The colloquium and masterclass on early-warning signals held in Amsterdam last week was an inspiring event for a diverse group of people studying critical transitions in social systems, medicine, and epidemics.

There was an avalanche of new work on early-warning indicators. A review paper in Science by Scheffer et al summarized work that has been developing in the field and set new research questions involving networks and critical transitions, whereas three research papers focused on raising awareness on the application of early-warnings: Boettiger and Hastings showed how transitions may occur with no critical point associated with them and how they can be discriminated, Perretti and Munch exemplified how high levels of noise may hinder the identification of early-warning signals, and Kéfi et al demonstrated how early-warning indicators are not specific for catastrophic transitions but can also be signaling non-catastrophic, continuous shifts. Lastly, Brock and Carpenter updated a non-parametric approach for identifying early-warning indicators that stands somewhere between the pure metric-based approaches and the model-based ones.

Our colloquium and masterclass on ‘Early-warning signals for critical transitions: bridging the gap between theory and practice’ will be hosted by the Dutch Royal Science and Arts Society (KNAW) from 10 to 12 of October 2012 in Amsterdam. This is going to be the first official kick-off of the Early Warning Signals Toolbox and SparcS – the Synergy Program for Analyzing Resilience and Critical transitionS!

The paper describes the details of how modified linear models with time-varying parameters can be used to extract an indicator of instability for a time series that may be drifting towards a regime shift. The paper is available online in Ecosphere and elaborates on time-varying AR(p) and threshold AR(p) models that are presented here. The code to execute all this is currently in Matlab, aiming to convert it into a routine in the R environment.

This fascinating paper by Dai et al demonstrates in a comprehensive way how a simple lab experiment confirms theoretical expectations over critical transitions and early-warning signals. The authors used a yeast population to show how it may shift to extinction at increasing dilution rates. At the same time they were able to demonstrate bistability by identifying the border of the basin of attraction to the extinction state, to measure generic indicators prior to extinction, and measure directly slowing down by perturbation experiments. In short, apply all theory and techniques in the same experiment at once!