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Investigating of stock status of longtail tuna (Thunnus tonggol Bleeker, 1851) based on limited data approach in Iranian southern waters (Persian Gulf and Oman Sea)
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Seyed Ahmadreza Hashemi *   , Mastoorh Doustdar |
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Abstract: (162 Views) |
| The purpose of this study to investigate the fishing trend and estimation of optimized catch limit of the T. tonggol species in the Iranian southern waters. In this research, the information of twenty-five years of catch of this species was prepared and with use of data limited approach, estimation of optimal catch was made. The average catch (Yi ± S. D) and logarithm of catch (LogYi ± S. D) for the years 1997 to 2021 were 44,609 ± 19,603 tons and 4.60 ± 0.23 tons, respectively, and the average catch was significantly (P<0.05) has increased during the studied period. Average (maximum-minimum) indices of biomass to maximum sustainable yield biomass (B/Bmsy) and biomass to carrying capacity or saturation (S=B/K) catch based on Catch-MSY (CMSY) model, Optimized catch-only model (OCOM) model and Zhou-Boosted regression tree models(Zhou-BRT) were calculated 0.94 (0.42-0.19), 0.92 (0.42-0.47), 0.98 (0.15-0.5) and 0.46 (0.21-0.59), 0.48 (0.37-0.58), 0.49 (0.07-0.74), respectively. The maximum sustainable yield (MSY) of Catch-MSY (CMSY) model and Optimized catch-only model (OCOM) model was estimated at 55 (43-69) and 58 (53-62) thousand tons, and the mean reference points not show significant difference (P>0.05). ARIMA, (p, d, q) combined prediction models were tested based on AIC index, and ARIMA model (0, 1, 0) had the best fit with the trend of T. tonggol catch changes in the Iranian southern waters (AIC=-42) and Based on this model, the trend of low and relatively stable changes for this speies catch in the coming years is predicted. This research shows that the annual harvest of Longtail tuna species has reached its maximum amount and no increase in fishing is suggested for this species. |
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| Keywords: Longtail tuna, Catch-MSY (CMSY), Optimized catch-only model (OCOM), Zhou-Boosted regression tree models(Zhou-BRT). |
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Type of Study: Research |
Subject:
Special
Received: 2023/02/25 | Accepted: 2024/03/22 | Published: 2024/04/20
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