Loliginid and ommastrephid stock prediction in Greek waters using time series analysis techniques

Time series of loliginid and ommastrephid landings were analysed taking into account spatio-temporal descriptors of sea surface temperature (SST). The data are based on fisheries statistics recorded from the three most important fishing ports in the Northern Aegean Sea (1984–1999) and NOAA satellite images processed using GIS and image analysis tools. Autocorrelation (AC) and partial autocorrelation (PAC) functions were estimated leading to the identification and construction of seasonal ARIMA models, suitable for explaining the time series and forecasting future abundance values. The performance of the models was tested by comparing the predicted against the observed data of the last year (1999) and by examining the distribution and the AC of the residuals. The analysis provided results characterizing the different fishing patterns in each geographic area, as well as new series containing seasonally adjusted values, trend, cycle and error components of the model. Time series of several statistical parameters describing spatio-temporal variations of the SST were estimated and analysed aiming at the detection of anomalies and possible stock-environment relationships. Cross-correlation analysis between SST parameters and stock biomass indexes showed significant correlation coefficients, before and after compensation of the seasonal fluctuations by seasonal differencing. The results suggest that SST can be a leading indicator for stock prediction of the target species in the survey area.