Honeybee-colony weakening and losses have been reported in the EU and worldwide. The way that stressors (biological, chemical and environmental) affect bees and contribute to the current observed trends of population declines is not well understood, neither are the underlying mechanisms, which remain complex given the potential number of combinations and interactions among stressors.
When assessing the risk of pesticides to honeybee colonies, a tiered approach is followed going from the most conservative (on individual bees and under laboratory conditions) to the most realistic (on colonies and under (semi)field conditions). Current tests do not reflect well the exposures of real colonies, which vary in time and space within a complex landscape. This is, in particular, because semi-field tests are too short in duration and tests in the field have plot sizes that are too small. The development of a mechanistic model reflecting this complexity can be a useful tool for the risk assessment of honeybee colonies exposed to multiple stressors that vary in time and space, at the landscape level.
The project is part of the EFSA’s MUST-B (MUltiple STressors in Bees) project. MUST-B aims to develop a holistic approach on the risk assessment on multiple stressors in bees which was formalized through an Opinion of the Scientific Committee (link). MUST-B provided both the original funding to develop ApisRAM, and now a new project to extend the capabilities of the model.
SESS is the developer of the ApisRAM model, utilizing the ALMaSS landscape simulation and provide the bees with a dynamic and realistic environment, including the multiple stressors (pesticides, diseases, parasites, and weather). The in silico colony will also simulate bee-keeping management and can be used to evaluate the likely effects of changed landscape use, bee keeping or pesticide exposure on honey bees.
ApisRAM is a highly innovative and detailed model, which attempts not only to simulate each individual bee in a colony, egg->adult, but also represent their interactions and behaviours at a highly detailed level. For example, a worker bee may engage in over 20 activities depending upon its experiences, local context, and physiological stage. Development of the model is also innovative with the application of machine learning to the development process itself.
1. EFSA: MUST-B project