After working hard, we are happy to announce that our earlywarning package is ready to use directly from R! No need to download and install locally, as from R you can find it and install it using your selected CRAN repository. Also, we keep a live update of the toolbox in github, as we have created an organization earlywarningstoolbox that aims to host the further development of the project.
Critical transitions in experimental and theoretical systems can be anticipated on the basis of specific warning signs, raising the prospect that it might also be possible to predict future real-world events on the scale of the 2007 global financial crisis and Arab spring. But what to measure? Recent work has focused on critical slowing down, in which a system’s recovery from perturbation is reduced as the transition is approached. Another possibility is flickering, in which increasing shifts between alternative stable states are seen in the run-up to the transition. This study uses long-term data from a real system, a Chinese lake, to show that flickering can be observed and that it occurs up to 20 years before a critical transition — in this case the deterioration of a lake towards a dead ‘eutrophic’ state as algal growth consumes the last available oxygen. (from the editor’s summary in Nature)
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!