Optimized exploitation of Pharaoh Cuttlefish (Sepia pharaonis Ehrenberg, 1831) stocks in the Iranian part of Persian Gulf and Oman Sea

Seyed Ahmadreza Hashemi, Mastooreh Doustdar

Abstract

The purpose of the present study was to investigate the trends in Pharaoh Cuttlefish (Sepia pharaonis) capture fisheries and determine the suitable range for optimized exploitation of S. pharaonis resources in the Iranian part of Persian Gulf and Oman Sea using catch data. The data on Pharaoh Cuttlefish capture fisheries in Iranian southern waters for the twenty-three years was collected and the suitable range for optimized exploitation of S. pharaonis was estimated using the R Software. The average values (95% confidence interval) using the Monte Carlo simulation method for intrinsic population growth rate (r), maximum sustainable yield (MSY), the biomass of maximum sustainable yield (Bmsy) and maximum fishing mortality rate of maximum sustainable yield (Fmsy) were 0.92 (0.73-1.17) per year, 5100 (4200-6200) tons, 1100 (8670-13900) tons, 0.46 (0.36-0.58) per year, respectively. The results showed that the annual catch of S. pharaonis exceeded the maximum sustainable yields and measures should be taken to reduce the number of capture fisheries and fishing effort. With results of the prediction model was observed moving average analysis (MAPE=2.85, MAD=0.10, MSD=0.02) and ARIMA (0, 0, 1) (AIC=9.79, BIC=6.38), are better than other models for a period of five years for modeling annual this species landing. It seems that reducing fishing permits and fishing effort will put the S. pharaonis stock situation in a more favorable condition in the long term and will further benefit the exploiters and the fishing community.

Keywords

Maximum sustainable yield, Monte Carlo simulation method, ARIMA model.

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References

Anderson S.C., Branch T.A., Ricard D., Lotze H.K. (2012). Assessing global marine fishery status with a revised dynamic catch-based method and stock-assessment reference points. Journal of Marine Science, 1(2): 20-26.

Arrizabalaga H., Murua M., Majkowski J. (2012). Global status of tuna stocks: summary sheets. Revista de Investigación Marina, AZTI-Tecnalia, 19(8): 645-676.

Branch T.A., Jensen O.P., Ricard D., Ye Y., Hilborn R. (2011). Contrasting global trends in marine fishery status obtained from catches and from stock assessments. Conservation Biology, 25(1): 777-786.

Bruska R., Moore W., shuster S. (2016). Invertebrates. Thrid edition. Sinauer associates bublisher, 1128 p.

Eagderi S., Fricke R., Esmaeilih R., Jalili P. (2019). Annotated checklist of the fishes of the Persian Gulf: Diversity and conservation status. Iranian Journal of Ichthyology, 6: 1-171.

FAO. (2018). The State of World Fisheries and Aquaculture 2018 - Meeting the sustainable development goals. Rome. Licenses: CC BY-NC-SA 3.0 IGO. 227 p.

Fisheries Statistical Yearbook, Iranian Area. (2020). Fisheries Administration, Council of Agriculture, Executive Tehran. 20 p.

Froese R., Demirel N., Sampang A. (2015). An overall indicator for the good environmental status of marine waters based on commercially exploited species. Marine Policy, 51(1): 230-237.

Froese R., Demirel N., Gianpaolo C., Kleisner K.M., Winker H. (2016). Estimating fisheries reference points from catch and resilience. Fish and Fisheries, 18(3): 506-526.

Froese R., Pauly D. (2015). FishBase. World Wide Web electronic publication. www.fishbase.org, version. (10/2015), accessed at www.fishbase.org in November /December 2015.

Gabriel W.L., Mace M.M. (1999). A Review of Biological Reference Points in the Context of the Precautionary Approach. 5th NMFS NSAW. NOAA Tech. Memo. NMFS-F/SPO-40. pp: 35-45.

Ghaitaranpour M., Poorbagher H., Eagderi S., Feghhi J. (2019). Modelling the spatial distribution of the yellowfin tuna, Thunnus Albacares in the Persian Gulf using a fuzzy rule-based classification. International Journal of Aquatic Biology, 7(6): 351-356.

Jereb P., Roper C.F.E. (2005). Cephalopods of the world. FAO species catalogue for fishery purposes. 262 p.

Karmaker C.L., Halder P.K., Sarker E. (2017). A study of time series model for predicting jute yarn demand: Case study. Journal of Industrial Engineering. Article ID 2061260. 8 p.

Lawer E.A. (2016). Empirical Modeling of Annual Fishery Landings. Natural Resources, 7: 193-204.

Martell S., Froese R. (2013). A simple method for estimating MSY from catch and resilience. Fish and Fisheries, 14(4): 504-514.

Palomares M.L.D., Froese R. (2017). Training on the use of CMSY for the assessment of fish stocks in data-poor environments. Workshop report submitted to the GIZ by Quantitative Aquatics, Inc. Q-quatics Technical Report No. 2. Bay, Laguna, Philippines. 58 p.

Rajaei M., Poorbagher H., Farahmand H., Mortazavi M.S., Eagderi S. (2014). Interpopulation differences in shell forms of the pearl oyster, Pinctada imbricata radiata (Bivalvia: Pterioida), in the northern Persian Gulf inferred from principal component analysis and elliptic Fourier analysis. Turkish Journal of Zoology, 38(1): 42-48.

Riede K. (2004). Global register of migratory species - from global to regional scales. Final Report of the R&D-Project 808 05 081. Federal Agency for Nature Conservation, Bonn, Germany. 329 p.

Roper C.F.E., Sweeney M.J., Nauen C.E. (1984). Cephalopods of the world. An annotated and illustrated catalogue of species of interest to fisheries. FAO Fisheries Synopsis, 125(1): 277 p.

Rosenberg A.A., Bolster W.J., Alexander K.E., Leavenworth W.B., Cooper A.B., McKenzie M.G. (2005). The history of ocean resources: modeling cod biomass using historical records. Frontiers in Ecology and the Environment, 3(2): 84-90.

Shabri A., Samsudin R. (2015). Fishery landing forecasting using wavelet-based autoregressive integrated moving average models. Hindawi Publishing Corporation Mathematical Problems in Engineering, Article ID 969450, 9 p.

Taghvimotlagh S.A., Akhundi M., Shiraei A.S. (2006). Analysis of fishing process and fishing potential based on statistics and catch data in Gulf and Oman waters. Journal of Fisheries Research, 15(3): 45-35.

Tsitsika E., Maravelias C., Haralabous J. (2007). Modeling and forecasting pelagic fish production using univariate and multivariate ARIMA models. Fisheries Science, 73: 979-988.

Valinasab T. (1993). Pharaoh cuttlefish biology and cephalopods species identification. Iranian Fisheries Science and Research Institute, Tehran. 65 p.

Valinasab T. (1999). Investigating the population diversity of Pharaoh Cuttlefish (Sepia pharaonis) in the Persian Gulf and Oman Sea. Dissertation, Islamic Azad University. 173 p.

Wetzel C.R., Punt A.E. (2015). Evaluating the performance of data-moderate and catch-only assessment methods for U.S. west coast groundfish. Fisheries Research, 171: 170-187.

Zhou S., Chen Z., Dichmont C.M., Ellis A.N., Haddon M., Punt A.E., Smith A.D.M., Smith D.C., Ye Y. (2016). Catch-based methods for data-poor fisheries. Report to FAO. CSIRO, Brisbane, Australia. 74 p.

Zhou S., Punt A.E., Smith A.D.M., Ye Y., Haddon M., Dichmont C.M., Smith D.C. (2017). An optimized catch-only assessment method for data poor fisheries. ICES Journal of Marine Science, 1(2): 20-26.

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