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In a fluctuating environment, population genetics models show that rapid environmental changes can create nonlinear fitness, leading to stable genetic diversity (polymorphism). This polymorphism can emerge even if mean population fitness decreases, enhancing robustness.

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Area of Science:

  • Evolutionary Biology
  • Mathematical Genetics
  • Population Dynamics

Background:

  • Replicator dynamics model selection in evolutionary game theory.
  • Fisher's equation describes gene frequency changes in mathematical genetics.
  • Previous models often assumed static environments.

Purpose of the Study:

  • To model genetic selection in time-periodic environments using replicator dynamics.
  • To investigate how environmental fluctuations induce nonlinear fitness.
  • To analyze the impact of these nonlinearities on population polymorphism and robustness.

Main Methods:

  • Utilized continuous-time two-player replicator dynamics.
  • Analyzed symmetric payoff scenarios reducing to the Fisher equation.
  • Focused on rapid, cyclic (fine-grained) environmental changes.

Main Results:

  • Time-averaged population frequencies follow replicator dynamics with environmentally induced nonlinear fitness.
  • Nonlinear fitness terms, arising from population tracking of the environment, can stabilize polymorphism.
  • Polymorphism can emerge with decreasing mean population fitness, indicating increased robustness.

Conclusions:

  • Time-periodic environments can drive the emergence of stable genetic polymorphism.
  • Polymorphic populations exhibit enhanced robustness in fluctuating environments.
  • This phenomenon was exemplified using rock-paper-scissors and prisoner's dilemma models.