Operationalising ecological resilience

Given the increasing pressures and perturbations on ecosystem due to climatic variability, there has been a developing interest in determining the resilience of ecosystems, particularly given the potential of abrupt and possibly irreversible shifts between alternative ecosystem states. There are numerous conceptual definitions of resilience in environmental systems and, even when resilience is clearly described for a particular ecosystem, it can challenging to quantify a priori to a state change in the ecosystem of interest. Ecosystems that approach transition exhibit generic changes in dynamical behaviour that can be used to signal the approach of a critical transition. When an ecosystem approaches a critical transition, its dynamics “slowdown”, and start exhibiting properties associated to the process critical slowing down (CSD), such as increased recovery time, variance, and autocorrelation. Here we develop a set of analytical methods based on measures of variance and autocorrelation to determine the current state of ecosystem and their likelihood to be at CSD, so to demonstrate how to operationalise what to date has been developed as a theoretical framework. We use wavelets as a measure of identifying changes in the variance term, and autocorrelation was modelled using an Bayesian dynamic linear model. We illustrate this on three case studies; i) on an experimental soil system subjected to dry wet cycles; ii) on an intensely managed ecological system in the Everglades and iii) on extensively managed grassland systems in the UK. We illustrate that although any of the resilience characteristics can be used to define resilience, the identified properties of a resilient response must be described for different contexts.