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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Evolutionary shift detection with ensemble variable selection.

Wensha Zhang1, Toby Kenney2, Lam Si Tung Ho2

  • 1Department of Mathematics and Statistics, Dalhousie University, Nova Scotia, Canada. wn209685@dal.ca.

BMC Ecology and Evolution
|January 20, 2024
PubMed
Summary
This summary is machine-generated.

Identifying evolutionary trait shifts is crucial for understanding adaptation. Simulation studies reveal that method performance depends on shift characteristics and data quality, with an ensemble method offering balanced detection.

Keywords:
ELPASOEnsemble methodEvolutionary shift detectionLASSOOrnstein-Uhlenbeck modelPhylogenetic comparative methodsTrait evolution

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Area of Science:

  • Evolutionary biology
  • Phylogenetics
  • Quantitative genetics

Background:

  • Abrupt environmental changes drive evolutionary shifts in traits.
  • Understanding these shifts is key to reconstructing phenotype evolutionary history.
  • Current trait evolution shift detection methods have limitations due to model oversimplification and sensitivity to various factors.

Purpose of the Study:

  • To assess the impact of factors like shift number, size, location, and phylogenetic structure on trait evolution shift detection.
  • To compare the performance of existing methods (e.g., R packages `ape` and `PhylogeneticEM`) with a newly proposed ensemble variable selection method (R package `ELPASO`).

Main Methods:

  • Conducting extensive simulations to evaluate shift detection method performance under diverse scenarios.
  • Comparing the proposed ensemble method (`ELPASO`) against established methods (`ape` and `PhylogeneticEM`).
  • Analyzing the influence of factors such as shift size, number of shifts, tree topology, and model misspecification on detection accuracy.

Main Results:

  • Method performance is highly sensitive to the chosen selection criterion.
  • `ape`+pBIC is conservative, excelling with large signal sizes; `ape`+BIC is less conservative, performing well with small signal sizes.
  • The ensemble method (`ELPASO`) offers a balanced approach between conservativeness and sensitivity.
  • Measurement error, tree reconstruction error, and variance shifts significantly impact the performance of all tested methods.

Conclusions:

  • The choice of selection criterion critically influences the reliability of evolutionary shift detection.
  • An ensemble approach like `ELPASO` provides a robust and balanced alternative for identifying trait evolution shifts.
  • Robustness of trait evolution shift detection is challenged by data imperfections and inherent biological variability.