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DOI:	10.1371/journal.pone.0109210
论文题名:	Bayesian Analysis Using a Simple Likelihood Model Outperforms Parsimony for Estimation of Phylogeny from Discrete Morphological Data
作者:	April M. Wright; David M. Hillis
刊名:	PLOS ONE
ISSN:	1932-6203
出版年:	2014
发表日期:	2014-10-3
卷:	9, 期:10
语种:	英语
英文关键词:	Phylogenetic analysis ; Bayesian method ; Paleogenetics ; Phylogenetics ; Fossils ; Evolutionary developmental biology ; Paleontology ; Evolutionary rate
英文摘要:	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://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0109210&type=printable
Citation statistics:
资源类型:	期刊论文
标识符:	http://119.78.100.158/handle/2HF3EXSE/18303
Appears in Collections:	过去全球变化的重建影响、适应和脆弱性科学计划与规划气候变化与战略全球变化的国际研究计划气候减缓与适应气候变化事实与影响

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File Name/ File Size	Content Type	Version	Access	License
journal.pone.0109210.PDF（656KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	View Download

作者单位:

Department of Integrative Biology, University of Texas at Austin, Austin, Texas, United States of America;Department of Integrative Biology, University of Texas at Austin, Austin, Texas, United States of America

Recommended Citation:

April M. Wright,David M. Hillis. Bayesian Analysis Using a Simple Likelihood Model Outperforms Parsimony for Estimation of Phylogeny from Discrete Morphological Data[J]. PLOS ONE,2014-01-01,9(10)

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