Status Identification in Support of Fishing Effort Estimation for Tuna Longliners in Waters near the Marshall Islands Based on AIS Data

• Published in: Fishes 2024-02, Vol.9 (2), p.66
• Main Authors: Lu, Zhengwei, Song, Liming, Jiang, Keji
• Format: Article
• Language: English
• Subjects: Accuracy; AIS; Back propagation; Classification; Coefficients; correlation analyses; Correlation analysis; Correlation coefficient; Data collection; Datasets; Distribution; Environmental aspects; Fisheries; Fisheries management; Fishery data; Fishery management; Fishery resources; Fishing; Fishing boats; Fishing effort; Fishing vessels; Forecasts and trends; Hooks; Identification; Islands; Logbooks; Longline fishing; Longliners; Longlining (Fisheries); Machine learning; Neural networks; Propagation; Research methodology; Screening; Spatial analysis; Spatial distribution; status identification; Temporal distribution; tuna longliner
• ISSN: 2410-3888; 2410-3888
• DOI: 10.3390/fishes9020066

Visualising the fishing behaviour of vessels and quantifying the spatial distribution of fishing effort is the scientific basis for assessing and managing fisheries resources. The information on the dynamics of fishing vessel voyages provided by the automatic identification system (AIS) of vessels serves as high-precision fishery data and provides a means of quantifying fishing effort with high spatial and temporal resolution in the tuna longline fishery. Based on the AIS data of five tuna longliners operating in the waters near the Marshall Islands from 2020 to 2021, this study used three methods, namely the threshold screening method, the construction of a BP neural network and the support vector machine (SVM) to identify the fishing and non-fishing status of the tuna longliners, respectively. This study investigates the status identification and fishing effort estimation of the tuna longliner (VESSEL A) in 2021 based on the constructed optimal model, and spatial correlation analyses are performed between the fishing effort estimated in hours based on AIS data and in hooks based on fishing logbook data, by month. The results showed (1) the recognition accuracy of the threshold screening method is 89.9%, the recognition accuracy of the BP neural network classification model is 95.11%, the kappa coefficient is 0.51, the recognition accuracy of the SVM classification model is 95.74% and the kappa coefficient is 0.52; (2) in comparison, the SVM classification model performs better than the other two status identification methods for tuna longliners; and (3) the correlation coefficients between the two types of effort of VESSEL A were greater than 0.79 on all fishing months, indicating that there was no significant difference in the spatial and temporal distribution between the two types of effort. This study suggests that the SVM model can be used to identify the status and estimate the fishing effort of longliners.