Literature DB >> 24312723

From behavioural analyses to models of collective motion in fish schools.

Ugo Lopez1, Jacques Gautrais, Iain D Couzin, Guy Theraulaz.   

Abstract

Fish schooling is a phenomenon of long-lasting interest in ethology and ecology, widely spread across taxa and ecological contexts, and has attracted much interest from statistical physics and theoretical biology as a case of self-organized behaviour. One topic of intense interest is the search of specific behavioural mechanisms at stake at the individual level and from which the school properties emerges. This is fundamental for understanding how selective pressure acting at the individual level promotes adaptive properties of schools and in trying to disambiguate functional properties from non-adaptive epiphenomena. Decades of studies on collective motion by means of individual-based modelling have allowed a qualitative understanding of the self-organization processes leading to collective properties at school level, and provided an insight into the behavioural mechanisms that result in coordinated motion. Here, we emphasize a set of paradigmatic modelling assumptions whose validity remains unclear, both from a behavioural point of view and in terms of quantitative agreement between model outcome and empirical data. We advocate for a specific and biologically oriented re-examination of these assumptions through experimental-based behavioural analysis and modelling.

Entities:  

Keywords:  animal groups; collective behaviour; coordination; fish schools; individual-based model; self-organization

Year:  2012        PMID: 24312723      PMCID: PMC3499128          DOI: 10.1098/rsfs.2012.0033

Source DB:  PubMed          Journal:  Interface Focus        ISSN: 2042-8898            Impact factor:   3.906


  48 in total

1.  Onset of collective and cohesive motion.

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2.  A model of animal movements in a bounded space.

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Journal:  J Theor Biol       Date:  2003-12-21       Impact factor: 2.691

3.  Inferring individual rules from collective behavior.

Authors:  Ryan Lukeman; Yue-Xian Li; Leah Edelstein-Keshet
Journal:  Proc Natl Acad Sci U S A       Date:  2010-06-28       Impact factor: 11.205

4.  Boltzmann and hydrodynamic description for self-propelled particles.

Authors:  Eric Bertin; Michel Droz; Guillaume Grégoire
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2006-08-02

5.  Collective memory and spatial sorting in animal groups.

Authors:  Iain D Couzin; Jens Krause; Richard James; Graeme D Ruxton; Nigel R Franks
Journal:  J Theor Biol       Date:  2002-09-07       Impact factor: 2.691

6.  Collective motion from local attraction.

Authors:  Daniel Strömbom
Journal:  J Theor Biol       Date:  2011-05-23       Impact factor: 2.691

7.  Inferring the structure and dynamics of interactions in schooling fish.

Authors:  Yael Katz; Kolbjørn Tunstrøm; Christos C Ioannou; Cristián Huepe; Iain D Couzin
Journal:  Proc Natl Acad Sci U S A       Date:  2011-07-27       Impact factor: 11.205

8.  Analyzing fish movement as a persistent turning walker.

Authors:  Jacques Gautrais; Christian Jost; Marc Soria; Alexandre Campo; Sébastien Motsch; Richard Fournier; Stéphane Blanco; Guy Theraulaz
Journal:  J Math Biol       Date:  2008-06-28       Impact factor: 2.259

9.  Deciphering interactions in moving animal groups.

Authors:  Jacques Gautrais; Francesco Ginelli; Richard Fournier; Stéphane Blanco; Marc Soria; Hugues Chaté; Guy Theraulaz
Journal:  PLoS Comput Biol       Date:  2012-09-13       Impact factor: 4.475

10.  Bayesian inference for identifying interaction rules in moving animal groups.

Authors:  Richard P Mann
Journal:  PLoS One       Date:  2011-08-04       Impact factor: 3.240

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  38 in total

1.  A data-driven method for reconstructing and modelling social interactions in moving animal groups.

Authors:  R Escobedo; V Lecheval; V Papaspyros; F Bonnet; F Mondada; C Sire; G Theraulaz
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2020-07-27       Impact factor: 6.237

2.  Ontogeny of collective behavior reveals a simple attraction rule.

Authors:  Robert C Hinz; Gonzalo G de Polavieja
Journal:  Proc Natl Acad Sci U S A       Date:  2017-02-13       Impact factor: 11.205

3.  Inferring collective behaviour from a fossilized fish shoal.

Authors:  Nobuaki Mizumoto; Shinya Miyata; Stephen C Pratt
Journal:  Proc Biol Sci       Date:  2019-05-29       Impact factor: 5.349

4.  Emergent oscillations assist obstacle negotiation during ant cooperative transport.

Authors:  Aviram Gelblum; Itai Pinkoviezky; Ehud Fonio; Nir S Gov; Ofer Feinerman
Journal:  Proc Natl Acad Sci U S A       Date:  2016-12-05       Impact factor: 11.205

5.  Efficient collective swimming by harnessing vortices through deep reinforcement learning.

Authors:  Siddhartha Verma; Guido Novati; Petros Koumoutsakos
Journal:  Proc Natl Acad Sci U S A       Date:  2018-05-21       Impact factor: 11.205

6.  Stable formations of self-propelled fish-like swimmers induced by hydrodynamic interactions.

Authors:  Longzhen Dai; Guowei He; Xiang Zhang; Xing Zhang
Journal:  J R Soc Interface       Date:  2018-10-17       Impact factor: 4.118

7.  Pair formation in insect swarms driven by adaptive long-range interactions.

Authors:  Dan Gorbonos; James G Puckett; Kasper van der Vaart; Michael Sinhuber; Nicholas T Ouellette; Nir S Gov
Journal:  J R Soc Interface       Date:  2020-10-07       Impact factor: 4.118

8.  Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish.

Authors:  Liu Lei; Ramón Escobedo; Clément Sire; Guy Theraulaz
Journal:  PLoS Comput Biol       Date:  2020-03-16       Impact factor: 4.475

Review 9.  The rise of intelligent matter.

Authors:  C Kaspar; B J Ravoo; W G van der Wiel; S V Wegner; W H P Pernice
Journal:  Nature       Date:  2021-06-16       Impact factor: 49.962

10.  Honeybee communication during collective defence is shaped by predation.

Authors:  Andrea López-Incera; Morgane Nouvian; Katja Ried; Thomas Müller; Hans J Briegel
Journal:  BMC Biol       Date:  2021-05-25       Impact factor: 7.431

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