基于红外相机的不可个体识别动物种群密度估算方法

doi:10.17520/biods.2022422

生物多样性 ›› 2023, Vol. 31 ›› Issue (3): 22422. DOI: 10.17520/biods.2022422

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¹^,²^,³, 杜梦甜¹^,²^,³, 朱原辛¹^,²^,³, 王大伟¹^,²^,³, 李治霖⁴^,^*(), 王天明¹^,²^,³^,^*()

1.东北虎豹国家公园保护生态学国家林业和草原局重点实验室, 北京 100875
2.生物多样性与生态工程教育部重点实验室, 北京 100875
3.北京师范大学生命科学学院, 北京 100875
4.天津师范大学生命科学学院天津市动物多样性保护与利用重点实验室, 天津 300387

收稿日期:2022-07-22 接受日期:2023-01-04 出版日期:2023-03-20 发布日期:2023-03-20
通讯作者: 李治霖,王天明
作者简介:wangtianming@bnu.edu.cn
* E-mail: lizhilin0319@tjnu.edu.cn;
基金资助:
国家自然科学基金(31971539);国家科技基础资源调查专项(2019FY101700);国家科技基础资源调查专项(2021FY100702)

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

Zhenzhen Li¹^,²^,³, Mengtian Du¹^,²^,³, Yuanxin Zhu¹^,²^,³, Dawei Wang¹^,²^,³, Zhilin Li⁴^,^*(), Tianming Wang¹^,²^,³^,^*()

1 National Forestry and Grassland Administration Key Laboratory for Conservation Ecology of Northeast Tiger and Leopard National Park, Beijing 100875
2 Ministry of Education Key Laboratory for Biodiversity Science and Engineering, Beijing 100875
3 College of Life Sciences, Beijing Normal University, Beijing 100875
4 Tianjin Key Laboratory of Conservation and Utilization of Animal Diversity, College of Life Sciences, Tianjin Normal University, Tianjin 300387

Received:2022-07-22 Accepted:2023-01-04 Online:2023-03-20 Published:2023-03-20
Contact: Zhilin Li,Tianming Wang

摘要/Abstract

摘要：

种群密度估计对野生动物的保护和管理至关重要, 也是动物生态学和保护生物学备受关注的研究热点, 但对大中型兽类种群数量的准确估算一直面临挑战。红外相机是哺乳动物调查中普遍采用的工具, 也是克服这一挑战的一种经济有效的方法。目前国际上已有多种方法采用红外相机数据估算不可个体识别动物的种群密度, 但相关技术在我国的应用案例较少, 本文旨在为国内研究者应用红外相机数据估算动物种群密度提供参考。首先, 我们介绍了随机相遇模型(random encounter model, REM)、随机相遇与停留时间(random encounter and staying time, REST)模型、相机前停留时间(time in front of the camera, TIFC)模型以及红外相机距离取样(camera trap distance sampling, CTDS)这四种模型的基本原理和假设; 其次, 描述了这些模型在野外调查中的技术要点, 并给出数据处理与分析的建议; 最后, 总结了每个模型的数据需求、优点和缺点。虽然我国目前拥有估算种群密度的大量红外相机数据源, 但有很多物种的数量尚未知晓, 也没有一种方法对所有红外相机数据都是最优的, 所以我们建议研究者在了解所研究动物类群的生活史和生态需求基础上, 根据模型假设确定合理的采样和分析方案, 扩大这些方法的应用, 为我国重要物种的保护和保护地建设提供科学指导。

关键词: 生物多样性监测, 红外相机, 种群密度, 种群建模, 基于遇见率的模型, 距离取样, 预测

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

李珍珍, 杜梦甜, 朱原辛, 王大伟, 李治霖, 王天明 (2023) 基于红外相机的不可个体识别动物种群密度估算方法. 生物多样性, 31, 22422. DOI: 10.17520/biods.2022422.

Zhenzhen Li, Mengtian Du, Yuanxin Zhu, Dawei Wang, Zhilin Li, Tianming Wang (2023) A practical guide for estimating the density of unmarked populations using camera traps. Biodiversity Science, 31, 22422. DOI: 10.17520/biods.2022422.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2022422

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

图/表 6

图1 基于红外相机估算动物种群密度的模型

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

图2 相机以类似动物速度运动所覆盖面积(A)和动物从6个典型角度接近相机探测区域的剖面图(B) (修改自Rowcliffe et al, 2008)。A图中阴影部分为圆形区域以动物运动速度移动时所覆盖的面积, 其中v为动物运动速度(m/h), H为相机工作时间(h), R为圆形区域的半径(m)。B图中扇形表示相机探测区域, R为相机探测区域半径(m), θ为相机探测区域角度, 箭头表示动物接近相机探测区域的方向, 动物接近相机探测区域的剖面用橙色粗线表示。

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.

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	√	√

图3 四种模型相机探测区域示意图。(A)随机相遇模型(REM): 根据动物在相机视野初次停留位置测量计算该物种的平均探测半径R和平均探测角度θ; (B)随机相遇与停留时间(REST)模型: 探测区域为阴影部分(焦点区域), 认为该区域相机对目标物种的探测率最高; (C)相机前停留时间(TIFC)模型: 探测半径由标记距离与其范围内动物探测概率计算得到(以5 m距离为例); (D)红外相机距离取样法(CTDS): 以图中的距离标志为例, 2 m、4 m、6 m、8 m为标志距离, 其中8 m为截断距离(最远标志距离, 也被认为是探测区域半径), 该距离范围内为探测区域。

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.

图4 四种模型估计种群密度的使用方法

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

图5 随机取样、系统网格取样与分层随机取样示意图

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

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基于红外相机的不可个体识别动物种群密度估算方法

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

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