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Licht M, Burns AL, Pacher K, Krause S, Bartashevich P, Romanczuk P, Hansen MJ, Then AY, Krause J. Sailfish generate foraging opportunities for seabirds in multi-species predator aggregations. Biol Lett 2024; 20:20240177. [PMID: 38982849 PMCID: PMC11252846 DOI: 10.1098/rsbl.2024.0177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 05/30/2024] [Indexed: 07/11/2024] Open
Abstract
While various marine predators form associations, the most commonly studied are those between subsurface predators and seabirds, with gulls, shearwaters or terns frequently co-occurring with dolphins, billfish or tuna. However, the mechanisms underlying these associations remain poorly understood. Three hypotheses have been proposed to explain the prevalence of these associations: (1) subsurface predators herd prey to the surface and make prey accessible to birds, (2) subsurface predators damage prey close to the surface and thereby provide food scraps to birds, and (3) attacks of underwater predators lower the cohesion of prey groups and thereby their collective defences making the prey easier to be captured by birds. Using drone footage, we investigated the interaction between Indo-Pacific sailfish (Istiophorus platypterus) and terns (Onychoprion sp.) preying on schooling fish off the eastern coast of the Malaysian peninsula. Through spatio-temporal analysis of the hunting behaviour of the two predatory species and direct measures of prey cohesion we showed that terns attacked when school cohesion was low, and that this decrease in cohesion was frequently caused by sailfish attacks. Therefore, we propose that sailfish created a by-product benefit for the bird species, lending support to the hypothesis that lowering cohesion can facilitate associations between subsurface predators and seabirds.
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Affiliation(s)
- M. Licht
- Faculty of Life Sciences, Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt-Universität zu Berlin, Invalidenstrasse 42, Berlin 10115, Germany
- Cluster of Excellence ‘Science of Intelligence’, Berlin, Germany, Marchstr. 23, Berlin 10587, Germany
| | - A. L. Burns
- Faculty of Life Sciences, Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt-Universität zu Berlin, Invalidenstrasse 42, Berlin 10115, Germany
- Cluster of Excellence ‘Science of Intelligence’, Berlin, Germany, Marchstr. 23, Berlin 10587, Germany
- Department Fish Biology, Fisheries and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, Berlin 12587, Germany
| | - K. Pacher
- Faculty of Life Sciences, Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt-Universität zu Berlin, Invalidenstrasse 42, Berlin 10115, Germany
- Department Fish Biology, Fisheries and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, Berlin 12587, Germany
| | - S. Krause
- Department of Electrical Engineering and Computer Science, Lübeck University of Applied Sciences, Lübeck 23562, Germany
| | - P. Bartashevich
- Cluster of Excellence ‘Science of Intelligence’, Berlin, Germany, Marchstr. 23, Berlin 10587, Germany
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - P. Romanczuk
- Cluster of Excellence ‘Science of Intelligence’, Berlin, Germany, Marchstr. 23, Berlin 10587, Germany
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - M. J. Hansen
- Department Fish Biology, Fisheries and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, Berlin 12587, Germany
| | - A. Y. Then
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Lembah Pantai, Kuala Lumpur 50603, Malaysia
| | - J. Krause
- Faculty of Life Sciences, Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt-Universität zu Berlin, Invalidenstrasse 42, Berlin 10115, Germany
- Cluster of Excellence ‘Science of Intelligence’, Berlin, Germany, Marchstr. 23, Berlin 10587, Germany
- Department Fish Biology, Fisheries and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, Berlin 12587, Germany
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2
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Chakrabortty T, Bhamla S. Controlling noisy herds. ARXIV 2024:arXiv:2406.06912v1. [PMID: 38947931 PMCID: PMC11213128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
The wisdom of the crowd breaks down in small groups. While large flocks exhibit swarm intelligence to evade predators, small groups display erratic behavior, oscillating between unity and discord. We investigate these dynamics using small groups of sheep controlled by shepherd dogs in century-old sheepdog trials, proposing a two-parameter stochastic dynamic framework. Our model employs pressure (stimulus intensity) and lightness (response isotropy) to simulate herding and shedding behaviors. Light sheep rapidly achieve a stable herding state, while heavy sheep exhibit intermittent herding and orthogonal alignment to the dog. High response isotropy enhances group cohesion but complicates group splitting. We construct a unified phase diagram for sheep behavior, identifying three regimes-fleeing, flocking, and grazing-based on group size and stimulus specificity. Increasing stimulus specificity shifts small group behavior from grazing to fleeing, while larger groups exhibit flocking. This transition underscores the challenge of controlling small indecisive collectives. Introducing the Indecisive Collective Algorithm (ICA), we show that deliberate indecisiveness and stochasticity improve control efficiency. ICA outperforms traditional averaging-based algorithms in high-noise settings and excels in tasks requiring group splitting. Our study offers a foundational framework for controlling small, indecisive groups, applicable to biochemical reactions, cell populations, and opinion dynamics.
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3
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Xiao Y, Lei X, Zheng Z, Xiang Y, Liu YY, Peng X. Perception of motion salience shapes the emergence of collective motions. Nat Commun 2024; 15:4779. [PMID: 38839782 PMCID: PMC11153630 DOI: 10.1038/s41467-024-49151-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 05/24/2024] [Indexed: 06/07/2024] Open
Abstract
Despite the profound implications of self-organization in animal groups for collective behaviors, understanding the fundamental principles and applying them to swarm robotics remains incomplete. Here we propose a heuristic measure of perception of motion salience (MS) to quantify relative motion changes of neighbors from first-person view. Leveraging three large bird-flocking datasets, we explore how this perception of MS relates to the structure of leader-follower (LF) relations, and further perform an individual-level correlation analysis between past perception of MS and future change rate of velocity consensus. We observe prevalence of the positive correlations in real flocks, which demonstrates that individuals will accelerate the convergence of velocity with neighbors who have higher MS. This empirical finding motivates us to introduce the concept of adaptive MS-based (AMS) interaction in swarm model. Finally, we implement AMS in a swarm of ~102 miniature robots. Swarm experiments show the significant advantage of AMS in enhancing self-organization of the swarm for smooth evacuations from confined environments.
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Affiliation(s)
- Yandong Xiao
- College of System Engineering, National University of Defense Technology, Changsha, Hunan, China.
| | - Xiaokang Lei
- College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an, Shaanxi, China
| | - Zhicheng Zheng
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Yalun Xiang
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Xingguang Peng
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi, China.
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4
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Zhao D, Luo H, Tu Y, Meng C, Lam TL. Snail-inspired robotic swarms: a hybrid connector drives collective adaptation in unstructured outdoor environments. Nat Commun 2024; 15:3647. [PMID: 38684822 PMCID: PMC11058817 DOI: 10.1038/s41467-024-47788-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 04/11/2024] [Indexed: 05/02/2024] Open
Abstract
Terrestrial self-reconfigurable robot swarms offer adaptable solutions for various tasks. However, most existing swarms are limited to controlled indoor settings, and often compromise stability due to their freeform connections. To address these issues, we present a snail robotic swarm system inspired by land snails, tailored for unstructured environments. Our system also employs a two-mode connection mechanism, drawing from the adhesive capabilities of land snails. The free mode, mirroring a snail's natural locomotion, leverages magnet-embedded tracks for freeform mobility, thereby enhancing adaptability and efficiency. The strong mode, analogous to a snail's response to disturbance, employs a vacuum sucker with directional polymer stalks for robust adhesion. By assigning specific functions to each mode, our system achieves a balance between mobility and secure connections. Outdoor experiments demonstrate the capabilities of individual robots and the exceptional synergy within the swarm. This research advances the real-world applications of terrestrial robotic swarms in unstructured environments.
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Affiliation(s)
- Da Zhao
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China
- Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China
| | - Haobo Luo
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China
- Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China
| | - Yuxiao Tu
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China
- Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China
| | - Chongxi Meng
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China
- Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China
| | - Tin Lun Lam
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China.
- Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China.
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5
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Tao Y, Zhou Y, Zheng Z, Lei X, Peng X. Characterizing Pairwise U-Turn Behavior in Fish: A Data-Driven Analysis. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1639. [PMID: 38136518 PMCID: PMC10742800 DOI: 10.3390/e25121639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 12/04/2023] [Accepted: 12/07/2023] [Indexed: 12/24/2023]
Abstract
We applied the time-series clustering method to analyze the trajectory data of rummy-nose tetra (Hemigrammus rhodostomus), with a particular focus on their spontaneous paired turning behavior. Firstly, an automated U-turn maneuver identification method was proposed to extract turning behaviors from the open trajectory data of two fish swimming in an annular tank. We revealed two distinct ways of pairwise U-turn swimming, named dominated turn and non-dominated turn. Upon comparison, the dominated turn is smoother and more efficient, with a fixed leader-follower relationship, i.e., the leader dominates the turning process. Because these two distinct ways corresponded to different patterns of turning feature parameters over time, we incorporated the Toeplitz inverse covariance-based clustering (TICC) method to gain deeper insights into this process. Pairwise turning behavior was decomposed into some elemental state compositions. Specifically, we found that the main influencing factor for a spontaneous U-turn is collision avoidance with the wall. In dominated turn, when inter-individual distances were appropriate, fish adjusted their positions and movement directions to achieve turning. Conversely, in closely spaced non-dominated turn, various factors such as changes in distance, velocity, and movement direction resulted in more complex behaviors. The purpose of our study is to integrate common location-based analysis methods with time-series clustering methods to analyze biological behavioral data. The study provides valuable insights into the U-turn behavior, motion characteristics, and decision factors of rummy-nose tetra during pairwise swimming. Additionally, the study extends the analysis of fish interaction features through the application of time-series clustering methods, offering a fresh perspective for the analysis of biological collective data.
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Affiliation(s)
- Yuan Tao
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
| | - Yuchen Zhou
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
| | - Zhicheng Zheng
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
| | - Xiaokang Lei
- College of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
| | - Xingguang Peng
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
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6
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Williams HJ, Sridhar VH, Hurme E, Gall GE, Borrego N, Finerty GE, Couzin ID, Galizia CG, Dominy NJ, Rowland HM, Hauber ME, Higham JP, Strandburg-Peshkin A, Melin AD. Sensory collectives in natural systems. eLife 2023; 12:e88028. [PMID: 38019274 PMCID: PMC10686622 DOI: 10.7554/elife.88028] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 11/10/2023] [Indexed: 11/30/2023] Open
Abstract
Groups of animals inhabit vastly different sensory worlds, or umwelten, which shape fundamental aspects of their behaviour. Yet the sensory ecology of species is rarely incorporated into the emerging field of collective behaviour, which studies the movements, population-level behaviours, and emergent properties of animal groups. Here, we review the contributions of sensory ecology and collective behaviour to understanding how animals move and interact within the context of their social and physical environments. Our goal is to advance and bridge these two areas of inquiry and highlight the potential for their creative integration. To achieve this goal, we organise our review around the following themes: (1) identifying the promise of integrating collective behaviour and sensory ecology; (2) defining and exploring the concept of a 'sensory collective'; (3) considering the potential for sensory collectives to shape the evolution of sensory systems; (4) exploring examples from diverse taxa to illustrate neural circuits involved in sensing and collective behaviour; and (5) suggesting the need for creative conceptual and methodological advances to quantify 'sensescapes'. In the final section, (6) applications to biological conservation, we argue that these topics are timely, given the ongoing anthropogenic changes to sensory stimuli (e.g. via light, sound, and chemical pollution) which are anticipated to impact animal collectives and group-level behaviour and, in turn, ecosystem composition and function. Our synthesis seeks to provide a forward-looking perspective on how sensory ecologists and collective behaviourists can both learn from and inspire one another to advance our understanding of animal behaviour, ecology, adaptation, and evolution.
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Affiliation(s)
- Hannah J Williams
- Max Planck Institute of Animal BehaviorKonstanzGermany
- Centre for the Advanced Study of Collective Behaviour, University of KonstanzKonstanzGermany
- Biology Department, University of KonstanzKonstanzGermany
| | - Vivek H Sridhar
- Max Planck Institute of Animal BehaviorKonstanzGermany
- Centre for the Advanced Study of Collective Behaviour, University of KonstanzKonstanzGermany
- Biology Department, University of KonstanzKonstanzGermany
| | - Edward Hurme
- Max Planck Institute of Animal BehaviorKonstanzGermany
- Centre for the Advanced Study of Collective Behaviour, University of KonstanzKonstanzGermany
- Biology Department, University of KonstanzKonstanzGermany
| | - Gabriella E Gall
- Max Planck Institute of Animal BehaviorKonstanzGermany
- Centre for the Advanced Study of Collective Behaviour, University of KonstanzKonstanzGermany
- Biology Department, University of KonstanzKonstanzGermany
- Zukunftskolleg, University of KonstanzKonstanzGermany
| | | | | | - Iain D Couzin
- Max Planck Institute of Animal BehaviorKonstanzGermany
- Centre for the Advanced Study of Collective Behaviour, University of KonstanzKonstanzGermany
- Biology Department, University of KonstanzKonstanzGermany
| | - C Giovanni Galizia
- Biology Department, University of KonstanzKonstanzGermany
- Zukunftskolleg, University of KonstanzKonstanzGermany
| | - Nathaniel J Dominy
- Zukunftskolleg, University of KonstanzKonstanzGermany
- Department of Anthropology, Dartmouth CollegeHanoverUnited States
| | - Hannah M Rowland
- Max Planck Research Group Predators and Toxic Prey, Max Planck Institute for Chemical EcologyJenaGermany
| | - Mark E Hauber
- Department of Evolution, Ecology, and Behavior, School of Integrative Biology, University of Illinois at Urbana-ChampaignUrbana-ChampaignUnited States
| | - James P Higham
- Zukunftskolleg, University of KonstanzKonstanzGermany
- Department of Anthropology, New York UniversityNew YorkUnited States
| | - Ariana Strandburg-Peshkin
- Max Planck Institute of Animal BehaviorKonstanzGermany
- Centre for the Advanced Study of Collective Behaviour, University of KonstanzKonstanzGermany
- Biology Department, University of KonstanzKonstanzGermany
| | - Amanda D Melin
- Zukunftskolleg, University of KonstanzKonstanzGermany
- Department of Anthropology and Archaeology, University of CalgaryCalgaryCanada
- Alberta Children’s Hospital Research Institute, University of CalgaryCalgaryCanada
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7
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Masila DR, Mahore R. Emergence of intelligent collective motion in a group of agents with memory. CHAOS (WOODBURY, N.Y.) 2023; 33:093131. [PMID: 37729097 DOI: 10.1063/5.0148977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 08/30/2023] [Indexed: 09/22/2023]
Abstract
Intelligent agents collect and process information from their dynamically evolving neighborhood to efficiently navigate through it. However, agent-level intelligence does not guarantee that at the level of a collective; a common example is the jamming we observe in traffic flows. In this study, we ask: how and when do the interactions between intelligent agents translate to desirable or intelligent collective outcomes? To explore this question, we choose a collective consisting of two kinds of agents with opposing desired directions of movement. Agents in this collective are minimally intelligent: they possess only a single facet of intelligence, viz., memory, where the agents remember how well they were able to travel in their desired directions and make up for their non-optimal past. We find that dynamics due to the agent's memory influences the collective, giving rise to diverse outcomes at the level of the group: from those that are undesirable to those that can be called "intelligent." When memory is short term, local rearrangement of agents leads to the formation of symmetrically jammed arrangements that take longer to unjam. However, when agents remember across longer time-scales, their dynamics become sensitive to small differences in their movement history. This gives rise to heterogeneity in the movement that causes agents to unjam more readily and form lanes.
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Affiliation(s)
- Danny Raj Masila
- Lab 10, Department of Chemical Engineering, IISc Bangalore, Bangalore 560012, Karnataka, India
| | - Rupesh Mahore
- Lab 10, Department of Chemical Engineering, IISc Bangalore, Bangalore 560012, Karnataka, India
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8
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Papadopoulou M, Fürtbauer I, O'Bryan LR, Garnier S, Georgopoulou DG, Bracken AM, Christensen C, King AJ. Dynamics of collective motion across time and species. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220068. [PMID: 36802781 PMCID: PMC9939269 DOI: 10.1098/rstb.2022.0068] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/17/2022] [Indexed: 02/21/2023] Open
Abstract
Most studies of collective animal behaviour rely on short-term observations, and comparisons of collective behaviour across different species and contexts are rare. We therefore have a limited understanding of intra- and interspecific variation in collective behaviour over time, which is crucial if we are to understand the ecological and evolutionary processes that shape collective behaviour. Here, we study the collective motion of four species: shoals of stickleback fish (Gasterosteus aculeatus), flocks of homing pigeons (Columba livia), a herd of goats (Capra aegagrus hircus) and a troop of chacma baboons (Papio ursinus). First, we describe how local patterns (inter-neighbour distances and positions), and group patterns (group shape, speed and polarization) during collective motion differ across each system. Based on these, we place data from each species within a 'swarm space', affording comparisons and generating predictions about the collective motion across species and contexts. We encourage researchers to add their own data to update the 'swarm space' for future comparative work. Second, we investigate intraspecific variation in collective motion over time and provide guidance for researchers on when observations made over different time scales can result in confident inferences regarding species collective motion. This article is part of a discussion meeting issue 'Collective behaviour through time'.
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Affiliation(s)
- Marina Papadopoulou
- Biosciences, School of Biosciences, Geography and Physics, Faculty of Science and Engineering, Swansea University, SA2 8PP Swansea, UK
| | - Ines Fürtbauer
- Biosciences, School of Biosciences, Geography and Physics, Faculty of Science and Engineering, Swansea University, SA2 8PP Swansea, UK
| | - Lisa R. O'Bryan
- Department of Psychological Sciences, Rice University, Houston, TX 77005, USA
| | - Simon Garnier
- Department of Biological Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Dimitra G. Georgopoulou
- Biosciences, School of Biosciences, Geography and Physics, Faculty of Science and Engineering, Swansea University, SA2 8PP Swansea, UK
- Institute of Marine Biology, Biotechnology & Aquaculture, HCMR, 71500 Hersonissos, Crete, Greece
| | - Anna M. Bracken
- Biosciences, School of Biosciences, Geography and Physics, Faculty of Science and Engineering, Swansea University, SA2 8PP Swansea, UK
- School of Biodiversity, One Health and Veterinary Medicine, Graham Kerr Building, Glasgow G12 8QQ, UK
| | - Charlotte Christensen
- Biosciences, School of Biosciences, Geography and Physics, Faculty of Science and Engineering, Swansea University, SA2 8PP Swansea, UK
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, 8057 Zürich, Switzerland
| | - Andrew J. King
- Biosciences, School of Biosciences, Geography and Physics, Faculty of Science and Engineering, Swansea University, SA2 8PP Swansea, UK
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9
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Ioannou CC, Laskowski KL. A multi-scale review of the dynamics of collective behaviour: from rapid responses to ontogeny and evolution. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220059. [PMID: 36802782 PMCID: PMC9939272 DOI: 10.1098/rstb.2022.0059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 01/26/2023] [Indexed: 02/21/2023] Open
Abstract
Collective behaviours, such as flocking in birds or decision making by bee colonies, are some of the most intriguing behavioural phenomena in the animal kingdom. The study of collective behaviour focuses on the interactions between individuals within groups, which typically occur over close ranges and short timescales, and how these interactions drive larger scale properties such as group size, information transfer within groups and group-level decision making. To date, however, most studies have focused on snapshots, typically studying collective behaviour over short timescales up to minutes or hours. However, being a biological trait, much longer timescales are important in animal collective behaviour, particularly how individuals change over their lifetime (the domain of developmental biology) and how individuals change from one generation to the next (the domain of evolutionary biology). Here, we give an overview of collective behaviour across timescales from the short to the long, illustrating how a full understanding of this behaviour in animals requires much more research attention on its developmental and evolutionary biology. Our review forms the prologue of this special issue, which addresses and pushes forward understanding the development and evolution of collective behaviour, encouraging a new direction for collective behaviour research. This article is part of a discussion meeting issue 'Collective behaviour through time'.
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Affiliation(s)
| | - Kate L. Laskowski
- Department of Evolution and Ecology, University of California Davis, Davis, CA 95616, USA
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10
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Sridhar VH, Davidson JD, Twomey CR, Sosna MMG, Nagy M, Couzin ID. Inferring social influence in animal groups across multiple timescales. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220062. [PMID: 36802787 PMCID: PMC9939267 DOI: 10.1098/rstb.2022.0062] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023] Open
Abstract
Many animal behaviours exhibit complex temporal dynamics, suggesting there are multiple timescales at which they should be studied. However, researchers often focus on behaviours that occur over relatively restricted temporal scales, typically ones that are more accessible to human observation. The situation becomes even more complex when considering multiple animals interacting, where behavioural coupling can introduce new timescales of importance. Here, we present a technique to study the time-varying nature of social influence in mobile animal groups across multiple temporal scales. As case studies, we analyse golden shiner fish and homing pigeons, which move in different media. By analysing pairwise interactions among individuals, we show that predictive power of the factors affecting social influence depends on the timescale of analysis. Over short timescales the relative position of a neighbour best predicts its influence and the distribution of influence across group members is relatively linear, with a small slope. At longer timescales, however, both relative position and kinematics are found to predict influence, and nonlinearity in the influence distribution increases, with a small number of individuals being disproportionately influential. Our results demonstrate that different interpretations of social influence arise from analysing behaviour at different timescales, highlighting the importance of considering its multiscale nature. This article is part of a discussion meeting issue 'Collective behaviour through time'.
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Affiliation(s)
- Vivek H. Sridhar
- Department of Biology, University of Konstanz, 78464 Konstanz, Germany,Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany,Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany,Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, 78467 Konstanz, Germany
| | - Jacob D. Davidson
- Department of Biology, University of Konstanz, 78464 Konstanz, Germany,Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany,Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany
| | - Colin R. Twomey
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA,Mind Center for Outreach, Research, and Education, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew M. G. Sosna
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Máté Nagy
- Department of Biology, University of Konstanz, 78464 Konstanz, Germany,Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany,Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany,MTA-ELTE Statistical and Biological Physics Research Group, Hungarian Academy of Sciences, Budapest 1117, Hungary,MTA-ELTE ‘Lendület’ Collective Behaviour Research Group, Hungarian Academy of Sciences, Eötvös Loránd University, Budapest 1117, Hungary,Department of Biological Physics, Eötvös Loránd University, Pázmány Péter sétány 1A, Budapest 1117, Hungary
| | - Iain D. Couzin
- Department of Biology, University of Konstanz, 78464 Konstanz, Germany,Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany,Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany
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11
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King AJ, Portugal SJ, Strömbom D, Mann RP, Carrillo JA, Kalise D, de Croon G, Barnett H, Scerri P, Groß R, Chadwick DR, Papadopoulou M. Biologically inspired herding of animal groups by robots. Methods Ecol Evol 2023. [DOI: 10.1111/2041-210x.14049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Andrew J. King
- Department of Biosciences, Faculty of Science and Engineering Swansea University Swansea UK
| | - Steven J. Portugal
- Department of Biological Sciences, School of Life and Environmental Sciences Royal Holloway University of London Egham UK
| | - Daniel Strömbom
- Department of Biology Lafayette College Easton Pennsylvania USA
| | - Richard P. Mann
- Department of Statistics, School of Mathematics University of Leeds Leeds UK
| | | | - Dante Kalise
- Department of Mathematics Imperial College London London UK
| | - Guido de Croon
- Faculty of Aerospace Engineering Delft University of Technology Delft The Netherlands
| | - Heather Barnett
- Central Saint Martins University of the Arts London London UK
| | - Paul Scerri
- Perceptronics Solutions Los Angeles California USA
| | - Roderich Groß
- Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield UK
| | - David R. Chadwick
- Environment Centre Wales, School of Natural Sciences Bangor University Bangor UK
| | - Marina Papadopoulou
- Department of Biosciences, Faculty of Science and Engineering Swansea University Swansea UK
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12
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Abstract
Despite significant efforts devoted to understanding the underlying complexity and emergence of collective movement in animal groups, the role of different external settings on this type of movement remains largely unexplored. Here, by combining time series analysis and complex network tools, we present an extensive investigation of the effects of shady environments on the behavior of a fish species (Silver Carp Hypophthalmichthys molitrix) within earthen ponds. We find that shade encourages fish residence during daylight hours, but the degree of preference for shade varies substantially among trials and ponds. Silver Carp are much slower and exhibit lower persistence in their speeds when under shade than out of it during daytime and nighttime, with fish displaying the highest persistence degree and speeds at night. Furthermore, our research shows that shade affects fish schooling behavior by reducing their polarization, number of interactions among individuals, and the stability among local neighbors; however, fish keep a higher local degree of order when under shade compared to nighttime positions.
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13
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Joshi V, Popp S, Werfel J, McCreery HF. Alignment with neighbours enables escape from dead ends in flocking models. J R Soc Interface 2022; 19:20220356. [PMID: 35975561 PMCID: PMC9382454 DOI: 10.1098/rsif.2022.0356] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/25/2022] [Indexed: 11/12/2022] Open
Abstract
Coordinated movement in animal groups (flocks, schools, herds, etc.) is a classic and well-studied form of collective behaviour. Most theoretical studies consider agents in unobstructed spaces; however, many animals move in often complicated environments and must navigate around and through obstacles. Here we consider simulated agents behaving according to typical flocking rules, with the addition of repulsion from obstacles, and study their collective behaviour in environments with concave obstacles (dead ends). We find that groups of such agents heading for a goal can spontaneously escape dead ends without wall-following or other specialized behaviours, in what we term 'flocking escapes'. The mechanism arises when agents align with one another while heading away from the goal, forming a self-stable cluster that persists long enough to exit the obstacle and avoids becoming trapped again when turning back towards the goal. Solitary agents under the same conditions are never observed to escape. We show that alignment with neighbours reduces the effective turning speed of the group while letting individuals maintain high manoeuvrability when needed. The relative robustness of flocking escapes in our studies suggests that this emergent behaviour may be relevant for a variety of animal species.
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Affiliation(s)
- Varun Joshi
- School of Kinesiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Stefan Popp
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721, USA
| | - Justin Werfel
- School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | - Helen F. McCreery
- School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
- Biology Department, University of Massachusetts, Boston, MA 02125, USA
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14
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Pigeon leadership hierarchies are not dependent on environmental contexts or individual phenotypes. Behav Processes 2022; 198:104629. [DOI: 10.1016/j.beproc.2022.104629] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/10/2022] [Accepted: 03/25/2022] [Indexed: 01/03/2023]
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15
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Papadopoulou M, Hildenbrandt H, Sankey DWE, Portugal SJ, Hemelrijk CK. Emergence of splits and collective turns in pigeon flocks under predation. ROYAL SOCIETY OPEN SCIENCE 2022; 9:211898. [PMID: 35223068 PMCID: PMC8864349 DOI: 10.1098/rsos.211898] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 01/25/2022] [Indexed: 05/03/2023]
Abstract
Complex patterns of collective behaviour may emerge through self-organization, from local interactions among individuals in a group. To understand what behavioural rules underlie these patterns, computational models are often necessary. These rules have not yet been systematically studied for bird flocks under predation. Here, we study airborne flocks of homing pigeons attacked by a robotic falcon, combining empirical data with a species-specific computational model of collective escape. By analysing GPS trajectories of flocking individuals, we identify two new patterns of collective escape: early splits and collective turns, occurring even at large distances from the predator. To examine their formation, we extend an agent-based model of pigeons with a 'discrete' escape manoeuvre by a single initiator, namely a sudden turn interrupting the continuous coordinated motion of the group. Both splits and collective turns emerge from this rule. Their relative frequency depends on the angular velocity and position of the initiator in the flock: sharp turns by individuals at the periphery lead to more splits than collective turns. We confirm this association in the empirical data. Our study highlights the importance of discrete and uncoordinated manoeuvres in the collective escape of bird flocks and advocates the systematic study of their patterns across species.
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Affiliation(s)
- Marina Papadopoulou
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
| | - Hanno Hildenbrandt
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
| | | | - Steven J. Portugal
- Department of Biological Sciences, School of Life and Environmental Sciences, Royal Holloway University of London, Egham, UK
| | - Charlotte K. Hemelrijk
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
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