Dynamic interactions among badgers: implications for sociality and disease transmission

1 Direct interactions between individuals play an important part in the sociality of group-living animals, their mating system and disease transmission. Here, we devise a methodology to quantify relative rates of proximity interaction from radio-tracking data and highlight potential asymmetries within the contact network of a moderate-density badger population in the north-east of England. 2 We analysed radio-tracking data from four contiguous social groups, collected over a 3-year period. Dynamic interaction analysis of badger dyads was used to assess the movement of individuals in relation to the movement of others, both within and between social groups. Dyads were assessed with regard to season, sex, age and sett use pattern of the badgers involved. 3 Intragroup separation distances were significantly shorter than intergroup separation distances, and interactions between groups were rare. Within groups, individuals interacted with each other more often than expected, and interaction patterns varied significantly with season and sett use pattern. Non-mover dyads (using the main sett for day-resting on > 50% of occasions) interacted more frequently than mover dyads (using an outlier sett for day-resting on > 50% of occasions) or mover–non-mover dyads. Interactions between group members occurred most frequently in winter. 4 Of close intragroup interactions (< 50 m separation distance), 88·6% were associated with a main sett and only 4·4% with outlier setts. Non-mover dyads and non-mover–mover dyads interacted significantly more often at the main sett than mover-only dyads. These results highlight the importance of the main sett to badger sociality and support the suggestion that badger social groups are comprised of different subgroups, in our case based on differential sett use patterns. 5 Asymmetries in contact structure within a population will affect the way in which diseases are transmitted through a social network. Assessment of these networks is essential for understanding the persistence and spread of disease within populations which do not mix freely or which exhibit heterogeneities in their spatial or social behaviour.