Coventry University Featured PhD Programmes
University of Sheffield Featured PhD Programmes
Engineering and Physical Sciences Research Council Featured PhD Programmes
FindA University Ltd Featured PhD Programmes
University of Southampton Featured PhD Programmes

Sustainable Marine Ecosystems and Offshore Energy: A Bayesian Modelling Approach

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  • Full or part time
    Dr B E Scott
    Dr Neda Trifonova
    Dr John Hartley
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

To avoid catastrophic climate change the world needs to switch to renewable energy. This will require the placement and operation of very large offshore windfarms (fixed and floating) as well as tidal stream, lagoons and wave energy developments. Concurrently there will be major decommissioning of the offshore fossil fuel industry. To do all of this relatively rapidly we need to be able to assess, with confidence, how to best to proceed while ensuring the conservation of our marine wildlife and ecosystems. The aim of this PhD project is to develop a Bayesian approach to Cumulative Effects Assessment (CEA) for marine systems.

The subject of CEA is challenging across industries and governments with industrial assessments currently done on a project by project basis while governments need to collaborate and plan at very large (whole North Sea) strategic levels. CEA is an active area of research, with multiple UK and international studies/initiatives, such as the SEANSE programme. However, CEA is complex and significant challenges remain in terms of developing assessment methods and the understanding of ecological effects. In particular what is needed is a comprehensive, more transparent modelling approach that can simultaneously assess how different actives such as the combined introduction of large scale windfarms, the displacement of fishing industries and climate change may effect whole ecosystems.

This interdisciplinary PhD would provide the student with a unique opportunity to interact with UK strategic environmental assessment (SEA) leaders, offshore energy industries and a wide range of academic and policy scientists in the field of marine ecology and planning. They will join a team at University of Aberdeen (UoA) in producing a modelling framework to assess the carrying capacity for energy extraction while protecting the sustainably of marine ecosystems. The Bayesian modelling framework will investigate how complex or simple the models need to be to give higher levels of certainty in predictions of ecosystem outcomes.

Rationale: The overall aim of the project is to investigate the potential for use of Bayesian approaches to CEA and linked models which can be expected to have applications to strategic assessment, spatial planning and consenting of offshore energy activities.

Outline: The PhD student needs to be someone that is eager to gain experience working across science disciplines, industry and government issues. They need to enjoy creating and exploring computer models while also understanding the ecological issues that underpin the models. Numerate ecologists, ecologically minded computing, statistical, bio and physical oceanographic students, are all welcome to apply. The project would begin with an extensive exploration and review of literature and examples of CEA in practice. The project will build on predicted ecosystem effects of very large scale wind and tidal renewables (Cazenave et al., 2016, Schuchert et al., 2018) that include effects of climate change (De Dominicis et al., 2018). The PhD student will join the research team at UoA that is part of the new Offshore Renewable Energy (ORE) Supergen Hub and is using Bayesian modelling techniques to predict effects for mobile species such as fish, seabirds, marine mammals as well as fishing fleets (Sadykova et al., 2017, Kafas 2018, Sadykova et al., in review).

Research Training:
- Bayesian ecosystem modelling and statistical analysis of outcomes of models
- R, C++, MATLAB
- Communication to industry, scientific, policy, and general audiences

Outcomes and Impacts: This innovative project will provide a Bayesian modelling framework for assessing cumulative effects of very large scale energy system deployments on marine ecosystems. The project will support ecosystem-based management, feeding into marine monitoring and assessment, supporting regulator decision-making, and regional marine plans in UK waters.

Funding Notes

Funded both by University of Aberdeen and Hartley Anderson (on behalf of the BEIS offshore energy SEA programme) each providing 50% funding. The studentship will cover stipend for the student, university fees, plus a research training support grant (RTSG) of £1000 per year with Hartley Anderson providing an additional £3,500 of RTSG.

-Please state the name of the lead supervisor on your application
-Please state the project title on your application
-Apply via our Postgraduate Application Portal at


Cazenave, P.W., Torres, R., Allen, J.I. (2016) Unstructured grid modelling of offshore wind farm impacts on seasonally stratified shelf seas. Progress in Oceanography 145:25–41

De Dominicis, M., Wolf, J., & O’Hara Murray, R. B. (2018) Comparative effects of climate change and tidal stream energy extraction in a shelf sea. Journal of Geophysical Research: Oceans, 123:5041–5067.

Kafas, A. (2018) Space competition between marine resource users. PhD Thesis, University of Aberdeen.

Sadykova, D., Scott, B. E., De Dominicis, M., Wakelin, S. L., Sadykov, A., & Wolf, J. (2017). Bayesian joint models with INLA exploring marine mobile predator–prey and competitor species habitat overlap. Ecology and Evolution, 7(14), 5212–5226.

Sadykova, D., Scott, B. E., De Dominicis, M., Wakelin, S. L., Sadykov, A., & Wolf, J. (in review) Ecological costs of
climate change on marine predator-prey population distributions by 2050. Ecology and Evolution.

Schuchert, P., Kregting, L., Pritchard, D., Savidge, G., & Elsäßer, B. (2018). Using coupled hydrodynamic biogeochemical models to predict the effects of tidal turbine arrays on phytoplankton dynamics. Journal of Marine Science and Engineering, 6(2).

FindAPhD. Copyright 2005-2019
All rights reserved.