his fully-funded studentship will remain open only until filled, and so we recommend applying immediately.
The Firth of Clyde has notoriously been referred to as an ‘ecological desert’ having been intensively overfished (Thurstan and Roberts, 2010). Circumstantially, it appears that intensive fishing may have driven the community to undergo the change (Heath and Speirs, 2012). However, given that targeted fishing for white-fish has all but ceased in the area, why has the community not recovered over an almost 20 year period? One hypothesis for the lack of recovery of the Clyde demersal fish community is that by-catch of demersal fish in the trawl fishery for prawns is sufficient to maintain a high, but hidden, mortality rate on the stocks despite best efforts to reduce by-catch by the industry.
The purpose of this project is to conduct a comprehensive extraction and synthesis of all the relevant data for demersal fish species in the Firth of Clyde. Then, combined with data from the archive of scientific trawl surveys, develop stock assessment models for the main fish species that will enable us to determine the role fishing in the dynamics of the fish community, and better define what remediation strategies are necessary to restore a healthy stock status in the Clyde.
Marine Scotland Science has maintained an observer programme in the Firth of Clyde since at least 1982, sampling landings and discards of demersal fish from both demersal otter trawlers and prawn trawlers. The student would need to spend some time at MSS in Aberdeen to compile data from the observer and market sampling databases. Thereafter, the time would be spent at Strathclyde, with regular trips to Aberdeen, working on the data and developing the stock assessments.
The stock assessment methodology will build on recent publications describing new methods for data-sparse stocks, and length-based methods (Cook and Heath, 2018). These methodologies are set in a Bayesian parameter estimation framework so as to provide robust credible intervals around the results.
The data handling and statistical modelling skills learned during the project will prepare the student for a wide range of careers in business, industry and government, as well as in science
Applicants should have or expect to obtain a good honours degree (1, 2.1, or equivalent) in a subject with a strong quantitative component. This may include biology, applied mathematics, statistics, or other quantitative science. Experience of numerical modelling and/or programming in R would be beneficial. Some experience in mathematical ecology/modelling is desirable, but not essential.
If you are interested in this PhD project then please contact one of the supervisors by phone or email to discuss it.
Formal application is via the University of Strathclyde postgraduate research application process at https://but.mis.strath.ac.uk/pguserprofile/control/enterDetailsPage
making sure that you clearly state your interest in this project with the named supervisors.
The formal start date for the student is 30th September 2019, and they will be expected to attend a SUPER DTP induction event in Glasgow on 1st October. The student will be enrolled in the SUPER Graduate School and onto the SUPER Post Graduate Certificate in Researcher Professional Development.