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Life-History traits and the replicator equation.

Johannes Müller1, Aurélien Tellier2

  • 1Center for Mathematics, Technische Universität München, 85748 Garching, Germany; Institute for Computational Biology, Helmholtz Center Munich, 85764 Neuherberg, Germany.

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Summary

This study generalizes the replicator equation to include plant and microbial life-history traits like quiescence and seed banks. Results show that these traits can stabilize cooperation in populations, aiding conservation biology efforts.

Keywords:
Adaptive dynamicsLife-history traitsPersisterPrisoner’s dilemmaQuiescenceReplicator equationSeed bank

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Area of Science:

  • Evolutionary biology
  • Population genomics
  • Theoretical ecology

Background:

  • Conservation biology necessitates evolutionary genomics models for diverse species beyond humans.
  • Life-history traits absent in humans, such as quiescence and seed banks, impact genomic evolution.
  • Generalizing the replicator equation is crucial for population genomics but complicated by large state spaces.

Purpose of the Study:

  • To develop a low-dimensional replicator equation incorporating plant and microbial life-history traits.
  • To investigate the evolution and maintenance of cooperation using generalized replicator equations and adaptive dynamics.
  • To understand how quiescence and seed banks influence genomic evolution and cooperation.

Main Methods:

  • Developed a method for a low-dimensional replicator equation under neutrality and weak frequency-dependent selection.
  • Applied the generalized replicator equation to analyze cooperation in Prisoner's dilemma and snowdrift games.
  • Utilized adaptive dynamics to refine the investigation of cooperation maintenance.

Main Results:

  • A generalized replicator equation was derived, accounting for quiescence and seed banks.
  • Cooperation can be stabilized in homogeneous populations depending on the timing and structure of dormancy traits.
  • The study provides insights into cooperation dynamics in plant, invertebrate, and microbial communities.

Conclusions:

  • The developed method simplifies complex evolutionary models by reducing dimensionality.
  • Life-history traits like quiescence and seed banks play a significant role in the evolution of cooperation.
  • Findings are relevant for understanding and managing ecological communities in conservation biology.