Background/Question/Methods
Linking evolutionary processes to community dynamics has become a major research topic in recent years. Clearly, just as genes determine the fitness of an individual, the success or failure of an individual within a community is dependent on genetics. Likewise, the success or failure of a species within a community depends on the genetic composition of its population. Often the fitness of an individual — and perhaps more generally a species — is determined by biotic interactions within the community, thus community composition shapes the evolutionary forces on individuals. Understanding this interplay between communities shaping evolutionary pressures, and evolution altering the community, is critical to bridging the gap between evolution and ecology. Despite the obvious and long-recognized importance of evolution within communities (since at least Darwin's era), there is currently very little theoretical work to provide predictions about the consequences of this complicated interplay, perhaps because there are no, or at least very few, modeling frameworks capable of making such predictions. In order to fill this need, we created a novel theoretical framework, based on previously established theory, to understand and make predictions about evolution in a community context.
Results/Conclusions
Our approach to modeling community evolution combines well established (population) genetic mechanisms that affect interspecific interactions (e.g. matching-alleles models, inverse matching-alleles models, and gene-for-gene models) with a statistical genetics approach to understand community-level dynamics. Specifically, we modeled simple three species communities consisting of a single focal species that interacts with 2 other species and examined how evolution at the species level changed the degree to which species interacted within the community. We also examined what conditions lead to correlated interaction among species within our community. Our results show several important points: The nature of the interaction (e.g. parasitism, mutualism, competition) changes the community's dynamics. The number of loci underlying the interacting traits plays a critical role in the rate at which evolution affects interactions. And, finally, genetic mechanisms and several other parameters (e.g. strength of selection and rate of recombination) determine the persistence of and correlation among interactions within a community. In short, our work builds a theoretical framework on which to examine the effects of evolution on community dynamics and, for a simple community,  makes predictions about how the interplay between evolution and communities is important.