Behavioral Ecology and Sociobiology

, Volume 64, Issue 8, pp 1211–1218 | Cite as

A novel method for investigating the collective behaviour of fish: introducing ‘Robofish’

  • Jolyon J. FariaEmail author
  • John R. G. Dyer
  • Romain O. Clément
  • Iain D. Couzin
  • Natalie Holt
  • Ashley J. W. Ward
  • Dean Waters
  • Jens Krause


Collective animal behaviour has attracted much attention recently, but cause-and-effect within interaction sequences has often been difficult to establish. To tackle this problem, we constructed a robotic fish (‘Robofish’) with which three-spined sticklebacks (Gasterosteus aculeatus L.) interact. Robofish is a computer-controlled replica stickleback that can be programmed to move around a tank. First, we demonstrated the functioning of the method: that the sticklebacks interacted with Robofish. We examined two types of interaction: recruitment and leadership. We found that Robofish could recruit a single fish from a refuge and could initiate a turn in singletons and in groups of ten, i.e. act as a leader. We also showed that the influence of Robofish diminished after the first 30 min that fish spent in a new environment. Second, using this method, we investigated the effects of metric and topological inter-individual distance on the influence that Robofish had on the orientation of fish in a shoal of ten. We found that inter-individual interactions during this turn were predominantly mediated by topological, rather than metric, distance. Finally, we discussed the potential of this novel method and the importance of our findings for the study of collective animal behaviour.


Collective animal behaviour Leadership Inter-individual distance Recruitment Robot Gasterosteus aculeatus 



JJF was funded by a Biotechnology and Biological Sciences Research Council Doctoral Training Grant. JRGD was funded by a grant from the Engineering and Physical Sciences Research Council to JK. JK also acknowledges funding from the Natural Environment Research Council. We are grateful to Julius Goldthorpe, Daryl van Cauwelaert and Nicola Atton for help with the experiments.

Supplementary material

265_2010_988_MOESM1_ESM.mpg (3.5 mb)
Online resource 1 Video of a Robofish experiment (plan view) in the test tank (width × length × depth 86 × 81 × 5 cm). Before the start of the trial, we placed ten three-spined sticklebacks and a robotic fish: ‘Robofish’, in the enclosed refuge (upper right corner of the tank on the video), and left them for 2 min. At the start of the trial (and after 2.5 s on the video), one wall of the refuge was raised remotely by fishing line. Robofish was then activated (after 4 s on the video) and moved along a standardised route and can be identified from sticklebacks as it is the first fish to move its entire body from beneath the refuge (judged from the viewpoint of the camera). Soon after Robofish made the first of its 90° turns, the sticklebacks made a sharp turn towards the lower side of the tank. The trial was stopped when Robofish had returned to the refuge. (MPG 3580 kb)


  1. Abramoff MD, Magelhaes PJ, Ram SJ (2004) Image processing with ImageJ. Biophoton Int 11:36–42Google Scholar
  2. Aoki I (1982) A simulation study on the schooling mechanism in fish. Bull Jpn Soc Sci Fish 48:1081–1088Google Scholar
  3. Ballerini M, Cabibbo N, Candelier R, Cavagna A, Cisbani E, Giardina I, Orlandi A, Parisi G, Procaccini A, Viale M, Zdravkovic V (2008a) Empirical investigation of starling flocks: a benchmark study in collective animal behaviour. Anim Behav 76:201–215. doi: 10.1016/j.anbehav.2008.02.004 CrossRefGoogle Scholar
  4. Ballerini M, Calbibbo N, Candeleir R, Cavagna A, Cisbani E, Giardina I, Lecomte V, Orlandi A, Parisi G, Procaccini A, Viale M, Zdravkovic V (2008b) Interaction ruling animal collective behavior depends on topological rather than metric distance: evidence from a field study. Proc Natl Acad Sci USA 105:1232–1237. doi: 10.1073/pnas.0711437105 CrossRefPubMedGoogle Scholar
  5. Barbaro A, Einarsson B, Birnir B, Sigurðsson S, Valdimarsson S, Pálsson ÓK, Sveinbjörnsson S, Sigurðsson P (2009) Modelling and simulations of the migration of pelagic fish. J Mar Sci 66:826–838. doi: 10.1093/icesjms/fsp067 Google Scholar
  6. Biro D, Sumpter DJT, Meade J, Guilford T (2006) From compromise to leadership in pigeon homing. Curr Biol 16:2123–2128. doi: 10.1016/j.cub.2006.08.087 CrossRefPubMedGoogle Scholar
  7. Buhl J, Sumpter DJT, Couzin ID, Hale JJ, Despland E, Miller ER, Simpson SJ (2006) From disorder to order in marching locusts. Science 312:1402–1406. doi: 10.1126/science.1125142 CrossRefPubMedGoogle Scholar
  8. Bumann D, Krause J (1993) Front individuals lead in shoals of 3-spined sticklebacks (Gasterosteus aculeatus) and juvenile roach (Rutilus rutilus). Behaviour 125:189–198CrossRefGoogle Scholar
  9. Couzin ID, Krause J (2003) Self-organization and collective behavior in vertebrates. Adv Study Behav 32:1–75. doi: 10.1016/S0065-3454(03)01001-5 CrossRefGoogle Scholar
  10. Couzin ID, Krause J, Franks NR, Levin SA (2005) Effective leadership and decision-making in animal groups on the move. Nature 433:513–516. doi: 10.1038/nature03236 CrossRefPubMedGoogle Scholar
  11. Couzin ID, Krause J, James R, Ruxton GD, Franks NR (2002) Collective memory and spatial sorting in animal groups. J Theor Biol 218:1–11. doi: 10.1006/jtbi.2002.3065 CrossRefPubMedGoogle Scholar
  12. Dyer JRG, Croft DP, Morrell LJ, Krause J (2009a) Shoal composition determines foraging success in the guppy. Behav Ecol 20:165–171. doi: 10.1093/beheco/arn129 CrossRefGoogle Scholar
  13. Dyer JRG, Johansson A, Helbing D, Couzin ID, Krause J (2009b) Leadership, consensus decision making and collective behaviour in humans. Philo Trans R Soc B-Biol Sci 364:781–789. doi: 10.1098/rstb.2008.0233 CrossRefGoogle Scholar
  14. Faria JJ, Codling EA, Dyer JRG, Trillmich F, Krause J (2009) Navigation in human crowds; testing the many-wrongs principle. Anim Behav 78:587–591. doi: 10.1016/j.anbehav.2009.05.019 CrossRefGoogle Scholar
  15. Faria JJ, Dyer JRG, Tosh C-JK (2010) Leadership and social information use in human crowds. Anim Behav 79:895–901. doi: 10.1016/j.anbehav.2009.12.039 CrossRefGoogle Scholar
  16. Franks NR, Dechaume-Moncharmont FX, Hanmore E, Reynolds JK (2009) Speed versus accuracy in decision-making ants: expediting politics and policy implementation. Philo Trans R Soc B-Biol Sci 364:845–852. doi: 10.1098/rstb.2008.0224 CrossRefGoogle Scholar
  17. Gautrais J, Jost C, Theraulaz G (2008) Key behavioural factors in a self-organised fish school model. Ann Zool Fenn 45:415–428Google Scholar
  18. Halloy J, Sempo G, Caprari G, Rivault C, Asadpour M, Tache F, Said I, Durier V, Canonge S, Ame JM, Detrain C, Correll N, Martinoli A, Mondada F, Siegwart R, Deneubourg JL (2007) Social integration of robots into groups of cockroaches to control self-organized choices. Science 318:1155–1158. doi: 10.1126/science.1144259 CrossRefPubMedGoogle Scholar
  19. Hensor EMA, Godin J-GJ, Hoare DJ-JK (2004) Effects of nutritional state on the shoaling tendency of banded killifish, Fundulus diaphanus, in the field. Anim Behav 65:663–669. doi: 10.1006/anbe.2003.2075 CrossRefGoogle Scholar
  20. Hoare DJ, Couzin ID, Godin J-GJ, Krause J (2004) Context-dependent group size choice in fish. Anim Behav 67:155–164. doi: 10.1016/j.anbehav.2003.04.004 CrossRefGoogle Scholar
  21. Huth A, Wissel C (1992) The simulation of movement of fish schools. J Theor Biol 156:365–385. doi: 10.1016/S0022-5193(05)80681-2 CrossRefGoogle Scholar
  22. King AJ, Johnson DDP, Van Vugt M (2009) The origins and evolution of leadership. Curr Biol 19:R911–R916. doi: 10.1016/j.cub.2009.07.027 CrossRefPubMedGoogle Scholar
  23. Krause J, Bumann D, Todt D (1992) Relationship between the position preference and nutritional state of individuals in schools of juvenile roach (Rutilus rutilus). Behav Ecol Sociobiol 30:177–180. doi: 10.1007/BF00166700 CrossRefGoogle Scholar
  24. Krause J, Hoare D, Krause S, Hemelrijk CK, Rubenstein DI (2000) Leadership in fish shoals. Fish Fish 1:82–89Google Scholar
  25. Krause J, Ruxton GD (2002) Living in groups, 1st edn. Oxford University Press, OxfordGoogle Scholar
  26. Künzler R, Bakker TCM (1998) Computer animations as a tool in the study of mating preferences. Behaviour 135:1137–1159Google Scholar
  27. Lee A (2009) VirtualDub home page. Software hosted by ‘’
  28. Liu J, Dukes I, Hu H (2005) Novel mechantronics design for a robotic fish. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS05), Edmonton, Canada, pp 2077–2082Google Scholar
  29. Magurran AE, Pitcher TJ (1983) Foraging, timidity, and shoal size in minnows and goldfish. Behav Ecol Sociobiol 12:147–152. doi: 10.1007/BF00343206 CrossRefGoogle Scholar
  30. R Core Development Team (2008) R: a language and environment for statistical computing. In: Computing RFfS (ed), Vienna, AustriaGoogle Scholar
  31. Rands SA, Cowlishaw G, Pettifor RA, Rowcliffe JM, Johnstone RA (2003) Spontaneous emergence of leaders and followers in foraging pairs. Nature 423:432–434. doi: 10.1038/nature01630 CrossRefPubMedGoogle Scholar
  32. Reebs SG (2000) Can a minority of informed leaders determine the foraging movements of a fish shoal? Anim Behav 59:403–409. doi: 10.1006/anbe.1999.1314 CrossRefPubMedGoogle Scholar
  33. Reebs SG (2001) Influence of body size on leadership in shoals of golden shiners, Notemigonus crysoleucas. Behaviour 138:797–809. doi: 10.1163/156853901753172656 CrossRefGoogle Scholar
  34. Robinson CJ, Pitcher TJ (1989) The influence of hunger and ration level on shoal density, polarization and swimming speed of herring, Clupea harengus L. J Fish Biol 34:631–633. doi: 10.1111/j.1095-8649.1989.tb03341.x CrossRefGoogle Scholar
  35. Romey WL (1996) Individual differences make a difference in the trajectories of simulated schools of fish. Ecol Model 92:65–77. doi: 10.1016/0304-3800(95)00202-2 CrossRefGoogle Scholar
  36. Seeley TD, Visscher PK, Passino KM (2006) Group decision making in honey bee swarms. Am Sci 94:220–229Google Scholar
  37. Streitlien K, Triantafyllou GS, Triantafyllou MS (1996) Efficient foil propulsion through vortex control. American Institute of Aeronautics and Astronautics 34:2315–2319. doi: 10.2514/3.13396 Google Scholar
  38. Sumpter DJ (2006) The principles of collective animal behaviour. Philos Trans R Soc Lond B Biol Sci 361:5–22. doi: 10.1098/rstb.2005.1733 CrossRefPubMedGoogle Scholar
  39. Sumpter DJ, Krause J, James R, Couzin ID, Ward AJ (2008) Consensus decision making by fish. Curr Biol 18:1773–1777. doi: 10.1016/j.cub.2008.09.064 CrossRefPubMedGoogle Scholar
  40. Tudorache C, Blust R, De Boeck G (2007) Swimming capacity and energetics of migrating and non-migrating morphs of three-spined stickleback Gasterosteus aculeatus L. and their ecological implications. J Fish Biol 71:1448–1456. doi: 10.1111/j.1095-8649.2007.01612.x CrossRefGoogle Scholar
  41. Ward AJW, Sumpter DJT, Couzin LD, Hart PJB, Krause J (2008) Quorum decision-making facilitates information transfer in fish shoals. Proc Natl Acad Sci USA 105:6948–6953. doi: 10.1073/pnas.0710344105 CrossRefPubMedGoogle Scholar
  42. Webb B (2000) What does robotics offer animal behaviour? Anim Behav 60:545–558. doi: 10.1006/anbe.2000.1514 CrossRefPubMedGoogle Scholar
  43. Webster M, Laland KN (2008) Social learning strategies and predation risk: minnows copy only when using private information would be costly. Proc R Soc Lond B Biol Sci 275:2869–2876. doi: 10.1098/rspb.2008.0817 CrossRefGoogle Scholar
  44. Wilson ADM, Godin J-GJ (2010) Boldness and intermittent locomotion in the bluegill sunfish, Lepomis macrochirus. Behav Ecol 21:57–62. doi: 10.1093/beheco/arp157 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Jolyon J. Faria
    • 1
    Email author
  • John R. G. Dyer
    • 1
  • Romain O. Clément
    • 2
  • Iain D. Couzin
    • 3
  • Natalie Holt
    • 1
  • Ashley J. W. Ward
    • 4
  • Dean Waters
    • 1
  • Jens Krause
    • 2
  1. 1.Institute of Integrative and Comparative BiologyUniversity of LeedsLeedsUK
  2. 2.Department of Biology and Ecology of FishesLeibniz-Institute of Freshwater Ecology & Inland FisheriesBerlinGermany
  3. 3.Department of Ecology and Evolutionary BiologyPrinceton UniversityPrincetonUSA
  4. 4.School of Biological SciencesUniversity of SydneySydneyAustralia

Personalised recommendations