Deep learning-based species-area models reveal multi-scale patterns of species richness and turnover

The number of species within ecosystems is influenced not only by their intrinsic characteristics but also by the spatial scale considered. As the sampled area expands, species richness increases, a phenomenon described by the species-area relationship (SAR). The accumulation dynamics of the SAR results from a complex interplay of biotic and abiotic processes operating at various spatial scales. However, the challenge of collecting exhaustive biodiversity records across spatial scales has hindered a comprehensive understanding of these dynamics. Here, we develop a deep learning approach that leverages sampling theory and small-scale ecological surveys to spatially resolve the scale-dependency of species richness. We demonstrate its performance by predicting the species richness of vascular plant communities across Europe, and evaluate the predictions against an independent dataset of plant community inventories. Our model improves species richness estimates by 32\% and delivers spatially explicit patterns of species richness and turnover for sampling areas ranging from square meters to hundreds of square kilometers. Explainable AI techniques further disentangle how drivers of species richness operate across spatial scales. The ability of our model to represent the multi-scale nature of biodiversity is essential to deliver robust biodiversity assessments and forecasts under global change.