Dumb and Lazy? A Comparison of Color Learning and Memory Retrieval in Drones and Workers of the Buff-Tailed Bumblebee, Bombus terrestris, by Means of PER Conditioning

More than 100 years ago, Karl von Frisch showed that honeybee workers learn and discriminate colors. Since then, many studies confirmed the color learning capabilities of females from various hymenopteran species. Yet, little is known about visual learning and memory in males despite the fact that in most bee species males must take care of their own needs and must find rewarding flowers to obtain food. Here we used the proboscis extension response (PER) paradigm to study the color learning capacities of workers and drones of the bumblebee, Bombus terrestris. Light stimuli were paired with sucrose reward delivered to the insects’ antennae and inducing a reflexive extension of the proboscis. We evaluated color learning (i.e. conditioned PER to color stimuli) in absolute and differential conditioning protocols and mid-term memory retention was measured two hours after conditioning. Different monochromatic light stimuli in combination with neutral density filters were used to ensure that the bumblebees could only use chromatic and not achromatic (e.g. brightness) information. Furthermore, we tested if bees were able to transfer the learned information from the PER conditioning to a novel discrimination task in a Y-maze. Both workers and drones were capable of learning and discriminating between monochromatic light stimuli and retrieved the learned stimulus after two hours. Drones performed as well as workers during conditioning and in the memory test, but failed in the transfer test in contrast to workers. Our data clearly show that bumblebees can learn to associate a color stimulus with a sugar reward in PER conditioning and that both workers and drones reach similar acquisition and mid-term retention performances. Additionally, we provide evidence that only workers transfer the learned information from a Pavlovian to an operant situation

Matters of Size: Behavioral, Morphological, and Physiological Performance Scaling Among Stingless Bees (Meliponini)

abstract: Body size plays a pervasive role in determining physiological and behavioral performance across animals. It is generally thought that smaller animals are limited in performance measures compared to larger animals; yet, the vast majority of animals on earth are small and evolutionary trends like miniaturization occur in every animal clade. Therefore, there must be some evolutionary advantages to being small and/or compensatory mechanisms that allow small animals to compete with larger species. In this dissertation I specifically explore the scaling of flight performance (flight metabolic rate, wing beat frequency, load-carrying capacity) and learning behaviors (visual differentiation visual Y-maze learning) across stingless bee species that vary by three orders of magnitude in body size. I also test whether eye morphology and calculated visual acuity match visual differentiation and learning abilities using honeybees and stingless bees. In order to determine what morphological and physiological factors contribute to scaling of these performance parameters I measure the scaling of head, thorax, and abdomen mass, wing size, brain size, and eye size. I find that small stingless bee species are not limited in visual learning compared to larger species, and even have some energetic advantages in flight. These insights are essential to understanding how small size evolved repeatedly in all animal clades and why it persists. Finally, I test flight performance across stingless bee species while varying temperature in accordance with thermal changes that are predicted with climate change. I find that thermal performance curves varied greatly among species, that smaller species conform closely to air temperature, and that larger bees may be better equipped to cope with rising temperatures due to more frequent exposure to high temperatures. This information may help us predict whether small or large species might fare better in future thermal climate conditions, and which body-size related traits might be expected to evolve.Dissertation/ThesisDoctoral Dissertation Biology 201

Assessment of pesticides risk for bees: methods for PNEC measurements

Background: An individual honeybee shows a complex behavioral structure. Each bee takes part in the collective behavioral set up that ensures bee colony survival and development. Contaminants are likely to have effects on individual bees’ behavior with consequences at the level of the whole colony. They also are likely to alter bees’ physiology, including lifespan, fertility or fecundity, leading to colony weakness or colony collapse. Results: Peer-reviewed scientific literature provides a wide range of methods used for testing honeybees’ behavioral or physiological parameters. Apart from alterations that may appear during the conduction of acute or chronic toxicity tests, specific tests could be conducted to complement the risk assessment in order to evaluate the impact of sublethal doses of contaminants on bees. Such tests can be developed both in laboratory conditions or as part of the semi-field and field tests that are currently required as higher tier tests of risk assessment schemes. Conclusion: The purpose of this work is to review some of these methods and discuss their relevance in the evaluation of pesticide active substances and/or products in view to propose their future inclusion in pesticides risk assessment to bees. Keywords: honey bee, sublethal effects, risk assessmen

Living in the slow or fast lane: cognitive phenotypes in honeybees

2021 Spring.Includes bibliographical references.The evolution and maintenance of cognitive variation is a question of fundamental interest in animal behavior because differences in cognition are predicted to underlie differences in behavior. The correlation between behavioral and cognitive variation has largely been conceptualized in terms of the speed-accuracy trade-off driving alternative cognitive strategies where 'fast' individuals are superficial learners that make inaccurate, risk-prone decisions relative to 'slow' individuals. My research has explored the factors that select for different cognitive abilities across species and the mechanisms that maintain variation in cognitive ability within species. To address these questions, I have identified how individuals of four honeybee species (Apis mellifera, A. cerana, A. dorsata, A. florea) differ in performance on multiple cognitive tasks and explored how such variation translates to behavioral outcomes and is shaped by ecology. In chapter one, I tested for the presence of variation in two different learning abilities in honeybee foragers and whether any component of learning influenced wing damage, an indicator of survival. My results demonstrated considerable interindividual variation in different types of learning abilities such that landmark and olfactory learning were negatively correlated. Additionally, I found that olfactory learning was positively correlated with maneuverability performance during flight, a measure which in turn positively influenced wing damage, a proxy for survival. This experiment demonstrated that individuals differ considerably in how they perform on two cognitive tasks and that cognitive ability has important implications for behaviors associated with survival. This work was further explored in chapter 2, where I studied how differences in learning preference relate to decision making during foraging. I measured individual latency to learn on a solitary foraging task and latency to learn on a social foraging task and found that individuals that perform well in a solitary learning task perform poorly in a social learning task. These findings suggest that honeybees specialize in one type of learning strategy when making foraging decisions, and such differences may have important implications for how individuals provision their colony. The first two chapters focused on how differences in performance on cognitive tasks may represent a trade-off that correlates to different behaviors. In the latter half of my dissertation, I first used multiple cognitive traits to define a cognitive phenotype in an individual and then investigated how such differences might impact performance on multiple behaviors and life history traits to determine functional consequences of cognitive variation. I then expanded this research to determine how differences in ecology shape cognitive phenotypes. In chapter three, I tested for the presence of distinct cognitive phenotypes in A. mellifera foragers by measuring multiple cognitive traits and determining whether these traits covary to produce distinct slow and fast cognitive phenotypes. I then compared performance on multiple behavioral and life history tasks to see if there were functional differences between these cognitive types. My results indicate the presence of two cognitive phenotypes that meet the predictions of the speed-accuracy trade-off and that are conserved across colonies. Compared to slow bees, fast bees were described by high associative learning, high preference for novelty and high preference for variance, bees which also engage in more nursing behavior and transition to becoming a forager at an earlier age. In chapter four, which explored how ecological and life history differences shape cognitive phenotypes between closely related honeybee species, I tested for differences in the cognitive phenotype in four honeybee species, each of which occupied a unique ecological niche that was correlated to their position on the slow-fast life history axis. My results indicate that a set of cognitive traits consistently covary within each species, resulting in slow and fast cognitive phenotypes that meet the predictions of the speed-accuracy tradeoff. I also found that the four species do not align on a slow-fast cognitive axis due to known differences in their life history and nesting ecology. Rather, cognitive differences among the species appear correlated to their brain size, which may be driven by differences in foraging range. Taken together, this work indicates that cognitive variation at the individual level has important behavioral and life history outcomes that may impact how the individual interacts with their environment and how the colony performs. At the species level, cognitive variation appears to be driven by a complex relationship with the species unique environment as well as underlying trade-offs associated with costs of cognition

Numerical cognition in bees and other insects

The ability to perceive the number of objects has been known to exist in vertebrates for a few decades, but recent behavioral investigations have demonstrated that several invertebrate species can also be placed on the continuum of numerical abilities shared with birds, mammals, and reptiles. In this review article, we present the main experimental studies that have examined the ability of insects to use numerical information. These studies have made use of a wide range of methodologies, and for this reason it is striking that a common finding is the inability of the tested animals to discriminate numerical quantities greater than four. Furthermore, the finding that bees can not only transfer learnt numerical discrimination to novel objects, but also to novel numerosities, is strongly suggestive of a true, albeit limited, ability to count. Later in the review, we evaluate the available evidence to narrow down the possible mechanisms that the animals might be using to solve the number-based experimental tasks presented to them. We conclude by suggesting avenues of further research that take into account variables such as the animals' age and experience, as well as complementary cognitive systems such as attention and the time sense.This publication was funded by the German Research Foundation (DFG) and the University of Wuerzburg in the funding program Open Access Publishing. Shaowu Zhang was supported by the ARC-CoE in Vision Science

What does the honeybee see? And how do we know?

This book is the only account of what the bee, as an example of an insect, actually detects with its eyes. Bees detect some visual features such as edges and colours, but there is no sign that they reconstruct patterns or put together features to form objects. Bees detect motion but have no perception of what it is that moves, and certainly they do not recognize “things” by their shapes. Yet they clearly see well enough to fly and find food with a minute brain. Bee vision is therefore relevant to the construction of simple artificial visual systems, for example for mobile robots. The surprising conclusion is that bee vision is adapted to the recognition of places, not things. In this volume, Adrian Horridge also sets out the curious and contentious history of how bee vision came to be understood, with an account of a century of neglect of old experimental results, errors of interpretation, sharp disagreements, and failures of the scientific method. The design of the experiments and the methods of making inferences from observations are also critically examined, with the conclusion that scientists are often hesitant, imperfect and misleading, ignore the work of others, and fail to consider alternative explanations. The erratic path to understanding makes interesting reading for anyone with an analytical mind who thinks about the methods of science or the engineering of seeing machines

Exposure to acetylcholinesterase inhibitors alters the physiology and motor function of honeybees

Cholinergic signalling is fundamental to neuro-muscular function in most organisms. Sub-lethal doses of neurotoxic pesticides that target cholinergic signalling can alter the behaviour of insects in subtle ways; their influence on non-target organisms may not be readily apparent in simple mortality studies. Beneficial arthropods such as honeybees perform sophisticated behavioural sequences during foraging that, if influenced by pesticides, could impair foraging success and reduce colony health. Here, we investigate the behavioural effects on honeybees of exposure to a selection of pesticides that target cholinergic signalling by inhibiting acetylcholinesterase (AChE). To examine how continued exposure to AChE inhibitors affected motor function, we fed adult foraging worker honeybees sub-lethal concentrations of these compounds in sucrose solution for 24 h. Using an assay for locomotion in bees, we scored walking, stopped, grooming, and upside down behaviour continuously for 15 min. At a 10nM concentration, all the AChE inhibitors caused similar effects on behaviour, notably increased grooming activity and changes in the frequency of bouts of behaviour such as head grooming. Coumaphos caused dose-dependent effects on locomotion as well as grooming behaviour, and a 1µM concentration of coumaphos induced symptoms of malaise such as abdomen grooming and defecation. Biochemical assays confirmed that the 4 compounds we assayed (coumaphos, aldicarb, chlorpyrifos, and donepezil) or their metabolites acted as AChE inhibitors in bees. Furthermore, we show that transcript expression levels of two honeybee acetylcholinesterase inhibitors were selectively upregulated in the brain and in gut tissues in response to AChE inhibitor exposure. The results of our study imply that the effects of pesticides that rely on this mode of action have subtle yet profound effects on physiological effects on behaviour that could lead to reduced survival

Apprentissage visuel en réalité virtuelle chez Apis mellifera

Dotées d'un cerveau de moins d'un millimètre cube et contenant environ 950 000 neurones, les abeilles présentent un riche répertoire comportemental, parmi lesquels l'apprentissage appétitif et la mémoire jouent un rôle fondamental dans le contexte des activités de recherche de nourriture. Outre les formes élémentaires d'apprentissage, où les abeilles apprennent une association spécifique entre des événements de leur environnement, les abeilles maîtrisent également différentes formes d'apprentissage non-élémentaire, à la fois dans le domaine visuel et olfactif, y compris la catégorisation, l'apprentissage contextuel et l'abstraction de règles. Ces caractéristiques en font un modèle idéal pour l'étude de l'apprentissage visuel et pour explorer les mécanismes neuronaux qui sous-tendent leurs capacités d'apprentissage. Afin d'accéder au cerveau d'une abeille lors d'une tâche d'apprentissage visuel, l'insecte doit être immobilisé. Par conséquent, des systèmes de réalité virtuelle (VR) ont été développés pour permettre aux abeilles d'agir dans un monde virtuel, tout en restant stationnaires dans le monde réel. Au cours de mon doctorat, j'ai développé un logiciel de réalité virtuelle 3D flexible et open source pour étudier l'apprentissage visuel, et je l'ai utilisé pour améliorer les protocoles de conditionnement existants en VR et pour étudier le mécanisme neuronal de l'apprentissage visuel. En étudiant l'influence du flux optique sur l'apprentissage associatif des couleurs, j'ai découvert que l'augmentation des signaux de mouvement de l'arrière-plan nuisait aux performances des abeilles. Ce qui m'a amené à identifier des problèmes pouvant affecter la prise de décision dans les paysages virtuels, qui nécessitent un contrôle spécifique par les expérimentateurs. Au moyen de la VR, j'ai induit l'apprentissage visuel chez des abeilles et quantifié l'expression immédiate des gènes précoces (IEG) dans des zones spécifiques de leur cerveau pour détecter les régions impliquées dans l'apprentissage visuel. En particulier, je me suis concentré sur kakusei, Hr38 et Egr1, trois IEG liés à la recherche de nourriture et à l'orientation des abeilles et qui peuvent donc également être pertinents pour la formation d'association visuelle appétitive. Cette analyse suggère que les corps pédonculés sont impliqués dans l'apprentissage associatif des couleurs. Enfin, j'ai exploré la possibilité d'utiliser la VR sur d'autres modèles d'insectes et effectué un conditionnement différentiel sur des bourdons. Cette étude a montré que non seulement les bourdons sont capables de résoudre cette tâche cognitive aussi bien que les abeilles, mais aussi qu'ils interagissent davantage avec la réalité virtuelle, ce qui entraîne un ratio plus faible d'individus rejetés de l'expérience par manque de mouvement. Ces résultats indiquent que les protocoles VR que j'ai établis au cours de cette thèse peuvent être appliqués à d'autres insectes, et que le bourdon est un bon candidat pour l'étude de l'apprentissage visuel en VR.Equipped with a brain smaller than one cubic millimeter and containing ~950,000 neurons, honeybees display a rich behavioral repertoire, among which appetitive learning and memory play a fundamental role in the context of foraging activities. Besides elemental forms of learning, where bees learn specific association between environmental features, bees also master different forms of non-elemental learning, including categorization, contextual learning and rule abstraction. These characteristics make them an ideal model for the study of visual learning and its underlying neural mechanisms. In order to access the working brain of a bee during visual learning the insect needs to be immobilized. To do so, virtual reality (VR) setups have been developed to allow bees to behave within a virtual world, while remaining stationary within the real world. During my PhD, I developed a flexible and open source 3D VR software to study visual learning, and used it to improve existing conditioning protocols and to investigate the neural mechanism of visual learning. By developing a true 3D environment, we opened the possibility to add frontal background cues, which were also subjected to 3D updating based on the bee movements. We thus studied if and how the presence of such motion cues affected visual discrimination in our VR landscape. Our results showed that the presence of frontal background motion cues impaired the bees' performance. Whenever these cues were suppressed, color discrimination learning became possible. Our results point towards deficits in attentional processes underlying color discrimination whenever motion cues from the background were frontally available in our VR setup. VR allows to present insects with a tightly controlled visual experience during visual learning. We took advantage of this feature to perform ex-vivo analysis of immediate early gene (IEG) expression in specific brain area, comparing learner and non-learner bees. Using both 3D VR and a lore restrictive 2D version of the same task we tackled two questions, first what are the brain region involved in visual learning? And second, is the pattern of activation of the brain dependent on the modality of learning? Learner bees that solved the task in 3D showed an increased activity of the Mushroom Bodies (MB), which is coherent with the role of the MB in sensory integration and learning. Surprisingly we also found a completely different pattern of IEGs expression in the bees that solved the task in 2D conditions. We observed a neural signature that spanned the optic lobes and MB calyces and was characterized by IEG downregulation, consistent with an inhibitory trace. The study of visual learning's neural mechanisms requires invasive approach to access the brain of the insects, which induces stress in the animals and can thus impair behaviors in itself. To potentially mitigate this effect, bumble bees Bombus terrestris could constitute a good alternative to Apis mellifera as bumble bees are more robust. That's why in the last part of this work we explored the performances of bumblebees in a differential learning task in VR and compared them to those of honey bees. We found that, not only bumble bees are able to solve the task as well as honey bees, but they also engage more with the virtual environment, leading to a lower ratio of discarded individuals. We also found no correlation between the size of bumble bees and their learning performances. This is surprising as larger bumble bees, that assume the role of foragers in the colony, have been shown to be better at learning visual tasks in the literature