Background
Fish stock advice provision in all ICES countries depends on having an ability to assess stocks and make some prediction about how fisheries catches are likely to affect future stock states. For stocks where there exists large amounts of process knowledge and data time series including fishery independent surveys and rigorous catch data collection programs, there is often enough information to fit statistical stock assessment models. Stocks with this kind of data are usually classified as ICES Category 1 and 2 stocks. Unfortunately, most ICES stocks fall under categories 3-6 where both process knowledge and data are less well known. The situation is similar in Canada and the USA. Despite a stock being classified as data-limited does advice on sustainable stock exploitation and sustainability reference points are still required.
There is an ever-growing suite of methods and tools available now for assessing data-limited stocks. ICES has developed many methods through its WKLIFE workshop series and many of these have been coded into publically available tools on the ICES GitHub site, though there remains many more available methods.
Course information
This course aims to explore the principles and theory behind data-limited methods used for stock assessment and how sustainability reference points can be derived from some of them. The skills learned from this course will be directly applicable in the ICES advisory process as well as fisheries management jurisdictions in Canada, the USA and elsewhere.
The format of the course will be interactive with lectures followed by practical application of the methods. Students will be encouraged to bring their own problems and data to the course in order to confront real data situations relevant to the participants.
Level
The course will be taught at an intermediate level. It is expected that participants will have had some experience with stock assessment such that they know if their stocks of interest might be data-limited. Advanced statistical or fisheries assessment knowledge will not be required. The ability to use Excel and an introductory level of R is expected.
