Using maximum entropy to predict the potential distribution of an invasive freshwater snail

Abstract

SummaryEcological niche modelling is a technique used to estimate potential distributions of invasive species based on available occurrence data and associated environmental conditions. Maximum entropy (Maxent) is a powerful method for ecological niche modelling and yet has only rarely been applied to aquatic species.Here we applied Maxent to estimate the potential distribution of the invasive Chinese mystery snail (Cipangopaludina chinesis) in Wisconsin and analysed several methodological issues associated with using Maxent for lentic species.To generate Maxent estimates of the potential distribution ofC. chinesis, we used presence records from 292 lakes, five spatially explicit climatic variables, and two lake‐specific environmental data sets (area, conductivity) from 7995 lakes. Our investigations included three aspects that could affect model prediction accuracy and transferability: (i) combining climate and lake variables into a single data set in two different ways, using each lake as a single observation and as a grid of 1 ha cells; (ii) varying the size of the background data set (locations without presences); and (iii) contrasting environmental conditions between locations with and withoutC. chinesis.The lake‐based model had higher accuracy than the grid‐based model, although both models had accuracy values indicative of good performance. Conductivity and lake area were important predictor variables for both models, but had higher contribution to the lake‐based model accuracy. Decreasing the background sample size minimally affected model accuracy and thus Maxent can be used even when background sampling does not meet the algorithm's default settings. Lastly, lakes that were environmentally dissimilar from lakes with knownC. chinesisrecords were more likely to be predicted unsuitable by both grid‐based and lake‐based models.Overall, the models predicted high potential suitability across Wisconsin lakes forC. chinesis, especially in lakes ≥60 ha. Our study provides evidence that small or environmentally biased presence data sets may underestimate the number of environmentally suitable locations of invasive species.