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Feeding experiments are standard tools in the pollinator risk assessment. The design (Oomen et al. 1992) was developed to test insect growth regulators and herbicides. In recent years there was an update (Lückmann & Schmitzer 2015) on the outline in order to also focus on the advantage of different rates making a dose response design possible where exposure levels are known. Additionally, this design gives the possibility to test different rates for honey bee colonies foraging in the same landscape. The main objective of the experiment presented here was to determine the natural variability of foragers losses of hives fed with a sub-lethal neonicotinoid concentration compared to an untreated control. Other objectives were to see if the neurotoxic exposure results in any observable sub-lethal effects and to find out if losses can be correlated to hive development. This was assessed with traditional methods and a novel, visual monitoring device.

A new study design, according to the guideline SANTE11956/2016 rev:9 (2018), was established to determine the maximum residue level (MRL) of plant protection products in honey. The guideline describes a syrup bee feeding study designed as a worst-case scenario for transferring plant protection products into honey. Previously, field and semi-field studies designs were used. The objectives of this study were to validate the suitability of this feeding semi-field studies according to the new guideline.

Colony strength, food storage and brood development are a fundamental part of each honeybee field study. Colony assessments are used to compare and assess those for beehive over time. At present, most colony assessments are made by experienced beekeepers according to Liebefeld method. This method is based on an estimation of areas covered by honeybees, food and brood stages on each side of a comb. Areas are counted from a grid separating the comb side into 8 sections which are protocolled with an accuracy of 0.5 sections. An assessment for a hive takes up to 20 min and even with two field locations, it is necessary to split assessments between beekeepers. So, it is important to make estimates as comparable as possible. For this purpose, beekeepers practice the assessments on pre-determined photographs to “calibrate themselves”. The advantage of the Liebefeld assessment is that the condition of bee hive is estimated with minimum disturbance of the bees. Digital photography is under discussion to gain data with high precision and accuracy with one major disadvantage. To be able to see food and brood stages in photographs, bees have to be removed from combs. This, however, results in a disturbance of the colony – especially if the assessments take place in short time intervals of 7 ± 1 days. An experiment was performed to evaluate the variation between individual beekeepers and to compare the results to data generated with photographs. For the experiment, five colonies were assessed each by four beekeepers independently according to Liebefeld method. Each comb side of the five colonies was photographed with and without honeybees sitting on it for precise analysis at the computer for a number of bees, nectar cells, pollen cells, eggs, open brood and capped brood. The number of bees and cells with the different contents were generated by an area-based assessment in ImageJ as well as a detailed counting with help of HiveAnalyzer® Software. Data from beekeeper estimations were then compared with assessments based on digital photography. With the results of the experiment, we tried to answer several questions. With the study, we wanted to determine the level of variation between the beekeepers for the live stages and food stores estimated.

There is a strong desire to enhance the knowledge creation on bee health and to get a better understanding of how available feed resources contribute to it. New technologies such as automatic carry a vast potential to gain insights into these questions. As of now, they were used to detect changes in activity and pollen foraging at colony level, but have not yet been applied to generate data at the level of individual bees. One new technology to observe survival, flight duration and frequency at colony level are radio-frequency identification (RFID) chips. With their help, the homing flight behaviour of bees equipped with sensors can be observed to find out if there is an influence of a plant protection product on the orientation of the bees, thus their ability to return to their hive (OECD GD 332). Combining data about the flight activity and life cycle of individual honey bees with data at colony level from an automatic bee counter could be very insightful for a better understanding of effects and their magnitude. The companies apic.ai and EAS Ecotox are partners in the improvement of a visual bee monitoring technology in the research project OCELI (FKZ 281C307B19). Automated visual identification of individual bees could enable the inclusion of life cycle changes, homing success, flight duration and frequency as well as individual behaviour in studies where visual monitoring technology is already in use to assess other behavioural endpoints like activity, pollen collection or share of pollen foragers. As part of the project, a proof-of-concept experiment was performed in 2021, where the apic.ai monitoring systems with computer vision technology were used to observe individual foraging bees leaving and entering the hive. Queen markers (opalite plates) with different numbers and colours were attached to bees to identify them individually. For the study, bees from two colonies were marked twice at the interval of one week with a different colour. At each marking, both freshly hatched bees young and forager bees were marked. The marking was successful. Over Time there was no difference between a more experienced marking team than a less experienced team. In a follow up tunnel trial in 2022 in Germany, marking with opalite plates was conducted to determine whether chemicals have an influence on foraging start and survival of individual foragers. apic.ai monitoring systems with computer vision technology were used to observe the activity and foraging of pollen at colony level and at the level of individuals through the marked bees. The first cohort marked were experienced foragers and the second cohort in hive bees. An algorithm saved multiple images of every bee entering and leaving the hive. These images were subsequently analyzed for markers using a neural network. Picture were checked by a person to identify the colour and the number on the plate. Thus, data could be collected on: ● the first time the bee was seen leaving the hive ● every time the bee was detected to enter and leave ● every time the bee was detected bringing pollen into the hive ● the last time the bee was seen leaving the hive Survival curves and changes in foraging and recruiting behaviour were studied using this data. The first results will be presented here to display and discuss the benefits of additional insights at the level of individual bees and the potential of the data to enhance simulation models such as BEEHAVE. [ABSTRACT FROM AUTHOR]