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High variation in developmental instability under non-normal developmental error: a Bayesian perspective.

Stefan Van Dongen1, Willem Talloen, Luc Lens

  • 1Group of Evolutionary Biology, Department of Biology, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp, Belgium. stefan.vandongen@ua.ac.be

Journal of Theoretical Biology
|May 19, 2005
PubMed
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Developmental instability (DI) is poorly understood, with fluctuating asymmetry (FA) often used as a proxy. This study reveals that assuming normal error distributions can overestimate DI variation, highlighting the need for accurate developmental error modeling.

Area of Science:

  • Developmental Biology
  • Evolutionary Biology
  • Biostatistics

Background:

  • Developmental instability (DI) mechanisms remain unclear, with fluctuating asymmetry (FA) commonly used as a surrogate measure.
  • Statistical assumptions about developmental error distributions (e.g., normal distribution) influence the interpretation of FA patterns and estimates of DI variation.
  • Leptokurtic FA distributions have been interpreted as high between-individual variation in DI, potentially leading to overestimation.

Purpose of the Study:

  • To develop a statistical model to assess the sensitivity of DI variation estimates to different developmental error distributions (normal, log-normal, gamma).
  • To investigate the impact of low measurement resolution on FA and DI variation estimates.
  • To provide a more accurate framework for interpreting FA patterns and their relationship to DI.

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Main Methods:

  • Development of a statistical model to compare DI variation estimates under normal, log-normal, and gamma error distributions.
  • Evaluation of the model's sensitivity using simulated and empirical datasets.
  • Assessment of the influence of low measurement resolution on FA data.

Main Results:

  • Bias due to misspecification of developmental error distribution can be substantial.
  • Alternative error distributions (log-normal, gamma) did not significantly reduce DI variation estimates in empirical data.
  • The effects of low measurement resolution on FA were found to be negligible.

Conclusions:

  • Incorrect assumptions about developmental error distributions can lead to significant biases in DI variation estimates.
  • While bias exists, it may not drastically alter DI variation estimates in empirical FA data.
  • Accurate modeling of developmental error distributions is crucial for reliable interpretation of FA patterns in relation to DI.