A powerful method for analysing and predicting nature’s dynamic and interconnected systems is providing new forecasting and management tools for Canada’s premier sockeye salmon fishery.
In a new paper published in the Proceedings of the National Academy of Sciences, scientists describe how empirical dynamic modelling, or EDM, can improve forecasting for Fraser River sockeye salmon in British Columbia. It uses archives of field data to drive predictions of future performance.
Professor George Sugihara from the Scripps Institution of Oceanography at UC San Diego, who co-authored the paper alongside graduate student Hao Ye and other fisheries in Canada, said: “This approach allows the data to speak for itself, instead of shoe-horning ill-fitting data into preconceived equations. The bottom line is that the EDM approach forecasts accurately in real time.”
Salmon populations at the Fraser River sockeye salmon fishery can exhibit dramatic and seemingly unpredictable changes in annual recruitment, notoriously difficult to predict.
For the study, the researchers applied EDM methods in advance of the 2014 recruitment for Late Shuswap. The EDM technique predicted returns of between 4.5m and 9.1m fish, while the official forecast indicated a much broader range of 6.9m to 20m. The actual tally was listed at around 8.8m fish, which meant that EDM outperformed traditional forecasts with a much smaller error margin.
The study was funded by the National Science Foundation (NSF), the Foundation for the Advancement of Outstanding Scholarship and the Ministry of Science and Technology of Taiwan, among others.
