Automatic methods for long-term tracking and the detection and decoding of communication dances in honeybees

The honeybee waggle dance communication system is an intriguing example of abstract animal communication and has been investigated thoroughly throughout the last seven decades. Typically, observables such as waggle durations or body angles are extracted manually either directly from the observation hive or from video recordings to quantify properties of the dance and related behaviors. In recent years, biology has profited from automation, improving measurement precision, removing human bias, and accelerating data collection. We have developed technologies to track all individuals of a honeybee colony and to detect and decode communication dances automatically. In strong contrast to conventional approaches that focus on a small subset of the hive life, whether this regards time, space, or animal identity, our more inclusive system will help the understanding of the dance comprehensively in its spatial, temporal, and social context. In this contribution, we present full specifications of the recording setup and the software for automatic recognition of individually tagged bees and the decoding of dances. We discuss potential research directions that may benefit from the proposed automation. Lastly, to exemplify the power of the methodology, we show experimental data and respective analyses from a continuous, experimental recording of 9 weeks duration

Life history tracking of social communication and navigation behaviors in honeybees (Apis mellifera L.)

The honeybee (Apis mellifera L.) is an ideal model for studying social behaviors and navigation. Social activities and navigational flights are two key aspects to regulate the function of social community. These complex processes usually involve dance communication, antennation, trophallaxis (social behaviors), and orientation and foraging flights (navigation). As group-living animals, honeybees are known to rely mainly on social information to help make decisions on whether, how and where to forage for food. However, honeybees may also constantly integrate their own experience with the information from other bees to make a final decision. Therefore, the degree to which bees follow the information from other individuals or apply their own knowledge would be age-dependent and experience-dependent on an individual basis. Meanwhile, honeybees, in particular living in a colony with small size, may be vulnerable to the external natural environment. There is no knowledge yet about how the development of the indoor and outdoor behaviors is and how the previously mentioned social and non-social factors influence bees’ behaviors indoors and outdoors, in particular how social behaviors influence the outdoor activities and vice versa. Therefore, the aim of the current study is to find the answers to these questions. This study combined the advantages of Raspberry Pi with video cameras by aid of infra-red illumination on one side, and harmonic radar on the other side to record the social behaviors inside of the colony without disruption and monitor flight trajectories outdoors in real-time. The social behaviors and flights were recorded over the bees’ lifetime within 15 days. In summary, each individual bee possesses their own rhythms with different levels of variation in responding to both social and non-social factors at both group and individual levels. The age dependence and experience dependence of the indoor and outdoor behaviors were found, however, of which the degrees of such dependence were various for different behaviors among different individuals and within an individual over the lifetime. Within the small community, my results showed that there was a small group of ‘elite’ bees that outperformed in both social interaction and flights, which in some sense reflect the collective characteristics and exquisite labor division in the eusocial community. Dance communication is known to convey vector information about the food sources that bees discover during foraging flights. Importantly, my studies firstly discovered that dance communication transmit both motivational and instructive role in the orientation and foraging flights, of which, the influence of information of direction and distance on the orientation and foraging flights in some degree was different. My result firstly discovered that dance communication plays important roles in both motivation and vector roles in bees’ orientation and foraging flights. Noise of information transfer is universal in dance communication. However, its influence on the orientation and foraging flights were not similar which depended on the different purposes of orientation and foraging flights. Honeybees could selectively determine to use flight information form dance communication. For the future, I suggest collecting more datasets about social behaviors to enrich the current conclusions. However, this is critically necessary to rely on an automatically tracking method with high accuracy and fast computing speed.Die Honigbiene (Apis mellifera L.) ist ein idealer Modellorganismus zur Untersuchung des Sozialverhaltens und der Navigation. Soziale Aktivitäten und Navigationsflüge sind zwei Schlüsselaspekte, die das Funktionieren der sozialen Gemeinschaft regeln. Zu diesen komplexen Prozessen gehören die Tanzkommunikation, Antennation und Trophallaxis (Sozialverhalten) sowie Orientierungs- und Sammelflüge (Navigation). Als in Gruppen lebende Tiere verlassen sich Honigbienen bekanntermaßen hauptsächlich auf soziale Informationen, um zu entscheiden, ob, wie und wo sie auf Nahrungssuche gehen. Allerdings können Honigbienen auch unentwegt ihre eigenen Erfahrungen mit den Informationen anderer Bienen kombinieren, um eine endgültige Entscheidung zu treffen. Inwieweit Bienen den Informationen anderer Individuen folgen oder ihr eigenes Wissen anwenden, ist daher individuell alters- und erfahrungsabhängig. In der Zwischenzeit sind Honigbienen, insbesondere wenn sie in einem kleinen Volk leben, anfällig für die äußere natürliche Umgebung sein. Es gibt noch keine Erkenntnisse darüber, wie sich das Verhalten in innerhalb und außerhalb des Volkes entwickelt und wie die zuvor genannten sozialen und nicht-sozialen Faktoren das Verhalten der Bienen innerhalb und außerhalb beeinflussen, insbesondere wie das soziale Verhalten die Aktivitäten im Freien beeinflusst und umgekehrt. Ziel der vorliegenden Studie ist es daher, Antworten auf diese Fragen zu finden. In dieser Studie wurden die Vorteile des Raspberry Pi mit Videokameras mit Hilfe von Infrarot-Beleuchtung auf der einen Seite und harmonischem Radar auf der anderen Seite kombiniert, um das Sozialverhalten innerhalb der Kolonie ohne Unterbrechung aufzuzeichnen und die Flugbahnen im Freien in Echtzeit zu überwachen. Das Sozialverhalten und die Flüge wurden über die gesamte Lebensdauer der Bienen innerhalb von 15 Tagen aufgezeichnet. Zusammenfassend hat jede einzelne Biene ihren eigenen Rhythmus, der sowohl auf Gruppen- als auch auf Individualebene unterschiedlich stark auf soziale und nicht- soziale Faktoren reagiert. Es wurde eine Alters- und Erfahrungsabhängigkeit des Innen- und Außenverhaltens festgestellt, wobei das Ausmaß dieser Abhängigkeit für verschiedene Verhaltensweisen bei verschiedenen Individuen und innerhalb eines Individuums im Laufe des Lebens unterschiedlich war. Innerhalb der kleinen Gemeinschaft des Versuchsstockes zeigten meine Ergebnisse, dass es eine kleine Gruppe von "Elite"-Bienen gab, die sowohl bei der sozialen Interaktion als auch bei den Flügen die Leistungen anderer übertrafen, was in gewisser Weise die kollektiven Merkmale und die exquisite Arbeitsteilung in der eusozialen Gemeinschaft widerspiegelt. Weiter ist bekannt, dass die Tanzkommunikation Vektorinformationen über die Nahrungsquellen vermittelt, die die Bienen während ihrer Flüge zur Futtersuche entdecken. Bedeutsam ist, dass meine Studien zunächst zeigen, dass die Tanzkommunikation sowohl eine motivierende als auch eine anweisende Rolle bei der Orientierung und den Futterflügen spielt, wobei der Einfluss von Richtungs- und Entfernungsinformationen auf die Orientierungs- und Sammelflüge zu einem gewissen Maße unterschiedlich war. Meine Ergebnisse zeigen weiterhin, dass die Tanzkommunikation sowohl eine motivierende als auch eine weisende Rolle bei den Orientierungs- und Sammelflügen der Bienen spielt. Ein Rauschen ist universell in der Informationsübertragung der Tanzkommunikation. Der Einfluss auf die Orientierungs- und Suchflüge war jedoch nicht gleich, was von den unterschiedlichen Zielen der Orientierungs- und Sammelflüge abhing. Honigbienen konnten selektiv entscheiden, ob sie Fluginformationen aus der Tanzkommunikation verwenden. Für zukünftige Studien schlage ich vor, weitere Datensätze über das Sozialverhalten zusammen um die aktuellen Schlussfolgerungen zu ergänzen. Dazu ist es jedoch unbedingt erforderlich, sich auf eine automatische Trackingmethode mit hoher Genauigkeit und schneller Rechengeschwindigkeit zu stützen

Automatic Analysis of Bees’ Waggle Dance

This work describe an algorithm for the automatic analysis of the waggle dance of honeybees. The algorithm analyses a video of a beehive with 13,624 frames, acquired at 25 frames/second. The algorithm employs the following traditional image processing steps: conversion to grayscale, low pass filtering, background subtraction, thresholding, tracking and clustering to detect run of bees that perform waggle dances. The algorithm detected 44,530 waggle events, i.e. one bee waggling in one time frame, which were then clustered into 511 waggle runs. Most of these were concentrated in one section of the hive. The accuracy of the tracking was 90% and a series of metrics like intra-dance variation in angle and duration were found to be consistent with literature. Whilst this algorithm was tested on a single video, the ideas and steps, which are simple as compared with Machine and Deep Learning techniques, should be attractive for researchers in this field who are not specialists in more complex techniques

From dyads to collectives: a review of honeybee signalling

From Springer Nature via Jisc Publications RouterHistory: received 2022-03-11, rev-recd 2022-07-12, accepted 2022-07-24, registration 2022-07-26, pub-electronic 2022-08-22, online 2022-08-22, pub-print 2022-09Publication status: PublishedFunder: H2020 European Research Council; doi: http://dx.doi.org/10.13039/100010663; Grant(s): 638873Abstract: The societies of honeybees (Apis spp.) are microcosms of divided labour where the fitness interests of individuals are so closely aligned that, in some contexts, the colony behaves as an entity in itself. Self-organization at this extraordinary level requires sophisticated communication networks, so it is not surprising that the celebrated waggle dance, by which bees share information about locations outside the hive, evolved here. Yet bees within the colony respond to several other lesser-known signalling systems, including the tremble dance, the stop signal and the shaking signal, whose roles in coordinating worker behaviour are not yet fully understood. Here, we firstly bring together the large but disparate historical body of work that has investigated the “meaning” of such signals for individual bees, before going on to discuss how network-based approaches can show how such signals function as a complex system to control the collective foraging effort of these remarkable social insect societies

Social networks predict the life and death of honey bees

In complex societies, individuals' roles are reflected by interactions with other conspecifics. Honey bees (Apis mellifera) generally change tasks as they age, but developmental trajectories of individuals can vary drastically due to physiological and environmental factors. We introduce a succinct descriptor of an individual's social network that can be obtained without interfering with the colony. This 'network age' accurately predicts task allocation, survival, activity patterns, and future behavior. We analyze developmental trajectories of multiple cohorts of individuals in a natural setting and identify distinct developmental pathways and critical life changes. Our findings suggest a high stability in task allocation on an individual level. We show that our method is versatile and can extract different properties from social networks, opening up a broad range of future studies. Our approach highlights the relationship of social interactions and individual traits, and provides a scalable technique for understanding how complex social systems function. Honey bee workers take on different tasks for the colony as they age. Here, the authors develop a method to extract a descriptor of the individuals' social networks and show that interaction patterns predict task allocation and distinguish different developmental trajectories

Behavioral variation across the days and lives of honey bees

In honey bee colonies, workers generally change tasks with age (from brood care, to nest work, to foraging). While these trends are well established, our understanding of how individuals distribute tasks during a day, and how individuals differ in their lifetime behavioral trajectories, is limited. Here, we use automated tracking to obtain long-term data on 4,100+ bees tracked continuously at 3 Hz, across an entire summer, and use behavioral metrics to compare behavior at different timescales. Considering single days, we describe how bees differ in space use, detection, and movement. Analyzing the behavior exhibited across their entire lives, we find consistent inter-individual differences in the movement characteristics of individuals. Bees also differ in how quickly they transition through behavioral space to ultimately become foragers, with fast-transitioning bees living the shortest lives. Our analysis framework provides a quantitative approach to describe individual behavioral variation within a colony from single days to entire lifetimes