Western myall groves (Acacia papyrocarpa) determine the fine‐scale distribution of soil Collembola in semi‐arid South Australia

Vegetation can exert a strong influence on the distribution and activity of biotic communities across a broad range of spatial scales, especially in arid and semi‐arid ecosystems. At fine spatial scales, patches created by individual plants can support different faunal and floral communities even at locations distant from the plant. These differences can have profound effects on a range of ecosystem processes, including seed dispersal, nutrient cycling and resource distribution. In semi‐arid Australia, areas surrounding groves of western myall (Acacia papyrocarpa) trees are largely devoid of vegetation, being referred to as ‘halos’. Here, we investigate the soil‐dwelling Collembola in groves of western myall trees, the surrounding halos and nearby chenopod shrubland. We also investigated whether the abundance of Collembola was influenced by soil depth (0–5 cm layer vs. 6–10 cm layer) in groves. We found that collembolan density was approximately nine times lower and taxonomic richness half that in a halo compared with the grove and chenopod vegetation. Furthermore, analyses at finer taxonomic levels indicate that vegetation patches differed in species composition, with some species restricted to or preferring particular patches. In the grove, we found a higher abundance of Collembola in the 0–5 cm soil layer compared with the 6–10 layer. Our results indicate vegetation patches strongly influence collembolan abundance and species composition in bare patches around western myall. As patches created by vegetation are a common feature of semi‐arid and arid regions, we suspect that these effects are widespread although seldom reported. Furthermore, as Collembola are involved in the decomposition process, Acacia papyrocarpa patches will be influencing nutrient cycling through their effects on the soil biota. Our results also emphasize that comprehensive fauna survey and management of woodland ecosystems need to consider fine‐scale processes.     

Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem

Soil organic carbon (SOC) plays an important role in soil fertility and carbon sequestration, and a better understanding of the spatial patterns of SOC is essential for soil resource management. In this study, we used boosted regression tree (BRT) and random forest (RF) models to map the distribution of topsoil organic carbon content at the northeastern edge of the Tibetan Plateau in China. A set of 105 soil samples and 12 environmental variables (including topography, climate and vegetation) were analyzed. The performance of the models was evaluated using a 10-fold cross-validation procedure. Maps of the mean values and standard deviations of SOC were generated to illustrate model variability and uncertainty. The results indicate that the BRT and RF models exhibited very similar performance and yielded similar predicted distributions of SOC. The two models explained approximately 70% of the total SOC variability. The BRT and RF models robustly predicted the SOC at low observed SOC values, whereas they underestimated high observed SOC values. This underestimation may have been caused by biased distributions of soil samples in the SOC space. Vegetation-related variables were assigned the highest importance in both models, followed by climate and topography. Both models produced spatial distribution maps of SOC that were closely related to vegetation cover. The SOC content predicted by the BRT model was clearly higher than that of the RF model in areas with greater vegetation cover because the contributions of vegetation-related variables in the two models (65% and 43%, respectively) differed significantly. The predicted SOC content increased from the northwestern to the southeastern part of the study area, average values produced by the BRT and RF models were 27.3 g kg⁻¹ and 26.6 g kg⁻¹, respectively. We conclude that the BRT and RF methods should be calibrated and compared to obtain the best prediction of SOC spatial distribution in similar regions. In addition, vegetation variables, including those obtained from remote sensing imagery, should be taken as the main environmental indicators and explicitly included when generating SOC maps in Alpine environments.     

Comparative regional-scale soil salinity assessment with near-ground apparent electrical conductivity and remote sensing canopy reflectance

Soil salinity is recognized worldwide as a major threat to agriculture, particularly in arid and semi-arid regions. Producers and decision makers need updated and accurate maps of salinity in agronomically and environmentally relevant ranges (i.e., <20 dS m⁻¹, when salinity is measured as electrical conductivity of the saturation extract, EC_e). State-of-the-art approaches for creating accurate EC_e maps beyond field scale (i.e., 1 km²) include: (i) Analysis Of Covariance (ANOCOVA) of near-ground measurements of apparent soil electrical conductivity (EC_a) and (ii) regression modeling of multi-year remote sensing canopy reflectance and other co-variates (e.g., crop type, annual rainfall). This study presents a comparison of the two approaches to establish their viability and utility. The approaches were tested using 22 fields (total 542 ha) located in California’s western San Joaquin Valley. In 2013 EC_a-directed soil sampling resulted in the collection of 267 soil samples across the 22 fields, which were analyzed for EC_e, ranging from 0 to 38.6 dS m⁻¹. The ANOCOVA EC_a-EC_e model returned a coefficient of determination (R²) of 0.87 and root mean square prediction error (RMSPE) of 3.05 dS m⁻¹. For the remote sensing approach seven years (2007–2013) of Landsat 7 reflectance were considered. The remote sensing salinity model had R² = 0.73 and RMSPE = 3.63 dS m⁻¹. The robustness of the models was tested with a leave-one-field-out (lofo) cross-validation to assure maximum independence between training and validation datasets. For the ANOCOVA model, lofo cross-validation provided a range of scenarios in terms of RMSPE. The worst, median, and best fit scenarios provided global cross-validation R² of 0.52, 0.80, and 0.81, respectively. The lofo cross-validation for the remote sensing approach returned a R² of 0.65. The ANOCOVA approach performs particularly well at EC_e values <10 dS m⁻¹, but requires extensive field work. Field work is reduced considerably with the remote sensing approach, but due to the larger errors at low EC_e values, the methodology is less suitable for crop selection, and other practices that require accurate knowledge of salinity variation within a field, making it more useful for assessing trends in salinity across a regional scale. The two models proved to be viable solutions at large spatial scales, with the ANOCOVA approach more appropriate for multiple-field to landscape scales (1–10 km²) and the remote sensing approach best for landscape to regional scales (>10 km²).     

Bioremediation of Oil-contaminated Soil Using Chicken Manure

In less developed countries, the prevalence of soil contaminated with used lubricating oil is high and the situation worsens with the economic advancement. The contamination has been shown to adversely affect the environment and human health. To mitigate, bioremediation could be adopted to tackle the problem of hydrocarbon-contaminated soil. Thus, this experimental research carried out the bioremediation using chicken manure in soils contaminated with 5%, 10% and 20% w/w used lubricating oil for a 42-day composting period. To compare, this research also experimented with the 5%, 10% and 20% oil-contaminated soils untreated with chicken manure. The results showed that the highest total petroleum hydrocarbons (TPHs) reduction efficiency of >60% was achieved in the 5% oil-contaminated compost remediated with chicken manure. The highest biodegradation rate of lubricating oil of 0.023–0.0025 day^?1 as measured by the first-order kinetics could also be achieved under the 5% oil contamination condition with the application of chicken manure. The findings highlight the prospect of chicken manure as a proper nutrient for enhanced remediation of hydrocarbon-contaminated soils, particularly of low contamination concentrations.     

Hydrocarbon Degradation and Biosurfactant Production by an Acenaphthene-degrading Pseudomonas Species

An acenaphthene-degrading bacterium putatively identified as Pseudomonas sp. strain KR3 and isolated from diesel-contaminated soil in Lagos, Nigeria was investigated for its degradative and biosurfactant production potentials on crude oil. Physicochemical analysis of the sampling site indicates gross pollution of the soil with high hydrocarbon content (2100 mg/kg) and detection of various heavy metals. The isolate grew luxuriantly on crude oil, engine oil and acenaphthene. It was resistant to septrin, amoxicillin and augmentin but was susceptible to pefloxacin, streptomycin and gentamycin. It was also resistant to elevated concentration of heavy metals such as 1–15 mM lead, nickel and molybdenum. On acenaphthene, the isolate exhibited specific growth rate and doubling time of 0.098 day^?1 and 3.06 days, respectively. It degraded 62.44% (31.2 mg/l) and 91.78% (45.89 mg/l) of 50 mg/l acenaphthene within 12 and 21 days. On crude oil, the specific growth rate and doubling time were 0.375 day^?1 and 1.85 days with corresponding percentage degradation of 33.01% (903.99 mg/l) and 87.79% (2403.71 mg/l) of crude oil (2738.16 mg/l) within 9 and 18 days. Gas chromatographic analysis of residual crude oil at the end of 18 days incubation showed significant reductions in the aliphatic, alicyclic and aromatic fractions with complete disappearance of benzene, propylbenzene, pristane, phytane, and nC₁₈-octadecane fractions of the crude oil. The isolate produced growth-associated biosurfactant on crude oil with the highest emulsification index (E₂₄) value of 72% ± 0.23 on Day 10 of incubation. The partially purified biosurfactant showed zero tolerance for salinity and had its optimal activity at 27°C and pH 2.0.     

Enhanced Immobilization of Cr(VI) in Soils by the Amendment of Rice Straw Char

This study investigated the effect of rice straw char (RSC) on the immobilization of Cr(VI) in soils. The Cr(VI) sorption experiments on the RSC and RSC-amended soils were conducted using the batch method. RSC exhibited Cr(VI) reduction capacity due to its black carbon content. The addition of RSC to the soils enhanced the overall Cr(VI) immobilization of the soils, which is primarily attributed to the Cr(VI) reduction capacity of RSC. The effects of RSC amendment on the Cr(VI) sorption of the soils increased with increasing RSC content in the soils and decreased with increasing pH or anion contents in the soil solutions. After Cr(VI) was sorbed by the soils, a portion of the Cr(VI) was converted to Cr(III) and the remainder was sorbed onto the soils. The presence of RSC in the soils decreased the portion of sorbed Cr(VI) in the soils and therefore lowered the potential remobilization of Cr(VI) from the soils. The results suggested that RSC amendment can be applied to develop a cost-effective method for immobilizing Cr(VI) in polluted soils, thus lowering the environmental risk from Cr(VI) toxicity.     

The Remediation of Chromium (VI)-Contaminated Soils Using Microbial Fuel Cells

Chromium (VI) is a priority pollutant in soil and water and poses serious threats to the environment. Microbial fuel cells (MFCs), as a sustainable technology, have been applied to treat heavy-metal-contaminated wastewater. To study MFC application in soil remediation, red clay soil and fluvo-aquic soil were spiked with Cr(VI) and packed into a cathode chamber of MFCs, which were then operated at external resistances of 100 and 1000 Ω for 16 days, with open circuit condition as a control treatment. After the operation, the concentration of dissolved Cr(VI) in supernatant and total Cr(VI) in soil was decreased. Soil type and external resistance significantly affected the current, removal efficiency of Cr(VI), and cathode efficiency. Reducing external resistance improved the removal efficiency. The red soil generated a higher current of MFCs, but showed a lower removal efficiency and cathode efficiency than fluvo-aquic soil, implying that the red soil may contain more electron acceptors that competed with Cr(VI) reduction reaction. Our study demonstrated that MFC-based technology has the potential to remediate Cr(VI)-contaminated soil; the efficiency varied between soil types and can be improved with high current.     

Effect of bacterial inoculation of strains of pseudomonas aeruginosa,alcaligenes feacalis and bacillus subtilis on germination,growth and heavy metal (cd,cr, and ni) uptake of brassica juncea

Bacterial inoculation may influence Brassica juncea growth and heavy metal (Ni, Cr, and Cd) accumulation. Three metal tolerant bacterial isolates (BCr3, BCd33, and BNi11) recovered from mine tailings, identified as Pseudomonas aeruginosa KP717554, Alcaligenes feacalis KP717561, and Bacillus subtilis KP717559 were used. The isolates exhibited multiple plant growth beneficial characteristics including the production of indole-3-acetic acid, hydrogen cyanide, ammonia, insoluble phosphate solubilization together with the potential to protect plants against fungal pathogens. Bacterial inoculation improved seeds germination of B. juncea plant in the presence of 0.1 mM Cr, Cd, and Ni, as compared to the control treatment. Compared with control treatment, soil inoculation with bacterial isolates significantly increased the amount of soluble heavy metals in soil by 51% (Cr), 50% (Cd), and 44% (Ni) respectively. Pot experiment of B. juncea grown in soil spiked with 100 mg kg^?1 of NiCl₂, 100 mg kg^?1 of CdCl₂, and 150 mg kg^?1 of K₂Cr₂O₇, revealed that inoculation with metal tolerant bacteria not only protected plants against the toxic effects of heavy metals, but also increased growth and metal accumulation of plants significantly. These findings suggest that such metal tolerant, plant growth promoting bacteria are valuable tools which could be used to develop bio-inoculants for enhancing the efficiency of phytoextraction.     

Inoculation methods using Rhodococcus erythropolis strain P30 affects bacterial assisted phytoextraction capacity of Nicotiana tabacum

In this study different bacterial inoculation methods were tested for tobacco plants growing in a mine-soil contaminated with Pb, Zn, and Cd. The inoculation methods evaluated were: seed inoculation, soil inoculation, dual soil inoculation event, and seed+soil inoculation. Each inoculum was added at two bacterial densities (10⁶ CFUs mL^?1 and 10⁸ CFUs mL^?1). The objectives were to evaluate whether or not the mode of inoculation or the number of applied microorganisms influences plant response. The most pronounced bacterial-induced effect was found for biomass production, and the soil inoculation treatment (using 10⁶ CFUs mL^?1) led to the highest increase in shoot dry weight yield (up to 45%). Bacterial-induced effects on shoot metal concentrations were less pronounced; although a positive effect was found on shoot Pb concentration when using 10⁸ CFUs mL^?1 in the soil inoculation (29% increase) and in the seed+soil inoculation (34% increase). Also shoot Zn concentration increased by 24% after seed inoculation with 10⁶ CFUs mL^?1. The best effects on the total metal yield were not correlated with an increasing number of inoculated bacteria. In fact the best results were found after a single soil inoculation using the lower cellular density of 10⁶ CFUs mL^?1.     

Phytomass change in natural phytocenosis as an indicator of technogenic pollution of soils with heavy metals

This study considered the possibility of using plant community phytomass for the assessment of soil pollution with heavy metals (HM) from industrial wastes. The three-year-long field experiment was run under the regional natural meadow vegetation; the polymetallic galvanic slime was used as an industrial waste contaminant. It is shown that soil contamination primarily causes decrease of phytomass in the growing phytocenosis. The vegetation experiments determined nonlinear dependence of cultivated and wild plant biomass on the level of soil contamination; it is described by the equations of logistic and Gaussian regression. In the absence of permanent contaminants, the soil is self-cleaned over time. It reproduces phytomass mainly due to the productivity increase of the most pollution-tolerant species in the remaining phytocenosis. This phenomenon is defined as environmental hysteresis. Soil pollution by industrial waste leads to the loss of plant biodiversity. The research shows that the study of the HM impact on ecosystems is expedient given the consideration of the “soil–phytocenosis–pollutant” complex in the “dose–response” aspect. The reaction of phytocenosis on HM showing decline in phytomass leads to serious limitations in the choice of accumulating plants, because the adsorbed HM are rejected through phytomass.