Bayesian phylogenetic analysis of combined data

The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameter-rich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new types of data, such as morphology. Based on this foundation, we developed a Bayesian MCMC approach to the analysis of combined data sets and explored its utility in inferring relationships among gall wasps based on data from morphology and four genes (nuclear and mitochondrial, ribosomal and protein coding). Examined models range in complexity from those recognizing only a morphological and a molecular partition to those having complex substitution models with independent parameters for each gene. Bayesian MCMC analysis deals efficiently with complex models: convergence occurs faster and more predictably for complex models, mixing is adequate for all parameters even under very complex models, and the parame...近年来，借助马尔可夫链蒙特卡洛（Markov chain Monte Carlo, MCMC）技术开展贝叶斯系统发育推断（Bayesian phylogenetic inference）的研究进展，有力推动了高参数进化模型的探索与应用。与此同时，随机模型愈发贴合真实演化场景（复杂度亦不断提升），并被拓展至形态学等新型数据类型。基于上述研究基础，我们开发了一套用于联合数据集分析的贝叶斯MCMC分析方法，并基于瘿蜂的形态学数据与4类基因（核基因、线粒体基因、核糖体基因与蛋白编码基因）序列，探究了该方法在推断瘿蜂物种亲缘关系中的应用价值。本次研究涵盖的模型复杂度跨度较大：从仅区分形态学与分子数据分区的基础模型，到为每个基因设置独立参数的复杂替换模型均有涉及。贝叶斯MCMC分析可高效处理复杂模型：针对复杂模型，其收敛速度更快且结果更具可预测性；即便在极高复杂度的模型设定下，所有参数的马尔可夫链混合性仍可满足分析要求，且参数...