A Likelihood Approach to Estimating Phylogeny from Discrete Morphological Character Data

TL;DR: Several new avenues of research are opened by an explicitly model-based approach to phylogenetic analysis of discrete morphological data, including combined-data likelihood analyses (morphology + sequence data), likelihood ratio tests, and Bayesian analyses.

Abstract: Evolutionary biologists have adopted simplelikelihood models for purposes of estimating ancestral states and evaluating character independence on specieed phylogenies; however, for pur- poses of estimating phylogenies byusing 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. AnimportantmodiecationofstandardMarkovmodelsinvolvesmakingthelikelihoodconditionalon characters being variable, because constant characters are absent in morphological data sets. Without this modiecation, 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 (morphologyCsequence 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- lated the development of stochastic models describing evolutionary change in molecu- lar sequences over time. Such models are not only useful for estimating molecular evolutionary parameters of interest but also important as the basis for phylogenetic inference using the method of maximum likelihood (ML) and Bayesian inference. ML provides a very general framework for esti- mation and has been extensively applied in diverse eelds of science (Casella and Berger, 1990); however, the popularity of ML in phylogenetic inference has lagged behind thatofotheroptimality criteria(suchas max- imum parsimony), primarily because of its much greater computational cost for evalu- ating any givencandidate tree.Recent devel- opments on the algorithmic aspects of ML inference as applied to phylogeny recon- struction (Olsen et al., 1994; Lewis, 1998; Salter and Pearl, 2001; Swofford, 2001) have succeeded in reducing this computational cost substantially, and ML phylogeny esti- mates involving hundreds of terminal taxa are now entering the realm of feasibility. Bayesian methods (based on a likelihood foundation) offer the prospect of obtaining meaningful nodal support measures with- out the unreasonable computational burden imposed by existing methods such as boot- strapping (Rannala and Yang, 1996; Yang and Rannala, 1997; Larget and Simon, 1999;