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)

Limited resources? Let the habitat priority planner help!

In this time of scarce resources, coastal resource managers must find ways to prioritize conservation, land use, and restoration efforts. The Habitat Priority Planner (HPP) is a free geospatial tool created by the National Oceanic and Atmospheric Administration’s Coastal Services Center that has received wide praise for its ease of use and broad applicability to conservation strategic planning, restoration, climate change scenarios, and other natural resource management actions. Not a geographic information system (GIS) user? Don’t worry―this tool was designed to be used in a team setting. One intermediate-level GIS user can push the buttons to show quick results while a roomful of resource managers and stakeholders provide input criteria that determine the results. The Habitat Priority Planner is a toolbar for ESRI’s ArcGIS platform that is composed of three modules: Habitat Classification, Habitat Analysis, and Data Explorer. The tool calculates basic ecological statistics that are used to examine how habitats function within a landscape. The tool pre‐packages several common landscape metrics into a user‐friendly interface for intermediate GIS users. In addition, HPP allows the user to build queries interactively using a graphical interface for demonstrating criteria selections quickly in a visual manner that is useful in stakeholder interactions. Tool advocates and users include land trusts, conservation alliances, nonprofit organizations, and select National Estuarine Research Reserves and refuges of the U.S. Fish and Wildlife Service. Participants in this session will learn the basic requirements for HPP use and the multiple ways the HPP has been applied to geographies nationwide. (PDF contains 5 pages)

Coastal erosion as a natural resource management problem: An economic perspective

Unremitting waves and occasional storms bring dynamic forces to bear on the coast. Sediment flux results in various patterns of erosion and accretion, with an overwhelming majority (80 to 90 percent) of coastline in the eastern U.S. exhibiting net erosion in recent decades. Climate change threatens to increase the intensity of storms and raise sea level 18 to 59 centimeters over the next century. Following a lengthy tradition of economic models for natural resource management, this paper provides a dynamic optimization model for managing coastal erosion and explores the types of data necessary to employ the model for normative policy analysis. The model conceptualizes benefits of beach and dune sediments as service flows accruing to nearby residential property owners, local businesses, recreational beach users, and perhaps others. Benefits can also include improvements in habitat for beach- and dune-dependent plant and animal species. The costs of maintaining beach sediment in the presence of coastal erosion include expenditures on dredging, pumping, and placing sand on the beach to maintain width and height. Other costs can include negative impacts on the nearshore environment. Employing these constructs, an optimal control model is specified that provides a framework for identifying the conditions under which beach replenishment enhances economic welfare and an optimal schedule for replenishment can be derived under a constant sea level and erosion rate (short term) as well as an increasing sea level and erosion rate (long term). Under some simplifying assumptions, the conceptual framework can examine the time horizon of management responses under sea level rise, identifying the timing of shift to passive management (shoreline retreat) and exploring factors that influence this potential shift. (PDF contains 4 pages)