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Demographic noise can reverse the direction of deterministic selection

Author(s): Constable, George W. A.; Rogers, Tim; McKane, Alan J.; Tarnita, Corina E.

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Abstract: Deterministic evolutionary theory robustly predicts that populations displaying altruistic behaviors will be driven to extinction by mutant cheats that absorb common benefits but do not themselves contribute. Here we show that when demographic stochasticity is accounted for, selection can in fact act in the reverse direction to that predicted deterministically, instead favoring cooperative behaviors that appreciably increase the carrying capacity of the population. Populations that exist in larger numbers experience a selective advantage by being more stochastically robust to invasions than smaller populations, and this advantage can persist even in the presence of reproductive costs. We investigate this general effect in the specific context of public goods production and find conditions for stochastic selection reversal leading to the success of public good producers. This insight, developed here analytically, is missed by both the deterministic analysis as well as standard game theoretic models that enforce a fixed population size. The effect is found to be amplified by space; in this scenario we find that selection reversal occurs within biologically reasonable parameter regimes for microbial populations. Beyond the public good problem, we formulate a general mathematical framework for models that may exhibit stochastic selection reversal. In this context, we describe a stochastic analogue to r − K theory, by which small populations can evolve to higher densities in the absence of disturbance.
Publication Date: 9-Aug-2016
Electronic Publication Date: 22-Jul-2016
Citation: Constable, George W. A., Rogers, Tim, McKane, Alan J., Tarnita, Corina E. (2016). Demographic noise can reverse the direction of deterministic selection. Proceedings of the National Academy of Sciences, 113 (32), E4745 - E4754. doi:10.1073/pnas.1603693113
DOI: doi:10.1073/pnas.1603693113
ISSN: 0027-8424
EISSN: 1091-6490
Pages: E4745 - E4754
Type of Material: Journal Article
Journal/Proceeding Title: Proceedings of the National Academy of Sciences
Version: Author's manuscript

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