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Behavioral Ecology Advance Access originally published online on December 20, 2006
Behavioral Ecology 2007 18(2):384-392; doi:10.1093/beheco/arl095
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© The Author 2006. Published by Oxford University Press on behalf of the International Society for Behavioral Ecology. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

Searching for a new home—scouting behavior of honeybee swarms

Stefan Jansona, Martin Middendorfa and Madeleine Beekmanb

a Parallel Computing and Complex Systems Group, Faculty of Mathematics and Computer Science, University of Leipzig, Augustusplatz 10/11, D-04109 Leipzig, Germany b Behaviour and Genetics of Social Insects Laboratory, School of Biological Sciences, University of Sydney, A12, Sydney, NSW 2006, Australia

Address correspondence to M. Beekman. E-mail: mbeekman{at}bio.usyd.edu.au.


   Abstract

Honeybee scouting, where individual bees search the environment without prior information about the possible location of food sources or nest sites, is notoriously difficult to study. Yet, understanding scouting behavior is important as it provides insights into how social insects trade-off exploitation with exploration. The use of simulation models is an ideal way to investigate the possible mechanisms behind the regulation of scouting at the group level as well as the ways in which the swarm searches its environment. We used an individual-based simulation model to study the scouting behavior of honeybee swarms. In our model, we implemented a simple decision rule that regulates the number of scouts: individual bees first attempt to find a dance to follow but become scouts if they fail to do so. We show that this rule neatly allows the swarm to adjust the number of scouts depending on the quality of the nest sites known to the swarm. We further explored different search strategies that allow the swarm to select good-quality nest sites independent of their distance from the swarm. Assuming that it is costly to move to a site that is far away, the best search strategy would be to give precedence to nearby sites while still allowing the discovery of better sites at distances farther away.

Key words: Apis, decentralized decision making, honeybees, individual-based model, nest-site selection, swarming.

Received 12 June 2006; revised 9 November 2006; accepted 26 November 2006.


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