Individual variations lead to universal and cross-species patterns of social behavior.

The duration of interaction events in a society is a fundamental measure of its collective nature and potentially reflects variability in individual behavior. Here we performed a high-throughput measurement of trophallaxis and face-to-face event durations experienced by a colony of honeybees over their entire lifetimes. The interaction time distribution is heavy-tailed, as previously reported for human face-to-face interactions. We developed a theory of pair interactions that takes into account individual variability and predicts the scaling behavior for both bee and extant human datasets. The individual variability of worker honeybees was nonzero but less than that of humans, possibly reflecting their greater genetic relatedness. Our work shows how individual differences can lead to universal patterns of behavior that transcend species and specific mechanisms for social interactions.

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

Social networks mediate the spread of information and disease. The dynamics of spreading depends, among other factors, on the distribution of times between successive contacts in the network. Heavy-tailed (bursty) time distributions are characteristic of human communication networks, including face-to-face contacts and electronic communication via mobile phone calls, email, and internet communities. Burstiness has been cited as a possible cause for slow spreading in these networks relative to a randomized reference network. However, it is not known whether burstiness is an epiphenomenon of human-specific patterns of communication. Moreover, theory predicts that fast, bursty communication networks should also exist. Here, we present a high-throughput technology for automated monitoring of social interactions of individual honeybees and the analysis of a rich and detailed dataset consisting of more than 1.2 million interactions in five honeybee colonies. We find that bees, like humans, also interact in bursts but that spreading is significantly faster than in a randomized reference network and remains so even after an experimental demographic perturbation. Thus, while burstiness may be an intrinsic property of social interactions, it does not always inhibit spreading in real-world communication networks. We anticipate that these results will inform future models of large-scale social organization and information and disease transmission, and may impact health management of threatened honeybee populations.

Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks.

Understanding the regulatory architecture of phenotypic variation is a fundamental goal in biology, but connections between gene regulatory network (GRN) activity and individual differences in behavior are poorly understood. We characterized the molecular basis of behavioral plasticity in queenless honey bee (Apis mellifera) colonies, where individuals engage in both reproductive and non-reproductive behaviors. Using high-throughput behavioral tracking, we discovered these colonies contain a continuum of phenotypes, with some individuals specialized for either egg-laying or foraging and 'generalists' that perform both. Brain gene expression and chromatin accessibility profiles were correlated with behavioral variation, with generalists intermediate in behavior and molecular profiles. Models of brain GRNs constructed for individuals revealed that transcription factor (TF) activity was highly predictive of behavior, and behavior-associated regulatory regions had more TF motifs. These results provide new insights into the important role played by brain GRN plasticity in the regulation of behavior, with implications for social evolution.

Lifetime-preserving reference models for characterizing spreading dynamics on temporal networks.

To study how a certain network feature affects processes occurring on a temporal network, one often compares properties of the original network against those of a randomized reference model that lacks the feature in question. The randomly permuted times (PT) reference model is widely used to probe how temporal features affect spreading dynamics on temporal networks. However, PT implicitly assumes that edges and nodes are continuously active during the network sampling period - an assumption that does not always hold in real networks. We systematically analyze a recently-proposed restriction of PT that preserves node lifetimes (PTN), and a similar restriction (PTE) that also preserves edge lifetimes. We use PT, PTN, and PTE to characterize spreading dynamics on (i) synthetic networks with heterogeneous edge lifespans and tunable burstiness, and (ii) four real-world networks, including two in which nodes enter and leave the network dynamically. We find that predictions of spreading speed can change considerably with the choice of reference model. Moreover, the degree of disparity in the predictions reflects the extent of node/edge turnover, highlighting the importance of using lifetime-preserving reference models when nodes or edges are not continuously present in the network.

Caste-specific differences in hindgut microbial communities of honey bees (Apis mellifera).

Host-symbiont dynamics are known to influence host phenotype, but their role in social behavior has yet to be investigated. Variation in life history across honey bee (Apis mellifera) castes may influence community composition of gut symbionts, which may in turn influence caste phenotypes. We investigated the relationship between host-symbiont dynamics and social behavior by characterizing the hindgut microbiome among distinct honey bee castes: queens, males and two types of workers, nurses and foragers. Despite a shared hive environment and mouth-to-mouth food transfer among nestmates, we detected separation among gut microbiomes of queens, workers, and males. Gut microbiomes of nurses and foragers were similar to previously characterized honey bee worker microbiomes and to each other, despite differences in diet, activity, and exposure to the external environment. Queen microbiomes were enriched for bacteria that may enhance metabolic conversion of energy from food to egg production. We propose that the two types of workers, which have the highest diversity of operational taxonomic units (OTUs) of bacteria, are central to the maintenance of the colony microbiome. Foragers may introduce new strains of bacteria to the colony from the environment and transfer them to nurses, who filter and distribute them to the rest of the colony. Our results support the idea that host-symbiont dynamics influence microbiome composition and, reciprocally, host social behavior.