Résumé
Biodiversity monitoring using AI-powered tools has become vital for tracking species distributions and assessing ecosystem health on a large scale. Automated image- and sound-based species recognition, in particular, continues to accelerate conservation efforts by enabling rapid, low-cost surveys of vulnerable populations. However, the ever-growing variety of algorithms and data sources underscores the need for standardized benchmarks to assess real-world performance. Since 2011, the LifeCLEF lab has filled this role by organizing annual evaluations that promote collaboration among AI experts, citizen science, and ecologists. In this overview, we report on the LifeCLEF 2025 edition, which featured five distinct, data-driven tasks: (i) AnimalCLEF, focusing on open-set individual animal re-identification; (ii) BirdCLEF+, about species recognition in complex acoustic soundscape recordings; (iii) FungiCLEF, addressing few-shot classification of rare fungi species; (iv) GeoLifeCLEF, combining environmental and high-resolution remote sensing with occurrence records to predict plant species presence; and (v) PlantCLEF, aiming to identify multiple co-occurring plant species in vegetation-plot imagery. This paper provides an overview of the motivation, methodology, and main outcomes of the five challenges.