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Assessing and comparing early warning signal performance in spatially-structured systems.

George E Robinson1, Graham M Donovan1

  • 1Department of Mathematics, The University of Auckland, Auckland, New Zealand.

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Summary
This summary is machine-generated.

Early warning signals (EWS) can predict critical transitions in systems. Spatially-informed EWS generally outperform temporal EWS, but the best choice depends on the specific system.

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Area of Science:

  • Complex systems science
  • Ecological dynamics
  • Time series analysis

Background:

  • Critical transitions represent abrupt shifts in system states.
  • Early warning signals (EWS) aim to predict these transitions in temporal and spatio-temporal systems.
  • Spatially-informed EWS leverage spatial data, potentially outperforming temporal EWS.

Purpose of the Study:

  • To compare the performance of spatially-informed EWS against traditional temporal EWS.
  • To establish a framework for measuring and comparing EWS performance.
  • To determine the applicability of different EWS to various complex systems.

Main Methods:

  • Quantified EWS performance using the strength of trends (Kendall's τ).
  • Assessed robustness to detrending parameters and statistical significance.
  • Evaluated agreement with expected EWS behavior for temporal and spatial systems.

Main Results:

  • Spatially-informed EWS generally demonstrated superior performance compared to temporal EWS in the studied systems.
  • EWS performance varied, indicating that no single method is universally optimal.
  • The choice of EWS is system-specific and requires careful consideration.

Conclusions:

  • Spatially-informed EWS offer a promising approach for anticipating critical transitions by utilizing spatial information.
  • A comprehensive assessment of EWS performance is crucial for selecting appropriate indicators for specific systems.
  • Further research is needed to refine EWS methodologies for diverse complex systems.