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1. Best practices for instrumenting honey bees

   https://doi.org/10.1038/s41598-022-16168-5  

   Koenig, Phoebe A.; Petersen, Kirstin H. (July 2022, Scientific Reports)

   Abstract Honey bees are vital pollinators and can be used to monitor the landscape. Consequently, interest in mounting technologies onto bees to track foraging behaviors is increasing. The barrier to entry is steep, in part because the methodology for fastening tags to bees, and the success rates, are often missing from publications. We tested six factors suspected to influence the presence and tag retention rates of nurse honey bees after their introduction to hives, and followed bees until foraging age. We also compared reintroducing foragers to their maternal colony using the best method for nurse bees to releasing them in front of their maternal hive and allowing them to fly back unaided. Nurses were most likely to be present in the hive with their tag still attached when introduced using an introduction cage at night. Glue type was important, but may further be influenced by tag material. Foragers were most likely to be present with a tag attached if released in front of their colony. Preparation and introduction techniques influence the likelihood of tagged honey bee survival and of the tags remaining attached, which should be considered when executing honey bee tagging and tracking experiments. 

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2. Automated Video Monitoring of Unmarked and Marked Honey Bees at the Hive Entrance

   https://doi.org/10.3389/fcomp.2021.769338  

   Rodriguez, Iván F.; Chan, Jeffrey; Alvarez Rios, Manuel; Branson, Kristin; Agosto-Rivera, José L.; Giray, Tugrul; Mégret, Rémi (February 2022, Frontiers in Computer Science)

   We present a novel system for the automatic video monitoring of honey bee foraging activity at the hive entrance. This monitoring system is built upon convolutional neural networks that perform multiple animal pose estimation without the need for marking. This precise detection of honey bee body parts is a key element of the system to provide detection of entrance and exit events at the entrance of the hive including accurate pollen detection. A detailed evaluation of the quality of the detection and a study of the effect of the parameters are presented. The complete system also integrates identification of barcode marked bees, which enables the monitoring at both aggregate and individual levels. The results obtained on multiple days of video recordings show the applicability of the approach for large-scale deployment. This is an important step forward for the understanding of complex behaviors exhibited by honey bees and the automatic assessment of colony health. 

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3. Genomic Signatures of Honey Bee Association in an Acetic Acid Symbiont

   https://doi.org/10.1093/gbe/evaa183  

   Smith, Eric A; Newton, Irene L (October 2020, Genome Biology and Evolution)

   Daniel, Sloan (Ed.)

   Abstract Recent declines in the health of the honey bee have startled researchers and lay people alike as honey bees are agriculture’s most important pollinator. Honey bees are important pollinators of many major crops and add billions of dollars annually to the US economy through their services. One factor that may influence colony health is the microbial community. Indeed, the honey bee worker digestive tract harbors a characteristic community of bee-specific microbes, and the composition of this community is known to impact honey bee health. However, the honey bee is a superorganism, a colony of eusocial insects with overlapping generations where nestmates cooperate, building a hive, gathering and storing food, and raising brood. In contrast to what is known regarding the honey bee worker gut microbiome, less is known of the microbes associated with developing brood, with food stores, and with the rest of the built hive environment. More recently, the microbe Bombella apis was identified as associated with nectar, with developing larvae, and with honey bee queens. This bacterium is related to flower-associated microbes such as Saccharibacter floricola and other species in the genus Saccharibacter, and initial phylogenetic analyses placed it as sister to these environmental bacteria. Here, we used comparative genomics of multiple honey bee-associated strains and the nectar-associated Saccharibacter to identify genomic changes that may be associated with the ecological transition to honey bee association. We identified several genomic differences in the honey bee-associated strains, including a complete CRISPR/Cas system. Many of the changes we note here are predicted to confer upon Bombella the ability to survive in royal jelly and defend themselves against mobile elements, including phages. Our results are a first step toward identifying potential function of this microbe in the honey bee superorganism. 

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4. Genomic regions influencing aggressive behavior in honey bees are defined by colony allele frequencies

   https://doi.org/10.1073/pnas.1922927117  

   Avalos, Arián; Fang, Miaoquan; Pan, Hailin; Ramirez Lluch, Aixa; Lipka, Alexander E.; Zhao, Sihai Dave; Giray, Tugrul; Robinson, Gene E.; Zhang, Guojie; Hudson, Matthew E. (July 2020, Proceedings of the National Academy of Sciences)

   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. 

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5. System design for inferring colony-level pollination activity through miniature bee-mounted sensors

   https://doi.org/10.1038/s41598-021-82537-1  

   Abdel-Raziq, Haron M.; Palmer, Daniel M.; Koenig, Phoebe A.; Molnar, Alyosha C.; Petersen, Kirstin H. (December 2021, Scientific Reports)

   Abstract In digital agriculture, large-scale data acquisition and analysis can improve farm management by allowing growers to constantly monitor the state of a field. Deploying large autonomous robot teams to navigate and monitor cluttered environments, however, is difficult and costly. Here, we present methods that would allow us to leverage managed colonies of honey bees equipped with miniature flight recorders to monitor orchard pollination activity. Tracking honey bee flights can inform estimates of crop pollination, allowing growers to improve yield and resource allocation. Honey bees are adept at maneuvering complex environments and collectively pool information about nectar and pollen sources through thousands of daily flights. Additionally, colonies are present in orchards before and during bloom for many crops, as growers often rent hives to ensure successful pollination. We characterize existing Angle-Sensitive Pixels (ASPs) for use in flight recorders and calculate memory and resolution trade-offs. We further integrate ASP data into a colony foraging simulator and show how large numbers of flights refine system accuracy, using methods from robotic mapping literature. Our results indicate promising potential for such agricultural monitoring, where we leverage the superiority of social insects to sense the physical world, while providing data acquisition on par with explicitly engineered systems. 

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