Data Management Plan (Deprecated)

We compared nekton use of Vallisneria americana Michx. (submerged aquatic vegetation, SAV) with marsh shoreline vegetation and subtidal nonvegetated bottom (SNB) using a 1-m2 drop sampler in the oligohaline area of Barataria Bay, Louisiana. Mean densities of most abundant species were significantly different among six habitat types. Harris mud crab Rhithropanopeus harrisii, Ohio shrimp Macrobrachium ohione, blue crab Callinectes sapidus, daggerblade grass shrimp Palaemonetes pugio, white shrimp Litopenaeus setiferus (fall), rainwater killifish Lucania parva, naked goby Gobiosoma bosc, code goby Gobiosoma robustum (fall), speckled worm eel Myrophis punctatus (fall), and gulf pipefish Syngnathus scovelli (spring), were much more abundant, and species richness also was greater, in Vallisneria than over SNB. Vallisneria supported densities of most species that were similar to those in marsh vegetation, although naked goby and gulf pipefish were more abundant in Vallisneria, and speckled worm eel and saltmarsh topminnow Fundulus jenkinsi were more abundant in marsh. Within the Vallisneria bed, densities of Harris mud crab, rainwater killifish, and speckled worm eel were higher at sites near the marsh (SAV Inside Edge) than at sites more distant from the marsh (SAV Outside Edge), and Ohio shrimp (fall) densities were higher in the interior of the bed than along the edges. The mean size of blue crab was larger in marsh than Vallisneria and larger in Vallisneria than SNB. White shrimp did not differ in size among habitat types. Vallisneria beds may provide an important nursery habitat for young blue crab and white shrimp that use oligohaline estuarine areas. These SAV beds can provide an alternative structural habitat to emergent vegetation during periods of low water, because Vallisneria occurs in the subtidal and generally persists throughout the year on the Gulf coast. Species whose young thrive in low-salinity waters and also depend on structure would benefit most from Vallisneria habitat in estuaries.

Gulf Of Mexico

Process Steps:

• After sample collection, field data were entered into an Excel spreadsheet or database file (DBF) using database manager software. A text file was created to describe these data and associated variables. Entered data were checked against the field sheets by two biologists to minimize entry errors. Samples were processed in the laboratory, sorted, specimens identified and measured, and information was entered into an Excel spreadsheet or DBF file. Files were printed out and compared against original data sheets by two biologists for data entry errors. Corrections were made at this time, the electronic file was saved, and a back-up copy made. Hard copies of the QCd files were printed and stored in the project folder along with the original field and laboratory data sheets. The electronic file was also sorted and examined by the Lab Supervisor or other project personnel in a variety of ways to look for outliers, missing data, and other potential errors. Verified data files were then saved electronically on the Galveston Laboratory server and backed-up as needed.

Each sample was assigned a unique identification code. Field collected samples were tagged redundantly (e.g. one label inside of the collection vessel and a matching label attached to the outside of the vessel). The identifier and its associated information (e.g. date, location, habitat) was recorded on field data sheets. Once a sample arrived at the laboratory, the label remained with the sample throughout the various stages of sample processing. After data were entered into an Excel spreadsheet or similar database file, the information was printed out and compared against the field data sheets by two biologists. Corrections were made at this time and saved. The electronic file was also sorted and examined by the Lab Supervisor or other project personnel in a variety of ways to look for outliers, missing data, and other potential errors.

The data will be available from a public web server once an access methodology has been developed.

The data resides on a secure government network requiring multi-factor authentication for network access.