Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study

By M. Ballerini, N. Cabibbo, Raphaël Candelier, Andrea Cavagna, E. Cisbani, Irene Giardina, V. Lecomte, A. Orlandi, Giorgio Parisi, Andrea Procaccini and 2 more. Proceedings of the National Academy of Sciences of the United States of America, 2007

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Numerical models indicate that collective animal behavior may emerge from simple local rules of interaction among the individuals. However, very little is known about the nature of such interaction, so that models and theories mostly rely on aprioristic assumptions. By reconstructing the three-dimensional positions of individual birds in airborne flocks of a few thousand members, we show that the interaction does not depend on the metric distance, as most current models and theories assume, but rather on the topological distance. In fact, we discovered that each bird interacts on average with a fixed number of neighbors (six to seven), rather than with all neighbors within a fixed metric distance. We argue that a topological interaction is indispensable to maintain a flock's cohesion against the large density changes caused by external perturbations, typically predation. We support this hypothesis by numerical simulations, showing that a topological interaction grants significantly higher cohesion of the aggregation compared with a standard metric one.

Subfields: Ecology, Evolution, Behavior and Systematics, Public Health, Environmental and Occupational Health, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/dd1543f2d39a2eeadc739b1a0537508e22019183

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Citations in the corpus

Are Biological Systems Poised at Criticality? (2010, background): “[17] M Ballerini, N Cabibbo, R Candelier, A Cavagna, E Cis-”
Symmetry, Thermodynamics, and Topology in Active Matter (2021, background): “Examples of this behavior abound in the living world [13], from the coordinated motion of cells in wound healing [14], to bird flocks [15] and human crowds [16, 17].”
Propagating waves in starling, Sturnus vulgaris, flocks under predation (2011, background): “Despite some recent insight into interindividual interactions in bird flocks (Ballerini et al. 2008b), much remains to be understood and future research in individualbased modelling of collective animal behaviour could benefit from our empirical data (Hildenbrandt et al. 2010; Hemelrijk &…”
Deciphering Interactions in Moving Animal Groups (2012, methodology): “Even if we claim that a Voronoi neighborhood was the best choice to account for our data thus extending the relevance of topological interactions, we also checked that our conclusions were robust against this choice, by testing a simple K-Nearest Neighbors network of interactions (which remains topo”
Mirroring and beyond: coupled dynamics as a generalized framework for modelling social interactions (2016, background): “This enables the group to move as an entity without the need for any centralized control [11].”
Emergent dynamics of the Cucker-Smale flocking model and its variants (2016, background): “[6, 64], and they have been extensively studied recently because of their possible applications to sensor networks, controls of robots and unmanned aerial vehicles [49, 55, 57], and opinion formation of social networks.”
Active Brownian particles (2012, background): “A major driving force of the active matter research are the continuously improving experimental techniques such as for example automated digital tracking [321,313,17] or the realization of active granular and colloidal systems (see e.”
Scale-free correlations in starling flocks (2009, neutral): “b C∞ðrÞ≈ 1 rγ [13]”