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A Markov jump process modelling animal group size statistics

Authors :
Robert L. Pego
Maximilian Engel
Jian-Guo Liu
Pierre Degond
Mathématiques pour l'Industrie et la Physique (MIP)
Université Toulouse 1 Capitole (UT1)
Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse)
Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3)
Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)
department of mathematics
University of Maryland [College Park]
University of Maryland System-University of Maryland System
Center for Nonlinear Analysis [Pittsburgh] (CNA)
Carnegie Mellon University [Pittsburgh] (CMU)
Université Toulouse Capitole (UT Capitole)
Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse)
Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3)
Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)
The Royal Society
Engineering & Physical Science Research Council (EPSRC)
Source :
Communications in Mathematical Sciences, Communications in Mathematical Sciences, International Press, 2020, 18, pp.55-89, Communications in Mathematical Sciences, 2020, 18, pp.55-89
Publication Year :
2020
Publisher :
International Press of Boston, 2020.

Abstract

International audience; We translate a coagulation-framentation model, describing the dynamics of animal group size distributions , into a model for the population distribution and associate the nonlinear evolution equation with a Markov jump process of a type introduced in classic work of H. McKean. In particular this formalizes a model suggested by H.-S. Niwa [J. Theo. Biol. 224 (2003)] with simple coagulation and fragmentation rates. Based on the jump process, we develop a numerical scheme that allows us to approximate the equilibrium for the Niwa model, validated by comparison to analytical results by Degond et al. [J. Nonlinear Sci. 27 (2017)], and study the population and size distributions for more complicated rates. Furthermore, the simulations are used to describe statistical properties of the underlying jump process. We additionally discuss the relation of the jump process to models expressed in stochastic differential equations and demonstrate that such a connection is justified in the case of nearest-neighbour interactions, as opposed to global interactions as in the Niwa model.

Details

ISSN :
19450796 and 15396746
Volume :
18
Database :
OpenAIRE
Journal :
Communications in Mathematical Sciences
Accession number :
edsair.doi.dedup.....86e8de06553335058d5742e8802b43ea
Full Text :
https://doi.org/10.4310/cms.2020.v18.n1.a3