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 wide-spread in many animal societies. It represents active information transfer that allows a given social species to reach collective decisions in the presence of limited information. Although leading-following behavior has received much scientific interest in the form of field studies, there is a need for systematic methods to quantify and study the individual contributions in this information transfer, which would eventually lead us to hypotheses about the individual mechanisms underlying this behaviour. In this paper we propose a general methodology that allows us to (a) infer individual leading-following behaviour from discrete observational data and (b) quantify individual influence based on methods from social network analysis. To demonstrate our methodology, we analyze longitudinal data of the roosting behavior of two different colonies of Bechstein’s bats in different years. Regarding (a) we show how the inference of leading-following events can be calibrated from data making it a general approach when only discrete observations are available. This allows us to address (b) by constructing social networks in which nodes represent individual bats and directed and weighted links—the leading-following events. We then show how social network theory can be used to define and quantify individual influence in a way that reflects the dynamics of the specific social network. We find that individuals can be consistently ranked regarding their influence in the information transfer. Moreover, we identify a small set of individuals that play a central role in leading other bats to roosts. In the case of Bechstein’s bats this finding can direct future studies on the individual-level mechanisms that result in such collective pattern. More generally, we posit that our data-driven methodology can be used to quantify leading-following behavior and individual impact in other animal systems, solely based on discrete observational data.