Abstract
Social interactions are a significant factor that influence the decision-making of species ranging from humans to bacteria. In the context of animal migration, social interactions may lead to improved decision-making, greater ability to respond to environmental cues, and the cultural transmission of optimal routes. Despite their significance, the precise nature of social interactions in migrating species remains largely unknown. Here we deploy unmanned aerial systems to collect aerial footage of caribou as they undertake their migration from Victoria Island to mainland Canada. Through a Bayesian analysis of trajectories we reveal the fine-scale interaction rules of migrating caribou and show they are attracted to one another and copy directional choices of neighbours, but do not interact through clearly defined metric or topological interaction ranges. By explicitly considering the role of social information on movement decisions we construct a map of near neighbour influence that quantifies the nature of information flow in these herds. These results will inform more realistic, mechanism-based models of migration in caribou and other social ungulates, leading to better predictions of spatial use patterns and responses to changing environmental conditions. Moreover, we anticipate that the protocol we developed here will be broadly applicable to study social behaviour in a wide range of migratory and non-migratory taxa.This article is part of the theme issue ‘Collective movement ecology’.
Highlights
Migrating species play a keystone role in the functioning of many ecosystems; they transport nutrients, connect disparate communities and act as both major resource consumers and prey for resident species [1]
Despite the fact that many species migrate in groups [3,4], most studies of animal migration neglect the potential role of social interactions on movement decisions
Make greater use of the directional cues provided by neighbours. These results are consistent with prior expectations about how different individuals are influenced by social interaction; they reveal that the framework we employ is able to detect and quantify this variation
Summary
Migrating species play a keystone role in the functioning of many ecosystems; they transport nutrients, connect disparate communities and act as both major resource consumers and prey for resident species [1]. Despite the fact that many species migrate in groups [3,4], most studies of animal migration neglect the potential role of social interactions on movement decisions. While the potential importance of interactions in the decision-making of animal groups is recognized, the barrier to quantifying these interactions among wild, free-ranging animals is the difficulty in obtaining simultaneous, fine-scale trajectories for every animal in a group [10] Such trajectories are more obtained for small-bodied animals in a laboratory setting, and have been used to infer rules of social interactions in several species [11,12,13]. Recent advances in unmanned aerial systems (UAS) [30 –33] and automated computer vision [34,35] offer a complementary technology to the use of individual telemetry with the potential to deliver the simultaneous trajectories needed to infer interaction rules of wild populations in a variety of settings [10]. A change in the rate of decay in correlation is apparent after 2 s so we select this interval as the time scale for our analysis; our results were not sensitive to this particular value (see electronic supplementary material, figure S4)
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