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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.

IntroductionLocal biodiversity is increasing in many temperate and subtropical waters due to climate change. It is often caused by shifting fish distributions, thus the biodiversity gradient, from lower to higher latitudes. However, these shifts in distributions do not occur uniformly across all species. Consequently, communities are not only shifting their spatial distributions, but species compositions are also changing. We investigated spatiotemporal differences in the compositions of fish species in the bays of the northeastern Gulf of Mexico and identified species that contribute to the temporal changes.MethodsWe used fish count data collected using gillnets in eight major bays, encompassing over 600 km of coastline, during spring and fall seasons from 1982 to 2019. The nonmetric multidimensional scaling (NMDS) on the Bray-Curtis dissimilarity index among species composition vectors was used to detect the differences in species composition, and the similarity percentages (SIMPER) were used to determine the contribution of species to the differences. ResultsThe result shows there was a gradual change in species composition in all bays over the years, and the composition was different among bays and seasons. The species contributing to the temporal changes included those that are expanding (e.g., Common snook, Centropomus undecimalis, and Smallscale fat snook, C. parallelus) as well as retracting (e.g., Southern flounder, Paralichthys lethostigma, and Spanish mackerel, Scomberomorus maculatus) their distributions toward the north. The species observed only in recent years in these bays tended to have a preference for warmer water (e.g., Gulf pipefish, Syngnathus scovelli, and Chain pipefish, S. louisiana). DiscussionThe results are consistent with the potential effects of climate change. However, the salinity of the bays in the study area generally exhibits an increasing trend from the northern to southern bays. The spatial salinity gradient has a substantial impact on species compositions, indicating that species distributions are influenced by multiple environmental conditions. This complexity makes our ability to accurately predict future species compositions under changing environmental conditions challenging.