Polycyclic aromatic hydrocarbons (PAHs) are pervasive organic pollutants in coastal ecosystems, especially in tidal flat wetlands. However, the mechanisms through which PAHs impact the soil bacterial communities of wetlands featuring a simple vegetation structure in the Yellow River Delta (China) remain largely unclear. In this study, we examined soil samples from two sites featuring a single vegetation type (Suaeda salsa) in the Yellow River Delta. Specifically, we investigated the impacts of PAHs on the diversity and composition of soil bacteria communities through high-throughput 16 S rRNA sequencing. PAHs significantly increased the soil organic carbon content but decreased the total phosphorus content (p = 0.02). PAH contamination notably reduced soil bacterial community α diversity (Shannon index) and β diversity. Furthermore, PAHs significantly altered the relative abundance of bacterial phyla, classes, and genera (p < 0.05). Specifically, PAHs increased the relative abundance of the bacterial phyla Acidobacteriota and Gemmatimonadota (p < 0.05), while decreasing the relative abundance of Bacteroidota, Desulfobacterota, and Firmicutes compared to the control wetland (p < 0.05). Moreover, PAHs and certain soil properties [total nitrogen (TN), soil organic carbon (SOC), total phosphorus (TP), and total salt (TS)] were identified as key parameters affecting the community of soil bacteria, with the abundance of specific bacteria being both negatively and positively affected by PAHs, SOC, and TN. In summary, our findings could facilitate the identification of existing environmental problems and offer insights for improving the protection and management of tidal flat wetland ecosystems in the Yellow River Delta of China.

Precisely estimating the grassland biomass carbon storage is vital for evaluating grassland carbon sequestration potential and the monitoring and management of grassland resources. With the increasing intensity of climate change (CC) and human activities (HA), it is necessary to explore spatiotemporal variations in biomass carbon storage and its response to CC and HA. In this study, we focused on the Hulunbuir Grassland, utilizing sample plots data, MODIS data, environmental factors (terrain, soil, and climate), location factor, and texture characteristics to assess the performance of four machine learning algorithms: random forest, support vector machine, gradient boosting decision tree, and extreme gradient boosting in estimating grassland aboveground biomass (AGB). Based on the optimal model combined with root-shoot ratio data, grassland distribution data, and carbon content coefficients, the spatiotemporal characteristics and driving factors of biomass carbon storage from 2001–2022 were analyzed. The results showed that (1) the random forest achieved the highest prediction accuracy for grassland AGB, making it appropriate for AGB estimation in the Hulunbuir Grassland. (2) The spectral indices were the key variables of the grassland AGB, especially the enhanced vegetation index and difference vegetation index. (3) The 22-year average total biomass (TB) of the study area was 1037.10 gC/m², of which the 22-year average AGB was 48.73 gC/m² and 22-year average belowground biomass was 988.37 gC/m², showing a spatial distribution feature of gradual increase from west to east. (4) From 2001–2022, TB carbon storage showed an insignificant growth trend (p > 0.05). The 22-year average carbon storage of TB was 72.34 ± 18.07 gC. (5) Climate factors were the main driving factors for the spatial pattern of grassland TB carbon density, while the combined effects of CC and HA were the main contributors to the interannual increase in grassland TB carbon density.

Grassland degradation could affect the composition, structure, and ecological function of plant communities and threaten the stability of their ecosystems. It is essential to accurately evaluate grassland degradation and elucidate its impacts on the vegetation–soil relationship. In this study, remote sensing data based on vegetation coverage were used to assess the degradation status of Hulunbuir grassland, and five different grassland degradation degrees were classified. Vegetation community composition, diversity, biomass, soil nutrient status, and their relationships in different degraded grasslands were investigated using field survey data. The results showed that grassland degradation significantly affected the species composition of the vegetation community. As degradation intensified, species richness declined, with the proportion of Gramineae and Legume species decreasing and Asteraceae species increasing. Additionally, the proportion of annual species initially increased and then decreased. Degradation also markedly reduced aboveground, belowground, and litter biomass within the communities. Soil moisture, electrical conductivity, organic carbon, total carbon, total potassium, and hydrolyzable nitrogen contents in non-degraded areas were higher than those in severely degraded areas. Conversely, soil total phosphorus content and bulk density gradually increased with degradation. Nitrate nitrogen and ammonium nitrogen levels in severely degraded soils were significantly higher than those in non-degraded soils. Plant diversity in the study area was significantly positively correlated with aboveground biomass and belowground biomass, and it positively correlated with soil nutrient total carbon and available carbon but negatively correlated with soil bulk density. Results of the partial least squares path model showed that grassland degradation had significant negative effects on plant diversity, soil nutrients, and biomass. Soil nutrients were the main factors affecting ecosystem productivity. The direct effect of plant diversity on biomass was not significant, suggesting that soil nutrients may play a more important role than plant diversity in determining biomass during grassland degradation. The results illustrated the relationships among soil nutrients, plant diversity, and biomass in degraded grasslands and emphasized the importance of an integrated approach in the effective management and restoration of degraded grasslands.

The soil microbiome plays an important role in wetland ecosystem services and functions. However, the impact of soil hydrological conditions on wetland microorganisms is not well understood. This study investigated the effects of wetted state (WS); wetting–drying state (WDS); and dried state (DS) on the diversity of soil bacteria, fungi, and archaea. The Shannon index of bacterial diversity was not significantly different in various flooding conditions (p > 0.05), however, fungal diversity and archaeal communities were significantly different in different flooding conditions (p < 0.05). Significant differences were found in the beta diversity of bacterial, fungal, and archaeal communities (p < 0.05). Additionally, the composition of bacteria, fungi, and archaea varied. Bacteria were predominantly composed of Proteobacteria and Actinobacteria, fungi mainly consisted of Ascomycota and Mucoromycota, and archaea were primarily represented by Crenarchaeota and Euryarchaeota. Bacteria exhibited correlations with vegetation coverage, fungi with plant diversity, and archaea with aboveground vegetation biomass. The pH influenced bacterial and archaeal communities, while soil bulk density, moisture, soil carbon, soil nitrogen, and plant community diversity impacted fungal communities. This study provides a scientific basis for understanding the effects of different hydrological conditions on microbial communities in the Huihe Nature Reserve; highlighting their relationship with vegetation and soil properties, and offers insights for the ecological protection of the Huihe wetland.

Rhizosphere microbiota, serving as pivotal drivers of multifunctionality in grassland ecosystems, are jointly shaped by the dual influences of biotic and abiotic factors. Among biotic components, plant functional types selectively modulate microbial communities through root exudate specificity, while soil fauna (e.g., nematodes and earthworms) drive microbial interaction networks via biophysical disturbances and trophic cascades. However, excessive nematode grazing suppresses the hyphal extension of arbuscular mycorrhizal fungi (AMF). Moderate grazing facilitates the proliferation of ammonia-oxidizing bacteria through fecal input, whereas intensive grazing induces topsoil compaction, leading to a dramatic 40–60% reduction in lipopolysaccharide content in Gram-negative bacteria. Long-term chemical fertilization significantly decreases the fungal-to-bacterial ratio, while organic amendments enhance microbial carbon use efficiency by activating extracellular enzymatic activities. Regarding abiotic factors, the stoichiometric characteristics of soil carbon, nitrogen, and phosphorus directly regulate microbial metabolic strategies. Hydrological dynamics influence microbial respiratory pathways through oxygen partial pressure shifts—drought stress inhibits mycelial network development. Future research should focus on predicting the emissions of gases such as N₂O (ozone monomer) and optimizing nitrogen fertilizer management to significantly reduce greenhouse gas emissions at the source. The soil organic carbon storage in grassland ecosystems is extremely large. Effective prediction and management can make these soils become important carbon “sinks”, offsetting the carbon dioxide in the atmosphere. At the same time, transcriptomics and metabolic flux analysis should be combined with multi-omics technologies and in situ labeling methods to provide theoretical basis and technical support for developing mechanism-based and predictable grassland restoration and adaptive management strategies from both macroscopic and microscopic perspectives.

The Yellow River Delta (YRD) is the most complete wetland ecosystem in the warm temperate zone of China and is rich in oil resources. However, with petroleum extraction and the development of the economy, pollution of the YRD has been paid increasing attention, in particular, pollution via polycyclic aromatic hydrocarbons (PAHs), as they have caused great harm to human health and the ecosystem balance. Based on the investigations of a research group in 2009, this study re-collected samples according to the same sampling points and analyzed the concentration, composition, source, ecological risk and health risk of PAHs in 2021. The concentration of ΣPAH₁₆ in the surface soil of YRD in 2009 ranged from 2.6 to 8275.46 ng/g, with an average of 1744.41 ng/g. The concentration of ΣPAH₁₆ in 2021 ranged from 56.25 to 582.56 ng/g, with an average of 149.63 ng/g. Therefore, the pollution situation in the YRD in 2021 was significantly improved compared with 2009. The composition of PAHs in soil in 2009 and 2021 was similar, which was dominated by low-ring PAHs. The evaluation results of the toxicity equivalent factor method showed that there was no potential ecological risk in the soil in 2009 and 2021. The evaluation results of the lifetime cancer risk increment model showed that the incremental lifetime cancer risk models (ILCRs) of soil PAHs in 2009 and 2021 were lower than the safety threshold of 10⁻⁶; therefore, there was no carcinogenic risk. The existing management measures for oil wells need to be further promoted to protect the regional ecological environment in the YRD.