Programs for Bayesian inference of phylogeny currently implement a unique and fixed suite of models. Consequently, users of these software packages are simultaneously forced to use a number of programs for a given study, while also lacking the freedom to explore models that have not been implemented by the developers of those programs. We developed a new open-source software package, RevBayes, to address these problems. RevBayes is entirely based on probabilistic graphical models, a powerful generic framework for specifying and analyzing statistical models. Phylogenetic-graphical models can be specified interactively in RevBayes, piece by piece, using a new succinct and intuitive language called Rev. Rev is similar to the R language and the BUGS model-specification language, and should be easy to learn for most users. The strength of RevBayes is the simplicity with which one can design, specify, and implement new and complex models. Fortunately, this tremendous flexibility does not come at the cost of slower computation; as we demonstrate, RevBayes outperforms competing software for several standard analyses. Compared with other programs, RevBayes has fewer black-box elements. Users need to explicitly specify each part of the model and analysis. Although this explicitness may initially be unfamiliar, we are convinced that this transparency will improve understanding of phylogenetic models in our field. Moreover, it will motivate the search for improvements to existing methods by brazenly exposing the model choices that we make to critical scrutiny. RevBayes is freely available at http://www.RevBayes.com. [Bayesian inference; Graphical models; MCMC; statistical phylogenetics.]

At a time when historical biogeography appears to be again expanding its scope after a period of focusing primarily on discerning area relationships using cladograms, new inference methods are needed to bring more kinds of data to bear on questions about the geographic history of lineages. Here we describe a likelihood framework for inferring the evolution of geographic range on phylogenies that models lineage dispersal and local extinction in a set of discrete areas as stochastic events in continuous time. Unlike existing methods for estimating ancestral areas, such as dispersal-vicariance analysis, this approach incorporates information on the timing of both lineage divergences and the availability of connections between areas (dispersal routes). Monte Carlo methods are used to estimate branch-specific transition probabilities for geographic ranges, enabling the likelihood of the data (observed species distributions) to be evaluated for a given phylogeny and parameterized paleogeographic model. We demonstrate how the method can be used to address two biogeographic questions: What were the ancestral geographic ranges on a phylogenetic tree? How were those ancestral ranges affected by speciation and inherited by the daughter lineages at cladogenesis events? For illustration we use hypothetical examples and an analysis of a Northern Hemisphere plant clade (Cercis), comparing and contrasting inferences to those obtained from dispersal-vicariance analysis. Although the particular model we implement is somewhat simplistic, the framework itself is flexible and could readily be modified to incorporate additional sources of information and also be extended to address other aspects of historical biogeography.

Historical biogeography is increasingly studied from an explicitly statistical perspective, using stochastic models to describe the evolution of species range as a continuous-time Markov process of dispersal between and extinction within a set of discrete geographic areas. The main constraint of these methods is the computational limit on the number of areas that can be specified. We propose a Bayesian approach for inferring biogeographic history that extends the application of biogeographic models to the analysis of more realistic problems that involve a large number of areas. Our solution is based on a "data-augmentation" approach, in which we first populate the tree with a history of biogeographic events that is consistent with the observed species ranges at the tips of the tree. We then calculate the likelihood of a given history by adopting a mechanistic interpretation of the instantaneous-rate matrix, which specifies both the exponential waiting times between biogeographic events and the relative probabilities of each biogeographic change. We develop this approach in a Bayesian framework, marginalizing over all possible biogeographic histories using Markov chain Monte Carlo (MCMC). Besides dramatically increasing the number of areas that can be accommodated in a biogeographic analysis, our method allows the parameters of a given biogeographic model to be estimated and different biogeographic models to be objectively compared. Our approach is implemented in the program, BayArea.

Bayesian analysis of macroevolutionary mixtures (BAMM) has recently taken the study of lineage diversification by storm. BAMM estimates the diversification-rate parameters (speciation and extinction) for every branch of a study phylogeny and infers the number and location of diversification-rate shifts across branches of a tree. Our evaluation of BAMM reveals two major theoretical errors: (i) the likelihood function (which estimates the model parameters from the data) is incorrect, and (ii) the compound Poisson process prior model (which describes the prior distribution of diversification-rate shifts across branches) is incoherent. Using simulation, we demonstrate that these theoretical issues cause statistical pathologies; posterior estimates of the number of diversification-rate shifts are strongly influenced by the assumed prior, and estimates of diversificationrate parameters are unreliable. Moreover, the inability to correctly compute the likelihood or to correctly specify the prior for rate-variable trees precludes the use of Bayesian approaches for testing hypotheses regarding the number and location of diversification-rate shifts using BAMM.volutionary biologists have long sought to detect patterns and understand the causes of variation in rates of lineage diversification (speciation − extinction). This has motivated the development of several statistical methods for detecting whether (and where) diversification rates have changed across the branches of a phylogeny (1-4). A recent approach-Bayesian analysis of macroevolutionary mixtures (BAMM) (5)-promises to greatly enhance our ability to study this problem.This important new method offers several key advantages. (i) BAMM is based on an explicit model that describes how diversification rates shift across the branches of a tree. (ii) The underlying branching process is more complex (and presumably more realistic) than those used in previous methods. Specifically, BAMM not only includes parameters for the rate of speciation and extinction, but also accommodates possible time-dependent effects (where the age of a lineage may affect its diversification rate). This is intended to approximate the phenomenon of diversity-dependent diversification (where the number of species in a lineage may affect its diversification rate), which is believed to be a prevalent feature of empirical phylogenies (6). (iii) By virtue of developing this method in a Bayesian statistical framework, BAMM allows us to gauge the uncertainty in our inferences by providing marginal posterior probability densities rather than point estimates of parameters. (iv) By averaging inferences over any number of diversification-rate shifts, BAMM both accommodates uncertainty in the choice of model and avoids potential complications associated with model selection.BAMM provides estimates of the number and location of diversification-rate shifts across the branches of a tree and also estimates the diversification-rate parameters-speciation, extinction, and time dependence-on each branch of the tree. ...

Maternally transmitted Wolbachia, Spiroplasma, and Cardinium bacteria are common in insects [1], but their interspecific spread is poorly understood. Endosymbionts can spread rapidly within host species by manipulating host reproduction, as typified by the global spread of wRi Wolbachia observed in Drosophila simulans [2, 3]. However, because Wolbachia cannot survive outside host cells, spread between distantly related host species requires horizontal transfers that are presumably rare [4-7]. Here, we document spread of wRi-like Wolbachia among eight highly diverged Drosophila hosts (10-50 million years) over only about 14,000 years (5,000-27,000). Comparing 110 wRi-like genomes, we find ≤0.02% divergence from the wRi variant that spread rapidly through California populations of D. simulans. The hosts include both globally invasive species (D. simulans, D. suzukii, and D. ananassae) and narrowly distributed Australian endemics (D. anomalata and D. pandora) [8]. Phylogenetic analyses that include mtDNA genomes indicate introgressive transfer of wRi-like Wolbachia between closely related species D. ananassae, D. anomalata, and D. pandora but no horizontal transmission within species. Our analyses suggest D. ananassae as the Wolbachia source for the recent wRi invasion of D. simulans and D. suzukii as the source of Wolbachia in its sister species D. subpulchrella. Although six of these wRi-like variants cause strong cytoplasmic incompatibility, two cause no detectable reproductive effects, indicating that pervasive mutualistic effects [9, 10] complement the reproductive manipulations for which Wolbachia are best known. "Super spreader" variants like wRi may be particularly useful for controlling insect pests and vector-borne diseases with Wolbachia transinfections [11].