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Productivity increases with variation in aggression among group members in Temnothorax ants

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  • Andreas P. Modlmeier
  • Susanne Foitzik

Abstract

Social insect societies are characterized not only by a reproductive division of labor between the queen and workers but also by a specialization of workers on different tasks. However, how this variation in behavior or morphology among workers influences colony fitness is largely unknown. We investigated in the ant Temnothorax longispinosus whether aggressive and exploratory behavior and/or variation among nest mates in these behavioral traits are associated with an important fitness measure, that is, per worker offspring production. In addition, we studied how body size and variation in size among workers affect this colony fitness correlate. First, we found strong differences in worker body size, aggression, and exploration behavior among colonies. Most notably, intracolonial variance in aggression was positively correlated with per worker productivity, suggesting a selective advantage of colonies with a higher variability in worker aggression. Because ant colonies in dense patches were both more aggressive and more productive, we cannot exclude the possibility that higher productivity and greater variability in aggression could both be results of good habitat quality and not causal influences on one another. This study suggests that social insect societies with stronger behavioral variation among nest members, and possibly a more efficient task allocation, are more productive in the field. Copyright 2011, Oxford University Press.

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  • Andreas P. Modlmeier & Susanne Foitzik, 2011. "Productivity increases with variation in aggression among group members in Temnothorax ants," Behavioral Ecology, International Society for Behavioral Ecology, vol. 22(5), pages 1026-1032.
  • Handle: RePEc:oup:beheco:v:22:y:2011:i:5:p:1026-1032
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    File URL: http://hdl.handle.net/10.1093/beheco/arr086
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    1. Bates, Douglas & M├Ąchler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
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