Methods For Detecting Early Warnings Of Critical Transitions In Time Series Illustrated Using Simulated Ecological Data
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Methods For Detecting Early Warnings Of Critical Transitions In Time Series Illustrated Using Simulated Ecological Data
| Title: | Methods For Detecting Early Warnings Of Critical Transitions In Time Series Illustrated Using Simulated Ecological Data |
| Author: |
Ellison, Aaron M.; Dakos, Vasilis; Carpenter, Stephen R.; Brock, William A.; Guttal, Vishwesha; Ives, Anthony R.; Kéfi, Sonia; Livina, Valerie; Seekell, David A.; van Nes, Egbert H.; Scheffer, Marten
Note: Order does not necessarily reflect citation order of authors. |
| Citation: | Dakos, Vasilis, Stephen R. Carpenter, William A. Brock, Aaron M. Ellison, Vishwesha Guttal, Anthony R. Ives, Sonia Kéfi, et al. 2012. Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS ONE 7(7): e41010. |
| Full Text & Related Files: |
Ellison_Methods.pdf (1.011Mb; PDF) 
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| Abstract: | 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 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. |
| Published Version: | doi:10.1371/journal.pone.0041010 |
| Terms of Use: | This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA |
| Citable link to this page: | http://nrs.harvard.edu/urn-3:HUL.InstRepos:9637972 |
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Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
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