An Analysis of Dredge Efficiency for Surfclam and Ocean Quahog Commercial Dredges
Between 1997 and 2011, The National Marine Fisheries Service conducted 50 depletion experiments to estimate survey gear efficiency and stock density for Atlantic surfclam (Spisula solidissima) and ocean quahog (Arctica islandica) populations using commercial hydraulic dredges. The Patch Model was fo...
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| Format: | Text |
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The Aquila Digital Community
2020
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| Online Access: | https://aquila.usm.edu/masters_theses/743 https://aquila.usm.edu/context/masters_theses/article/1762/viewcontent/auto_convert.pdf |
| Summary: | Between 1997 and 2011, The National Marine Fisheries Service conducted 50 depletion experiments to estimate survey gear efficiency and stock density for Atlantic surfclam (Spisula solidissima) and ocean quahog (Arctica islandica) populations using commercial hydraulic dredges. The Patch Model was formulated to estimate gear efficiency and organism density from the data. The range of efficiencies estimated is substantial, leading to uncertainty in the application of these estimates in stock assessment. Analysis of depletion experiment simulations showed that uncertainty in the estimates of gear efficiency from depletion experiments was reduced by higher numbers of dredge tows per experiment, more tow overlap in the experimental area, a homogeneous as opposed to patchy distribution of clams in the experimental area, and the use of gear of inherently high efficiency. Simulations suggest that adapting the experimental protocol during the depletion experiment by adjusting tow number and degree and dispersion of tow overlap may substantively reduce uncertainty in the final efficiency estimates. Known values of four metrics for each field experiment were compared to metrics from the 9,000 simulations in the simulation dataset to determine which experiments diverge from those in the simulation dataset, and which experiments were likely to have high error in the efficiency estimate. The error metrics used implicate a subset of experiments that are outliers, biasing the efficiency estimates for the entire dataset. |
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