Biological soil crusts determine soil properties and salt dynamics under arid climatic condition in Qara Qir, Iran

Distinguishing natural and anthropogenic contributions to biological soil crust distribution in China's drylands

Desertification caused by natural factors and human activities seriously threatens dryland biological communities. However, the impact of these factors on non-vascular plants in drylands has not been fully documented. This study proposed a framework to distinguish the natural and anthropogenic contributions to the distribution of the biological soil crust (BSC) coverage. The 20 model-simulated environmental datasets, including climate, soil characteristics and terrain, were selected to explore the internal relationship between these environmental drivers and BSC coverage. Random forest classification and regression models were developed to calculate the BSC coverage in the drylands of China under natural conditions. By subtracting the predicted natural BSC coverage from the observed BSC coverage, the spatial distribution of changes in BSC coverage attributed to human activities was mapped. The results showed that in the limited vegetation areas of China's drylands, human activities had a positive impact on BSC coverage in only 11.3 % of the regions while having a negative effect on 25.4 % of the regions. Moreover, human activities led to a 33 % reduction in BSC coverage in these regions. The positive impacts of large-scale ecological restoration projects on BSC coverage in the drylands of China were limited due to land use changes caused by human economic activities. This framework provides support for assessing regional variations in anthropogenic impacts on dryland BSC communities and contributes to the development of appropriate dryland management policies.

Interplay of moss cover and seasonal variation regulate soil physicochemical properties and net nitrogen mineralization rates in Central Himalayas, India

Mosses (Class- Bryopsida) are vital to ecosystem dynamics in numerous biomes, although their effects on soil processes are poorly understood. The interplay of moss cover and seasonal variations in soil processes is still unclear in the Indian Central Himalayas. Therefore, we examined the seasonal variations in net nitrogen (N) mineralization rates and several soil properties under two ground covers (with and without moss cover). We used the ex-situ incubation technique to determine N mineralization rates (Rmin) and standard methodology for soil physical and chemical analysis. During the rainy season, the physical properties of the soil and its nutrients, apart from phosphorus, were higher under moss cover. The winter season, however, showed a different pattern, with soil properties exhibiting higher values in soils without moss cover. Ammonium concentrations were higher under moss cover, while nitrate concentrations were higher in soil without moss cover during rainy and winter seasons. The Rmin rates were higher in soil under moss cover, indicating that moss cover promotes N transformation. In contrast, Rmin rates were negative in soil without moss cover, indicating that N immobilization was dominant in N transformation under this ground cover during the rainy season. Our research shows that mosses positively impact the nutrient status and N mineralization rates in various temperate forest types. The seasonal patterns of soil properties are strongly influenced by soil temperature, moisture, and organic carbon. Therefore, we advocate the conservation of mosses and their integration into forest management plans for better ecosystem processes and services in the ecologically fragile Himalayas.

Effects of Vertically Heterogeneous Soil Salinity on Genetic Polymorphism and Productivity of the Widespread Halophyte Bassia prostrata

Salinity is one of the environmental factors that affects both productivity and genetic diversity in plant species. Within the soil profile, salinity is a dynamic indicator and significantly changes with depth. The present study examined the effects of the vertical heterogeneity of soil salinity chemistry on the plant height, fresh and dry biomass accumulation, water content, level of genetic polymorphism, and observed and expected heterozygosity in seven populations of halophyte Bassia prostrata in natural habitats. Soil salinity ranged from slight (S_salts = 0.11-0.25%) to extreme (S_salts = 1.35-2.57%). The main contributors to salinity were Na⁺, Ca²⁺, and Mg²⁺. Multivariate analysis revealed that biomass accumulation is positively affected by moderate/high salinity in 20-60 cm soil layers, which may be associated with the salt required for the optimal growth of the halophyte B. prostrata. The formation of seed genetic diversity is negatively affected by slight/moderate salinity in the 0-40 cm layers. An increase in divalent ion content can reduce genetic diversity and increase the local adaptation of B. prostrata to magnesium-calcium sulfate salinity. The effect of the in-depth distribution of soil salinity on productivity and genetic diversity may be related to seasonal variables during biomass accumulation (summer) and seed formation (autumn).

Anomalous concentrations and environmental implications of rare earth elements in the rock-soil-moss system in the black shale area

Nowadays, rare earth elements (REEs) pollution caused by anthropogenic activities has aroused great attention, but the contribution of natural geological sources remains unclear. In this study, parent rocks, corresponding soil, and overlying moss (Pohlia flexuosa Harv. In Hook) were collected to identify the release, transportation, and environmental exposure of REEs in the black shale areas. The results showed that black shales had elevated REEs levels (245 ± 124 mg kg^-1) and served as a geogenic source of REEs. The released REEs were temporarily enriched in the acidized soil (327 ± 91.8 mg kg^-1, pH 4.87 ± 0.810) and were still highly bioavailable, thereby resulting in REEs accumulated up to a high level in moss P. flexuosa (86.2 ± 64.3 mg kg^-1). Hence, the ecological risks of REEs in black shale areas were assuredly enhanced. The shale-normalized results of REEs concentrations in the parent rock - soil - moss system followed a remarkably identical pattern and were characterized by strong enrichment in HREEs (La_N/Yb_N 0.520 ± 0.274), indicating that geogenic input was responsible for the concentration and composition of REEs in mosses. Moreover, REEs were readily preserved and not easily fractionated in moss P. flexuosa due to its special morphological features, indicating the lithological signatures of REEs in black shales were readily inherited by moss P. flexuosa, and underwent only minor losses. Overall, moss P. flexuosa could be used as a powerful tool to reflect the content and composition of REEs in black shale areas.

Assessment of the uncertainty and interpretability of deep learning models for mapping soil salinity using DeepQuantreg and game theory

This research introduces a new combined modelling approach for mapping soil salinity in the Minab plain in southern Iran. This study assessed the uncertainty (with 95% confidence limits) and interpretability of two deep learning (DL) models (deep boltzmann machine—DBM) and a one dimensional convolutional neural networks (1DCNN)—long short-term memory (LSTM) hybrid model (1DCNN-LSTM) for mapping soil salinity by applying DeepQuantreg and game theory (Shapely Additive exPlanations (SHAP) and permutation feature importance measure (PFIM)), respectively. Based on stepwise forward regression (SFR)—a technique for controlling factor selection, 18 of 47 potential controls were selected as effective factors. Inventory maps of soil salinity were generated based on 476 surface soil samples collected for measuring electrical conductivity (ECe). Based on Taylor diagrams, both DL models performed well (RMSE < 20%), but the 1DCNN-LSTM hybrid model performed slightly better than the DBM model. The uncertainty range associated with the ECe values predicted by both models estimated using DeepQuantilreg were similar (0–25 dS/m for the 1DCNN-LSTM hybrid model and 2–27 dS/m for DBM model). Based on the SFR and PFIM (permutation feature importance measure)—a measure in game theory, four controls (evaporation, sand content, precipitation and vertical distance to channel) were selected as the most important factors for soil salinity in the study area. The results of SHAP (Shapely Additive exPlanations)—the second measure used in game theory—suggested that five factors (evaporation, vertical distance to channel, sand content, cation exchange capacity (CEC) and digital elevation model (DEM)) have the strongest impact on model outputs. Overall, the methodology used in this study is recommend for applications in other regions for mapping environmental problems.

Global Soil Hydraulic Properties dataset based on legacy site observations and robust parameterization

The representation of land surface processes in hydrological and climatic models critically depends on the soil water characteristics curve (SWCC) that defines the plant availability and water storage in the vadose zone. Despite the availability of SWCC datasets in the literature, significant efforts are required to harmonize reported data before SWCC parameters can be determined and implemented in modeling applications. In this work, a total of 15,259 SWCCs from 2,702 sites were assembled from published literature, harmonized, and quality-checked. The assembled SWCC data provide a global soil hydraulic properties (GSHP) database. Parameters of the van Genuchten (vG) SWCC model were estimated from the data using the R package 'soilhypfit'. In many cases, information on the wet- or dry-end of the SWCC measurements were missing, and we used pedotransfer functions (PTFs) to estimate saturated and residual water contents. The new database quantifies the differences of SWCCs across climatic regions and can be used to create global maps of soil hydraulic properties.

Natural field freeze-thaw process leads to different performances of soil amendments towards Cd immobilization and enrichment

Cadmium (Cd) soil pollution is a global issue affecting crop production and food safety. Remediation methods involving in-situ Cd immobilization have been developed, but their effectiveness can diminish under seasonal freeze-thaw aging processes. In this study, we assessed the field performance of four soil treatments at a seasonally frozen rice paddy. Amendments were applied at 2 wt%, including: (i) sepiolite (a 2:1 clay mineral), (ii) superphosphate, (iii) biochar (produced by rice husk at 500 °C for 2 h), and (iv) joint application of biochar & superphosphate (1:1 mixture by weight). Immobilization performance was determined as DTPA extractable Cd and plant uptake in various organs. Overall, the four treatments significantly reduced Cd bioavailability during the plant growth period, with average DTPA-extractable concentrations decreasing by 43%, 34%, 39% and 45% for the four treatments, respectively, relative to untreated soil (control). Rice grain yields from the superphosphate and the joint application treatments increased by 8.0% and 11.8%, respectively, and Cd accumulation within those grains reduced by 14.3% and 48.9%, respectively. During the winter non-growth period, freeze-thaw aging facilitated Cd mobilization, with DTPA-extractable Cd increasing by 16.9% in the control soil, relative to the initial period. However, this reduced to 10.9%, 14.4%, 7.6% and 5.0%, for the sepiolite, superphosphate, biochar and joint application treatments, respectively. Overall, the joint application of biochar and superphosphate provided the best performance in terms of both long-term Cd immobilization and rice production enhancement, offering a green remediation option for risk management at Cd contaminated rice paddies in seasonally frozen regions.

Physical Disturbance Reduces Cyanobacterial Relative Abundance and Substrate Metabolism Potential of Biological Soil Crusts on a Gold Mine Tailing of Central China

As the critical ecological engineers, biological soil crusts (biocrusts) are considered to play essential roles in improving substrate conditions during ecological rehabilitation processes. Physical disturbance, however, often leads to the degradation of biocrusts, and it remains unclear how the physical disturbance affects biocrust microorganisms and their related metabolism. In this study, the photosynthetic biomass (indicated by chlorophyll a), nutrients, enzyme activities, and bacterial communities of biocrusts were investigated in a gold mine tailing of Central China to evaluate the impact of physical disturbance on biocrusts during the rehabilitation process of gold mine tailings. The results show that physical disturbance significantly reduced the photosynthetic biomass, nutrient contents (organic carbon, ammonium nitrogen, nitrate nitrogen, and total phosphorus), and enzyme activities (β-glucosidase, sucrase, nitrogenase, neutral phosphatase, and urease) of biocrusts in the mine tailings. Furthermore, 16S rDNA sequencing showed that physical disturbance strongly changed the composition, structure, and interactions of the bacterial community, leading to a shift from a cyanobacteria dominated community to a heterotrophic bacteria (proteobacteria, actinobacteria, and acidobacteria) dominated community and a more complex bacterial network (higher complexity, nodes, and edges). Altogether, our results show that the biocrusts dominated by cyanobacteria could also develop in the tailings of humid region, and the dominants (e.g., Microcoleus) were the same as those from dryland biocrusts; nevertheless, physical disturbance significantly reduced cyanobacterial relative abundance in biocrusts. Based on our findings, we propose the future work on cyanobacterial inoculation (e.g., Microcoleus), which is expected to promote substrate metabolism and accumulation, ultimately accelerating the development of biocrusts and the subsequent ecological restoration of tailings.

Biochar Addition Alters C: N: P Stoichiometry in Moss Crust-Soil Continuum in Gurbantünggüt Desert

The biogeochemical cycling of soil elements in ecosystems has changed under global changes, including nutrients essential for plant growth. The application of biochar can improve the utilization of soil nutrients by plants and change the stoichiometry of carbon (C), nitrogen (N), and phosphorus (P) in plants and soil. However, the response of ecological stoichiometry in a moss crust-soil continuum to local plant biochar addition in a desert ecosystem has not been comprehensively explored. Here, we conducted a four-level Seriphidium terrae-albae biochar addition experiment (CK, 0 t ha−1; T1, 3.185 t ha−1; T2, 6.37 t ha−1; T3, 12.74 t ha−1) to elucidate the influence of biochar input on C: N: P stoichiometry in moss crusts (surface) and their underlying soil (subsurface). The results showed that biochar addition significantly affected the C, N, and P both of moss crusts and their underlying soil (p < 0.001). Biochar addition increased soil C, N, and P concentrations, and the soil N content showed a monthly trend in T3. The C, N, and P concentrations of moss crusts increased with the addition levels of biochar, and the moss crust P concentrations showed an overall increasing trend by the month. Moreover, the soil and moss crust C: P and N: P ratios both increased. There was a significant correlation between moss crust C, N, and P and soil C and N. Additionally, nitrate nitrogen (NO3−N), N: P, C: P, EC, pH, soil moisture content (SMC), and N have significant effects on the C, N, and P of moss crusts in turn. This study revealed the contribution of biochar to the nutrient cycle of desert system plants and their underlying soil from the perspective of stoichiometric characteristics, which is a supplement to the theory of plant soil nutrition in desert ecosystems.

Applicability Evaluation of Soil Algae Pipe Assay in Silver Nanoparticle-Contaminated Soils

Due to pervasive and resilient soil contaminants, heterogeneously contaminated soil poses unpredictable potential threats to ecosystems. In this study, the extension of a previously developed soil algae pipe assay for evaluating heterogeneously contaminated soil under an open system is described. The assay can be used in soil that is heterogeneously contaminated with silver nanoparticles in combination with the examination of morphological changes (e.g., in vivo chlorophyll a, cell granularity, cell size, and mucilaginous sheath) and lipid contents. In addition, we attempted to extend the exposure duration under an open system. We evaluated the applicability of this soil algae pipe assay using green alga Chlamydomonas reinhardtii exposed to heterogeneous and homogeneous polyvinylpyrrolidone capping silver nanoparticles in contaminated soils. The results demonstrated that this method is an applicable bioassay that can be employed to better evaluate soil algal toxicity under an open system, with significant changes in the measured endpoints. The developed assay showed decent predictivity, which can be a useful tool when evaluating heterogeneous soil algae contamination.

Distribution and dispersion of heavy metals in the rock-soil-moss system of the black shale areas in the southeast of Guizhou Province, China

Black shales are easily exposed due to human activities such as mining, road construction, and shale gas development, which results in several environmental issues including heavy metal (HM) pollution, soil erosion, and the destruction of vegetation. Mosses are widely used to monitor metal pollution in the atmosphere, but few studies on the distribution and dispersion of HMs in the rock-soil-moss system are available. Here, mosses (Pohlia flexuosa Harv. in Hook), growing soils, and corresponding parent rocks were collected from black shale areas. After appropriate pretreatment, samples were analyzed for multiple elemental concentrations by ICP-AES and ICP-MS. The results show that black shale parent rocks have elevated HM concentration and act as a source of multiple metals. The overlying soil significantly inherits and accumulates heavy metals released from black shale. Significant positive correlations between HMs in P. flexuosa and the growing soils indicate that HMs are mainly originating from geological source rather than atmospheric deposition. Differential accumulation of HMs is observed between rhizoids and stems in our study. Moreover, P. flexuosa is able to cope with high concentrations of toxic metals without any visible negative effect on its growth and development. Finally, the bioconcentration factor (BCF) for all the HMs in P. flexuosa is less than 1, indicating that it has a tolerance and exclusion mechanism for these metals, especially for the non-essential elements As and Pb. Therefore, the luxuriant and spontaneous growth of P. flexuosa could be used as a phytostabilization pioneer plant in the black shale outcrop where vascular plants are rare.

Identification of the key functional genes in salt-stress tolerance of Cyanobacterium Phormidium tenue using in silico analysis

The development of artificial biocrust using cyanobacterium Phormidium tenue has been suggested as an effective strategy to prevent soil degradation. Here, a combination of in silico approaches with growth rate, photosynthetic pigment, morphology, and transcript analysis was used to identify specific genes and their protein products in response to 500 mM NaCl in P. tenue. The results show that 500 mM NaCl induces the expression of genes encoding glycerol-3-phosphate dehydrogenase (glpD) as a Flavoprotein, ribosomal protein S12 methylthiotransferase (rimO), and a hypothetical protein (sll0939). The constructed co-expression network revealed a group of abiotic stress-responsive genes. Using the Basic Local Alignment Search Tool (BLAST), the homologous proteins of rimO, glpD, and sll0939 were identified in the P. tenue genome. Encoded proteins of glpD, rimO, and DUF1622 genes, respectively, contain (DAO and DAO C), (UPF0004, Radical SAM and TRAM 2), and (DUF1622) domains. The predicted ligand included 22B and MG for DUF1622, FS5 for rimO, and FAD for glpD protein. There was no direct disruption in ligand-binding sites of these proteins by Na+, Cl-, or NaCl. The growth rate, photosynthetic pigment, and morphology of P. tenue were investigated, and the result showed an acceptable tolerance rate of this microorganism under salt stress. The quantitative real-time polymerase chain reaction (qRT-PCR) results revealed the up-regulation of glpD, rimO, and DUF1622 genes under salt stress. This is the first report on computational and experimental analyses of the glpD, rimO, and DUF1622 genes in P. tenue under salt stress to the best of our knowledge.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-03050-w.

Spatial modelling of soil salinity: deep or shallow learning models?

Understanding the spatial distribution of soil salinity is required to conserve land against degradation and desertification. Against this background, this study is the first attempt to predict soil salinity in the Jaghin basin, in southern Iran, by applying and comparing the performance of four deep learning (DL) models (deep convolutional neural networks-DCNNs, dense connected deep neural networks-DenseDNNs, recurrent neural networks-long short-term memory-RNN-LSTM and recurrent neural networks-gated recurrent unit-RNN-GRU) and six shallow machine learning (ML) models (bagged classification and regression tree-BCART, cforest, cubist, quantile regression with LASSO penalty-QR-LASSO, ridge regression-RR and support vectore machine-SVM). To do this, 49 environmental landsat8-derived variables including digital elevation model (DEM)-extracted covariates, soil-salinity indices, and other variables (e.g., soil order, lithology, land use) were mapped spatially. For assessing the relationships between soil salinity (EC) and factors controlling EC, we collected 319 surficial (0-5 cm depth) soil samples for measuring soil salinity on the basis of electrical conductivity (EC). We then selected the most important features (covariates) controlling soil salinity by applying a MARS model. The performance of the DL and shallow ML models for generating soil salinity spatial maps (SSSMs) was assessed using a Taylor diagram and the Nash Sutcliff coefficient (NSE). Among all 10 predictive models, DL models with NSE ≥ 0.9 (DCNNs was the most accurate model with NSE = 0.96) were selected as the four best models, and performed better than the six shallow ML models with NSE ≤ 0.83 (QR-LASSO was the weakest predictive model with NSE = 0.50). Based on DCNNs-, the values of the EC ranged between 0.67 and 14.73 dS/m, whereas for QR-LASSO the corresponding EC values were 0.37 to 19.6 dS/m. Overall, DL models performed better than shallow ML models for production of the SSSMs and therefore we recommend applying DL models for prediction purposes in environmental sciences.