Natural selection on fecundity variance in subdivided populations: kin selection meets bet hedging.
In a series of seminal articles in 1974, 1975, and 1977, J. H. Gillespie challenged the notion that the "fittest" individuals are those that produce on average the highest number of offspring. He showed that in small populations, the variance in fecundity can determine fitness as much as mean fecundity. One likely reason why Gillespie's concept of within-generation bet hedging has been largely ignored is the general consensus that natural populations are of large size. As a consequence, essentially no work has investigated the role of the fecundity variance on the evolutionary stable state of life-history strategies. While typically large, natural populations also tend to be subdivided in local demes connected by migration. Here, we integrate Gillespie's measure of selection for within-generation bet hedging into the inclusive fitness and game theoretic measure of selection for structured populations. The resulting framework demonstrates that selection against high variance in offspring number is a potent force in large, but structured populations. More generally, the results highlight that variance in offspring number will directly affect various life-history strategies, especially those involving kin interaction. The selective pressures on three key traits are directly investigated here, namely within-generation bet hedging, helping behaviors, and the evolutionary stable dispersal rate. The evolutionary dynamics of all three traits are markedly affected by variance in offspring number, although to a different extent and under different demographic conditions.
Lehmann
Laurent
L
Department of Genetics, University of Cambridge, CB2 3EH Cambridge, United Kingdom. lehmann@stanford.edu
Balloux
François
F
D000818:Animals; D005075:Biological Evolution; D005298:Fertility / Q000235:genetics*; D005787:Gene Frequency; D014644:Genetic Variation / Q000235:genetics*; D006801:Humans; D008018:Life Cycle Stages; D008957:Models, Genetic; D012641:Selection, Genetic