Acknowledging the existence of critical transitions is only the first step for avoiding them. It would be tremendously valuable, if we could predict when a critical transition will happen. Unfortunately, for most systems, we neither have enough records of past transitions nor reliable models to study their behavior. Despite our rapidly increasing knowledge, we are still lacking enough understanding of the feedbacks and mechanisms that trigger these transitions. Most available models either lack realism for predictive purposes, or are too complex and typically very uncertain. Alternatively, it has been recently proposed that one could measure the resilience of a system- and thus its proximity to a critical transition- using so-called generic early-warning signals (EWS). We are developing and applying such indicators in systems ranging from ecology to climate both theoretically and empirically.
- Dakos V, Carpenter SR, Brock WA, Ellison AM, Guttal V, Ives AR, Kéfi S, Livina V, Seekell DA, van Nes EH, Scheffer M (2012) Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data. PLoS ONE 7(7): e41010 doi:10.1371/journal.pone.0041010
- Kéfi S, Guttal V, Brock WA, Carpenter SR, Ellison AM, Livina VN, Seekell DA, Scheffer M, van Nes EH, Dakos V (2014) Early Warning Signals of Ecological Transitions: Methods for Spatial Patterns. PLoS ONE 9(3): e92097. doi:10.1371/journal.pone.0092097
There is a dedicated website that tracks the development of EWS: http://www.early-warning-signals.org
More on the toolboxes can be found in “Earlywarnings: spatial and temporal methods”: https://github.com/earlywarningtoolbox
Also available to be installed directly in R: http://cran.r-project.org/web/packages/earlywarnings/index.html