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The Point of No Return: Evolution of Excess Mutation Rate Is Possible Even for Simple Mutation Models

Author

Listed:
  • Brian Mintz

    (Department of Mathematics, Dartmouth College, Hanover, NH 03755, USA)

  • Feng Fu

    (Department of Mathematics, Dartmouth College, Hanover, NH 03755, USA
    Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA)

Abstract

Under constant selection, each trait has a fixed fitness, and small mutation rates allow populations to efficiently exploit the optimal trait. Therefore, it is reasonable to expect that mutation rates will evolve downwards. However, we find that this need not be the case, examining several models of mutation. While upwards evolution of the mutation rate has been found with frequency- or time-dependent fitness, we demonstrate its possibility in a much simpler context. This work uses adaptive dynamics to study the evolution of the mutation rate, and the replicator–mutator equation to model trait evolution. Our approach differs from previous studies by considering a wide variety of methods to represent mutation. We use a finite string approach inspired by genetics as well as a model of local mutation on a discretization of the unit intervals, handling mutation beyond the endpoints in three ways. The main contribution of this work is a demonstration that the evolution of the mutation rate can be significantly more complicated than what is usually expected in relatively simple models.

Suggested Citation

  • Brian Mintz & Feng Fu, 2022. "The Point of No Return: Evolution of Excess Mutation Rate Is Possible Even for Simple Mutation Models," Mathematics, MDPI, vol. 10(24), pages 1-9, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4818-:d:1007506
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    References listed on IDEAS

    as
    1. Liberman, Uri & Behar, Hilla & Feldman, Marcus W., 2016. "Evolution of reduced mutation under frequency-dependent selection," Theoretical Population Biology, Elsevier, vol. 112(C), pages 52-59.
    2. Te Wu & Feng Fu & Long Wang, 2011. "Moving Away from Nasty Encounters Enhances Cooperation in Ecological Prisoner's Dilemma Game," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-7, November.
    3. Segismundo S. Izquierdo & Luis R. Izquierdo, 2011. "Strictly Dominated Strategies in the Replicator-Mutator Dynamics," Games, MDPI, vol. 2(3), pages 1-10, September.
    Full references (including those not matched with items on IDEAS)

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