| Statistical Programs |
| College of Agriculture | University of Idaho |
| Seminar Announcement |
| "Applied Statistics in Agriculture" |
|
A Fuzzy Logic Approach to Analyze Fish
Stock-Recruitment Relationships
Presented By |
| Dr. Din G. Chen |
|
International Pacific Halibut Commission University of Washington |
|
Tuesday, Oct. 17 3:30 P. M. White Water Room University Commons |
| Accurate fish stock assessment is a key requirement for successfully implementing fisheries management policies. It is now known that fishery stock-recruitment relationships have been masked by environmental interventions. Following this consensus, a machine-learning method, specifically a fuzzy logic approach is presented to analyze and classify the stock-recruitment relationships under different regimes of interventions and a hybrid global optimization is adopted for the estimation of parameters. To address the lack of uncertainty estimation in the fuzzy logic machine- learning method, a bootstrap re-sampling scheme is proposed to produce the sampling probability distributions for the stock-recruitment parameters related to fishery management policies so that the associated uncertainty measures can be obtained. Three stock-recruitment applications: 1) USA Southeast Alaska pink salmon, 2) West Coast Vancouver Island, BC, Canada, herring, and 3) Pacific Halibut, are examined to demonstrate the advantages of this new model to the traditional approaches. In the examples, the annual mean sea-surface temperature is incorporated as an environmental intervention. Accordingly, this new fuzzy logic approach could improve the management of the fisheries given a more accurate and more realistic fish stock assessment. |
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