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Animal behaviour

Trust in fish

Naturevolume 441pages937938 (2006) | Download Citation

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A mutually beneficial interaction between two species of fish turns out to involve the careful appraisal of one by the other — and the appropriately virtuous behaviour by the former while being watched.

Many animals, including humans, are scorekeepers, paying close attention to what others do to them and responding in kind. This sort of tit-for-tat reciprocity works well, but is limited to instances involving some sort of direct, one-on-one, interaction. What, however, if organisms could learn about who was trustworthy (or not) by observing others from afar? Such ‘eavesdropping’, as Bshary and Grutter show on page 975 of this issue1, characterizes an amazing case of cooperation through social networking in two species of fish: the cleaner wrasse and the bridled monocle bream.

Social scientists have long realized that humans are part of complex social networks, within which information travels in many directions. It took time, however, for this view of behaviour to migrate to other disciplines, particularly behavioural ecology. Although the sociobiologist Richard Alexander2 discussed indirect ways of obtaining information back in 1987, only recently have evolutionary and behavioural ecologists begun to take the study of such social networks in non-humans seriously. This has occurred along two distinct, but parallel, paths.

One path towards understanding social networks centres on the evolution of cooperation. Here, tit-for-tat scorekeeping is expanded, such that individuals can glean information on the cooperative tendencies of others by observing behavioural interactions in their group. They then create what is called an ‘image score’ of those around them — this score increases when an observed individual cooperates, and otherwise decreases3,4. Image scoring allows cooperators to preferentially help other cooperators, and sets the stage for the evolution of reputation5. Such a scoring system may also create selection pressures for individuals to advertise their cooperative tendencies6.

The second path for studying social networks involves eavesdropper effects and audience effects7. These effects often centre on territorial behaviour, and more generally aggression, in which eavesdroppers learn about the fighting abilities of those they monitor, and use this information when they subsequently interact with such individuals. In principle, eavesdropper effects, as well as audience effects — in which the individual being observed behaves differently as a consequence of being watched — are not restricted to aggressive behaviour, but can evolve in almost any context.

Bshary and Grutter1 merge the image-scoring and eavesdropper approaches to uncover a fascinating example of a complex aquatic social network involving the cleaner wrasse (Labroides dimidiatus) and its ‘client’ bream (Scolopsis bilineatus) (Fig. 1). In this system, the wrasse ‘cleans’ the bream by eating the parasites that live on its body. The catch is that the cleaner wrasse actually prefers eating the client's mucus, and such cheating is not in the client's best interest. Yet, clients never eat their cleaners in Bshary and Grutter's experimental set-up — so what can clients do to prevent cheating, and why doesn't such cheating characterize this system? The answers are steeped in a social-networking system that has evolved between the two species.

Figure 1: Client and cleaner.
Figure 1

The relationship between these two species, the bridled monocle bream and the cleaner wrasse, constitutes a complex social network.

Credit: A. GRUTTER

To disentangle this network, Bshary and Grutter constructed an ingenious experiment in which a client fish could observe two cleaners. One of the cleaners appeared to be interacting cooperatively with another client fish, whereas no information about the cooperative tendencies of the second cleaner was provided. Given a choice between two such cleaners, the client preferred to spend its time near the cooperator. That is, clients chose their cleaners by eavesdropping on them, and preferentially interacting with those most likely to actually eat parasites, rather than with unknown individuals, who might very well try to get a quick meal of nutritious mucus off the client.

On the other side of the social network, Bshary and Grutter examined whether cleaner fish act in a more cooperative manner when they are being watched. Such audience effects would make cooperation all the more likely to evolve. Bshary and Grutter searched for such effects by setting up an artificial pulley system attached to a series of plates that contained two food items — flake food and prawns. Of these two items, cleaners prefer prawns over flake food (just as they prefer mucus over parasites on their clients). The plates, then, acted as proxies for the client fish: that is, the plates ‘watched’ the cleaner fish and adjusted what food was available as a function of the behaviour of the cleaners. By using the pulleys, Bshary and Grutter could test whether cleaner fish were more likely to take the less preferred food, if taking the more preferred food from a plate meant that they would lose access to other plates (just as they would lose access to other clients in the wild if they cheated). And indeed, the authors did find audience effects under these admittedly contrived conditions, as cleaners were induced into eating the less preferred foods.

If social networks such as those uncovered by Bshary and Grutter in fish are common in other animals, the prospect for researchers is both exciting and daunting. It is exciting because it suggests that animal behaviour has to be seen as being embedded in a rich social tapestry, composed of interconnected direct and indirect streams of information. And it is logistically daunting for exactly the same reason.

References

  1. 1

    Bshary, R. & Grutter, A. S. Nature 441, 975–978 (2006).

  2. 2

    Alexander, R. The Biology of Moral Systems (Aldine De Gruyter, New York, 1987).

  3. 3

    Nowak, M. A. & Sigmund, K. Nature 393, 573–577 (1998).

  4. 4

    Wedekind, C. & Milinski, M. Science 288, 850–852 (2000).

  5. 5

    Pollock, G. & Dugatkin, L. A. J. Theor. Biol. 159, 25–37 (1992).

  6. 6

    Zahavi, A. & Zahavi, A. The Handicap Principle (Oxford Univ. Press, 1997).

  7. 7

    McGregor, P. (ed.) Animal Communication Networks (Cambridge Univ. Press, 2005).

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  1. Lee Alan Dugatkin is in the Department of Biology, University of Louisville, Louisville, 40202, Kentucky, USA

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https://doi.org/10.1038/441937a

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