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This course will provide instruction, case studies, and hands-on exercises to demonstrate different quantitative approaches to common problems in modern fishery management, especially stock assessment methods and management strategy evaluation (MSE).
Objective
This course provides scientists an overview of quantitative approaches required to address common problems in modern fishery management. Using basic quantitative stock assessment methods as a launch point, we explore case studies where there is insufficient data for a stock assessment model; where risk is represented; where simulation and scenario analysis are used to improve monitoring and management; and where the harvesting and processing sector outcomes need to be integrated to understand the effects of alternative measures. A hands-on modeling exercise will provide a demonstration of application of MSE methods. It is anticipated this exercise will be complementary to the material given in the lectures/case-studies.
Course segments
– Introduction to management strategy evaluation, stock assessment methods, and other quantitative approaches to common problems in fishery management
– Case studies
- Assessment approaches for a data-deficient tropical fishery.
- Management strategy evaluations of a Gulf of Mexico multi-species fishery using Atlantis and Ecosim.
- Assessment of Southern Bluefin Tuna using a multiple model approach.
- Simulating movements and tagging rates in a PIT tag array river network to compare estimates of natal stream salmon abundance under alternative management scenarios.
- Increasing escapements goals in the Bristol Bay salmon fishery, which increases volatility because processing capacity contrains benefits from higher average runs.
– Hands-on modeling exercise in R.
Level
This course is appropriate for both graduate students and managers with minimal experience in quantitative approaches to management, or for quantitative modelers who desire exposure to applications of quantitative methods to a broader range of data and modeling applications to providing scientific advice for management. Participating directly in the introductory-level hands-on modelling exercise requires familiarity with R programming, though participants not familiar with R will be able to engage fully in other segments of the workshop, and may be able follow along with other participants during the exercise.