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Assessing natural capital in highly variable coastal ecosystems: a novel machine learning, high resolution imaging and field sampling approach


Project Description

We are looking for an enthusiastic and ambitious student to develop an exciting
project that will combine UAV imaging, biogeochemical field data and machine
learning to accurately determine ecosystem function within a highly variable
landscape. The ideal candidate will enjoy interacting with academics and
stakeholders from a range of backgrounds and want to apply their scientific
training to an important applied question.
Understanding salt marsh behaviour is vital if we want to maintain similar
ecosystem services as sea level rises and UK climate changes. Effective future
conservation and management will rely on accurate estimates of current day salt
marsh function. However, accurate assessments are limited by the highly
variable nature of salt marshes, and it is necessary to develop effective tools that
can address this variability, providing more accurate estimates.
We will combine cutting-edge machine learning, drone imaging and field
sampling approaches to address this challenge. We will compare drone images
of regional salt marshes with an established database for UK salt marshes;
including a range of climate, vegetation and sediment information (e.g.-
vegetation coverage, carbon storage, trace gas fluxes, and N/P content).
Machine learning based on UAV imagery and the salt marsh data will allow us to
constrain ecosystem behaviour and suggest sampling locations to best address

remaining uncertainty. UK salt marshes are an ideal test case to develop these
tools due to their known heterogeneity and substantial sampling coverage.
Applications and benefits
The student will receive thorough postgraduate training supported by a
multidisciplinary team of supervisors with strong research backgrounds and
experience in postgraduate supervision. The student will gain ecological research
skills including: empirical field techniques; cutting-edge laboratory equipment
techniques; spatial analysis methods. This study will provide a much needed tool
to address highly variable behaviour within complex ecosystems: it will benefit

the academic ecology/conservation/biogeochemistry communities, policy-
makers and coastal managers.

Funding Notes

Funding: This is a 3.5 year fully-funded studentship part of the NERC Doctoral Training Partnership in Adapting to the Challenges of a Changing Environment (ACCE). The studentship covers: (i) a tax-free stipend at the standard Research Council rate (around £15,000 per year), (ii) tuition fees at UK/EU rate, (iii) research consumables and training necessary for the project.

Entry requirements: At least an upper second class honours degree, or equivalent in any relevant subject that provides the necessary skills, knowledge and experience for the DTP, including environmental, biological, chemical, mathematical, physical and social sciences.

References

Eligibility: The studentships are available to UK and EU students who meet the UK residency requirements. Students from EU countries who do not meet the residency requirements may still be eligible for a fees-only award. Further information about eligibility for Research Council UK funding

Shortlisting: Applicants will be notified if they have been selected for interview in the week commencing on Monday 28 January 2019.

Interviews: Shortlisted applicants will be invited for an interview to take place in the Department of Biology at the University of York in the week beginning 11 February 2019 (or the following week). Prior to the interview candidates will be asked to give a 5 minute presentation on a research project carried out by them.

How good is research at University of York in Biological Sciences?

FTE Category A staff submitted: 44.37

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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