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Nutrient amendment diminished bacterial functional diversity, consolidating carbon flow through fewer bacterial taxa. Here, we show strong differences in the bacterial taxa responsible for respiration from four ecosystems, indicating the potential for taxon-specific control over soil carbon cycling. Trends in functional diversity, defined as the richness of bacteria contributing to carbon flux and their equitability of carbon use, paralleled trends in taxonomic diversity although functional diversity was lower overall. Among genera common to all ecosystems, Bradyrhizobium , the Acidobacteria genus RB41 , and Streptomyces together composed 45–57% of carbon flow through bacterial productivity and respiration. Bacteria that utilized the most carbon amendment (glucose) were also those that utilized the most native soil carbon, suggesting that the behavior of key soil taxa may influence carbon balance. Mapping carbon flow through different microbial taxa as demonstrated here is crucial in developing taxon-sensitive soil carbon models that may reduce the uncertainty in climate change projections. The fate of soil carbon depends on microbial processes, but whether different microbial taxa have individualistic effects on carbon fluxes is unknown. Here the authors use 16 S amplicon sequencing and stable isotopes to show how taxonomic differences influence bacterial respiration and carbon cycling across four ecosystems.

This work documents the first version of the U.S. Department of Energy (DOE) new Energy Exascale Earth System Model (E3SMv1). We focus on the standard resolution of the fully--coupled physical model designed to address DOE mission-relevant water cycle questions. Its components include atmosphere and land (110km grid spacing), ocean and sea ice (60km in the mid-latitudes and 30km at the equator and poles), and river transport (55km) models. This base configuration will also serve as a foundation for additional configurations exploring higher horizontal resolution as well as augmented capabilities in the form of biogeochemistry and cryosphere configurations. The performance of E3SMv1 is evaluated by means of a standard set of Coupled Model Intercomparison Project Phase 6 Diagnosis, Evaluation, and Characterization of Klima (CMIP6 DECK) simulations consisting of a long pre-industrial control, historical simulations (ensembles of fully coupled and prescribed SSTs) as well as idealized CO2 forcing simulations. The model performs well overall with biases typical of other CMIP-class models, although the simulated Atlantic Meridional Overturning Circulation is weaker than many CMIP-class models. While the E3SMv1 historical ensemble captures the bulk of the observed warming between pre-industrial (1850) and present-day, the trajectory of the warming diverges from observations in the second half of the 20th century with a period of delayed warming followed by an excessive warming trend. Using a two-layer energy balance model, we attribute this divergence to the model's strong aerosol-related effective radiative forcing (ERFari+aci = -1.65 W m-2) and high equilibrium climate sensitivity (ECS = 5.3 K).

One of the biggest challenges in microbial ecology is correlating the identity of microorganisms with the roles they fulfill in natural environmental systems. Studies of microbes in pure culture reveal much about their genomic content and potential functions but may not reflect an organism’s activity within its natural community. Culture-independent studies supply a community-wide view of composition and function in the context of community interactions but often fail to link the two. Quantitative stable isotope probing (qSIP) is a method that can link the identity and functional activity of specific microbes within a naturally occurring community. Here, we explore how the resolution of density gradient fractionation affects the error and precision of qSIP results, how they may be improved via additional experimental replication, and discuss cost-benefit balanced scenarios for SIP experimental design. Quantitative stable isotope probing (qSIP) estimates isotope tracer incorporation into DNA of individual microbes and can link microbial biodiversity and biogeochemistry in complex communities. As with any quantitative estimation technique, qSIP involves measurement error, and a fuller understanding of error, precision, and statistical power benefits qSIP experimental design and data interpretation. We used several qSIP data sets—from soil and seawater microbiomes—to evaluate how variance in isotope incorporation estimates depends on organism abundance and resolution of the density fractionation scheme. We assessed statistical power for replicated qSIP studies, plus sensitivity and specificity for unreplicated designs. As a taxon’s abundance increases, the variance of its weighted mean density declines. Nine fractions appear to be a reasonable trade-off between cost and precision for most qSIP applications. Increasing the number of density fractions beyond that reduces variance, although the magnitude of this benefit declines with additional fractions. Our analysis suggests that, if a taxon has an isotope enrichment of 10 atom% excess, there is a 60% chance that this will be detected as significantly different from zero (with alpha 0.1). With five replicates, isotope enrichment of 5 atom% could be detected with power (0.6) and alpha (0.1). Finally, we illustrate the importance of internal standards, which can help to calibrate per sample conversions of %GC to mean weighted density. These results should benefit researchers designing future SIP experiments and provide a useful reference for metagenomic SIP applications where both financial and computational limitations constrain experimental scope. IMPORTANCE One of the biggest challenges in microbial ecology is correlating the identity of microorganisms with the roles they fulfill in natural environmental systems. Studies of microbes in pure culture reveal much about their genomic content and potential functions but may not reflect an organism’s activity within its natural community. Culture-independent studies supply a community-wide view of composition and function in the context of community interactions but often fail to link the two. Quantitative stable isotope probing (qSIP) is a method that can link the identity and functional activity of specific microbes within a naturally occurring community. Here, we explore how the resolution of density gradient fractionation affects the error and precision of qSIP results, how they may be improved via additional experimental replication, and discuss cost-benefit balanced scenarios for SIP experimental design.

The application of the cosmogenic radioisotope sulfur-35 (35S) as a chronometer near spreading basins is evaluated at two well-established Managed Aquifer Recharge (MAR) sites: the Atlantis facility (South Africa) and Orange County Water District’s (OCWD’s) Kraemer Basin (Northern Orange County, CA, USA). Source water for both of these sites includes recycled wastewater. Despite lying nearer to the outlet end of their respective watersheds than to the headwaters, 35S was detected in most of the water sampled, including from wells found close to the spreading ponds and in the source water. Dilution with 35S-dead continental SO4 was minimal, a surprising finding given its short ~3 month half-life. The initial work at the Atlantis MAR site demonstrated that remote laboratories could be set up and that small volume samples—saline solutions collected after the resin elution step from the recently developed batch method described below—can be stored and transported to the counting laboratory. This study also showed that the batch method needed to be altered to remove unknown compounds eluted from the resin along with SO4. Using the improved batch method, times series measurements of both source and well water from OCWD’s MAR site showed significant temporal variations. This result indicates that during future studies, monthly to semi-monthly sampling should be conducted. Nevertheless, both of these initial studies suggest the 35S chronometer may become a valuable tool for managing MAR sites where regulations require minimum retention times.

Background Despite their widespread distribution and ecological importance, protists remain one of the least understood components of the soil and rhizosphere microbiome. Knowledge of the roles that protists play in stimulating organic matter decomposition and shaping microbiome dynamics continues to grow, but there remains a need to understand the extent to which biological and environmental factors mediate protist community assembly and dynamics. We hypothesize that protists communities are filtered by the influence of plants on their rhizosphere biological and physicochemical environment, resulting in patterns of protist diversity and composition that mirror previously observed diversity and successional dynamics in rhizosphere bacterial communities. Results We analyzed protist communities associated with the rhizosphere and bulk soil of switchgrass (SG) plants ( Panicum virgatum ) at different phenological stages, grown in two marginal soils as part of a large-scale field experiment. Our results reveal that the diversity of protists is lower in rhizosphere than bulk soils, and that temporal variations depend on soil properties but are less pronounced in rhizosphere soil. Patterns of significantly prevalent protists groups in the rhizosphere suggest that most protists play varied ecological roles across plant growth stages and that some plant pathogenic protists and protists with omnivorous diets reoccur over time in the rhizosphere. We found that protist co-occurrence network dynamics are more complex in the rhizosphere compared to bulk soil. A phylogenetic bin-based null model analysis showed that protists’ community assembly in our study sites is mainly controlled by homogenous selection and dispersal limitation, with stronger selection in rhizosphere than bulk soil as SG grew and senesced. Conclusions We demonstrate that environmental filtering is a dominant determinant of overall protist community properties and that at the rhizosphere level, plant control on the physical and biological environment is a critical driver of protist community composition and dynamics. Since protists are key contributors to plant nutrient availability and bacterial community composition and abundance, mapping and understanding their patterns in rhizosphere soil is foundational to understanding the ecology of the root-microbe-soil system. 5stCu76cmNPcXaeF7W4bD3 Video Abstract

As a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

Salt's deposition in the subsoil is known as salinization. It is caused by natural processes such as mineral weathering or human-made activities such as irrigation with saline water. This environmental issue has grown more critical and is frequently occurring in the Hungarian Great Plain, adversely influencing agricultural productivity. This study aims to predict soil salinity in the Great Hungarian Plain, located in the east of Hungary, using Landsat 8 OLI data combined with four state-of-the-art regression models, i.e., Multiple Linear Regression, Partial Least Squares Regression, Ridge Regression, and Feedforward Artificial Neural Network. For this purpose, seventy-six soil samples were collected during a field survey conducted by the Research Institute for Soil Sciences and Agricultural Chemistry between the 15 of September and the 15 of October, 2016. We used the min–max accuracy, the root-mean-square error (RMSE), and the mean squared error (MSE) to evaluate and compare the four models' performance. The results showed that the ridge regression model performed the best in terms of prediction (MSE training  = 0.006, MSE test  = 0.0007, RMSE = 0.081), with a min–max accuracy equal to 0.75. Hence, the application of regression modeling on spectral indices, principal component analysis, and land surface temperature derived from multispectral data is an efficient method for soil salinity assessment at local scales. The resulting map can provide an overview of salinity levels and evaluate the efficiency of land management strategies in irrigated areas. An increase in sampling density will be recommended to validate this approach on the regional scale.

This paper investigates the bandgap properties of two-dimensional elastic problem with pile barrier for seismic surface waves. The main objective of this proposed work is to investigate the propagation of surface waves through periodically arranged piles in a single soil medium and to study the feasibility of surface waves attenuation by finite element technique. We consider the idea of seismic metamaterials in this study. One class of seismic metamaterials is explored with a periodic arrangement of vertical pile inclusions inside of the ground. The unit-cell analysis of the pile-soil system has been discussed in this work to attain the bandgaps with the low-frequency range. We varied all geometrical and mechanical parameters with shape of the pile to display the productivity of the weakening zone. These low-frequency ranges bandgaps suggest that the planned pile-soil system is efficient in reducing surface waves. The possibility study exposes that this type of pile-soil system can be applied as seismic barricades for mitigating seismic waves to guard critical civil structures from earthquake dangers. This comes as an interesting approach to potentially shield the LIGO detectors in the low-frequency range and can have an impact on the sensitivity of existing and future ground-based detectors. The present result will open a new prospect for the wave barricades designed with a low cost of money.

Due to the increasing presence of problematic soils, expansive clays and highly compressive sand engineers are using a verity of soil improvement techniques to treat such soils. While geosynthetics are extensively used for improving soil characteristics in roads, pavements and embankments, it can also be used to increase the lack of bearing capacity of residential housing or lightweight structures constructed on sandy soils. In order to simulate site conditions in the laboratory environment, a laboratory-scale testing platform has been manufactured to assess the behaviour of geosynthetics reinforced and un-reinforced strip footing. The first group of tests were performed on the unreinforced compacted sandy soils with different densities where the other group of tests were carried out in the soil that has been reinforced individually with three different types of geosynthetic materials having distinct tensile strengths. Furthermore, interface direct shear tests and consolidated undrained triaxial tests have been carried out to determine the shear parameters which is directly influencing the bearing capacity a strip footing. Geosynthetic reinforcement has considerably enhanced the mechanical behaviour of sandy soil in regarding the type of geosynthetic. Furthermore, it was observed that coir geosynthetic has provided increased interfacial friction when compared to other geosynthetic types and improved bearing capacity. Moreover, the adopted testing method found to represent well the behaviour of such materials in the laboratory environment.

This paper illustrates application of a locally produced geogrid material for strength improvement of expansive subgrade soil. Samples of black, soft soil predominating the study area were collected from south western parts of Modjo town, inside the rift valley region of central parts of Ethiopia. X-Ray diffraction as well as index property tests were executed to identify and categorize the expansiveness of the highly plastic soft soil. The effects of two locally manufactured geogrid reinforcement materials; namely, polypropylene (PP) and high density polyethylene (HDPE) on the California bearing ratio (CBR) values of the expansive soil have been investigated. The test results indicated that the use of the geogrid reinforcement can significantly improve the bearing capacity of weak subgrade soil. The soaked CBR of the untreated soil sample, which was about 2.98%, was able to be raised to 10.16% and 7.48% by the application of PP and HDPE type of geogrid respectively, that were placed at 0.35H from the top of specimen. The research demonstrated the potential of using locally produced geogrid material for the improvement of weak subgrade soil. Article Highlights The strength of weak subgrade soil was strongly improved after the introduction of two locally produced geogrid materials made of polypropylene and high density polyethylene, respectively The geogrid made from polypropylene raw material was found to improve the strength (CBR) of the subgrade better than that made from high density polyethylene Experimental investigations about the effectiveness of chemical stabilization using cement kiln dust (CKD) indicated that geogrid reinforcement is relatively more promising