Collective behavior in animal groups: Theoretical models and empirical studies

Collective phenomena in animal groups have attracted much attention in the last years, becoming one of the hottest topics in ethology. There are various reasons for this. On the one hand, animal grouping provides a paradigmatic example of self‐organization, where collective behavior emerges in absence of centralized control. The mechanism of group formation, where local rules for the individuals lead to a coherent global state, is very general and transcends the detailed nature of its components. In this respect, collective animal behavior is a subject of great interdisciplinary interest. On the other hand, there are several important issues related to the biological function of grouping and its evolutionary success. Research in this field boasts a number of theoretical models, but much less empirical results to compare with. For this reason, even if the general mechanisms through which self‐organization is achieved are qualitatively well understood, a quantitative test of the models assumptions is still lacking. New analysis on large groups, which require sophisticated technological procedures, can provide the necessary empirical data.

[1]  J. Kennedy,et al.  The migration of the Desert Locust (Schistocerca gregaria Forsk.) I. The behaviour of swarms. II. A theory of long-range migrations , 1951, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences.

[2]  J. T. Emlen Flocking Behavior in Birds , 1952 .

[3]  H. A. Baldwin,et al.  Methods for measuring the three-dimensional structure of fish schools. , 1965, Animal behaviour.

[4]  Tets G F Van A photographic method of estimating densities of bird flocks in flight , 1966 .

[5]  Richard S. Miller,et al.  Spatial Relationships in Flocks of Sandhill Cranes (Grus Canadensis) , 1966 .

[6]  I. Vine,et al.  Risk of visual detection and pursuitby a predator and the selective advantage of flocking behaviour. , 1971, Journal of theoretical biology.

[7]  E. Wilson The Insect Societies , 1974 .

[8]  D. V. Radakov Schooling in the ecology of fish , 1973 .

[9]  A. Sinclair,et al.  The African Buffalo: A Study of Resource Limitation of Populations , 1978 .

[10]  H. Haken Synergetics: an Introduction, Nonequilibrium Phase Transitions and Self-organization in Physics, Chemistry, and Biology , 1977 .

[11]  T. Pitcher,et al.  Fish school density and volume , 1979 .

[12]  K. Kaplan H. Haken, Synergetics. An Introduction. Nonequilibrium Phase Transitions and Self-Organization in Physics, Chemistry, and Biology (2nd Edition). XI + 355 S., 152 Abb. Berlin—Heidelberg—New York 1978. Springer-Verlag. DM 66,00 , 1980 .

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

[14]  I. Aoki An analysis of the schooling behavior of fish: Internal organization and communication process. , 1980 .

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

[16]  B. Partridge,et al.  The effect of school size on the structure and dynamics of minnow schools , 1980, Animal Behaviour.

[17]  I. Aoki A simulation study on the schooling mechanism in fish. , 1982 .

[18]  T. Pitcher,et al.  Shoal size, patch profitability and information exchange in foraging goldfish , 1983, Animal Behaviour.

[19]  T. Pitcher Heuristic definitions of fish shoaling behaviour , 1983, Animal Behaviour.

[20]  Lê Văn Long,et al.  A stereo photographic method for measuring the spatial position of fish. , 1985 .

[21]  T. Pitcher Functions of Shoaling Behaviour in Teleosts , 1986 .

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

[23]  J. Banavar,et al.  Computer Simulation of Liquids , 1988 .

[24]  U. Grenander,et al.  A stochastic nonlinear model for coordinated bird flocks , 1990 .

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

[26]  J. Parrish Do Predators 'Shape' Fish Schools: Interactions Between Predators and Their Schooling Prey , 1991 .

[27]  J. Deneubourg,et al.  Trails and U-turns in the Selection of a Path by the Ant Lasius niger , 1992 .

[28]  F. Heppner,et al.  Structure of Turning in Airborne Rock Dove (Columba livia) Flocks , 1992 .

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

[30]  Jens Krause DIFFERENTIAL FITNESS RETURNS IN RELATION TO SPATIAL POSITION IN GROUPS , 1994, Biological reviews of the Cambridge Philosophical Society.

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

[32]  R. Durrett,et al.  The Importance of Being Discrete (and Spatial) , 1994 .

[33]  Leah Edelstein-Keshet,et al.  Simple models for trail-following behaviour; Trunk trails versus individual foragers , 1994 .

[34]  Alex Fukunaga,et al.  Cooperative mobile robotics: antecedents and directions , 1995 .

[35]  D. Chialvo,et al.  Pattern Formation and Functionality in Swarm Models , 1995, adap-org/9507003.

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

[37]  J. Cardy Scaling and Renormalization in Statistical Physics , 1996 .

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

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

[40]  T. Seeley,et al.  The honey bee’s tremble dance stimulates additional bees to function as nectar receivers , 1996, Behavioral Ecology and Sociobiology.

[41]  T. Vicsek,et al.  Spontaneously ordered motion of self-propelled particles , 1997, cond-mat/0611741.

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

[43]  J. Osborn Animal Groups in Three Dimensions: Analytical and digital photogrammetry , 1997 .

[44]  J. Parrish,et al.  Animal Groups in Three Dimensions: Individual decisions, traffic rules, and emergent pattern in schooling fish , 1997 .

[45]  J. Bouchaud,et al.  Herd Behavior and Aggregate Fluctuations in Financial Markets , 1997 .

[46]  J. Parrish,et al.  Animal Groups in Three Dimensions: Is the sum of the parts equal to the whole: The conflict between individuality and group membership , 1997 .

[47]  E. Bonabeau,et al.  The synchronization of recruitment-based activities in ants. , 1998, Bio Systems.

[48]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1998 .

[49]  J. Toner,et al.  Flocks, herds, and schools: A quantitative theory of flocking , 1998, cond-mat/9804180.

[50]  A. Mogilner,et al.  A non-local model for a swarm , 1999 .

[51]  G. F.,et al.  From individuals to aggregations: the interplay between behavior and physics. , 1999, Journal of theoretical biology.

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

[53]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[54]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[55]  D. Sumpter,et al.  Phase transition between disordered and ordered foraging in Pharaoh's ants , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[56]  D. Helbing Traffic and related self-driven many-particle systems , 2000, cond-mat/0012229.

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

[58]  E. Bonabeau,et al.  Spatial patterns in ant colonies , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[59]  R. R. Krausz Living in Groups , 2013 .

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

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

[62]  S. Torquato Random Heterogeneous Materials , 2002 .

[63]  Guy Theraulaz,et al.  Self-Organization in Biological Systems , 2001, Princeton studies in complexity.

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

[65]  I. Couzin,et al.  Self-Organization and Collective Behavior in Vertebrates , 2003 .

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

[67]  D. Grünbaum Schooling as a strategy for taxis in a noisy environment , 1998, Evolutionary Ecology.

[68]  J. Deneubourg,et al.  Self-organized shortcuts in the Argentine ant , 1989, Naturwissenschaften.

[69]  A. S. Morse,et al.  Coordination of Groups of Mobile Autonomous Agents , 2004 .

[70]  T. Pitcher,et al.  The three-dimensional structure of fish schools , 1980, Behavioral Ecology and Sociobiology.

[71]  Dirk Helbing,et al.  Optimal traffic organization in ants under crowded conditions , 2004, Nature.

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

[73]  T. Seeley,et al.  Collective decision-making in honey bees: how colonies choose among nectar sources , 1991, Behavioral Ecology and Sociobiology.

[74]  S. Levin,et al.  Dynamics of fish shoals: identifying key decision rules , 2004 .

[75]  H. Chaté,et al.  Onset of collective and cohesive motion. , 2004, Physical review letters.

[76]  L. Dill,et al.  The three-dimensional structure of airborne bird flocks , 1978, Behavioral Ecology and Sociobiology.

[77]  A. Nieder Counting on neurons: the neurobiology of numerical competence , 2005, Nature Reviews Neuroscience.

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

[79]  C. Hemelrijk,et al.  Density distribution and size sorting in fish schools: an individual-based model , 2005 .

[80]  D. Sumpter The principles of collective animal behaviour , 2006, Philosophical Transactions of the Royal Society B: Biological Sciences.

[81]  José Halloy,et al.  Collegial decision making based on social amplification leads to optimal group formation. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[82]  Christophe Becco,et al.  Experimental evidences of a structural and dynamical transition in fish school , 2006 .

[83]  J. Deneubourg,et al.  Self-organized structures in a superorganism: do ants "behave" like molecules? , 2006 .

[84]  E. Bertin,et al.  Boltzmann and hydrodynamic description for self-propelled particles. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[85]  Joseph J. Hale,et al.  From Disorder to Order in Marching Locusts , 2006, Science.

[86]  T. Vicsek,et al.  Phase transition in the collective migration of tissue cells: experiment and model. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[88]  Guy Theraulaz,et al.  The biological principles of swarm intelligence , 2007, Swarm Intelligence.

[89]  R. Sepulchre,et al.  Oscillator Models and Collective Motion , 2007, IEEE Control Systems.

[90]  Naomi Ehrich Leonard,et al.  SPATIAL PATTERNS IN THE DYNAMICS OF ENGINEERED AND BIOLOGICAL NETWORKS , 2007 .

[91]  George J. Pappas,et al.  Flocking in Fixed and Switching Networks , 2007, IEEE Transactions on Automatic Control.

[92]  T. Vicsek,et al.  Phase transition in the scalar noise model of collective motion in three dimensions , 2007, 0801.0151.

[93]  H. Chaté,et al.  Collective motion of self-propelled particles interacting without cohesion. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[94]  C. Hemelrijk,et al.  Self-Organized Shape and Frontal Density of Fish Schools , 2008 .

[95]  G. Parisi,et al.  The STARFLAG handbook on collective animal behaviour: Part II, three-dimensional analysis , 2008, 0802.1674.

[96]  G. Parisi,et al.  New statistical tools for analyzing the structure of animal groups. , 2008, Mathematical biosciences.

[97]  G. Parisi,et al.  Empirical investigation of starling flocks: a benchmark study in collective animal behaviour , 2008, Animal Behaviour.

[98]  Giorgio Parisi,et al.  The STARFLAG handbook on collective animal behaviour: 1. Empirical methods , 2008, Animal Behaviour.

[99]  G. Parisi,et al.  Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study , 2007, Proceedings of the National Academy of Sciences.