Factors affecting the character stability of classifications

Abstract

The effect of six resemblance coefficients (taxonomic distance, Manhattan distance, correlations, cosines, and two new general dissimilarity coefficients) on the character stability of classifications based on six data sets was evaluated. The six data sets represent a variety of organisms, and of ratios of number of characters to number of OTUs, and were randomly bipartitioned 100 times. The results of matrix correlations, cophenetic correlations and two consensus measures indicate that no one resemblance coefficient is uniformly better than all others when evaluated in terms of the stability of a classification, although taxonomic distance and Manhattan distance produce relatively more stable classifications than the other resemblance coefficients. An index of dimensionality, the stemminess and cophenetic correlations of classifications were calculated for the six data sets and also for 20 data sets analyzed in an earlier study. Regression analysis of stability on the ratio of number of characters to the number of OTUs, dimensionality, stemminess, and cophenetic correlations explained more than 70% of the variance in stability. Of the four factors, the ratio was by far the most important. Stemminess and dimensionality contributed little when considered singly, and did not add appreciably to the variance explained by ratio and cophenetic correlations.