Developing an evidence base and methodology for the assessment of fish and shellfish stocks using Remote Electronic Monitoring data.

The UK is committed to managing fish stocks at levels capable of producing their maximum sustainable yield(1). Achieving this goal requires an understanding of abundance, size composition, and biological characteristics of commercial fish stocks. These data are currently acquired from research vessel surveys and from the fishing industry, through port sampling and on-board catch sampling by observers.

Remote Electronic Monitoring (REM) is the term used to refer to the automated collection of data from fishing vessels. REM encompasses closed-circuit TV systems (CCTV), global positioning system data (GPS) information, and electronic logbooks. Because it is a more economical alternative to observer programmes there is global interest in making greater use of REM in fisheries science(2). One application of REM is the use of CCTV systems for estimating the discarding of unwanted fish at sea; other applications include quantifying fish and shellfish catches. This PhD project focusses specifically on the use of CCTV to estimate the quantities of fish that are discarded at sea.

Discard estimates are typically obtained through observer-based sampling programmes. Due to limited resources, observer sampling coverage is generally low which can result in highly uncertain discard estimates(3). CCTV monitoring has the capacity to cover many vessels and sample large numbers of fish for length, potentially improving the precision of abundance estimates. This project will focus on operationalizing the use of CCTV as a source of input data for standard stock assessment by identifying feasible sampling schemes and evaluating the accuracy of resulting discard estimates through simulation and statistical modelling. The extent to which the use of CCTV-derived discard estimates impacts the accuracy of stock assessment results will be explored. The emphasis of the project will be on commercially important stocks where discarding is especially problematic, such as West of Scotland whiting and cod, and northeast Atlantic saithe fisheries. There will be opportunities to gain additional practical skills through participating in research vessel surveys and engaging with a diverse range of stakeholders in Scotland. This work also links to the recently commissioned EU H2020 project [Smartfish] and will include participation in international meetings and conferences.

The studentship is based principally at Marine Scotland in Aberdeen, working with supervisors there. There will also be regular contact with scientists and students at the University of Aberdeen. The project represents an excellent opportunity to gain expertise in a key issue for applied fisheries management. Mathematical and statistical modelling will be a key feature of the work. We therefore seek applicants with a background in mathematics, computer science, statistics or quantitative biology. Knowledge of one or more programming languages is desirable. Knowledge about fish is useful but not essential as it will be extensively developed through the project work. If required, the student will take a statistics course and a course on the statistical programming language R in the first year.

The project will provide support for training in state-of-the-art image processing, population modelling and statistical analyses. Depending on the research questions that are identified, this project has additional scope for developing complementary analytical skills in other fields including GIS, data visualisation and stakeholder engagement. The student will also be enrolled in the MASTS Graduate school (http://www.masts.ac.uk/graduate-school/ ). A key component of the studentship will be communication of results within Marine Scotland, at conferences, industry events and through publishing in the peer-reviewed literature. It provides an excellent opportunity to develop advanced technical skills in data processing and analysis in the context of applied marine conservation, management and policy development. Through the project there will be opportunities to engage with involving government, industry, and academic stake-holders and end-users. The skills, insights and networks developed through this PhD are highly transferable and especially well-suited to a career in fisheries and resource management, information and communication technology, data manipulation and quantitative analysis.

Please contact any of the co-supervisors if you wish more information about the project. Information about applying to the University of Aberdeen can be found at: https://www.abdn.ac.uk/study/postgraduate-research/

Application Process
Please apply for admission to the ’Degree of Doctor of Philosophy in Biological Science’ to ensure that your application is passed to the correct college for processing.
Please provide a copy of the degree certificate and transcript for each previous degree undertaken, a copy of your English language proficiency certificate (if relevant), and contact details of two referees who can comment on your previous academic performance (at least one should be from your current degree programme). References will be requested if you are selected for interview. Incomplete applications will not be considered.