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)

What is the Code of Conduct for Responsible Fisheries?

(PDF contains 17 pages)

Ecosystem exploitation, sustainability and biodiversity: Are they compatible?

This articles offers a basis for describing sustainability and then seeks to place this concept on an energetic basis by reference to recent advances in the understanding of patterns and processes in (mainly pelagic) fresh waters. Finally, by relating these to terrestrial ecosystems, it is shown how their sustainability may be attained through encouraging healthy fresh waters. Features of population succession are taken from observations on phytoplankton ecology.

James's rule and causes and consequences of a latitudinal cline in the demography of John's Snapper (Lutjanus johnii) in coastal waters of Australia

Demographic parameters were derived from sectioned otoliths of John’s Snapper (Lutjanus johnii) from 4 regions across 9° of latitude and 23° of longitude in northern Australia. Latitudinal variation in size and growth rates of this species greatly exceeded longitudinal variation. Populations of John’s Snapper farthest from the equator had the largest body sizes, in line with James’s rule, and the fastest growth rates, contrary to the temperature-size rule for ectotherms. A maximum age of 28.6 years, nearly 3 times previous estimates, was recorded and the largest individual was 990 mm in fork length. Females grew to a larger mean asymptotic fork length (L∞) than did males, a finding consistent with functional gonochorism. Otolith weight at age and gonad weight at length followed the same latitudinal trends seen in length at age. Length at maturity was ~72–87% of L∞ and varied by ~23% across the full latitudinal gradient, but age at first maturity was consistently in the range of 6–10 years, indicating that basic growth trajectories were similar across vastly different environments. We discuss both the need for complementary reproductive data in age-based studies and the insights gained from experiments where the concept of oxygen- and capacity-limited thermal tolerance is applied to explain the mechanistic causes of James’s rule in tropical fish species.

Toward a rule-based biome model

EXTRACT (SEE PDF FOR FULL ABSTRACT): Current projections of the response of the biosphere to global climatic change indicate as much as 50 to 90% spatial displacement of extratropical biomes. The mechanism of spatial shift could be dominated either by competitive displacement of northern biomes by southern biomes or by drought-induced dieback of areas susceptible to change. The current suite of global biosphere models cannot distinguish between these two processes, hence the need for a mechanistically based biome model. The first steps have been taken toward development of a rule-based, mechanistic model of regional biomes at a continental scale. ... The model is in an early stage of development and will require several enhancements, including: explicit simulation of potential evapotranspiration, extension to boreal and tropical biomes, a shift from steady-state to transient dynamics, and validation on other continents.