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A Practical Guide to Phylogenetics for Nonexperts
Published on: February 5, 2014
Phylograms better fit neutrally evolving traits than chronograms
Thibault Latrille1, Théo Gaboriau1, Nicolas Salamin1
1Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
Evolution Letters
|June 5, 2026
Summary
Phylograms, representing sequence divergence, better model neutral trait evolution than chronograms (time-based). Chronograms remain suitable for studying selection on quantitative traits.
Area of Science:
- Evolutionary Biology
- Phylogenetics
- Quantitative Genetics
Background:
- Trait evolution is shaped by both selective and neutral forces.
- Phylogenetic trees are crucial for modeling trait evolution, with branch lengths typically representing time (chronograms).
- Varying generation times across species can introduce biases when using chronograms.
Purpose of the Study:
- To investigate the impact of using phylograms (branch lengths in sequence divergence) versus chronograms on modeling trait evolution.
- To determine the appropriate phylogenetic tree representation for different evolutionary models (neutral drift vs. selection).
Main Methods:
- Simulations were used to compare the fit of evolutionary models on phylograms and chronograms.
- Quantitative trait evolution was modeled under both neutral and selective scenarios.
- Model performance was evaluated based on prediction accuracy and support for different evolutionary processes.
Main Results:
- Phylograms provided better support for neutral evolution models compared to chronograms.
- Using phylograms for analyzing selection on quantitative traits led to inaccurate predictions.
- Chronograms demonstrated continued suitability for modeling selection acting on quantitative traits.
Conclusions:
- Phylograms are recommended over chronograms when inferring neutral trait evolution (genetic drift).
- Chronograms should be preferentially used for studying the effects of selection on quantitative traits.
- The choice of phylogenetic tree representation is critical for accurate evolutionary inference.
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