Many nations are developing offshore wind farms as part of the drive towards net zero carbon emissions. Installation and operation of turbines can affect marine fauna, and quantifying the potential risk through environmental impact assessments is an essential part of the permitting process. Risk assessment can also help to guide decisions about where and when to undertake installations, and where further research investment may be warranted to reduce uncertainties. In this PhD project, the student will work with their supervisors and as part of a large research team to compare different approaches to risk assessment (qualitative, semi-quantitative and fully quantitative) and undertake case studies on avian and marine mammal examples.
Potential research questions:
· What is the optimal risk assessment framework in different contexts (e.g., species, region)?
· How do we quantify different risks and what heuristics are robust in data-poor contexts?
· How do we assess aggregate risk from multiple installations?
· How should uncertainty be quantified and propagated in risk assessments? How should research efforts be targeted to reduce uncertainty?
The PhD student will drive the emphasis and structure of their thesis, but potential topics could include:
· Comparing existing approaches to risk assessment in a data-rich case study (e.g., northern gannet, harbour porpoise) to demonstrate the consequences of different model assumptions and identify areas of methodological improvement.
· Developing and testing risk assessment frameworks for contexts with little or no empirical data, e.g., no baseline monitoring. Investigating applicability of transferring risk assessments between different wind technologies, e.g., fixed versus floating wind farms.
· Undertaking sensitivity analysis to identify parameters leading to the greatest uncertainty in assessment endpoints.
· Developing and testing meta-analytical methods to support risk assessments, e.g., a method to estimate a dose-response function, with uncertainty, from multiple studies.
· Using baseline assessments to inform/evidence post-construction mitigation/compensation.
· Software development - further developing bespoke impact assessment software developed within CREEM, or developing new software for presenting assessment outputs.
For a review of risk assessment in the context of wind energy development, see May et al. (2018). For a more general review of approaches see Linkov et al. (2009), and for a review of a quantitative approach we have pursued in a different context, see Pirotta et al. (2018).
The PhD is offered within the School of Mathematics and Statistics; the candidate will be part of an inter-disciplinary research centre, the Centre for Research into Ecological and Environmental Modelling (CREEM, https://www.creem.st-andrews.ac.uk/). The PhD project is part of a multi-institution inter-disciplinary project, WOW (“Wildlife and Offshore Wind”), that is developing and demonstrating methods for evaluating effects of offshore wind development along the East coast of the USA. For more information about the WOW project, see the web page https://offshorewind.env.duke.edu/, which is hosted by the lead institution, Duke University. WOW is funded by the US Department of Energy and Bureau of Ocean Energy Management.
The project is fully funded for 3.5 years via a scholarship from the University of St Andrews covering tuition fees, and from the WOW project covering living expenses at the UKRI stipend rate (level for 2023-24 set at time of writing, but 2022-23 rate was £17,668; rate increases annually). UK and overseas applicants are equally welcome and eligible.
Training and environment:
We will offer training and learning opportunities depending on the student’s background and needs. These might include, but are not limited to the Academy of Postgraduate Training in Statistics (APTS, https://warwick.ac.uk/fac/sci/statistics/apts/) and Scottish Mathematical Sciences Training Centre (SMSTC, https://smstc.ac.uk/). Professional development support is also available through the University’s Centre for Educational Enhancement and Development (CEED https://www.st-andrews.ac.uk/ceed/).
The student will join a welcoming and stimulating work and study environment in CREEM, with a lively programme of seminars, discussion groups, visiting scholars, and get-togethers. As part of CREEM and WOW, the student will be able to strengthen and grow their professional networks across different disciplines both within the University and worldwide. We actively nurture, and expect the student to contribute to, a positive research culture as outlined at the web site https://www.st-andrews.ac.uk/research/environment/culture
Applicant qualifications and experience:
The ideal candidate will be someone with a passion for using quantitative methods to answer pressing ecological issues. They will have formal training in both statistics and ecology, and will possess excellent programming skills (ideally in R).
· A good first degree in mathematics, statistics or another discipline (e.g., biology or computer science) that includes a substantial statistical component.
· Good computing and programming skills (e.g., in R, Python, C++ or Matlab).
· Excellent verbal and written communication skills in English. Those who do not have English as a first language and who have not undertaken an undergraduate or graduate degree taught in English should provide evidence of English proficiency (minimum IELTS overall 6.5 or equivalent).
· Experience programming in R.
· Training in ecology, conservation, population biology, or a related subject.
· Experience of computer simulation, data analysis and meta-analysis.
In addition to the above selection criteria for this project, the following generic criteria are assessed for all applicants to the School of Mathematics and Statistics and will be used used as part of candidate ranking:
· Academic merit (degree level, classification/grade, and relevance). This is a major criterion: successful candidates will typically have a good to very good 1st class undergraduate degree and/or a distinction at MSc level.
· Research potential and engagement (employment history, papers, etc).
· Alignment of research interests with topic applied for; evidence of scientific curiosity.
· Relevant personal and professional development.
More information and how to apply:
For more information, please see this document (pdf file): Postgraduate Opportunities in Statistics (https://stats.wp.st-andrews.ac.uk/files/2022/10/StAndrewsStatisticsPhDOpportunities.pdf). The last page of that document contains specific information about the application process.
The closing date for applications for this PhD opportunity is 6th February 2023.