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

Shape Dynamical Models for Activity Recognition and Coded Aperture Imaging for Light-Field Capture

Classical applications of Pattern recognition in image processing and computer vision have typically dealt with modeling, learning and recognizing static patterns in images and videos. There are, of course, in nature, a whole class of patterns that dynamically evolve over time. Human activities, behaviors of insects and animals, facial expression changes, lip reading, genetic expression profiles are some examples of patterns that are dynamic. Models and algorithms to study these patterns must take into account the dynamics of these patterns while exploiting the classical pattern recognition techniques. The first part of this dissertation is an attempt to model and recognize such dynamically evolving patterns. We will look at specific instances of such dynamic patterns like human activities, and behaviors of insects and develop algorithms to learn models of such patterns and classify such patterns. The models and algorithms proposed are validated by extensive experiments on gait-based person identification, activity recognition and simultaneous tracking and behavior analysis of insects. The problem of comparing dynamically deforming shape sequences arises repeatedly in problems like activity recognition and lip reading. We describe and evaluate parametric and non-parametric models for shape sequences. In particular, we emphasize the need to model activity execution rate variations and propose a non-parametric model that is insensitive to such variations. These models and the resulting algorithms are shown to be extremely effective for a wide range of applications from gait-based person identification to human action recognition. We further show that the shape dynamical models are not only effective for the problem of recognition, but also can be used as effective priors for the problem of simultaneous tracking and behavior analysis. We validate the proposed algorithm for performing simultaneous behavior analysis and tracking on videos of bees dancing in a hive. In the last part of this dissertaion, we investigate computational imaging, an emerging field where the process of image formation involves the use of a computer. The current trend in computational imaging is to capture as much information about the scene as possible during capture time so that appropriate images with varying focus, aperture, blur and colorimetric settings may be rendered as required. In this regard, capturing the 4D light-field as opposed to a 2D image allows us to freely vary viewpoint and focus at the time of rendering an image. In this dissertation, we describe a theoretical framework for reversibly modulating {4D} light fields using an attenuating mask in the optical path of a lens based camera. Based on this framework, we present a novel design to reconstruct the {4D} light field from a {2D} camera image without any additional refractive elements as required by previous light field cameras. The patterned mask attenuates light rays inside the camera instead of bending them, and the attenuation recoverably encodes the rays on the {2D} sensor. Our mask-equipped camera focuses just as a traditional camera to capture conventional {2D} photos at full sensor resolution, but the raw pixel values also hold a modulated {4D} light field. The light field can be recovered by rearranging the tiles of the {2D} Fourier transform of sensor values into {4D} planes, and computing the inverse Fourier transform. In addition, one can also recover the full resolution image information for the in-focus parts of the scene

Spatial aspects of foraging behaviour in Eastern honeybees, Apis cerana

The majority of plants in Asian tropical ecosystems depend on bee pollination. However, there is a substantial lack of knowledge of the behaviour and ecology of native tropical bees. In the present study I explored how the Eastern honeybee, Apis cerana, distributes its foragers in the local environment analysing waggle dances of foragers in four rural and urban locations in Kerala, South India. Similar to their well-studied close relatives, the Western honeybee A. mellifera, returning A. cerana foragers recruit nest mates through these dances communicating the distance and direction from the hive to a food source. I decoded the locations of food sources for which pollen and nectar foragers danced. The results suggest that the bees tend to forage over shorter distances as compared to the Western honeybees. Furthermore, I have found that the foraging distances, in which dancing foragers have travelled, can notably differ for pollen and nectar resources. However, there is no significant difference in the direction in which nectar and pollen foragers travel. The results also show that despite floral abundance in the proximity of the hive in the rubber plantation, foragers travelled significantly further in this location when compared to the distance that they travelled in the other locations. This may indicate that these floral resources might actually represent a nutritionally poor floral resource for the honeybees. Throughout all of the four locations, the honeybee colonies showed variable patterns of foraging distribution, focusing their recruitment towards areas which seemed to offer both pollen and nectar rewards. This is likely to be in response to the spatial clustering of their food sources, which may be a characteristic of landscapes that are dominated by human agri- and horticultural activities.The UK-India Education and Research Initiative (British Council, UK and DST, India)

Towards dense object tracking in a 2D honeybee hive

From human crowds to cells in tissue, the detection and efficient tracking of multiple objects in dense configurations is an important and unsolved problem. In the past, limitations of image analysis have restricted studies of dense groups to tracking a single or subset of marked individuals, or to coarse-grained group-level dynamics, all of which yield incomplete information. Here, we combine convolutional neural networks (CNNs) with the model environment of a honeybee hive to automatically recognize all individuals in a dense group from raw image data. We create new, adapted individual labeling and use the segmentation architecture U-Net with a loss function dependent on both object identity and orientation. We additionally exploit temporal regularities of the video recording in a recurrent manner and achieve near human-level performance while reducing the network size by 94% compared to the original U-Net architecture. Given our novel application of CNNs, we generate extensive problem-specific image data in which labeled examples are produced through a custom interface with Amazon Mechanical Turk. This dataset contains over 375,000 labeled bee instances across 720 video frames at 2FPS, representing an extensive resource for the development and testing of tracking methods. We correctly detect 96% of individuals with a location error of ~ 7% of a typical body dimension, and orientation error of 12°, approximating the variability of human raters. Our results provide an important step towards efficient image-based dense object tracking by allowing for the accurate determination of object location and orientation across time-series image data efficiently within one network architecture.Funding for this work was provided by the OIST Graduate University to ASM and GS. Additional funding was provided by KAKENHI grants 16H06209 and 16KK0175 from the Japan Society for the Promotion of Science to AS

Automated home-cage behavioral phenotyping of mice

We describe a trainable computer vision system enabling the automated analysis of complex mouse behaviors. We provide software and a very large manually annotated video database used for training and testing the system. Our system outperforms leading commercial software and performs on par with human scoring, as measured from the ground-truth manual annotations of thousands of clips of freely behaving animals. We show that the home-cage behavior profiles provided by the system is sufficient to accurately predict the strain identity of individual animals in the case of two standard inbred and two non-standard mouse strains. Our software should complement existing sensor-based automated approaches and help develop an adaptable, comprehensive, high-throughput, fine-grained, automated analysis of rodent behavior

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