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Testing hypotheses in evolutionary ecology with imperfect detection: capture-recapture structural equation modeling.

Sarah Cubaynes1, Claire Doutrelant, Arnaud Grégoire

  • 1Centre d'Ecologie Evolutive et Fonctionnelle UMR 5175, 1919 Route de Mende, 34293 Montpellier, Cedex 5, France. sarah.cubaynes@gmail.com

Ecology
|May 26, 2012
PubMed
Summary

This study introduces a new method, capture-recapture structural equation models (CR-SEM), to analyze evolutionary processes in wild populations. CR-SEM helps understand how individual and environmental factors influence population dynamics, even with imperfect detection.

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Area of Science:

  • Ecology and Evolutionary Biology
  • Quantitative Biology
  • Population Genetics

Background:

  • Studying evolutionary mechanisms in natural populations requires testing complex causal scenarios.
  • Accurate demographic parameter estimation necessitates accounting for imperfect individual detection.

Purpose of the Study:

  • To develop and illustrate a novel approach combining structural equation models with capture-recapture models (CR-SEM).
  • To investigate competing hypotheses on individual and environmental variability affecting demographic parameters.
  • To provide unbiased estimates of demographic parameters while accounting for imperfect detection.

Main Methods:

  • Developed a new approach: capture-recapture structural equation models (CR-SEM).
  • Employed Markov chain Monte Carlo sampling within a Bayesian framework.
  • Utilized model selection and posterior predictive checks for hypothesis evaluation and model fit assessment.

Main Results:

  • Demonstrated the utility of CR-SEM in quantifying selection gradients on phenotypic traits in Common Blackbirds (Turdus merula).
  • Illustrated the application of CR-SEM for studying evolutionary trade-offs in Blue Tits (Cyanistes caeruleus) under varying environmental conditions.

Conclusions:

  • CR-SEM is a valuable tool for investigating evolutionary causality in natural populations.
  • The approach allows for the simultaneous analysis of demographic parameters, individual variation, environmental influences, and imperfect detection.
  • CR-SEM facilitates a deeper understanding of selection and evolutionary trade-offs in the wild.