Simulation and Identification of the Habitat of Antarctic Krill Based on Vessel Position Data and Integrated Species Distribution Model: A Case Study of Pumping-Suction Beam Trawl Fishing Vessels
文献类型： 外文期刊
作者： Zhang, Heng 1 ; Sun, Yuyan 1 ; Zhu, Hanji 1 ; Xiang, Delong 1 ; Wang, Jianhua 1 ; Zhang, Famou 1 ; Huang, Sisi 1 ; Li, Yang 1 ;
作者机构： 1.Chinese Acad Fishery Sci, East China Sea Fisheries Res Inst, Key Lab Ocean & Polar Fisheries, Minist Agr & Rural Affairs, Shanghai 200090, Peoples R China
2.Qingdao Marine Sci & Technol Ctr, Laoshan Lab, Qingdao 266237, Peoples R China
3.Dalian Ocean Univ, Coll Nav & Ship Engn, Dalian 116023, Peoples R China
4.Shanghai Ocean Univ, Coll Marine Living Resource Sci & Management, Shanghai 201306, Peoples R China
关键词： Antarctic krill; vessel position data; environmental data; species distribution model; habitat research
期刊名称：ANIMALS （ 影响因子：2.7； 五年影响因子：3.2 ）
ISSN： 2076-2615
年卷期： 2025 年 15 卷 11 期
页码：
收录情况： SCI
摘要： This study, based on the vessel position data of pump-suction beam trawlers and the integrated species distribution model (ISDM), deeply analyzes the spatio-temporal distribution characteristics of the habitat of Antarctic krill and the contributions of key environmental factors. The Convolutional Neural Network-attention model (CNN-attention model) was used to identify the fishing status of the vessel position data of Norwegian pump-suction beam trawlers for Antarctic krill during the fishing seasons from 2021 to 2023. Variables of marine environment, including sea surface temperature (SST), sea surface height (SSH), chlorophyll concentration (CHL), sea ice concentration (SIC), sea surface salinity (SSS), and spatial factor Geographical Offshore Linear Distance (GLD) were combined and input into the ISDM for simulating and predicting the spatial distribution of the habitat. The model results show that the Area Under the Curve (AUC) and True Skill Statistic (TSS) indices for all months exceed 0.9, with an average AUC of 0.997 and a TSS of 0.973, indicating extremely high accuracy of the model in habitat prediction. Further analysis of environmental factors reveals that Geographical Offshore Linear Distance (GLD) and chlorophyll concentration (CHL) are the main factors affecting habitat suitability, contributing 34.9% and 25.2%, respectively, and their combined contribution exceeds 60%. In addition, factors such as sea surface height (SSH), sea surface temperature (SST), sea ice concentration (SIC), and sea surface salinity (SSS) have impacts on the habitat distribution to varying degrees, and each factor exhibits different suitability response characteristics in different seasons and sub-regions. There is no significant correlation between the habitat area of Antarctic krill and catch (p > 0.05), while there is a significant positive correlation between the fishing duration and the catch (p < 0.001), indicating that a longer fishing duration can effectively increase the Antarctic krill catch.
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