Key message: Increasing human impacts on forests, including unintentional movement of pathogens, climate change, and large-scale intensive plantations, are associated with an unprecedented rate of new diseases. An evolutionary ecology perspective can help address these challenges and provide direction for sustainable forest management.Context: Forest pathology has historically relied on an ecological approach to understand and address the practical management of forest diseases. A widening of this perspective to include evolutionary considerations has been increasingly developed in response to the rising rates of genetic change in both pathogen populations and tree populations due to human activities.Aims: Here, five topics for which the evolutionary perspective is especially relevant are highlighted.Results: The first relates to the evolutionary diversity of fungi and fungal-like organisms, with issues linked to the identification of species and their ecological niches. The second theme deals with the evolutionary processes that allow forest pathogens to adapt to new hosts after introductions or to become more virulent in homogeneous plantations. The third theme presents issues linked to disease resistance in tree breeding programs (e.g., growth-defense trade-offs) and proposes new criteria and methods for more durable resistance. The last two themes are dedicated to the biotic environment of the tree–pathogen system, namely, hyperparasites and tree microbiota, as possible solutions for health management.Conclusion: We conclude by highlighting three major conceptual advances brought by evolutionary biology, i.e., that (i) “not everything is everywhere”, (ii) evolution of pathogen populations can occur on short time scales, and (iii) the tree is a multitrophic community. We further translate these into a framework for immediate policy recommendations and future directions for research.

Summary: 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 anad hocdata 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.