The third version of an agent‐based honey bee colony model (ApisRAM.03) for the risk assessment of pesticides

The third version of an agent‐based honey bee colony model (ApisRAM.03) for the risk assessment of pesticides

Meta data
DOI
https://doi.org/10.2903/sp.efsa.2025.EN-9293
QUESTION NUMBER
EFSA-Q-2022-00786
CONTACT
PLANTS@efsa.europa.eu

Abstract

This report presents the implementation of the third version of an agent‐based system model designed for the risk assessment of pesticides on honey bees colonies (Apis mellifera) – ApisRAM.03. The third version of ApisRAM focuses on scenarios involving a single plant protection product and a single use. The application of pesticide is implemented using the pesticide engine module in The Animal, Landscape and Man Simulation System (ALMaSS). The pesticide engine supports three application types: spray, seed coating, and granular, with consideration of spray and dust drift. The ApisRAM.03 model consists of the colony development, vitality, beekeeping management practices, and activity modules. Among the modelled activities, the foraging activity establishes a dynamic link between colony development and the surrounding landscape, capturing the complexities of the foraging behaviour, resource availability, and pesticide exposure. Incorporating a vitality method, the ApisRAM.03 integrates the effects from exposure to multiple stressors that bees encounter in their environment. This method evaluates the combined effects of pesticides with nutritional stress on mortality, along with possible combined effect from exposure to infectious agents. Additionally, the beekeeping management practices module supports management activities, such as chemical treatment and supplementary feeding. ApisRAM.03 contains a model designed to store measurement endpoints. It stores the hourly population and resource dynamics, foraging activities and pesticide exposure status for advanced simulation results analysis. To enhance computational efficiency, ApisRAM.03 was implemented to support multithreading. This capability enables faster simulations, allowing for more extensive scenario tests and more timely assessments.

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