Fisheries and Aquatic Sciences

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.

Fisheries and Aquatic Sciences

Fisheries and Aquatic Sciences

In commercial abalone production, sperm quality is a key determinant of fertilization success and, consequently, the long-term sustainability of the industry. However, human activities, such as the release of acidic and alkaline industrial wastes, have altered ocean pH levels, particularly affecting coastal waters, which may reduce abalone sperm quality and prevent fertilization. Currently, most studies regarding the effects of pH have focused on the development of embryos and larvae. Research on Pacific abalone sperm quality responses to pH remains limited. This study investigates the impact of pH on the quality of Pacific abalone sperm and the success of fertilization. Fresh sperm were incubated for 15 min in artificial seawater at pH values ranging from 6 to 10. Sperm quality was then assessed using computer-assisted sperm analysis (CASA), which included measurements of motility, progressive motility, beat cross frequency (BCF), the amplitude of lateral displacement (ALH), curvilinear velocity (VCL), straight-line velocity (VSL), the straightness index (STR), linearity (LIN), and average path velocity (VAP). We also assessed fertilization rates, live/dead sperm viability, acrosome integrity, hydrogen peroxide levels, and DNA damage levels. Compared to the control, decreases in motility and progressive motility were observed at pH levels of 6, 9, and 10. However, other CASA metrics (BCF, ALH, VCL, VSL, STR, LIN, and VAP) were not significantly affected. The percentage of cells exhibiting DNA damage was impacted considerably by pH. Similarly, the proportion of cells with damaged acrosomes rose at pH 10, and the proportion of dead sperm cells increased dramatically at pH 6, 9, and 10. At pH 6, hydrogen peroxide levels increased. Fertilization rates were reduced by exposure to the extreme pH levels tested, pH 6 and pH 10. In conclusion, Pacific abalone sperm quality may be adversely affected in seawater at pH values of 6, 9, and 10. These findings suggest that changes in coastal seawater pH may impact sperm quality, potentially affecting both natural abalone populations and aquaculture yields.