Quantifying individual influence in leading-following behavior of Bechstein's bats

Abstract

Leading-following behavior as a way of transferring information about the location of resources is widespread in different animal societies. However, it cannot always be observed directly. Here, we develop a general method to infer leading-following events from observational data if only the discrete appearance of individuals is recorded. Our method further allows to distinguish such events from local enhancement at the resource, such as swarming behavior in case of bats, which is another widespread way of transferring information among animals. To test our methodology, we analyze longitudinal data about the roosting behavior of Bechstein's bats from two different colonies and different years. The detection of leading-following events allows us, in a second step, to construct social networks in which nodes represent individual bats and directed and weighted links the leading-following events. We analyze the topology of these networks on the level of the colony, to see whether all individuals participate in leading-following behavior. Further, based on the leading-following network we measure the importance of individuals in this leading-following behavior by calculating different centrality measures. We find that individuals can be consistently ranked regarding their influence on others. Moreover, we identify a small set of individuals that play a central role in leading other bats to roosts. Our methodology can be used to understand the leading-following behavior and the individual impact of group members on the spread of information in animal groups in general.