Individual error correction drives responsive self-assembly of army ant scaffolds

Matthew J. Lutz*, Chris R. Reid*, Christopher J. Lustri, Albert B. Kao, Simon Garnier, Iain D. Couzin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

An inherent strength of evolved collective systems is their ability to rapidly adapt to dynamic environmental conditions, offering resilience in the face of disruption. This is thought to arise when individual sensory inputs are filtered through local interactions, producing an adaptive response at the group level. To understand how simple rules encoded at the individual level can lead to the emergence of robust group-level (or distributed) control, we examined structures we call “scaffolds,” self-assembled by Eciton burchellii army ants on inclined surfaces that aid travel during foraging and migration. We conducted field experiments with wild E. burchellii colonies, manipulating the slope over which ants traversed, to examine the formation of scaffolds and their effects on foraging traffic. Our results show that scaffolds regularly form on inclined surfaces and that they reduce losses of foragers and prey, by reducing slipping and/or falling of ants, thus facilitating traffic flow. We describe the relative effects of environmental geometry and traffic on their growth and present a theoretical model to examine how the individual behaviors underlying scaffold formation drive group-level effects. Our model describes scaffold growth as a control response at the collective level that can emerge from individual error correction, requiring no complex communication among ants. We show that this model captures the dynamics observed in our experiments and is able to predict the growth—and final size—of scaffolds, and we show how the analytical solution allows for estimation of these dynamics.
Original languageEnglish
Article numbere2013741118
Pages (from-to)1-8
Number of pages8
JournalProceedings of the National Academy of Sciences of the United States of America
Volume118
Issue number17
DOIs
Publication statusPublished - 27 Apr 2021

Bibliographical note

Copyright the Author(s) 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • self-assembly
  • resilience
  • collective behavior
  • distributed control
  • infrastructure
  • Self-assembly
  • Distributed control
  • Infrastructure
  • Collective behavior
  • Resilience

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