Software for Systematics and Evolution Syst. Biol. 61(3):539–542, 2012 c The Author(s) 2012. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. DOI:10.1093/sysbio/sys029 Advance Access publication on February 22, 2012 MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space FREDRIK RONQUIST1,∗ , MAXIM TESLENKO1, PAUL VAN DER MARK2, DANIEL L. AYRES3, AARON DARLING 4 , SEBASTIAN H O¨HNA5 , BRET LARGET6, LIANG LIU7, MARC A. SUCHARD8, AND JOHN P. HUELSENBECK9 1Department of Biodiversity Informatics, Swedish Museum of Natural History, SE-10405 Stockholm, Sweden; 2Department of Scientific Computing, Florida State University, FL 32306, USA; 3Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA; 4Genome Center, University of California, Davis, CA 95616, USA; 5Department of Mathematics, Stockholm University, SE-10691 Stockholm, Sweden; 6Departments of Statistics and Botany, University of Wisconsin, Madison, WI 53706, USA; 7Departments of Agriculture and Natural Resources, Delaware State University, Dover, DE 19901, USA; 8Departments of Biomathematics, Biostatistics and Human Genetics, University of California, Los Angeles, CA 90095, USA; and 9Department of Integrative Biology, University of California, Berkeley, CA 94720, USA; ∗Correspondence to be sent to: Department of Biodiversity Informatics, Swedish Museum of Natural History, SE-10405 Stockholm, Sweden; E-mail: fredrik.ronquist@nrm.se. Received 13 August 2011; reviews returned 20 September 2011; accepted 6 February 2012 Associate Editor: David Posada Abstract.—Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon prob- lems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incor- poration of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site dN/dS rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software. [Bayes factor; Bayesian inference; MCMC; model averaging; model choice.] Bayesian Markov chain Monte Carlo (MCMC) meth- ods quickly gained in popularity after they were intro- duced in statistical phylogenetics in the late 1990’s (Mau and Newton 1997; Yang and Rannala 1997; Larget and Simon 1999; Mau et al. 1999). This was due to the inherent advantages of the approach but also to the availability of easy-to-use software packages, such as MrBayes (Huelsenbeck and Ronquist 2001). Originally, MrBayes only supported simple phylogenetic models, but the model space expanded considerably in version 3.0 (Ronquist and Huelsenbeck 2003). In addition to a wide range of models on binary, “standard” (morphol- ogy), nucleotide and amino acid data, version 3.0 also supported mixed models. The latter allow different data partitions to be combined in the same model, with pa- rameters linked or unlinked across partitions according to user specifications. MrBayes 3.0 was apparently the first statistical phylogenetics package to support such models (Rannala and Yang 2008). Bayesian phylogenetic inference using MCMC has developed in leaps and bounds since the release of MrBayes 3.0. In particular, the relative ease with which complex models can be tackled using the MCMC machinery has led to an explosion in the develop- ment of probabilistic evolutionary models (for a re- view, see Ronquist and Deans 2010). We have also seen the appearance of better MCMC algorithms and more sophisticated convergence diagnostics for phylogenetic models, and methods for Bayesian model choice have improved considerably. With this note, we announce the official release of ver- sion 3.2 of MrBayes. Version 3.2 was originally intended as a relatively modest expansion of version 3.1, which added convergence diagnostics to the original features in version 3.0. Over the years, however, a number of significant new features were added to version 3.2, and large parts of the program were rewritten. When we now officially release version 3.2, it is every bit as significant in the evolution of the program as the release of version 3.0 almost a decade ago. DESCRIPTION OF NEW FEATURES Convergence The phylogenetics community has come to ac- cept as good practice that Bayesian MCMC results 539