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USGS2013

Chemical transfers along slowly eroding catenas developed on granitic cratons in southern Africa

A catena is a series of distinct but co-evolving soils arrayed along a slope. On low-slope, slowly eroding catenas the redistribution of mass occurs predominantly as plasma, the dissolved and suspended constituents in soil water. We applied mass balance methods to track how redistribution via plasma contributed to physical and geochemical differentiation of nine slowly eroding (~ 5 mm ky − 1 ) granitic catenas. The catenas were arrayed in a 3 × 3 climate by relief matrix and located in Kruger National Park, South Africa. Most of the catenas contained at least one illuviated soil profile that had undergone more volumetric expansion and less mass loss, and these soils were located in the lower halves of the slopes. By comparison, the majority of slope positions were eluviated. Soils from the wetter climates (550 and 730 mm precipitation yr − 1 ) generally had undergone greater collapse and lost more mass, while soils in the drier climate (470 mm yr − 1 ) had undergone expansion and lost less mass. Effects of differences in catena relief were less clear. Within each climate zone, soil horizon mass loss and strain were correlated, as were losses of most major elements, illustrating the predominant influence of primary mineral weathering. Nevertheless, mass loss and volumetric collapse did not become extreme because of the skeleton of resistant primary mineral grains inherited from the granite. Colloidal clay redistribution, as traced by the ratio of Ti to Zr in soil, suggested clay losses via suspension from catena eluvial zones. Thus illuviation of colloidal clays into downslope soils may be crucial to catena development by restricting subsurface flow there. Our analysis provides quantitative support for the conceptual understanding of catenas in cratonic landscapes and provides an endmember reference point in understanding the development of slowly eroding soil landscapes.

Geoderma↗

USGS2013

Quantifying mineral abundances of complex mixtures by coupling spectral deconvolution of SWIR spectra (2.1-2.4 μm) and regression tree analysis

This paper presents a methodology for assessing mineral abundances of mixtures having more than two constituents using absorption features in the 2.1-2.4 μm wavelength region. In the first step, the absorption behaviour of mineral mixtures is parameterised by exponential Gaussian optimisation. Next, mineral abundances are predicted by regression tree analysis using these parameters as inputs. The approach is demonstrated on a range of prepared samples with known abundances of kaolinite, dioctahedral mica, smectite, calcite and quartz and on a set of field samples from Morocco. The latter contained varying quantities of other minerals, some of which did not have diagnostic absorption features in the 2.1-2.4 μm region. Cross validation showed that the prepared samples of kaolinite, dioctahedral mica, smectite and calcite were predicted with a root mean square error (RMSE) less than 9 wt.%. For the field samples, the RMSE was less than 8 wt.% for calcite, dioctahedral mica and kaolinite abundances. Smectite could not be well predicted, which was attributed to spectral variation of the cations within the dioctahedral layered smectites. Substitution of part of the quartz by chlorite at the prediction phase hardly affected the accuracy of the predicted mineral content; this suggests that the method is robust in handling the omission of minerals during the training phase. The degree of expression of absorption components was different between the field sample and the laboratory mixtures. This demonstrates that the method should be calibrated and trained on local samples. Our method allows the simultaneous quantification of more than two minerals within a complex mixture and thereby enhances the perspectives of spectral analysis for mineral abundances.

Geoderma↗

USGS2014

Distribution and landscape controls of organic layer thickness and carbon within the Alaskan Yukon River Basin

Understanding of the organic layer thickness (OLT) and organic layer carbon (OLC) stocks in subarctic ecosystems is critical due to their importance in the global carbon cycle. Moreover, post-fire OLT provides an indicator of long-term successional trajectories and permafrost susceptibility to thaw. To these ends, we 1) mapped OLT and associated uncertainty at 30 m resolution in the Yukon River Basin (YRB), Alaska, employing decision tree models linking remotely sensed imagery with field and ancillary data, 2) converted OLT to OLC using a non-linear regression, 3) evaluate landscape controls on OLT and OLC, and 4) quantified the post-fire recovery of OLT and OLC. Areas of shallow (< 10 cm), moderate (≥ 10 cm and < 20 cm), moderately thick (≥ 20 cm and < 30 cm), and thick (≥ 30 cm) OLT, composed 34, 20, 14, and 18% of the YRB, respectively; the average OLT was 19.4 cm. Total OLC was estimated to be 3.38 Pg. A regional chronosequence analysis over 30 years revealed that OLT and OLC increased with stand age (OLT: R 2 = 0.68; OLC: R 2 = 0.66), where an average of 16 cm OLT and 5.3 kg/m 2 OLC were consumed by fires. Strong predictors of OLT included climate, topography, near-surface permafrost distributions, soil wetness, and spectral information. Our modeling approach enabled us to produce regional maps of OLT and OLC, which will be useful in understanding risks and feedbacks associated with fires and climate feedbacks.

Alaska↗

USGS2014

Evaluation of a model framework to estimate soil and soil organic carbon redistribution by water and tillage using 137 Cs in two U.S. Midwest agricultural fields

Cultivated lands in the U.S. Midwest have been affected by soil erosion, causing soil organic carbon (SOC) redistribution in the landscape and other environmental and agricultural problems. The importance of SOC redistribution on soil productivity and crop yield, however, is still uncertain. In this study, we used a model framework, which includes the Unit Stream Power-based Erosion Deposition (USPED) and the Tillage Erosion Prediction (TEP) models, to understand the soil and SOC redistribution caused by water and tillage erosion in two agricultural fields in the U.S. Midwest. This model framework was evaluated for different digital elevation model (DEM) spatial resolutions (10-m, 24-m, 30-m, and 56-m) and topographic exponents ( m = 1.0–1.6 and n = 1.0–1.3) using soil redistribution rates from 137 Cs measurements. The results showed that the aggregated 24-m DEM, m = 1.4 and n = 1.0 for rill erosion, and m = 1.0 and n = 1.0 for sheet erosion, provided the best fit with the observation data at both sites. Moreover, estimated average SOC redistributions were 1.3 ± 9.8 g C m − 2 yr − 1 in field site 1 and 3.6 ± 14.3 g C m − 2 yr − 1 in field site 2. Spatial distribution patterns showed SOC loss (negative values) in the eroded areas and SOC gain (positive value) in the deposition areas. This study demonstrated the importance of the spatial resolution and the topographic exponents to estimate and map soil redistribution and the SOC dynamics throughout the landscape, helping to identify places where erosion and deposition from water and tillage are occurring at high rates. Additional research is needed to improve the application of the model framework for use in local and regional studies where rainfall erosivity and cover management factors vary. Therefore, using this model framework can help to improve the information about the spatial distribution of soil erosion across agricultural landscapes and to gain a better understanding of SOC dynamics within eroding and previously eroded fields.

Iowa↗

USGS2015

Machine learning for predicting soil classes in three semi-arid landscapes

Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes. Our objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were the focus of digital soil mapping studies. Sampling sites at each study area were selected using conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural networks, tree based methods, and support vector machine classifiers. Tested machine learning models were divided into three groups based on model complexity: simple, moderate, and complex. We also compared environmental covariates derived from digital elevation models and Landsat imagery that were divided into three different sets: 1) covariates selected a priori by soil scientists familiar with each area and used as input into cLHS, 2) the covariates in set 1 plus 113 additional covariates, and 3) covariates selected using recursive feature elimination. Overall, complex models were consistently more accurate than simple or moderately complex models. Random forests (RF) using covariates selected via recursive feature elimination was consistently the most accurate, or was among the most accurate, classifiers between study areas and between covariate sets within each study area. We recommend that for soil taxonomic class prediction, complex models and covariates selected by recursive feature elimination be used. Overall classification accuracy in each study area was largely dependent upon the number of soil taxonomic classes and the frequency distribution of pedon observations between taxonomic classes. Individual subgroup class accuracy was generally dependent upon the number of soil pedon observations in each taxonomic class. The number of soil classes is related to the inherent variability of a given area. The imbalance of soil pedon observations between classes is likely related to cLHS. Imbalanced frequency distributions of soil pedon observations between classes must be addressed to improve model accuracy. Solutions include increasing the number of soil pedon observations in classes with few observations or decreasing the number of classes. Spatial predictions using the most accurate models generally agree with expected soil–landscape relationships. Spatial prediction uncertainty was lowest in areas of relatively low relief for each study area.

New Mexico, Utah, Wyoming↗

USGS2015

Long-term controls of soil organic carbon with depth and time: a case study from the Cowlitz River Chronosequence, WA USA

Over timescales of soil development (millennia), the capacity of soils to stabilize soil organic carbon (SOC) is linked to soil development through changes in soil mineralogy and other soil properties. In this study, an extensive dataset of soil profile chemistry and mineralogy is compiled from the Cowlitz River Chronosequence (CRC), WA USA. The CRC soils range in age from 0.25 to 1200 kyr, spanning a developmental gradient encompassing clear changes in soil mineralogy, chemistry, and surface area. Comparison of these and other metrics of soil development with SOC properties reveal several relationships that may be diagnostic of the long-term coupling of soil development and C cycling. Specifically, SOC content was significantly correlated with sodium pyrophosphate extractable metals emphasizing the relevance of organo-metal complexes in volcanic soils. The depth distributions of organo-metals and other secondary weathering products, including the kaolin and short-range order (SRO) minerals, support the so-called “binary composition” of volcanic soils. The formation of organo-metal complexes limits the accumulation of secondary minerals in shallow soils, whereas in deep soils with lower SOC content, secondary minerals accumulate. In the CRC soils, secondary minerals formed in deep soils (below 50 cm) including smectite, allophane, Fe-oxides and dominated by the kaolin mineral halloysite. The abundance of halloysite was significantly correlated with bulk soil surface area and 14 C content (a proxy for the mean age of SOC), implying enhanced stability of C in deep soils. Allophane, an SRO mineral commonly associated with SOC storage, was not correlated with SOC content or 14 C values in CRC soils. We propose conceptual framework to describe these observations based on a general understanding of pedogenesis in volcanic soils, where SOC cycling is coupled with soil development through the formation of and fate of organo-metal or other mobile weathering products. This framework highlights interactions between SOC and soil development, which may be applicable to other soils where organic inputs interact with the products of chemical weathering.

Washington↗

USGS2016

POLARIS: A 30-meter probabilistic soil series map of the contiguous United States

A new complete map of soil series probabilities has been produced for the contiguous United States at a 30 m spatial resolution. This innovative database, named POLARIS, is constructed using available high-resolution geospatial environmental data and a state-of-the-art machine learning algorithm (DSMART-HPC) to remap the Soil Survey Geographic (SSURGO) database. This 9 billion grid cell database is possible using available high performance computing resources. POLARIS provides a spatially continuous, internally consistent, quantitative prediction of soil series. It offers potential solutions to the primary weaknesses in SSURGO: 1) unmapped areas are gap-filled using survey data from the surrounding regions, 2) the artificial discontinuities at political boundaries are removed, and 3) the use of high resolution environmental covariate data leads to a spatial disaggregation of the coarse polygons. The geospatial environmental covariates that have the largest role in assembling POLARIS over the contiguous United States (CONUS) are fine-scale (30 m) elevation data and coarse-scale (~ 2 km) estimates of the geographic distribution of uranium, thorium, and potassium. A preliminary validation of POLARIS using the NRCS National Soil Information System (NASIS) database shows variable performance over CONUS. In general, the best performance is obtained at grid cells where DSMART-HPC is most able to reduce the chance of misclassification. The important role of environmental covariates in limiting prediction uncertainty suggests including additional covariates is pivotal to improving POLARIS' accuracy. This database has the potential to improve the modeling of biogeochemical, water, and energy cycles in environmental models; enhance availability of data for precision agriculture; and assist hydrologic monitoring and forecasting to ensure food and water security.

Geoderma↗

USGS2017

Pyrogenic carbon distribution in mineral topsoils of the northeastern United States

Due to its slow turnover rates in soil, pyrogenic carbon (PyC) is considered an important C pool and relevant to climate change processes. Therefore, the amounts of soil PyC were compared to environmental covariates over an area of 327,757 km 2 in the northeastern United States in order to understand the controls on PyC distribution over large areas. Topsoil (defined as the soil A horizon, after removal of any organic horizons) samples were collected at 165 field sites in a generalised random tessellation stratified design that corresponded to approximately 1 site per 1600 km 2 and PyC was estimated from diffuse reflectance mid-infrared spectroscopy measurements using a partial least-squares regression analysis in conjunction with a large database of PyC measurements based on a solid-state 13 C nuclear magnetic resonance spectroscopy technique. Three spatial models were applied to the data in order to relate critical environmental covariates to the changes in spatial density of PyC over the landscape. Regional mean density estimates of PyC were 11.0 g kg − 1 (0.84 Gg km − 2 ) for Ordinary Kriging, 25.8 g kg − 1 (12.2 Gg km − 2 ) for Multivariate Linear Regression, and 26.1 g kg − 1 (12.4 Gg km − 2 ) for Bayesian Regression Kriging. Akaike Information Criterion (AIC) indicated that the Multivariate Linear Regression model performed best (AIC = 842.6; n = 165) compared to Ordinary Kriging (AIC = 982.4) and Bayesian Regression Kriging (AIC = 979.2). Soil PyC concentrations correlated well with total soil sulphur (P < 0.001; n = 165), plant tissue lignin (P = 0.003), and drainage class (P = 0.008). This suggests the opportunity of including related environmental parameters in the spatial assessment of PyC in soils. Better estimates of the contribution of PyC to the global carbon cycle will thus also require more accurate assessments of these covariates.

Geoderma↗

USGS2018

A sequential selective dissolution method to quantify storage and stability of organic carbon associated with Al and Fe hydroxide phases

Stabilization of SOM (soil organic matter) is regulated in part by sorption and desorption reactions happening at mineral surfaces, as well as precipitation and dissolution of organo-metal complexes. Fe and Al hydroxides play a particularly significant role in SOM stabilization in soils due to their ubiquitous distribution and their highly reactive surface properties. Iron and Al hydroxides exist in soils across a wide spectrum of crystallinity, ranging from dissolved Fe and Al cations which combine with organics to form organo-metal precipitates to the more crystalline end members, goethite and gibbsite, which sorb SOM through a variety of molecular interactions. Though the importance of these sorption and precipitation reactions has long been recognized, the distribution of SOM among Fe and Al hydroxides of differing crystallinity has not been well quantified, nor has the timescale over which these stabilization mechanisms operate. In an attempt to measure the distribution of organic C among (i) Al- and Fe-humus complexes (ii) short-range-order (SRO) Al and Fe hydroxide surfaces and (iii) crystalline Fe oxyhydroxide surfaces, a single method combining several selective mineral dissolutions was applied to soils of four different geneses (a tropical forest Andisol, a temperate forest basaltic Mollisol, a Mediterranean coastal prairie Mollisol, and a northern mixed hardwood forest Spodosol). The traditional reactants used in selective dissolutions were replaced with carbon-free analogues so that the carbon released along with the Fe and Al at each stage of the selective dissolution process could be measured. Selective dissolutions were performed sequentially: Na-pyrophosphate (organo-Al and Fe complexes) followed by hydroxylamine (SRO Al and Fe hydroxides) followed by dithionite-HCl (crystalline Fe hydroxides). Carbon, Al, and Fe concentrations, as well as radiocarbon abundance were measured in the solutions yielded by each stage of the selective dissolution process. Results suggest that precipitation of organo-metal complexes (Na-pyrophosphate extractable C) often accounts for the largest pool of stabilized C among the three selectively dissolved pools, but these complexes were 14 C enriched in comparison to C from the other selectively dissolved pools and the residual C left on crystalline mineral surfaces after all three stages of selective dissolution. Hydroxylamine and dithionite-HCl extractable C pools were, on average, small and often below detection level in temperate soils. However, radiocarbon values for these C pools were generally depleted in comparison to other pools. These results suggest variation in organo-mineral complex stability is associated with degree of crystallinity of the mineral phase. Overall, this work suggests that sequential selective dissolution methods are a promising tool for characterizing the content and isotopic composition of soil C associated with distinct organo-mineral and organo-metal associations.

Geoderma↗

USGS2019

Controls of the spatial variability of denitrification potential in nontidal floodplains of the Chesapeake Bay watershed, USA

Identifying floodplains with high rates of denitrification will help prioritize restoration projects for the removal of nitrogen. Currently, relationships of denitrification with hydrogeomorphic, physiographic, and climate ( i.e. , largescale) characteristics of floodplains are relatively unknown, even though these characteristics have datasets ( e.g. , geographic mapping tools) that are publicly available (or soon-to-become) that could be used to understand denitrification variability. Thus, we investigated control of denitrification by these largescale characteristics in eighteen nontidal floodplains of the Chesapeake Bay watershed ( i.e. , at regional scale, >100 km, scale), using measurements or compiled data at the scales of the stream reach and respective catchment; floodplain soil and herbaceous vegetation ( i.e. , local) characteristics were additionally investigated. Soil denitrification potentials were measured in May, July, and August using complementary acetylene-based techniques under an anoxic environment. Linear largescale predictors of denitrification potential measurements included stream nitrogen and phosphorus concentrations (+), channel width-to-depth ratio (+), floodplain sedimentation (+), forested (−) and urban (+) catchment land cover, and seasonal air temperature (−). Three predictors, catchment forested land cover (strongly related to agricultural land cover), catchment urban land cover, and floodplain sedimentation were related to the most number of denitrification potential measurements. Soil structure, soil nutrient concentrations, and herbaceous vegetation characteristics that were seasonally measured (with a few exceptions) were linear predictors of denitrification potentials in May and August, with nitrogen and carbon characteristics the most consistent (positive) predictors across measurements. Nutrient amendment assays further supported the importance of nitrogen and carbon controls. Using the local characteristics as statistical mediators in path analysis, greater non-forested catchment land cover indirectly increased denitrification through greater floodplain soil nitrate, total phosphorus, and herbaceous aboveground biomass. Additionally, greater floodplain sedimentation indirectly increased denitrification through greater soil pH, total phosphorus, and potential carbon mineralization. Due to the consistency of relationships across denitrification potential measurements along with path modeling results, hotspots of floodplain denitrification should be found in urban and agricultural catchments where river-floodplain hydrologic connectivity promotes sedimentation. Largescale predictors explained 43–57% of the variation in denitrification potentials and should be useful for prediction in floodplains. Siting restoration projects in watersheds for maximum nitrate removal using publicly available largescale datasets is both feasible and effective.

Chesapeake Bay watershed↗

USGS2019

Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data

Fine scale maps of soil properties enable efficient land management and inform earth system models. Recent efforts to create soil property maps from field observations tend to use similar tree-based machine learning interpolation approaches, but often deal with depth of predictions, validation, and uncertainty differently. One of the main differences in approaches is whether to model individual depths of interest separately as ‘2D’ models, or to create models that incorporate depth as a predictor variable creating a ‘3D’ model that can make pre-dictions for all depths. It is unclear how choice of 2D or 3D approach influences model accuracy and uncertainty due to lack of direct comparison and inconsistent presentation of results in past studies. This study compares 2D and 3D methods for mapping soil electrical conductivity (salinity), pH, sum of fine and very fine sands, and organic carbon at 30 m resolution for the upper 432,000 km 2 of the Colorado River Watershed of the United States of America. A new, simple, model-agnostic relative prediction interval (RPI) approach to report un-certainty is presented that scales prediction interval width to the 95% interquantile width of the original training sample distribution. The RPI approach enables direct comparison of uncertainty between properties and depths and is easily interpretable by end users. Results indicate that 3D mapping of soil properties with strong variation with depth can result in substantial areas with much higher uncertainty that coincide with unrealistic predictions relative to 2D models, even though 3D models had slightly better global cross-validation scores. Maps and global model summaries of RPI proved helpful in identifying these issues with 3D models. These results suggest that the use of RPI or similar approaches to evaluate models can identify accuracy problems not evident in global va-lidation diagnostics.

Geoderma↗

USGS2021

Regional ensemble modeling reduces uncertainty for digital soil mapping

Recent country and continental-scale digital soil mapping efforts have used a single model to predict soil properties across large regions. However, different ecophysiographic regions within large-extent areas are likely to have different soil-landscape relationships so models built specifically for these regions may more accurately capture these relationships relative to a ‘global’ model. We ask the question: Is a single ‘global’ model sufficient or are regionally-specific models useful for accurate digital soil mapping? We test this question by modeling soil depth classes across the 432,000 km 2 upper Colorado River Basin in the Western USA using a single global model, multiple ecophysiographic models, and ensembles of the ecophysiographic models. Effective soil depth class observations ( n = 12,194) were derived from multiple soil databases. Fifty-seven environmental covariates were derived from a 30 m digital elevation model, climate data, satellite imagery, and aeroradiometric data. Three independent land classifications were used to stratify the area. Two expert-derived land classifications, USDA Major Land Resource Areas (MLRA) and US-EPA Level III ecoregions, divided the study area into multiple ecophysiographic regions based on vegetation and broad-scale physiographic differences. The third land classification divided the study area into broad landforms. Soil depth observations were split into separate training ( n = 10,470) and validation ( n = 1,724) datasets. First, a ‘global’ random forest model was used to model soil depth classes using all training observations and covariates. ‘Global’ denotes a model built with all training data across the extent of the area, not a model at world extent. Second, the land classifications were used to subset the observations into ecophysiographic sub-datasets and random forest models were refit for each region. Models fit by ecophysiographic region are referred to as regional models. Thirdly, predictions from each regional model were fused into regional-ensemble models. Accuracy, Brier scores, and Shannon’s entropy were used to compare model accuracy and uncertainty. Regional ecophysiographic models were also compared to models built for geographic areas that were defined solely to be approximately equal in area. Training dataset density and the imbalance ratio were investigated to determine if data characteristics influenced regional accuracy/uncertainty metrics. Accuracy for the global model using the validation set was 62.8%. Regional model accuracies ranged between 56.1% and 75.0%. We found: 1) useful inter-regional differences in global model accuracy were revealed when the global model was validated by region, 2) no consistent relationship between training observation density and accuracy/uncertainty metrics, 3) no meaningful differences in accuracy and uncertainty metrics between physiographic and geographic regions, 4) ensembles of regionally-specific models were approximately as accurate as global models, and 5) both region-specific models and ensembles of regional models were less uncertain than the global model. Overall, we recommend the use of soil depth class predictions made from MLRA regional ensemble models because this prediction had higher accuracy than the ecoregion ensemble model prediction, but lower uncertainty than both the global model and the landform ensemble model predictions. We answer our question: Ensembles of regionally-specific models are approximately as accurate as global models, but result in less uncertainty.

Arizona, Colorado, Nevada, New Mexico, Utah, Wyomi↗

USGS2021

Plant growth and biocrust-fire interactions across five North American deserts

Biological soil crusts (biocrusts) are communities predominately comprised of lichens, bryophytes, fungi, algae, and cyanobacteria that form at the soil surface in dryland ecosystems worldwide. Biocrusts can influence the vascular plant community by altering surface hydrology, nutrient cycling, and the availability of microsites suitable for germination. Fire frequency has increased in many dryland systems, but the potential impacts of fire on biocrust-plant interactions remains unclear. Our study explores how biocrusts and the heating associated with fire affect plant growth across five North American desert sites: the Chihuahuan, Colorado Plateau, Great Basin, Mojave, and Sonoran. Using field-collected biocrusts and mineral soil samples from each of these five deserts, we investigated soil biogeochemical differences and the implications of soil heating and biocrust cover on greenhouse grown Elymus elymoides plants. Results showed plant biomass and leaf production were largely determined by the desert where soils originated, and that the soils collected from the Great Basin site, whether heated or not, were generally higher in nutrients and distinct from the other North American desert sites. In contrast, the Chihuahuan site was lower in nutrients and plant biomass growth compared with the other desert sites. In the short term, biocrusts and heating did not significantly affect the biogeochemical profile of individual desert site soils. However, biocrusts and soil heating positively influenced plant growth, and the combination of these factors influenced plants more strongly than either factor considered separately. These findings highlight the importance of biocrusts in mediating resources and suggest additional mechanisms through which fire may alter or accentuate dynamics between biocrusts and vascular plants.

Arizona, Colorado, New Mexico, Nevada↗

USGS2021

Responses of soil extracellular enzyme activities and bacterial community composition to seasonal stages of drought in a semiarid grassland

Extreme drought can strongly impact belowground communities and biogeochemical processes, including soil microbial community composition and extracellular enzyme activities (EEAs), which are considered key agents in ecosystem carbon (C) and nutrient cycling. However, our understanding of how seasonal timing of drought during the growing season affects soil microbial communities and their activity remains notably poor. In this study, we investigated the responses of soil physicochemical properties, EEAs, and bacterial community composition to extreme-duration drought imposed in the early-, mid-, or late-stages of the growing season in a semiarid grassland ecosystem in Inner Mongolia, China. Compared with the ambient control, the activities of C-, nitrogen (N)-, and phosphorus (P)-acquisition enzymes were significantly decreased in the mid- and/or late-stages of drought. Bacterial community diversity also significantly decreased in the mid- and late-stage drought treatments. Soil water content was the most important factor explaining changes in soil EEAs and bacterial community composition. At the end of the growing season, the activities of C-, N-, and P-acquisition enzymes had mostly recovered, while the bacterial community diversity in the mid- and late-stage drought treatments was still lower than the ambient control. Overall, our study demonstrates that the effects of extreme drought on soil EEAs and bacterial community composition depend on the timing of drought. Our results highlight that understanding the effects of extreme-duration drought at different stages of the growing season may play a vital role in predicting the responses of belowground function to global changes in grassland ecosystems.

Geoderma↗

USGS2021

Biocrust and the soil surface: Influence of climate, disturbance, and biocrust recovery on soil surface roughness

Biocrust communities promote soil surface roughness, a key functional characteristic for soil ecology. However, the spatial scales at which biocrust communities contribute to surface roughness are not well understood. To refine our understanding of the spatial dynamics between biocrust and soil surface roughness, we used mm-resolution terrestrial LiDAR to measure micro-topographic roughness at seven sub-meter, 3-dimensional kernels (spatial scales) for undisturbed and disturbed biocrusts within the cool Great Basin and the hot Chihuahuan Deserts of western North America. This multi-scalar approach applied within the different desert regions allowed us to explore two objectives: 1) assess the relative importance of climate and disturbance on biocrust roughness, and 2) evaluate how soil surface roughness evolves with biocrust recovery. For objective 1, we found that undisturbed cool desert biocrust was up to three times rougher than hot desert biocrust. Much of the difference in roughness between the two desert biocrust communities appeared to be from climate or other regional factors. However, positive correlations between roughness and biocrust indicators, including soil chlorophyll-a and the field-based Level of Development (LOD) index, suggested that differences in roughness at spatial scales ≤ 10 cm are directly related to biocrust development. Mechanical disturbance aimed at removing biocrust resulted in significant reductions in roughness and removed much of the observed differences in roughness between cool and hot desert soils. We evaluated biocrust recovery within the cool desert study area two years after mechanical disturbance and found that the disturbed soil increased in roughness up-to 300%. The increased surface roughness at spatial scales ≤ 10 cm were positively correlated with increased aggregate stability and indicators of biocrust reestablishment. We found that topographic change area was also an important contributor to roughness at all spatial scales, particularly at spatial scales ≥ 20 cm where it was the most important factor evaluated. These results provide insight into how biocrust interacts with other biophysical processes to influence soil surface roughness and how soil surfaces evolve at time scales relevant to soil restoration activities.

New Mexico, Utah↗

USGS2021

The trajectory of soil development and its relationship to soil carbon dynamics

It has been postulated that the amount of soil organic carbon (SOC) associated with soil minerals exhibits a threshold relationship in response to effective soil moisture (estimated as precipitation less evapotranspiration). To better characterize the role of moisture in influencing mechanisms of SOC storage during pedogenesis, we compare soils from two different chronosequence sites: the Santa Cruz and Mattole River marine terraces that together form a soil age-by-climate gradient (i.e., climo-chronosequence). Our results demonstrate how variation in the effective soil moisture may drive soil development along divergent pedogenic trajectories, resulting in variations in the form and depth distribution of secondary weathering products. In particular, the residual metals Fe and Al are directly related to the type of secondary minerals that accumulate during weathering, and these variations are coupled to differences in the storage and long-term preservation of SOC both within and between soils. Over time, these differences in soil development may lead to ‘pedogenic thresholds’ that further differentiate soil characteristics and influence SOC dynamics. In this case, the pedogenic threshold takes the form of clay-rich argillic horizons that once formed, inhibit aqueous transport, decouple shallow and deep soil environments, and potentially limit SOC inputs and increase microbial recycling in deep soils. Our data suggest argillic horizon development is favorable in the drier Santa Cruz soils, where kaolinite is the dominant secondary weathering product. In contrast, greater available moisture in soils of the Mattole chronosequence drive a different weathering trajectory characterized by the accumulation of more amorphous secondary minerals. As a result, the Mattole soils and do not exhibit argillic horizon development but are instead characterized by greater accumulation of SOC across all depths sampled. Overall, our results illustrate how the interaction of climate (i.e., moisture) and time may shape the trajectory of soil development and the dynamics of SOC storage and preservation.

California↗

USGS2022

Vertical movement of soluble carbon and nutrients from biocrusts to subsurface mineral soils

Dryland ecosystems can be constrained by low soil fertility. Within drylands, the soil nutrient and organic carbon (C) cycling that does occur is often mediated by soil surface communities known as biological soil crusts (biocrusts), which cycle C and nutrients in the top ca. 0–2 cm of soil. However, the degree to which biocrusts are influencing soil fertility and biogeochemical cycling in deeper, subsurface mineral soils is unclear. The movement of dissolved resources from biocrusts to deeper soil layers in leachate may be one of the main mechanisms through which biocrust fertility is transferred downward towards deeper microbial communities and plant roots occurring within mineral soil. Here we examined the role of biocrust leachate in contributing to subsurface nutrient and soluble C pools and subsurface microbial cycling. We collected biocrusts from three biocrust successional stages and explored resource pools in situ at multiple soil depths, while collecting leachate and measuring nutrient and organic C concentrations and metabolite composition from each successional stage in the laboratory. After four leachate collections, we conducted an incubation of mineral soil collected from below each biocrust successional stage to measure heterotrophic microbial CO 2 flux and biomass. Overall, our findings observed that the degree of nutrient and C connectivity between biocrusts and the sub-crust mineral soil depended on the biocrust successional stage and the element being considered, and the influence of biocrust successional stage on mineral soil CO 2 flux is likely related to long-term resource build up. Together, our results suggest that the influence of biocrust leachate on subsurface mineral soil is complex and context dependent, but, over longer time periods and at later successional stages, can have measurable effects on dryland soil biogeochemical cycling with feedbacks to resource availability and CO 2 flux.

Geoderma↗

USGS2022

Quantifying the influence of different biocrust community states and their responses to warming temperatures on soil biogeochemistry in field and mesocosm studies

Biocrusts influence soil biogeochemistry by fixing carbon (C) and nitrogen (N) and through leachate inputs to soils. Functional rates can vary among biocrust community states and in response to edaphic properties, heterotrophic microbial activity, and global change. Using soils and biocrusts from the Colorado Plateau, Utah, USA, we aimed to quantify the influence of early-successional (ES) and late-successional (LS) biocrust community states on soil biogeochemistry. In a field setting, we found soil was less “fertile” under ES than LS biocrust, but ES biocrust had a relative influence 1.3 times greater than LS biocrust on soil fertility. Leachate collected from LS biocrust had, on average, 6 times more organic C and 1.7 times more dissolved N than ES biocrust, but concentrations of phosphorus (P) and inorganic N did not differ among the two biocrust types. To disentangle influences of biocrusts and soil properties on biogeochemical pools, we constructed mesocosms from homogenized soil and left the surface bare or covered with ES or LS biocrust, before assignment to ambient or warmed (+5 °C) temperature treatments for 3 months. Multivariate biogeochemical properties differed among cover types, yet all exhibited losses of P, N, and organic C and nearly half of the biogeochemical variables considered did not differ among cover types. Mesocosms with LS biocrust retained more dissolved N, supported 8 additional, significant correlations among biogeochemical pools of the biocrust and mineral soil layer on average, and lost fewer of these correlations under warming. Overall, while soils under LS biocrusts were more fertile (i.e., had higher nutrient concentrations) than under ES, we did not find evidence implicating leachate as the primary driver of this difference. Biocrust influences on soil fertility were greater when mineral soil nutrients were in lower concentrations, highlighting the value of even incipient biocrusts for dryland functioning.

Arizona, Utah↗