Brood indicators are an early warning signal of honey bee colony loss-a simulation-based study.

Honey bees (Apis mellifera) are exposed to multiple stressors such as pesticides, lack of forage, and diseases. It is therefore a long-standing aim to develop robust and meaningful indicators of bee vitality to assist beekeepers While established indicators often focus on expected colony winter mortality based on adult bee abundance and honey reserves at the beginning of the winter, it would be useful to have indicators that allow detection of stress effects earlier in the year to allow for adaptive management. We used the established honey bee simulation model BEEHAVE to explore the potential of different indicators such as population size, number of capped brood cells, flight activity, abundance of Varroa mites, honey stores and a brood-bee ratio. We implemented two types of stressors in our simulations: 1) parasite pressure, i.e. sub-optimal Varroa treatment by the beekeeper (hereafter referred as Biotic stress) and 2) temporal forage gaps in spring and autumn (hereafter referred as Environmental stress). Neither stressor type could be detected by bee abundance or honey reserves at the end of the first year. However, all response variables used in this study did reveal early warning signals during the course of the year. The most reliable and useful measures seem to be related to brood and the abundance of Varroa mites at the end of the year. However, while in the model we have full access to time series of variables from stressed and unstressed colonies, knowledge of these variables in the field is challenging. We discuss how our findings can nevertheless be used to develop practical early warning indicators. As a next step in the interactive development of such indicators we suggest empirical studies on the importance of the number of capped brood cells at certain times of the year on bee population vitality.

Effective pest management approaches can mitigate honey bee (Apis mellifera) colony winter loss across a range of weather conditions in small-scale, stationary apiaries.

Managed honey bee (Apis mellifera L.) colonies in North America and Europe have experienced high losses in recent years, which have been linked to weather conditions, lack of quality forage, and high parasite loads, particularly the obligate brood parasite, Varroa destructor. These factors may interact at various scales to have compounding effects on honey bee health, but few studies have been able to simultaneously investigate the effects of weather conditions, landscape factors, and management of parasites. We analyzed a dataset of 3,210 survey responses from beekeepers in Pennsylvania from 2017 to 2022 and combined these with remotely sensed weather variables and novel datasets about seasonal forage availability into a Random Forest model to investigate drivers of winter loss. We found that beekeepers who used treatment against Varroa had higher colony survival than those who did not treat. Moreover, beekeepers who used multiple types of Varroa treatment had higher colony survival rates than those who used 1 type of treatment. Our models found weather conditions are strongly associated with survival, but multiple-treatment type colonies had higher survival across a broader range of climate conditions. These findings suggest that the integrated pest management approach of combining treatment types can potentially buffer managed honey bee colonies from adverse weather conditions.

An Analysis of Honeybee Population Dynamics.

Colony Collapse disorder (the CCD) is the term used to describe the global decline in bee populations. The research mission of this article is to identify which factors contribute to the CCD and understand how these factors contribute to the decline of bee populations, which may provide methods for restoring global bee populations. Two parts of the study will be mentioned in this article. The first half of our study was to understand such collective intelligence (and habits such as seasonal behavioral change) and use a mathematical model to simulate it. We then input the variables that we used to simulate honeybee collective intelligence into a time-dependent model to predict the population of a honey colony over time. In this model, we excluded the factors that might cause the CCD on purpose, so we could use it as a controlled set of honeybee natural population dynamics. We compared the results of this population model to experimental data we found, and they matched within certain degrees. The second half of our study was to perform a sensitivity analysis by introducing back the three factors that might cause the CCD to the population model including climate change, pesticides, and habitat destruction. The paper further discussed the strength and weaknesses of the mathematical model and used this model to predict how many honeybee hives were needed to support the pollination of a 20-acre parcel of land containing crops that benefit from pollination. Additionally, an infographic of our method was illustrated.

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.

Impacts of seasonality and parasitism on honey bee population dynamics.

The honeybee plays an extremely important role in ecosystem stability and diversity and in the production of bee pollinated crops. Honey bees and other pollinators are under threat from the combined effects of nutritional stress, parasitism, pesticides, and climate change that impact the timing, duration, and variability of seasonal events. To understand how parasitism and seasonality influence honey bee colonies separately and interactively, we developed a non-autonomous nonlinear honeybee-parasite interaction differential equation model that incorporates seasonality into the egg-laying rate of the queen. Our theoretical results show that parasitism negatively impacts the honey bee population either by decreasing colony size or destabilizing population dynamics through supercritical or subcritical Hopf-bifurcations depending on conditions. Our bifurcation analysis and simulations suggest that seasonality alone may have positive or negative impacts on the survival of honey bee colonies. More specifically, our study indicates that (1) the timing of the maximum egg-laying rate seems to determine when seasonality has positive or negative impacts; and (2) when the period of seasonality is large it can lead to the colony collapsing. Our study further suggests that the synergistic influences of parasitism and seasonality can lead to complicated dynamics that may positively and negatively impact the honey bee colony's survival. Our work partially uncovers the intrinsic effects of climate change and parasites, which potentially provide essential insights into how best to maintain or improve a honey bee colony's health.

Comparison of individual hive and apiary-level sample types for spores of Paenibacillus larvae in Saskatchewan honey bee operations.

Three commercial honey bee operations in Saskatchewan, Canada, with outbreaks of American foulbrood (AFB) and recent or ongoing metaphylactic antibiotic use were intensively sampled to detect spores of Paenibacillus larvae during the summer of 2019. Here, we compared spore concentrations in different sample types within individual hives, assessed the surrogacy potential of honey collected from honey supers in place of brood chamber honey or adult bees within hives, and evaluated the ability of pooled, extracted honey to predict the degree of spore contamination identified through individual hive testing. Samples of honey and bees from hives within apiaries with a recent, confirmed case of AFB in a single hive (index apiaries) and apiaries without clinical evidence of AFB (unaffected apiaries), as well as pooled, apiary-level honey samples from end-of-season extraction, were collected and cultured to detect and enumerate spores. Only a few hives were heavily contaminated by spores in any given apiary. All operations were different from one another with regard to both the overall degree of spore contamination across apiaries and the distribution of spores between index apiaries and unaffected apiaries. Within operations, individual hive spore concentrations in unaffected apiaries were significantly different from index apiaries in the brood chamber (BC) honey, honey super (HS) honey, and BC bees of one of three operations. Across all operations, BC honey was best for discriminating index apiaries from unaffected apiaries (p = 0.001), followed by HS honey (p = 0.06), and BC bees (p = 0.398). HS honey positively correlated with both BC honey (rs = 0.76, p < 0.0001) and bees (rs = 0.50, p < 0.0001) and may be useful as a surrogate for either. Spore concentrations in pooled, extracted honey seem to have predictive potential for overall spore contamination within each operation and may have prognostic value in assessing the risk of future AFB outbreaks at the apiary (or operation) level.

Modelling the impact of Apivar treatment on a Varroa mite population and the influence of resistance.

BACKGROUND: Varroa destructor is a parasite of honeybees. It causes biological damage leading to the colony collapse in the absence of treatment. In recent years, acaricide resistance has emerged in Varroa mites, leading to a decrease in treatment efficacy. We modelled the action of Apivar (amitraz) treatment, using three input parameters: treatment duration, treatment period, and daily mortality due to the treatment. The output parameters were cumulative mite mortality during treatment, the residual number of Varroa mites, and treatment efficacy, expressed as a percentage. RESULTS: The model was validated by monitoring efficacy in the field, in 36 treated hives. According to the model, treatment in the absence of brood is optimal. For a long period without egg laying during the winter, an initial infestation of 100 mites and a start date for treatment of August 7, a minimal treatment efficacy of 98.8% is required for stabilization of the mite population for year to year. More effective treatment is associated with lower cumulative numbers of dead Varroa mites over the entire treatment period. Thus, the total number of dead mites observed during the monitoring of field efficacy provides information about more than just the initial level of colony infestation. The proportion of resistant mites can be modelized by a decrease of daily mortality rate influencing treatment efficacy. Management of the initial Varroa mite infestation of the colony by the beekeeper can compensate for the decrease in treatment efficacy for resistance thresholds of up to 40% of resistant mites. CONCLUSION: Treatment efficacy depends on several parameters, including initial level of infestation, treatment period and the presence of acaricide resistance. Amitraz resistance may lead to treatment failure, even if the beekeeper is able to keep initial infestation rates low. © 2021 Society of Chemical Industry.

Effects of Insecticides and Microbiological Contaminants on Apis mellifera Health.

Over the past two decades, there has been an alarming decline in the number of honey bee colonies. This phenomenon is called Colony Collapse Disorder (CCD). Bee products play a significant role in human life and have a huge impact on agriculture, therefore bees are an economically important species. Honey has found its healing application in various sectors of human life, as well as other bee products such as royal jelly, propolis, and bee pollen. There are many putative factors of CCD, such as air pollution, GMO, viruses, or predators (such as wasps and hornets). It is, however, believed that pesticides and microorganisms play a huge role in the mass extinction of bee colonies. Insecticides are chemicals that are dangerous to both humans and the environment. They can cause enormous damage to bees' nervous system and permanently weaken their immune system, making them vulnerable to other factors. Some of the insecticides that negatively affect bees are, for example, neonicotinoids, coumaphos, and chlorpyrifos. Microorganisms can cause various diseases in bees, weakening the health of the colony and often resulting in its extinction. Infection with microorganisms may result in the need to dispose of the entire hive to prevent the spread of pathogens to other hives. Many aspects of the impact of pesticides and microorganisms on bees are still unclear. The need to deepen knowledge in this matter is crucial, bearing in mind how important these animals are for human life.

Honey Bee Colony Population Daily Loss Rate Forecasting and an Early Warning Method Using Temporal Convolutional Networks.

The population loss rate of a honey bee colony is a critical index to verify its health condition. Forecasting models for the population loss rate of a honey bee colony can be an essential tool in honey bee health management and pave a way to early warning methods in the understanding of potential abnormalities affecting a honey bee colony. This work presents a forecasting and early warning algorithm for the population daily loss rate of honey bee colonies and determining warning levels based on the predictions. Honey bee colony population daily loss rate data were obtained through embedded image systems to automatically monitor in real-time the in-and-out activity of honey bees at hive entrances. A forecasting model was trained based on temporal convolutional neural networks (TCN) to predict the following day's population loss rate. The forecasting model was optimized by conducting feature importance analysis, feature selection, and hyperparameter optimization. A warning level determination method using an isolation forest algorithm was applied to classify the population daily loss rate as normal or abnormal. The integrated algorithm was tested on two population loss rate datasets collected from multiple honey bee colonies in a honey bee farm. The test results show that the forecasting model can achieve a weighted mean average percentage error (WMAPE) of 17.1 ± 1.6%, while the warning level determination method reached 90.0 ± 8.5% accuracy. The forecasting model developed through this study can be used to facilitate efficient management of honey bee colonies and prevent colony collapse.

Accelerated Varroa destructor population growth in honey bee (Apis mellifera) colonies is associated with visitation from non-natal bees.

A leading cause of managed honey bee colony mortality in the US, Varroa destructor populations typically exceed damaging levels in the fall. One explanation for rapid population increases is migration of mite carrying bees between colonies. Here, the degree to which bees from high and low mite donor colonies move between apiaries, and the effect visitation has on Varroa populations was monitored. More bees from low mite colonies (n = 37) were detected in receiver apiaries than bees from high mite colonies (n = 10, p < 0.001). Receiver colony Varroa population growth was associated with visitation by non-natal bees (p = 0.03), but not high mite bees alone (p = 0.19). Finally, colonies lacking robbing screens experienced faster Varroa population growth than screened neighbors (p = 0.01). Results indicate visiting non-natal bees may vector mites to receiver colonies. These results do not support the current two leading theories regarding mite immigration - the "mite bomb" theory (bees from high mite colonies emigrating to collapsing colonies), or the "robbing" theory (natal robbing bees return home with mites from collapsing colonies). Potential host-parasite effects to bee behavior, as well as important management implications both for Varroa treatment regimens and breeding Varroa resistant bees are discussed.

Impact of Varroa destructor and associated pathologies on the colony collapse disorder affecting honey bees.

Varroa mite is the major threat to the western honey bee, Apis mellifera, and the cause of significant economic losses in the apiculture industry. Varroa destructor feeds on brood and adult bees being responsible for vectoring virus infections and other diseases. This study analyses the role of Varroa and other associated pathogens, such as viruses or the fungus Nosema ceranae, and their relationships regarding the viability of the bee colony. It has been carried out during one beekeeping season, with the subspecies A. m. iberiensis, commonly used in the apiculture industry of Spain. Our study shows a significant relationship between the presence of Varroa destructor and viral infection by deformed wing virus and acute bee paralysis virus. Nosema ceranae behaved as an opportunistic pathogen. In addition, this study explored a potential naturally occurring subset of peptides, responsible for the humoral immunity of the bees. The expression of the antimicrobial peptides abaecin and melittin showed a significant relationship with the levels of Varroa mite and the deformed wing virus.

Poor hive thermoregulation produces an Allee effect and leads to colony collapse.

In recent years the honey bee industry has been experiencing increased loss of hives. The accumulation of multiple stressors on a hive potentially drives hive loss in various ways, including winter loss and colony collapse disorder. One of these stressors is the breakdown of thermoregulation inside the hive. For pupae to develop correctly into healthy adult bees, the temperature within the hive must be regulated by the hive bees to within a narrow range that ensures optimal development. Suboptimal development in adults affects their brain and flight muscles so bees becomes inefficient foragers with shorter life spans. We model the effect of thermoregulation on hive health using a system of delay differential equations that show that thermoregulatory stress has the capacity to drive colony loss in the model via a saddle-node bifurcation with an associated Allee effect.

Longitudinal analysis on parasite diversity in honeybee colonies: new taxa, high frequency of mixed infections and seasonal patterns of variation.

To evaluate the influence that parasites have on the losses of Apis mellifera it is essential to monitor their presence in the colonies over time. Here we analysed the occurrence of nosematids, trypanosomatids and neogregarines in five homogeneous colonies for up to 21 months until they collapsed. The study, which combined the use of several molecular markers with the application of a massive parallel sequencing technology, provided valuable insights into the epidemiology of these parasites: (I) it enabled the detection of parasite species rarely reported in honeybees (Nosema thomsoni, Crithidia bombi, Crithidia acanthocephali) and the identification of two novel taxa; (II) it revealed the existence of a high rate of co-infections (80% of the samples harboured more than one parasite species); (III) it uncovered an identical pattern of seasonal variation for nosematids and trypanosomatids, that was different from that of neogregarines; (IV) it showed that there were no significant differences in the fraction of positive samples, nor in the levels of species diversity, between interior and exterior bees; and (V) it unveiled that the variation in the number of parasite species was not directly linked with the failure of the colonies.

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.

Honey bee virus causes context-dependent changes in host social behavior.

Anthropogenic changes create evolutionarily novel environments that present opportunities for emerging diseases, potentially changing the balance between host and pathogen. Honey bees provide essential pollination services, but intensification and globalization of honey bee management has coincided with increased pathogen pressure, primarily due to a parasitic mite/virus complex. Here, we investigated how honey bee individual and group phenotypes are altered by a virus of concern, Israeli acute paralysis virus (IAPV). Using automated and manual behavioral monitoring of IAPV-inoculated individuals, we find evidence for pathogen manipulation of worker behavior by IAPV, and reveal that this effect depends on social context; that is, within versus between colony interactions. Experimental inoculation reduced social contacts between honey bee colony members, suggesting an adaptive host social immune response to diminish transmission. Parallel analyses with double-stranded RNA (dsRNA)-immunostimulated bees revealed these behaviors are part of a generalized social immune defensive response. Conversely, inoculated bees presented to groups of bees from other colonies experienced reduced aggression compared with dsRNA-immunostimulated bees, facilitating entry into susceptible colonies. This reduction was associated with a shift in cuticular hydrocarbons, the chemical signatures used by bees to discriminate colony members from intruders. These responses were specific to IAPV infection, suggestive of pathogen manipulation of the host. Emerging bee pathogens may thus shape host phenotypes to increase transmission, a strategy especially well-suited to the unnaturally high colony densities of modern apiculture. These findings demonstrate how anthropogenic changes could affect arms races between human-managed hosts and their pathogens to potentially affect global food security.

Detecting varroosis using a gas sensor system as a way to face the environmental threat.

Colony Collapse Disorder (CCD) is an environmental threat on a global scale due to the irreplaceable role of bees in crop pollination. Varroa destructor (V.d.), a parasite that attacks honeybee colonies, is one of the primary causes of honey bee population decline and the most serious threat to the beekeeping sector. This work demonstrates the possibility of quantitatively determining bee colony infestation by V.d. using gas sensing. The results are based on analysing the experimental data acquired for eighteen bee colonies in field conditions. Their infestation rate was in the 0 to 24.76% range. The experimental data consisted of measurements of beehive air with a semiconductor gas sensor array and the results of bee colony V.d. infestation assessment using a flotation method. The two kinds of data were collected in parallel. Partial Least Square regression was applied to identify the relationship between the highly multivariate measurement data provided by the gas sensor array and the V.d. infestation rate. The quality of the developed quantitative models was very high, as demonstrated by the coefficient of determination exceeding R2 = 0.99. Moreover, the prediction error was <0.6% for V.d. infestation rate predictions based on the measurement data that was unknown to the model. The presented work has considerable novelty. To our knowledge, the ability to determine the V.d. infestation rate of bee colony quantitatively based on beehive air measurements using a semiconductor gas sensor array has not been previously demonstrated.

A mathematical model of honey bee colony dynamics to predict the effect of pollen on colony failure.

The decline in colony populations of the honey bee, known as the Colony Collapse Disorder (CCD), is a global concern. Numerous studies have reported possible causes, including pesticides, parasites, and nutritional stress. Poor nutrition affects the immune system at both the individual and colony level, amplifying effects of other stress factors. Pollen is the only source of ten amino acids that are essential to honey bee development, brood rearing and reproduction. This paper presents a new mathematical model to explore the effect of pollen on honey bee colony dynamics. In this model, we considered pollen and nectar as the required food for the colony. The effect of pollen and nectar collected by foragers was evaluated at different mortality rates of pupa, pollen and nectar foragers.

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

We develop a model of honey bee colony collapse based on the contamination of forager bees in environmental regions contaminated with pesticides. An important feature of the model is the daily homing capacity each day of foragers bees. The model consists of difference equations describing the daily homing of uncontaminated and contaminated forager bees, with an increased homing failure of contaminated bees. The model quantifies colony collapse in terms of the fraction of contaminated bees subject to this increased homing failure. If the fraction is sufficiently high, then the hive falls below a viability threshold population size that leads to rapid disintegration. If the fraction is sufficiently low, then the hive can rise above the viability threshold and attain a stable population level.

Impact of nutritional stress on the honeybee colony health.

Honeybees Apis mellifera are important pollinators of wild plants and commercial crops. For more than a decade, high percentages of honeybee colony losses have been reported worldwide. Nutritional stress due to habitat depletion, infection by different pests and pathogens and pesticide exposure has been proposed as the major causes. In this study we analyzed how nutritional stress affects colony strength and health. Two groups of colonies were set in a Eucalyptus grandis plantation at the beginning of the flowering period (autumn), replicating a natural scenario with a nutritionally poor food source. While both groups of colonies had access to the pollen available in this plantation, one was supplemented with a polyfloral pollen patty during the entire flowering period. In the short-term, colonies under nutritional stress (which consumed mainly E. grandis pollen) showed higher infection level with Nosema spp. and lower brood and adult bee population, compared to supplemented colonies. On the other hand, these supplemented colonies showed higher infection level with RNA viruses although infection levels were low compared to countries were viral infections have negative impacts. Nutritional stress also had long-term colony effects, because bee population did not recover in spring, as in supplemented colonies did. In conclusion, nutritional stress and Nosema spp. infection had a severe impact on colony strength with consequences in both short and long-term.