Time Series Modelling of the Caspian Kutum (Rutilus frisii) Catch Using SARIMA Model

Document Type : Original Article

Authors

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

Abstract

Introduction: The Caspian Kutum (Rutilus frisii) is one of the most important bony fish species
of the Caspian Sea and has high conservation and commercial value. 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 commercial catch data of Caspian Kutum, over the seine net
fishing points of the northern coastal regions of Iran during catch seasons 2002/03 to 2011/12,
were used as catch-per-unit-of-effort (CPUE). 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. Time series modeling was conducted using
the seasonal autoregressive integrated moving average (SARIMA) model based on seasonal 3-
month intervals. The performance and predictive ability of the models were assessed using a
set of indices, including Akaike’s information criteria (AIC), Bayesian information criterion
(BIC), root mean squared error (RMSE), normalized root mean squared error (nRMSE), mean
absolute error (MAE), normalized mean absolute error (nMAE) and the Pearson correlation
coefficient (r). CPUE trends over the five years of 2013 to 2017 were predicted using the bestfitted
SARIMA models.
Results and Discussion: Five optimum (HR) and six non-optimum ranges (CR) were identified
over the whole fishing points range (WR). 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. The obtained predictions from the models for data sets having
sudden temporal fluctuations were less accurate. In contrast, higher accuracy levels of
predictions and trends were observed for fish catch time series with no sudden alterations in
CPUE levels over the studied period. Most of the obtained predictions for 2013-2017 similarly
presented stationary fluctuation trends with apparent seasonal increases in CPUEs.
Conclusion: Time-series modeling for 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 nonseasonal
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. The findings of this research could lead to a better
understanding of the temporal trends in catch levels of Caspian Kutum and use them by fisheries
managers to adopt efficient management plans regarding the available stocks of this species in
the future.

Keywords

References

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Environmental Sciences
Volume 22, Issue 2
2024
Pages 273-288

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