NOAA/MLML Radiometric Data Acquisition and Processing

This report is a detailed description of data processing of NOAA/MLML spectroradiometry data. It introduces the MLML_DBASE programs, describes the assembly of diverse data fues, and describes general algorithms and how individual routines are used. Definitions of data structures are presented in Appendices. [PDF contains 48 pages]

User’s Guide to ADMB2R: A Set of AD Model Builder Output Routines Compatible with the R Statistics Language

ADMB2R is a collection of AD Model Builder routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 ADMB2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the ADMB2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer ADMB2R to others in the hope that they will find it useful. (PDF contains 30 pages)

User’s Guide to C2R: A Set of C Language Output Routines Compatible with the R Statistics Language

C2R is a collection of C routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 C2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the C2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer C2R to others in the hope that they will find it useful. (PDF contains 27 pages)

User’s Guide to For2R: A Module of Fortran 95 Output Routines Compatible with the R Statistics Language

For2R is a collection of Fortran routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 For2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the For2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer For2R to others in the hope that they will find it useful. (PDF contains 31 pages)

Variation saisonniere des structures hydrologique et dynamique du plateau continental ivoirien (VASPI I, II, III)

This data report contains all the meteorological, hydrological and dynamic data gathered during the VASPI cruises (I : from December 8th to December 13th 1982, II : from March 5th to March 9th 1983, and III : from October 6th to October 10th 1983) carried out along the continental shelf of Ivory Coast by the oceanographic ship "Andre NIZERY". These cruises, which represent a part of a coastal programme, are included in the more general scientific program FOCAL, whose main scientific objective is the study of the thermal context of the intertropical atlantic area.

Sea Turtle Observations at Explosive Removals of Energy Structures

Observers were placed at offshore sites to monitor and protect sea turtles during explosive removals of oil and gas structures in the Gulf of Mexico off Louisiana and Texas. Data collected during more than 6,500 hours of monitoring at 106 structure removals in 1992 provided information on sea turtle distribution. Eighteen individuals were observed including 10 loggerheads, 2 leatherbacks, 1 hawksbill, and 5 unidentified sea turtles. The observation rate (individuals per monitoring hour) of sea turtles was about 30 times higher during aerial surveys than during day or night suiface surveys.

Improving pinniped diet analyses through identification of multiple skeletal structures in fecal samples

Marine mammal diet is typically characterized by identifying fish otoliths and cephalopod beaks retrieved from stomachs and fecal material (scats). The use and applicability of these techniques has been the matter of some debate given inherent biases associated with the method. Recent attempts to identify prey using skeletal remains in addition to beaks and otoliths are an improvement; however, difficulties incorporating these data into quantitative analyses have limited results for descriptive analyses such as frequency of occurrence. We attempted to characterize harbor seal (Phoca vitulina) diet in an area where seals co-occur with several salmon species, some endangered and all managed by state or federal agencies, or both. Although diet was extremely variable within sampling date, season, year, and between years, the frequency and number of individual prey were at least two times greater for most taxa when prey structures in addition to otoliths were identified. Estimating prey mass in addition to frequency and number resulted in an extremely different relative importance of prey in harbor seal diet. These data analyses are a necessary step in generating estimates of the size, total number, and annual biomass of a prey species eaten by pinnipeds for inclusion in fisheries management plans.