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Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection

Overview of attention for article published in PLoS Computational Biology, March 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

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1 news outlet
facebook
1 Facebook page

Citations

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24 Dimensions

Readers on

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103 Mendeley
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Title
Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection
Published in
PLoS Computational Biology, March 2013
DOI 10.1371/journal.pcbi.1002961
Pubmed ID
Authors

Richard P. Mann, Andrea Perna, Daniel Strömbom, Roman Garnett, James E. Herbert-Read, David J. T. Sumpter, Ashley J. W. Ward

Abstract

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 the observed locality of interactions. Traditional self-propelled particle models fail to capture the fine scale dynamics of the system. 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, while maintaining a biologically plausible perceptual range. 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.

Mendeley readers

The data shown below were compiled from readership statistics for 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 5%
Switzerland 3 3%
China 1 <1%
Germany 1 <1%
India 1 <1%
Sweden 1 <1%
New Zealand 1 <1%
Unknown 90 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 26%
Researcher 25 24%
Professor > Associate Professor 10 10%
Student > Master 8 8%
Professor 7 7%
Other 26 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 39 38%
Physics and Astronomy 13 13%
Unspecified 11 11%
Computer Science 9 9%
Engineering 8 8%
Other 23 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 June 2017.
All research outputs
#1,206,708
of 12,091,568 outputs
Outputs from PLoS Computational Biology
#1,523
of 4,829 outputs
Outputs of similar age
#14,754
of 131,416 outputs
Outputs of similar age from PLoS Computational Biology
#25
of 132 outputs
Altmetric has tracked 12,091,568 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,829 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.2. This one has gotten more attention than average, scoring higher than 68% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 131,416 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.