The impact of kinship composition on social structure

Abstract

AbstractThe relatedness between group members is a potential driver of variation in social structure. Relatedness predicts biases in partner choice and formation of strong relationships among group members. As such, groups that differ in their percentage of non-kin dyads, i.e., in their kinship composition, should therefore differ in the structure of their social networks. Yet the relationship between kinship composition and social structure remains unclear. Here, we used long-term social and pedigree data from a population of rhesus macaques to investigate the relationship between kinship composition and the connectivity, cohesion, potential for transmission and social differentiation of the social networks of adult female macaques. We found no evidence that the social structure of groups composed of greater proportion of unrelated females differ from that of groups with a lower proportion of non-kin. To investigate this unexpected finding, we built agent-based models parameterised with the empirical data to further explore (1) the expected relationship between kinship composition and social structure and (2) why we did not find such a relationship in the empirical data. Agent-based models showed that kinship composition can influence social structure in populations similar to the one studied, but that this effect may only be detectable with a sample size even larger than ours (19 group-years) and with greater variance in kinship composition (proportion of non-kin varied between 0.830-0.922 in the empirical data). The relationship between kinship composition and social structure might be more apparent when comparing data from species that differ strongly in their social organisation, translating into marked differences in kinship composition. This further emphasises the importance of reporting existing and future kinship composition data to deepen our understanding of the evolution of sociality and highlights the potential of agent-based models to better understand empirical results.