Bee Health and Productivity in Apis mellifera, a Consequence of Multiple Factors

Investigating the Impact of Nosema Infection in Beehives on Honey Quality Using Fluorescence Spectroscopy and Chemometrics

This study investigates the impact of Nosema infection in beehives on the physico-chemical and biochemical properties and spectral characteristics of honey as indicators of honey quality. Comprehensive analyses were performed on honey samples from hives with varying levels of Nosema infection, examining water content, free acidity, optical rotation, electrical conductivity, sugar composition, catalase activity, and pollen content. Honey from highly infected hives showed higher water content (up to 17.3%), lower optical rotation, reduced electrical conductivity, decreased glucose levels, and increased sucrose levels. Principal component analysis (PCA) identified distinct clustering of samples based on infection levels, with changes in the sugar profile, particularly higher phenolic compounds, correlating with increased infection levels. Fluorescence spectroscopy combined with PARAFAC modeling identified proteins and phenolic compounds as key discriminators of honey from infected hives. Correlation and PLS modeling further demonstrated strong relationships between spectral features and honey properties, including catalase activity and pollen content. This research presents a novel approach to evaluating the impact of Nosema infection on honey quality by integrating physico-chemical and biochemical analyses and sugar composition profiling with advanced spectroscopic techniques. These insights are invaluable for improving bee health monitoring practices and advancing sustainability in the beekeeping and honey production industries.

An Overview of the Nutritional Requirements of Honey Bees (Apis mellifera Linnaeus, 1758)

Honey bees are known for their wide global distribution, their ease of handling, and their economic and ecological value. However, they are often exposed to a wide variety of stress factors. Therefore, it is essential for beekeepers to maintain healthy bee colonies. In this context, a balanced diet is recommended to support the growth of strong and healthy honey bee colonies. The purpose of this review is therefore to provide an overview of the nutritional requirements of Apis mellifera and their importance for the maintenance of healthy bee colonies. An adequate diet includes the consumption of sufficient amounts of proteins, carbohydrates, lipids, amino acids, vitamins, minerals, water, and essential sterols, and a diet based on multi-floral pollen is desirable. However, when honey bee colonies are located near agroecosystems with lower resource diversity, both brood rearing and colony longevity may decrease, making them more susceptible to parasites and diseases. On the other hand, efforts have been made to improve the health of honey bee colonies with the help of nutritional supplements consisting of a variety of components. Nevertheless, studies have shown that even with these supplements, a lack of nutrients can still be an issue for honey bee colonies. Furthermore, future research should focus on identifying nutritional supplements that can better replicate natural diet diversity and assessing long-term effects on honey bee colony resilience, especially in low-flowering areas. This review discusses the interaction between nutrient requirements and the effects of supplements on colony health.

Africanized honey bee colonies in Costa Rica: first evidence of its management, brood nest structure and factors associated with varroa mite infestation

Management, brood nest structure and factors associated with varroa mite infestation were studied in 60 apiaries of Africanized honey bees in the northwest region of the Central Valley of Costa Rica. Apiaries were monitored two times. The first monitoring was taken forward during the rainy season between May and November 2019. The second monitoring during the dry season between February and March 2020. Information about the beekeepers, apiaries and management was collected through a survey. Amount of open and capped brood, honey and pollen were measured in the field. The infestation rate of varroa (IRV) was quantified using standard laboratory methods. A determination of multi-residue pesticides in bee bread was made through GC-MS/MS and LC-MS/MS techniques. According to the results, most of the beekeepers produce honey (96.7%), participate in training activities (82.2%), and change the bee queens annually (70%). The first monitoring was characterized by a lower amount of capped brood and honey reserves compared to the second one. IRV was significantly higher in the first monitoring (6.0 ± 0.4) in comparison with the second one (3.0 ± 0.3) (U Mann-Whitney p < 0.001). The maximum value for the first monitoring exceeds 40%, while this value was close to 25% in the second monitoring. Mite infestation exposed significant differences in relation to the variables associated to the beekeeper's management, i.e., change of bee queen (p = 0.002) or when beekeepers monitor varroa mites (p = 0.004). Additionally, the IRV had inverse correlations (p < 0.01) with the number of comb sides with capped brood (Spearman's rho coefficient = - 0.190), and honey reserves (Spearman's rho coefficient = - 0.168). Furthermore, 23 of 60 bee bread samples presented one to five pesticide residues, being the most frequent antifungal agrochemicals.

Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies' exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony's health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project's data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping.

Challenges and Advances in Bee Health and Diseases

Understanding the health status of bees is crucial in assessing the epidemiology of pathogens that cause diseases in honey bee (Apis mellifera) colonies and wild bees [...]

Increased survival of honey bees consuming pollen and beebread is associated with elevated biomarkers of oxidative stress

Introduction

Significant losses of honey bee colonies have been observed worldwide in recent decades. Inadequate nutrition is considered to be one of the factors that can reduce honey bee resistance to abiotic and biotic environmental stresses. Accordingly, we assessed the impact of food composition on worker bee survival.

Methods

Bees in cages were fed six different diets, and then their survival, levels of thiobarbituric acid reactive substances and protein carbonyl groups, catalase and lysozyme activities were evaluated.

Results and Discussion

After 17 days of feeding, the lowest mortality was observed in the group of bees that received sucrose solution with the addition of willow pollen or artificial rapeseed beebread or artificial willow beebread (diets 4–6). The highest mortality was found in bees that consumed only sucrose solution (diet 1) or the sucrose solution supplemented with a mixture of amino acids (diet 2), which can be explained by the lack of vitamins and microelements in these diets. In the group of bees that received the sucrose solution with rapeseed pollen (diet 3), mortality was intermediate. To check whether the decrease in insect survival could be related to oxidative damage, we evaluated biomarkers of oxidative stress. Consumption of pollen (diets 3 and 5) and artificial beebread (diets 4 and 6) enhances protein carbonylation in worker bees. Feeding bees artificial beebread also resulted in increase in lipid peroxidation and catalase activity, which is probably due to the presence of hydrogen peroxide in the honey contained in beebread. Remarkably, the increase in biomarkers of oxidative stress was not accompanied by adverse but positive effects on insect survival. A lack of amino acids and proteins in the diet 1 did not cause oxidative stress, but led to an increase in lysozyme activity in hemolymph, a biomarker of immune system status. In conclusion, we believe that the increase in oxidative stress biomarkers we found do not indicate oxidative damage, but rather reflect the changes in redox balance due to consumption of certain dietary options.

Deep Learning Beehive Monitoring System for Early Detection of the Varroa Mite

One of the most critical causes of colony collapse disorder in beekeeping is caused by the Varroa mite. This paper presents an embedded camera module supported by a deep learning algorithm for the process of early detecting of Varroa infestations. This is achieved using a deep learning algorithm that tries to identify bees inside the brood frames carrying the mite in real-time. The end-node device camera module is placed inside the brood box. It is equipped with offline detection in remote areas of limited network coverage or online imagery data transmission and mite detection over the cloud. The proposed deep learning algorithm uses a deep learning network for bee object detection and an image processing step to identify the mite on the previously detected objects. Finally, the authors present their proof of concept experimentation of their approach that can offer a total bee and varroa detection accuracy of close to 70%. The authors present in detail and discuss their experimental results.

Proposed Smart Monitoring System for the Detection of Bee Swarming

This paper presents a bee-condition-monitoring system incorporated with a deep-learning process to detect bee swarming. This system includes easy-to-use image acquisition and various end node approaches for either on-site or cloud-based mechanisms. This system also incorporates a new smart CNN engine called Swarm-engine for detecting bees and the issue of notifications in cases of bee swarming conditions to the apiarists. First, this paper presents the authors’ proposed implementation system architecture and end node versions that put it to the test. Then, several pre-trained networks of the authors’ proposed CNN Swarm-engine were also validated to detect bee-clustering events that may lead to swarming. Finally, their accuracy and performance towards detection were evaluated using both cloud cores and embedded ARM devices on parts of the system’s different end-node implementations.