Next-Generation Marine Ecosystem Indicators: Machine Learning for Smarter Marine Spatial Planning in a Changing Climate

Next-Generation Marine Ecosystem Indicators: Machine Learning for Smarter Marine Spatial Planning in a Changing Climate

Project Description

Supervisors

Dr Neda Trifonova, School of Environmental Sciences, University of East Anglia from January 2026

Professor Carol Robinson, School of Environmental Sciences

Dr David Righton, Centre for Environment, Fisheries and Aquaculture Science (Cefas)

Dr Oliver Hogg, Centre for Environment, Fisheries and Aquaculture Science (Cefas)

 

Scientific Background

The UK marine environment is a complex, high-demand space. It provides vital services, including food provision through fisheries and aquaculture, and energy security via offshore wind generation. Balancing these uses while preserving biodiversity and protecting marine ecosystem health is increasingly challenging. As human pressures and climate change accelerate, we urgently need smart, evidence-based tools to plan, manage, and protect our marine ecosystems.

At the forefront of this innovation is machine learning. Its ability to process complex, multidimensional datasets is transforming marine ecology and redefining how we detect and respond to ecosystem change.

This PhD will place you at the forefront of this emerging field. You will address a key challenge: assessing what makes a reliable measurable indicator of change in marine ecosystems (e.g., shift in species populations) and interpreting what these indicators reveal about ecosystem health.

(Image produced by Ella Benninghaus, PhD candidate at the University of Aberdeen)

 

Research Methodology

You will develop and apply cutting-edge machine-learning techniques to identify the most informative indicators of ecosystem change and use them to build dynamic Bayesian network (DBN) ecosystem models. The project’s key objectives are to: 1) Identify critical indicators relating to ecosystem health and resilience; 2) Incorporate indicators into DBN models to simulate how ecosystems respond to future climate and human use scenarios; 3) Investigate the merits and drawbacks of DBN models in different geographic locations and over varying timescales; 4) Translate DBN model outputs into policy-relevant insights supporting the design of effective Marine Protected Areas (MPAs) and informed Marine Spatial Planning.

 

Training

Based at the University of East Anglia, you will gain expertise in marine ecosystem modelling using both field-based and modelled physical and biological data. Collaboration with Centre for the Environment, Fisheries and Aquaculture Science will provide exposure to how science informs UK policy on marine biodiversity and MPAs design. You will gain sea-going field experience and be trained in a range of state-of-the-art instruments aboard the RV CEFAS Endeavour.

 

Person Specification

We seek an enthusiastic individual who is interested in marine ecology, computing, with some prior experience in programming and data handling, eager to communicate findings to wider stakeholders and help shape the UK’s marine conservation strategies and the sustainable use of the marine environment.

Acceptable first degree subjects: Suitable for someone passionate about environmental research and sustainable use of the marine environment with an aptitude for numerical descriptions of ecological processes and qualification(s) in environmental, marine or mathematical sciences (or similar)

Project code: TRIFONOVA_UEA_ARIES26

References

  • Trifonova, N., Scott, B., De Dominicis, M. and Wolf, J., 2022. Use of our future seas: relevance of spatial and temporal scale for physical and biological indicators. Frontiers in Marine Science, 8, p.769680.
  • Trifonova, N.I., Scott, B.E., De Dominicis, M., Waggitt, J.J. and Wolf, J., 2021. Bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas. Ecological Indicators, 129, p.107997.
  • Hogg, O.T., Kerr, M., Fronkova, L., Martinez, R., Procter, W., Readdy, L. and Darby, C., 2024. Assessing efficacy in MPA design decisions using a bespoke and interactive fisheries management tool. Biological Conservation, 300, p.110848.
  • Hogg, O.T., Huvenne, V.A.I., Griffiths, H.J., Linse, K. (2018) On the ecological relevance of landscape mapping and its application in the spatial planning of very large marine protected areas. Science of The Total Environment, 626: 384-398.
  • O’Leary, B.C., Copping, J.P., Mukherjee, N., Dorning, S.L., Stewart, B.D., McKinley, E., Addison, P.F.E., Williams, C., Carpenter, G., Righton, D., Yates, K.L. (2021) The nature and extent of evidence on methodologies for monitoring and evaluating marine spatial management measures in the UK and similar coastal waters: a systematic map. Environ Evidence, 10, 13.

Key Information

  • This studentship has been shortlisted for funding under the UKRI NERC DLA funding scheme and will commence on 1 October 2026. The closing date for applications is 23:59 on 7 January 2026.
  • Successful candidates who meet UKRI’s eligibility criteria will be awarded a fully-funded studentship, which covers fees, maintenance stipend (£20,780 p.a. for 2025/26) and a research training and support grant (RTSG). A limited number of studentships are available for international applicants, with the difference between 'home' and 'international' fees being waived by the registering university. Please note, however, that ARIES funding does not cover additional costs associated with relocation to, and living in, the UK, such as visa costs or the health surcharge.
  • ARIES postgraduate researchers (PGRs) benefit from bespoke training and ARIES provides £2,500 to every student for access to external training, travel and conferences, on top of all Research Costs associated with the project. Excellent applicants from quantitative disciplines with limited experience in environmental sciences may be considered for an additional 3-month stipend to take advanced-level courses. Excellent applicants from quantitative disciplines with limited experience in environmental sciences may be considered for an additional 3-month stipend to take advanced-level courses.
  • ARIES is committed to equality, diversity, widening participation and inclusion in all areas of its operation. We encourage enquiries and applications from all sections of the community regardless of gender, ethnicity, disability, age, sexual orientation and transgender status. Academic qualifications are considered alongside non-academic experience, and our recruitment process considers potential with the same weighting as past experience.
  • All ARIES studentships may be undertaken on a part-time or full-time basis. International applicants should check whether there are any conditions of visa or immigration permission that preclude part-time study. All advertised project proposals have been developed with consideration of a safe, inclusive and appropriate research and fieldwork environment with respect to protected characteristics. If you have any concerns, please contact us.
  • For further information, please contact the supervisor. To apply for this Studentship, follow the instructions at the bottom of the page or click the 'apply now' link.
  • ARIES is required by our funders to collect Equality and Diversity Information from all of our applicants. The information you provide will be used solely for monitoring and statistical purposes; it will remain confidential and will be stored on the UEA SharePoint server. Data will not be shared with those involved in making decisions on the award of Studentships and will have no influence on the success of your application. It will only be shared outside of this group in an anonymised and aggregated form. You will be asked to complete the form by the University to which you apply.
  • ARIES studentships are subject to UKRI terms and conditions. Postgraduate Researchers are expected to live within reasonable distance of their host organisation for the duration of their studentship. Please see https://www.ukri.org/publications/terms-and-conditions-for-training-funding/ for more information.

Apply Now

Apply now via the  University of East Anglia Application Portal