Predicting the landscape-scale distribution of alien plants and their threat to plant diversity

Invasive alien organisms pose a major threat to global biodiversity. The Cape Peninsula, South Africa, provides a case study of the threat of alien plants to native plant diversity. We sought to identify where alien plants would invade the landscape and what their threat to plant diversity could be. This information is needed to develop a strategy for managing these invasions at the landscape scale. We used logistic regression models to predict the potential distribution of six important invasive alien plants in relation to several environmental variables. The logistic regression models showed that alien plants could cover over 89% of the Cape Peninsula. Acacia cyclops and Pinus Pinaster were predicted to cover the greatest area. These predictions were overlaid on the current distribution of native plant diversity for the Cape Peninsula in order to quantify the threat of alien plants to native plant diversity. We defined the threat to native plant diversity as the number of native plant species (divided into all species, rare and threatened species, and endemic species) whose entire range is covered by the predicted distribution of alien plant species. We used a null model, which assumed a random distribution of invaded sites, to assess whether area invaded is confounded with threat to native plant diversity. The null model showed that most alien species threaten more plant species than might be suggested by the area they are predicted to invade. For instance, the logistic regression model predicted that P. pinaster threatens 350 more native species, 29 more rare and threatened species, and 21 more endemic species than the null model would predict. Comparisons between the null and logistic regression models suggest that species richness and invasibility are positively correlated and that species richness is a poor indicator of invasive resistance in the study site. Our results emphasize the importance of adopting a spatially explicit approach to quantifying threats to biodiversity, and they provide the information needed to prioritize threats from alien species and the sites that need urgent management intervention.