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.

SSL4Eco: A Global Seasonal Dataset for Geospatial Foundation Models in Ecology

With the exacerbation of the biodiversity and climate crises, macroecological pursuits such as global biodiversity mapping become more urgent. Remote sensing offers a wealth of Earth observation data for ecological studies, but the scarcity of labeled datasets remains a major challenge. Recently, self-supervised learning has enabled learning representations from unlabeled data, triggering the development of pretrained geospatial models with generalizable features. However, these models are often trained on datasets biased toward areas of high human activity, leaving entire ecological regions underrepresented. Additionally, while some datasets attempt to address seasonality through multi-date imagery, they typically follow calendar seasons rather than local phenological cycles. To better capture vegetation seasonality at a global scale, we propose a simple phenology-informed sampling strategy and introduce corresponding SSL4Eco, a multi-date Sentinel-2 dataset, on which we train an existing model with a season-contrastive objective. We compare representations learned from SSL4Eco against other datasets on diverse ecological downstream tasks and demonstrate that our straightforward sampling method consistently improves representation quality, highlighting the importance of dataset construction. The model pretrained on SSL4Eco reaches state of the art performance on 7 out of 8 downstream tasks spanning (multi-label) classification and regression. We release our code, data, and model weights to support macroecological and computer vision research at https://github.com/PlekhanovaElena/ssl4eco.

Digital Taxonomist: Identifying Plant Species in Citizen Scientists' Photographs

Automatic identification of plant specimens from amateur photographs could improve species range maps, thus supporting ecosystems research as well as conservation efforts. However, classifying plant specimens based on image data alone is challenging: some species exhibit large variations in visual appearance, while at the same time different species are often visually similar; additionally, species observations follow a highly imbalanced, long-tailed distribution due to differences in abundance as well as observer biases. On the other hand, most species observations are accompanied by side information about the spatial, temporal and ecological context. Moreover, biological species are not an unordered list of classes but embedded in a hierarchical taxonomic structure. We propose a machine learning model that takes into account these additional cues in a unified framework. Our Digital Taxonomist is able to identify plant species in photographs more correctly.