Invasive alien species are among the main global drivers of biodiversity loss, posing major challenges to nature conservation and causing substantial ecological and economic impacts.

This study, part of the PRIN PREVALIEN project funded by MUR & Next Generation EU, examines 130 alien vascular plants, including invasive species of European Union concern (EU Regulation No 1143/2014) and potential future candidates, to identify functional patterns and predict habitat suitability beyond their native ranges. Global occurrence data for these species were retrieved from the GBIF and iNaturalist platforms and classified according to positional accuracy into local (<500 m) and global (>500 m) scales. These occurrence data were then used to calibrate hierarchical species distribution models (SDMs).

To identify functional groups (FGs) based on ecological similarities among species, we compiled a comprehensive database of morphological traits (e.g., height, seed mass), ecological traits
(e.g., ecological indicator values for nutrients and temperature), and phenological traits (e.g., blooming period). An optimized Gower distance was used to calculate functional dissimilarities among species. Functional clusters were identified using multiple clustering algorithms and validated with bootstrap randomization to ensure robustness. Principal Coordinates Analysis (PCoA), combined with trait-environment vector fitting, was used to elucidate key traits driving cluster differentiation.

By integrating these functional groups with hierarchical SDMs, habitat suitability will be projected for each species and mapped according to functional cluster memberships. Pixel-wise values will represent Functional Groups Weighted Means (FG-WMs), aggregating habitat suitability, with each species’ contribution weighted by its functional group and constrained by its dispersal capacity.

This combined trait-based and spatially explicit methodology enhances the ability to forecast invasion risks, thereby facilitating the development of early-warning systems and targeted management interventions.