Inferring the rules of interaction of shoaling fish

Collective motion, where large numbers of individuals move synchronously together, is achieved when individuals adopt interaction rules that determine how they respond to their neighbors’ movements and positions. These rules determine how group-living animals move, make decisions, and transmit information between individuals. Nonetheless, few studies have explicitly determined these interaction rules in moving groups, and very little is known about the interaction rules of fish. Here, we identify three key rules for the social interactions of mosquitofish (Gambusia holbrooki): (i) Attraction forces are important in maintaining group cohesion, while we find only weak evidence that fish align with their neighbor’s orientation; (ii) repulsion is mediated principally by changes in speed; (iii) although the positions and directions of all shoal members are highly correlated, individuals only respond to their single nearest neighbor. The last two of these rules are different from the classical models of collective animal motion, raising new questions about how fish and other animals self-organize on the move.

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

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

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

[4]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

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

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

[7]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[8]  M. Stamp Dawkins,et al.  Cognitive ecology: the evolutionary ecology of information processing and decision making. , 1998, Trends in cognitive sciences.

[9]  Michael I. Jordan.  Learning in Graphical Models , 1999, NATO ASI Series.

[10]  A. Czirók,et al.  Collective Motion , 1999, physics/9902023.

[11]  Dirk Helbing,et al.  Application of statistical mechanics to collective motion in biology , 1999 .

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

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

[14]  Ian T. Nabney,et al.  Netlab: Algorithms for Pattern Recognition , 2002 .

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

[16]  T. Pitcher,et al.  The sensory basis of fish schools: Relative roles of lateral line and vision , 1980, Journal of comparative physiology.

[17]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[18]  Yuhong Yang,et al.  Information Theory, Inference, and Learning Algorithms , 2005 .

[19]  D. Sumpter,et al.  From Compromise to Leadership in Pigeon Homing , 2006, Current Biology.

[20]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[21]  A. J. Wood,et al.  Evolving the selfish herd: emergence of distinct aggregating strategies in an individual-based model , 2007, Proceedings of the Royal Society B: Biological Sciences.

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

[23]  David J. T. Sumpter,et al.  Information transfer in moving animal groups , 2008, Theory in Biosciences.

[24]  L. Williams,et al.  Contents , 2020, Ophthalmology (Rochester, Minn.).

[25]  Irene Giardina,et al.  Collective behavior in animal groups: Theoretical models and empirical studies , 2008, HFSP journal.

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

[27]  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.

[28]  Pietro Perona,et al.  High-throughput Ethomics in Large Groups of Drosophila , 2009, Nature Methods.

[29]  James J. Anderson,et al.  Collective motion in animal groups from a neurobiological perspective: the adaptive benefits of dynamic sensory loads and selective attention. , 2009, Journal of theoretical biology.

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

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

[32]  G. Parisi,et al.  Scale-free correlations in starling flocks , 2009, Proceedings of the National Academy of Sciences.

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

[34]  Martin Nilsson Jacobi,et al.  Determining interaction rules in animal swarms , 2010 .

[35]  H. Chaté,et al.  Relevance of metric-free interactions in flocking phenomena. , 2010, Physical review letters.

[36]  D. Sumpter.  Collective Animal Behavior , 2010 .

[37]  D. Biro,et al.  Group decisions and individual differences: route fidelity predicts flight leadership in homing pigeons (Columba livia) , 2010, Biology Letters.

[38]  Leah Edelstein-Keshet,et al.  Inferring individual rules from collective behavior , 2010, Proceedings of the National Academy of Sciences.

[39]  I. Couzin,et al.  Inferring the structure and dynamics of interactions in schooling fish , 2011, Proceedings of the National Academy of Sciences.

[40]  Dirk Helbing,et al.  How simple rules determine pedestrian behavior and crowd disasters , 2011, Proceedings of the National Academy of Sciences.

[41]  D. Strömbom.  Collective motion from local attraction. , 2011, Journal of theoretical biology.

[42]  D. Sumpter,et al.  Fast and accurate decisions through collective vigilance in fish shoals , 2011, Proceedings of the National Academy of Sciences.

[43]  Richard P. Mann,et al.  Bayesian Inference for Identifying Interaction Rules in Moving Animal Groups , 2011, PloS one.