eprintid: 10069294
rev_number: 26
eprint_status: archive
userid: 608
dir: disk0/10/06/92/94
datestamp: 2019-03-04 13:02:53
lastmod: 2021-11-29 00:15:06
status_changed: 2019-03-04 13:02:53
type: article
metadata_visibility: show
creators_name: Álvarez-Carretero, S
creators_name: Goswami, A
creators_name: Yang, Z
creators_name: Dos Reis, M
title: Bayesian Estimation of Species Divergence Times Using Correlated Quantitative Characters
ispublished: pub
divisions: UCL
divisions: B02
divisions: C08
divisions: D09
divisions: F99
keywords: Bayesian inference, continuous morphological characters, geometric morphometrics, Procrustes alignment, molecular clock, divergence times, phylogeny
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Discrete morphological data have been widely used to study species evolution, but the use of quantitative (or continuous) morphological characters is less common. Here, we implement a Bayesian method to estimate species divergence times using quantitative characters. Quantitative character evolution is modelled using Brownian diffusion with character correlation and character variation within populations. Through simulations, we demonstrate that ignoring the population variation (or population “noise”) and the correlation among characters leads to biased estimates of divergence times and rate, especially if the correlation and population noise are high. We apply our new method to the analysis of quantitative characters (cranium landmarks) and molecular data from carnivoran mammals. Our results show that time estimates are affected by whether the correlations and population noise are accounted for or ignored in the analysis. The estimates are also affected by the type of data analysed, with analyses of morphological characters only, molecular data only, or a combination of both; showing noticeable differences among the time estimates. Rate variation of morphological characters among the carnivoran species appears to be very high, with Bayesian model selection indicating that the independent-rates model fits the morphological data better than the autocorrelated-rates model. We suggest that using morphological continuous characters, together with molecular data, can bring a new perspective to the study of species evolution. Our new model is implemented in the MCMCtree computer program for Bayesian inference of divergence times.
date: 2019-11
date_type: published
publisher: Society of Systematic Biologists
official_url: https://doi.org/10.1093/sysbio/syz015
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1634101
doi: 10.1093/sysbio/syz015
lyricists_name: Alvarez-Carretero, Sandra
lyricists_name: Goswami, Anjali
lyricists_name: Yang, Ziheng
lyricists_id: SALVA56
lyricists_id: AGOSW54
lyricists_id: ZYANG48
actors_name: Yang, Ziheng
actors_id: ZYANG48
actors_role: owner
full_text_status: public
publication: Systematic Biology
volume: 68
number: 6
pagerange: 967-986
issn: 1063-5157
citation:        Álvarez-Carretero, S;    Goswami, A;    Yang, Z;    Dos Reis, M;      (2019)    Bayesian Estimation of Species Divergence Times Using Correlated Quantitative Characters.                   Systematic Biology , 68  (6)   pp. 967-986.    10.1093/sysbio/syz015 <https://doi.org/10.1093/sysbio%2Fsyz015>.       Green open access   
 
document_url: https://discovery-pp.ucl.ac.uk/id/eprint/10069294/1/2019Alvarez-CarreteroSB.pdf