Explainable Artificial Intelligence Reveals Spatially Divergent Effects of Global Change on Mammals

Understanding how global change reshapes species distributions remains a central challenge in ecology, particularly as environmental drivers exert uneven effects across space and time. Most assessments of the hypothesized impacts of climate change and conservation of future biodiversity emphasize climate-induced risks but overlook where environmental changes may also relax constraints and improve habitat suitability. Here, we apply explainable machine learning to assess how climate averages, variability, extremes, and land cover are projected to reshape future distributions of 1,992 terrestrial mammals worldwide. Leveraging Shapley Additive Explanations (SHAP) applied to species distribution models (SDMs), we quantify the directional contribution of 16 environmental drivers and track how these contributions change over space and time. Our work enables strong tests of past hypotheses and shows that climate extremes produce more localized but intense effects than means or variability; temperature-related drivers dominate, with the strongest and most uncertain impacts for endangered species; and individual drivers can simultaneously increase or reduce suitability across regions. These findings reveal that ecological risks and gains are spatially divergent, highlighting the need for driver-specific, regionally tailored conservation strategies under global change.