Understanding social resilience in honeybee colonies

Transfer learning-based approach to individual Apis cerana segmentation

Honey bees play a crucial role in natural ecosystems, mainly through their pollination services. Within a hive, they exhibit intricate social behaviors and communicate among thousands of individuals. Accurate detection and segmentation of honey bees are crucial for automated behavior analysis, as they significantly enhance object tracking and behavior recognition by yielding high-quality results. This study is specifically centered on the detection and segmentation of individual bees, particularly Apis cerana, within a hive environment, employing the Mask R-CNN deep learning model. We used transfer learning weights from our previously trained Apis mellifera model and explored data preprocessing techniques, such as brightness and contrast enhancement, to enhance model performance. Our proposed approach offers an optimal solution with a minimal dataset size and computational time while maintaining high model performance. Mean average precision (mAP) served as the evaluation metric for both detection and segmentation tasks. Our solution for A. cerana segmentation achieves the highest performance with a mAP of 0.728. Moreover, the number of training and validation sets was reduced by 85% compared to our previous study on the A. mellifera segmentation model.

Remodeling of Cellular Respiration and Insulin Signaling Are Part of a Shared Stress Response in Divergent Bee Species

The honey bee (Apis mellifera) is of paramount importance to human activities through the pollination services they provide in agricultural settings. Honey bee colonies in the United States have suffered from an increased rate of annual die-off in recent years, stemming from a complex set of interacting stressors that remain poorly described. Defining the cellular responses that are perturbed by divergent stressors represents a key step in understanding these synergies. We found that multiple model stressors induce upregulated expression of the lactate dehydrogenase (Ldh) gene in the midgut of the eusocial honey bee and that the Ldh gene family is expanded in diverse bee species. Alterations in Ldh expression were concomitant with changes in the expression of other genes involved in cellular respiration and genes encoding insulin/insulin-like growth factor signaling (IIS) pathway components. Additionally, changes in metabolites in the midgut after stress, including increased levels of lactate, linked metabolic changes with the observed changes in gene expression. Select transcriptional changes in response to stress were similarly observed in the solitary alfalfa leafcutting bee (Megachile rotundata). Thus, increased Ldh expression may be part of a core stress response remodeling cellular respiration and insulin signaling. These findings suggest that a conserved cellular response that regulates metabolic demands under diverse stressful conditions may play a protective role in bees regardless of life history.

Shift in distribution of division of labour in chronically stressed honeybee colonies after perturbation

Division of labour (DOL) in eusocial insects plays an important role in colony fitness. Honeybees face a variety of stressors that compromise the homeostasis of the colony and reduce survival and reproduction. Considering the significance of DOL in colony homeostasis, it is important to understand whether and how DOL may be altered as a result of chronic stress. Therefore, we tested whether honeybee colonies shift DOL in response to high infestation with the parasitic mite Varroa destructor. For this, we monitored chronically stressed and presumably low-stress colonies from April till December 2022. During the experiment, we applied a cold shock to test whether a perturbation resulted in a larger alteration in DOL in chronically stressed colonies. We found that after cold shock, there was a lower proportion of nurses in the chronically stressed colonies. For foragers, we found higher activity post-cold shock in chronically stressed colonies, but no difference between treatments in nectar inflow, suggesting less efficient foragers. Furthermore, we found that there was an accelerated task switch in chronically stressed colonies after the cold shock. The large changes after the perturbation may indicate inefficient task allocation due to chronic stress. Our study contributes to the understanding of social resilience and chronic stress responses in eusocial animals.

Japanese honey bees (Apis cerana japonica) have swarmed more often over the last two decades

The impacts of temperature increase are a concern for honey bees, which are major pollinators of crops and wild plants. Swarming is the reproductive behavior of honey bees that increases colony numbers. Honey bee colonies sometimes swarm multiple times, with each swarming termed a "swarming event" and a series of these events called a "swarming cycle." The number of swarming events per swarming cycle varies widely depending on climatic conditions and subspecies, and the recent temperature increase due to global warming might be affecting the number of swarming events per swarming cycle of native honey bees. We clarified long-term changes in the number of swarming events per swarming cycle of Japanese honey bees (Apis cerana japonica) by collecting beekeepers' swarming logbooks. The survey showed that between 2000 and 2022, Japanese honey bees swarmed 1 to 8 times per swarming cycle. Generalized linear model analysis indicated that year had a significant positive effect (coefficient, 0.03; 95% CI, 0.01-0.04); that is, the number of swarming events per swarming cycle showed a moderate increase over time. In addition, we found that colonies swarmed more often in a cycle when the swarming process began in early spring, especially in March. Considering the notably strong trend in Japan of warmer temperatures in March, the number of swarming events per swarming cycle may be increasing because reproduction is beginning earlier in the year. Further analyses are needed to verify the causal relationship of temperature increase on the number of swarming events per swarming cycle.

Thinking inside the box: Restoring the propolis envelope facilitates honey bee social immunity

When wild honey bee colonies (Apis mellifera) nest in hollow tree cavities, they coat the rough cavity walls with a continuous layer of propolis, a substance comprised primarily of plant resins. Studies have shown that the resulting "propolis envelope" leads to both individual- and colony-level health benefits. Unfortunately, the smooth wooden boxes most commonly used in beekeeping do little to stimulate propolis collection. As a result, most managed bees live in hives that are propolis-poor. In this study, we assessed different surface texture treatments (rough wood boxes, boxes outfitted with propolis traps, and standard, smooth wood boxes) in terms of their ability to stimulate propolis collection, and we examined the effect of propolis on colony health, pathogen loads, immune gene expression, bacterial gene expression, survivorship, and honey production in both stationary and migratory beekeeping contexts. We found that rough wood boxes are the most effective box type for stimulating propolis deposition. Although the use of rough wood boxes did not improve colony survivorship overall, Melissococcus plutonius detections via gene expression were significantly lower in rough wood boxes, and viral loads for multiple viruses tended to decrease as propolis deposition increased. By the end of year one, honey bee populations in migratory rough box colonies were also significantly larger than those in migratory control colonies. The use of rough wood boxes did correspond with decreased honey production in year one migratory colonies but had no effect during year two. Finally, in both stationary and migratory operations, propolis deposition was correlated with a seasonal decrease and/or stabilization in the expression of multiple immune and bacterial genes, suggesting that propolis-rich environments contribute to hive homeostasis. These findings provide support for the practical implementation of rough box hives as a means to enhance propolis collection and colony health in multiple beekeeping contexts.

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.

Stress-induced loss of social resilience in honeybee colonies and its implications on fitness

Stressors may lead to a shift in the timing of life-history events of species, causing a mismatch with optimal environmental conditions, potentially reducing fitness. In honeybees, the timing of brood rearing and nest emergence in late winter/early spring is critical as colonies need to grow fast after winter to prepare for reproduction. However, the effects of stress on these life-history events in late winter/early spring and the possible consequences are not well understood. Therefore, we tested whether (i) honeybee colonies shift timing of brood rearing and nest emergence as response to stressors, and (ii) if there is a consequent loss of social resilience, reflected in colony fitness (survival, growth and reproduction). We monitored stressed (high load of the parasitic mite Varroa destructor or nutrition restricted) colonies and presumably non-stressed colonies from the beginning of 2020 till spring of 2021. We found that honeybee colonies do not shift the timing of brood rearing and nest emergence in spring as a coping mechanism to stressors. However, we show that there is loss of social resilience in stressed colonies, leading to reduced growth and reproduction. Our study contributes to better understanding the effects of stressors on social resilience in eusocial organisms.