Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks

Abstract

SignificanceInteraction patterns in human communication networks are characterized by intermittency and unpredictable timing (burstiness). Simulated spreading dynamics through such networks are slower than expected. A technology for automated recording of social interactions of individual honeybees, developed by the authors, enables one to study these two phenomena in a nonhuman society. Specifically, by analyzing more than 1.2 million bee social interactions, we demonstrate that burstiness is not a human-specific interaction pattern. We furthermore show that spreading dynamics on bee social networks are faster than expected, confirming earlier theoretical predictions that burstiness and fast spreading can co-occur. We expect that these findings will inform future models of large-scale social organization, spread of disease, and information transmission.

Interdisciplinary approaches for uncovering the impacts of architecture on collective behaviour

“…Furthermore, there could be different types of interactions. In social insects, brief antennal interactions, and longer trophallactic interactions, are used for different purposes and only a few automated image analysis software can distinguish between the two [42]. In human studies, tracking hardware may capture audio so that communication can be recorded, or, at least, documented (e.g.…”

Impact of temporal network structures on the speed of consensus formation in opinion dynamics

“…As summarized in Table 2, each successive reference model listed below destroys additional temporal structure. • Permuted-time model with preserved node lifetime (PTN) [39,23]: Start times of interactions are randomly permuted if the activation and deactivation times of each node are preserved after permutation. Permutations that change node lifetimes are rejected.…”

Markerless tracking of an entire insect colony

“…A natural honey bee colony contains a high density of visually similar members, rapidly moving and occluding on the uneven and changing honeycomb surface, and whose numbers change in time. These factors present substantial difficulties for automated image analysis for which a common solution is to apply physical tags to some 8 or most 9,10 of the colony members. Barcoded tags allow for the distinct marking of a sufficiently large number of individuals to track a naturally-sized colony and have been exploited to unravel important aspects of bee communication 8,11 and information spread 10 .…”

“…These factors present substantial difficulties for automated image analysis for which a common solution is to apply physical tags to some 8 or most 9,10 of the colony members. Barcoded tags allow for the distinct marking of a sufficiently large number of individuals to track a naturally-sized colony and have been exploited to unravel important aspects of bee communication 8,11 and information spread 10 . However, the burden of manually tagging hundreds or thousands of small insects, without harm or inhibition to their motion, carries severe limitations.…”