Effects of sample size on accuracy and stability of species distribution models: A comparison of GARP and Maxent

Abstract

Prediction of species distribution and its changes play an increasingly important role in the fields of ecological protection and application as well as global climate changes. It is impracticable to survey species distribution in large area, especially for rare species; therefore species distribution models are very useful for predicting species distribution. Sampling size has an essential influence on the cost of actual survey and the accuracy of model prediction. Generally, the larger sample size, the higher accuracy of models, but with the more cost of survey. It is necessary to research species distribution models to get the highest accuracy but with the least sample size. Taking 31 different sample sizes (5, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 120, 150, 180, 200, 220, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 800, 900, 1000 and 1200) of 4 species as an example, this study analyzed the influence of different sample sizes on accuracy and stability of both MaxEnt (Maximum Entropy Species Prediction Model) and GARP(Genetic Algorithm for Rule-set Production). The results showed that both the accuracy and stability of two models increased with the sample size. And the two models had different predictive results to different species. The omission values of GARP were always smaller than that of MaxEnt at the same sample size to all species. © 2012 Springer-Verlag GmbH.