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The ghost of nestedness in ecological networks

Author

Listed:
  • Phillip P. A. Staniczenko

    (University of Chicago)

  • Jason C. Kopp

    (University of Chicago)

  • Stefano Allesina

    (University of Chicago
    Computation Institute, University of Chicago)

Abstract

Ecologists are fascinated by the prevalence of nestedness in biogeographic and community data, where it is thought to promote biodiversity in mutualistic systems. Traditionally, nestedness has been treated in a binary sense: species and their interactions are either present or absent, neglecting information on abundances and interaction frequencies. Extending nestedness to quantitative data facilitates the study of species preferences, and we propose a new detection method that follows from a basic property of bipartite networks: large dominant eigenvalues are associated with highly nested configurations. We show that complex ecological networks are binary nested, but quantitative preferences are non-nested, indicating limited consumer overlap of favoured resources. The spectral graph approach provides a formal link to local dynamical stability analysis, where we demonstrate that nested mutualistic structures are minimally stable. We conclude that, within the binary constraint of interaction plausibility, species preferences are partitioned to avoid competition, thereby benefiting system-wide resource allocation.

Suggested Citation

  • Phillip P. A. Staniczenko & Jason C. Kopp & Stefano Allesina, 2013. "The ghost of nestedness in ecological networks," Nature Communications, Nature, vol. 4(1), pages 1-6, June.
    • Handle: RePEc:nat:natcom:v:4:y:2013:i:1:d:10.1038_ncomms2422
    DOI: 10.1038/ncomms2422
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    Cited by:

    1. Tad Dallas & Andrew W Park & John M Drake, 2017. "Predicting cryptic links in host-parasite networks," PLOS Computational Biology, Public Library of Science, vol. 13(5), pages 1-15, May.
    2. Vincent Miele & Catherine Matias & Stéphane Robin & Stéphane Dray, 2019. "Nine quick tips for analyzing network data," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-10, December.
    3. Mika J. Straka & Guido Caldarelli & Tiziano Squartini & Fabio Saracco, 2017. "From Ecology to Finance (and Back?): Recent Advancements in the Analysis of Bipartite Networks," Papers 1710.10143, arXiv.org.
    4. Michel Alexandre & Felipe Jordão Xavier & Thiago Christiano Silva & Francisco A. Rodrigues, 2022. "Nestedness in the Brazilian Financial System," Working Papers Series 566, Central Bank of Brazil, Research Department.
    5. 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.
    6. Luiz G. A. Alves & Giuseppe Mangioni & Isabella Cingolani & Francisco A. Rodrigues & Pietro Panzarasa & Yamir Moreno, 2018. "The nested structural organization of the worldwide trade multi-layer network," Papers 1803.02872, arXiv.org, revised Sep 2019.
    7. Fabio Saracco & Riccardo Di Clemente & Andrea Gabrielli & Tiziano Squartini, 2015. "Detecting early signs of the 2007-2008 crisis in the world trade," Papers 1508.03533, arXiv.org, revised Jul 2016.
    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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