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Bayesian phylodynamic inference of population dynamics with dormancy.

Lorenzo Cappello1,2, Wai Tung 'Jack' Lo3, Joy Z Zhang4

  • 1Departments of Economics and Business, Universitat Pompeu Fabra, Barcelona, Spain.

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Summary

Organisms use reversible dormancy, or seedbanks, influencing genetic diversity. This study introduces a new Bayesian framework to infer population dynamics and evolutionary parameters from molecular data under the strong seedbank model.

Keywords:
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Area of Science:

  • Evolutionary biology
  • Population genetics
  • Computational biology

Background:

  • Reversible dormancy (seedbanks) is a key life-history strategy impacting genetic diversity.
  • Two models exist: weak seedbank (seasonal) and strong seedbank (stochastic switching).
  • Existing methods cannot infer population dynamics under the strong seedbank model.

Purpose of the Study:

  • To develop a Bayesian framework for inferring population dynamics under the strong seedbank coalescent.
  • To enable joint inference of latent genealogy, seedbank, and evolutionary parameters.
  • To provide a practical tool for studying dormancy's eco-evolutionary impacts.

Main Methods:

  • Derived the exact probability density of genealogies under the strong seedbank coalescent.
  • Developed efficient computational algorithms for likelihood evaluation.
  • Created a tailored Markov chain Monte Carlo sampler and implemented it in BEAST2 (SeedbankTree package).

Main Results:

  • Presented a novel Bayesian framework for strong seedbank inference.
  • Provided theoretical underpinnings and computational tools for analyzing dormancy.
  • Enabled joint inference of genetic and demographic parameters.

Conclusions:

  • The developed framework offers a robust method for studying dormancy's influence on genetic diversity.
  • SeedbankTree facilitates research into the eco-evolutionary dynamics of organisms with strong seedbank strategies.
  • This work bridges theoretical population genetics and practical phylogenetic inference.