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Sustainable Oceans Courses

Natural and Human Systems: Graduate Course in Marine Science and Policy.

WFC 298 (3 units) – CRN 52739.

Fall Quarter 2022.

Tuesdays 2:10-4:00pm and Thursdays 3:10-4:00pm

1350 Storer Hall.

Course Description: Understand the development and implementation of ecosystem-based fisheries management through integrating oceanography, marine ecology and evolution, social ecological systems, Native American studies, and resource economics.Instructors: Sustainable Oceans Faculty.

For questions contact: Pernille Sporon Boving

Causal Chain Course: Balancing Conservation, Fishery Exploitation and Community Resilience

ECL 298 (2 units) – CRN 29929 

Fall Quarter 2022

Thursdays 1:10 PM - 3:00 PM  

2120B Wickson Hall

Course Title: Balancing Conservation, Fishery exploitation, and Community Resilience

Course Description: One of the greatest challenges of modern ocean governance is finding the right balance of conservation and fishery exploitation under changing oceanic conditions while at the same time promoting resilient coastal communities. To gain insights into this balance, the class will develop a causal chain(s) to explore the tradeoffs for a focal species or group (e.g., salmon, forage fish) in a region (e.g., California, Alaska, Arctic). The causal chain provides the scaffolding for the suite of natural and social science research necessary to inform these trade-offs and decisions in a changing ocean. The students will work in teams researching the scientific basis for different parts of the chain and the remaining important unknowns. The teams will present their research to the class throughout the quarter.

Instructor: James Sanchirico.   

Bayesian Models:  A Statistical Primer for Ecologists

Graduate Course

ECL 298 (3 units) – CRN TBD

Offered TBD 

Time and location TBD

Course Description: This course is to deliver practical model-building and model-criticizing skills, to help students build intuition and raise their confidence in statistical modeling, and to make inferences from complicated ecological data. An essential part of the course is to teach how to construct accurate mathematical expressions for Bayesian models linking observation to specific hypothesis about how ecological systems work. We will cover logical foundations of Bayesian inference, causal inference, multilevel models, and model comparison. The course is meant to equip students with (1) the skills to engineer the model structure that is best for their research questions, conditioned on the status of data, and (2) the ability to evaluate, critique, and refine their models and use them responsibly. The principle audience is researchers in the natural and social sciences, who have had a basic course on regression but nevertheless remain uneasy about statistical thinking.

Instructor: TBD

For questions contact program coordinator: