Tag Archives: early warnings

Detecting tipping points in ecological networks

In this just published paper we develop a framework of detecting tipping points in the context of  mutualistic networks. Under a scenario of global environmental change that might affect species interactions we show how indicators of resilience could provide early warning in complex communities such as those represented by the network of interactions between plants and their animal mutualists. This work is a first step towards quantifying the risk of network collapse and the possibility to monitor community resilience based on best-indicator species.

The use and misuse of resilience indicators as early warnings for regime shifts

Regime shifts have been a long sought theme of research in marine ecosystems. Controversies, new methods and alternative hypotheses on how to study and understand such marine regime shifts are summarized in the special issue of the Philosophical Transactions of the Royal Society on Marine regime shifts around the globe: theory, drivers and impacts that just appeared online. Thus, we couldn’t think of a better place to publish a review-research paper on the use and misuse of resilience indicators as early warnings for regime shifts in marine but not only systems.

Spatial methods of early warnings for tipping points

Recently our paper on spatial indicators for critical transitions was published in PloS One. In this paper we summarize methods and create a flowchart for looking for indicators of upcoming transitions in spatial data. It is a natural follow-up paper from our previous work on methods for timeseries. The methods of the paper are now summarized in the spatial indicators section of the EWS toolbox website together with the actual R code.

EWS package gets into WICI Data challenge finalists!

Our submission of 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 we didn’t get the first place, the judges were very flattering and Leo Lahti and myself are really proud to have made it that far in a short time. We hope that this will be a kick into developing more the package and making it more interactive and available. You can read more on it here.

Are Warning Signals specific to Catastrophic Transitions?

There is a lot of interest on the limits of resilience indicators and on whether they are uniquely associated with catastrophic transitions. We tried to shed light on that question in a short piece that just appeared in Oikos. There, we show that the same early warnings may signal non catastrophic transitions, but the same transitions are as well bifurcation points. Thus, it is not surprising that the same expectations for signals of deteriorating resilience are universal prior to any (local) bifurcation. The challenge remains in finding signals that would be specific to the catastrophic, unexpected, and irreversible shifts.

highlighted in Editor’s choice in Oikos

earlywarnings package in R libraries

logoEWS Together with Leo Lahti, we fixed bugs and moved the earlywarnings toolbox in R. It is now a library ready to be installed from your preferable CRAN repository. In the process, we also migrated the earlywarnings toolbox to github for shifitng towards open-source, community-based project development. We hope this will facilitate the use of the toolbox both for research and education. More details on the Early Warning Signals Toolbox webpage.

Flickering before a shift to eutrophication

Together with colleagues from China and the UK we just published work on a paleo limnological record in a big chinese lake that shows a transition to eutrophication during the last 30 years. Interestingly, the data offer the possibility to show that the system exhibits bimodality and that approaching to the permanent shift ‘flickering’ between the oligotrophic and eutrophic state may be observed. We compared these results to model simulations and we conclude that flickering may be more possible to detect in the most common ecological records at hand.

Review on anticipating critical transitions in last week´s issue in Science

Our review paper on Anticipating Critical Transitions summarizes the advancement and popularity in estimating early-warning signals for approaching transitions in a variety of disciplines together with some ground-breaking experimental demonstrations that followed the earlier review on early-warnings. In addition, new ideas are mapped out and the challenge of merging network perspectives on stability and collapse with early-warnign research is pioneered.

KNAW colloquium, masterclass and SparcS

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 also going to be the official kick-off of SparcS – the Synergy Program for Analyzing Resilience and Critical transitionS: an initiative on getting people to work on issues of critical transitions and resilience in a broad range of scientific fields.

Model-based leading indicators for critical transitions

In a recent work with Tony Ives, we showed 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. The idea is simply that instead of fitting an autoregressive model and finding a fixed value for its parameters, to fit an autoregressive model with parameters that change based on the point one is along the time series. This is possible due to a Kalman filter fitting proceedure and seems to not require a too much long time series. We also show that fitting threshold autoregressive models can distinguish alternative attractors in a flickering time series. The code to execute all this is currently in Matlab, but we aim in converting it to an easy to use routine in the R environment.