Dancing on the edge: honey bee recruitment networks are sparse and affected by individuality in waggle dance behavior - McHenry, Laura C.; Schürch, Roger; Johnson, Lindsay E.; Ohlinger, Bradley D.; Couvillon, Margaret J. (Frontiers in Bee Science, 2025)
Social network analysis is increasingly and fruitfully applied to study the collective structure and function of animal societies across space and time. Honey bees (Apis mellifera L.) are a particularly tractable model system that is rich in social relationships and dynamics. Despite the rich body of literature describing the social life of the honey bee, including the famous waggle dance by which foragers recruit nestmates to profitable resources, relatively little is known about the networks that arise from waggle dance communication. Here we conducted a field experiment with fully-marked experimental colonies (N = 2 colonies, 3,000 bees each) to characterize the honey bee waggle dance recruitment network structure and function. Particularly, we studied network density, burstiness in waggle dance bouts, and the effect of individuality in waggle dance communication behavior on network structure. We simulated a maximally-efficient honey bee recruitment network using a deterministic susceptible-infected model. Then we used this simulated network as an upper bound for network density to calculate the proportion of successful recruitment events in observed networks compared to the simulated maximal network. Next, we characterized the burstiness, or temporal distribution, of waggle dance bouts. Finally, we tested whether inter-bee differences, or individuality, in waggle dance communication affected the recruitment network structure. We found that (1) real recruitment networks are sparse, with each individual recruiting up to 3.5% as many nestmates as predicted by the simulated maximal network; (2) individual bees danced steadily, not in bursts, and (3) that individuality in waggle dance calibrations was positively associated with successful recruitment and thus the propagation of the recruitment network (p = 0.008). Our results offer the first empirical and biologically-informed descriptive statistics for honey bee waggle dance networks and may be informative in the parameterization of bio-inspired computing models.