Models based on chronological data correctly predict the spread of freshwater aliens, and reveal a strong influence of river access, anthropogenic activities and climate regimes

Alien species constitute one of the main threats to freshwater ecosystems, negatively impacting biodiversity, economy, biosecurity and ecosystem services. Predicting the arrival and spread of alien species is of paramount importance to prevent new introductions and control the expansion and establishment of already introduced species. We modelled the distribution of four freshwater invaders in Great Britain, using environmental and anthropogenic predictors, to help focus management actions. The species grouped different taxa including signal crayfish ( Pacifastacus leniusculus ), the marsh frog ( Pelophylax ridibundus ), the red-eared slider ( Trachemys scripta ) and the pike-perch ( Sander lucioperca ). The modelling approach accounted for methodological limitations and implemented two evaluations, a temporal evaluation using data corresponding to 70% of the oldest records to calibrate models and the remaining 30% for evaluation using various performance metrics (the common AUC, TSS and also null models) and an independent evaluation using the most recent range expansion of the species in the last six years. The distribution of the species was facilitated by multiple environmental and anthropogenic predictors. Road density was the second most important predictor of the occurrence of signal crayfish and red-eared slider preceded by the distance to ports and isothermality for each species respectively. Human population density was the most important predictor of marsh frog presence whereas pike-perch was mostly related to the proximity of boat ramps and precipitation regimes. Our distribution models were accurate and predicted the most recent range expansion of all of the species, highlighting their usefulness for preventing alien species spread and the value of using historical projections, usually available for non-native species, to calibrate and evaluate Species Distribution Models.