A framework for climate-resilient forest planning and restoration: advances linking species distribution modelling, genetic adaptation and future climate scenarios

Introduction Climate change is reshaping the distribution, resilience, and genetic pool of European forests, posing major challenges for long-term forest restoration and adaptive management. Addressing these challenges requires approaches that integrate species-level responses, intra-specific genetic differentiation, and uncertainty associated with future climate projections. Methods An integrated statistical modelling framework was developed and applied to support climate-informed forest planning across Spain. The framework was applied to 44 forest species, 12 of which were further analysed at the genetic-group level, resulting in a total of 49 genetic groups (93 taxa in total), representing Mediterranean, Atlantic, montane, and restricted-distribution taxa. Climatic predictors were derived from 10 CMIP6 Earth System Models under four Shared Socioeconomic Pathways and three future time horizons (2021–2050, 2041–2070, 2071–2,100). Climate data were statistically downscaled to 1 km 2 resolution using observations from more than 5,500 meteorological stations and topographic covariates. Species distribution models were calibrated using an ensemble of algorithms and evaluated with robust performance metrics. Uncertainty was explicitly quantified using the Percentage of Climate Models Predicting Suitability (PCM–PS), providing spatially consistent measures of agreement across climate projections. Results Projections indicate a biogeographical reorganisation of forest climatic suitability across the Iberian Peninsula. Temperate and montane species, such as Fagus sylvatica, Abies alba, and Taxus baccata , are projected to experience widespread suitability contraction, whereas Mediterranean and drought-tolerant taxa, including Quercus ilex and Pinus halepensis , show persistence or expansion towards northern latitudes and higher elevations. Modelling at the genetic-group level improved spatial precision and identified climate-resilient refugia where specific lineages may persist despite overall species-level declines. Under the high-emission SSP3-7.0 scenario, approximately 78% of species and 73% of genetic groups exhibit contraction trends. Conclusion By integrating species-level responses, genetic differentiation, climate projections, and explicit uncertainty analysis, this framework translates complex modelling outputs into decision-ready information for forest restoration and management. The results underscore the importance of local adaptive provenance transfer and multi-scenario planning to enhance forest resilience under climate change. Overall, the framework offers a replicable approach for evidence-based adaptation planning in forest ecosystems of southern Europe.