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The determination of rates of body growth is the first step in many aquaticpopulation studies and fisheries stock assessments. ELEFAN (Electronic LEngthFrequency ANalysis) is a widely used method to fit a growth curve tolength-frequency distribution (LFD) data. However, up to now, it was notpossible to assess its accuracy or the uncertainty inherent of this method, orto obtain confidence intervals for growth parameters within an unconstrainedsearch space. In this study, experiments were conducted to assess the precisionand accuracy of bootstrapped and single-fit ELEFAN-based curve fitting methods,using synthetic LFDs with known input parameters and a real data set of Abraalba shell lengths. The comparison of several types of bootstrap experimentsand 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 fitalgorithms, seed effects, sample size and matrix information content) could beassessed from partial bootstraps. Uncertainty was mainly determined by LFDmatrix size, total number of non-zero bins and the sampling of large-sizedindividuals. A new pseudo-Rsquared index for the goodness-of-fit of VBGF modelsto LFD data is proposed. For a large, perfect synthetic data set,pseudo-RsquaredPhi was very high (88 to 100%), indicating an excellent fit ofthe VBGF model. The small Abra alba data set showed a low pseudo-RsquaredPhi,from to 54% to 68%, indicating the need for more samples and a larger LFD datamatrix. New, robust, bootstrap-based methods for curve fitting are presentedand discussed. This study demonstrates a promising new path for length-basedanalyses of growth and mortality in natural populations, which are the basisfor a new suite of methods that are included in the new fishboot package.

Assessing sources of uncertainty in length-based estimates of body growth in populations of fishes and macroinvertebrates with bootstrapped ELEFAN