Journal of Plant Ecology Advance Access published online on April 15, 2008
Journal of Plant Ecology, doi:10.1093/jpe/rtn005
© The Author 2008. Published by Oxford University Press on behalf of the Institute of Botany, Chinese Academy of Sciences and the Botanical Society of China. All rights reserved. For permissions, please email: journals.permissions@oxfordjournals.org
GeoSVM: an efficient and effective tool to predict species' potential distributions
1 State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
2 School of Software, Tsinghua University, Beijing 100084, China
* Correspondence address. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China. E-mail: kpma@ibcas.ac.cn
| The first 150 words of the full text of this article appear below. |
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 program—GeoSVM—that 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.
Patterns of species distribution can potentially answer a bunch of fundamental questions in ecology, such as where are the original habitats of the species; how do the species distribute on earth;