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Time-Ordered Networks Reveal Limitations to Information Flow in Ant Colonies

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  • Benjamin Blonder
  • Anna Dornhaus

Abstract

Background: An important function of many complex networks is to inhibit or promote the transmission of disease, resources, or information between individuals. However, little is known about how the temporal dynamics of individual-level interactions affect these networks and constrain their function. Ant colonies are a model comparative system for understanding general principles linking individual-level interactions to network-level functions because interactions among individuals enable integration of multiple sources of information to collectively make decisions, and allocate tasks and resources. Methodology/Findings: Here we show how the temporal and spatial dynamics of such individual interactions provide upper bounds to rates of colony-level information flow in the ant Temnothorax rugatulus. We develop a general framework for analyzing dynamic networks and a mathematical model that predicts how information flow scales with individual mobility and group size. Conclusions/Significance: Using thousands of time-stamped interactions between uniquely marked ants in four colonies of a range of sizes, we demonstrate that observed maximum rates of information flow are always slower than predicted, and are constrained by regulation of individual mobility and contact rate. By accounting for the ordering and timing of interactions, we can resolve important difficulties with network sampling frequency and duration, enabling a broader understanding of interaction network functioning across systems and scales.

Suggested Citation

  • Benjamin Blonder & Anna Dornhaus, 2011. "Time-Ordered Networks Reveal Limitations to Information Flow in Ant Colonies," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-8, May.
  • Handle: RePEc:plo:pone00:0020298
    DOI: 10.1371/journal.pone.0020298
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    References listed on IDEAS

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    1. Marcel Salathé & James H Jones, 2010. "Dynamics and Control of Diseases in Networks with Community Structure," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-11, April.
    2. Ciro Cattuto & Wouter Van den Broeck & Alain Barrat & Vittoria Colizza & Jean-François Pinton & Alessandro Vespignani, 2010. "Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks," PLOS ONE, Public Library of Science, vol. 5(7), pages 1-9, July.
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    Cited by:

    1. Viana, Matheus P. & Fourcassié, Vincent & Perna, Andrea & Costa, Luciano da F. & Jost, Christian, 2013. "Accessibility in networks: A useful measure for understanding social insect nest architecture," Chaos, Solitons & Fractals, Elsevier, vol. 46(C), pages 38-45.
    2. Daniel Charbonneau & Anna Dornhaus, 2015. "When doing nothing is something. How task allocation strategies compromise between flexibility, efficiency, and inactive agents," Journal of Bioeconomics, Springer, vol. 17(3), pages 217-242, October.
    3. Aming Li & Yang-Yu Liu, 2020. "Controlling Network Dynamics," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-19, February.
    4. Amos Korman & Efrat Greenwald & Ofer Feinerman, 2014. "Confidence Sharing: An Economic Strategy for Efficient Information Flows in Animal Groups," PLOS Computational Biology, Public Library of Science, vol. 10(10), pages 1-10, October.
    5. Li, Mingwu & Dankowicz, Harry, 2019. "Impact of temporal network structures on the speed of consensus formation in opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1355-1370.
    6. Mulder, Joris & Leenders, Roger Th.A.J., 2019. "Modeling the evolution of interaction behavior in social networks: A dynamic relational event approach for real-time analysis," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 73-85.

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