skip to content

Cambridge NERC Doctoral Training Partnerships

Graduate Research Opportunities

Lead Supervisor: David Coomes, Plant Sciences

Co-Supervisor: Mark Hancock, Cairngorms Connect & RSPB Centre for Conservation Science

Brief summary: 
Use process-based modelling and high-resolution imagery to help a major restoration projects in the Scottish Highlands understand how forest regeneration and carbon sequestration processes are affected by management decisions
Importance of the area of research concerned: 
Natural habitat restoration offers solutions to two environmental crises: the loss of biodiversity and climate change. For this reason, the UN has declared the 2020s as the decade of restoration. Exciting large-scale restoration projects are underway in the Scottish Highlands, aiming to bring back native forests, healthy peatlands and floodplains by reducing grazing pressure and restructuring forestry plantations. In particular, Cairngorms Connect ( is working to restore over 600 km2 of habitat in one of the most spectacular upland landscapes in Britain. However, the effectiveness of conservation interventions remain poorly understood and the long-term consequences for carbon storage require critical evaluation
Project summary : 
In collaboration with the Cairngorms Connect partnership, the project aims to improve understanding of forest regeneration processes in the Scottish Highlands, using process-based modelling to simulate the expansion of native forest under different scenarios (see Tanentzap 2013). Models will be constructed (parameterised) using data on the recruitment, growth and mortality of trees collected in the field and high-resolution map of habitats and tree locations from drone and satellite imagery. The models will make predictions about landscape-level native woodland restoration over the coming century under different browsing and climate change scenarios. By evaluating soil and biomass carbon stock changes, the climate benefits of forest recovery will be assessed. It is hoped that this research will feed directly into the management of the project area.
What will the student do?: 
a) Parameterise forest dynamics model: Collect data on growth, mortality, dispersal and recruitment processes. Data from long-term experimental plots (e.g. Hancock et al. 2005, 2009) and new field surveys will be used for parameterisation. b) Map tree regeneration and vegetation types: use drone surveying techniques developed in the Coomes group to identify tree species and habitat types; these methods using spectral and textural properties to classify vegetation. c) Simulate future woodland recovery across the landscape under alternative scenarios (e.g. management decisions that alter patterns of deer browsing pressure), using the model and the baseline maps from (a) and (b) (d) Test model performance: Fixed-point photos are available, some dating back to 1950s. Patches of trees will be mapped using these images, and these will be used as initial conditions for simulations running to the present day, which can be compared with field data. (e) Carbon sequestration: Drone and satellite imagery will be used to quantify forest carbon and soil sampling to quantify belowground carbon, allowing the climate benefits of restoration to be evaluated
References - references should provide further reading about the project: 
Tanentzap, A.J., Zou, J, & Coomes, D.A. 2013. Getting the Biggest Birch for the Bang: Restoring and Expanding Upland Birchwoods in the Scottish Highlands by Managing Red Deer. Ecology and Evolution 3 (7): 1890–1901.
Hancock, M.H. et al. 2005. The Effect of Experimental Prescribed Fire on the Establishment of Scots Pine Seedlings on Heather Moorland.” Forest Ecology and Management vol. 212 pp. 199–213.
Hancock, M.H. et al. 2009. “Testing Prescribed Fire as a Tool to Promote Scots Pine Pinus Sylvestris Regeneration.” European Journal of Forest Research vol. 128 pp 319.
You can find out about applying for this project on the Department of Plant Sciences page.
Department of Plant Sciences Graduate Administrator
David Coomes