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Honey bees (Apis melliferaL.) are the primary commercial pollinators across the world. The subspeciesA. m. scutellataoriginated in Africa and was introduced to the Americas in 1956. For the last 60 years, it hybridized successfully with European subspecies, previous residents in the area. The result of this hybridization was called Africanized honey bee (AHB). AHB has spread since then, arriving to Puerto Rico (PR) in 1994. The honey bee population on the island acquired a mosaic of features from AHB or the European honey bee (EHB). AHB in Puerto Rico shows a major distinctive characteristic, docile behavior, and is called gentle Africanized honey bees (gAHB). We used 917 SNPs to examine the population structure, genetic differentiation, origin, and history of range expansion and colonization of gAHB in PR. We compared gAHB to populations that span the current distribution ofA. melliferaworldwide. The gAHB population is shown to be a single population that differs genetically from the examined populations of AHB. Texas and PR groups are the closest genetically. Our results support the hypothesis that the Texas AHB population is the source of gAHB in Puerto Rico.

 

ABSTRACT Visual learning is vital to the behavioral ecology of the Western honey bee (Apis mellifera). Honey bee workers forage for floral resources, a behavior that requires the learning and long-term memory of visual landmarks, but how these memories are mapped to the brain remains poorly understood. To address this gap in our understanding, we collected bees that successfully learned visual associations in a conditioned aversion paradigm and compared gene expression correlates of memory formation in the mushroom bodies, a higher-order sensory integration center classically thought to contribute to learning, as well as the optic lobes, the primary visual neuropil responsible for sensory transduction of visual information. We quantified expression of CREB and CaMKII, two classical genetic markers of learning, and fen-1, a gene specifically associated with punishment learning in vertebrates. As expected, we found substantial involvement of the mushroom bodies for all three markers but additionally report the involvement of the optic lobes across a similar time course. Our findings imply the molecular involvement of a sensory neuropil during visual associative learning parallel to a higher-order brain region, furthering our understanding of how a tiny brain processes environmental signals. 

Males in Hymenopteran societies are understudied in many aspects and it is assumed that they only have a reproductive function. We studied the time budget of male honey bees, drones, using multiple methods. Changes in the activities of animals provide important information on biological clocks and their health. Yet, in nature, these changes are subtle and often unobservable without the development and use of modern technology. During the spring and summer mating season, drones emerge from the hive, perform orientation flights, and search for drone congregation areas for mating. This search may lead drones to return to their colony, drift to other colonies (vectoring diseases and parasites), or simply get lost to predation. In a low percentage of cases, the search is successful, and drones mate and die. Our objective was to describe the activity of Apis mellifera drones during the mating season in Northwestern Argentina using three methods: direct observation, video recording, and radio frequency identification (RFID). The use of RFID tagging allows the tracking of a bee for 24 h but does not reveal the detailed activity of drones. We quantified the average number of drones’ departure and arrival flights and the time outside the hive. All three methods confirmed that drones were mostly active in the afternoon. We found no differences in results between those obtained by direct observation and by video recording. RFID technology enabled us to discover previously unknown drone behavior such as activity at dawn and during the morning. We also discovered that drones may stay inside the hive for many days, even after initiation of search flights (up to four days). Likewise, we observed drones to leave the hive for several days to return later (up to three days). The three methods were complementary and should be considered for the study of bee drone activity, which may be associated with the diverse factors influencing hive health. 

Recurrent honey bee losses make it critical to understand the impact of human interventions, such as antibiotics use in apiculture. Antibiotics are used to prevent or treat bacterial infections in colonies. However, little is known about their effects on honey bee development. We studied the effect of two commercial beekeeping antibiotics on the bee physiology and behavior throughout development. Our results show that antibiotic treatments have an effect on amount of lipids and rate of behavioral development. Lipid amount in treated bees was higher than those not treated. Also, the timing of antibiotic treatment had distinct effects for the age of onset of behaviors starting with cleaning, then nursing and lastly foraging. Bees treated during larva-pupa stages demonstrated an accelerated behavioral development and loss of lipids, while bees treated from larva to adulthood had a delay in behavioral development and loss of lipids. The effects were shared across the two antibiotics tested, TerramycinR (oxytetracycline) and TylanR (tylosin tartrate). These results on effects of antibiotic treatments suggest a role of microbiota in the interaction between the fat body and brain that is important for honey bee behavioral development. 

For social animals, the genotypes of group members affect the social environment, and thus individual behavior, often indirectly. We used genome-wide association studies (GWAS) to determine the influence of individual vs. group genotypes on aggression in honey bees. Aggression in honey bees arises from the coordinated actions of colony members, primarily nonreproductive “soldier” bees, and thus, experiences evolutionary selection at the colony level. Here, we show that individual behavior is influenced by colony environment, which in turn, is shaped by allele frequency within colonies. Using a population with a range of aggression, we sequenced individual whole genomes and looked for genotype–behavior associations within colonies in a common environment. There were no significant correlations between individual aggression and specific alleles. By contrast, we found strong correlations between colony aggression and the frequencies of specific alleles within colonies, despite a small number of colonies. Associations at the colony level were highly significant and were very similar among both soldiers and foragers, but they covaried with one another. One strongly significant association peak, containing an ortholog of the Drosophila sensory gene dpr4 on linkage group (chromosome) 7, showed strong signals of both selection and admixture during the evolution of gentleness in a honey bee population. We thus found links between colony genetics and group behavior and also, molecular evidence for group-level selection, acting at the colony level. We conclude that group genetics dominates individual genetics in determining the fatal decision of honey bees to sting. 

The LabelBee system is a web application designed to facilitate the collection, annotation and analysis of large amounts of honeybee behavior data from video monitoring. It is developed as part of NSF BIGDATA project “Large-scale multi-parameter analysis of honeybee behavior in their natural habitat”, where we analyze continuous video of the entrance of bee colonies. Due to the large volume of data and its complexity, LabelBee provides advanced Artificial Intelligence and visualization capabilities to enable the construction of good quality datasets necessary for the discovery of complex behavior patterns. It integrates several levels of information: raw video, honeybee positions, decoded tags, individual trajectories and behavior events (entrance/exit, presence of pollen, fanning, etc.). This integration enables the combination of manual and automatic processing by the biologist end-users, who also share and correct their annotation through a centralized server. These annotations are used by the Computer Scientists to create new automatic models, and improve the quality of the automatic modules. The data constructed by this semi-automatized approach can then be exported for the analytic part, which is taking place on the same server using Jupyter notebooks for the extraction and exploration of behavior patterns.