Zezheng Liu

Understanding how soil arthropod communities respond to nutrient enrichment is important for assessing grassland ecosystem health, but such knowledge remains limited for degraded Sophora alopecuroides grasslands. To address this gap, a two-year field experiment was conducted in the Tuhulusu grassland (Xinjiang, China) with four treatments: nitrogen (N) addition, phosphorus (P) addition, combined N and P (NP) addition, and an unamended control (CK). Soil arthropod communities and environmental variables were monitored during the flowering, maturity, and senescence stages of S. alopecuroides. Across all treatments, three taxa—Oppiidae, Hypoaspidae, and Rhagidiidae—remained dominant, indicating wide ecological tolerance. Nutrient addition significantly altered arthropod individual density (response variable) and soil properties, including total phosphorus, available phosphorus, nitrate−N, ammonium−N, and pH (all p < 0.001), and these effects were strongly linked to plant phenology. The dominance, evenness, and Shannon diversity indices ranked as NP > CK > P > N. The key environmental drivers varied by treatment: total phosphorus and soil moisture under N addition, soil moisture under P and NP addition, and pH and electrical conductivity under CK. Collectively, these findings provide evidence that soil arthropod communities in S. alopecuroides grasslands are sensitive to nutrient enrichment in a phenology−dependent manner, with soil moisture content emerging as a critical limiting factor under nutrient−added conditions.

Grassland degradation, exacerbated by climate change and anthropogenic disturbances, poses a substantial barrier to ecological restoration, largely due to the invasion of toxic weeds. In the degraded grasslands of the Ili River Valley, Xinjiang, Sophora alopecuroides has emerged as the dominant toxic species; yet, its expansion mechanisms and sensitivity to management interventions remain poorly understood. This study utilized a three-year (2023–2025) field experiment to evaluate the impacts of nitrogen addition (N), mowing (M), and their combination (NM) on the stoichiometric characteristics and homeostasis of the plant–soil system. The results demonstrated that while M suppressed aboveground biomass, it facilitated the accumulation of root carbon (RC) and phosphorus (RP). Nitrogen enrichment significantly lowered soil C:N and C:P ratios, thereby alleviating phosphorus limitation. Crucially, the NM treatment effectively counteracted N-induced weed proliferation and mitigated M-induced biomass reductions. Analysis of stoichiometric homeostasis revealed that NM optimized plant adaptive strategies, maintaining strict homeostasis for RC and RP (H > 4) while preserving the sensitivity of the root N:P ratio of S. alopecuroides (RN:P). Structural equation modeling further indicated that soil C:P and N:P indirectly regulated total biomass by modulating the root C:P ratio of S. alopecuroides (RC:P). Consequently, stoichiometric coupling within the plant–soil system is essential for maintaining ecosystem functions. Integrated management (NM) optimizes soil nutrient balance and harnesses compensatory growth to suppress weed expansion, providing a robust scientific framework for the restoration of S. alopecuroides-invaded grasslands.

The resilience of arid ecosystems to climate change hinges on their carbon-water dynamics. This study investigates the spatiotemporal patterns of ecosystem water use efficiency (WUE) and its resilience in the ecologically vulnerable Yili-Balkhash Basin, a critical watershed in Central Asia. Contrary to a basin-wide trend of increasing WUE, we identify a significant decline in the WUE of high-productivity forest ecosystems. We demonstrate that this decline stems from a fundamental decoupling between the drivers of carbon (GPP) and water (ET) cycles during drought periods. While GPP shows a positive response to atmospheric aridity (vapor pressure deficit), likely driven by co-varying high radiation and temperature, ET remains primarily controlled by soil moisture and surface thermal conditions. This driver asynchrony results in ET-dominated control over WUE across 65.8% of the basin, rendering forests particularly vulnerable. Machine learning-based attribution reveals that ecosystem resilience is not determined by long-term drought legacy but by the combined effects of immediate thermal stress and a one-month ecological memory. Our findings highlight an emerging vulnerability of high-productivity forest ecosystems to atmospheric aridity and underscore the necessity of process-based frameworks for assessing ecosystem stability under a changing climate.