Critical transitions are sudden, often irreversible, changes that can occur in a large variety of complex systems. Signals that warn of critical transitions are highly desirable, but their construction can be impeded by limited availability of data. We propose a method that can significantly reduce the amount of time series data required for a robust early warning signal by using other information about the system. This information is integrated through the framework of a generalized model. We demonstrate the applicability of the proposed approach through several examples, including a previously published fisheries model.
|Translated title of the contribution||Early warning signals for critical transitions: A generalized modelling approach|
|Number of pages||6|
|Journal||PLoS Computational Biology|
|Publication status||Published - 2012|