Aquaculture technology transfer to smallholder farmers in Malawi, Southern Africa

Details are given of activities conducted in Zomba, Malawi, in order to demonstrate new aquaculture technologies and encourage their use by smallholder fish farmers. The following technologies were introduced: napier grass as a pond output; use of a reed fence for harvesting fish; developing a high-quality compost as a pond input; vegetable-pond integration; chicken-pond integration; smoking kiln; pond stirring; and rice-fish integration. The reactions of the farmers to these technologies and their testing by the farmers are outlined briefly.

Brevetoxin in two planktivorous fishes after exposure to Karenia brevis: implications for food-web transfer to bottlenose dolphins

Brevetoxin uptake was analyzed in 2 common planktivorous fish that are likely foodweb vectors for dolphin mortality events associated with brevetoxin-producing red tides. Fish were exposed to brevetoxin-producing Karenia brevis for 10 h under conditions previously reported to produce optimal uptake of toxin in blood after oral exposure. Striped mullet Mugil cephalus were exposed to a low dose of brevetoxin, and uptake and depuration by specific organs were evaluated over a 2 mo period. Atlantic menhaden Brevoortia tyrannus specimens were used to characterize a higher brevetoxin dose uptake into whole body components and evaluate depuration over 1 mo. We found a high uptake of toxin by menhaden, with a body to water ratio of 57 after a 10 h exposure and a slow elimination with a half life (t1/2) of 24 d. Elimination occurred rapidly from the intestine (t1/2 < 1 wk) and muscle (t1/2 ≈ 1 wk) compartments and redistributed to liver which continued to accumulate body stores of toxin for 4 wk. The accumulation and elimination characteristics of the vectoring capacity of these 2 fish species are interpreted in relation to data from the Florida Panhandle dolphin mortality event of 2004. We show that due to slow elimination rate of brevetoxin in planktivorous fish, brevetoxin-related dolphin mortality events may occur without evidence of a concurrent harmful algal bloom event.

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)