The multidemsionality of ecological stability

Abstract

Ecological stability is a multifaceted concept, incorporating components such as variability, resistance, resilience, persistence, and robustness. Understanding and predicting the relationships among these many components of stability is fundamental to the optimal management of both biodiversity and ecosystem functioning. In spite of this, the multidimensionality of ecological stability remains remarkably understudied, with most research focussing on one or two components in isolation. We know worryingly little about the mechanisms underpinning relationships among components of stability and whether there are any general features of these that are common across different types of ecosystem or disturbance. In this thesis, I explore the effect of perturbations on both the relationships between stability components and the predictability of these components. In Chapter 2, I examine the effects of perturbation intensity using theoretical simulations. By analysing the dynamics of food-webs following perturbations of different strengths, I discover that the predictability of stability components and the strength of relationships between them decrease with increasing perturbation strength. Importantly, this decoupling effect of strong perturbations was consistent across a variety of food-web structures. In Chapter 3, I test these predictions in natural communities using data from the Nutrient Network - a globally distributed grassland experimental system - to examine whether perturbations decouple relationships between components of stability at the global scale. Consistent with theory, I found weaker relationships between stability components in perturbed treatments compared to the unperturbed controls. Natural systems encounter a large variety of perturbations that vary in their spatial extents, durations, frequencies, and intensities. While most models and experiments predicting ecological responses have typically applied static steady- state approaches that focus on a single perturbation event or the mean level of environmental change, few have incorporated environmental stochasticity. Moreover, those that do tend to incorporate it as white noise. In Chapter 4, using food- web modeling I explore how the response and predictability of different stability components are regulated by key characteristics of environmental stochasticity, including its temporal autocorrelation (colour), and the strength of correlations in the responses of species to it. I found that different stability components showed distinct responding patterns to changing temporal autocorrelation of environmental noise. Increasing environmental autocorrelation stabilize communities in some dimensions yet simultaneously destabilize them in others. In contrast, the predictability of stability decreases consistently as the temporal autocorrelation of environmental noise becomes increasingly positive. This finding demonstrates the fundamental role played by environmental stochasticity in determining the dynamics and stability of ecosystems and challenges the credibility of models that overlook it or simply incorporate it as white noise. Taken together, results of the research described in this thesis highlight important difference between different components of ecological stability in their response to external perturbations and environmental stochasticity, and emphasize the necessity of exploring further the multifaceted nature of ecological stability.