Evaluating tuna stock sustainability in Iranian waters using data-limited methods

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.

Authors

Institutions

Publication Details

Journal
Aquatic Ecology
Published
2025-12-09
DOI
https://doi.org/10.1007/s10452-025-10243-5
Primary Topic
Marine and fisheries research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluating tuna stock sustainability in Iranian waters using data-limited methods

Sachinandan Dutta, Rahimeh Rahmati, Rishi Sharma, Mastooreh Doustdar et al.
Aquatic Ecology
Marine and fisheries research
article

Evaluating tuna stock sustainability in Iranian waters using data-limited methods

Sachinandan Dutta, Rahimeh Rahmati, Rishi Sharma, Mastooreh Doustdar, Seyed Ahmadreza Hashemi, Somayeh Mollaee
article en

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.

Aquatic EcologyVol. 60(1)
University of Waterloo (CA), Sari Agricultural Sciences and Natural Resources University (IR), UNICEF East Asia and Pacific Regional Office (TH), Agricultural Research & Education Organization (IR), Agricultural Biotechnology Research Institute of Iran (IR), Sultan Qaboos University (OM)
Openalex Percentile: Top 64%
Marine and fisheries research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.