An improved approximate-Bayesian model-choice method for estimating shared evolutionary history.

Jamie R Oaks

doi:10.1186/1471-2148-14-150

Abstract

BackgroundTo understand biological diversification, it is important to account for large-scale processes that affect the evolutionary history of groups of co-distributed populations of organisms. Such events predict temporally clustered divergences times, a pattern that can be estimated using genetic data from co-distributed species. I introduce a new approximate-Bayesian method for comparative phylogeographical model-choice that estimates the temporal distribution of divergences across taxa from multi-locus DNA sequence data. The model is an extension of that implemented in msBayes.ResultsBy reparameterizing the model, introducing more flexible priors on demographic and divergence-time parameters, and implementing a non-parametric Dirichlet-process prior over divergence models, I improved the robustness, accuracy, and power of the method for estimating shared evolutionary history across taxa.ConclusionsThe results demonstrate the improved performance of the new method is due to (1) more appropriate priors on divergence-time and demographic parameters that avoid prohibitively small marginal likelihoods for models with more divergence events, and (2) the Dirichlet-process providing a flexible prior on divergence histories that does not strongly disfavor models with intermediate numbers of divergence events. The new method yields more robust estimates of posterior uncertainty, and thus greatly reduces the tendency to incorrectly estimate models of shared evolutionary history with strong support.

Highlights

To understand biological diversification, it is important to account for large-scale processes that affect the evolutionary history of groups of co-distributed populations of organisms
Results are similar for the general linear model (GLM)-adjusted estimates of divergence times (DT), albeit the regression adjustment tends to improve estimates of this continuous statistic for all the models (Additional file 1: Figure S2)
There is a trend of MDPP > MUniform > MUshaped > MmsBayes in terms of estimation accuracy as measured by root mean square error (RMSE) when the models are applied to data generated under most of the models (Additional file 1: Figure S5)

Summary

Introduction

It is important to account for large-scale processes that affect the evolutionary history of groups of co-distributed populations of organisms. Such events predict temporally clustered divergences times, a pattern that can be estimated using genetic data from co-distributed species. I introduce a new approximate-Bayesian method for comparative phylogeographical model-choice that estimates the temporal distribution of divergences across taxa from multi-locus DNA sequence data. By partitioning communities, such an event can isolate groups of populations and cause a temporal cluster of speciation events across co-distributed taxa. Methods for accurately estimating models of shared evolutionary events across co-distributed taxa from genetic data are important for better understanding how regional and global biogeographical processes affect biodiversity

Methods

Results

Discussion

Conclusion