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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

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

Tom Carruthers¹, Robert W Scotland²

¹The Jodrell Building, Royal Botanic Gardens Kew, Richmond, London TW9 3AE, UK.

|February 11, 2022

Summary

We developed exTREEmaTIME, a new method for estimating divergence times that accounts for uncertainty. This approach provides the oldest and youngest possible divergence times consistent with minimal assumptions, improving evolutionary timescale accuracy.

Keywords:

Assumptions Divergence times Uncertainty

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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.