Parsimony and maximum-likelihood phylogenetic analyses of morphology do not generally integrate uncertainty in inferring evolutionary history: a response to Brown et al.
Our recent study evaluated the performance of parsimony and probabilistic models of phylogenetic inference based on categorical data [1]. We found that a Bayesian implementation of a probabilistic Markov model produced more accurate results than either of the competing parsimony approaches (the main...
| Autores: | , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión enviada para evaluación y publicación |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/217174 |
| Acceso en línea: | http://hdl.handle.net/10261/217174 |
| Access Level: | acceso abierto |
| Palabra clave: | Taxonomy and systematics Evolution Palaeontology |
| Sumario: | Our recent study evaluated the performance of parsimony and probabilistic models of phylogenetic inference based on categorical data [1]. We found that a Bayesian implementation of a probabilistic Markov model produced more accurate results than either of the competing parsimony approaches (the main method currently employed), and the maximum-likelihood implementation of the same model. This occurs principally because the results of Bayesian analyses are less resolved (less precise) as a measure of topological uncertainty is intrinsically recovered in this MCMC-based approach and can be used to construct a majority-rule consensus tree that reflects this. Of the three main methods, maximum likelihood performed theworst of all as a single exclusively bifurcating tree is estimated in this framework which does not integrate an intrinsic measure of support. |
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