Abstract
Many studies on ecosystems have shown that gradual environmental change can lead to discontinuous, catastrophic shifts between alternative stable ecosystem states with concomitant losses of ecological and economic resources. Because of the non-linear response of these ecosystems on different temporal and spatial scales, there is no predictive theory for catastrophic shifts. However, recent findings provide a new perspective on such theory, in that the occurrence of catastrophes is associated with the emergence of self-organized spatial patterning of communities and their resources. In order to link the concepts of catastrophes and self-organization mechanistically, I will focus on Turing-like symmetry breaking instabilities. These are the classical mechanisms explaining shifts in chemical and physical structures, as well as morphogenesis in biology, but new is their use in explaining shifts in ecosystem structures. Hence, my central hypothesis is that catastrophic shifts in ecosystems can be predicted on the basis of self-organized spatial patterning. A new unifying framework for both catastrophes and self-organization will be developed and tested in arid and peatland ecosystems. These ecosystems are known to be vulnerable to catastrophic shifts, and they exhibit spatial self-organization of vegetation. Spatially explicit models will be developed using a combination of a reaction-diffusion and cellular automata approach. The models will be parameterized and validated using existing data and new data from field measurements. The models will be made quantitatively testable with enough spatial and temporal resolution to separate endogeneous and exogeneous influences on pattern formation. In order to formulate a predictive ecosystem theory, I will investigate whether the identified principles may also explain self-organization and catastrophic shifts in other ecosystems. By this I hope to contribute to a better scientific understanding needed for the development of innovative strategies for sustainable management of ecosystems that are vulnerable to catastrophic shifts.
Netherlands