Primary Supervisor - Dr James Gilroy
Secondary Supervisor - Prof Andrew Lovett
Supervisory Team - Prof David Edwards (University of Sheffield)
Reforestation is a prominent climate mitigation strategy, exemplified by the Bonn Challenge to restore 350 million hectares of degraded land. Forest regrowth can deliver significant carbon draw-down while offering co-benefits for biodiversity, but also risks negative consequences if the wrong trees are grown in the wrong places. This studentship will quantify spatial variation in carbon and biodiversity co-benefits and trade-offs of reforestation on a global scale, to identify the most effective regions and mechanisms for future reforestation initiatives. It will address two core objectives:
Objective1: Determine the drivers of variation in carbon and biodiversity outcomes of forest regrowth
You will use a combination of meta-analysis and geospatial modelling to analyse the co-benefits of reforestation and how these differ between regions. You will conduct the first global meta-analysis of the impacts of recent forest growth on net emissions and biodiversity, using meta-regression techniques to model how co-benefits vary along environmental gradients (e.g. climate/soil conditions), among forest types (plantations versus natural regrowth), and across degradation levels.
Objective2: Quantify the benefits of recent pan-tropical reforestation for carbon sequestration and reduced extinction risk
Using recently-developed pantropical datasets of forest change, you will develop geospatial models to estimate and map how local gains in tropical forest cover have impacted net emissions and biodiversity. Combining spatially-explicit estimates of greenhouse gas flux with models of habitat suitability for wild species (using tropical birds as an indicator group), you will quantify the co-benefits arising from different mechanisms of reforestation and examine how these vary across tropical biomes, pinpointing situations where win-win outcomes are maximised.
Training:
The supervisory team will provide one-to-one training in highly transferable skills including advanced data analysis, machine-learning and GIS in R. You will be trained in the theoretical and practical aspects of research, including study design and hypothesis testing, data visualisation, and scientific writing.
Person specification:
You will have a core interest in applying scientific knowledge to address major global challenges, with a background in climate science, ecology, and/or spatial analysis. You will be keen to develop skills in data science and GIS, and work with interdisciplinary research teams to solve global problems.
For more information on the supervisor for this project, please visit the UEA website www.uea.ac.uk
The start date is 1 October 2022
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