Impacts of survey design on a Gulf of Mexico bottom longline survey and the transition to a unified, stratified - random design

Le résumé fourni par la source

The Southeast Area Monitoring and Assessment Program (SEAMAP) Bottom Longline (BLL) survey was established to provide a nearshore complement to the offshore National Marine Fisheries Service (NMFS) BLL survey. SEAMAP state partners (i.e., Texas, Louisiana, Mississippi, and Alabama) used identical gear and sampling protocol to NMFS; however, temporal window, sampling universe, sampling frequency, and station selection were determined independently by each state based on available resources and capabilities. Although each state collected high quality data, the lack of a unified design complicated the efforts to combine state partner data to develop an index of abundance for stock assessment purposes. To improve the value of the survey and prioritize the quality and utility of the resultant data, the SEAMAP BLL survey was modified to implement a unified design that included consistency in spatial coverage and sampling frequency, and proportional allocation of sampling effort. Data from the early (2008-2014) and modified (2015-2021) SEAMAP BLL surveys, and from the modified SEAMAP and NMFS surveys were compared to determine the effects of this unified design on data precision. Overall catch composition slightly differed between the early and modified SEAMAP BLL surveys; however, taxa with declined abundance under the modified SEAMAP BLL survey were adequately sampled by the complementary NMFS BLL survey. Size composition was compared for three managed species. The size composition of Atlantic Sharpnose Sharks and Blacktip Sharks differed significantly between the modified SEAMAP BLL survey and the NMFS BLL survey, indicating that the modified survey is indeed providing complementary data. Further, implementing the modified design reduced the coefficient of variation for the indices of abundance for both Blacktip Sharks and Red Drum. The evolution of this survey highlights the benefit of unifying survey designs that build upon existing efforts to enhance the utility of survey data for multiple applications.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.