DataSheet_1_Evaluating the impacts of reduced sampling density in a systematic fisheries-independent survey design.docx

• Main Authors: Lukas DeFilippo, Stan Kotwicki, Lewis Barnett, Jon Richar, Michael A. Litzow, William T. Stockhausen, Katie Palof
• Format: Dataset
• Language: unknown
• Published: 2023
• Subjects: Oceanography; Marine Biology; Marine Geoscience; Biological Oceanography; Chemical Oceanography; Physical Oceanography; Marine Engineering; survey design; stock assessment; groundfish; crab; spatiotemporal model; Bering Sea; blue king crab; Paralithodes platypus; St Matthew Island
• Online Access: https://doi.org/10.3389/fmars.2023.1219283.s001; https://figshare.com/articles/dataset/DataSheet_1_Evaluating_the_impacts_of_reduced_sampling_density_in_a_systematic_fisheries-independent_survey_design_docx/24031338

Fisheries-independent surveys provide critical data products used to estimate stock status and inform management decisions. While it can be possible to redistribute sampling effort to improve survey efficiency and address changing monitoring needs in the face of unforeseen challenges, it is important to assess the consequences of such changes. Here, we present an approach that relies on existing survey data and simulations to evaluate the impacts of strategic reductions in survey sampling effort. We apply this approach to assess the potential effects of reducing high density sampling near St. Matthew Island and the Pribilof Islands in the NOAA eastern Bering Sea (EBS) bottom trawl survey. These areas contain high density “corner stations” that were implemented for finer-scale monitoring of associated blue king crab stocks (Paralithodes platypus) which historically supported commercial fisheries but have since declined and are seldom eligible for harvest. We investigate the effects of removing these corner stations on survey data quality for focal P. platypus stocks and other crab and groundfish species monitored by the EBS survey. We find that removing the St. Matthew and Pribilof Islands corner stations has negligible effects on data quality for most stocks, except for those whose distributions are concentrated in these areas. However, the data quality for such stocks was relatively low even with higher density sampling, and corner station removal had only minor effects on stock assessment outcomes. The analysis we present here provides a generic approach for evaluating strategic reductions in sampling effort for systematic survey designs and can be applied by scientists and managers facing similar decisions elsewhere.