Impacts of environmental change on the structure and function of macroecological communities

The mission to understand and predict the effects of climate and land-use change on ecological communities has most often focused on theoretical models or experimental approaches with potentially limited relevance to complex multi-species systems operating at larger spatial and temporal scales. Attempts to generalise from underlying theory and experiments to broad-scale predictions of biodiversity and associated processes under climate change have proved challenging, largely because mechanistic models struggle to capture the complexity of whole ecosystems.

This studentship aims to take a broader macroecological perspective by combining recently compiled global bioinformatics datasets with newly developed models designed to understand current and future responses of biodiversity to environmental change accounting for evolutionary history, species traits and biotic interactions. The student will have access to existing future GIS-based range projections under climate change developed by Prof Willis and colleagues, and the PREDICTS database of land-use change impacts on global biodiversity. The project will focus on birds because they are ecologically important and relatively well known in terms of geographic distribution, phylogeny, and functional traits, as well as being sufficiently diverse (11,000 species) to provide deeper insight into complex multi-dimensional processes at global scales.

The main research goals are as follows:
1) Combining and refining estimates of spatial occurrence, population density and ecology for all birds to quantify the current condition and resilience of ecosystems in terms of key ecological processes, including seed dispersal, pollination and pest control.
2) Integrating functional trait and phylogenetic datasets with land-use change and climate change scenarios to understand the likely implications of future biodiversity responses (population declines, range shifts and contractions) for the stability of ecosystems and the provision of ecosystem services.
3) Capitalising on recent advances in data availability and computational power to develop biologically realistic forecasting models with applications to environmental policy.