Staying ahead of invaders: using species distribution modeling to predict alien species’ potential niche shifts

Abstract

Early detection and rapid response are essential to prevent invasive species from thriving in marine environments following their introduction. Species distribution models (SDMs) are widely used to predict the potential distribution of invasive species, providing excellent tools for the design of strategies to prevent or mitigate impacts of non-native species. Niche shifts are among the major drawbacks in the use of SDMs, leading scientists to formulate inaccurate predictions. In this work, we tested the performance of 3 different SDMs (Bioclim, Mahalanobis distance and Maxent) to predict the distribution of a niche-shifting invasive species using native data only. As a model organism, we used the neurotoxic sea-slug Pleurobranchaea maculata, which was recently introduced into the southwestern Atlantic, where it has undergone a niche shift. Our results show that Maxent outperforms the other modeling techniques in predicting the invasive distribution, but that Bioclim provides the most accurate outputs, minimizing over- and underpredictions. Our study strongly suggests that niche decomposition can provide important evidence for the underlying causes of niche shifts, aiding our understanding of why they occur and how they can be addressed by SDMs. This approach will improve the interpretation of SDMs in order to predict the potential spread of invasive species worldwide.