Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Fish pool their experience to solve problems collectively

Nature Ecology & Evolution volume 1, Article number: 0135 (2017) Cite this article

Subjects

Abstract

Access to information is a key advantage of grouping. Although experienced animals can lead others to solve problems, less is known about whether partially informed individuals can pool experiences to overcome challenges collectively. Here we provide evidence of such ‘experience-pooling’. We presented shoals of sticklebacks (Gasterosteus aculeatus) with a two-stage foraging task requiring them to find and access hidden food. Individual fish were either inexperienced or had knowledge of just one of the stages. Shoals containing individuals trained in each of the stages pooled their expertise, allowing more fish to access the food, and to do so more rapidly, compared with other shoal compositions. Strong social effects were identified: the presence of experienced individuals increased the likelihood of untrained fish completing each stage. These findings demonstrate that animal groups can integrate individual experience to solve multi-stage problems, and have implications for our understanding of social foraging, migration and social systems.

Group-living provides animals with both ready access to valuable social information and the potential, by processing information through social interactions, to achieve solutions to cognitive problems that might lie beyond the reach of lone individuals1–11. Information processing by groups can occur through several different mechanisms, and distinguishing between these is a key challenge faced by researchers10. Such mechanisms include swarm intelligence, facilitation and pool-of-competence effects. Swarm intelligence refers to improved cognitive performance that stems from distributed, self-organized decision making, with decisions emerging from repeated local interactions between individuals12,13. The ‘many-wrongs’ principle of collective navigation is an example of swarm intelligence. Here, individuals’ motivation to follow their varied and imperfect estimates of the correct travel direction interacts with their drive to remain in close proximity to their neighbours, resulting in a group-level compromise on preferred direction that is more accurate than the separate estimates of most individuals14,15. A second mechanism, facilitation, occurs when necessary costs such as vigilance for predators are shared among group members, allowing individuals to allocate more effort to other problems, such as searching for resources11. Finally, pool-of-competence describes effects arising from group size and diversity, with larger groups being statistically more likely to include more experienced, motivated, persistent or bold individuals that are more likely to solve problems and from which others in the group can acquire information10,16.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Number and rate of goal zone and feeder entries.
Figure 2: Proportion of naive fish entering the goal and feeder areas.

References

  1. Krause, J. & Ruxton, G. D. Living in Groups (Oxford Univ. Press, 2002).

    Google Scholar 

  2. Danchin, É. et al. Public information: from nosy neighbors to cultural evolution. Science 305, 487–491 (2004).

    Article  CAS  Google Scholar 

  3. Couzin, I. D. et al. Effective leadership and decision-making in animal groups on the move. Nature 433, 513–516 (2005).

    Article  CAS  Google Scholar 

  4. Couzin, I. D. Collective cognition in animal groups. Trends Cogn. Sci. 13, 36–43 (2009).

    Article  Google Scholar 

  5. Krause, J., Ruxton, G. D. & Krause, S. Swarm intelligence in animals and humans. Trends Ecol. Evol. 25, 28–34 (2010).

    Article  Google Scholar 

  6. Sumpter, D. J. Collective Animal Behavior (Princeton Univ. Press, 2010).

    Book  Google Scholar 

  7. Laland, K. N., Atton, N. & Webster, M. M. From fish to fashion: experimental and theoretical insights into the evolution of culture. Phil. Trans. R. Soc. Lond. B 366, 958–968 (2011).

    Article  CAS  Google Scholar 

  8. Ward, A. J. et al. Fast and accurate decisions through collective vigilance in fish shoals. Proc. Natl Acad. Sci. USA 108, 2312–2315 (2011).

    Article  CAS  Google Scholar 

  9. Berdahl, A. et al. Emergent sensing of complex environments by mobile animal groups. Science 339, 574–576 (2013).

    Article  CAS  Google Scholar 

  10. Ioannou, C. C. Swarm intelligence in fish? The difficulty in demonstrating distributed and self-organised collective intelligence in (some) animal groups. Behav. Process. http://dx.doi.org/10.1016/j.beproc.2016.10.005 (2016).

  11. Ward, A. J. W. & Webster, M. M. Sociality: The Behaviour of Group Living Animals (Springer, 2016).

    Book  Google Scholar 

  12. Bonabeau, E., Dorigo, M. & Theraulaz, G. Swarm Intelligence: From Natural to Artificial Systems (Oxford Univ. Press, 1999).

    Google Scholar 

  13. Garnier, S., Gautrais, J. & Theraulaz, G. The biological principles of swarm intelligence. Swarm Intel. 1, 3–31 (2007).

    Article  Google Scholar 

  14. Codling, E. A., Pitchford, J. W. & Simpson, S. D. Group navigation and the ‘many-wrongs principle’ in models of animal movement. Ecology 88, 1864–1870 (2007).

    Article  CAS  Google Scholar 

  15. Codling, E. A. & Bode, N. W. Balancing direct and indirect sources of navigational information in a leaderless model of collective animal movement. J. Theor. Biol. 394, 32–42 (2016).

    Article  Google Scholar 

  16. Morand-Ferron, J. & Quinn, J. L. Larger groups of passerines are more efficient problem solvers in the wild. Proc. Natl Acad. Sci. USA 108, 15898–15903 (2011).

    Article  CAS  Google Scholar 

  17. Dyer, J. R. et al. Leadership, consensus decision making and collective behaviour in humans. Phil. Trans. R. Soc. Lond. B 364, 781–789 (2009).

    Article  Google Scholar 

  18. Ioannou, C. C., Singh, M. & Couzin, I. D. Potential leaders trade off goal-oriented and socially oriented behavior in mobile animal groups. Am. Nat. 186, 284–293 (2015).

    Article  Google Scholar 

  19. Jolles, J. W. et al. The role of social attraction and its link with boldness in the collective movements of three-spined sticklebacks. Anim. Behav. 99, 147–153 (2015).

    Article  Google Scholar 

  20. Webster, M. M. Experience and motivation shape leader–follower interactions in fish shoals. Behav. Ecol. 28, 77–84 (2017).

    Article  Google Scholar 

  21. Conradt, L. et al. ‘Leading according to need’ in self-organizing groups. Am. Nat. 173, 304–312 (2009).

    Article  CAS  Google Scholar 

  22. Day, R. L. et al. Interactions between shoal size and conformity in guppy social foraging. Anim. Behav. 62, 917–925 (2001).

    Article  Google Scholar 

  23. Atton, N. et al. Information flow through threespine stickleback networks without social transmission. Proc. R. Soc. Lond. B 279, 4272–4278 (2012).

    Article  CAS  Google Scholar 

  24. Krause, S. et al. Swarm intelligence in humans: diversity can trump ability. Anim. Behav. 81, 941–948 (2011).

    Article  Google Scholar 

  25. Reader, S. M. & Laland, K. N. Diffusion of foraging innovations in the guppy. Anim. Behav. 60, 175–180 (2000).

    Article  CAS  Google Scholar 

  26. Atton, N. et al. Familiarity affects social network structure and discovery of prey patch locations in foraging stickleback shoals. Proc. R. Soc. Lond. B 281, 20140579 (2014).

    Article  CAS  Google Scholar 

  27. Webster, M. M. & Laland, K. N. Evaluation of a non-invasive tagging system for laboratory studies using three-spined sticklebacks (Gasterosteus aculeatus). J. Fish Biol. 75, 1868–1873 (2009).

    Article  CAS  Google Scholar 

  28. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).

  29. Therneau, T. M. & Lumley, T. survival: Survival analysis v. 2.41-2. https://cran.r-project.org/web/packages/survival/index.html (2015).

Download references

Acknowledgements

This work was funded by an ERC Advanced Grant to K.N.L. (EVOCULTURE, ref. 232823). We thank K. Meacham for assistance in preparing the manuscript.

Author information

Authors and Affiliations

  1. School of Biology, Harold Mitchell Building, University of St Andrews, St Andrews, KY16 9ST, Fife, UK.

    Mike M. Webster, Andrew Whalen & Kevin N. Laland

  2. Roslin Institute, University of Edinburgh, Easter Bush, EH25 9RG, Edinburgh, UK.

    Andrew Whalen

Authors
  1. Mike M. Webster
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew Whalen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kevin N. Laland
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.M.W. designed and performed the experiments. M.M.W., A.W. and K.N.L. analysed the data and co-authored the paper.

Corresponding author

Correspondence to Mike M. Webster.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Methods; Supplementary Figures 1–4; Supplementary Tables 1–3 (PDF 308 kb)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Webster, M., Whalen, A. & Laland, K. Fish pool their experience to solve problems collectively. Nat Ecol Evol 1, 0135 (2017). https://doi.org/10.1038/s41559-017-0135

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41559-017-0135

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing