Distribution data of 4638 species in seven geographic regions of Shanxi Province were examined as a small sample, of 7766 species in 14 geographic regions of Inner Mongolia as a medium sample, and of 16804 genera in 67 ecological regions of China as a large sample. Statistical analyses of the three data groups were conducted separately, using a traditional merged method ( similarity clustering analysis, SCA) and a new non鄄merged method ( multivariate similarity clustering analysis ( MSCA) ) . A critical comparison of the two methods demonstrates that the non鄄merged method can attain a result suitable for both logistics of biological statistics and geography, regardless of the scale of the data. The merged method ( SCA) may achieve a result closely resembling that of the non鄄merged method when dealing with a fewer number of geographic regions. However, with an increased number of geographic regions, the clustering structure with the merged method may create a change at a different level -so much as to cause a complete loss of functionality. Regardless
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