Spatial Prediction and Environmental Response of Skipjack Tuna Resources from the Perspective of Geographic Similarity: A Case Study of Purse Seine Fisheries in the Western and Central Pacific

• Published in: Journal of marine science and engineering Vol. 13; no. 8; p. 1444
• Main Authors: Feng, Shuyang, Yang, Xiaoming, Li, Menghao, Hua, Zhoujia, Tian, Siquan, Zhu, Jiangfeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 29.07.2025
• Subjects: Commercial fishing; Environmental factors; Exploitation; Fish; Fisheries; Fisheries management; Fishery data; Fishery resources; Fishing; Fishing grounds; Fishing zones; Geography; Machine learning; Marine ecosystems; Marine environment; Marine resources; Methods; Net Primary Productivity; Overfishing; Predictions; Primary production; Purse seines; Salinity; Sea surface temperature; Similarity theory; Spatial data; Spatial distribution; Surface salinity; System theory; Tuna; Uncertainty; Uncertainty analysis; Variables
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse13081444

Skipjack tuna constitutes a crucial fishery resource in the Western and Central Pacific Ocean (WCPO) purse seine fishery, with high economic value and exploitation potential. It also serves as an essential subject for studying the interaction between fishery resource dynamics and marine ecosystems, as its resource abundance is significantly influenced by marine environmental factors. Skipjack tuna can be categorized into unassociated schools and associated schools, with the latter being predominant. Overfishing of the associated schools can adversely affect population health and the ecological environment. In-depth exploration of the spatial distribution responses of these two fish schools to environmental variables is significant for the rational development and utilization of tuna resources and for enhancing the sustainability of fishery resources. In sparsely sampled and complex marine environments, geographic similarity methods effectively predict tuna resources by quantifying local fishing ground environmental similarities. This study introduces geographical similarity theory. This study focused on 1° × 1° fishery data (2004–2021) released by the Western and Central Pacific Fisheries Commission (WCPFC) combined with relevant marine environmental data. We employed Geographical Convergent Cross Mapping (GCCM) to explore significant environmental factors influencing catch and variations in causal intensity and employed a Geographically Optimal Similarity (GOS) model to predict the spatial distribution of catch for the two types of tuna schools. The research findings indicate that the following: (1) Sea surface temperature (SST), sea surface salinity (SSS), and net primary productivity (NPP) are key factors in GCCM model analysis, significantly influencing the catch of two fish schools. (2) The GOS model exhibits higher prediction accuracy and stability compared to the Generalized Additive Model (GAM) and the Basic Configuration Similarity (BCS) model. R2 values reaching 0.656 and 0.649 for the two types of schools, respectively, suggest that the geographical similarity method has certain applicability and application potential in the spatial prediction of fishery resources. (3) Uncertainty analysis revealed more stable predictions for unassociated schools, with 72.65% of the results falling within the low-uncertainty range (0.00–0.25), compared to 52.65% for associated schools. This study, based on geographical similarity theory, elucidates differential spatial responses of distinct schools to environmental factors and provides a novel approach for fishing ground prediction. It also provides a scientific basis for the dynamic assessment and rational exploitation and utilization of skipjack tuna resources in the Pacific Ocean.