Understanding animal behaviour is central to predicting, understanding, and miti-gating impacts of natural and anthropogenic changes on animal populations andecosystems. However, the challenges of acquiring and processing long-term, eco-logically relevant data in wild settings have constrained the scope of behaviouralresearch. The increasing availability of Unmanned Aerial Vehicles (UAVs), cou-pled with advances in machine learning, has opened new opportunities for wildlifemonitoring using aerial tracking. However, the limited availability of datasets with wildanimals in natural habitats has hindered progress in automated computer visionsolutions for long-term animal tracking. Here, we introduce the first large-scaleUAV dataset designed to solve multi-object tracking (MOT) and re-identification(Re-ID) problem in wild animals, specifically the mating behaviour (or lekking) ofblackbuck antelopes. Collected in collaboration with biologists, the MOT datasetincludes over 1.2 million annotations including 680 tracks across 12 high-resolution(5.4K) videos, each averaging 66 seconds and featuring 30 to 130 individuals. TheRe-ID dataset includes 730 individuals captured with two UAVs simultaneously.The dataset is designed to drive scalable, long-term animal behavior tracking usingmultiple camera sensors. By providing baseline performance with two detectors,and benchmarking several state-of-the-art tracking methods, our dataset reflects thereal-world challenges of tracking wild animals in socially and ecologically relevantcontexts. In making these data widely available, we hope to catalyze progress inMOT and Re-ID for wild animals, fostering insights into animal behaviour, conser-vation efforts, and ecosystem dynamics through automated, long-term monitoring.