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Optimizing phylogenetic eigenvector regression: union eigenvectors, robust estimation, and flexible application to

Zheng-Lin Chen1, Deng-Ke Niu1

  • 1MOE Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, China.

Evolution; International Journal of Organic Evolution
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Summary
This summary is machine-generated.

This study optimizes Phylogenetic Eigenvector Regression (PVR) by improving eigenvector selection and using robust regression. The enhanced PVR framework offers greater accuracy and reliability for evolutionary analyses.

Keywords:
evolutionary shiftphylogenetic autocorrelationphylogenetic comparative methodsphylogenetic signalrobust regressiontrait correlation

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Area of Science:

  • Ecology and evolutionary biology
  • Phylogenetic comparative methods

Background:

  • Phylogenetic eigenvector regression (PVR) is a common tool for analyzing evolutionary relationships.
  • Current PVR implementations face challenges in eigenvector selection, regression robustness, and model complexity guidelines.

Purpose of the Study:

  • To develop an optimized PVR framework addressing key implementation challenges.
  • To enhance the accuracy and robustness of PVR for ecological and evolutionary studies.

Main Methods:

  • Evaluated trait-specific eigenvector selection versus using their union.
  • Assessed robust regression estimators (PVR-MM, PVR-L2) under evolutionary heterogeneity.
  • Reassessed the samples-per-variable (SPV) guideline through simulations.

Main Results:

  • Using the union of eigenvectors provides better control of phylogenetic non-independence.
  • PVR-MM and PVR-L2 maintain accuracy under evolutionary shifts where OLS fails.
  • PVR can tolerate more eigenvectors than suggested by the SPV guideline, with careful consideration for overfitting.

Conclusions:

  • The optimized PVR framework improves accuracy and robustness across diverse evolutionary scenarios.
  • Addressing eigenvector selection, regression, and model complexity enhances PVR's reliability.
  • This work strengthens the applicability of PVR in ecological and evolutionary research.