Bigeye grunt (Brachydeuterus auritus) is a dominant fish species and mostly a major target species in both artisanal and industrial fisheries in the coastal waters of Sierra Leone. It was listed as near threatened in 2015 by the International Union for Conservation of Nature (IUCN) Red List. Although this species has been repeatedly assessed as overexploited by the Fishery Committee for the Eastern Central Atlantic (CECAF) in the majority of its range in the Eastern Central Atlantic, there have never been studies of stock assessment in the coastal waters of Sierra Leone. We conducted a study on the population dynamics of bigeye grunt in the coastal waters of Sierra Leone, which is crucial for completing the resource status of this species in the Eastern Central Atlantic. The results showed that the bigeye grunt had a wide distribution in the coastal waters of Sierra Leone, with significant spatiotemporal variation characteristics in biomass and abundance. The growth parameters of bigeye grunt varied across different months, but all E values were below 0.5, indicating that no overfishing occurred. These findings were further corroborated by the results of the Length-Based Bayesian Biomass Estimation method (LBB). The results of the Generalized Additive Model (GAM) show that there is a certain nonlinear relationship between the resource abundance of the bigeye grunt and both environmental factors and geographical locations, among which the influence of latitude is the greatest. This study posits that the bigeye grunt in Sierra Leone’s coastal waters exhibits moderate exploitation potential. The findings are anticipated to provide a scientific framework for informing evidence-based management strategies for this fishery resource.

To accurately predict the fishing intensity of the Northwest Pacific squid fishing grounds and address the limitations of traditional models in capturing long-term temporal and spatial correlations and neglecting the coupling relationships of deep environmental factors, this study constructs a 3DCNN model and three fusion models incorporating residual, attention, and Transformer mechanisms. Using the 2017–2024 AIS fishing data and ocean environmental variables from the North Pacific squid fishing industry, the models’ performance is compared at 12 different temporal and spatial scales, and key core environmental variables are identified. The results show that the ResNet3D model exhibits the best overall performance, achieving an F1 score of 0.7909 at the 1.0°-7 days temporal–spatial scale. The residual connections effectively mitigate the gradient vanishing problem, balancing prediction accuracy and stability. The optimal spatial resolution is 1.0°, and the key environmental variables include S₁₀₀, Chl-a₁₀₀, PP₁₀₀, and DO₁₀₀. S₁₀₀ is the core driving variable, consistently exhibiting the highest feature importance value at all time scales. It should be noted that Chl-a is considered an indirect indicator of primary productivity, which may influence squid distribution through trophic transfer processes rather than direct biological effects. This study demonstrates the prediction accuracy and applicability of the multi-mechanism fusion 3DCNN model, reveals the temporal and spatial distribution patterns of fishing intensity in the Northwest Pacific squid fishing grounds, and provides scientific methods and technical support for dynamic monitoring, intelligent management, and sustainable utilization of squid resources.

Yellowfin tuna (Thunnus albacares) is a key component of the Indian Ocean tuna fishery, constituting approximately 25% of the total tuna catch in the region. However, persistent high-intensity fishing pressure has posed severe threats to its stock status. This study integrated length-frequency data and historical catch records of Indian Ocean yellowfin tuna to construct an integrated assessment workflow incorporating the fishboot, LBB (Length-Based Bayesian Biomass Estimation), and CMSY (Catch-Maximum Sustainable Yield) models. By sequentially propagating prior information across models, this workflow mitigates uncertainties inherent in single-model approaches—as evidenced by low prior-posterior variance ratios (PPVR) in the CMSY analysis, indicating substantial information gain from the data—and enables a comprehensive reconstruction of both current stock status and historical exploitation dynamics under data-limited conditions. The workflow was applied to systematically analyze long-term exploitation dynamics, current resource status, and identify critical overfishing risk phases along with their primary drivers. Results demonstrate that the rapid expansion of fishing effort and widespread adoption of highly efficient gears, particularly purse seine nets, are closely associated with resource changes. Assessments indicate that the Indian Ocean yellowfin tuna stock is currently overfished (B/B_msy = 0.79) and continues to experience excessive fishing pressure (F/F_msy = 1.41). The population decline is attributed to several factors: slow growth (K = 0.25 year⁻¹) leading to low reproductive resilience, continued use of non-selective gear resulting in high catches of immature individuals, and fishing intensity far exceeding sustainable levels. Key parameters estimated in this study–including optimal length at first capture (L_{c_opt}), biological reference points (B/B_msy, F/F_msy), and maximum sustainable yield (MSY)–provide a scientific basis for developing conservation and management strategies for Indian Ocean yellowfin tuna.