All Change: Research on early warning signals for critical transitions in complex systems such as ecosystems, climate, and global finance systems recently has been gathering pace. At the same time, studies on complex networks are starting to reveal which architecture may cause systems to be vulnerable to systemic collapse. Scheffer et al. (p. 344 ) review how previously isolated lines of work can be connected, conclude that many critical transitions (such as escape from the poverty trap) can have positive outcomes, and highlight how the new approaches to sensing fragility can help to detect both risks and opportunities for desired change.

Tree distributions across continents indicate three distinct stable states in tree cover―forest, savanna, and treeless.

In the Earth's history, periods of relatively stable climate have often been interrupted by sharp transitions to a contrasting state. One explanation for such events of abrupt change is that they happened when the earth system reached a critical tipping point. However, this remains hard to prove for events in the remote past, and it is even more difficult to predict if and when we might reach a tipping point for abrupt climate change in the future. Here, we analyze eight ancient abrupt climate shifts and show that they were all preceded by a characteristic slowing down of the fluctuations starting well before the actual shift. Such slowing down, measured as increased autocorrelation, can be mathematically shown to be a hallmark of tipping points. Therefore, our results imply independent empirical evidence for the idea that past abrupt shifts were associated with the passing of critical thresholds. Because the mechanism causing slowing down is fundamentally inherent to tipping points, it follows that our way to detect slowing down might be used as a universal early warning signal for upcoming catastrophic change. Because tipping points in ecosystems and other complex systems are notoriously hard to predict in other ways, this is a promising perspective.

Recent studies suggest size-selective removal of small plastic particles from the ocean surface, an observation that remains unexplained. We studied one of the hypotheses regarding this size-selective removal: the formation of a biofilm on the microplastics (biofouling). We developed the first theoretical model that is capable of simulating the effect of biofouling on the fate of microplastic. The model is based on settling, biofilm growth, and ocean depth profiles for light, water density, temperature, salinity, and viscosity. Using realistic parameters, the model simulates the vertical transport of small microplastic particles over time, and predicts that the particles either float, sink to the ocean floor, or oscillate vertically, depending on the size and density of the particle. The predicted size-dependent vertical movement of microplastic particles results in a maximum concentration at intermediate depths. Consequently, relatively low abundances of small particles are predicted at the ocean surface, while at the same time these small particles may never reach the ocean floor. Our results hint at the fate of “lost” plastic in the ocean, and provide a start for predicting risks of exposure to microplastics for potentially vulnerable species living at these depths.