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Honeybees play a vital role in preservation of an healthy environment. Bees not only provide pollination services but also produce honey, beeswax, and royal jelly. Beekeeping has a rich history and substantial economic potential worldwide, but swarming remains a crucial challenge for maintaining profitability. Swarming, a typical colony reproductive process in honeybees, significantly impacts beekeepers profitability by lowering the number of bees in hives and thus effecting honey production. Monitoring of these beehives is therefore of paramount importance to keep an eye on their irregular behavior. Swarm prediction can be done by visually inspecting hives, monitoring temperature, or analyzing acoustic features with machine learning. Acoustic monitoring is instrumental in detecting changes in colony behavior since it overcomes the constraints of visual inspections and is not affected by external factors like temperature. In this paper, we aim to evaluate various state-of-the-art machine learning and deep learning models for swarm prediction by studying wave plot features, Mel Spectrogram, and Melfrequency Cepstral coefficients (MFCC). We use Naive Bayes, K-nearest Neighbors (KNN), and Support Vector Machines (SVM) as machine learning models and Convolution Neural Networks (CNN), Long Short Term Memory (LSTM), and Transformers as deep learning models for comparison purposes. We apply these models on a well-known honey bees audio dataset provided by the NU-hive project and consider classification metrics such as accuracy, precision, recall, and F1 score for the comparative evaluation of our models. Our evaluation demonstrates SVM as the best-performing machine learning algorithm. In particular, SVM with Mel Spectrogram as input data, achieved an accuracy of around 97%. On the other hand, CNN outperformed all the models and achieved an accuracy of 99%, using MFCC features as input data. As a result of these encouraging outcomes, we understand that our results can help the researchers to choose which AI model is more suitable for them to design beehive monitoring systems for accurate identification of abnormal situations in beehives.

This paper presents a machine-learning approach for detecting swarming events. Three different classification algorithms are tested: The k-Nearest Neighbors algorithm (k-NN) and Support Vector Machine (SVM), and a newly proposed by the authors, U-Net Convolutional Neural Network (CNN), developed for biomedical image segmentation. Next, the authors present their experimental scenario of collecting audio data of swarming and non-swarming events and evaluating the results from the k-NN and SVM classifiers and their proposed CNN algorithm. Finally, the authors compare these three methods and present the cross-comparison results of the optimal method for early and late/close-to-the-event detection of swarming.

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The swarming of a bee colony is guided by a small group of scout individuals, which are informed of the target destination (the new nest). However, little is known on the underlying mechanisms, i.e. on how the information is passed within the population. In this respect, we here present a discrete mathematical model to investigate these aspects. In particular, each bee, represented by a material point, is assigned its status within the colony and set to move according to individual strategies and social interactions. More specifically, we propose alternative assumptions on the flight synchronization mechanism of uninformed individuals and on the characteristic dynamics of the scout insects. Numerical realizations then point out the combinations of behavioural hypotheses resulting in collective productive movement. An analysis of the role of the scout bee percentage and of the phenomenology of the swarm in domains with structural elements is finally performed.

The swarming of a bee colony is guided by a small group of scout individuals, which are informed of the target destination (the new nest). However, little is known on the underlying mechanisms, i.e. on how the information is passed within the population. In this respect, we here present a discrete mathematical model to investigate these aspects. In particular, each bee, represented by a material point, is assigned its status within the colony and set to move according to individual strategies and social interactions. More specifically, we propose alternative assumptions on the flight synchronization mechanism of uninformed individuals and on the characteristic dynamics of the scout insects. Numerical realizations then point out the combinations of behavioural hypotheses resulting in collective productive movement. An analysis of the role of the scout bee percentage and of the phenomenology of the swarm in domains with structural elements is finally performed. [ABSTRACT FROM AUTHOR]

Considering that extending the concept of bees partitioning into subgroups of foragers to the onlooker phase of the cooperative learning artificial bee colony (CLABC) strategy is not only feasible from algorithmic viewpoint but might reflect real behavioral foraging characteristics of bee swarms, this paper studies whether the performance of CLABC can be enhanced by developing a new model for the proposed cooperative foraging scheme. Relying on this idea, we design a modified cooperative learning artificial bee colony algorithm, referred to as mCLABC. The design procedure is built upon the definition of a partitioning scheme of onlookers allowing the generation of subgroups of foragers that might evolve differently by using specific solution search rules. In order to improve the involving of local and global search at both employed and onlooker levels, the multiple search mechanism is further tuned and scheduled according to a random selection strategy defined on the evolving parameters. Moreover, a detailed performance and robustness study of the proposed algorithm dealing with the analysis of the impact of different structural and parametric settings is conducted on benchmark optimization problems. Superior convergence performance, better solution quality, and strong robustness are the main features of the proposed strategy in comparison with recent ABC variants and advanced methods. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • We propose an optimization algorithm based on bees’ nest-site selection behavior. • We validated the algorithm performance on the traveling salesman and clustering problems. • A quorum mechanism provides the stopping criterion instead of maximum cycle number. • The convergent speed was gained without reduction in solution quality. • The results show promising performance in solving combinatorial problems. Abstract This paper proposes a new version of the Artificial Bee Colony (ABC) optimization algorithm based on the natural nest-site selection behavior of honey bee swarms. The algorithm offers three advantages. First, the responsibility of bees in the proposed algorithm is dynamic, meaning that bee agents may change states (explorer, resting, observer, and committed) during the execution. New feasible solutions are randomly generated in every iteration by varying the numbers of bee agents, thus introducing diversified candidate solutions into the solution space. Second, each bee records its own satisfaction with each nest-site and this satisfaction value is added to the quality measure for the nest site, allowing lower quality nest sites to be retained as candidate solutions and again providing greater diversity. Third, a quorum mechanism provides the stopping criterion, instead of the maximum cycle number (MCN). This mechanism helps the algorithm to converge more quickly. Experiments on the traveling salesman and clustering problems have been conducted in order to observe the algorithm performance. The results show promising performance in solving combinatorial problems when compared with other state-of-the-art algorithms. In addition, the proposed algorithm can converge more quickly with the quorum mechanism. [ABSTRACT FROM AUTHOR]

During reproductive swarming, a honeybee swarm needs to decide on a new nest site and then move to the chosen site collectively. Most studies of swarming and nest-site selection are based on one species, Apis mellifera. Natural colonies of A. mellifera live in tree cavities. The quality of the cavity is critical to the survival of a swarm. Other honeybee species nest in the open, and have less strict nest-site requirements, such as the opennesting dwarf honeybee Apis florea. Apis florea builds a nest comprised of a single comb suspended from a twig. For a cavity-nesting species, there is only a limited number of potential nest sites that can be located by a swarm, because suitable sites are scarce. By contrast, for an open-nesting species, there is an abundance of equally suitable twigs. While the decision-making process of cavity-nesting bees is geared towards selecting the best site possible, open-nesting species need to coordinate collective movement towards areas with potential nest sites. Here, we argue that the nest-site selection processes of A. florea and A. mellifera have been shaped by each species' specific nest-site requirements. Both species use the same behavioural algorithm, tuned to allow each species to solve their species-specific problem. [ABSTRACT FROM AUTHOR]

A literary criticism of the book "Aeneid" by Vergil is presented. It focuses on an omen that appears in the sacred laurel of Apollo located within the palace of Latinus. It examines the behavior of bees and reveals that the prophet's interpretation recalls that of historical Roman haruspices who viewed the appearance of a swarm of bees in a military camp as a negative omen.

The article offers information on reproductive and nonreproductive swarming in honey bees and discusses whether it adapts to pathogens or not. Topics discussed include the incidence of pathogens in honey bee colonies and honey bee swarming behavior, the importance of the population health of honey bees, and the role of nonreproductive swarming in behavioral immunity for the honey bees.

Photovoltaic (PV) energy generation is one of the more promising renewable energy technologies. However, determining optimal specifications (e.g., size, location) for PV systems is a significant challenge for planners and investigators of distribution systems. In this paper, a framework is provided for integrating geographical information systems, artificial bee swarm optimization, and simulation to determine the optimal size and location for PV panels on an annual basis. A geographical information system module is developed first to determine the suitable location and capacity for a stand-alone PV system in rural areas, taking into account the technical, environmental and socio-economic criteria affecting the site selection process of stand-alone PV systems. Then an efficient heuristic approach, namely artificial bee swarm optimization, is proposed for optimum sizing of a stand-alone PV system to continuously satisfy the load demand with minimal cost and enhanced energy system reliability. For this aim, the maximum allowable loss of power supply probability is also considered. The proposed framework is applied to a real case study in eastern Iran and the results show that it can effectively be used for off-grid photovoltaic projects in rural areas considering both power quality and cost. [ABSTRACT FROM AUTHOR]

Wireless sensor network refers to distributed sets of embedded devices, all of them having processing units, wireless transmission interface and sensors or actuators. Data accumulation through effective network organizations helps nodes to be split into small sets known as clusters. This grouping of sensor nodes as clusters is known as clustering. All clusters have leaders known as cluster heads (CHs). Clustering networks for minimizing total distance is an NP-hard issue. For a particular network topology, it is hard to discover optimum quantity of cluster-heads as well as their positions. The current article suggests a hybrid differential evolution with multi objective bee swam optimization (MOBSO-DE) for efficient clustering. CH selection process is based on communication energy and factors like residual energy and energy constraint metric. Simulation shows that the new MOBSO-DE method outperformed LEACH and MOBSO for packet delivery ratio and network lifetime. [ABSTRACT FROM AUTHOR]

The study was undertaken in Amhara National Regional State, northeastern zones of Ethiopia in 2013/2014. The objectives of the study were to identify the existing local honeybees and their productive characteristics and behavior and generate baseline information for further research and development. Out of two zones, three potential districts have been purposively selected based on their potential. A total of 260 beekeepers personal interviewees were administered using structured and semi-structured questionnaires. The study was designed to address behavioral characteristics and productivities of different local honeybees. About 98.8% of interviewees were male and about 73.9% were at stages of literacy ranging from read and write to diploma level. Nearly 73.4% of beekeepers have more than 5 years of beekeeping experience. The average honeybee colony holding was 4.6 in highlands, 2.8 in mid altitude, and 2.7 in lowlands. About 34% of respondents harvest honey more than two times a year. Based on the honeybees individual color appearance, aggressiveness, and honey productivity, about 36.7% of the respondents have identified three local honeybee types, namely, brown red, black, and mixed. Brown red colored honeybee ecotype has been selected as best due to their better honey production, low aggressiveness, and relatively high reproductive swarming tendency. [ABSTRACT FROM AUTHOR]

The article provides observations of multiple honey bee (Apis mellifera) swarms aboard the USNS Comfort (TAH-20) during the Continuing Promise 2015 mission. A brief overview of swarming biology is given along with control/removal recommendations to reduce sting exposures. The observations suggest that preventive medicine personnel should provide adequate risk communications about the potential occurrence of bee swarms aboard military ships, and medical department personnel should be prepared for the possibility of treating of multiple sting exposures, especially in the Southern Command Area of Operations where the Africanized genotype of A mellifera is common. [ABSTRACT FROM AUTHOR]

Precision Beekeeping (or Precision Apiculture) aims to help beekeepers monitor bee colonies remotely and identify different colony states including deviant behaviour. One monitoring target is the remote identification of bee colony swarming since this is one of the factors that can significantly reduce profitability. To identify temperature dynamics and its patterns for swarming detection, ten colonies were constantly monitored for four months from 1 May to 31 August 2015. Nine swarms were observed during experiments. During the warm-up stage, in the last 10–20 min before take-off, a temperature rise by 1.5–3.4 °C from typical range 34–35 °C to range 37–38 °C was registered by a temperature sensor placed above the polyethylene foil covering the upper hive body under the pillow. For all swarming events it was common that a bee colony needs a relatively small amount of time (from 8 to 20 min) to warm up before take-off. It was concluded that a single temperature sensor above the bee nest combined with a proposed decision support algorithm can be used for automatic remote detection of swarming at take-off stage. [ABSTRACT FROM AUTHOR]

The article explores the early growth and founding of new nests of Plebeia remota, a genus of stingless bees. It mentions the dependence of Plebeia remota on cavities for nest-building that are excellent to lure swarms. It also emphasizes the importance of available resources in determining the success of swarms.

During reproductive swarming, honey bee scouts perform two very important functions. Firstly, they find new nesting locations and return to the swarm cluster to communicate their discoveries. Secondly, once the swarm is ready to depart, informed scout bees act as guides, leading the swarm to its final destination. We have previously hypothesised that the two processes, selecting a new nest site and swarm guidance, are tightly linked in honey bees. When swarms can be laissez faire about where they nest, reaching directional consensus prior to lift off seems unnecessary. If, in contrast, it is essential that the swarm reaches a precise location, either directional consensus must be near unanimous prior to swarm departure or only a select subgroup of the scouts guide the swarm. Here, we tested experimentally whether directional consensus is necessary for the successful guidance of swarms of the Western honey bee Apis mellifera by forcing swarms into the air prior to the completion of the decision-making process. Our results show that swarms were unable to guide themselves prior to the swarm reaching the pre-flight buzzing phase of the decision-making process, even when directional consensus was high. We therefore suggest that not all scouts involved in the decision-making process attempt to guide the swarm. [ABSTRACT FROM AUTHOR]

Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Scheduling of tasks in cloud computing is an NP-hard optimization problem. Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing (HBB-LB), which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue. [Copyright &y& Elsevier]

When a colony of honeybees relocates to a new nest site, less than 5 % of the bees (the scout bees) know the location of the new nest. Nevertheless, the small minority of informed bees manages to provide guidance to the rest and the entire swarm is able to fly to the new nest intact. The streaker bee hypothesis, one of the several theories proposed to explain the guidance mechanism in bee swarms, seems to be supported by recent experimental observations. The theory suggests that the informed bees make high-speed flights through the swarm in the direction of the new nest, hence conspicuously pointing to the desired direction of travel. This work presents a mathematical model of flight guidance in bee swarms based on the streaker bee hypothesis. Numerical experiments, parameter studies, and comparison with experimental data are presented. [ABSTRACT FROM AUTHOR]

In the spring of 1863, Lev Tolstoi, newly married and soon to be a father, began to conceive of the work that would eventually become War and Peace. That same spring he also took up beekeeping. While in practical terms his "bee passion" proved relatively short-lived, it was an exceptionally intense engagement with a miniaturized and uniquely observable biological and social universe. In this article, Thomas Newlin explores how Tolstoi's dual enmeshment in "swarm life"—that is, in the biologically fraught realms of marriage and beekeeping—influenced both the unconventional form of War and Peace and its equally unconventional ideas (in particular Tolstoi's linked conceptions of the nature of history and of consciousness). The implications of a "swarm" model of history ultimately troubled Tolstoi, however; his doubts about the imperatives of biology do not play themselves out fully in War and Peace but instead lurk just beneath its surface. [ABSTRACT FROM PUBLISHER]

Summary: According to kin selection theory, the colony kin structure of eusocial insects motivates workers'' altruistic behaviors and therefore their sterility or restricted reproduction []. Indeed, theory and cross-species comparison confirm that workers engage in their own reproduction depending on relatedness among colony members []. We show that in a honeybee colony, the workers switch from their typical altruistic role to a more selfish one if at their larval stage there are environmental cues of an upcoming decline in intracolony relatedness. This happens inevitably when a colony multiplies by swarming and replaces the mother queen with her daughter, because the mother queen''s workers are faced with rearing the sister queen''s offspring related to them half as much as between sisters. Workers developing from the mother queen''s eggs immediately after swarming, in a temporarily queenless colony, had more ovarioles in their ovaries and less-developed hypopharyngeal glands producing brood food than control workers reared in queenright conditions. These “rebel” workers were more engaged in laying their own male-determined eggs than in rearing offspring, whether or not the sister queen was present in the colony. The finding of this previously unknown rebel strategy confirms that kin selection shapes both cooperation and conflict in honeybee societies. [Copyright &y& Elsevier]

We present an agent-based model for the nest site selection process of the open-nesting red dwarf honeybee, Apis florea. Our main aim was to determine how nest site requirements affect the bees’ decision-making process. We either calculated our model parameters from experimental data or chose them so that our model would generate similar numbers of dancing bees and dance followers to those observed in real swarms with access to an abundance of suitable nest sites in all directions. We found that A. florea is less capable of making a collective decision on a new nest site when the area occupied by suitable sites is small compared to when suitable sites are abundant. Increasing the use of information regarding the location of potential nest sites or the accuracy of the information available enhanced the decision-making ability of A. florea when nest sites were scarce. We also found that swarm guidance might be hindered when suitable nest site areas are wide apart. We therefore examined two possible mechanisms for increasing directional agreement among dancers: mimicry of unverified dance information and self-regulation by inhibiting or changing dance behaviour based on observations of other dances. We show that, even at low levels, dance mimicry greatly enhances the ability of an A. florea swarm to make a decision and reduces the time to make a decision. However, in the presence of mimicry errors propagate through the swarm. Self-regulation had little or no effect, probably because of the overall low levels of dance activity present on the swarm at any given time. Our model results suggest that A. florea’s decision-making process allows swarms to locate a new nest site provided nest sites are abundant, even when they are of similar quality. [ABSTRACT FROM AUTHOR]

The circular walk of whirligig beetles, in the absence of swarming behaviour, is described by a coupled system of transport equations. For this system, an approximate description is derived in the form of coupled damped wave equations and ultimately in the form of a scalar diffusion equation. The resulting diffusion coefficient is quite different from what one would get for a walk on straight lines with tumbling (as in bacterial motion). [ABSTRACT FROM AUTHOR]

The honeybee dance ''language'' is one of the most popular examples of information transfer in the animal world. Today, more than 60 years after its discovery it still remains unknown how follower bees decode the information contained in the dance. In order to build a robotic honeybee that allows a deeper investigation of the communication process we have recorded hundreds of videos of waggle dances. In this paper we analyze the statistics of visually captured high-precision dance trajectories of European honeybees (Apis mellifera carnica). The trajectories were produced using a novel automatic tracking system and represent the most detailed honeybee dance motion information available. Although honeybee dances seem very variable, some properties turned out to be invariant. We use these properties as a minimal set of parameters that enables us to model the honeybee dance motion. We provide a detailed statistical description of various dance properties that have not been characterized before and discuss the role of particular dance components in the commmunication process [ABSTRACT FROM AUTHOR]

I am going to compare the strategies and communication bees use in order to locate and retrieve nectar to the world of science and the scientist. The analogy is intentionally anthropomorphic but I wish to argue that if successful bees made assumptions they would be similar to those of the scientist: flowers can be regarded as facts, nectar as knowledge, honey as technology and their 'waggle-dance' as communication of ideas. I would like to say that this is to be used as an analogy and should not be taken to be a statement of the scientific method as an emergent property of nature, as evolution ultimately does not care about what is true or false, whereas science does. However, what i do wish to convey is that in the same way that the life of bees can be limited by the process of their enquiries; science can also limit itself by the assumptions that are taken to be true or worthwhile in the quest for new knowledge. [ABSTRACT FROM AUTHOR]

The vibration signal may influence nest-site selection by honey bee swarms by enhancing scouting and recruitment. We investigated this hypothesis by comparing (1) the number of nest sites and the distances communicated by nest-site dancers on swarms from which vibrators were and were not removed and (2) the behavior of scouts visiting higher-quality (HQ) and lower-quality (LQ) nest boxes. The removal of vibrators from swarms did not alter the number of nest sites investigated, the distances traveled to nest sites, or the time required to select a new nest cavity. In contrast, vibrator removal tripled the time required for swarms to achieve liftoff after a cavity had been selected, although all swarm eventually became airborne and moved to a new site. About 14% of the scouts that visited the HQ and LQ nest boxes performed vibration signals; however, nest-box quality did not influence the tendency to produce the signal or intermix vibration signals and recruitment dances. However, we did find a significant, positive correlation between overall levels of vibration signal activity and nest-site recruitment during the house-hunting process. When viewed in concert, our results suggest that the vibration signal contributes to the house-hunting process by operating in a non-specific manner that may enhance scouting and recruitment in general during nest-site selection and facilitate rapid swarm liftoff after a new nest site has been chosen. The vibration signal is therefore a component in the cascade of communication signals that orchestrate house-hunting and colony relocation decisions. [ABSTRACT FROM AUTHOR]

Abstract: Honey bees live in groups of approximately 40,000 individuals and go through their reproductive cycle by the swarming process, during which the old queen leaves the nest with numerous workers and drones to form a new colony. In the spring time, many clues can be seen in the hive, which sometimes demonstrate the proximity to swarming, such as the presence of more or less mature queen cells. In spite of this the actual date and time of swarming cannot be predicted accurately, as we still need to better understand this important physiological event. Here we show that, by means of a simple transducer secured to the outside wall of a hive, a set of statistically independent instantaneous vibration signals of honey bees can be identified and monitored in time using a fully automated and non-invasive method. The amplitudes of the independent signals form a multi-dimensional time-varying vector which was logged continuously for eight months. We found that combined with specifically tailored weighting factors, this vector provides a signature highly specific to the swarming process and its build up in time, thereby shedding new light on it and allowing its prediction several days in advance. The output of our monitoring method could be used to provide other signatures highly specific to other physiological processes in honey bees, and applied to better understand health issues recently encountered by pollinators. [Copyright &y& Elsevier]

In this paper, we introduce a method for determining local interaction rules in animal swarms. The method is based on the assumption that the behavior of individuals in a swarm can be treated as a set of mechanistic rules. The principal idea behind the technique is to vary parameters that define a set of hypothetical interactions, as for example, a rule for aligning with neighbors. The parameter values are optimized so that the deviation between the observed movements in an animal swarm and the movements predicted by the assumed rule set is minimal. We demonstrate the method by reconstructing the interaction rules from the trajectories produced by a computer simulation. [ABSTRACT FROM AUTHOR]

Abstract: We introduce a pair of compartment models for the honey bee nest-site selection process that lend themselves to analytic methods. The first model represents a swarm of bees deciding whether a site is viable, and the second characterizes its ability to select between two viable sites. We find that the one-site assessment process has two equilibrium states: a disinterested equilibrium (DE) in which the bees show no interest in the site and an interested equilibrium (IE) in which bees show interest. In analogy with epidemic models, we define basic and absolute recruitment numbers ( and ) as measures of the swarm''s sensitivity to dancing by a single bee. If is less than one then the DE is locally stable, and if is less than one then it is globally stable. If is greater than one then the DE is unstable and the IE is stable under realistic conditions. In addition, there exists a critical site quality threshold above which the site can attract some interest (at equilibrium) and below which it cannot. We also find the existence of a second critical site quality threshold above which the site can attract a quorum (at equilibrium) and below which it cannot. The two-site discrimination process, in which we examine a swarm''s ability to simultaneously consider two sites differing in both site quality and discovery time, has a stable DE if and only if both sites’ individual basic recruitment numbers are less than one. Numerical experiments are performed to study the influences of site quality on quorum time and the outcome of competition between a lower quality site discovered first and a higher quality site discovered second. [Copyright &y& Elsevier]

When honeybee, Apis mellifera, swarms lift off to fly to their new nest site, only a small proportion of individuals know the location of the new site, yet these few individuals must guide the entire swarm to the correct place. How do scouts transfer directional information to their swarm-mates? The vision hypothesis proposes that scout bees fly rapidly through swarms ‘pointing’ in the direction of travel. We examined the vision hypothesis by exposing flying swarms to a large number of fast-moving bees headed in the wrong direction. These bees mimicked scouts and created conflicting directional information. Swarms that were exposed to conflicting information from fast-moving bees showed clear evidence of disrupted guidance including splitting and failure to reach the nestbox. We discuss the implications of our findings for hypotheses about the mechanisms of swarm guidance, and for our knowledge of how collectively moving groups deal with conflicting information. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Animals that travel in groups must synchronize the timing of impending departures to ensure group cohesion. The mechanisms used by a honeybee, Apis mellifera, colony to organize the departure of a swarm from its nest remain a mystery. We examined the signals that trigger a swarm''s explosive exodus from the parental nest, and we documented the concurrent changes in bee density and mobility. Using video recordings of swarms exiting observation hives, we analysed how bees in three swarming colonies prepared for and then performed their sudden departures. Over the 60min before swarm exodus, the production of piping signals gradually increased and ultimately peaked at the start of the swarm departure. Also, during swarm exodus, bee density (number of bees in 100cm2) dropped markedly, whereas the average bee velocity (mm/s), and the production of buzz-run signals, spiked dramatically. Neither waggle runs nor shaking signals showed increases before or during swarm exodus. The explosive departure of a honeybee swarm from its parental nest shows how animals can use the same communication signals in different contexts; we now know that honeybees use piping and buzz-run signals to initiate both a swarm''s departure from its nest and a swarm''s take-off from its bivouac site. This study also shows how a small minority of individuals in a social insect colony can operate as an oligarchy to make an important decision, i.e. when a swarm should leave its nest to found a new colony. [Copyright &y& Elsevier]

Abstract: Beekeeping, known as one of the oldest forms of agriculture, in its complexity requires control for honey production with what modern technology can offer. Honey is included in animal production implying that farmers have interest in big productions according to the best blooming time, the presence of parasites, the genetic strain of his bees and the swarming periods of the honeybees (queen and her workers leaving the hive). This last fact has a big economic interest for the beekeeper as swarming means honey loss since bees start collecting the honey to migrate. Here for a method that enables the prediction of the swarming is required to prevent the queen from leaving the hives. In this experiment an acoustic method based on labelling of sounds is proposed to predict the swarming period. Three hives were monitored during 270h. The microphones were sited inside the hives together with a temperature and humidity sensor. The sounds were recorded with a sample rate of 2kHz, and analyzed via Matlab and Cool Edit Pro. During this period 9 swarming activities occurred. Swarming is indicated by an increase in the power spectral density at about 110Hz; approaching to swarm the sound augmented in amplitude and frequency to 300Hz, occasionally a rapid change occurred from 150Hz to 500Hz. Another finding indicating the initiation of a swarming period is the raise in temperature from 33°C to 35°C until the actual time of swarming when the temperature starts dropping to 32°C. With more activity, ventilation from bee wings causes drop of temperature. Less information comes from the correlation between sound and humidity since this parameter is too much influenced by the external conditions and no significant variation occurred according to a swarm. This increase of temperature, together with the changes in acoustical features of the sound recorded in the hive, may be used as a predictor for swarming of the bees to reduce honey loss. [Copyright &y& Elsevier]

The impact of Varroa destructor Anderson & Trueman (Mesostigmata: Varroidae) on colonies of Apis mellifera L. (Hymenoptera: Apidae) in southern Louisiana was evaluated by analyzing changes in swarming and longevity of colonies for 17 yr. Swarming rates were calculated from yearly captures of swarms in bait hives placed in five areas of Louisiana from 1991 to 2006. Colony longevity was monitored in 104 swarms established from 1990 to 2000 and followed until 2004. In the first years, before V. destructor, average swarm capture rates ranged from 0.85 to 0.95 swarms per bait hive-year, and survival of colonies established from swarms averaged 14 mo. In years immediately after the arrival of V. destructor (1993-1996), swarming rates and colony longevity decreased to 0.36-0.60 swarms per bait hive-year and 10 too, respectively. After ≈5 yr in the presence of V. destructor, both rates recovered to levels at least as high as those seen before varroa arrived; swarm capture rates were 0.75-1.04 swarms per bait hive-year and average longevity was 26 mo. Analysis of varroa infestations in three colonies established from swarms in 1997 showed the presence of varroa at oscillating densities for 5 to 8 yr. Possible causes for this apparent recovery are natural selection for resistance in honey bees, introgression of selected resistant genetic material or reduced virulence of the mites. [ABSTRACT FROM AUTHOR]