1
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Kuntz G, Huang J, Rask M, Lindgren-Ruby A, Shinsato JY, Bi D, Tabatabai AP. Spatial confinement affects the heterogeneity and interactions between shoaling fish. Sci Rep 2024; 14:12296. [PMID: 38811673 DOI: 10.1038/s41598-024-63245-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 05/27/2024] [Indexed: 05/31/2024] Open
Abstract
Living objects are able to consume chemical energy and process information independently from others. However, living objects can coordinate to form ordered groups such as schools of fish. This work considers these complex groups as living materials and presents imaging-based experiments of laboratory schools of fish to understand how activity, which is a non-equilibrium feature, affects the structure and dynamics of a group. We use spatial confinement to control the motion and structure of fish within quasi-2D shoals of fish and use image analysis techniques to make quantitative observations of the structures, their spatial heterogeneity, and their temporal fluctuations. Furthermore, we utilize Monte Carlo simulations to replicate the experimentally observed data which provides insight into the effective interactions between fish and confirms the presence of a confinement-based behavioral preference transition. In addition, unlike in short-range interacting systems, here structural heterogeneity and dynamic activities are positively correlated as a result of complex interplay between spatial arrangement and behavioral dynamics in fish collectives.
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Affiliation(s)
- Gabriel Kuntz
- Department of Physics, Seattle University, Seattle, WA, 98122, USA
| | - Junxiang Huang
- Department of Physics, Northeastern University, Boston, MA, 02115, USA
| | - Mitchell Rask
- Department of Physics, Seattle University, Seattle, WA, 98122, USA
| | | | - Jacob Y Shinsato
- Department of Physics, Seattle University, Seattle, WA, 98122, USA
| | - Dapeng Bi
- Department of Physics, Northeastern University, Boston, MA, 02115, USA
| | - A Pasha Tabatabai
- Department of Physics, Seattle University, Seattle, WA, 98122, USA.
- Physics Department, California Polytechnic State University, San Luis Obispo, CA, 93410, USA.
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2
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Friston KJ, Parr T, Heins C, Constant A, Friedman D, Isomura T, Fields C, Verbelen T, Ramstead M, Clippinger J, Frith CD. Federated inference and belief sharing. Neurosci Biobehav Rev 2024; 156:105500. [PMID: 38056542 PMCID: PMC11139662 DOI: 10.1016/j.neubiorev.2023.105500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/08/2023] [Accepted: 12/01/2023] [Indexed: 12/08/2023]
Abstract
This paper concerns the distributed intelligence or federated inference that emerges under belief-sharing among agents who share a common world-and world model. Imagine, for example, several animals keeping a lookout for predators. Their collective surveillance rests upon being able to communicate their beliefs-about what they see-among themselves. But, how is this possible? Here, we show how all the necessary components arise from minimising free energy. We use numerical studies to simulate the generation, acquisition and emergence of language in synthetic agents. Specifically, we consider inference, learning and selection as minimising the variational free energy of posterior (i.e., Bayesian) beliefs about the states, parameters and structure of generative models, respectively. The common theme-that attends these optimisation processes-is the selection of actions that minimise expected free energy, leading to active inference, learning and model selection (a.k.a., structure learning). We first illustrate the role of communication in resolving uncertainty about the latent states of a partially observed world, on which agents have complementary perspectives. We then consider the acquisition of the requisite language-entailed by a likelihood mapping from an agent's beliefs to their overt expression (e.g., speech)-showing that language can be transmitted across generations by active learning. Finally, we show that language is an emergent property of free energy minimisation, when agents operate within the same econiche. We conclude with a discussion of various perspectives on these phenomena; ranging from cultural niche construction, through federated learning, to the emergence of complexity in ensembles of self-organising systems.
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Affiliation(s)
- Karl J Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK; VERSES AI Research Lab, Los Angeles, CA 90016, USA.
| | - Thomas Parr
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK
| | - Conor Heins
- VERSES AI Research Lab, Los Angeles, CA 90016, USA; Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78457 Konstanz, Germany; Centre for the Advanced Study of Collective Behaviour, 78457 Konstanz, Germany; Department of Biology, University of Konstanz, 78457 Konstanz, Germany
| | - Axel Constant
- VERSES AI Research Lab, Los Angeles, CA 90016, USA; School of Engineering and Informatics, The University of Sussex, Brighton, UK
| | - Daniel Friedman
- Department of Entomology and Nematology, University of California, Davis, Davis, CA, USA; Active Inference Institute, Davis, CA 95616, USA
| | - Takuya Isomura
- Brain Intelligence Theory Unit, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
| | - Chris Fields
- Allen Discovery Center at Tufts University, Medford, MA 02155, USA
| | - Tim Verbelen
- VERSES AI Research Lab, Los Angeles, CA 90016, USA
| | - Maxwell Ramstead
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK; VERSES AI Research Lab, Los Angeles, CA 90016, USA
| | | | - Christopher D Frith
- Institute of Philosophy, School of Advanced Studies, University of London, UK
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3
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Hawkins RD, Berdahl AM, Pentland A'S, Tenenbaum JB, Goodman ND, Krafft PM. Flexible social inference facilitates targeted social learning when rewards are not observable. Nat Hum Behav 2023; 7:1767-1776. [PMID: 37591983 DOI: 10.1038/s41562-023-01682-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 07/20/2023] [Indexed: 08/19/2023]
Abstract
Groups coordinate more effectively when individuals are able to learn from others' successes. But acquiring such knowledge is not always easy, especially in real-world environments where success is hidden from public view. We suggest that social inference capacities may help bridge this gap, allowing individuals to update their beliefs about others' underlying knowledge and success from observable trajectories of behaviour. We compared our social inference model against simpler heuristics in three studies of human behaviour in a collective-sensing task. Experiment 1 demonstrated that average performance improved as a function of group size at a rate greater than predicted by heuristic models. Experiment 2 introduced artificial agents to evaluate how individuals selectively rely on social information. Experiment 3 generalized these findings to a more complex reward landscape. Taken together, our findings provide insight into the relationship between individual social cognition and the flexibility of collective behaviour.
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Affiliation(s)
- Robert D Hawkins
- Department of Psychology, Stanford University, Stanford, CA, USA.
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA.
| | - Andrew M Berdahl
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, USA
| | | | | | - Noah D Goodman
- Department of Psychology, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - P M Krafft
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Creative Computing Institute, University of Arts London, London, UK
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4
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Galesic M, Barkoczi D, Berdahl AM, Biro D, Carbone G, Giannoccaro I, Goldstone RL, Gonzalez C, Kandler A, Kao AB, Kendal R, Kline M, Lee E, Massari GF, Mesoudi A, Olsson H, Pescetelli N, Sloman SJ, Smaldino PE, Stein DL. Beyond collective intelligence: Collective adaptation. J R Soc Interface 2023; 20:20220736. [PMID: 36946092 PMCID: PMC10031425 DOI: 10.1098/rsif.2022.0736] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 02/27/2023] [Indexed: 03/23/2023] Open
Abstract
We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for 'intelligent' collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.
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Affiliation(s)
- Mirta Galesic
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Complexity Science Hub Vienna, 1080 Vienna, Austria
- Vermont Complex Systems Center, University of Vermont, Burlington, VM 05405, USA
| | | | - Andrew M. Berdahl
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195, USA
| | - Dora Biro
- Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
| | - Giuseppe Carbone
- Department of Mechanics, Mathematics and Management, Politecnico di Bari, Bari 70125, Italy
| | - Ilaria Giannoccaro
- Department of Mechanics, Mathematics and Management, Politecnico di Bari, Bari 70125, Italy
| | - Robert L. Goldstone
- Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Cleotilde Gonzalez
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Anne Kandler
- Department of Mathematics, Max-Planck-Institute for Evolutionary Anthropology, Leipzig 04103, Germany
| | - Albert B. Kao
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Biology Department, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Rachel Kendal
- Centre for Coevolution of Biology and Culture, Durham University, Anthropology Department, Durham, DH1 3LE, UK
| | - Michelle Kline
- Centre for Culture and Evolution, Division of Psychology, Brunel University London, Uxbridge, UB8 3PH, UK
| | - Eun Lee
- Department of Scientific Computing, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan, 48513, Republic of Korea
| | | | - Alex Mesoudi
- Department of Ecology and Conservation, University of Exeter, Penryn TR10 9FE, UK
| | | | | | - Sabina J. Sloman
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Department of Computer Science, University of Manchester, Manchester, M13 9PL, UK
| | - Paul E. Smaldino
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Department of Cognitive and Information Sciences, University of California, Merced, CA 95343, USA
| | - Daniel L. Stein
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Department of Physics and Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA
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5
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Abstract
The analogy of mitochondria as powerhouses has expired. Mitochondria are living, dynamic, maternally inherited, energy-transforming, biosynthetic, and signaling organelles that actively transduce biological information. We argue that mitochondria are the processor of the cell, and together with the nucleus and other organelles they constitute the mitochondrial information processing system (MIPS). In a three-step process, mitochondria (1) sense and respond to both endogenous and environmental inputs through morphological and functional remodeling; (2) integrate information through dynamic, network-based physical interactions and diffusion mechanisms; and (3) produce output signals that tune the functions of other organelles and systemically regulate physiology. This input-to-output transformation allows mitochondria to transduce metabolic, biochemical, neuroendocrine, and other local or systemic signals that enhance organismal adaptation. An explicit focus on mitochondrial signal transduction emphasizes the role of communication in mitochondrial biology. This framework also opens new avenues to understand how mitochondria mediate inter-organ processes underlying human health.
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Affiliation(s)
- Martin Picard
- Department of Psychiatry, Division of Behavioral Medicine, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Neurology, H. Houston Merritt Center, Columbia Translational Neuroscience Initiative, Columbia University Irving Medical Center, New York, NY 10032, USA; New York State Psychiatric Institute, New York, NY 10032, USA.
| | - Orian S Shirihai
- Department of Medicine, Endocrinology, and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Metabolism Theme, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
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6
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Horsevad N, Kwa HL, Bouffanais R. Beyond Bio-Inspired Robotics: How Multi-Robot Systems Can Support Research on Collective Animal Behavior. Front Robot AI 2022; 9:865414. [PMID: 35795475 PMCID: PMC9252458 DOI: 10.3389/frobt.2022.865414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 05/11/2022] [Indexed: 11/17/2022] Open
Abstract
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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Affiliation(s)
| | - Hian Lee Kwa
- Singapore University of Technology and Design, Singapore, Singapore
- Thales Solutions Asia, Singapore, Singapore
| | - Roland Bouffanais
- University of Ottawa, Ottawa, ON, Canada
- *Correspondence: Roland Bouffanais,
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7
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Fernández Velasco P. Group navigation and procedural metacognition. PHILOSOPHICAL PSYCHOLOGY 2022. [DOI: 10.1080/09515089.2022.2062316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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8
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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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9
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Kashetsky T, Avgar T, Dukas R. The Cognitive Ecology of Animal Movement: Evidence From Birds and Mammals. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.724887] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Cognition, defined as the processes concerned with the acquisition, retention and use of information, underlies animals’ abilities to navigate their local surroundings, embark on long-distance seasonal migrations, and socially learn information relevant to movement. Hence, in order to fully understand and predict animal movement, researchers must know the cognitive mechanisms that generate such movement. Work on a few model systems indicates that most animals possess excellent spatial learning and memory abilities, meaning that they can acquire and later recall information about distances and directions among relevant objects. Similarly, field work on several species has revealed some of the mechanisms that enable them to navigate over distances of up to several thousand kilometers. Key behaviors related to movement such as the choice of nest location, home range location and migration route are often affected by parents and other conspecifics. In some species, such social influence leads to the formation of aggregations, which in turn may lead to further social learning about food locations or other resources. Throughout the review, we note a variety of topics at the interface of cognition and movement that invite further investigation. These include the use of social information embedded in trails, the likely important roles of soundscapes and smellscapes, the mechanisms that large mammals rely on for long-distance migration, and the effects of expertise acquired over extended periods.
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10
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Davidson JD, Sosna MMG, Twomey CR, Sridhar VH, Leblanc SP, Couzin ID. Collective detection based on visual information in animal groups. J R Soc Interface 2021; 18:20210142. [PMID: 34229461 PMCID: PMC8261228 DOI: 10.1098/rsif.2021.0142] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 06/10/2021] [Indexed: 01/14/2023] Open
Abstract
We investigate key principles underlying individual, and collective, visual detection of stimuli, and how this relates to the internal structure of groups. While the individual and collective detection principles are generally applicable, we employ a model experimental system of schooling golden shiner fish (Notemigonus crysoleucas) to relate theory directly to empirical data, using computational reconstruction of the visual fields of all individuals. This reveals how the external visual information available to each group member depends on the number of individuals in the group, the position within the group, and the location of the external visually detectable stimulus. We find that in small groups, individuals have detection capability in nearly all directions, while in large groups, occlusion by neighbours causes detection capability to vary with position within the group. To understand the principles that drive detection in groups, we formulate a simple, and generally applicable, model that captures how visual detection properties emerge due to geometric scaling of the space occupied by the group and occlusion caused by neighbours. We employ these insights to discuss principles that extend beyond our specific system, such as how collective detection depends on individual body shape, and the size and structure of the group.
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Affiliation(s)
- Jacob D. Davidson
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Matthew M. G. Sosna
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Colin R. Twomey
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
- Mind Center for Outreach, Research, and Education, University of Pennsylvania, Philadelphia, PA, USA
| | - Vivek H. Sridhar
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Simon P. Leblanc
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Iain D. Couzin
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
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11
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Roundtree KA, Cody JR, Leaf J, Demirel HO, Adams JA. Human-collective visualization transparency. SWARM INTELLIGENCE 2021. [DOI: 10.1007/s11721-021-00194-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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12
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Klamser PP, Romanczuk P. Collective predator evasion: Putting the criticality hypothesis to the test. PLoS Comput Biol 2021; 17:e1008832. [PMID: 33720926 PMCID: PMC7993868 DOI: 10.1371/journal.pcbi.1008832] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 03/25/2021] [Accepted: 02/24/2021] [Indexed: 11/19/2022] Open
Abstract
According to the criticality hypothesis, collective biological systems should operate in a special parameter region, close to so-called critical points, where the collective behavior undergoes a qualitative change between different dynamical regimes. Critical systems exhibit unique properties, which may benefit collective information processing such as maximal responsiveness to external stimuli. Besides neuronal and gene-regulatory networks, recent empirical data suggests that also animal collectives may be examples of self-organized critical systems. However, open questions about self-organization mechanisms in animal groups remain: Evolutionary adaptation towards a group-level optimum (group-level selection), implicitly assumed in the "criticality hypothesis", appears in general not reasonable for fission-fusion groups composed of non-related individuals. Furthermore, previous theoretical work relies on non-spatial models, which ignore potentially important self-organization and spatial sorting effects. Using a generic, spatially-explicit model of schooling prey being attacked by a predator, we show first that schools operating at criticality perform best. However, this is not due to optimal response of the prey to the predator, as suggested by the "criticality hypothesis", but rather due to the spatial structure of the prey school at criticality. Secondly, by investigating individual-level evolution, we show that strong spatial self-sorting effects at the critical point lead to strong selection gradients, and make it an evolutionary unstable state. Our results demonstrate the decisive role of spatio-temporal phenomena in collective behavior, and that individual-level selection is in general not a viable mechanism for self-tuning of unrelated animal groups towards criticality.
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Affiliation(s)
- Pascal P. Klamser
- Department of Biology, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Pawel Romanczuk
- Department of Biology, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
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13
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Ogawa S, Pfaff DW, Parhar IS. Fish as a model in social neuroscience: conservation and diversity in the social brain network. Biol Rev Camb Philos Soc 2021; 96:999-1020. [PMID: 33559323 DOI: 10.1111/brv.12689] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 01/19/2021] [Accepted: 01/20/2021] [Indexed: 12/21/2022]
Abstract
Mechanisms for fish social behaviours involve a social brain network (SBN) which is evolutionarily conserved among vertebrates. However, considerable diversity is observed in the actual behaviour patterns amongst nearly 30000 fish species. The huge variation found in socio-sexual behaviours and strategies is likely generated by a morphologically and genetically well-conserved small forebrain system. Hence, teleost fish provide a useful model to study the fundamental mechanisms underlying social brain functions. Herein we review the foundations underlying fish social behaviours including sensory, hormonal, molecular and neuroanatomical features. Gonadotropin-releasing hormone neurons clearly play important roles, but the participation of vasotocin and isotocin is also highlighted. Genetic investigations of developing fish brain have revealed the molecular complexity of neural development of the SBN. In addition to straightforward social behaviours such as sex and aggression, new experiments have revealed higher order and unique phenomena such as social eavesdropping and social buffering in fish. Finally, observations interpreted as 'collective cognition' in fish can likely be explained by careful observation of sensory determinants and analyses using the dynamics of quantitative scaling. Understanding of the functions of the SBN in fish provide clues for understanding the origin and evolution of higher social functions in vertebrates.
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Affiliation(s)
- Satoshi Ogawa
- Brain Research Institute, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway, Selangor, 47500, Malaysia
| | - Donald W Pfaff
- Laboratory of Neurobiology and Behavior, Rockefeller University, New York, NY, 10065, U.S.A
| | - Ishwar S Parhar
- Brain Research Institute, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway, Selangor, 47500, Malaysia
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14
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Ribeiro TL, Chialvo DR, Plenz D. Scale-Free Dynamics in Animal Groups and Brain Networks. Front Syst Neurosci 2021; 14:591210. [PMID: 33551759 PMCID: PMC7854533 DOI: 10.3389/fnsys.2020.591210] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 12/21/2020] [Indexed: 11/13/2022] Open
Abstract
Collective phenomena fascinate by the emergence of order in systems composed of a myriad of small entities. They are ubiquitous in nature and can be found over a vast range of scales in physical and biological systems. Their key feature is the seemingly effortless emergence of adaptive collective behavior that cannot be trivially explained by the properties of the system's individual components. This perspective focuses on recent insights into the similarities of correlations for two apparently disparate phenomena: flocking in animal groups and neuronal ensemble activity in the brain. We first will summarize findings on the spontaneous organization in bird flocks and macro-scale human brain activity utilizing correlation functions and insights from critical dynamics. We then will discuss recent experimental findings that apply these approaches to the collective response of neurons to visual and motor processing, i.e., to local perturbations of neuronal networks at the meso- and microscale. We show how scale-free correlation functions capture the collective organization of neuronal avalanches in evoked neuronal populations in nonhuman primates and between neurons during visual processing in rodents. These experimental findings suggest that the coherent collective neural activity observed at scales much larger than the length of the direct neuronal interactions is demonstrative of a phase transition and we discuss the experimental support for either discontinuous or continuous phase transitions. We conclude that at or near a phase-transition neuronal information can propagate in the brain with similar efficiency as proposed to occur in the collective adaptive response observed in some animal groups.
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Affiliation(s)
- Tiago L. Ribeiro
- Section on Critical Brain Dynamics, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Dante R. Chialvo
- Center for Complex Systems and Brain Sciences (CEMSC3), Instituto de Ciencias Físicas, (ICIFI) Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín (UNSAM), Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Dietmar Plenz
- Section on Critical Brain Dynamics, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
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15
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Kotrschal A, Szorkovszky A, Herbert-Read J, Bloch NI, Romenskyy M, Buechel SD, Eslava AF, Alòs LS, Zeng H, Le Foll A, Braux G, Pelckmans K, Mank JE, Sumpter D, Kolm N. Rapid evolution of coordinated and collective movement in response to artificial selection. SCIENCE ADVANCES 2020; 6:6/49/eaba3148. [PMID: 33268362 PMCID: PMC7710366 DOI: 10.1126/sciadv.aba3148] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 10/21/2020] [Indexed: 05/28/2023]
Abstract
Collective motion occurs when individuals use social interaction rules to respond to the movements and positions of their neighbors. How readily these social decisions are shaped by selection remains unknown. Through artificial selection on fish (guppies, Poecilia reticulata) for increased group polarization, we demonstrate rapid evolution in how individuals use social interaction rules. Within only three generations, groups of polarization-selected females showed a 15% increase in polarization, coupled with increased cohesiveness, compared to fish from control lines. Although lines did not differ in their physical swimming ability or exploratory behavior, polarization-selected fish adopted faster speeds, particularly in social contexts, and showed stronger alignment and attraction responses to multiple neighbors. Our results reveal the social interaction rules that change when collective behavior evolves.
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Affiliation(s)
- Alexander Kotrschal
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden.
- Behavioural Ecology, Wageningen University, Wageningen, Netherlands
| | - Alexander Szorkovszky
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden
- Department of Mathematics, Uppsala University, Uppsala, Sweden
| | - James Herbert-Read
- Department of Zoology, University of Cambridge, Cambridge, UK
- Aquatic Ecology, Lund University, Lund, Sweden
| | - Natasha I Bloch
- Department of Biomedical Engineering, University of Los Andes, Bogotá, Colombia
| | - Maksym Romenskyy
- Department of Life Sciences, Imperial College London, London, UK
| | | | - Ada Fontrodona Eslava
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden
- Centre for Biological Diversity, University of St. Andrews, St. Andrews, UK
| | | | - Hongli Zeng
- School of Science, Nanjing University of Posts and Telecommmunications, Nanjing, China
| | - Audrey Le Foll
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden
| | - Ganaël Braux
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden
| | | | - Judith E Mank
- University College London, London, UK
- Department of Zoology, University of British Columbia, Vancouver, Canada
| | - David Sumpter
- Department of Mathematics, Uppsala University, Uppsala, Sweden
| | - Niclas Kolm
- Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden
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16
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Guerra AS, Kao AB, McCauley DJ, Berdahl AM. Fisheries-induced selection against schooling behaviour in marine fishes. Proc Biol Sci 2020; 287:20201752. [PMID: 32993472 DOI: 10.1098/rspb.2020.1752] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Group living is a common strategy used by fishes to improve their fitness. While sociality is associated with many benefits in natural environments, including predator avoidance, this behaviour may be maladaptive in the Anthropocene. Humans have become the dominant predator in many marine systems, with modern fishing gear developed to specifically target groups of schooling species. Therefore, ironically, behavioural strategies which evolved to avoid non-human predators may now actually make certain fish more vulnerable to predation by humans. Here, we use an individual-based model to explore the evolution of fish schooling behaviour in a range of environments, including natural and human-dominated predation conditions. In our model, individual fish may leave or join groups depending on their group-size preferences, but their experienced group size is also a function of the preferences of others in the population. Our model predicts that industrial fishing selects against individual-level behaviours that produce large groups. However, the relationship between fishing pressure and sociality is nonlinear, and we observe discontinuities and hysteresis as fishing pressure is increased or decreased. Our results suggest that industrial fishing practices could be altering fishes' tendency to school, and that social behaviour should be added to the list of traits subject to fishery-induced evolution.
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Affiliation(s)
- Ana Sofia Guerra
- Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, CA 93106, USA
| | - Albert B Kao
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.,Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Douglas J McCauley
- Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, CA 93106, USA
| | - Andrew M Berdahl
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195, USA
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17
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Cheng L, Zhou L, Bao Y, Mahtab N. Effect of conspecific neighbors on the foraging activity levels of the wintering Oriental Storks ( Ciconia boyciana): Benefits of social information. Ecol Evol 2020; 10:10384-10394. [PMID: 33072267 PMCID: PMC7548187 DOI: 10.1002/ece3.6693] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 07/15/2020] [Accepted: 07/29/2020] [Indexed: 11/07/2022] Open
Abstract
Animals prefer to aggregate in patches with high abundance and availability of food resources. Group foragers typically receive information about food resources by monitoring external events and the behavior of neighbors. The Information Centre Hypothesis proposes that aggregations increase foraging activity levels as a result of social information provided by conspecifics. Increasing the foraging rate has as a result decreasing time devoted to anti-predator vigilance and may intensify competition among group members. Studies have shown that foraging activities are influenced by factors other than flock size, such as the number and foraging intensity of neighbors. To test these hypotheses, we examined the effect of number and foraging intensity of neighbors on the foraging activity levels (foraging rate, foraging effort, and foraging success rate) of the wintering Oriental Storks (Ciconia boyciana). In this study, we collected focal sampling data on the foraging behavior of storks at Shengjin Lake during winter from 2017 to 2019, controlling the effects of other variables (group identity, wintering years, and wintering periods). We found that foraging activity levels were higher in the presence of foraging neighbors than in their absence. Moreover, individuals adjusted their foraging activity levels according to social information gathered from the behavior of neighboring conspecifics. Focal individuals' foraging rate and foraging effort were positively correlated with the average foraging rate of neighbors. Their foraging success rate was not influenced by the average foraging rate and foraging success rate of neighbors; however, it was positively correlated with the average foraging effort of neighbors. In conclusion, foraging activity levels of individuals are primarily driven by the intensity of the foraging activity of neighbors. This result differs from the results of previous studies that suggested that flock size was the most important factor determining individual foraging activity levels.
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Affiliation(s)
- Lei Cheng
- School of Resources and Environmental Engineering Anhui University Hefei China.,Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration (Anhui University) Hefei China.,Anhui Biodiversity Information Center Anhui University Hefei China
| | - Lizhi Zhou
- School of Resources and Environmental Engineering Anhui University Hefei China.,Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration (Anhui University) Hefei China.,Anhui Biodiversity Information Center Anhui University Hefei China
| | - Yiwei Bao
- School of Resources and Environmental Engineering Anhui University Hefei China.,Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration (Anhui University) Hefei China.,Anhui Biodiversity Information Center Anhui University Hefei China
| | - Nazia Mahtab
- School of Resources and Environmental Engineering Anhui University Hefei China.,Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration (Anhui University) Hefei China.,Anhui Biodiversity Information Center Anhui University Hefei China
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18
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Harpaz R, Schneidman E. Social interactions drive efficient foraging and income equality in groups of fish. eLife 2020; 9:e56196. [PMID: 32838839 PMCID: PMC7492088 DOI: 10.7554/elife.56196] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 08/05/2020] [Indexed: 12/14/2022] Open
Abstract
The social interactions underlying group foraging and their benefits have been mostly studied using mechanistic models replicating qualitative features of group behavior, and focused on a single resource or a few clustered ones. Here, we tracked groups of freely foraging adult zebrafish with spatially dispersed food items and found that fish perform stereotypical maneuvers when consuming food, which attract neighboring fish. We then present a mathematical model, based on inferred functional interactions between fish, which accurately describes individual and group foraging of real fish. We show that these interactions allow fish to combine individual and social information to achieve near-optimal foraging efficiency and promote income equality within groups. We further show that the interactions that would maximize efficiency in these social foraging models depend on group size, but not on food distribution, and hypothesize that fish may adaptively pick the subgroup of neighbors they 'listen to' to determine their own behavior.
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Affiliation(s)
- Roy Harpaz
- Department of Neurobiology, Weizmann Institute of ScienceRehovotIsrael
- Department of Molecular and Cellular Biology, Harvard UniversityCambridge MAUnited States
| | - Elad Schneidman
- Department of Neurobiology, Weizmann Institute of ScienceRehovotIsrael
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19
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Ding SS, Muhle LS, Brown AEX, Schumacher LJ, Endres RG. Comparison of solitary and collective foraging strategies of Caenorhabditis elegans in patchy food distributions. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190382. [PMID: 32713303 DOI: 10.1098/rstb.2019.0382] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Collective foraging has been shown to benefit organisms in environments where food is patchily distributed, but whether this is true in the case where organisms do not rely on long-range communications to coordinate their collective behaviour has been understudied. To address this question, we use the tractable laboratory model organism Caenorhabditis elegans, where a social strain (npr-1 mutant) and a solitary strain (N2) are available for direct comparison of foraging strategies. We first developed an on-lattice minimal model for comparing collective and solitary foraging strategies, finding that social agents benefit from feeding faster and more efficiently simply owing to group formation. Our laboratory foraging experiments with npr-1 and N2 worm populations, however, show an advantage for solitary N2 in all food distribution environments that we tested. We incorporated additional strain-specific behavioural parameters of npr-1 and N2 worms into our model and computationally identified N2's higher feeding rate to be the key factor underlying its advantage, without which it is possible to recapitulate the advantage of collective foraging in patchy environments. Our work highlights the theoretical advantage of collective foraging owing to group formation alone without long-range interactions and the valuable role of modelling to guide experiments. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.
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Affiliation(s)
- Siyu Serena Ding
- Institute of Clinical Sciences, Imperial College London, London, UK.,MRC London Institute of Medical Sciences, London, UK
| | - Leah S Muhle
- Department of Life Sciences, Imperial College London, London, UK.,Department of Physics, Faculty of Science, Eberhard-Karls-Universität, Tübingen, Germany
| | - André E X Brown
- Institute of Clinical Sciences, Imperial College London, London, UK.,MRC London Institute of Medical Sciences, London, UK
| | | | - Robert G Endres
- Department of Life Sciences, Imperial College London, London, UK
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20
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Ramos-Fernandez G, Smith Aguilar SE, Krakauer DC, Flack JC. Collective Computation in Animal Fission-Fusion Dynamics. Front Robot AI 2020; 7:90. [PMID: 33501257 PMCID: PMC7805913 DOI: 10.3389/frobt.2020.00090] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 06/05/2020] [Indexed: 11/15/2022] Open
Abstract
Recent work suggests that collective computation of social structure can minimize uncertainty about the social and physical environment, facilitating adaptation. We explore these ideas by studying how fission-fusion social structure arises in spider monkey (Ateles geoffroyi) groups, exploring whether monkeys use social knowledge to collectively compute subgroup size distributions adaptive for foraging in variable environments. We assess whether individual decisions to stay in or leave subgroups are conditioned on strategies based on the presence or absence of others. We search for this evidence in a time series of subgroup membership. We find that individuals have multiple strategies, suggesting that the social knowledge of different individuals is important. These stay-leave strategies provide microscopic inputs to a stochastic model of collective computation encoded in a family of circuits. Each circuit represents an hypothesis for how collectives combine strategies to make decisions, and how these produce various subgroup size distributions. By running these circuits forward in simulation we generate new subgroup size distributions and measure how well they match food abundance in the environment using transfer entropies. We find that spider monkeys decide to stay or go using information from multiple individuals and that they can collectively compute a distribution of subgroup size that makes efficient use of ephemeral sources of nutrition. We are able to artificially tune circuits with subgroup size distributions that are a better fit to the environment than the observed. This suggests that a combination of measurement error, constraint, and adaptive lag are diminishing the power of collective computation in this system. These results are relevant for a more general understanding of the emergence of ordered states in multi-scale social systems with adaptive properties-both natural and engineered.
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Affiliation(s)
- Gabriel Ramos-Fernandez
- Departamento de Modelación Matemática de Sistemas Sociales, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
- Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, Instituto Politécnico Nacional, Ciudad de México, Mexico
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21
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Information can explain the dynamics of group order in animal collective behaviour. Nat Commun 2020; 11:2737. [PMID: 32483141 PMCID: PMC7264142 DOI: 10.1038/s41467-020-16578-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 05/05/2020] [Indexed: 11/08/2022] Open
Abstract
Animal groups vary in their collective order (or state), forming disordered swarms to highly polarized groups. One explanation for this variation is that individuals face differential benefits or costs depending on the group's order, but empirical evidence for this is lacking. Here we show that in three-spined sticklebacks (Gasterosteus aculeatus), fish that are first to respond to an ephemeral food source do so faster when shoals are in a disordered, swarm-like state. This is because individuals' visual fields collectively cover more of their environment, meaning private information is more readily available in disordered groups. Once social information becomes available, however, the arrival times of subsequent group members to the food are faster in more ordered, polarized groups. Our data further suggest that first responding individuals (those that benefit from group disorder) maintain larger differences in heading angle to their nearest neighbours when shoaling, thereby explaining how conflict over whether private or social information is favoured can drive dynamic changes in collective behaviour.
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22
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White TP, Veit RR. Spatial ecology of long‐tailed ducks and white‐winged scoters wintering on Nantucket Shoals. Ecosphere 2020. [DOI: 10.1002/ecs2.3002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Affiliation(s)
- Timothy P. White
- Environmental Studies Program, Bureau of Ocean Energy Management U.S. Department of the Interior Sterling Virginia 20166 USA
| | - Richard R. Veit
- Department of Biology CSI/CUNY Staten Island New York 10314 USA
- The Graduate Center CUNY New York New York 10016 USA
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23
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Sosna MMG, Twomey CR, Bak-Coleman J, Poel W, Daniels BC, Romanczuk P, Couzin ID. Individual and collective encoding of risk in animal groups. Proc Natl Acad Sci U S A 2019; 116:20556-20561. [PMID: 31548427 PMCID: PMC6789631 DOI: 10.1073/pnas.1905585116] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The need to make fast decisions under risky and uncertain conditions is a widespread problem in the natural world. While there has been extensive work on how individual organisms dynamically modify their behavior to respond appropriately to changing environmental conditions (and how this is encoded in the brain), we know remarkably little about the corresponding aspects of collective information processing in animal groups. For example, many groups appear to show increased "sensitivity" in the presence of perceived threat, as evidenced by the increased frequency and magnitude of repeated cascading waves of behavioral change often observed in fish schools and bird flocks under such circumstances. How such context-dependent changes in collective sensitivity are mediated, however, is unknown. Here we address this question using schooling fish as a model system, focusing on 2 nonexclusive hypotheses: 1) that changes in collective responsiveness result from changes in how individuals respond to social cues (i.e., changes to the properties of the "nodes" in the social network), and 2) that they result from changes made to the structural connectivity of the network itself (i.e., the computation is encoded in the "edges" of the network). We find that despite the fact that perceived risk increases the probability for individuals to initiate an alarm, the context-dependent change in collective sensitivity predominantly results not from changes in how individuals respond to social cues, but instead from how individuals modify the spatial structure, and correspondingly the topology of the network of interactions, within the group. Risk is thus encoded as a collective property, emphasizing that in group-living species individual fitness can depend strongly on coupling between scales of behavioral organization.
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Affiliation(s)
- Matthew M G Sosna
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544;
| | - Colin R Twomey
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104
| | - Joseph Bak-Coleman
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544
| | - Winnie Poel
- Institute for Theoretical Biology, Department of Biology, Humboldt Universität zu Berlin, D-10099 Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Humboldt Universität zu Berlin, D-10115 Berlin, Germany
| | - Bryan C Daniels
- Arizona State University-Santa Fe Institute (ASU-SFI) Center for Biosocial Complex Systems, Arizona State University, Tempe, AZ 85287
| | - Pawel Romanczuk
- Institute for Theoretical Biology, Department of Biology, Humboldt Universität zu Berlin, D-10099 Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Humboldt Universität zu Berlin, D-10115 Berlin, Germany
| | - Iain D Couzin
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, D-78547 Konstanz, Germany;
- Department of Biology, University of Konstanz, D-78547 Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, D-78547 Konstanz, Germany
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24
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Tempos and modes of collectivity in the history of life. Theory Biosci 2019; 140:343-351. [PMID: 31529373 DOI: 10.1007/s12064-019-00303-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 09/04/2019] [Indexed: 10/26/2022]
Abstract
Collective integration and processing of information have increased through the history of life, through both the formation of aggregates in which the entities may have very different properties and which jointly coarse-grained environmental variables (ranging from widely varying metabolism in microbial consortia to the ecological diversity of species on reefs) and through collectives of similar entities (such as cells within an organism or social groups). Such increases have been implicated in significant transitions in the history of life, including aspects of the origin of life, the generation of pangenomes among microbes and microbial communities such as stromatolites, multicellularity and social insects. This contribution provides a preliminary overview of the dominant modes of collective information processing in the history of life, their phylogenetic distribution and extent of convergence, and the effects of new modes for integrating and acting upon information on the tempo of evolutionary change.
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25
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Mohapatra S, Mahapatra PS. Confined System Analysis of a Predator-Prey Minimalistic Model. Sci Rep 2019; 9:11258. [PMID: 31375724 PMCID: PMC6677773 DOI: 10.1038/s41598-019-47603-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Accepted: 07/18/2019] [Indexed: 11/15/2022] Open
Abstract
In nature exists a properly defined food chain- an order of hunting and getting hunted. One such hunter-hunted pair is considered in this context and coordinated escape manoeuvres in response to predation is studied in case of a rarely examined confined system. Both the predator agent and prey agents are considered to be self-propelled particles moving in a viscous fluid. The state of motility when alive and passivity on death has been accounted for. A novel individual-based combination of Vicsek model and Boids flocking model is used for defining the self-propelling action and inter-agent interactions. The regimes observed at differing levels of co-ordination segregated by quantification of global order parameter are found to be in agreement with the extant literature. This study strives to understand the penalty on the collective motion due to the restraints employed by the rigid walls of the confinement and the predator’s hunting tactics. The success of any escape manoeuvre is dependent on the rate of information transfer and the strength of the agitation at the source of the manoeuvre. The rate of information transfer is studied as a function of co-ordination and the size of the influence zone and the source strength is studied as a function of escape acceleration activated on the agitated prey. The role of these factors in affecting survival rate of prey is given due coverage.
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Affiliation(s)
- Siddhant Mohapatra
- Department of Mechanical Engineering, National Institute of Technology Silchar, Silchar, India
| | - Pallab Sinha Mahapatra
- Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, India.
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26
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Ling H, Mclvor GE, van der Vaart K, Vaughan RT, Thornton A, Ouellette NT. Local interactions and their group-level consequences in flocking jackdaws. Proc Biol Sci 2019; 286:20190865. [PMID: 31266425 PMCID: PMC6650722 DOI: 10.1098/rspb.2019.0865] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 06/07/2019] [Indexed: 11/12/2022] Open
Abstract
As one of nature's most striking examples of collective behaviour, bird flocks have attracted extensive research. However, we still lack an understanding of the attractive and repulsive forces that govern interactions between individuals within flocks and how these forces influence neighbours' relative positions and ultimately determine the shape of flocks. We address these issues by analysing the three-dimensional movements of wild jackdaws ( Corvus monedula) in flocks containing 2-338 individuals. We quantify the social interaction forces in large, airborne flocks and find that these forces are highly anisotropic. The long-range attraction in the direction perpendicular to the movement direction is stronger than that along it, and the short-range repulsion is generated mainly by turning rather than changing speed. We explain this phenomenon by considering wingbeat frequency and the change in kinetic and gravitational potential energy during flight, and find that changing the direction of movement is less energetically costly than adjusting speed for birds. Furthermore, our data show that collision avoidance by turning can alter local neighbour distributions and ultimately change the group shape. Our results illustrate the macroscopic consequences of anisotropic interaction forces in bird flocks, and help to draw links between group structure, local interactions and the biophysics of animal locomotion.
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Affiliation(s)
- Hangjian Ling
- Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA
| | - Guillam E. Mclvor
- Center for Ecology and Conservation, University of Exeter, Penryn, UK
| | - Kasper van der Vaart
- Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA
| | | | - Alex Thornton
- Center for Ecology and Conservation, University of Exeter, Penryn, UK
| | - Nicholas T. Ouellette
- Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA
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27
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Berdahl AM, Kao AB, Flack A, Westley PAH, Codling EA, Couzin ID, Dell AI, Biro D. Collective animal navigation and migratory culture: from theoretical models to empirical evidence. Philos Trans R Soc Lond B Biol Sci 2019; 373:rstb.2017.0009. [PMID: 29581394 PMCID: PMC5882979 DOI: 10.1098/rstb.2017.0009] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/01/2017] [Indexed: 12/31/2022] Open
Abstract
Animals often travel in groups, and their navigational decisions can be influenced by social interactions. Both theory and empirical observations suggest that such collective navigation can result in individuals improving their ability to find their way and could be one of the key benefits of sociality for these species. Here, we provide an overview of the potential mechanisms underlying collective navigation, review the known, and supposed, empirical evidence for such behaviour and highlight interesting directions for future research. We further explore how both social and collective learning during group navigation could lead to the accumulation of knowledge at the population level, resulting in the emergence of migratory culture. This article is part of the theme issue ‘Collective movement ecology’.
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Affiliation(s)
- Andrew M Berdahl
- Santa Fe Institute, Santa Fe, NM 87501, USA .,School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195, USA
| | - Albert B Kao
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Andrea Flack
- Department of Migration and Immuno-Ecology, Max Planck Institute for Ornithology, 78315 Radolfzell, Germany.,Department of Biology, University of Konstanz, 78457 Konstanz, Germany
| | - Peter A H Westley
- Department of Fisheries, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
| | - Edward A Codling
- Department of Mathematical Sciences, University of Essex, Colchester, CO4 3SQ, UK
| | - Iain D Couzin
- Department of Biology, University of Konstanz, 78457 Konstanz, Germany.,Department of Collective Behaviour, Max Planck Institute for Ornithology, Konstanz, Germany.,Chair of Biodiversity and Collective Behaviour, University of Konstanz, 78457 Konstanz, Germany
| | - Anthony I Dell
- National Great Rivers Research and Education Center, Alton, IL 62024, USA.,Department of Biology, Washington University in St Louis, St Louis, MO 63130, USA
| | - Dora Biro
- Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
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28
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Fryxell JM, Berdahl AM. Fitness trade-offs of group formation and movement by Thomson's gazelles in the Serengeti ecosystem. Philos Trans R Soc Lond B Biol Sci 2019; 373:rstb.2017.0013. [PMID: 29581398 PMCID: PMC5882983 DOI: 10.1098/rstb.2017.0013] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/03/2018] [Indexed: 11/22/2022] Open
Abstract
Collective behaviours contributing to patterns of group formation and coordinated movement are common across many ecosystems and taxa. Their ubiquity is presumably due to altering interactions between individuals and their predators, resources and physical environment in ways that enhance individual fitness. On the other hand, fitness costs are also often associated with group formation. Modifications to these interactions have the potential to dramatically impact population-level processes, such as trophic interactions or patterns of space use in relation to abiotic environmental variation. In a wide variety of empirical systems and models, collective behaviour has been shown to enhance access to ephemeral patches of resources, reduce the risk of predation and reduce vulnerability to environmental fluctuation. Evolution of collective behaviour should accordingly depend on the advantages of collective behaviour weighed against the costs experienced at the individual level. As an illustrative case study, we consider the potential trade-offs on Malthusian fitness associated with patterns of group formation and movement by migratory Thomson's gazelles in the Serengeti ecosystem. This article is part of the theme issue ‘Collective movement ecology’.
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Affiliation(s)
- John M Fryxell
- Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada N1G 2W1
| | - Andrew M Berdahl
- Santa Fe Institute, Santa Fe, NM 87501, USA.,School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98105, USA
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29
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Cohen IR, Efroni S. The Immune System Computes the State of the Body: Crowd Wisdom, Machine Learning, and Immune Cell Reference Repertoires Help Manage Inflammation. Front Immunol 2019; 10:10. [PMID: 30723470 PMCID: PMC6349705 DOI: 10.3389/fimmu.2019.00010] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 01/04/2019] [Indexed: 11/29/2022] Open
Abstract
Here, we outline an overview of the mammalian immune system that updates and extends the classical clonal selection paradigm. Rather than focusing on strict self-not-self discrimination, we propose that the system orchestrates variable inflammatory responses that maintain the body and its symbiosis with the microbiome while eliminating the threat from pathogenic infectious agents and from tumors. The paper makes four points:
The immune system classifies healthy and pathologic states of the body—including both self and foreign elements—by deploying individual lymphocytes as cellular computing machines; immune cells transform input signals from the body into an output of specific immune reactions. Rather than independent clonal responses, groups of individually activated immune-system cells co-react in lymphoid organs to make collective decisions through a type of self-organizing swarm intelligence or crowd wisdom. Collective choices by swarms of immune cells, like those of schools of fish, are modified by relatively small numbers of individual regulators responding to shifting conditions—such collective inflammatory responses are dynamically responsive. Self-reactive autoantibody and T-cell receptor (TCR) repertoires shared by healthy individuals function in a biological version of experience-based supervised machine learning. Immune system decisions are primed by formative experience with training sets of self-antigens encountered during lymphocyte development; these initially trained T cell and B cell repertoires form a Wellness Profile that then guides immune responses to test sets of antigens encountered later. This experience-based machine learning strategy is analogous to that deployed by supervised machine-learning algorithms.
We propose experiments to test these ideas. This overview of the immune system bears clinical implications for monitoring wellness and for treating autoimmune disease, cancer, and allograft reactions.
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Affiliation(s)
- Irun R Cohen
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Sol Efroni
- Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
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30
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Abstract
Most living systems, from individual cells to tissues and swarms, display collective self-organization on length scales that are much larger than those of the individual units that drive this organization. A fundamental challenge is to understand how properties of microscopic components determine macroscopic, multicellular biological function. Our study connects intracellular physiology to macroscale collective behaviors during multicellular development, spanning five orders of magnitude in length and six orders of magnitude in time, using bacterial swarming as a model system. This work is enabled by a high-throughput adaptive microscopy technique, which we combined with genetics, machine learning, and mathematical modeling to reveal the phase diagram of bacterial swarming and that cell–cell interactions within each swarming phase are dominated by mechanical interactions. Coordinated dynamics of individual components in active matter are an essential aspect of life on all scales. Establishing a comprehensive, causal connection between intracellular, intercellular, and macroscopic behaviors has remained a major challenge due to limitations in data acquisition and analysis techniques suitable for multiscale dynamics. Here, we combine a high-throughput adaptive microscopy approach with machine learning, to identify key biological and physical mechanisms that determine distinct microscopic and macroscopic collective behavior phases which develop as Bacillus subtilis swarms expand over five orders of magnitude in space. Our experiments, continuum modeling, and particle-based simulations reveal that macroscopic swarm expansion is primarily driven by cellular growth kinetics, whereas the microscopic swarming motility phases are dominated by physical cell–cell interactions. These results provide a unified understanding of bacterial multiscale behavioral complexity in swarms.
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31
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Abstract
Interest in robotic swarms has increased exponentially. Prior research determined that humans perceive biological swarm motions as a single entity, rather than perceiving the individuals. An open question is how the swarm’s visual representation and the associated task impact human performance when identifying current swarm tasks. The majority of the existing swarm visualizations present each robot individually. Swarms typically incorporate large numbers of individuals, where the individuals exhibit simple behaviors, but the swarm appears to exhibit more intelligent behavior. As the swarm size increases, it becomes increasingly difficult for the human operator to understand the swarm’s current state, the emergent behaviors, and predict future outcomes. Alternative swarm visualizations are one means of mitigating high operator workload and risk of human error. Five visualizations were evaluated for two tasks, go to and avoid, in the presence or absence of obstacles. The results indicate that visualizations incorporating representations of individual agents resulted in higher accuracy when identifying tasks.
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Pruitt JN, Berdahl A, Riehl C, Pinter-Wollman N, Moeller HV, Pringle EG, Aplin LM, Robinson EJH, Grilli J, Yeh P, Savage VM, Price MH, Garland J, Gilby IC, Crofoot MC, Doering GN, Hobson EA. Social tipping points in animal societies. Proc Biol Sci 2018; 285:20181282. [PMID: 30232162 PMCID: PMC6170811 DOI: 10.1098/rspb.2018.1282] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 08/31/2018] [Indexed: 11/12/2022] Open
Abstract
Animal social groups are complex systems that are likely to exhibit tipping points-which are defined as drastic shifts in the dynamics of systems that arise from small changes in environmental conditions-yet this concept has not been carefully applied to these systems. Here, we summarize the concepts behind tipping points and describe instances in which they are likely to occur in animal societies. We also offer ways in which the study of social tipping points can open up new lines of inquiry in behavioural ecology and generate novel questions, methods, and approaches in animal behaviour and other fields, including community and ecosystem ecology. While some behaviours of living systems are hard to predict, we argue that probing tipping points across animal societies and across tiers of biological organization-populations, communities, ecosystems-may help to reveal principles that transcend traditional disciplinary boundaries.
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Affiliation(s)
- Jonathan N Pruitt
- Department of Ecology, Evolution and Marine Biology, University of California - Santa Barbara, Santa Barbara, CA 93106, USA
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario L8S 4K1, Canada
| | - Andrew Berdahl
- School of Aquatic and Fisheries Sciences, University of Washington, Seattle, WA 98195, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Christina Riehl
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Noa Pinter-Wollman
- Department of Ecology and Evolutionary Biology, University of California - Los Angeles, Los Angeles, CA 90095, USA
| | - Holly V Moeller
- Department of Ecology, Evolution and Marine Biology, University of California - Santa Barbara, Santa Barbara, CA 93106, USA
| | | | - Lucy M Aplin
- Edward Grey Institute of Field Ornithology, Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
- Cognitive and Cultural Ecology Research Group, Max Planck Institute of Ornithology, Radolfzell, 78315, Germany
| | - Elva J H Robinson
- Department of Biology, University of York, Heslington, York YO10 5DD, UK
| | | | - Pamela Yeh
- Department of Ecology and Evolutionary Biology, University of California - Los Angeles, Los Angeles, CA 90095, USA
| | - Van M Savage
- Department of Ecology and Evolutionary Biology, University of California - Los Angeles, Los Angeles, CA 90095, USA
| | | | | | - Ian C Gilby
- School of Human Evolution and Social Change, and Institute of Human Origins, Arizona State University, Tempe, AZ 85287, USA
| | - Margaret C Crofoot
- Department of Anthropology, University of California Davis, Davis, CA 95616, USA
| | - Grant N Doering
- Department of Ecology, Evolution and Marine Biology, University of California - Santa Barbara, Santa Barbara, CA 93106, USA
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario L8S 4K1, Canada
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33
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Gil MA, Hein AM, Spiegel O, Baskett ML, Sih A. Social Information Links Individual Behavior to Population and Community Dynamics. Trends Ecol Evol 2018; 33:535-548. [DOI: 10.1016/j.tree.2018.04.010] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 04/13/2018] [Accepted: 04/16/2018] [Indexed: 11/17/2022]
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34
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Fu X, Kato S, Long J, Mattingly HH, He C, Vural DC, Zucker SW, Emonet T. Spatial self-organization resolves conflicts between individuality and collective migration. Nat Commun 2018; 9:2177. [PMID: 29872053 PMCID: PMC5988668 DOI: 10.1038/s41467-018-04539-4] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Accepted: 05/03/2018] [Indexed: 12/24/2022] Open
Abstract
Collective behavior can spontaneously emerge when individuals follow common rules of interaction. However, the behavior of each individual differs due to existing genetic and non-genetic variation within the population. It remains unclear how this individuality is managed to achieve collective behavior. We quantify individuality in bands of clonal Escherichia coli cells that migrate collectively along a channel by following a self-generated gradient of attractant. We discover that despite substantial differences in individual chemotactic abilities, the cells are able to migrate as a coherent group by spontaneously sorting themselves within the moving band. This sorting mechanism ensures that differences between individual chemotactic abilities are compensated by differences in the local steepness of the traveling gradient each individual must navigate, and determines the minimum performance required to travel with the band. By resolving conflicts between individuality and collective migration, this mechanism enables populations to maintain advantageous diversity while on the move. How bacteria migrate collectively despite individual phenotypic variation is not understood. Here, the authors show that cells spontaneously sort themselves within moving bands such that variations in individual tumble bias, a determinant of gradient climbing speed, are compensated by the local gradient steepness experienced by individuals.
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Affiliation(s)
- X Fu
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, 06520, USA.,Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - S Kato
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, 06520, USA.,Department of Molecular Biotechnology, Graduate School of Advanced Sciences of Matter, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-8530, Japan
| | - J Long
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, 06520, USA.,Department of Physics, Yale University, New Haven, CT, 06520, USA
| | - H H Mattingly
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, 06520, USA
| | - C He
- Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - D C Vural
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, 06520, USA.,Department of Physics, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - S W Zucker
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA.,Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - T Emonet
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, 06520, USA. .,Department of Physics, Yale University, New Haven, CT, 06520, USA.
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35
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Del Mar Delgado M, Miranda M, Alvarez SJ, Gurarie E, Fagan WF, Penteriani V, di Virgilio A, Morales JM. The importance of individual variation in the dynamics of animal collective movements. Philos Trans R Soc Lond B Biol Sci 2018; 373:20170008. [PMID: 29581393 PMCID: PMC5882978 DOI: 10.1098/rstb.2017.0008] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2017] [Indexed: 11/12/2022] Open
Abstract
Animal collective movements are a key example of a system that links two clearly defined levels of organization: the individual and the group. Most models investigating collective movements have generated coherent collective behaviours without the inclusion of individual variability. However, new individual-based models, together with emerging empirical information, emphasize that within-group heterogeneity may strongly influence collective movement behaviour. Here we (i) review the empirical evidence for individual variation in animal collective movements, (ii) explore how theoretical investigations have represented individual heterogeneity when modelling collective movements and (iii) present a model to show how within-group heterogeneity influences the collective properties of a group. Our review underscores the need to consider variability at the level of the individual to improve our understanding of how individual decision rules lead to emergent movement patterns, and also to yield better quantitative predictions of collective behaviour.This article is part of the theme issue 'Collective movement ecology'.
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Affiliation(s)
- Maria Del Mar Delgado
- Research Unit of Biodiversity (UMIB, UO-CSIC-PA), Oviedo University, Campus Mieres, 33600 Mieres, Spain
| | - Maria Miranda
- Research Unit of Biodiversity (UMIB, UO-CSIC-PA), Oviedo University, Campus Mieres, 33600 Mieres, Spain
| | - Silvia J Alvarez
- Department of Biology, University of Maryland, 1210 Biology-Psychology Building, College Park, MD 20742, USA
- Wildlife Conservation Society, Carrera 7 No. 82-66, Bogota, Colombia
| | - Eliezer Gurarie
- Department of Biology, University of Maryland, 1210 Biology-Psychology Building, College Park, MD 20742, USA
| | - William F Fagan
- Department of Biology, University of Maryland, 1210 Biology-Psychology Building, College Park, MD 20742, USA
| | - Vincenzo Penteriani
- Research Unit of Biodiversity (UMIB, UO-CSIC-PA), Oviedo University, Campus Mieres, 33600 Mieres, Spain
- Pyrenean Institute of Ecology (IPE), CSIC, Avda. Montañana 1005, 50059, Zaragoza, Spain
| | - Agustina di Virgilio
- Ecotono, INIBIOMA-CONICET, Universidad Nacional del Camahue, Quintral 1250, Bariloche 8400, Argentina
| | - Juan Manuel Morales
- Ecotono, INIBIOMA-CONICET, Universidad Nacional del Camahue, Quintral 1250, Bariloche 8400, Argentina
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36
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Abstract
Throughout the animal kingdom, animals frequently benefit from living in groups. Models of collective behaviour show that simple local interactions are sufficient to generate group morphologies found in nature (swarms, flocks and mills). However, individuals also interact with the complex noisy environment in which they live. In this work, we experimentally investigate the group performance in navigating a noisy light gradient of two unrelated freshwater species: golden shiners (Notemigonuscrysoleucas) and rummy nose tetra (Hemigrammus bleheri). We find that tetras outperform shiners due to their innate individual ability to sense the environmental gradient. Using numerical simulations, we examine how group performance depends on the relative weight of social and environmental information. Our results highlight the importance of balancing of social and environmental information to promote optimal group morphologies and performance.
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37
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van Vliet S, Dal Co A, Winkler AR, Spriewald S, Stecher B, Ackermann M. Spatially Correlated Gene Expression in Bacterial Groups: The Role of Lineage History, Spatial Gradients, and Cell-Cell Interactions. Cell Syst 2018; 6:496-507.e6. [PMID: 29655705 DOI: 10.1016/j.cels.2018.03.009] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Revised: 01/24/2018] [Accepted: 03/14/2018] [Indexed: 10/17/2022]
Abstract
Gene expression levels in clonal bacterial groups have been found to be spatially correlated. These correlations can partly be explained by the shared lineage history of nearby cells, although they could also arise from local cell-cell interactions. Here, we present a quantitative framework that allows us to disentangle the contributions of lineage history, long-range spatial gradients, and local cell-cell interactions to spatial correlations in gene expression. We study pathways involved in toxin production, SOS stress response, and metabolism in Escherichia coli microcolonies and find for all pathways that shared lineage history is the main cause of spatial correlations in gene expression levels. However, long-range spatial gradients and local cell-cell interactions also contributed to spatial correlations in SOS response, amino acid biosynthesis, and overall metabolic activity. Together, our data show that the phenotype of a cell is influenced by its lineage history and population context, raising the question of whether bacteria can arrange their activities in space to perform functions they cannot achieve alone.
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Affiliation(s)
- Simon van Vliet
- Institute of Biogeochemistry and Pollutant Dynamics, Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland; Department of Environmental Microbiology, Eawag, 8600 Dübendorf, Switzerland.
| | - Alma Dal Co
- Institute of Biogeochemistry and Pollutant Dynamics, Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland; Department of Environmental Microbiology, Eawag, 8600 Dübendorf, Switzerland
| | - Annina R Winkler
- Institute of Biogeochemistry and Pollutant Dynamics, Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland; Department of Environmental Microbiology, Eawag, 8600 Dübendorf, Switzerland
| | | | - Bärbel Stecher
- Max-von-Pettenkofer Institute, LMU Munich, 80336 Munich, Germany; German Center for Infection Research (DZIF), Partner Site LMU Munich, 80336 Munich, Germany
| | - Martin Ackermann
- Institute of Biogeochemistry and Pollutant Dynamics, Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland; Department of Environmental Microbiology, Eawag, 8600 Dübendorf, Switzerland
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38
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On the role of collective sensing and evolution in group formation. SWARM INTELLIGENCE 2018. [DOI: 10.1007/s11721-018-0156-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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39
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Richter S, Gerum RC, Schneider W, Fabry B, Le Bohec C, Zitterbart DP. A remote‐controlled observatory for behavioural and ecological research: A case study on emperor penguins. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.12971] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
| | | | | | - Ben Fabry
- Biophysics GroupFriedrich‐Alexander University Erlangen Germany
| | - Céline Le Bohec
- Département de Biologie PolaireCentre Scientifique de Monaco Monaco Principality of Monaco
- Université de StrasbourgCNRSIPHC Strasbourg France
| | - Daniel P. Zitterbart
- Biophysics GroupFriedrich‐Alexander University Erlangen Germany
- Applied Ocean Physics and EngineeringWoods Hole Oceanographic Institution Woods Hole USA
- Alfred‐Wegener‐Institute for Polar and Marine Research Bremerhaven Germany
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40
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Brush ER, Krakauer DC, Flack JC. Conflicts of interest improve collective computation of adaptive social structures. SCIENCE ADVANCES 2018; 4:e1603311. [PMID: 29376116 PMCID: PMC5777398 DOI: 10.1126/sciadv.1603311] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2016] [Accepted: 12/14/2017] [Indexed: 05/31/2023]
Abstract
In many biological systems, the functional behavior of a group is collectively computed by the system's individual components. An example is the brain's ability to make decisions via the activity of billions of neurons. A long-standing puzzle is how the components' decisions combine to produce beneficial group-level outputs, despite conflicts of interest and imperfect information. We derive a theoretical model of collective computation from mechanistic first principles, using results from previous work on the computation of power structure in a primate model system. Collective computation has two phases: an information accumulation phase, in which (in this study) pairs of individuals gather information about their fighting abilities and make decisions about their dominance relationships, and an information aggregation phase, in which these decisions are combined to produce a collective computation. To model information accumulation, we extend a stochastic decision-making model-the leaky integrator model used to study neural decision-making-to a multiagent game-theoretic framework. We then test alternative algorithms for aggregating information-in this study, decisions about dominance resulting from the stochastic model-and measure the mutual information between the resultant power structure and the "true" fighting abilities. We find that conflicts of interest can improve accuracy to the benefit of all agents. We also find that the computation can be tuned to produce different power structures by changing the cost of waiting for a decision. The successful application of a similar stochastic decision-making model in neural and social contexts suggests general principles of collective computation across substrates and scales.
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Affiliation(s)
- Eleanor R. Brush
- Program in Quantitative and Computational Biology, Princeton University, Princeton, NJ 08544, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
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41
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Laan A, Gil de Sagredo R, de Polavieja GG. Signatures of optimal control in pairs of schooling zebrafish. Proc Biol Sci 2017; 284:20170224. [PMID: 28404782 PMCID: PMC5394674 DOI: 10.1098/rspb.2017.0224] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Accepted: 03/13/2017] [Indexed: 11/12/2022] Open
Abstract
Animals moving in groups coordinate their motion to remain cohesive. A large amount of data and analysis of movement coordination has been obtained in several species, but we are lacking theoretical frameworks that can derive the form of coordination rules. Here, we examine whether optimal control theory can predict the rules underlying social interactions from first principles. We find that a control rule which is designed to minimize the time it would take a pair of schooling fish to form a cohesively moving unit correctly predicts the characteristics of social interactions in fish. Our methodology explains why social attraction is negatively modulated by self-motion velocity and positively modulated by partner motion velocity, and how the biomechanics of fish swimming can shape the form of social forces. Crucially, the values of all parameters in our model can be estimated from independent experiments that need not relate to measurement of social interactions. We test our theory by showing a good match with experimentally observed social interaction rules in zebrafish. In addition to providing a theoretical rationale for observed decision rules, we suggest that this framework opens new questions about tuning problems and learnability of collective behaviours.
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Affiliation(s)
- Andress Laan
- Champalimaud Neuroscience Programme, Champalimaud Center for the Unknown, Lisbon, Portugal
| | - Raul Gil de Sagredo
- Champalimaud Neuroscience Programme, Champalimaud Center for the Unknown, Lisbon, Portugal
| | - Gonzalo G de Polavieja
- Champalimaud Neuroscience Programme, Champalimaud Center for the Unknown, Lisbon, Portugal
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42
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Daniels BC, Krakauer DC, Flack JC. Control of finite critical behaviour in a small-scale social system. Nat Commun 2017; 8:14301. [PMID: 28186194 PMCID: PMC5309824 DOI: 10.1038/ncomms14301] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Accepted: 12/16/2016] [Indexed: 12/02/2022] Open
Abstract
Many adaptive systems sit near a tipping or critical point. For systems near a critical point small changes to component behaviour can induce large-scale changes in aggregate structure and function. Criticality can be adaptive when the environment is changing, but entails reduced robustness through sensitivity. This tradeoff can be resolved when criticality can be tuned. We address the control of finite measures of criticality using data on fight sizes from an animal society model system (Macaca nemestrina, n=48). We find that a heterogeneous, socially organized system, like homogeneous, spatial systems (flocks and schools), sits near a critical point; the contributions individuals make to collective phenomena can be quantified; there is heterogeneity in these contributions; and distance from the critical point (DFC) can be controlled through biologically plausible mechanisms exploiting heterogeneity. We propose two alternative hypotheses for why a system decreases the distance from the critical point. Proximity to criticality can be advantageous under changing conditions, but it also entails reduced robustness. Here, the authors analyse fight sizes in a macaque society and find not only that it sits near criticality, but also that the distance from the critical point is tunable through adjustment of individual behaviour and social conflict management.
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Affiliation(s)
- Bryan C Daniels
- ASU-SFI Center for Biosocial Complex Systems, Arizona State University, Tempe, Arizona 85287, USA
| | - David C Krakauer
- ASU-SFI Center for Biosocial Complex Systems, Arizona State University, Tempe, Arizona 85287, USA.,Santa Fe Institute, Santa Fe, New Mexico 87501, USA
| | - Jessica C Flack
- ASU-SFI Center for Biosocial Complex Systems, Arizona State University, Tempe, Arizona 85287, USA.,Santa Fe Institute, Santa Fe, New Mexico 87501, USA
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43
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Hagstrom GI, Levin SA. Marine Ecosystems as Complex Adaptive Systems: Emergent Patterns, Critical Transitions, and Public Goods. Ecosystems 2017. [DOI: 10.1007/s10021-017-0114-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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44
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Ramdya P, Schneider J, Levine JD. The neurogenetics of group behavior in Drosophila melanogaster. J Exp Biol 2017; 220:35-41. [DOI: 10.1242/jeb.141457] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
ABSTRACT
Organisms rarely act in isolation. Their decisions and movements are often heavily influenced by direct and indirect interactions with conspecifics. For example, we each represent a single node within a social network of family and friends, and an even larger network of strangers. This group membership can affect our opinions and actions. Similarly, when in a crowd, we often coordinate our movements with others like fish in a school, or birds in a flock. Contributions of the group to individual behaviors are observed across a wide variety of taxa but their biological mechanisms remain largely unknown. With the advent of powerful computational tools as well as the unparalleled genetic accessibility and surprisingly rich social life of Drosophila melanogaster, researchers now have a unique opportunity to investigate molecular and neuronal determinants of group behavior. Conserved mechanisms and/or selective pressures in D. melanogaster can likely inform a much wider phylogenetic scale. Here, we highlight two examples to illustrate how quantitative and genetic tools can be combined to uncover mechanisms of two group behaviors in D. melanogaster: social network formation and collective behavior. Lastly, we discuss future challenges towards a full understanding how coordinated brain activity across many individuals gives rise to the behavioral patterns of animal societies.
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Affiliation(s)
- Pavan Ramdya
- Department of Biology and Bioengineering, California Institute of Technology, Pasadena, CA 91106, USA
| | - Jonathan Schneider
- Department of Biology, University of Toronto at Mississauga, Mississauga, Ontario, CanadaL5L1C6
| | - Joel D. Levine
- Department of Biology, University of Toronto at Mississauga, Mississauga, Ontario, CanadaL5L1C6
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45
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Balancing general principles with fine-scale interactions in understanding the emergence of movement-driven spatial patterns. Phys Life Rev 2016; 19:125-127. [DOI: 10.1016/j.plrev.2016.10.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Accepted: 10/24/2016] [Indexed: 11/23/2022]
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46
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Natural search algorithms as a bridge between organisms, evolution, and ecology. Proc Natl Acad Sci U S A 2016; 113:9413-20. [PMID: 27496324 DOI: 10.1073/pnas.1606195113] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The ability to navigate is a hallmark of living systems, from single cells to higher animals. Searching for targets, such as food or mates in particular, is one of the fundamental navigational tasks many organisms must execute to survive and reproduce. Here, we argue that a recent surge of studies of the proximate mechanisms that underlie search behavior offers a new opportunity to integrate the biophysics and neuroscience of sensory systems with ecological and evolutionary processes, closing a feedback loop that promises exciting new avenues of scientific exploration at the frontier of systems biology.
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47
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Abstract
A model based on shoaling fish suggests how a group can show decision-making properties beyond those of any one individual.
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Affiliation(s)
- Yaroslav Ispolatov
- Departamento de Fisica, Universidad de Santiago de Chile, Santiago, Chile
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