The challenge of monitoring elusive large carnivores: An accurate and cost-effective tool to identify and sex pumas (Puma concolor) from footprints

Acquiring reliable data on large felid populations is crucial for effective conservation and management. However, large felids, typically solitary, elusive and nocturnal, are difficult to survey. Tagging and following individuals with VHF or GPS technology is the standard approach, but costs are high and these methodologies can compromise animal welfare. Such limitations can restrict the use of these techniques at population or landscape levels. In this paper we describe a robust technique to identify and sex individual pumas from footprints. We used a standardized image collection protocol to collect a reference database of 535 footprints from 35 captive pumas over 10 facilities; 19 females (300 footprints) and 16 males (235 footprints), ranging in age from 1–20 yrs. Images were processed in JMP data visualization software, generating one hundred and twenty three measurements from each footprint. Data were analyzed using a customized model based on a pairwise trail comparison using robust cross-validated discriminant analysis with a Ward’s clustering method. Classification accuracy was consistently > 90% for individuals, and for the correct classification of footprints within trails, and > 99% for sex classification. The technique has the potential to greatly augment the methods available for studying puma and other elusive felids, and is amenable to both citizen-science and opportunistic/local community data collection efforts, particularly as the data collection protocol is inexpensive and intuitive.

获取大型猫科动物种群的可靠数据，对于开展高效的物种保护与种群管理工作至关重要。然而，大型猫科动物通常独居、生性机警且夜行性，因此种群调查难度极大。当前主流的调查方法是利用甚高频（VHF）或全球定位系统（GPS）技术对个体进行标记与追踪，但此类方法成本高昂，且可能对动物福利造成损害。这类局限性使得上述技术难以在种群或景观尺度上推广应用。本研究提出了一种基于足迹的稳健方法，用于识别美洲狮个体并鉴定其性别。我们采用标准化图像采集规程，从10家饲养机构的35只圈养美洲狮中采集了共计535份足迹样本：其中雌性19只（提供300份足迹），雄性16只（提供235份足迹），受试个体年龄跨度为1至20岁。所有足迹图像均通过JMP数据可视化软件进行处理，为每份足迹提取123项测量指标。本研究基于成对轨迹比较，结合稳健交叉验证判别分析与沃德（Ward）聚类算法构建定制化模型以分析数据。实验结果显示，个体识别准确率始终高于90%，轨迹内足迹的正确分类准确率同样达到该水平，而性别鉴定准确率则超过99%。该方法有望大幅扩充美洲狮及其他难以观测的猫科动物的研究手段，且适用于公民科学项目与机会性采集、本地社区参与的数据收集工作，尤其是其数据采集规程成本低廉、操作直观。