An interactive visualization version of the earlywarnings R package for critical transitions in the Data Challenge competition organised by the Waterloo Institute for Complexity and Innovation got into the 4 finalists. Although not reaching the first place, the reviews were flattering and Leo Lahti and myself got the kick into developing the package further. You can read more on it here and soon there will be more to follow.
The current issue of Theoretical Ecology is dedicated on Early Warnings and Tipping Points in Ecology. This special issue was co-edited by Vasilis Dakos and Alan Hastings and contains 11 original research papers from key contributors of the topic. You can find a complete list of content here. We hope it will have a strong impact in the further development of anticipating regime shifts in complex systems. It will be highlighted in the upcoming ESA conference in Minneapolis.
Two interesting papers on empirically demonstrating the detection of critical transitions using the generic early warnings have been recently published in high profile journals. Dai et al showed experimentally that a disturbance in connected yeast populations affects longer spatial scales close to a critical transition. The authors introduced this effect as the analogue of ‘recovery time‘ in space: namely ‘recovery length’ to show that it increases prior to a critical transition. Streeter and Dugmore used transects to substitute time for space and measured early warnings for land surface transitions. They found signatures of variance and autocorrelation in tephra deposits along transects that cut across 6 types of landscape thresholds. This is the first study that early warning have been used for critical transitions across space in field observations for such types of transitions. Also in this study the authors have used our recently developed earlywarnings package for R. These two studies are brilliant examples of applying early warning research in creative ways performing experiments in the lab, or looking for experiments conducted by nature.
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)