Coupled land use and ecological models reveal emergence and feedbacks in socio-ecological systems

Acknowledgements: This work was supported by an EPSRC Doctoral Training Centre grant (EP/G03690X/1). Supplementary material (Appendix ECOG‐04039 at ). Appendix 1.Peer reviewedPublisher PD

Virtual prey with Lévy motion are preferentially attacked by predatory fish

This work was funded by a NERC Independent Research Fellowship (NE/K009370/1) and a Leverhulme Trust grant (RPG-2017-041 V) awarded to C.C.I.Of widespread interest in animal behavior and ecology is how animals search their environment for resources, and whether these search strategies are optimal. However, movement also affects predation risk through effects on encounter rates, the conspicuousness of prey, and the success of attacks. Here, we use predatory fish attacking a simulation of virtual prey to test whether predation risk is associated with movement behavior. Despite often being demonstrated to be a more efficient strategy for finding resources such as food, we find that prey displaying Lévy motion are twice as likely to be targeted by predators than prey utilizing Brownian motion. This can be explained by the predators, at the moment of the attack, preferentially targeting prey that were moving with straighter trajectories rather than prey that were turning more. Our results emphasize that costs of predation risk need to be considered alongside the foraging benefits when comparing different movement strategies.Publisher PDFPeer reviewe

Finding the Gap:Neuromorphic Motion Vision in Cluttered Environments

Many animals meander in environments and avoid collisions. How the underlying neuronal machinery can yield robust behaviour in a variety of environments remains unclear. In the fly brain, motion-sensitive neurons indicate the presence of nearby objects and directional cues are integrated within an area known as the central complex. Such neuronal machinery, in contrast with the traditional stream-based approach to signal processing, uses an event-based approach, with events occurring when changes are sensed by the animal. Contrary to von Neumann computing architectures, event-based neuromorphic hardware is designed to process information in an asynchronous and distributed manner. Inspired by the fly brain, we model, for the first time, a neuromorphic closed-loop system mimicking essential behaviours observed in flying insects, such as meandering in clutter and gap crossing, which are highly relevant for autonomous vehicles. We implemented our system both in software and on neuromorphic hardware. While moving through an environment, our agent perceives changes in its surroundings and uses this information for collision avoidance. The agent's manoeuvres result from a closed action-perception loop implementing probabilistic decision-making processes. This loop-closure is thought to have driven the development of neural circuitry in biological agents since the Cambrian explosion. In the fundamental quest to understand neural computation in artificial agents, we come closer to understanding and modelling biological intelligence by closing the loop also in neuromorphic systems. As a closed-loop system, our system deepens our understanding of processing in neural networks and computations in biological and artificial systems. With these investigations, we aim to set the foundations for neuromorphic intelligence in the future, moving towards leveraging the full potential of neuromorphic systems.Comment: 7 main pages with two figures, including appendix 26 pages with 14 figure

Insect-Inspired Visual Perception for Flight Control and Collision Avoidance

Flying robots are increasingly used for tasks such as aerial mapping, fast exploration, video footage and monitoring of buildings. Autonomous flight at low altitude in cluttered and unknown environments is an active research topic because it poses challenging perception and control problems. Traditional methods for collision-free navigation at low altitude require heavy resources to deal with the complexity of natural environments, something that limits the autonomy and the payload of flying robots. Flying insects, however, are able to navigate safely and efficiently using vision as the main sensory modality. Flying insects rely on low resolution, high refresh rate, and wide-angle compound eyes to extract angular image motion and move in unstructured environments. These strategies result in systems that are physically and computationally lighter than those often found in high-definition stereovision. Taking inspiration from insects offers great potential for building small flying robots capable of navigating in cluttered environments using lightweight vision sensors. In this thesis, we investigate insect perception of visual motion and insect vision based flight control in cluttered environments. We use the knowledge gained through the modelling of neural circuits and behavioural experiments to develop flying robots with insect-inspired control strategies for goal-oriented navigation in complex environments. We start by exploring insect perception of visual motion. We present a study that reconciles an apparent contradiction in the literature for insect visual control: current models developed to explain insect flight behaviour rely on the measurement of optic flow, however the most prominent neural model for visual motion extraction (the Elementary Motion Detector, or EMD) does not measure optic flow. We propose a model for unbiased optic flow estimation that relies on comparing the output of multiple EMDs pointed in varying viewing directions. Our model is of interest of both engineers and biologists because it is computationally more efficient than other optic flow estimation algorithms, and because it represents a biologically plausible model for optic flow extraction in insect neural systems. We then focus on insect flight control strategies in the presence of obstacles. By recording the trajectories of bumblebees (Bombus terrestris), and by comparing them to simulated flights, we show that bumblebees rely primarily on the frontal part of their field of view, and that they pool optic flow in two different manners for the control of flight speed and of lateral position. For the control of lateral position, our results suggest that bumblebees selectively react to the portions of the visual field where optic flow is the highest, which correspond to the closest obstacles. Finally, we tackle goal-oriented navigation with a novel algorithm that combines aspects of insect perception and flight control presented in this thesis -- like the detection of fastest moving objects in the frontal visual field -- with other aspects of insect flight known from the literature such as saccadic flight pattern. Through simulations, we demonstrate autonomous navigation in forest-like environments using only local optic flow information and assuming knowledge about the direction to the navigation goal

Causes and consequences of individual forager variability in social bees

Chez les pollinisateurs sociaux, comme l'abeille domestique (Apis mellifera L.) et le bourdon terrestre (Bombus terrestris L.), mes deux modèles d'étude, différents individus sont spécialisés dans différentes tâches. Il est admis que différents types de comportement de butinage contribuent à une optimisation des performances de la colonie. Actuellement, les populations de pollinisateurs sont exposées à des stress environnementaux, qui sont connus pour perturber le comportement des individus en visant directement leur cognition. Il est ainsi crucial de mieux comprendre comment les colonies d'abeilles et de bourdons maintiennent une activité de butinage efficace, et quels sont les effets de stress environnementaux sur les butineuses. Dans cette thèse, j'ai donc examiné les différentes stratégies de butinage pour différentes sources de nourriture, pollen et nectar, et les variabilités interindividuelles dans le comportement de butinage. Je me suis aussi intéressé à l'impact de stress tels que les pesticides sur l'efficacité de butinage. J'ai utilisé la technologie RFID pour suivre le comportement des abeilles tout au long de leur vie. J'ai trouvé que les colonies d'abeilles et de bourdons reposent sur un petit groupe d'individus très actifs qui fournissent la majorité de la nourriture pour la colonie. Chez les abeilles, ces individus très actifs sont aussi plus efficaces pour collecter nectar et pollen. J'ai aussi identifié l'existence de différentes stratégies pour la collecte de pollen ou de nectar. Ensuite, j'ai pu montrer que les bourdons ont des différences interindividuelles très marquées dans un test de navigation, une tâche cruciale dans le comportement de butinage. Finalement, j'ai testé l'effet néfaste de pesticides sur l'apprentissage visuel chez l'abeille. Cette thèse a pour but de mieux comprendre les causes de vulnérabilité des pollinisateurs aux stress environnementaux. Mes résultats soulignent le besoin de considérer la diversité comportementale comme une adaptation des espèces de pollinisateurs sociaux, mais aussi comme une potentielle cause de vulnérabilité de la colonie vis-à-vis des stress.In social insects, such as bees, different individuals specialise in the collection of different resources, and it is assumed that natural behavioural variability among foragers contributes to a self-organised optimisation of colony performance. Currently, bee populations are facing an increasing number of environmental stressors, known to disturb the behaviour of individuals, presumably upon their impact on cognitive capacities. Hence it is important to learn more about how stressors impact on individual foraging behaviour to understand how a colony maintains effective nutrition and development. In this thesis in cognitive ecology, I examined the different foraging strategies for the different macronutrient sources, pollen and nectar, and the inter-individual variation in bee foraging performance. I also looked at how stressors, such as pesticides, can impact on bee foraging efficiency. I compared two social Hymenoptera that vary in their level of social complexity: the European honey bee (Apis mellifera L.) and the buffed-tailed bumblebee (Bombus terrestris L.). I used Radio Frequency Identification (RFID) to automatically track the foraging behaviour of bees throughout their life. I found that honey bee and bumblebee colonies rely on a subset of very active bees to supply the whole colony needs. In honey bees, these foragers are more efficient and collect more pollen. I also identified different strategies for pollen or nectar collection in both species. Using manipulative experiments, I then showed that bees exhibit consistent inter-individual different behaviours in a spatial learning task and that pesticides impair visual learning. My thesis aims at better explaining the causes of vulnerability of pollinators to sublethal pesticides and other environmental stressors. The results highlight the need for considering behavioural diversity as an adaptation for social insects, as well as a potential dimension of colony-level vulnerability to environmental stressors that can impair the whole colony nutritional balance