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A Quantitative Fitness Analysis Workflow
11:39

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Published on: August 13, 2012

Estimating and visualizing fitness surfaces using mark-recapture data.

Olivier Gimenez1, Arnaud Grégoire, Thomas Lenormand

  • 1Centre d'Ecologie Evolutive et Fonctionnelle, UMR 5175, 1919 Route de Mende, 34293 Montpellier, Cedex 5, France. olivier.gimenez@cefe.cnrs.fr

Evolution; International Journal of Organic Evolution
|August 7, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework using mark-recapture data to estimate and visualize natural selection on multiple traits in wild populations. It reveals correlational selection on common blackbird survival and morphology.

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Area of Science:

  • Evolutionary Biology
  • Ecology
  • Quantitative Genetics

Background:

  • Estimating natural selection on phenotypic traits is crucial in evolutionary biology.
  • Challenges arise in natural populations due to imperfect detection and difficulties in tracking individuals.
  • Mark-recapture data offers a solution for studying fitness-related traits with imperfect detection.

Purpose of the Study:

  • To develop a general framework for estimating linear and nonlinear selection gradients using mark-recapture data.
  • To visualize fitness surfaces and understand selection acting on multivariate phenotypes.
  • To apply this methodology to investigate survival selection in wild populations.

Main Methods:

  • Integration of second-order polynomial regression within mark-recapture models to quantify selection strength.
  • Utilizing splines for flexible visualization of selection on multiple traits.
  • Employing Bayesian inference with Markov chain Monte Carlo (MCMC) sampling for parameter estimation and model assessment.

Main Results:

  • The framework successfully estimates selection gradients and visualizes fitness surfaces.
  • Evidence of correlational selection was found in a wild population of Common blackbirds (Turdus merula).
  • The approach effectively assesses trait relevance and determines optimal smoothing for spline models.

Conclusions:

  • The proposed framework enhances the utility of mark-recapture data for studying natural selection.
  • It provides a robust method for exploring complex selection patterns in natural populations.
  • This approach facilitates a deeper understanding of evolutionary processes driven by selection on phenotypic traits.