Institute for Bee Research

The Western honey bee (Apis mellifera) is among the most important commercial pollinators in agriculture, but also plays a central role as pollinator in natural ecosystems. The globally occurring brood disease American foulbrood (AFB), caused by the gram-positive, spore-forming bacterium Paenibacillus larvae, poses a serious threat to colony health and productivity. Early and accurate diagnosis is therefore essential to effectively contain disease outbreaks. This study aimed to develop a rapid, sensitive point-of-care test in lateral flow format for the simultaneous detection and differentiation of the clinically relevant P. larvae genotypes ERIC I and II. To achieve this aim, two target antigens were selected, produced recombinantly or purified, and used to generate monoclonal antibodies. These antibodies formed the basis for the development of two sandwich ELISAs and, building on this, a duplex lateral flow assay. Both immunoassay formats were evaluated with infected larvae samples. The results showed high specificity, sensitivity, and accuracy, allowing reliable detection of P. larvae and differentiation between the genotypes ERIC I and II, thereby providing a valuable addition to existing diagnostic methods. In particular, the lateral flow assay enables rapid on-site diagnosis, facilitating timely intervention, and thus supports effective measures to control the spread of the disease.

Abstract Geographically isolated mating stations are deployed across Europe to facilitate controlled mating with selected drone-producing colonies. To assess the reliability of these stations, we developed a paternity assignment test using a custom Illumina genotyping chip with 6457 SNPs based on two metrics (number of mismatch alleles and kinship). The method demonstrated remarkable accuracy during validation with an independent dataset of known parent-offspring pairs, with an accuracy rate of 97.7%. We then applied the developed paternity assignment test to theApis mellifera iberiensismating station in the Ataun valley, Basque Country, Spain, in 2021. Drone-producing colonies in the valley were sampled and genotyped, as well as 156 worker offspring of queens mated at the station, and 56 drones collected in the drone congregation area. Out of the 156 worker samples, we could assign paternity of 120 (76.9%) to one of the drone-producing colonies in the valley, while 23.1% were of unknown patriline. Out of the 56 drones collected in the air, 52 (92.9%) were assigned to drone-producing colonies. We were also able to determine the colonies and apiaries that made the most significant contributions to the matings. This information aids in effective apiary management, including the selection of suitable mating station locations and the positioning of drone-producing colonies therein. Overall, our SNP-based paternity assignment test offers a valuable tool for evaluating mating station effectiveness across Europe, crucial for advancing breeding objectives in honey bee populations.

Honey bees (Apis mellifera) have a peculiar and complex reproductive biology, with queens being polyandrous and mating with several drones during one or more mating (nuptial) flights in so-called drone congregation areas. Observing the virgin queens’ and drones’ flight behaviour provides data to understand and interpret a portion of the honey bees’ complex reproductive process. Observing the behaviour of the virgin queens on the hive entrance also serves to estimate the distance from the mating place or potential drone congregation areas (DCAs) as well as to detect the presence of airborne drones in the area. In this paper, we provide a detailed description of the methodology used for observing queens’ and drones’ flights during the period of expected mating. In addition, we provide information about required equipment, tools as well as step by step description of the observation and recordkeeping process.

In preparation of genomic selection, the genomic data is compared to pedigree information, and samples causing Mendelian inconsistencies are removed. Traditionally, opposing homozygotes and coefficients of inbreeding and relationships were used for this purpose. Here, we adapted these methods to the peculiar mating biology in honey bees. We developed a rule system for the removal of Mendelian inconsistencies in honey bees based on the empirical distributions of opposing homozygotes and coefficients of inbreeding and relationship in real and simulated data. To initiate genomic selection in the honey bee, 2,970 phenotyped queens were recently genotyped for about 100,000 markers. The samples of 207 and 135 queens were removed by the checks for opposing homozygotes and abnormalities in the relationship matrices, respectively. We suggest, that our approach should be used to prepare data for genomic selection in the honey bee.

Machine-learning techniques are shifting the boundaries of feasibility in many fields of ethological research. Here, we describe an application of machine learning to the detection/measurement of hygienic behaviour, an important breeding trait in the honey bee (Apis mellifera). Hygienic worker bees are able to detect and destroy diseased brood, thereby reducing the reproduction of economically important pathogens and parasites such as the Varroa mite (Varroa destructor). Video observation of this behaviour on infested combs has many advantages over other methods of measurement, but analysing the recorded material is extremely time-consuming. We approached this problem by combining automatic tracking of bees in the video recordings, extracting relevant features, and training a multi-layer discriminator on positive and negative examples of the behaviour of interest. Including expert knowledge into the design of the features lead to an efficient model for identifying the uninteresting parts of the video which can be safely skipped. This algorithm was then used to semiautomatically identify individual worker bees involved in the behaviour. Application of the machine-learning method allowed to save 70% of the time required for manual analysis, and substantially increased the number of cell openings correctly identified. It thereby turns video-observation of individual cell opening events into an economically competitive method for selecting potentially resistant bees. This method presents an example of how machine learning can be used to boost ethological research, and how it can generate new knowledge by explaining the learned decision rule in form of meaningful parameters.

Abstract Honey bee subspecies originate from specific geographic areas in Africa, Europe and the Middle East. The interest of beekeepers in specific phenotypes has led them to import subspecies to regions outside of their original range. The resulting admixture complicates population genetics analyses and population stratification can be a major problem for association studies. As a typical example, the case of the French population is studied here. We sequenced 870 haploid drones for SNP detection and identified nine genetic backgrounds in 629 samples. Five correspond to subspecies, two to isolated populations and two to human-mediated population management. We also highlight several large haplotype blocks, some of which coincide with the position of centromeres. The largest is 3.6 Mb long on chromosome 11, representing 1.6 % of the genome and has two major haplotypes, corresponding to the two dominant genetic backgrounds identified.

According to legislation, unifloral honeys are characterized by their organoleptic, physicochemical, and microscopic properties. Melissopalynology is the established method for identifying the pollen taken up with the floral nectar by forager bees and is used for authentication of the nectar sources in honey. For cornflower honey (Centaurea cyanus), the pollen input does not correlate with the nectar input, because the nectar is produced both in floral and in extrafloral nectaries. The well-known cornflower marker lumichrome has now also been detected in the extrafloral nectar. Therefore, lumichrome is a suitable marker substance for cornflower honey. Four different methods for the sole analysis of lumichrome in honey were validated and compared. Studies over nine years have shown that unifloral cornflower honey should contain approximately 35 mg/kg lumichrome. For a further differentiated cornflower honey specific verification, other nonvolatile compounds like 7-carboxylumichrome and volatiles, such as 3,4-dihydro-3-oxoedulan I and 3,4-dihydro-3-oxoedulan II, should be analyzed. This enables a more specific accuracy for the classification of unifloral cornflower honey.

The most effective strategy against brood diseases, such as those stemming from infestation by the mite Varroa destructor, is the early detection and removal of sick brood. Recent findings suggest that genes associated with worker bee olfactory perception play a central role in Varroa-sensitive hygiene (VSH). In this study, the odour sensitivity of Apis mellifera drones was examined through proboscis extension response (PER) conditioning. Individuals sensitive/insensitive to the two Varroa-parasitised-brood odours (extract-low and extract-high) were used for breeding. Twenty-one queens from a VSH-selected line (SelQ) and nineteen queens from a nonselected line (ConQ) were single-drone-inseminated with sperm from drones that showed either sensitivity (SenD+) or insensitivity (SenD−) to the two extracts. Individual VSH behaviour in a total of 5072 offspring of these combinations (SelQ × SenD+, SelQ× SenD−, ConQ × SenD+, ConQ × SenD−) was subsequently observed in a specially designed observation unit with infrared light. The results from the video observation were also separately examined, considering the genetic origin (VSH-selected or nonselected line) of the participating queens and drones. While the drone PER conditioning results were not significantly reflected in the VSH results of the respective offspring, the genetic origin of the participating queens/drones was crucial for VSH manifestation.

Simple SummaryThe Apis mellifera carnica subspecies of the honeybee is known for its gentleness and good honey yield. In the early 20th century, systematic breeding efforts began. Breeding progress was slow before the introduction of modern techniques of genetic evaluation in the mid 1990s. Here, the results of the official breeding value estimation in BeeBreed.eu are analyzed. From about 2000 onward, breeding progress accelerated. The result is a considerable gain in honey yield and desirable properties without increased inbreeding coefficients. The success of A. m. carnica breeding shows the potential of genetic evaluation.The Apis mellifera carnica subspecies of the honeybee has long been praised for its gentleness and good honey yield before systematic breeding efforts began in the early 20th century. However, before the introduction of modern techniques of genetic evaluation (best linear unbiased prediction, BLUP) and a computerized data management in the mid 1990s, genetic progress was slow. Here, the results of the official breeding value estimation in BeeBreed.eu are analyzed to characterize breeding progress and inbreeding. From about the year 2000 onward, the genetic progression accelerated and resulted in a considerable gain in honey yield and desirable properties without increased inbreeding coefficients. The prognostic quality of breeding values is demonstrated by a retrospective analysis. The success of A. m. carnica breeding shows the potential of BLUP-based breeding values and serves as an example for a large-scale breeding program.