Projects per year
Complex systems in ecology and the climate can have tipping points. The term ‘tipping point’ is loosely defined as a threshold point in the conditions after which runaway change brings the system to a new stable state. Such a transition can have long-term dire consequences, and therefore it is of critical importance to understand why and when these transitions occur. Under the ongoing climate change, these critical transitions are projected to increase. However, little is understood about how relative timescales of the rate of environmental change and variability affect the occurrence and detectability of these critical transitions in nature and society. The aim of this thesis is to provide some insights in how differences in these timescales may affect critical transitions.
This thesis starts with the analysis of bistability of marine anoxic events in the Mediterranean Sea. Reconstructed time series have been used to detect changes in resilience indicators prior to several abrupt shifts in the past climate. This is, however, only possible under a limited set of conditions. For the past marine anoxic events in the Mediterranean Sea, these conditions are met. Recent technological advances made it possible to construct high-resolution and (almost) evenly spaced time series of past widespread anoxic events in the Mediterranean Sea. In Chapter 2, we analysed whether past transitions in the Mediterranean Sea could have been predicted using the resilience indicators autocorrelation and variance. We show that the repeated shifts into marine anoxia in the Mediterranean Sea had the character of critical transitions, because there was a gradual increase in the temporal autocorrelation and variance in the deep cores (>1600 meter depth) before the onset of most events. Our results imply that future widespread anoxia in marine systems might be recognizable using an appropriate statistical approach and high-resolution records.
These shifts to an anoxic state occurred relatively fast, but not all shifts to an alternative stable state unfold rapidly. Slowly responding systems show a gradual shift to the alternative state, once the tipping point has been passed. As the rate of the current environmental change is unprecedented, more system respond relatively slow to changes in the environment. The current resilience indicators are based on the theoretical finding that the system slows down close to the tipping point. But in these relatively slow systems, the recovery rates are always slow. Therefore, it is the question whether these resilience indicators flag that a relatively slow system is approaching a tipping point. In Chapter 3, we show that it is more difficult to quantify the resilience of a system that responds relatively slow. These results indicate that as the rates of environmental change keep increasing, it become more and more difficult to detect whether systems are approaching a tipping point.
Another risk under current rates of environmental change is that the rate of change in the conditions triggers a shift to an alternative stable state, whereas a change of the same magnitude but at slower rates would not. Only few studies describe this so-called ‘rate-tipping’ in ecological systems, but understanding rate-tipping is needed to understand and predict ecosystem response to the ongoing rapid environmental change. Therefore, we show in Chapter 4 that there can be rate-induced tipping for a range of initial conditions in a model of cyanobacteria with realistic parameter settings. A pulse in the environmental conditions, for example as a result of an extreme event, can cause a temporary collapse, depending on both the rate and the duration of the pulse. In addition, we showed that the type of environmental variability can influence the probability of inducing rate-tipping. These results imply that we need to incorporate critical rates of change in our ecosystems assessment and management.
In addition to affecting the probability of inducing rate-tipping, environmental variability itself can bring a system past a tipping point. The variability is different in different parts of the climate, but because of climate change, the climatic variability is changing systematically in different parts of the world. Therefore, we analysed in Chapter 5 what the effect of changes in the memory of the climatic variability is on the chance of undergoing a critical transition. We show that chances of invoking such critical transitions are strongly affected by the climate memory as measured for instance by temporal autocorrelation in climatic variables. We illustrate the implications of this prediction with evidence from forests, corals reefs, poverty traps, violent conflict and ice-sheet instability. In all of these examples, the duration of anomalous dry or warm events increases the chance of invoking a critical transition. Our results imply that understanding the effects of altered climate variability requires research on climate memory.
In the Afterthoughts I conclude that fast rates of environmental change and changes in environmental variability can affect the detectability and predictability of critical transitions. While the exact impacts of climate change are likely system-specific, interacting timescales make it difficult to untangle system dynamics from external forcing. The relations I describe throughout this thesis, however, are relative. This means that it is impossible to make general rules about whether resilience indicators can be observed, or if the conditions can be restored. Therefore, we should not give up on a priori detecting and reversing critical transitions driven by climate change.
|Qualification||Doctor of Philosophy|
|Award date||10 Jan 2020|
|Place of Publication||Wageningen|
|Publication status||Published - 2020|