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Conventional evolutionary theories assume well-mixed populations, but simulations show chaotic flows disrupt this. A new model accurately predicts selection suppression in moving populations, offering insights for biological and social systems.

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Area of Science:

  • Evolutionary dynamics
  • Mathematical modeling
  • Population genetics

Background:

  • The 'well-mixed' population assumption simplifies evolutionary dynamics, enabling analytical solutions.
  • This assumption implies that physical stirring can achieve a state of uniform interaction.
  • However, the validity of this assumption in dynamic environments is questionable.

Purpose of the Study:

  • To investigate the accuracy of conventional well-mixed theories in populations undergoing chaotic motion.
  • To develop a new analytical framework for evolutionary dynamics in fluid environments.
  • To provide a predictive tool for systems with mobile individuals.

Main Methods:

  • Simulations of populations within chaotic fluid flows.
  • Development of a new analytical description for the fast-flow limit.
  • Comparison of theoretical predictions with simulation outcomes.

Main Results:

  • Conventional well-mixed theories inaccurately predict fixation probabilities in chaotic flows, irrespective of stirring intensity.
  • The new analytical model accurately predicts the suppression of selection as competition becomes more localized.
  • The model demonstrates effectiveness for both global and local selection processes.

Conclusions:

  • The 'well-mixed' assumption is often invalid for populations in motion, challenging traditional evolutionary models.
  • A novel analytical approach in the fast-flow limit offers accurate predictions for evolutionary dynamics in fluid systems.
  • This work provides a valuable modeling tool for understanding evolutionary processes in mobile biological and social systems.