10.5061/DRYAD.Q7RF263
Álvarez-Carretero, Sandra
University of London
Goswami, Anjali
University College London
Yang, Ziheng
University College London
dos Reis, Mario
University of London
Data from: Bayesian estimation of species divergence times using
correlated quantitative characters
Dryad
dataset
2019
Continuous morphological characters
divergence times
Procrustes alignment
2019-02-21T15:04:44Z
2019-02-21T15:04:44Z
en
https://doi.org/10.1093/sysbio/syz015
1633102 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
Supplementary FiguresSupplementary figures S1-S5 with the corresponding
captions.SuppFigs.pdfSupplementary TablesSupplementary tables S1-S7
corresponding to the performance measures for the estimated nodes and rate
for the simulated data sets. Table S1 contains the performance measures
when assessing the effect of sample size, Table S2 for the effect of
fossil age, Table S3 for the effect of low population noise (c=0.25),
Table S4 for the effect of high population noise (c=0.5), Table S5 for the
effect of low population noise (rho = 0.5), and Table S6 for the effect of
high population noise (rho = 0.9). Tables S7A-E contain the performance
measures for different correlation values ranging from 0 to 0.5 and from
0.5 to 0.9. These measures were only used to better explore the results in
Fig. 7C and 7C', which could not be properly understood when only
plotting the estimated parameters when rho = 0, 0.5, and
0.9.Morpho_TablesS1S2S3S4S5S6S7.xlsxSupplementary dataThis zip file
contains (i) the raw data from the carnivoran data set, (ii) the alignment
used with morphology-only and molecule-only partitions, (iii) the control
file used for the divergence times estimation with the combined data set,
(iv) the tree files used to calculate Bayes factors and estimate
divergence times, and (v) a README.md file with the details about the
data. In addition, each data file contains also a description of its
format at the end.Supp_data_ms.zip