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Performance surfaces and adaptive landscapes.

Stevan J Arnold1

  • 1Department of Zoology, Oregon State University, Corvallis, Oregon 97331.

Integrative and Comparative Biology
|June 18, 2011
PubMed
Summary
This summary is machine-generated.

This study extends the morphology-performance-fitness relationship to nonlinear selection, using adaptive landscapes to analyze evolutionary biology. It details methods for estimating selection impacts on traits and performance, offering insights into evolutionary trajectories.

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Area of Science:

  • Evolutionary Biology
  • Biomechanics
  • Quantitative Genetics

Background:

  • Previous work characterized morphology-performance-fitness under directional selection.
  • Nonlinear selection, including stabilizing selection, requires a more general framework.

Purpose of the Study:

  • To extend the morphology-performance-fitness relationship to include nonlinear selection.
  • To conceptualize the role of performance studies within the adaptive landscape framework.
  • To explore the evolution of performance and adaptive landscapes.

Main Methods:

  • Reviewing the adaptive landscape concept and its importance in evolutionary biology.
  • Estimating adaptive landscape descriptors via selection impacts on phenotypic trait means, variances, and covariances.
  • Employing quadratic approximations of selection and performance surfaces to analyze trait-performance-fitness relationships.

Main Results:

  • Key adaptive landscape descriptors can be estimated by measuring selection's impact on phenotypic traits.
  • Quadratic approximations of performance surfaces yield descriptors for the performance landscape.
  • The performance landscape may evolve along lines of least resistance, supported by biomechanical principles.

Conclusions:

  • The study provides a general framework for analyzing morphology-performance-fitness relationships under various selection types.
  • Understanding adaptive and performance landscapes is crucial for evolutionary biology.
  • Performance landscapes may evolve predictably, offering insights into evolutionary pathways.