A Likelihood Approach to Estimating Phylogeny from Discrete Morphological Character Data

TLDR

Evolutionary biologists use simple likelihood models for ancestral state estimation and character independence, but for estimating phylogenies from discrete morphological data, maximum parsimony remains the sole option. This study investigates whether standard Markov models can estimate morphological phylogenies, including branch lengths, under a likelihood framework. The authors modify standard Markov models by conditioning likelihood on variable characters, excluding constant characters that are absent in morphological datasets. The modification prevents branch length overestimation and bias in tree topology, and it opens avenues for combined-data likelihood analyses, likelihood ratio tests, and Bayesian approaches.

Abstract

Evolutionary biologists have adopted simple likelihood models for purposes of estimating ancestral states and evaluating character independence on specified phylogenies; however, for purposes 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 important modification of standard Markov models involves making the likelihood conditional on characters being variable, because constant characters are absent in morphological data sets. Without this modification, 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 likelihood analyses (morphology + sequence data), likelihood ratio tests, and Bayesian analyses.