A likelihood approach to estimating phylogeny from discrete morphological character data
A likelihood approach to estimating phylogeny from discrete morphological character data
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DOI：
10.1080/106351501753462876
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发表时间：
2001-11-01
影响因子：
6.5
通讯作者：
Lewis, PO
中科院分区：
文献类型：
--
作者：
Lewis, PO
进化生物学家采用简单的似然模型来估计祖先状态和评估特定的遗传特征的独立性;然而，为了通过使用离散形态数据来估计遗传特征，最大简约性仍然是唯一的选择。本文探讨了使用标准的，良好的表现马尔可夫模型的可能性，估计形态的似然性（包括分支长度）下的标准。一个重要的修改标准马尔可夫模型涉及的可能性条件的字符是可变的，因为恒定的字符是不存在的形态数据集。如果没有这种修改，分支长度往往被高估，导致在树拓扑结构选择的潜在严重的偏见。几个新的研究途径是开放的一个明确的基于模型的方法，离散形态学数据的系统发育分析，包括组合数据似然分析（形态+序列数据），似然比检验，和Bavesian分析。
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 Bavesian analyses.