Invasive species threaten biodiversity; hence, predicting where they may establish is vital for conservation. Our aimisto provide a robustpredictive model for an invasive species suitable for managers acting at both global and regional scales. Specifically, we investigate one of the world’s worst invasive species [the red-eared slider turtle (RES) Trachemys scripta elegans] and one of the world’s biodiversity hotspots (New Zealand) as our representative systems. We used climate data and location records to define a bioclimatic envelope for the species. Multimodel inference was used to predict areas suitable for RES establishment, weighting in favour of models with low false-negative and high true-positive rates in predictive cross-validation tests. Our performance criterion was the partial area under the curve of a receiver operating characteristic plot where sensitivity exceeded 0.95. We generated both conservative (best-case scenario) and liberal (worst-case scenario) predictions, based on different levels of information about breeding potential. All predictions were expressed on a standardscale of suitabilityrelative to existing known distribution. Globally, the best climate matches for RES outside of their native range in North America include south-eastAsia, and parts of Europe, areas whereRES have already established. The best available site in New Zealand is considered climatically more suitable than 16% of global sites where RES have bred successfully. While RES can survive in several areas throughout New Zealand, the potential to establish a self-sustaining (i.e. breeding) population appears restricted to the upper areas of the north island where the mean daily temperatures in the hottest month exceed 181C. The methods developed here were designed to reduce false-negative predictions as that represents a precautionary approach for species that pose a biosecurity risk. They could readily be adapted, however, to reduce false-positives when predicting areas suitable for translocation of rare and endangered species.
