Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/40612
Title: A random acceleration model of individual animal movement allowing for diffusive, superdiffusive and superballistic regimes
Authors: Tilles, Paulo F. C.
Petrovskii, Sergei V.
Natti, Paulo L.
First Published: 30-Oct-2017
Publisher: Nature Publishing Group
Citation: Scientific Reports, 2017, 7, Article number: 14364
Abstract: Patterns of individual animal movement attracted considerable attention over the last two decades. In particular, question as to whether animal movement is predominantly diffusive or superdiffusive has been a focus of discussion and controversy. We consider this problem using a theory of stochastic motion based on the Langevin equation with non-Wiener stochastic forcing that originates in animal's response to environmental noise. We show that diffusive and superdiffusive types of motion are inherent parts of the same general movement process that arises as interplay between the force exerted by animals (essentially, by animal's muscles) and the environmental drag. The movement is superballistic with the mean square displacement growing with time as 〈x 2 (t)〉 ∼ t 4 at the beginning and eventually slowing down to the diffusive spread 〈x 2 (t)〉 ∼ t. We show that the duration of the superballistic and superdiffusive stages can be long depending on the properties of the environmental noise and the intensity of drag. Our findings demonstrate theoretically how the movement pattern that includes diffusive and superdiffusive/superballistic motion arises naturally as a result of the interplay between the dissipative properties of the environment and the animal's biological traits such as the body mass, typical movement velocity and the typical duration of uninterrupted movement.
DOI Link: 10.1038/s41598-017-14511-9
ISSN: 2045-2322
eISSN: 2045-2322
Links: https://www.nature.com/articles/s41598-017-14511-9
http://hdl.handle.net/2381/40612
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2017. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Description: Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-017-14511-9.
Appears in Collections:Published Articles, Dept. of Mathematics

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