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      Army ants dynamically adjust living bridges in response to a cost-benefit trade-off.

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          Abstract

          The ability of individual animals to create functional structures by joining together is rare and confined to the social insects. Army ants (Eciton) form collective assemblages out of their own bodies to perform a variety of functions that benefit the entire colony. Here we examine ‟bridges" of linked individuals that are constructed to span gaps in the colony's foraging trail. How these living structures adjust themselves to varied and changing conditions remains poorly understood. Our field experiments show that the ants continuously modify their bridges, such that these structures lengthen, widen, and change position in response to traffic levels and environmental geometry. Ants initiate bridges where their path deviates from their incoming direction and move the bridges over time to create shortcuts over large gaps. The final position of the structure depended on the intensity of the traffic and the extent of path deviation and was influenced by a cost-benefit trade-off at the colony level, where the benefit of increased foraging trail efficiency was balanced by the cost of removing workers from the foraging pool to form the structure. To examine this trade-off, we quantified the geometric relationship between costs and benefits revealed by our experiments. We then constructed a model to determine the bridge location that maximized foraging rate, which qualitatively matched the observed movement of bridges. Our results highlight how animal self-assemblages can be dynamically modified in response to a group-level cost-benefit trade-off, without any individual unit's having information on global benefits or costs.

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          Most cited references 22

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          Traffic and Related Self-Driven Many-Particle Systems

           Dirk Helbing (2000)
          Since the subject of traffic dynamics has captured the interest of physicists, many astonishing effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by so-called ``phantom traffic jams'', although they all like to drive fast? What are the mechanisms behind stop-and-go traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction of the traffic volume cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize in lanes, while similar systems are ``freezing by heating''? Why do self-organizing systems tend to reach an optimal state? Why do panicking pedestrians produce dangerous deadlocks? All these questions have been answered by applying and extending methods from statistical physics and non-linear dynamics to self-driven many-particle systems. This review article on traffic introduces (i) empirically data, facts, and observations, (ii) the main approaches to pedestrian, highway, and city traffic, (iii) microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts like a general modelling framework for self-driven many-particle systems, including spin systems. Subjects such as the optimization of traffic flows and relations to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are discussed as well.
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            Collective cognition in animal groups.

             Iain Couzin (2009)
            The remarkable collective action of organisms such as swarming ants, schooling fish and flocking birds has long captivated the attention of artists, naturalists, philosophers and scientists. Despite a long history of scientific investigation, only now are we beginning to decipher the relationship between individuals and group-level properties. This interdisciplinary effort is beginning to reveal the underlying principles of collective decision-making in animal groups, demonstrating how social interactions, individual state, environmental modification and processes of informational amplification and decay can all play a part in tuning adaptive response. It is proposed that important commonalities exist with the understanding of neuronal processes and that much could be learned by considering collective animal behavior in the framework of cognitive science.
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              Self-organized lane formation and optimized traffic flow in army ants.

               Iain Couzin,  N Franks (2003)
              We show how the movement rules of individual ants on trails can lead to a collective choice of direction and the formation of distinct traffic lanes that minimize congestion. We develop and evaluate the results of a new model with a quantitative study of the behaviour of the army ant Eciton burchelli. Colonies of this species have up to 200 000 foragers and transport more than 3000 prey items per hour over raiding columns that exceed 100 m. It is an ideal species in which to test the predictions of our model because it forms pheromone trails that are densely populated with very swift ants. The model explores the influences of turning rates and local perception on traffic flow. The behaviour of real army ants is such that they occupy the specific region of parameter space in which lanes form and traffic flow is maximized.
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                Author and article information

                Journal
                Proc. Natl. Acad. Sci. U.S.A.
                Proceedings of the National Academy of Sciences of the United States of America
                Proceedings of the National Academy of Sciences
                1091-6490
                0027-8424
                Dec 08 2015
                : 112
                : 49
                Affiliations
                [1 ] Department of Biological Sciences, New Jersey Institute of Technology, Newark, NJ 07102; chrisreidresearch@gmail.com mlutz@princeton.edu.
                [2 ] Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544; chrisreidresearch@gmail.com mlutz@princeton.edu.
                [3 ] Department of Biological Sciences, George Washington University, Washington, DC 20052;
                [4 ] Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138;
                [5 ] Department of Collective Behaviour, Max Planck Institute for Ornithology, Konstanz D-78457, Germany; Chair of Biodiversity and Collective Behaviour, Department of Biology, University of Konstanz, Konstanz D-78457, Germany.
                [6 ] Department of Biological Sciences, New Jersey Institute of Technology, Newark, NJ 07102;
                Article
                1512241112
                10.1073/pnas.1512241112
                4679032
                26598673

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