Tuna Tracking

Karen Stocks – San Diego Supercomputer Center
and Scripps Institution of Oceanography
Carl Wenning – Illinois State University

Scenario

You are a commercial fisherman from Gloucester, MA, in search of albacore tuna. Recently the catch has been quite poor, and the cost of taking available fish has exceeded the price for which they can be sold upon landing. In short, you are losing money and, unless things turn around, you could lose your operation as well. You need to find a way to increase the likelihood that you will return to port with a significantly improved catch. Your goal is to use available oceanographic, landings, and survey data (National Marine Fisheries Service) to determine (a) one which of two dates would be preferable for you to go out to fish, and (b) where you should go to fish in order to catch tuna.

Learning Goals

1) Students will accurately identify and characterize physical and biological feedback systems within the ocean, and how they affect fish migration and general availability to fishermen on the basis of available data. Available data include feeding habits and reproduction data, breeding grounds, bathymetry, water temperature, water salinity, chlorophyll concentration, and current flow) on the basis of available data. 2) Giving landing statistics and environmental data for multiple years, students will characterize conditions that lead to "good" versus "bad" fishing years.

Procedure

General problem-based learning strategies are employed throughout. Small cooperative learning groups are also employed. Students begin effort by going through a cycle of "what we know," "what we need to know," and "how we are going to find out." Given a basic understanding of this problem, students will begin to formulate alternative hypotheses that might account for increased or decreased yields of tuna taken by commercial fishermen. Students will identify data need to test the various hypotheses.

Data needs

10 years worth of landings data for tuna (from the National Marine Fisheries Service); monthly data sets on water temperature, salinity, chlorophyll content, precipitation (from satellite data in the National Oceanographic Data Center); bathymetry maps (NODC); 1 year worth of individual tuna tracks from multiple tuna (GPS); weekly environmental data sets for that year (fro NODC or Goddard DAAC).

Data tools

ability to overlay tuna tracks with environmental data; method for finding correlations between environmental data and tuna locations.

Additional resources

FishBanks by Dennis Meadows and William Prothero (see University of California Santa Barbara)

Level

Non-majors at university level, high school biology students

Assessment

Determine most reasonable answer to question, "What factors affect the migration of tuna fish?" Then, students must justify from data choice of better day and better location to fish for tuna. Format might include research paper, class presentation, etc.

Spin off

Daily lead in to an oceanography class. Where is (are) tuna each day? Can be used to introduce major topics in oceanography.

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