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Many recent studies in invasion science have identified species traits that determine either invasiveness or impact. Such analyses underpin risk assessments and attempts to prioritise management actions. However, the factors that mediate the capacity of an introduced species to establish and spread (i.e. its invasiveness) can differ from those that affect the nature and severity of impacts. Here we compare those traits correlated with invasiveness with those correlated with impact for Cactaceae (“cacti”) in South Africa. To assess impact magnitude, we scored 70 cacti (35 invasive and 35 non-invasive species) using the Generic Impact Scoring System (GISS) and identified traits correlated with impact using a decision tree approach. We then compared the traits correlated with impact with those identified in a recent study as correlated with invasiveness (i.e. native range size and growth form). We found that there is a significant correlation between native range size and both invasiveness and impact. Cacti with larger native ranges were more likely to become invasive (p=0.001) and cause substantial impacts (p=0.01). These results are important for prioritising efforts on the management of cactus species. Understanding when and why impact and invasiveness are correlated (as they appear to be for Cactaceae) is likely to be an important area of future research in risk assessment.
Climate is frequently used to predict the outcome of species introductions based on the results from species distribution models (SDMs). However, despite the widespread use of SDMs for pre- and post-border risk assessments, data that can be used to validate predictions is often not available until after an invasion has occurred. Here we explore the potential for using historical forestry trials to assess the performance of climate-based SDMs. SDMs were parameterized based on the native range distribution of 36 Australian acacias, and predictions were compared against both the results of 150 years of government forestry trials, and current invasive distribution in southern Africa using true skill statistic, sensitivity and specificity. Classification tree analysis was used to evaluate why some Australian acacias failed in trials while others were successful. Predicted suitability was significantly related to the invaded range (sensitivity = 0.87) and success in forestry trials (sensitivity = 0.80), but forestry trial failures were under-predicted (specificity = 0.35). Notably, for forestry trials, the success in trials was greater for species invasive somewhere in the world. SDM predictions also indicate a considerable invasion potential of eight species that are currently naturalized but not yet widespread. Forestry trial data clearly provides a useful additional source of data to validate and refine SDMs in the context of risk assessment. Our study identified the climatic factors required for successful invasion of acacias, and accentuates the importance of integration of status elsewhere for risk assessment.