The Environmental Niche of the Light Purse Seine Fleet in the Northwest Pacific Ocean Based on Automatic Identification System Data

Ecosystem-based fisheries management requires high-precision fisheries information to provide relevant data for natural resource management, assessment, and marine spatial planning. This study utilizes Automatic Identification System (AIS) data from light purse seine vessels from the Chinese mainland that were collected from May to November between 2020 and 2022, along with the corresponding environmental data. By applying boosted regression trees (BRTs) and generalized additive models (GAMs), this study establishes nonlinear relationships between fishing intensity and predictor variables and explores the ecological and environmental drivers behind the spatial distribution of light purse seine vessels from the Chinese mainland in the Northwest Pacific. This research identifies the key influencing factors and reveals significant seasonal preferences for different marine environments in various months, with chlorophyll-a being the primary influencing factor. The predicted fishing effort closely resembles observed data, providing valuable information to support fisheries resource management and planning.

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Publication Details

Journal
Journal of Marine Science and Engineering
Published
2024-10-31
DOI
https://doi.org/10.3390/jmse12111944
Citations
2
Primary Topic
Maritime Navigation and Safety
Type
article
Field-Weighted Citation Impact
0.67
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The Environmental Niche of the Light Purse Seine Fleet in the Northwest Pacific Ocean Based on Automatic Identification System Data

Zuli Wu, Shenglong Yang, Keji Jiang, Shengmao Zhang et al.
2 citations
Journal of Marine Science and Engineering
Maritime Navigation and Safety
0.67
article

The Environmental Niche of the Light Purse Seine Fleet in the Northwest Pacific Ocean Based on Automatic Identification System Data

Zuli Wu, Shenglong Yang, Keji Jiang, Shengmao Zhang, Lijun Wan, Wei Fan, Fei Wang, Jiashu Shi, Yang Dai, Linlin Yu, Weifeng Zhou
article en
2 citations

Abstract

Ecosystem-based fisheries management requires high-precision fisheries information to provide relevant data for natural resource management, assessment, and marine spatial planning. This study utilizes Automatic Identification System (AIS) data from light purse seine vessels from the Chinese mainland that were collected from May to November between 2020 and 2022, along with the corresponding environmental data. By applying boosted regression trees (BRTs) and generalized additive models (GAMs), this study establishes nonlinear relationships between fishing intensity and predictor variables and explores the ecological and environmental drivers behind the spatial distribution of light purse seine vessels from the Chinese mainland in the Northwest Pacific. This research identifies the key influencing factors and reveals significant seasonal preferences for different marine environments in various months, with chlorophyll-a being the primary influencing factor. The predicted fishing effort closely resembles observed data, providing valuable information to support fisheries resource management and planning.

Journal of Marine Science and EngineeringVol. 12(11)
Dalian Ocean University (CN), Ministry of Agriculture and Rural Affairs (CN), Laoshan Laboratory, Shanghai Ocean University (CN), Chinese Academy of Fishery Sciences (CN)
Life below water
Openalex Percentile: Top 30%
Maritime Navigation and Safety
0.67
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