Department of Ecology and Evolutionary Biology, University of Kansas, 1200 Sunnyside Avenue, Lawrence Kansas 66045, USA.
BMC Evol Biol. 2014 Jul 3;14:150. doi: 10.1186/1471-2148-14-150.
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. 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.
By 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.
The 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.
为了理解生物多样性，重要的是要解释影响生物分布群体进化历史的大规模过程。这些事件预测了时间聚类的分歧时间，这一模式可以通过来自共同分布物种的遗传数据来估计。我引入了一种新的近似贝叶斯方法，用于比较系统地理学模型选择，该方法从多基因座 DNA 序列数据估计跨分类单元的分歧时间分布。该模型是在 msBayes 中实现的模型的扩展。
通过重新参数化模型，对人口和分歧时间参数引入更灵活的先验，并对分歧模型实施非参数狄利克雷过程先验，我提高了该方法估计跨分类单元共享进化历史的稳健性、准确性和能力。
结果表明，新方法的改进性能是由于（1）在分歧时间和人口参数上采用了更合适的先验，避免了具有更多分歧事件的模型的边际似然率过高，以及（2）狄利克雷过程对分歧历史提供了灵活的先验，不会强烈偏向具有中等数量分歧事件的模型。新方法产生了更稳健的后验不确定性估计，从而大大降低了错误估计具有强烈支持的共享进化历史模型的倾向。
