Extending electronic length frequency analysis in R

Electronic length frequency analysis (ELEFAN) is a system of stock assessment methods using length-frequency (LFQ) data. One step is the estimation of growth from the progression of LFQ modes through time using the von Bertalanffy growth function (VBGF). The option to fit a seasonally oscillating VBGF (soVBGF) requires a more intensive search due to two additional parameters. This work describes the implementation of two optimisation approaches (simulated annealing and genetic algorithm) for growth function fitting using the open-source software R. Using a generated LFQ data set with known values, the accuracy of the soVBGF parameter estimation was evaluated. The results indicate that both optimisation approaches are capable of finding high scoring solutions, yet settings regarding the initial restructuring process for LFQ bin scoring (i.e. moving average,) and the fixing of the asymptotic length parameter (L-infinity) are found to have significant effects on parameter estimation error. An outlook provides context as to the significance of the R-based implementation for further testing and development, as well as the general relevance of the method for data-limited stock assessment.