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A fast and unbiased procedure to randomize ecological binary matrices with fixed row and column totals

Author

Listed:
  • Giovanni Strona

    (European Commission, Joint Research Centre, Institute for Environment and Sustainability, Forest Resources and Climate Unit)

  • Domenico Nappo

    (European Commission, Joint Research Centre, Institute for Environment and Sustainability, Forest Resources and Climate Unit)

  • Francesco Boccacci

    (European Commission, Joint Research Centre, Institute for Environment and Sustainability, Forest Resources and Climate Unit)

  • Simone Fattorini

    (Azorean Biodiversity Group, (CITA-A) and Portuguese Platform for Enhancing Ecological Research & Sustainability, University of the Azores, Pico da Urze, 9700-042)

  • Jesus San-Miguel-Ayanz

    (European Commission, Joint Research Centre, Institute for Environment and Sustainability, Forest Resources and Climate Unit)

Abstract

A well-known problem in numerical ecology is how to recombine presence-absence matrices without altering row and column totals. A few solutions have been proposed, but all of them present some issues in terms of statistical robustness (that is, their capability to generate different matrix configurations with the same probability) and their performance (that is, the computational effort that they require to generate a null matrix). Here we introduce the ‘Curveball algorithm’, a new procedure that differs from existing methods in that it focuses rather on matrix information content than on matrix structure. We demonstrate that the algorithm can sample uniformly the set of all possible matrix configurations requiring a computational effort orders of magnitude lower than that required by available methods, making it possible to easily randomize matrices larger than 108 cells.

Suggested Citation

  • Giovanni Strona & Domenico Nappo & Francesco Boccacci & Simone Fattorini & Jesus San-Miguel-Ayanz, 2014. "A fast and unbiased procedure to randomize ecological binary matrices with fixed row and column totals," Nature Communications, Nature, vol. 5(1), pages 1-9, September.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms5114
    DOI: 10.1038/ncomms5114
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    Cited by:

    1. Marjan Cugmas & Aleš Žiberna & Anuška Ferligoj, 2021. "The Relative Fit measure for evaluating a blockmodel," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1315-1335, December.
    2. Rivest, Louis-Paul & Ebouele, Sergio Ewane, 2020. "Sampling a two dimensional matrix," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
    3. Stock, Michiel & Piot, Niels & Vanbesien, Sarah & Meys, Joris & Smagghe, Guy & De Baets, Bernard, 2021. "Pairwise learning for predicting pollination interactions based on traits and phylogeny," Ecological Modelling, Elsevier, vol. 451(C).
    4. Matthew J Michalska-Smith & Stefano Allesina, 2019. "Telling ecological networks apart by their structure: A computational challenge," PLOS Computational Biology, Public Library of Science, vol. 15(6), pages 1-13, June.
    5. Jeroen van Lidth de Jeude & Riccardo Di Clemente & Guido Caldarelli & Fabio Saracco & Tiziano Squartini, 2019. "Reconstructing Mesoscale Network Structures," Complexity, Hindawi, vol. 2019, pages 1-13, January.
    6. Marco Bardoscia & Paolo Barucca & Stefano Battiston & Fabio Caccioli & Giulio Cimini & Diego Garlaschelli & Fabio Saracco & Tiziano Squartini & Guido Caldarelli, 2021. "The Physics of Financial Networks," Papers 2103.05623, arXiv.org.
    7. Isaac Trindade-Santos & Faye Moyes & Anne E. Magurran, 2022. "Global patterns in functional rarity of marine fish," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    8. Geut Galai & Xie He & Barak Rotblat & Shai Pilosof, 2023. "Ecological network analysis reveals cancer-dependent chaperone-client interaction structure and robustness," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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