The widespread collapse of an invasive species: Argentine ants (Linepithema humile) in New Zealand.

Synergies between invasive species and climate change are widely considered to be a major biodiversity threat. However, invasive species are also hypothesized to be susceptible to population collapse, as we demonstrate for a globally important invasive species in New Zealand. We observed Argentine ant populations to have collapsed in 40 per cent of surveyed sites. Populations had a mean survival time of 14.1 years (95% CI = 12.9-15.3 years). Resident ant communities had recovered or partly recovered after their collapse. Our models suggest that climate change will delay colony collapse, as increasing temperature and decreasing rainfall significantly increased their longevity, but only by a few years. Economic and environmental costs of invasive species may be small if populations collapse on their own accord.

Quantifying uncertainty in the potential distribution of an invasive species: climate and the Argentine ant.

Maps of a species' potential range make an important contribution to conservation and invasive species risk analysis. Spatial predictions, however, should be accompanied by an assessment of their uncertainty. Here, we use multimodel inference to generate confidence intervals that incorporate both the uncertainty involved in model selection as well as the error associated with model fitting. In the case of the invasive Argentine ant, we found that it was most likely to occur where the mean daily temperature in mid-winter was 7-14 degrees C and maximum daily temperatures during the hottest month averaged 19-30 degrees C. Uninvaded regions vulnerable to future establishment include: southern China, Taiwan, Zimbabwe, central Madagascar, Morocco, high-elevation Ethiopia, Yemen and a number of oceanic islands. Greatest uncertainty exists over predictions for China, north-east India, Angola, Bolivia, Lord Howe Island and New Caledonia. Quantifying the costs of different errors (false negatives vs. false positives) was considered central for connecting modelling to decision-making and management processes.