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Sea cucumber catch has followed “boom-and-bust” patterns over the period of 60 years from 1950-2010, and sea cucumber fisheries have had important ecological, economic and societal roles. However, sea cucumber fisheries have not been explored systematically, especially in terms of catch change trends. Sea cucumbers are relatively sedentary species. An attempt was made to explore whether the time series analysis approach (exponential smoothing models and autoregressive integrated moving average (ARIMA) models) is also applicable to relatively sedentary species. This study was conducted to develop exponential smoothing and ARIMA models to predict the short-term change trends (2011-2020), according to the time series data for 1950-2010 collected from the FAO Fishstat Plus database. The study results show that the single exponential smoothing and ARIMA (1, 1, 1) models are best for predicting sea cucumber short-term catches, and the predictive powers of both models are good. However, the accuracies of the models would be better if the data quality was resolved and the variables influencing sea cucumber capture production were fully considered.

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The relationship between the Lower Danube River level and Romanian annual catches of Pontic shad (Alosa immaculata, Bennett 1835) were analyzed. For analysis of long term data on the Danube River water level and Pontic shad catch, combinations of different methods were applied using statistical programs, SPSS 13.0 and MATLAB 6. Periodograms, containing cyclic patterns, were obtained using Fourier analysis. Significant oscillations were determined with Fisher-Whittle’s tests and residuals were calculated after subtracting these significant oscillations from the original signals. Autoregressive moving average (ARMA) models of residuals were finally applied. Results indicated that river water levels, and especially those in May, greatly explained the fluctuations of Pontic shad catch. Annual landings varied greatly and appeared to be cyclic. Varying river flow was considered to be one of the most important factors that cause fluctuations in the size of populations. Forecast indicates gradual increase of the catch in the next decade, followed by a decrease in other decades. Estimated as a vulnerable species of fish by the IUCN, development of the forecasting model of the future catch oscillations could be very helpful to regulate fishing efforts towards the sustainable use of stocks and species conservation.