An Environmental Model of Honey Bee Colony Collapse Due to Pesticide Contamination

Pollen foraging mediates exposure to dichotomous stressor syndromes in honey bees

Recent declines in the health of honey bee colonies used for crop pollination pose a considerable threat to global food security. Foraging by honey bee workers represents the primary route of exposure to a plethora of toxins and pathogens known to affect bee health, but it remains unclear how foraging preferences impact colony-level patterns of stressor exposure. Resolving this knowledge gap is crucial for enhancing the health of honey bees and the agricultural systems that rely on them for pollination. To address this, we carried out a national-scale experiment encompassing 456 Canadian honey bee colonies to first characterize pollen foraging preferences in relation to major crops and then explore how foraging behavior influences patterns of stressor exposure. We used a metagenetic approach to quantify honey bee dietary breadth and found that bees display distinct foraging preferences that vary substantially relative to crop type and proximity, and the breadth of foraging interactions can be used to predict the abundance and diversity of stressors a colony is exposed to. Foraging on diverse plant communities was associated with increased exposure to pathogens, while the opposite was associated with increased exposure to xenobiotics. Our work provides the first large-scale empirical evidence that pollen foraging behavior plays an influential role in determining exposure to dichotomous stressor syndromes in honey bees.

Enhancing knowledge of chemical exposures and fate in honey bee hives: Insights from colony structure and interactions

Honey bees are unintentionally exposed to a wide range of chemicals through various routes in their natural environment, yet research on the cumulative effects of multi-chemical and sublethal exposures on important caste members, including the queen bee and brood, is still in its infancy. The hive's social structure and food-sharing (trophallaxis) practices are important aspects to consider when identifying primary and secondary exposure pathways for residential hive members and possible chemical reservoirs within the colony. Secondary exposures may also occur through chemical transfer (maternal offloading) to the brood and by contact through possible chemical diffusion from wax cells to all hive members. The lack of research on peer-to-peer exposures to contaminants and their metabolites may be in part due to the limitations in sensitive analytical techniques for monitoring chemical fate and dispersion. Combined application of automated honey bee monitoring and modern chemical trace analysis techniques could offer rapid progress in quantifying chemical transfer and accumulation within the hive environment and developing effective mitigation strategies for toxic chemical co-exposures. To enhance the understanding of chemical fate and toxicity within the entire colony, it is crucial to consider both the intricate interactions among hive members and the potential synergistic effects arising from combinations of chemical and their metabolites.

A return-to-home model with commuting people and workers

This article proposes a new model to describe human intra-city mobility. The goal is to combine the convection-diffusion equation to describe commuting people's movement and the density of individuals at home. We propose a new model extending our previous work with a compartment of office workers. To understand such a model, we use semi-group theory and obtain a convergence result of the solutions to an equilibrium distribution. We conclude this article by presenting some numerical simulations of the model.

Generalized Stressors on Hive and Forager Bee Colonies

Hive-forming bees play an integral role in promoting agricultural sustainability and ecosystem preservation. The recent worldwide decline of several species of bees, and in particular, the honeybee in the United States, highlights the value in understanding possible causes. Over the past decade, numerous mathematical models and empirical experiments have worked to understand the causes of colony stress, with a particular focus on colony collapse disorder. We integrate and enhance major mathematical models of the past decade to create a single, analytically tractable model using a traditional disease modeling framework that incorporates both lethal and sublethal stressors. On top of this synthesis, a major innovation of our model is the generalization of stressor attributes including their transmissibility, impairment level, lethality, duration, and temporal-occurrence. Our model is validated against numerous emergent, biological characteristics and demonstrates that precocious foraging and labor destabilization can produce colony collapse disorder. The thresholds for these phenomena to occur depend on the characteristics and timing of the stressor, thus motivating further empirical and theoretical studies into stressor characteristics.

Negative but antagonistic effects of neonicotinoid insecticides and ectoparasitic mites Varroa destructor on Apis mellifera honey bee food glands

Collaborative brood care by workers is essential for the functionality of eusocial Apis mellifera honey bee colonies. The hypopharyngeal food glands of workers play a crucial role in this context. Even though there is consensus that ubiquitous ectoparasitic mites Varroa destructor and widespread insecticides, such as neonicotinoids, are major stressors for honey bee health, their impact alone and in combination on the feeding glands of workers is poorly understood. Here, we show that combined exposure to V. destructor and neonicotinoids antagonistically interacted on hypopharyngeal gland size, yet they did not interact on emergence body mass or survival. While the observed effects of the antagonistic interaction were less negative than expected based on the sum of the individual effects, hypopharyngeal gland size was still significantly reduced. Alone, V. destructor parasitism negatively affected emergence body mass, survival, and hypopharyngeal gland size, whereas neonicotinoid exposure reduced hypopharyngeal gland size only. Since size is associated with hypopharyngeal gland functionality, a reduction could result in inadequate brood care. As cooperative brood care is a cornerstone of eusociality, smaller glands could have adverse down-stream effects on inclusive fitness of honey bee colonies. Therefore, our findings highlight the need to further study how ubiquitous stressors like V. destructor and neonicotinoids interact to affect honey bees.

Return-to-home model for short-range human travel

In this work, we develop a mathematical model to describe the local movement of individuals by taking into account their return to home after a period of travel. We provide a suitable functional framework to handle this system and study the large-time behavior of the solutions. We extend our model by incorporating a colonization process and applying the return to home process to an epidemic.

Effects of glyphosate exposure on honeybees

Honeybees show an important pollination ability and play vital roles in improving crop yields and increasing plant genetic diversity, thereby generating tremendous economic benefits for humans. However, honeybee survival is affected by a number of biological and abiotic stresses, including the effects of fungi, bacteria, viruses, parasites, and especially agrochemicals. Glyphosate, a broad-spectrum herbicide that is primarily used for weed control in agriculture, has been reported to have lethal and sublethal effects on honeybees. Here, we summarize recent advances in research on the effects of glyphosate on honeybees, including effects on their behaviors, growth and development, metabolic processes, and immune defense, providing a detailed reference for studying the mechanism of action of pesticides. Furthermore, we provide possible directions for future research on glyphosate toxicity to honeybees.

Overwintering honeybees maintained dynamic and stable intestinal bacteria

Honeybee is an important pollinator for maintaining ecological balance. However, scientist found the bizarre mass death of bees in winter. Meanwhile, some reported that the differences composed of intestinal bacteria between healthy honeybees and CCD honeybees. It is essential that explored dynamic changes to the intestinal bacteria in overwintering honeybees. We collected bee samples before overwintering, during prophase of overwintering, metaphase of overwintering, anaphase of overwintering, telophase of overwintering, and after overwintering. By using high-throughput sequencing targeting the V3-V4 regions of the 16S rDNA, the abundance of the intestinal bacteria were analyzed in overwintering honeybees. A total of 1,373,886 high-quality sequences were acquired and Proteobacteria (85.69%), Firmicutes (10.40%), Actinobacteria (3.66%), and Cyanobacteria (1.87%) were identified as major components of the intestinal bacteria. All core honeybee intestinal bacteria genera, such as Gilliamella, Bartonella, Snodgrassella, Lactobacillus, Frischella, Commensalibacter, and Bifidobacterium were detected. The abundance of Actinobacteria, Bartonella, and Bifidobacterium increased initially and then decreased in winter honeybees. There were no significant differences in the richness and evenness of the microbiota in overwintering honeybees; however, there was a statistically significant difference in the beta diversity of the intestinal bacteria after overwintering compared with that in other groups. Our results suggested that honeybees maintained their intestinal ecosystem balance, and increased the abundance of gut probiotics in response to environmental and nutrition pressures in winter.

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.

Dynamics of task allocation in social insect colonies: scaling effects of colony size versus work activities

The mechanisms through which work is organized are central to understanding how complex systems function. Previous studies suggest that task organization can emerge via nonlinear dynamical processes wherein individuals interact and modify their behavior through simple rules. However, there is very limited theory about how those processes are shaped by behavioral variation within social groups. In this work, we propose an adaptive modeling framework on task allocation by incorporating variation both in task performance and task-related metabolic rates. We study the scaling effects of colony size on the resting probability as well as task allocation. We also numerically explore the effects of stochastic noise on task allocation in social insect colonies. Our theoretical and numerical results show that: (a) changes in colony size can regulate the probability of colony resting and the allocation of tasks, and the direction of regulation depends on the nonlinear metabolic scaling effects of tasks; (b) increased response thresholds may cause colonies to rest in varied patterns such as periodicity. In this case, we observed an interesting bubble phenomenon in the task allocation of social insect colonies for the first time; (c) stochastic noise can cause work activities and task demand to fluctuate within a range, where the amplitude of the fluctuation is positively correlated with the intensity of noise.

Markerless tracking of an entire honey bee colony

From cells in tissue, to bird flocks, to human crowds, living systems display a stunning variety of collective behaviors. Yet quantifying such phenomena first requires tracking a significant fraction of the group members in natural conditions, a substantial and ongoing challenge. We present a comprehensive, computational method for tracking an entire colony of the honey bee Apis mellifera using high-resolution video on a natural honeycomb background. We adapt a convolutional neural network (CNN) segmentation architecture to automatically identify bee and brood cell positions, body orientations and within-cell states. We achieve high accuracy (~10% body width error in position, ~10° error in orientation, and true positive rate > 90%) and demonstrate months-long monitoring of sociometric colony fluctuations. These fluctuations include ~24 h cycles in the counted detections, negative correlation between bee and brood, and nightly enhancement of bees inside comb cells. We combine detected positions with visual features of organism-centered images to track individuals over time and through challenging occluding events, recovering ~79% of bee trajectories from five observation hives over 5 min timespans. The trajectories reveal important individual behaviors, including waggle dances and crawling inside comb cells. Our results provide opportunities for the quantitative study of collective bee behavior and for advancing tracking techniques of crowded systems.

Neonicotinoids disrupt circadian rhythms and sleep in honey bees

Honey bees are critical pollinators in ecosystems and agriculture, but their numbers have significantly declined. Declines in pollinator populations are thought to be due to multiple factors including habitat loss, climate change, increased vulnerability to disease and parasites, and pesticide use. Neonicotinoid pesticides are agonists of insect nicotinic cholinergic receptors, and sub-lethal exposures are linked to reduced honey bee hive survival. Honey bees are highly dependent on circadian clocks to regulate critical behaviors, such as foraging orientation and navigation, time-memory for food sources, sleep, and learning/memory processes. Because circadian clock neurons in insects receive light input through cholinergic signaling we tested for effects of neonicotinoids on honey bee circadian rhythms and sleep. Neonicotinoid ingestion by feeding over several days results in neonicotinoid accumulation in the bee brain, disrupts circadian rhythmicity in many individual bees, shifts the timing of behavioral circadian rhythms in bees that remain rhythmic, and impairs sleep. Neonicotinoids and light input act synergistically to disrupt bee circadian behavior, and neonicotinoids directly stimulate wake-promoting clock neurons in the fruit fly brain. Neonicotinoids disrupt honey bee circadian rhythms and sleep, likely by aberrant stimulation of clock neurons, to potentially impair honey bee navigation, time-memory, and social communication.

A spatial model of honey bee colony collapse due to pesticide contamination of foraging bees

We develop a model of honey bee colony collapse based on contamination of forager bees in pesticide contaminated spatial environments. The model consists of differential and difference equations for the spatial distributions of the uncontaminated and contaminated forager bees. A key feature of the model is incorporation of the return to the hive each day of forager bees. The model quantifies colony collapse in terms of two significant properties of honey bee colonies: (1) the fraction of contaminated forager bees that fail to return home due to pesticide contamination, and (2) the fraction of forager bees in the total forager bee population that return to the sites visited on the previous day. If the fraction of contaminated foragers failing to return home is high, then the total population falls below a critical threshold and colony collapse ensues. If the fraction of all foragers that return to previous foraging sites is high, then foragers who visit contaminated sites multiple times have a higher probability of becoming contaminated, and colony collapse ensues. This quantification of colony collapse provides guidance for implementing measures for its avoidance.