Li Ma

Mangrove ecosystems play a critical role in sequestering heavy metals pollutants, yet the dynamics of heavy metals accumulation during mixed litter decomposition remain poorly understood. This study investigated the seasonal and species-specific variations in heavy metals accumulation during the decomposition of Kandelia obovata (KO) and Avicennia marina (AM) leaf litter mixtures in a subtropical mangrove forest in the Jiulong River Estuary, Fujian, China. Using the litterbag technique, we monitored eight heavy metals (V, Cr, Ni, Cu, Zn, As, Se, Cd) across three mixing ratios (KO:AM = 1:2, 1:1, 2:1) in summer and winter. Results revealed that V concentrations were influenced by both season and litter ratio, with higher KO proportions enhancing V accumulation in summer but reducing it in winter. In contrast, Cr, Ni, Cu, As, Se, and Cd were primarily regulated by litter ratios: KO-dominated mixtures promoted Cr and Ni accumulation, while AM-dominated mixtures favored Cu, As, Se, and Cd. Zn exhibited the highest variability and was unaffected by season or ratio. Total organic carbon (TOC) and carbon/metal (C/M) ratios significantly correlated with reduced bioavailability of most heavy metals, whereas total nitrogen (TN) and C/N ratios showed no consistent relationship. The heavy metals accumulation index (MAI) indicated higher accumulation in summer than in winter, with the highest MAI observed in the KO:AM = 2:1 treatment group during summer (MAI = 1.36), whereas winter decomposition slowed accumulation rates. These findings highlight the dual regulatory roles of species composition and environmental factors in mangrove heavy metals cycling, offering critical insights for ecological risk assessment and contaminated soil remediation strategies in coastal ecosystems.

This study employs an improved fractional-order grey multivariable convolution model (FGMC(1,N,2r)) to predict abalone aquaculture output in Fujian, Shandong, and Guangdong. By integrating fractional-order accumulation (r₁, r₂) with a particle-swarm-optimization (PSO) algorithm, the model addresses limitations of handling multivariable interactions and sequence heterogeneity within small-sample regional datasets. Grey relational analysis (GRA) first identified key factors exhibiting the strongest associations with production: abalone production in Fujian and Shandong is predominantly influenced by funding for aquatic-technology extension (GRA degrees of 0.9156 and 0.8357, respectively), while in Guangdong, production was most strongly associated with import volume (GRA degree of 0.9312). Validation confirms that FGMC(1,N,2r) achieves superior predictive accuracy, with mean absolute percentage errors (MAPE) of 0.51% in Fujian, 3.51% in Shandong, and 2.12% in Guangdong, significantly outperforming benchmark models. Prediction of abalone production for 2024–2028 project sustained growth across Fujian, Shandong, and Guangdong. However, risks associated with typhoon disasters (X₆ and import dependency (X₅) require attention. The study demonstrates that the FGMC(1,N,2r) model achieves high predictive accuracy for regional aquaculture output. It identifies the primary drivers of abalone production: technology-extension funding in Fujian and Shandong, and import volume in Guangdong. These findings support the formulation of region-specific strategies, such as enhancing technological investment in Fujian and Shandong, and strengthening seed supply chains while reducing import dependency in Guangdong. Furthermore, by identifying vulnerabilities such as typhoon disasters and import reliance, the study underscores the need for resilient infrastructure and diversified seed sources, thereby providing a robust scientific basis for production optimization and policy guidance towards sustainable and environmentally sound aquaculture development.

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.

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.