Literature DB >> 35440203

Stochastic modelling of bird flocks: accounting for the cohesiveness of collective motion.

Andy M Reynolds1, Guillam E McIvor2, Alex Thornton2, Patricia Yang3, Nicholas T Ouellette3.   

Abstract

Collective behaviour can be difficult to discern because it is not limited to animal aggregations such as flocks of birds and schools of fish wherein individuals spontaneously move in the same way despite the absence of leadership. Insect swarms are, for example, a form of collective behaviour, albeit one lacking the global order seen in bird flocks and fish schools. Their collective behaviour is evident in their emergent macroscopic properties. These properties are predicted by close relatives of Okubo's 1986 [Adv. Biophys. 22, 1-94. (doi:10.1016/0065-227X(86)90003-1)] stochastic model. Here, we argue that Okubo's stochastic model also encapsulates the cohesiveness mechanism at play in bird flocks, namely the fact that birds within a flock behave on average as if they are trapped in an elastic potential well. That is, each bird effectively behaves as if it is bound to the flock by a force that on average increases linearly as the distance from the flock centre increases. We uncover this key, but until now overlooked, feature of flocking in empirical data. This gives us a means of identifying what makes a given system collective. We show how the model can be extended to account for intrinsic velocity correlations and differentiated social relationships.

Entities:  

Keywords:  cohesiveness; collective behaviours; flocks; stochastic modelling; swarms

Mesh:

Year:  2022        PMID: 35440203      PMCID: PMC9019524          DOI: 10.1098/rsif.2021.0745

Source DB:  PubMed          Journal:  J R Soc Interface        ISSN: 1742-5662            Impact factor:   4.293


  36 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.  Fold-change detection and scalar symmetry of sensory input fields.

Authors:  Oren Shoval; Lea Goentoro; Yuval Hart; Avi Mayo; Eduardo Sontag; Uri Alon
Journal:  Proc Natl Acad Sci U S A       Date:  2010-08-20       Impact factor: 11.205

3.  Intrinsic fluctuations and driven response of insect swarms.

Authors:  Rui Ni; James G Puckett; Eric R Dufresne; Nicholas T Ouellette
Journal:  Phys Rev Lett       Date:  2015-09-10       Impact factor: 9.161

4.  Response of insect swarms to dynamic illumination perturbations.

Authors:  Michael Sinhuber; Kasper van der Vaart; Nicholas T Ouellette
Journal:  J R Soc Interface       Date:  2019-01-31       Impact factor: 4.118

5.  The measure of spatial position within groups that best predicts predation risk depends on group movement.

Authors:  Poppy J Lambert; James E Herbert-Read; Christos C Ioannou
Journal:  Proc Biol Sci       Date:  2021-09-15       Impact factor: 5.530

6.  Simultaneous measurements of three-dimensional trajectories and wingbeat frequencies of birds in the field.

Authors:  Hangjian Ling; Guillam E Mclvor; Geoff Nagy; Sepehr MohaimenianPour; Richard T Vaughan; Alex Thornton; Nicholas T Ouellette
Journal:  J R Soc Interface       Date:  2018-10-24       Impact factor: 4.118

7.  Behavioural plasticity and the transition to order in jackdaw flocks.

Authors:  Hangjian Ling; Guillam E Mclvor; Joseph Westley; Kasper van der Vaart; Richard T Vaughan; Alex Thornton; Nicholas T Ouellette
Journal:  Nat Commun       Date:  2019-11-15       Impact factor: 14.919

8.  Harmonic radar tracking reveals that honeybee drones navigate between multiple aerial leks.

Authors:  Joseph L Woodgate; James C Makinson; Natacha Rossi; Ka S Lim; Andrew M Reynolds; Christopher J Rawlings; Lars Chittka
Journal:  iScience       Date:  2021-05-20

9.  Emergent dynamics of laboratory insect swarms.

Authors:  Douglas H Kelley; Nicholas T Ouellette
Journal:  Sci Rep       Date:  2013-01-15       Impact factor: 4.379

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