Behavior Of Bees Research Articles

Keypoint-Based Bee Orientation Estimation and Ramp Detection at the Hive Entrance for Bee Behavior Identification System

This paper addresses the challenge of accurately estimating bee orientations on beehive landing boards, which is crucial for optimizing beekeeping practices and enhancing agricultural productivity. The research utilizes YOLOv8 pose models, trained on a dataset created using an open-source computer vision annotation tool. The annotation process involves associating bounding boxes with keypoints to represent bee orientations, with each bee annotated using two keypoints: one for the head and one for the stinger. The YOLOv8-pose models demonstrate high precision, achieving 98% accuracy for both bounding box and keypoint detection in 1024×576 px images. However, trade-offs between model size and processing speed are addressed, with the smaller nano model reaching 67 frames per second on 640×384 px images. The entrance ramp detection model achieves 91.7% intersection over union across four keypoints, making it effective for detecting the hive’s landing board. The paper concludes with plans for future research, including the behavioral analysis of bee colonies and model optimization for real-time applications.

Autonomous tracking of honey bee behaviors over long-term periods with cooperating robots.

Digital and mechatronic methods, paired with artificial intelligence and machine learning, are transformative technologies in behavioral science and biology. The central element of the most important pollinator species-honey bees-is the colony's queen. Because honey bee self-regulation is complex and studying queens in their natural colony context is difficult, the behavioral strategies of these organisms have not been widely studied. We created an autonomous robotic observation and behavioral analysis system aimed at continuous observation of the queen and her interactions with worker bees and comb cells, generating behavioral datasets of exceptional length and quality. Key behavioral metrics of the queen and her social embedding within the colony were gathered using our robotic system. Data were collected continuously for 24 hours a day over a period of 30 days, demonstrating our system's capability to extract key behavioral metrics at microscopic, mesoscopic, and macroscopic system levels. Additionally, interactions among the queen, worker bees, and brood were observed and quantified. Long-term continuous observations performed by the robot yielded large amounts of high-definition video data that are beyond the observation capabilities of humans or stationary cameras. Our robotic system can enable a deeper understanding of the innermost mechanisms of honey bees' swarm-intelligent self-regulation. Moreover, it offers the possibility to study other social insect colonies, biocoenoses, and ecosystems in an automated manner. Social insects are keystone species in all terrestrial ecosystems; thus, developing a better understanding of their behaviors will be invaluable for the protection and even the restoration of our fragile ecosystems globally.

Learning to handle flowers increases pollen collection for bees but does not affect pollination success for plants

Abstract Cooperation and conflict are common in plant–pollinator interactions. Flowering plants often entice pollinators to visit by offering floral food rewards, thereby facilitating pollination. However, pollinators such as bees can learn to improve their collection of floral rewards (such as pollen), changing how they interact with the flower’s reproductive organs, which together could reduce pollination success. Consequently, complex flowers that slow pollinator learning might benefit the plant. Yet how pollinator learning and flower complexity interact to affect pollination success is unknown. We therefore asked how differences in complexity of 4 flower types (Phacelia campanularia, Exacum affine, Solanum elaeagnifolium, and Erythranthe guttata) affected learning by pollen-foraging generalist bumble bees (Bombus impatiens) and how learning affected pollen collection and pollen deposition on these flowers. We found that bees generally learned how to efficiently handle more complex flower types more slowly. Bees that required more visits to become efficient foragers collected less pollen, with no effect on pollen deposition. Except for the simplest flower type, learning also involved development of motor routines unique to each flower type. Experienced bees overall collected more pollen, but individual differences in motor routines did not affect pollen collection. Conversely, individual differences in motor routines affected pollen deposition, but there was no overall effect of experience. Thus, even though learning overall benefits the bee, it does not alter female (and potentially male) fitness benefits for the plant. We discuss potential reasons for these patterns and consequences for bee behavior and flower evolution.

Determining the Effect of Thiacloprid Exposure on Honey Bees (Apis mellifera)

The effect of thiacloprid (21.7% SC), an N-cyanoamidine neonicotinoid exposure on honey bees, Apis mellifera was evaluated under field conditions. A study on foraging behaviour, hygienic behaviour, mortality, and food stores was carried out using different concentrations of thiacloprid viz., 500 ppm, 325 ppm, 250 ppm, 125 ppm, 62.50 ppm, 31.25 ppm, and 0 ppm. A significant effect was observed from the use of concentrations of 500 and 325 ppm only. This was evident for all the behaviours studied. This indicated that thiacloprid remained safer for the honey bees at lower concentrations of 250, 125, 62.50, and 31.25 ppm as the effect of these concentrations was not significantly different from that of the control. Thus, an impact to a great extent on the behaviour of honey bees was possible with the higher concentrations of thiacloprid only.

Physically stressed bees expect less reward in an active choice judgement bias test.

Emotion-like states in animals are commonly assessed using judgment bias tests that measure judgements of ambiguous cues. Some studies have used these tests to argue for emotion-like states in insects. However, most of these results could have other explanations, including changes in motivation and attention. To control for these explanations, we developed a novel judgment bias test, requiring bumblebees to make an active choice indicating their interpretation of ambiguous stimuli. Bumblebees were trained to associate high or low rewards, in two different reward chambers, with distinct colours. We subsequently presented bees with ambiguous colours between the two learnt colours. In response, physically stressed bees were less likely than control bees to enter the reward chamber associated with high reward. Signal detection and drift diffusion models showed that stressed bees were more likely to choose low reward locations in response to ambiguous cues. The signal detection model further showed that the behaviour of stressed bees was explained by a reduction in the estimated probability of high rewards. We thus provide strong evidence for judgement biases in bees and suggest that their stress-induced behaviour is explained by reduced expectation of higher rewards, as expected for a pessimistic judgement bias.

Differences between uncapping and removal behaviors in Apis cerana from the perspective of long non-coding RNAs

BackgroundHygienic behavior, a specialized form of immune response evolved in social insects, plays a crucial role in safeguarding colonies from disease spread. In honeybee colonies, such behavior typically entails the dual steps of uncapping and removal of unhealthy and deceased brood. Although in recent years, numerous studies have examined the development of hygienic behavior, the mechanisms underlying the division in the performance of uncapping and removal have yet to be sufficiently elucidated. In this regard, long non-coding RNAs (lncRNAs) have been evidenced to be engaged in regulating the physiological activities of honeybees; however, whether lncRNAs are likewise involved in the uncapping and removal tasks has not been clarified.ResultsIn this study, the strong hygienic Apis cerana worker bees were used and the processes of uncapping and removal behaviors in three colonies were assayed with freeze-killed brood in the field. We then sequenced the antennal RNAs of honeybees to identify differentially expressed lncRNAs and performed lncRNA-mRNA association analysis to establish the differences between uncapping and removal. We detected 1,323 differentially expressed lncRNAs in the antennae, and the findings of lncRNA-mRNA association analyses revealed that the target genes of differentially expressed lncRNAs between uncapping and removal worker bees were predominantly linked to response to stimulus, receptor activity, and synapse. Notably, among the lncRNAs enriched in cellular response to stimulus, XR_001766094.2 was exclusively expressed in the uncapping worker bees. Based on these findings, we hypothesize that XR_001766094.2 plays a key role in distinguishing uncapping from removal behaviors by responding to external stimulus, thereby suggesting that the division of hygienic behaviors is governed by differential thresholds of responsiveness to environmental cues.ConclusionWe characterized differences in the uncapping and removal behaviors of worker bees from a perspective of lncRNAs. Uncapping bees may be equipped with a more rapid stimulatory response and more acute olfactory sensitivity, contributing to the rapid hygienic behavior in honeybee colonies. Our results thus establish a foundation for potential lncRNA-mediated gene expression regulation in hygienic behavior.

Identification of alarm pheromone components of the southern giant Asian hornet, Vespa soror, a major pest of honey bees.

The rise of biological invasions threatens biodiversity and food security, with the vespid family, including Vespa soror, being of particular concern. Our study focused on the alarm pheromone components of V. soror. By using gas chromatography-mass spectrometry (GC-MS) chemical analyses, electroantennograms, and field bioassays, we identified 5 compounds-2-pentanol, 3-methyl-1-butanol, 2-heptanol, 2-nonanol (2-N), and isopentyl acetate (IPA)-in hornet sting venom that elicited defensive behavior from hornets. IPA and 2-N also serve as alarm pheromone components in multiple honey bee species that are important prey for V. soror. This shared chemical signaling may allow cross-detection by each species on the other's alarm cues. While it should be advantageous for bees to detect V. soror alarm pheromone, the benefits to V. soror of using IPA and 2-N are unclear. V. soror may manipulate bee behavior, potentially distracting defenders, because they mark victim bee colonies by rubbing their abdomens, which contain their sting glands, at bee hive entrances. Our findings pose new evolutionary questions about the role of manipulation in the arms races.

Hierarchical decision-making approach for the task planning of the BWBUG cluster with three-dimensional time-varying ocean currents

This study concerns the task planning problem for a Blended-Wing-Body Underwater Glider (BWBUG) cluster with three-dimensional, time-varying ocean currents. The objective is to achieve task allocation and three-dimensional glide path planning for the BWBUG cluster in a manner that ensures safety and cost-effectiveness. First, the task planning problem for the BWBUG cluster is analyzed, detailing the task requirements and operational space. Next, a hierarchical decision-making approach is proposed, which comprises an upstream planner and a downstream planner. The upstream planner aims to determine a feasible task space for the BWBUG cluster while satisfying task constraints. The downstream planner fully considers the impact of ocean currents on BWBUG movement, as well as the safety and kinematic constraints of BWBUGs. It optimizes the three-dimensional movement trajectories of each BWBUG with the objective of minimizing energy consumption. Inspired by the nest-seeking behavior of bees, a novel heuristic algorithm called the Bee Nesting Optimization Algorithm (BNOA) is proposed to solve both upstream and downstream planning problems. The results of the simulations demonstrate the efficacy of the hierarchical decision-making approach developed in this study. Additionally, the proposed BNOA is highly competitive, offering reasonable task allocations and optimal three-dimensional glide paths for the BWBUG cluster.

Conventional agriculture affects sex communication and impacts local population size in a wild bee

Man-made agricultural stressors have been identified to compromise the reproductive dynamics of bee populations within agricultural environments. With the aid of bee hotels, we explored the influence of conventional and organic farming systems on local population size and body traits of the mason bee, Osmia bicornis, in southern Germany. We further used a chemical ecology approach and bioassays to test whether farming management influence male pre-copulatory behaviors. We observed a positive relationship between the extent of organic agriculture in the landscape and both overall brood cell production and nesting frequency. Moreover, farming systems were found to influence body traits, with bees from organic sites being smaller in size and having a different cuticular hydrocarbon composition compared with those at conventional sites. Bioassays revealed that males were more sexually attracted to freeze-killed females from conventional sites compared with those from organic sites. Intriguingly, treating females from organic fields with synthetic semiochemicals enhanced their sexual attraction to levels comparable with females from conventional sites. Our findings shed light on the intricate interplay between farming practices and the reproductive behaviors of wild mason bees, emphasizing the need for a comprehensive understanding of these dynamics for effective conservation and management strategies.

Pollination ecology in Centrosema brasilianum (L.) Benth. (Fabaceae: Papilionoideae): bee size influences pollen deposition

Here, were observed the interaction between Centrosema brasilianum (Fabaceae, Papilionoideae) and their floral visitors and quantified pollen removal and visitor traits. Aims: for to determine whether the behavior, quantity, and location of deposited pollen on the body of visiting bees differs according to the body size of bees. Methods: the morphological and behavioral attributes of floral and bees observed in interaction were measured in 20 individuals of C. brasilianum. The place of deposit and the amount of polinic charge in each bee were identified. The relationship between the attributes of the interactants and of pollen deposited was determined using simple linear models. Results: Bee behavior and pollen deposition on the body varied according to bee body size. Larger-sized bees with long tongues raised floral keels during visits and pollen deposition occurred on their heads and dorsal surfaces. Smaller individuals with short tongues did not raise the keel, and pollen was deposited on the dorsal surface, but mostly over the wings. Additionally, larger bees had twice as many pollen grains deposited on their body surfaces than smaller bees. Conclusions: we assumed that larger bees may provide a more effective pollination service than smaller bees in C. brasilianum. Implications: Future evaluations should be conducted that include more morphological and behavioral attributes in a larger sample number, which would provide a more nuanced understanding of pollination efficiency, the relationship between different behaviors and body sizes of bees, and the consequences of differential pollen deposition on the reproductive success of C. brasilianum.

Routing algorithm for sparse unstructured P2P networks using honey bee behavior

SummaryUnstructured peer‐to‐peer (P2P) networks pose unique challenges for efficient and scalable routing. In this study, we introduce a novel routing algorithm inspired by the foraging behavior of honey bees named Honey Bee Optimization in P2P Networks (HBO_P2P) to address the inherent limitations of routing in unstructured P2P networks, focusing on improving packet delivery, minimizing hop count, reducing message overhead, and optimizing overall throughput. To evaluate the performance of our proposed algorithm, we conducted comprehensive experiments comparing it with existing algorithms commonly used in P2P networks, namely, particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimization (ACO). After the simulation, we got the results as follows: Our algorithm outperforms ACO, GA, and PSO by exhibiting the highest number of data hops indicating potential efficiency in route optimization. Routing overhead is also minimal as compared to ACO, GA, and PSO. The average data packet delay is also low in our algorithm as compared to ACO, GA, and PSO. HBO_P2P achieves the highest throughput, nearly reaching 100 Mbps. While ACO and GA exhibit similar throughput of around 80 Mbps, and PSO has the lowest throughput, approximately 60 Mbps.

Improved unmanned aerial vehicle control for efficient obstacle detection and data protection

<p>The article centers on the research objectives and tasks associated with developing a swarm control system for unmanned aerial vehicles (UAVs) utilizing artificial intelligence (AI). A comprehensive literature review was undertaken to assess the effectiveness of the "swarm" method in UAV management and identify key challenges in this domain. Swarm algorithms were implemented in the MATLAB/Simulink environment for modeling and simulation purposes. The study successfully instantiated and simulated a UAV swarm control system adhering to fundamental principles and laws. Each UAV operates autonomously, following target-swarm principles inspired by the collective behavior of bees and ants. The collective movement and behavior of the swarm are controlled by an AI-based program. The system demonstrated effective obstacle detection and avoidance through computer simulations. Results obtained highlight key features contributing to success, including decentralized autonomy, collective intelligence, UAV coordination, scalability, and flexibility. The deployment of a local radio communication system in UAV swarm control and remote object monitoring is also discussed. The research findings hold practical significance as they enable the effective execution of complex tasks and have potential applications in various fields.</p>

PENYELESAIAN CAPACITATED VEHICLE ROUTING PROBLEM PADA PENDISTRIBUSIAN AIR GALLON MENGGUNAKAN ALGORITMA KOLONI LEBAH

Distribution is one of the problems in the business and industrial world, for example the problem of delivering supplies of raw materials and other goods to several customers, where the delivery must be carried out exactly once by one or more vehicles to each customer with a certain capacity. In its development, this problem was formulated as the Capacitated Vehicle Routing Problem (CVRP). Based on the definition, CVRP is one of the most common variations of the Vehicle Routing Problem (VRP) problem to determine the optimal route with the addition of constraints in the form of homogeneous vehicle capacity. Here, we discussed the CVRP problem of the distribution route for Pak Nurrofiq's gallons of water from the depot to its customers in the Taman Sei Raya Complex, Serdam Asri Complex and Kapuas Mas Complex. The aim of this research is to create a mathematical model for the CVRP problem in the distribution of Pak Nurrofiq's drinking water and solve it using the bee colony algorithm. The bee colony algorithm is a metaheuristic algorithm based on the behavior of honey bees in searching for food sources. The research results show that the case of gallon water distribution was successfully implemented into CVRP and completed using the bee colony algorithm, 4 routes were obtained with a total distance of all routes, namely 9,068 km.

Educational Application of Artificial Intelligence for Diagnosing the State of Railway Tracks

The aim of the work is to present an innovative solution based on artificial intelligence for examining the condition of railway tracks in real time. The system, based on fuzzy logic and metaheuristics such as Fuzzy Logic, Neural Networks and Bee Behavior Optimization, combines hardware and software to provide reliable data on the technical characteristics of the railway. Installed in rail vehicles, hardware collects this data, while software uses artificial intelligence to improve operational reliability and safety. The aforementioned technology is not only useful for infrastructure diagnostics, but also for urban railways such as trams and metros, ensuring a high level of passenger safety. The introduction of artificial intelligence in the railway sector is a key step towards modernisation, improving efficiency, resource optimization and safety. Although still in its infancy, artificial intelligence already shows great potential in transforming the railway sector towards a more efficient, reliable and sustainable future.

Machine learning-based bee recognition and tracking for advancing insect behavior research

The study of insect behavior, particularly that of honey bees, has a broad scope and significance. Tracking bee flying patterns grants much helpful information about bee behavior. However, tracking a small yet fast-moving object, such as a bee, is difficult. Hence, we present artificial intelligence, machine-learning-based bee recognition, and tracking systems to assist the researcher in studying the bee’s behavior. To develop a machine learning system, a labeled database is required for model training. To address this, we implemented an automated system for analyzing and labeling bee videos. This labeled database served as the foundation for two distinct bee-tracking solutions. The first solution (planar bee tracking system) tracked individual bees in closed mazes using a neural network. The second solution (spatial bee tracking system) utilized a neural network and a tracking algorithm to identify and track flying bees in open environments. Both systems tackle the challenge of tracking small-bodied creatures with rapid and diverse movement patterns. Although we applied these systems to entomological cognition research in this paper, their relevance extends to general insect research and developing tracking solutions for small organisms with swift movements. We present the complete architecture and detailed methodologies to facilitate the utilization of these models in future research endeavors. Our approach is a simple and inexpensive method that contributes to the growing number of image-analysis tools used for tracking animal movement, with future potential applications under less sterile field conditions. The tools presented in this paper could assist the study of movement ecology, specifically in insects, by providing accurate movement specifications. Following the movement of pollinators or natural enemies, for example, greatly contributes to the study of pollination or biological control, respectively, in natural and agro-ecosystems.

Field Exploration for Colony Selection: Evaluating Hygienic Behavior in Apis cerana indica Colonies.

Hygienic behavior (HB) emerges as a pivotal trait, impacting colony resistance to diseases. This study aimed to understand the behavioral traits of Apis cerana indica colonies, with a focus on HB and other key characteristics crucial for colony health, and to screen and identify colonies with superior hygienic behavior and better performance to combat prevailing diseases and pests. This research spans a comprehensive field analysis with different seasons and locations, encompassing the distinct environmental and management factors that influence honey bee behavior. The inclusion of A. cerana indica colonies from various locations provides a novel perspective, offering valuable insights regarding the hygienic behavior of A. cerana indica. Several statistical analyses, including descriptive statistics, principal component analysis (PCA), and Aligned Rank Transformation-Analysis of Variance (ART-ANOVA) for repeated measures, shed light on the distribution of hive metrics, emphasizing the significance of considering seasonality and location-specific factors. PCA highlights unique characteristics in Tirupur and Coimbatore colonies, while correlation analyses uncover relationships among HB, honey, pollen, brood area, and adult population. Moreover, the study's nuanced findings gave the status of hygienic behavior of A. cerana indica colonies and identified colonies with better colony performance, which will be useful for future breeding programs with A. cerana indica.

Early exposure to glyphosate during larval development induces late behavioural effects on adult honey bees

As the most abundant pollinator insect in crops, Apis mellifera is a sentinel species of the pollinator communities. In these ecosystems, honey bees of different ages and developmental stages are exposed to diverse agrochemicals. However, most toxicological studies analyse the immediate effects during exposure. Late effects during adulthood after early exposure to pollutants during larval development are poorly studied in bees. The herbicide glyphosate (GLY) is the most applied pesticide worldwide. GLY has been detected in honey and beebread from hives near treated crops. Alterations in growth, morphogenesis or organogenesis during pre-imaginal development could induce late adverse effects after the emergence. Previous studies have demonstrated that GLY alters honey bee development, immediately affecting survival, growth and metabolism, followed by late teratogenic effects. The present study aims to determine the late impact on the behaviour and physiology of adult bees after pre-imaginal exposure to GLY. For that, we reared brood in vitro or in the hive with sub-chronic exposure to the herbicide with the average detected concentration in hives. Then, all newly emerged bees were reared in an incubator until maturity and tested when they became nurse-aged bees. Three behavioural responses were assessed as markers of cognitive and physiological impairment. Our results show i) decreased sensitivity to sucrose regardless of the rearing procedure, ii) increased choice latency and locomotor alterations during chemotaxis and iii) impaired associative learning. These late toxicity signs could indicate adverse effects on task performance and colony efficiency.

Research on multi-source separation method of partial discharge based on ABC-JADE

Abstract The presence of multiple PD power sources in the switchgear will produce mixed PD signals, which will bring difficulties to the subsequent signal identification work. To solve this problem, this paper uses the exponential signal model to perform hybrid separation and makes ABC (Artificial Bee Colony Algorithm)-JADE (Joint Approximative Diagonalization of Eigen matrices), i.e., the ABC algorithm is a part of the JADE algorithm, and the ABC algorithm introduces the foraging behavior of bees in the search space, which can help the algorithm better explore the solution space. The JADE algorithm is a differential evolution algorithm, which can adjust the algorithm parameters through an adaptive mechanism. In this paper, the similarity coefficient and amplitude relative error index are used to evaluate the signal separation performance, and the experimental results show that the proposed JADE-ABC algorithm can effectively separate mixed signals. The amplitude relative error and phase accuracy of the separated signal and the source signal are within a certain range, and have a certain degree of robustness.

Influence of Some Factors on Propolis Dilution by Honey Bees

Propolis is a sticky resinous substance collected from buds, leaves, stems of wild plants and processed by bees. Currently, bee behaviour during the collection of propolis, its botanical origin and sources of production have been studied. The process of propolis accumulation in bee nests and the factors influencing the intensity and productivity of bee colonies need to be further studied, which will allow to control the propolis productivity of bees in the future. To better understand the behavior of bees when liquefying propolis with enzymes from the mandibular glands, we conducted studies using specially designed collectors. Three different types of mesh were used in the collectors: ethylene vinyl acetate (EVA), polyethylene, and nylon. All three types of meshes had different hole diameters. As a result of the study, we determined the diameter of propolis droplets formed in all collectors, deviations from roundness, and the distance at which the droplets fly away from the edge of the glass substrates. We conclude that the type of mesh materials does not influence the behavior of bees in liquefying propolis. The size of the mesh holes affects the distance from the edge of the glass substrate to the centre of the propolis drops.

Frequency and behavior of Melipona stingless bees and orchid bees (Hymenoptera: Apidae) in relation to floral characteristics of vanilla in the Yucatán region of Mexico.

Vanilla planifolia is native to the Mexican tropics. Despite its worldwide economic importance as a source of vanilla for flavoring and other uses, almost all vanilla is produced by expensive hand-pollination, and minimal documentation exists for its natural pollination and floral visitors. There is a claim that vanilla is pollinated by Melipona stingless bees, but vanilla is more likely pollinated by orchid bees. Natural pollination has not been tested in the Yucatán region of Mexico, where both vanilla and potential native bee pollinators are endemic. We document for the first time the flowering process, nectar production and natural pollination of V. planiflora, using bagged flower experiments in a commercial planting. We also assessed the frequency and visitation rates of stingless bees and orchid bees on flowers. Our results showed low natural pollination rates of V. planifolia (~ 5%). Only small stingless bees (Trigona fulviventris and Nannotrigona perilampoides) were seen on flowers, but no legitimate visits were witnessed. We verified that there were abundant Euglossa and fewer Eulaema male orchid bees around the vanilla plants, but neither visited the flowers. The introduction of a colony of the stingless bee Melipona beecheii and the application of chemical lures to attract orchid bees failed to induce floral visitations. Melipona beecheii, and male orchid bees of Euglossa viridissima and E. dilemma may not be natural pollinators of vanilla, due to lack of attraction to flowers. It seems that the lack of nectar in V. planifolia flowers reduces the spectrum of potential pollinators. In addition, there may be a mismatch between the attractiveness of vanilla floral fragrances to the species of orchid bees registered in the studied area. Chemical studies with controlled experiments in different regions would be important to further elucidate the potential pollinators of vanilla in southern Mexico.