Data from: 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. Not only does this novel approach identify 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 meters from their social group. Although the genetic network approach we have used is an excellent tool for visualising 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.

出生扩散（natal dispersal）是驱动个体适合度变异的重要生活史特征，因此，明晰扩散行为背后的影响因素，对种群动态、行为生态学、保护生物学等诸多研究领域均具有关键意义。然而，尽管已有多年采用直接与间接研究方法开展的相关探索，个体扩散模式仍难以被精准量化。本研究以长期密集研究的合作繁殖红顶鹛（Pomatostomus ruficeps）种群为研究对象，结合两两亲缘关系数据与社会网络分析方法构建遗传网络，以此量化种群内的扩散情况，并将其与基于重目击数据得到的扩散估计结果进行对比。该创新性方法不仅可识别研究样地内不同社会群体间的个体移动，还能估算源自研究样地外部的个体迁入率。遗传数据与重目击数据均显示，扩散存在显著的雌性偏倚，但基于遗传数据得到的扩散程度估算值远高于后者。这表明诸多以往依赖标记-重捕数据的研究，可能显著低估了扩散水平。对采样种群内空间遗传结构的分析同样支持雌性扩散能力更强这一结论：雌性个体的遗传结构未超出其所在社会群体的范围，而雄性个体的遗传结构可延伸至距离其社会群体750米的区域。尽管本研究采用的遗传网络方法，是可视化群居动物社会与遗传微观结构、识别扩散个体的优质工具，但研究结果也表明，将该方法与行为学及生活史数据结合应用的重要性。