Applying BioMod for Model-Ensemble in Species Distributions: a Case Study for Tsuga chinensis in China

The integration of new statistical techniques and increasing availability of multi-sources and multi-scale
data sets promote the development of species distribution modeling. Yet, choice of data sets, different model types
and their underlying ecological theories and assumptions can cause uncertainty in model predictions. In order to decrease prediction uncertainty, studies using model ensemble are gaining in popularity. In this paper we apply the BioMod package developed under R environment to predict the spatial distribution of Tsuga chinensis using nine different models. Our aims were to evaluate model performance, select explanatory variables, and assemble the best predictive output. Random Forest, MARS and GAM performed the best amongst the nine models compared, while SRE
was the worst. The ensemble models predicted that the areas of high probability for T. chinensis presence lie mainly in
Southwest China and the periphery of the Sichuan basin, and are also distributed sporadically in South China and Taiwan. These predictions reflect the actual distribution pattern of T. chinensis, and show high agreement with other analyses. The application of BioMod for model ensemble lowers uncertainty and improves the prediction performance.