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)

NMFS / Interagency Working Group Evaluation of CITES Criteria and Guidelines.

EXECUTIVE SUMMARY: At present, the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) criteria used to assess whether a population qualifies for inclusion in the CITES Appendices relate to (A) size of the population, (B) area of distribution of the population, and (C) declines in the size of the population. Numeric guidelines are provided as indicators of a small population (less than 5,000 individuals), a small subpopulation (less than 500 individuals), a restricted area of distribution for a population (less than 10,000 km2), a restricted area of distribution for a subpopula-tion (less than 500 km2), a high rate of decline (a decrease of 50% or more in total within 5 years or two generations whichever is longer or, for a small wild population, a decline of 20% or more in total within ten years or three generations whichever is longer), large fluctuations (population size or area of distribution varies widely, rapidly and frequently, with a variation greater than one order of magnitude), and a short-term fluctuation (one of two years or less). The Working Group discussed several broad issues of relevance to the CITES criteria and guidelines. These included the importance of the historical extent of decline versus the recent rate of decline; the utility and validity of incorporating relative population productivity into decline criteria; the utility of absolute numbers for defining small populations or small areas; the appropriateness of generation times as time frames for examining declines; the importance of the magnitude and frequency of fluctuations as factors affecting risk of extinction; and the overall utility of numeric thresh-olds or guidelines.

On the arrival time, waiting time to unload, unloading time and waiting time for departure for purse seiners at Mangalore landing centre

Fifty four purse-seine boats at Mangalore landing centre were observed during different stages of unloading fish catch. It was found that a boat takes 75% of the berthing time to unload an average quantity of 2.4 tons of fish. Further, unloading period and catch were found to be directly related where it was estimated that 5 to 7 minutes are spent in unloading about half a ton of fish to a nearby tempo by employing 9 ± 2 laborers.