Interacting phenotypes are traits whose expression is affected by interactions with conspecifics. Commonly-studied interacting phenotypes include aggression, courtship, and communication. More extreme examples of interacting phenotypes-traits that exist exclusively as a product of interactions-include social dominance, intraspecific competitive ability, and mating systems. We adopt a quantitative genetic approach to assess genetic influences on interacting phenotypes. We partition genetic and environmental effects so that traits in conspecifics that influence the expression of interacting phenotypes are a component of the environment. When the trait having the effect is heritable, the environmental influence arising from the interaction has a genetic basis and can be incorporated as an indirect genetic effect. However, because it has a genetic basis, this environmental component can evolve. Therefore, to consider the evolution of interacting phenotypes we simultaneously consider changes in the direct genetic contributions to a trait (as a standard quantitative genetic approach would evaluate) as well as changes in the environmental (indirect genetic) contribution to the phenotype. We then explore the ramifications of this model of inheritance on the evolution of interacting phenotypes. The relative rate of evolution in interacting phenotypes can be quite different from that predicted by a standard quantitative genetic analysis. Phenotypic evolution is greatly enhanced or inhibited depending on the nature of the direct and indirect genetic effects. Further, unlike most models of phenotypic evolution, a lack of variation in direct genetic effects does not preclude evolution if there is genetic variance in the indirect genetic contributions. The available empirical evidence regarding the evolution of behavior expressed in interactions, although limited, supports the predictions of our model.

No abstract

Interactions among conspecifics influence social evolution through two distinct but intimately related paths. First, they provide the opportunity for indirect genetic effects (IGEs), where genes expressed in one individual influence the expression of traits in others. Second, interactions can generate social selection when traits expressed in one individual influence the fitness of others. Here, we present a quantitative genetic model of multivariate trait evolution that integrates the effects of both IGEs and social selection, which have previously been modeled independently. We show that social selection affects evolutionary change whenever the breeding value of one individual covaries with the phenotype of its social partners. This covariance can be created by both relatedness and IGEs, which are shown to have parallel roles in determining evolutionary response. We show that social selection is central to the estimation of inclusive fitness and derive a version of Hamilton's rule showing the symmetrical effects of relatedness and IGEs on the evolution of altruism. We illustrate the utility of our approach using altruism, greenbeards, aggression, and weapons as examples. Our model provides a general predictive equation for the evolution of social phenotypes that encompasses specific cases such as kin selection and reciprocity. The parameters can be measured empirically, and we emphasize the importance of considering both IGEs and social selection, in addition to relatedness, when testing hypotheses about social evolution.

Interacting phenotypes are traits whose expression is affected by interactions with conspecifics. Commonly-studied interacting phenotypes include aggression, courtship, and communication. More extreme examples of interacting phenotypes-traits that exist exclusively as a product of interactions-include social dominance, intraspecific competitive ability, and mating systems. We adopt a quantitative genetic approach to assess genetic influences on interacting phenotypes. We partition genetic and environmental effects so that traits in conspecifics that influence the expression of interacting phenotypes are a component of the environment. When the trait having the effect is heritable, the environmental influence arising from the interaction has a genetic basis and can be incorporated as an indirect genetic effect. However, because it has a genetic basis, this environmental component can evolve. Therefore, to consider the evolution of interacting phenotypes we simultaneously consider changes in the direct genetic contributions to a trait (as a standard quantitative genetic approach would evaluate) as well as changes in the environmental (indirect genetic) contribution to the phenotype. We then explore the ramifications of this model of inheritance on the evolution of interacting phenotypes. The relative rate of evolution in interacting phenotypes can be quite different from that predicted by a standard quantitative genetic analysis. Phenotypic evolution is greatly enhanced or inhibited depending on the nature of the direct and indirect genetic effects. Further, unlike most models of phenotypic evolution, a lack of variation in direct genetic effects does not preclude evolution if there is genetic variance in the indirect genetic contributions. The available empirical evidence regarding the evolution of behavior expressed in interactions, although limited, supports the predictions of our model.

The "geographic mosaic" approach to understanding coevolution is predicated on the existence of variable selection across the landscape of an interaction between species. A range of ecological factors, from differences in resource availability to differences in community composition, can generate such a mosaic of selection among populations, and thereby differences in the strength of coevolution. The result is a mixture of hotspots, where reciprocal selection is strong, and coldspots, where reciprocal selection is weak or absent, throughout the ranges of species. Population subdivision further provides the opportunity for nonadaptive forces, including gene flow, drift, and metapopulation dynamics, to influence the coevolutionary interaction between species. Some predicted results of this geographic mosaic of coevolution include maladapted or mismatched phenotypes, maintenance of high levels of polymorphism, and prevention of stable equilibrium trait combinations. To evaluate the potential for the geographic mosaic to influence predator-prey coevolution, we investigated the geographic pattern of genetically determined TTX resistance in the garter snake Thamnophis sirtalis over much of the range of its ecological interaction with toxic newts of genus Taricha. We assayed TTX resistance in over 2900 garter snakes representing 333 families from 40 populations throughout western North America. Our results provide dramatic evidence that geographic structure is an important component in coevolutionary interactions between predators and prey. Resistance levels vary substantially (over three orders of magnitude) among populations and over short distances. The spatial array of variation is consistent with two areas of intense evolutionary response by predators ("hotspots") surrounded by clines of decreasing resistance. Some general predictions of the geographic mosaic process are supported, including clinal variation in phenotypes, polymorphism in some populations, and divergent outcomes of the interaction between predator and prey. Conversely, our data provide little support for one of the major predictions, mismatched values of interacting traits. Two lines of evidence suggest selection is paramount in determining population variation in resistance. First, phylogenetic information indicates that two hotspots of TTX resistance have evolved independently. Second, in the one region that TTX levels in prey have been quantified, resistance and toxicity levels match almost perfectly over a wide phenotypic and geographic range. However, these results do not preclude the role the nonadaptive forces in generating the overall geographic mosaic of TTX resistance. Much work remains to fill in the geographic pattern of variation among prey populations and, just as importantly, to explore the variation in the ecology of the interaction that occurs within populations.