Posada David, Buckley Thomas R
Departamento de Bioquímica, Genética e Inmunología, Facultad de Biología, Universidad de Vigo, Vigo 36200, Spain.
Syst Biol. 2004 Oct;53(5):793-808. doi: 10.1080/10635150490522304.
Model selection is a topic of special relevance in molecular phylogenetics that affects many, if not all, stages of phylogenetic inference. Here we discuss some fundamental concepts and techniques of model selection in the context of phylogenetics. We start by reviewing different aspects of the selection of substitution models in phylogenetics from a theoretical, philosophical and practical point of view, and summarize this comparison in table format. We argue that the most commonly implemented model selection approach, the hierarchical likelihood ratio test, is not the optimal strategy for model selection in phylogenetics, and that approaches like the Akaike Information Criterion (AIC) and Bayesian methods offer important advantages. In particular, the latter two methods are able to simultaneously compare multiple nested or nonnested models, assess model selection uncertainty, and allow for the estimation of phylogenies and model parameters using all available models (model-averaged inference or multimodel inference). We also describe how the relative importance of the different parameters included in substitution models can be depicted. To illustrate some of these points, we have applied AIC-based model averaging to 37 mitochondrial DNA sequences from the subgenus Ohomopterus(genus Carabus) ground beetles described by Sota and Vogler (2001).
模型选择是分子系统发育学中一个特别相关的主题，它影响着系统发育推断的许多（如果不是所有）阶段。在这里，我们将在系统发育学的背景下讨论模型选择的一些基本概念和技术。我们首先从理论、哲学和实践的角度回顾系统发育学中替代模型选择的不同方面，并以表格形式总结这种比较。我们认为，最常用的模型选择方法，即层次似然比检验，并不是系统发育学中模型选择的最优策略，而赤池信息准则（AIC）和贝叶斯方法等方法具有重要优势。特别是，后两种方法能够同时比较多个嵌套或非嵌套模型，评估模型选择的不确定性，并允许使用所有可用模型估计系统发育和模型参数（模型平均推断或多模型推断）。我们还描述了如何描绘替代模型中包含的不同参数的相对重要性。为了说明其中的一些要点，我们将基于AIC的模型平均应用于Sota和Vogler（2001）描述的步甲属Ohomopterus亚属的37个线粒体DNA序列。
