Addressing the biodiversity crisis requires spatial data products to measure current state, assess trends, and evaluate scenarios for decision-making. Use cases range from global monitoring and national reporting to conservation prioritization and corporate disclosures. Today, massive volumes of heterogeneous data (eDNA, bioacoustics, citizen science, remote sensing, text and ecological networks) call for models that can leverage high-dimensional data to learn relationships between biodiversity, the environment and anthropogenic pressures, to create meaningful biodiversity indicators and impact metrics.

In this session, we deep dive into the state-of-the-art of spatial biodiversity modeling on local to global scales, ranging from populations through communities to ecosystems. This includes a multitude of models that integrate in-situ biodiversity data with remote sensing, such as species distribution models, macroecological models, natural value segmentation, causal inference methods, and beyond. It covers a broad spectrum of data-driven modeling techniques, from time-tested statistical models to modern deep learning frameworks that can facilitate learning across species and environments.

We will examine how such models can fuse multi-source inputs into ready-to-use metrics, such as species richness, community turnover, and functional diversity. Discussions will cover best practices for evaluation and uncertainty quantification, strategies to address gaps in biodiversity data, and the roles of data management, benchmarking and explainable AI in building transparent, trustworthy models.

Combining scientific talks, panel discussions and audience engagement, the session aims to identify current limitations of and outline key priorities for improving the state-of-the-art in this field.

Co-Convener: Tobias Andermann, Jan Borgelt, C. Vanalli, Sara Si-Moussi, Pierre Bonnet, Florian Hartwig