Individual Honeybees Research Articles

The Improving Effects of Probiotic-Added Pollen Substitute Diets on the Gut Microbiota and Individual Health of Honey Bee (Apis mellifera L.).

Honey bee (Apis mellifera L.) health is crucial for honey bee products and effective pollination, and it is closely associated with gut bacteria. Various factors such as reduced habitat, temperature, disease, and diet affect the health of honey bees by disturbing the homeostasis of the gut microbiota. In this study, high-throughput 16S rRNA gene sequencing was used to analyze the gut microbiota of honey bees subjected to seven diets over 5 days. Lactobacillus dominated the microbiota in all diets. Cage experiments (consumption, head protein content, and vitellogenin gene expression level) were conducted to verify the effect of the diet. Through a heatmap, the Diet2 (probiotic-supplemented) group was clustered together with the Beebread and honey group, showing high consumption (177.50 ± 26.16 mg/bee), moderately higher survival duration (29.00 ± 2.83 days), protein content in the head (312.62 ± 28.71 µg/mL), and diet digestibility (48.41 ± 1.90%). Additionally, we analyzed the correlation between gut microbiota and health-related indicators in honey bees fed each diet. Based on the overall results, we identified that probiotic-supplemented diets increased gut microbiota diversity and positively affected the overall health of individual honey bees.

Behavioral and genetic correlates of heterogeneity in learning performance in individual honeybees, Apis mellifera.

Learning an olfactory discrimination task leads to heterogeneous results in honeybees with some bees performing very well and others at low rates. Here we investigated this behavioral heterogeneity and asked whether it was associated with particular gene expression patterns in the bee's brain. Bees were individually conditioned using a sequential conditioning protocol involving several phases of olfactory learning and retention tests. A cumulative score was used to differentiate the tested bees into high and low performers. The rate of CS+ odor learning was found to correlate most strongly with a cumulative performance score extracted from all learning and retention tests. Microarray analysis of gene expression in the mushroom body area of the brains of these bees identified a number of differentially expressed genes between high and low performers. These genes are associated with diverse biological functions, such as neurotransmission, memory formation, cargo trafficking and development.

Eggs sampling as an effective tool for identifying the incidence of viruses in honey bees involved in artificial queen rearing

The Carniolan honey bee (Apis mellifera carnica) plays an essential role in crop pollination, environment diversity, and the production of honey bee products. However, the health of individual honey bees and their colonies is under pressure due to multiple stressors, including viruses as a significant threat to bees. Monitoring various virus infections could be a crucial selection tool during queen rearing. In the present study, samples from all developmental stages (eggs, larvae, pupae, and queens) were screened for the incidence of seven viruses during queen rearing in Slovenia. The screening of a total of 108 samples from five queen breeders was performed by the RT-qPCR assays. The results showed that the highest incidence was observed for black queen cell virus (BQCV), Lake Sinai virus 3 (LSV3), deformed wing virus B (DWV-B), and sacbrood virus (SBV). The highest viral load was detected in queens (6.07 log10 copies/queen) and larvae (5.50 log10 copies/larva) for BQCV, followed by SBV in larvae (5.47 log10 copies/larva). When comparing all the honey bee developmental stages, the eggs exhibited general screening for virus incidence and load in queen mother colonies. The results suggest that analyzing eggs is a good indicator of resilience to virus infection during queen development.

Individual honey bee tracking in a beehive environment using deep learning and Kalman filter

The honey bee is the most essential pollinator and a key contributor to the natural ecosystem. There are numerous ways for thousands of bees in a hive to communicate with one another. Individual trajectories and social interactions are thus complex behavioral features that can provide valuable information for an ecological study. To study honey bee behavior, the key challenges that have resulted from unreliable studies include complexity (high density of similar objects, small objects, and occlusion), the variety of background scenes, the dynamism of individual bee movements, and the similarity between the bee body and the background in the beehive. This study investigated the tracking of individual bees in a beehive environment using a deep learning approach and a Kalman filter. Detection of multiple bees and individual object segmentation were performed using Mask R-CNN with a ResNet-101 backbone network. Subsequently, the Kalman filter was employed for tracking multiple bees by tracking the body of each bee across a sequence of image frames. Three metrics were used to assess the proposed framework: mean average precision (mAP) for multiple-object detection and segmentation tasks, CLEAR MOT for multiple object tracking tasks, and MOTS for multiple object tracking and segmentation tasks. For CLEAR MOT and MOTS metrics, accuracy (MOTA and MOTSA) and precision (MOTP and MOTSP) are considered. By employing videos from a custom-designed observation beehive, recorded at a frame rate of 30 frames per second (fps) and utilizing a continuous frame rate of 10 fps as input data, our system displayed impressive performance. It yielded satisfactory outcomes for tasks involving segmentation and tracking of multiple instances of bee behavior. For the multiple-object segmentation task based on Mask R-CNN, we achieved a 0.85 mAP. For the multiple-object-tracking task with the Kalman filter, we achieved 77.48% MOTA, 79.79% MOTSP, and 79.56% recall. For the overall system for multiple-object tracking and segmentation tasks, we achieved 77.00% MOTSA, 75.60% MOTSP, and 80.30% recall.

A tale of two parasites: Responses of honey bees infected with Nosema ceranae and Lotmaria passim

Nosema ceranae and Lotmaria passim are two commonly encountered digestive tract parasites of the honey bee that have been associated with colony losses in Canada, the United States, and Europe. Though honey bees can be co-infected with these parasites, we still lack basic information regarding how they impact bee health at the individual and colony level. Using locally-isolated parasite strains, we investigated the effect of single and co-infections of these parasites on individual honey bee survival, and their responsiveness to sucrose. Results showed that a single N. ceranae infection is more virulent than both single L. passim infections and co-infections. Honey bees singly infected with N. ceranae reached < 50% survival eight days earlier than those inoculated with L. passim alone, and four days earlier than those inoculated with both parasites. Honey bees infected with either one, or both, parasites had increased responsiveness to sucrose compared to uninfected bees, which could correspond to higher levels of hunger and increased energetic stress. Together, these findings suggest that N. ceranae and L. passim pose threats to bee health, and that the beekeeping industry should monitor for both parasites in an effort correlate pathogen status with changes in colony-level productivity and survival.

Detection of deformed wing virus in the greenhouse for possible horizontal transmission of virus in honey bee colony

AbstractThe honey bee Apis mellifera L. is a crucial insect in the agricultural industry and natural ecosystem by being a major pollinator. Nevertheless, honey bee population has been recently facing a decline. Among the several factors responsible for this decline, deformed wing virus (DWV) is considered a primary cause that negatively affects honey bee health. DWV is a cosmopolitan honey bee pathogen and causes morphological disadvantages in individual honey bees and colony collapse. Regarding the horizontal transmission of DWV, in addition to Varroa destructor, a well‐known major vector of DWV, flowers have recently been implied as a transmission route. Therefore, in this study, we detected DWV from various substances, including flowers, honey bee feces, pupa, larva, nurse bee, surface of nurse bee, pollen collected by forager bee, and forager bee samples in four strawberry greenhouses, which could suggest the potential for the horizontal transmission of DWV in the semi‐field condition. We also detected DWV in pollen collected by DWV‐negative forager bees, implying that flowers can serve as a potential source of virus infection. These findings suggest that the surrounding environment such as shared floral sources affects the spread of DWV.

Variability of Vairimorpha (=Nosema) ceranae infection level in individual honey bees and its implications on the pooled sample size

Nosemosis is caused by two microsporidian species of the genus recently renamed Vairimorpha (=Nosema) that parasitize the midgut of Apis mellifera. Usually, pooled samples of 60 bees are used for diagnosis. Reported differences in the efficiency of treatments against Vairimorpha sp. led us to wonder if the sample size may be one of the causes of these unpredictable results. This study focuses on the distribution of the spore load of V. ceranae among forager bees within a colony using statistical approaches in relation to sample size. Spore counts per bee were used to describe the spore distribution and to test whether they fit different statistical distributions. Standard errors were calculated and two-sided 95% confidence intervals were constructed for the spore count means, using standard (Student’s t) and non-parametric bootstrap methods (Percentil and bias-corrected and accelerated-BCa). Bees with a high degree of infection appear in low percentage but are very influential on the mean value. Data showed a poor fit to all of the distributions evaluated. To obtain a relative standard error below 10% of the mean, pooled samples should have more than 1,500 bees. The BCa interval showed better coverage than others being acceptable starting at 100 data of individual foragers. Results show that, when using the traditional sample size (pooled samples n = 60), the accuracy for estimating the mean spore load is low in V. ceranae infected hives, regardless of the statistical method applied. Thus, this work could justify some erratic results reported for treatments with commercial products.

Tyramine and its Amtyr1 receptor modulate attention in honey bees (Apis mellifera).

Animals must learn to ignore stimuli that are irrelevant to survival and attend to ones that enhance survival. When a stimulus regularly fails to be associated with an important consequence, subsequent excitatory learning about that stimulus can be delayed, which is a form of nonassociative conditioning called 'latent inhibition'. Honey bees show latent inhibition toward an odor they have experienced without association with food reinforcement. Moreover, individual honey bees from the same colony differ in the degree to which they show latent inhibition, and these individual differences have a genetic basis. To investigate the mechanisms that underly individual differences in latent inhibition, we selected two honey bee lines for high and low latent inhibition, respectively. We crossed those lines and mapped a Quantitative Trait Locus for latent inhibition to a region of the genome that contains the tyramine receptor gene Amtyr1 [We use Amtyr1 to denote the gene and AmTYR1 the receptor throughout the text.]. We then show that disruption of Amtyr1 signaling either pharmacologically or through RNAi qualitatively changes the expression of latent inhibition but has little or slight effects on appetitive conditioning, and these results suggest that AmTYR1 modulates inhibitory processing in the CNS. Electrophysiological recordings from the brain during pharmacological blockade are consistent with a model that AmTYR1 indirectly regulates at inhibitory synapses in the CNS. Our results therefore identify a distinct Amtyr1-based modulatory pathway for this type of nonassociative learning, and we propose a model for how Amtyr1 acts as a gain control to modulate hebbian plasticity at defined synapses in the CNS. We have shown elsewhere how this modulation also underlies potentially adaptive intracolonial learning differences among individuals that benefit colony survival. Finally, our neural model suggests a mechanism for the broad pleiotropy this gene has on several different behaviors.

Overlapping exposure effects of pathogen and dimethoate on honeybee (Apis mellifera Linnaeus) metabolic rate and longevity.

Introduction: Declines in honeybee abundance have been observed worldwide during last decades. This is partly due to plant protection agents used in intensive farming, landscaping and infrastructure maintenance. Another type of factors negatively affecting honeybees is the spread of diseases caused by different pathogens and pests. Lately, more focus has been paid to the interactions between different overlapping stressors affecting honeybee health, the combination of these often being more detrimental compared to individual stressors. The most widely used stress-evaluating methods take into account lethal- or motorial changes of the individuals or colonies. Comparatively little honeybee research has examined changes in initial recovery potential and physiological symptoms of toxification. The aim of this study was to examine the combined effect of Nosema apis and N. ceranae (according to a newer classification Vairimorpha apis and V. ceranae), the common causes of nosemosis in the honeybee Apis mellifera L., with the insecticide dimethoate. Methods: In this study, honeybee mortality and metabolic rate were used to assess the combined effects interactions of Nosema ssp. and dimethoate. Results: Our results showed that exposure to the low concentration of either dimethoate, either one or both species of Nosema ssp as single factors or in the combination had no significant effect on honeybee metabolic rate. The mortality increased with the two Nosema spp., as well as with infection by N. ceranae alone. The effect of dimethoate was observed only in combination with N. apis infection, which alone had no effect on individual honeybee mortality. Conclusion: This study demonstrates that the overlapping exposure to a non-lethal concentration of a pesticide and a pathogen can be hidden by stronger stressor but become observable with milder stressors.

SIMplyBee: an R package to simulate honeybee populations and breeding programs

BackgroundThe Western honeybee is an economically important species globally, but has been experiencing colony losses that lead to economical damage and decreased genetic variability. This situation is spurring additional interest in honeybee breeding and conservation programs. Stochastic simulators are essential tools for rapid and low-cost testing of breeding programs and methods, yet no existing simulator allows for a detailed simulation of honeybee populations. Here we describe SIMplyBee, a holistic simulator of honeybee populations and breeding programs. SIMplyBee is an R package and hence freely available for installation from CRAN http://cran.r-project.org/package=SIMplyBee.ImplementationSIMplyBee builds upon the stochastic simulator AlphaSimR that simulates individuals with their corresponding genomes and quantitative genetic values. To enable honeybee-specific simulations, we extended AlphaSimR by developing classes for global simulation parameters, SimParamBee, for a honeybee colony, Colony, and multiple colonies, MultiColony. We also developed functions to address major honeybee specificities: honeybee genome, haplodiploid inheritance, social organisation, complementary sex determination, polyandry, colony events, and quantitative genetics at the individual- and colony-levels.ResultsWe describe its implementation for simulating a honeybee genome, creating a honeybee colony and its members, addressing haplodiploid inheritance and complementary sex determination, simulating colony events, creating and managing multiple colonies at the same time, and obtaining genomic data and honeybee quantitative genetics. Further documentation, available at http://www.SIMplyBee.info, provides details on these operations and describes additional operations related to genomics, quantitative genetics, and other functionalities.DiscussionSIMplyBee is a holistic simulator of honeybee populations and breeding programs. It simulates individual honeybees with their genomes, colonies with colony events, and individual- and colony-level genetic and breeding values. Regarding the latter, SIMplyBee takes a user-defined function to combine individual- into colony-level values and hence allows for modeling any type of interaction within a colony. SIMplyBee provides a research platform for testing breeding and conservation strategies and their effect on future genetic gain and genetic variability. Future developments of SIMplyBee will focus on improving the simulation of honeybee genomes, optimizing the simulator’s performance, and including spatial awareness in mating functions and phenotype simulation. We invite the honeybee genetics and breeding community to join us in the future development of SIMplyBee.

A Perspective on Urban Beekeeping with &lt;i&gt;Apis cerana&lt;/i&gt; F. in Bengaluru, Karnataka with Special Reference to Bee Flora and Hive Suitability

Urban beekeeping is nowadays attracting immense popularity. Many people in large cities are installing hives on the rooftops. A significant drawback for beekeeping in urban areas of India is limited bee flora which may lead to poor honey yield and performance. As more people take to urban beekeeping, the competition for resources between honey bee colonies increases. Since the space covered by vegetation in the city remains relatively constant, each honey bee colony ends with less honey production per year. It slows the growth of individual urban honey bee colonies and affects the foraging potential of the honey bee colonies. Thus, urban beekeeping could be made effective by providing sufficient bee forage in surrounding beekeeping areas and by the selection of hives suitable for urban areas. Therefore, the present work was undertaken to study the diversity of nectariferous and polleniferous honey bee flora found in the major cosmopolitan city of India: Bengaluru. We recorded 58 major bee floras grown in the Bengaluru urban to make beekeeping activities throughout the year. In addition, hives with different frame numbers (5, 6 and 8) were evaluated for their performance in 3 different locations. Results clearly showed that hives with six frames are equally good compared to the conventional eight frame hive. Therefore, hives with six frames are more suitable for urban beekeeping, which would be efficient by reducing energy requirements in activities like comb construction of the colony. Appropriate hive selection was an important factor for successful beekeeping, especially in urban areas of Bengaluru.

A combination of the frequent fungicides boscalid and dimoxystrobin with the neonicotinoid acetamiprid in field-realistic concentrations does not affect sucrose responsiveness and learning behavior of honeybees

The increasing loss of pollinators over the last decades has become more and more evident. Intensive use of plant protection products is one key factor contributing to this decline. Especially the mixture of different plant protection products can pose an increased risk for pollinators as synergistic effects may occur. In this study we investigated the effect of the fungicide Cantus® Gold (boscalid/dimoxystrobin), the neonicotinoid insecticide Mospilan® (acetamiprid) and their mixture on honeybees. Since both plant protection products are frequently applied sequentially to the same plants (e.g. oilseed rape), their combination is a realistic scenario for honeybees. We investigated the mortality, the sucrose responsiveness and the differential olfactory learning performance of honeybees under controlled conditions in the laboratory to reduce environmental noise. Intact sucrose responsiveness and learning performance are of pivotal importance for the survival of individual honeybees as well as for the functioning of the entire colony. Treatment with two sublethal and field relevant concentrations of each plant protection product did not lead to any significant effects on these behaviors but affected the mortality rate. However, our study cannot exclude possible negative sublethal effects of these substances in higher concentrations. In addition, the honeybee seems to be quite robust when it comes to effects of plant protection products, while wild bees might be more sensitive.

Decoupling the effects of nutrition, age, and behavioral caste on honey bee physiology, immunity, and colony health.

Nutritional stress, especially a dearth of pollen, has been linked to honey bee colony losses. Colony-level experiments are critical for understanding the mechanisms by which nutritional stress affects individual honey bee physiology and pushes honey bee colonies to collapse. In this study, we investigated the impact of pollen restriction on key markers of honey bee physiology, main elements of the immune system, and predominant honey bee viruses. To achieve this objective, we uncoupled the effects of behavior, age, and nutritional conditions using a new colony establishment technique designed to control size, demography, and genetic background. Our results showed that the expression of storage proteins, including vitellogenin (vg) and royal jelly major protein 1 (mrjp1), were significantly associated with nursing, pollen ingestion, and older age. On the other hand, genes involved in hormonal regulation including insulin-like peptides (ilp1 and ilp2) and methyl farnesoate epoxidase (mfe), exhibited higher expression levels in young foragers from colonies not experiencing pollen restriction. In contrast, pollen restriction induced higher levels of insulin-like peptides in old nurses. On the other hand, we found a strong effect of behavior on the expression of all immune genes, with higher expression levels in foragers. In contrast, the effects of nutrition and age were significant only the expression of the regulatory gene dorsal. We also found multiple interactions of the experimental variables on viral titers, including higher Deformed wing virus (DWV) titers associated with foraging and age-related decline. In addition, nutrition significantly affected DWV titers in young nurses, with higher titers induced by pollen ingestion. In contrast, higher levels of Black queen cell virus (BQCV) were associated with pollen restriction. Finally, correlation, PCA, and NMDS analyses proved that behavior had had the strongest effect on gene expression and viral titers, followed by age and nutrition. These analyses also support multiple interactions among genes and virus analyzed, including negative correlations between the expression of genes encoding storage proteins associated with pollen ingestion and nursing (vg and mrjp1) with the expression of immune genes and DWV titers. Our results provide new insights into the proximal mechanisms by which nutritional stress is associated with changes in honey bee physiology, immunity, and viral titers.

Fungicide Exposure in Honey Bee Hives Varies By Time, Worker Role, and Proximity to Orchards in Spring.

Fungicides are commonly applied to prevent diseases in eastern North American cherry orchards at the same time that honey bees (Apis mellifera L. (Hymenoptera: Apidae)) are rented for pollination services. Fungicide exposure in honey bees can cause negative health effects. To measure fungicide exposure, we sampled commercial honey bee colonies during orchard bloom at two commercial tart cherry orchards and one holding yard in northern Michigan over two seasons. Nurse bees, foragers, larvae, pollen, bee bread, and wax were screened for captan, chlorothalonil, and thiophanate-methyl. We also looked at the composition of pollens collected by foragers during spring bloom. We found differences in fungicide residue levels between nurse bees and foragers, with higher captan levels in nurse bees. We also found that residue levels of chlorothalonil in workers were significantly increased during tart cherry bloom, and that nurse bees from hives adjacent to orchards had significantly higher chlorothalonil residues than nurse bees from hives kept in a holding yard. Our results suggest that fungicide exposure of individual honey bees depends greatly on hive location in relation to mass-flowering crops, and worker role (life stage) at the time of collection. In some pollen samples, captan and chlorothalonil were detected at levels known to cause negative health effects for honey bees. This study increases our understanding of exposure risk for bees under current bloom time orchard management in this region. Further research is needed to balance crop disease management requirements with necessary pollination services and long-term pollinator health.

A genotyping by sequencing approach can disclose Apis mellifera population genomic information contained in honey environmental DNA

Awareness has been raised over the last years on the genetic integrity of autochthonous honey bee subspecies. Genomic tools available in Apis mellifera can make it possible to measure this information by targeting individual honey bee DNA. Honey contains DNA traces from all organisms that contributed or were involved in its production steps, including the honey bees of the colony. In this study, we designed and tested a genotyping by sequencing (GBS) assay to analyse single nucleotide polymorphisms (SNPs) of A. mellifera nuclear genome using environmental DNA extracted from honey. A total of 121 SNPs (97 SNPs informative for honey bee subspecies identification and 24 SNPs associated with relevant traits of the colonies) were used in the assay to genotype honey DNA, which derives from thousands of honey bees. Results were integrated with information derived from previous studies and whole genome resequencing datasets. This GBS method is highly reliable in estimating honey bee SNP allele frequencies of the whole colony from which the honey derived. This assay can be used to identify the honey bee subspecies of the colony that produced the honey and, in turn, to authenticate the entomological origin of the honey.

The BEEHAVEecotox Model-Integrating a Mechanistic Effect Module into the Honeybee Colony Model.

Mechanistic effect models are powerful tools for extrapolating from laboratory studies to field conditions. For bees, several good models are available that can simulate colony dynamics. Controlled and reliable experimental systems are also available to estimate the inherent toxicity of pesticides to individuals. However, there is currently no systematic and mechanistic way of linking the output of experimental ecotoxicological testing to bee models for bee risk assessment. We introduce an ecotoxicological module that mechanistically links exposure with the hazard profile of a pesticide for individual honeybees so that colony effects emerge. This mechanistic link allows the translation of results from standard laboratory studies to relevant parameters and processes for simulating bee colony dynamics. The module was integrated into the state-of-the-art honeybee model BEEHAVE. For the integration, BEEHAVE was adapted to mechanistically link the exposure and effects on different cohorts to colony dynamics. The BEEHAVEecotox model was tested against semifield (tunnel) studies, which were deemed the best study type to test whether BEEHAVEecotox predicted realistic effect sizes under controlled conditions. Two pesticides used as toxic standards were chosen for this validation to represent two different modes of action: acute mortality of foragers and chronic brood effects. The ecotoxicological module was able to predict effect sizes in the tunnel studies based on information from standard laboratory tests. In conclusion, the BEEHAVEecotox model is an excellent tool to be used for honeybee risk assessment, interpretation of field and semifield studies, and exploring the efficiency of different mitigation measures. The principles for exposure and effect modules are portable and could be used for any well-constructed honeybee model. Environ Toxicol Chem 2022;41:2870-2882. © 2022 Bayer AG & Sygenta, et al. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Bees under interactive stressors: the novel insecticides flupyradifurone and sulfoxaflor along with the fungicide azoxystrobin disrupt the gut microbiota of honey bees and increase opportunistic bacterial pathogens

The gut microbiome plays an important role in bee health and disease. But it can be disrupted by pesticides and in-hive chemicals, putting honey bee health in danger. We used a controlled and fully crossed laboratory experimental design to test the effects of a 10-day period of chronic exposure to field-realistic sublethal concentrations of two nicotinic acetylcholine receptor agonist insecticides (nACHRs), namely flupyradifurone (FPF) and sulfoxaflor (Sulf), and a fungicide, azoxystrobin (Azoxy), individually and in combination, on the survival of individual honey bee workers and the composition of their gut microbiota (fungal and bacterial diversity). Metabarcoding was used to examine the gut microbiota on days 0, 5, and 10 of pesticide exposure to determine how the microbial response varies over time; to do so, the fungal ITS2 fragment and the V4 region of the bacterial 16S rRNA were targeted. We found that FPF has a negative impact on honey bee survival, but interactive (additive or synergistic) effects between either insecticide and the fungicide on honey bee survival were not statistically significant. Pesticide treatments significantly impacted the microbial community composition. The fungicide Azoxy substantially reduced the Shannon diversity of fungi after chronic exposure for 10 days. The relative abundance of the top 10 genera of the bee gut microbiota was also differentially affected by the fungicide, insecticides, and fungicide-insecticide combinations. Gut microbiota dysbiosis was associated with an increase in the relative abundance of opportunistic pathogens such as Serratia spp. (e.g. S. marcescens), which can have devastating consequences for host health such as increased susceptibility to infection and reduced lifespan. Our findings raise concerns about the long-term impact of novel nACHR insecticides, particularly FPF, on pollinator health and recommend a novel methodology for a refined risk assessment that includes the potential effects of agrochemicals on the gut microbiome of bees.

Why honeybees are poor pollinators of a mass-flowering plant: Experimental support for the low pollen quality hypothesis.

PremiseHoneybees dominate the flower‐visitor assemblages of many plant species, yet their efficiency in terms of the quality of pollen delivered to stigmas is largely unknown. We investigated why honeybees are poor pollinators of Aloe ferox, a self‐incompatible succulent treelet with large numbers of flowers. Honeybees are very frequent visitors to flowers of this species, yet contribute very little to seed production.MethodsWe assessed pollen loads on honeybees, studied their visitation behavior, selectively excluded birds from plants to determine direct effects of bees on pollen deposition, seed set, and ovule abortion, and used a novel “split‐pollinator” method to test whether honeybees deposit mainly low‐quality self pollen. For the latter, we captured honeybees, and with their existing pollen loads, used them to either pollinate virgin flowers on the plant on which they were caught or to pollinate virgin flowers on different plants.ResultsHoneybees cumulatively deposit as much pollen on stigmas as do birds, but our experiments showed that the pollen deposited by honeybees is mostly low‐quality self pollen that leads to substantial ovule discounting and depressed seed set.ConclusionsLack of movement among A. ferox plants during individual honeybee foraging bouts is the most likely explanation for their deposition of low‐quality self pollen on stigmas. The “split‐pollinator” method is a simple and cost‐effective technique to test the quality of pollination.

IMPACT OF THE PRECISION BEEKEEPING ON THE LIVING ENVIRONMENT

Bees are insects of great importance to humanity and the living environment. Bees are the main pollinators and plants are dependent on them. Consequently, global food production is dependent on bees and their work. In order to have a healthy living environment, it is of great importance for humans to help bees to be as healthy and productive as possible. The relationship between humans and bees is more than 9000 years old, but beekeeping principles and tools invented more than 200 years ago are still in use. The new principle that taking momentum in the beekeeping industry is Precision Beekeeping (PB). This is an apiary management strategy based on the monitoring of individual honeybee colonies to minimize resource consumption, maximize the productivity of bees and improve the quality of the living environment. In this research, we analyze the state-of-the-art systems for Precision Beekeeping. We make a comparative analysis of commercial solutions for precision beekeeping, with the goal to understand and determine the impact of device features on honeybees and the living environment.

Evaluating the foraging performance of individual honey bees in different environments with automated field RFID systems

AbstractMeasuring individual foraging performance of pollinators is crucial to guide environmental policies that aim at enhancing pollinator health and pollination services. Automated systems have been developed to track the activity of individual honey bees, but their deployment is extremely challenging. This has limited the assessment of individual foraging performance in full‐strength bee colonies in the field. Most studies available to date have been constrained to use downsized bee colonies located in urban and suburban areas. Environmental policy‐making, on the other hand, needs a more comprehensive assessment of honey bee performance in a broader range of environments, including in remote agricultural and wild areas. Here, we detail a new autonomous field method to record high‐quality data on the flight ontogeny and foraging performance of honey bees, using radio frequency identification (RFID). We separate bee traffic into returning and exiting tunnels to improve data quality solving many previous limitations of RFID systems caused by traffic jams and the parasitic coupling of RFID antennae. With this method, we assembled a large RFID dataset made of control bee colonies from experiments conducted in different locations and seasons. We hope our results will be a starting point to understand how ontogenetic and environmental factors affect the individual performance of honey bees and that our method will enable large‐scale replication of individual pollinator performance studies.