Early warning signals of recovery in complex systems

Theory and observation tell us that many complex systems exhibit tipping points—thresholds involving an abrupt and irreversible transition to a contrasting dynamical regime. Such events are commonly referred to as critical transitions. Current research seeks to develop early warning signals (EWS) of critical transitions that could help prevent undesirable events such as ecosystem collapse. However, conventional EWS do not indicate the type of transition, since they are based on the generic phenomena of critical slowing down. For instance, they may fail to distinguish the onset of oscillations (e.g. Hopf bifurcation) from a transition to a distant attractor (e.g. Fold bifurcation). Moreover, conventional EWS are less reliable in systems with density-dependent noise. Other EWS based on the power spectrum (spectral EWS) have been proposed, but they rely upon spectral reddening, which does not occur prior to critical transitions with an oscillatory component. Here, we use Ornstein–Uhlenbeck theory to derive analytic approximations for EWS prior to each type of local bifurcation, thereby creating new spectral EWS that provide greater sensitivity to transition proximity; higher robustness to density-dependent noise and bifurcation type; and clues to the type of approaching transition. We demonstrate the advantage of applying these spectral EWS in concert with conventional EWS using a population model, and show that they provide a characteristic signal prior to two different Hopf bifurcations in data from a predator–prey chemostat experiment. The ability to better infer and differentiate the nature of upcoming transitions in complex systems will help humanity manage critical transitions in the Anthropocene Era.

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Early warning signals in chemical reaction networks

Chemical Communications ◽

10.1039/d0cc01010c ◽

2020 ◽

Vol 56 (26) ◽

pp. 3725-3728

Author(s):

Oliver R. Maguire ◽

Albert S. Y. Wong ◽

Jan Harm Westerdiep ◽

Wilhelm T. S. Huck

Keyword(s):

Complex Systems ◽

Chemical Reaction ◽

Early Warning ◽

Reaction Network ◽

Chemical Reaction Networks ◽

Chemical Reaction Network ◽

Reaction Networks ◽

Warning Signals ◽

Early Warning Signals ◽

Complex Chemical

Many natural and man-made complex systems display early warning signals when close to an abrupt shift in behaviour. Here we show that such early warning signals appear in a complex chemical reaction network.

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Faculty Opinions recommendation of Early warning signals of recovery in complex systems.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.735539450.793560446 ◽

2019 ◽

Author(s):

Ulrich Brose

Keyword(s):

Complex Systems ◽

Early Warning ◽

Warning Signals ◽

Early Warning Signals

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Machine learning dismantling and early-warning signals of disintegration in complex systems

Nature Communications ◽

10.1038/s41467-021-25485-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Marco Grassia ◽

Manlio De Domenico ◽

Giuseppe Mangioni

Keyword(s):

Complex Systems ◽

Early Warning ◽

Large Scale ◽

Minimal Set ◽

Warning Signals ◽

Early Warning Signals ◽

External Perturbations ◽

Targeted Attacks ◽

Np Hard Problem ◽

Assisted Analysis

AbstractFrom physics to engineering, biology and social science, natural and artificial systems are characterized by interconnected topologies whose features – e.g., heterogeneous connectivity, mesoscale organization, hierarchy – affect their robustness to external perturbations, such as targeted attacks to their units. Identifying the minimal set of units to attack to disintegrate a complex network, i.e. network dismantling, is a computationally challenging (NP-hard) problem which is usually attacked with heuristics. Here, we show that a machine trained to dismantle relatively small systems is able to identify higher-order topological patterns, allowing to disintegrate large-scale social, infrastructural and technological networks more efficiently than human-based heuristics. Remarkably, the machine assesses the probability that next attacks will disintegrate the system, providing a quantitative method to quantify systemic risk and detect early-warning signals of system’s collapse. This demonstrates that machine-assisted analysis can be effectively used for policy and decision-making to better quantify the fragility of complex systems and their response to shocks.

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Early warning signals of regime shifts for aquatic systems: Can experiments help to bridge the gap between theory and real-world application?

Ecological Complexity ◽

10.1016/j.ecocom.2021.100944 ◽

2021 ◽

Vol 47 ◽

pp. 100944

Author(s):

Julio Alberto Alegre Stelzer ◽

Jorrit Padric Mesman ◽

Rita Adrian ◽

Bastiaan Willem Ibelings

Keyword(s):

Early Warning ◽

Real World ◽

Regime Shifts ◽

Aquatic Systems ◽

Warning Signals ◽

Early Warning Signals ◽

Real World Application

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Efficacy of early warning signals and spectral periodicity for predicting transitions in bipolar patients: An actigraphy study

Translational Psychiatry ◽

10.1038/s41398-021-01465-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yoram K. Kunkels ◽

Harriëtte Riese ◽

Stefan E. Knapen ◽

Rixt F. Riemersma - van der Lek ◽

Sandip V. George ◽

...

Keyword(s):

Spectral Analysis ◽

Early Warning ◽

Type I ◽

Warning Signals ◽

Early Warning Signals ◽

Research Fields ◽

Mood Episode ◽

Final Sample ◽

Bipolar Patients ◽

Direction Opposite

AbstractEarly-warning signals (EWS) have been successfully employed to predict transitions in research fields such as biology, ecology, and psychiatry. The predictive properties of EWS might aid in foreseeing transitions in mood episodes (i.e. recurrent episodes of mania and depression) in bipolar disorder (BD) patients. We analyzed actigraphy data assessed during normal daily life to investigate the feasibility of using EWS to predict mood transitions in bipolar patients. Actigraphy data of 15 patients diagnosed with BD Type I collected continuously for 180 days were used. Our final sample included eight patients that experienced a mood episode, three manic episodes and five depressed episodes. Actigraphy data derived generic EWS (variance and kurtosis) and context-driven EWS (autocorrelation at lag-720) were used to determine if these were associated to upcoming bipolar episodes. Spectral analysis was used to predict changes in the periodicity of the sleep/wake cycle. The study procedures were pre-registered. Results indicated that in seven out of eight patients at least one of the EWS did show a significant change-up till four weeks before episode onset. For the generic EWS the direction of change was always in the expected direction, whereas for the context-driven EWS the observed effect was often in the direction opposite of what was expected. The actigraphy data derived EWS and spectral analysis showed promise for the prediction of upcoming transitions in mood episodes in bipolar patients. Further studies into false positive rates are suggested to improve effectiveness for EWS to identify upcoming bipolar episode onsets.

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