Literature DB >> 21620861

Collective motion from local attraction.

Daniel Strömbom1.   

Abstract

Many animal groups, for example schools of fish or flocks of birds, exhibit complex dynamic patterns while moving cohesively in the same direction. These flocking patterns have been studied using self-propelled particle models, most of which assume that collective motion arises from individuals aligning with their neighbours. Here, we propose a self-propelled particle model in which the only social force between individuals is attraction. We show that this model generates three different phases: swarms, undirected mills and moving aligned groups. By studying our model in the zero noise limit, we show how these phases depend on the relative strength of attraction and individual inertia. Moreover, by restricting the field of vision of the individuals and increasing the degree of noise in the system, we find that the groups generate both directed mills and three dynamically moving, 'rotating chain' structures. A rich diversity of patterns is generated by social attraction alone, which may provide insight into the dynamics of natural flocks.
Copyright © 2011 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2011        PMID: 21620861     DOI: 10.1016/j.jtbi.2011.05.019

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  25 in total

1.  Inferring the rules of interaction of shoaling fish.

Authors:  James E Herbert-Read; Andrea Perna; Richard P Mann; Timothy M Schaerf; David J T Sumpter; Ashley J W Ward
Journal:  Proc Natl Acad Sci U S A       Date:  2011-11-07       Impact factor: 11.205

Review 2.  From behavioural analyses to models of collective motion in fish schools.

Authors:  Ugo Lopez; Jacques Gautrais; Iain D Couzin; Guy Theraulaz
Journal:  Interface Focus       Date:  2012-10-03       Impact factor: 3.906

3.  Swarming and pattern formation due to selective attraction and repulsion.

Authors:  Pawel Romanczuk; Lutz Schimansky-Geier
Journal:  Interface Focus       Date:  2012-09-26       Impact factor: 3.906

4.  Local interactions and their group-level consequences in flocking jackdaws.

Authors:  Hangjian Ling; Guillam E Mclvor; Kasper van der Vaart; Richard T Vaughan; Alex Thornton; Nicholas T Ouellette
Journal:  Proc Biol Sci       Date:  2019-07-03       Impact factor: 5.349

5.  Randomness in the choice of neighbours promotes cohesion in mobile animal groups.

Authors:  Vivek Jadhav; Vishwesha Guttal; Danny Raj Masila
Journal:  R Soc Open Sci       Date:  2022-03-23       Impact factor: 2.963

6.  Multi-scale inference of interaction rules in animal groups using Bayesian model selection.

Authors:  Richard P Mann; Andrea Perna; Daniel Strömbom; Roman Garnett; James E Herbert-Read; David J T Sumpter; Ashley J W Ward
Journal:  PLoS Comput Biol       Date:  2013-03-21       Impact factor: 4.475

7.  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

8.  Multi-scale inference of interaction rules in animal groups using Bayesian model selection.

Authors:  Richard P Mann; Andrea Perna; Daniel Strömbom; Roman Garnett; James E Herbert-Read; David J T Sumpter; Ashley J W Ward
Journal:  PLoS Comput Biol       Date:  2012-01-05       Impact factor: 4.475

9.  Data-driven stochastic modelling of zebrafish locomotion.

Authors:  Adam Zienkiewicz; David A W Barton; Maurizio Porfiri; Mario di Bernardo
Journal:  J Math Biol       Date:  2014-10-31       Impact factor: 2.259

10.  The modelling cycle for collective animal behaviour.

Authors:  David J T Sumpter; Richard P Mann; Andrea Perna
Journal:  Interface Focus       Date:  2012-08-15       Impact factor: 3.906

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