Identifying Key Factors Influencing Regional Fishing Footprints in Coastal Waters
Chen, Renli1,2; Wang, Yueqi1,3; Wu, Xiaoqing1,3; Gao, Zhiqiang1,3
Source PublicationSSRN
ISSN15565068
2023
KeywordAutomation Environmental management Environmental technology Information management Oceanography Remote sensing Surface temperature Surface waters Sustainable development
DOI10.2139/ssrn.4485869
AbstractFishing is one of the most widespread anthropogenic disturbance activities in the marine environment. Accurate understanding of dynamic changes in fishing activities and their influence mechanisms is crucial for marine environmental protection and fisheries management. Although the temporal and spatial changes in fishing activities are increasingly well understood, little research has comprehensively investigated the reasons for these changes. The advance of Automatic Identification System (AIS) and remote sensing technologies has provided opportunities to obtain high-precision fishery data and marine environmental data. Here, taking the Bohai Sea and its adjacent seas (BSAW) in China as an example, the spatial association of fishing activities with the marine environment, geographical position, socio-economic conditions, and marine management policies was constructed based on AIS data, geographic information system (GIS), and statistical models. The results showed that the temporal and spatial distribution of fishing intensity was not homogeneous in the BSAW. These changes in fishing activities were the result of a combination of factors. Overall, the average explanatory power of socio-economic and geographical position factors on the spatial stratified heterogeneity of fishing activities was higher than that of marine environmental factors. Among them, 39.6% of the variance in fishing activities can be explained by combining marine environmental and geographical position factors with a Generalized Additive Model (GAM). Intensive fishing was mainly concentrated in a certain range of the sea surface temperature frontal gradient (SSTF, 0.20~0.60 ℃/km) or sea surface chlorophyll-a frontal gradient (Chl-aF, 1.06~1.25). Moreover, fishing activities responded well to marine management policies, with more than 70% of fishing activities located within restricted or allowable fishing areas. This study shed new light on key influencing factors on fishing activities in coastal waters and the complexity of their interaction. These findings can provide a theoretical basis for marine environmental management and sustainable development of fisheries. © 2023, The Authors. All rights reserved.
Indexed ByEI
Language英语
EI KeywordsAutomation ; Environmental management ; Environmental technology ; Information management ; Oceanography ; Remote sensing ; Surface temperature ; Surface waters ; Sustainable development
EI Accession Number20230205948
Citation statistics
Document Type期刊论文
Identifierhttp://ir.yic.ac.cn/handle/133337/34301
Collection中国科学院海岸带环境过程与生态修复重点实验室
中国科学院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心
Affiliation1.Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai; 264003, China;
2.University of Chinese Academy of Sciences, Beijing; 100049, China;
3.Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Chinese Academy of Sciences, Yantai; 264003, China
Recommended Citation
GB/T 7714
Chen, Renli,Wang, Yueqi,Wu, Xiaoqing,et al. Identifying Key Factors Influencing Regional Fishing Footprints in Coastal Waters[J]. SSRN,2023.
APA Chen, Renli,Wang, Yueqi,Wu, Xiaoqing,&Gao, Zhiqiang.(2023).Identifying Key Factors Influencing Regional Fishing Footprints in Coastal Waters.SSRN.
MLA Chen, Renli,et al."Identifying Key Factors Influencing Regional Fishing Footprints in Coastal Waters".SSRN (2023).
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