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《Acta Ecologica Sinica》 2007-03
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Prediction of the spatial patterns of species richness based on the plant-topography relationship:An application of GAMs approach

SHEN Ze-Hao, Zhao JunDepartment of Ecology, College of Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China  
A new approach is forming internationally for the quantitative study of vegetation-environment relationship, by combining the field sampling-based regression analysis of community vs. habitat attributes and GIS supported spatial prediction of biological indices. As one of the non-parameter model types, Generalized Additive Models GAMs is becoming an efficient tool in the "regression analysis-spatial prediction" approach, partly for its flexibility to a wide variety of data types. A spatially explicit database for environment factors that frequently rely on digitalized elevation model is the requisite background for spatial prediction. This approach is introduced and applied here, in a prediction and analysis of the spatial pattern of a group of biodiversity indices in the study area. Species richness of sampling plot (include 6 categories as total species, tree species, shrub species, herb species, evergreen woody species, and rare species) was used as responsive variables, with five topographic indices was predictive variables in GAMs. Spatial environment database came from DEM of predicting area with 10m resolution. Model analysis and validation were done to the results of modeling. The results suggest that: (1) The structure and D2 values of models are different for different biodiversity indices, so are the model stability in modeling repeats, indicating their differences in response to the gradients of topographic indices. (2) Generally, the most prominent topographic effects come from slope position and slope angle, which contribute to habitat heterogeneity mainly at smaller scale, while the effect of elevation, acting at a much larger scale, is not always significant. (3) The predictions of the richness patterns of tree species, herb species, and rare species passed the validation with an independent sampling data, that of evergreen woody species and total species succeeded partially, but the prediction for the richness of shrub species failed. (4) Efficient and adequate predictive variables are crucial for the interpretative capability of the models, the sample size also has prominently effects on the robustness of GAM. Discussion is then made about the efficiency of topographic indices as habitat indicators, sources of error, the advantage/disadvantage and potential applications of GAMs.
【Fund】: 国家自然科学基金资助项目(30000024 30470313)~~
【CateGory Index】: Q948
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