Deep Learning and Survival Analysis Reveal Foraging-Driven Habitat Use in Pacific Saury Fisheries

Understanding the alignment between fisher behavior and habitat dynamics is essential for data-driven fisheries management. This study analyzed high-resolution Automatic Identification System (AIS) and Vessel Monitoring System (VMS) data, integrated with logbooks from 10 stick-held dipnet vessels targeting Pacific saury (Cololabis saira) in the North Pacific high seas. We developed an optimized CNN-LSTM-SE model to classify vessel trajectories into eight operational states, achieving 91% accuracy. This model generated a high-confidence presence dataset, addressing spatiotemporal data limitations in pelagic species research. A dynamic Ensemble Species Distribution Model (ESDM) mapped habitat suitability index (HSI) for the primary fishing seasons (June–September) of 2023–2024, revealing seasonal northward migrations and an interannual eastward shift in core habitats, primarily driven by sea surface temperature (SST: 6.4–19.1 °C), chlorophyll-a (CHL: 0.2–2.0 mg/m3), mixed layer depth (MLD: 14–30 m), and dissolved oxygen (DO: 220–290 mmol/m3). Receiver operating characteristic (ROC) sensitivity analysis identified an HSI threshold of ≥0.4 for suitable habitats, where 98.4% of fishing effort was concentrated. Kaplan–Meier survival analysis demonstrated that vessels in high-quality habitats (HSI ≥ 0.8) exhibited significantly longer fishing bout durations and lower cessation probabilities (log-rank test, χ2 = 20.9, p < 0.001), providing empirical evidence for the Marginal Value Theorem and Optimal Foraging Theory. Although HSI showed a weak direct correlation with catch rates (R2 = 0.007), it effectively delineated high-potential fishing grounds (>90% of high-catch days > 30 tonnes in HSI ≥ 0.6). By demonstrating that fishers’ spatial decisions appear to reflect environmental gradients, suggesting that fishing effort may indirectly act as an ecological indicator, this integrated framework bridges fisher behavior with ecological theory, supporting dynamic ocean management in climate-variable fisheries.

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Publication Details

Journal
Fishes
Published
2025-11-21
DOI
https://doi.org/10.3390/fishes10120597
Primary Topic
Marine and fisheries research
Type
article
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article

Deep Learning and Survival Analysis Reveal Foraging-Driven Habitat Use in Pacific Saury Fisheries

Ming Gao, Jianhua Wang, Sisi Huang, Zhao Guo-qing et al.
Fishes
Marine and fisheries research
article

Deep Learning and Survival Analysis Reveal Foraging-Driven Habitat Use in Pacific Saury Fisheries

Ming Gao, Jianhua Wang, Sisi Huang, Zhao Guo-qing, Famou Zhang, Hanji Zhu, Heng Zhang
article en

Abstract

Understanding the alignment between fisher behavior and habitat dynamics is essential for data-driven fisheries management. This study analyzed high-resolution Automatic Identification System (AIS) and Vessel Monitoring System (VMS) data, integrated with logbooks from 10 stick-held dipnet vessels targeting Pacific saury (Cololabis saira) in the North Pacific high seas. We developed an optimized CNN-LSTM-SE model to classify vessel trajectories into eight operational states, achieving 91% accuracy. This model generated a high-confidence presence dataset, addressing spatiotemporal data limitations in pelagic species research. A dynamic Ensemble Species Distribution Model (ESDM) mapped habitat suitability index (HSI) for the primary fishing seasons (June–September) of 2023–2024, revealing seasonal northward migrations and an interannual eastward shift in core habitats, primarily driven by sea surface temperature (SST: 6.4–19.1 °C), chlorophyll-a (CHL: 0.2–2.0 mg/m3), mixed layer depth (MLD: 14–30 m), and dissolved oxygen (DO: 220–290 mmol/m3). Receiver operating characteristic (ROC) sensitivity analysis identified an HSI threshold of ≥0.4 for suitable habitats, where 98.4% of fishing effort was concentrated. Kaplan–Meier survival analysis demonstrated that vessels in high-quality habitats (HSI ≥ 0.8) exhibited significantly longer fishing bout durations and lower cessation probabilities (log-rank test, χ2 = 20.9, p < 0.001), providing empirical evidence for the Marginal Value Theorem and Optimal Foraging Theory. Although HSI showed a weak direct correlation with catch rates (R2 = 0.007), it effectively delineated high-potential fishing grounds (>90% of high-catch days > 30 tonnes in HSI ≥ 0.6). By demonstrating that fishers’ spatial decisions appear to reflect environmental gradients, suggesting that fishing effort may indirectly act as an ecological indicator, this integrated framework bridges fisher behavior with ecological theory, supporting dynamic ocean management in climate-variable fisheries.

FishesVol. 10(12)
Dalian Ocean University (CN), Shanghai Ocean University (CN), Anhui Normal University (CN), Chinese Academy of Fishery Sciences (CN)
Openalex Percentile: Top 67%
Marine and fisheries research
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