Ecology of fear in highly invasive fish revealed by robots

Polverino, Giovanni; Soman, Vrishin R.; Karakaya, Mert; Gasparini, Clelia; Evans, Jonathan P.; Porfiri, Maurizio

doi:10.1016/j.isci.2021.103529

Cited by 18 publications

(12 citation statements)

References 64 publications

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“…Animal behaviour studies can again offer insight into potential modelling rules, for instance, through experiments of fish shoals interacting with biologically inspired and interactive robotic predators (e.g. Polverino et al, 2019, 2022). However, the interaction dynamics of a herding robot will need to be different to a natural (or simulated) predator, since the goal of bio‐herding is to keep the group together and manoeuvre it, while a predator's goal is typically the opposite, to break up groups and isolate individuals as prey (Zoratto et al, 2010).…”

Section: Uavs (Drones) For Bio‐herdingmentioning

confidence: 99%

Biologically inspired herding of animal groups by robots

et al. 2023

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1. A single sheepdog can bring together and manoeuvre hundreds of sheep from one location to another. Engineers and ecologists are fascinated by this sheepdog herding because of the potential it provides for 'bio-herding': a biologically inspired herding of animal groups by robots. Although many herding algorithms have been proposed, most are studied via simulation.2. There are a variety of ecological problems where management of wild animal groups is currently impossible, dangerous and/or costly for humans to manage directly, and which may benefit from bio-herding solutions.3. Unmanned aerial vehicles (UAVs) now deliver significant benefits to the economy and society. Here, we suggest the use of UAVs for bio-herding. Given their mobility and speed, UAVs can be used in a wide range of environments and interact with animal groups at sea, over the land and in the air.4. We present a potential roadmap for achieving bio-herding using a pair of UAVs.In our framework, one UAV performs 'surveillance' of animal groups, informing the movement of a second UAV that herds them. We highlight the promise and flexibility of a paired UAV approach while emphasising its practical and ethical challenges. We start by describing the types of experiments and data required to understand individual and collective responses to UAVs. Next, we describe how to develop appropriate herding algorithms. Finally, we describe the integration of bio-herding algorithms into software and hardware architecture.

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Section: Uavs (Drones) For Bio‐herdingmentioning

confidence: 99%

Biologically inspired herding of animal groups by robots

et al. 2023

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“…For instance, state‐of‐the‐art robots can now be used to interact with fish in real time to study the underpinnings of social and anxiety‐related behaviours (e.g. Polverino et al ., 2019), and even reveal the long‐lasting consequences of brief behavioural alterations on the life‐history, reproduction, and ecological success of targeted animals (Polverino et al ., 2022). Indeed, despite being underexploited in behavioural ecotoxicology to date, bioinspired robots offer an autonomous, customisable, and repeatable approach that overcomes challenges associated with more traditional methods, in which live stimuli are an extra source of variation, although at the cost of some degree of biological and ecological realism.…”

Section: Tools and Technologies For Advancing Behavioural Ecotoxicologymentioning

confidence: 99%

Frontiers in quantifying wildlife behavioural responses to chemical pollution

et al. 2022

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Animal behaviour is remarkably sensitive to disruption by chemical pollution, with widespread implications for ecological and evolutionary processes in contaminated wildlife populations. However, conventional approaches applied to study the impacts of chemical pollutants on wildlife behaviour seldom address the complexity of natural environments in which contamination occurs. The aim of this review is to guide the rapidly developing field of behavioural ecotoxicology towards increased environmental realism, ecological complexity, and mechanistic understanding. We identify research areas in ecology that to date have been largely overlooked within behavioural ecotoxicology but which promise to yield valuable insights, including within-and among-individual variation, social networks and collective behaviour, and multi-stressor interactions. Further, we feature methodological and technological innovations that enable the collection of data on pollutant-induced behavioural changes at an unprecedented resolution and scale in the laboratory and the field. In an era of rapid environmental change, there is an urgent need to advance our understanding of the real-world impacts of chemical pollution on wildlife behaviour. This review therefore provides a roadmap of the major outstanding questions in behavioural ecotoxicology and highlights the need for increased cross-talk with other disciplines in order to find the answers.

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“…Indeed, such simple models have helped to explain certain behaviors observed in animal collectives. For example, the SPP model was used by Yates et al (2009) to show how noise affects the level of coherence in locust swarms and by Polverino et al (2021) to study the movements of animal groups when threatened by a predator. Similarly, the Ising model was also used by Feinerman et al (2018) to study the transport of food by ants.…”

Section: Simulating Collective Behaviormentioning

confidence: 99%

Beyond Bio-Inspired Robotics: How Multi-Robot Systems Can Support Research on Collective Animal Behavior

2022

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In the study of collective animal behavior, researchers usually rely on gathering empirical data from animals in the wild. While the data gathered can be highly accurate, researchers have limited control over both the test environment and the agents under study. Further aggravating the data gathering problem is the fact that empirical studies of animal groups typically involve a large number of conspecifics. In these groups, collective dynamics may occur over long periods of time interspersed with excessively rapid events such as collective evasive maneuvers following a predator’s attack. All these factors stress the steep challenges faced by biologists seeking to uncover the fundamental mechanisms and functions of social organization in a given taxon. Here, we argue that beyond commonly used simulations, experiments with multi-robot systems offer a powerful toolkit to deepen our understanding of various forms of swarming and other social animal organizations. Indeed, the advances in multi-robot systems and swarm robotics over the past decade pave the way for the development of a new hybrid form of scientific investigation of social organization in biology. We believe that by fostering such interdisciplinary research, a feedback loop can be created where agent behaviors designed and tested in robotico can assist in identifying hypotheses worth being validated through the observation of animal collectives in nature. In turn, these observations can be used as a novel source of inspiration for even more innovative behaviors in engineered systems, thereby perpetuating the feedback loop.

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Ecology of fear in highly invasive fish revealed by robots

Cited by 18 publications

References 64 publications

Biologically inspired herding of animal groups by robots

Biologically inspired herding of animal groups by robots

Frontiers in quantifying wildlife behavioural responses to chemical pollution

Beyond Bio-Inspired Robotics: How Multi-Robot Systems Can Support Research on Collective Animal Behavior

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