Spatial structures in simulations of animal grouping

We present numerical simulations of an animal grouping model based on individual behaviours of attraction, alignment and repulsion. We study the consequences on the simulated groups’ internal structures, of using different functions. These different functions which are adapted from the literature define the intensity, associated with these behaviours, as a distance function between individuals. We also investigate here the impacts of: the number of individuals, the number of influential neighbours and the strength of the alignment behaviour on the structures. We show that homogeneous groups can be identified when: the different functions used lead to a smooth transition from attraction to repulsion; alignment overcomes repulsion and attraction, in particular within this transition zone; and when there is a low number of influential neighbours. We also point out the fact that otherwise, the model results in heterogeneous internal structures, which take the form of a concentration of individuals in subgroups, in lines, or at the periphery of the groups.

[1]  François Gerlotto,et al.  The three-dimensional morphology and internal structure of clupeid schools as observed using vertical scanning multibeam sonar , 2003 .

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

[3]  T. Vicsek,et al.  Collective motion of organisms in three dimensions , 1999, physics/9902021.

[4]  Simon A. Levin,et al.  Frontiers in Mathematical Biology , 1995 .

[5]  W. L. Romey Individual differences make a difference in the trajectories of simulated schools of fish , 1996 .

[6]  Y. Tu,et al.  Moving and staying together without a leader , 2003, cond-mat/0401257.

[7]  Julia K. Parrish,et al.  Animal Groups in Three Dimensions: Analysis , 1997 .

[8]  Maureen C. Stone,et al.  Proceedings of the 14th annual conference on Computer graphics and interactive techniques , 1987, International Conference on Computer Graphics and Interactive Techniques.

[9]  Steven V. Viscido,et al.  The effect of population size and number of influential neighbors on the emergent properties of fish schools , 2005 .

[10]  A. Deutsch A new mechanism of aggregation in a lattice-gas cellular automaton model , 2000 .

[11]  Rune Vabø,et al.  An individual based model of fish school reactions: predicting antipredator behaviour as observed in nature , 1997 .

[12]  A. Ōkubo,et al.  Di?usion and ecological problems: mathematical models , 1980 .

[13]  Vicsek,et al.  Lattice-gas model for collective biological motion. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[14]  D. Grünbaum Translating stochastic density-dependent individual behavior with sensory constraints to an Eulerian model of animal swarming , 1994, Journal of mathematical biology.

[15]  Pierre Fréon,et al.  Changes in school structure according to external stimuli: description and influence on acoustic assessment , 1992 .

[16]  Hauke Reuter,et al.  SELFORGANIZATION OF FISH SCHOOLS : AN OBJECT-ORIENTED MODEL , 1994 .

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

[18]  S. Gueron,et al.  The Dynamics of Herds: From Individuals to Aggregations , 1996 .

[19]  Demetri Terzopoulos,et al.  Artificial fishes: physics, locomotion, perception, behavior , 1994, SIGGRAPH.

[20]  D. Grünbaum,et al.  From individuals to aggregations: the interplay between behavior and physics. , 1999, Journal of theoretical biology.

[21]  Charlotte K. Hemelrijk,et al.  Artificial Fish Schools: Collective Effects of School Size, Body Size, and Body Form , 2003, Artificial Life.

[22]  H. Bussemaker,et al.  Mean-Field Analysis of a Dynamical Phase Transition in a Cellular Automaton Model for Collective Motion , 1997, physics/9706008.

[23]  M. Barangé,et al.  Trends in the abundance and distribution of anchovy and sardine on the South African continental shelf in the 1990s, deduced from acoustic surveys , 1999 .

[24]  Ovide Arino,et al.  Alignment in a fish school: a mixed Lagrangian–Eulerian approach , 2003 .

[25]  T. Vicsek,et al.  Collective behavior of interacting self-propelled particles , 2000, cond-mat/0611742.

[26]  David A. Fournier,et al.  An advection-diffusion-reaction model for the estimation of fish movement parameters from tagging data, with application to skipjack tuna (Katsuwonus pelamis) , 1999 .

[27]  J. Sibert,et al.  Population dynamics and movements of skipjack tuna ( Katsuwonus pelamis ) in the Maldivian fishery: analysis of tagging data from an advection-diffusion-reaction model , 2002 .

[28]  Kevin Warburton Animal Groups in Three Dimensions: Social forces in animal congregations: Interactive, motivational, and sensory aspects , 1997 .

[29]  J. Godin,et al.  Body size and shoaling in fish , 2000 .

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

[31]  K. Warburton,et al.  Tendency-distance models of social cohesion in animal groups. , 1991, Journal of Theoretical Biology.

[32]  Yoshinobu Inada,et al.  Order and flexibility in the motion of fish schools. , 2002, Journal of theoretical biology.

[33]  Simon Hubbard,et al.  A model of the formation of fish schools and migrations of fish , 2004 .

[34]  A. Ōkubo Dynamical aspects of animal grouping: swarms, schools, flocks, and herds. , 1986, Advances in biophysics.

[35]  Birgitt Schönfisch,et al.  Simple individual based models of movement, alignment and schooling behaviour , 2001, Future Gener. Comput. Syst..

[36]  Bussemaker Analysis of a pattern-forming lattice-gas automaton: Mean-field theory and beyond. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[37]  Magnar Aksland,et al.  Schooling dynamics of norwegian spring spawning herring (Clupea harengus L.) in a coastal spawning area , 1996 .

[38]  Andreas Huth,et al.  THE SIMULATION OF FISH SCHOOLS IN COMPARISON WITH EXPERIMENTAL DATA , 1994 .

[39]  Steven V. Viscido,et al.  Individual behavior and emergent properties of fish schools: a comparison of observation and theory , 2004 .

[40]  S. McRobert,et al.  The influence of body coloration on shoaling preferences in fish , 1998, Animal Behaviour.

[41]  G. Huse,et al.  Modelling changes in migration pattern of herring: collective behaviour and numerical domination , 2002 .

[42]  I. Couzin,et al.  Effective leadership and decision-making in animal groups on the move , 2005, Nature.

[43]  A. Ōkubo,et al.  MODELLING SOCIAL ANIMAL AGGREGATIONS , 1994 .

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

[45]  S Stöcker,et al.  Models for tuna school formation. , 1999, Mathematical biosciences.

[46]  R. Veit,et al.  Partial Differential Equations in Ecology: Spatial Interactions and Population Dynamics , 1994 .

[47]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.