Using electronic monitoring to improve data-limited catch estimates for sharks
Channel: Food and Agriculture Organization of the United Nations
Date: 07/10/2021

The stock assessment for sharks in federal waters of Alaska are considered severely data-limited, with harvest recommendations based solely on historical catch. Catch for large sharks is believed to be inaccurate due to gaps in the data available for total catch estimation in both longline and trawl fisheries. Electronic monitoring (EM) may provide new tools to close those gaps and provide previously unavailable data to improve the estimates of total catch of sharks. The project presented here has four objectives: 1) verify that can accurately detect sharks; 2) develop automated species identification to aid in video review; 3) use machine learning tools to size grade sharks based on visual cues; and 4) integrate categorical size data into total catch estimates. This project began in March 2021 and has already had success with industry participation and initial versions of species identification software.

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