Identification of Environmental Predictors and Their Spatial Heterogeneity for VMS-Derived Fishing Duration in the Argentine Shortfin Squid Fishery Across the Southwest Atlantic Ocean.
5 affiliations
- 1Key Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China.
- 2College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China.
- 3School of ecology and environment, Anhui Normal University, Wuhu 241002, China.
- 4Zhejiang Intertion Information Technology Co., Ltd., Zhoushan 316100, China.
- 5Wenchang Innovation Research Center of East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Wenchang 571343, China.
Abstract
Argentine shortfin squid (Illex argentinus) fishing grounds in the Southwest Atlantic extend across the Argentine Exclusive Economic Zone (EEZ) and adjacent high seas, where environmental contrasts may lead to spatial heterogeneity. In this study, Vessel Monitoring System data from December 2020 to June 2025 were used to derive fishing duration as an indicator of fishing-ground use intensity. Conventional and derived environmental variables described vertical temperature and salinity differences, surface gradients, and topography. K-means clustering identified fishing-ground units, while CatBoost, SHapley Additive exPlanations, and accumulated local effects identified major environmental predictors and high-response ranges. No persistent fishing-ground center was identified within the EEZ, whereas the high seas were divided at 44.27° S into northern and southern fishing grounds with persistent centers. Seabed slope, bathymetry, and sea surface temperature were important predictors across the full area and subregions, but their rankings and high-response ranges differed among regions. Under LOSO validation, the full-study-area model yielded R² = 0.083 ± 0.104 and Spearman's ρ = 0.4485 ± 0.0903, indicating limited absolute predictive performance but some relative ranking capability. Modeling the full area as a single unit obscures regional differences in predictor combinations and response ranges. The results demonstrate that region-specific modeling can characterize spatially varying environmental associations and support differentiated management.
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BibTeX
@article{zhang2026identification,
title = {{Identification of Environmental Predictors and Their Spatial Heterogeneity for VMS-Derived Fishing Duration in the Argentine Shortfin Squid Fishery Across the Southwest Atlantic Ocean.}},
author = {Famou Zhang and Kangbo Li and Ming Gao and Chen Liu and Chenshuai Dong and Chen Li and Shuaiwen Zhang and Heng Zhang},
journal = {Biology},
year = {2026},
month = oct,
doi = {10.3390/biology15191735},
url = {https://europepmc.org/article/MED/42883407},
note = {CC BY},
}
RIS
TY - JOUR
TI - Identification of Environmental Predictors and Their Spatial Heterogeneity for VMS-Derived Fishing Duration in the Argentine Shortfin Squid Fishery Across the Southwest Atlantic Ocean.
AU - Famou Zhang
AU - Kangbo Li
AU - Ming Gao
AU - Chen Liu
AU - Chenshuai Dong
AU - Chen Li
AU - Shuaiwen Zhang
AU - Heng Zhang
PY - 2026
DA - 2026/10/01
JO - Biology
DO - 10.3390/biology15191735
UR - https://europepmc.org/article/MED/42883407
AB - Argentine shortfin squid (Illex argentinus) fishing grounds in the Southwest Atlantic extend across the Argentine Exclusive Economic Zone (EEZ) and adjacent high seas, where environmental contrasts may lead to spatial heterogeneity. In this study, Vessel Monitoring System data from December 2020 to June 2025 were used to derive fishing duration as an indicator of fishing-ground use intensity. Conventional and derived environmental variables described vertical temperature and salinity differences, surface gradients, and topography. K-means clustering identified fishing-ground units, while CatBoost, SHapley Additive exPlanations, and accumulated local effects identified major environmental predictors and high-response ranges. No persistent fishing-ground center was identified within the EEZ, whereas the high seas were divided at 44.27° S into northern and southern fishing grounds with persistent centers. Seabed slope, bathymetry, and sea surface temperature were important predictors across the full area and subregions, but their rankings and high-response ranges differed among regions. Under LOSO validation, the full-study-area model yielded R² = 0.083 ± 0.104 and Spearman's ρ = 0.4485 ± 0.0903, indicating limited absolute predictive performance but some relative ranking capability. Modeling the full area as a single unit obscures regional differences in predictor combinations and response ranges. The results demonstrate that region-specific modeling can characterize spatially varying environmental associations and support differentiated management.
SN - 2079-7737
ER -