Simulating the changes of the habitats suitability of chub mackerel (Scomber japonicus) in the high seas of the North Pacific Ocean using ensemble models under medium to long-term future climate scenarios.

Understanding and forecasting changes in marine habitats due to global climate warming is crucial for sustainable fisheries. Using future environmental data provided by Global Climate Models (GCMs) and occurrence records of Chub mackerel in the North Pacific Ocean (2014-2023), we built eight individual models and four ensemble models to simulate current habitat distribution and forecast changes under three future climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) for the 2050s and 2100s. Ensemble models outperformed individual ones, with the weighted average algorithm model achieving the highest accuracy (AUC 0.994, TSS 0.929). Sea Surface Temperature (SST) and chlorophyll-a (Chla) significantly influenced habitat distribution. Predictions indicate current high suitability areas for Chub mackerel are concentrated beyond the 200-nautical-mile baseline. Under future climate scenarios, habitat suitability is expected to decline, with a shift towards higher latitudes and deeper waters. High suitability areas will be significantly reduced.

Spatial information extraction of fishing grounds for light purse seine vessels in the Northwest Pacific Ocean based on AIS data.

Ecological fishery management requires high-precision fishery information to support resource management and marine spatial planning. In this paper, the Automatic Identification System (AIS) was adopted to extract the spatial information on the fishing grounds of light purse seine vessels in the Northwest Pacific Ocean. The spatial distributions of fishing grounds mapped by the data mining, kernel density analysis and hotspot analysis methods were compared. The spatial similarity index was applied to determine the spatial consistency between the computed spatial information and fisheries resource information. Finally, the spatial information derived by the best method was used to investigate the characteristics of fishing activity. The results showed that: the speed of light purse seine vessels related to operations was lower than 1.6 knots. The spatial information extracted by the three methods was consistent with the catch data distribution, and the spatial similarity between the fishing effort and catch data was the highest. The spatial variation in fishing activity was similar to that in the chub mackerel migration route. AIS data could be used to provide high-resolution fishery information. Light purse seine fishing vessels typically operate and travel along the exclusive economic zone boundary, and increased attention must be given to fishing vessel operation supervision. A comprehensive supervision system can be employed to monitor the operations of fishing vessels more effectively. The results of this study can provide technical support for the management of fishing activities and conservation of marine resources in this region using AIS data.

Analysis of the Distribution Characteristics of Jellyfish and Environmental Factors in the Seawater Intake Area of the Haiyang Nuclear Power Plant in China.

In recent years, there have been frequent jellyfish outbreaks in Chinese coastal waters, significantly impacting the structure, functionality, safety, and economy of nuclear power plant cooling water intake and nearby ecosystems. Therefore, this study focuses on jellyfish outbreaks in Chinese coastal waters, particularly near the Shandong Peninsula. By analyzing jellyfish abundance data, a Generalized Additive Model integrating environmental factors reveals that temperature and salinity greatly influence jellyfish density. The results show variations in jellyfish density among years, with higher densities in coastal areas. The model explains 42.2% of the variance, highlighting the positive correlation between temperature (20-26 °C) and jellyfish density, as well as the impact of salinity (27.5-29‱). Additionally, ocean currents play a significant role in nearshore jellyfish aggregation, with a correlation between ocean currents and site coordinates. This study aims to investigate the relationship between jellyfish blooms and environmental factors. The results obtained from the study provide data support for the prevention and control of blockages in nuclear power plant cooling systems, and provide a data basis for the implementation of monitoring measures in nuclear power plants.

Construction of chub mackerel (Scomber japonicus) fishing ground prediction model in the northwestern Pacific Ocean based on deep learning and marine environmental variables.

Accurate prediction of the central fishing grounds of chub mackerel is substantial for assessing and managing marine fishery resources. Based on the high-seas chub mackerel fishery statistics and multi-factor ocean remote-sensing environmental data in the Northwest Pacific Ocean from 2014 to 2021, this article applied the gravity center of the fishing grounds, 2DCNN, and 3DCNN models to analyze the spatial and temporal variability of the chub mackerel catches and fishing grounds. Results:1) the primary fishing season of chub mackerel fishery was April-November which catches were mainly concentrated in 39°âˆ¼43°N, 149°âˆ¼154°E. 2) Since 2019, the annual gravity center of the fishing grounds has continued to move northeastward; the monthly gravity center has prominent seasonal migratory characteristics. 3) 3DCNN model was better than the 2DCNN model. 4) For 3DCNN, the model prioritized learning information on the most easily distinguishable ocean remote-sensing environmental variables in different classifications.