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