Abstract Summary
AI is increasingly central to understanding and managing biodiversity and ecosystems. Since 2011, the LifeCLEF lab has provided large-scale benchmarks that stimulate progress in multimodal species recognition, ecological prediction, and knowledge extraction. The 2026 edition expands this scope with five complementary challenges spanning visual, acoustic, and textual data: (i) \textbf{AnimalCLEF}: discovery and re-identification of individual animals, (ii) \textbf{BirdCLEF+}: multi-taxonomic species recognition in complex soundscapes, (iii) \textbf{MarineCLEF}: detection of marine species in underwater imagery under positive-unlabeled constraints, (iv) \textbf{PestCLEF}: extraction of information on plant pests from heterogeneous textual sources, (v) \textbf{PlantCLEF}: multi-species plant identification in quadrat images. Together, these challenges address critical dimensions of biodiversity science and ecosystem management, while fostering collaboration between AI researchers, ecologists, and practitioners. This paper provides an overview of the LifeCLEF 2026 lab and its tasks, outlining their motivation, data, and evaluation methodology to guide participants and inform the wider research community.
