Marine ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has significant ecological implications for predicting nature’s response to changes in climate and biodiversity. The increasing frequency of weather events like heat waves, the emergence of cold-water anomalies and freshening events, and the emergence of signs of ecosystem “breakdown” (e.g., avian flu, harmful algal blooms) are becoming more common. Accommodating these gradual and stochastic types of variability, comprising environmental (including climate change) and direct human pressures (e.g., development, resource exploitation) will require ecosystem-level approaches to provide objective information for policy and marine spatial planning. Ecosystem modelling encapsulating the complexities of ecological relationships and such variabilities will allow us to drive better and more timely decisions for securing ecosystem sustainability in the face of variability across a range of timeframes, magnitudes, and predictabilities.
This project will develop ecosystem models capable of incorporating extreme events at different spatial and temporal scales, representing the complexities of ecological relationships and variability (environmental and direct human pressures) of key parameters. Underpinning objectives will include to 1) consolidate and improve information on the probability/frequency of extreme events driven by environmental (including climate) or direct human-induced change (e.g., development, exploitation), including the identification of conditions that facilitate certain events to occur; 2) Incorporate variability and extreme events in ecosystem modelling approaches to improve predictions on where, when, and how ecosystem components changes are and/or will be driven by such events; 3) Investigate the merits and drawbacks of ecosystem models of different spatial and temporal scales.
The proposed project will investigate the use of ecosystem modelling approaches, such as dynamic Bayesian networks that will allow the modelling of ecosystem components across spatial scales and over time and their multiplicity of interactions. The application of machine learning methods and other data-oriented predictive approaches will be investigated to incorporate and model variability and extreme events in ecosystem models. Focus on the use of state-of-the-art powerful computational and analytical techniques, yet pragmatic approaches with a whole system perspective based upon Bayesian inference and Bayesian networks with hidden variables will be investigated to improve predictions on how ecosystem components are and/or will be driven by variability and extreme events.
The proposed project will develop ecosystem models that will allow for the inclusion of variability and extreme events to improve understanding both on the processes behind such events but mainly on the ecological consequences and also to enable a deeper understanding of ecosystem “uncertainty” in the face of future variability and events. Advanced ecosystem models will therefore provide greater ecological realisms with a more holistic view of marine ecosystems to inform the development of effective marine spatial planning in a dynamic environment with a multitude of stressors. The proposed project offers the potential to develop information that will enable policy and marine spatial planning to better prepare for and respond to natural and human-induced variability and extreme events.
This PhD will also be linked officially to the Supergen ORE Hub and will benefit from direct interaction with the community of experts in offshore renewables.
Applicants for this studentship must have obtained a First or Upper Second Class UK Honours degree (or international equivalent) with a Masters or about to obtain a Masters degree. The ideal candidate will have some experience in computer programming, with a background in marine ecology, environmental, and/or computer science. Candidates from other fields may be considered if your experience is relevant, but please do state this in your cover letter. A strong background in data handling, statistical modelling, computational and/or analytical techniques would be beneficial.
We encourage applications from all backgrounds and communities, and are committed to having a diverse, inclusive team.
- Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php
- You should apply for Biological Sciences (PhD) to ensure your application is passed to the correct team for processing.
- Please include the name of the supervisor and project title in the respective fields on the application form. If you do not mention the project title and the supervisor on your application, it may not be considered for the studentship.
- Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts (Undergraduate and postgraduate).
- Please note: you DO NOT need to provide a research proposal with this application
- If you require any additional assistance in submitting your application or have any queries about the application process, please don’t hesitate to contact us at [email protected]
This 42 Month, fully funded PhD project includes full funding to cover tuition fees at the home/UK rate (this includes EU nationals that hold UK settled or pre-settled status), full research costs, and a doctoral stipend for living costs (£18,622 pa. For the 23/24 academic year).
The project funding is linked to the ORE Supergen Hub Phase 2 project (part of EPSRC Hubs for Net Zero).
The expected start date is January 2024 or soon thereafter.
• Horne, J.K. and Schneider, D.C., 1994. Analysis of scale-dependent processes with dimensionless ratios. Oikos, pp.201-211.
• Tucker, A. and Duplisea, D., 2012. Bioinformatics tools in predictive ecology: applications to fisheries. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1586), pp.279-290.
• 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.
• 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.