exTREEmaTIME: a method for incorporating uncertainty into divergence time estimates.

Area of Science:

• Evolutionary Biology
• Computational Biology
• Phylogenetics

Background:

• Estimating divergence times is crucial for understanding evolutionary history.
• Existing methods often rely on complex, biologically difficult-to-justify assumptions.
• These assumptions can lead to inaccurate divergence time estimates and uncertainty.

Purpose of the Study:

• To present exTREEmaTIME, a novel method for divergence time estimation.
• To effectively quantify and represent uncertainty in divergence time estimates.
• To provide a framework for evaluating the impact of stricter assumptions in other methods.

Main Methods:

• exTREEmaTIME requires a minimal set of assumptions.
• It estimates the oldest and youngest possible divergence times consistent with these assumptions.
• The method's effectiveness was validated through simulations and empirical analyses.

Main Results:

• exTREEmaTIME effectively represents uncertainty in divergence time estimates.
• Simulations and empirical analyses confirmed its efficacy.
• The method provides a basis for assessing the implications of more stringent assumptions.

Conclusions:

• exTREEmaTIME offers a robust approach to divergence time estimation by explicitly handling uncertainty.
• It serves as a valuable tool for comparative analyses with methods that yield more precise but potentially less accurate estimates.
• This work highlights the importance of acknowledging and quantifying uncertainty in phylogenetic analyses.