Generalized "avatar" niche shifts improve distribution models for invasive species

Aim: Species distribution models are an invaluable tool for anticipating the potential range of invasive species. These models often improve when both native and non-native occurrences are available for model development and validation. Therefore, how might ecologists anticipate the potential distributions for emerging invasive species that lack any or abundant non-native range occurrences? Here, we evaluate the recent suggestion of transferring niche shifts from well-established 'avatar' invaders to emerging invaders by testing if ensemble niche shifts from a group of globally invasive plants improve model predictions when each of these species is iteratively treated as an 'emerging' invader. Location: Global. Methods: We built species distribution models using Mahalanobis distance and four climatic predictors (maximum and minimum temperature and precipitation) for 26 invasive terrestrial plants from an Australian priority list of weeds. Models using only native range occurrences for each species were modified with avatar niche shifts from the remaining ensemble of 25 species based on both a typical (median) niche shift and a large (extreme) niche shift (or niche expansion). Native range and both median and extreme avatar models were then compared with total range models (developed with both native and non-native occurrences) for performance by measures of discrimination and an approximation of calibration. Results: Avatar niche shifts reduced errors of omission for known non-native occurrences relative to native range models, with a trade-off of increased errors of commission of lesser magnitude. Further, our approximation of model calibration measured relative to total range models improved with avatar niche shifts. Differences between native range and avatar models were most pronounced for the larger 'extreme' avatar niche shifts (or expansion) based on increased niche size and decreased (towards 0) covariance among climatic axes. Main conclusions: We suggest that researchers and managers evaluating risk of invasion of their jurisdiction by emerging data-poor invaders modify native range models with observed avatar niche shifts from ensembles of well-studied invaders. Alternative implementations of the avatar invader concept are discussed and research needs for methodological improvements proposed. Despite these opportunities for improved implementation of avatar niche shifts, ample evidence now supports that researchers should expect models based on only native ranges to underestimate or misrepresent the total range for data-poor emerging invaders. Avatar niche shifts (and specifically expansion) from well-studied species offer a precautionary means to anticipate the extent to which native range models may underestimate total ranges.