Zengfeng Zhao

Shellfish aquaculture can alter sediment conditions and affect benthic ecosystem functioning, so dredging is widely applied as a management strategy to mitigate sediment deterioration. However, its ecological effectiveness remains uncertain. This study evaluated ecological quality under the disturbance of dredging in shellfish farming areas of Gamak Bay, South Korea, using multiple macrobenthic indices. Macrobenthic samples and environmental data were collected before (May 2025) and after dredging (August 2025). Five macrobenthic indices, including the AZTI Marine Biotic Index (AMBI), BENTIX, Benthic Polychaete/Amphipod ratio (BPA), Benthic Pollution Index (BPI), and Multivariate AMBI (M-AMBI), along with a composite index, were used to assess ecological quality. Temporal changes within groups were tested using Wilcoxon signed-rank tests, and differences between dredged and control stations were examined using Mann–Whitney U tests. Multivariate analyses were used to explore environmental gradients and community responses. Results showed clear seasonal variation in environmental conditions and macrobenthic community structure. Most indices indicated a decline in ecological quality after dredging, with higher AMBI values and lower BENTIX, BPI, and M-AMBI values at dredged stations. However, these changes were not statistically significant (p > 0.05), suggesting limited short-term effects of dredging. The proportion of stations with acceptable ecological status decreased slightly from May to August. Seasonal factors, particularly temperature and salinity, played a dominant role in structuring benthic communities. Overall, the findings indicate that the short-term dredging effects were weaker than seasonal environmental variability. A multi-index approach is recommended for robust ecological assessment, and long-term monitoring is necessary to fully evaluate the effectiveness of dredging in shellfish aquaculture systems.

This study systematically investigated the driving mechanisms and feedback effects of fluoride pollution gradients on fungal communities in water-soil systems, using the Qingshui River basin in Ningxia, China, as a case study. In 2022, 66 sets of samples, each comprising water, sediment, and riparian soil, were collected across three phases (May, July, December). High-throughput sequencing combined with fluoride speciation analysis revealed that fluoride pollution significantly reduced fungal alpha diversity (low-fluoride group > high-fluoride group I > high-fluoride group II), with aquatic habitats exhibiting the most sensitive response. Ascomycota and Chytridiomycota were identified as dominant fluoride-tolerant phyla, and Ascobolus and Cladosporium as representative tolerant genera. Fungi influenced fluoride speciation through mediating mineral weathering and organic matter metabolism; for instance, Humicola promoted fluoride immobilization, while Archaeorhizomyces participated in organic matter-bound fluoride (O.M.-F) metabolism. Fungi in sediments tended to promote the accumulation of residual fixed fluoride (Res-F), whereas those in riparian soils exhibited dual regulatory effects on the release of bioavailable fluoride (Ba-F). This research elucidates the succession patterns of fungal communities under fluoride pollution and their feedback mechanisms on fluoride biogeochemical cycling, offering a theoretical basis for ecological restoration in high-fluoride regions.

The clarification of community assembly mechanisms in benthic macroinvertebrates and their respective contributions to the development of beta diversity is a fundamental concern in aquatic ecology. Nonetheless, the intrinsic complexity of community alterations and their non-linear reactions to gradients of explanatory variables present considerable obstacles to measuring the determinants of beta diversity. Fifty sampling points were set up along the major rivers of the Yellow River Irrigation Area (YRIA), the Central Arid Zone (CAZ), and the Southern Mountainous Area (SMA) in Ningxia in April, July, and October 2023. The findings demonstrate that the optimal parameter-based geographical detector (OPGD) model identified a 3000 m circular buffer as the spatial scale at which landscape structure most significantly influences water quality. A degradation in water quality presumably results in diminished differences in species composition among communities. The Sørensen index was determined to be more appropriate for this investigation, and the total beta diversity of the communities was relatively high (β_SOR ≥ 0.82), with no identifiable nested spatial patterns detected. Except in the YRIA, environmental variability contributed more significantly to the variance in beta diversity than spatial factors, and deterministic mechanisms dominated the community assembly of benthic macroinvertebrates across all three months. To improve biodiversity and aquatic ecosystem health, the study region should optimize its landscape structure by reducing the amount of bare land and increasing the percentage of forest land within buffer zones. Additionally, a multi-site conservation strategy should be put into place.

As the foundational and core resource for agricultural development, arable land plays a critical role in optimizing regional agricultural resource allocation and ensuring food security. This study employs Morphological Spatial Pattern Analysis (MSPA) and the Landscape Pattern Index (LPI) to comprehensively analyze the erosion and fragmentation of arable land in Shandong Province from 1990 to 2023. Bivariate spatial autocorrelation (BSA), Pearson correlation coefficients and the Self-Organizing Map (SOM) algorithm are applied to examine the relationships among landscape pattern indices and spatial clustering patterns. Finally, the driving factors are explored using Multiscale Geographically Weighted Regression (MGWR). The results demonstrate the following: (1) The results indicate a rapid decline in core cultivated zones across central and southeastern coastal Shandong between 1990 and 2023. The proportion of primary core areas (Level 1) dropped sharply from 82.44 to 30.28% of total arable land, suggesting marked degradation of the agricultural landscape. (2) An assessment of landscape pattern metrics reveals increasing fragmentation of cultivated land. In contrast, the western regions have retained more intact agricultural landscapes, marked by a higher share of cultivated land, more cohesive patch patterns, and stronger spatial aggregation. (3) Bivariate spatial autocorrelation (mean Moran’s I = 0.6516) and Pearson correlation (mean r = 0.9225) both point to a strong positive association between the Aggregation Index (AI) and the Percentage of Landscape (PLAND). Among the six cluster types, Clusters C, D, and F—characterized by large area shares, high aggregation, and strong connectivity—represent the most suitable regions for agriculture. Although the proportions of Clusters C and D declined (from 26.5 to 3.7% and 36.8 to 25.7%, respectively), farmland protection policies contributed to a notable rise in Cluster F, from 17.6 to 27.9% over the study period. (4) The influence of key drivers varies across different landscape metrics. Slope gradient shows the greatest explanatory power for the Perimeter-Area Fractal Dimension (PAFRAC), Patch Density (PD), and AI. Meanwhile, NDVI, nighttime light index, and GDP emerge as primary drivers for Connectance Index (CONNECT), Patch Cohesion Index (COHESION), and PLAND respectively, with regression coefficients ranging [0.43, 0.76], [−0.99, −0.67], and [−0.98, −0.12].