Behavior Research Methods

, Volume 38, Issue 4, pp 704–710 | Cite as

Comparing the EthoVision 2.3 system and a new computerized multitracking prototype system to measure the swimming behavior in fry fish

  • Johann Delcourt
  • Christophe Beco
  • Marc Y. Ylieff
  • Hervé Caps
  • Nicolas Vandewalle
  • Pascal Poncin
Article
  • 442 Downloads

Abstract

Coming from the framework of unmarked fry tracking, we compared the capacities, advantages, and disadvantages of two recent video tracking systems: EthoVision 2.3 and a new prototype of multitracking. The EthoVision system has proved to be impressive for tracking a fry using the detection by gray scaling. Detection by subtraction has given less accurate results. Our video multitracking system is able to detect and track more than 100 unmarked fish by gray scaling technique. It permits an analysis at the group level as well as at the individual level. The multitracking program is able to attribute a number to each fish and to follow each one for the whole duration of the track. Our system permits the analysis of the movement of each individual, even if the trajectories of two fish cross each other. This is possible thanks to the theoretical estimation of the trajectory of each fish, which can be compared with the real trajectory (analysis with feedback). However, the period of the track is limited for our system (about 1 min), whereas EthoVision is able to track for numerous hours. In spite of these limitations, these two systems allow an almost continuous automatic sampling of the movement behaviors during the track.

References

  1. Axelsen, B. E., Anker-Nilssen, T., Fossum, P., Kvamme, C., &Nøttestad, L. (2001). Pretty patterns but a simple strategy: Predator-prey interactions between juvenile herring and Atlantic puffins observed with multibeam sonar.Canadian Journal of Zoology,79, 1586–1596.CrossRefGoogle Scholar
  2. Becco, C., Vandewalle, N., Delcourt, J., &Poncin, P. (2006). Experimental evidences of a structural and dynamical transition in fish school.Physica A,367, 487–493.CrossRefGoogle Scholar
  3. Buma, M. O. S., Moskal, J., & Liang, D. (1998, August).EthoVision MultiPro: Improved animal identification during automatic multiobject tracking. Measuring Behavior ’98, 2nd International Conference on Methods and Techniques in Behavioral Research. Groningen, The Netherlands.Google Scholar
  4. Buma, M. O. S., Moskal, J., Thomas, G., & Jongbloed, S. (1996, October).Automatic video tracking of multiple animals without the need for marking. Paper presented at Measuring Behavior ’96, 1st International Conference on Methods and Techniques in Behavioral Research. Utrecht, The Netherlands.Google Scholar
  5. Bumann, D., &Krause, J. (1993). Front individuals lead in shoals of three-spined sticklebacks (Gasterosteus aculeatus) and juvenile roach(Rutilus rutilus).Behaviour,125, 189–198.CrossRefGoogle Scholar
  6. Delcourt, J., Ylieff, M. [Y.], & Poncin, P. (2004, August).Measuring the ontogeny of swimming behaviour with a computerised video tracking system: Development of spontaneous activity at embryonic and early juvenile periods in the mouthbrooder Nile tilapia. Paper presented at the 2nd European Conference on Behavioural Biology. Groningen, The Netherlands.Google Scholar
  7. Derry, J. F., & Elliott, C. J. H. (1997). Automated 3-D tracking of video-captured movement using the example of an aquatic mollusk.Behavior Research Methods, Instruments, & Computers,29, 353-357.Google Scholar
  8. Gerlai, R. (2003). Zebra fish: An uncharted behavior genetic model.Behavior Genetics,33, 461–468.CrossRefPubMedGoogle Scholar
  9. Gerlai, R., Lahav, M., Guo, S., &Rosenthal, A. (2000). Drinks like a fish: Zebra fish (Danio rerio) as a behavior genetic model to study alcohol effects.Pharmacology Biochemistry & Behavior,67, 773–782.CrossRefGoogle Scholar
  10. Grégoire, G., &Chaté, H. (2004). Onset of collective and cohesive motion.Physical Review Letters,92, 025702.1–025702.4.CrossRefGoogle Scholar
  11. Jadot, C., Donnay, A., Ylieff, M. [Y.], &Poncin, P. (2005). Impact implantation of a transmitter onSarpa salpa behaviour: Study with a computerized video tracking system.Journal of Fish Biology,67, 589–595.CrossRefGoogle Scholar
  12. Kato, S., Tamada, K., Shimada, Y., &Chujo, T. (1996). A quantification of goldfish behavior by an image processing system.Behavioural Brain Research,80, 51–55.CrossRefPubMedGoogle Scholar
  13. Krause, J. (1993). The relationship between foraging and shoal position in a mixed shoal of roach (Rutilus rutilus) and chub (Leuciscus cephalus): A field study.Oecologia,93, 356–359.CrossRefGoogle Scholar
  14. Laurel, B. J., Laurel, C. J., Brown, J. A., &Gregory, R. S. (2005). A new technique to gather 3-D spatial information using a single camera.Journal of Fish Biology,66, 429–441.CrossRefGoogle Scholar
  15. Martin, B. R., Prescott, W. R., &Zhu, M. (1992). Quantification of rodent catalepsy by a computer-imaging technique.Pharmacology Biochemistry & Behavior,43, 381–386.CrossRefGoogle Scholar
  16. Mélard, C. (1986). Les bases biologiques de l’élevage intensif du tilapia du Nil.Cahiers d’Ethologie Appliqué,6 (3).Google Scholar
  17. Mukhina, T. V., Bachurin, S. O., Lermontova, N. N., &Zefirov, N. S. (2001). Versatile computerized system for tracking and analysis of water maze tests.Behavior Research Methods, Instruments, & Computers,33, 371–380.CrossRefGoogle Scholar
  18. Nilsson, G. E., Rosén, P., &Johansson, D. (1993). Anoxic depression of spontaneous locomotor activity in crucian carp quantified by a computerized imaging technique.Journal of Experimental Biology,180, 153–162.Google Scholar
  19. Noldus, L. P. J. J., Spink, A. J., &Tegelenbosch, R. A. J. (2001). EthoVision: A versatile video tracking system for automation of behavioral experiments.Behavior Research Methods, Instruments, & Computers,33, 398–414.CrossRefGoogle Scholar
  20. Noldus, L. P. J. J., Spink, A. J., &Tegelenbosch, R. A. J. (2002). Computerized video tracking, movement analysis and behavior recognition in insects.Computers & Electronics in Agriculture,35, 201–227.CrossRefGoogle Scholar
  21. Nøttestad, L., &Axelsen, B. E. (1999). Herring schooling manoeuvres in response to killer whale attacks.Canadian Journal of Zoology,77, 1540–1546.CrossRefGoogle Scholar
  22. Olivo, R. F., &Thompson, M. C. (1988). Monitoring animals’ movements using digitized video images.Behavior Research Methods, Instruments, & Computers,20, 485–490.CrossRefGoogle Scholar
  23. Parrish, J. K., Viscido, S. V., &Grünbaum, D. (2002). Self-organized fish schools: An examination of emergent properties.Biological Bulletin,202, 296–305.CrossRefPubMedGoogle Scholar
  24. Pereira, P., &Oliveira, R. F. (1994). A simple method using a single video camera to determine the three-dimensional position of a fish.Behavior Research Methods, Instruments, & Computers,26, 443–446.CrossRefGoogle Scholar
  25. Reynolds, C. W. (1987). Flocks, herds, and schools: A distributed behavioral model.Computer Graphics Quarterly,21, 25–34.CrossRefGoogle Scholar
  26. Spink, A. J., Tegelenbosch, R. A. J., Buma, M. O. S., &Noldus, L. P. J. J. (2001). The EthoVision video tracking system—A tool for behavioral phenotyping of transgenic mice.Physiology & Behavior,73, 731–744.CrossRefGoogle Scholar
  27. Suzuki, K., Tsumonu, T., &Hiraishi, T. (2003). Video analysis of fish schooling behavior in finite space using a mathematical model.Fisheries Research,60, 3–10.CrossRefGoogle Scholar
  28. Vandewalle, N., Trabelsi, S., &Caps, H. (2004). Block-to-granularlike transition in dense bubble flows.Europhysics Letters,65, 316–322.CrossRefGoogle Scholar
  29. Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I., &Shochet, O. (1995). Novel type of phase transition in a system of self-driven particles.Physical Review Letters,75, 1226–1229.CrossRefPubMedGoogle Scholar
  30. Vicsek, T., Czirók, A., Farkas, I. J., &Helbing, D. (1999). Application of statistical mechanics to collective motion in biology.Physica A,274, 182–189.CrossRefGoogle Scholar
  31. Viscido, S. V., Parrish, J. K., &Grünbaum, D. (2004). Individual behavior and emergent properties of fish schools: A comparison of observation and theory.Marine Ecology Progress Series,273, 239–249.CrossRefGoogle Scholar
  32. Winberg, S., Nilsson, G. E., Spruijt, B. M., &Höglund, U. (1993). Spontaneous locomotor activity in Arctic charr measured by a computerized imaging technique: Role of brain serotonergic activity.Journal of Experimental Biology,179, 213–232.Google Scholar
  33. Ylieff, M. [Y.] (2002).Validation et exploitation de nouvelles techniques d’imagerie numérique pour la caractérisation des profils comportementaux chez les poissons: Etude de l’influence de facteurs abiotiques et biotiques chez Symphodus ocellatus (Forsskål, 1775) et Chromis chromis Linné, 1758, Labridé et Pomacentridé méditerranéens. Unpublished doctoral dissertation, Université de Liège.Google Scholar
  34. Ylieff, M. Y., Bosch, V., Kestemont, P., Poncin, P., & Thomé, J. P. (2003, November).Impact of a contamination by xenobiotics (PCBs and atrazine) on the locomotor behaviour and the activity of a biomarker of exposure (EROD) in the goldfish Carassius auratus,Linnaeus, 1758. Paper presented the 10th Benelux Congress of Zoology, Leiden, The Netherlands.Google Scholar
  35. Ylieff, M. Y., &Poncin, P. (2003). Quantifying spontaneous swimming activity in fish with a computerized color video tracking system, a laboratory device using last imaging techniques.Fish Physiology & Biochemistry,28, 281–282.CrossRefGoogle Scholar
  36. Ylieff, M. Y., Sanchez-Colero, C., Poncin, P., Voss, J., & Ruwet, J. C. (2000). Measuring effects of different temperatures on swimming activity and social behavior in groups of Mediterranean marine fish with theEthoVision Color-Pro video tracking system. InProceedings of Measuring Behavior 2000, 3rd International Conference on the Methods and Techniques in Behavioral Research (pp. 350–351). Wageningen, The Netherlands: Noldus Information Technology.Google Scholar

Copyright information

© Psychonomic Society, Inc. 2006

Authors and Affiliations

  • Johann Delcourt
    • 1
  • Christophe Beco
    • 1
  • Marc Y. Ylieff
    • 1
  • Hervé Caps
    • 1
  • Nicolas Vandewalle
    • 1
  • Pascal Poncin
    • 1
  1. 1.Behavioral Biology Unit: Ethology and Animal Psychology, Department of Environmental Sciences & Management Faculty of SciencesUniversity of LiègeLiègeBelgium

Personalised recommendations