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Uncovering the plant‐soil feedback mechanisms underlying the assembly of belowground microbial communities is essential for terrestrial biodiversity conservation. However, little is known about the small‐scale spatial assembly processes of distinct soil microorganisms, especially during natural restoration of ex‐arable ecosystems. We examined the spatial structure of soil microbiomes in arable land and reforested soils to elucidate the underlying assembly processes at a small spatial scale. The analysis was based on a MiSeq sequencing database, detecting the diversity of archaeal, bacterial and fungal communities, simultaneously, from 300 soil samples along vertical and horizontal profiles during 30‐year reforestation. Compared with environmental filtering, dispersal limitation made crucial contributions to microbial community assembly. Archaeal and bacterial communities with a wider niche breadth were governed more by dispersal limitation than were fungal communities. The effect of dispersal limitation on archaeal and bacterial communities increased first and then decreased over time, while the effect on fungi temporally increased. Synthesis and applications. Our results highlight the variation of assembly processes governing distinct soil microbiomes during reforestation, with dispersal limitation playing a prominent role. This finding suggests that the increase in soil microbial diversity during natural restoration is mainly due to the stochastic influx and dispersal of microorganisms. This greater understanding of microbial community assembly can contribute to more targeted and efficient environmental management practices for the restoration of terrestrial ecosystems, for example, by promoting the restoration practices and shortening the restoration period. These practices may thus be incorporated into policies developed for effective biodiversity conservation, especially the restoration and maintenance of subsurface soil microbial diversity and associated functions. Our results highlight the variation of assembly processes governing distinct soil microbiomes during reforestation, with dispersal limitation playing a prominent role. This finding suggests that the increase in soil microbial diversity during natural restoration is mainly due to the stochastic influx and dispersal of microorganisms. This greater understanding of microbial community assembly can contribute to more targeted and efficient environmental management practices for the restoration of terrestrial ecosystems, for example, by promoting the restoration practices and shortening the restoration period. These practices may thus be incorporated into policies developed for effective biodiversity conservation, especially the restoration and maintenance of subsurface soil microbial diversity and associated functions.

Accurate subsurface soil moisture (SM) estimation is critical for vegetation growth, drought monitoring, and climate change mitigation, yet remains a significant challenge. Previous data assimilation (DA) approaches are limited to only surface SM assimilation. In this study, we utilized the proxy subsurface SM estimated via the exponential filter method (ExpF) as another assimilation variable in our 2‐dimensional DA. Meanwhile, the dual updating DA scheme was implemented to simultaneously update model parameters and states. The two DA pathways were incorporated into the proposed framework (DA_1E_D), which enhanced the subsurface SM accuracy, with the effects of 2‐dimensional assimilation being more significant. Under 2‐dimensional DA, the information transfer between layers was more accurately characterized, leading to overall improvements with unbiased root‐mean‐square error (ubRMSE) reductions of 0.015 and 0.005 m3 · m−3, and Kling–Gupta efficiency (KGE) increases of 0.248 and 0.067 for surface and subsurface SM, respectively, across five SM networks. The soil thickness (d2) and hydraulic conductivity exponent (expt2) are the most influential parameters affecting subsurface SM dynamics through model propagation. DA_1E_D also outperformed ExpF in subsurface SM accuracy, particularly in SM networks with weak surface‐subsurface correlation, achieving an average ubRMSE reduction of 0.003 m3 · m−3 and an average KGE increase of 0.202. It was also applied to Soil Moisture Active Passive data at regional scale, demonstrating significant improvements. The model surface‐subsurface SM coupling was adjusted toward the actual coupling after subsurface assimilation and dual updating. This study may provide new insights into the diagnosis and refinements of the model representation of surface‐subsurface processes. Plain Language Summary Soil moisture (SM) is a key variable in the fields of hydrology, ecology, and agriculture, and in characterizing Earth's climate. Particularly, accurate subsurface SM estimation remains challenging for the Earth science community. Here, we used the empirical subsurface SM estimates derived from an exponential filter method (ExpF) as another assimilation variable, in combination with the data assimilation (DA) technique which updates model states and parameters simultaneously, to constrain the model estimates. We found these two pathways lead to significant improvements in subsurface SM estimation. The accuracy of subsurface SM was improved with biases (ubRMSE) significantly reduced by 0.005 m3 · m−3 on average over the modeling grid cells, due to the assimilation of empirical subsurface SM. The effects of parameter updating were relatively smaller but also positive. The proposed DA framework demonstrated superior accuracy to ExpF, with an average ubRMSE reduction of 0.003 m3 · m−3 and an average KGE increase of 0.202. This framework was also applicable to regional scale using satellite products. Moreover, both pathways proved effective in adjusting the physical surface‐subsurface correlation in the land surface model toward actual coupling. Our analysis has important implications for future studies on estimating subsurface SM using land surface models at larger spatiotemporal scales. Key Points A 2‐dimensional data assimilation (DA) framework which accounted for a dual state‐parameter updating scheme was proposed The accuracy of subsurface soil moisture (SM) was improved by assimilating empirical subsurface estimates and dual updating The proposed DA framework can also be applied to Soil Moisture Active Passive data to improve subsurface SM estimation at regional scale

Monitoring heavy metals (HMs) across source distance and depth distribution near coal mining sites is essential for preventing environmental pollution and health risks. This study investigated the distribution of selected HMs, cadmium (Cd 2+ ), chromium (Cr 2+ ), copper (Cu 2+ ), manganese (Mn 2+ ), nickel (Ni 2+ ), lead (Pb 2+ ), and zinc (Zn 2+ ), in soil samples collected from ten sites (S-1–S-10) at two different depths (0–15 and 15–30 cm) and distances of 50, 100, and 200 m from a mining source. Additionally, three plant species, Prosopis spp., Justicia spp., and wheat, were collected to assess HM bioavailability and leaf accumulation. Coal mine activities’ impact on soil properties and their HM associations were also explored. Results reveal HM concentrations except for Cr 2+ exceeding World Health Organization (WHO) limits. In surface soil, Cd 2+ (58%), Cu 2+ (93%), Mn 2+ (68%), Ni 2+ (80%), Pb 2+ (35%), and Zn 2+ (88%) surpassed permissible limits. Subsurface soil also exhibited elevated Cd 2+ (53%), Cu 2+ (83%), Mn 2+ (60%), Ni 2+ (80%), Pb 2+ (35%), and Zn 2+ (77%). Plant species displayed varying HM levels, exceeding permissible limits, with average concentrations of 1.4, 1.34, 1.42, 4.1, 2.74, 2.0, and 1.98 mg kg −1 for Cd 2+ , Pb 2+ , Cr 2+ , Cu 2+ , Mn 2+ , Ni 2+ , and Zn 2+ , respectively. Bioaccumulation factors were highest in wheat, Prosopis spp., and Justicia spp. Source distance and depth distribution significantly influenced soil pH, electrical conductivity (EC), and soil organic carbon (SOC). Soil pH and EC increased with an increase in soil depth, while SOC decreased. Pearson correlation analysis revealed varying relationships between soil properties and HMs, showing a considerably negative correlation. Concentrations of HMs decreased with increasing depth and distance from mining activities, validated by regression analysis. Findings suggest crops from these soils may pose health risks for consumption. Graphical abstract

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.

AimsCramped and sealed sites common in compact city areas limit tree growth due to multiple physical restrictions and physiological stresses. Fast urbanization and densification have intensified the pressure on urban trees, demanding innovative methods and solutions. The subaerial tree-growth space attracts more attention, but the more intractable subterranean rootability constraints are often overlooked. They are expressed as external (macro-scale) soil-body volume and internal (micro-scale) soil-pore volume limitations. The double jeopardy of urban soil insularity acutely restricts root growth, root spread, tree health, and stability.MethodsSome novel solutions can be distilled from a comprehensive review of recent research findings to bring effective relief.ResultsPedestrians and vehicles can co-use the expanded soil area in dense urban areas. Various creative soil expansion techniques can allow tree roots to break out from conventional confined tree pits or tree strips. Subsurface connections can link a planting site to an adjacent one or a nearby green patch. The soil union could be realized by subsurface soil conduits (large-diameter buried pipes) or subsurface soil corridors covered by pier-supported paving. In the spirit of landscape altruism, soil sharing by neighbor trees optimizes using the scarce rootable soil resource. Internal soil volume expansion can be accompanied by high-quality soil mix and compaction-prevention measures to resolve porosity and rootability deficit.ConclusionsUrban tree managers can adopt out-of-the-box thinking in managing critical physical soil deficiencies. New research findings can more promptly inform policymakers and practitioners. Close interactions between science and practice can be proactively cultivated.

Post agricultural grasslands are thought to accumulate soil organic carbon (SOC) after cultivation cessation. The Conservation Reserve Program (CRP) in the U.S. is a wide-scale, covering approximately 8.9 Mha as of 2020, example of row-crop to grassland conversion. To date, changes in SOC stock in CRP lands have mostly been evaluated at local scales and focused on the surface 20–30 cm of the soil profile. Thus, we lack knowledge of SOC dynamics in CRP lands on a continental scale, especially in the subsurface soil, after agricultural cessation. The Rapid Carbon Assessment (RaCA) project is the most recent effort by the United States Department of Agriculture (USDA) to systematically quantify C stock in the 0–100 cm soil profile across the conterminous US. Here we analyzed data from RaCA to evaluate the SOC stocks of both surface and subsurface soil of the CRP on a continental scale. We found there was no difference in SOC stock between croplands and CRP lands when comparing the 0–100 cm soil profiles, which indicates that the C sequestration in CRP lands is insignificant overall. We did find that CRP lands have higher SOC stocks in the surface soil (0–5 cm). However, such higher SOC levels in surface (0–5 cm) soil were offset by the lower SOC stock in the subsurface (30–100 cm) of the CRP. We also found that CRP lands in humid and warm regions may have net soil C sequestration because they have much more SOC in the surface as compared with croplands in the same regions. Whether the lower SOC in the subsurface of CRP lands is caused by legacy effects or is a result of C losses needs to be verified by long-term repeated sampling in both surface and subsurface soil. This analysis highlights the importance of examining C dynamics in subsurface soil after agricultural cessation to accurately measure and improve C sequestration rates in CRP lands.

Eutrophication of natural water bodies is moderated by transformation of nitrate (NO₃⁻) in riparian wetlands, which serve as filters of infiltrating drain water from upland agricultural areas. The present study comprised field observations, laboratory experiments and metagenomic studies to describe NO₃⁻ removing transformation pathways and interactions with the cycling of iron (Fe) in a temperate riparian wetland soil profile down to 1 m depth. Water samples from piezometers showed a distinct plume of riparian wetland soil profile down to 1 m depth. Water samples from piezometers showed a distinct plume of NO₃⁻ in the subsurface soil where agricultural drain water was infiltrating. However, within a distance of few meters in the water flow direction, NO₃⁻ was depleted from the percolating water. Sampling and analyses of soil from the active zone of the biogeochemical NO₃⁻ removal showed that denitrifying enzyme activity was ~ tenfold higher in the upper 0–25 cm than in the lower 25–100 cm. Yet, net transformation of NO₃⁻ was substantial also at 25–100 cm when assayed with relatively undisturbed soil samples and by ¹⁵N tracer techniques in soil slurries. Transformation pathways of dissimilatory 3 was substantial also at 25–100 cm when assayed with relatively undisturbed soil samples and by 15 N tracer techniques in soil slurries. Transformation pathways of dissimilatory nitrate reduction to ammonium and anaerobic ammonium oxidation were identified, but were quantitatively minor as compared to denitrification. Heterotrophic denitrification and denitrification mediated by oxidation of ferrous iron, Fe(II), were identified as important processes in the wetland soil. The latter was substantiated by geochemical observations, by rates of NO₃⁻ depletion in slurry incubations with added FeCl 2, and by identification of microorganisms with known capacity of NO₃⁻ reduction coupled to Fe (II) oxidation (Acidovorax sp.). The transformation pathway of iron-mediated NO₃⁻ reduction could involve biotic and abiotic reactions, and N₂O, which is a potent greenhouse gas, was a major product of the process. It remains to be seen under field conditions if N₂O emission hotspots are linked to specific sites of dynamic NO₃⁻ reduction coupled to Fe(II) oxidation. process. It remains to be seen under field conditions if N 2 O emission hotspots are linked to specific sites of dynamic NO-3 reduction coupled to Fe (II) oxidation.

Abandoned agricultural fields (old fields) are thought to accumulate soil organic matter (SOM) after cultivation cessation. However, most research on old fields soil carbon (C) and nitrogen (N) sequestration has focused on the surface (10 or 30 cm depth) and overlooked their dynamics below 30 cm. This study quantified C and N stock change in both the surface and subsurface with repeated inventories over 13 years. We conducted repeated soil surveys in 8 old fields that form a 64-year chronosequence at Cedar Creek Ecosystem Science Reserve (CCESR), Minnesota in 2001 and 2014. On average, soil C and N accumulated by 16.5 ± 14.5 g C m−2 y−1 and 1.0 ± 1.1 g N m−2 y−1 in the surface (0–20 cm). In contrast, we found soil C and N decreased by 78.9 ± 26.3 g C m−2 y−1 and 12.9 ± 2.42 g N m−2 y−1 in the subsurface (20–100 cm). The C and N losses in the subsurface soil were correlated with low deep root biomass; the majority of roots are located in the top 20 cm of soil. Such root distribution may be attributed to the continuing dominance of nonnative and shallow-rooted C3 grasses and the lack of legumes after field abandonment. This study shows that agriculture has a long legacy effect after abandonment on subsurface soil C and N. Some abandoned agricultural fields can continue to lose C and N because surface C and N accumulation does not offset the ongoing deeper soil C and N losses.

The objectives of this study were to explore the effects of long-term and continued application of fertilizers and manures on microbial biomass, soil biological activity and their seasonal variations in surface and subsurface soils in relation to soil fertility. For this, soils were sampled in spring, summer and autumn from Shenyang Long-term Experimental Station, northeastern China. The results showed that soil total nitrogen (N), organic carbon (C), basal respiration, microbial biomass and enzymatic activity increased in manure-amended surface soils, but decreased with soil depth. Long-term application of inorganic fertilizers significantly decreased soil pH value, sucrase activity and microbial biomass C, but increased soil metabolic quotient (qCO₂). However, no significant effect of inorganic fertilizers on soil total N, urease activity and microbial biomass N was observed in comparison with CK0 (neither tillage nor fertilization) and CK (no fertilizers). There was no significant difference between CK0 and CK in soil total N, organic C and microbial activity in surface soil layer (0-20 cm), but these parameters in subsurface soil layer (20-40 cm) were higher in CK than in CK0. Moreover, seasonal changes were observed in terms of soil nutrient contents, enzymatic activity, microbial biomass and soil respiration. There were significant correlations between soil microbial biomass C and N, between organic C and sucrase activity and between total N and urease activity, respectively. It is recommended that combined use of organic manure with inorganic fertilizers should be considered to maintain higher microbial biomass, soil biological activity and soil fertility. Considering considerably high nutrients reserve and microbial activity in subsurface layers of soil and wind-erosion-caused nutrient loss in spring in north China, we also propose that low tillage should be considered to make use of nutrients in soils.

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.