Predicting global habitat suitability for Corbicula fluminea using species distribution models: The importance of different environmental datasets

Abstract

tNiche-based models (NBMs) are increasingly being used to predict the biological distribution of species,as well as the importance of different environmental variables on their habitat adequability. Here, weinvestigate the reliability of these models in predicting habitat suitability for Corbicula fluminea, an impor-tant freshwater bivalve invasive species. In order to determine the influence of topographic vs. climaticvariables, three datasets were used: (1) CorbiculaTOPO with topographic variables (altitude, slope and acompound topographical index); (2) CorbiculaMIX, combining climatic (annual mean temperature, meantemperature of warmest quarter, mean temperature of coldest quarter and annual precipitation) andtopographic variables and (3) CorbiculaCLIM with only the climatic variables. Nine different types ofmodels, implemented in BIOMOD2, were used and an ensemble of NBMs was built. We aimed to knowhow climatic suitability for these invaders changes when using different datasets of environmental vari-ables; if the predictive reliability is similar between datasets; and which environmental variables betterexplain habitat adequability. Model performance was very similar between CorbiculaMIX and Corbicula-CLIM. CorbiculaTOPO was the dataset with the least accurate predictions. Mean temperature of the coldestquarter and altitude were the variables that influenced C. fluminea distribution the most. The use of anensemble of predictions allowed us to clearly identify areas with potential to be invaded by the bivalve,in which records are not yet detected. This information can be used in management, to implement meas-ures to delay or prevent invasions, as well as for the identification of the environmental variables thatfavor that invasive potential.