Time series modelling for the Caspian Kutum (Rutilus frisii) catch using SARIMA model

Document Type : Original Article


1 Department of Fisheries, Faculty of Natural Resources, University of Tehran, Karaj, Iran

2 Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran



Introduction: Recognizing temporal trends of fluctuations in fish stocks and using them to predict population changes in the future is one of the practical tools in fisheries stock management. The Caspian Kutum (Rutilus frisii) is the most important bony fish species in the southern Caspian Sea and has high conservation and commercial value. However, there were decreasing trends in its catch levels in the last years. Identifying temporal trends of its catch could help adopt proper plans to maintain the stocks of this important species and achieve sustainable exploitation goals. In the present study, we conducted a time-series analysis for catch data of the species over a decadal period.
Material and methods: The catch data as catch-per-unit-of-effort (CPUE) for the Kutum for catch seasons of 2002/3 to 2011/12 of sein net fishing points over northern coastal regains of Iran were used. A 5-point moving average of CPUE was used to distinguish the fishing points as optimum (with normalized CPUEs ≥ 0/6) and non-optimum (with normalized CPUEs < 0/6) fishing locations. The seasonal autoregressive integrated moving average (SARIMA) model was used to model time series based on seasonal 3-month intervals. The performance and predictive ability of models were assessed using a set of indices, including AIC, BIC, RMSE, nRMSE, MAE, nMAE and the Pearson correlation coefficient (r). CPUE trends over the five years 2013 to 2017 were predicted by applying the best-fitted SARIMA models.
Results and discussion: The fitted SARIMA models based on the whole data of all fishing locations as well as classified optimum and non-optimum ranges of fishing locations did not have significant non-seasonal autoregressive and moving average components, indicating no increasing nor decreasing trends for CPUE over the study period, while for some of the ranges of fishing points, there were significant autoregressive and moving average components with clear seasonal increasing trends. The overall trend of CPUEs showed mainly an increase from 2002 to 2006, and then after relatively constant levels, there were decreases from 2009 to 2013. Using the Classified fishing points as optimum and non-optimum ranges led to SARIMA models with better performance and more detailed identification of CPUE time-series components than to the whole set of fishing points. Most of the obtained predictions for 2013-2017 similarly presented stationary fluctuation trends with apparent seasonal increases in CPUEs.
Conclusion: Time-series modelling for the Kutum (R. frisii) using the SARIMA method mainly indicated clear increasing seasonal trends without any general trend of change over the whole fishing points. The simplicity of the obtained models considering the obtained seasonal and non-seasonal components could be explained by the short time frame and the low number of data points; however, spatial classification of fishing points resulted in more detailed models and higher recognition potential of them.