The Science of Bluefin Tuna
Every two years, the International Commission for the Conservation of Atlantic Tunas (ICCAT) meets to set catch limits and other management measures for the Atlantic bluefin tuna fishery. The Commission relies on scientific information and advice from its Standing Committee on Research and Statistics (SCRS), which is made up of scientists from ICCAT's 48 member governments. Based on recommendations from the SCRS, the Commission then agrees to management measures that should allow the populations to grow and recover.
Stock assessments undertaken by the SCRS provide a key source of information. These assessments use historical catch data, scientific studies, and mathematical models to simulate and track a population as fish are produced, grow, reproduce, and die. They also allow scientists to predict how various management options will affect bluefin tuna in the future.
Stock assessments, though a valuable tool, are only as good as the data that go into them. Pew and its partners aim to improve both the data and the mathematical models that underpin the stock assessments. Better documentation of fishing catch and effort, incorporation of new data on bluefin tuna biology, and more complete and comprehensive models will lead to an accurate picture of the health of bluefin tuna populations and allow managers to implement more effective conservation measures.
A particular challenge for the management of the western Atlantic population of bluefin tuna is the use of two models to predict the number of fish produced each year. One model assumes that the number of young fish is linked to the number of adults in the population: More adults equal more young. The second model puts a cap on the number of young fish based on the premise that something has changed in the ocean environment and it can no longer support as many bluefin tuna as it once did. Right now, SCRS scientists consider both scenarios equally likely when making their recommendations to ICCAT, even though they produce contrasting results. This leads to different recommendations for management. A single model, based on the best available science, would help scientists make clear recommendations and allow managers to make better-informed decisions.