ABSTRACT
Genetic relatedness in animal societies is often a factor that drives the structure of social groups. In the marine world, most studies which have investigated this question have focused on marine mammals such as whales and dolphins. For sharks, recent studies have demonstrated preferential associations among individuals from which social communities emerge. Assortment patterns have been found according to phenotypic or behavioural traits but the role of genetic relatedness in shaping the social structure of adult shark populations has, to the best our knowledge, never been investigated. Here, we used a social network analysis crossed with DNA microsatellite genotyping to investigate the role of the genetic relatedness in the social structure of a blacktip reef shark (Carcharhinus melanopterus) population. Based on data from 156 groups of sharks, we used generalized affiliation indices to isolate social preferences from non-social associations, controlling for the contribution of sex, size, gregariousness, spatial and temporal overlap on social associations, to test for the influence of genetic relatedness on social structure. Kinship was not a predictor of associations and affiliations among sharks at the dyad or community levels as individuals tended to associate independently of the genetic relatedness among them. The lack of parental care in this species may contribute to the breakdown of family links in the population early in life, thereby preventing the formation of kin-based social networks.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Major changes in order to make the analyses more robust: 1)We restricted the network to individuals sighted more than 14 times (median number of sightings) which is now highly conservative for potential true associations 2)We changed the association index HWI to SRI because it is assumed to be less biased 3)We used Generalized Affiliation Indices (GAI) in complement to SRI in order to remove the structural components of associations (i.e. spatial and temporal overlap) and keep only true social affiliations and avoidances. 4)We tested more relatedness estimators including maximum-likelihood estimators and finally selected a new estimator (TrioML) that performed best. 5)We included analyses on the influence of sex in relationships between association patterns related and genetic relatedness. 6)We analysed the entire network without separation in mating and non-mating periods in order to keep sufficient information due to the new conservative thresholding of our analysis.