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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
Dissimilarity measures for generalized Lotka-Volterra systems on networks
Nicolás A Márquez1, Maryam Chaib De Mares2, Alejandro P Riascos3
1Departamento de Física, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá, 111321, Colombia.
Scientific Reports
|May 15, 2026
Summary
We developed a framework to measure differences in generalized Lotka-Volterra dynamics. This tool reveals how network structure and interactions impact system stability and predict potential instabilities in ecological communities.
Area of Science:
- Ecology
- Theoretical Biology
- Network Science
Background:
- Generalized Lotka-Volterra models describe complex ecological interactions.
- Quantifying dissimilarities between these dynamical systems is crucial for understanding ecological stability and resilience.
- Existing methods may not fully capture transient dynamics or network structural influences.
Purpose of the Study:
- Introduce a general framework to quantify dissimilarities between generalized Lotka-Volterra dynamical processes.
- Enable systematic comparisons across diverse ecological systems, including varying interaction parameters, network weights, and topologies.
- Provide a tool for analyzing robustness, detecting structural sensitivity, and predicting instabilities in nonlinear systems.
Main Methods:
- Developed a novel framework for quantifying dynamical dissimilarities.
- Applied measures to capture both transient and stationary dynamics.
- Analyzed systems ranging from two-species interactions to multispecies communities on networks.
Main Results:
- Subtle structural changes in networks can lead to markedly distinct dynamical outcomes.
- In two-species systems, interaction strength and initial conditions significantly affect divergence.
- In modular networks, the distribution of negative interactions critically influences stability transitions.
Conclusions:
- Dynamical dissimilarity measures offer a powerful approach for analyzing nonlinear systems.
- The framework is versatile, applicable to systems with different nonlinear equations and network structures.
- This approach facilitates comparative analysis of biological systems, emphasizing the role of interaction networks and nonlinear dynamics in stability and resilience.
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