Aim Niche-based distribution models are often used to predict the spread of invasive species. These models assume niche conservation during invasion, but invasive species can have different requirements from populations in their native range for many reasons, including niche evolution. I used distribution modelling to investigate niche conservatism for the Asian tiger mosquito (Aedes albopictus Skuse) during its invasion of three continents. I also used this approach to predict areas at risk of invasion from propagules originating from invasive populations.
Location Models were created for Southeast Asia, North and South America, and Europe.
Methods I used maximum entropy (Maxent) to create distribution models using occurrence data and 18 environmental datasets. One native model was created for Southeast Asia; this model was projected onto North America, South America and Europe. Three models were created independently for the non-native ranges and projected onto the native range. Niche overlap between native and non-native predictions was evaluated by comparing probability surfaces between models using real data and random models generated using a permutation approach.
Results The native model failed to predict an entire region of occurrences in South America, approximately 20% of occurrences in North America and nearly all Italian occurrences of A. albopictus. Non-native models poorly predict the native range, but predict additional areas at risk for invasion globally. Niche overlap metrics indicate that non-native distributions are more similar to the native niche than a random prediction, but they are not equivalent. Multivariate analyses support modelled differences in niche characteristics among continents, and reveal important variables explaining these differences.
Main conclusions The niche of A. albopictus has shifted on invaded continents relative to its native range (Southeast Asia). Statistical comparisons reveal that the niche for introduced distributions is not equivalent to the native niche. Furthermore, reciprocal models highlight the importance of controlling bi-directional dispersal between native and non-native distributions.