Journal
PLOS ONE
Volume 7, Issue 7, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0041010
Keywords
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Categories
Funding
- Spinoza Award
- European Research Council
- Vilas Trust
- Center for Robust Decision Making in Climate and Energy Policy
- National Science Foundation (NSF) through the Decision Making Under Uncertainty Program
- NSF
- Hilldale Fund of UW-Madison
- Natural Environment Research Council [NE/F005474/1]
- AXA Research Fund
- US National Science Foundation (GRFP)
- US National Science Foundation through its LTER program [DEB 06-20443]
- Santa Fe Institute
- Arizona State University
- Natural Environment Research Council [NE/F005474/1] Funding Source: researchfish
- NERC [NE/F005474/1] Funding Source: UKRI
- Division Of Environmental Biology
- Direct For Biological Sciences [1144624] Funding Source: National Science Foundation
- Division Of Environmental Biology
- Direct For Biological Sciences [1144627, 1144683] Funding Source: National Science Foundation
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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.
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