Spatial and temporal distribution characteristics and influencing factors of tourism eco-efficiency in the Yellow River Basin based on the geographical and temporal weighted regression model.

With economic progression in China, Yellow River Basin serves as a critical economic belt, which has also been recognized as a cradle of Chinese culture. A watershed is a complex structure of social, economic, and natural factors, and the diversity of its components determines its complexity. Studies on the spatiotemporal distribution characteristics and factors influencing the tourism eco-efficiency at the watershed scale are crucial for the sustainable regional socio-economic development, maintaining a virtuous cycle of various ecosystems, and comprehensively considering the utilization and coordinated development of various elements. Based on tourism eco-efficiency, the coordination degree of regional human-land system and the sustainable development levels can be accurately measured. With the tourism eco-efficiency in the Yellow River Basin from 2009 to 2019, the present study considers 63 cities in the Yellow River Basin as the research area by adopting the super-efficiency slacks-based measure (Super-SBM) model. Methods such as trend surface analysis, spatial autocorrelation analysis, elliptic standard deviation analysis, and hot spot analysis were used to explore their spatiotemporal distribution and evolution characteristics. The geographical and temporal weighted regression (GTWR) model was used to determine the factors influencing the tourism eco-efficiency value. The findings are as follows: ①The level of tourism eco-efficiency in the Yellow River Basin is not high, exhibiting a fluctuating upward trend. ②The tourism eco-efficiency in the Yellow River Basin shows significant spatial interdependence and agglomeration. Furthermore, the track of the center of gravity moves from northeast to southwest. ③ The tourism eco-efficiency in the Yellow River Basin is affected by various factors, with the economic development level having the greatest influence.

Deciphering roles of microbiota in arsenic biotransformation from the earthworm gut and skin.

Biotransformation mediated by microbes can affect the biogeochemical cycle of arsenic. However, arsenic biotransformation mediated by earthworm-related microorganisms has not been well explored, especially the role played by earthworm skin microbiota. Herein, we reveal the profiles of arsenic biotransformation genes (ABGs) and elucidate the microbial communities of the earthworm gut, skin, and surrounding soil from five different soil environments in China. The relative abundance of ABGs in the earthworm skin microbiota, which were dominated by genes associated with arsenate reduction and transport, was approximately three times higher than that in the surrounding soil and earthworm gut microbiota. The composition and diversity of earthworm skin microbiota differed significantly from those of the soil and earthworm gut, comprising a core bacterial community with a relative abundance of 96% Firmicutes and a fungal community with relative abundances of 50% Ascomycota and 13% Mucoromycota. In addition, stochastic processes mainly contributed to the microbial community assembly across all samples. Moreover, fungal genera such as Vishniacozyma and Oomyces were important mediators of ABGs involved in the biogeochemical cycle of arsenic. This is the first study to investigate earthworm skin as a reservoir of microbial diversity in arsenic biotransformation. Our findings broaden the current scientific knowledge of the involvement of earthworms in the arsenic biogeochemical cycle.

Arsenic bioaccumulation in the soil fauna alters its gut microbiome and microbial arsenic biotransformation capacity.

The biotransformation of arsenic mediated by microorganisms plays an important role in the arsenic biogeochemical cycle. However, the fate and biotransformation of arsenic in different soil fauna gut microbiota are largely unknown. Herein the effects of arsenic contamination on five types of soil fauna were compared by examining variations in arsenic bioaccumulation, gut microbiota, and arsenic biotransformation genes (ABGs). Significant difference was observed in the arsenic bioaccumulation across several fauna body tissues, and Metaphire californica had the highest arsenic bioaccumulation, with a value of 107 ± 1.41 mg kg-1. Arsenic exposure significantly altered overall patterns of ABGs; however, dominant genes involved in arsenic redox and other genes involved in arsenic methylation and demethylation were not significantly changed across animals. Except for M. californica, the abundance of ABGs in other animal guts firstly increased and then decreased with increasing arsenic concentrations. In addition, exposure of soil fauna to arsenic led to shifts in the unique gut-associated bacterial community, but the magnitude of these changes varied significantly across ecological groups of soil fauna. A good correlation between the gut bacterial communities and ABG profiles was observed, suggesting that gut microbiota plays important roles in the biotransformation of arsenic. Overall, these results provide a universal profiling of a microbial community capable of arsenic biotransformation in different fauna guts. Considering the global distribution of soil fauna in the terrestrial ecosystem, this finding broadens our understanding of the hidden role of soil fauna in the arsenic bioaccumulation and biogeochemical cycle.

High-resolution three-dimensional mapping of soil organic carbon in China: Effects of SoilGrids products on national modeling.

Soil organic carbon (SOC) is a key factor in soil fertility and structure and plays an important role in the global carbon cycle. However, SOC causes a large uncertainty in Earth System Models for predicting future climate change. The GlobalSoilMap (GSM) project aims to provide global digital soil maps of primary functional soil properties at six standard depth intervals (0-5, 5-15, 15-30, 30-60, 60-100, and 100-200 cm) with a grid resolution of 90 × 90 m. Currently, few SOC national products that meet the GSM specifications are available. This study describes the three-dimensional spatial modeling of SOC maps according to GSM specifications. We used 5982 soil profiles collected during the Second National Soil Survey of China, along with 16 environmental covariates related to soil formation. The results were obtained by parallel computing over tiles of 100 × 100 km, and the predictions for the tiles were subsequently merged into a single SOC map for the whole of China per standard GSM depth interval. For each standard GSM depth interval, SOC contents and their uncertainties were predicted and mapped at a spatial resolution of approximately 90 m using bootstrapping. Southwestern and northeastern China had higher SOC contents than the rest of China did, whereas northwestern China had a lower SOC content. The range of the coefficient of determination for the six depth intervals ranged from 0.35 to 0.02, and the mean SOC content was 17.86-8.67 g kg-1. Both these values decreased strongly with increasing soil depth. Cropland SOC content was lower than that of forest and grassland. The results of variable importance show that SoilGrids data were the best predictors for defining the soil-landscape relationship during regression modeling for SOC. These SOC maps can provide a data source for environmental modeling, a benchmark against which to evaluate and monitor SOC dynamics, and a guide for the design of future soil surveys.

Land use and climate change effects on soil organic carbon in North and Northeast China.

Soil is recognized as the largest carbon reservoir in the terrestrial ecosystem. Soil organic carbon (SOC) is vulnerable to changes in land use and climate. For a better understanding of the SOC dynamics and its driving factors, we collected data of the 1980s and 2000s in the North and Northeast China and conducted the digital soil mapping for spatial variation of SOC for the respective period. In the 1980s, 585 soils were sampled and the area was resampled in 2003 and 2004 (1062 samples) in a 30-km grid. The main land use in the area was cropland, forest and grassland. The random forest was used to predict the SOC concentration and its temporal change using land use, terrain factors, vegetation index, vis-NIR spectra and climate factors as predictors. The average SOC concentration in 1985 was 10.0 g kg-1 compared to 12.5 g kg-1 in 2004. The SOC variation was similar over the two periods, and levels increased from south to north. The estimated SOC stock was 1.68 Pg in 1985 and 1.66 Pg in 2004, but the SOC changes were different under different land uses. Over the twenty-year period, average temperatures increased and large areas of forests and grassland were converted to cropland. SOC under cropland was increased by 0.094 Pg (+9%) whereas 0.089 Pg SOC was lost under forests (-25%) and 0.037 Pg in the soils under grassland (-25%). It is concluded that land use is the main drivers for SOC changes in this area while climate change had different contributions in different regions. SOC loss was remarkable under the land use conversion while cropland has considerable potential to sequester SOC.

A high-resolution map of soil pH in China made by hybrid modelling of sparse soil data and environmental covariates and its implications for pollution.

The soil's pH is the single most important indicator of the soil's quality, whether for agriculture, pollution control or environmental health and ecosystem functioning. Well documented data on soil pH are sparse for the whole of China - data for only 4700 soil profiles were available from China's Second National Soil Inventory. By combining those data, standardized for the topsoil (0-20 cm), with 17 environmental covariates at a fine resolution (3 arc-second or 90 m) we have predicted the soil's pH at that resolution, that is at more than 109 points. We did so by parallel computing over tiles, each 100 km × 100 km, with two machine learning techniques, namely Random Forest and XGBoost. The predictions for the tiles were then merged into a single map of soil pH for the whole of China. The quality of the predictions were assessed by cross-validation. The root mean squared error (RMSE) was an acceptable 0.71 pH units per point, and Lin's Concordance Correlation Coefficient was 0.84. The hybrid model revealed that climate (mean annual precipitation and mean annual temperature) and soil type were the main factors determining the soil's pH. The pH map showed acid soil mainly in southern and north-eastern China, and alkaline soil dominant in northern and western China. This map can provide a benchmark against which to evaluate the impacts of changes in land use and climate on the soil's pH, and it can guide advisors and agencies who make decisions on remediation and prevention of soil acidification, salinization and pollution by heavy metals, for which we provide examples for cadmium and mercury.

Current and future assessments of soil erosion by water on the Tibetan Plateau based on RUSLE and CMIP5 climate models.

Soil erosion by water is accelerated by a warming climate and negatively impacts water security and ecological conservation. The Tibetan Plateau (TP) has experienced warming at a rate approximately twice that observed globally, and heavy precipitation events lead to an increased risk of erosion. In this study, we assessed current erosion on the TP and predicted potential soil erosion by water in 2050. The study was conducted in three steps. During the first step, we used the Revised Universal Soil Equation (RUSLE), publicly available data, and the most recent earth observations to derive estimates of annual erosion from 2002 to 2016 on the TP at 1-km resolution. During the second step, we used a multiple linear regression (MLR) model and a set of climatic covariates to predict rainfall erosivity on the TP in 2050. The MLR was used to establish the relationship between current rainfall erosivity data and a set of current climatic and other covariates. The coefficients of the MLR were generalised with climate covariates for 2050 derived from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) models to estimate rainfall erosivity in 2050. During the third step, soil erosion by water in 2050 was predicted using rainfall erosivity in 2050 and other erosion factors. The results show that the mean annual soil erosion rate on the TP under current conditions is 2.76tha-1y-1, which is equivalent to an annual soil loss of 559.59×106t. Our 2050 projections suggested that erosion on the TP will increase to 3.17tha-1y-1 and 3.91tha-1y-1 under conditions represented by RCP2.6 and RCP8.5, respectively. The current assessment and future prediction of soil erosion by water on the TP should be valuable for environment protection and soil conservation in this unique region and elsewhere.