Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI, 53706, U.S.A.
Wisconsin Department of Natural Resources, 2901 Progress Drive, Madison, WI, 53716, U.S.A.
Conserv Biol. 2021 Feb;35(1):88-100. doi: 10.1111/cobi.13517. Epub 2020 Jun 27.
The rapid improvement of camera traps in recent decades has revolutionized biodiversity monitoring. Despite clear applications in conservation science, camera traps have seldom been used to model the abundance of unmarked animal populations. We sought to summarize the challenges facing abundance estimation of unmarked animals, compile an overview of existing analytical frameworks, and provide guidance for practitioners seeking a suitable method. When a camera records multiple detections of an unmarked animal, one cannot determine whether the images represent multiple mobile individuals or a single individual repeatedly entering the camera viewshed. Furthermore, animal movement obfuscates a clear definition of the sampling area and, as a result, the area to which an abundance estimate corresponds. Recognizing these challenges, we identified 6 analytical approaches and reviewed 927 camera-trap studies published from 2014 to 2019 to assess the use and prevalence of each method. Only about 5% of the studies used any of the abundance-estimation methods we identified. Most of these studies estimated local abundance or covariate relationships rather than predicting abundance or density over broader areas. Next, for each analytical approach, we compiled the data requirements, assumptions, advantages, and disadvantages to help practitioners navigate the landscape of abundance estimation methods. When seeking an appropriate method, practitioners should evaluate the life history of the focal taxa, carefully define the area of the sampling frame, and consider what types of data collection are possible. The challenge of estimating abundance of unmarked animal populations persists; although multiple methods exist, no one method is optimal for camera-trap data under all circumstances. As analytical frameworks continue to evolve and abundance estimation of unmarked animals becomes increasingly common, camera traps will become even more important for informing conservation decision-making.
近几十年来，相机陷阱技术的快速发展彻底改变了生物多样性监测。尽管在保护科学中有明确的应用，但相机陷阱很少被用于对无标记动物种群数量进行建模。我们试图总结无标记动物数量估计所面临的挑战，编制现有分析框架的概述，并为寻求合适方法的从业者提供指导。当相机记录到无标记动物的多次检测时，无法确定这些图像是代表多个移动个体还是单个个体反复进入相机视场。此外，动物的运动使采样区域的定义变得模糊，因此，无法确定数量估计所对应的区域。认识到这些挑战，我们确定了 6 种分析方法，并回顾了 2014 年至 2019 年期间发表的 927 项相机陷阱研究，以评估每种方法的使用和普及程度。只有约 5%的研究使用了我们确定的任何一种数量估计方法。这些研究大多估计了局部丰度或协变量关系，而不是预测更广泛区域的丰度或密度。接下来，针对每种分析方法，我们汇总了数据要求、假设、优点和缺点，以帮助从业者了解数量估计方法的全貌。在寻求合适的方法时，从业者应评估研究对象的生活史，仔细定义采样框架的区域，并考虑可能进行的哪种类型的数据收集。无标记动物种群数量估计的挑战仍然存在；尽管存在多种方法，但没有一种方法在所有情况下都是相机陷阱数据的最佳选择。随着分析框架的不断发展，以及无标记动物数量估计变得越来越普遍，相机陷阱将在为保护决策提供信息方面变得更加重要。
