Hysteresis stabilizes dynamic control of self-assembled army ant constructions.


Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
04 03 2022
Historique:
received: 11 12 2020
accepted: 07 02 2022
entrez: 5 3 2022
pubmed: 6 3 2022
medline: 14 4 2022
Statut: epublish

Résumé

Biological systems must adjust to changing external conditions, and their resilience depends on their control mechanisms. How is dynamic control implemented in noisy, decentralized systems? Army ants' self-assembled bridges are built on unstable features, like leaves, which frequently move. Using field experiments and simulations, we characterize the bridges' response as the gaps they span change in size, identify the control mechanism, and explore how this emerges from individuals' decisions. For a given gap size, bridges were larger after the gap increased rather than decreased. This hysteresis was best explained by an accumulator model, in which individual decisions to join or leave a bridge depend on the difference between its current and equilibrium state. This produces robust collective structures that adjust to lasting perturbations while ignoring small, momentary shifts. Our field data support separate joining and leaving cues; joining is prompted by high bridge performance and leaving by an excess of ants. This leads to stabilizing hysteresis, an important feature of many biological and engineered systems.

Identifiants

pubmed: 35246567
doi: 10.1038/s41467-022-28773-z
pii: 10.1038/s41467-022-28773-z
pmc: PMC8897433
doi:

Banques de données

figshare
['10.6084/m9.figshare.13337255.v1']

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1160

Informations de copyright

© 2022. The Author(s).

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Auteurs

Helen F McCreery (HF)

School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA. hmccreery@gmail.com.

Georgina Gemayel (G)

University of Southern California, Los Angeles, CA, 90007, USA.

Ana Isabel Pais (AI)

Department of Biological Sciences, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
Rutgers University, Newark, Newark, NJ, 07102, USA.

Simon Garnier (S)

Department of Biological Sciences, New Jersey Institute of Technology, Newark, NJ, 07102, USA.

Radhika Nagpal (R)

School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA.
Wyss Institute for Biologically Inspired Engineering, Boston, MA, 02115, USA.
School of Engineering and Applied Sciences, Princeton University, Princeton, NJ, 08544, USA.

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Classifications MeSH