Project Description:
Predicting the spread of alien species is an urgent challenge. Most approaches use models of a species’ ecological niche to identify climatically suitable areas for invasion, but the reliability of these predictions may be limited. We recently developed a model that predicts alien species spread using a measure of biotic similarity to the site where it established (“environmental resistance”, ER). We found that alien birds have a lower probability of spreading to areas with higher ER (i.e. low biotic similarity) compared to areas with lower ER (i.e. high biotic similarity). ER does not require any information on the ecological niche of the invading species, but still predicts patterns of spread better than a model based on climate matching to the species’ native range.
These results are tantalising because they suggest that ER may provide a general approach for predicting risks of species’ invasions. However, because this approach is so new, a number of important questions about the method remain, and would be the focus of this PhD project. For example, the accuracy of ER predictions appears to vary across species. What then are the conditions under which ER performs well or less well in predicting alien spread? Our analysis used birds, but what about taxa for which native distributions are less well known? Could the spread of invasive plants be predicted from patterns of biotic similarity of birds? Could the spread of invasive insect pests be predicted from patterns of biotic similarity of their hosts? Would incorporating information on functional traits or phylogenetic relatedness improve predictions compared to models considering only species identity?
Policy Impact of Research:
The number of alien species is increasing unabated, with major negative impacts on biodiversity and human society. This project would potentially provide a simple tool to predict where and how far alien species will spread, which would be very useful for conservation practitioners, land managers, and policy makers.
