Prediction of fishing intensity and trends across South China Sea biogeographic zones

Bin He; Fengqin Yan; Fenzhen Su; Vincent Lyne; Jiasheng Tang

doi:10.1016/j.scitotenv.2023.165691

Prediction of fishing intensity and trends across South China Sea biogeographic zones

Sci Total Environ. 2023 Nov 15:899:165691. doi: 10.1016/j.scitotenv.2023.165691. Epub 2023 Jul 22.

Authors

Bin He¹, Fengqin Yan², Fenzhen Su³, Vincent Lyne⁴, Jiasheng Tang⁵

Affiliations

¹ School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Collaborative Innovation Center for the South China Sea Studies, Nanjing University, Nanjing 210023, China. Electronic address: heb@lreis.ac.cn.
² State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Collaborative Innovation Center for the South China Sea Studies, Nanjing University, Nanjing 210023, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China. Electronic address: yanfq@lreis.ac.cn.
³ School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Collaborative Innovation Center for the South China Sea Studies, Nanjing University, Nanjing 210023, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China. Electronic address: sufz@lreis.ac.cn.
⁴ State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; IMAS-Hobart, University of Tasmania, Hobart, TAS 7004, Australia. Electronic address: vincent.lyne@utas.edu.au.
⁵ State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, China. Electronic address: tangjs@lreis.ac.cn.

PMID: 37482352
DOI: 10.1016/j.scitotenv.2023.165691

Abstract

The volume of industrial fishing in the South China Sea ranks among the top global sustainable fisheries concerns of the Food and Agriculture Organization (FAO). To better understand the scale of management challenges, biogeographic zones of the SCS were characterized, and within each a multivariate GAM (General Additive Model) was fitted to predict and map the complete fishing activities from 2017 to 2020. Model variables, some incomplete or with gaps, included: VIIRS DNB night-time light imagery; Global Fisheries Watch (GFW) data; satellite Ocean Colour; Sea Surface Temperature; and bathymetry data. Four biogeographic zones with differing fishing patterns and trends were identified. We used cross-validation and the GAM model's own tuning method for model prediction accuracy determination, which performed well in four biogeographic zones (R² respectively: 0.62, 0.68, 0.74 and 0.71). High-intensity fishing grounds are mainly distributed in offshore continental shelf areas. From 2017 to 2019, high-intensity fishing grounds were located near the Beibu Gulf of Vietnam, south Vietnam, part of the Gulf of Thailand and the central Java Sea, where fishing effort greater than 50 h exceeded average annual SCS fishing intensity for several years. By season, intensity and extent of fishing in Spring were largest. In 2020, due to the impact of COVID-19, except for Spring, fishing volume generally decreased. Our experimental results provide new insights and an adaptable biogeographic modelling methodology to map the scale and intensity of regional fishing activities more accurately and completely. This more comprehensive database, that takes account of intrinsic biogeographic fishery context, will help improve and strengthen the regulation of fishing activities around the world.

Keywords: Automatic identification system (AIS); Fishing effort; GAM prediction; South China Sea; Spatio-temporal analysis; VIIRS DNB.

MeSH terms

COVID-19*
China
Fisheries
Humans
Hunting*
Seasons