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Peer-reviewedCC BY 4.0Royal Society Open Science

Using matrix analysis to understand competition in food webs and antimicrobial resistance networks

  • Hussein Al-Taani1
  • Mohammad Abudayah1
  • Walid A Al-Zyoud2
  • Ismail Abushaikha3
  1. 1School of Computing, German Jordanian University 1 , Amman, Amman Governorate, Jordan
  2. 2School of Applied Medical Sciences, German Jordanian University 2 , Amman, Amman Governorate, Jordan
  3. 3Business School, German Jordanian University 3 , Amman, Amman Governorate, Jordan
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Abstract

Indirect competition is a fundamental mechanism shaping the structure of biological systems, from food webs to antimicrobial resistance (AMR) networks. Translating directed interaction data into a quantitative measure of competition remains a modelling challenge. This paper develops a spectral framework that maps directed bipartite networks into symmetric, positive semidefinite (PSD) competition matrices via the shared-target principle (C=AA⊤). We establish a duality between exploitative competition (consumer overlap) and apparent competition (target overlap) using singular value decomposition (SVD), allowing for a unified spectral analysis of both trophic levels. We further prove that using this data-driven matrix in Lotka–Volterra dynamics guarantees Lyapunov stability at any positive equilibrium, with explicit conditions for global asymptotic stability versus convergence to an equilibrium continuum in the presence of exact guilds. The framework is validated on synthetic AMR strain–gene networks, where we demonstrate that spectral competitive centrality provides a more robust measure of influence than simple gene counts. This approach offers a rigorous, reproducible workflow for converting qualitative interaction structure into quantitative stability predictions.

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Cite

BibTeXRIS
BibTeX
@article{altaani2026using,
  title = {{Using matrix analysis to understand competition in food webs and antimicrobial resistance networks}},
  author = {Hussein Al-Taani and Mohammad Abudayah and Walid A Al-Zyoud and Ismail Abushaikha},
  journal = {Royal Society Open Science},
  year = {2026},
  month = sep,
  doi = {10.1098/rsos.260035},
  url = {https://royalsocietypublishing.org/rsos/article/13/9/260035/483423/Using-matrix-analysis-to-understand-competition-in},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Using matrix analysis to understand competition in food webs and antimicrobial resistance networks
AU  - Hussein Al-Taani
AU  - Mohammad Abudayah
AU  - Walid A Al-Zyoud
AU  - Ismail Abushaikha
PY  - 2026
DA  - 2026/09/30
JO  - Royal Society Open Science
DO  - 10.1098/rsos.260035
UR  - https://royalsocietypublishing.org/rsos/article/13/9/260035/483423/Using-matrix-analysis-to-understand-competition-in
AB  - Indirect competition is a fundamental mechanism shaping the structure of biological systems, from food webs to antimicrobial resistance (AMR) networks. Translating directed interaction data into a quantitative measure of competition remains a modelling challenge. This paper develops a spectral framework that maps directed bipartite networks into symmetric, positive semidefinite (PSD) competition matrices via the shared-target principle (C=AA⊤). We establish a duality between exploitative competition (consumer overlap) and apparent competition (target overlap) using singular value decomposition (SVD), allowing for a unified spectral analysis of both trophic levels. We further prove that using this data-driven matrix in Lotka–Volterra dynamics guarantees Lyapunov stability at any positive equilibrium, with explicit conditions for global asymptotic stability versus convergence to an equilibrium continuum in the presence of exact guilds. The framework is validated on synthetic AMR strain–gene networks, where we demonstrate that spectral competitive centrality provides a more robust measure of influence than simple gene counts. This approach offers a rigorous, reproducible workflow for converting qualitative interaction structure into quantitative stability predictions.
SN  - 2054-5703
ER  -