Beehive Smart Detector Device for the Detection of Critical Conditions That Utilize Edge Device Computations and Deep Learning Inferences.

This paper presents a new edge detection process implemented in an embedded IoT device called Bee Smart Detection node to detect catastrophic apiary events. Such events include swarming, queen loss, and the detection of Colony Collapse Disorder (CCD) conditions. Two deep learning sub-processes are used for this purpose. The first uses a fuzzy multi-layered neural network of variable depths called fuzzy-stranded-NN to detect CCD conditions based on temperature and humidity measurements inside the beehive. The second utilizes a deep learning CNN model to detect swarming and queen loss cases based on sound recordings. The proposed processes have been implemented into autonomous Bee Smart Detection IoT devices that transmit their measurements and the detection results to the cloud over Wi-Fi. The BeeSD devices have been tested for easy-to-use functionality, autonomous operation, deep learning model inference accuracy, and inference execution speeds. The author presents the experimental results of the fuzzy-stranded-NN model for detecting critical conditions and deep learning CNN models for detecting swarming and queen loss. From the presented experimental results, the stranded-NN achieved accuracy results up to 95%, while the ResNet-50 model presented accuracy results up to 99% for detecting swarming or queen loss events. The ResNet-18 model is also the fastest inference speed replacement of the ResNet-50 model, achieving up to 93% accuracy results. Finally, cross-comparison of the deep learning models with machine learning ones shows that deep learning models can provide at least 3-5% better accuracy results.

Automated precision beekeeping for accessing bee brood development and behaviour using deep CNN.

Bees play a significant role in the health of terrestrial ecosystems. The decline of bee populations due to colony collapse disorder around the world constitutes a severe ecological danger. Maintaining high yield of honey and understanding of bee behaviour necessitate constant attention to the hives. Research initiatives have been taken to establish monitoring programs to study the behaviour of bees in accessing their habitat. Monitoring the sanitation and development of bee brood allows for preventative measures to be taken against mite infections and an overall improvement in the brood's health. This study proposed a precision beekeeping method that aims to reduce bee colony mortality and improve conventional apiculture through the use of technological tools to gather, analyse, and understand bee colony characteristics. This research presents the application of advanced digital image processing with computer vision techniques for the visual identification and analysis of bee brood at various developing stages. The beehive images are first preprocessed to enhance the important features of object. Further, object is segmented and classified using computer vision techniques. The research is carried out with the images containing variety of immature brood stages. The suggested method and existing methods are tested and compared to evaluate efficiency of proposed methodology.

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.

Metagenomic analysis of viromes in honey bee colonies (Apis mellifera; Hymenoptera: Apidae) after mass disappearance in Korea.

After the nationwide, massive winter losses of honey bees in Korea during the winter of 2021, samplings were conducted from live honey bees in colonies and dead honey bees nearby colonies in the same bee-farms in six regions in Korea. Each sample was subjected to virome analysis using high-throughput sequencing technology. The number of viral reads was the lowest in the live honey bee group sample with 370,503 reads and the highest in the dead honey bee group sample with 42,659,622 reads. Viral contigs were matched with the viral genomes of the black queen cell virus, deformed wing virus, Israeli acute paralysis virus, and sacbrood virus, all of which have been previously reported in Korea. However, Apis rhabdovirus 5, bee macula-like virus, Varroa orthomyxovirus-1, Hubei partiti-like virus 34, Lake Sinai virus 2, 3, and 4, and the Ditton virus, were also discovered in this study, which are the first records in Korea. Plant viral sequences resembling those of Arabidopsis latent virus 1, and a novel viral sequence was also discovered. In the present study 55 complete viral genome sequences were identified. This study is the first virome analysis of domestic honey bees and provides the latest information on the diversity of honey bee viruses in Korea.

The honey bees immune memory.

Invertebrates' immune priming or innate immune memory is an analogous response to the vertebrates' adaptive memory. We investigated if honey bees have immune memory. We compared survival and immune response between bees that were: 1) manipulated (Naïve), 2) challenged twice with the same pathogen Escherichia coli (Memory), 3) challenged twice with different pathogens (Staphylococcus aureus versus E. coli, Micrococcus lysodeikticus versus E. coli), or 4) with PBS (the diluent of bacteria) versus E. coli (heterologous challenge; Control). Results indicate better survival in the Memory than the Control group, and the Memory group showed a similar survival than Naïve insects. The Memory group had higher lytic activity but lower prophenoloxidase, phenoloxidase activity, and hemocyte count than the Control and Naïve groups. No differences were found in relative expression of defensin-1. This first demonstration of immune memory opens the questions about its molecular mechanisms and whether, immune memory could be used against natural parasites that affect honey bees, hence, if they could be "vaccinated" against some natural parasites.

Sunflower-Associated Reductions in Varroa Mite Infestation of Honey Bee Colonies.

Landscapes can affect parasite epidemiology in wild and agricultural animals. Honey bees are threatened by loss of floral resources and by parasites, principally the mite Varroa destructor and the viruses it vectors. Existing mite control relies heavily on chemical treatments that can adversely affect bees. Alternative, pesticide-free control methods are needed to mitigate infestation with these ectoparasites. Many flowering plants provide nectar and pollen that confer resistance to parasites. Enrichment of landscapes with antiparasitic floral resources could therefore provide a sustainable means of parasite control in pollinators. Floral rewards of Asteraceae plants can reduce parasitic infection in diverse bee species, including honey and bumble bees. Here, we tested the effects of sunflower (Helianthus annuus) cropland and pollen supplementation on honey bee resistance to macro- and microparasites. Although sunflower had nonsignificant effects on microparasites, We found that increased sunflower pollen availability correlated with reduced Varroa mite infestation in landscapes and pollen-supplemented colonies. At the landscape level, each doubling of sunflower crop area was associated with a 28% reduction in mite infestation. In field trials, late-summer supplementation of colonies with sunflower pollen reduced mite infestation by 2.75-fold relative to artificial pollen. United States sunflower crop acreage has declined by 2% per year since 1980, however, suggesting reduced availability of this floral resource. Although further research is needed to determine whether the observed effects represent direct inhibition of mite fecundity or mite-limiting reductions in honey bee brood-rearing, our findings suggest the potential for sunflower plantings or pollen supplements to counteract a major driver of honey bee losses worldwide.

Antiparasitic effects of three floral volatiles on trypanosomatid infection in honey bees.

Trypanosomatid gut parasites are common in pollinators and costly for social bees. The recently described honey bee trypanosomatid Lotmaria passim is widespread, abundant, and correlated with colony losses in some studies. The potential for amelioration of infection by antimicrobial plant compounds has been thoroughly studied for closely related trypanosomatids of humans and is an area of active research in bumble bees, but remains relatively unexplored in honey bees. We recently identified several floral volatiles that inhibited growth of L. passim in vitro. Here, we tested the dose-dependent effects of four such compounds on infection, mortality, and food consumption in parasite-inoculated honey bees. We found that diets containing the monoterpenoid carvacrol and the phenylpropanoids cinnamaldehyde and eugenol at > 10-fold the inhibitory concentrations for cell cultures reduced infection, with parasite numbers decreased by > 90 % for carvacrol and cinnamaldehyde and > 99 % for eugenol; effects of the carvacrol isomer thymol were non-significant. However, both carvacrol and eugenol also reduced bee survival, whereas parasite inoculation did not, indicating costs of phytochemical exposure that could exceed those of infection itself. To our knowledge, this is the first controlled screening of phytochemicals for effects on honey bee trypanosomatid infection, identifying potential treatments for managed bees afflicted with a newly characterized, cosmopolitan intestinal parasite.

Insects in Art during an Age of Environmental Turmoil.

Humans are reshaping the planet in impressive, and impressively self-destructive, ways. Evidence and awareness of our environmental impact has failed to elicit meaningful change in reversing our behavior. A multifaceted approach to communicating human-induced environmental destruction is critical, and art can affect our behavior by its power to evoke emotions. Artists often use insects in their works because of our intimate and varied relationship with this diverse, abundant lineage of animals. We surveyed work by 73 artists featuring insects or insect bodily products to gauge how extensively artists are addressing anthropogenic environmental distress, and what insects they are choosing as subjects in the process. Categories often cited as contributing to species extinction are (1) habitat destruction, (2) invasive species, (3) pollution, (4) human population, and (5) overharvesting. After adding insect-specific categories of (6) decline of insect pollinators and (7) the intentional modification or extermination of insects, we categorized our surveyed works, confirming categorizations with 53 of the living artists. Forty-seven percent of the artists addressed habitat destruction or climate change, but some other categories were severely underrepresented, with almost no work explicitly addressing overpopulation or overharvesting. Artists favored Hymenoptera (62%) over potentially more species-rich orders. Recognizing these biases could alert scientists, artists, and others to more effectively communicate messages of universal importance.

Environmental exposures associated with honey bee health.

Bee health is declining on a global scale, yet the exact causes and their interactions responsible for the decline remain unknown. To more objectively study bee health, recently biomarkers have been proposed as an essential tool, because they can be rapidly quantified and standardized, serving as a comparable measure across bee species and varying environments. Here, we used a systems biology approach to draw associations between endogenous and exogenous chemical profiles, with pesticide exposure, or the abundance of the 21 most common honey bee diseases. From the analysis we identified chemical biomarkers for both pesticide exposure and bee diseases along with the mechanistic biological pathways that may influence disease onset and progression. We found a total of 2352 chemical features, from 30 different hives, sampled from seven different locations. Of these, a total of 1088 significant associations were found that could serve as chemical biomarker profiles for predicting both pesticide exposure and the presence of diseases in a bee colony. In almost all cases we found novel external environmental exposures within the top seven associations with bee diseases and pesticide exposures, with the majority having previously unknown connections to bee health. We highlight the exposure-outcome paradigm and its ability to identify previously uncategorized interactions from different environmental exposures associated with bee diseases, pesticides, mechanisms, and potential synergistic interactions of these that are responsible for honey bee health decline.

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.

Increased alarm pheromone component is associated with Nosema ceranae infected honeybee colonies.

Use of chemicals, such as alarm pheromones, for rapid communication with conspecifics is widespread throughout evolutionary history. Such chemicals are particularly important for social insects, such as the honeybee (Apis mellifera), because they are used for collective decision-making, coordinating activities and self-organization of the group. What is less understood is how these pheromones change due to an infection and what the implications might be for social communication. We used semiquantitative polymerase chain reaction (sqPCR) to screen for a common microsporidian gut parasite, Nosema ceranae, for 30 hives, across 10 different locations. We then used high-resolution accurate mass gas chromatography-quadrupole time of flight mass spectrometry to generate an exposome profile for each hive. Of the 2352 chemical features identified, chemicals associated with infection were filtered for cosanes or cosenes. A significant association was found between N. ceranae and the presence of (Z)-11-eicosen-1-ol, a known alarm pheromone component. The increase in (Z)-11-eicosen-1-ol could be the recognition mechanism for healthy individuals to care for, kill, or quarantine infected nestmates. Nosema ceranae has contributed to the global decline in bee health. Therefore, altered alarm pheromones might play a role in disrupting social harmony and have potential impacts on colony health.

Complete mitochondrial DNA sequence of the parasitic honey bee mite Varroa destructor (Mesostigmata: Varroidae).

Varroa destructor is a parasite mite of the eastern honey bee Apis cerana, which is native to Asia. The European honey bee Apis mellifera was imported to Asia from Europe and the USA for apiculture in the 19th century. In a short period of time, V. destructor parasitized the artificially introduced honey bees. Varroa destructor was estimated to have spread around the world with A. mellifera when it was exported from Asia to locations worldwide about 50 years ago. The mitochondrial DNA of the parasitic honey bee mite V. destructor was analyzed using next-generation sequencing. The complete mitochondrial genome of V. destructor was identified as a 16,476-bp circular molecule containing 13 protein-coding genes (PCGs), 22 tRNA genes, two rRNA genes, and one AT-rich control region. The heavy strand was predicted to have nine PCGs and 13 tRNA genes, whereas the light strand was predicted to contain four PCGs, nine tRNA genes, and two rRNA genes. All PCGs began with ATA as the start codon, except COIII and CytB, which had ATG as the start codon. Stop codons were of two types: TAA for eight genes and TAG for five genes. Molecular phylogenetic analysis revealed that V. destructor from Japan was genetically distant from that of France. A high base substitution rate of 2.82% was also confirmed between the complete mitochondrial DNA sequences of V. destructor from Japan and the USA, suggesting that one Varroa mite strain found in the USA is not from Japan.

Selective Breeding for Low and High Varroa destructor Growth in Honey Bee (Apis mellifera) Colonies: Initial Results of Two Generations.

After two years of bidirectional selection for low and high rates of Varroa destructor population growth (LVG and HVG, respectively) in honey bee (Apis mellifera) colonies in Ontario, Canada, significant differences between the two genotypes were observed. LVG colonies had V. destructor population increases over the summer of 1.7 fold compared to 9.6 fold for HVG colonies by Generation 2. Additionally, HVG colonies had significantly higher mite infestation rates in adult bees compared to LVG colonies for both selected generations. DWV prevalence and levels were significantly higher in HVG colonies than in LVG colonies in Generation 1 but not in Generation 2. Winter mortality rates of Generation 1 colonies were significantly different at 26% and 14% for the HVG and LVG genotypes, respectively. The results of this study thus far indicate that selection for LVG may result in colonies with lower V. destructor infestation rates, lower prevalence, and levels of DWV and higher colony winter survivorship. Future work will focus on determining what mechanisms are responsible for the genotypic differences, estimating genetic parameters, and molecular analyses of the genotypes to identify candidate genes associated with resistance to V. destructor and DWV that could potentially be used for marker-assisted selection.

Factors Associated with Honey Bee Colony Losses: A Mini-Review.

The Western honey bee (Apis mellifera L., Hymenoptera: Apidae) is a species of crucial economic, agricultural and environmental importance. In the last ten years, some regions of the world have suffered from a significant reduction of honey bee colonies. In fact, honey bee losses are not an unusual phenomenon, but in many countries worldwide there has been a notable decrease in honey bee colonies. The cases in the USA, in many European countries, and in the Middle East have received considerable attention, mostly due to the absence of an easily identifiable cause. It has been difficult to determine the main factors leading to colony losses because of honey bees' diverse social behavior. Moreover, in their daily routine, they make contact with many agents of the environment and are exposed to a plethora of human activities and their consequences. Nevertheless, various factors have been considered to be contributing to honey bee losses, and recent investigations have established some of the most important ones, in particular, pests and diseases, bee management, including bee keeping practices and breeding, the change in climatic conditions, agricultural practices, and the use of pesticides. The global picture highlights the ectoparasitic mite Varroa destructor as a major factor in colony loss. Last but not least, microsporidian parasites, mainly Nosema ceranae, also contribute to the problem. Thus, it is obvious that there are many factors affecting honey bee colony losses globally. Increased monitoring and scientific research should throw new light on the factors involved in recent honey bee colony losses. The present review focuses on the main factors which have been found to have an impact on the increase in honey bee colony losses.

Using Manual and Computer-Based Text-Mining to Uncover Research Trends for Apis mellifera.

Honey bee research is believed to be influenced dramatically by colony collapse disorder (CCD) and the sequenced genome release in 2006, but this assertion has never been tested. By employing text-mining approaches, research trends were tested by analyzing over 14,000 publications during the period of 1957 to 2017. Quantitatively, the data revealed an exponential growth until 2010 when the number of articles published per year ceased following the trend. Analysis of author-assigned keywords revealed that changes in keywords occurred roughly every decade with the most fundamental change in 1991-1992, instead of 2006. This change might be due to several factors including the research intensification on the Varroa mite. The genome release and CCD had quantitively only minor effects, mainly on honey bee health-related topics post-2006. Further analysis revealed that computational topic modeling can provide potentially hidden information and connections between some topics that might be ignored in author-assigned keywords.

Interactions Between Thiamethoxam and Deformed Wing Virus Can Drastically Impair Flight Behavior of Honey Bees.

Exposure to multiple stress factors is believed to contribute to honey bee colony decline. However, little is known about how co-exposure to stress factors can alter the survival and behavior of free-living honey bees in colony conditions. We therefore studied the potential interaction between a neonicotinoid pesticide, thiamethoxam, and a highly prevalent honey bee pathogen, Deformed wing virus (DWV). For this purpose, tagged bees were exposed to DWV by feeding or injection, and/or to field-relevant doses of thiamethoxam, then left in colonies equipped with optical bee counters to monitor flight activity. DWV loads and the expression of immune genes were quantified. A reduction in vitellogenin expression level was observed in DWV-injected bees and was associated with precocious onset of foraging. Combined exposure to DWV and thiamethoxam did not result in higher DWV loads compared to bees only exposed to DWV, but induced precocious foraging, increased the risk of not returning to the hive after the first flight, and decreased survival when compared to single stress exposures. We therefore provided the first evidence for deleterious interactions between DWV and thiamethoxam in natural conditions.

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.

Predictors of colony extinction vary by habitat type in social spiders.

Many animal societies are susceptible to mass mortality events and collapse. Elucidating how environmental pressures determine patterns of collapse is important for understanding how such societies function and evolve. Using the social spider Stegodyphus dumicola, we investigated the environmental drivers of colony extinction along two precipitation gradients across southern Africa, using the Namib and Kalahari deserts versus wetter savanna habitats to the north and east. We deployed experimental colonies (n = 242) along two ~ 800-km transects and returned to assess colony success in the field after 2 months. Specifically, we noted colony extinction events after the 2-month duration and collected environmental data on the correlates of those extinction events (e.g., evidence of ant attacks, no. of prey captured). We found that colony extinction events at desert sites were more frequently associated with attacks by predatory ants as compared with savanna sites, while colony extinctions in wetter savannas sites were more tightly associated with fungal outbreaks. Our findings support the hypothesis that environments vary in the selection pressures that they impose on social organisms, which may explain why different social phenotypes are often favored in each habitat.

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.