Oceans

Toxic dinoflagellate blooms and microplastics are widespread coastal pollutants. In this study, the scallop, Argopecten irradians, was selected as an experimental organism to systematically investigate the single and combined toxic effects of polystyrene (PS) and the toxic dinoflagellate, Alexandrium pacificum. The results showed that both PS and algal cells could be ingested by A. irradians. The survival rate of A. irradians remained above 90% in both the single and combined treatment groups, indicating that 1 mg/L PS and 1500 cells/mL A. pacificum cells did not pose a serious threat to scallop survival in the short term. However, CAT, SOD, and GSH-ST activities, as well as MDA content, were all elevated in the combined treatment group. Transcriptomic analysis further revealed that A. pacificum primarily affected immune-related pathways, whereas PS might interfere with endocrine function through the release of additives. Combined exposure to PS and A. pacificum induced more complex synergistic effects, reflected in the metabolic stress of exogenous substances, and the disruption of developmental and homeostasis regulatory pathways. This study provides important theoretical support for assessing the threats posed by composite coastal pollution to aquaculture and marine ecological security.

Underwater imaging suffers from significant degradation due to scattering by suspended particles, selective absorption by the medium, and depth-dependent noise, leading to issues such as contrast reduction, color distortion, and blurring. Existing enhancement methods typically address only one aspect of these problems, relying on unrealistic assumptions of uniform noise, and fail to jointly handle the spatially heterogeneous noise and spectral channel attenuation. To address these challenges, we propose the variational-based spatial–spectral joint enhancement method (VSJE). This method is based on the physical principles of underwater optical imaging and constructs a depth-aware noise heterogeneity model to accurately capture the differences in noise intensity between near and far regions. Additionally, we propose a channel-sensitive adaptive regularization mechanism based on multidimensional statistics to accommodate the spectral attenuation characteristics of the red, green, and blue channels. A unified variational energy function is then formulated to integrate noise suppression, data fidelity, and color consistency constraints within a collaborative optimization framework, where the depth-aware noise model and channel-sensitive regularization serve as the core adaptive components of the variational formulation. This design enables the joint restoration of multidimensional degradation in underwater images by leveraging the variational framework’s capability to balance multiple enhancement objectives in a mathematically rigorous manner. Experimental results using the UIEBD-VAL dataset demonstrate that VSJE achieves a URanker score of 2.4651 and a UICM score of 9.0740, representing a 30.9% improvement over the state-of-the-art method GDCP in the URanker metric—a key indicator for evaluating the overall visual quality of underwater images. VSJE exhibits superior performance in metrics related to color uniformity (UICM), perceptual quality (CNNIQA, PAQ2PIQ), and overall visual ranking (URanker).

The deep sea is often depicted as a barren environment. Using the abyssal plain as a baseline system characterized by high pressure, extreme nutrient limitation, and slow growth rates, this review contrasts these conditions with specialized habitats that serve as oases of life such as whale falls, cold seeps, and hydrothermal vents. These environments retain the high-pressure characteristic of deep-sea habitats, but other unique environmental factors select for organisms with distinct life-history strategies and growth rates. This review examines the environmental constraints, organism physiological adaptations, and life-history strategies that define each habitat. Through synthesizing these factors, we identify patterns that influence not only growth and succession, but broader ecosystem vulnerability and resilience, defined here as the capacity of these communities to recover from disturbance. By evaluating how biological traits contribute to resilience across the four habitats in response to specific environmental constraints, this comparative framework identifies trade-offs between growth specialization and habitat stability. Understanding these environmental factors is critical in evaluating the resilience of these habitats to growing anthropogenic disturbances and determining future directions of study. This review concludes that while hydrostatic pressure and temperature impose fundamental metabolic constraints, nutrient availability and habitat stability are the primary determinants of organismal growth rates and life-history strategies. In the context of each ecosystem, both these variables can play a large role in the ability and time to recover from disturbance and may be good indicators of resilience at both a community and an organismal level. Consequently, slow-growing, long-lived fauna may possess far lower intrinsic resilience to anthropogenic disturbance compared to rapidly growing organisms with shorter life histories. Varying resilience of these habitats may necessitate habitat-specific strategies for assessment and protection.