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Systematic analysis and optimization of early warning signals for critical transitions using distribution data
Daniele Proverbio1,2, Alexander Skupin1,3,4, Jorge Gonçalves1,5
1Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue Du Swing, 4367 Belvaux, Luxembourg.
Iscience
|July 17, 2023
Summary
This study enhances early warning signals (EWS) for detecting critical transitions in complex systems. An optimized composite indicator, validated on experimental data, improves EWS performance and explains discrepancies between models and real-world observations.
Area of Science:
- Complex Systems Dynamics
- Ecological Resilience
- Systems Biology
Background:
- Complex systems exhibit abrupt shifts between alternative states, impacting fields from cell regulation to ecosystems.
- Model-free early warning signals (EWS) aim to predict these critical transitions but face challenges in generic applicability and data extraction, particularly from biological experiments.
Purpose of the Study:
- To systematically investigate the properties and performance of dynamical EWS under various deteriorating conditions.
- To propose an optimized combination of EWS for early detection of impending transitions.
- To reconcile discrepancies between EWS performance in simulated models versus real experimental data.
Main Methods:
- Systematic investigation of dynamical EWS properties and performance across different deteriorating conditions.
- Development and testing of an optimized composite indicator for early warning signals.
- Validation of the proposed methods using experimental data from microbiological populations.
Main Results:
- Identified factors influencing EWS performance in deteriorating complex systems.
- Demonstrated that an optimized composite indicator significantly enhances early detection of critical transitions.
- Provided explanations for observed differences between simulated and experimental EWS data.
Conclusions:
- The study offers guidance for selecting appropriate EWS based on specific system characteristics.
- The proposed composite indicator offers a more robust approach to alerting for impending critical transitions.
- Results clarify the utility and limitations of EWS in real-world biological systems.
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