Mechanisms and prediction of large-scale ecological responses to environmental change

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Each natural ecosystem and each landscape on earth is unique. Yet, a range of generic biodiversity patterns is consistently observed: regularities in the numbers of co-existing species, their abundances, and their spatial distributions. How do ecosystems respond to wide-ranging stressors such as climate warming, habitat degradation and fragmentation, harvesting of biomass, and the accumulation of environmental pollutants? What do we mean by stability in an ecological context and how does it interact with these stressors? In order to address these pressing questions and be able to predict the responses of biodiversity to climate change and other stressors, it is essential to understand the fundamental processes that structure ecological systems over large spatial scales. Recent breakthroughs in our understanding of spatial biodiversity patterns have been made possible by the development of models that reproduce large-scale ecological phenomena. The major known natural biodiversity patterns consistently observed in real ecological systems have recently been shown to emerge naturally in one such spatial model, the Lotka Volterra Metacommunity Model (LVMCM), which unifies the species-sorting, mass-effect, and patch-dynamics paradigms of metacommunity theory. In this project we build upon this prototype and use it to explore key fundamental and applied questions in macrecology. The LVMCM represents reality at the level of abstraction at which policy formulates environmental targets, decision makers think about environmental impacts, and ecologists ask fundamental questions. By further exploring this modelling approach, we will address all five grand questions raised by NERC in the call text for this grant, which ask how, why and where ecosystems respond to large-scale stressors, and what role ecological (in)stability plays for these responses. The model's simplicity will permit us to develop novel analytic theory for the processes controlling spatial biodiversity patterns and ecosystem responses to stressors on large scales, thus exposing principles that can inform an intuitive understanding of the mechanisms at work and--importantly--the conditions on which these mechanisms depend. Comparing field data with outputs from a process-based model like the LVMCM we will also develop novel ecological indicators and protocols for their computation from empirical datasets, together with guidelines on data requirements and reproducibility, for use by conservation ecologists and policy makers.