Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data.

Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data.

Despite the introduction of likelihood-based methods for estimating phylogenetic trees from phenotypic data, parsimony remains the most widely-used optimality criterion for building trees from discrete morphological data. However, it has been known for decades that there are regions of solution spac...

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Bibliographic Details
Main Authors: April M Wright, David M Hillis
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4184849?pdf=render
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spelling doaj-a1fd98e1cfbc430face46f8d0991e2592020-11-24T21:45:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01910e10921010.1371/journal.pone.0109210Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data.April M WrightDavid M HillisDespite the introduction of likelihood-based methods for estimating phylogenetic trees from phenotypic data, parsimony remains the most widely-used optimality criterion for building trees from discrete morphological data. However, it has been known for decades that there are regions of solution space in which parsimony is a poor estimator of tree topology. Numerous software implementations of likelihood-based models for the estimation of phylogeny from discrete morphological data exist, especially for the Mk model of discrete character evolution. Here we explore the efficacy of Bayesian estimation of phylogeny, using the Mk model, under conditions that are commonly encountered in paleontological studies. Using simulated data, we describe the relative performances of parsimony and the Mk model under a range of realistic conditions that include common scenarios of missing data and rate heterogeneity.http://europepmc.org/articles/PMC4184849?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author April M Wright
David M Hillis
spellingShingle April M Wright
David M Hillis
Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data.
PLoS ONE
author_facet April M Wright
David M Hillis
author_sort April M Wright
title Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data.
title_short Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data.
title_full Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data.
title_fullStr Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data.
title_full_unstemmed Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data.
title_sort bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2014-01-01
description Despite the introduction of likelihood-based methods for estimating phylogenetic trees from phenotypic data, parsimony remains the most widely-used optimality criterion for building trees from discrete morphological data. However, it has been known for decades that there are regions of solution space in which parsimony is a poor estimator of tree topology. Numerous software implementations of likelihood-based models for the estimation of phylogeny from discrete morphological data exist, especially for the Mk model of discrete character evolution. Here we explore the efficacy of Bayesian estimation of phylogeny, using the Mk model, under conditions that are commonly encountered in paleontological studies. Using simulated data, we describe the relative performances of parsimony and the Mk model under a range of realistic conditions that include common scenarios of missing data and rate heterogeneity.
url http://europepmc.org/articles/PMC4184849?pdf=render
work_keys_str_mv AT aprilmwright bayesiananalysisusingasimplelikelihoodmodeloutperformsparsimonyforestimationofphylogenyfromdiscretemorphologicaldata
AT davidmhillis bayesiananalysisusingasimplelikelihoodmodeloutperformsparsimonyforestimationofphylogenyfromdiscretemorphologicaldata
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