A Conceptual Model for Milling Formations in Biological Aggregates

Collective behavior of swarms and flocks has been studied from several perspectives, including continuous (Eulerian) and individual-based (Lagrangian) models. Here, we use the latter approach to examine a minimal model for the formation and maintenance of group structure, with specific emphasis on a simple milling pattern in which particles follow one another around a closed circular path.We explore how rules and interactions at the level of the individuals lead to this pattern at the level of the group. In contrast to many studies based on simulation results, our model is sufficiently simple that we can obtain analytical predictions. We consider a Newtonian framework with distance-dependent pairwise interaction-force. Unlike some other studies, our mill formations do not depend on domain boundaries, nor on centrally attracting force-fields or rotor chemotaxis.By focusing on a simple geometry and simple distant-dependent interactions, we characterize mill formations and derive existence conditions in terms of model parameters. An eigenvalue equation specifies stability regions based on properties of the interaction function. We explore this equation numerically, and validate the stability conclusions via simulation, showing distinct behavior inside, outside, and on the boundary of stability regions. Moving mill formations are then investigated, showing the effect of individual autonomous self-propulsion on group-level motion. The simplified framework allows us to clearly relate individual properties (via model parameters) to group-level structure. These relationships provide insight into the more complicated milling formations observed in nature, and suggest design properties of artificial schools where such rotational motion is desired.

[1]  Vicsek,et al.  Novel type of phase transition in a system of self-driven particles. , 1995, Physical review letters.

[2]  Andrea L. Bertozzi,et al.  Swarming Patterns in a Two-Dimensional Kinematic Model for Biological Groups , 2004, SIAM J. Appl. Math..

[3]  A. Bertozzi,et al.  Self-propelled particles with soft-core interactions: patterns, stability, and collapse. , 2006, Physical review letters.

[4]  A. Czirók,et al.  Hydrodynamics of bacterial motion , 1997, cond-mat/9811247.

[5]  A. Parr A contribution to the theoretical analysis of the schooling behavior of fishes , 1927 .

[6]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[7]  Niwa Migration Dynamics of Fish Schools in Heterothermal Environments. , 1998, Journal of theoretical biology.

[8]  Sumiko Sakai,et al.  A Model for group structure and its behavior , 1973 .

[9]  Neha Bhooshan,et al.  The Simulation of the Movement of Fish Schools , 2001 .

[10]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[11]  I. Couzin,et al.  Collective memory and spatial sorting in animal groups. , 2002, Journal of theoretical biology.

[12]  T. C. . Schneirla,et al.  A unique case of circular milling in ants, considered in relation to trail following and the general problem of orientation. American Museum novitates ; no. 1253 , 1944 .

[13]  A. Bertozzi,et al.  State Transitions and the Continuum Limit for a 2D Interacting, Self-Propelled Particle System , 2006, nlin/0606031.

[14]  Leah Edelstein-Keshet,et al.  The Dynamics of Animal Grouping , 2001 .

[15]  Mireille E. Broucke,et al.  Formations of vehicles in cyclic pursuit , 2004, IEEE Transactions on Automatic Control.

[16]  N H-S,et al.  Newtonian Dynamical Approach to Fish Schooling , 2022 .

[17]  S. Wilson Basking sharks (Cetorhinus maximus) schooling in the southern Gulf of Maine , 2004 .

[18]  J. Hemmingsson Modellization of self-propelling particles with a coupled map lattice model , 1995 .

[19]  J. Silvester Determinants of block matrices , 2000, The Mathematical Gazette.

[20]  A. Huth,et al.  The simulation of the movement of fish schools , 1992 .

[21]  L. Edelstein-Keshet,et al.  Complexity, pattern, and evolutionary trade-offs in animal aggregation. , 1999, Science.

[22]  Leah Edelstein-Keshet,et al.  Minimal mechanisms for school formation in self-propelled particles , 2008 .

[23]  Dick Bedeaux,et al.  Hydrodynamic model for a system of self-propelling particles with conservative kinematic constraints , 2005 .

[24]  A. Blaurock X-ray diffraction pattern from a bilayer with protein outside. , 1973, Biophysical journal.

[25]  W. Rappel,et al.  Self-organization in systems of self-propelled particles. , 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  A. Mogilner,et al.  Mathematical Biology Mutual Interactions, Potentials, and Individual Distance in a Social Aggregation , 2003 .

[27]  Steven V. Viscido,et al.  Self-Organized Fish Schools: An Examination of Emergent Properties , 2002, The Biological Bulletin.

[28]  D. Weihs,et al.  Energetic advantages of burst swimming of fish. , 1974, Journal of theoretical biology.

[29]  Stability properties of the collective stationary motion of self-propelling particles with conservative kinematic constraints , 2006, physics/0611210.

[30]  明 大久保,et al.  Diffusion and ecological problems : mathematical models , 1980 .

[31]  W. T. Stobo,et al.  Putative Mating Behavior in Basking Sharks off the Nova Scotia Coast , 1999 .

[32]  Frank Schweitzer,et al.  Modeling Vortex Swarming In Daphnia , 2004, Bulletin of mathematical biology.

[33]  Tamás Vicsek,et al.  Chemomodulation of cellular movement, collective formation of vortices by swarming bacteria, and colonial development , 1997 .

[34]  Igor S. Aranson,et al.  Emergence of agent swarm migration and vortex formation through inelastic collisions , 2008 .

[35]  Hiro-Sato Niwa Self-organizing Dynamic Model of Fish Schooling , 1994 .

[36]  Vicsek,et al.  Formation of complex bacterial colonies via self-generated vortices. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.