To date, most studies focus on the ecological and environmental effects of land-based photovoltaic (PV) power plants, while there is a dearth of studies examining the impacts of water-based PV power plants. The effects of a fishery complementary PV power plant, a kind of water-based PV technology, on the near-surface meteorology and aquaculture water environment were investigated in coastal aquaculture ponds in southeast China. The results showed that PV prevented 89~93% of the solar radiation on the surface of the pond, resulting in an average reduction in water temperature of 1.5 °C and a substantial decrease in light intensity of 94%. Furthermore, it weakened the wind speed by 41~50% and elevated the surface air temperature by an average of 0.6 °C. In addition, PV power results in an impressive decrease in chlorophyll-α of 72~94% and a notable increase in dissolved oxygen (DO) concentrations of 8~24%. PV power also reduced the concentration of labile phosphate, active silicate, total nitrogen, total phosphorus, and total organic carbon. However, the PV power did not have a substantial influence on the concentrations of nitrate and ammonium. Our results highlight that fishery complementary PV power plants may be able to improve water quality and benefit shade-loving species.

The decomposition of mangrove litter plays a crucial role in material circulation and energy flow within mangrove forests. Evaluating the decomposition-based variation in biogenic elements in litter is important for improving our understanding about their biogeochemical cycling in ecosystems. The main objective of this study was to examine the interaction effect during the decomposition process of mixed Kandelia obovata and Avicennia marina litter. Variations in C and N were also determined in the decomposing leaf litter mixtures. Our findings revealed that the decomposition rates were faster in summer than in winter, and increased with the proportion of A. marina litter. After 35 days of decomposition in summer, the remaining weights for different proportions of K. obovata (KO) and A. marina (AM) were 22.9% (KO:AM = 1:2), 27.2% (KO:AM = 1:1), and 31.2% (KO:AM = 2:1), respectively. Similarly, after 49 days of decomposition in winter, the remaining weights for the different KO:AM proportions were 27.7%, 35.4%, and 44.0%, respectively. Additionally, the decomposition of mixed K. obovata and A. marina litter had an influence on C content and N release dynamics. These results provide a scientific basis for understanding the decomposition of mixed mangrove litter and its implications for material circulation and energy flow within these ecosystems.

In this study, grey system theory is applied through the implementation of GM(1,1) modelling and Grey Relational Analysis (GRA) to forecast and evaluate sturgeon aquaculture production dynamics in Guizhou Province. The results demonstrate a marked temporal dependency in predictive efficacy, with GM(1,1) exhibiting a superior short-term forecasting performance that progressively diminishes with temporal extension. Utilizing 2018–2022 observational data, the GM(1,1) framework achieved Grade 2 precision (mean absolute percentage error, MAPE = 4.172%; 1% <$\overline{ ∆k}$ ≤ 5%), projecting sustained annual production growth. The decade-long forecast (2023–2032) yielded the following production estimates (×10³ tons): 32.3, 39.1, 47.3, 57.2, 69.2, 83.7, 101.2, 122.4, 148.1, and 179.2. GRA identified three principal determinants: the aquatic seed production value (X₉, r = 0.8336), freshwater fishery output (X₂, r = 0.8019), and per capita fisher income (X₅, r = 0.8003). Furthermore, technological promotion funding (X₆) and fishery workforce parameters (X₄), while demonstrating weaker correlations (r < 0.75), maintain critical roles in technological advancement and labour competency enhancement. This methodological framework provides empirical support for sustainable development strategies in Guizhou’s sturgeon aquaculture sector, emphasizing the necessity of temporal-scale considerations and multifactorial optimization in production management.