Data-driven speciation tree prior for better species divergence times in calibration-poor molecular phylogenies

Abstract

MotivationPrecise 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 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. ResultsWe present a simple method to estimate parameters of the birth-death 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. ConclusionEmpirical Bayesian dating analyses with ddBD tree priors enable more accurate and precise node age estimates for calibration-poor phylogenies. Our 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. AvailabilityAn R module for computing the ddBD tree prior, simulated datasets, and empirical datasets are available at https://github.com/cathyqqtao/ddBD-tree-prior.