BEESCOUT: A model of bee scouting behaviour and a software tool for characterizing nectar/pollen landscapes for BEEHAVE

Whole-Colony Dynamic Energy Budget Model for Bumble Bees: Assessing the Impact of Wildflower Patches on Crop Pollination

Bumble bees are important pollinators of many crops around the world. In recent decades, agricultural intensification has resulted in significant declines in bumble bee populations and the pollination services they provide. Empirical studies have shown that this trend can be reversed by enhancing the agricultural landscape, for example, by placing wildflower patches adjacent to crops. Despite the empirical evidence, the mechanisms behind these positive effects are not fully understood. Theoretical studies, in the form of mathematical or computational models, have proven useful in providing insights, but the complexity of the underlying system means that certain factors remain unexplored. In this work, we build a unique model coupling a whole-colony Dynamic Energy Budget (DEB) approach for population dynamics to a Maximum Entropy (MaxEnt) principle formulation for the spatial distribution of foraging bees. The use of a DEB to asses whole-colony energy budgets, and its coupling to a spacial model is novel. The use of MaxEnt to predict foraging spatial distributions is still in its early stages, and our work highlights its potential to advance and expand upon the traditional assumptions of the Ideal Free Distribution. We use the developed model to asses the possible benefits and drawbacks of planting wildflower nearby crops for crop pollination services. We answer questions of when should wildflowers bloom, how many should we plant, which type of wildflowers, and where should we place them.

Expanding the Scope of the Bumblebee Model BEE-STEWARD: A Simple Foraging Module Facilitates the Parameterization

The BEE-STEWARD model simulates the population dynamics and behavior of bumblebees, including foraging, in remarkable detail, allowing the impact of various stressors on their populations to be assessed. To support the underlying detailed mechanistic descriptions, BEE-STEWARD requires extensive parameterization, including corolla depth, which affects the handling time of foraging bees, for each flower species in the simulated landscape. However, this detailed approach limits the applicability of BEE-STEWARD due to the lack of data for corolla depths, while also resulting in unrealistic foraging trip durations. Here we present a simplified foraging module that uses a constant handling time for foraging, thus eliminating the need to parameterize corolla depth. This simplification allows us both to apply the model to large scales and to assume handling times that reproduce observed foraging trip durations. Our new foraging module allows large-scale population projections with BEE-STEWARD. This increases its value in policy contexts and contributes to understanding and mitigating bumblebee declines.

National-scale mapping of potential floral resources for honeybees and native pollinators in New Zealand

Floral resources are important food resources for pollinators. These resources are produced in different quantities depending on land cover and plant species composition, and the quantity of production varies seasonally. As such, land use change and management of natural resources can have substantial impacts on conservation through resource provision for pollinators, and also commercial enterprises through resources for honeybee hives which require adequate forage to be successful. In New Zealand, locations with vegetation that produce high-value honey also suffer from overcrowding of hives, as beekeepers compete for this valuable resource. At present, there is a lack of quantitative spatial data describing the production of these resources, especially over large spatial scales. Here, using maps of land cover and environment, and a large vegetation plot dataset, we show that the provision of floral resources for pollinators can be estimated spatially at national scales. These maps can be used to estimate the consequences of changing land cover, both historical and with future management actions, and to understand potential threats to floral resource provision. We find that the production of floral resources across New Zealand is highly seasonal, and overwhelmingly produced by indigenous land cover types, especially within public conservation land. Within forests, we show that floral production is dominated by a small number of plant families. Our results show the importance of native land cover for the provision of floral resources for commercial honeybee enterprises and also native pollinators. We anticipate our results will be a starting point to inform management decisions regarding the placement and stocking density of honeybee hives, and also the concession process for honeybee permits on public land. We also show how the restoration of woody ecosystems on cleared land can benefit the conservation of native pollinators by providing abundant and high-quality forage across all seasons.

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.

Will biomimetic robots be able to change a hivemind to guide honeybees' ecosystem services?

We study whether or not a group of biomimetic waggle dancing robots is able to significantly influence the swarm-intelligent decision making of a honeybee colony, e.g. to avoid foraging at dangerous food patches using a mathematical model. Our model was successfully validated against data from two empirical experiments: one examined the selection of foraging targets and the other cross inhibition between foraging targets. We found that such biomimetic robots have a significant effect on a honeybee colony's foraging decision. This effect correlates with the number of applied robots up to several dozens of robots and then saturates quickly with higher robot numbers. These robots can reallocate the bees' pollination service in a directed way towards desired locations or boost it at specific locations, without having a significant negative effect on the colony's nectar economy. Additionally, we found that such robots may be able to lower the influx of toxic substances from potentially harmful foraging sites by guiding the bees to alternative places. These effects also depend on the saturation level of the colony's nectar stores. The more nectar is already stored in the colony, the easier the bees are guided by the robots to alternative foraging targets. Our study shows that biomimetic and socially immersive biomimetic robots are a relevant future research target in order to support (a) the bees by guiding them to safe (pesticide free) places, (b) the ecosystem via boosted and directed pollination services and (c) human society by supporting agricultural crop pollination, thus increasing our food security this way.

Bee species perform distinct foraging behaviors that are best described by different movement models

In insect-pollinated plants, the foraging behavior of pollinators affects their pattern of movement. If distinct bee species vary in their foraging behaviors, different models may best describe their movement. In this study, we quantified and compared the fine scale movement of three bee species foraging on patches of Medicago sativa. Bee movement was described using distances and directions traveled between consecutive racemes. Bumble bees and honey bees traveled shorter distances after visiting many flowers on a raceme, while the distance traveled by leafcutting bees was independent of flower number. Transition matrices and vectors were calculated for bumble bees and honey bees to reflect their directionality of movement within foraging bouts; leafcutting bees were as likely to move in any direction. Bee species varied in their foraging behaviors, and for each bee species, we tested four movement models that differed in how distances and directions were selected, and identified the model that best explained the movement data. The fine-scale, within-patch movement of bees could not always be explained by a random movement model, and a general model of movement could not be applied to all bee species.

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.

The FloRes Database: A floral resources trait database for pollinator habitat-assessment generated by a multistep workflow

Background

The decline of pollinating insects in agricultural landscapes proceeds due to intensive land use and the associated loss of habitat and food sources. The feeding of those insects depends on the spatial and temporal distribution of nectar and pollen as food resource. Hence, to protect insect biodiversity, a spatio-temporal assessment of food quantity of their habitats is necessary. Therefore, sufficient data on traits of floral resources are required.

New information

As floral resources' traits of plants are important to quantify food availability, we present two databases, the FloRes Database (Floral Resources Database) and the raw database, from where FloRes was derived. Both databases contain the plant traits: (1) flowering period, (2) floral-unit density per day, (3) nectar volume per floral unit per day, (4) sugar content per floral unit, (5) sugar concentration in nectar, (6) pollen mass or volume per floral unit and per day, (7) protein content of pollen and (8) corolla depth. All traits were sampled from literature and online databases. The raw database consists of 702 specified plant species, 138 unspecified species 37 species (spec., sp), 22 species pluralis (spp) and, for 79, only the genus was identified) and two species complexes (agg.). Those 842 taxa belong to 488 genera and 102 families. Finally, only 27 taxa have a complete set of traits, too few for a sufficient assessment of spatio-temporal availability of floral food-resources.As information on floral resources is scattered throughout many publications with different units, we also present our multistep workflow implemented in five consecutive R-scripts. The multistep workflow standardises the trait units of the raw database to comparable entities with identical units and aggregates them on a reasonable taxonomic level into the second application database, the FloRes Database. Finally, the FloRes Database contains aggregated information of traits for 42 taxa and, when corolla depth is excluded, for 72 taxa.This is the first attempt to gather these eight traits from different literature sources into one database with a multistep workflow. The publication of the multistep workflow enables the users to extend the FloRes Database on their own demands with other literature data or newly-gathered data to improve quantification of food resources. Especially, the combination of pollen, nectar and the open flowers per square metre is, as far as we know, a novelty.The FloRes Database can be used to evaluate the quantity of food-resource habitats available for pollinators, for example, to compare seed mixtures of agri-environmental measures, such as flower strips, considering flower phenology on a daily basis.

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.

Memory-guided foraging and landscape design interact to determine ecosystem services

Many studies examine how the landscape affects memory-informed movement patterns, but very few examine how memory-informed foragers influence the landscape. This reverse relationship is an important factor in preventing the continued decline of many ecosystem services. We investigate this question in the context of crop pollination services by wild bees, a critical ecosystem service that is in steep decline. Many studies suggest that adding wild flower patches near crops can result in higher crop pollination services, but specific advice pertaining to the optimal location and density of these wild flower patches is lacking, as well as any estimate of the expected change in crop pollination services. In this work, we seek to understand what is the optimal placement of a flower patch relative to a single crop field, during crop bloom and considering spatial factors alone. We develop an individual based model of memory-based foraging by bumble bees to simulate bee movement from a single nest while the crop is in bloom, and measure the resulting crop pollination services. We consider a single crop field enhanced with a wild flower patch in a variable location, and measure crop flower visitation over the course of a single day. We analyze the pollination intensity and spatial distribution of flower visits to determine optimal wild flower patch placement for an isolated crop field. We find that the spatial arrangement of crop and wild flower patch have a significant effect on the number of crop flower visits, and that these effects arise from the memory-informed foraging pattern. The most effective planting locations are either in the centre of the crop field or on the far side of the crop field, away from the single bumble bee nest.

The use of ecological models to assess the effects of a plant protection product on ecosystem services provided by an orchard

The objective of this case study was to explore the feasibility of using ecological models for applying an ecosystem services-based approach to environmental risk assessment using currently available data and methodologies. For this we used a 5 step approach: 1) selection of environmental scenario, 2) ecosystem service selection, 3) development of logic chains, 4) selection and application of ecological models and 5) detailed ecosystem service assessment. The study system is a European apple orchard managed according to integrated pest management principles. An organophosphate insecticide was used as the case study chemical. Four ecosystem services are included in this case study: soil quality regulation, pest control, pollination and recreation. Logic chains were developed for each ecosystem service and describe the link between toxicant effects on service providing units and ecosystem services delivery. For the soil quality regulation ecosystem service, springtails and earthworms were the service providing units, for the pest control ecosystem service it was ladybirds, for the pollination ecosystem service it was honeybees and for the recreation ecosystem service it was the meadow brown butterfly. All the ecological models addressed the spatio-temporal magnitude of the direct effects of the insecticide on the service providing units and ecological production functions were used to extrapolate these outcomes to the delivery of ecosystem services. For all ecosystem services a decision on the acceptability of the modelled and extrapolated effects on the service providing units could be made using the protection goals as set by the European Food Safety Authority (EFSA). Developing quantitative ecological production functions for extrapolation of ecosystem services delivery from population endpoints remains one of the major challenges. We feel that the use of ecological models can greatly add to this development, although the further development of existing ecological models, and of new models, is needed for this.

Review on mathematical modeling of honeybee population dynamics

Honeybees have an irreplaceable position in agricultural production and the stabilization of natural ecosystems. Unfortunately, honeybee populations have been declining globally. Parasites, diseases, poor nutrition, pesticides, and climate changes contribute greatly to the global crisis of honeybee colony losses. Mathematical models have been used to provide useful insights on potential factors and important processes for improving the survival rate of colonies. In this review, we present various mathematical tractable models from different aspects: 1) simple bee-only models with features such as age segmentation, food collection, and nutrient absorption; 2) models of bees with other species such as parasites and/or pathogens; and 3) models of bees affected by pesticide exposure. We aim to review those mathematical models to emphasize the power of mathematical modeling in helping us understand honeybee population dynamics and its related ecological communities. We also provide a review of computational models such as VARROAPOP and BEEHAVE that describe the bee population dynamics in environments that include factors such as temperature, rainfall, light, distance and quality of food, and their effects on colony growth and survival. In addition, we propose a future outlook on important directions regarding mathematical modeling of honeybees. We particularly encourage collaborations between mathematicians and biologists so that mathematical models could be more useful through validation with experimental data.

Differential equation model for central-place foragers with memory: implications for bumble bee crop pollination

Bumble bees provide valuable pollination services to crops around the world. However, their populations are declining in intensively farmed landscapes. Understanding the dispersal behaviour of these bees is a key step in determining how agricultural landscapes can best be enhanced for bumble bee survival. Here we develop a partial integro-differential equation model to predict the spatial distribution of foraging bumble bees in dynamic heterogeneous landscapes. In our model, the foraging population is divided into two subpopulations, one engaged in an intensive search mode (modeled by diffusion) and the other engaged in an extensive search mode (modeled by advection). Our model considers the effects of resource-dependent switching rates between movement modes, resource depletion, central-place foraging behaviour, and memory. We use our model to investigate how crop pollination services are affected by wildflower enhancements. We find that planting wildflowers such that the crop is located in between the wildflowers and the nest site can benefit crop pollination in two different scenarios. If the bees do not have a strong preference for wildflowers, a small or low density wildflower patch is beneficial. If, on the other hand, the bees strongly prefer the wildflowers, then a large or high density wildflower patch is beneficial. The increase of the crop pollination services in the later scenario is of remarkable magnitude.

A model of resource partitioning between foraging bees based on learning

Central place foraging pollinators tend to develop multi-destination routes (traplines) to exploit patchily distributed plant resources. While the formation of traplines by individual pollinators has been studied in detail, how populations of foragers use resources in a common area is an open question, difficult to address experimentally. We explored conditions for the emergence of resource partitioning among traplining bees using agent-based models built from experimental data of bumblebees foraging on artificial flowers. In the models, bees learn to develop routes as a consequence of feedback loops that change their probabilities of moving between flowers. While a positive reinforcement of movements leading to rewarding flowers is sufficient for the emergence of resource partitioning when flowers are evenly distributed, the addition of a negative reinforcement of movements leading to unrewarding flowers is necessary when flowers are patchily distributed. In environments with more complex spatial structures, the negative experiences of individual bees on flowers favour spatial segregation and efficient collective foraging. Our study fills a major gap in modelling pollinator behaviour and constitutes a unique tool to guide future experimental programs.

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.

EFSA Scientific Committee, More S, Bampidis V, Benford D, Bragard C, Halldorsson T, Hernández‐Jerez A, Bennekou SH, Koutsoumanis K, Machera K, Naegeli H, Nielsen SS, Schlatter J, Schrenk D, Silano V, Turck D, Younes M, Arnold G, Dorne J, Maggiore A, Pagani S, Szentes C, Terry S, Tosi S, Vrbos D, Zamariola G, Rortais A. 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.

Honeybee colonies compensate for pesticide-induced effects on royal jelly composition and brood survival with increased brood production

Sublethal doses of pesticides affect individual honeybees, but colony-level effects are less well understood and it is unclear how the two levels integrate. We studied the effect of the neonicotinoid pesticide clothianidin at field realistic concentrations on small colonies. We found that exposure to clothianidin affected worker jelly production of individual workers and created a strong dose-dependent increase in mortality of individual larvae, but strikingly the population size of capped brood remained stable. Thus, hives exhibited short-term resilience. Using a demographic matrix model, we found that the basis of resilience in dosed colonies was a substantive increase in brood initiation rate to compensate for increased brood mortality. However, computer simulation of full size colonies revealed that the increase in brood initiation led to severe reductions in colony reproduction (swarming) and long-term survival. This experiment reveals social regulatory mechanisms on colony-level that enable honeybees to partly compensate for effects on individual level.

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.

Translocation of pharmaceuticals from wastewater into beehives

There has been a substantial research focus on the presence of pesticides in flowers and the subsequent exposure to honeybees. Here we demonstrate for the first time that honeybees can also be exposed to pharmaceuticals, commonly present in wastewater. Residues of carbamazepine (an anti-epileptic drug) up to 371 ng/mL and 30 µg/g were detected in nectar and pollen sampled from zucchini flowers (Cucurbita pepo) grown in carbamazepine spiked soil (0.5-20 µg/g). Under realistic exposure conditions from the use of recycled wastewater, carbamazepine concentrations were estimated to be 0.37 ng/L and 30 ng/kg in nectar and pollen, respectively. Incorporation of environmentally relevant carbamazepine residues in nectar and pollen into a modelling framework able to simulate beehive dynamics including the honeybee foraging activity at the landscape scale (BEEHAVE and BEESCOUT) enabled the simulation of carbamazepine translocation from zucchini fields into honeybee hives. Carbamazepine accumulation was modelled in 11 beehives across a 25 km2 landscape over three years chosen to represent distinct climatic conditions. During a single flowering period, carbamazepine concentrations were simulated to range between 0 and 2478 ng per beehive. The amount of carbamazepine gathered not only varied across the simulated years but there were also differences in accumulation of carbamazepine between beehives within the same year. This work illustrates a fundamental first step in assessing the risk of pharmaceuticals to bees through realistic scenarios by demonstrating a method to quantify potential exposure of honeybees at the landscape scale. Pharmaceuticals are being inadvertently but increasingly applied to agricultural lands globally via the use of wastewater for agricultural irrigation in response to water scarcity problems. We have demonstrated a route of pharmaceutical exposure to honeybees via contaminated nectar and pollen. Given the biological potency of pharmaceuticals, accumulation of these chemicals in nectar and pollen suggest potential implications for honeybee health, with unknown ecosystem consequences.

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

A lack of standard and internationally agreed procedures for higher-tier risk assessment of plant protection products for bees makes coherent availability of data, their interpretation, and their use for risk assessment challenging. Focus has been given to the development of modeling approaches, which in the future could fill this gap. The BEEHAVE model, and its submodels, is the first model framework attempting to link 2 processes vital for the assessment of bee colonies: the within-hive dynamics for honey bee colonies and bee foraging in heterogeneous and dynamic landscapes. We use empirical data from a honey bee field study to conduct a model evaluation using the control data set. Simultaneously, we are testing several model setups for the interlinkage between the within-hive dynamics and the landscape foraging module. Overall, predictions of beehive dynamics fit observations made in the field. This result underpins the European Food Safety Authority's evaluation of the BEEHAVE model that the most important in-hive dynamics are represented and correctly implemented. We show that starting conditions of a colony drive the simulated colony dynamics almost entirely within the first few weeks, whereas the impact is increasingly substituted by the impact of foraging activity. Common among field studies is that data availability for hive observations and landscape characterizations is focused on the proportionally short exposure phase (i.e., the phase where colony starting conditions drive the colony dynamics) in comparison to the postexposure phase that lasts several months. It is vital to redistribute experimental efforts toward more equal data aquisition throughout the experiment to assess the suitability of using BEEHAVE for the prediction of bee colony overwintering survival. Environ Toxicol Chem 2019;38:2535-2545. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Honey bee colony-level exposure and effects in realistic landscapes: An application of BEEHAVE simulating clothianidin residues in corn pollen

Discerning potential effects of insecticides on honey bee colonies in field studies conducted under realistic conditions can be challenging because of concurrent interactions with other environmental conditions. Honey bee colony models can control exposures and other environmental factors, as well as assess links among pollen and nectar residues in the landscape, their influx into the colony, and the resulting exposures and effects on bees at different developmental stages. We extended the colony model BEEHAVE to represent exposure to the insecticide clothianidin via residues in pollen from treated cornfields set in real agricultural landscapes in the US Midwest. We assessed their potential risks to honey bee colonies over a 1-yr cycle. Clothianidin effects on colony strength were only observed if unrealistically high residue levels in the pollen were simulated. The landscape composition significantly impacted the collection of pollen (residue exposure) from the cornfields, resulting in higher colony-level effects in landscapes with lower proportions of semi-natural land. The application of the extended BEEHAVE model with a pollen exposure-effects module provides a case study for the application of a mechanistic honey bee colony model in pesticide risk assessment integrating the impact of a range of landscape compositions. Environ Toxicol Chem 2019;38:423-435. © 2018 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Bombus terrestris in a mass-flowering pollinator-dependent crop: A mutualistic relationship?

Bumblebees (Bombus spp.) rely on an abundant and diverse selection of floral resources to meet their nutritional requirements. In farmed landscapes, mass-flowering crops can provide an important forage resource for bumblebees, with increased visitation from bumblebees into mass-flowering crops having an additional benefit to growers who require pollination services. This study explores the mutualistic relationship between Bombus terrestris L. (buff-tailed bumblebee), a common species in European farmland, and the mass-flowering crop courgette (Cucurbita pepo L.) to see how effective B. terrestris is at pollinating courgette and in return how courgette may affect B. terrestris colony dynamics. By combining empirical data on nectar and pollen availability with model simulations using the novel bumblebee model Bumble-BEEHAVE, we were able to quantify and simulate for the first time, the importance of courgette as a mass-flowering forage resource for bumblebees. Courgette provides vast quantities of nectar to ensure a high visitation rate, which combined with abundant pollen grains, enables B. terrestris to have a high pollination potential. While B. terrestris showed a strong fidelity to courgette flowers for nectar, courgette pollen was not found in any pollen loads from returning foragers. Nonetheless, model simulations showed that early season courgette (nectar) increased the number of hibernating queens, colonies, and adult workers in the modeled landscapes. Synthesis and applications. Courgette has the potential to improve bumblebee population dynamics; however, the lack of evidence of the bees collecting courgette pollen in this study suggests that bees can only benefit from this transient nectar source if alternative floral resources, particularly pollen, are also available to fulfill bees' nutritional requirements in space and time. Therefore, providing additional forage resources could simultaneously improve pollination services and bumblebee populations.

Impacts of deforestation on plant-pollinator networks assessed using an agent based model

Plant-pollinator networks have been widely used to understand the ecology of mutualistic interactions between plants and animals. While a number of general patterns have been identified, the mechanisms underlying the structure of plant-pollinator networks are poorly understood. Here we present an agent based model (ABM) that simulates the movement of bees over heterogeneous landscapes and captures pollination events, enabling the influence of landscape pattern on pollination networks to be explored. Using the model, we conducted a series of experiments using virtual landscapes representing a gradient of forest loss and fragmentation. The ABM was able to produce expected trends in network structure, from simulations of interactions between individual plants and pollinators. For example, results indicated an increase in the index of complementary specialization (H2') and a decline in network connectance with increasing forest cover. Furthermore, network nestedness was not associated with the degree of forest cover, but was positively related to forest patch size, further supporting results obtained in the field. This illustrates the potential value of ABMs for exploring the structure and dynamics of plant-pollinator networks, and for understanding the mechanisms that underlie them. We attribute the results obtained primarily to a shift from specialist to generalist pollinators with increasing forest loss, a trend that has been observed in some field situations.

The prevalence of olfactory- versus visual-signal encounter by searching bumblebees

While the phrase 'foraging bumblebee' brings to mind a bumbling bee flying flower to flower in a sunny meadow, foraging is a complicated series of behaviors such as: locating a floral patch; selecting a flower-type; learning handling skills for pollen and nectar extraction; determining when to move-on from a patch; learning within-patch paths (traplining); and learning efficient hive-to-patch routes (spatial navigation). Thus the term 'forager' encompasses multiple distinct behaviors that rely on different sensory modalities. Despite a robust literature on bumblebee foraging behavior, few studies are directly relevant to sensory-guided search; i.e. how workers locate novel patches. The first step in answering this question is to determine what sensory information is available to searching bumblebees. This manuscript presents a computational model that elucidates the relative frequency of visual and olfactory cues that are available to workers searching for floral resources under a range of ecologically relevant scenarios. Model results indicate that odor is the most common sensory cue encountered during search flights. When the likelihood of odor-plume contact is higher, odor-encounter is ubiquitous. While integrative (visual + olfactory) cues are common when foragers are searching for larger flowers (e.g. Echinacea), they become rare when foragers are searching for small flowers (e.g. Penstemon). Visual cues are only encountered in isolation when foragers are seeking large flowers with a low odor-plume contact probability. These results indicate that despite the multisensory nature of floral signals, different modalities may be encountered in isolation during search-behavior, as opposed to the reliably multimodal signals encountered during patch-exploitation or nectar/ pollen acquisition.

Bumble-BEEHAVE: A systems model for exploring multifactorial causes of bumblebee decline at individual, colony, population and community level

World‐wide declines in pollinators, including bumblebees, are attributed to a multitude of stressors such as habitat loss, resource availability, emerging viruses and parasites, exposure to pesticides, and climate change, operating at various spatial and temporal scales. Disentangling individual and interacting effects of these stressors, and understanding their impact at the individual, colony and population level are a challenge for systems ecology. Empirical testing of all combinations and contexts is not feasible. A mechanistic multilevel systems model (individual‐colony‐population‐community) is required to explore resilience mechanisms of populations and communities under stress.

We present a model which can simulate the growth, behaviour and survival of six UK bumblebee species living in any mapped landscape. Bumble‐BEEHAVE simulates, in an agent‐based approach, the colony development of bumblebees in a realistic landscape to study how multiple stressors affect bee numbers and population dynamics. We provide extensive documentation, including sensitivity analysis and validation, based on data from literature. The model is freely available, has flexible settings and includes a user manual to ensure it can be used by researchers, farmers, policy‐makers, NGOs or other interested parties.

Model outcomes compare well with empirical data for individual foraging behaviour, colony growth and reproduction, and estimated nest densities.

Simulating the impact of reproductive depression caused by pesticide exposure shows that the complex feedback mechanisms captured in this model predict higher colony resilience to stress than suggested by a previous, simpler model.

Synthesis and applications. The Bumble‐BEEHAVE model represents a significant step towards predicting bumblebee population dynamics in a spatially explicit way. It enables researchers to understand the individual and interacting effects of the multiple stressors affecting bumblebee survival and the feedback mechanisms that may buffer a colony against environmental stress, or indeed lead to spiralling colony collapse. The model can be used to aid the design of field experiments, for risk assessments, to inform conservation and farming decisions and for assigning bespoke management recommendations at a landscape scale.

The Bumble‐BEEHAVE model represents a significant step towards predicting bumblebee population dynamics in a spatially explicit way. It enables researchers to understand the individual and interacting effects of the multiple stressors affecting bumblebee survival and the feedback mechanisms that may buffer a colony against environmental stress, or indeed lead to spiralling colony collapse. The model can be used to aid the design of field experiments, for risk assessments, to inform conservation and farming decisions and for assigning bespoke management recommendations at a landscape scale.

Inter-individual variability in the foraging behaviour of traplining bumblebees

Workers of social insects, such as bees, ants and wasps, show some degree of inter-individual variability in decision-making, learning and memory. Whether these natural cognitive differences translate into distinct adaptive behavioural strategies is virtually unknown. Here we examined variability in the movement patterns of bumblebee foragers establishing routes between artificial flowers. We recorded all flower visitation sequences performed by 29 bees tested for 20 consecutive foraging bouts in three experimental arrays, each characterised by a unique spatial configuration of artificial flowers and three-dimensional landmarks. All bees started to develop efficient routes as they accumulated foraging experience in each array, and showed consistent inter-individual differences in their levels of route fidelity and foraging performance, as measured by travel speed and the frequency of revisits to flowers. While the tendency of bees to repeat the same route was influenced by their colony origin, foraging performance was correlated to body size. The largest foragers travelled faster and made less revisits to empty flowers. We discuss the possible adaptive value of such inter-individual variability within the forager caste for optimisation of colony-level foraging performances in social pollinators.