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Behavioral Ecology and Sociobiology

, Volume 65, Issue 5, pp 1147–1157 | Cite as

Novel methods for discriminating behavioral differences between stickleback individuals and populations in a laboratory shoaling assay

  • Abigail R. Wark
  • Barry J. Wark
  • Tessa J. Lageson
  • Catherine L. PeichelEmail author
Methods

Abstract

Threespine sticklebacks (Gasterosteus aculeatus) from different habitats have been observed to differ in shoaling behavior, both in the wild and in laboratory studies. In the present study, we surveyed the shoaling behavior of sticklebacks from a variety of marine, lake, and stream habitats throughout the Pacific Northwest. We tested the shoaling tendencies of 113 wild-caught sticklebacks from 13 populations using a laboratory assay that was based on other published shoaling assays in sticklebacks. Using traditional behavioral measures for this assay, such as time spent shoaling and mean position in the tank, we were unable to find population differences in shoaling behavior. However, simple plotting techniques revealed differences in spatial distributions during the assay. When we collapsed individual trials into population-level data sets and applied information theoretic measurements, we found significant behavioral differences between populations. For example, entropy estimates confirm that populations display differences in the extent of clustering at various tank positions. Using log-likelihood analysis, we show that these population-level observations reflect consistent differences in individual behavioral patterns that can be difficult to discriminate using standard measures. The analytical techniques we describe may help improve the detection of potential behavioral differences between fish groups in future studies.

Keywords

Social behavior Shoaling assay Stickleback Entropy Information theory 

Notes

Acknowledgments

We would like to thank Matt Arnegard, Susan Foster, Andrew Hendry, Jean-Sebastien Moore, Dolph Schluter, and Mike Shapiro for their help in collecting sticklebacks, and to Anna Greenwood for the advice and support throughout the study. We are also grateful to Adrienne Fairhall and Joe Sisneros for their helpful comments on the manuscript. This research was supported by a grant from the National Institutes of Health HG002568 to C.L.P.

Supplementary material

265_2010_1130_MOESM1_ESM.pdf (1.4 mb)
Fig. S1 Tracked fish positions are correlated in time in both horizontal and vertical dimensions for approximately 60 s, the width of the correlation peak at half-maximum. a Auto-correlation function shows correlation coefficient between each fish's horizontal position in the tank and that fish's horizontal position at a given time lag (thin gray lines). Average auto-correlation across all 113 fish is shown in bold. b Auto-correlation function shows correlation coefficient between each fish's vertical position in the tank and that fish's vertical position at a given time lag (thin gray lines). Average auto-correlation across all 113 fish is shown in bold (PDF 1404 kb)

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Abigail R. Wark
    • 1
    • 2
  • Barry J. Wark
    • 2
    • 3
  • Tessa J. Lageson
    • 1
  • Catherine L. Peichel
    • 1
    Email author
  1. 1.Division of Human Biology, Fred Hutchinson Cancer Research CenterSeattleUSA
  2. 2.Program in Neurobiology and Behavior, T471 Health Sciences CenterUniversity of WashingtonSeattleUSA
  3. 3.Physion ConsultingBostonUSA

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