A likelihood approach to estimating phylogeny from discrete morphological character data

Abstract

Abstract.—Evolutionary biologists have adopted simple likelihoodmodels for purposes of estimating ancestral states and evaluating character independence on specied phylogenies; however, for pur-poses of estimating phylogenies by using discrete morphological data, maximum parsimony remains the only option. This paper explores the possibility of using standard, well-behaved Markov models for estimating morphological phylogenies (including branch lengths) under the likelihood criterion. An importantmodication of standardMarkovmodels involvesmaking the likelihood conditional on characters being variable, because constant characters are absent in morphological data sets. Without this modication, branch lengths are often overestimated, resulting in potentially serious biases in tree topology selection. Several new avenues of research are opened by an explicitly model-based approach to phylogenetic analysis of discrete morphological data, including combined-data likeli-hood analyses (morphologyC sequence data), likelihood ratio tests, and Bayesian analyses. [Discrete morphological character; Markov model; maximum likelihood; phylogeny.] The increased availability of nucleotide and protein sequences from a diversity of both organisms and genes has stimu