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

By Vasilis Dakos, Stephen R. Carpenter, William A. Brock, Aaron M. Ellison, Vishwesha Guttal, Anthony R. Ives, Sonia Kefi, Valerie N. Livina, D. Seekell, E. V. van Nes and 1 more. PLoS ONE, 2012

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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-called ‘early warning signals’, and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data.

Subfields: Economics and Econometrics, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/b22b40906454d3eec7fdbd79bef269e2ad2fe096

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Citations in the corpus

Transient phenomena in ecology (2018, background): “Intense study of one kind of exogenously triggered regime shift (those caused by saddle-node bifurcations) has provided important insights (3, 5, 6) across a range of ecological systems.”
Do early warning indicators consistently predict nonlinear change in long‐term ecological data? (2016, methodology): “…time series in terms of quality and length, our study supports the assertion that there is unlikely to be a ‘silver bullet’ that meets this challenge (Dakos et al. 2012a; Lindegren et al. 2012) and that early detection of nonlinear change using variance and autocorrelation as early warnings may be…”
Safe and just operating spaces for regional social-ecological systems (2014, background): “A number of new analytical techniques defined by changes in magnitude-frequency, variability, skewness or autocorrelation metrics (Lenton, 2013; Dakos et al., 2012; Carstensen and Weydmann, 2012) offer the promise of providing Type IV early warning signals.”
Resilience indicators: prospects and limitations for early warnings of regime shifts (2015, methodology): “Despite the fact that this is an obstacle to the application of early warnings, recent advances in significance testing [35,36,61], modelbased approaches [35,62,63] and combinations of spatial and temporal indicators [25,29,64] offer promising alternatives in identifying a nearby transition.”
Critical slowing down as early warning for the onset and termination of depression (2013, neutral): “However, the examples we analyzed may serve to illustrate the general phenomenon that indicators of critical slowing down can be found at tipping points independently of the precise underlying complex mechanisms involved, and on the way the variables are measured (27, 28, 43).”
Ecological resilience: what to measure and how (2022, methodology): “…methods and statistical software for conducting breakpoint analysis based either on statistical tests (Beaulieu et al 2012), fitting threshold-general additivemodels (Ciannelli et al 2004) or threshold auto-regressive models to the data (Gröger et al 2011, Ives and Dakos 2012, Laitinen et al 2021).”