The roughscale sole, Clidoderma asperrimum is categorized as an endangered species. Sperm freezing is essential for preserving gametes. This study examined the CPA concentration, diluent, dilution ratio, and thawing temperature to design a sperm cryopreservation protocol for roughscale sole. The variables examined included sperm motility and kinematics, cell survival, fertilization, and DNA fragmentation. Sperm motility parameters were assessed via computer-assisted sperm analysis using a CEROS II instrument. Cell survival rate and DNA damage were assessed using the Cell Counting Kit-8 and single-cell gel electrophoresis assay, respectively. Sperm preservation was tested using several CPAs, including ethylene glycol, dimethyl sulfoxide (DMSO), glycerol, propylene glycol, and methanol. The diluents tested were 300 mM sucrose, 300 mM glucose, Stein’s solution, Ringer’s solution, and Hank’s solution. The optimal conditions for sperm cryopreservation were 10% DMSO + Stein’s solution. After thawing, sperm motility was highest with a 1:1 dilution ratio (sperm to CPA + diluent), at 69.20 ± 0.32%; thawing at 10 °C was optimal for post-thaw motility (72.03 ± 0.95%). The highest fertilization rate (40.00 ± 1.22%) was obtained using DMSO. The fresh sperm had the lowest tail DNA, followed by 10% DMSO + Stein’s solution. The developed cryopreservation methods can be used in roughscale sole hatcheries.

Intermediate-term preservation of sperm assists the reproductive management of fish spermatozoa; however, no information is available on sperm of the spotted halibut, Verasper variegatus. We aimed to identify the optimum diluents, temperatures, dilution ratios, antibiotics, and antioxidants for sperm motility and cell viability. The diluents evaluated were marine fish Ringer’s solution (MFRS), Stein’s solution, 300 mM sucrose, and 300 mM glucose (diluted 1:1 [sperm: diluent], 1:2, 1:4, and 1:10 and stored at 0, 2, 4, and 6 °C). Neomycin and gentamycin (100, 200, 400, and 800 mg/L) and antioxidants (Mito-TEMPO [0, 25, 50, 75, 100, 125, 150, 175, and 200 µM], reduced glutathione [0, 2, 4, 6, 8, and 10 mM], and trehalose [0, 50, 100, 150, 200, and 250 mM]) were assessed in terms of sperm preservation. The most effective condition for cold storage of spotted halibut sperm was Stein’s solution at a dilution ratio of 1:4 at 2 °C, with a combination of neomycin 800 mg/L and 250 mM trehalose that showed spermatozoa motility of > 43% after 60 days. These storage conditions will be valuable for spotted halibut hatcheries.

The cultivation of abalone, a species with high economic value, faces significant challenges due to its slow growth rate and sensitivity to environmental conditions, resulting in prolonged cultivation periods and increased mortality risks. To address these challenges, we propose a novel probabilistic machine learning approach based on a Bayesian framework to predict abalone growth by modeling key environmental factors, including water temperature, pH, salinity, nutrient supply, and dissolved oxygen levels. The proposed method employs a weighted Bayesian kernel machine regression model, integrating Gaussian processes with a spike-and-slab prior to identify influential variables. This approach accommodates heteroscedasticity, capturing varying levels of variance across observations, and models complex, non-linear relationships between environmental factors and abalone growth. Our analysis reveals that time, dissolved oxygen, salinity, and nutrient supply are the most critical factors influencing growth, while water temperature and pH play relatively minor roles under controlled indoor farming conditions. Interaction analysis highlights the non-linear dependencies among factors, such as the combined effects of salinity and nutrient supply. The proposed model not only improves prediction accuracy compared to baseline methods, but also provides actionable insights into the environmental dynamics that optimize abalone growth. These findings underscore the potential of advanced machine learning techniques in enhancing aquaculture practices and offer a robust framework for managing complex, multi-variable systems in sustainable farming.

Olive flounder (Paralichthys olivaceus) is a key aquaculture species in South Korea, but its production has been challenged by rising mortality under environmental stress from key environmental factors such as water temperature, dissolved oxygen, and feeding conditions. To support adaptive management, this study proposes a Bayesian Deep Kernel Machine Regression (BDKMR) model that integrates Gaussian process regression with neural network-based feature learning. Using longitudinal data from commercial farms, we model fish growth as a function of water temperature, dissolved oxygen, and feed quantity. Model performance is assessed via Leave-One-Out Cross-Validation and compared against kernel ridge regression and Bayesian kernel machine regression. Results show that BDKMR achieves substantially lower prediction errors, indicating superior accuracy and robustness. These findings suggest that BDKMR offers a flexible and effective framework for predictive modeling in aquaculture systems.