How Do Fish Use the Movement of Other Fish to Make Decisions?

From Individual Movement to Collective Decision Making
  • Arianna Bottinelli
  • Andrea Perna
  • Ashley Ward
  • David Sumpter
Part of the Springer Proceedings in Complexity book series (SPCOM)


Recent experiments by Ward et al. have shown that fish a moving fish group detects hidden predators faster and more accurately than isolated individuals. The increase in speed, in particular, seems to be a consequence of the movement-mediated nature of the interactions used by fish to share information. The present work aims at investigating the link between movement and information transfer underlying collective decisions in fish. We define an individual-based self-propelled particle (SPP) model of the decision-making process analyzed by Ward et al. We fit it to data in order to deduce the smallest set of interaction rules consistent with the experimentally observed behaviour. We infer the relative weight of different social forces on fish movement during the decision-making process. We find that, in order to reproduce the observed experimental trends, both the social forces of alignment and attraction have to be introduced in the model, alignment playing a more important role than attraction. We finally apply this model to make theoretical predictions about fish ability to detect and avoid a moving predator in a natural environment such as open water.


Collective animal behaviour Decision making SPP models Fish 


  1. 1.
    Dall SRX, Giraldeau LA, Olsson O, McNamara JM, Stephens DW (2005) Information and its use by animals in evolutionary ecology. Trends Ecol Evol 20(4):187–193 CrossRefGoogle Scholar
  2. 2.
    King AJ, Cowlishaw G (2007) When to use social information: the advantage of large group size in individual decision making. Biol Lett 3(2):137–139 CrossRefGoogle Scholar
  3. 3.
    Couzin ID (2009) Collective cognition in animal groups. Trends Cogn Sci 13(1):36–43 CrossRefGoogle Scholar
  4. 4.
    Treherne J, Foster W (1980) The effects of group size on predator avoidance in a marine insect. Anim Behav 28(4):1119–1122 CrossRefGoogle Scholar
  5. 5.
    Lima SL (1995) Back to the basics of anti-predatory vigilance: the group-size effect. Anim Behav 49(1):11–20 MathSciNetCrossRefGoogle Scholar
  6. 6.
    Ward AJW, Herbert-Read JE, Sumpter DJT, Krause J (2011) Fast and accurate decisions through collective vigilance in fish shoals. Proc Natl Acad Sci USA 108(6):2312–2315 ADSCrossRefGoogle Scholar
  7. 7.
    Sumpter D, Buhl J, Biro D, Couzin I (2008) Information transfer in moving animal groups. Theory Biosci 127(2):177–186 CrossRefGoogle Scholar
  8. 8.
    Sumpter DJT, Krause J, James R, Couzin ID, Ward AJW (2008) Consensus decision making by fish. Curr Biol 18(22):1773–1777 CrossRefGoogle Scholar
  9. 9.
    Ward AJW, Sumpter DJT, Couzin ID, Hart PJB, Krause J (2008) Quorum decision-making facilitates information transfer in fish shoals. Proc Natl Acad Sci USA 105(19):6948–6953 ADSCrossRefGoogle Scholar
  10. 10.
    Vicsek T, András Czirók EBJ, Cohen I (1995) Novel type of phase transition in a system of self-driven particles. Phys Rev Lett 75:1226 ADSCrossRefGoogle Scholar
  11. 11.
    Czirók A, Vicsek T (2000) Collective behavior of interacting self-propelled particles. Physica A 281:17–29 ADSCrossRefGoogle Scholar
  12. 12.
    Czirok A, Barabasi A, Vicsek T (1999) Collective motion of self-propelled particles: kinetic phase transition in one dimension. Phys Rev Lett 82(1):209–212 ADSCrossRefGoogle Scholar
  13. 13.
    Couzin ID, Krause J, James R, Ruxton GD, Franks NR (2002) Collective memory and spatial sorting in animal groups. J Theor Biol 218(1):1–11 MathSciNetCrossRefGoogle Scholar
  14. 14.
    Vicsek T, Zafeiris A (2012) Collective motion. Phys Rep. doi: 10.1016/j.physrep.2012.03.004 Google Scholar
  15. 15.
    Mann RP (2011) Bayesian inference for identifying interaction rules in moving animal groups. PLoS ONE 6(8):e22827 CrossRefGoogle Scholar
  16. 16.
    Strombom D (2011) Collective motion from local attraction. J Theor Biol 283(1):145–151 MathSciNetCrossRefGoogle Scholar
  17. 17.
    Rountree RA, Sedberry GR (2009) A theoretical model of shoaling behavior based on a consideration of patterns of overlap among the visual fields of individual members. Acta Ethol 12(2):61–70 CrossRefGoogle Scholar
  18. 18.
    Herbert-Read JE, Perna A, Mann RP, Schaerf TM, Sumpter DJT, Ward AJW (2011) Inferring the rules of interaction of shoaling fish. Proc Natl Acad Sci USA 108(46):18726–18731 ADSCrossRefGoogle Scholar
  19. 19.
    Radakov D (1973) Schooling in the ecology of fish. Wiley, New York Google Scholar
  20. 20.
    Romey WL (1996) Individual differences make a difference in the trajectories of simulated schools of fish. Ecol Model 92(1):65–77 CrossRefGoogle Scholar
  21. 21.
    Lemasson B, Anderson J, Goodwin R (2009) Collective motion in animal groups from a neurobiological perspective: the adaptive benefits of dynamic sensory loads and selective attention. J Theor Biol 261(4):501–510 MathSciNetCrossRefGoogle Scholar
  22. 22.
    Moussaïd M, Guillot EG, Moreau M, Fehrenbach J, Chabiron O, Lemercier S, Pettré J, Appert-Rolland C, Degond P, Theraulaz G (2012) Traffic instabilities in self-organized pedestrian crowds. PLoS Comput Biol 8(3):e1002442 CrossRefGoogle Scholar
  23. 23.
    Hemelrijk CK, Hildenbrandt H, Reinders J, Stamhuis EJ (2010) Emergence of oblong school shape: models and empirical data of fish. Ethology 116(11):1099–1112 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Arianna Bottinelli
    • 1
  • Andrea Perna
    • 1
  • Ashley Ward
    • 2
  • David Sumpter
    • 1
  1. 1.Uppsala UniversityUppsalaSweden
  2. 2.University of SidneySidneyAustralia

Personalised recommendations