Distance Methods and the Approximation of Most-Parsimonious Trees

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Abstract
Distance data have posed a number of problems for phylogenetic analysis. Among these are the loss of information about individual character states, and the frequent departures from metric properties of distance matrices derived by molecular techniques. The common degree-of-fit methods for the analysis of such data imply possibly unrealistic assumptions about these distances. As an alternative, a minimum-length criterion is considered. This has the appeal of requiring more conservative assumptions about distance data and represents the equivalent criterion to that for the analysis of character data by numerical cladistic techniques. Based upon its similarity to the character-Wagner algorithm, the distance-Wagner algorithm is suggested as a possible heuristic method for the approximation of most-parsimonious trees from distance data. Both the distance-Wagner algorithm and a recent modification have weaknesses in this role. Computer simulations demonstrate that the new algorithm developed in this study compares favorably with not only the distance-Wagner algorithm but also the character-Wagner algorithmin the approximation of most-parsimonious trees.

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