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Monitoring resilience in bursts.

Clara Delecroix1, Egbert H van Nes1, Marten Scheffer1

  • 1Department of Environmental Sciences, Wageningen University and Research, Wageningen 6700 AA, The Netherlands.

Proceedings of the National Academy of Sciences of the United States of America
|July 24, 2024
PubMed
Summary
This summary is machine-generated.

Detecting loss of resilience can anticipate critical transitions. Using data bursts, not continuous time series, improved resilience indicator performance, suggesting better early warning signals for systems like patient mood.

Keywords:
DIORsmonitoringresilienceresource allocationtipping points

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Area of Science:

  • Complex systems science
  • Mathematical ecology
  • Computational psychiatry

Background:

  • Anticipating critical transitions is crucial across disciplines.
  • Loss of resilience, indicated by critical slowing down, precedes tipping points.
  • Traditional resilience indicators use continuous time series and moving windows.

Purpose of the Study:

  • Compare the effectiveness of resilience indicators using data bursts versus a moving window approach.
  • Evaluate the potential of burst sampling for early warning signals of critical transitions.
  • Provide guidelines for optimal sampling strategies in resilience monitoring.

Main Methods:

  • Simulated data analysis to compare moving window and burst approaches.
  • Calculation of resilience indicators (autocorrelation, variance) for both methods.
  • Case study using high-resolution mood data to predict depressive episodes using burst sampling.

Main Results:

  • The burst data approach outperformed the moving window method in detecting changes in resilience with equal data points.
  • Burst sampling can effectively downsample continuous time series while retaining early warning signal capabilities.
  • The burst method showed promise in a case study for anticipating mood transitions.

Conclusions:

  • Sampling data in bursts is a viable and potentially superior alternative to continuous time series for resilience monitoring.
  • This approach enhances the capacity to detect critical transitions in systems where continuous monitoring is challenging.
  • The findings have broad implications for fields including human health, epidemiology, and ecology.