The spatial distribution of species is one of the important research projects in ecology. In recent years,much progress has been made in predictive models of species with the development of applied ecology, but on the other hand, it makes more difficult to use the species distribution models because the number of available techniques or models is large and is increasing steadily, making it confused for users to select the most appropriate methodology for their needs. So evaluat ing and comparing the predictive accuracy of different distribution models has great significances for selecting and using these models. BIOMOD ( BIOdiversity MODelling ), a new computation framework based on R language, is presented. Yanhe River basin was selected as the study area. In order to select a suitable model for simulating and predict ing the typical species distributions in the study area, nine widely used modeling techniques in species predictions, such as artificial neural networks ( ANN), were compared to predict the representative species distribution. The comparison of their dif ferences in predict ion accuracy can not only provide evidence for select ing species dist ribution models, but also lay a foundation for projecting the species spatial distributions into different environmental conditions ( e. g. climate change scenarios) . Three available techniques of Roc, Kappa, and TSS were used to assess each model's performance. Results from the model comparison showed that the relative model performance and simulation accuracy of different models were quite different across species. The evaluation indicated that the nine models had the highest predictive accuracy for the Thymus mongolicus dist ribution, yet the predictive accuracy of the Artemisia gmelinii distribution was the lowest. Furthermore, the nine models can predict the distribution of the rest species very well. It is concluded that the RF model which has the highest predictive accuracy using the three methods is the best one among the nine models.