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Nonconvergence in Bayesian estimation of migration rates.

Patrick G Meirmans1

  • 1Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, P.O. Box 94248, 1090GE Amsterdam.

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Summary

BAYESASS, a migration estimation method, shows Markov chain Monte Carlo (MCMC) convergence issues with real data, often trapping estimates near prior distribution bounds. Recommendations focus on realistic expectations and study design for improved migration rate inference.

Keywords:
Markov chain Monte Carloassignment testsbayesassconvergencemicrosatellitesmigration

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Area of Science:

  • Population Genetics
  • Molecular Ecology
  • Bioinformatics

Background:

  • Estimating migration rates is crucial in population genetics.
  • Existing methods often rely on restrictive assumptions.
  • BAYESASS offers an assumption-light Bayesian approach using assignment methods.

Purpose of the Study:

  • To investigate Markov chain Monte Carlo (MCMC) convergence issues of BAYESASS with empirical data.
  • To assess the impact of study design on BAYESASS performance.
  • To provide recommendations for improving migration rate estimation using BAYESASS.

Main Methods:

  • Literature review of 100 studies utilizing BAYESASS for migration rate estimation.
  • Analysis of MCMC convergence patterns in empirical datasets.
  • Evaluation of factors influencing inference quality, including sample size and population structure (FST).

Main Results:

  • BAYESASS exhibits MCMC convergence problems with empirical data, similar to simulated data.
  • Estimated nonmigrant proportions frequently converged near 2/3 or 1, indicating MCMC trapping.
  • Inference quality decreased with more sampled populations but improved with more individuals and stronger population structure.

Conclusions:

  • BAYESASS convergence issues are prevalent in empirical studies.
  • Study design significantly impacts the reliability of migration rate estimates.
  • Researchers should adopt realistic expectations and tailor experimental design for optimal BAYESASS application.