Explore the vast range of titles available.

Microorganisms inhabit the entire soil profile and play important roles in nutrient cycling and soil formation. Recent studies have found that soil bacterial diversity and composition differ significantly among soil layers. However, little is known about the vertical variation in soil bacterial communities and how it may change along an elevation gradient. In this study, we collected soil samples from 5 forest types along an elevation gradient in Taibai Mountain to characterize the bacterial communities and their vertical patterns and variations across soil profiles. The richness and Shannon index of soil bacterial communities decreased from surface soils to deep soils in three forest types, and were comparable among soil layers in the other two forests at the medium elevation. The composition of soil bacterial communities differed significantly between soil layers in all forest types, and was primarily affected by soil C availability. Oligotrophic members of the bacterial taxa, such as Chloroflexi , Gemmatimonadetes , Nitrospirae , and AD3 , were more abundant in the deep layers. The assembly of soil bacterial communities within each soil profile was mainly governed by deterministic processes based on environmental heterogeneity. The vertical variations in soil bacterial communities differed among forest types, and the soil bacterial communities in the Betula albo-sinensis forest at the medium elevation had the lowest vertical variation. The vertical variation was negatively correlated with mean annual precipitation (MAP), weighted rock content, and weighted sand particle content in soils, among which MAP had the highest explanatory power. These results indicated that the vertical mobilization of microbes with preferential and matrix flows likely enhanced bacterial homogeneity. Overall, our results suggest that the vertical variations in soil bacterial communities differ along the elevation gradient and potentially affect soil biological processes across soil profiles.

Our aim was to determine the effect of soil characteristics and root traits on soil aggregate stability at an inter- and intra-site scale in a range of agro-ecosystems. We also evaluated the effect of soil depth and the type of land use on aggregate stability. Soil aggregate stability, soil physicochemical properties and fine root traits were measured along land use gradients (from monocultures to agroforestry systems and forests), at two soil depths at four sites (Mediterranean and tropical climates) with contrasting soils (Andosol, Ferralsol, Leptosol and Fluvisol). Aggregate stability was much lower in deep than in surface soil layers, likely linked to lower soil organic carbon (SOC) and lower root mass density (RMD). Locally, and consistently in all sites, land use intensification degrades soil aggregate stability, mainly in surface soil layers. Soil organic carbon, cation exchange capacity and root traits: water-soluble compounds, lignin and medium root length proportion were the most important drivers of aggregate stability at the inter-site level, whereas SOC, root mass and root length densities (RMD, RLD) were the main drivers at the intra-site level. Overall, the data suggest different controls on soil aggregate stability globally (soil) and locally (roots). Conversion from forests to agricultural land will likely lead to greater C losses through a loss of aggregate stability and increased soil erosion.

Widespread shrub encroachment in global drylands may increase plant biomass and change soil organic carbon stocks of grassland ecosystems. However, the response of soil inorganic carbon (SIC), which is a major component of dryland carbon pools, to this vegetation shift remains unknown. We conducted a systematic field survey in 75 pairs of shrub‐encroached grassland (SEG) and control plots at 25 sites in the grasslands of the Inner Mongolia Plateau to evaluate how shrub encroachment affects SIC density (SICD) in these ecosystems. We found that shrub encroachment significantly reduced SICD in the upper 100 cm (3.85 vs. 4.74 kg C m−2, p < .05), especially in the subsurface soil (20–50 cm layer). The magnitude of SICD changes was related to the change in soil pH, shrub patch size and initial SICD, reflecting that the reduction in SICD might be attributed to the shrub encroachment‐related soil acidification. Our results also revealed that the lost SIC was mainly released into the atmosphere rather than redistributed into deeper soil layers. Synthesis. We provide the first evidence for the soil acidification‐induced SIC loss caused by shrub encroachment. Our findings highlight the non‐negligible role of SIC dynamics in the C budget of SEG ecosystems and the need to consider these dynamics in terrestrial C cycle research. We provide the first evidence for the soil acidification‐induced soil inorganic carbon (SIC) loss caused by shrub encroachment. Our findings highlight the non‐negligible role of SIC dynamics in the C budget of shrub‐encroached grassland ecosystems and the need to consider these dynamics in terrestrial C cycle research.

Soil moisture affects the spatial variation of land–atmosphere interactions through its influence on the balance of latent and sensible heat fluxes.Wetter soils aremore prone to flooding because a smaller fraction of rainfall can infiltrate into the soil. The Soil Moisture Ocean Salinity (SMOS) satellite carries a remote sensing instrument able to make estimates of near-surface soilmoisture on a global scale. Oneway to validate satellite observations is by comparing them with observations made with sparse networks of in situ soil moisture sensors that match the extent of satellite footprints. The rate of soil drying after significant rainfall observed by SMOS is found to be higher than the rate observed by a U.S. Department of Agriculture (USDA) soil moisture network in the watershed of the South Fork Iowa River. This leads to the conclusion that SMOS and the network observe different layers of the soil: SMOS observes a layer of soil at the soil surface that is a few centimeters thick, while the network observes a deeper soil layer centered at the depth at which the in situ soil moisture sensors are buried. It is also found that SMOS near-surface soil moisture is drier than the South Fork network soil moisture, on average. The conclusion that SMOS and the network observe different layers of the soil, and therefore different soil moisture dynamics, cannot explain the dry bias. However, it can account for some of the root-mean-square error in the relationship. In addition, SMOS observations are noisier than the network observations.

The influence of shelterbelt afforestation on soils in different-depth profiles and possible interaction with climatic conditions is important for evaluating ecological effects of large-scale afforestation programs. In the Songnen Plain, northeastern China, 720 soil samples were collected from five different soil layers (0-20, 20-40, 40-60, 60-80, and 80-100 cm) in shelterbelt poplar forests and neighboring farmlands. Soil physiochemical properties [pH, electrical conductivity (EC), soil porosity, soil moisture and bulk density], soil carbon and nutrients [soil organic carbon (SOC), N, alkaline-hydrolyzed N, P, available P, K and available K], forest characteristics [tree height, diameter at breast height (DBH), and density], climatic conditions [mean annual temperature (MAT), mean annual precipitation (MAP), and aridity index (ARID)], and soil texture (percentage of silt, clay, and sand) were measured. We found that the effects of shelterbelt afforestation on bulk density, porosity, available K, and total P were observed up to 100 cm deep; while the changes in available K and P were several-fold higher in the 0-20 cm soil layer than that in deeper layers ( < 0.05). For other parameters (soil pH and EC), shelterbelt-influences were mainly observed in surface soils, e.g., EC was 14.7% lower in shelterbelt plantations than that in farmlands in the 0-20 cm layer, about 2.5-3.5-fold higher than 60-100 cm soil inclusion. For soil moisture, shelterbelt afforestation decreased soil water by 7.3-8.7% in deep soils ( < 0.05), while no significant change was in 0-20 cm soil. For SOC and N, no significant differences between shelterbelt and farmlands were found in all five-depth soil profiles. Large inter-site variations were found for all shelterbelt-induced soil changes ( < 0.05) except for total K in the 0-20 cm layer. MAT and silt content provided the greatest explanation powers for inter-site variations in shelterbelt-induced soil properties changes. However, in deeper soils, water (ARID and MAP) explained more of the variation than that in surface soils. Therefore, shelterbelt afforestation in northeastern China could affect aspects of soil properties down to 100 cm deep, with inter-site variations mainly controlled by climate and soil texture, and greater contribution from water characteristics in deeper soils.

Arid and semi‐arid grassland ecosystems cover about 15% of the global land surface and provide vital soil carbon (C) and nitrogen (N) sequestration. Although half of the soil C and N is stored in deep soils (below 30 cm), no regional‐scale study of microbial properties and their functions through the soil profile has been conducted in these drylands. To explore the distribution and determinants of microbial properties and C and N mineralization rates through soil profile along aridity gradient at a regional scale, we investigated these variables for four soil layers (0–20, 20–40, 40–60 and 60–100 cm) in 132 plots on the Mongolia Plateau. Soil microbial properties (biomass and bacteria:fungi ratio) and C and N mineralization rates decreased with increasing soil depth and aridity at the regional scale. Aridity‐induced declines in soil microbial properties mainly resulted from the negative effects of aridity on ANPP/root biomass and soil organic C (SOC) in the surface soil layers (0–20 and 20–40 cm) but from the direct and indirect (via SOC and soil C/N) negative effects of aridity in the deep soil layers (40–60 and 60–100 cm). Aridity‐induced declines in soil C mineralization rates mainly resulted from the negative indirect effect of aridity on SOC and microbial properties in each soil layer, with weaker effects of SOC and stronger effects of soil microbes in the deep soil layers. Aridity‐induced declines in soil N mineralization rates mainly resulted from the negative indirect effect of aridity on SOC in the three soil layers above 60 cm and mainly resulted from the negative direct effect of aridity in the 60–100 cm soil layer. Aridity via direct or indirect effects strongly determined the patterns of soil microbial properties and C and N mineralization throughout soil profiles on the Mongolian Plateau. These findings suggest that the increases in aridity are likely to induce changes in soil micro‐organisms and their associated functions across soil depths of semi‐arid grasslands, and future models should consider the dynamic interactions between substrates and microbial properties across soil depths in global drylands. A plain language summary is available for this article. Plain Language Summary

Soil salinization is a crucial type in the degradation of coastal land, but its spatial distribution and drivers have not been sufficiently explored especially at the depth scale owing to its multidimensional nature. In this study, we proposed a multi-depth soil salinity prediction model (0–10 cm, 10–20 cm, 20–40 cm, and 40–60 cm) fully using the advantages of satellite image data and field sampling to rapidly estimate the multi-depth soil salinity in the Yellow River Delta, China. Firstly, a multi-depth soil salinity predictive factor system was developed through correlation analysis of soil sample electrical conductivity with a series of remote-sensing parameters containing heat, moisture, salinity, vegetation indices, spectral value, and spatial location. Then, three machine learning methods including back propagation neural network (BPNN), support vector machine (SVM), and random forest (RF) were adopted to construct a coastal soil salinity inversion model. By using the best inversion model, we obtain the spatial distribution of soil salinity in the Yellow River Delta. The results show the following: (1) Environmental variables in this study are all effective variables for soil salinity prediction. The most sensitive indicators to multi-depth soil salinity are GDVI, ENDVI, SI-T, NDWI, and LST. (2) The RF model was chosen as the optimal approach for predicting and mapping soil salinity based on performance at four soil depths. (3) The soil salinity profiles exhibited intricate coexistence of two distinct types: surface aggregated and homogeneous. The former was dominant in the east, where salinity was higher. The central and southwestern parts were mostly homogeneous, with lower soil salinity. (4) The soil salinity throughout the four depths examined was found to be most elevated in saltern and bare land and lowest in wetland vegetation and farmland, according to land-cover type. This study proposed a remote sensing prediction method for salinization in multiple soil layers in the coastal plain, which could provide decision support for spatial monitoring of land salinization and achieving land degradation neutrality targets.

• Disturbances have altered community dynamics in boreal forests with unknown consequences for belowground ecological processes. Soil fungi are particularly sensitive to such disturbances; however, the individual response of fungal guilds to different disturbance types is poorly understood. • Here, we profiled soil fungal communities in lodgepole pine forests following a bark beetle outbreak, wildfire, clear-cut logging, and salvage-logging. Using Illumina MiSeq to sequence ITS1 and SSU rDNA, we characterized communities of ectomycorrhizal, arbuscular mycorrhizal, saprotrophic, and pathogenic fungi in sites representing each disturbance type paired with intact forests. We also quantified soil fungal biomass by measuring ergosterol. • Abiotic disturbances changed the community composition of ectomycorrhizal fungi and shifted the dominance from ectomycorrhizal to saprotrophic fungi compared to intact forests. The disruption of the soil organic layer with disturbances correlated with the decline of ectomycorrhizal and the increase of arbuscular mycorrhizal fungi. Wildfire changed the community composition of pathogenic fungi but did not affect their proportion and diversity. Fungal biomass declined with disturbances that disrupted the forest floor. • Our results suggest that the disruption of the forest floor with disturbances, and the changes in C and nutrient dynamics it may promote, structure the fungal community with implications for fungal biomass–C.

Aims To investigate the effects of the spatial distribution of rice root systems on dissolved CH 4 and CH 4 emissions and the CH 4 transport efficiency of aboveground plant parts in paddy fields. Methods A two-year field and leaf cutting experiment was conducted on seven rice varieties, and we determined the dynamics of CH 4 emissions, root system traits and dissolved CH 4 concentrations in different soil layers, and the CH 4 transport efficiencies of the leaf and stem sheath. Results CH 4 emissions, the root distribution and the distribution of dissolved CH 4 concentration showed large discrepancies among the different rice varieties. Correlation analysis and structural equation modeling (SEM) revealed that CH 4 emissions had strong negative associations with root morphological traits (root dry weight, root area index and root volume density) and a clear positive correlation with dissolved CH 4 concentrations in the 0–20 cm soil layer. In addition, the root system had an indirect negative correlation with CH 4 emissions by influencing the dissolved CH 4 concentrations. Furthermore, root traits had strongly positive correlations with grain yield. In the aboveground parts, the CH 4 transport efficiencies of the leaf (20–70%) and stem sheath (30–80%) presented large differences among the different rice varieties, and CH 4 emissions exhibited significant positive correlations with leaf CH 4 transport efficiency and leaf dry weight. Conclusions Our results suggest that varieties with larger and deeper root distributions and lower leaf dry weight can decrease CH 4 emissions in paddy fields and maintain higher grain yield.

PurposeSubsurface soil acidity in conjunction with aluminium (Al3+) toxicity is a major limitation to agricultural production globally. The conventional use of surface-applied lime is often insufficient at correcting subsurface acidity; therefore, new practices and ameliorants are required.MethodsThis 3-month leaching experiment investigated whether animal wastes and other novel ameliorants in combination with lime could improve alkalinity movement, leading to greater amelioration of acid subsurface soil layers compared with lime alone. Five ameliorants (mature dairy compost, vegetable garden compost, poultry litter, potassium humate and gypsum) were added at a rate of 18 mg dry matter g−1 to the topsoil layer either without or with lime (target pH 5.5).ResultsAll ameliorants with lime improved alkalinity movement below the amended layer (0–10 cm), with pH increases of 0.03–0.10 units at 1 month and 0.02–0.20 units at 3 months. In comparison, the Al3+ concentrations in 10–12-cm and 12–15-cm layers were significantly decreased by 2–5.5 μg g−1. With lime, the improvements in alkalinity movement with ameliorants were in the order of gypsum > vegetable garden compost > potassium humate > poultry litter > mature dairy compost. Without lime, each amendment increased soil pH in the subsoil, with their effectiveness decreasing in the order of poultry litter > vegetable garden compost > mature dairy compost > gypsum > potassium humate.ConclusionSome organic amendments are effective in addressing subsoil acidity. When combined with lime, their additive effects are limited.