Theoretical Ecology

, Volume 6, Issue 3, pp 255–264 | Cite as

Early warning signals: the charted and uncharted territories

  • Carl BoettigerEmail author
  • Noam Ross
  • Alan Hastings
Original Paper


The realization that complex systems such as ecological communities can collapse or shift regimes suddenly and without rapid external forcing poses a serious challenge to our understanding and management of the natural world. The potential to identify early warning signals that would allow researchers and managers to predict such events before they happen has therefore been an invaluable discovery that offers a way forward in spite of such seemingly unpredictable behavior. Research into early warning signals has demonstrated that it is possible to define and detect such early warning signals in advance of a transition in certain contexts. Here, we describe the pattern emerging as research continues to explore just how far we can generalize these results. A core of examples emerges that shares three properties: the phenomenon of rapid regime shifts, a pattern of “critical slowing down” that can be used to detect the approaching shift, and a mechanism of bifurcation driving the sudden change. As research has expanded beyond these core examples, it is becoming clear that not all systems that show regime shifts exhibit critical slowing down, or vice versa. Even when systems exhibit critical slowing down, statistical detection is a challenge. We review the literature that explores these edge cases and highlight the need for (a) new early warning behaviors that can be used in cases where rapid shifts do not exhibit critical slowing down; (b) the development of methods to identify which behavior might be an appropriate signal when encountering a novel system, bearing in mind that a positive indication for some systems is a negative indication in others; and (c) statistical methods that can distinguish between signatures of early warning behaviors and noise.


Early warning signals Regime shifts Bifurcation Critical slowing down 



This work was partially supported by the Center for Stock Assessment Research, a partnership between the University of California Santa Cruz and the Fisheries Ecology Division, Southwest Fisheries Science Center, Santa Cruz, CA, to CB; the NSF Integrative Graduate Education and Research Traineeship Program to NR; and by funding from NSF Grant EF 0742674 to AH.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Center for Stock Assessment Research, Department of Applied Math and StatisticsUniversity of CaliforniaSanta CruzUSA
  2. 2.Department of Environmental Science and PolicyUniversity of California DavisDavisUSA

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