Ecological Niche Modelling of Forest Tree Species in the Alpine Space: a Stacked SDM Approach at Regional Scale

Climate change is deeply altering the structure and composition of forest ecosystems, particularly in mountain landscapes. Ecological Niche Modelling (ENM) provides a valuable tool to estimate the potential distribution of species under current and future climate scenarios. Although many studies have assessed tree-species distributions at continental or national extents with coarse spatial resolution, few have addressed regional, high-resolution modelling – despite its clear relevance for local forest planning and conservation. Key challenges for fine-scale modelling include limited field data availability and high computational and methodological complexity. This thesis implements Ecological Niche Modelling at 50-m resolution to estimate the potential distribution of 23 dominant forest tree species in the Autonomous Province of Trento (Italian Alps). The modelling was performed using the R package SSDM which combines an ensemble of statistical and machine-learning algorithms to produce individual species models and community-level outputs via Stacked Species Distribution Modelling (SSDM). Models were calibrated using downscaled, high-resolution climate projections (ECLIPS2.0), terrain metrics from a Digital Terrain Model (DTM), and detailed occurrence data from local forest inventories, remote sensing, and a citizen-science platform. Model performance was evaluated using metrics including Area Under the receiver-operating-characteristic Curve (AUC), Cohen’s Kappa, sensitivity, and specificity. Species richness maps were produced using the SSDM framework, providing a replicable protocol for modelling both individual species distributions and community composition. Additionally, future projections for the 23 species under two IPCC emission scenarios (RCP4.5 and RCP8.5) were generated to assess potential vegetation shifts across three time periods (2041–2060, 2061–2080, and 2081–2100). The analysis also included the assessment of feature importance and modelling uncertainty, which improves the interpretability and robustness of the projections. Species were classified according to projected changes in their distributional ranges. Taxa such as Fagus sylvatica L., Fraxinus ornus L. and Quercus ilex L. are predicted to exhibit range expansion (“gainers”), whereas Picea abies (L.) H.Karst., Pinus cembra L. and Fraxinus excelsior L. are anticipated to contract (“losers”). Other taxa, including Betula pendula Roth, Larix decidua Mill., and Populus tremula L., display non-linear temporal trend, with initial gains followed by subsequent losses of suitable areas. All the studied species will experience an upward elevation shift of their climate optimum. Species richness is projected to remain largely stable under the RCP4.5 scenario, whereas a decline is expected under RCP8.5. Results can support adaptive forest planning and biodiversity conservation in the Alpine contexts. The approach adopted in this study contributes to bridging the gap between broad-scale modelling and local forest management needs. The work provides spatial insights into biodiversity patterns and could help to identify areas of high ecological significance or conservation concern.