Geo-environmental determinants of vascular plant richness in Western Anatolia: a species distribution modelling approach for Manisa Spil Mountain, Türkiye

Understanding the spatial drivers of plant richness in mountainous regions is essential for biodiversity conservation. This study explores vascular plant richness in Spil Mountain, western Türkiye, by modelling 1831 occurrence records of 38 species using MaxEnt and stacked species distribution models (SSDMs). Environmental predictors included high-resolution topographic, climatic, and geomorphological variables. Landforms were classified with the geomorphons method, and geomorphological diversity was quantified via the Shannon diversity index. Results reveal that flat and low-slope areas harbour the highest richness, while species diversity declines in steeper, high-altitude zones. Elevation and slope were the most influential factors, showing strong negative correlations with richness, whereas climatic variables had weaker and spatially variable effects. This study demonstrates the utility of geomorphological predictors in biodiversity modelling and shows how expanding the modelling extent can address data limitations. The findings provide a spatial foundation for conservation in Spil Mountain National Park and inform future models integrating land-use, soil, and climate change variables. Flat and low-slope terrains support the highest vascular plant richness and should be prioritized in regional conservation planning.Elevation and slope are the most influential environmental filters, showing strong negative correlations with species richness.Geomorphological predictors, such as landform diversity, enhance the spatial accuracy of species distribution models in complex terrains.Expanding the modelling extent to ecologically similar regions helps overcome data limitations in global-scale biodiversity studies. Flat and low-slope terrains support the highest vascular plant richness and should be prioritized in regional conservation planning. Elevation and slope are the most influential environmental filters, showing strong negative correlations with species richness. Geomorphological predictors, such as landform diversity, enhance the spatial accuracy of species distribution models in complex terrains. Expanding the modelling extent to ecologically similar regions helps overcome data limitations in global-scale biodiversity studies.