Modeling Skipjack Tuna Purse Seine Fishery Distribution in the Western and Central Pacific Ocean Under ENSO Scenarios: An Integrated MGWR-BME Framework

The Western and Central Pacific Ocean (WCPO), the key global purse seine fishing ground for skipjack tuna (Katsuwonus pelamis), sees frequent ENSO events. These events drastically alter marine ecosystems and fishery resource patterns, complicating fisheries management—given skipjack tuna’s high mobility and sensitivity to marine environmental changes. To address this, the study proposes an improved spatial prediction framework that incorporates the MGWR model to capture environmental changes. The spatial regression results generated by the MGWR model are incorporated as the mean-field input for the BME model. Additionally, the interannual standard deviation of skipjack tuna resources is fed into the BME model as a measure of spatial uncertainty. The results indicate that the mean field and uncertainty field exhibit a strong correlation, with an R2 of 0.54, an RMSE of 583.32, an MAE of 377.22, and an ME of 334.77. Compared to the single prediction models BME and MGWR, the MGWR-BME integrated framework has improved R2 by 12%, 30%, and 13% in the 2021–2023 predictions, respectively. Additionally, its prediction performance for distinguishing El Niño, La Niña, and normal years has significantly improved, with R2 increasing from 0.6 to 0.67 in 2021, from 0.34 to 0.62 in 2022, and from 0.30 to 0.40 in 2023. According to the evaluation results based on Kernel Density Estimation (KDE) curves, the model performs well in fitting low values but shows weaker performance in fitting high values. By applying this approach, we have clarified the multiscale driving mechanisms through which marine environmental heterogeneity affects the distribution of skipjack tuna under ENSO conditions. This insight enables fishery managers to more accurately predict the dynamic changes in skipjack tuna fishing grounds under different climatic scenarios, thereby providing a reliable scientific basis for formulating rational fishing quotas, optimizing fishing operation layouts, and implementing targeted conservation measures—ultimately contributing to the balanced development of fishery resource utilization and ecological protection.

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Publication Details

Journal
Fishes
Published
2025-09-04
DOI
https://doi.org/10.3390/fishes10090450
Citations
1
Primary Topic
Marine and fisheries research
Type
article
Field-Weighted Citation Impact
1.11

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article

Modeling Skipjack Tuna Purse Seine Fishery Distribution in the Western and Central Pacific Ocean Under ENSO Scenarios: An Integrated MGWR-BME Framework

Menghao Li, Yiheng Wang, Jiangfeng Zhu, Xiaoming Yang
1 citations
Fishes
Marine and fisheries research
1.11
article

Modeling Skipjack Tuna Purse Seine Fishery Distribution in the Western and Central Pacific Ocean Under ENSO Scenarios: An Integrated MGWR-BME Framework

Menghao Li, Yiheng Wang, Jiangfeng Zhu, Xiaoming Yang
article en
1 citations

Abstract

The Western and Central Pacific Ocean (WCPO), the key global purse seine fishing ground for skipjack tuna (Katsuwonus pelamis), sees frequent ENSO events. These events drastically alter marine ecosystems and fishery resource patterns, complicating fisheries management—given skipjack tuna’s high mobility and sensitivity to marine environmental changes. To address this, the study proposes an improved spatial prediction framework that incorporates the MGWR model to capture environmental changes. The spatial regression results generated by the MGWR model are incorporated as the mean-field input for the BME model. Additionally, the interannual standard deviation of skipjack tuna resources is fed into the BME model as a measure of spatial uncertainty. The results indicate that the mean field and uncertainty field exhibit a strong correlation, with an R2 of 0.54, an RMSE of 583.32, an MAE of 377.22, and an ME of 334.77. Compared to the single prediction models BME and MGWR, the MGWR-BME integrated framework has improved R2 by 12%, 30%, and 13% in the 2021–2023 predictions, respectively. Additionally, its prediction performance for distinguishing El Niño, La Niña, and normal years has significantly improved, with R2 increasing from 0.6 to 0.67 in 2021, from 0.34 to 0.62 in 2022, and from 0.30 to 0.40 in 2023. According to the evaluation results based on Kernel Density Estimation (KDE) curves, the model performs well in fitting low values but shows weaker performance in fitting high values. By applying this approach, we have clarified the multiscale driving mechanisms through which marine environmental heterogeneity affects the distribution of skipjack tuna under ENSO conditions. This insight enables fishery managers to more accurately predict the dynamic changes in skipjack tuna fishing grounds under different climatic scenarios, thereby providing a reliable scientific basis for formulating rational fishing quotas, optimizing fishing operation layouts, and implementing targeted conservation measures—ultimately contributing to the balanced development of fishery resource utilization and ecological protection.

FishesVol. 10(9)
Ministry of Agriculture and Rural Affairs (CN), Shanghai Ocean University (CN)
National Key Research and Development Program of China
Openalex Percentile: Top 20%
Marine and fisheries research
1.11
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