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Data from: Emergence of long-term balanced polymorphism under cyclic selection of spatially variable magnitude

Cite this dataset

Gulisija, Davorka; Kim, Yuseob (2023). Data from: Emergence of long-term balanced polymorphism under cyclic selection of spatially variable magnitude [Dataset]. Dryad. https://doi.org/10.5061/dryad.539j4

Abstract

A fundamental question in evolutionary biology is what promotes genetic variation at non-neutral loci, a major precursor to adaptation in changing environments. In particular, balanced polymorphism under realistic evolutionary models of temporally varying environments in finite natural populations remains to be demonstrated. Here, we propose a novel mechanism of balancing selection under temporally varying fitnesses. Using forward-in-time computer simulations and mathematical analysis, we show that cyclic selection that spatially varies in magnitude, such as along an environmental gradient, can lead to elevated levels of non-neutral genetic polymorphism in finite populations. Balanced polymorphism is more likely with an increase in gene flow, magnitude and period of fitness oscillations, and spatial heterogeneity. This polymorphism-promoting effect is robust to small systematic fitness differences between competing alleles or to random environmental perturbation. Furthermore, we demonstrate analytically that protected polymorphism arises as spatially heterogeneous cyclic fitness oscillations generate a type of storage effect that leads to negative frequency-dependent selection. Our findings imply that spatially variable cyclic environments can promote elevated levels of non-neutral genetic variation in natural populations.

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