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Estimating evolutionary parameters when viability selection is operating.

Jarrod D Hadfield1

  • 1University of Edinburgh, Edinburgh EH8 9YL, UK. j.hadfield@ed.ac.uk

Proceedings. Biological Sciences
|January 24, 2008
PubMed
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Evolutionary inferences require explicit modeling of missing data, including the invisible fraction (individuals dying before measurement) and missing traits. Ignoring these can lead to biased parameter estimates in evolutionary and quantitative genetics.

Area of Science:

  • Evolutionary Biology
  • Quantitative Genetics
  • Biostatistics

Background:

  • The invisible fraction (individuals dying before trait measurement) and missing traits present challenges in evolutionary studies.
  • These issues can be conceptualized as missing data problems in statistical analysis.

Purpose of the Study:

  • To formally define conditions for valid evolutionary inference when the invisible fraction or missing traits are ignored.
  • To investigate methods for accurate estimation of evolutionary and quantitative genetic parameters in the presence of missing data.

Main Methods:

  • Application of missing data theory from statistics to evolutionary biology.
  • Extension of survival analysis models to address the invisible fraction and viability selection.
  • Development of quantitative genetic approaches to handle missing traits.

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Main Results:

  • Conditions for ignoring missing data are restrictive and rarely met in long-term studies.
  • Ignoring the invisible fraction or missing traits leads to biased parameter estimates without explicit modeling.
  • Quantitative genetics approaches recover missing information from relatives, outperforming phenotypic selection studies.

Conclusions:

  • Explicitly modeling missing data processes is crucial for accurate evolutionary and genetic parameter estimation.
  • Survival analysis models offer a framework for the invisible fraction, while quantitative genetics methods address missing traits.
  • Quantitative genetics provides a more robust framework than phenotypic selection studies when dealing with missing data in evolutionary contexts.