Abstract

AbstractAimBiological invasions threaten biodiversity globally. Large‐scale studies of non‐native plant species invasiveness typically focus on identifying ecological differences between naturalized and invasive species that account for their spread from sites of initial establishment (i.e., invasion success). However, invasive species differ widely in the magnitude of their impacts, suggesting the characteristics that favour invasion success might not necessarily predict the consequences of that invasion. Here we test whether those factors that increase the probability of plant species invasion also explain the severity of impacts.LocationChina.MethodsWe compiled a database of the invasiveness, biogeographic origins, life history traits, and introduction history for 538 non‐native plants in China and modelled differences in (a) naturalized and invasive species; (b) the spatial extent of invasion; and, (c) the severity of invasion impacts among successful invaders.ResultsInvasion success and the spatial extent of invasion shared similar influencing factors. However, these clearly differed from the predictors of severe invasion impacts. Unintentionally introduced non‐native plants with shorter life cycles and longer residence times were more likely to become invasive and to invade a larger area, while taller plants introduced from the Americas tended to have more severe impacts on the native ecosystems of China.Main ConclusionsThese results illustrate the different roles of introduction history, biogeographical origin and biological traits in determining the invasion success and spatial extent of invasion versus the severity of invasive species impacts. We suggest that factors associated with evolutionary adaptation and population expansion might determine invasion success and extent, while traits related to the relative competitive ability of invasive species determine the severity of impacts. Identifying specific characteristics of species that distinguish among successful invaders most likely to result in more severe impacts could help with planning more effective interventions.

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