000164185 001__ 164185 000164185 005__ 20251127172930.0 000164185 0247_ $$2doi$$a10.3354/cr01757 000164185 0248_ $$2sideral$$a146363 000164185 037__ $$aART-2025-146363 000164185 041__ $$aeng 000164185 100__ $$aBlanco-Domínguez, Minia$$uUniversidad de Zaragoza 000164185 245__ $$aLost and gained: climate-induced redistribution of black truffle in Spain 000164185 260__ $$c2025 000164185 5060_ $$aAccess copy available to the general public$$fUnrestricted 000164185 5203_ $$aSpain is the world’s leading producer of black truffle Tuber melanosporum Vittad. because of a distinct combination of edaphic properties and climatic conditions in the growing season. Relatively cold winters and moderate summer precipitation are key for a productive harvest; however, climate projections foresee a spatially diverse warming, favoring extreme temperatures and drought conditions which will affect the future distribution of the species. We used a combination of 3 machine-learning-based species distribution modeling methods—maxent, random forest, and boosted regression trees—to estimate the current and future habitat potentiality in mainland Spain, based on historical observations and a collection of environmental variables previously demonstrated as critical parameters for the modeling of black truffle presence. Results showed a notable change in the distribution under 2 future scenarios, with a total loss of between 22 and 32% of the current habitat, especially in zones with hot temperatures and low precipitation. This was partially compensated with the colonization of new areas (+25%) in colder and more humid climates. Overall, the greatest changes in future distribution scenarios were associated with maximum summer temperatures, summer precipitation, elevation, and edaphic pH. While significant differences were found in the contribution of the predictors to the 3 models, the spatial distribution results were similar. By illustrating the climate-driven redistribution of a high-value crop, our findings underscore the importance of integrating ecological modeling into agricultural policy and land-use planning to ensure long-term resilience of rural economies. 000164185 536__ $$9info:eu-repo/grantAgreement/ES/DGA/S74-23R$$9info:eu-repo/grantAgreement/ES/MICINN/RYC2021-034330-I 000164185 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es 000164185 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000164185 700__ $$0(orcid)0000-0002-7248-234X$$aGarcía-Barreda, Sergi 000164185 700__ $$0(orcid)0000-0003-4331-9794$$aSánchez, Sergio 000164185 700__ $$0(orcid)0000-0001-7663-1202$$aSerrano-Notivoli, Roberto$$uUniversidad de Zaragoza 000164185 7102_ $$13006$$2430$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Geografía Física 000164185 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi. 000164185 773__ $$g95 (2025), 27-41$$pClim. res.$$tCLIMATE RESEARCH$$x0936-577X 000164185 8564_ $$s4839409$$uhttps://zaguan.unizar.es/record/164185/files/texto_completo.pdf$$yVersión publicada 000164185 8564_ $$s2348518$$uhttps://zaguan.unizar.es/record/164185/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000164185 909CO $$ooai:zaguan.unizar.es:164185$$particulos$$pdriver 000164185 951__ $$a2025-11-27-15:16:40 000164185 980__ $$aARTICLE