Literature DB >> 1943130

Tendency-distance models of social cohesion in animal groups.

K Warburton1, J Lazarus.   

Abstract

Although it has been assumed that attraction and repulsion between social individuals constitute a basis for group cohesion, there has been no systematic study of the possible ways in which these tendencies might vary with inter-individual distance (IID), or of associated implications for group structure. In this paper, a family of attraction/repulsion--distance functions is described. Computer simulation was used to examine the effects of each function on group cohesion, as reflected by mean values and variability in IID and group shape. Our results showed that: (a) all models led to stability in group structure, but differed significantly in terms of stable IID and group shape characteristics; (b) cohesion was best served by an upwardly convex behaviour--distance function in which maximum attraction equaled maximum repulsion (and the biological plausibility of this function is discussed); (c) group elongation and variability in mean IID were significantly positively correlated; (d) although dyads maintained an equilibrial separation distance, at which attraction balanced repulsion, in larger groups stable nearest neighbour distances were often less than the equilibrium distance; and (e) individuals needed to monitor and respond to only relatively few of their companions in order to avoid group fragmentation.

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Year:  1991        PMID: 1943130     DOI: 10.1016/s0022-5193(05)80441-2

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


  15 in total

1.  Mutual interactions, potentials, and individual distance in a social aggregation.

Authors:  A Mogilner; L Edelstein-Keshet; L Bent; A Spiros
Journal:  J Math Biol       Date:  2003-05-15       Impact factor: 2.259

2.  Effects of anisotropic interactions on the structure of animal groups.

Authors:  Emiliano Cristiani; Paolo Frasca; Benedetto Piccoli
Journal:  J Math Biol       Date:  2010-05-19       Impact factor: 2.259

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.  An interacting particle system modelling aggregation behavior: from individuals to populations.

Authors:  Daniela Morale; Vincenzo Capasso; Karl Oelschläger
Journal:  J Math Biol       Date:  2004-07-05       Impact factor: 2.259

5.  Coarse-grained analysis of stochasticity-induced switching between collective motion states.

Authors:  Allison Kolpas; Jeff Moehlis; Ioannis G Kevrekidis
Journal:  Proc Natl Acad Sci U S A       Date:  2007-03-27       Impact factor: 11.205

6.  Collective behavior in animal groups: theoretical models and empirical studies.

Authors:  Irene Giardina
Journal:  HFSP J       Date:  2008-08-01

7.  Spatially balanced topological interaction grants optimal cohesion in flocking models.

Authors:  Marcelo Camperi; Andrea Cavagna; Irene Giardina; Giorgio Parisi; Edmondo Silvestri
Journal:  Interface Focus       Date:  2012-08-08       Impact factor: 3.906

Review 8.  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

9.  Translating stochastic density-dependent individual behavior with sensory constraints to an Eulerian model of animal swarming.

Authors:  D Grünbaum
Journal:  J Math Biol       Date:  1994       Impact factor: 2.259

10.  Oscillations in shoal cohesion in zebrafish (Danio rerio).

Authors:  Noam Y Miller; Robert Gerlai
Journal:  Behav Brain Res       Date:  2008-05-16       Impact factor: 3.332

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