ArticleSystematic biology2016
Computational Performance and Statistical Accuracy of *BEAST and Comparisons with Other Methods.
Article in Systematic biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed.
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- QuCo: quartet-based co-estimation of species trees and gene trees.Bioinformatics (Oxford, England) · 2022Article
- StarBeast3: Adaptive Parallelized Bayesian Inference under the Multispecies Coalescent.Systematic biology · 2022Article
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- Using Robinson-Foulds supertrees in divide-and-conquer phylogeny estimation.Algorithms for molecular biology : AMB · 2021Article
- UglyTrees: a browser-based multispecies coalescent tree visualizer.Bioinformatics (Oxford, England) · 2021Article
- Molecular dating for phylogenies containing a mix of populations and species by using Bayesian and RelTime approaches.Molecular ecology resources · 2021Article
- A new phylogenetic protocol: dealing with model misspecification and confirmation bias in molecular phylogenetics.NAR genomics and bioinformatics · 2020Article
- Practical Speedup of Bayesian Inference of Species Phylogenies by Restricting the Space of Gene Trees.Molecular biology and evolution · 2020Article
- Phylogeny and character evolution in thePersoonia · 2020Article
- Optimizing Phylogenomics with Rapidly Evolving Long Exons: Comparison with Anchored Hybrid Enrichment and Ultraconserved Elements.Molecular biology and evolution · 2020Article
- Article
- Habitat preference modulates trans-oceanic dispersal in a terrestrial vertebrate.Proceedings. Biological sciences · 2019Article
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4 authors.
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Abstract
Under the multispecies coalescent model of molecular evolution, gene trees have independent evolutionary histories within a shared species tree. In comparison, supermatrix concatenation methods assume that gene trees share a single common genealogical history, thereby equating gene coalescence with species divergence. The multispecies coalescent is supported by previous studies which found that its predicted distributions fit empirical data, and that concatenation is not a consistent estimator of the species tree. *BEAST, a fully Bayesian implementation of the multispecies coalescent, is popular but computationally intensive, so the increasing size of phylogenetic data sets is both a computational challenge and an opportunity for better systematics. Using simulation studies, we characterize the scaling behavior of *BEAST, and enable quantitative prediction of the impact increasing the number of loci has on both computational performance and statistical accuracy. Follow-up simulations over a wide range of parameters show that the statistical performance of *BEAST relative to concatenation improves both as branch length is reduced and as the number of loci is increased. Finally, using simulations based on estimated parameters from two phylogenomic data sets, we compare the performance of a range of species tree and concatenation methods to show that using *BEAST with tens of loci can be preferable to using concatenation with thousands of loci. Our results provide insight into the practicalities of Bayesian species tree estimation, the number of loci required to obtain a given level of accuracy and the situations in which supermatrix or summary methods will be outperformed by the fully Bayesian multispecies coalescent.
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