Data from: Bayesian estimation of species divergence times using correlated quantitative characters

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.zi