Litter decomposition is important for understanding the effects of habitat on nutrient cycling. In this study, we investigated the decomposition characteristics, decomposition variability, and regulatory factors restricting the decomposition rates of leaf litter in three different habitats: a flood disturbance habitat, an arid habitat, and a high-salinity habitat. The litter decomposition rates of the habitats decreased in the following order: flood disturbance habit > arid habitat > high-salinity habitat. The organic carbon, total nitrogen, and lignin residues of the litter during the decomposition period were highest in the high-salinity habitat. The litter quality was the main regulator of the release of phosphorus and cellulose residues, which exhibited different release processes and patterns in these three habitats. The litter decomposition coefficient was negatively correlated with litter carbon residue in the flood disturbance habitats, the lignocellulose index in the arid habitats, and soil urease in the high-salinity habitats. It was positively correlated with the lignocellulose index in flood disturbance habitats and litter carbon residue in high-salinity habitats. The litter quality in the flood disturbance area played a significant role in litter decomposition, while environmental quality and litter quality were the dominant factors under arid and high-salt conditions. Litter quality in the flood disturbance area played a significant role in litter decomposition, while both environmental quality and litter quality were the dominant factors under arid and salt conditions.

Farmland shelterbelts are crucial elements in safeguarding agricultural ecological security and sustainable development, with their precise extraction being vital for regional ecological monitoring and precision agriculture management. However, constrained by their narrow linear distribution, complex farmland backgrounds, and spectral confusion issues, traditional remote sensing methods encounter significant challenges in terms of accuracy and generalization capability. In this study, six representative deep learning semantic segmentation models—U-Net, Attention U-Net (AttU_Net), ResU-Net, U²-Net, SwinUNet, and TransUNet—were systematically evaluated for farmland shelterbelt extraction using high-resolution Gaofen-6 imagery. Model performance was assessed through four-fold cross-validation and independent test set validation. The results indicate that convolutional neural network (CNN)-based models show overall better performance than Transformer-based architectures; on the independent test set, the best-performing CNN model (U-Net) achieved a Dice Similarity Coefficient (DSC) of 91.45%, while the lowest DSC (88.86%) was obtained by the Transformer-based TransUNet model. Among the evaluated models, U-Net demonstrated a favorable balance between accuracy, stability, and computational efficiency. The trained U-Net was applied to large-scale farmland shelterbelt mapping in the study area (Alar City, Xinjiang), achieving a belt-level visual accuracy of 95.58% based on 385 manually interpreted samples. Qualitative demonstrations in Aksu City and Shaya County illustrated model transferability. This study provides empirical guidance for model selection in high-resolution agricultural remote sensing and offers a feasible technical solution for large-scale and precise farmland shelterbelt extraction.

Arid and semi-arid shelterbelts must provide long-term ecological protection under chronic water scarcity, high evaporative demand, and rising salinization risk, yet management still lacks an integrated framework linking irrigation, root-zone salt dynamics, and woody plant performance. Here, we synthesize evidence on water–salt–root linkages in drip-irrigated shelterbelts and related dryland woody systems from a structured Web of Science Core Collection search (1 January 2000–1 January 2026). The evidence shows that shelterbelt performance is governed not by water or salinity alone, but by a coupled root-zone system: localized irrigation creates moisture–salt heterogeneity, salts accumulate near evaporative fronts and emitter margins, and roots redistribute depth, density, and uptake zones. In hyper-arid saline-drip systems, precipitation may be only ~24.6 to 3000–3639 mm yr⁻¹, groundwater salinity 2.8–29.7 g L⁻¹, active roots 20–80 cm, and salt mainly in the 0–20 cm surface layer. Irrigation thus acts as both the basis of establishment and a source of long-term vulnerability, particularly where saline groundwater or other non-conventional water sources are used. Management options can improve root-zone habitability, but shelterbelt-specific thresholds and integrated indicators remain limited. This review proposes a root-zone-centered framework supporting predictive regulation.

Populus euphratica (P. euphratica) is a dominant tree species in the arid and semi-arid regions along the main stem of the Tarim River. This study aims to explore the response of microbial communities in the rhizosphere soil of P. euphratica to varying groundwater depths (GWD) and to elucidate the ecological functions of key microbial groups in drought resistance. We established three groundwater depth levels (3.8 m, 5.4 m, and 7.35 m) and employed metagenomic sequencing technology to systematically analyze the topological characteristics of functional microbial community networks, as well as the types and quantities of key microbial groups in the rhizosphere soil of P. euphratica under different GWD conditions. The results indicate that compared to GWDs of 3.8 m and 7.35 m, the average degree and graph density of microbial communities in the rhizosphere soil of P. euphratica at a depth of 5.4 m are the highest. This suggests that at a GWD of 5.4 m, the connectivity and stability of the microbial network structure in the rhizosphere soil of P. euphratica are significantly enhanced. Analysis of the Zi-Pi values within the microbial network structure reveals that, compared to GWDs of 3.8 m and 7.35 m, a depth of 5.4 m supports the greatest variety and quantity of key microbial species in the rhizosphere soil of P. euphratica. The four connecting nodes identified are Actinophytocola, Haladaptatus, Devosia and Pseudonocardia. Spearman correlation analysis demonstrates that the relative abundance of the key bacterial genus Mesorhizobium in the rhizosphere soil of P. euphratica at different GWD is significantly positively correlated with soil catalase (CAT) and urease (UE) activity. Furthermore, the relative abundance of the key bacterial genus Pseudonocardia shows a significant positive correlation with soil total nitrogen (TN) and ammonium nitrogen (NH₄⁺-N) (p < 0.05). The relative abundance of the key bacterial genus Devosia exhibits a highly significant positive correlation with soil water content (SWC) (p < 0.01) and a significant negative correlation with soil NH₄⁺-N (p < 0.05). Additionally, the relative abundance of Devosia is significantly positively correlated with soil CAT (p < 0.05). This study provides a theoretical foundation for the conservation of desert poplar forests in arid regions and for the identification and cultivation of specific key microbial communities in the rhizosphere soil of P. euphratica.

In the restoration of extreme arid desert ecosystems, plant-rhizosphere microorganism-soil synergy determines vegetation colonization and stability. This study focused on Atriplex canescens , divided soil into 0–20, 20–30, 30–40 cm layers, and explored spatiotemporal dynamics of its rhizosphere bacterial community and driving mechanisms. Results showed: ① Dominant phyla were Pseudomonadota , Actinomycetota , Bacillota , Bacteroidota (cumulative relative abundance >70%); Ralstonia was enriched in early growth period (EP), while Halomonas and Arthrobacter dominated middle (MP) and late (LP) periods. ② Significant spatiotemporal variation existed: temporally, α -diversity, network complexity and stability in MP/LP were higher than EP; spatially, bacteria enriched in root-intensive layers (20–30, 30–40 cm), with a “suitable microhabitat” in 20–30 cm layer during EP. ③ Growth period and soil depth synergistically regulated the community by altering spatiotemporal heterogeneity of soil nitrogen, dissolved organic carbon and pH, and nitrogen and dissolved organic carbon were core factors in root-intensive layers, while pH played a greater role in surface and deep layers. ④ The core regulatory chain: growth period and soil depth formed a two-dimensional framework, modifying root-intensive layer microenvironment to induce soil physicochemical heterogeneity, driving bacterial community differentiation and forming patterns adapted to Atriplex canescens at different growth stages. This study clarified the community’s spatiotemporal dynamics and driving mechanisms, providing theoretical support for microecological regulation in desert restoration.

Aims: Land-use types and soil salinization in arid desert–oasis ecotones are associated with differences in soil CO₂ efflux, yet the combined effects of multiple environmental factors remain poorly understood. This study aims to characterise the seasonal patterns, temperature sensitivity, and major correlates of soil respiration across four typical land-use types, thereby supporting regional carbon budget assessments. Methods: We conducted in situ soil respiration monitoring during the 2024 growing season (April–October) in four representative habitats (Populus euphratica forest, jujube orchard, grassland, and wetland) in the Aksu desert–oasis ecotone, Xinjiang. Concurrent measurements of topsoil (0–20 cm) physicochemical properties and extracellular enzyme activities were taken. Pearson correlation, variation partitioning, and the Van’t Hoff temperature-response model were employed. Results: Soil respiration exhibited a unimodal seasonal pattern peaking in July, with growing-season mean fluxes ranked as grassland > wetland > jujube orchard > Populus euphratica forest. Temperature sensitivity (Q₁₀) followed wetland (3.23) > grassland (2.44) > orchard (2.08) > forest (1.57), a pattern consistent with differences in soil moisture and salinity among the sites. Respiration correlated positively with pH, electrical conductivity, total salt, organic matter, urease, and sucrase, and negatively with available N, P, K, and peroxidase (p < 0.05). Physicochemical properties and enzyme activities jointly explained 70.7% of variation, with total soil salt (12.5%), sucrase (12.2%), and available N (11.1%) as the top contributors. Root and heterotrophic respiration accounted for 37.2% and 62.8% of total soil respiration, respectively, with heterotrophic respiration dominating in all seasons. Conclusions: Soil CO₂ emissions differed markedly among the four land-use types, and these differences were associated with water–salt–thermal–nutrient–enzyme conditions. Carbon cycling mechanisms in wetlands and grasslands differ markedly from those in woody habitats, highlighting the need for differentiated land-use planning and salinity-adapted carbon management strategies in arid regions.