Advertisement

Automated Tracking of Zebrafish Shoals and the Analysis of Shoaling Behavior

  • Noam Miller
  • Robert Gerlai
Protocol
Part of the Neuromethods book series (NM, volume 66)

Abstract

Zebrafish spend the majority of their time in groups, called shoals. Shoaling behavior is complex and dynamic: fish leave and rejoin the shoal, distances between shoal-members oscillate, and the speed and polarization of the shoal change on timescales of seconds to minutes. All these features of shoals can be modified by various pharmacological and environmental—and possibly also genetic—manipulations and a thorough characterization of shoaling behavior can therefore be used as an effective assay for complex aspects of vertebrate social behavior. We present methods for acquiring and analyzing detailed trajectory data from shoals of zebrafish and demonstrate how these methods can be used to distinguish episodes of shoaling under different conditions. These methods could be further developed to create a standardized assay of shoaling behavior that will allow for an in-depth exploration of social behaviors in zebrafish.

Key words

Shoaling Schooling Automated video tracking Time-series analysis Zebrafish 

References

  1. 1.
    Miller NY, Gerlai R (2011) Shoaling in zebrafish: what we don’t know. Rev Neurosci 22:17–25PubMedGoogle Scholar
  2. 2.
    Wright D, Rimmer LB, Pritchard VL, Krause J, Butlin RK (2003) Inter and intra-population variation in shoaling and boldness in the zebrafish (Danio rerio). Naturwissenschaften 90:374–377PubMedCrossRefGoogle Scholar
  3. 3.
    Wright D, Nakamichi R, Krause J, Butlin R (2006) QTL analysis of behavioral and morphological differentiation between wild and laboratory zebrafish (Danio rerio). Behav Genet 36:271–284PubMedCrossRefGoogle Scholar
  4. 4.
    Dlugos CA, Rabin RA (2003) Ethanol effects on three strains of zebrafish: model system for genetic investigations. Pharmacol Biochem Behav 74:471–480PubMedCrossRefGoogle Scholar
  5. 5.
    Echevarria DJ, Hammack CM, Pratt DW, Hosemann JD (2008) A novel behavioral test battery to assess global drug effects using the zebrafish. Int J Comp Psychol 21:19–34Google Scholar
  6. 6.
    Gerlai R, Ahmad F, Prajapati S (2008) Differences in acute alcohol-induced behavioral responses among zebrafish populations. Alcohol Clin Exp Res 32:1–11CrossRefGoogle Scholar
  7. 7.
    Kurta A, Palestis BG (2010) Effects of ethanol on the shoaling behavior of zebrafish (Danio rerio). Dose Response 8:527–533PubMedCrossRefGoogle Scholar
  8. 8.
    Krause J, Ruxton GD (2002) Living in groups. Oxford University Press, OxfordGoogle Scholar
  9. 9.
    Shaw E (1978) Schooling fishes. Am Sci 66:166–175Google Scholar
  10. 10.
    Pitcher TJ, Parrsih JK (1993) Functions of shoaling behavior in teleosts. In: Pitcher TJ (ed) Behavior of teleost fishes. Chapman & Hall, LondonCrossRefGoogle Scholar
  11. 11.
    Magurran AE, Pitcher TJ (1983) Foraging, timidity and shoal size in minnows and goldfish. Behav Ecol Sociobiol 12:147–152CrossRefGoogle Scholar
  12. 12.
    Viscido SV, Parrish JK, Grunbaum D (2004) Individual behavior and emergent properties of fish schools: a comparison of observation and theory. Mar Ecol Prog Ser 273:239–249CrossRefGoogle Scholar
  13. 13.
    Delcourt J, Becco C, Vandewalle N, Poncin P (2009) A video multitracking system for quantification of individual behavior in a large fish shoal: advantages and limits. Behav Res Methods 41:228–235PubMedCrossRefGoogle Scholar
  14. 14.
    Ballerini M, Cabibbo N, Candelier R, Cavagna A, Cisbani E, Giardina I et al (2008) Empirical investigation of starling flocks: a benchmark study in collective animal behaviour. Anim Behav 76:201–215CrossRefGoogle Scholar
  15. 15.
    Miller N, Gerlai R (2007) Quantification of shoaling behavior in zebrafish. Behav Brain Res 184:157–166PubMedCrossRefGoogle Scholar
  16. 16.
    Wu HS, Zhao Q, Zou D, Chen YQ (2011) Automated 3D trajectory measuring of large numbers of moving particles. Opt Express 19:7646–7663PubMedCrossRefGoogle Scholar
  17. 17.
    Buske C, Gerlai R (2011) Shoaling develops with age in zebrafish (Danio rerio). Prog Neuropsychopharmacol Biol Psychiatry 35(6): 1409–1415PubMedCrossRefGoogle Scholar
  18. 18.
    Gerlach G, Lysiak N (2006) Kin recognition and inbreeding avoidance in zebrafish, Danio rerio, is based on phenotype matching. Anim Behav 71:1371–1377CrossRefGoogle Scholar
  19. 19.
    Cavagna A, Giardina I, Orlandi A, Parisi G, Procaccini A (2008) The STARFLAG handbook on collective animal behaviour: 2. Three-dimensional analysis. Anim Behav 76:237–248CrossRefGoogle Scholar
  20. 20.
    Kato S, Nakagawa T, Ohkawa M, Muramoto K, Oyama O, Watanabe A et al (2004) A computer image processing system for quantification of zebrafish behavior. J Neurosci Methods 134:1–7PubMedCrossRefGoogle Scholar
  21. 21.
    Tien JH, Levin SA, Rubinstein DI (2004) Dynamics of fish shoals: identifying key decision rules. Evol Ecol Res 6:555–565Google Scholar
  22. 22.
    Becco Ch, Vandewalle N, Delcourt J, Poncin P (2006) Experimental evidences of a structural and dynamical transition in fish school. Physica A 367:487–493CrossRefGoogle Scholar
  23. 23.
    Inman AJ (1990) Group foraging in starlings: distributions of unequal competitors. Anim Behav 40:801–810CrossRefGoogle Scholar
  24. 24.
    Chatfield C (2002) The analysis of time series: an introduction, 6th edn. Chapman & Hall, LondonGoogle Scholar
  25. 25.
    Miller N, Gerlai R (2011) Redefining membership in animal groups. Behav Res Methods 43(4):964–970PubMedCrossRefGoogle Scholar
  26. 26.
    Buhl J, Sumpter DJT, Couzin ID, Hale JJ, Despland E, Miller ER et al (2006) From disorder to order in marching locusts. Science 312:1402–1406PubMedCrossRefGoogle Scholar
  27. 27.
    Miller N, Gerlai R (2008) Oscillations in shoal cohesion in zebrafish (Danio rerio). Behav Brain Res 193:148–151PubMedCrossRefGoogle Scholar
  28. 28.
    Aoki I (1980) An analysis of the schooling behavior of fish: internal organization and communication process. Bull Ocean Res Inst Univ Tokyo 12:1–65Google Scholar
  29. 29.
    Hernandez G (1999) Time series, periodograms, and significance. J Geophys Res 104:10355–10368CrossRefGoogle Scholar
  30. 30.
    Frescura FAM, Engelbrecht CA, Frank BS (2007) Significance tests for periodogram peaks. NASA Astrophys Data Sys Arxiv: 0706.2225vlGoogle Scholar
  31. 31.
    Lukeman R, Li Y-X, Edelstein-Keshet L (2010) Inferring individual rules from collective behavior. PNAS 107:12576–12580PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Princeton UniversityPrincetonUSA
  2. 2.University of Toronto at MississaugaMississaugaCanada

Personalised recommendations