Nouvian, Morgane
0000-0003-4882-2372 Berufsbeschreibung
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Glyphosate impairs aversive learning in bumblebees
Agrochemicals represent prominent anthropogenic stressors contributing to the ongoing global insect decline. While their impact is generally assessed in terms of mortality rates, non-lethal effects on fitness are equally important to insect conservation. Glyphosate, a commonly used herbicide, is toxic to many animal species, and thought to impact a range of physiological functions. In this study, we investigate the impact of long-term exposure to glyphosate on locomotion, phototaxis and learning abilities in bumblebees, using a fully automated high-throughput assay. We find that glyphosate exposure had a very slight and transient impact on locomotion, while leaving the phototactic drive unaffected. Glyphosate exposure also reduced attraction towards UV light when blue was given as an alternative and, most strikingly, impaired learning of aversive stimuli. Thus, glyphosate had specific actions on sensory and cognitive processes. These non-lethal perceptual and cognitive impairments likely represent a significant obstacle to foraging and predator avoidance for wild bumblebees exposed to glyphosate. Similar effects in other species could contribute to a widespread reduction in foraging efficiency across ecosystems, driven by the large-scale application of this herbicide. The high-throughput paradigm presented in this study can be adapted to investigate sublethal effects of other agrochemicals on bumblebees or other important pollinator species, opening up a critical new avenue for the study of anthropogenic stressors.
Extracting individual characteristics from population data reveals a negative social effect during honeybee defence
Honeybees protect their colony against vertebrates by mass stinging and they coordinate their actions during this crucial event thanks to an alarm pheromone carried directly on the stinger, which is therefore released upon stinging. The pheromone then recruits nearby bees so that more and more bees participate in the defence. However, a quantitative understanding of how an individual bee adapts its stinging response during the course of an attack is still a challenge: Typically, only the group behaviour is effectively measurable in experiment; Further, linking the observed group behaviour with individual responses requires a probabilistic model enumerating a combinatorial number of possible group contexts during the defence; Finally, extracting the individual characteristics from group observations requires novel methods for parameter inference.
We first experimentally observed the behaviour of groups of bees confronted with a fake predator inside an arena and quantified their defensive reaction by counting the number of stingers embedded in the dummy at the end of a trial. We propose a biologically plausible model of this phenomenon, which transparently links the choice of each individual bee to sting or not, to its group context at the time of the decision. Then, we propose an efficient method for inferring the parameters of the model from the experimental data. Finally, we use this methodology to investigate the effect of group size on stinging initiation and alarm pheromone recruitment.
Our findings shed light on how the social context influences stinging behaviour, by quantifying how the alarm pheromone concentration level affects the decision of each bee to sting or not in a given group size. We show that recruitment is curbed as group size grows, thus suggesting that the presence of nestmates is integrated as a negative cue by individual bees. Moreover, the unique integration of exact and statistical methods provides a quantitative characterisation of uncertainty associated to each of the inferred parameters.
Data-Informed Parameter Synthesis for Population Markov Chains
Population models are widely used to model different phenomena: animal collectives such as social insects, flocking birds, schooling fish, or humans within societies, as well as molecular species inside a cell, cells forming a tissue. Animal collectives show remarkable self-organisation towards emergent behaviours without centralised control. Quantitative models of the underlying mechanisms can directly serve important societal concerns (for example, prediction of seismic activity [5]), inspire the design of distributed algorithms (for example, ant colony algorithm [1]), or aid robust design and engineering of collective, adaptive systems under given functionality and resources, which is recently gaining attention in vision of smart cities [3, 4]. Quantitative prediction of the behaviour of a population of agents over time and space, each having several behavioural modes, results in a high-dimensional, non-linear, and stochastic system [2]. Hence, computational modelling with population models is challenging, especially when the model parameters are unknown and experiments are expensive.
Seasonality, alarm pheromone and serotonin : insights on the neurobiology of honeybee defence from winter bees
Honeybees maintain their colony throughout the cold winters, a strategy that enables them to make the most of early spring flowers. During this period, their activity is mostly limited to thermoregulation, while foraging and brood rearing are stopped. Less is known about seasonal changes to the essential task of defending the colony against intruders, which is regulated by the sting alarm pheromone. We studied the stinging responsiveness of winter bees exposed to this scent or a control (solvent). Surprisingly, winter bees, while maintaining their responsiveness in control conditions, did not increase stinging frequency in response to the alarm pheromone. This was not owing to the bees not perceiving the pheromone, as shown by calcium imaging of the antennal lobes. As the alarm pheromone is thought to act through an increase in brain serotonin levels, ultimately causing heightened defensiveness, we checked if serotonin treatments would affect the stinging behaviour of winter bees. Indeed, treated winter bees became more inclined to sting. Thus, we postulate that loss of responsiveness to the sting alarm pheromone is based on a partial or total disruption of the mechanism converting alarm pheromone perception into high serotonin levels in winter bees.
Geosmin suppresses defensive behaviour and elicits unusual neural responses in honey bees
Geosmin is an odorant produced by bacteria in moist soil. It has been found to be extraordinarily relevant to some insects, but the reasons for this are not yet fully understood. Here we report the first tests of the effect of geosmin on honey bees. A stinging assay showed that the defensive behaviour elicited by the bee’s alarm pheromone component isoamyl acetate (IAA) is strongly suppressed by geosmin. Surprisingly, the suppression is, however, only present at very low geosmin concentrations, and disappears at higher concentrations. We investigated the underlying mechanisms at the level of the olfactory receptor neurons by means of electroantennography, finding the responses to mixtures of geosmin and IAA to be lower than to pure IAA, suggesting an interaction of both compounds at the olfactory receptor level. Calcium imaging of the antennal lobe (AL) revealed that neuronal responses to geosmin decreased with increasing concentration, correlating well with the observed behaviour. Computational modelling of odour transduction and coding in the AL suggests that a broader activation of olfactory receptor types by geosmin in combination with lateral inhibition could lead to the observed non-monotonic increasing–decreasing responses to geosmin and thus underlie the specificity of the behavioural response to low geosmin concentrations.
Honeybee communication during collective defence is shaped by predation
Background
Social insect colonies routinely face large vertebrate predators, against which they need to mount a collective defence. To do so, honeybees use an alarm pheromone that recruits nearby bees into mass stinging of the perceived threat. This alarm pheromone is carried directly on the stinger; hence, its concentration builds up during the course of the attack. We investigate how bees react to different alarm pheromone concentrations and how this evolved response pattern leads to better coordination at the group level.
Results
We first present a dose-response curve to the alarm pheromone, obtained experimentally. This data reveals two phases in the bees’ response: initially, bees become more likely to sting as the alarm pheromone concentration increases, but aggressiveness drops back when very high concentrations are reached. Second, we apply Projective Simulation to model each bee as an artificial learning agent that relies on the pheromone concentration to decide whether to sting or not. Individuals are rewarded based on the collective performance, thus emulating natural selection in these complex societies. By also modelling predators in a detailed way, we are able to identify the main selection pressures that shaped the response pattern observed experimentally. In particular, the likelihood to sting in the absence of alarm pheromone (starting point of the dose-response curve) is inversely related to the rate of false alarms, such that bees in environments with low predator density are less likely to waste efforts responding to irrelevant stimuli. This is compensated for by a steep increase in aggressiveness when the alarm pheromone concentration starts rising. The later decay in aggressiveness may be explained as a curbing mechanism preventing worker loss.
Conclusions
Our work provides a detailed understanding of alarm pheromone responses in honeybees and sheds light on the selection pressures that brought them about. In addition, it establishes our approach as a powerful tool to explore how selection based on a collective outcome shapes individual responses, which remains a challenging issue in the field of evolutionary biology.
Data-Informed Parameter Synthesis for Population Markov Chains
Stochastic population models are widely used to model phenomena in different areas such as chemical kinetics or collective animal behaviour. Quantitative analysis of stochastic population models easily becomes challenging, due to the combinatorial propagation of dependencies across the population. The complexity becomes especially prominent when model’s parameters are not known and available measurements are limited. In this paper, we illustrate this challenge in a concrete scenario: we assume a simple communication scheme among identical individuals, inspired by how social honeybees emit the alarm pheromone to protect the colony in case of danger. Together, n individuals induce a population Markov chain with n parameters. In addition, we assume to be able to experimentally observe the states only after the steady-state is reached. In order to obtain the parameters of the individual’s behaviour, by utilising the data measurements for population, we combine two existing techniques. First, we use the tools for parameter synthesis for Markov chains with respect to temporal logic properties, and then we employ CEGAR-like reasoning to find the viable parameter space up to desired coverage. We report the performance on a number of synthetic data sets.
Olfactory Strategies in the Defensive Behaviour of Insects
Most animals must defend themselves in order to survive. Defensive behaviour includes detecting predators or intruders, avoiding them by staying low-key or escaping or deterring them away by means of aggressive behaviour, i.e., attacking them. Responses vary across insect species, ranging from individual responses to coordinated group attacks in group-living species. Among different modalities of sensory perception, insects predominantly use the sense of smell to detect predators, intruders, and other threats. Furthermore, social insects, such as honeybees and ants, communicate about danger by means of alarm pheromones. In this review, we focus on how olfaction is put to use by insects in defensive behaviour. We review the knowledge of how chemical signals such as the alarm pheromone are processed in the insect brain. We further discuss future studies for understanding defensive behaviour and the role of olfaction.
Complexity and plasticity in honey bee phototactic behaviour
The ability to move towards or away from a light source, namely phototaxis, is essential for a number of species to find the right environmental niche and may have driven the appearance of simple visual systems. In this study we ask if the later evolution of more complex visual systems was accompanied by a sophistication of phototactic behaviour. The honey bee is an ideal model organism to tackle this question, as it has an elaborate visual system, demonstrates exquisite abilities for visual learning and performs phototaxis. Our data suggest that in this insect, phototaxis has wavelength specific properties and is a highly dynamical response including multiple decision steps. In addition, we show that previous experience with a light (through exposure or classical aversive conditioning) modulates the phototactic response. This plasticity is dependent on the wavelength used, with blue being more labile than green or ultraviolet. Wavelength, intensity and past experience are integrated into an overall valence for each light that determines phototactic behaviour in honey bees. Thus, our results support the idea that complex visual systems allow sophisticated phototaxis. Future studies could take advantage of these findings to better understand the neuronal circuits underlying this processing of the visual information.
Aversive training of honey bees in an automated Y-maze
Honeybees have remarkable learning abilities given their small brains, and have thus been established as a powerful model organism for the study of learning and memory. Most of our current knowledge is based on appetitive paradigms, in which a previously neutral stimulus (e.g. a visual, olfactory, or tactile stimulus) is paired with a reward. Here we present a novel apparatus, the yAPIS, for aversive training of walking honey bees. This system consists in 3 arms of equal length and at 120° from each other. Within each arm, colored lights (λ=375, 465 or 520 nm) or odors (here limonene or linalool) can be delivered to provide conditioned stimuli (CS). A metal grid placed on the floor and roof delivers the punishment in the form of mild electric shocks (unconditioned stimulus, US). Our training protocol followed a fully classical procedure, in which the bee was exposed sequentially to a CS paired with shocks (CS+) and to another CS not punished (CS-). Learning performance was measured during a second phase, which took advantage of the Y-shape of the apparatus and of real-time tracking to present the bee with a choice situation, e.g. between the CS+ and the CS-. Bees reliably chose the CS- over the CS+ after only a few training trials with either colors or odors, and retained this memory for at least a day, except for the shorter wavelength (λ=375nm) that produced mixed results. This behavior was largely the result of the bees avoiding the CS+, as no evidence was found for attraction to the CS-. Interestingly, trained bees initially placed in the CS+ spontaneously escaped to a CS- arm if given the opportunity, even though they could never do so during the training. Finally, honey bees trained with compound stimuli (color + odor) later avoided either components of the CS+. Thus, the yAPIS is a fast, versatile and high-throughput way to train honey bees in aversive paradigms. It also opens the door for controlled laboratory experiments investigating bimodal integration and learning, a field that remains in its infancy.