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A review was given of the role of traditional morphometrics in plant morphological studies using 54 published studies in three major journals and others in Korea, such as Journal of Korean Forestry Society, Korean Journal of Plant Taxonomy, Korean Journal of Breeding, Korean Journal of Apiculture, J...

A review was given of the role of traditional morphometrics in plant morphological studies using 54 published studies in three major journals and others in Korea, such as Journal of Korean Forestry Society, Korean Journal of Plant Taxonomy, Korean Journal of Breeding, Korean Journal of Apiculture, Journal of Life Science, and Korean Journal of Plant Resources from 1997 to 2008. The two most commonly used techniques of data analysis, cluster analysis (CA) and principal components analysis (PCA) with other statistical tests were discussed. The common problem of PCA is the underlying assumptions of methods, like random sampling and multivariate normal distribution of data. The procedure was intended mainly for continuous data and was not efficient for data which were not well summarized by variances or covariances. Likewise CA was most appropriate for categorical rather than continuous data. Also, the CA produced clusters whether or not natural groupings existed, and the results depended on both the similarity measure chosen and the algorithm used for clustering. An additional problems of the PCA and the CA arised with both qualitative and quantitative data with a limited number of variables and/or too few numbers of samples. Some of these problems may be avoided if a certain number of variables (more than 20 at least) and sufficient samples (40-50 at least) are considered for morphometric analyses, but we do not think that the methods are all mighty tools for data analysts. Instead, we do believe that reasonable applications combined with focus on objectives and limitations of each procedure would be a step forward.

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