Chin J Plant Ecol ›› 2009, Vol. 33 ›› Issue (5): 870-877.DOI: 10.3773/j.issn.1005-264x.2009.05.005

• Research Articles • Previous Articles     Next Articles

EFFECTS OF SAMPLE SIZE AND SPECIES TRAITS ON PERFORMANCE OF BIOCLIM IN PREDICTING GEOGRAPHICAL DISTRIBUTION OF TREE SPECIES—A CASE STUDY WITH 12 DECIDUOUS QUERCUS SPECIES INDIGENOUS TO CHINA

SHAO Hui1,3, TIAN Jia-Qian1, GUO Ke1, Osbert Jianxin Sun2,*()   

  1. 1State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
    2Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China
    3Graduate University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2009-03-23 Revised:2009-05-15 Online:2009-03-23 Published:2009-09-30
  • Contact: Osbert Jianxin Sun

Abstract:

Aims Our objective was to evaluate the performance of BIOCLIM in predicting geographical distributions of tree species in China and to determine the impacts of sample size and species traits on predictive accuracy.
Methods BIOCLIM model was used to predict the geographical distribution of 12 deciduous Quercus species differing in ecological traits and sample sizes. We used data from museum or herbarium collections and 18 layers of grid-data of meteorological variables at 6′×6′ resolution to run the model. We evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC) and Kappa statistic.
Important findings Model performance was improved and the variability in predictive accuracy was reduced with increasing sample size. Reasonable predictions to county-level resolution can be achieved with a sample size of 25 data points. On average, 75-100 observations were found to be sufficient to obtain maximal accuracy. Furthermore, we found that accuracy of BIOCLIM was greater for species with narrower geographical range and limited environmental tolerance than those with broader range and greater tolerance.

Key words: prediction of species distribution, deciduous oak, BIOCLIM model, predictive accuracy, sample size, species traits