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Updated: May 15, 2025

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

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Organisms use reversible dormancy (seedbanks) to survive environmental changes, impacting genetic diversity. A new Bayesian framework infers population dynamics and evolutionary parameters under the strong seedbank model.

Keywords:
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Area of Science:

  • Population genetics
  • Evolutionary biology
  • Computational biology

Background:

  • Reversible dormancy (seedbanks) is a key life-history strategy influencing genetic diversity.
  • Existing coalescent models do not fully capture the complexities of strong seedbank dynamics.

Purpose of the Study:

  • To develop a statistical framework for inferring population dynamics under the strong seedbank model.
  • To provide tools for analyzing genetic data from organisms with stochastic dormancy.

Main Methods:

  • Developed a Bayesian framework for joint inference of genealogies and seedbank parameters.
  • Derived exact probability densities and likelihood functions for the strong seedbank coalescent.
  • Implemented a Markov chain Monte Carlo sampler in the SeedbankTree package for BEAST2.

Main Results:

  • Presented a novel theoretical foundation for the strong seedbank coalescent.
  • Established efficient computational algorithms for evaluating the likelihood function.
  • Created a practical inference framework for studying dormancy's ecoevolutionary impacts.

Conclusions:

  • The developed framework enables robust inference of population genetic and genealogical parameters under strong dormancy.
  • This work bridges the gap between theoretical models of dormancy and empirical genetic data analysis.
  • Facilitates a deeper understanding of dormancy's role in shaping biodiversity.