IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1009523.html
   My bibliography  Save this article

Better tired than lost: Turtle ant trail networks favor coherence over short edges

Author

Listed:
  • Arjun Chandrasekhar
  • James A R Marshall
  • Cortnea Austin
  • Saket Navlakha
  • Deborah M Gordon

Abstract

Creating a routing backbone is a fundamental problem in both biology and engineering. The routing backbone of the trail networks of arboreal turtle ants (Cephalotes goniodontus) connects many nests and food sources using trail pheromone deposited by ants as they walk. Unlike species that forage on the ground, the trail networks of arboreal ants are constrained by the vegetation. We examined what objectives the trail networks meet by comparing the observed ant trail networks with networks of random, hypothetical trail networks in the same surrounding vegetation and with trails optimized for four objectives: minimizing path length, minimizing average edge length, minimizing number of nodes, and minimizing opportunities to get lost. The ants’ trails minimized path length by minimizing the number of nodes traversed rather than choosing short edges. In addition, the ants’ trails reduced the opportunity for ants to get lost at each node, favoring nodes with 3D configurations most likely to be reinforced by pheromone. Thus, rather than finding the shortest edges, turtle ant trail networks take advantage of natural variation in the environment to favor coherence, keeping the ants together on the trails.Author summary: We investigated the trail networks of arboreal turtle ants in the canopy of the tropical forest, to ask what characterizes the colony’s choice of foraging paths within the vegetation. We monitored day to day changes in the junctions and edges of trail networks of colonies in the dry forest of western Mexico. We compared the paths used by the ants to simulated random paths in the surrounding vegetation. We found that the paths of turtle ants prioritize coherence, keeping ants together on the trail, over minimizing the average edge length. The choice of paths reduces the number of junctions in the trail where ants could get lost, and favors junctions with a physical configuration that makes it likely that successive ants will reinforce the same path. Our work suggests that design principles that emphasize keeping information flow constrained to streamlined, coherent trails may be useful in human-designed distributed routing and transport networks or robot swarms.

Suggested Citation

  • Arjun Chandrasekhar & James A R Marshall & Cortnea Austin & Saket Navlakha & Deborah M Gordon, 2021. "Better tired than lost: Turtle ant trail networks favor coherence over short edges," PLOS Computational Biology, Public Library of Science, vol. 17(10), pages 1-24, October.
  • Handle: RePEc:plo:pcbi00:1009523
    DOI: 10.1371/journal.pcbi.1009523
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009523
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009523&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1009523?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. John Vandermeer & Ivette Perfecto & Stacy M. Philpott, 2008. "Clusters of ant colonies and robust criticality in a tropical agroecosystem," Nature, Nature, vol. 451(7177), pages 457-459, January.
    2. Tatiana P Flanagan & Noa M Pinter-Wollman & Melanie E Moses & Deborah M Gordon, 2013. "Fast and Flexible: Argentine Ants Recruit from Nearby Trails," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-7, August.
    3. Iain D. Couzin & Jens Krause & Nigel R. Franks & Simon A. Levin, 2005. "Effective leadership and decision-making in animal groups on the move," Nature, Nature, vol. 433(7025), pages 513-516, February.
    4. Gambardella, L.M. & Montemanni, R. & Weyland, D., 2012. "Coupling ant colony systems with strong local searches," European Journal of Operational Research, Elsevier, vol. 220(3), pages 831-843.
    5. Audrey Dussutour & Vincent Fourcassié & Dirk Helbing & Jean-Louis Deneubourg, 2004. "Optimal traffic organization in ants under crowded conditions," Nature, Nature, vol. 428(6978), pages 70-73, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Simon Levin & Anastasios Xepapadeas, 2021. "On the Coevolution of Economic and Ecological Systems," Annual Review of Resource Economics, Annual Reviews, vol. 13(1), pages 355-377, October.
    2. Becco, Ch. & Vandewalle, N. & Delcourt, J. & Poncin, P., 2006. "Experimental evidences of a structural and dynamical transition in fish school," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 367(C), pages 487-493.
    3. Zhang, Zhe & Song, Xiaoling & Gong, Xue & Yin, Yong & Lev, Benjamin & Zhou, Xiaoyang, 2024. "Coordinated seru scheduling and distribution operation problems with DeJong’s learning effects," European Journal of Operational Research, Elsevier, vol. 313(2), pages 452-464.
    4. Long-Hai Wang & Alexander Ulrich Ernst & Duo An & Ashim Kumar Datta & Boris Epel & Mrignayani Kotecha & Minglin Ma, 2021. "A bioinspired scaffold for rapid oxygenation of cell encapsulation systems," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    5. Richard P Mann, 2011. "Bayesian Inference for Identifying Interaction Rules in Moving Animal Groups," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-10, August.
    6. Ernesto Tarantino & Ivanoe De Falco & Umberto Scafuri, 2019. "A mobile personalized tourist guide and its user evaluation," Information Technology & Tourism, Springer, vol. 21(3), pages 413-455, September.
    7. Ma, Jian & Song, Wei-guo & Zhang, Jun & Lo, Siu-ming & Liao, Guang-xuan, 2010. "k-Nearest-Neighbor interaction induced self-organized pedestrian counter flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(10), pages 2101-2117.
    8. Andrew Hoegh & Frank T. Manen & Mark Haroldson, 2021. "Agent-Based Models for Collective Animal Movement: Proximity-Induced State Switching," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 560-579, December.
    9. Jinxiao Duan & Guanwen Zeng & Nimrod Serok & Daqing Li & Efrat Blumenfeld Lieberthal & Hai-Jun Huang & Shlomo Havlin, 2023. "Spatiotemporal dynamics of traffic bottlenecks yields an early signal of heavy congestions," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    10. Tamás Nepusz & Tamás Vicsek, 2013. "Hierarchical Self-Organization of Non-Cooperating Individuals," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-9, December.
    11. 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.
    12. Shiwakoti, Nirajan & Sarvi, Majid, 2013. "Understanding pedestrian crowd panic: a review on model organisms approach," Journal of Transport Geography, Elsevier, vol. 26(C), pages 12-17.
    13. Roy Harpaz & Minh Nguyet Nguyen & Armin Bahl & Florian Engert, 2021. "Precise visuomotor transformations underlying collective behavior in larval zebrafish," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    14. Li, Qing & Zhang, Lingwei & Jia, Yongnan & Lu, Tianzhao & Chen, Xiaojie, 2022. "Modeling, analysis, and optimization of three-dimensional restricted visual field metric-free swarms," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    15. Mathew Titus & George Hagstrom & James R Watson, 2021. "Unsupervised manifold learning of collective behavior," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-20, February.
    16. Armbruster, D. & de Beer, C. & Freitag, M. & Jagalski, T. & Ringhofer, C., 2006. "Autonomous control of production networks using a pheromone approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(1), pages 104-114.
    17. Sophie Lardy & Daniel Fortin & Olivier Pays, 2016. "Increased Exploration Capacity Promotes Group Fission in Gregarious Foraging Herbivores," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-14, December.
    18. Fan, Kangqi & Pedrycz, Witold, 2016. "Opinion evolution influenced by informed agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 431-441.
    19. De Rosis, Alessandro, 2014. "Hydrodynamic effects on a predator approaching a group of preys," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 329-339.
    20. Shao, Zhi-Gang & Yang, Yan-Yan, 2015. "Effective strategies of collective evacuation from an enclosed space," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 34-39.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1009523. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.