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)

Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions

This paper analyzes the use of artificial neural networks (ANNs) for predicting the received power/path loss in both outdoor and indoor links. The approach followed has been a combined use of ANNs and ray-tracing, the latter allowing the identification and parameterization of the so-called dominant path. A complete description of the process for creating and training an ANN-based model is presented with special emphasis on the training process. More specifically, we will be discussing various techniques to arrive at valid predictions focusing on an optimum selection of the training set. A quantitative analysis based on results from two narrowband measurement campaigns, one outdoors and the other indoors, is also presented.

Distribution and sighting frequency of reef fishes in the Florida Keys National Marine Sanctuary

This study analyzed species richness, distribution, and sighting frequency of selected reef fishes to describe species assemblage composition, abundance, and spatial distribution patterns among sites and regions (Upper Keys, Middle Keys, Lower Keys, and Dry Tortugas) within the Florida Keys National Marine Sanctuary (FKNMS) barrier reef ecosystem. Data were obtained from the Reef Environmental Education Foundation (REEF) Fish Survey Project, a volunteer fish-monitoring program. A total of 4,324 visual fish surveys conducted at 112 sites throughout the FKNMS were used in these analyses. The data set contained sighting information on 341 fish species comprising 68 families. Species richness was generally highest in the Upper Keys sites (maximum was 220 species at Molasses Reef) and lowest in the Dry Tortugas sites. Encounter rates differed among regions, with the Dry Tortugas having the highest rate, potentially a result of differences in the evenness in fishes and the lower diversity of habitat types in the Dry Tortugas region. Geographic coverage maps were developed for 29 frequently observed species. Fourteen of these species showed significant regional variation in mean sighting frequency (%SF). Six species had significantly lower mean %SF and eight species had significantly higher mean %SF in the Dry Tortugas compared with other regions. Hierarchical clustering based on species composition (presence-absence) and species % SF revealed interesting patterns of similarities among sites that varied across spatial scales. Results presented here indicate that phenomena affecting reef fish composition in the FKNMS operate at multiple spatial scales, including a biogeographic scale that defines the character of the region as a whole, a reef scale (~50-100 km) that include meso-scale physical oceanographic processes and regional variation in reef structure and associated reef habitats, and a local scale that includes level of protection, cross-shelf location and a suite of physical characteristics of a given reef. It is likely that at both regional and local scales, species habitat requirements strongly influence the patterns revealed in this study, and are particularly limiting for species that are less frequently observed in the Dry Tortugas. The results of this report serve as a benchmark for the current status of the reef fishes in the FKNMS. In addition, these data provide the basis for analyses on reserve effects and the biogeographic coupling of benthic habitats and fish assemblages that are currently underway. (PDF contains 61 pages.)

Viewpoints cards - full set

Viewpoints cards for developing successful student-staff partnerships.