Multi-scale inference of interaction rules in animal groups using Bayesian model selection.

Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of...

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Autores principales:	Richard P Mann, Andrea Perna, Daniel Strömbom, Roman Garnett, James E Herbert-Read, David J T Sumpter, Ashley J W Ward
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/02b4eabc03b04bca86c50547dca185ac
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spelling	oai:doaj.org-article:02b4eabc03b04bca86c50547dca185ac2021-11-18T05:51:40ZMulti-scale inference of interaction rules in animal groups using Bayesian model selection.1553-734X1553-735810.1371/journal.pcbi.1002308https://doaj.org/article/02b4eabc03b04bca86c50547dca185ac2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22241970/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis). We show that these exhibit a stereotypical 'phase transition', whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have 'memory' of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture fine scale rules of interaction, which are primarily mediated by physical contact. Conversely, the Markovian self-propelled particle model captures the fine scale rules of interaction but fails to reproduce global dynamics. The most sophisticated model, the non-Markovian model, provides a good match to the data at both the fine scale and in terms of reproducing global dynamics. We conclude that prawns' movements are influenced by not just the current direction of nearby conspecifics, but also those encountered in the recent past. Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should, if they are to be realistic at multiple biological scales, include memory of previous interactions and other non-Markovian effects.Richard P MannAndrea PernaDaniel StrömbomRoman GarnettJames E Herbert-ReadDavid J T SumpterAshley J W WardPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 1, p e1002308 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Richard P Mann Andrea Perna Daniel Strömbom Roman Garnett James E Herbert-Read David J T Sumpter Ashley J W Ward Multi-scale inference of interaction rules in animal groups using Bayesian model selection.
description	Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis). We show that these exhibit a stereotypical 'phase transition', whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have 'memory' of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture fine scale rules of interaction, which are primarily mediated by physical contact. Conversely, the Markovian self-propelled particle model captures the fine scale rules of interaction but fails to reproduce global dynamics. The most sophisticated model, the non-Markovian model, provides a good match to the data at both the fine scale and in terms of reproducing global dynamics. We conclude that prawns' movements are influenced by not just the current direction of nearby conspecifics, but also those encountered in the recent past. Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should, if they are to be realistic at multiple biological scales, include memory of previous interactions and other non-Markovian effects.
format	article
author	Richard P Mann Andrea Perna Daniel Strömbom Roman Garnett James E Herbert-Read David J T Sumpter Ashley J W Ward
author_facet	Richard P Mann Andrea Perna Daniel Strömbom Roman Garnett James E Herbert-Read David J T Sumpter Ashley J W Ward
author_sort	Richard P Mann
title	Multi-scale inference of interaction rules in animal groups using Bayesian model selection.
title_short	Multi-scale inference of interaction rules in animal groups using Bayesian model selection.
title_full	Multi-scale inference of interaction rules in animal groups using Bayesian model selection.
title_fullStr	Multi-scale inference of interaction rules in animal groups using Bayesian model selection.
title_full_unstemmed	Multi-scale inference of interaction rules in animal groups using Bayesian model selection.
title_sort	multi-scale inference of interaction rules in animal groups using bayesian model selection.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/02b4eabc03b04bca86c50547dca185ac
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Multi-scale inference of interaction rules in animal groups using Bayesian model selection.

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