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Gut microbiomes are increasingly recognized for mediating diverse biological aspects of their hosts, including complex behavioral phenotypes. Although many studies have reported that experimental disruptions to the gut microbial community result in atypical host behavior, studies that address how gut microbes contribute to adaptive behavioral trait variation are rare. Eusocial insects represent a powerful model to test this, because of their simple gut microbiota and complex division of labor characterized by colony-level variation in behavioral phenotypes. Although previous studies report correlational differences in gut microbial community associated with division of labor, here, we provide evidence that gut microbes play a causal role in defining differences in foraging behavior between European honey bees (Apis mellifera). We found that gut microbial community structure differed between hive-based nurse bees and bees that leave the hive to forage for floral resources. These differences were associated with variation in the abundance of individual microbes, including Bifidobacterium asteroides, Bombilactobacillus mellis, and Lactobacillus melliventris. Manipulations of colony demography and individual foraging experience suggested that differences in gut microbial community composition were associated with task experience. Moreover, single-microbe inoculations with B. asteroides, B. mellis, and L. melliventris caused effects on foraging intensity. These results demonstrate that gut microbes contribute to division of labor in a social insect, and support a role of gut microbes in modulating host behavioral trait variation., (© The Author(s) 2024. Published by Oxford University Press on behalf of the International Society for Microbial Ecology.)

Barcode-based tracking of individuals is revolutionizing animal behavior studies, but further progress hinges on whether in addition to determining an individual's location, specific behaviors can be identified and monitored. We achieve this goal using information from the barcodes to identify tightly bounded image regions that potentially show the behavior of interest. These image regions are then analyzed with convolutional neural networks to verify that the behavior occurred. When applied to a challenging test case, detecting social liquid transfer (trophallaxis) in the honey bee hive, this approach yielded a 67% higher sensitivity and an 11% lower error rate than the best detector for honey bee trophallaxis so far. We were furthermore able to automatically detect whether a bee donates or receives liquid, which previously required manual observations. By applying our trophallaxis detector to recordings from three honey bee colonies and performing simulations, we discovered that liquid exchanges among bees generate two distinct social networks with different transmission capabilities. Finally, we demonstrate that our approach generalizes to detecting other specific behaviors. We envision that its broad application will enable automatic, high-resolution behavioral studies that address a broad range of previously intractable questions in evolutionary biology, ethology, neuroscience, and molecular biology., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Honey bee queens show extreme fecundity, commonly laying more than a thousand eggs in a single day. It has proven challenging to study the temporal organization of egg-laying behavior because queens are typically active around the clock in the dark cavity of a densely populated nest. To contend with this challenge, we developed two novel methods allowing detailed monitoring of queen activity and egg laying. We first adapted a high-resolution, continuous, tracking system allowing to track the position of barcode-tagged queens in observation hives with colonies foraging outside. We found that the queen is active ~96% of the day with typically no diurnal rhythm. Next, we developed a new laboratory procedure to monitor egg laying at single egg resolution under different light regimes. We found that under constant darkness (DD) and temperature conditions, queens laid eggs with no circadian rhythms. Queen fecundity was severely reduced under constant light (LL). Under a 12:12 illumination regime, queen fecundity was comparable to under constant darkness, with a higher number of eggs during the light phase. These daily rhythms in egg laying continued when these queens were released to DD conditions, suggesting that egg-laying rhythms are influenced by endogenous circadian clocks. These results suggest that honey bee queens are active and lay eggs around the clock with no diurnal rhythms. Light has complex influences on these behaviors, but more studies are needed to determine whether these effects reflect the influence of light directly on the queen or indirectly by affecting workers that interact with the queen.

Adverse social experience affects social structure by modifying the behavior of individuals, but the relationship between an individual's behavioral state and its response to adversity is poorly understood. We leveraged naturally occurring division of labor in honey bees and studied the biological embedding of environmental threat using laboratory assays and automated behavioral tracking of whole colonies. Guard bees showed low intrinsic levels of sociability compared with foragers and nurse bees, but large increases in sociability following exposure to a threat. Threat experience also modified the expression of caregiving-related genes in a brain region called the mushroom bodies. These results demonstrate that the biological embedding of environmental experience depends on an individual's societal role and, in turn, affects its future sociability., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2022. Published by The Company of Biologists Ltd.)