Unraveling emergent network indeterminacy in complex ecosystems: A random matrix approach
1Graduate School of Life Sciences, Tohoku University, Sendai 980-8578, Japan.
Summary
Ecological network indeterminacy, or unpredictability, is common in food webs but rare in competitive communities. Random matrix theory (RMT) helps predict ecosystem responses to environmental changes.
Area of Science:
- Ecology
- Network Theory
- Mathematical Biology
Background:
- Ecosystem unpredictability, termed indeterminacy, arises from indirect species interactions.
- Forecasting environmental change impacts is difficult due to complex indirect pathways in ecological networks.
Purpose of the Study:
- To develop mathematical criteria for predicting ecological network indeterminacy using random matrix theory (RMT).
- To understand how species interaction characteristics influence network indeterminacy.
Main Methods:
- Applied random matrix theory (RMT) for analytical and simulation-based network analysis.
- Investigated criteria for indeterminacy emergence across diverse ecological community structures.
Main Results:
- Network indeterminacy is uncommon in large competitive and mutualistic communities.
- Indeterminacy is prevalent in top-down regulated food webs, especially those dominated by predator-prey interactions.
- Predictable and unpredictable perturbations can coexist within the same ecosystem.
Conclusions:
- RMT provides a framework to determine if ecological networks are indeterminate and identify perturbation types causing unpredictability.
- Understanding direct species interactions and applying RMT aids in ecological forecasting and network identification.
- The framework has potential applications in microbial and medical sciences for network reconstruction.
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