A likelihood approach to estimating phylogeny from discrete morphological character data.

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.

journal_name

Syst Biol

journal_title

Systematic biology

authors

Lewis PO

doi

10.1080/106351501753462876

keywords:

subject

Has Abstract

pub_date

2001-11-01 00:00:00

pages

913-25

issue

6

eissn

1063-5157

issn

1076-836X

journal_volume

50

pub_type

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