An Evaluation of the BEEHAVE Model Using Honey Bee Field Study Data: Insights and Recommendations

Models of bee responses to land use and land cover changes in agricultural landscapes - a review and research agenda

Predictive modelling tools can be used to support the design of agricultural landscapes to promote pollinator biodiversity and pollination services. Despite the proliferation of such modelling tools in recent decades, there remains a gap in synthesising their main characteristics and representation capacities. Here, we reviewed 42 studies that developed non-correlative models to explore the impact of land use and land cover changes on bee populations, and synthesised information about the modelled systems, modelling approaches, and key model characteristics like spatiotemporal extent and resolution. Various modelling approaches are employed to predict the biodiversity of bees and the pollination services they provide, with a prevalence of models focusing on wild populations compared to managed ones. Of these models, landscape indicators and distance decay models are relatively simple, with few parameters. They allow mapping bee visitation probabilities using basic land cover data and considering bee foraging ranges. Conversely, mechanistic or agent-based models delineate, with varying degrees of complexity, a multitude of processes that characterise, among others, the foraging behaviour and population dynamics of bees. The reviewed models collectively encompass 38 ecological, agronomic, and economic processes, producing various outputs including bee abundance, habitat visitation rate, and crop yield. To advance the development of predictive modelling tools aimed at fostering pollinator biodiversity and pollination services in agricultural landscapes, we highlight future avenues for increasing biophysical realism in models predicting the impact of land use and land cover changes on bees. Additionally, we address the challenges associated with balancing model complexity and practical usability.

Honey bee stressor networks are complex and dependent on crop and region

Honey bees play a major role in crop pollination but have experienced declining health throughout most of the globe. Despite decades of research on key honey bee stressors (e.g., parasitic Varroa destructor mites and viruses), researchers cannot fully explain or predict colony mortality, potentially because it is caused by exposure to multiple interacting stressors in the field. Understanding which honey bee stressors co-occur and have the potential to interact is therefore of profound importance. Here, we used the emerging field of systems theory to characterize the stressor networks found in honey bee colonies after they were placed in fields containing economically valuable crops across Canada. Honey bee stressor networks were often highly complex, with hundreds of potential interactions between stressors. Their placement in crops for the pollination season generally exposed colonies to more complex stressor networks, with an average of 23 stressors and 307 interactions. We discovered that the most influential stressors in a network-those that substantively impacted network architecture-are not currently addressed by beekeepers. Finally, the stressor networks showed substantial divergence among crop systems from different regions, which is consistent with the knowledge that some crops (e.g., highbush blueberry) are traditionally riskier to honey bees than others. Our approach sheds light on the stressor networks that honey bees encounter in the field and underscores the importance of considering interactions among stressors. Clearly, addressing and managing these issues will require solutions that are tailored to specific crops and regions and their associated stressor networks.

Simulation of Varroa mite control in honey bee colonies without synthetic acaricides: Demonstration of Good Beekeeping Practice for Germany in the BEEHAVE model

The BEEHAVE model simulates the population dynamics and foraging activity of a single honey bee colony (Apis mellifera) in great detail. Although it still makes numerous simplifying assumptions, it appears to capture a wide range of empirical observations. It could, therefore, in principle, also be used as a tool in beekeeper education, as it allows the implementation and comparison of different management options. Here, we focus on treatments aimed at controlling the mite Varroa destructor. However, since BEEHAVE was developed in the UK, mite treatment includes the use of a synthetic acaricide, which is not part of Good Beekeeping Practice in Germany. A practice that consists of drone brood removal from April to June, treatment with formic acid in August/September, and treatment with oxalic acid in November/December. We implemented these measures, focusing on the timing, frequency, and spacing between drone brood removals. The effect of drone brood removal and acid treatment, individually or in combination, on a mite-infested colony was examined. We quantify the efficacy of Varroa mite control as the reduction of mites in treated bee colonies compared to untreated bee colonies. We found that drone brood removal was very effective, reducing mites by 90% at the end of the first simulation year after the introduction of mites. This value was significantly higher than the 50-67% reduction expected by bee experts and confirmed by empirical studies. However, literature reports varying percent reductions in mite numbers from 10 to 85% after drone brood removal. The discrepancy between model results, empirical data, and expert estimates indicate that these three sources should be reviewed and refined, as all are based on simplifying assumptions. These results and the adaptation of BEEHAVE to the Good Beekeeping Practice are a decisive step forward for the future use of BEEHAVE in beekeeper education in Germany and anywhere where organic acids and drone brood removal are utilized.

The BEEHAVEecotox Model-Integrating a Mechanistic Effect Module into the Honeybee Colony Model

Mechanistic effect models are powerful tools for extrapolating from laboratory studies to field conditions. For bees, several good models are available that can simulate colony dynamics. Controlled and reliable experimental systems are also available to estimate the inherent toxicity of pesticides to individuals. However, there is currently no systematic and mechanistic way of linking the output of experimental ecotoxicological testing to bee models for bee risk assessment. We introduce an ecotoxicological module that mechanistically links exposure with the hazard profile of a pesticide for individual honeybees so that colony effects emerge. This mechanistic link allows the translation of results from standard laboratory studies to relevant parameters and processes for simulating bee colony dynamics. The module was integrated into the state-of-the-art honeybee model BEEHAVE. For the integration, BEEHAVE was adapted to mechanistically link the exposure and effects on different cohorts to colony dynamics. The BEEHAVEecotox model was tested against semifield (tunnel) studies, which were deemed the best study type to test whether BEEHAVEecotox predicted realistic effect sizes under controlled conditions. Two pesticides used as toxic standards were chosen for this validation to represent two different modes of action: acute mortality of foragers and chronic brood effects. The ecotoxicological module was able to predict effect sizes in the tunnel studies based on information from standard laboratory tests. In conclusion, the BEEHAVEecotox model is an excellent tool to be used for honeybee risk assessment, interpretation of field and semifield studies, and exploring the efficiency of different mitigation measures. The principles for exposure and effect modules are portable and could be used for any well-constructed honeybee model. Environ Toxicol Chem 2022;41:2870-2882. © 2022 Bayer AG & Sygenta, et al. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Chronic and Acute Effects of Imidacloprid on a Simulated BEEHAVE Honeybee Colony

Honeybees (Apis mellifera) are important pollinators for wild plants as well as for crops, but honeybee performance is threatened by several stressors including varroa mites, gaps in foraging supply, and pesticides. The consequences of bee colony longtime exposure to multiple stressors are not well understood. The vast number of possible stressor combinations and necessary study duration require research comprising field, laboratory, and simulation experiments. We simulated long-term exposure of a honeybee colony to the insecticide imidacloprid and to varroa mites carrying the deformed wing virus in landscapes with different temporal gaps in resource availability as single stressors and in combinations. Furthermore, we put a strong emphasis on chronic lethal, acute sublethal, and acute lethal effects of imidacloprid on honeybees. We have chosen conservative published values to parameterize our model (e.g., highest reported imidacloprid contamination). As expected, combinations of stressors had a stronger negative effect on bee performance than each single stressor alone, and effect sizes were larger after 3 years of exposure than after the first year. Imidacloprid-caused reduction in bee performance was almost exclusively due to chronic lethal effects because the thresholds for acute effects were rarely met in simulations. In addition, honeybee colony extinctions were observed by the last day of the first year but more pronounced on the last days of the second and third simulation year. In conclusion, our study highlights the need for more long-term studies on chronic lethal effects of pesticides on honeybees. Environ Toxicol Chem 2022;41:2318-2327. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Binary and ternary toxicological interactions of clothianidin and eight commonly used pesticides on honey bees (Apis mellifera)

Although many toxicological evaluations have been conducted for honey bees (Apis mellifera), most of these studies have only focused on the effects of individual chemicals. However, honey bees are usually exposed to pesticide mixtures under field conditions. In this study, we examined the effects of individual pesticides and mixtures of clothianidin (CLO) with eight other pesticides [carbaryl (CAR), thiodicarb (THI), chlorpyrifos (CHL), beta-cyfluthrin (BCY), gamma-cyhalothrin (GCY), tetraconazole (TET), spinosad (SPI) and indoxacarb (IND)] on honey bees using a feeding method. Toxicity tests of a 4-day exposure to individual pesticides revealed that CLO had the highest toxicity to A. mellifera, with an LC₅₀ value of 0.24 μg a.i. mL^-1, followed by IND and CHL with LC₅₀ values of 3.40 and 3.56 μg a.i. mL^-1, respectively. SPI and CAR had relatively low toxicities, with LC₅₀ values of 7.19 and 8.42 μg a.i. mL^-1, respectively. In contrast, TET exhibited the least toxicity, with an LC₅₀ value of 258.7 μg a.i. mL^-1. Most binary mixtures of CLO with other pesticides exerted additive and antagonistic effects. However, all the ternary mixtures containing CLO and TET (except for CLO+TET+THD) elicited synergistic responses to bees. Either increased numbers of components in the mixture or/and a unique mode of action appeared to be responsible for the higher toxicity of mixtures. Our findings emphasized the need for risk assessment of pesticide mixtures rather than the individual chemicals. Our data also provided information that might help growers avoid increased toxicity and unnecessary injury to pollinators.

A New Approach to Inform Restoration and Management Decisions for Sustainable Apiculture

Habitat loss has reduced the available resources for apiarists and is a key driver of poor colony health, colony loss, and reduced honey yields. The biggest challenge for apiarists in the future will be meeting increasing demands for pollination services, honey, and other bee products with limited resources. Targeted landscape restoration focusing on high-value or high-yielding forage could ensure adequate floral resources are available to sustain the growing industry. Tools are currently needed to evaluate the likely productivity of potential sites for restoration and inform decisions about plant selections and arrangements and hive stocking rates, movements, and placements. We propose a new approach for designing sites for apiculture, centred on a model of honey production that predicts how changes to plant and hive decisions affect the resource supply, potential for bees to collect resources, consumption of resources by the colonies, and subsequently, amount of honey that may be produced. The proposed model is discussed with reference to existing models, and data input requirements are discussed with reference to an Australian case study area. We conclude that no existing model exactly meets the requirements of our proposed approach, but components of several existing models could be combined to achieve these needs.

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.

Long-term effects of antibiotic treatments on honeybee colony fitness: A modelling approach

Gut microbiome disequilibrium is increasingly implicated in host fitness reductions, including for the economically important and disease-challenged western honey bee Apis mellifera. In laboratory experiments, the antibiotic tetracycline, which is used to prevent American Foulbrood Disease in countries including the US, elevates honey bee mortality by disturbing the microbiome. It is unclear, however, how elevated individual mortality affects colony-level fitness.We used an agent-based model (BEEHAVE) and empirical data to assess colony-level effects of antibiotic-induced worker bee mortality, by measuring colony size. We investigated the relationship between the duration that the antibiotic-induced mortality probability is imposed for and colony size.We found that when simulating antibiotic-induced mortality of worker bees from just 60 days per year, up to a permanent effect, the colony is reduced such that tetracycline treatment would not meet the European Food Safety Authority's (EFSA) honey bee protection goals. When antibiotic mortality was imposed for the hypothetical minimal exposure time, which assumes that antibiotics only impact the bee's fitness during the recommended treatment period of 15 days in both spring and autumn, the colony fitness reduction was only marginally under the EFSA's threshold. Synthesis and Applications. Modelling colony-level impacts of antibiotic treatment shows that individual honey bee worker mortality can lead to colony mortality. To assess the full impact, the persistence of antibiotic-induced mortality in honey bees must be determined experimentally, in vivo. We caution that as the domestication of new insect species increases, maintaining healthy gut microbiomes is of paramount importance to insect health and commercial productivity. The recommendation from this work is to limit prophylactic use of antibiotics and to not exceed recommended treatment strategies for domesticated insects. This is especially important for highly social insects as excess antibiotic use will likely decrease colony growth and an increase in colony mortality.

Honey bee colony performance affected by crop diversity and farmland structure: a modeling framework

Forage availability has been suggested as one driver of the observed decline in honey bees. However, little is known about the effects of its spatiotemporal variation on colony success. We present a modeling framework for assessing honey bee colony viability in cropping systems. Based on two real farmland structures, we developed a landscape generator to design cropping systems varying in crop species identity, diversity, and relative abundance. The landscape scenarios generated were evaluated using the existing honey bee colony model BEEHAVE, which links foraging to in-hive dynamics. We thereby explored how different cropping systems determine spatiotemporal forage availability and, in turn, honey bee colony viability (e.g., time to extinction, TTE) and resilience (indicated by, e.g., brood mortality). To assess overall colony viability, we developed metrics, PH and PP, which quantified how much nectar and pollen provided by a cropping system per year was converted into a colony's adult worker population. Both crop species identity and diversity determined the temporal continuity in nectar and pollen supply and thus colony viability. Overall farmland structure and relative crop abundance were less important, but details mattered. For monocultures and for four-crop species systems composed of cereals, oilseed rape, maize, and sunflower, PH and PP were below the viability threshold. Such cropping systems showed frequent, badly timed, and prolonged forage gaps leading to detrimental cascading effects on life stages and in-hive work force, which critically reduced colony resilience. Four-crop systems composed of rye-grass-dandelion pasture, trefoil-grass pasture, sunflower, and phacelia ensured continuous nectar and pollen supply resulting in TTE > 5 yr, and PH (269.5 kg) and PP (108 kg) being above viability thresholds for 5 yr. Overall, trefoil-grass pasture, oilseed rape, buckwheat, and phacelia improved the temporal continuity in forage supply and colony's viability. Our results are hypothetical as they are obtained from simplified landscape settings, but they nevertheless match empirical observations, in particular the viability threshold. Our framework can be used to assess the effects of cropping systems on honey bee viability and to develop land-use strategies that help maintain pollination services by avoiding prolonged and badly timed forage gaps.

Simulating Honey Bee Large-Scale Colony Feeding Studies Using the BEEHAVE Model-Part I: Model Validation

In pesticide risk assessments, semifield studies, such as large-scale colony feeding studies (LSCFSs), are conducted to assess potential risks at the honey bee colony level. However, such studies are very cost and time intensive, and high overwintering losses of untreated control hives have been observed in some studies. Honey bee colony models such as BEEHAVE may provide tools to systematically assess multiple factors influencing colony outcomes, to inform study design, and to estimate pesticide impacts under varying environmental conditions. Before they can be used reliably, models should be validated to demonstrate they can appropriately reproduce patterns observed in the field. Despite the recognized need for validation, methodologies to be used in the context of applied ecological models are not agreed on. For the parameterization, calibration, and validation of BEEHAVE, we used control data from multiple LSCFSs. We conducted detailed visual and quantitative performance analyses as a demonstration of validation methodologies. The BEEHAVE outputs showed good agreement with apiary-specific validation data sets representing the first year of the studies. However, the simulations of colony dynamics in the spring periods following overwintering were identified as less reliable. The comprehensive validation effort applied provides important insights that can inform the usability of BEEHAVE in applications related to higher tier risk assessments. In addition, the validation methodology applied could be used in a wider context of ecological models. Environ Toxicol Chem 2020;39:2269-2285. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.