Population Viscosity and the Evolution of Altruism

Mitteldorf, Josh; Wilson, David Sloan

doi:10.1006/jtbi.2000.2007

Cited by 178 publications

(155 citation statements)

References 10 publications

Supporting

Mentioning

147

Contrasting

Order By: Relevance

“…While early formulations of evolutionary game theory focused on notions of evolutionary stability (Maynard Smith and Price, 1973) and on frequency dynamics in infinite populations (Taylor and Jonker, 1978), much recent attention has been devoted to individual-based evolutionary game modelswhich we abbreviate as "IBEG models"-in which births, deaths, game interactions, and other events are represented explicitly (Nowak and May, 1992;Durrett and Levin, 1994;Killingback and Doebeli, 1996;Nakamaru et al, 1997;van Baalen and Rand, 1998;Mitteldorf and Wilson, 2000;Nowak et al, 2004;Santos and Pacheco, 2005;Ohtsuki et al, 2006;Traulsen and Nowak, 2006;Szabó and Fáth, 2007;Taylor et al, 2007a;Antal et al, 2009;Tarnita et al, 2009a;Nowak et al, 2010a;Perc and Szolnoki, 2010;van Veelen et al, 2012). In contrast to traditional evolutionary game theory, IBEG models allow investigation of how localized interaction, population size, and other factors affect evolutionary game competition.…”

Section: Individual-based Evolutionary Game (Ibeg) Modelsmentioning

confidence: 99%

“…Well-studied examples include evolution in spatially structured populations (Wright, 1943;Kimura and Weiss, 1964;Barton and Slatkin, 1986;Durrett and Levin, 1994;Tilman and Kareiva, 1997;Dieckmann et al, 2000;Rousset, 2004;Lieberman et al, 2005;Lion and van Baalen, 2008) in group-or demestructured populations (Wright, 1931;Wilson, 1977;Taylor, 1992;Hanski and Gilpin, 1997;Traulsen and Nowak, 2006;Ohtsuki, 2010), and in populations with active assortment by kin (Hamilton, 1971;Eshel and Cavalli-Sforza, 1982) or phenotype (Antal et al, 2009). These forms of localized interaction have significant consequences for the evolution of cooperation (Nowak and May, 1992;Killingback and Doebeli, 1996;Nakamaru et al, 1997;Mitteldorf and Wilson, 2000;Santos and Pacheco, 2005;Ohtsuki et al, 2006;Traulsen and Nowak, 2006;Taylor et al, 2007a;Fletcher and Doebeli, 2009;Helbing and Yu, 2009;van Veelen et al, 2012), host-parasite interactions (Hassell et al, 1991;Herre, 1993;Boots and Sasaki, 1999;Haraguchi and Sasaki, 2000;van Baalen, 2002;Read and Keeling, 2003;Boots et al, 2004), signaling …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Dynamics with Interaction Structure

Allen

Nowak

Dieckmann

2013

The American Naturalist

View full text Add to dashboard Cite

Evolutionary dynamics depend critically on a population's interaction structure-the pattern of which individuals interact with which others, depending on the state of the population and the environment. Previous research has shown, for example, that cooperative behaviors disfavored in well-mixed populations can be favored when interactions occur only between spatial neighbors or group members. Combining the adaptive dynamics approach with recent advances in evolutionary game theory, we here introduce a general mathematical framework for analyzing the long-term evolution of continuous game strategies for a broad class of evolutionary models, encompassing many varieties of interaction structure. Our main result, the "canonical equation of adaptive dynamics with interaction structure", characterizes expected evolutionary trajectories resulting from any such model, thereby generalizing a central tool of adaptive dynamics theory. Interestingly, the effects of different interaction structures and update rules on evolutionary trajectories are fully captured by just two real numbers associated with each model, which are independent of the considered game. The first, a structure coefficient, quantifies the effects on selection pressures, and thus on the shapes of expected evolutionary trajectories. The second, an effective population size, quantifies the effects on selection responses, and thus on the expected rates of adaptation. Applying our results to two social dilemmas, we show how the range of evolutionarily stable cooperative behaviors systematically varies with a model's structure coefficient.2

show abstract

Section: Individual-based Evolutionary Game (Ibeg) Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Dynamics with Interaction Structure

Allen

Nowak

Dieckmann

2013

The American Naturalist

View full text Add to dashboard Cite

show abstract

“…Taylor's results has lead to the widespread view that limited dispersal (population viscosity) cannot favour the evolution of altruism when helping occurs after dispersal (e.g. Queller 1992;Taylor 1992a;Chapuisat et al 1997;Mittledorf & Wilson 2000;West et al 2002West et al , 2006Yamamura et al 2004;Trontti et al 2005). However, it has also been emphasized (Rousset 2004, pp.…”

Section: Introductionmentioning

confidence: 99%

Population viscosity can promote the evolution of altruistic sterile helpers and eusociality

2008

View full text Add to dashboard Cite

Because it increases relatedness between interacting individuals, population viscosity has been proposed to favour the evolution of altruistic helping. However, because it increases local competition between relatives, population viscosity may also act as a brake for the evolution of helping behaviours. In simple models, the kin selected fecundity benefits of helping are exactly cancelled out by the cost of increased competition between relatives when helping occurs after dispersal. This result has lead to the widespread view, especially among people working with social organisms, that special conditions are required for the evolution of altruism. Here, we re-examine this result by constructing a simple population genetic model where we analyse whether the evolution of a sterile worker caste (i.e. an extreme case of altruism) can be selected for by limited dispersal. We show that a sterile worker caste can be selected for even under the simplest life-cycle assumptions. This has relevant consequences for our understanding of the evolution of altruism in social organisms, as many social insects are characterized by limited dispersal and significant genetic population structure.

show abstract

“…The effect of environmental stochasticity was studied through simulations by Mittledorf and Wilson (2000). In this model individuals are living on a lattice, interact locally, and environmental disturbances occur randomly through the introduction of vacant breeding sites in each generation.…”

mentioning

confidence: 99%

Population Demography and the Evolution of Helping Behaviors

2006

View full text Add to dashboard Cite

Abstract. Limited dispersal may favor the evolution of helping behaviors between relatives as it increases their relatedness, and it may inhibit such evolution as it increases local competition between these relatives. Here, we explore one way out of this dilemma: if the helping behavior allows groups to expand in size, then the kin-competition pressure opposing its evolution can be greatly reduced. We explore the effects of two kinds of stochasticity allowing for such deme expansion. First, we study the evolution of helping under environmental stochasticity that may induce complete patch extinction. Helping evolves if it results in a decrease in the probability of extinction or if it enhances the rate of patch recolonization through propagules formed by fission of nonextinct groups. This mode of dispersal is indeed commonly found in social species. Second, we consider the evolution of helping in the presence of demographic stochasticity. When fecundity is below its value maximizing deme size (undersaturation), helping evolves, but under stringent conditions unless positive density dependence (Allee effect) interferes with demographic stochasticity. When fecundity is above its value maximizing deme size (oversaturation), helping may also evolve, but only if it reduces negative density-dependent competition.Key words. Allee effect, altruism, demographic stochasticity, environmental stochasticity, kin selection, recolonization. Behaviors by which an individual sacrifices a fraction of its resources or itself to the benefit of another individual are of central importance to the evolution of sociality. Evaluating the conditions under which such behaviors are selected for requires a careful account of how the behavioral effects translate into fitness costs and benefits. A considerable amount of theoretical work has been devoted to this issue, usually under the heading of ''evolution of altruism.'' Inclusive fitness theory (Hamilton 1964) emphasizes that altruism may evolve if recipient individuals tend to bear the genes underlying the behavior. Thus, genetic relatedness between actor and recipients matters for the evolution of such behaviors. However, it has been more recently emphasized that genetically related neighbors are also more strongly competing for the same local resources, which could at least partially offset the fecundity benefits to neighbors. Indeed, in a simple model where relatedness between group members is induced by population structure, the direction of selection on helping is determined solely by direct fecundity benefits, the behavior being selected for only if the actor's fecundity, that is, the number of juveniles counted before any competition stage, is increased (Taylor 1992a). This result takes the form B/N Ϫ C Ͼ 0, where N is the group size, C is the fecundity cost of the act, and B is the total fecundity benefit to the group, including the actor, so that a benefit B/N is received by the actor.Taylor's model assumes a fixed number of adults. With this assumption, the behavior may affect t...

show abstract

Population Viscosity and the Evolution of Altruism

Cited by 178 publications

References 10 publications

Adaptive Dynamics with Interaction Structure

Adaptive Dynamics with Interaction Structure

Population viscosity can promote the evolution of altruistic sterile helpers and eusociality

Population Demography and the Evolution of Helping Behaviors

Contact Info

Product

Resources

About