Specifications for field data collection contributing to honey bee model corroboration and verification

First application of an Integrated Biological Response index to assess the ecotoxicological status of honeybees from rural and urban areas

Understanding the effects of environmental contaminants on honeybees is essential to minimize their impacts on these important pollinating insects. The aim of this study was to assess the ecotoxicological status of honeybees in environments undergoing different anthropic pressure: a wood (reference site), an orchard, an agricultural area, and an urban site, using a multi-biomarker approach. To synthetically represent the ecotoxicological status of the honeybees, the responses of the single biomarkers were integrated by the Integrated Biological Response (IBRv2) index. Overall, the strongest alteration of the ecotoxicological status (IBRv2 = 7.52) was detected in the bees from the orchard due to the alteration of metabolic and genotoxicity biomarkers indicating the presence of pesticides, metals, and lipophilic compounds. Honeybees from the cultivated area (IBRv2 = 7.18) revealed an alteration especially in neurotoxicity, metabolic, and genotoxicity biomarkers probably related to the presence of pesticides, especially fungicides. Finally, in the urban area (IBRv2 = 6.60), the biomarker results (GST, lysozyme, and hemocytes) indicated immunosuppression in the honeybees and the effects of the presence of lipophilic compounds and metals in the environment.

Infrared Irradiation Drying Impact on Bee Pollen: Case Study on the Phenolic Composition of Eucalyptus globulus Labill and Salix atrocinerea Brot. Pollens

Bee pollen is commonly reputed as a rich source of nutrients, both for bees and humans. Its composition is well balanced and can be taken as a stand-alone food or as supplement, including for the elderly owing its low caloric value. However, storage conditions frequently lead to product degradation, namely due to the high moisture content that enable the proliferation of molds and bacteria. Herein, an infrared (IR)-based technology is proposed as a mean to determine moisture content, setting also a new scalable approach for the development of a drying technology to be used for bee pollen processing, which can be carried out in a short time, without impacting the phenolic and flavonoid content and associated bioactive effects. Proof-of-concept was attained with an IR moisture analyzer, bee pollen samples from Eucalyptus globulus Labill and Salix atrocinerea Brot. being selected as models. Impact of the IR radiation towards the phenolic and flavonoid profiles was screened by HPLC/DAD profiling and radical scavenging ability by the DPPH assay. The IR-based approach shows good reproducibility while simultaneously reducing drying time and energy consumption, thus implying a low environmental impact and being suitable for industrial scale-up once no degradation has been found to occur during the radiation process.

A systems-based approach to the environmental risk assessment of multiple stressors in honey bees

The European Parliament requested EFSA to develop a holistic risk assessment of multiple stressors in honey bees. To this end, a systems-based approach that is composed of two core components: a monitoring system and a modelling system are put forward with honey bees taken as a showcase. Key developments in the current scientific opinion (including systematic data collection from sentinel beehives and an agent-based simulation) have the potential to substantially contribute to future development of environmental risk assessments of multiple stressors at larger spatial and temporal scales. For the monitoring, sentinel hives would be placed across representative climatic zones and landscapes in the EU and connected to a platform for data storage and analysis. Data on bee health status, chemical residues and the immediate or broader landscape around the hives would be collected in a harmonised and standardised manner, and would be used to inform stakeholders, and the modelling system, ApisRAM, which simulates as accurately as possible a honey bee colony. ApisRAM would be calibrated and continuously updated with incoming monitoring data and emerging scientific knowledge from research. It will be a supportive tool for beekeeping, farming, research, risk assessment and risk management, and it will benefit the wider society. A societal outlook on the proposed approach is included and this was conducted with targeted social science research with 64 beekeepers from eight EU Member States and with members of the EU Bee Partnership. Gaps and opportunities are identified to further implement the approach. Conclusions and recommendations are made on a way forward, both for the application of the approach and its use in a broader context.

Predicting acute contact toxicity of organic binary mixtures in honey bees (A. mellifera) through innovative QSAR models

Pollinators such as honey bees are of considerable importance, because of the crucial pollination services they provide for food crops and wild plants. Since bees are exposed to a wide range of multiple chemicals "mixtures" both of anthropogenic (e.g. plant protection products) and natural origin (e.g. plant toxins), understanding their combined toxicity is critical. Although honey bees are employed worldwide as surrogate species for Apis and non-Apis bees in toxicity tests, it is practically unfeasible to perform in vivo tests for all mixtures of chemicals. Therefore, Quantitative Structure-Activity Relationships (QSAR) models can be developed using available data and can provide useful tools to predict such combined toxicity. Here, three different QSAR models within the CORAL software have been calibrated and validated for honey bees (A. mellifera) to predict the acute contact mixtures potency (LD_50-mix), in two regression based-models, and the nature of combined toxicity (synergism / non-synergism) in a classification-based model. Experimental data on binary mixtures (n = 123) (LD_50-mix) including dose response data (n = 97) and corresponding Toxic Unit values were retrieved from EFSA databases. The models were built using the principle of extraction of attributes from SMILES (or quasi-SMILES) while calculating so-called correlation weights for these attributes using Monte Carlo techniques. The two regression models were validated for their reliability and robustness (R² = 0.89, CCC = 0.92, Q² = 0.81; R² = 0.87, CCC = 0.89, Q² = 0.75). The classification model was validated using sensitivity (=0.86), specificity (=1), accuracy (=0.96), and Matthews correlation coefficient (MCC = 0.90) as qualitative statistical validation parameters. Results indicate that these QSAR models successfully predict acute contact toxicity of binary mixtures in honey bees and can support prioritisation of multiple chemicals of concerns. Data gaps and further development of QSAR models for honey bees are highlighted particularly for chronic and sub-lethal effects.