Title: BioAnalyst: A Foundation Model for Biodiversity
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: foundation model, multimodal, deep learning, representation learning, ecology, biodiversity

Abstract: Multimodal Foundation Models (FMs) offer a path to learn general-purpose representations from heterogeneous ecological data, easily transferable  to downstream tasks. However, practical biodiversity modelling remains fragmented; separate pipelines and models are built for each dataset and objective, which limits reuse across regions and taxa. In response, we present BioAnalyst, to our knowledge the first multimodal Foundation Model tailored to biodiversity analysis and conservation planning in Europe at $0.25^{\circ}$ spatial resolution. BioAnalyst employs a transformer-based architecture, pre-trained on extensive multimodal datasets that align species occurrence records with remote sensing indicators, climate and environmental variables. Post pre-training, the model is adapted via lightweight fine-tuning to a range of downstream tasks, including species distribution modelling, habitat suitability assessment and population trend forecasting. The model is evaluated on two representative use cases: (i) species distribution modelling and (ii) monthly climate linear probing. Our findings show that BioAnalyst outperformed Microsoft's Aurora climate foundation model, especially in data-scarce scenarios, setting a new accuracy benchmark for ecological forecasting. We have open-sourced the model weights, training and fine-tuning pipelines to advance AI-driven ecological research.