Adaptive Metropolis-coupled MCMC for BEAST 2

With ever more complex models used to study evolutionary patterns, approaches that facilitate efficient inference under such models are needed. Metropolis-coupled Markov chain Monte Carlo (MCMC) has long been used to speed up phylogenetic analyses and to make use of multi-core CPUs. Metropolis-coupled MCMC essentially runs multiple MCMC chains in parallel. All chains are heated except for one cold chain that explores the posterior probability space like a regular MCMC chain. This heating allows chains to make bigger jumps in phylogenetic state space. The heated chains can then be used to propose new states for other chains, including the cold chain. One of the practical challenges using this approach, is to find optimal temperatures of the heated chains to efficiently explore state spaces. We here provide an adaptive Metropolis-coupled MCMC scheme to Bayesian phylogenetics, where the temperature difference between heated chains is automatically tuned to achieve a target acceptance probability of states being exchanged between individual chains. We first show the validity of this approach by comparing inferences of adaptive Metropolis-coupled MCMC to MCMC on several datasets. We then explore where Metropolis-coupled MCMC provides benefits over MCMC. We implemented this adaptive Metropolis-coupled MCMC approach as an open source package licenced under GPL 3.0 to the Bayesian phylogenetics software BEAST 2, available from https://github.com/nicfel/CoupledMCMC.

Quantifying the impact of an inference model in Bayesian phylogenetics

SummaryPhylogenetic trees are currently routinely reconstructed from an alignment of character sequences (usually nucleotide sequences). Bayesian tools, such as MrBayes, RevBayes and BEAST2, have gained much popularity over the last decade, as they allow joint estimation of the posterior distribution of the phylogenetic trees and the parameters of the underlying inference model. An important ingredient of these Bayesian approaches is the species tree prior. In principle, the Bayesian framework allows for comparing different tree priors, which may elucidate the macroevolutionary processes underlying the species tree. In practice, however, only macroevolutionary models that allow for fast computation of the prior probability are used. The question is how accurate the tree estimation is when the real macroevolutionary processes are substantially different from those assumed in the tree prior.Here we present pirouette, a free and open-source R package that assesses the inference error made by Bayesian phylogenetics for a given macroevolutionary diversification model. pirouette makes use of BEAST2, but its philosophy applies to any Bayesian phylogenetic inference tool.We describe pirouette’s usage providing full examples in which we interrogate a model for its power to describe another.Last, we discuss the results obtained by the examples and their interpretation.

Subgrouping the Timor-Alor-Pantar languages using systematic Bayesian inference

This paper refines the subgroupings of the Timor-Alor-Pantar (TAP) family of Papuan languages, using a systematic Bayesian phylogenetics study. While recent work indicates that the TAP family comprises a Timor (T) branch and an Alor Pantar (AP) branch (Holton et al. 2012, Schapper et al. 2017) the internal structure of the AP branch has proven to be a challenging issue, and earlier studies leave large gaps in our understanding. Our Bayesian inference study is based on an extensive set of TAP lexical data from the online LexiRumah database (Kaiping & Klamer, 2019). Systematically testing different approaches to cognate coding, loan exclusion, and explicit modelling choices, we arrive at a subgrouping structure of the TAP family that is based on features of the phylogenies shared across the different analyses. Our TAP tree differs from all earlier proposals by inferring the East-Alor subgroup as an early split-off from all other AP languages, instead of the most deeply embedded subgroup inside that branch.

Adaptive parallel tempering for BEAST 2

With ever more complex models used to study evolutionary patterns, approaches that facilitate efficient inference under such models are needed. Parallel tempering has long been used to speed up phylogenetic analyses and to make use of multi-core CPUs. Parallel tempering essentially runs multiple MCMC chains in parallel. All chains are heated except for one cold chain that explores the posterior probability space like a regular MCMC chain. This heating allows chains to make bigger jumps in phylogenetic state space. The heated chains can then be used to propose new states for other chains, including the cold chain. One of the practical challenges using this approach, is to find optimal temperatures of the heated chains to efficiently explore state spaces. We here provide an adaptive parallel tempering scheme to Bayesian phylogenetics, where the temperature difference between heated chains is automatically tuned to achieve a target acceptance probability of states being exchanged between individual chains. We first show the validity of this approach by comparing inferences of adaptive parallel tempering to MCMC on several datasets. We then explore where parallel tempering provides benefits over MCMC. We implemented this adaptive parallel tempering approach as an open source package licensed under GPL 3.0 to the Bayesian phylogenetics software BEAST2, available from https://github.com/nicfel/CoupledMCMC.

Not endemic after all: Imparipecten Freeman, 1961 (Diptera: Chironomidae) described from the Neotropical Region

Imparipecten, a previously monotypic genus, was considered endemic to Australia. Here, we report Imparipecten from the Neotropical region for the first time and describe Imparipecten sychnacanthus sp. n. from Brazil. The association between larvae and adults was established by sequencing a fragment of one ribosomal gene (28S), two fragments of a nuclear protein-coding gene (CAD1 and CAD4), and one mitochondrial protein-coding gene (COI). We also show the close molecular proximity with Imparipecten pictipes through analyses of genetic distances and Bayesian phylogenetics.