Integrating and predicting responses of natural systems to disturbances

Lead Research Organisation: University of Oxford


Identifying the mechanisms that provide ecological systems with the ability to resist and recover from disturbances, or transition to alternative states, is fundamental to quantify and project biodiversity trends. Resilience, a system's ability to respond to disturbances, has attracted the attention of ecologists since the 1970s. Yet, we still do not understand how resilience emerges, scales across levels of biological organisation, nor how it shapes their structure and dynamics. This knowledge gap is at odds with international policies (e.g., UN Sustainable Development Goals) that deem preserving the resilience of ecological systems key for sustainable development. In this project, we will:

Obj 1. Develop a novel framework to quantify and predict the resilience of natural populations to realistic disturbance regimes and develop new theory to scale responses to disturbances across individuals, populations and communities,

Obj 2. Reveal the biotic and abiotic factors, and their temporal scales, that confer resilience across thousands of taxa, life history strategies, and functional groups worldwide, and

Obj 3. Apply the novel developments from Objs 1 & 2 to experimentally test theoretical community predictions based on individual and population-level responses to disturbances in a new global network of experimental disturbances in grasslands.

Through this ambitious research programme, novel methods, metrics, and knowledge will emerge to identify the most/least resilient species, functional groups, and regions of the world to different disturbance regimes. Grounded in structured demography, community matrix theory, pseudospectral theory, and coexistence theory, we will apply the framework to simulations, large datasets, long-term records, and field experiments to identify the mechanisms that confer resilience to ecological systems. As such, this project addresses fundamental questions in Biology and will help inform best practices to achieve key UN Sustainable Development Goals, thus Pushing the Frontiers of our understanding in these key areas.


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