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Stochastic evolution of staying together.

Whan Ghang1, Martin A Nowak2

  • 1Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138, USA; Department of Mathematics, Harvard University, Cambridge, MA 02138, USA.

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|July 4, 2014
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Summary
This summary is machine-generated.

Staying together, where replicating units remain connected after reproduction, is key to evolutionary complexity. This study analyzes the probability of "staying together" mutants emerging through natural selection and genetic drift in finite populations.

Keywords:
Evolution of complexityFinite populationFixation probabilityMulti-cellularity

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Area of Science:

  • Evolutionary biology
  • Theoretical biology
  • Population genetics

Background:

  • Staying together, a phenomenon where replicating units remain aggregated post-reproduction, is a fundamental driver for evolutionary complexity.
  • This includes the emergence of multicellularity and eusociality, representing significant leaps in biological organization.

Purpose of the Study:

  • To analyze the fixation probability of a mutant exhibiting the
  • staying together
  • trait.

Main Methods:

  • Mathematical modeling of a stochastic process to examine fixation probability.
  • Analysis incorporates the effects of population size, reproductive rate, and the probability of staying together.
  • Development of a general framework for varying selection intensities, with closed-form solutions for specific cases, including weak selection limits.

Main Results:

  • The study quantifies how natural selection and random drift interact to influence the emergence of staying together in finite populations.
  • The complexity of the stochastic process scales exponentially with population size.
  • General results are derived for weak selection scenarios.

Conclusions:

  • The "staying together" trait can emerge through the interplay of selection and drift.
  • The framework developed allows for analysis across different selection intensities.
  • Understanding these dynamics is crucial for comprehending the evolution of biological complexity.