Plant Diversity ›› 2013, Vol. 35 ›› Issue (5): 647-655.DOI: 10.7677/ynzwyj201312127

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

 BI  Ying-Feng-1、2, HU  Jian-Chu-3, LI  Qiao-Hong-1, Antoine  Guisan4, Wilfried  Thuiller5   

  1. 1 Key Laboratory of Economic Plants and Biotechnology, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming
    650201, China; 2 University of Chinese Academy of Sciences, Beijing 100049, China; 3 Centre for Mountain Ecosystem Studies,
    Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China; 4 Département d′Ecologie
    et Evolution Faculté de Biologie et Médecine Université, de Lausanne CH1015 Lausanne, Switzerland;
    5 Laboratoire d’Ecologie Alpine, University Grenoble 1J. Fourier, France
  • Received:2012-10-19 Online:2013-09-25 Published:2013-02-26
  • Supported by:

    中国科学院知识创新工程重要方向项目——西南野生生物资源的挖掘与利用 (KSCX2-EW-J-24)


 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.

Key words: Species distribution model Tsuga chinensis, Model assembly, Biogeography, BioMod

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