How fragmented are the world’s savannas?
Supervisory team: Casey Ryan1, Edward Mitchard1, Kate Parr2 and Mahesh Sankaran3
Contact: [Email Address Removed], https://blogs.ed.ac.uk/land-system-science/
1 School of GeoSciences, University of Edinburgh; 2University of Liverpool; 3 School of Biology, University of Leeds.
Fragmentation is known to be widespread in the world’s tropical forests, with negative impacts on biodiversity and resilience. However, the situation in savannas is poorly known – there are no maps of how intact or fragmented savannas are, and as a result it is impossible to target conservation work appropriately. It is thought that savanna biodiversity might be particularly sensitive to fragmentation, as many savanna animals need large home ranges1. It is also likely that fragmentation disrupts the provision of many ecosystem services that are critical to the livelihoods of 100s of millions of people who live in or near savannas2.
This project will address these major knowledge gap by quantifying the intactness, connectivity and fragmentation of the world’s savannas. This is both a conceptual and practical challenge: conceptually it is hard to define fragmentation in a naturally patchy and sparsely wooded landscape; practically it is difficult to distinguish open savannas from small scale agricultural landscapes using common remote sensing techniques.
This work builds on recent developments in mapping savanna structure3 made possible by a new generation of radar satellites and combines remote sensing with fieldwork at long term sites. You will be closely linked to the SECO project (a new NERC Large Grant) in which a large team is addressing complementary research questions. The work will also be a collaboration with Yamaguchi University and the Japanese space agency (JAXA), to ensure access to the latest satellite radar data and products.
1. How much of the savanna biome is fragmented, and to what extent?
2. What opportunities are there for increasing connectivity in savannas, and where is most threated by ongoing fragmentation?
This project will work at a variety of scales, starting with a case study in Mozambique where the student can understand the reality of fragmentation in savanna landscapes and collect ground truth data for upscaling. This will make use of long term socio-ecological studies of three landscapes in Mozambique [ref1] and will use a variety of methods including field ecology and drones to map fragmentation at landscape scale. At larger scales, you will use a suite of tools created by the University of Edinburgh LANDteam to map the structure of savannas using data from radar satellites (ALOS and Sentinel 1), with high resolution optical and field data as a reference. It is likely that this work will start with a regional focus (e.g. ref 3) and move to global mapping once the methods have been developed and evaluated.
The student will gain state of the art skills much in demand across the environmental sector, including remote sensing, image analysis, spatial modelling, and ecological fieldwork. You will join a research group that uses R, Python and Google Earth Engine for most of its analysis, and will work closely with post docs and other PhD students conducing similar work. Full training will be provided in all the required methods.
The successful student will need to have enthusiasm for learning new methods and be comfortable with quantitative analysis. As such this project would suit a student with a background in most natural sciences, and is also suitable for people with a physics, informatics or maths background, as long as you have an interest in ecosystem science. Fieldwork duration is flexible but is likely to involve 1-2 months away.
Part of the Centre for Satellite Data in Environmental Science
This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org
This 3 year 9 month long NERC SENSE CDT award will provide tuition fees (£4,500 for 2021/22), tax-free stipend at the UK research council rate (£15,609 for 2021/22), and a research training and support grant to support national and international conference travel. http://www.eo-cdt.org/apply-now