Building genetic networks using relatedness information: a novel approach for the estimation of dispersal and characterization of group structure in social animals.

Natal dispersal is an important life history trait driving variation in individual fitness, and therefore, a proper understanding of the factors underlying dispersal behaviour is critical to many fields including population dynamics, behavioural ecology and conservation biology. However, individual dispersal patterns remain difficult to quantify despite many years of research using direct and indirect methods. Here, we quantify dispersal in a single intensively studied population of the cooperatively breeding chestnut-crowned babbler (Pomatostomus ruficeps) using genetic networks created from the combination of pairwise relatedness data and social networking methods and compare this to dispersal estimates from re-sighting data. This novel approach not only identifies movements between social groups within our study sites but also provides an estimation of immigration rates of individuals originating outside the study site. Both genetic and re-sighting data indicated that dispersal was strongly female biased, but the magnitude of dispersal estimates was much greater using genetic data. This suggests that many previous studies relying on mark-recapture data may have significantly underestimated dispersal. An analysis of spatial genetic structure within the sampled population also supports the idea that females are more dispersive, with females having no structure beyond the bounds of their own social group, while male genetic structure expands for 750 m from their social group. Although the genetic network approach we have used is an excellent tool for visualizing the social and genetic microstructure of social animals and identifying dispersers, our results also indicate the importance of applying them in parallel with behavioural and life history data.