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Detecting patterns of correlational selection with sampling error: A simulation study.

Monique Nouailhetas Simon1, Gabriel Marroig1, Stevan J Arnold2

  • 1Department of Genetics and Evolutionary Biology, University of São Paulo, São Paulo, Brazil.

Evolution; International Journal of Organic Evolution
|December 10, 2021
PubMed
Summary
This summary is machine-generated.

Estimating correlational selection requires large sample sizes (≥500 individuals) for accurate multivariate nonlinear selection (γ-matrix) analysis. Distinct trait-function patterns improve detection, but sample sizes near 1000 are still needed.

Keywords:
Canonical analysisfunctional complexesnonlinear multivariate selectionsample sizetrait coadaptationtrait correlations

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Area of Science:

  • Evolutionary biology
  • Quantitative genetics

Background:

  • Multivariate selection theory predicts adaptive peaks and trait co-adaptation via correlational selection.
  • Estimating the multivariate nonlinear selection (γ-matrix) is crucial for testing correlational selection's ubiquity.
  • Sampling error can impact the precision of estimated γ-matrices.

Purpose of the Study:

  • To investigate the sampling properties of estimated γ-matrices.
  • To determine the minimum sample size needed for accurate estimation of correlational selection.
  • To identify factors influencing the detection of correlational selection patterns.

Main Methods:

  • Simulated individual traits and fitness under varying selection strengths, sample sizes, and numbers of traits.
  • Employed nonlinear regressions to estimate γ-matrices from simulated data with added random error.
  • Assessed the similarity between imposed and estimated γ-matrices to evaluate detection accuracy.

Main Results:

  • Correlational selection detection strongly depends on total selection strength and residual fitness variance.
  • A minimum sample size of 500 individuals is necessary for precise γ-matrix estimation.
  • Selection patterns involving distinct functional trait sets are easier to detect, even with many traits, but require ~1000 individuals.

Conclusions:

  • Accurate estimation of correlational selection is feasible but sample size dependent.
  • Researchers should consider trait functional complexes and robust fitness proxies to minimize sample size requirements.
  • Future studies should prioritize sufficient sample sizes and appropriate trait/fitness measures to reliably detect correlational selection.