The plankton was examined as an indicator of water quality in 14 shrimp Litopenaeus vannamei farms in Brazil in 2003. The ponds were categorized by high stocking density (>30 PL m(-2)) of phytoplankton, consisting of 51 species with concentrations ranging from 365,218+/-416,615 cells mL(-1) to 1,961,675+/-3,160,172 cells mL(-1). Diatoms contributed to almost 70% of the species number and high densities resulted from Cyanophyta blooms, mainly Pseudanabaena cf limnetica. Forty zooplankton taxa were registered and were essentially composed of typical marine euryhaline species and suspension-feeders. Copepoda dominated (45%) the make-up, followed by Protozoa (18%), Rotifera (12%), and Mollusca (12%) larvae. Zooplankton varied from 972+/-209 ind m(-3) to 4235+/-2877 ind m(-3). Enhanced nutrient input affected plankton density and composition. Diatom and Copepoda dominance was replaced by cyanobacteria, protozoan, and rotifers as nutrient concentrations increased with the cultured period, indicating that plankton structure is affected by eutrophic conditions.

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The determination of rates of body growth is the first step in many aquatic population studies and fisheries stock assessments. ELEFAN (Electronic LEngth Frequency ANalysis) is a widely used method to fit a growth curve to length-frequency distribution (LFD) data. However, up to now, it was not possible to assess its accuracy or the uncertainty inherent of this method, or to obtain confidence intervals for growth parameters within an unconstrained search space. In this study, experiments were conducted to assess the precision and accuracy of bootstrapped and single-fit ELEFAN-based curve fitting methods, using synthetic LFDs with known input parameters and a real data set of Abra alba shell lengths. The comparison of several types of bootstrap experiments and their outputs (95% confidence intervals and confidence contour plots) provided a first glimpse into the accuracy of modern ELEFAN-based fit methods. The main components of uncertainty (precision and reproducibility of fit algorithms, seed effects, sample size and matrix information content) could be assessed from partial bootstraps. Uncertainty was mainly determined by LFD matrix size (months x size bins), total number of non-zero bins and the sampling of large-sized individuals. A new pseudo-Rsquared index for the goodness-of-fit of VBGF models to LFD data is proposed. For a large, perfect synthetic data set, pseudo-RsquaredPhi' was very high (88 to 100%), indicating an excellent fit of the VBGF model. The small Abra alba data set showed a low pseudo-RsquaredPhi', from to 54% to 68%, indicating the need for more samples (length measurements) and a larger LFD data matrix. New, robust, bootstrap-based methods for curve fitting are presented and discussed. This study demonstrates a promising new path for length-based analyses of growth and mortality in natural populations, which are the basis for a new suite of methods that are included in the new fishboot package. Highlights: ELEFAN-based fit methods for the analysis of length-frequency data were tested and improved. The new, bootstrapped approach provides best fits and 95% confidence intervals for all parameters. This new approach showed a high level of reproducibility and accuracy. A new statistic for the information content of length-frequency data is introduced (pseudo-R²). This study demonstrates a promising new path for length-based studies of aquatic populations. al., 2017a; 2017b), which also contain numerous other functions. ELEFAN I (Pauly and David, 1981) uses a high-pass filter to identify peaks in LFDs. This filter is based on a moving average, where the frequencies of the original LFD that reach above the moving average are detected as peaks (black bars in Fig. 1) and those that are below the moving average are detected as troughs (white bars in Fig. 1). The score of peaks (black bars) that are being crossed by a VBGF curve (Fig. 1) is the basis for calculating a goodness-of-fit indicator ('Rn' score). Any ELEFAN-based fit procedure is thus nothing else but the search for the single optimum...