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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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

1 news outlet
1 blog
1 Facebook page


32 Dimensions

Readers on

95 Mendeley
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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

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


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 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
Switzerland 2 2%
Germany 1 1%
China 1 1%
Sweden 1 1%
Unknown 86 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 28%
Researcher 21 22%
Professor > Associate Professor 10 11%
Student > Master 9 9%
Professor 6 6%
Other 15 16%
Unknown 7 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 36%
Physics and Astronomy 13 14%
Computer Science 12 13%
Engineering 7 7%
Mathematics 6 6%
Other 11 12%
Unknown 12 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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
of 18,457,061 outputs
Outputs from PLoS Computational Biology
of 6,994 outputs
Outputs of similar age
of 165,871 outputs
Outputs of similar age from PLoS Computational Biology
of 137 outputs
Altmetric has tracked 18,457,061 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,994 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.5. This one has done well, scoring higher than 76% 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 165,871 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 137 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.