Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/5835
Title: Dynamical modeling of collective behavior from pigeon flight data : flock cohesion and dispersion
Authors: Dieck Kattas, G
Xu, X
Small, M
Keywords: Animal groups
Time-series
Identification
Systems
Rules
Issue Date: 29-Mar-2012
Publisher: Public Library of Science (PLoS)
Source: PLoS computational biology, 29 Mar., 2012, v. 8, no. 3, e1002449, p. 1-15 How to cite?
Journal: PLoS computational biology 
Abstract: Several models of flocking have been promoted based on simulations with qualitatively naturalistic behavior. In this paper we provide the first direct application of computational modeling methods to infer flocking behavior from experimental field data. We show that this approach is able to infer general rules for interaction, or lack of interaction, among members of a flock or, more generally, any community. Using experimental field measurements of homing pigeons in flight we demonstrate the existence of a basic distance dependent attraction/repulsion relationship and show that this rule is sufficient to explain collective behavior observed in nature. Positional data of individuals over time are used as input data to a computational algorithm capable of building complex nonlinear functions that can represent the system behavior. Topological nearest neighbor interactions are considered to characterize the components within this model. The efficacy of this method is demonstrated with simulated noisy data generated from the classical (two dimensional) Vicsek model. When applied to experimental data from homing pigeon flights we show that the more complex three dimensional models are capable of simulating trajectories, as well as exhibiting realistic collective dynamics. The simulations of the reconstructed models are used to extract properties of the collective behavior in pigeons, and how it is affected by changing the initial conditions of the system. Our results demonstrate that this approach may be applied to construct models capable of simulating trajectories and collective dynamics using experimental field measurements of herd movement. From these models, the behavior of the individual agents (animals) may be inferred.
URI: http://hdl.handle.net/10397/5835
ISSN: 1553-7358 (online)
DOI: 10.1371/journal.pcbi.1002449
Rights: © 2012 Dieck Kattas et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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