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Estimating interaction matrices from performance data for diverse systems

View ORCID ProfileMalyon D. Bimler, View ORCID ProfileMargaret M. Mayfield, View ORCID ProfileTrace E. Martyn, View ORCID ProfileDaniel B. Stouffer
doi: https://doi.org/10.1101/2022.03.28.486154
Malyon D. Bimler
1School of Biological Sciences, The University of Queensland, St Lucia, Queensland, Australia. Email:
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  • For correspondence: malyonbimler@gmail.com malyonbimler@gmail.com
Margaret M. Mayfield
2School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia. Email:
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  • For correspondence: margie.mayfield@unimelb.edu.au
Trace E. Martyn
3School of Natural Resources and the Environment, The University of Arizona, Tucson, USA. Email:
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  • For correspondence: tmartyn@arizona.edu
Daniel B. Stouffer
4Centre for Integrative Ecology, School of Biological Sciences, University of Canterbury, Christchurch, New Zealand. Email:
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  • For correspondence: daniel.stouffer@canterbury.ac.nz
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Abstract

  1. Network theory allows us to understand complex systems by evaluating how their constituent elements interact with one another. Such networks are built from matrices which describe the effect of each element on all others. Quantifying the strength of these interactions from empirical data can be difficult, however, because the number of potential interactions increases non-linearly as more elements are included in the system, and not all interactions may be empirically observable when some elements are rare.

  2. We present a novel modelling framework which estimates the strength of pairwise interactions in diverse horizontal systems, using measures of species performance in the presence of varying densities of their potential interaction partners.

  3. Our method allows us to directly estimate pairwise effects when they are statistically identifiable and approximate pairwise effects when they would otherwise be statistically unidentifiable. The resulting interaction matrices can include positive and negative effects, the effect of a species on itself, and are non-symmetrical.

  4. The advantages of these features are illustrated with a case study on an annual wildflower community of 22 focal and 52 neighbouring species, and a discussion of potential applications of this framework extending well beyond plant community ecology.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Data accessibility, Data will be submitted to Dryad upon publication.

  • https://github.com/malbion/JointModelFramework

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 29, 2022.
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Estimating interaction matrices from performance data for diverse systems
Malyon D. Bimler, Margaret M. Mayfield, Trace E. Martyn, Daniel B. Stouffer
bioRxiv 2022.03.28.486154; doi: https://doi.org/10.1101/2022.03.28.486154
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Estimating interaction matrices from performance data for diverse systems
Malyon D. Bimler, Margaret M. Mayfield, Trace E. Martyn, Daniel B. Stouffer
bioRxiv 2022.03.28.486154; doi: https://doi.org/10.1101/2022.03.28.486154

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