Calibrated birth-death phylogenetic time-tree priors for bayesian inference

Area of Science:

• Phylogenetics
• Computational Biology
• Evolutionary Biology

Background:

• Bayesian phylogenetic inference relies on accurate tree priors.
• Existing methods for incorporating calibration data can be restrictive.
• Accurate divergence time dating is crucial for evolutionary studies.

Purpose of the Study:

• To introduce a general class of multiple calibration birth-death tree priors.
• To provide flexible methods for incorporating calibration information in phylogenetic analyses.
• To improve the accuracy of divergence time estimation.

Main Methods:

• Developed a class of tree priors separating ancestral node heights into calibrated and uncalibrated nodes.
• Proposed two formulations: one informing ranked topologies, another factorizing divergence times and topologies.
• Implemented algorithms for prior density computation, with one formulation being computationally efficient.

Main Results:

• The second formulation offers computational efficiency for up to six calibrations.
• Demonstrated utility through simulations and a real-world phylogenetic analysis.
• The new priors provide greater control over prior specification in time-tree inference.

Conclusions:

• The introduced multiple-calibration tree priors enhance flexibility and control in phylogenetic analyses.
• These priors simplify the assessment of calibration densities and tree prior interactions.
• Facilitates more robust divergence time dating and phylogenetic inference.