Phosphorus (P) is a crucial limiting nutrient in soil ecosystems, significantly influencing soil fertility and plant productivity. Soil microorganisms adapt to phosphorus deficiency and enhance soil phosphorus effectiveness through various mechanisms, which are notably influenced by land use practices. This study examined the impact of different land use types (long-term continuous maize farmland, abandoned evolving grassland, artificial tamarisk forests, artificial ash forests, and wetlands) on soil phosphorus-cycling functional genes within the Tanyang Forest Farm in a typical region of the Yellow River alluvial plain using macro genome sequencing technology. The gene cluster related to inorganic phosphorus solubilization and organic phosphorus mineralization exhibited the highest relative abundance across different land use types (2.24 × 10⁻³), followed by the gene cluster associated with phosphorus transport and uptake (1.42 × 10⁻³), with the lowest relative abundance observed for the P-starvation response regulation gene cluster (5.52 × 10⁻⁴). Significant differences were found in the physical and chemical properties of the soils and the relative abundance of phosphorus-cycling functional genes among various land use types. The lowest relative abundance of soil phosphorus-cycling functional genes was observed in forestland, with both forestland types showing significantly lower gene abundance compared to wetland, farmland, and grassland. Correlation analysis and redundancy analysis (RDA) revealed a significant relationship between soil physicochemical properties and soil phosphorus-cycling functional genes, with ammonium nitrogen, organic carbon, total nitrogen, and pH being the main environmental factors influencing the abundance of these genes, explaining 70% of the variation in their relative abundance. Our study reveals land use’s impact on soil phosphorus-cycling genes, offering genetic insights into microbial responses to land use changes.

The drive towards carbon neutrality has prompted the worldwide expansion of utility-scale solar facilities. Previous studies have reported the positive effects of solar facilities’ installation on pasture productivity and biodiversity in arid regions. However, our understanding of how solar facilities influence a wide range of ecosystem functions simultaneously, and the relative contributions of soil microbial attributes, remains incomplete. To address this gap, we assessed the changes in ecosystem multifunctionality following solar facility installation in an alpine desert grassland in the Qinghai–Tibet plateau by measuring twenty-three ecosystem function indicators comprising primary production, the soil nutrient pool, carbon cycling, nitrogen cycling, phosphorus cycling and oxidation–reduction. Furthermore, we estimated the soil microbial diversity, microbial indicator taxa and microbial activity to identify the primary driving factors. The results showed that solar facilities had positive effects on ecosystem multifunctionality; the positive effect size was higher in the initial installation period (31.4%) than in the constant running period (3.5%). The enhancements in ecosystem multifunctionality were mainly due to enhanced nutrient cycling induced by the increased abundance of fungal indicator taxa and microbial activity. Moreover, the structural equation model revealed distinct regulatory paths between the two periods and a transition in the primary driving factors of ecosystem multifunctionality from microbial indicator taxa to microbial activity. In conclusion, our study demonstrates the positive influence of solar facilities on multiple ecosystem functions, emphasizing the critical role of soil microbial mechanisms in regulating ecosystem multifunctionality. These findings provide valuable insights into soil biota-driven processes that could inform strategies aimed at enhancing soil health and ecosystem functionality in arid grasslands under human-managed systems.

Mountain forest greenness varies across seasons, yet growing-season averages can mask season-specific climate associations. We assessed seasonal and elevational variation in forest greenness, temporal variation in spring climate associations, and whether nighttime light (NTL)–greenness covariation persisted after detrending in Changbai Mountain from 2000 to 2024. Analyses used MODIS normalized difference vegetation index (NDVI), 1 km temperature and precipitation grids, elevation zones, and annual NTL data, restricted to pixels with at least 70% forest cover based on the 2024 Annual China Land Cover Dataset. Forest NDVI increased significantly during the growing season (slope = 0.0006 yr⁻¹, R² = 0.166, P < 0.05), summer (slope = 0.0006 yr⁻¹, R² = 0.188, P < 0.05), and autumn (slope = 0.0019 yr⁻¹, R² = 0.280, P < 0.01), with the steepest increase in autumn. Spring showed no significant long-term trend (slope = 0.0011 yr⁻¹, R² = 0.087, P = 0.152), but its NDVI had the strongest temperature association at the regional scale (r = 0.760, P < 0.001), remaining positive across all elevation zones. In spring regressions, temperature represented 84.1–97.8% of the summed absolute standardised coefficient magnitude across elevation zones, with all variance inflation factors ≤1.22. Exploratory 5-year sliding-window correlations suggested that spring NDVI–climate correlations varied over time, but each estimate was based on only five observations. At low elevation, the raw NTL–NDVI regression was significant (R² = 0.198, P < 0.05), whereas the detrended regression was not (R² = 0.042, P = 0.324), indicating shared long-term trends. Growing-season averaging therefore obscures strong autumn greening and the distinct spring temperature association; raw NTL–NDVI covariation should not be interpreted without accounting for temporal trends.