Honeybee Waggle Dance Research Articles

Foraging distance distributions reveal how honeybee waggle dance recruitment varies with landscape

Honeybee (Apis mellifera) colonies use a unique collective foraging system, the waggle dance, to communicate and process the location of resources. Here, we present a means to quantify the effect of recruitment on colony forager allocation across the landscape by simply observing the waggle dance on the dancefloor. We show first, through a theoretical model, that recruitment leaves a characteristic imprint on the distance distribution of foraging sites that a colony visits, which varies according to the proportion of trips driven by individual search. Next, we fit this model to the real-world empirical distance distribution of forage sites visited by 20 honeybee colonies in urban and rural landscapes across South East England, obtained via dance decoding. We show that there is considerable variation in the use of dancing information in colony foraging, particularly in agri-rural landscapes. In our dataset, reliance on dancing increases as arable land gives way to built-up areas, suggesting that dancing may have the greatest impact on colony foraging in the complex and heterogeneous landscapes of forage-rich urban areas. Our model provides a tool to assess the relevance of this extraordinary behaviour across modern anthropogenic landscape types.

Neuroethology: Decoding the waggle dance

A new study combining high-speed video recordings and computational modeling has revealed an overlooked feature of the famous honeybee waggle dance, yielding the first biologically plausible neural circuit model of how the information transmitted via the waggle dance could be assimilated by the follower bees.

Dynamic antennal positioning allows honeybee followers to decode the dance

The honeybee waggle dance has been widely studied as a communication system, yet we know little about how nestmates assimilate the information needed to navigate toward the signaled resource. They are required to detect the dancer's orientation relative to gravity and duration of the waggle phase and translate this into a flight vector with a direction relative to the sun1 and distance from the hive.2,3 Moreover, they appear capable of doing so from varied, dynamically changing positions around the dancer. Using high-speed, high-resolution video, we have uncovered a previously unremarked correlation between antennal position and the relative body axes of dancer and follower bees. Combined with new information about antennal inputs4,5 and spatial encoding in the insect central complex,6,7 we show how a neural circuit first proposed to underlie path integration could be adapted to decoding the dance and acquiring the signaled information as a flight vector that can be followed to the resource. This provides the first plausible account of how the bee brain could support the interpretation of its dance language.

Machine learning reveals the waggle drift's role in the honey bee dance communication system.

The honey bee waggle dance is one of the most prominent examples of abstract communication among animals: successful foragers convey new resource locations to interested followers via characteristic "dance" movements in the nest, where dances advertise different locations on different overlapping subregions of the "dance floor." To this day, this spatial separation has not been described in detail, and it remains unknown how it affects the dance communication. Here, we evaluate long-term recordings of Apis mellifera foraging at natural and artificial food sites. Using machine learning, we detect and decode waggle dances, and we individually identify and track dancers and dance followers in the hive and at artificial feeders. We record more than a hundred thousand waggle phases, and thousands of dances and dance-following interactions to quantitatively describe the spatial separation of dances on the dance floor. We find that the separation of dancers increases throughout a dance and present a motion model based on a positional drift of the dancer between subsequent waggle phases that fits our observations. We show that this separation affects follower bees as well and results in them more likely following subsequent dances to similar food source locations, constituting a positive feedback loop. Our work provides evidence that the positional drift between subsequent waggle phases modulates the information that is available to dance followers, leading to an emergent optimization of the waggle dance communication system.

Honey Bee Waggle Dance as a Model of Swarm Intelligence

Honey bees are social insects that form colonies (hives), which often consist of more than 10,000 individuals. In a colony, bees allocate jobs (division of labor) and work cooperatively and intelligently to maintain the colony’s activity, such as nursing broods, cleaning, and guarding against enemies. Among worker bees, only forager bees collect food, and success in finding food directly influences colony survival. For more efficient foraging, honey bees share location information pertaining to profitable food sources through specific behavior called “waggle dances.” During such dances, the direction and distance from the hive to the food source are encoded as body movements. Other foragers follow the dancing bees and receive location information. Some of these bees then fly to the advertised location to find the food source. Some of these “recruited bees” subsequently dance to recruit new bees. This process is then repeated. Consequently, many foragers visit the food source, and a colony can rapidly and flexibly collect large amounts of food even in foraging environment that can suddenly change (e.g., flowers disappear or nectar flux increases/decreases). To achieve effective food collection through the waggle dance, the behavior of both the dancers and followers probably contains information for an implementation of “swarm intelligence.” In this review, we introduce the properties of dance behavior at the levels of dancers, followers, and colonies. We found that errors in waggle dance information play an important role in adaptive foraging in dynamically changing environments.

Learning in the honey bee waggle dance.

The waggle dance of honey bees is a classic example of complex behavior and communication in animals. Despite long being considered a completely fixed and innate behavior, recent work is showing a role for social learning in tuning components of the waggle dance in naïve bees.

Bees learn to dance

Experience yields precision in the waggle dance of honey bees.

Honey bee waggle dances facilitate shorter foraging distances and increased foraging aggregation

Honey bee waggle dances facilitate shorter foraging distances and increased foraging aggregation

Soybean is a Common Nectar Source for Honey Bees (Hymenoptera: Apidae) in a Midwestern Agricultural Landscape.

Large-scale soybean [Glycine max (L.) Merr.] cultivation has substantially transformed the Midwestern landscape in recent decades. Floral nectar produced by immense fields of soybeans has the potential to influence foraging ecology and resource accumulation of honey bee (Apis mellifera L.) colonies. In this study, we combined microscopic and molecular pollen analysis of honey samples with waggle dance inference of spatial foraging patterns to demonstrate that honey bees routinely forage on soybeans in Ohio. In analyzing honey samples from across the state, we found ubiquitous presence of soybean pollen in honey collected from agricultural lands during soybean bloom. The abundance of soybean pollen in honey increased with the amount of soybean fields surrounding the apiaries. Honey bee waggle dances recorded during soybean bloom revealed that honey bees preferred soybean fields for foraging over other habitat types. With these results, future research efforts aimed at enhancing mutual interactions between soybeans and honey bees may represent an unexplored pathway for increasing soybean production while supporting honey bees and other pollinators in the surrounding landscape.

Machine learning models identify gene predictors of waggle dance behaviour in honeybees.

The molecular characterization of complex behaviours is a challenging task as a range of different factors are often involved to produce the observed phenotype. An established approach is to look at the overall levels of expression of brain genes-or 'neurogenomics'-to select the best candidates that associate with patterns of interest. However, traditional neurogenomic analyses have some well-known limitations: above all, the usually limited number of biological replicates compared to the number of genes tested-known as the "curse of dimensionality." In this study we implemented a machine learning (ML) approach that can be used as a complement to more established methods of transcriptomic analyses. We tested three supervised learning algorithms (Random Forests, Lasso and Elastic net Regularized Generalized Linear Model, and Support Vector Machine) for their performance in the characterization of transcriptomic patterns and identification of genes associated with honeybee waggle dance. We then matched the results of these analyses with traditional outputs of differential gene expression analyses and identified two promising candidates for the neural regulation of the waggle dance: boss and hnRNP A1. Overall, our study demonstrates the application of ML to analyse transcriptomics data and identify candidate genes underlying social behaviour. This approach has great potential for application to a wide range of different scenarios in evolutionary ecology, when investigating the genomic basis for complex phenotypic traits, and can present some clear advantages compared to the established tools of gene expression analysis, making it a valuable complement for future studies.

Do honey bees modulate dance following according to foraging distance?

Honey bees famously use waggle dances to communicate foraging locations to nestmates in the hive, thereby recruiting them to those sites. The decision to dance is governed by rules that, when operating collectively, are assumed to direct foragers to the most profitable locations with little input from potential recruits, who are presumed to respond similarly to any dance regardless of its information content. Yet, variation in receiver responses can qualitatively alter collective outcomes. Here, we use network-based diffusion analysis to compare the collective influence of dance information during recruitment to feeders at different distances. We further assess how any such effects might be achieved at the individual level by dance followers either persisting with known sites when novel targets are distant and/or seeking more accurate spatial information to guide long-distance searches. Contrary to predictions, we found little evidence that dance followers’ responses depended on target distance over the foraging distances considered here (100–500 m). While dance information was always key to feeder discovery, its importance did not vary with feeder distance, and bees were in fact quicker to abandon previously rewarding sites for distant alternatives. These findings provide empirical support for the long-standing assumption that self-organized foraging by honey bee colonies relies heavily on signal performance rules with limited input from recipients. • Honey bee waggle dances indicate the direction of and distance to food sources. • Whether dance follower responses account for the indicated distance is unclear. • We compared responses to waggle dances indicating different foraging distances. • Dance-following behaviour did not differ according to the dance-indicated distance. • Results are consistent with honey bee foraging being driven by signalling rules.

Research project on field data collection for honey bee colony model evaluation

As part of the MUST-B project, a research project on field data collection for honey bee colony model evaluation was carried out in 2018-2020. In a preparatory phase (2018), methods for monitoring of honey bee colonies were tested, field operators trained, and experimental colonies established. The main field experiment was conducted in 2019-2020, during which bee colonies in six experimental apiaries were closely monitored in both Denmark and Portugal. An experimental spraying (spraying of Pirimor G in 6 ha of flowering oilseed rape) was carried out at one of the sites in Denmark in 2019. During the two-year experiment, climate variables were recorded continuously, and availability of floral resources was mapped regularly in the landscapes surrounding each apiary (within an area of 1.5 km radius). Adult bee population, brood and provision were assessed approximately every three weeks in experimental colonies. Furthermore, the weight of colonies was logged continuously during the field seasons by automatic hive scales. At four sites, foraging activity was monitored continuously in 1-2 colonies in 2019 and 2020. Spatial foraging was decoded from honey bee waggle dances observed once per month in four apiaries, at the same time as floral mapping. Finally, samples for analysis of diseases (varroa, Nosema and viruses), pesticide residues and botanical composition of pollen were collected. All data were organized in a relational database. Whereas previous studies have monitored similar aspects of honey bee colony development and health, the current dataset is unique in encompassing a large number of variables measured simultaneously. In particular, the current study emphasized a detailed data collection on population dynamics and development for the testing and calibration of the ApisRAM model developed in the MUST-B project. Methods used encompassed manual and automatic monitoring. Recommendations for future data collection include an assessment of variables currently collected with confidence and variables in need of further development.

External validation of the new calibration for mapping honey bee waggle dances

• We tested a new method for mapping honey bee waggle dances using two data sets. • This method was faster than previous calibration and avoided some mapping artefacts. • Its accuracy was similar to previous calibration but it was affected by distance. • The new method outperformed the old method for resources at closer distances.

Similarities in dance follower behaviour across honey bee species suggest a conserved mechanism of dance communication

Social communication systems are predominantly multimodal and can combine modulatory and information-bearing signals. The honey bee waggle dance, one of the most elaborate forms of social communication in animals, activates nestmates to search for food and communicates symbolic information about the location of the food source. Previous studies on the dance behaviour in diverse honey bee species demonstrated distinct differences in the concurrence of visual, auditory, olfactory and tactile signals produced by the dancer. We studied the behaviour of dance signal receivers, the dance followers, to explore the potential significance of different signals in the communication process. In particular, we asked whether the behaviour of dance followers differs between the three major Asian honey bee species, Apis florea, Apis dorsata and Apis cerana, and whether this might correlate with the differences in the signals produced by the dancing foragers. Our comparison demonstrated that the behaviour of the dance followers is highly similar across all three species. The mean body orientation of the dance followers with respect to the waggle dancer was close to 90° throughout the run, with the majority positioning themselves lateral to the dancer. These findings suggest that the communication of spatial information might be more conserved than implied by the differences in the signals produced by the dancer. Along with studies in Apis mellifera, our results suggest that all honey bee species rely on tactile contacts between the dancer and follower to communicate spatial information. The cues and signals that differ between the species may be involved in attracting the followers towards the dancer or increasing their motivation to start foraging in the different nest environments.

Path integration: how details of the honeybee waggle dance and the foraging strategies of desert ants might help in understanding its mechanisms.

Path integration is a navigational strategy that gives an animal an estimate of its position relative to some starting point. For many decades, ingenious and probing behavioural experiments have been the only window onto the operation of path integration in arthropods. New methods have now made it possible to visualise the activity of neural circuits in Drosophila while they fly or walk in virtual reality. Studies of this kind, as well as electrophysiological recordings from single neurons in the brains of other insects, are revealing details of the neural mechanisms that control an insect's direction of travel and other aspects of path integration. The aim here is first to review the major features of path integration in foraging desert ants and honeybees, the current champion path integrators of the insect world, and second consider how the elaborate behaviour of these insects might be accommodated within the framework of the newly understood neural circuits. The discussion focuses particularly on the ability of ants and honeybees to use a celestial compass to give direction in Earth-based coordinates, and of honeybees to use a landscape panorama to provide directional guidance for path integration. The possibility is raised that well-ordered behaviour might in some cases substitute for complex circuitry.

Dismantling Babel: creation of a universal calibration for honey bee waggle dance decoding

Noisy animal communication presents a unique challenge for biologists interpreting a produced signal precisely and accurately. One past approach has been to reduce noise. Lately, with the advance of quantitative tools, an alternative is to use the noise to improve model predictions. Here we take the latter approach to improve our ability to recover honey bee ( Apis mellifera spp.) foraging locations, in particular the distance at which they forage, from observed waggle dance communications, which encode the vector from the hive to the forage. This vector gives both the distance from the hive to the forage, which is encoded in dance duration, and the direction from the hive to the forage, which is encoded in the angle that the bee makes while dancing. We analysed the waggle dances from individually marked honey bees ( N = 859 dances from 85 bees from 3 hives) that foraged at multiple known locations to determine a distance-to-duration calibration. Next we compared this calibration to a previously published calibration, which demonstrated that while their slopes were similar ( P = 0.82), their intercepts differed ( P N = 84) (performance comparison: P = 0.36) and second with a linear discriminant analysis, which failed to assign dances to their originating population. Both results confirm that the universal calibration may be used, irrespective of landscape or subspecies. Therefore, by allowing noise to be part of our analysis, the usability of our calibration is broadened for use in future settings.

Social modulation of individual differences in dance communication in honey bees

Group-living animals must constantly integrate and respond to information from other individuals within the group. The degree to which consistent inter-individual behavioural differences are influenced by social cues in such groups is largely unanswered. We used the honey bee waggle dance as an experimental paradigm to explore this question. Honey bee foragers use the waggle dance behaviour to communicate information about food sources in the environment to their nest mates. This recruitment process incorporates information about the food reward, the colony food stores and the environmental food availability and plays a major role in ensuring efficient exploitation of the food sources available to the colony. We first observed individual foragers visiting the same food source and quantified the probability and intensity of their dance activity. We found that there are consistent inter-individual differences in both measures of dance activity within a forager group. Next, we removed foragers and observed that this led to a significant increase in the average dance activity of some foragers. The individuals which increased their dance activity were the ones which were more active before the removal. Finally, we allowed recruits to join the foragers at the food source, which had a strong inhibitory effect on the dance activity of all the individual foragers. Our study shows that a complex interplay between individual behavioural differences and social interactions drives the dance communication needed to effectively organise the colony’s collective foraging behaviour. Very little is known about the effect of social cues and signals on consistent inter-individual differences in behaviour amongst workers in the same colony in social insects. We studied this in the recruitment behaviour of honey bee foragers, the dance communication. Foragers visiting the same food source consistently differed in their dance activity, suggesting that there were strong inter-individual differences in the perception of the food reward. We then changed the social cues experienced by the foragers by either removing some of the foragers or allowing recruits to forage at the same food source. We found that the removal of some foragers had an individual specific effect, whereas the presence of all recruits affected all foragers. Our results show that the regulation of foraging in honey bees involves the social modulation of consistent differences in recruitment activity.

Follower Position Does Not Affect Waggle Dance Information Transfer

It is known that the honey bee waggle dance communicates the distance and direction of some item of interest, most commonly a food source, to nestmates. Previous work suggests that, in order to successfully acquire the information contained in a dance, other honey bees must follow the dancer from behind. We revisit this topic using updated methodology, including a greater distance from the hive to the feeder, which produced longer, more easily-read dances. Our results are not congruent with those of earlier work, and we did not conclude that honey bees must follow a dancer from behind in order to obtain the dance information. Rather, it is more likely that a follower can successfully acquire a dance’s information regardless of where she may be located about a dancer.

Attribution of Information in Animal Interaction

This article establishes grounds on which attributions of information and encoding in animal signals are warranted. As common interest increases between evolutionary agents, the theoretical approach best suited to describing their interaction shifts from evolutionary game theory to communication theory, which warrants informational language. The take-home positive message is that in cooperative settings, signals can appropriately be described as transmitting encoded information, regardless of the cognitive powers of signalers. The canonical example is the honeybee waggle dance, which is discussed extensively in the second and third sections. The take-home negative message is that signals are not always a consequence of coadaptation. The communication theory approach is just one end of a continuum explored more thoroughly by evolutionary game theory. The fourth and fifth sections explore this wider framework, as well as overturning some widely held misconceptions about information theory.

Applications of Nature-Inspired Algorithms in Different Aspects of Semantic Web

Nature has always inspired us all the waggle dance of Honey bee, the school of whales and the swarm of ants, each element when observed carefully has the abundance of teachings. If we carefully observe nature, we find that although Nature seems to be very simple and systematic, it hides many complexities underneath it. As technology also follows the same principle of ‘simple-yet-complex’, the researchers have always tried to apply the learning from Nature to complex technological Algorithms used to solve few real life human problems. Since the past decade, there has been a rapid increase of research in this field. Today Nature Inspired algorithms have permeated into almost all areas of sciences. Although it had been applied to various areas of sciences, the scope of this paper is limited to its application in the domain of The Semantic Web. The main objective of Semantic web applications is to obtain, manage and utilize the huge amount of information that is available in either structured semistructured or unstructured databases in distributed environment. This is an emerging domain and is advancing towards more and more intelligent and human oriented applications. This paper presents a survey of vital nature-Inspired techniques that can be used for optimizing various areas of Semantic web applications such as knowledge base, content filtering, Information Retrieval and Inference mechanism.