Content area

Abstract

High-throughput sequencing projects generate genome-scale sequence data for species-level phylogenies1-3. However, state-of-the-art Bayesian methods for inferring timetrees are computationally limited to small datasets and cannot exploit the growing number of available genomes4. In the case of mammals, molecular-clock analyses of limited datasets have produced conflicting estimates of clade ages with large uncertainties5,6, and thus the timescale of placental mammal evolution remains contentious7-10. Here we develop a Bayesian molecular-clock dating approach to estimate a timetree of4,705 mammal species integrating information from 72 mammal genomes. We show that increasingly larger phylogenomic datasets produce diversification time estimates with progressively smaller uncertainties, facilitating precise tests of macroevolutionary hypotheses. For example, we confidently reject an explosive model of placental mammal origination in the Palaeogene8 and show that crown Placentalia originated in the Late Cretaceous with unambiguous ordinal diversification in the Palaeocene/Eocene. Our Bayesian methodology facilitates analysis of complete genomes and thousands of species within an integrated framework, making it possible to address hitherto intractable research questions on species diversifications. This approach can be used to address other contentious cases of animal and plant diversifications that require analysis of species-level phylogenomic datasets.

Details

Title
A species-level timeline of mammal evolution integrating phylogenomic data
Author
Álvarez-Carretero, Sandra 1 ; Tamuri, Asif U 2 ; Battini, Matteo 3 ; Nascimento, Fabricia F 4 ; Carlisle, Emily 3 ; Asher, Robert J; Yang, Ziheng; Donoghue, Philip C J; Reis, Mario Dos

 School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK 
 Centre for Advanced Research Computing, University College London, London, UK 
 School of Earth Sciences, University of Bristol, Bristol, UK 
 MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK 
Pages
263-2,267A-267F
Section
Article
Publication year
2022
Publication date
Feb 10, 2022
Publisher
Nature Publishing Group
ISSN
00280836
e-ISSN
14764687
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2627994215
Copyright
Copyright Nature Publishing Group Feb 10, 2022