Search Results (6)

Yellowfin tuna (Thunnus albacares) constitutes a critical global fishery resource, and its distribution pattern is correlated to varying degrees with the marine environment. This study utilized longline fishing data from the Western and Central Pacific Fisheries Commission (WCPFC) and the Inter-American Tropical Tuna Commission (IATTC) spanning 2004 to 2020, categorized by quarter and combined with surface and 0–200 m depth environmental variables. Geographical random forests (GRF) were employed to examine spatially non-stationary relationships between yellowfin tuna resources and environmental factors. Additionally, by integrating GRF with GeoShapley explainable methods, we quantitatively evaluated the mechanistic impacts of environmental drivers on tuna distribution across spatial scales. The findings indicated that (1) the GRF model demonstrated superior performance throughout all four quarters, with the goodness of fit on the 20% test set (R² = 0.72–0.85) consistently surpassing that of conventional random forest (RF) (R² = 0.68–0.79) and extreme gradient boosting random forest (XGBRF) (R² = 0.68–0.80). Moreover, in most cases, it had a lower RMSE and MAE, while effectively addressing spatial heterogeneity issues in yellowfin tuna fishery resources across most regions. (2) GeoShapley spatial explainable analysis revealed distinct environmental drivers, showing that the sea surface temperature and temperature at 105 m depth significantly influenced yellowfin tuna resources across all quarters, following a “high-value promotion, low-value inhibition” pattern, with salinity and dissolved oxygen at 105 m depth in Q2–Q3 and mixed-layer depth in Q3 also demonstrating notable effects. (3) Significant spatiotemporal heterogeneity was observed. The main spatial effects and temperature–depth–locality interactions remained significant throughout the year; mixed-layer depth–locality interactions were prominent in Q1, Q3, and Q4, dissolved oxygen–locality interactions in Q2 and Q4, and 105 m salinity–locality interactions exclusively in Q2. This study used geographical random forests (GRF) to integrate spatial statistics and machine learning to model the relationship between Pacific yellowfin tuna fishery resources and environmental factors. This approach demonstrates potential in improving spatial predictions of heterogeneous tuna resources and may help to identify key environmental drivers influencing their distribution. These findings provide essential insights for the formulation of science-based management strategies for Pacific yellowfin tuna fisheries. Full article

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. Full article

The yellowfin tuna represents a significant fishery resource in the Pacific Ocean. Its resource endowment status and spatial variation mechanisms are intricately influenced by marine environments, particularly under varying climate events. Consequently, investigating the spatial variation patterns of dominant environmental factors under diverse climate conditions, and understanding the response of yellowfin tuna catch volume based on the spatial heterogeneity among these environmental factors, presents a formidable challenge. This paper utilizes comprehensive 5°×5° yellowfin tuna longline fishing data and environmental data, including seawater temperature and salinity, published by the Western and Central Pacific Fisheries Commission (WCPFC) and the Inter-American Tropical Tuna Commission (IATTC) for the period 2000–2021 in the Pacific Ocean. In conjunction with the Niño index, a multiscale geographically weighted regression model based on geographically weighted principal component analysis (GWPCA-MGWR) and spatial association between zones (SABZ) is employed for this study. The results indicate the following: (1) The spatial distribution of dominant environmental factors affecting the catch of Pacific yellowfin tuna is primarily divided into two types: seawater temperature dominates in the western Pacific Ocean, while salinity dominates in the eastern Pacific Ocean. When El Niño occurs, the area with seawater temperature as the dominant environmental factor in the western Pacific Ocean further extends eastward, and the water layers where the dominant environmental factors are located develop to deeper depths; when La Niña occurs, there is a clear westward expansion in the area with seawater salinity as the dominant factor in the eastern Pacific Ocean. This change in the spatial distribution pattern of dominant factors is closely related to the movement of the position of the warm pool and cold tongue under ENSO events. (2) The areas with a higher catch of Pacific yellowfin tuna are spatially associated with the dominant environmental factor of mid-deep seawater temperature (105–155 m temperature) to a greater extent than other factors, the highest correlation exceeds 70%, and remain relatively stable under different ENSO events. The formation of this spatial association pattern is related to the vertical movement of yellowfin tuna as affected by subsurface seawater temperature. (3) The GWPCA-MGWR model can fully capture the differences in environmental variability among subregions in the Pacific Ocean under different climatic backgrounds, intuitively reflect the changing areas and influencing boundaries from a macro perspective, and has a relatively accurate prediction on the trend of yellowfin tuna catch in the Pacific Ocean. Full article

Studies have shown the abundance of fisheries resources in the waters of the northern part of the Philippines bordering southern Taiwan. However, discrepancies in legal frameworks, enforcement mechanisms, and cultural practices, as well as maritime boundary issues, contribute to complexities in collaboration. This paper thus aims to provide an understanding into the intricacies and challenges faced by both countries in managing their shared fishing resources. By analyzing the relevant international laws and instruments on fisheries cooperation, the paper shows what coastal states and entities fishing in the high seas could do to manage and conserve fishery resources in disputed areas. Existing fisheries agreements in the region such as the Taiwan–Japan Fisheries Cooperation provide a template of the kind of cooperation that can be concluded within the overlapping waters of both nations. Results of the analysis show how important it is for both nations to acknowledge the significance of fisheries cooperation in the overlapping waters. By recognizing the mutual benefits of sustainable resource management through peaceful dialogue, establishing a fisheries cooperation under the legal framework of the WCPFC is the logical solution. The findings contribute to understanding the complexities of cross-border fisheries cooperation and provide valuable insights for policymakers and stakeholders in the region. Full article

The ecosystem approach to fisheries management (EAFM) is considered one of the key management approaches for addressing global resource decline and promoting the health and resilience of ecosystems. This paper explores how the Western and Central Pacific Fisheries Commission (WCPFC), which manages tuna fisheries, has incorporated the ecosystem approach into its management and decision-making system. This study finds that (1) the WCPFC lacks incentives to adopt EAFM as a whole due to its management priorities on target species and some key bycatch species; (2) inadequate scientific information on associated species and the environment hinders ecosystem risk assessments, leading to delays in EAFM-related decisions; and (3) the organization has given little consideration to human factors. The authors suggest developing an EAFM vision with a clear roadmap to guide the implementation of EAFM and applying area-based management tools in cases where there is limited data and scientific information. The proposed coordination mechanism aims to address growing concerns about labor issues by involving multiple stakeholders in the development of a management measure on labor standards. Full article

Understanding the spatial pattern of human fishing activity is very important for fisheries resource monitoring and spatial management. The environmental preferences of tropical tuna purse seine fleet in the Western and Central Pacific Ocean (WCPO) were constructed and compared at different spatial scales based on the fishing effort (FE) data from the available automatic identification system (AIS) and commercial fishery data compiled from the Western and Central Pacific Fisheries Commission (WCPFC), using maximum entropy (MaxEnt) methods. The MaxEnt models were fitted with FE and commercial fishery data and remote sensing environmental data. Our results showed that the area under the curve (AUC) value each month based on the commercial fishery data (1°) and FE at 0.25° and 0.5° spatial scales was greater than 0.8. The AUC values each month based on the FE data at a 1° scale ranged from 0.775 to 0.829. The AUC values based on commercial fishing data at the 1° scale were comparable to the model results based on FE data at the 0.5° scale and inferior to the model results based on FE data at the 0.25° scales. Overall, the sea surface temperature (SST), temperature at 100 metres (T100), oxygen concentration at 100 metres (O100) and total primary production (PP) had the greatest influence on the distribution of the purse seine tuna fleet. The oxygen concentration at 200 metres (O200), distance to shore (DSH), dissolved oxygen (Dox), EKE, mixed layer depth (Mld), sea surface salinity (SSS), salinity at 100 metres (S100) and salinity at 200 metres (S200) had moderate influences, and other environmental variables had little influence. The suitable habitat areas varied in response to environmental conditions. The purse seine tuna fleet was mostly present at locations where the SST, T100, O100, O200 and PP were 28–30 °C, 27–29 °C, 150–200 mmol/m³ and 5–10 mg/m⁻³, respectively. The MaxEnt models enable the integration of AIS data and high-resolution environmental data from satellite remote sensing to describe the spatiotemporal distribution of the tuna purse seine fishery and the influence of environmental variables on the distribution, and can provide forecasts for fishing ground distributions based on future remote sensing environmental data. Full article