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.

The extraordinary olfactory capabilities in detection and rescue dogs are well-known. However, the olfactory performance varies by breed and search environment (Jezierski et al., 2014), as well as by the quantity of training (Horowitz et al., 2013). While detection of an olfactory cue inherently demands a judgment regarding the presence or absence of a cue at a given location, olfactory discrimination requires an assessment of quantity, a task demanding more attention and, hence, decreasing reliability as an informational source (Horowitz et al., 2013). This study aims at gaining more clarity on detection and discrimination of olfactory cues in untrained dogs and in a variety of dog breeds. Using a two-alternative forced choice (2AFC) paradigm, we assessed olfactory detection scores by presenting a varied quantity of food reward under one or the other hidden cup, and discrimination scores by presenting two varied quantities of food reward under both hidden cups. We found relatively reliable detection performances across all breeds and limited discrimination abilities, modulated by breed. We discuss our findings in relation to the cognitive demands imposed by the tasks and the cephalic index of the dog breeds.

AbstractOne 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, a pattern mediated by dominance, kin relations, sex, the relative time of the day and external events, such as feeding. 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.

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