Jon Helge Vølstad & Mary Christman, instructors for Design and analysis of statistically sound catch sampling; 12–16 September
Anyone who wishes to either analyze, design, or understand the various approaches to designing statistically defensible sampling strategies.
Without statistically-designed sampling efforts, scientist runs the risk of not specifying important fisheries characteristics correctly and specifying erroneous measures of precision in quantities of interest. As a result, management decisions would be based on incorrect information, possibly leading to loss of yield or conversely overfishing.
Anne Cooper, instructor for Data-limited stock assessment; 12-16 September
Fish stock assessment and scientific advice are key inputs for achieving conservation and management objectives. That said, over 90% of fisheries around the world are unassessed and data-limited. Improving the input from science on these fisheries is a high priority, and this is being done through a variety of data-limited assessment methods that will be taught in the course.
Anyone who wants not only to learn about but to be able to apply and use cutting-edge tools to inform the conservation and management of the vast majority of wild fisheries around the world. There will also be scope to apply these methods to any stock-specific data brought to the course.
José de Oliveira & Carryn de Moore, instructors for Management Strategy Evaluation; 17-22 October
Who should attend this course?Scientists who want to learn about the development of Management Strategy Evaluation, why it has become an important tool, what fundamental concepts underlie the approach, and how it is used and applied in various contexts. It's also for scientists who want to learn how to build basic Management Strategy Evaluation examples based on real-world data.
Management Strategy Evaluation has become one of the most fundamental tools in a fisheries scientist's toolkit, allowing management strategies to be evaluated against their ability to satisfy quantifiable objectives against a range of plausible uncertainties.