One of the hardest problems in studying animal behaviour is to quantify patterns of social interaction at the group level. Recent technological developments in global positioning system (GPS) devices have opened up new avenues for locating animals with unprecedented spatial and temporal resolution. Likewise, advances in computing power have enabled new levels of data analyses with complex mathematical models to address unresolved problems in animal behaviour, such as the nature of group geometry and the impact of group-level interactions on individuals. Here, we present an information theory-based tool for the analysis of group behaviour. We illustrate its affordances with GPS data collected from a freely interacting pack of 15 Siberian huskies (Canis lupus familiaris). We found that individual freedom in movement decisions was limited to about 4%, while a subject’s location could be predicted with 96% median accuracy by the locations of other group members. Dominant individuals were less affected by other individuals’ locations than subordinate ones, and same-sex individuals influenced each other more strongly than opposite-sex individuals. We also found that kinship relationships increased the mutual dependencies of individuals. Moreover, the network stability of the pack deteriorated with an upcoming feeding event. Together, we conclude that information theory-based approaches, coupled with state-of-the-art bio-logging technology, provide a powerful tool for future studies of animal social interactions beyond the dyadic level.
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
- Experimental and Cognitive Psychology