Generic early warning signals for catastrophic shifts in natural systems
02 / 2007 - 12 / 2011
Introduction: The usual state of affair in most natural systems is one of fluctuations around gradual trends. Incidentally, however, this pattern can be interrupted by sudden dramatic shifts to a different regime. Such regime shifts may be related to the existence of alternative attractors in the system (Scheffer et al., Scheffer and Carpenter 2003). Examples of systems that appear to have this property include shallow lakes, coastal oceans, rangelands, coral reefs and regional climate systems (Scheffer et al. 2001, Folke et al. 2004). Gradually changing conditions may have seemingly little effect on such systems, but still reduce the size of the basin of attraction around the current state, referred to as ecological resilience (Holling 1973, 1996). Systems with a low ecological resilience are more brittle in the sense that a minor perturbation may cause a drastic shift to a contrasting state. It would be very interesting to have ways of estimating ecological resilience, as this could provide early warning signals for increased risk of catastrophic shifts and allow adaptive management aimed at to maximizing resilience (Scheffer et al. 2001, Peterson 2002). Aim: Our central objective is to develop generic indicators of ecological resilience, i.e. the size of the basin of attraction around equilibria in complex systems, and evaluate their practical applicability. Among others we will address research questions such as: 1. What are the clearest and most generic indicators for predicting ecological resilience in stochastically forced simple models that represent homogeneous, completely mixed systems? 2. What is the effect of spatial complexity (heterogeneity and limited spatial exchange) on the predictive power of such indicators? 3. How well do the indicators perform in realistic models of natural systems? 4. Can the indicators be used to signal reduced resilience before catastrophic shifts appeared in time series from real systems? Research: The major part of the approach in our project is to analyze and develop mathematical models of various complexity. By analyzing both simple and complex models we attempt to get the best of both worlds (Van Nes and Scheffer 2005b). Simple models are easy to understand, while complex models are considered to be more realistic. We use simple models to develop theory and to generate hypotheses. These hypotheses are tested in models with increasing complexity and realism. In addition we will analyze time series from natural systems that appear to have gone through a regime shift, and from especially designed laboratory experiments that we will deliberately push through a regime shift.