Fish Population Dynamics and Assessment (Online)

Target Audience:

Federal and state fisheries managers and researchers interested in using fish population dynamics concepts and models to better assess and manage fish resources.

Summary and Objectives:

The goal of this course is to build a set of skills and tools useful for managing fish resources.

This course will focus on techniques associated with the two fundamental activities for successful management:

• Monitoring fish populations with sufficient rigor to characterize key population attributes important to management objectives (e.g., growth, mortality, abundance, and size distribution).
• Predicting how key population attributes might change with implementation of management actions.

Participants will learn basic parameter estimation, fisheries modeling and a framework to estimate key population attributes using monitoring data. The methods covered in this course could apply to exploited fisheries applications (e.g., managing harvest) as well as conservation (e.g., endangered species). Students will learn how to use models to communicate ideas about the behavior of fish populations and to predict how populations might vary with changing environmental or management drivers. The course will utilize case histories and actual fisheries data to elaborate these concepts. Students will build estimation and simulation models in MS Excel.

Course Structure:

Daily independent or group work employing instructional videos finishing with an instructor-led live session from 4 - 6 PM eastern time. A forum will provide communication access with other participants and instructors throughout each day. The expected daily time obligation is 3 - 5 hours spent on independent work and up to 2 hours on the live session. Total time is 5 - 7 hours per day over a 2-week period.

Upon completion of this course, participants will be able to:

• Build fisheries population models to describe dynamics, growth, mortality, recruitment, and harvest/take.
• Conduct parameter estimation using least squares and likelihood approaches in a spreadsheet framework.
• Use simulation modeling to predict the relative outcome of management actions, such as harvest policies and/or changes in habitat quality and quantity.
• Incorporate uncertainty into population models.
• Diagnosing growth and recruitment overfishing.
• Run simulations improve monitoring programs.