PLEASE NOTE: Application deadline date 08 Jan 2024. Applications are no longer being accepted for this project
Project Overview
Climate change may lead to tipping points where ecosystems shift to alternative states (i.e. grassland to desert). New theory suggests that tipping points may not be uniform but display distinctive spatial patterns. In this project, you will test if such patterns occur for vegetation globally, using new spatio-temporal global datasets.
Project Description
Tipping points – critical thresholds at which a small additional change in a variable such as temperature leads to a large change across a system (i.e. a shift from from a grassland to shrubland or glacier to no glacier) – are of keen scientific interest given their potential impacts on society and ecosystems under environmental change. However, new theory suggests that ecosystems may shift less abruptly than thought as they are able to re-organize spatially leading to distinctive stable patterns in large envelopes of parameter space (Rietkerk et al. 2021). The patterns are the result of extensive, large scale negative feedbacks to a driver (e.g., biomass response to temperature) coupled to local positive feedbacks (like microclimates, soil percolation). In this PhD, you will use global multi-year satellite derived vegetation monitoring datasets to undertake the first large-scale tests to see if such distinctive spatial patterns exist in different ecoregions or biomes. The theoretical prediction is that if an ecosystem is gradually shifting to a new state we would expect the patterning to change in trend as a series of steps over time. We anticipate that looking at changes in phenology (timings of seasonal vegetation change) may be particularly useful, as these are less affected by non-climate disturbances (i.e. logging) than other types of vegetation data. The work is highly novel and is at the cutting edge of applied climate science and will involve using advanced remote sensing and data analytics techniques.
The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. The student will be registered at the University of Southampton and hosted at the School of Geography and Environmental Sciences (SOGES) at the University of Southampton, which is ranked joint 3rd in the UK for its research.
Specific training will include the use of advanced techniques to process time series of remote sensing data and to analyse and process very large spatial datasets in environments such as R, Python and Google Earth Engine. This advanced quantitative training will be greatly facilitated by large pool of postgraduate students and researchers working in related fields and methods in SOGES,. The student will also be trained in academic publishing and be encouraged to present at large international conferences.
Rietkerk et al. (2021) Science: https://www.science.org/doi/10.1126/science.abj0359
Lenton et al. (2008) PNAS: https://www.pnas.org/doi/10.1073/pnas.0705414105#:~:text=Abstract,may%20....
