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On the methods to assess significance in nestedness analyses.

Giovanni Strona1, Simone Fattorini

  • 1European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, 21027, Ispra, VA, Italy, giovanni.strona@jrc.ec.europa.eu.

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Evaluating nestedness significance using Z values or relative nestedness (RN) yielded similar results. However, the choice of null models significantly impacts outcomes, necessitating standardized methods for ecological network analysis.

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Area of Science:

  • Ecology
  • Network Analysis
  • Quantitative Biology

Background:

  • Nestedness significance is commonly assessed using Z values.
  • Relative nestedness (RN) offers an alternative, but null matrix generation lacks standardization.
  • Restrictive null models (FF, CE) accounting for row/column totals are recommended but differ in restrictiveness.

Purpose of the Study:

  • To compare Z values and RN for nestedness significance.
  • To evaluate the impact of FF and CE null models on nestedness analysis.
  • To investigate the performance of NODF and ρ(A) metrics with different null models.

Main Methods:

  • Comparative analyses on theoretical and real matrices.
  • Calculation of Z and RN values using FF and CE null models.
  • Measurement of nestedness using NODF and ρ(A).

Main Results:

  • No significant difference was found between Z and RN values.
  • Inconsistent outcomes arise from combinations of nestedness measures and null models.
  • The choice of null model (FF vs. CE) critically influences nestedness analysis results.

Conclusions:

  • Standardization of null model procedures is crucial for reproducible nestedness analysis.
  • The study clarifies issues in practical nestedness analysis, guiding future research.
  • Further development of commonly accepted standards for null model generation is needed.