Invasive plants have broader physiological niches

Significance Invasibility (the vulnerability of a receiving environment to invasion) and invasiveness (the capacity of an organism to invade) are fundamental metrics in theoretical ecology. However, attempts to apply these concepts to the question of whether an introduced species will invade have failed. Here, we use a model that characterizes the physiological niche of plants to successfully predict invasive success of 749 species. We find that, although invasive success is dependent on different factors in different ecological contexts, the modeled global potential range of species integrates these contingencies to produce a reliable and interpretable indicator of the likelihood that a species will reproduce and spread. Invasive species cost the global economy billions of dollars each year, but ecologists have struggled to predict the risk of an introduced species naturalizing and invading. Although carefully designed experiments are needed to fully elucidate what makes some species invasive, much can be learned from unintentional experiments involving the introduction of species beyond their native ranges. Here, we assess invasion risk by linking a physiologically based species distribution model with data on the invasive success of 749 Australian acacia and eucalypt tree species that have, over more than a century, been introduced around the world. The model correctly predicts 92% of occurrences observed outside of Australia from an independent dataset. We found that invasiveness is positively associated with the projection of physiological niche volume in geographic space, thereby illustrating that species tolerant of a broader range of environmental conditions are more likely to be invasive. Species achieve this broader tolerance in different ways, meaning that the traits that define invasive success are context-specific. Hence, our study reconciles studies that have failed to identify the traits that define invasive success with the urgent and pragmatic need to predict invasive success.