Objective
This course will enable participants to identify data information content and how they interact in a variety of assessment settings – from data-limited to data-rich.
Students will also be taught to recognize a diverse range of data-limited assessment methodologies and the data needs for each. In addition, they'll be able to understand each method's assumptions, benefits, limitations, and prior applications and performances.
Participants will work to apply decision trees to identify which methods are applicable for any specific data-limited dataset, and interpret results of data-limited stock assessments and understand what can be inferred from them. The course will also look at quantifing uncertainty in data-limited methods by running sensitivities and combining results across methods.
ICES approved methods and procedures for working with data-limited stocks will also be explored, meaning participants can understand these and learn how they are different from other approaches.
Students will also evaluate which data-limited assessment techniques are most appropriate for a given stock and available data and run a range of datasets through software to apply data-limited methodologies. There will also be scope to apply these methods to any stock-specific data brought to the course.
