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Variation, selection and evolution of function-valued traits.

J G Kingsolver1, R Gomulkiewicz, P A Carter

  • 1Department of Biology, University of North Carolina, Chapel Hill 27599, USA. jgking@bio.unc.edu

Genetica
|February 13, 2002
PubMed
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This study introduces a new framework for analyzing how mathematical function traits evolve. It uses caterpillar thermal performance curves to show how variation, selection, and evolution of these traits can be modeled.

Area of Science:

  • Evolutionary Biology
  • Quantitative Genetics
  • Mathematical Modeling

Background:

  • Understanding phenotypic trait evolution typically uses multivariate models for quantitative traits.
  • Function-valued traits, which are mathematical functions, present unique challenges and require advanced modeling approaches.
  • Existing models may not fully capture the constraints on variation inherent in continuous, function-valued traits.

Purpose of the Study:

  • To present a novel framework for analyzing the variation, selection, and evolution of function-valued phenotypic traits.
  • To demonstrate the application of this framework using thermal performance curves (TPCs) of caterpillar growth rates.
  • To extend existing quantitative genetic models to accommodate function-valued traits.

Main Methods:

Related Experiment Videos

  • Quantified phenotypic and genetic variation using variance-covariance functions and eigenfunctions for TPCs.
  • Defined selection on function-valued traits using selection gradient functions, relating performance to fitness across environmental conditions.
  • Predicted evolutionary responses by integrating genetic variation and selection gradient functions.

    • Main Results:

    • Demonstrated that function-valued traits have unique variation patterns not captured by standard multivariate models.
    • Showed that selection gradients for TPCs are influenced by environmental state distributions (e.g., temperatures).
    • Predicted potentially non-linear evolutionary responses of TPCs, even with linear mean phenotypes and selection gradients.

    Conclusions:

    • The proposed framework offers a robust method for studying the evolution of function-valued traits.
    • Function-valued traits exhibit complex variation and selection dynamics that necessitate specialized modeling.
    • Future research should address methodological and empirical challenges in studying the evolution of these traits.