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bioRxiv · 10.1101/679084

Mismatch of the morphology model is mostly unproblematic in total-evidence dating: insights from an extensive simulation study

Abstract

Calibrating the molecular clock is the most contentious step in every dating analysis, but the emerging total-evidence dating approach promises increased objectivity. It combines molecular and morphological data of extant and fossil taxa in a Bayesian framework. Information about absolute node ages stems from the inferred fossil placements and associated branch lengths, under the assumption of a morphological clock. We here use computer simulations to assess the impact of mismatch of the morphology model, such as misspecification of character states and transition rates, non-stationarity of the evolutionary process, and extensive variation of evolutionary rates among branches. Comparisons with published datasets suggest that, at least for evolutionary rates typically observed in discrete morphological characters, the total-evidence dating framework is surprisingly robust to these factors. We show that even with relatively low numbers of morphological characters sampled, extensive model mismatch is mostly irrelevant for the performance of the method. The only exception we found are cases of highly asymmetric state frequencies and thus transition rates, but these can be accounted for by appropriate morphology models. In contrast, we find that the temporal scope of fossil sampling has a major impact on divergence time estimates, with the time signal quickly eroding if only rather young fossils are included in an analysis. Our results suggest that total-evidence dating might work even without a good understanding of morphological evolution and that study design should instead focus on an adequate sampling of all relevant fossils, even those with highly incomplete preservation.

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BibTeXRIS

Klopfstein, S., Ryser, R., Corio, M., Spasejovic, T.. 2019-06-21. Mismatch of the morphology model is mostly unproblematic in total-evidence dating: insights from an extensive simulation study. https://doi.org/10.1101/679084

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