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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Groundwater regulates interannual variations in evapotranspiration in a riparian semiarid ecosystem

Dryland ecosystems are an important component of Earth’s carbon, water, and energy budgets. Changes in hydroclimatic conditions can significantly alter land surface fluxes in these ecosystems. In regions with groundwater-river water exchange, land surface fluxes are strongly modulated by groundwater table fluctuations in response to river stage variations; however, interactions between fluxes and groundwater remain poorly understood in drylands. Here, we use eddy covariance data collected in paired upland and riparian semiarid ecosystems during two years with contrasting river flow conditions to examine interactions among river stage conditions, water table dynamics, meteorological drivers, and ecosystem fluxes. Elevated groundwater availability resulting from groundwater-surface water exchange at the riparian site enhanced the latent heat flux (LE, the energy form of evapotranspiration), which was sustained longer in the growing season during both years. At the upland site, mean LE during the dry season differed by less than 2% between the two years. In contrast, at the riparian site contrasting river flow conditions resulted in a 41% difference in peak monthly LE between the two years. The two-year mean LE in the summer at the riparian site was twice that at the upland site. Carbon uptake (NEP) and LE were well correlated at the upland site during both years, but at the riparian site enhanced summertime LE during the high river flow year did not correspond t enhanced NEP. Our results demonstrate that enhanced groundwater availability modulated by river stage variability is a critical driver of land surface fluxes in semiarid riparian ecosystems.

Missik, Justine E.↗
DOE OSTI2025

Legacy Effects of Cropping System and Precipitation Influence the Core Camelina sativa Microbiome

Camelina ( Camelina sativa L.) is a potential biofuel crop and beneficial rotation crop in dryland cropping systems. Little is known about camelina microbiota or the legacy effect of soil origin/cropping system zones on camelina-associated microbiome assembly. To explore camelina-microbe associations, we grew camelina in the greenhouse using soil transplanted from 33 locations in the dryland wheat production area of eastern Washington. Bacterial, archaeal, and fungal communities from bulk soil, rhizosphere, and endosphere were characterized with 16S rRNA and internal transcribed spacer amplicon sequencing and were analyzed alongside site-specific climatic and edaphic data. We found that soil from the highest precipitation zone had higher alpha diversity than soil from the driest zone, but this effect was not seen in the greenhouse rhizosphere or endosphere. Plant compartment, cropping system zone, and soil origin all significantly influenced microbial composition, with soil pH and organic matter, as well as precipitation at origin, as major predictors. Analysis of abundance–occupancy distributions showed that the Actinobacteriota Aeromicrobium and Marmoricola and the fungus Pseudogymnoascus in the rhizosphere were plant-selected, while the endosphere was characterized by a number of Actinobacteriota, Rhizobium, and Clostridium. Sphingomonas amplicon sequence variants were also consistently enriched in the rhizosphere, suggesting that they are present in soils collected throughout eastern Washington and may represent good candidate biostimulants. Several lignin decomposing fungi had site-specific rhizospheric distributions, suggesting that they may be dispersal-limited or result from the legacy effect of long-term wheat cropping. Overall, this study contributes to our understanding of microbiome assembly in and on camelina roots while also highlighting the potential impact of cropping history on soil- and plant-associated microbiomes. [Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 “No Rights Reserved” license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2025.

Barnes, Elle M↗
DOE OSTI2024

Dynamic regulation of water potential in Juniperus osteosperma mediates ecosystem carbon fluxes

Summary Some plants exhibit dynamic hydraulic regulation, in which the strictness of hydraulic regulation (i.e. iso/anisohydry) changes in response to environmental conditions. However, the environmental controls over iso/anisohydry and the implications of flexible hydraulic regulation for plant productivity remain unknown. InJuniperus osteosperma, a drought‐resistant dryland conifer, we collected a 5‐month growing season time series ofin situ, high temporal‐resolution plant water potential () and stand gross primary productivity (GPP). We quantified the stringency of hydraulic regulation associated with environmental covariates and evaluated how predawn water potential contributes to empirically predicting carbon uptake. Juniperus osteospermashowed less stringent hydraulic regulation (more anisohydric) after monsoon precipitation pulses, when soil moisture and atmospheric demand were high, and corresponded with GPP pulses. Predawn water potential matched the timing of GPP fluxes and improved estimates of GPP more strongly than soil and/or atmospheric moisture, notably resolving GPP underestimation before vegetation green‐up. Flexible hydraulic regulation appears to allowJ. osteospermato prolong soil water extraction and, therefore, the period of high carbon uptake following monsoon precipitation pulses. Water potential and its dynamic regulation may account for why process‐based and empirical models commonly underestimate the magnitude and temporal variability of dryland GPP.

Plant Sciences↗
DOE OSTI2021

Technical note: Uncertainties in eddy covariance CO 2 fluxes in a semiarid sagebrush ecosystem caused by gap-filling approaches

Abstract. Gap-filling eddy covariance CO2 fluxes is challenging at dryland sites due to small CO2 fluxes. Here, four machine learning (ML) algorithms including artificial neural network (ANN), k-nearest neighbors (KNNs), random forest (RF), and support vector machine (SVM) are employed and evaluated for gap-filling CO2 fluxes over a semiarid sagebrush ecosystem with different lengths of artificial gaps. The ANN and RF algorithms outperform the KNN and SVM in filling gaps ranging from hours to days, with the RF being more time efficient than the ANN. Performances of the ANN and RF are largely degraded for extremely long gaps of 2 months. In addition, our results suggest that there is no need to fill the daytime and nighttime net ecosystem exchange (NEE) gaps separately when using the ANN and RF. With the ANN and RF, the gap-filling-induced uncertainties in the annual NEE at this site are estimated to be within 16 g C m−2, whereas the uncertainties by the KNN and SVM can be as large as 27 g C m−2. To better fill extremely long gaps of a few months, we test a two-layer gap-filling framework based on the RF. With this framework, the model performance is improved significantly, especially for the nighttime data. Therefore, this approach provides an alternative in filling extremely long gaps to characterize annual carbon budgets and interannual variability in dryland ecosystems.

Yao, Jingyu↗
DOE OSTI2026

The relative importance of wind and hydroclimate drivers in modulating the interannual variability of dust emissions in Earth system models

Windblown dust emissions are controlled by near-surface wind speed and sediment erodibility, the latter modulated by hydroclimate and land-use conditions. Accurate representations of these drivers are critical for reproducing historical dust variability and projecting future dust changes in Earth system models (ESMs). This study examines the discrepancies among 21 ESMs in the relative importance of wind speed versus five hydroclimate drivers in explaining the historical (1980–2014) variability of dust emissions from global drylands. In hyperarid areas, models show poor agreement in the simulated dust variability, with only 9 % out of 210 inter-model comparisons exhibiting significant positive correlations. In contrast, arid and semiarid areas exhibit a dual pattern driven by a “double-edged sword” effect of land surface memory: models with coherent hydroclimate variability show better agreement, whereas those with divergent hydroclimate representations show larger disagreement. While the ESMs capture the dominant role of wind speed in hyperarid areas, they diverge markedly in the relative contributions of wind and hydroclimate drivers in arid and semiarid areas. Replacing the Zender et al. (2003) dust scheme with the Kok et al. (2014) scheme in CESM and E3SM generally strengthens hydroclimate influences while reducing wind speed contributions to simulated dust variability. MERRA-2 reanalysis produces stronger wind influences than most ESMs across all dryland regions. These results underscore the need for improved near-surface wind simulations in hyperarid areas and more realistic land surface and hydroclimate representations in arid and semiarid areas to reduce uncertainties in global dust emission simulations.

Li, Xinzhu [Michigan Technological University, Hou↗
NASA NTRS2004

Ecosystem Impacts of Woody Encroachment In Texas: A Spatial Analysis Using AVIRIS

Woody encroachment, the increase of woody plant density relative to herbaceous vegetation, has been documented in drylands of Texas as well as worldwide (Archer 1994, Harrington and Harman 1995, Moleele et al. 2002). Over-grazing, fire suppression and climate change are implicated in the shift from open grasslands to ecosystems now populated by trees and shrubs (Scholes and Archer 1997, Archer et al. 2001), such as Prosopis glandulosa var. glandulosa (honey mesquite) in north Texas (Teague et al. 1997, Ansley et al. 2001, Asner et al. 2003a). Several studies have examined changes in ecosystem properties accompanying woody vegetation encroachment in the Southwest U.S., with research focused on increases in plant and soil carbon (C) and nitrogen (N) stores (Hoffman and Jackson 2000, Asner et al. 2003a), isotopic shifts in these pools (Boutton 1999, Archer et al. 2001), and increases in N cycling rates (Rundel et al. 1982, Hibbard et al. 2001). However, little is known regarding the impact of woody encroachment on N trace gas emissions from dryland regions such as Texas. NOx is produced in the soil during the processes of nitrification and denitrification (Firestone and Davidson 1989). The total N efflux from soils is most directly influenced by the internal cycling of N, which at a regionalscale, is controlled by the inputs and availability of N from vegetation via litterfall and subsequent decomposition (Robertson et al. 1989). Although plot-scale studies are critical to understanding controls over N oxide emissions, regionalization of the measurements is impeded by spatial variation in the factors contributing most to N cycling processes: soil properties (affecting soil moisture regimes and N stocks) and vegetation cover (affecting litter inputs and N uptake). While broad patterns in ecosystem structure and vegetation composition co-vary with general patterns of trace gas emissions (Matson 1997), there is no easily measured index of N availability that can be applied for regional-scale studies of N oxide fluxes. Remote sensing is arguably the only approach available to develop a spatially-explicit understanding of ecosystem processes. More specifically, remotely detectable spatial patterns in the distal controls over soil N properties, such as vegetation cover, land use and soil type (Robertson et al. 1989), should be exploited for regional studies of N oxide emissions. The woody encroachment phenomenon provides an opportunity to test the strength of the relationship between N oxide emissions and those factors controlling the fluxes that can be remotely measured. If such linkages can be firmly established, and if the spatial pattern of distal controls is relevant, then the combination of field measurements and remote sensing offers to improve regional-scale N oxide estimates. The paper presents the utility of linking field based sampling of soil NOx emissions with very high resolution remote sensing estimates of woody vegetation cover from the NASA AVIRIS, Airborne Visible-Infrared Imaging Spectrometer (Green et al. 1998, Asner and Green 2001) and automated spectral mixture analysis (Asner and Lobell 2000, Asner and Heidebrecht 2002) that provide a means to spatially extrapolate soil NOx emissions to the regional scale.

Martin, Roberta E.↗
NASA NTRS2011

Vegetation Water Content Mapping in a Diverse Agricultural Landscape: National Airborne Field Experiment 2006

Mapping land cover and vegetation characteristics on a regional scale is critical to soil moisture retrieval using microwave remote sensing. In aircraft-based experiments such as the National Airborne Field Experiment 2006 (NAFE 06), it is challenging to provide accurate high resolution vegetation information, especially on a daily basis. A technique proposed in previous studies was adapted here to the heterogenous conditions encountered in NAFE 06, which included a hydrologically complex landscape consisting of both irrigated and dryland agriculture. Using field vegetation sampling and ground-based reflectance measurements, the knowledge base for relating the Normalized Difference Water Index (NDWI) and the vegetation water content was extended to a greater diversity of agricultural crops, which included dryland and irrigated wheat, alfalfa, and canola. Critical to the generation of vegetation water content maps, the land cover for this region was determined from satellite visible/infrared imagery and ground surveys with an accuracy of 95.5% and a kappa coefficient of 0.95. The vegetation water content was estimated with a root mean square error of 0.33 kg/sq m. The results of this investigation contribute to a more robust database of global vegetation water content observations and demonstrate that the approach can be applied with high accuracy. Keywords: Vegetation, field experimentation, thematic mapper, NDWI, agriculture.

Cosh, Michael H.↗
NASA NTRS2022

Critical Soil Moisture Thresholds of Plant Water Stress in Terrestrial Ecosystems

Plant water stress occurs at the point when soil moisture (SM) limits transpiration, defining a critical SM threshold (θ crit ). Knowledge of the spatial distribution of θ crit is crucial for future projections of climate and water resources. Here, we use global eddy covariance observations to quantify θcrit and evaporative fraction (EF) regimes. Three canonical variables describe how EF is controlled by SM: the maximum EF (EF max ), θ crit , and slope (S) between EF and SM. We find systematic differences of these three variables across biomes. Variation in θ crit , S, and EFmax is mostly explained by soil texture, vapor pressure deficit, and precipitation, respectively, as well as vegetation structure. Dryland ecosystems tend to operate at low θ crit and show adaptation to water deficits. The negative relationship between θ crit and S indicates that dryland ecosystems minimize θcrit through mechanisms of sustained SM extraction and transport by xylem. Our results further suggest an optimal adaptation of local EF-SM response that maximizes growing-season evapotranspiration and photosynthesis.

Zheng Fu↗
DOE OSTI2024

Relating flow resistance to equivalent roughness

Describing flow resistance using the physical properties of an underlying surface is a recalcitrant problem in overland flow models. If discharge measurements are available, an equivalent roughness (e.g., Manning’s n) can be calibrated to represent the effects of surface properties within the domain with a single numerical value. Alternatively, the flow resistance can be estimated from discharge and velocity measured at a point, typically a runoff plot outlet. However, such experimental estimates are often inconsistent with the equivalent roughness determined from calibration to discharge, even if both derive from the same dataset. For example, if Manning’s equation is used to parameterize flow resistance, the Manning’s n obtained by calibrating a model to discharge differs from the value of n calculated from measured flow and velocity at the hillslope outlet. Here, this discrepancy is resolved by deriving a correction factor relating experimentally-determined flow resistance to the equivalent roughness. The derived correction factor is tested for four commonly-used resistance formulations using 129 rainfall simulator experiments. The correction factor is necessary to reproduce measured velocities, and yields minor improvements in discharge prediction. Plain Language Summary: Accurate runoff prediction is needed for land and water management in dryland regions, where sporadic and limited rainfall necessitate efficient water use and drought mitigation strategies. The skill of runoff models is known to be hindered by out ability to estimate flow resistance, which is the quantity that describes how energy is lost from flowing water to the underlying surface. Typically, models represent flow resistance with an equivalent roughness, e.g., Manning’s n, that is adjusted until the model can reproduce available discharge observations at watershed scale. However, the flow resistance measured in plot-scale experiments (1–10 m) often exceeds equivalent roughness coefficients by a factor of 10. This means that the direct use of plot-scale experimental data to parameterize runoff models could cause errors in discharge and runoff velocity predictions. Here, we resolve these differences by deriving an analytic correction factor that relates flow resistance to the equivalent roughness required for models to reproduce experimental velocity and discharge data. This correction factor is tested using rainfall simulator data from 129 experiments performed in the US Southwest covering a wide range of precipitation intensities, soil textures and vegetation types. Use of the correction factor substantially improves model prediction of flow velocity, which is needed for reproducing the timing of flood events and the estimation of erosion.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Interannual variability of spring and summer monsoon growing season carbon exchange at a semiarid savanna over nearly two decades

Eddy covariance measurements of land-atmosphere energy, carbon, and water exchange now span multiple decades at some sites, supporting an improved understanding of flux interannual variability (IAV) and its ecophysiological and physical controls. Most eddy covariance IAV studies have focused on temperate forest ecosystems, where carbon fluxes are large and flux records are longest – but also where IAV is much lower than in dryland regions, which have been identified as an essential driver of the trend and variability in the global terrestrial carbon sink. In this study, we leveraged 19 years of continuous micrometeorological measurements at the AmeriFlux US-SRM mesquite savanna site in southern Arizona, USA to quantify the IAV, trends, and drivers of carbon fluxes during the distinct spring and summer growing seasons. We also assessed the ability of modern satellite and land surface models to capture the IAV of seasonal water and carbon fluxes. Annual net ecosystem production (NEP) was small and highly variable (23 +/- 64 gC m –2 yr –1 ). Precipitation and associated measures of water availability determined most of the variability in NEP, largely through their influence on annual and seasonal gross ecosystem productivity (GEP) as opposed to ecosystem respiration (ER). Root-zone soil moisture captured between 73% (spring) and 85% (summer) of GEP variability and between 73% (spring) and 58% (summer) of ER variability. Throughout the study period, soil moisture and greenness increased with associated increases in GEP, ER and NEP. These trends were strongly influenced by very productive and wet summer growing seasons during the last two years, which were characterized by abundant understory grass cover. Typically, less than half of the variability in growing season GEP and evapotranspiration was captured by satellite-based estimates and land surface model simulations with local site forcing and calibration, highlighting the ongoing utility of long-term datasets to support careful model testing and improvement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

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. Here, 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.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

While legume-based rotation influences the chemical composition of mineral-associated organic matter, tillage has little effect on its persistence

Legume-based rotations are key to enhancing soil carbon (C) storage in stable forms such as mineral-associated organic matter (MAOM), improving soil health, and helping maintain long-term C stock. This study investigates how plant inputs and tillage practices influence the distribution, composition, and persistence of MAOM in wheat cropping systems when legumes are incorporated into the rotation. Soil samples were collected from Walla Walla silty loam of northeast Oregon, including conventional tillage (CT) and no-till (NT) treatments in a long-term dryland winter wheat (Triticum aestivum L.) and spring pea (Pisum sativum L.) rotation experiment, with an adjacent grass pasture (GP) plot serving as the baseline. Bulk soil, particulate organic matter (POM), and MAOM fractions were scanned using mid-infrared (Mid-IR) spectroscopy and analyzed for total C and nitrogen (N) as well as radiocarbon age, aggregate stability, and mineralizable C. In WP systems, NT promoted greater C accumulation and stabilization in MAOM than CT, reaching levels similar to those under native GP conditions. Furthermore, our findings indicate that decades of continuous legume-based rotation were insufficient to restore total bulk C content to levels observed in the undisturbed reference site (GP) in the 0–30 cm layer, with increases limited to the surface layer. The Mid-IR data provided insights into how cultivation may alter the chemical composition of MAOM. An increase in amide bands was observed in GP, while legume-based rotation systems favored an abundance of aliphatic (C-H) components in the MAOM fraction. In cultivated soils, C in MAOM exhibited longer residence times under arable conditions than in grassland, while POM remained an actively cycled pool across all treatments. Despite variations in the chemical composition of MAOM across treatments, mineralizable C emissions from MAOM fractions remained statistically unchanged during the 96-hour incubation period. Our findings suggest that soil physical protection, enhanced by reduced tillage, is the primary factor influencing MAOM cycling.

Carbon stabilization↗
NASA NTRS2020

Synergistic Use of SMAP and OCO-2 Data in Assessing the Responses of Ecosystem Productivity to the 2018 U.S. Drought

Soil moisture and gross primary productivity (GPP) estimates from the Soil Moisture Active Passive (SMAP) and solar-induced chlorophyll fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) provide new opportunities for understanding the relationship between soil moisture and terrestrial photosynthesis over large regions. Here we explored the potential of the synergistic use of SMAP and OCO-2 based data for monitoring the responses of ecosystem productivity to drought. We used complementary observational information on root-zone soil moisture and GPP (9 km) from SMAP and fine-resolution SIF (0.05°; GOSIF) derived from OCO-2 SIF soundings. We compared the spatial pattern and temporal evolution of anomalies of these variables over the conterminous U.S. during the 2018 drought, and examined to what extent they could characterize the drought-induced variations of flux tower GPP and crop yield data. Our results showed that SMAP GPP and GOSIF could well capture the spatial extent and dynamics of drought indicated by the U.S. Drought Monitor maps and the SMAP root-zone soil moisture deficit. Over the U.S. Southwest, monthly anomalies of soil moisture showed significant positive correlations with those of SMAP GPP (R2=0.44, p<0.001) and GOSIF (R2=0.76, p<0.001), demonstrating strong water availability constraints on plant productivity across dryland ecosystems. We further found that SMAP GPP and GOSIF captured the impact of drought on tower GPP and crop yield. Our results suggest that synergistic use of SMAP and OCO-2 data products can reveal the drought evolution and its impact on ecosystem productivity and carbon uptake at multiple spatial and temporal scales, and demonstrate the value of SMAP and OCO-2 for studying ecosystem function, carbon cycling, and climate change.

Soil Moisture Active Passive↗
NASA NTRSDate not supplied

FORWARD AND INVERSE L-BAND RADIATIVE TRANSFER MODELING OVER THE DRY CHACO, USING SMOS OBSERVATIONS, LAND SURFACE MODELING AND IN SITU DATA

Passive microwave L-band remote sensing is well known for its sensitivity to surface soil moisture over land.The signal is also affected by other dynamic variables such as vegetation and soil salinity. In this research, L-band microwave observations of the Soil Moisture Ocean Salinity (SMOS) mission are used to explore soil surface salinity, moisture and vegetation, in the Argentinean Dry Chaco, an area with possible emerging dryland salinity. A Radiative Transfer Model (RTM) with inclusion of a correction to the soil’s dielectric constant for salinity was used in forward and inverse mode, using either in situ data or Catchment Land Surface Model (CLSM) simulations as RTM input. The forward analysis pointed out shortcomings in the modeled soil moisture and soil surface temperature estimates. The impact of salinity on forward simulations over the Dry Chaco was limited.The RTM inversion using 10 years of SMOS brightness temperature observations resulted in realistic estimates of vegetation and roughness, both with and withouto ptimizing a correction term for the dielectric constant in terms of salinity equivalents. However,the retrieval of the correction term to the dielectric constant was very uncertain and not representative of soil surface salinity, but rather of open water, texture uncertainty or soil moisture bias.

L-band↗
DOE OSTI2020

Long-term Accumulation, Depth Distribution, and Speciation of Silver Nanoparticles in Biosolids-Amended Soils

Biosolids, when applied to soil, can be a source of metals and metal nanoparticles. One of the metals that forms nanoparticles is silver (Ag), which is commonly used in elemental (Ag(0)) form as an engineered nanomaterial in industrial or consumer products. The objective of this study was to quantify and characterize the accumulation and transport of Ag in a natural soil that has received agronomically-recommended rates of biosolids as fertilizer for the past 23 years (1994{2017). Total Ag concentrations were measured in biosolids and soil samples collected from 0 to 10 cm between 1996 and 2017. In addition, the depth distribution of Ag in the soil down to 60-cm depth was measured in 2017. Electron microscopy, in combination with X-ray spectroscopy, and X-ray absorption spectroscopy were used to identify the elemental association and oxidation state of the Ag in the samples. The Ag concentrations in the biosolids-amended soil increased steadily from 1996 until 2007, after which the concentrations leveled off at about 1.25 mg Ag kg-1 soil. This corresponded with a decrease of Ag concentrations in the biosolids over time. The majority of the Ag (82%) was confined to the top 10 cm of the soil, small amounts (14%) were detected in 10 to 20-cm depth, and trace amounts (4%) in 30 to 40-cm depth. The Ag in the biosolids and soil was identified as Ag-containing nanoparticles with a diameter of 10 to 12 nm. Ag was associated with S suggest that these 19 nanoparticles are Ag2S. This could be corroborated in biosolids with X-ray absorption spectroscopy (XANES); however, the Ag concentrations in the soil samples were too low to allow identification with XANES. Biosolids, when applied at agronomic rates in dryland cropping systems, represent an economically viable source of crop nutrients. In our study, long-term application of biosolids did not increase the concentration of total Ag in soil above a maximum of 1.5 mg Ag kg-1, and the Ag is present in the sulfide form. This concentration is below ecotoxicity limits for Ag2S in soil.

Taylor, Stephen E.↗
DOE OSTI2022

The Brazilian Amazonian rainforest harbors a high diversity of yeasts associated with rotting wood, including many candidates for new yeast species

This study investigated the diversity of yeast species associated with rotting wood in Brazilian Amazonian rainforests. Herein, a total of 569 yeast strains were isolated from rotting wood samples collected in three Amazonian areas (Universidade Federal do Amazonas-Universidade Federal do Amazonas [UFAM], Piquiá, and Carú) in the municipality of Itacoatiara, Amazon state. The samples were cultured in yeast nitrogen base (YNB)-d-xylose, YNB-xylan, and sugarcane bagasse and corncob hemicellulosic hydrolysates (undiluted and diluted 1:2 and 1:5). Sugiyamaella was the most prevalent genus identified in this work, followed by Kazachstania. The most frequently isolated yeast species were Schwanniomyces polymorphus, Scheffersomyces amazonensis, and Wickerhamomyces sp., respectively. The alpha diversity analyses showed that the dryland forest of UFAM was the most diverse area, while the floodplain forest of Carú was the least. Additionally, the difference in diversity between UFAM and Carú was the highest among the comparisons. Thirty candidates for new yeast species were obtained, representing 36% of the species identified and totaling 101 isolates. Among them were species belonging to the clades Spathaspora, Scheffersomyces, and Sugiyamaella, which are recognized as genera with natural xylose-fermenting yeasts that are often studied for biotechnological and ecological purposes. The results of this work showed that rotting wood collected from the Amazonian rainforest is a tremendous source of diverse yeasts, including candidates for new species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A micrometeorological flux perspective on brush management in a shrub-encroached Sonoran Desert grassland

Woody plant encroachment typically limits the forage productivity of managed rangelands and alters a panoply of semiarid ecosystem processes and services. Intervention strategies to reduce woody plant abundance, collectively termed “brush management”, often lack observations to quantify and interpret changes in ecosystem processes. Furthermore, comparative studies between treated and untreated areas should account for heterogeneity since plant composition, microclimate, topoedaphic factors, and historical land use can substantially vary over short distances in drylands. Here, in this work, we quantify ecosystem responses to brush management after a single aerial herbicide application on an 18 hectare shrub-encroached grassland (savanna) in southern Arizona, USA. We conducted a pre- and post-treatment comparison of a flux tower site in the treated area with that of a tower in a nearby control site. The comparison, spanning a seven year period, included: (1) ground, airborne, and satellite-based measurements of vegetation structure, and (2) eddy covariance measurements. The herbicide treatment defoliated the dominant shrub (velvet mesquite, Prosopis velutina) and led to a temporary reduction in summer greening, but full foliar recovery occurred within two years. Contrary to expectations, perennial grass cover decreased and bare soil cover increased on the treated site. Relative amounts of evapotranspiration were reduced, while carbon uptake increased during the 2 year post-treatment period at the treated site due to a higher water use efficiency in the following spring. During mesquite recovery, carbon uptake was enhanced by higher gross primary productivity and accompanied by a decrease in ecosystem respiration relative to the untreated site. Mesquite recovery was facilitated by access to deep soil water, carbohydrate reserves in rooting systems, and a lower competition from reduced perennial grass cover.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Integrating very-high-resolution imagery, Sentinel-2 time-series data, and machine learning to map shrub fractional abundance across arid and semi-arid ecosystems in China

Shrub fractional abundance (SFA), the proportion of shrub cover per unit area, serves as a critical indicator of environmental aridity and ecosystem health in arid and semi-arid regions, particularly across the Mongolian steppe. However, large-scale SFA mapping in Mongolian steppe ecosystems remains challenging due to the small crown size of shrubs, their sparse distribution, and spectral overlap with coexisting low vegetation (e.g., grasses and herbs), which hinders accurate detection using coarser-resolution satellite data or traditional field surveys. To address these challenges, we developed a two-step approach that integrates very-high-resolution (VHR) imagery, time-series Sentinel-2 data, and deep learning techniques. First, we generated high-accuracy benchmark maps of individual shrub crowns from 0.5 m VHR imagery by combining manual segmentation with a hybrid deep learning framework (Dino V2 and convolutional neural networks). Second, we used these shrub crown maps as training data to build an XGBoost model for predicting SFA from 20 m Sentinel-2 time-series data, leveraging phenological information to improve estimation. We validated our approach across 70 sites (1km 2 each) in the Inner Mongolia Autonomous Region, which is representative of Mongolian steppe ecosystems. From VHR imagery, we mapped 1.31 million shrub crowns with an accuracy of R 2 = 0.92. Scaling up with Sentinel-2 data yielded regional SFA maps with an R 2 = 0.60. Further SHAP (SHapley Additive exPlanations) analysis on the developed XGBoost model revealed that phenological metrics (particularly observations in early-May, mid-July, and late-September), which distinguish shrub phenology from that of other land cover types (e.g., grasses and bare soil), were the most influential predictors of SFA. Finally, our regional SFA maps uncovered unimodal relationships between shrub distribution and climate variables, peaking at mean annual minimum temperatures near 0 °C and annual precipitation around 200 mm. Collectively, these findings demonstrate how the integration of multi-source remote sensing and machine learning can overcome historical limitations in SFA mapping, enabling accurate, spatially continuous assessments across vast Inner-Mongolian steppe ecosystems. Our framework has the potential to be applied to other steppe ecosystems and dryland ecosystems across the Mongolian steppe and beyond, offering a foundation for improved monitoring and ecological impact assessments in the face of global climate changes.

Arid and semi-arid landscapes↗