GeoSVM: an efficient and effective tool to predict species' potential distributions

Abstract

Patterns of species distribution have long been one of the important topics of ecological study (Brown and Lomonilo 1998). In this brief communication, we introduce a new programGeoSVMthat uses support vector machine (SVM) to predict species' potential distributions. (GeoSVM is now available at http://www.unm.edu/~wyzuo/GEO.htm.) Here, we also give the results of our evaluation of the performance of GeoSVM. We used data for 30 species of Rhododendron in China as a case study to compare GeoSVM and Genetic Algorithm for Rule-Set Prediction (GARP), one of the most popular models to predict species' potential distributions. We found that GeoSVM is more accurate and efficient than GARP. Furthermore, GeoSVM can handle more environmental information, which significantly improves the prediction accuracy.