What is the role of discarding in the dynamics of the demersal fish community of the Firth of Clyde?

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.