Jingkuan Sun

The Yellow River Delta, as a typical coastal saline soil distribution area in northern China, also faces increasingly prominent nitrogen input problems. To investigate the effects of salt–nitrogen interaction on the growth and photosynthetic physiology of the typical halophyte Limonium bicolor in the Yellow River Delta, this study established a pot-controlled experiment with three salt gradients (no salt addition, 0.3%, and 0.6% salinity) and four nitrogen addition levels (0, 15, 30, and 45 kg·N·ha ⁻¹ ·yr ⁻¹ ). Growth parameters, photosynthetic characteristics, element contents, and soil physicochemical properties of L. bicolor were measured. The results showed that salt and nitrogen addition had significant interactive effects on plant height, biomass, net photosynthetic rate, and other indicators. Compared with the no-salt and 0 kg·N·ha ⁻¹ ·yr ⁻¹ treatment, the treatment combining 0.6% salinity and 45 kg·N·ha ⁻¹ ·yr ⁻¹ significantly increased plant height, aboveground biomass, and net photosynthetic rate. Nitrogen addition increased the net photosynthetic rate, stomatal conductance, SPAD value, and total chlorophyll content of L. bicolor in a saline environment. Salt–nitrogen interaction significantly regulated total nitrogen in aboveground and belowground parts, and total phosphorus in belowground parts, of L. bicolor . Salt significantly increased soil pH, electrical conductivity, and organic matter content. Foliar nitrogen input, accompanied by plant–soil feedback processes, altered certain soil nutrient indices, with soil total nitrogen and inorganic nitrogen contents showing significant responses; however, it should be noted that long-term high nitrogen input may drive a shift toward phosphorus limitation. In ecological restoration practices, coordinated management of nitrogen and phosphorus should be given due attention. Structural equation modeling indicated that nitrogen addition was the primary exogenous factor driving L. bicolor growth. In conclusion, moderate salt–nitrogen interaction (i.e., the 0.6% salinity and 45 kg·N·ha ⁻¹ ·yr ⁻¹ treatment) synergistically promoted the growth and photosynthesis of L. bicolor , although higher nitrogen addition may induce a potential nitrogen saturation-like response.

Habitat quality (HQ) is a crucial indicator for assessing ecosystem services. Investigating HQ in the Yellow River Delta (YRD) and elucidating its relationships with climate change and human activities is crucial for supporting regional eco‐economic sustainability. Based on land use data from 1980, 1990, 2000, 2010, and 2020, the impacts and correlations of climate change and human activities on the ecological environment were analyzed using the integrated valuation of ecosystem services and tradeoffs (InVEST), the bivariate spatial autocorrelation, the human activity intensity, and the geographical detector models. From 1980 to 2020, the HQ in the YRD showed a fluctuating trend with alternating declines and increases. Overall, medium‐grade habitat consistently dominated the landscape, while the proportion of high‐grade habitat increased markedly by 2020. Notably, the net annual habitat transitions shifted toward lower‐grade habitats during 1980–1990, whereas the period from 2010 to 2020 witnessed a reversal, with net transitions increasingly directed toward higher‐grade habitats. Over the 40‐year period, HQ in the YRD exhibited significant correlations with climate change and human activities. Temperature was negatively correlated with HQ, while precipitation showed a positive correlation during the first three decades but shifted to a negative correlation in the last decade. Human activities exerted the strongest explanatory power on the spatial heterogeneity of HQ, with significant interactive effects between human activities and climatic factors. During the first three decades of the study period, this interaction demonstrated a nonlinearly enhanced influence, while in the last decade, a synergistic enhancement by both factors became more pronounced. This study elucidated the impacts of climate change and human activities on HQ in the Yellow River Delta and provided recommendations for ecological conservation, restoration, and promoting a positive interaction between ecological and economic development in the region.

The composition, architecture, and plant traits of temperate steppe communities are intricately associated with environmental factors. However, most studies primarily focus on aboveground observations, often overlooking the critical role of belowground root systems. Here we conducted a field survey at a large-regional scale to investigate the composition of temperate steppe communities and plant root traits. Cluster analysis, correspondence analysis and Pearson correlation coefficient matrix method were employed to classify vegetation associations based on plant community composition and root traits. The principal driving and limiting factors shaping plant root communities were systematically investigated. The results showed that the temperate steppe was categorized into three community subtypes: meadow steppe, typical steppe, and desert steppe, comprising five plant groups and thirteen plant associations. The RLFS analysis, based on belowground architectural and functional traits, demonstrated a spatial gradient differentiation with three ecological adaptations: tufted herbs, rhizome herbs, and non-tufted or rhizome herbs. Key environmental driving factors for meadow steppe included precipitation, soil carbon, nitrogen, and phosphorus content, while the average growing-season temperature as a limiting factor. The environmental driving factors for the typical steppe were not apparent, and the limiting factor was water. For the desert steppe, the environmental driving factors were altitude and average growing-season temperature. These findings reveal notable spatial heterogeneity and a distinct distribution pattern in community composition and vegetation classification based on belowground root traits in the Inner Mongolia steppes.

Exploring carbon (C), nitrogen (N), and phosphorus (P) contents as well as dynamic balances in soil are important for understanding ecological characteristics and stability. However, the substantial costs associated with soil surveys limited the possibility of large‐scale surveys. The accurate predictive capability of machine learning (ML) supported this possibility. In this study, ML models (Random Forest, RF; support vector machine, SVM; Extreme Gradient Boosting, XGboost; Gradient Boosting Decision Tree, GBDT) and remote sensing data were used to predict soil C, N, and P as well as ecological stoichiometric ratio (ESR) in the Yellow River Delta (YRD). The purpose of this study was to assess the performance of four MLs in predicting soil C, N, and P as well as ESRs and to assess the performance of indirect and direct predictions in ESRs. The results showed that RF and SVM have higher accuracy than XGboost and GBDT. The model was the main factor affecting accuracy, and there were differences in the applicability of different elements to the model. The ESR prediction performance was weaker than that of total elements due to the fact that ESR is controlled by two elements. In the localized prediction of farmland and wetland vegetation, the performance of the models was substantially enhanced compared to the global prediction. The predictive performance of total elements was higher and the predictive performance of ESRs was poorer in soil in farmland. However, this pattern was reversed in wetland vegetated soil. The prediction followed by calculation method improved the prediction accuracy of ESRs. Although not generalizable, this approach still offered a possibility for accurate prediction of multi‐element variables such as ESRs. Land‐use type had a significant effect on soil C, N, and P as well as ESRs. The mean values of TC, TN, and TP in the study area were 17.730 ± 2.395, 0.710 ± 0.253, and 0.691 ± 0.089 g/kg, respectively. The highest TC was found in farmland and wetland vegetation soils with mean values of 18.228 and 18.138 g/kg, respectively. The Yellow River as well as its old channel had a significant effect on the spatial distribution of C, N, P, and ESRs. This study clarified the spatial distribution pattern of soil C, N, and P as well as ESR in the YRD. In addition, this study provided an indirect prediction method for the prediction of multi‐element variables that improved the prediction accuracy.

Nitrogen (N) deposition from human activities leads to an imbalance in the N and phosphorus (P) ratios of natural ecosystems, which has a series of negative impacts on ecosystems. In this study, we used 16s rRNA sequencing technology to investigate the effect of the N-P supply ratio on the bulk soil (BS) and rhizosphere soil (RS) bacterial community of halophytes in coastal wetlands through manipulated field experiments. The response of soil bacterial communities to changing N and P ratios was influenced by plants. The N:P ratio increased the α-diversity of the RS bacterial community and changed the structure of the BS bacterial community. P addition may increase the threshold, causing decreased α-diversity of the bacterial community. The co-occurrence network of the RS community is more complex, but it is more fragile than that of BS. The co-occurrence network in BS has more modules and fewer network hubs. The increased N:P ratio can increase chemoheterotrophy and denitrification processes in the RS bacterial community, while the N:P ratio can decrease the N-fixing processes and increase the nitration processes. The response of the BS and the RS bacterial community to the N:P ratio differed, as influenced by soil organic carbon (SOC) content in terms of diversity, community composition, mutualistic networks, and functional composition. This study demonstrates that the effect of the N:P ratio on soil bacterial community is different for plant roots and emphasizes the role of plant roots in shaping soil bacterial community during environmental change.

Human activities have increased nitrogen (N) and phosphorus (P) inputs to the Yellow River Delta and the supply level of N and P affects plant growth as well as ecosystem structure and function directly. However, the root growth, stoichiometry, and antioxidant system of plants in response to N and P additions, especially for herbaceous halophyte in the Yellow River Delta (YRD), remain unknown. A field experiment with N addition (0, 5, 15, and 45 g N m^-2 yr^-1, respectively) as the main plot, and P addition (0 and 1 g N m^-2 yr^-1, respectively) as the subplot, was carried out with a split-plot design to investigate the effects on the root morphology, stoichiometry, and antioxidant system of Suaeda salsa. The results showed that N addition significantly increased the above-ground and root biomass as well as shoot-root ratio of S. salsa, which had a significant interaction with P addition. The highest biomass was found in the treatment with 45 g N m^-2 yr^-1 combined with P addition. N addition significantly increased TN content and decreased C:N ratio of root, while P addition significantly increased TP content and decreased C:P ratio. The main root length (MRL), total root length (TRL), specific root length (SRL), and root tissue density (RTD) of S. salsa root were significantly affected by N addition and P addition, as well as their interaction. The treatments with or without P addition at the 45 g N m^-2 yr^-1 of N addition significantly increased the superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) activities and soluble protein content of roots, decreased malondialdehyde (MDA) content. And there was a significant interaction between the N and P addition on SOD activity. Therefore, N and P additions could improve the growth of S. salsa by altering the root morphology, increasing the root nutrient content, and stimulating antioxidant system.