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)

Bestimmung der Begriffe Gesamtfang, Fang, Beifang und Rückwürfe

Due to the increasing environmental awareness of the public, marine environmental protection became a general interest. Since fishery is one of the strongest direct influences of man on the marine ecosystem, it became criticised, especially because of the discarding of unused fish, which is considered as a waste of the resource. In the different types of fisheries the calculation of the amounts of discards in relationship to the landings vary strongly. One of the reasons is, however, the different use of the terms 'bycatch' and 'discard'. Thus it is important to work with equal terms and definitions, so that fishermen, protectionists and ecologists talk the same language. This contribution tries to define the following terms achieving a generally acceptable terminology which is also explained by the attached diagram.

Education and awareness programme on marine protected area management, Bar Reef Marine Sanctuary, Sri Lanka

The objectives of the education programme included: making the fishing community and the younger generation aware of the importance of conserving marine resources; to obtain their suggestions and opinions; and to identify gaps and needs to strengthen local participation.

Strengthening effectiveness of policy directives of the LVFO Council of Ministers on Lake Victoria, Uganda

Research was done to assess the dissemination and implementation by the Fisheries Department, Local Govemments and beach management units and the awareness, acceptance and compliance among fishers to the CoM Directives on management of Lake Victoria fisheries. Conducted by the National Fisheries Resources Research Institute (NaFIRRI), the research focused on the implementation and effectiveness of measures following the LYFO Council of Ministers (CoM) Directives for improved management of the fisheries of Lake Victoria, with particular reference to the 2009 CoM Directives as a case study, it was established that many of the Directives have not been implemented. In cases where the directives were implemented, their effectiveness remains questionable. While steps were taken to disseminate and implement the Directives, there were some challenges, including the unclear legal status of the directives, limited dissemination materials and poor methods of dissemination, language barriers and inadequate resources for enforcement.