Interim report on the ground-water resources of Seminole County, Florida

Salt-water encroachment is undoubtedly the problem of most concern to users of ground water in Florida. This is a problem in many coastal areas where water levels are lowered excessively by heavy pumping. It is a problem also in some inland areas where the water-bearing formations contain salty water at relatively shallow depths. Among the coastal areas where wells have become contaminated with salt water are Pinellas County and the Miami area of Dade County. Inland areas where wells are likely to become contaminated with salt water include Seminole County and the southwestern part of Volusia County. The purpose of the investigation is to make a detailed study of the geology and ground-water resources of the county with special emphasis on the problems associated with declining water levels and salt-water contamination. This report reviews briefly the progress of the investigation through February 1954. (PDF contains 43 pages.)

Population Characteristics of Largemouth Bass Associated with Changes in Abundance of Submersed Aquatic Vegetation in Lake Seminole, Georgia

Population characteristics of largemouth bass ( Micropterus salmoides L.) including growth, body condition (relative weight), survival, and egg production were examined in relation to abundance of submersed aquatic vegetation (SAV) coverage (primarily hydrilla [ Hydrilla verticillata L.f. Royle]) in three embayments of Lake Seminole, GA, and compared to a previous study conducted in 1998. (PDF has 8 pages.)

Effect of Aquathol K Treatments on Activity Patterns of Largemouth Bass in Two Coves of Lake Seminole, Georgia

Thirty largemouth bass ( Micropterus salmoide s Lacepde) were implanted with radio tags in late October 2003 in two coves of Lake Seminole, Georgia, and tracked over a 24-hour period about every 10 days to determine their response to herbicide application. After five weeks of tracking, hydrilla ( Hydrilla verticillata Royle) in each cove was treated in early December 2003 with dipotassium salt of endothall (Aquathol K; 7-oxabicyclo [2.2.1] heptane-2,3-dicarboxylic acid) at a rate of 3.5 ppm. Largemouth bass were tracked during application and tracking continued for three months post treatment to assess effects of herbicide treatment on activity patterns. The treatment in Desser Cove successfully reduced hydrilla in approximately half the cove. However, the treatment in Peacock Lake completely eliminated all submersed aquatic vegetation (SAV) by April 2004. Movement and activity centers remained similar between treatment periods in Desser Cove, but increased after treatment in Peacock Lake. Depth occupied by telemetered fish decreased after Aquathol K treatment in both coves. In general, behavior of largemouth bass did not change appreciably during treatment, and only minor changes were observed in the posttreatment period in Peacock Lake, where all SAV was eliminated. Fish showed little attraction to or movement away from treatment areas, and fish migration from either cove was nil after treatment. Application of Aquathol K and subsequent reduction of SAV had little effect on largemouth bass behavior or movement. (PDF has 8 pages.)

Changes in Behavior, Movement, and Home Ranges of Largemouth Bass Following Large-scale Hydrilla Removal in Lake Seminole, Georgia

About 1,200 ha of hydrilla ( Hydrilla verticillata L.f. Royle) was eliminated in the Spring Creek embayment of Lake Seminole, Georgia, using a drip-delivery application of fluridone (1- methyl-3-phenyl-5-[3-(trifluoromethl) phenyl]-4(1H)-pyridinone) in 2000 and 2001. Two groups of 15 and 20 largemouth bass (Micropterus salmoides Lacepede) were implanted with 400-day radio tags in February 2000 and 2001 to determine changes in movement and behavior before and after hydrilla reduction.(PDF contains 8 pages.)

The Use of Herbicides to Control Hydrilla and the Effects on Young Largemouth Bass Population Characteristics and Aquatic Vegetation in Lake Seminole, Georgia

From 1997 to 2003, we examined the impacts of two aquatic herbicides, fluridone (Sonar; 1-methyl-3-phenyl-5-[3-(trifluromethl) phenyl]-4(1H)-pyridinone), and dipotassium salt of endothall (Aquathol K; 7-oxabicyclo[2.2.1]heptane-2,3-dicarboxylic acid), used to control dense hydrilla (Hydrilla verticillata L. f. Royle), on population characteristics of juvenile largemouth bass (Micropterus salmoides Lacepede) in small coves (<10 ha) in Lake Seminole, Georgia. In addition, we estimated areal coverage and species composition of submersed aquatic vegetation (SAV) communities in each cove. Fish and plants were sampled in both control (hydrilla infested)and herbicide treated coves in November and March- April each year. Electrofishing catch-per-effort for both number and weight of age-0 and age-1 fish for the 1997 to 2002 year classes was either the same or higher (p < 0.05) in herbicide treated than in control coves. Age-0 fish were larger (p <0.05) in treated, than in control coves in November, but at age-1 in the following spring, fish were slightly longer (p <0.05) in the control coves. Higher age-0 catches were associated with greater percent reductions in numeric catch between age-0 and age-1 and reduced lengths of fish in November indicating density-dependent effects. Age-0 fish lengths were also negatively correlated to percent cover of both total and native SAV. Total or native SAV coverages were not associated with catch-per-unit effort for number and weight, but nearly all control and herbicide treated coves had total SAV coverage greater than 40%. Applications of both Sonar and Aquathol K reduced total SAV coverage and hydrilla, permitted the establishment of native SAVs, and had either neutral or positive impacts on young largemouth bass in small coves in Lake Seminole. (PDF contains 7 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)