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Setting Limits on Supersymmetry Using Simplified Models
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All models are wrong.

Michael J Hickerson1

  • 1Department of Biology, City College of New York, 160 Convent Ave., MR 526, New York, NY 10031, USA; The Graduate Center of the City University of New York, Subprogram in Ecology, Evolution and Behavior, 365 5th Ave, New York, NY, 10016, USA.

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Summary
This summary is machine-generated.

Researchers face challenges in creating phylogeographic models for population genetics. Approximate Bayesian computation (ABC) helps evaluate demographic models, but results depend on the chosen model set.

Keywords:
amphibiansmodel choicephylogeographypopulation genetics - empiricalpopulation genetics - theoretical

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Area of Science:

  • Evolutionary biology
  • Population genetics
  • Phylogeography

Background:

  • Model-based inference is crucial in phylogeography, yet developing sufficiently complex yet tractable models remains a challenge.
  • Exploring model space and testing competing hypotheses are essential for understanding evolutionary processes and biological communities.
  • Approximate Bayesian computation (ABC) has emerged as a popular method for evaluating historical demographic models using population genetic data.

Purpose of the Study:

  • To demonstrate the application of Approximate Bayesian computation (ABC) for evaluating phylogeographic models.
  • To test a large set of competing phylogeographic submodels using empirical genetic data.
  • To highlight the influence of model set selection on the outcomes of ABC model choice procedures.

Main Methods:

  • Utilized Approximate Bayesian computation (ABC) for model selection.
  • Applied ABC to analyze geographically widespread genetic samples from the salamander species Plethodon idahoensis.
  • Evaluated 143 distinct phylogeographic submodels.

Main Results:

  • The study successfully applied ABC to a complex phylogeographic dataset.
  • Demonstrated that the results of ABC model choice are sensitive to the specific set of models being compared.
  • Provided empirical evidence for the impact of model set composition on inferring demographic history.

Conclusions:

  • Approximate Bayesian computation is a valuable tool for phylogeographic inference.
  • The selection of an appropriate model set is critical for robust conclusions in phylogeographic studies.
  • Future research should carefully consider and justify the scope of models evaluated using ABC.