a field survey and analyzing the obtained visual data. Finally, for each analytical approach, we compiled the data requirements, advantages, and disadvantages of each to help practitioners navigate the landscape of abundance estimation methods.

Perspectives: Although multiple methods exist, no one method is optimal for every camera-trap data scenario. While there has been rapid improvement of camera traps in recent decades throughout China, we encourage researchers to evaluate the life history of the focal taxa, carefully define the area of the sampling frame, and enhance the use of camera trapping for estimating densities of unmarked populations.

Key words: biodiversity monitoring, camera traps, population density, population modeling, encounter-based model, distance sampling, prediction

Zhenzhen Li, Mengtian Du, Yuanxin Zhu, Dawei Wang, Zhilin Li, Tianming Wang. A practical guide for estimating the density of unmarked populations using camera traps[J]. Biodiv Sci, 2023, 31(3): 22422.

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URL: https://www.biodiversity-science.net/EN/10.17520/biods.2022422

https://www.biodiversity-science.net/EN/Y2023/V31/I3/22422

Figures/Tables 6

Fig. 1 Models for estimating animal population density based on camera trap

Fig. 2 Coverage area of a camera moving at a speed similar to that of an animal (A) and the profile of an animal approaching the probe area from six typical angles (B) (Modified from Rowcliffe et al, 2008). The shadow part in A is the area covered by the circular region moving at the animal speed, where v is the animal speed, H is the camera working time, and R is the radius of the circular region. In B, the sector represents the camera detection area; R is the radius of the camera detection area; θ is the angle of the camera detection area; the arrow indicates the direction of the animal approaching the camera detection area; and the profile of the animal approaching the camera detection area is represented by orange rough line.

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	√	√

Fig. 3 Camera detection area of four models. (A) Random encounter model (REM): the average detection radius (R) and the average detection angle (θ) of the species were calculated according to the measurement of the initial residence position of the animal. (B) Random encounter and staying time (REST) model: The detection area is the shadow part, which is considered to be the area with the highest detection rate of the focal species by the camera. (C) Time in front of the camera (TIFC) model: the detection radius is calculated by the marker distance and the detection probability of animals within its range (taking the distance of 5 m as an example). (D) Camera trap distance sampling (CTDS) model: In the graph, 2 m, 4 m, 6 m and 8 m are marked distances, of which 8 m is the truncated distance (also known as the radius of the detection area), within which the detection area is located.

Fig. 4 The method of estimating population density for four models

Fig. 5 Schematic diagram of random placement, systematic grid placement and stratified-random placement

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