BEEHAVE: a systems model of honeybee colony dynamics and foraging to explore multifactorial causes of colony failure

Abstract

A notable increase in failure of managed European honeybee Apis mellifera L. colonies has been reported in various regions in recent years. Although the underlying causes remain unclear, it is likely that a combination of stressors act together, particularly varroa mites and other pathogens, forage availability and potentially pesticides. It is experimentally challenging to address causality at the colony scale when multiple factors interact. In silico experiments offer a fast and cost-effective way to begin to address these challenges and inform experiments. However, none of the published bee models combine colony dynamics with foraging patterns and varroa dynamics.We have developed a honeybee model, BEEHAVE, which integrates colony dynamics, population dynamics of the varroa mite, epidemiology of varroa-transmitted viruses and allows foragers in an agent-based foraging model to collect food from a representation of a spatially explicit landscape.We describe the model, which is freely available online (www.beehave-model.net). Extensive sensitivity analyses and tests illustrate the model's robustness and realism. Simulation experiments with various combinations of stressors demonstrate, in simplified landscape settings, the model's potential: predicting colony dynamics and potential losses with and without varroa mites under different foraging conditions and under pesticide application. We also show how mitigation measures can be tested.Synthesis and applications. BEEHAVE offers a valuable tool for researchers to design and focus field experiments, for regulators to explore the relative importance of stressors to devise management and policy advice and for beekeepers to understand and predict varroa dynamics and effects of management interventions. We expect that scientists and stakeholders will find a variety of applications for BEEHAVE, stimulating further model development and the possible inclusion of other stressors of potential importance to honeybee colony dynamics.

Weight of evidence evaluation of a network of adverse outcome pathways linking activation of the nicotinic acetylcholine receptor in honey bees to colony death

“…Accounting for the dynamics of the colony, this model predicts that reduction of the foraging force by approximately two-thirds will put the colony at risk of failure (Khoury et al, 2011). Another honey bee model, BEEHAVE, takes into account colony dynamics, foraging landscape and performance, bee development, and impacts of the Varroa mite and Varroa-transmitted viruses, allowing for evaluation of multiple-stressor scenarios (Becher et al, 2014). Due to the difficulty in actually testing different multi-stressor scenarios at the colony level, such models provide the biologically plausible predictions of honey bee colony dynamics that can be useful for managing and understanding where a weakened colony, caused by multiple factors, may lead to colony death/failure.…”

Toward a Complete Agent-Based Model of a Honeybee Colony

“…Some of these phenomena may be described at the colony-level by using dierential equations and by not considering bees as individuals [1,12,11]. However, in order to investigate these phenomena and to allow interactions with bees, a bottom-up approach is required to deduce the eects of these micro actions in the short and long run on the colony.…”

“…In Beehave [1], the agent-based model, dedicated to the foraging activity, interacts with two other population-based modules simulating the life of the colony and the propagation of the parasite Varroa destructor in the colony. The agent-based model includes a spatially explicit landscape that the forager agents explore and on which they forage at ower patches.…”

Statement on the suitability of the BEEHAVE model for its potential use in a regulatory context and for the risk assessment of multiple stressors in honeybees at the landscape level

“…Modelling tools such as BEEHAVE (Becher et al, 2014) offer the potential to investigate the relative importance of different stressors without conducting resource-intensive experiments.…”