Models of Density‐Dependent Genic Selection and a New Rock‐Paper‐Scissors Social System

Abstract

We describe new ESS models of density regulation driven by genic selection to explain the cyclical dynamics of a social system that exhibits a rock-paper-scissors (RPS) set of three alternative strategies. We tracked changes in morph frequency and fitness of Lacerta vivipara and found conspicuous RPS cycles. Morphs of Uta and Lacerta exhibited parallel survival-performance trade-offs. Frequency cycles in both species of lizards are driven by genic selection. In Lacerta, frequency of each allele in adult cohorts had significant impacts on juvenile recruitment, similar to mutualistic, altruistic, and antagonistic relations of RPS alleles in Uta. We constructed evolutionarily stable strategy (ESS) models in which adults impact juvenile recruitment as a function of self versus nonself color recognition. ESS models suggest that the rapid 4-year RPS cycles exhibited by Lacerta are not possible unless three factors are present: behaviors evolve that discriminate self versus nonself morphs at higher rates than random, self- versus non-self-recognition contributes to density regulation, and context-dependent mate choice evolves in females, which choose sire genotypes to enhance progeny survival. We suggest genic selection coupled to density regulation is widespread and thus fundamental to theories of social system evolution as well as theories of population regulation in diverse animal taxa.

Hormonally mediated maternal effects, individual strategy and global change

“…At least two species of lizards (Uta stansburiana and Zootoca vivipara) have been described with cyclical changes in the frequency of adult male phenotypes. Interestingly, these cycles are ascribed to be the result of changes in male phenotypes that have behavioural traits organized by the action of testosterone [198,199]. Because organizational effects are, in part, responsible for the expression of male behavioural traits, the disruption of hormonal cascades by environmental change can potential result in chaotic population dynamics, given the link between testosterone and fitness, and the elimination of one or more morphs.…”

The role of pleiotropy vs signaller–receiver gene epistasis in life history trade-offs: dissecting the genomic architecture of organismal design in social systems

“…RPS orbits for five populations are depicted for Lacerta (2002-2006: three at Louvie (circles), Aubisque (hexagons), Gabas (triangles)), and one population is shown for Uta (1990Uta ( -2006. Both species exhibit rapid fourgeneration RPS orbits (from Sinervo et al, 2007).…”

“…Crustaceans, insects, lizards, bacteria and humans exhibit variations on the RPS Sinervo et al, 2007;Figure 4). Many other species exhibit trimorphisms, which have not yet been verified to exhibit RPS dynamics (elephant seals, Gouldian finch, the ruff, trisytlous plants).…”

“…Thus resource allocation per se and life history trade-offs can have a social cause. These social types are a permanent fixture of fitness landscapes and very general for all organisms Sinervo et al, 2007). The impacts of these social types on the genomic architecture of life history trade-offs must be addressed.…”

The Generation and Maintenance of Genetic Variation by Frequency-Dependent Selection: Constructing Polymorphisms Under the Pairwise Interaction Model

“…In models of multispecies community assembly, intransitive competitive interactions increase the likelihood of multispecies coexistence (Laird and Schamp 2006). In a perfectly intransitive competitive network, all competitors win and lose to an equal number of competing strategies (A beats B beats C beats A) as in rock-paper-scissors (RPS), whereas in a transitive/hierarchical network, some strategies are universally superior (A beats B and C, B beats C.) Several naturally polymorphic systems have been identified where variation is maintained by genetic RPS games (Shuster and Wade 1991;Sinervo and Lively 1996;Sinervo et al 2007). In our construction approach to the PIM, each new mutant's interaction fitnesses are drawn from the uniform distribution on [0, 1], so that on average half its interactions will have values .0.5 (a competitive ''win'') and the other half ,0.5 (a competitive ''loss'').…”