Behavioral Ecology Advance Access originally published online on March 24, 2006
Behavioral Ecology 2006 17(4):532-538; doi:10.1093/beheco/arj060
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Toward an individual-level understanding of vigilance: the role of social information
Department of Environmental and Evolutionary Biology, Institute of Biomedical and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK
Address correspondence to A.L. Jackson who is now at Medical Research Council Social and Public Health Sciences Unit, 4 Lilybank Gardens, Glasgow G12 8RZ, UK. E-mail: andrew{at}msoc.mrc.gla.ac.uk.
A gregarious lifestyle affords the benefit of collective detection of predators through the many-eyes effect. Studies of vigilance are generally concerned with exploring the relationship between vigilance rates and group size. However, a mechanistic understanding of the rules individual animals use to achieve this group-level behavior is lacking. Building on a previous modeling approach, we suggest that individuals reconcile their own private information against the social information they receive from their group mates in order to decide whether to feed or be vigilant at any one time. We present a novel modeling approach utilizing a Markov chain Monte Carlo process to describe the transition between vigilant and nonvigilant states. Many of our assumptions are based qualitatively on recently published experimental observations. We vary the amount of social information and the fidelity with which individuals process this information and show that this has a profound effect on the individual vigilance rate, the individual vigilant bout length, and the proportion of vigilant individuals at any one time. A wide range of group-level vigilance patterns can be obtained by varying simple behavioral characteristics of individual animals. We find that generally, increasing the amount of, and sensitivity to, social information generates a more cooperative vigilance behavior. This model potentially provides a theoretical and conceptual framework for examining specific real-life systems. We propose analyzing individual-based data from real animals by considering their group to be a connected network of individuals, with information transfer between them.
Key words: collective behavior, computational model, group living, information transfer, network.
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