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      Individual and collective encoding of risk in animal groups

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          Significance

          Many biological systems exhibit an emergent ability to process information about their environment. This collective cognition emerges as a result of both the behavior of system components and their interactions, yet the relative importance of the two is often hard to disentangle. Here, we combined experiments and modeling to examine how fish schools collectively encode information about the external environment. We demonstrate that risk is predominantly encoded in the physical structure of groups, which individuals modulate in a way that augments or dampens behavioral cascades. We show that this modulation is necessary for behavioral cascades to spread and that it allows collective systems to be responsive to their environments even without changes in individual computation.

          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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          Most cited references53

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          Temporal Variation in Danger Drives Antipredator Behavior: The Predation Risk Allocation Hypothesis

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            Self-Organization and Collective Behavior in Vertebrates

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              Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion.

              Coordination among social animals requires rapid and efficient transfer of information among individuals, which may depend crucially on the underlying structure of the communication network. Establishing the decision-making circuits and networks that give rise to individual behavior has been a central goal of neuroscience. However, the analogous problem of determining the structure of the communication network among organisms that gives rise to coordinated collective behavior, such as is exhibited by schooling fish and flocking birds, has remained almost entirely neglected. Here, we study collective evasion maneuvers, manifested through rapid waves, or cascades, of behavioral change (a ubiquitous behavior among taxa) in schooling fish (Notemigonus crysoleucas). We automatically track the positions and body postures, calculate visual fields of all individuals in schools of ∼150 fish, and determine the functional mapping between socially generated sensory input and motor response during collective evasion. We find that individuals use simple, robust measures to assess behavioral changes in neighbors, and that the resulting networks by which behavior propagates throughout groups are complex, being weighted, directed, and heterogeneous. By studying these interaction networks, we reveal the (complex, fractional) nature of social contagion and establish that individuals with relatively few, but strongly connected, neighbors are both most socially influential and most susceptible to social influence. Furthermore, we demonstrate that we can predict complex cascades of behavioral change at their moment of initiation, before they actually occur. Consequently, despite the intrinsic stochasticity of individual behavior, establishing the hidden communication networks in large self-organized groups facilitates a quantitative understanding of behavioral contagion.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                8 October 2019
                23 September 2019
                23 September 2019
                : 116
                : 41
                : 20556-20561
                Affiliations
                [1] aDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544;
                [2] bDepartment of Biology, University of Pennsylvania, Philadelphia, PA 19104;
                [3] cInstitute for Theoretical Biology, Department of Biology, Humboldt Universität zu Berlin, D-10099 Berlin, Germany;
                [4] dBernstein Center for Computational Neuroscience Berlin, Humboldt Universität zu Berlin , D-10115 Berlin, Germany;
                [5] eArizona State University-Santa Fe Institute (ASU–SFI) Center for Biosocial Complex Systems, Arizona State University, Tempe, AZ 85287;
                [6] fDepartment of Collective Behaviour, Max Planck Institute of Animal Behavior, D-78547 Konstanz, Germany;
                [7] gDepartment of Biology, University of Konstanz, D-78547 Konstanz, Germany;
                [8] hCentre for the Advanced Study of Collective Behaviour, University of Konstanz, D-78547 Konstanz, Germany
                Author notes
                1To whom correspondence may be addressed. Email: matt.g.sosna@ 123456gmail.com or icouzin@ 123456ab.mpg.de .

                Edited by Gene E. Robinson, University of Illinois at Urbana–Champaign, Urbana, IL, and approved August 28, 2019 (received for review April 1, 2019)

                Author contributions: M.M.G.S., J.B.-C., and I.D.C. designed research; M.M.G.S. performed research; C.R.T., W.P., B.C.D., and P.R. contributed new reagents/analytic tools; M.M.G.S. and W.P. analyzed data; M.M.G.S., C.R.T., J.B.-C., W.P., B.C.D., P.R., and I.D.C. wrote the paper; and C.R.T., J.B.-C., W.P., B.C.D., and P.R. developed the mathematical model and performed and analyzed numerical simulations.

                Author information
                http://orcid.org/0000-0002-7590-3824
                http://orcid.org/0000-0002-5870-1059
                http://orcid.org/0000-0002-4733-998X
                http://orcid.org/0000-0001-8556-4558
                Article
                201905585
                10.1073/pnas.1905585116
                6789631
                31548427
                d947829e-101a-40dc-a3ad-78c436b5ec48
                Copyright © 2019 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 6
                Funding
                Funded by: Deutsche Forschungsgemeinschaft (DFG) 501100001659
                Award ID: EXC 2002/1
                Award Recipient : Pawel Romanczuk Award Recipient : Iain D Couzin
                Funded by: Deutsche Forschungsgemeinschaft (DFG) 501100001659
                Award ID: RO47766/2-1
                Award Recipient : Pawel Romanczuk Award Recipient : Iain D Couzin
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: IOS-1355061
                Award Recipient : Iain D Couzin
                Funded by: DOD | United States Navy | Office of Naval Research (ONR) 100000006
                Award ID: N00014-09-1-1074
                Award Recipient : Iain D Couzin
                Funded by: DOD | United States Navy | Office of Naval Research (ONR) 100000006
                Award ID: N00014-14-1-0635
                Award Recipient : Iain D Couzin
                Funded by: Deutsche Forschungsgemeinschaft (DFG) 501100001659
                Award ID: 422037984
                Award Recipient : Pawel Romanczuk Award Recipient : Iain D Couzin
                Categories
                Biological Sciences
                Ecology

                group structure,antipredator behavior,social contagion

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