Bayesian Estimation of Species Divergence Times Using Correlated Quantitative Characters

Systematic Biology
Sandra Álvarez-CarreteroMario Dos Reis

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 modeled 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 analyzed, 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,...Continue Reading

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Citations

Jan 28, 2020·Systematic Biology·Ceferino Varón-GonzálezChristian Peter Klingenberg
Jul 30, 2019·Insect Systematics and Diversity·April M Wright
Jan 15, 2021·Nature·Angela R PerriLaurent A F Frantz
May 14, 2021·BMC Ecology and Evolution·Jose Barba-MontoyaSudhir Kumar

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