Camera traps have become an important research tool for both conservation biologists and wildlife managers. Recent advances in spatially explicit capture-recapture (SECR) methods have increasingly put camera traps at the forefront of population monitoring programs. These methods allow for benchmark analysis of species density without the need for invasive fieldwork techniques. We conducted a review of SECR studies using camera traps to summarize the current focus of these investigations, as well as provide recommendations for future studies and identify areas in need of future investigation. Our analysis shows a strong bias in species preference, with a large proportion of studies focusing on large felids, many of which provide the only baseline estimates of population density for these species. Furthermore, we found that a majority of studies produced density estimates that may not be precise enough for long-term population monitoring. We recommend simulation and power analysis be conducted before initiating any particular study design and provide examples using readily available software. Furthermore, we show that precision can be increased by including a larger study area that will subsequently increase the number of individuals photo-captured. As many current studies lack the resources or manpower to accomplish such an increase in effort, we recommend that researchers incorporate new technologies such as machine-learning, web-based data entry, and online deployment management into their study design. We also cautiously recommend the potential of citizen science to help address these study design concerns. In addition, modifications in SECR model development to include species that have only a subset of individuals available for individual identification (often called mark-resight models), can extend the process of explicit density estimation through camera trapping to species not individually identifiable.

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通过相机捕获进行空间显式捕获-重新捕获：野生动物密度估计的基准分析回顾

相机陷阱已成为保护生物学家和野生动物管理者的重要研究工具。空间显式捕获-重新捕获 (SECR) 方法的最新进展越来越多地将相机陷阱置于人口监测计划的最前沿。这些方法允许对物种密度进行基准分析，而无需侵入性实地工作技术。我们使用相机陷阱对 SECR 研究进行了审查，以总结这些调查的当前重点，并为未来的研究提供建议并确定未来需要调查的领域。我们的分析显示物种偏好存在很大偏差，大部分研究都集中在大型猫科动物上，其中许多提供了这些物种种群密度的唯一基线估计。此外，我们发现，大多数研究得出的密度估计对于长期人口监测来说可能不够精确。我们建议在启动任何特定研究设计之前进行模拟和功效分析，并使用现成的软件提供示例。此外，我们表明可以通过包括更大的研究区域来提高精度，这将随后增加照片捕获的个人数量。由于许多当前的研究缺乏资源或人力来完成这种增加的工作，我们建议研究人员将机器学习、基于网络的数据输入和在线部署管理等新技术纳入他们的研究设计中。我们还谨慎地推荐公民科学的潜力来帮助解决这些研究设计问题。此外，