Colour reverse learning and animal personalities: the advantage of behavioural diversity assessed with agent-based simulations

Foraging bees use colour cues to help identify rewarding from unrewarding flowers, but as conditions change, bees may require behavioural flexibility to reverse their learnt preferences. Perceptually similar colours are learnt slowly by honeybees and thus potentially pose a difficult task to reverse-learn. Free-flying honeybees (N = 32) were trained to learn a fine colour discrimination task that could be resolved at ca. 70% accuracy following extended differential conditioning, and were then tested for their ability to reverse-learn this visual problem multiple times. Subsequent analyses identified three different strategies: ‘Deliberative-decisive’ bees that could, after several flower visits, decisively make a large change to learnt preferences; ‘Fickle- circumspect’ bees that changed their preferences by a small amount every time they encountered evidence in their environment; and ‘Stay’ bees that did not change from their initially learnt preference. The next aim was to determine if there was any advantage to a colony in maintaining bees with a variety of decision-making strategies. To understand the potential benefits of the observed behavioural diversity agent-based computer simulations were conducted by systematically varying parameters for flower reward switch oscillation frequency, flower handling time, and fraction of defective ‘target’ stimuli. These simulations revealed that when there is a relatively high frequency of reward reversals, fickle-circumspect bees are more efficient at nectar collection. However, as the reward reversal frequency decreases the performance of deliberative-decisive bees becomes most efficient. These findings show there to be an evolutionary benefit for honeybee colonies with individuals exhibiting these different strategies for managing resource change. The strategies have similarities to some complex decision-making processes observed in humans, and algorithms implemented in artificial intelligence systems

Learning and decision making along a nutritional gradient

2019 Spring.Includes bibliographical references.Nutrition is fundamental to the life history of all animals and the behavioral processes by which animals acquire nutrition are of central interest to students of animal behavior. How an animal learns about available food resources, and the strategies adopted to acquire food resources are therefore of central importance. While animal nutrition is quite complex, energy is a fundamental nutrient and is the focus of this work. In chapter 1, honeybees were fed or starved before they were given a choice assay to determine how individual energetic state altered their choice between gathering information about food resources and consuming known food resources. It was found that bees which were relatively satiated prioritized the collection of information over energy. This work was expanded in chapter 2, in which the energetic states of honeybee colonies were manipulated, in addition to the manipulation of individual energetic state. This experiment provided insights into how group members make decisions in the presence of conflicting individual and group level interests and found that honeybee behavioral phenotypes vary in how they prioritize group and individual needs. The first two chapters focus on how animals make decisions after they have acquired some information, but differences in learning also play a vital role in the acquisition of nutrition. In chapter 3, bees were weighed early and late in their lifetimes, and it was found that bees with more stable weight percentile ranks performed better in a learning assay than bees with unstable weight percentile ranks. As nutritional environment plays a significant role on the body weight of individuals, this may indicate that consistent nutritional conditions contribute to bee cognition. Along with nutrition, body weight is also correlated with the metabolic rate of individuals. Metabolic rate is directly tied to the energy acquisition behavior of animals, as it determines how and at what rate energy is processed by an animal. In order to evaluate how metabolic rate alone influences nutrient acquisition, a model, presented in Chapter 4, was constructed that evaluated the performance of different metabolic rates in different nutritional environments. In general, high metabolic rates were more favorable in rich nutritional environments and low metabolic rates were more favorable in poor nutritional environments. It was also shown that diversity of metabolic rates within a group is advantageous in some environments. Taken together, this work indicates that nutrition, in the form of energy, plays a vital role in the how animals learn and make decisions. This is true for nutrition at both the individual and group level, over immediate and long-term timescales, and for physiological differences in the capacity of an animal to assimilate energy. These findings have broad implications in behavioral ecology and are discussed in terms of optimal foraging, group behavior, developmental plasticity, and gene-environment interactions

A particle model reproducing the effect of a conflicting flight information on the honeybee swarm guidance

The honeybee swarming process is steered by few scout individuals, which are the unique informed on the location of the target destination. Theoretical and experimental results suggest that bee coordinated flight arises from visual signals. However, how the information is passed within the population is still debated. Moreover, it has been observed that honeybees are highly sensitive to conflicting directional information. In fact, swarms exposed to fast-moving bees headed in the wrong direction show clear signs of disrupted guidance. In this respect, we here present a discrete mathematical model to investigate different hypotheses on the behaviour both of informed and uninformed bees. In this perspective, numerical realizations, specifically designed to mimic selected experiments, reveal that only one combination of the considered assumptions is able to reproduce the empirical outcomes, resulting thereby the most reliable mechanism underlying the swarm dynamics according to the proposed approach. Specifically, this study suggests that (i) leaders indicate the right flight direction by repeatedly streaking at high speed pointing towards the target and then slowly coming back to the trailing edge of the bee cloud; and (ii) uninformed bees, in turn, gather the route information by adapting their movement to all the bees sufficiently close to their position

Personal mobile grids with a honeybee inspired resource scheduler

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The overall aim of the thesis has been to introduce Personal Mobile Grids (PMGrids) as a novel paradigm in grid computing that scales grid infrastructures to mobile devices and extends grid entities to individual personal users. In this thesis, architectural designs as well as simulation models for PM-Grids are developed. The core of any grid system is its resource scheduler. However, virtually all current conventional grid schedulers do not address the non-clairvoyant scheduling problem, where job information is not available before the end of execution. Therefore, this thesis proposes a honeybee inspired resource scheduling heuristic for PM-Grids (HoPe) incorporating a radical approach to grid resource scheduling to tackle this problem. A detailed design and implementation of HoPe with a decentralised self-management and adaptive policy are initiated. Among the other main contributions are a comprehensive taxonomy of grid systems as well as a detailed analysis of the honeybee colony and its nectar acquisition process (NAP), from the resource scheduling perspective, which have not been presented in any previous work, to the best of our knowledge. PM-Grid designs and HoPe implementation were evaluated thoroughly through a strictly controlled empirical evaluation framework with a well-established heuristic in high throughput computing, the opportunistic scheduling heuristic (OSH), as a benchmark algorithm. Comparisons with optimal values and worst bounds are conducted to gain a clear insight into HoPe behaviour, in terms of stability, throughput, turnaround time and speedup, under different running conditions of number of jobs and grid scales. Experimental results demonstrate the superiority of HoPe performance where it has successfully maintained optimum stability and throughput in more than 95% of the experiments, with HoPe achieving three times better than the OSH under extremely heavy loads. Regarding the turnaround time and speedup, HoPe has effectively achieved less than 50% of the turnaround time incurred by the OSH, while doubling its speedup in more than 60% of the experiments. These results indicate the potential of both PM-Grids and HoPe in realising futuristic grid visions. Therefore considering the deployment of PM-Grids in real life scenarios and the utilisation of HoPe in other parallel processing and high throughput computing systems are recommended

A robotic honeycomb for interaction with a honeybee colony

Abstract: Robotic technologies have shown the capability to interact with living organisms and even to form integrated mixed societies comprised of living and artificial agents. Bio-compatible robots, incorporating sensing and actuation capable of generating and responding to relevant stimuli, can be a tool to study collective behaviors previously unattainable with traditional techniques. To investigate collective behaviors of the western honeybee (Apis mellifera), we designed a robotic system capable of observing and modulating the bee cluster using an array of thermal sensors and actuators. We initially integrated the system into a beehive populated with approximately 4,000 bees for several months. The robotic system was able to observe the colony by continuously collecting spatio- temporal thermal profiles of the winter cluster. Furthermore, we found that our robotic device reliably modulated the superorganism’s response to dynamic thermal stimulation, influencing its spatiotemporal re-organization. In addition, after identifying the thermal collapse of a colony, we used the robotic system in a “life-support” mode via its thermal actuators. Ultimately, we demonstrated a robotic device capable of autonomous closed-loop interaction with a cluster comprising thousands of individual bees. Such biohybrid societies open the door to investigation of collective behaviors that necessitate observing and interacting with the animals within a complete social context, as well as for potential applications in augmenting the survivability of these pollinators crucial to our ecosystems and our food supply. This is the author’s version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science Robotics, Vol. 8, 76, Mar 2023, DOI: 10.1126/scirobotics.add7385 https://doi.org/10.1126/scirobotics.add738

On Honey Bee Colony Dynamics and Disease Transmission

The work herein falls under the umbrella of mathematical modeling of disease transmission. The majority of this document focuses on the extent to which infection undermines the strength of a honey bee colony. These studies extend from simple mass-action ordinary differential equations models, to continuous age-structured partial differential equation models and finally a detailed agent-based model which accounts for vector transmission of infection between bees as well as a host of other influences and stressors on honey bee colony dynamics. These models offer a series of predictions relevant to the fate of honey bee colonies in the presence of disease and the nonlinear effects of disease, seasonality and the complicated dynamics of honey bee colonies. We are also able to extract from these models metrics that preempt colony failure. The analysis of disease dynamics in age-structured honey bee colony models required the study of next generation operators (NGO) and the basic reproduction number, $R_0$, for partial differential equations. This led us to the development of a coherent path from the NGO to its discrete compartmental counterpart, the next generation matrix (NGM) as well as the derivation of new closed-form formulae for the NGO for specific classes of disease models

Assessing the health status of managed honeybee colonies (HEALTHY-B): a toolbox to facilitate harmonised data collection

Tools are provided to assess the health status of managed honeybee colonies by facilitating further harmonisation of data collection and reporting, design of field surveys across the European Union (EU) and analysis of data on bee health. The toolbox is based on characteristics of a healthy managed honeybee colony: an adequate size, demographic structure and behaviour; an adequate production of bee products (both in relation to the annual life cycle of the colony and the geographical location); and provision of pollination services. The attributes ‘queen presence and performance’, ‘demography of the colony’, ‘in-hive products’ and ‘disease, infection and infestation’ could be directly measured in field conditions across the EU, whereas ‘behaviour and physiology’ is mainly assessed through experimental studies. Analysing the resource providing unit, in particular land cover/use, of a honeybee colony is very important when assessing its health status, but tools are currently lacking that could be used at apiary level in field surveys across the EU. Data on ‘beekeeping management practices’ and ‘environmental drivers’ can be collected via questionnaires and available databases, respectively. The capacity to provide pollination services is regarded as an indication of a healthy colony, but it is assessed only in relation to the provision of honey because technical limitations hamper the assessment of pollination as regulating service (e.g. to pollinate wild plants) in field surveys across the EU. Integrating multiple attributes of honeybee health, for instance, via a Health Status Index, is required to support a holistic assessment. Examples are provided on how the toolbox could be used by different stakeholders. Continued interaction between the Member State organisations, the EU Reference Laboratory and EFSA is required to further validate methods and facilitate the efficient use of precise and accurate bee health data that are collected by many initiatives throughout the EU