AI Reveals Climate Change Hits Mammals Differently Across the Globe

For decades, ecologists trying to forecast how climate change will reshape life on Earth have faced an uncomfortable truth: their models often behave like black boxes. They can predict that species will shift, shrink, or vanish, but they struggle to explain which environmental driver is responsible, where it matters most, and whether it helps or harms a particular animal in a particular place. A new study published in Nature Communications by Lei Song of Rutgers University and an international team of collaborators tackles this problem head-on, using explainable artificial intelligence to dissect how global change is projected to transform the distributions of nearly 2,000 terrestrial mammal species across the planet.

The research team applied a machine learning framework built around Shapley Additive Explanations, a technique borrowed from game theory that assigns each input variable a quantifiable share of a model’s output. Paired with species distribution models, this approach allowed the scientists to decompose projected changes in mammal habitat suitability into the individual contributions of 16 distinct environmental drivers. These drivers span climate averages, such as mean annual temperature and precipitation; climate variability, including seasonal fluctuations; climate extremes, such as heatwaves and droughts; and land cover characteristics, from forest extent to human-modified landscapes. Rather than treating the environment as a single blended force, the method reveals the fingerprint of each driver separately, across space and through time.

What emerges from this decomposition is a picture of ecological change far more nuanced than conventional global assessments have offered. The team introduced two novel diagnostic measures to interpret their results. The first, which they call directional turnover, identifies locations where a given driver flips from being favorable to unfavorable for a species, or the reverse. The second, favorability shift, captures places where a driver’s influence strengthens or weakens without changing its sign. Together, these diagnostics expose the geography of ecological risk and opportunity in a way that aggregate vulnerability metrics cannot.

One of the study’s most striking findings concerns climate extremes. Heatwaves, cold snaps, and droughts impose intense pressures on mammal populations, but those pressures are spatially restricted, concentrated in specific regions rather than spread uniformly across continents. This means that while extreme events may not affect the largest total area, the severity of their local impact can be profound, pushing species in those hotspots toward the edges of their physiological tolerance. Meanwhile, temperature-related drivers dominate the overall pattern of exposure, meaning that gradual warming trends shape the broad geography of risk more than any other factor the team measured.

Perhaps the most consequential insight, however, is that the same environmental driver can act as a threat in one region while creating opportunity in another. Rising temperatures that push a mammal beyond its thermal limits in the tropics may simultaneously open previously inhospitable habitat at higher latitudes. Precipitation changes that desiccate one ecosystem may green another. This spatial divergence has been largely invisible in traditional assessments that average effects across broad regions, and it carries direct implications for conservation planning. A species flagged as vulnerable based on range-wide averages may actually be gaining ground in parts of its distribution while collapsing in others, and interventions designed around aggregate metrics may misallocate scarce resources.

The framework also revealed that specialist species, those with narrow ecological tolerances, are disproportionately exposed to temperature-related drivers compared with generalists. This aligns with long-standing ecological theory, which predicts that specialists should be more sensitive to environmental change because they lack the behavioral and physiological flexibility to buffer against it. The fact that the explainable AI approach independently recovered this expected pattern serves as a check on the method’s plausibility, even though the authors are careful to note that their framework assesses whether projected spatial patterns align with ecological expectations rather than establishing strict causation.

Technically, the strength of the Shapley-based approach lies in its consistency and local accuracy. For any single prediction, the Shapley values of all input variables sum exactly to the difference between the model’s output and a baseline expectation, ensuring that contributions are neither double-counted nor lost. By computing these values at every grid cell across the globe and for every species, the team generated maps of driver-specific influence that can be compared across taxa, regions, and time periods. This turns what was previously a global summary statistic into a spatially explicit diagnostic tool, one that conservation practitioners can interrogate at the resolution relevant to on-the-ground decisions.

The implications extend beyond mammals. The analytical framework is fundamentally agnostic about taxonomic group; it could be applied to birds, amphibians, plants, or freshwater organisms, provided suitable occurrence data and environmental layers are available. As climate datasets grow finer in resolution and land cover products become more accurate, the same machinery could resolve driver contributions at scales relevant to individual protected areas or wildlife corridors. The authors suggest that their approach advances biodiversity forecasting toward a more actionable understanding of ecological risk and opportunity, shifting the conversation from how much change is coming to precisely where, why, and in which direction it will unfold.

For the 1,992 mammal species included in the analysis, the message is sobering but constructive. Climate change is not a uniform tide lifting or sinking all habitats equally. It is a mosaic of localized pressures and unexpected openings, shaped by the interplay of averages, variability, extremes, and land transformation. By making the invisible architecture of these forces visible, explainable machine learning offers conservation science something it has long needed: a way to see not just that biodiversity is changing, but exactly which lever of global change is pulling hardest, and where pulling back could make the greatest difference.

Subject of Research: Explainable machine learning analysis of how climate and land cover drivers reshape future distributions of terrestrial mammals

Article Title: Explainable artificial intelligence reveals spatially divergent effects of global change on mammals

Article References: Song, L., Frazier, A. E., Kedron, P., Araujo, D. S. A., Cui, D., Enquist, B. J., Hinojo-Hinojo, C., Krieger, J. M., Maitner, B. S., Merow, C., Moulatlet, G. M., Nikolopoulos, E. I., & Roehrdanz, P. R. (2026). Explainable artificial intelligence reveals spatially divergent effects of global change on mammals. Nature Communications. https://doi.org/10.1038/s41467-026-78677-5

Image Credits: AI Generated

DOI: 10.1038/s41467-026-78677-5

Keywords: explainable AI, Shapley values, species distribution models, climate change, mammals, biodiversity, climate extremes, land cover, ecological forecasting, conservation, biogeography, Nature Communications