生物多样性 ›› 2021, Vol. 29 ›› Issue (12): 1700-1717.DOI: 10.17520/biods.2021134

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大中型兽类种群数量估算的研究进展

李月辉*()   

  1. 中国科学院沈阳应用生态研究所中国科学院森林生态与管理重点实验室, 沈阳 110016
  • 收稿日期:2021-04-12 接受日期:2021-09-03 出版日期:2021-12-20 发布日期:2021-10-06
  • 通讯作者: 李月辉
  • 作者简介:*E-mail: liyh@iae.ac.cn
  • 基金资助:
    国家自然科学基金(41871197);国家自然科学基金(41271201)

A review on estimating population size of large and medium-sized mammals

Yuehui Li*()   

  1. CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016
  • Received:2021-04-12 Accepted:2021-09-03 Online:2021-12-20 Published:2021-10-06
  • Contact: Yuehui Li

摘要:

大中型兽类种群数量的估算是动物生态学中重要的基本问题, 受到研究者、管理者和公众的共同关注。国际上从20世纪中期开始研究该问题, 已出现了多种研究方法和相应案例, 且还在快速发展, 但世界各地仍有很多物种的种群数量尚未知晓。在我国, 从20世纪80年代开始调查大中型兽类种群数量, 取得了重要进展, 也还有很多物种的种群数量尚不清楚。因此, 有必要归纳国际上种群数量估算的研究进展, 同时, 总结国内研究的现状、优势和趋势, 供研究者参考。本文首先选择估算大中型兽类种群数量的原理、数据来源和模型这3个要素归纳出简明的研究框架, 将现有的多种方法置于其中予以阐述。在该框架下, 根据估算原理分为4大类方法, 为距离取样法、标志重捕法、基于遇见率法和遥感影像直接计数法。针对每一大类方法, 论述其基本原理模型和模型假设, 说明能实现该原理的相应数据来源(视觉观测、红外相机拍摄、DNA微卫星识别、卫星定位跟踪、声音监测或遥感影像)的特点及如何实现该原理, 评价其适用性及优缺点, 并选择其中具有可比性的方法予以比较评价。其次, 参照该研究框架, 总结我国的研究现状, 分析未来发展的优势和趋势: 我国的红外相机数据积累充分, 可以发展以此为数据源的距离取样法、标志重捕法和基于遇见率法; 发展以粪便样品为数据来源的距离取样法和粪便DNA标志重捕法; 相比地面调查数据, 获取高分辨率遥感影像数据更容易, 尽量以此估算符合适用条件的大中型兽类的种群数量。最后, 本文提出了适用于我国大中型兽类种群数量的估算方法的选择流程, 供研究者参考。

关键词: 种群数量, 种群密度, 大中型兽类, 距离取样法, 标志重捕法, 基于遇见率法, 遥感影像直接计数法

Abstract

Context: Estimating the population size of large and medium-sized mammals is a fundamental issue in animal ecology, attracting great attention from researchers, managers, and the public. However, despite the fact that it has been explored from the mid-20th century to now, the population sizes of numerous species worldwide are unknown. In China, the research targeting large and medium-sized mammals have been explored since 1980s. Although it has made great progress, the population size of many species in China are still unknown.
Aims: We aim to establish a framework to categorize existing estimation methods and further summarize the research development of population size estimation in China while highlighting strengths and trends under this framework.
Results & Conclusions: First, we establish a concise hierarchical framework according to the estimation theory, data resources, and models used. This framework indicates that there are four classes of methods including distance sampling method, capture-recapture method, encounter-based method, and direct count method from remotely sensed imagery according to estimation theory. Then for each of the four methods, we illustrate the basic model and its assumptions, explaining how existing data resources (including insight, camera trap, DNA microsatellite, satellite tracking, acoustic monitor, and remote sensing data) realize each theory respectively. We summarize unique features, advantages, and disadvantages of each method and compare size or density estimation resulted from different methods. Secondly, we summarize the development of population size estimation methods in China in the context of this framework while highlighting trends and strengths. Numerous data obtained from infrared cameras in many study areas during the last decade can be used to estimate the population size by employing distance sampling, capture-recapture models, and encounter-based methods. Meanwhile, the pellet distance sampling method, fecal-DNA capture-recapture method and direct count method from remotely sensed imagery are suggested to be developed. Finally, a guide to select the estimation methods appropriate for their studies is provided as a reference for future researchers.

Key words: population size, population density, large and medium-sized mammals, distance sampling, capture-recapture method, encounter-based method, direct count method from remotely sensed imagery