title: Bet-hedging--a triple trade-off between means, variances and correlations.
abstract: In unpredictably varying environments, strategies that have a reduced variance in fitness can invade a population consisting of individuals that on average do better. Such strategies 'hedge their evolutionary bets' against the variability of the environment. The idea of bet-hedging arises from the fact that appropriate measure of long-term fitness is sensitive to variance, leading to the potential for strategies with a reduced mean fitness to invade and increase in frequency. Our aim is to review the conceptual foundation of bet-hedging as a mechanism that influences short- and long-term evolutionary processes. We do so by presenting a general model showing how evolutionary changes are affected by variance in fitness and how genotypic variance in fitness can be separated into variance in fitness at the level of the individuals and correlations in fitness among them. By breaking down genotypic fitness variance in this way the traditional divisions between conservative and diversified strategies are more easily intuited, and it is also shown that this division can be considered a false dichotomy, and is better viewed as two extreme points on a continuum. The model also sheds light on the ideas of within- and between-generation bet-hedging, which can also be generalized to be seen as two ends of a different continuum. We use a simple example to illustrate the virtues of our general model, as well as discuss the implications for systems where bet-hedging has been invoked as an explanation.
authors.affiliation: Section for Climate and Environmental Modelling, Norwegian Institute for Water Research (NIVA), Gaustadalléen 21, N-0349 Oslo, Norway. jostein.starrfelt@niva.no
authors.forename: Jostein
authors.initials: J
authors.lastname: Starrfelt
authors.forename: Hanna
authors.initials: H
authors.lastname: Kokko
mesh_terms: D000818:Animals; D005075:Biological Evolution; D017753:Ecosystem; D008954:Models, Biological; D015233:Models, Statistical; D012641:Selection, Genetic