Looking for safe harbor in a crowded sea: Coastal space use conflict and marine renewable energy development

Technological advances in the marine renewable energy industry and increased clarity about the leasing and licensing process are fostering development proposals in both state and federal waters. The ocean is becoming more industrialized and competition among all marine space users is developing (Buck et al. 2004). More spatial competition can lead to conflict between ocean users themselves, and to tensions that spill over to include other stakeholders and the general public (McGrath 2004). Such conflict can wind up in litigation, which is costly and takes agency time and financial resources away from other priorities. As proposals for marine renewable energy developments are evaluated, too often decision-makers lack the tools and information to properly account for the cumulative effects and the tradeoffs associated with alternative human uses of the ocean. This paper highlights the nature of marine space conflicts associated with marine renewable energy literature highlights key issues for the growth of the marine renewable energy sector in the United States. (PDF contains 4 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)

The STREAM Initiative: support to regional aquatic resources management. Promoting participation, communication, and policies that support the livelihoods of poor aquatic resource users in the Asia-Pacific. Memorandum

Throughout the Asia-Pacific region capture fisheries and certain less intensive forms of aquaculture can and do play a vital role in livelihoods management, food security, and health and nutrition. Knowledge and experience exist that could be more effectively used in policy for poverty alleviation. (PDF contains 89 pages)

Fish pond management at the Onibonoje Agricultural Industries (Nigeria) Limited, RSH farm at ESA Oke Road'Ijebu Jesa Oyo State, Nigeria

A brief account is given of fish culture at the Anibonoje Agricultural Industries Ltd. fish farm in Nigeria. The pond culture of Tilapia, Clarias lazera, Heterotis niloticus and Cyprinus carpio is outlined, describing the feeding of the fish, the fertilization of the ponds, and pond management. The role of the government vis-a-vis that of the organized private sector is also examined

Supporting regional coastal and ocean managers: Linking ocean observing tools and capabilities to the priority needs of managers and users in the southeast region

Ocean observing has been recognized by the US Commission on Ocean Policy, the Ocean Research and Resources Advisory Panel, the Joint Ocean Commission Initiative, and many other ocean policy entities and initiatives as foundational to meeting the nation’s need for more effective coastal and ocean management. The Interim Report of the Interagency Task Force on Ocean Policy (September 2009) has called for strengthening the nation’s capacity for observing the nation’s ocean, coastal, and Great Lakes systems. (PDF contains 3 pages)