Evaluating tuna stock sustainability in Iranian waters using data-limited methods
Bibliographic record
Abstract
The primary objective of this study was to develop a scientific framework for analyzing catch patterns and determining the most suitable fishing quotas for tuna and tuna-like species in southern Iranian waters. Given the ecological and economic importance of these species, an effective assessment approach is essential for sustainable fisheries management. A 26-year dataset (1997–2022) on tuna and tuna-like catches from the Persian Gulf and the Sea of Oman was analyzed using the Transparent Analytical Framework (TAF) within the R programming environment. Time series forecasting models—specifically, ARIMA and Extreme Learning Machine (ELM) neural networks—were applied to predict future catch trends. Model performance was evaluated using statistical metrics including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results revealed a statistically significant increasing trend in total catch over the study period (significance level 0.9, P < 0.05). Among the species assessed, Yellowfin Tuna (YFT) and Longtail Tuna (LOT) were identified as overexploited and full exploited with high and medium depletion rates (“red” and “yellow” status), while Skipjack Tuna (SKJ), Kawakawa (KAW), Frigate Tuna (FRI), Narrow-barred Spanish Mackerel (COM), and Indo-Pacific Spanish Mackerel (GUT) showed low depletion rates and were categorized as underutilized (“green” status). The ARIMA (0,1,0) model outperformed the ELM model (MAE = 1.6 vs. 22; RMSE = 1.7 vs. 23), indicating its superior predictive accuracy. Current exploitation levels for YFT and LOT exceed sustainable thresholds, necessitating urgent reductions in fishing effort. In contrast, other species remain underexploited, offering potential for increased but controlled harvesting. The application of time-series modeling, particularly ARIMA, provides a robust tool for forecasting stock dynamics and supporting data-driven fisheries management in the region. These findings contribute to improved conservation strategies and policy-making for sustainable utilization of marine resources in the Persian Gulf and the Sea of Oman.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".