Britain, in common with many other parts of the globe, faces the challenge of managing its land for multiple ends in the face of a rising population and likely climate warming. Thus new science is needed to help search for socially, economically and ecologically optimal solutions to the way land is managed.
Models are a potentially useful way of tackling the problem hence we seek an enthusiastic student who will combine and develop new and existing models to help decision makers address real-world problems; from farm to catchment to region to nation. New approaches are also required that bring together modelled projections of different ecosystem services so that trade-offs in ecosystem services and impacts on biodiversity can be quantified and mapped.
This is especially important given the amounts of money being spent on management interventions in agricultural land across Britain and Europe.
You will have the chance to work with new high quality field datasets as well as new earth observation data and combine these using state-of-the-art ecological models. You will also contribute to the further development of a new high resolution landscape and ecosystem services modelling tool developed by Wellington University and CEH Bangor (http://www.lucitools.org/) and take part in ecological field surveys of soil, plants and habitats as part of your training programme.
The opportunities for research are highly novel simply because the datasets are so new and have yet to be explored as inputs to ecological models. Moreover, the proposed research will come to fruition in the lead up to reporting for the global Aichi 2020 targets. Since these focus on good stewardship of biodiversity and ecosystem services, model-based solutions will be in greater demand than ever. The final years of the PhD will therefore provide an unrivalled opportunity to publicise your work and to achieve lasting impact.
Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in Ecology, Geography or related discipline. Candidates with previous experience of statistical and/or spatial modelling are particularly encouraged to apply.