Abstract
A phylogeny is an evolutionary tree tracing the shared history, including common ancestors, of a set of extant species or “taxa”. Phylogenies are increasingly reconstructed on the basis of molecular data (DNA and protein sequences) using statistical techniques such as likelihood and Bayesian methods. Algorithmically, these techniques suffer from the discrete nature of tree topology space. Since the number of tree topologies increases exponentially as a function of the number of taxa, and each topology requires a separate likelihood calculation, it is important to restrict the search space and to design efficient heuristics. Distance methods for phylogeny reconstruction serve this purpose by inferring trees in a fraction of the time required for the more statistically rigorous methods. Distance methods also provide fairly accurate starting trees to be further refined by more sophisticated methods. Moreover, the input to a distance method is the matrix of pairwise evolutionary distances among taxa, which are estimated by maximum likelihood, so that distance methods also have sound statistical justifications.