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bioRxiv · 10.1101/2021.04.21.440863

Scalable total-evidence inference from molecular and continuous characters in a Bayesian framework

Zhang, R.·Drummond, A. J.·Mendes, F. K.
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Abstract

Time-scaled phylogenetic trees are an ultimate goal of evolutionary biology and a necessary ingredient in comparative studies. The accumulation of genomic data has resolved the tree of life to a great extent, yet timing evolutionary events remains challenging if not impossible without external information such as fossil ages and morphological characters. Methods for incorporating morphology in tree estimation have lagged behind their molecular counter-parts, especially in the case of continuous characters. Despite recent advances, such tools are still direly needed as we approach the limits of what molecules can teach us. Here, we implement a suite of state-of-the-art methods for leveraging continuous morphology in phylogenetics, and by conducting extensive simulation studies we thoroughly validate and explore our methods properties. While retaining model generality and scalability, we make it possible to estimate absolute and relative divergence times from multiple continuous characters while accounting for uncertainty. We compile and analyze one of the most data-type diverse data sets to date, comprised of contemporaneous and ancient molecular sequences, and discrete and continuous characters from living and extinct Carnivora taxa. We conclude by synthesizing lessons about our methods behavior, and suggest future research venues.

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Zhang, R., Drummond, A. J., Mendes, F. K.. 2021-04-22. Scalable total-evidence inference from molecular and continuous characters in a Bayesian framework. https://doi.org/10.1101/2021.04.21.440863

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