Validation of an automated data collection method for quantifying social networks in collective behaviours

The social network of preferences among group members can affect the distribution and consequences of collective behaviours. However, the behavioural contexts and taxa in which social network structure has been described are still limited because such studies require extensive data. Here, we highlight the use of an automated passive integrated transponder (PIT)-tag monitoring system for social network analyses and do so in a novel context-nestling provisioning in an avian cooperative breeder, for which direct observation of social behaviours is difficult. First, we used observers and cameras to arrive at a suitable metric of nest visit synchrony in the PIT-tag data. Second, we validated the use of this metric for social network analyses using internal nest video cameras. Third, we used hierarchical regression models with 'sociality' parameter to investigate structure of networks collected from multiple groups. Use of PIT tags led to nest visitation duration and frequency being obtained with a high degree of accuracy for all group members, except for the breeding female for whom accurate estimations required the use of a video camera due to her high variability in visitation time. The PIT-tag dataset uncovered significant variability in social network structure. Our results highlight the importance of combining complementary observation methods when conducting social network analyses of wild animals. Our methods can also be generalised to multiple contexts in social systems wherever repeated encounters with other individuals in closed space have ecological implications.