Calibrated birth-death phylogenetic time-tree priors for bayesian inference.
Here we introduce a general class of multiple calibration birth-death tree priors for use in Bayesian phylogenetic inference. All tree priors in this class separate ancestral node heights into a set of "calibrated nodes" and "uncalibrated nodes" such that the marginal distribution of the calibrated nodes is user-specified whereas the density ratio of the birth-death prior is retained for trees with equal values for the calibrated nodes. We describe two formulations, one in which the calibration information informs the prior on ranked tree topologies, through the (conditional) prior, and the other which factorizes the prior on divergence times and ranked topologies, thus allowing uniform, or any arbitrary prior distribution on ranked topologies. Although the first of these formulations has some attractive properties, the algorithm we present for computing its prior density is computationally intensive. However, the second formulation is always faster and computationally efficient for up to six calibrations. We demonstrate the utility of the new class of multiple-calibration tree priors using both small simulations and a real-world analysis and compare the results to existing schemes. The two new calibrated tree priors described in this article offer greater flexibility and control of prior specification in calibrated time-tree inference and divergence time dating, and will remove the need for indirect approaches to the assessment of the combined effect of calibration densities and tree priors in Bayesian phylogenetic inference.
Allan Wilson Centre for Molecular Ecology and Evolution, New Zealand; Department of Computer Science, The University of Auckland, Auckland, New Zealand jheled@gmail.com.
Joseph
J
Heled
Allan Wilson Centre for Molecular Ecology and Evolution, New Zealand; Department of Computer Science, The University of Auckland, Auckland, New Zealand Allan Wilson Centre for Molecular Ecology and Evolution, New Zealand; Department of Computer Science, The University of Auckland, Auckland, New Zealand.
Alexei J
AJ
Drummond
D000465:Algorithms; D000818:Animals; D001001:Anura / Q000145:classification; D001499:Bayes Theorem; D002138:Calibration; D002965:Classification / Q000379:methods*; D003198:Computer Simulation; D010802:Phylogeny; D013995:Time; D032401:Typhaceae / Q000145:classification
BEAST; Bayesian inference; Yule prior; birth–death tree prior; fossil calibrations; multiple calibrations