Main Article Content

Abstract

The aim of this study is to develop a framework for investigating the catch trend and estimating the optimized catch limit of Frigate tuna (FRI) stock by collecting catch data in the Iranian southern waters, including the Persian Gulf and the Oman Sea. Two methods were employed to determine the biological reference points (BRPs) of Frigate tuna in the Persian Gulf and Oman Sea.  Catch data spanning 26 years (1997-2022) was utilized, and the optimized catch limit was estimated using a limited data approach. The average catch (Ct) over this period was 22,439 tons (95% confidence interval 18,299-26,638 tons), showing a significant increase over the past two decades (R= 0.9, P<0.05). The average maximum sustainable yield (MSY), current biomass to the biomass of MSY (B/BMSY) ratio, the ratio of current fishing mortality to the fishing mortality rate of MSY (F/FMSY), and saturation (S= B/K) ratio were obtained using the extension of Catch-MSY (CMSY) and Stochastic Surplus Production model in Continuous-Time (SPiCT). The results from different models showed that the current B/BMSY ratio, F/FMSY ratio, and MSY were not significantly different based on a t-test (P<0.05). The findings from the last year of the study indicated that the exploitation ratio in the Frigate tuna stock is below sustainable levels (under exploitation), our result indicating that more exploitation of FRI species is possible in the Persian Gulf and Oman Sea.

Keywords

Frigate tuna Catch-MSY Surplus Production model Persian Gulf Oman Sea

Article Details

How to Cite
HASHEMI, S. A., DOUSTDAR, M., & DUTTA, S. (2025). Catch-based data-limited stock assessment of Frigate tuna (<i>Auxis thazard</i> Lacepède, 1800) in the Persian Gulf and Oman Sea (Iranian southern waters). Iranian Journal of Ichthyology, 12(2), 101–112. https://doi.org/10.22034/iji.v12i2.1026

References

  1. Ajik, J.O. & Tahiluddin, A.B. 2021. Size distribution, length-weight relationship, and catch per unit effort of frigate tuna, Auxis thazard (Lacepède, 1800) in Tawi-Tawi waters, southern Philippines, caught using multiple handlines. Marine Science and Technology Bulletin 10(4): 370-375.
  2. Al-Shehhi, A.M., Dutta, S. & Paul, S., 2021. Stock assessment of Scomberomorus commerson (Kingfish) fishery of Oman: Perspectives of sustainability. Regional Studies in Marine Science, 47, p.101970.
  3. 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.
  4. Arrizabalaga, H.; Murua, M. & Majkowski, J. 2012. Global status of tuna stocks: summary sheets. Revista de Investigación Marina, AZTI-Tecnalia 19(8): 645-676.
  5. Asgharieskoui, M. 2002. Application of neural network in time series forecasting. Iranian Economic Research Quarterly 12(1): 96-69.
  6. Azadtalaitepe, N.; Bahmanesh, J.; Mantsari, M. & Vardinjad, V. 2015. Comparison of time series and artificial neural network methods in forecasting reference evapotranspiration (case study: Urmia). Irrigation Science and Engineering (Scientific-Research Magazine) 38(4). 85-76.
  7. Bastardie, F.; Feary, D.A.; Brunel, T.; Kell, L.T.; Döring, R.; Metz, S.; Eigaard, O.R.; Basurko, O.C.; Bartolino, V.; Bentley, J. & Berges, B. 2022. Ten lessons on the resilience of the EU common fisheries policy towards climate change and fuel efficiency-A call for adaptive, flexible and well-informed fisheries management. Frontiers in Marine Science 9: 947150.
  8. Benzer, S. & Benzer, R. 2019. Alternative growth models in fisheries: Artificial Neural Network. Journal of Fisheries 7(3): 719-725.
  9. Biais, G. 2022. SPiCT runs for the northeast Atlantic porbeagle. Working document ICES WKELASMO workshop
  10. 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.
  11. Cai, K.; Kindong, R.; Ma, Q. & Tian, S. 2023. Stock Assessment of Chub Mackerel (Scomber japonicus) in the Northwest Pacific Using a Multi-Model Approach. Fishes 8: 80.
  12. Cayré, P.; Amon Kothias, J.B.; Diouf, T. & Stretta, J.M. 1993. Biology of tuna. p. 147-244. In A. Fonteneau and J. Marcille (eds.) Resources, fishing and biology of the tropical tunas of the Eastern Central Atlantic. FAO fisheries technical paper 292. Rome, FAO. 354 p.
  13. Cadrin, S.X. 2020. Defining spatial structure for fishery stock assessment. Fisheries Research 221: 105397.
  14. Castellano-Mendez, M.; Gonzalez- Manteiga, W.; Febrero- Bande, M.; Prada-Sanchez, J. M. & Lozano-Calderon, R. 2004. Modelling of monthly and daily behavior of the run off the Xallas River using Box-Jenkins and neural networks methods. Journal of Hydrology 296: 38-58.
  15. Chen, Y.; Song, L.; Liu, Y.; Yang, L. & Li, D. 2020. A Review of the Artificial Neural Network Models for Water Quality Prediction. Applied Sciences 10(5776): 2-49.
  16. Collette, B.B. & Nauen, C.E. 1983. FAO species catalogue. Vol. 2. Scombrids of the world. An annotated and illustrated catalogue of tunas, mackerels, bonitos and related species known to date. FAO Fish Synopses 125(2): 137 p.
  17. Darvishi, M.; Behzadi, S.; Salarpouri, A. & Momeni, M. 2020. Growth, mortality and exploitation ratio of Auxis thazard (Lacepède, 1800) in the Northern Persian Gulf and Oman Sea waters (Hormozgan Province Zone). Animal Environment Journal 12: 131-138.
  18. Deepti, V.I. & Sujatha, K. 2012. Fishery and some aspects of reproductive biology of two coastal species of tuna, Auxis thazard (Lacepède, 1800) and Euthynnus affinis (Cantor, 1849) off north Andhra Pradesh, India. Indian Journal of Fisheries 59(4): 67-76.
  19. Dick, E.J. & MacCall, A.D. 2011. Depletion-based stock reduction analysis: a catch-based method for determining sustainable yields for data-poor fish stocks. Fisheries Research 110: 331-341.
  20. FAO 1993. Reference points for fishery management: their potential application to straddling and highly migratory resources. FAO Fish.Circ.,864,52p.
  21. 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.
  22. Fisheries Statistical Yearbook, Iranian Area 2023. Fisheries Administration, Council of Agriculture, Executive Tehran. 20 P.
  23. Free, C.M.; Jensen, O.P.; Anderson, S.; Gutierrez, N.L.; Kleisner, K.; Longo, C.; Minto, C.; ChatoOsio, G. & Walsh, J. 2020. Blood from a stone: Performance of catch-only methods in estimating stock biomass status. Fisheries Research 223: 1-10.
  24. Froese, R.; Zeller, D.; Kleisner, K. & Pauly, D. 2012. What catch data can tell us about the status of global fisheries. Marine Biology 159: 1283-1292.
  25. 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.
  26. 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.
  27. Froese, R. & Pauly, D. eds. 2015. FishBase. World Wide Web electronic publication. www.fishbase.org, version. (10/2015), accessed at www.fishbase.org in November/December 2015.
  28. Froese, R.; Demirel, N.; Coro, G.; Kleisner, K.M. & Winker, H. 2017. Estimating fisheries reference points from catch and resilience. Fish and Fisheries, 18(3): 506-526.
  29. Froese, R.; Winker, H.; Coro, G.; Demirel, N.; Tsikliras, A. C. & Dimarchopoulou, D. et al. 2018. Status and rebuilding of European fisheries. Marine Policy 93: 159-170.
  30. Gebremedhin, S.; Bruneel, S.; Getahun, A.; Anteneh, W. & Goethals, P. 2021. Scientific methods to understand fish population dynamics and support sustainable fisheries management. Water 13(4): 574.
  31. Hashemi, S.A.R.; Doustdar, M. & Derakhsh, P.M. 2023. Stock assessment of Yellowfin tuna, Thunnus albacares (Bonnaterre, 1788) using the LBB and LB-SPR methods in the northern Oman Sea, Iran. International Journal of Aquatic Biology 11(4): 313-320.
  32. HaghiVayghan, A.; Hashemi, S.A. & Kaymaram, F. 2021. Estimation of fisheries reference points for Longtail tuna (Thunnus tonggol) in the Iranian southern waters (Persian Gulf and Oman Sea). Iranian Journal of Fisheries Sciences 20(3): 678-693.
  33. IFO 2023. Iran Fisheries Organization (IFO). Bureau of Statistics; Yearbook of Fisheries Statistics. 25 p.
  34. Jenning, S.; Kasier, M. & Reynold. J. 2000. Marine Fisheries Ecology. Black well Science.391 p.
  35. King, M. 2007. Fisheries biology, assessment and management. Fishing News Book, London, 342 p.
  36. Koutroumanidis, T.; Iliadis, L. & Sylaios, GK. 2006. Time-series modeling of fishery landings using ARIMA models and Fuzzy Expected Intervals software. Environmental Modelling & Software 21: 1711-1721.
  37. Lawer, E.A. 2016. Empirical Modeling of Annual Fishery Landings. Natural Resources 7: 193-204.
  38. Li, Y.N.; Zhu, J.; Dai, X.; Fu, D. & Chen, Y. 2022. Using data-limited approaches to assess data-rich Indian Ocean bigeye tuna: Data quantity evaluation and critical information for management implications. Acta Oceanologica Sinica 41(3): 11-23.
  39. Lucena-Frédou, F.; Frédou, T. & Ménard, F. 2017. Preliminary Ecological Risk Assessment of small tunas of the Atlantic Ocean. Collective Volume of Scientific Papers, ICCAT 73: 2663-2667.
  40. Lucena-Frédou, F.L.; B. Mourato, T.; Frédou, P.G.; Lino, R.; Muñoz-Lechuga, C.; Palma, A. & Soares, M. Pons. 2021. Review of the life history, fisheries and stock assessment for small tunas in the Atlantic Ocean. Reviews in Fish Biology and Fisheries 31: 709-736.
  41. Martell, S. & Froese, R. 2013. A simple method for estimating MSY from catch and resilience. Fish and Fisheries 14(4): 504-514.
  42. Martinez, F.; Charte, F.; Frías, M. & Martínez-Rodríguez, M. 2022. Strategies for time series forecasting with generalized regression neural networks. Neurocomputing 491(1): 509-521.
  43. Mildenberger, T.K.; Berg, C.W.; Pedersen, M.W.; Kokkalis, A. & Nielsen, J.R. 2020. Time-variant productivity in biomass dynamic models on seasonal and long-term scales, ICES Journal of Marine Science 77(1): 174-187.
  44. Ovando, D.; Free, C. M.; Jensen, O. P. & Hilborn, R. 2022. A history and evaluation of catch-only stock assessment models. Fish and Fisheries 23: 616-630.
  45. Pedersen, M.W. & Berg. C.W. 2017. A stochastic surplus production model in continuous time. Fish and Fisheries 18: 226-243.
  46. Pons, M.; Cope, J.M. & Kell, L.T. 2020. Comparing performance of catch-based and length-based stock assessment methods in data-limited fisheries. Canadian Journal of Fisheries and Aquatic Sciences 77(6): 1026-1037.
  47. R Core Team 2022. R: a language and environment for statistical computing [online]. R Foundation for Statistical Computing, Vienna. Available from https://www.R-project.org.
  48. Shabri, A. & Samsudin. R. 2015. Fishery Landing Forecasting Using Wavelet-Based Autoregressive Integrated Moving Average Models. Hindawi Publishing Corporation Mathematical Problems in Engineering, 2015, Article ID 969450: 9.
  49. Skaar, S. 2020. A Comprehensive Guide to Neural Network Modeling. Nova Science Pub Inc. 172 p.
  50. Su, S.; Tang, Y.; Chen, J.; Chang, B. & Chen, Y. 2021. A comprehensive framework for operating science‐based fisheries management: A checklist for using the best available science. Fish and Fisheries 22(4): 798-811.
  51. Tabatabaei, S.N.; Abdoli, A.; Ahmadzadeh, F.; Primmer, C.R.; Swatdipong, A. & Segherloo, I.H. 2020. Mixed stock assessment of lake-run Caspian Sea trout Salmo in the Lar National Park, Iran. Fisheries Research 230: 105644.
  52. Talebzadeh, A. 1997. Survy stocks of five species scomdride in Hormozgan Costal waters (Persan Gulf and Oman Sea). Institute research Oman sea.155 p.
  53. Tsitsika, E.; Maravelias, C. & Haralabous, J. 2007. Modeling and forecasting pelagic fish production using univariate and multivariate ARIMA models. Fisheries Science 73: 979–-988.
  54. Vieira, J.; Costa. P.; Braga, A.; São-Clemente, R.; Ferreira, C. & Silva, J. 2022. Age, growth and maturity of frigate tuna (Auxis thazard Lacepède, 1800) in the Southeastern Brazilian coast. Aquatic Living Resources 35: 11.
  55. Walsh, J.C.; Minto, C.; Jardim, E.; Anderson, S.C.; Jensen, O.P.; Afflerbach, J.; DickeyCollas, M.; Kleisner, K.M.; Longo, C.; Osio, G.C.; Selig, E.R.; Thorson, J.T.; Rudd, M.B.; Papacostas, K.J.; Kittinger, J.N.; Rosenberg, A.A. & Cooper, A.B. 2018. Trade-offs for data-limited fisheries when using harvest strategies based on catch-only models. Fish and Fisheries 4: e4570-17.
  56. 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.
  57. 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.