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新的不可应用形态特征的系统发育马尔可夫模型。

New Phylogenetic Markov Models for Inapplicable Morphological Characters.

机构信息

Finnish Museum of Natural History, Pohjoinen Rautatiekatu 13, FI-00014 Helsinki, Finland.

出版信息

Syst Biol. 2023 Jun 17;72(3):681-693. doi: 10.1093/sysbio/syad005.

Abstract

This article proposes new Markov models for phylogenetic inference with anatomically dependent (inapplicable) morphological characters. The proposed models can explicitly model an anatomical dependency in which one or several characters are allowed to evolve only within a specific state of the hierarchically upstream character. The new models come up in two main types depending on the type of character hierarchy. The functions for constructing custom character hierarchies are provided in the R package rphenoscate. The performance of the new models is assessed using theory and simulations. This article provides practical recommendations for using the new models in Bayesian phylogenetic inference with RevBayes. [Bayesian; inapplicable characters; likelihood; Markov models; morphology; parsimony; RevBayes.].

摘要

本文提出了新的马尔可夫模型,用于具有解剖学依赖性(不适用)形态特征的系统发育推断。所提出的模型可以明确地模拟一种解剖学依赖性,其中一个或几个特征仅允许在层次结构上游特征的特定状态下进化。新模型主要有两种类型,取决于特征层次的类型。构建自定义特征层次结构的功能在 R 包 rphenoscate 中提供。新模型的性能通过理论和模拟进行评估。本文提供了在使用 RevBayes 进行贝叶斯系统发育推断时使用新模型的实用建议。[贝叶斯;不适用特征;似然;马尔可夫模型;形态;简约;RevBayes。]

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c376/10276624/c67bb869b4a7/syad005_fig1.jpg

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