The impact of social networks on animal collective motion

Many group-living animals show social preferences for relatives, familiar conspecifics or individuals of similar attributes such as size, personality or sex. How such preferences could affect the collective motion of animal groups has been rather unexplored. We present a general model of collective animal motion that includes social connections as preferential reactions between individuals. Our conceptual examples illustrate the possible impact of underlying social networks on the collective motion of animals. Our approach shows that the structure of these networks could influence: (1) the cohesion of groups; (2) the spatial position of individuals within groups; and (3) the hierarchical dynamics within such groups. We argue that the position of individuals within a social network and the social network structure of populations could have important fitness implications for individual animals. Counterintuitive results from our conceptual examples show that social structures can result in unexpected group dynamics. This sharpens our understanding of the way in which collective movement can be interpreted as a result of social interactions.

[1]  Xiaolin Hu,et al.  Modeling group structures in pedestrian crowd simulation , 2010, Simul. Model. Pract. Theory.

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

[3]  I. Couzin,et al.  Mechanisms underlying shoal composition in the Trinidadian guppy, Poecilia reticulata , 2003 .

[4]  D. Franks,et al.  Sampling animal association networks with the gambit of the group , 2009, Behavioral Ecology and Sociobiology.

[5]  T. Vicsek,et al.  Hierarchical group dynamics in pigeon flocks , 2010, Nature.

[6]  B. Bollobás The evolution of random graphs , 1984 .

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

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

[9]  W. Hamilton Geometry for the selfish herd. , 1971, Journal of theoretical biology.

[10]  Jorge Cortés,et al.  Distributed Motion Constraints for Algebraic Connectivity of Robotic Networks , 2008, 2008 47th IEEE Conference on Decision and Control.

[11]  Peter K. McGregor,et al.  Analyzing Animal Societies: Quantitative Methods for Vertebrate Social Analysis , 2009 .

[12]  Daniel W Franks,et al.  Limited interactions in flocks: relating model simulations to empirical data , 2011, Journal of The Royal Society Interface.

[13]  A Jamie Wood,et al.  Strategy selection under predation; evolutionary analysis of the emergence of cohesive aggregations. , 2010, Journal of theoretical biology.

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

[15]  L. Morrell,et al.  Optimal individual positions within animal groups , 2008 .

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

[17]  I. Couzin,et al.  “Leading According to Need” in Self‐Organizing Groups , 2009, The American Naturalist.

[18]  P. K. McGregor Animal Communication Networks: Index , 2005 .

[19]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[20]  Mark Newman,et al.  Networks: An Introduction , 2010 .

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

[22]  Jens Krause,et al.  Mortality risk of spatial positions in animal groups: The danger of being in the front , 1997 .

[23]  J. Deneubourg,et al.  Short-term group fission processes in macaques: a social networking approach , 2010, Journal of Experimental Biology.

[24]  Jens Krause,et al.  How perceived threat increases synchronization in collectively moving animal groups , 2010, Proceedings of the Royal Society B: Biological Sciences.

[25]  Hal Whitehead,et al.  Techniques for Analyzing Vertebrate Social Structure Using Identified Individuals: Review and Recommendations , 1999 .

[26]  A. Magurran,et al.  Schooling decisions in guppies (Poecilia reticulata) are based on familiarity rather than kin recognition by phenotype matching , 1999, Behavioral Ecology and Sociobiology.

[27]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[28]  Marios M. Polycarpou,et al.  Stability analysis of M-dimensional asynchronous swarms with a fixed communication topology , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[29]  Daniel W Franks,et al.  Making noise: emergent stochasticity in collective motion. , 2010, Journal of theoretical biology.

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

[31]  D. Helbing,et al.  The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics , 2010, PloS one.

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

[33]  Soraia Raupp Musse,et al.  Modeling individual behaviors in crowd simulation , 2003, Proceedings 11th IEEE International Workshop on Program Comprehension.

[34]  Daniel W. Franks,et al.  Social networks and models for collective motion in animals , 2011, Behavioral Ecology and Sociobiology.

[35]  Charlotte K. Hemelrijk,et al.  Towards the integration of social dominance and spatial structure , 2000, Animal Behaviour.

[36]  George J. Pappas,et al.  Stable flocking of mobile agents, part I: fixed topology , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[37]  Marek Špinka,et al.  Graded leadership by dominant animals in a herd of female beef cattle on pasture , 2010, Animal Behaviour.

[38]  M. Mooring,et al.  Animal Grouping for Protection From Parasites: Selfish Herd and Encounter-Dilution Effects , 1992 .

[39]  J. Krause,et al.  Exploring Animal Social Networks , 2008 .

[40]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[41]  Jie Lin,et al.  Coordination of groups of mobile autonomous agents using nearest neighbor rules , 2003, IEEE Trans. Autom. Control..