Statistical approaches on discriminating spatial variation of species diversity

This study applied statistical approaches to the discrimination of spatial variations between sites and between forest types in the upper area of the Liukuei Experimental Forest of Taiwan Forestry Research Institute, Taiwan. The main purpose was to compare the effectiveness of various statistical approaches and then present the best strategy for discriminating the spatial variations of species diversity. The two methods used were (1) univariate methods by diversity measures, Shannon t-test, and (2) multivariate methods by cluster analysis, ordination by nonmetric multi-dimensional scaling, and principal component analysis. The results by univariate methods indicate that diversity differences exist between sites and between forest types. Meanwhile, the natural forest has more diversity than the plantation, and the hardwood plantation has more diversity than the conifer plantation. The differences between forest types are very significant at the 1% significance level according to the Shannon t-test. The results indicate that univariate methods by diversity measures are a flexible way to reduce the complexity of “species by sites” matrices into a single coefficient. The results of using multivariate methods indicate that cluster analysis and ordination by non-metric multi-dimensional scaling and principal component analysis are useful techniques for discriminating spatial variations. However, ordination by non-metric multi-dimensional scaling discriminates better than principal component analysis. In addition, ordination by non-metric multi-dimensional scaling is a more informative summary than cluster analysis, and the combination of both the analyses is more effective than either alone for the mutual consistency of representations. It is concluded that the most powerful tools for discriminating the spatial variations of species diversity are in the multivariate category. Among multivariate methods, ordination by non-metric multidimensional scaling is preferable, and its superimposition with cluster analysis is recommended in order to obtain more information regarding the relationship between sites and between forest types.