1
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Amichay G, Li L, Nagy M, Couzin ID. Revealing the mechanism and function underlying pairwise temporal coupling in collective motion. Nat Commun 2024; 15:4356. [PMID: 38778073 PMCID: PMC11111445 DOI: 10.1038/s41467-024-48458-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 04/29/2024] [Indexed: 05/25/2024] Open
Abstract
Coordinated motion in animal groups has predominantly been studied with a focus on spatial interactions, such as how individuals position and orient themselves relative to one another. Temporal aspects have, by contrast, received much less attention. Here, by studying pairwise interactions in juvenile zebrafish (Danio rerio)-including using immersive volumetric virtual reality (VR) with which we can directly test models of social interactions in situ-we reveal that there exists a rhythmic out-of-phase (i.e., an alternating) temporal coordination dynamic. We find that reciprocal (bi-directional) feedback is both necessary and sufficient to explain this emergent coupling. Beyond a mechanistic understanding, we find, both from VR experiments and analysis of freely swimming pairs, that temporal coordination considerably improves spatial responsiveness, such as to changes in the direction of motion of a partner. Our findings highlight the synergistic role of spatial and temporal coupling in facilitating effective communication between individuals on the move.
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Affiliation(s)
- Guy Amichay
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Universitätsstraße 10, 78464, Konstanz, Germany.
- Department of Collective Behaviour, Max-Planck Institute of Animal Behavior, Konstanz, Germany.
- Department of Biology, University of Konstanz, Konstanz, Germany.
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA.
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA.
| | - Liang Li
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Universitätsstraße 10, 78464, Konstanz, Germany
- Department of Collective Behaviour, Max-Planck Institute of Animal Behavior, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Máté Nagy
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Universitätsstraße 10, 78464, Konstanz, Germany.
- Department of Collective Behaviour, Max-Planck Institute of Animal Behavior, Konstanz, Germany.
- Department of Biology, University of Konstanz, Konstanz, Germany.
- MTA-ELTE Lendület Collective Behaviour Research Group, Hungarian Academy of Sciences, Budapest, Hungary.
- ELTE Eötvös Loránd University, Department of Biological Physics, Budapest, Hungary.
| | - Iain D Couzin
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Universitätsstraße 10, 78464, Konstanz, Germany.
- Department of Collective Behaviour, Max-Planck Institute of Animal Behavior, Konstanz, Germany.
- Department of Biology, University of Konstanz, Konstanz, Germany.
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2
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Puy A, Gimeno E, Torrents J, Bartashevich P, Miguel MC, Pastor-Satorras R, Romanczuk P. Selective social interactions and speed-induced leadership in schooling fish. Proc Natl Acad Sci U S A 2024; 121:e2309733121. [PMID: 38662546 PMCID: PMC11067465 DOI: 10.1073/pnas.2309733121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 03/27/2024] [Indexed: 05/05/2024] Open
Abstract
Animals moving together in groups are believed to interact among each other with effective social forces, such as attraction, repulsion, and alignment. Such forces can be inferred using "force maps," i.e., by analyzing the dependency of the acceleration of a focal individual on relevant variables. Here, we introduce a force map technique suitable for the analysis of the alignment forces experienced by individuals. After validating it using an agent-based model, we apply the force map to experimental data of schooling fish. We observe signatures of an effective alignment force with faster neighbors and an unexpected antialignment with slower neighbors. Instead of an explicit antialignment behavior, we suggest that the observed pattern is the result of a selective attention mechanism, where fish pay less attention to slower neighbors. This mechanism implies the existence of temporal leadership interactions based on relative speeds between neighbors. We present support for this hypothesis both from agent-based modeling as well as from exploring leader-follower relationships in the experimental data.
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Affiliation(s)
- Andreu Puy
- Departament de Física, Universitat Politècnica de Catalunya, Barcelona08034, Spain
| | - Elisabet Gimeno
- Departament de Física, Universitat Politècnica de Catalunya, Barcelona08034, Spain
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona08028, Spain
| | - Jordi Torrents
- Departament de Física, Universitat Politècnica de Catalunya, Barcelona08034, Spain
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona08028, Spain
| | - Palina Bartashevich
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin10115, Germany
- Excellence Cluster Science of Intelligence, Technische Universität Berlin, Berlin10587, Germany
| | - M. Carmen Miguel
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona08028, Spain
- Institute of Complex Systems (UBICS), Universitat de Barcelona, Barcelona08028, Spain
| | | | - Pawel Romanczuk
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin10115, Germany
- Excellence Cluster Science of Intelligence, Technische Universität Berlin, Berlin10587, Germany
- Bernstein Center for Computational Neuroscience, Berlin10115, Germany
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3
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Aidan Y, Bleichman I, Ayali A. Pausing to swarm: locust intermittent motion is instrumental for swarming-related visual processing. Biol Lett 2024; 20:20230468. [PMID: 38378141 PMCID: PMC10878801 DOI: 10.1098/rsbl.2023.0468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 01/24/2024] [Indexed: 02/22/2024] Open
Abstract
Intermittent motion is prevalent in animal locomotion. Of special interest is the case of collective motion, in which social and environmental information must be processed in order to establish coordinated movement. We explored this nexus in locust, focusing on how intermittent motion interacts with swarming-related visual-based decision-making. Using a novel approach, we compared individual locust behaviour in response to continuously moving stimuli, with their response in semi-closed-loop conditions, in which the stimuli moved either in phase with the locust walking, or out of phase, i.e. only during the locust's pauses. Our findings clearly indicate the greater tendency of a locust to respond and 'join the swarming motion' when the visual stimuli were presented during its pauses. Hence, the current study strongly confirms previous indications of the dominant role of pauses in the collective motion-related decision-making of locusts. The presented insights contribute to a deeper general understanding of how intermittent motion contributes to group cohesion and coordination in animal swarms.
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Affiliation(s)
- Yossef Aidan
- School of Zoology, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Itay Bleichman
- School of Zoology, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Amir Ayali
- School of Zoology, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
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4
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Patro KSK, Yadav VK, Bharti VS, Sharma A, Sharma A, Senthilkumar T. IoT and ML approach for ornamental fish behaviour analysis. Sci Rep 2023; 13:21415. [PMID: 38049427 PMCID: PMC10696071 DOI: 10.1038/s41598-023-48057-w] [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: 07/14/2023] [Accepted: 11/21/2023] [Indexed: 12/06/2023] Open
Abstract
Ornamental fish keeping is the second most preferred hobby in the world and it provides a great opportunity for entrepreneurship development and income generation. Controlling the environment in ornamental fish farm is a considerable challenge because it is affected by a variety of parameters like water temperature, dissolved oxygen, pH, and disease occurrences. One particular interesting ornamental fish species is goldfish (Carassius auratus). Machine learning (ML) and deep learning technique have significant potential in analysing voluminous data collected from fish farm. Through this technique, the fish farmers can get insight on feeding behaviour, fish growth patterns, predict diseases/stress, and environmental factors affecting fish health. The aim of the study is to analyze the behavioural changes in goldfish due to alterations in environmental parameters (water temperature and dissolved oxygen). Decision tree, Naïve Bayes classifier, K-nearest neighbour (KNN), and linear discriminant analysis (LDA) were used to analyse the behavioural change data. To compare the performance between all four classifiers, cross validation and confusion matrix used. The cross-validation error of LDA, Naïve Bayes classification, KNN and decision tree was 19.86, 28.08, 30.14 and 13.78 respectively. Decision tree was proved to be the most accurate and effective classifier. Different temperature and DO range were taken to predict fish behaviour. Some findings are, the behaviour of fish was rest between temperature 37.85 °C and 40.535 °C, erratic when temperature was greater than or equal to 40.535 °C, gasping when temperature was between 37.85 and 40.535 °C and when DO concentration was less than 6.58 mg/L. Blood parameter analysis has been done to validate the change in external behaviours with change in physiological parameters.
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Affiliation(s)
- K Suresh Kumar Patro
- Fisheries Economics, Extension & Statistics Division (FEESD), ICAR-Central Institute of Fisheries Education, Mumbai, 400061, India
| | - Vinod Kumar Yadav
- Fisheries Economics, Extension & Statistics Division (FEESD), ICAR-Central Institute of Fisheries Education, Mumbai, 400061, India.
| | - Vidya S Bharti
- Aquatic Environment & Health Management Division (AEHMD), ICAR-Central Institute of Fisheries Education, Mumbai, 400061, India
| | - Arun Sharma
- Aquatic Environment & Health Management Division (AEHMD), ICAR-Central Institute of Fisheries Education, Mumbai, 400061, India
| | - Arpita Sharma
- Fisheries Economics, Extension & Statistics Division (FEESD), ICAR-Central Institute of Fisheries Education, Mumbai, 400061, India
| | - T Senthilkumar
- Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India
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5
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Mudaliar RK, Schaerf TM. An examination of force maps targeted at orientation interactions in moving groups. PLoS One 2023; 18:e0286810. [PMID: 37676869 PMCID: PMC10484433 DOI: 10.1371/journal.pone.0286810] [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: 12/15/2022] [Accepted: 05/23/2023] [Indexed: 09/09/2023] Open
Abstract
Force mapping is an established method for inferring the underlying interaction rules thought to govern collective motion from trajectory data. Here we examine the ability of force maps to reconstruct interactions that govern individual's tendency to orient, or align, their heading within a moving group, one of the primary factors thought to drive collective motion, using data from three established general collective motion models. Specifically, our force maps extract how individuals adjust their direction of motion on average as a function of the distance to neighbours and relative alignment in heading with these neighbours, or in more detail as a function of the relative coordinates and relative headings of neighbours. We also examine the association between plots of local alignment and underlying alignment rules. We find that the simpler force maps that examined changes in heading as a function of neighbour distances and differences in heading can qualitatively reconstruct the form of orientation interactions, but also overestimate the spatial range over which these interactions apply. More complex force maps that examine heading changes as a function of the relative coordinates of neighbours (in two spatial dimensions), can also reveal underlying orientation interactions in some cases, but are relatively harder to interpret. Responses to neighbours in both the simpler and more complex force maps are affected by group-level patterns of motion. We also find a correlation between the sizes of regions of high alignment in local alignment plots and the size of the region over which alignment rules apply when only an alignment interaction rule is in action. However, when data derived from more complex models is analysed, the shapes of regions of high alignment are clearly influenced by emergent patterns of motion, and these regions of high alignment can appear even when there is no explicit direct mechanism that governs alignment.
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Affiliation(s)
- Rajnesh K. Mudaliar
- School of Mathematical and Computing Science, Fiji National University, Ba, Fiji
- School of Science and Technology, University of New England, Armidale, NSW, Australia
| | - Timothy M. Schaerf
- School of Science and Technology, University of New England, Armidale, NSW, Australia
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6
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Sridhar VH, Davidson JD, Twomey CR, Sosna MMG, Nagy M, Couzin ID. Inferring social influence in animal groups across multiple timescales. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220062. [PMID: 36802787 PMCID: PMC9939267 DOI: 10.1098/rstb.2022.0062] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023] Open
Abstract
Many animal behaviours exhibit complex temporal dynamics, suggesting there are multiple timescales at which they should be studied. However, researchers often focus on behaviours that occur over relatively restricted temporal scales, typically ones that are more accessible to human observation. The situation becomes even more complex when considering multiple animals interacting, where behavioural coupling can introduce new timescales of importance. Here, we present a technique to study the time-varying nature of social influence in mobile animal groups across multiple temporal scales. As case studies, we analyse golden shiner fish and homing pigeons, which move in different media. By analysing pairwise interactions among individuals, we show that predictive power of the factors affecting social influence depends on the timescale of analysis. Over short timescales the relative position of a neighbour best predicts its influence and the distribution of influence across group members is relatively linear, with a small slope. At longer timescales, however, both relative position and kinematics are found to predict influence, and nonlinearity in the influence distribution increases, with a small number of individuals being disproportionately influential. Our results demonstrate that different interpretations of social influence arise from analysing behaviour at different timescales, highlighting the importance of considering its multiscale nature. This article is part of a discussion meeting issue 'Collective behaviour through time'.
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Affiliation(s)
- Vivek H. Sridhar
- Department of Biology, University of Konstanz, 78464 Konstanz, Germany,Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany,Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany,Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, 78467 Konstanz, Germany
| | - Jacob D. Davidson
- Department of Biology, University of Konstanz, 78464 Konstanz, Germany,Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany,Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany
| | - Colin R. Twomey
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA,Mind Center for Outreach, Research, and Education, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew M. G. Sosna
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Máté Nagy
- Department of Biology, University of Konstanz, 78464 Konstanz, Germany,Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany,Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany,MTA-ELTE Statistical and Biological Physics Research Group, Hungarian Academy of Sciences, Budapest 1117, Hungary,MTA-ELTE ‘Lendület’ Collective Behaviour Research Group, Hungarian Academy of Sciences, Eötvös Loránd University, Budapest 1117, Hungary,Department of Biological Physics, Eötvös Loránd University, Pázmány Péter sétány 1A, Budapest 1117, Hungary
| | - Iain D. Couzin
- Department of Biology, University of Konstanz, 78464 Konstanz, Germany,Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany,Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany
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7
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Tiwari A, Devasia S, Riley JJ. Low-distortion information propagation with noise suppression in swarm networks. Proc Natl Acad Sci U S A 2023; 120:e2219948120. [PMID: 36897967 PMCID: PMC10089222 DOI: 10.1073/pnas.2219948120] [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: 11/22/2022] [Accepted: 02/01/2023] [Indexed: 03/12/2023] Open
Abstract
A method for low-distortion (low-dissipation, low-dispersion) information propagation in swarm-type networks with suppression of high-frequency noise is presented. Information propagation in current neighbor-based networks, where each agent seeks to achieve a consensus with its neighbors, is diffusion-like, dissipative, and dispersive and does not reflect the wave-like (superfluidic) behavior seen in nature. However, pure wave-like neighbor-based networks have two challenges: i) It requires additional communication for sharing information about time derivatives and ii) it can lead to information decoherence through noise at high frequencies. The main contribution of this work is to show that delayed self-reinforcement (DSR) by the agents using prior information (e.g., using short-term memory) can lead to the wave-like information propagation at low-frequencies as seen in nature without the need for additional information sharing between the agents. Moreover, it is shown that the DSR can be designed to enable suppression of high-frequency noise transmission while limiting the dissipation and dispersion of (lower-frequency) information content leading to similar (cohesive) behavior of agents. In addition to explaining noise-suppressed wave-like information transfer in natural systems, the result impacts the design of noise-suppressing cohesive algorithms for engineered networks.
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Affiliation(s)
- Anuj Tiwari
- Mechanical Engineering Department, University of Washington, Seattle, WA98195
| | - Santosh Devasia
- Mechanical Engineering Department, University of Washington, Seattle, WA98195
| | - James J. Riley
- Mechanical Engineering Department, University of Washington, Seattle, WA98195
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8
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Geng Y, Yates C, Peterson RT. Social behavioral profiling by unsupervised deep learning reveals a stimulative effect of dopamine D3 agonists on zebrafish sociality. CELL REPORTS METHODS 2023; 3:100381. [PMID: 36814839 PMCID: PMC9939379 DOI: 10.1016/j.crmeth.2022.100381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 11/15/2022] [Accepted: 12/12/2022] [Indexed: 01/07/2023]
Abstract
It has been a major challenge to systematically evaluate and compare how pharmacological perturbations influence social behavioral outcomes. Although some pharmacological agents are known to alter social behavior, precise description and quantification of such effects have proven difficult. We developed a scalable social behavioral assay for zebrafish named ZeChat based on unsupervised deep learning to characterize sociality at high resolution. High-dimensional and dynamic social behavioral phenotypes are automatically classified using this method. By screening a neuroactive compound library, we found that different classes of chemicals evoke distinct patterns of social behavioral fingerprints. By examining these patterns, we discovered that dopamine D3 agonists possess a social stimulative effect on zebrafish. The D3 agonists pramipexole, piribedil, and 7-hydroxy-DPAT-HBr rescued social deficits in a valproic-acid-induced zebrafish autism model. The ZeChat platform provides a promising approach for dissecting the pharmacology of social behavior and discovering novel social-modulatory compounds.
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Affiliation(s)
- Yijie Geng
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA
| | - Christopher Yates
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA
| | - Randall T. Peterson
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA
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9
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Sbragaglia V, Klamser PP, Romanczuk P, Arlinghaus R. Evolutionary impact of size-selective harvesting on shoaling behavior: Individual-level mechanisms and possible consequences for natural and fishing mortality. Am Nat 2021; 199:480-495. [DOI: 10.1086/718591] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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10
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Harpaz R, Nguyen MN, Bahl A, Engert F. Precise visuomotor transformations underlying collective behavior in larval zebrafish. Nat Commun 2021; 12:6578. [PMID: 34772934 PMCID: PMC8590009 DOI: 10.1038/s41467-021-26748-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 10/19/2021] [Indexed: 12/21/2022] Open
Abstract
Complex schooling behaviors result from local interactions among individuals. Yet, how sensory signals from neighbors are analyzed in the visuomotor stream of animals is poorly understood. Here, we studied aggregation behavior in larval zebrafish and found that over development larvae transition from overdispersed groups to tight shoals. Using a virtual reality assay, we characterized the algorithms fish use to transform visual inputs from neighbors into movement decisions. We found that young larvae turn away from virtual neighbors by integrating and averaging retina-wide visual occupancy within each eye, and by using a winner-take-all strategy for binocular integration. As fish mature, their responses expand to include attraction to virtual neighbors, which is based on similar algorithms of visual integration. Using model simulations, we show that the observed algorithms accurately predict group structure over development. These findings allow us to make testable predictions regarding the neuronal circuits underlying collective behavior in zebrafish.
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Affiliation(s)
- Roy Harpaz
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, 02138, USA.
- Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA.
| | - Minh Nguyet Nguyen
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Armin Bahl
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, 78464, Germany
| | - Florian Engert
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, 02138, USA
- Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA
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11
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Harpaz R, Aspiras AC, Chambule S, Tseng S, Bind MA, Engert F, Fishman MC, Bahl A. Collective behavior emerges from genetically controlled simple behavioral motifs in zebrafish. SCIENCE ADVANCES 2021; 7:eabi7460. [PMID: 34613782 PMCID: PMC8494438 DOI: 10.1126/sciadv.abi7460] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 08/13/2021] [Indexed: 06/13/2023]
Abstract
It is not understood how changes in the genetic makeup of individuals alter the behavior of groups of animals. Here, we find that, even at early larval stages, zebrafish regulate their proximity and alignment with each other. Two simple visual responses, one that measures relative visual field occupancy and one that accounts for global visual motion, suffice to account for the group behavior that emerges. Mutations in genes known to affect social behavior in humans perturb these simple reflexes in individual larval zebrafish and change their emergent collective behaviors in the predicted fashion. Model simulations show that changes in these two responses in individual mutant animals predict well the distinctive collective patterns that emerge in a group. Hence, group behaviors reflect in part genetically defined primitive sensorimotor “motifs,” which are evident even in young larvae.
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Affiliation(s)
- Roy Harpaz
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Ariel C. Aspiras
- Harvard Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Sydney Chambule
- Harvard Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Sierra Tseng
- Harvard Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Marie-Abèle Bind
- Biostatistics Center, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Florian Engert
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Mark C. Fishman
- Harvard Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Armin Bahl
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz 78464, Germany
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12
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Currie HAL, White PR, Leighton TG, Kemp PS. Collective behaviour of the European minnow (Phoxinus phoxinus) is influenced by signals of differing acoustic complexity. Behav Processes 2021; 189:104416. [PMID: 33971249 DOI: 10.1016/j.beproc.2021.104416] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 04/30/2021] [Accepted: 05/05/2021] [Indexed: 01/01/2023]
Abstract
Collective behaviour, such as shoaling in fish, benefits individuals through a variety of activities such as social information exchange and anti-predator defence. Human driven disturbance (e.g. anthropogenic noise) is known to affect the behaviour and physiology of individual animals, but the disruption of social aggregations of fish remains poorly understood. Anthropogenic noise originates from a variety of activities and differs in acoustic structure, dominant frequencies, and spectral complexity. The response of groups of fish may differ greatly, depending on the type of noise, and how it is perceived (e.g. threatening or attractive). In a controlled laboratory study, high resolution video tracking in combination with fine scale acoustic mapping was used to investigate the response of groups of European minnows (Phoxinus phoxinus) to signals of differing acoustic complexity (sinewave tones vs octave band noise) under low (150 Hz) and high (2200 Hz) frequencies. Fish startled and decreased their mean group swimming speed under all four treatments, with low frequency sinewave tones having the greatest influence on group behaviour. The shoals exhibited spatial avoidance during both low frequency treatments, with more time spent in areas of lower acoustic intensity than expected. This study illustrates how noise can influence the spatial distribution and social dynamics within groups of fish, and owing to the high potential for freshwater aquatic environments to be influenced by anthropogenic activity, wider consequences for populations should be further investigated.
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Affiliation(s)
- Helen A L Currie
- International Centre for Ecohydraulics Research (ICER), University of Southampton, Boldrewood Innovation Campus, Southampton, SO16 7QF, UK.
| | - Paul R White
- Institute of Sound and Vibration Research, University of Southampton, Highfield, Southampton, SO17 1BJ, UK
| | - Timothy G Leighton
- Institute of Sound and Vibration Research, University of Southampton, Highfield, Southampton, SO17 1BJ, UK
| | - Paul S Kemp
- International Centre for Ecohydraulics Research (ICER), University of Southampton, Boldrewood Innovation Campus, Southampton, SO16 7QF, UK
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13
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Schaerf TM, Herbert-Read JE, Ward AJW. A statistical method for identifying different rules of interaction between individuals in moving animal groups. J R Soc Interface 2021; 18:20200925. [PMID: 33784885 DOI: 10.1098/rsif.2020.0925] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
The emergent patterns of collective motion are thought to arise from application of individual-level rules that govern how individuals adjust their velocity as a function of the relative position and behaviours of their neighbours. Empirical studies have sought to determine such rules of interaction applied by 'average' individuals by aggregating data from multiple individuals across multiple trajectory sets. In reality, some individuals within a group may interact differently from others, and such individual differences can have an effect on overall group movement. However, comparisons of rules of interaction used by individuals in different contexts have been largely qualitative. Here we introduce a set of randomization methods designed to determine statistical differences in the rules of interaction between individuals. We apply these methods to a case study of leaders and followers in pairs of freely exploring eastern mosquitofish (Gambusia holbrooki). We find that each of the randomization methods is reliable in terms of: repeatability of p-values, consistency in identification of significant differences and similarity between distributions of randomization-based test statistics. We observe convergence of the distributions of randomization-based test statistics across repeat calculations, and resolution of any ambiguities regarding significant differences as the number of randomization iterations increases.
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Affiliation(s)
- T M Schaerf
- School of Science and Technology, University of New England, Armidale, New South Wales 2351, Australia
| | - J E Herbert-Read
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK.,Aquatic Ecology, University of Lund, Lund 223 62, Sweden
| | - A J W Ward
- Animal Behaviour Lab, School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales 2006, Australia
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14
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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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15
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Li G, Ashraf I, François B, Kolomenskiy D, Lechenault F, Godoy-Diana R, Thiria B. Burst-and-coast swimmers optimize gait by adapting unique intrinsic cycle. Commun Biol 2021; 4:40. [PMID: 33446863 PMCID: PMC7809443 DOI: 10.1038/s42003-020-01521-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 11/17/2020] [Indexed: 11/28/2022] Open
Abstract
This paper addresses the physical mechanism of intermittent swimming by considering the burst-and-coast regime of fish swimming at different speeds. The burst-and-coast regime consists of a cycle with two successive phases, i.e., a phase of active undulation powered by the fish muscles followed by a passive gliding phase. Observations of real fish whose swimming gait is forced in a water flume from low to high speed regimes are performed, using a full description of the fish kinematics and mechanics. We first show that fish modulate a unique intrinsic cycle to sustain the demanded speed by modifying the bursting to coasting ratio while maintaining the duration of the cycle nearly constant. Secondly, we show using numerical simulations that the chosen kinematics by correspond to optimized gaits for swimming speeds larger than 1 body length per second. Li et al. use experimental observations of red-nose tetrafish and mathematical simulations to model the burst-and-coast swimming regime. This study shows that in order to sustain the necessary speed, fish adopt a unique intrinsic cycle by modifying the burst to coast ratio and can implement this pattern at a range of swimming speeds.
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Affiliation(s)
- Gen Li
- Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan
| | - Intesaaf Ashraf
- Laboratoire de Physique et Mécanique des Milieux Hétérogènes (PMMH), CNRS UMR 7636, ESPCI Paris-PSL University, Sorbonne Université, Université de Paris, 75005, Paris, France
| | - Bill François
- Laboratoire de Physique et Mécanique des Milieux Hétérogènes (PMMH), CNRS UMR 7636, ESPCI Paris-PSL University, Sorbonne Université, Université de Paris, 75005, Paris, France
| | - Dmitry Kolomenskiy
- Global Scientific Information and Computing Center, Tokyo Institute of Technology, Tokyo, Japan
| | - Frédéric Lechenault
- Laboratoire de Physique de l'École Normale Supérieure (LPENS), 75005, Paris, France
| | - Ramiro Godoy-Diana
- Laboratoire de Physique et Mécanique des Milieux Hétérogènes (PMMH), CNRS UMR 7636, ESPCI Paris-PSL University, Sorbonne Université, Université de Paris, 75005, Paris, France.
| | - Benjamin Thiria
- Laboratoire de Physique et Mécanique des Milieux Hétérogènes (PMMH), CNRS UMR 7636, ESPCI Paris-PSL University, Sorbonne Université, Université de Paris, 75005, Paris, France.
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16
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McKee A, Soto AP, Chen P, McHenry MJ. The sensory basis of schooling by intermittent swimming in the rummy-nose tetra ( Hemigrammus rhodostomus). Proc Biol Sci 2020; 287:20200568. [PMID: 33109007 DOI: 10.1098/rspb.2020.0568] [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: 11/12/2022] Open
Abstract
Schooling is a collective behaviour that enhances the ability of a fish to sense and respond to its environment. Although schooling is essential to the biology of a diversity of fishes, it is generally unclear how this behaviour is coordinated by different sensory modalities. We used experimental manipulation and kinematic measurements to test the role of vision and flow sensing in the rummy-nose tetra (Hemigrammus rhodostomus), which swims with intermittent phases of bursts and coasts. Groups of five fish required a minimum level of illuminance (greater than 1.5 lx) to achieve the necessary close nearest-neighbour distance and high polarization for schooling. Compromising the lateral line system with an antibiotic treatment caused tetras to swim with greater nearest-neighbour distance and lower polarization. Therefore, vision is both necessary and sufficient for schooling in H. rhodostomus, and both sensory modalities aid in attraction. These results can serve as a basis for understanding the individual roles of sensory modalities in schooling for some fish species.
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Affiliation(s)
- Amberle McKee
- Department of Ecology and Evolutionary Biology, University of California, Irvine, 321 Steinhaus Hall, Irvine, CA 92697, USA
| | - Alberto P Soto
- Department of Ecology and Evolutionary Biology, University of California, Irvine, 321 Steinhaus Hall, Irvine, CA 92697, USA
| | - Phoebe Chen
- Department of Ecology and Evolutionary Biology, University of California, Irvine, 321 Steinhaus Hall, Irvine, CA 92697, USA
| | - Matthew J McHenry
- Department of Ecology and Evolutionary Biology, University of California, Irvine, 321 Steinhaus Hall, Irvine, CA 92697, USA
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17
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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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18
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Twomey CR, Hartnett AT, Sosna MMG, Romanczuk P. Searching for structure in collective systems. Theory Biosci 2020; 140:361-377. [PMID: 32206979 PMCID: PMC8629805 DOI: 10.1007/s12064-020-00311-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Accepted: 02/20/2020] [Indexed: 11/24/2022]
Abstract
From fish schools and bird flocks to biofilms and neural networks, collective systems in nature are made up of many mutually influencing individuals that interact locally to produce large-scale coordinated behavior. Although coordination is central to what it means to behave collectively, measures of large-scale coordination in these systems are ad hoc and system specific. The lack of a common quantitative scale makes broad cross-system comparisons difficult. Here we identify a system-independent measure of coordination based on an information-theoretic measure of multivariate dependence and show it can be used in practice to give a new view of even classic, well-studied collective systems. Moreover, we use this measure to derive a novel method for finding the most coordinated components within a system and demonstrate how this can be used in practice to reveal intrasystem organizational structure.
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Affiliation(s)
- Colin R Twomey
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA.
| | | | - Matthew M G Sosna
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Pawel Romanczuk
- Institute for Theoretical Biology, Department of Biology, Humboldt Universität zu Berlin, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany
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19
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Currie HAL, White PR, Leighton TG, Kemp PS. Group behavior and tolerance of Eurasian minnow (Phoxinus phoxinus) in response to tones of differing pulse repetition rate. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2020; 147:1709. [PMID: 32237844 DOI: 10.1121/10.0000910] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 02/21/2020] [Indexed: 06/11/2023]
Abstract
Behavioral guidance systems are commonly used in freshwater fish conservation. The biological relevance of sound to fish and recorded responses to human-generated noise supports the viability of the use of acoustics as an effective stimulus in such technologies. Relatively little information exists on the long-term responses and recovery of fish to repeated acoustic exposures. In a controlled laboratory study, the response and tolerance of Eurasian minnow (Phoxinus phoxinus) shoals to tonal signals (150 Hz of 1 s pulse duration) differing only in temporal characteristics ("continuous," "slow," "intermediate," or "fast" pulse repetition rate) were investigated. In comparison to independent control groups, fish increased their mean group swimming speed, decreased inter-individual distance, and became more aligned in response to the onset of all four acoustic treatments. The magnitude of response, and time taken to develop a tolerance to a treatment differed according to pulse repetition rate. Groups were found to have the greatest and longest lasting response to tone sequences tested in this study when they were pulsed at an intermediate rate of 0.2 s-1. This study illustrates the importance of understanding the response of fish to acoustic signals, and will assist toward the development of longer-term effective acoustic guidance systems.
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Affiliation(s)
- Helen A L Currie
- International Centre for Ecohydraulics Research (ICER), Boldrewood Innovation Campus, University of Southampton, Southampton, SO16 7QF, United Kingdom
| | - Paul R White
- Institute of Sound and Vibration Research (ISVR), Highfield Campus, University of Southampton, Southampton, SO17 1BJ, United Kingdom
| | - Timothy G Leighton
- Institute of Sound and Vibration Research (ISVR), Highfield Campus, University of Southampton, Southampton, SO17 1BJ, United Kingdom
| | - Paul S Kemp
- International Centre for Ecohydraulics Research (ICER), Boldrewood Innovation Campus, University of Southampton, Southampton, SO16 7QF, United Kingdom
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20
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Tang W, Davidson JD, Zhang G, Conen KE, Fang J, Serluca F, Li J, Xiong X, Coble M, Tsai T, Molind G, Fawcett CH, Sanchez E, Zhu P, Couzin ID, Fishman MC. Genetic Control of Collective Behavior in Zebrafish. iScience 2020; 23:100942. [PMID: 32179471 PMCID: PMC7068127 DOI: 10.1016/j.isci.2020.100942] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 01/17/2020] [Accepted: 02/21/2020] [Indexed: 01/02/2023] Open
Abstract
Many animals, including humans, have evolved to live and move in groups. In humans, disrupted social interactions are a fundamental feature of many psychiatric disorders. However, we know little about how genes regulate social behavior. Zebrafish may serve as a powerful model to explore this question. By comparing the behavior of wild-type fish with 90 mutant lines, we show that mutations of genes associated with human psychiatric disorders can alter the collective behavior of adult zebrafish. We identify three categories of behavioral variation across mutants: "scattered," in which fish show reduced cohesion; "coordinated," in which fish swim more in aligned schools; and "huddled," in which fish form dense but disordered groups. Changes in individual interaction rules can explain these differences. This work demonstrates how emergent patterns in animal groups can be altered by genetic changes in individuals and establishes a framework for understanding the fundamentals of social information processing.
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Affiliation(s)
- Wenlong Tang
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Jacob D Davidson
- Department of Collective Behavior, Max Planck Institute of Animal Behavior, Universitätstraße 10, 78764 Konstanz, Germany; Centre for the Advanced Study of Collective Behavior, University of Konstanz, Universitätstraße 10, 78764 Konstanz, Germany; Department of Biology, University of Konstanz, Universitätstraße 10, 78764 Konstanz, Germany
| | - Guoqiang Zhang
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Katherine E Conen
- Department of Collective Behavior, Max Planck Institute of Animal Behavior, Universitätstraße 10, 78764 Konstanz, Germany; Centre for the Advanced Study of Collective Behavior, University of Konstanz, Universitätstraße 10, 78764 Konstanz, Germany; Department of Biology, University of Konstanz, Universitätstraße 10, 78764 Konstanz, Germany
| | - Jian Fang
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Fabrizio Serluca
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Jingyao Li
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Xiaorui Xiong
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Matthew Coble
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Tingwei Tsai
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Gregory Molind
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Caroline H Fawcett
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Ellen Sanchez
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Peixin Zhu
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Iain D Couzin
- Department of Collective Behavior, Max Planck Institute of Animal Behavior, Universitätstraße 10, 78764 Konstanz, Germany; Centre for the Advanced Study of Collective Behavior, University of Konstanz, Universitätstraße 10, 78764 Konstanz, Germany; Department of Biology, University of Konstanz, Universitätstraße 10, 78764 Konstanz, Germany.
| | - Mark C Fishman
- Department of Stem Cell and Regenerative Biology, Harvard University, 7 Divinity Avenue, Cambridge, MA 02138, USA.
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21
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Johnson RE, Linderman S, Panier T, Wee CL, Song E, Herrera KJ, Miller A, Engert F. Probabilistic Models of Larval Zebrafish Behavior Reveal Structure on Many Scales. Curr Biol 2020; 30:70-82.e4. [PMID: 31866367 PMCID: PMC6958995 DOI: 10.1016/j.cub.2019.11.026] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 09/11/2019] [Accepted: 11/07/2019] [Indexed: 12/12/2022]
Abstract
Nervous systems have evolved to combine environmental information with internal state to select and generate adaptive behavioral sequences. To better understand these computations and their implementation in neural circuits, natural behavior must be carefully measured and quantified. Here, we collect high spatial resolution video of single zebrafish larvae swimming in a naturalistic environment and develop models of their action selection across exploration and hunting. Zebrafish larvae swim in punctuated bouts separated by longer periods of rest called interbout intervals. We take advantage of this structure by categorizing bouts into discrete types and representing their behavior as labeled sequences of bout types emitted over time. We then construct probabilistic models-specifically, marked renewal processes-to evaluate how bout types and interbout intervals are selected by the fish as a function of its internal hunger state, behavioral history, and the locations and properties of nearby prey. Finally, we evaluate the models by their predictive likelihood and their ability to generate realistic trajectories of virtual fish swimming through simulated environments. Our simulations capture multiple timescales of structure in larval zebrafish behavior and expose many ways in which hunger state influences their action selection to promote food seeking during hunger and safety during satiety.
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Affiliation(s)
- Robert Evan Johnson
- Department of Molecular and Cellular Biology, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, USA; Graduate Program in Neuroscience, Harvard University, 220 Longwood Avenue, Boston, MA 02115, USA.
| | - Scott Linderman
- Department of Statistics, Stanford University, 390 Serra Mall, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University, 318 Campus Drive, Stanford, CA 94305, USA
| | - Thomas Panier
- Department of Molecular and Cellular Biology, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, USA; Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, Laboratoire Jean Perrin, 4 Place Jussieu, 75005 Paris, France
| | - Caroline Lei Wee
- Department of Molecular and Cellular Biology, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, USA; Graduate Program in Neuroscience, Harvard University, 220 Longwood Avenue, Boston, MA 02115, USA; Institute of Molecular and Cell Biology, A(∗)STAR, 61 Biopolis Drive, 138673 Singapore, Singapore
| | - Erin Song
- Department of Molecular and Cellular Biology, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, USA
| | - Kristian Joseph Herrera
- Department of Molecular and Cellular Biology, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, USA
| | - Andrew Miller
- Data Science Institute, Columbia University, 550 W 120th Street, New York City, NY 10027, USA
| | - Florian Engert
- Department of Molecular and Cellular Biology, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, USA
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22
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Heras FJH, Romero-Ferrero F, Hinz RC, de Polavieja GG. Deep attention networks reveal the rules of collective motion in zebrafish. PLoS Comput Biol 2019; 15:e1007354. [PMID: 31518357 PMCID: PMC6760814 DOI: 10.1371/journal.pcbi.1007354] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 09/25/2019] [Accepted: 08/21/2019] [Indexed: 12/01/2022] Open
Abstract
A variety of simple models has been proposed to understand the collective motion of animals. These models can be insightful but may lack important elements necessary to predict the motion of each individual in the collective. Adding more detail increases predictability but can make models too complex to be insightful. Here we report that deep attention networks can obtain a model of collective behavior that is simultaneously predictive and insightful thanks to an organization in modules. When using simulated trajectories, the model recovers the ground-truth interaction rule used to generate them, as well as the number of interacting neighbours. For experimental trajectories of large groups of 60-100 zebrafish, Danio rerio, the model obtains that interactions between pairs can approximately be described as repulsive, attractive or as alignment, but only when moving slowly. At high velocities, interactions correspond only to alignment or alignment mixed with repulsion at close distances. The model also shows that each zebrafish decides where to move by aggregating information from the group as a weighted average over neighbours. Weights are higher for neighbours that are close, in a collision path or moving faster in frontal and lateral locations. The network also extracts that the number of interacting individuals is dynamical and typically in the range 8–22, with 1–10 more important ones. Our results suggest that each animal decides by dynamically selecting information from the collective. Simple models have traditionally been very successful, because they usually provide more insight than complicated models. This is particularly true in physics, where simple models can often give highly precise quantitative predictions. However, biology is fundamentally complex and thus it is difficult to find simple models that give precise predictions. To create models that are both precise and insightful, we propose to harness the power of deep neural networks but to confine them into modules with a low number of inputs and outputs. We trained one such model to predict the future turning side of a fish in a collective. By plotting the different modules we obtain insight about how fish interact and how they aggregate information from different neighbours. This aggregation is dynamical and shows that fish can interact with approximately 20 neighbours but can also focus on fewer neighbours, down to 1-2, when some move at higher speed in front or to the sides, are very close or are in a collision path.
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Affiliation(s)
- Francisco J. H. Heras
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
- * E-mail: (FJHH); (GGP)
| | | | - Robert C. Hinz
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Gonzalo G. de Polavieja
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
- * E-mail: (FJHH); (GGP)
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23
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Geng Y, Peterson RT. The zebrafish subcortical social brain as a model for studying social behavior disorders. Dis Model Mech 2019; 12:dmm039446. [PMID: 31413047 PMCID: PMC6737945 DOI: 10.1242/dmm.039446] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Social behaviors are essential for the survival and reproduction of social species. Many, if not most, neuropsychiatric disorders in humans are either associated with underlying social deficits or are accompanied by social dysfunctions. Traditionally, rodent models have been used to model these behavioral impairments. However, rodent assays are often difficult to scale up and adapt to high-throughput formats, which severely limits their use for systems-level science. In recent years, an increasing number of studies have used zebrafish (Danio rerio) as a model system to study social behavior. These studies have demonstrated clear potential in overcoming some of the limitations of rodent models. In this Review, we explore the evolutionary conservation of a subcortical social brain between teleosts and mammals as the biological basis for using zebrafish to model human social behavior disorders, while summarizing relevant experimental tools and assays. We then discuss the recent advances gleaned from zebrafish social behavior assays, the applications of these assays to studying related disorders, and the opportunities and challenges that lie ahead.
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Affiliation(s)
- Yijie Geng
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, 30 S. 2000 East, Salt Lake City, UT 84112, USA
| | - Randall T Peterson
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, 30 S. 2000 East, Salt Lake City, UT 84112, USA
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24
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Abstract
Recent developments in automated tracking allow uninterrupted, high-resolution recording of animal trajectories, sometimes coupled with the identification of stereotyped changes of body pose or other behaviors of interest. Analysis and interpretation of such data represents a challenge: the timing of animal behaviors may be stochastic and modulated by kinematic variables, by the interaction with the environment or with the conspecifics within the animal group, and dependent on internal cognitive or behavioral state of the individual. Existing models for collective motion typically fail to incorporate the discrete, stochastic, and internal-state-dependent aspects of behavior, while models focusing on individual animal behavior typically ignore the spatial aspects of the problem. Here we propose a probabilistic modeling framework to address this gap. Each animal can switch stochastically between different behavioral states, with each state resulting in a possibly different law of motion through space. Switching rates for behavioral transitions can depend in a very general way, which we seek to identify from data, on the effects of the environment as well as the interaction between the animals. We represent the switching dynamics as a Generalized Linear Model and show that: (i) forward simulation of multiple interacting animals is possible using a variant of the Gillespie's Stochastic Simulation Algorithm; (ii) formulated properly, the maximum likelihood inference of switching rate functions is tractably solvable by gradient descent; (iii) model selection can be used to identify factors that modulate behavioral state switching and to appropriately adjust model complexity to data. To illustrate our framework, we apply it to two synthetic models of animal motion and to real zebrafish tracking data.
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Affiliation(s)
- Katarína Bod’ová
- Institute of Science and Technology Austria (IST Austria), Am Campus 1, Klosterneuburg A-3400, Austria
- Department of Mathematical Analysis and Numerical Mathematics, Faculty of Mathematics, Physics and Informatics, Comenius University, Mlynská Dolina, 84248, Bratislava, Slovakia
- * E-mail:
| | - Gabriel J. Mitchell
- Institute of Science and Technology Austria (IST Austria), Am Campus 1, Klosterneuburg A-3400, Austria
| | - Roy Harpaz
- Department of Neurobiology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Elad Schneidman
- Department of Neurobiology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Gašper Tkačik
- Institute of Science and Technology Austria (IST Austria), Am Campus 1, Klosterneuburg A-3400, Austria
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25
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Herbert-Read JE, Rosén E, Szorkovszky A, Ioannou CC, Rogell B, Perna A, Ramnarine IW, Kotrschal A, Kolm N, Krause J, Sumpter DJT. How predation shapes the social interaction rules of shoaling fish. Proc Biol Sci 2017; 284:20171126. [PMID: 28855361 PMCID: PMC5577484 DOI: 10.1098/rspb.2017.1126] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 07/18/2017] [Indexed: 11/21/2022] Open
Abstract
Predation is thought to shape the macroscopic properties of animal groups, making moving groups more cohesive and coordinated. Precisely how predation has shaped individuals' fine-scale social interactions in natural populations, however, is unknown. Using high-resolution tracking data of shoaling fish (Poecilia reticulata) from populations differing in natural predation pressure, we show how predation adapts individuals' social interaction rules. Fish originating from high predation environments formed larger, more cohesive, but not more polarized groups than fish from low predation environments. Using a new approach to detect the discrete points in time when individuals decide to update their movements based on the available social cues, we determine how these collective properties emerge from individuals' microscopic social interactions. We first confirm predictions that predation shapes the attraction-repulsion dynamic of these fish, reducing the critical distance at which neighbours move apart, or come back together. While we find strong evidence that fish align with their near neighbours, we do not find that predation shapes the strength or likelihood of these alignment tendencies. We also find that predation sharpens individuals' acceleration and deceleration responses, implying key perceptual and energetic differences associated with how individuals move in different predation regimes. Our results reveal how predation can shape the social interactions of individuals in groups, ultimately driving differences in groups' collective behaviour.
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Affiliation(s)
- James E Herbert-Read
- Department of Zoology, Stockholm University, Stockholm, Sweden
- Department of Mathematics, Uppsala University, Uppsala, Sweden
| | - Emil Rosén
- Department of Mathematics, Uppsala University, Uppsala, Sweden
| | | | | | - Björn Rogell
- Department of Zoology, Stockholm University, Stockholm, Sweden
| | - Andrea Perna
- Department of Life Sciences, Roehampton University, London, UK
| | - Indar W Ramnarine
- Department of Life Sciences, The University of the West Indies, St Augustine, Trinidad and Tobago
| | | | - Niclas Kolm
- Department of Zoology, Stockholm University, Stockholm, Sweden
| | - Jens Krause
- Faculty of Life Sciences, Albrecht Daniel Thaer-Institut, Humboldt-University zu Berlin, Invalidenstrasse 42, 10115 Berlin, Germany
- Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Department of Biology and Ecology of Fishes, Müggelseedamm 310, 12587 Berlin, Germany
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