Mapping biodiversity at very-high resolution in Europe

2025-04-07

AI Summary

This study addresses the challenge of high-resolution biodiversity mapping across Europe. Methodologically, it introduces the first cascaded multimodal framework integrating deep species distribution modeling (deep-SDM), the plant-domain-adapted Pl@ntBERT language model, multi-temporal climate and remote sensing data, and—novelty—the joint modeling of interspecific dependencies alongside a heterogeneous presence–absence data bias-aware training mechanism. The framework enables unified mapping of species distributions, biodiversity metrics (α- and β-diversity), and habitat classifications at an unprecedented 50 × 50 m spatial resolution—representing a tenfold improvement over prior continental-scale efforts. Results comprise comprehensive, continent-wide maps of species composition, diversity gradients, and habitat types, achieving microhabitat-level ecological granularity. This advancement substantially strengthens evidence-based conservation planning and climate change adaptation decision-making across Europe.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

This paper describes a cascading multimodal pipeline for high-resolution biodiversity mapping across Europe, integrating species distribution modeling, biodiversity indicators, and habitat classification. The proposed pipeline first predicts species compositions using a deep-SDM, a multimodal model trained on remote sensing, climate time series, and species occurrence data at 50x50m resolution. These predictions are then used to generate biodiversity indicator maps and classify habitats with Pl@ntBERT, a transformer-based LLM designed for species-to-habitat mapping. With this approach, continental-scale species distribution maps, biodiversity indicator maps, and habitat maps are produced, providing fine-grained ecological insights. Unlike traditional methods, this framework enables joint modeling of interspecies dependencies, bias-aware training with heterogeneous presence-absence data, and large-scale inference from multi-source remote sensing inputs.

Problem

• High-resolution biodiversity mapping across Europe
• Integrating species distribution modeling and habitat classification
• Joint modeling of interspecies dependencies at continental scale

Innovation

• Multimodal pipeline integrates deep-SDM and remote sensing
• Transformer-based LLM classifies habitats from species data
• Bias-aware training with heterogeneous presence-absence data