Presentation Information
[POS-28]Mimetic load determines the evolution of imperfect mimicry or polymorphism in Batesian mimicry
*Haruto Tomizuka1, Yuuya Tachiki1,2 (1. Tokyo Metropolitan University (Japan), 2. Science Groove Inc. (Japan))
Keywords:
Mimicry,Predation,Polymorphism,Evolutionary branching
Batesian mimicry is a strategy in which palatable prey species (mimic species) resemble unpalatable species (model species). Although research on Batesian mimicry dates back to Darwin and Bates [1], many aspects of its evolution remain unclear. Batesian mimicry exhibits diverse forms. For example, the accuracy of mimicry ranges from highly precise to imperfect, with the latter referred to as imperfect mimicry. In addition, some species show polymorphism, where only a subset of individuals evolve mimicry, while others remain non-mimetic.
In this study, we modified and analyzed an eco-evolutionary dynamics model of Batesian mimicry [2] to explore the conditions under which imperfect mimicry or polymorphism evolves. We modeled predator foraging behavior based on signal detection theory [3]. Specifically, we assumed that palatable species evolve in one-dimensional trait space with two separate adaptive peaks: (1) a mimicry phenotype and (2) a non-mimetic phenotype with low physiological costs.
To analyze the evolutionary outcome of palatable species, adaptive dynamics under a quasi-steady-state approximation were applied, in which ecological processes were sufficiently fast compared to evolutionary changes. Our analysis revealed that mimetic load—defined as the relative abundance of palatable species to unpalatable model species—was a key factor determining the evolutionary outcome. When the mimetic load was very low, palatable species exhibited imperfect mimicry because even inaccurate mimicry was sufficient to avoid predation. In contrast, when the mimetic load was high, evolutionary branching occurred, leading to the stable coexistence of two phenotypes: a mimetic type and a non-mimetic type.
[1] Bates HW. 1862 XXXII. Contributions to an Insect Fauna of the Amazon Valley. Lepidoptera: Heliconidæ. Transactions of the Linnean Society of London 3, 495–566.
[2] Tomizuka H, Tachiki Y. 2024 The eco-evolutionary dynamics of Batesian mimicry. J. Theor. Biol. 577, 111683. (doi:10.1016/j.jtbi.2023.111683)
[3] Oaten, A., Pearce, C. E. M., & Smyth, M. E. B. (1975). Batesian mimicry and signal detection theory. Bulletin of Mathematical Biology, 37, 367-387.
In this study, we modified and analyzed an eco-evolutionary dynamics model of Batesian mimicry [2] to explore the conditions under which imperfect mimicry or polymorphism evolves. We modeled predator foraging behavior based on signal detection theory [3]. Specifically, we assumed that palatable species evolve in one-dimensional trait space with two separate adaptive peaks: (1) a mimicry phenotype and (2) a non-mimetic phenotype with low physiological costs.
To analyze the evolutionary outcome of palatable species, adaptive dynamics under a quasi-steady-state approximation were applied, in which ecological processes were sufficiently fast compared to evolutionary changes. Our analysis revealed that mimetic load—defined as the relative abundance of palatable species to unpalatable model species—was a key factor determining the evolutionary outcome. When the mimetic load was very low, palatable species exhibited imperfect mimicry because even inaccurate mimicry was sufficient to avoid predation. In contrast, when the mimetic load was high, evolutionary branching occurred, leading to the stable coexistence of two phenotypes: a mimetic type and a non-mimetic type.
[1] Bates HW. 1862 XXXII. Contributions to an Insect Fauna of the Amazon Valley. Lepidoptera: Heliconidæ. Transactions of the Linnean Society of London 3, 495–566.
[2] Tomizuka H, Tachiki Y. 2024 The eco-evolutionary dynamics of Batesian mimicry. J. Theor. Biol. 577, 111683. (doi:10.1016/j.jtbi.2023.111683)
[3] Oaten, A., Pearce, C. E. M., & Smyth, M. E. B. (1975). Batesian mimicry and signal detection theory. Bulletin of Mathematical Biology, 37, 367-387.