| Title: | Better tired than lost: Turtle ant trail networks favor coherence over short edges |
| Author(s): | Chandrasekhar A; Marshall JAR; Austin C; Navlakha S; Gordon DM; |
| Address: | "Department of Computer Science, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America. Department of Computer Science, University of Sheffield, Sheffield, United Kingdom. Central Washington University, Ellensburg, Washington, United States of America. Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America. Department of Biology, Stanford University, Stanford, California, United States of America" |
| DOI: | 10.1371/journal.pcbi.1009523 |
| ISSN/ISBN: | 1553-7358 (Electronic) 1553-734X (Print) 1553-734X (Linking) |
| 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" |
| Keywords: | "Algorithms Animals Ants/*physiology Behavior, Animal/*physiology Computational Biology Feeding Behavior/physiology *Models, Biological Pheromones Walking/*physiology;" |
| Notes: | "MedlineChandrasekhar, Arjun Marshall, James A R Austin, Cortnea Navlakha, Saket Gordon, Deborah M eng Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. 2021/10/22 PLoS Comput Biol. 2021 Oct 21; 17(10):e1009523. doi: 10.1371/journal.pcbi.1009523. eCollection 2021 Oct" |
