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Soil microbes are the key to revealing the mechanisms driving variation in soil biogeochemical processes. In recent decades, forests in Southeast China have been widely transformed into tea plantations due to the drivers of economic benefits. However, the changes in the soil microbial community and their potential function during the transition from a typical forest ecosystem to tea plantations remain poorly understood. This study investigated the soil microbial community in tea plantation soils with different planting ages, i.e., 6, 12, 23 and 35 years, and in an adjacent woodland control. We discovered that tea planting significantly increased soil bacterial richness (ACE and Chao1) and decreased fungal richness, the diversity of bacteria (Simpson and Shannon) show a trend of initially decreasing and then increasing while there was no significant effect on fungal diversity. After tea planting, the relative abundances of Actinobacteria and Proteobacteria increased by 180.9%–386.6% and 62.3%–97.5%, respectively; the relative abundances of Acidobacteria decreased by 11.4%–66.8%. However, the fungal phyla were not significantly different among different aged tea plantations and woodlands. FAPROTAX and FUNGuild revealed that the transition of natural woodland to tea plantations significantly increased the relative abundances of aerobic_chemoheterotrophy (14.66%–22.69%), chemoheterotrophy (34.36%–37.04%), ureolysis (0.68%–1.35%) and pathogenic fungi (26.17%–37.02%). db-RDA proved that the bacterial community structure was more strongly related to soil pH and available nitrogen (AN), while the main determinants of the fungal community composition were soil pH and soil organic matter (SOM). These findings indicate that tea planting has a strong effect on the soil microbial community and potential function. The change in soil pH during tea planting was the most important factor affecting the soil microbial community, while soil bacteria were more sensitive to tea planting than fungi.

The spatial differences in microbial communities have been widely investigated in many environments through culture-independent molecular biological methods. Currently, for archived soils, there is a concern whether the spatial differences are still preserved in them after long-term storage. Considering plenty of archived soils being stored by institutions, we tried to explore whether they could provide certain information about microbial biogeography. In the present study, high-throughput sequencing of the 16S rRNA gene was conducted for archived soil samples, which were collected in different years from four agricultural long-term experimental stations of the Chinese Ecosystem Research Network and had been stored for approximate 1, 6 and 11 years. After processing the raw high-throughput sequencing data, a total of 10,623 OTUs belonging to 46 phyla were detected across all 72 soil samples. The results of OTU analysis showed that the overlap ratio (>57.35%) of bacterial OTUs between sampling years at the same station was higher than that (

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 OTUs (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. This article is protected by copyright. All rights reserved.

Many precious air-dried soil samples are currently stored by institutions across the world, and they have been proved useful in retrieving historical information of soil microbial ecology, such as the shifts of microbial populations and the effects of agricultural managements on soil microbial communities. However, most of those retrospective studies on microorganisms in archived soils were mainly conducted through PCR-DGGE or T-RFLP analysis, which was less sensitive to low-abundance species. In order to assess whether archived soils could reveal the spatial distribution of microbial communities at large spatial scale, especially the rare microbial taxa, a total of 117 serial time-historical samples from eight long-term field experimental stations of the Chinese Ecosystem Research Network were analyzed using high-throughput sequencing. The non-metric multidimensional scaling (NMDS) analyses showed that both the total bacterial communities and the rare bacterial subcommunities were distinctly clustered into different groups corresponding to each experimental station, and that analogous distribution patterns of bacterial communities existed between each two sampling years for both the total and the rare taxa, though the microbial community composition changed over time. The corresponding hierarchical clustering analyses demonstrated that soil bacterial communities for both the total and the rare taxa could be clustered into five groups for all samples and for the sub-samples of each sampling year, which corresponded to five eco-geographic regions in the Eco-Geographic Regional System of China. The results from this investigation highlighted the great value of serial time-archived soils in revealing the spatial distribution patterns of bacterial communities for both the total and the rare taxa at large spatial scale.

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, P0.001) and croplands (R = 0.34, P = 0.004), while the coefficients were opposite; the richness-precipitation relationship was significant in deserts (R = 0.63, P0.001) 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.

The profound effects of land-use types on soil microbial communities have been uncovered extensively in areas where land-use conversions are unidirectional. However, little is known about the response of soil microbes to land-use types in areas undergoing frequent bidirectional land-use conversions. Here, 36 paired cropland and grassland soils across soil types were collected in the agro-pastoral ecotone of northern China (APEN), and soil microbial communities were examined by high-throughput sequencing. According to the land-use history interpreted from remote sensing images from 2008 to 2017, the average frequency of conversion between cropland and grassland was 3 to 4 at all sites, and the duration of current land uses ranged from 1 to 9 years. Paired t-tests revealed that soil microbial diversity, measured as observed OTUs and Faith’s PD, did not differ significantly between cropland and grassland. In contrast, soil microbial community composition remarkably varied with the land-use type based on the nonmetric multidimensional scaling (NMDS) and analysis of similarity (ANOSIM). The above phenomena were also observed for different soil types (Kastanozems and Cambisols). The similarity percentage (SIMPER) analysis showed that the microbial taxa contributing most to the variation in the community composition were Pseudarthrobacter, Azotobacter, Bacillus, and Blastococcus, which are closely associated with the vegetation type (maize vs. grass). Meanwhile, there were no significant differences in soil properties between cropland and grassland, suggesting that vegetation types alone might drive changes in the community composition. Moreover, soil microbial diversity decreased with the duration of the croplands but did not change with the duration of the grasslands. The community composition between cropland and grassland differed more over a long duration (5 to 9 years) than over a short duration (1–4 years). Therefore, the longer the land-use type lasted, the more pronounced the differences in soil microbes between the two land-use types became. In conclusion, compared to soil microbial diversity, the community composition was more susceptible to land-use types under frequent bidirectional conversions, but both responses were enhanced with the duration of current land uses.

Hyper-spectral technology has been proven to be an effective method for the fast and non-destructive monitoring of crop biomass. However, the biomass estimation accuracy of this method is limited due to the effects of background factors, such as soils and water. In this study, a spectral separation method, non-negative matrix factorization (NMF), was proposed to alleviate the effects of soil on spectra. With the application of the NMF method, pure vegetation spectra were extracted from the field-observed spectra of wheat canopy, which were collected in four growing seasons from the tillering to the booting stages of wheat. Then, prediction models of wheat biomass (WB) were established and validated using the extracted spectra with the partial least squares regression (PLSR) method. The results showed that the NMF method could effectively separate the vegetation spectra from the mixed canopy spectra. Based on the extracted vegetation spectra, the WB prediction accuracy could be greatly improved with an increase of 31.7% for the R2p and an increase of 46.6% for the ratio of performance to deviation (RPD) as compared to the original spectra, indicating that the NMF method could significantly improve the performance of the WB prediction model. This method has potential application in the estimation of biomass using remote sensing technology.

Multispectral remote sensing technique has been extensively applied in recent years for the detection of soil salinity on bare soil; however, multispectral remote sensing is restricted in areas covered with vegetation, largely due to the mixed pixel problem. In the present study, non-negative matrix factorization (NMF) was implemented to separate soil spectral signal from mixed pixels of Landsat 5 Thematic Mapper (TM) to further estimate the soil salinity in a partially vegetated area. Four methods, namely, partial least squares regression (PLSR), least-squares support vector machine (LS-SVM), back propagation neural network (BPNN), and random forest (RF), were applied and compared. The results showed that the NMF-separated soil spectra could greatly improve the prediction accuracy compared with the mixed spectra, and among the four modeling methods, RF performed better than the rest of the methods, with the averaged results of determination of the prediction R2p = 0.67, a root mean square error of the prediction RMSEp = 0.73 ms cm−1, and the ratio of the standard deviation to RMSEp RPD = 1.61 after 100 times of random sampling and modeling. This approach could propose a new method for accurate and timely monitoring of soil salinity in a partially vegetation-covered area.