Methods For Detecting Early Warnings Of Critical Transitions In Time Series Illustrated Using Simulated Ecological Data

DSpace/Manakin Repository

Methods For Detecting Early Warnings Of Critical Transitions In Time Series Illustrated Using Simulated Ecological Data

Show simple item record 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 2012-09-24T20:46:14Z 2012
dc.identifier.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. en_US
dc.identifier.issn 1932-6203 en_US
dc.description.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. en_US
dc.description.sponsorship Organismic and Evolutionary Biology en_US
dc.language.iso en_US en_US
dc.publisher Public Library of Science en_US
dc.relation.isversionof doi:10.1371/journal.pone.0041010 en_US
dash.license LAA
dc.subject leading indicator en_US
dc.subject resilience en_US
dc.subject critical transition en_US
dc.subject catastrophic shift en_US
dc.subject regime shift en_US
dc.subject 47 alternative states en_US
dc.subject autocorrelation en_US
dc.subject variance en_US
dc.subject skewness en_US
dc.subject kurtosis en_US
dc.subject spectral reddening en_US
dc.subject detrended 48 fluctuation analysis en_US
dc.subject conditional heteroskedasticity en_US
dc.subject time-varying autoregressive models en_US
dc.subject BDS test en_US
dc.subject potential analysis en_US
dc.subject 50 time-series analysis en_US
dc.subject nonlinearity en_US
dc.title Methods For Detecting Early Warnings Of Critical Transitions In Time Series Illustrated Using Simulated Ecological Data en_US
dc.type Journal Article en_US
dc.description.version Accepted Manuscript en_US
dc.relation.journal PLoS ONE en_US Ellison, Aaron M. 2012-09-24T20:46:14Z

Files in this item

Files Size Format View
Ellison_Methods.pdf 988.0Kb PDF View/Open

This item appears in the following Collection(s)

  • FAS Scholarly Articles [6463]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

Show simple item record


Search DASH

Advanced Search