1. Biological invasions are a major consequence of globalization and pose a significant threat to biodiversity. Because only a small fraction of introduced species become invasive, identification of those species most likely to become invasive after introduction is highly desirable to focus management efforts. The predictive potential of species-specific traits has been much investigated in plants and animals. However, despite the importance of fungi as a biological group and the potentially severe effects of pathogenic fungi on agrosystems and natural ecosystems, the specific identification of traits correlated with the invasion success of fungi has not been attempted previously.
2. We addressed this question by constructing an ad hoc data set including invasive and non-invasive species of forest pathogenic fungi introduced into Europe. Data were analysed with a machine learning method based on classification trees (Random Forest). The performance of the classification rule based on species traits was compared with that of several random decision rules, and the principal trait predictors associated with invasive species were identified.
3. Invasion success was more accurately predicted by the classification rule including biological traits than by random decision rules. The effect of species traits was maintained when confounding variables linked to residence time and habitat availability were included. The selected traits were unlikely to be affected by a phylogenetic bias as invasive and non-invasive species were evenly distributed in fungal clades.
4. The species-level predictors identified as useful for distinguishing between invasive and non-invasive species were traits related to long-distance dispersal, sexual reproduction (in addition to asexual reproduction), spore shape and size, number of cells in spores, optimal temperature for growth and parasitic specialization (host range and infected organs).
5. Synthesis and applications. This study demonstrates that some species-level traits are predictors of invasion success for forest pathogenic fungi in Europe. These traits could be used to refine current pest risk assessment (PRA) schemes. Our results suggest that current schemes, which are mostly based on sequential questionnaires, could be improved by taking into account trait interactions or combinations. More generally, our results confirm the interest of machine learning methods, such as Random Forest, for species classification in ecology.