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Subtropical Moso bamboo (Phyllostachys edulis) forests have become hot spots for nitrogen (N) deposition in China. However, the effect of long-term N deposition on the structure of soil microbial communities in different horizons of these forests remains unclear. To investigate the influence of extended N deposition on soil bacteria and fungi, a simulated N deposition field study was conducted in Fujian Province with four N deposition levels (0, 20, 40 and 80 kg N hm−2 a−1). The absolute abundances, diversity, community structure, and potential functions of soil bacteria and fungi were investigated after 10 years of simulated N deposition. The results indicated that there was no significant effect of N deposition on the abundance or diversity of soil microbes in either the organic or mineral horizons of Moso bamboo forests. However, under rates of 40 and 80 kg N hm−2 a−1, long-term N deposition altered the structure of the soil bacterial community in the organic horizon. Specifically, there was a 27.75∼36.40 % increase in the relative abundance of Actinobacteria, while that of Acidobacteria decreased by 24.40∼27.52 %. Redundancy analysis (RDA) demonstrated that soil total carbon (TC) was the key variable influencing the bacterial community structure in the organic horizon. Analysis of potential microbial functions showed that in the organic horizon, the rates of chemoheterotrophy significantly decreased under 80 kg N hm−2 a−1. However, N deposition did not noticeably impact the fungal community structure in either soil horizon. Our study suggested that under 10 years of N deposition, only the bacterial community composition and function in the organic horizon was affected, indicating that soil bacteria are more sensitive to N deposition than fungi in subtropical Moso bamboo forests. Therefore, further research is necessary to fully understand the impact of N deposition on fungal community composition in the soil organic horizon and on the microbial community in the mineral horizon of subtropical bamboo forests. This study provides valuable insights for informing future environmental management and conservation strategies.

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.

Previous studies have shown that the color red can affect basic motor functioning. However, these studies utilized simple gross motor tasks rather than those assessing complex fine motor skills. Moreover, these empirical studies were theoretically based on the threat–behavior link in human and non-human animals, and neglected the relationship between arousal and motor performance. According to the Yerkes–Dodson law and the inverted-U hypothesis in sport psychology, for simple motor tasks, high arousal (associated with the color red) is more advantageous than low arousal (associated with the color blue); for complex motor tasks, low arousal (blue color) is more advantageous than high arousal (red color). The current research examined the effect of color on different kinds of motor skills (fine motor and gross motor) based on the inverted U-hypothesis. In Experiment 1, we examined the effect of red and blue on dart-throwing performance, whereas in Experiment 2, we examined the effect of red and blue on grip strength performance. The results showed that performance of fine motor skill (dart-throwing) in the blue condition was better than in the red condition, and performance of gross motor skill (handgrip) in the red context was better than in the blue context. These results indicate that the type of motor skill assessed moderates the influence of red and blue on motor performance.

Remote sensing of land surface mostly obtains a mixture of spectral information of soil and vegetation. It is thus of great value if soil and vegetation information can be acquired simultaneously from one model. In this study, we designed a laboratory experiment to simulate land surface compositions, including various soil types with varying soil moisture and vegetation coverage. A model of a one-dimensional convolutional neural network (1DCNN) was established to simultaneously estimate soil properties (organic matter, soil moisture, clay, and sand) and vegetation coverage based on the hyperspectral data measured in the experiment. The results showed that the model achieved excellent predictions for soil properties (R2 = 0.88–0.91, RPIQ = 4.01–5.78) and vegetation coverage (R2 = 0.95, RPIQ = 7.75). Compared with the partial least-squares regression (PLSR), the prediction accuracy of 1DCNN improved 42.20%, 45.82%, 43.32%, and 36.46% in terms of the root-mean-squared error (RMSE) for predicting soil organic matter, sand, clay, and soil moisture, respectively. The improvement might be caused by the fact that the spectral preprocessing and spectral features useful for predicting soil properties were successfully identified in the 1DCNN model. For the prediction of vegetation coverage, although the prediction accuracy by 1DCNN was excellent, its performance (R2 = 0.95, RPIQ = 7.75, RMSE = 3.92%) was lower than the PLSR model (R2 = 0.98, RPIQ = 12.57, RMSE = 2.41%). These results indicate that 1DCNN can simultaneously predict soil properties and vegetation coverage. However, the factors such as surface roughness and vegetation type that could affect the prediction accuracy should be investigated in the future.

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 β-nearest taxon index (bNTI) 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. [ABSTRACT FROM AUTHOR]

Recently, visible and near-infrared (Vis-NIR) hyperspectral imaging has shown great potential in fine mapping of soil properties in laboratory. Whether it could be used to predict soil salt content (SSC) in the soil profile under field conditions still remained to be determined. In this study, hyperspectral images were acquired in situ from a soil profile with a Vis-NIR imaging spectrometer, and the optimum SSC prediction model was built to determine SSC of each pixel, and the fine SSC distribution maps were generated. The observed soil profile was located at an experimental station in Dongtai City, Jiangsu Province, China. Hyperspectral images with a spectral range of 397 to 1018 nm were obtained from 21 to 25 May 2015; a total of 140 soil samples were collected. Five spectral preprocessing methods, Daubechies wavelet (Db), LOG10(1/Db), Savitzky-Golay (SG), multiplicative scatter correction (MSC), and standard normal variate (SNV) were applied, and partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) models were developed. Results showed that the LS-SVM model predicted the SSC more accurately than the PLSR model, and the highest prediction accuracy was obtained with LOG10(1/Db) preprocessed spectra with R²p, RMSEp, RPIQ, and RPD values of 0.87, 0.58 g kg-1, 2.60 and 2.77, respectively. Based on the optimum prediction model, the fine distribution of SSC in soil profiles over 5 d were successfully obtained. This study indicated hyperspectral imaging is an efficient and nondestructive method for mapping SSC distribution in soil profiles and characterizing the vertical transportation of soil salt under field conditions with moderate soil moisture range. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Previous studies have shown that the color red can affect basic motor functioning. However, these studies utilized simple gross motor tasks rather than those assessing complex fine motor skills. Moreover, these empirical studies were theoretically based on the threat–behavior link in human and non-human animals, and neglected the relationship between arousal and motor performance. According to the Yerkes–Dodson law and the inverted-U hypothesis in sport psychology, for simple motor tasks, high arousal (associated with the color red) is more advantageous than low arousal (associated with the color blue); for complex motor tasks, low arousal (blue color) is more advantageous than high arousal (red color). The current research examined the effect of color on different kinds of motor skills (fine motor and gross motor) based on the inverted U-hypothesis. In Experiment 1, we examined the effect of red and blue on dart-throwing performance, whereas in Experiment 2, we examined the effect of red and blue on grip strength performance. The results showed that performance of fine motor skill (dart-throwing) in the blue condition was better than in the red condition, and performance of gross motor skill (handgrip) in the red context was better than in the blue context. These results indicate that the type of motor skill assessed moderates the influence of red and blue on motor performance. [ABSTRACT FROM AUTHOR]

Summary: 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. [ABSTRACT FROM AUTHOR]

Soil microbial communities respond significantly to long‐term agricultural development in desert areas. Because soil microbial communities are distinct in various deserts at different eco‐climate regions, their response patterns to agricultural development are diverse at a local scale. However, whether the different response patterns had some commonalities across various types of deserts at a larger spatial scale remained unclear. To address this question, historical soil samples, collected in different years from pairwise long‐term experimental plots (desert and farmland) at five field stations of the Chinese Ecosystem Research Network across northern China, were analyzed through high‐throughput sequencing approach. Here, we found that soil bacterial communities were sharply different between deserts and agricultural lands. Contrasting to desert soils with higher relative abundances of Actinobacteria and Firmicutes, agricultural soils harbored higher relative abundances of Proteobacteria, Acidobacteria, and Planctomycetes and exhibited higher bacterial α‐diversity with less variation. More importantly, higher community similarities were found in agricultural lands for each station and all stations, suggesting that soil microbiotic homogenization occurred at both local and regional scales after long‐term agricultural development in desert areas. Further analyses revealed that the homogenization was due to the loss of desert‐endemic species and the range expansion of generalist species, which might result from the introduction of crops combined with agricultural practices. The soil microbiotic homogenization associated with the loss of endemic species should be paid special attention to because this might imply the decrease in ecological resilience to perturbations and might lead to rapid desertification in desert margin areas. [ABSTRACT FROM AUTHOR]

Remote sensing of land surface mostly obtains a mixture of spectral information of soil and vegetation. It is thus of great value if soil and vegetation information can be acquired simultaneously from one model. In this study, we designed a laboratory experiment to simulate land surface compositions, including various soil types with varying soil moisture and vegetation coverage. A model of a one-dimensional convolutional neural network (1DCNN) was established to simultaneously estimate soil properties (organic matter, soil moisture, clay, and sand) and vegetation coverage based on the hyperspectral data measured in the experiment. The results showed that the model achieved excellent predictions for soil properties (R2 = 0.88–0.91, RPIQ = 4.01–5.78) and vegetation coverage (R2 = 0.95, RPIQ = 7.75). Compared with the partial least-squares regression (PLSR), the prediction accuracy of 1DCNN improved 42.20%, 45.82%, 43.32%, and 36.46% in terms of the root-mean-squared error (RMSE) for predicting soil organic matter, sand, clay, and soil moisture, respectively. The improvement might be caused by the fact that the spectral preprocessing and spectral features useful for predicting soil properties were successfully identified in the 1DCNN model. For the prediction of vegetation coverage, although the prediction accuracy by 1DCNN was excellent, its performance (R2 = 0.95, RPIQ = 7.75, RMSE = 3.92%) was lower than the PLSR model (R2 = 0.98, RPIQ = 12.57, RMSE = 2.41%). These results indicate that 1DCNN can simultaneously predict soil properties and vegetation coverage. However, the factors such as surface roughness and vegetation type that could affect the prediction accuracy should be investigated in the future. [ABSTRACT FROM AUTHOR]