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Abstract Time‐scaled phylogenies underpin the interrogation of evolutionary processes across deep timescales, as well as attempts to link these to Earth's history. By inferring the placement of fossils and using their ages as temporal constraints, tip dating under the fossilized birth–death (FBD) process provides a coherent prior on divergence times. At the same time, it also links topological and temporal accuracy, as incorrectly placed fossil terminals should misinform divergence times. This could pose serious issues for obtaining accurate node ages, yet the interaction between topological and temporal error has not been thoroughly explored. We simulate phylogenies and associated morphological datasets using methodologies that incorporate evolution under selection, and are benchmarked against empirical datasets. We find that datasets of 300 characters and realistic levels of missing data generally succeed in inferring the correct placement of fossils on a constrained extant backbone topology, and that true node ages are usually contained within Bayesian posterior distributions. While increased fossil sampling improves the accuracy of inferred ages, topological and temporal errors do not seem to be linked: analyses in which fossils resolve less accurately do not exhibit elevated errors in node age estimates. At the same time, inferred divergence times are biased, probably due to a mismatch between the FBD prior and the shape of our simulated trees. While these results are encouraging, suggesting that even fossils with uncertain affinities can provide useful temporal information, they also emphasize that palaeontological information cannot overturn discrepancies between model priors and the true diversification history.
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Data-driven speciation tree prior for better species divergence times in calibration-poor molecular phylogenies
Abstract Motivation Precise time calibrations needed to estimate ages of species divergence are not always available due to fossil records' incompleteness. Consequently, clock calibrations available for Bayesian dating analyses can be few and diffused, i.e. phylogenies are calibration-poor, impeding reliable inference of the timetree of life. We examined the role of speciation birth–death (BD) tree prior on Bayesian node age estimates in calibration-poor phylogenies and tested the usefulness of an informative, data-driven tree prior to enhancing the accuracy and precision of estimated times. Results We present a simple method to estimate parameters of the BD tree prior from the molecular phylogeny for use in Bayesian dating analyses. The use of a data-driven birth–death (ddBD) tree prior leads to improvement in Bayesian node age estimates for calibration-poor phylogenies. We show that the ddBD tree prior, along with only a few well-constrained calibrations, can produce excellent node ages and credibility intervals, whereas the use of an uninformative, uniform (flat) tree prior may require more calibrations. Relaxed clock dating with ddBD tree prior also produced better results than a flat tree prior when using diffused node calibrations. We also suggest using ddBD tree priors to improve the detection of outliers and influential calibrations in cross-validation analyses. These results have practical applications because the ddBD tree prior reduces the number of well-constrained calibrations necessary to obtain reliable node age estimates. This would help address key impediments in building the grand timetree of life, revealing the process of speciation and elucidating the dynamics of biological diversification. Availability and implementation An R module for computing the ddBD tree prior, simulated datasets and empirical datasets are available at https://github.com/cathyqqtao/ddBD-tree-prior.
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- PAR ID:
- 10294052
- Date Published:
- Journal Name:
- Bioinformatics
- Volume:
- 37
- Issue:
- Supplement_1
- ISSN:
- 1367-4803
- Page Range / eLocation ID:
- i102 to i110
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/bioinformatics/btab307
