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Related Experiment Videos

Bi-dimensional null model analysis of presence-absence binary matrices.

Giovanni Strona1, Werner Ulrich2, Nicholas J Gotelli3

  • 1Directorate D - Sustainable Resources, Joint Research Centre, European Commission, Via E. Fermi 2749, 21027, Ispra (VA), Italy.

Ecology
|October 13, 2017
PubMed
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Ecologists often compare binary matrices to randomized versions, but null model choices impact results. A new "Tuning Peg" algorithm offers flexible randomization, enabling a continuous exploration of matrix structures for clearer ecological pattern interpretation.

Area of Science:

  • Ecology
  • Computational Biology
  • Data Analysis

Background:

  • Comparing ecological matrices to randomized counterparts is standard practice.
  • Existing null models for binary matrix randomization vary in constraints, complicating pattern interpretation.
  • A need exists for methods exploring intermediate restrictiveness and synthesizing results.

Purpose of the Study:

  • Introduce a flexible matrix randomization framework using the "Tuning Peg" algorithm.
  • Address limitations of existing null models by allowing continuous tuning of marginal total discrepancies.
  • Enable comprehensive exploration of the null space for ecological matrix analysis.

Main Methods:

  • Developed the "Tuning Peg" algorithm, a modified swap procedure for binary matrix randomization.
Keywords:
NODFC-scoreP-valueco-occurrenceecological networkseffect sizenestedness

Related Experiment Videos

  • Incorporated a random walk procedure to explore the null space continuously.
  • Utilized two parameters to "tune" row and column marginal total discrepancies.

    • Main Results:

    • The "Tuning Peg" algorithm explores the complete null space, encompassing existing methods.
    • A bi-dimensional landscape visualizes matrix structural patterns based on significance and effect size.
    • Demonstrated the approach's informativeness with simulated and real ecological matrices.

    Conclusions:

    • The "Tuning Peg" algorithm provides a flexible and comprehensive approach to ecological matrix randomization.
    • Visualizing patterns in a continuous null space landscape offers novel insights.
    • This method can help resolve longstanding debates in ecological matrix analysis.