Atmospheric Trace Gas Oxidizers Contribute to Soil Carbon Fixation Driven by Key Soil Conditions in Terrestrial Ecosystems.

Microbial oxidizers of trace gases such as hydrogen (H2) and carbon monoxide (CO) are widely distributed in soil microbial communities and play a vital role in modulating biogeochemical cycles. However, the contribution of trace gas oxidizers to soil carbon fixation and the driving environmental factors remain unclear, especially on large scales. Here, we utilized biogeochemical and genome-resolved metagenomic profiling, assisted by machine learning analysis, to estimate the contributions of trace gas oxidizers to soil carbon fixation and to predict the key environmental factors driving this process in soils from five distinct ecosystems. The results showed that phylogenetically and physiologically diverse H2 and CO oxidizers and chemosynthetic carbon-fixing microbes are present in the soil in different terrestrial ecosystems. The large-scale variations in soil carbon fixation were highly positively correlated with both the abundance and the activity of H2 and CO oxidizers (p < 0.05-0.001). Furthermore, soil pH and moisture-induced shifts in the abundance of H2 and CO oxidizers partially explained the variation in soil carbon fixation (55%). The contributions of trace gas oxidizers to soil carbon fixation in the different terrestrial ecosystems were estimated to range from 1.1% to 35.0%. The estimated rate of trace gas carbon fixation varied from 0.04 to 1.56 mg kg-1 d-1. These findings reveal that atmospheric trace gas oxidizers may contribute to soil carbon fixation driven by key soil environmental factors, highlighting the non-negligible contribution of these microbes to terrestrial carbon cycling.

Linking soil bacterial diversity to satellite-derived vegetation productivity: a case study in arid and semi-arid desert areas.

Increasing studies have begun to focus on biodiversity-productivity relationships for soil microorganisms through molecular ecology methods. However, most of these studies involve controlled experiments, and whether the relationship remains at large spatial scales is still largely unknown. To unravel this issue, archived desert soils from long-term experiments were analysed using high-throughput sequencing, and satellite-derived vegetation datasets were acquired to quantify productivity. Most of the abundant genera were significantly different between low- and high-productivity conditions, and soil bacterial communities were strongly impacted by productivity. Soil bacterial biodiversity, including observed operational taxonomic units and the Chao1, Shannon, and Faith's PD indexes, increased rapidly with productivity at low levels and then reached a relatively stable state, and similar phenomena were observed at multiple taxonomic ranks and for most of the dominant groups. Furthermore, we discovered that the mechanisms resulting in the observed relationship might be ecosystem resource availability in large-scale regions and species competition in local regions. Collectively, these results enhance our understanding of the linkage between belowground microorganisms and aboveground vegetation in arid and semi-arid areas and confirm the potential value of satellite-derived datasets in research on soil microbial diversity at large spatial scales.

Distinct Assembly Processes and Determinants of Soil Microbial Communities between Farmland and Grassland in Arid and Semiarid Areas.

It is critical to identify the assembly processes and determinants of soil microbial communities to better predict soil microbial responses to environmental change in arid and semiarid areas. Here, soils from 16 grassland-only, 9 paired grassland and farmland, and 16 farmland-only sites were collected across the central Inner Mongolia Plateau, covering a steep environmental gradient. Through analyzing the paired samples, we discovered that land uses had strong effects on soil microbial communities but weak effects on their assembly processes. For all samples, although no environmental variables were significantly correlated with the net relatedness index (NRI), both the nearest taxon index (NTI) and the ß-nearest taxon index (ßNTI) were most related to mean annual precipitation (MAP). With the increase of MAP, soil microbial taxa at the tips of the phylogenetic tree were more clustered, and the contribution of determinism increased. Determinism (48.6%), especially variable selection (46.3%), and stochasticity (51.4%) were almost equal in farmland, while stochasticity (75.0%) was dominant in grassland. Additionally, Mantel tests and redundancy analyses (RDA) revealed that the main determinants of soil microbial community structure were MAP in grassland but mean annual temperature (MAT) in farmland. MAP and MAT were also good predictors of the community composition (the top 200 dominant operational taxonomic units) in grassland and farmland, respectively. Collectively, in arid and semiarid areas, soil microbial communities were more sensitive to environmental change in farmland than in grassland, and unlike the major impact of MAP on grassland microbial communities, MAT was the primary driver of farmland microbial communities. IMPORTANCE As one of the most diverse organisms, soil microbes play indispensable roles in many ecological processes in arid and semiarid areas with limited macrofaunal and plant diversity, yet the mechanisms underpinning soil microbial community are not fully understood. In this study, soil microbial communities were investigated along a 500-km transect covering a steep environmental gradient across farmland and grassland in the areas. The results showed that precipitation was the main factor mediating the assembly processes. Determinism was more influential in farmland, and variable selection of farmland was twice that of grassland. Temperature mainly drove farmland microbial communities, while precipitation mainly affected grassland microbial communities. These findings provide new information about the assembly processes and determinants of soil microbial communities in arid and semiarid areas, consequently improving the predictability of the community dynamics, which have implications for sustaining soil microbial diversity and ecosystem functioning, particularly under global climate change conditions.

Economics- and policy-driven organic carbon input enhancement dominates soil organic carbon accumulation in Chinese croplands.

China's croplands have experienced drastic changes in management practices, such as fertilization, tillage, and residue treatments, since the 1980s. There is an ongoing debate about the impact of these changes on soil organic carbon (SOC) and its implications. Here we report results from an extensive study that provided direct evidence of cropland SOC sequestration in China. Based on the soil sampling locations recorded by the Second National Soil Survey of China in 1980, we collected 4,060 soil samples in 2011 from 58 counties that represent the typical cropping systems across China. Our results showed that across the country, the average SOC stock in the topsoil (0-20 cm) increased from 28.6 Mg C ha-1 in 1980 to 32.9 Mg C ha-1 in 2011, representing a net increase of 140 kg C ha-1 year-1 However, the SOC change differed among the major agricultural regions: SOC increased in all major agronomic regions except in Northeast China. The SOC sequestration was largely attributed to increased organic inputs driven by economics and policy: while higher root biomass resulting from enhanced crop productivity by chemical fertilizers predominated before 2000, higher residue inputs following the large-scale implementation of crop straw/stover return policy took over thereafter. The SOC change was negatively related to N inputs in East China, suggesting that the excessive N inputs, plus the shallowness of plow layers, may constrain the future C sequestration in Chinese croplands. Our results indicate that cropland SOC sequestration can be achieved through effectively manipulating economic and policy incentives to farmers.

[Prediction of Cadmium Content in the Leaves of Navel Orange in Heavy Metal Contaminated Soil Using VIS-NIR Reflectance Spectroscopy].

Visual and Near-infrared (VIS-NIR) reflectance spectroscopy had been used widely in monitoring agricultural pollution in recent years, however, it was rarely applied in monitoring the contamination of heavy metal in orchards. In the present paper, Newhall navel orange (Citrus sinensis [L.] Osbeck cv. Newhall) were cultivated in the potted soil contaminated with cadmium (Cd) at different levels, and the spectral reflectance and Cd content in the leaves were measured simultaneously at different growing seasons, which then were used to establish the prediction model by partial least squares regression (PLSR) based on spectral reflectance and by linear regression based on spectral index. The results showed that Cd was more easily transferred to and cumulated in the new leaves, and this phenomenon was more obvious in heavily contaminated soils with Cd. Blue shift in red edge was found in the band of 700-730 nm in the new leaves, however, no such phenomenon was found in the old leaves. The coefficient of determination (R²) of linear regression model based on spectral index was nearly 0. 8, while the PLSR model had a better result in predicting Cd content in the new leaves than the linear regression with R²CV of approximately 0.9. Furthermore, the standard normal variate transformation(SNV) in spectral preprocessing can improve the precision significantly in PLSR model. These results suggest that the VIS-NIR method has a great potential in monitoring heavy metal pollution in the navel orange.

[Restoration of microbial ammonia oxidizers in air-dried forest soils upon wetting].

OBJECTIVE: This study was aimed to investigate the abundance and community shift of ammonia-oxidizing archaea (AOA) and bacteria (AOB) in air-dried forest soils in response to water addition, to explore the applicability of air-dried soil for microbial ecology study, and to elucidate whether AOA within the marine group 1. 1a dominate ammonia oxidizers communities in the acidic forest soils in China. METHODS: Soil samples were collected from 10 forest sites of the China Ecosystem Research Network (CERN) and kept under air-drying conditions in 2010. In 2013 the air-dried soil samples were adjusted to 60% of soil maximum water holding capacity for a 28-day incubation at 28 degrees C in darkness. DGGE fingerprinting, clone library construction, pyrosequencing and quantitative PCR of amoA genes were performed to assess community change of ammonia oxidizers in air-dried and re-wetted soils. RESULTS: After incubation for 28 days, the abundance of bacteria and archaea increased significantly, up to 3,230 and 568 times, respectively. AOA increased significantly in 8 samples, and AOB increased significantly in 5 of 10 samples. However, pyrosequencing of amoA genes reveals insignificant changes in composition of AOA and AOB communities. Phylogenetic analysis of amoA genes indicates that archaeal ammonia oxidizers were predominated by AOA within the soil group 1. 1b lineage, while the Nitrosospira-like AOB dominate bacteria ammonia oxidizer communities. There was a significantly positive correlation between AOA/AOB ratio and total nitrogen (r2 = 0.54, P < 0.05), implying that soil ammonia oxidation might be dominated by AOA in association with ammonium released from soil mineralization. CONCLUSION: Phylogenetic analysis suggest that AOA members within the soil group 1. 1b lineage were not restricted to non-acidic soils as previously thought. The abundance rather than composition of AOA and AOB changed in response to water addition. This indicates that air-dried soil could be of help for microbial biogeography study.

[Predicting soil salinity based on spectral symmetry under wet soil condition].

There has been a growing interest in using spectral reflectance as a rapid and inexpensive tool for soil salinity monitoring in recent years. However, since soil moisture often exerts a tremendous influence on soil reflectance, the monitoring accuracy under various moisture conditions cannot fully satisfy the requirements of agricultural practice. In the present paper, a linear model was built to relate the spectral symmetry in the band of 1 370 - 1 610 nm with the salt content and moisture content of the saline soil based on regularly measured data of reflectance, soil moisture and salt content of the surface of 5 soil columns during the simulated evaporation process in laboratory. The results showed that the model was good with r greater than 0.8. By inversing the model, soil salt content then was predicted after moisture content was determined. The results showed that the prediction accuracy was acceptable with a root mean square error (RMSE) of 2.059 g x kg(-1) and an r of 0.656. The results demonstrated the feasibility of using spectral symmetry to predict soil salt content under various moisture conditions.

The effects of climate on soil microbial diversity shift after intensive agriculture in arid and semiarid regions.

In arid and semiarid desert areas, climate factors distinctly impact soil microbial community, which can also be greatly altered after agricultural practices at multiple spatial scales. However, it is still poorly unknown whether the effects of climate on soil microbial diversity change after intensive agriculture at a large spatial scale. To uncover this concern, we used time-interval archived soils, taken from paired desert and agricultural experiments at five field stations of the Chinese Ecosystem Research Network across northern China, and performed high-throughput sequencing. Herein, we discovered that the clustering pattern of soil microbial communities was influenced by precipitation at some extent in desert ecosystem, while not impacted by climate factors in agricultural ecosystem. In addition, the analyses on microbial communities presented that the effects of climate factors on the communities decreased after agricultural practices. Soil microbial richness was significantly correlated with environmental temperature in deserts (R = -0.39, P < 0.001) and croplands (R = 0.34, P = 0.004), while the coefficients were opposite; the richness-precipitation relationship was significant in deserts (R = 0.63, P < 0.001) while nonsignificant in croplands (R = -0.03, P = 0.815). Moreover, for the dominant microbial groups (the top 10 phyla), the relationships between their richness and climate factors differed in two land use types, and fewer significant correlations were observed in croplands. In summary, it can be indicated that the influences of climate on soil microbial communities are shifted after intensive agriculture, and the relations of the richness with climate factors are also weakened for both the total and dominant microbial groups. These results improve our comprehension about the effects of climate on soil microbial diversity after intensive agriculture in desert areas, which can help to project microbial diversity in varied land uses under the context of global climate changes.

Analyzing the Variation of Building Density Using High Spatial Resolution Satellite Images: the Example of Shanghai City.

Building density is an important issue in urban planning and land management. In the article, building coverage ratio (BCR) and floor area ratio (FAR) values extracted from high resolution satellite images were used to indicate buildings' stretching on the surface and growth along the third dimension within areas of interest in Shanghai City, P.R. China. The results show that the variation of FAR is higher than that of BCR in the inner circle, and that the newer commercial centers have higher FAR and lower BCR values, while the traditional commercial areas have higher FAR and BCR ratios. By comparing different residential areas, it was found that the historical "Shikumen" areas and the old residential areas built before 1980s have higher BCR and lower FAR, while the new residential areas have higher FAR and lower BCR, except for the villa areas. These results suggest that both older building areas and villa areas use land resources in an inefficient way, and therefore better planning and management of urban land are needed for those fast economic growing regions.

Predicting Soil Salinity with Vis-NIR Spectra after Removing the Effects of Soil Moisture Using External Parameter Orthogonalization.

Robust models for predicting soil salinity that use visible and near-infrared (vis-NIR) reflectance spectroscopy are needed to better quantify soil salinity in agricultural fields. Currently available models are not sufficiently robust for variable soil moisture contents. Thus, we used external parameter orthogonalization (EPO), which effectively projects spectra onto the subspace orthogonal to unwanted variation, to remove the variations caused by an external factor, e.g., the influences of soil moisture on spectral reflectance. In this study, 570 spectra between 380 and 2400 nm were obtained from soils with various soil moisture contents and salt concentrations in the laboratory; 3 soil types × 10 salt concentrations × 19 soil moisture levels were used. To examine the effectiveness of EPO, we compared the partial least squares regression (PLSR) results established from spectra with and without EPO correction. The EPO method effectively removed the effects of moisture, and the accuracy and robustness of the soil salt contents (SSCs) prediction model, which was built using the EPO-corrected spectra under various soil moisture conditions, were significantly improved relative to the spectra without EPO correction. This study contributes to the removal of soil moisture effects from soil salinity estimations when using vis-NIR reflectance spectroscopy and can assist others in quantifying soil salinity in the future.

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS.

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data.