Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data

@article{Dakos2012MethodsFD,
  title={Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data},
  author={Vasilis Dakos and Stephen R. Carpenter and William A. Buz Brock and Aaron M. Ellison and Vishwesha Guttal and Anthony R. Ives and Sonia K{\'e}fi and Valerie N. Livina and David A. Seekell and Egbert H. van Nes and Marten Scheffer},
  journal={PLoS ONE},
  year={2012},
  volume={7}
}
Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so… 
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593 Citations
Highly Influential Citations
52
Background Citations
199
Methods Citations
108
Results Citations
9

593 Citations

Early Warning Signals of Ecological Transitions: Methods for Spatial Patterns
TLDR
A range of currently available spatial early warning signals are summarized, potential null models to interpret their trends are suggested, and a statistical toolbox that may be used to help detect approaching transitions in a wide range of spatial data is proposed.
Evaluating early-warning indicators of critical transitions in natural aquatic ecosystems
TLDR
Four commonly used early-warning indicators are assessed on long-term datasets of five freshwater ecosystems that have experienced sudden, persistent transitions and for which the relevant ecological mechanisms and drivers are well understood, suggesting a priori knowledge of the underlying mechanisms driving ecosystem transitions is necessary to identify relevant state variables for successfully monitoring EWIs.
Early Warnings for State Transitions
Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem
TLDR
The results revealed that MVWR in combination with autocorrelation at-lag-1 could successfully provide early warning signals for a critical transition in a wetland ecosystem, and showed a significantly improved performance compared to either vegetation (NDVI) or water (MNDWI) indices alone.
Identifying potential critical transitions in a forest ecosystem using satellite data
Ecosystems can undergo changes that are slow and gradual, or an abrupt change - a sudden nonlinear dynamics that can bring catastrophic changes and ultimately leads to the change in the structure and…
Robustness of early warning signals for catastrophic and non-catastrophic transitions
TLDR
It is found that EWS generally work well to signal an impending saddle-node bifurcation, regardless of the autocorrelation or intensity of the noise, but different methods should be used to predict other types of regime shifts.
Stochastic sensitivity analysis and early warning signals of critical transitions in a tri-stable prey-predator system with noise.
Near a tipping point, small changes in a certain parameter cause an irreversible shift in the behavior of a system, called critical transitions. Critical transitions can be observed in a variety of…
Implications of being discrete and spatial for detecting early warning signals of regime shifts
What is the robustness of early warning signals to temporal aggregation?
TLDR
High-resolution palaeoecological time series could be in a large extent suitable to support EWS analyses, and robustness of EWS detection to temporal aggregation was addressed using simulated time series mimicking ecological dynamics.
Eigenvalues of the covariance matrix as early warning signals for critical transitions in ecological systems
TLDR
This study proposes the use of eigenvalues of the covariance matrix of multiple time series as early warning signals and applies the method to simulated data from several spatial ecological models to demonstrate the method’s applicability.
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References

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