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

Author:

Reynolds Andy M.1ORCID,McIvor Guillam E.2,Thornton Alex2ORCID,Yang Patricia3,Ouellette Nicholas T.3

Affiliation:

1. Rothamsted Research, Harpenden AL5 2JQ, UK

2. Centre for Ecology and Conservation, University of Exeter, Penryn, Cornwall TR10 9FE, UK

3. Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA

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.

Funder

Human Frontier Science Program

Biotechnology and Biological Sciences Research Council

SCP

Publisher

The Royal Society

Subject

Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biophysics,Biotechnology

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1. Time cost estimation for flocking of Cucker–Smale type models with switching protocol;Physica A: Statistical Mechanics and its Applications;2024-02

2. Collective behavior of self-steering active particles with velocity alignment and visual perception;Physical Review Research;2024-01-29

3. Learning Agent Interactions from Density Evolution in 3D Regions With Obstacles;2023 62nd IEEE Conference on Decision and Control (CDC);2023-12-13

4. Flocking and swarming in a multi-agent dynamical system;Chaos: An Interdisciplinary Journal of Nonlinear Science;2023-12-01

5. Study of elastic interaction in collective motion phenomenon;Modern Physics Letters B;2023-08-18

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