Coalescent-Based Branch Length Estimation Improves Dating of Species Trees

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Authors: Yasamin Tabatabaee, Santiago Claramunt, Siavash Mirarab
Cited by: 3
References: 70

Abstract

Species trees need to be dated for many downstream applications. Some scalable molecular dating methods take a phylogenetic tree with branch lengths in substitution units, as well as a set of calibrations, as input and convert the branch lengths of the species tree to time units, while being consistent with the pre-specified calibrations. When dating species trees from multi-locus genome-scale datasets, the branch lengths and sometimes the topology of the species tree are estimated using concatenation. However, concatenation does not address gene tree heterogeneity across the genome. While Bayesian dating methods can address some forms of gene tree heterogeneity, such as incomplete lineage sorting, they are not scalable to large numbers of species. In this paper, we introduce a new scalable pipeline for dating species trees that addresses gene tree discordance for both topology and branch length estimation. The pipeline uses discordance-aware methods that account for incomplete lineage sorting for estimating the topology and branch lengths, and maximum likelihood-based methods for the dating step. Our simulation study on datasets with gene tree discordance shows that this pipeline produces more accurate and less biased date estimates than pipelines that use concatenation. Furthermore, it is substantially more scalable and can handle datasets with thousands of species and genes. Our results on two biological datasets demonstrate that this new pipeline improves the inference of node ages and branch lengths for certain nodes, particularly those closer to the tree tips, and improves downstream analyses of diversification.

Fields of Study & Subjects

Genomics and Phylogenetic Studies Genetic diversity and population structure Evolution and Paleontology Studies

Publication details

Journal / source
Systematic Biology
Publisher
Oxford University Press (OUP)
Publication date
20 May 2026
Language
EN