title: Enhanced kin recognition through population estimation.
abstract: Kin recognition systems enable organisms to predict genetic relatedness. In so doing, they help to maximize the fitness consequences of social actions. Recognition based on phenotypic similarity-a process known as phenotype matching-is thought to depend upon information about one's own phenotype and the phenotypes of one's partners. We provide a simple model of genetic relatedness conditioned upon phenotypic information, however, that demonstrates that individuals additionally require estimates of the distributions of phenotypes and genotypes in the population. Following the results of our model, we develop an expanded concept of phenotype matching that brings relatedness judgments closer in line with relatedness as it is currently understood and provides a heuristic mechanism by which individuals can discriminate positive from negative relatives, thereby increasing opportunities for the evolution of altruism and spite. Finally, we propose ways in which organisms might acquire population estimates and identify research that supports their use in phenotype matching.
authors.affiliation: Department of Mathematics and Statistics, Queen's University, Kingston, Ontario K7L 3N6, Canada. daniel.krupp@queensu.ca
authors.forename: Daniel Brian
authors.initials: DB
authors.lastname: Krupp
authors.forename: Peter D
authors.initials: PD
authors.lastname: Taylor
mesh_terms: D000818:Animals; D014644:Genetic Variation; D005838:Genotype; D008954:Models, Biological; D010641:Phenotype; D011157:Population Dynamics