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.

Chub mackerel (Scomber japonicus) is a commercially important fish species which are widely distributed in the North Pacific. Based on the fishery data from China’s high-sea light-purse seine fishing from 2014 to 2020 and the marine environment factors, a mixed linear model considering the actual spatiotemporal stratification of the catch per unit effort (CPUE) was established to analyze the fixed and random effects of marine environmental factors on the CPUE of chub mackerel and to investigate the relationship between the abundance of chub mackerel resources in the Northwest Pacific and two marine environmental factors: sea surface temperature (SST) and chlorophyll-a concentration (CHL). The results showed that SST had a significant fixed effect on the CPUE. In contrast, the natural logarithm of chlorophyll (logCHL) had no fixed effect on the CPUE. Based on the monthly analysis, random fluctuations were observed in the impact of logCHL on the CPUE. LogCHL and CPUE show a positive correlation during spawning and wintering periods and a negative correlation during the feeding period. The study showed that when fishery sampling data exhibit spatiotemporal stratification, linear mixed models can effectively incorporate both the fixed and random effects of environmental factors on the CPUE of chub mackerel. Linear mixed models can play an important role in analyzing the fluctuations in resource abundance and the mechanisms governing the formation of fishing grounds for chub mackerel in the Northwest Pacific.

In aquaculture production, manual or fixed-schedule feeding often fails to match the real-time feeding level of fish schools, and overfeeding can lead to feed wastage and water-quality deterioration, which has become a major bottleneck for both large-scale farming efficiency and environmental sustainability. During feeding, intense competition and jumping behaviors generate splashes of varying magnitudes, which can serve as an indirect visual proxy for hunger intensity. In this study, we constructed a frame-level splash-annotated dataset and performed data preprocessing. Building upon YOLO11 pretrained weights, we introduced a P2–P5 four-scale detection head to enhance small-splash recognition, injected EGMA into the backbone C3k2 blocks, and replaced stride-2 downsampling convolutions with a three-branch ADown operator. On the validation set, the proposed YOLO11-PEGA achieved a precision of 0.86 and a recall of 0.80, with mAP@0.5 exceeding 0.80 and mAP@0.5–0.95 exceeding 0.30. Compared with the baseline model, the parameter count was reduced by 72.3%. The results demonstrate that the proposed model maintains stable detection and evaluation performance under complex environmental conditions, providing actionable decision support for feeding-threshold setting, feeding-time determination, and feed-amount adjustment.

In recent years, with the development of pelagic fishing, the working environment and monitoring of crew (squid jigger) members have become increasingly important. However, traditional methods of pelagic human observers suffer from high costs, low coverage, poor timeliness, and susceptibility to subjective factors. In contrast, the Electronic Monitoring System (EMS) has advantages such as continuous operation under various weather conditions; more objective, transparent, and efficient data; and less interference with fishing operations. This paper shows how the 3DCNN model, LSTM+ResNet model, and TimeSformer model are applied to video-classification tasks, and for the first time, they are applied to an EMS. In addition, this paper tests and compares the application effects of the three models on video classification, and discusses the advantages and challenges of using them for video recognition. Through experiments, we obtained the accuracy and relevant indicators of video recognition using different models. The research results show that when NUM_FRAMES is set to 8, the LSTM+ResNet-50 model has the best performance, with an accuracy of 88.47%, an $F1$ score of 0.8881, and an $m_{ap}$ score of 0.8133. Analyzing the EMS for pelagic fishing can improve China’s performance level and management efficiency in pelagic fishing, and promote the development of the fishery knowledge service system and smart fishery engineering.

The cooling water intake systems of coastal nuclear power plants are frequently clogged by marine organisms in the water intake area. This study uses hydroacoustic (BioSonics DT-X, 199 kHz) and trawling methods (20 mm codend mesh size) to research fish spatiotemporal dynamics. Species composition, seasonal variations in fish abundance, and target strength were analyzed. Pearson’s correlation analysis revealed a significant negative correlation between water depth and resource density. Significant differences in fish abundance and biomass density were observed among seasons and stations. An analysis of dominant species showed that the IRI (index of relative importance) of Collichthys lucidus was highest in February 2023 (5736.54), while Harpadon nehereus had the highest IRI in November 2023 (2309.17). The distribution and abundance estimates of dominant species from acoustic surveys and biological trawling were highly consistent, demonstrating the applicability and reliability of hydroacoustic methods in fish resource assessments. Through the identification of dominant species as well as the distribution patterns of fish within the water intake area, valuable data support can be provided for the confirmation of those organisms that are prone to clogging the cooling water source. Moreover, it lays a solid foundation for the development of relevant protection work.

Fine-scale identification of Spartina alterniflora (S. alterniflora) is essential for coastal wetland conservation. However, in tidal-flat environments, lodged S. alterniflora often occurs together with upright S. alterniflora and native vegetation. The two-dimensional spectral features of S. alterniflora are easily affected by senescence, canopy posture, tidal stage and mixed pixels, leading to unstable classification. The integration of UAV multispectral imagery and LiDAR data can effectively address this problem. The study focused on Shangsha Island within Jiuduansha Wetland in the Yangtze Estuary and constructed multidimensional spectral–structural features by integrating the two data sources. The separability of upright S. alterniflora, lodged S. alterniflora, Phragmites australis (P. australis) and Scirpus mariqueter (S. mariqueter) was characterized using point-cloud elevation distributions, vertical organization and canopy density. The results showed that P. australis had a multilayered point-cloud structure with broad vertical extent, S. mariqueter showed a compact and sparse structure, and S. alterniflora was characterized by a continuous and dense single-layer point-cloud structure. Lodged S. alterniflora further showed a more concentrated, single-layered point-cloud structure and stronger grass-layer continuity. Multisource classification achieved an overall accuracy of 98.15% and a Kappa coefficient of 0.97. In the lodging-area comparison experiment, point-cloud fusion increased overall accuracy from 95.18% to 97.99% and Kappa from 0.89 to 0.95, improving boundary continuity and discrimination stability. Experimental results demonstrate that the fusion of UAV multispectral imagery and LiDAR data can improve the identification of lodged S. alterniflora in complex tidal-flat environments. Accurate identification and spatial delineation of S. alterniflora can help reduce field-survey effort and associated costs while supporting more targeted and efficient removal operations.