Abstract:
Larval catch data after the oil spill is being used to improve our understanding of the causes of temporal variability as it relates to the Deep Water Horizon oil spill (DWHOS). Bongo and neuston net tows were conducted at 48 stations in both June and July, 2015 in the northern Gulf of Mexico. Cruise data collected at each site included latitude/longitude, date, time and environmental data (temperature, salinity, dissolved oxygen). The occurrence and density of selected epipelagic (e.g., billfishes, tunas, dolphinfishes, flyingfishes) and deep pelagic (e.g., lanternfishes, bristlemouths, marine hatchetfishes) fish larvae were quantified and are being used to extend the pre- and post-DWHOS time series to better understand the drivers of natural variability in abundance for these taxa. Catch data are also being coupled with environmental data to identify high quality (highly suitable) habitat of each species or taxonomic group.
Suggested Citation:
Jay R. Rooker, R.J. David Wells. 2017. Presence/absence and density data for epipelagic tows from 48 stations in the northern Gulf of Mexico from R/V Blazing Seven cruises LF2015A and LF2015B June 2015 and July 2015. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/N76W98G7
Purpose:
Epipelagic surveys conducted by DEEPEND in shelf and slope waters represent the only larval indices of abundance for many taxa in the northern Gulf of Mexico during their primary spawning period (billfishes, dolphinfishes, swordfish, tunas). Natural variability in larval abundance and occurrence is high for post-spill years, and thus expanding this time series is critical for characterizing natural variability. This survey also examined the abundance/diversity of the entire epipelagic community, including an assessment of deep pelagic larvae present in surface waters. In addition, habitat suitability models are being developed for epipelagic and deep pelagic species from 2015 collections (+ 2016 and 2017) that will ultimately be used to predict the probability of occurrence of selected taxa in future years.
Data Parameters and Units:
Cruise, Year, Month, Collection date (MM/DD/YYYY), Station, Sample ID, Net Type, Start Latitude (DD), Start Longitude (DD), Stop Latitude (DD), Stop Longitude (DD), Time (HH:MM), Sea Surface temperature- SST (°C), Salinity, Dissolved Oxygen- DO (mg/L), Sargassum (kg), Tow time (MM:SS), Flowmeter (FM) Volume Sampled (m3), Enviro. Comments, Individual counts: Makaira nigricans, Tetrapturus albidus, Istiophorus platypterus, Frozen Istiophoridae, Istiophoridae Total, Xiphias gladius, Frozen Xiphias gladius, Total Xiphias gladius, Coryphaena hippurus, Coryphaena equiselis, Coryphaenidae, Frozen Coryphaenidae, Total Coryphaenidae, Exocoetidae, Scombridae, Frozen Scombridae, Total Scombridae, Pterois sp., Myctophidae, Gonostomatidae, Sternoptychidae, Non-Target Fish, Frozen Non-target Fish, Total Non-Target Fish, Squid, Octopus, Frozen Invert., Invert. Biomass (g)
Methods:
Sea surface temperature, salinity, and dissolved oxygen were measured at each station. GPS location was recorded at each station. Larvae at each station were collected with two different gear types: 1) neuston net (2 m width x 1 m height frame) with 1200 micron mesh; 2) paired 61-cm bongo nets with 333 and 500 micron mesh. Neuston nets were towed through the upper meter of the water column while oblique tows to ca. 100 m were made with the paired bongo nets. In addition, a paired ring net with 1000 micron mesh was deployed at night to qualitatively sample deep sea taxa. Ring net sampling was conducted at night on 2 stations to 500m depth. General Oceanics flow meters (Model 2030R, Miami, FL) were placed within net frames determine the volume of water sampled by each net. Fish larvae and early juveniles collected with net gears were preserved onboard in 95% ethanol; a subsample of the catch was placed on dry ice in the field for subsequent analysis. Larvae samples were sorted to the lowest possible taxonomic level using a Leica MZ stereomicroscope, and were identified to the species level using genetic markers. For genetic assays of select taxa (i.e., billfishes), a single eyeball was removed from each larva and DNA was extracted using the ZR-96 Genomic DNA Tissue MiniPrep Kit (Zymo Research #D3056, Irvine, CA). A multiplex polymerase chain reaction (PCR) was then performed using HotStarTaq DNA Polymerase (QIAGEN #203443, Valencia, CA) in 20uL reactions comprised of the following: 8.375uL PCR water, 10uL HotStar Master Mix, 0.625uL 10uM primer mix, and 1uL DNA. Thermocycling was performed using the manufacturer’s protocol and an annealing temperature of 55˚C. The primer mix used for PCR amplification was comprised of six primer pairs that amplify the mitochondrial (mt) DNA ND4 gene region: a universal billfish primer set, and species-specific primer sets for white marlin (Kajikia albida), blue marlin (Makaira nigricans), and sailfish (Istiophorus platypterus; Magnussen & Shivji, personal communication). DNA from specimens with separately validated species identifications were included as controls in all PCR amplifications. PCR amplification products were electrophoresed on 1.5% agarose gels to visualize species-specific banding patterns. Species identification was determined by comparing the banding pattern from specimens of unknown species identification to that of the control specimens (Simms et al. 2010). Species-specific primers have also been developed in our laboratories for tunas, flyingfishes, and dolphinfishes. These PCR reactions were examined via gel electrophoresis using 1% agarose gels containing ethidium bromide, with species identification based on gel banding patterns.
Instruments:
YSI EXO2 Multiparameter Sonde (Salinity, Dissolved Oxygen, Sea Surface Temperature); Garmin GPSmap76 (Latitude and Longitude); General Oceanic’s flow meters Model 2030R (determine surface area sampled by each net); Leica MZ stereomicroscope; ZR-96 Genomic DNA Tissue MiniPrep Kit (Zymo Research #D3056, Irvine, CA); HotStarTaq DNA Polymerase (QIAGEN #203443); Eppendorf mastercycler
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