Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data.
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...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0041010 |
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| _version_ | 1850119771949891584 |
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| author | Vasilis Dakos Stephen R Carpenter William A Brock Aaron M Ellison Vishwesha Guttal Anthony R Ives Sonia Kéfi Valerie Livina David A Seekell Egbert H van Nes Marten Scheffer |
| author_facet | Vasilis Dakos Stephen R Carpenter William A Brock Aaron M Ellison Vishwesha Guttal Anthony R Ives Sonia Kéfi Valerie Livina David A Seekell Egbert H van Nes Marten Scheffer |
| author_sort | Vasilis Dakos |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3872659110fd4c0dbcd63ceb91164f4b |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-3872659110fd4c0dbcd63ceb91164f4b2025-08-20T02:35:33ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0177e4101010.1371/journal.pone.0041010Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data.Vasilis DakosStephen R CarpenterWilliam A BrockAaron M EllisonVishwesha GuttalAnthony R IvesSonia KéfiValerie LivinaDavid A SeekellEgbert H van NesMarten SchefferMany 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.https://doi.org/10.1371/journal.pone.0041010 |
| spellingShingle | Vasilis Dakos Stephen R Carpenter William A Brock Aaron M Ellison Vishwesha Guttal Anthony R Ives Sonia Kéfi Valerie Livina David A Seekell Egbert H van Nes Marten Scheffer Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS ONE |
| title | Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. |
| title_full | Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. |
| title_fullStr | Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. |
| title_full_unstemmed | Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. |
| title_short | Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. |
| title_sort | methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data |
| url | https://doi.org/10.1371/journal.pone.0041010 |
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