Department of Anthropology, Princeton University, Princeton, NJ, USA.
Department of Chemistry, Columbia University, New York, NY, USA.
Sci Adv. 2024 May 24;10(21):eadn5390. doi: 10.1126/sciadv.adn5390.
Accurately estimating population sizes for free-ranging animals through noninvasive methods, such as camera trap images, remains particularly limited by small datasets. To overcome this, we developed a flexible model for estimating upper limit populations and exemplified it by studying a group-living synanthrope, the long-tailed macaque (). Habitat preference maps, based on environmental and GPS data, were generated with a maximum entropy model and combined with data obtained from camera traps, line transect distance sampling, and direct sightings to produce an expected number of individuals. The mapping between habitat preference and number of individuals was optimized through a tunable parameter ρ (inquisitiveness) that accounts for repeated observations of individuals. Benchmarking against published data highlights the high accuracy of the model. Overall, this approach combines citizen science with scientific observations and reveals the long-tailed macaque populations to be (up to 80%) smaller than expected. The model's flexibility makes it suitable for many species, providing a scalable, noninvasive tool for wildlife conservation.
通过非侵入性方法（如相机陷阱图像）准确估计自由放养动物的种群数量仍然受到小数据集的限制。为了克服这一问题，我们开发了一种灵活的模型来估计上限种群，并通过研究一种群居的共生动物——长尾猕猴（）为例进行了说明。基于环境和 GPS 数据的栖息地偏好图是使用最大熵模型生成的，并与相机陷阱、样线距离抽样和直接目击数据相结合，以产生预期的个体数量。通过可调节参数 ρ（好奇心）来优化栖息地偏好和个体数量之间的映射，该参数考虑了个体的重复观察。与已发表数据的基准测试突出了该模型的高精度。总的来说，这种方法将公民科学与科学观察相结合，揭示长尾猕猴的种群数量比预期的（高达 80%）要小。该模型的灵活性使其适用于许多物种，为野生动物保护提供了一种可扩展的、非侵入性的工具。
