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…

• NS
  National Science Foundation

  Awards: 0075404, 0075406, DEB-0075406
• V
  Vetenskapsrådet
• MD
  Ministerio de Ciencia y Tecnología