Bridging Individual Behavior and Global Biogeography: A Machine Learning Framework for Biodiversity Conservation

Abstract

Global biodiversity is currently experiencing a catastrophic average decline of 73%, yet a critical "knowledge shortfall" persists due to the disciplinary silo between macro-scale biogeography and individual-scale behavioral ecology. Traditional models often fail to account for the mechanistic behavioral responses that determine species' persistence in fragmented, climate-stressed landscapes. This paper proposes the "Digital Nature" framework, a transdisciplinary machine learning architecture designed to bridge these scales. The framework integrates multi-source data—including hyperspectral satellite imagery, edge-computing acoustic sensors, and citizen science—using an ensemble of Bipartite Graph Neural Networks (GNNs) for distribution modeling, Convolutional Neural Networks (CNNs) for behavioral pose estimation, and Reinforcement Learning (RL) for restoration policy optimization.

Evaluation using 2024 and 2025 empirical datasets demonstrates that the GNN approach achieves high predictive accuracy (0.82–0.94 AUCROC) in species distribution modeling. Case studies on model systems reveal that individual-level behavioral sentinels, such as a 50% plummet in juvenile pika recruitment and transgenerational dysfunction in sticklebacks, provide high-sensitivity early warning signals of biogeographic range collapse that are often missed by traditional structural metrics. Integrating behavioral dynamics into global conservation frameworks significantly enhances the precision of extinction risk assessments and spatial planning. The proposed framework offers a scalable decision-support system to operationalize the Kunming-Montreal Global Biodiversity Framework’s "30x30" targets, potentially improving conservation efficiency by 37% and reducing associated government spending by 40% through synergistic climate-biodiversity policy alignment.