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

Explainable Artificial Intelligence Reveals Spatially Divergent Effects of Global Change on Mammals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Explainable Artificial Intelligence Reveals Spatially Divergent Effects of Global Change on Mammals Lei Song, Amy Frazier, Peter Kedron, Diogo S. A. Araujo, Diyang Cui, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7500921/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract 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. Earth and environmental sciences/Environmental sciences/Environmental impact Biological sciences/Ecology/Ecological modelling Biological sciences/Ecology/Biogeography Full Text Additional Declarations There is NO Competing Interest. Supplementary Files supplementaryinformation.docx Supplementary information supplementarytable1.csv Supplementary table 1 supplementarytable2.csv Supplementary table 2 supplementarytable3.csv Supplementary table 3 supplementarytable4.csv Supplementary table 4 supplementarytable5.csv Supplementary table 5 extendeddata.docx Extended Data Tables and Figures Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7500921","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":508641431,"identity":"67dc4679-b88a-4d05-99ab-b37cc9a2fd54","order_by":0,"name":"Lei Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACPgbGBjCDH0nQAK8WNpgWyQYYi7AWmLIDRGthb25g/FFxx27z7TPmD79U3LNnYG/eJoFXC8/BBmaeM8+St53LMWyWOVOc2MBzrAy/FonEBmbGtsPJZmd4DJsl2xISGCRyzPBrkX/YwPgTqMW4B6TlX4I9g/wbAlokgJ7mbTtsZ8DDY9j4sSGBsUGCh4AWnsSGwzxnDidInGErnM1wLCGxjSet2AKfFn724w8f/qg4bM/fw7zh44+aBHt+9sMbb+DTAgIHgDixAUgw8zAgxRQhYA8iGH8Qq3wUjIJRMApGFAAAydVFvdAVpi0AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-4371-1473","institution":"Rutgers University-New Brunswick","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Song","suffix":""},{"id":508641432,"identity":"7894a97e-123c-4618-956b-0aad9bcd701a","order_by":1,"name":"Amy Frazier","email":"","orcid":"https://orcid.org/0000-0003-4552-4935","institution":"University of California, Santa Barbara","correspondingAuthor":false,"prefix":"","firstName":"Amy","middleName":"","lastName":"Frazier","suffix":""},{"id":508641433,"identity":"244e491b-07f4-4c5d-a96e-bac970f8e248","order_by":2,"name":"Peter Kedron","email":"","orcid":"","institution":"University of California, Santa Barbara","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Kedron","suffix":""},{"id":508641434,"identity":"02802444-4549-4260-a1f9-2229ede3e446","order_by":3,"name":"Diogo S. 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