Literature DB >> 33784885

A statistical method for identifying different rules of interaction between individuals in moving animal groups.

T M Schaerf1, J E Herbert-Read2,3, A J W Ward4.   

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.

Entities:  

Keywords:  Gambusia holbrooki; collective motion; followers; leaders; randomization methods; rules of interaction

Mesh:

Year:  2021        PMID: 33784885      PMCID: PMC8098707          DOI: 10.1098/rsif.2020.0925

Source DB:  PubMed          Journal:  J R Soc Interface        ISSN: 1742-5662            Impact factor:   4.118


  28 in total

1.  Novel type of phase transition in a system of self-driven particles.

Authors: 
Journal:  Phys Rev Lett       Date:  1995-08-07       Impact factor: 9.161

2.  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

3.  Self-propelled particles with soft-core interactions: patterns, stability, and collapse.

Authors:  M R D' Orsogna; Y L Chuang; A L Bertozzi; L S Chayes
Journal:  Phys Rev Lett       Date:  2006-03-17       Impact factor: 9.161

4.  Social feedback and the emergence of leaders and followers.

Authors:  Jennifer L Harcourt; Tzo Zen Ang; Gemma Sweetman; Rufus A Johnstone; Andrea Manica
Journal:  Curr Biol       Date:  2009-01-29       Impact factor: 10.834

5.  A method for estimating the interactions in dissipative particle dynamics from particle trajectories.

Authors:  Anders Eriksson; Martin Nilsson Jacobi; Johan Nyström; Kolbjørn Tunstrøm
Journal:  J Phys Condens Matter       Date:  2009-01-29       Impact factor: 2.333

6.  The mechanism of flight guidance in honeybee swarms: subtle guides or streaker bees?

Authors:  Kevin M Schultz; Kevin M Passino; Thomas D Seeley
Journal:  J Exp Biol       Date:  2008-10       Impact factor: 3.312

7.  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

8.  Disentangling and modeling interactions in fish with burst-and-coast swimming reveal distinct alignment and attraction behaviors.

Authors:  Daniel S Calovi; Alexandra Litchinko; Valentin Lecheval; Ugo Lopez; Alfonso Pérez Escudero; Hugues Chaté; Clément Sire; Guy Theraulaz
Journal:  PLoS Comput Biol       Date:  2018-01-11       Impact factor: 4.475

9.  Deep attention networks reveal the rules of collective motion in zebrafish.

Authors:  Francisco J H Heras; Francisco Romero-Ferrero; Robert C Hinz; Gonzalo G de Polavieja
Journal:  PLoS Comput Biol       Date:  2019-09-13       Impact factor: 4.475

10.  Examination of an averaging method for estimating repulsion and attraction interactions in moving groups.

Authors:  Rajnesh K Mudaliar; Timothy M Schaerf
Journal:  PLoS One       Date:  2020-12-09       Impact factor: 3.240

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  2 in total

1.  Network analysis of intra- and interspecific freshwater fish interactions using year-around tracking.

Authors:  Sara Vanovac; Dakota Howard; Christopher T Monk; Robert Arlinghaus; Philippe J Giabbanelli
Journal:  J R Soc Interface       Date:  2021-10-20       Impact factor: 4.293

2.  Coordination of movement via complementary interactions of leaders and followers in termite mating pairs.

Authors:  Nobuaki Mizumoto; Sang-Bin Lee; Gabriele Valentini; Thomas Chouvenc; Stephen C Pratt
Journal:  Proc Biol Sci       Date:  2021-07-14       Impact factor: 5.530

  2 in total

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