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At least 307 records · Page 17

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DOE OSTI2023

The predictive power of phylogeny on growth rates in soil bacterial communities

Abstract Predicting ecosystem function is critical to assess and mitigate the impacts of climate change. Quantitative predictions of microbially mediated ecosystem processes are typically uninformed by microbial biodiversity. Yet new tools allow the measurement of taxon-specific traits within natural microbial communities. There is mounting evidence of a phylogenetic signal in these traits, which may support prediction and microbiome management frameworks. We investigated phylogeny-based trait prediction using bacterial growth rates from soil communities in Arctic, boreal, temperate, and tropical ecosystems. Here we show that phylogeny predicts growth rates of soil bacteria, explaining an average of 31%, and up to 58%, of the variation within ecosystems. Despite limited overlap in community composition across these ecosystems, shared nodes in the phylogeny enabled ancestral trait reconstruction and cross-ecosystem predictions. Phylogenetic relationships could explain up to 38% (averaging 14%) of the variation in growth rates across the highly disparate ecosystems studied. Our results suggest that shared evolutionary history contributes to similarity in the relative growth rates of related bacteria in the wild, allowing phylogeny-based predictions to explain a substantial amount of the variation in taxon-specific functional traits, within and across ecosystems.

Walkup, Jeth (ORCID:0000000279082963)↗

DOE OSTI2024

Soil Moisture‐Cloud‐Precipitation Feedback in the Lower Atmosphere From Functional Decomposition of Satellite Observations

Abstract The feedback of topsoil moisture (SM) content on convective clouds and precipitation is not well understood and represented in the current generation of weather and climate models. Here, we use functional decomposition of satellite‐derived SM and cloud vertical profiles (CVP) to quantify the relationship between SM and the vertical distribution of cloud water in the central US. High‐dimensional model representation is used to disentangle the contributions of SM and other land‐surface and atmospheric variables to the CVP. Results show that the sign and strength of the SM‐cloud‐precipitation feedback varies with cloud height and time lag and displays a large spatial variability. Positive anomalies in antecedent 7‐hr SM and land‐surface temperature enhance cloud reflectivity up to 4 dBZ in the lower atmosphere about 1–3 km above the surface. Our approach presents new insights into the SM‐cloud‐precipitation feedback and aids in the diagnosis of land‐atmosphere interactions simulated by weather and climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Methane-derived carbon flows into host–virus networks at different trophic levels in soil

Significance The impact of soil viruses on prokaryotic hosts and their functional processes is largely unknown. While metagenomic sequencing of soil microbial communities enables identification of linkages between viruses and hosts, this does not necessarily identify contemporary interactions. To enable a detailed analysis of active virus–host interactions between individual populations, we focused on the critical biogeochemical process of methane (CH 4 ) oxidation and followed the transfer of carbon from hosts to their associated viruses in situ. Analysis of 13 C-enriched metagenomic DNA demonstrated that CH 4 -derived carbon is transferred into viral biomass via both primary and secondary utilizers of CH 4 and suggests viral predation is an important mechanism for releasing CH 4 -derived organic carbon into the soil matrix.

Lee, Sungeun↗

NASA NTRS2020

Using a Simple Water Balance Framework to Quantify the Impact of Soil Moisture Initialization on Subseasonal Evapotranspiration and Air Temperature Forecasts

Past studies have shown that accurate soil moisture initialization can contribute significant skill to near-surface air temperature (T2M) forecasts at subseasonal leads. The mechanisms by which soil moisture contributes such skill are examined here with a simple water balance-based model that captures the essence of soil moisture behavior in a state-of-the-art subseasonal-to-seasonal (S2S) forecasting system. The simple model successfully transforms initial soil moisture contents into average “forecasted” ET values at 16-30 day lead that agree well, during summer, with the values forecasted by the full NASA GEOS S2S system, indicating that soil moisture initialization dominates over forecasted meteorology in determining ET fluxes at subseasonal leads. When the simple model’s ET anomalies are interpreted in terms of T2M anomalies, a similar conclusion is reached for T2M: soil moisture initialization explains much (about 50% in the eastern half of the continental US) of the T2M anomalies produced by the full GEOS S2S system at 16-30 day lead, and the T2M forecasts produced by the simple model capture about half of the skill attained by the full system. The simple model’s framework is particularly conducive to an analysis of uncertainty in forecasts. Drier soils are generally found to induce larger uncertainty in ET (and thus T2M) forecasts, a result linked to the functional form relating ET to soil moisture in the simple model and verified by an analysis of the ensemble spreads within the forecasts produced by the full GEOS S2S system

Randal D Koster↗

DOE OSTI2021

Drought legacies mediated by trait trade‐offs in soil microbiomes

Abstract Soil microbiomes play a key role in driving biogeochemical cycles of the Earth system. As drought frequency and intensity increase due to climate change, soil microbes and the processes they control will be impacted. Even after a drought ends, microbiomes and other systems take time to recover and may display a memory of previous climate conditions. Still, the mechanisms involved in these legacy effects remain unclear, making it difficult to predict climate and biogeochemical rates in the future. Here, we used a trait‐based microbiome model (DEMENTpy) to implement trade‐off‐mediated mechanisms that may lead to drought legacy effects on litter decomposition. Trade‐offs were assumed to follow the Y‐A‐S framework that defines three primary life‐history strategies of microorganisms: high growth Yield, resource Acquisition, and Stress tolerance. We represented cellular trade‐offs between osmolytes required for drought tolerance and investment in enzymes involved in litter decomposition. Simulations were run under varying levels of drought severity and dispersal. With high levels of dispersal, no legacy effects were predicted by DEMENTpy following drought. With limited dispersal, severe drought resulted in a persistent legacy of altered community‐level traits and reduced litter decomposition. Moderate drought resulted in a transient legacy that disappeared after two years, consistent with recent empirical observations in Southern California ecosystems. These results imply that greater movement along the trade‐off between enzyme investment and osmolyte production resulted in stronger legacy effects. More generally, factors that shift the position of a microbiome in YAS space may alter the legacy outcome following drought. Our trait‐based modeling study motivates additional empirical measurements to quantify YAS traits and trade‐offs that are needed to make accurate predictions of soil microbiome resilience and functioning. Also, our study illustrates an emerging approach for representing trait trade‐offs in microbiomes and vegetation that dictate ecosystem responses to drought and other environmental perturbations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Structure-Driven Liquid Microjunction Surface-Sampling Probe Mass Spectrometry

The rhizosphere is the narrow region of soil surrounding the roots of plants that is influenced by root exudates, root secretions, and associated microbial communities. This region is crucial to plant growth and development and plays a critical role in nutrient uptake, disease resistance, and soil transformation. Understanding the function of exogenous compounds in the rhizosphere starts with determining the spatiotemporal distribution of these molecular components. Using liquid microjunction surface-sampling probe mass spectrometry (LMJ-SSP-MS) and microfluidic devices with attached microporous membranes enables in situ, nondisruptive, and nondestructive spatiotemporal measurement of exogenous compounds from plant roots. However, long imaging times (>2 h) can negatively affect plant heath and limit temporal studies. Here, we present a novel strategy to optimize the number and location of sampling sites on these microporous membrane-covered microfluidic devices. In conclusion, this novel, “structure-driven” sampling workflow takes into consideration the channel structure of the microfluidic device to maximize sampling from the channels and minimize acquisition time (~4× less time in some cases while providing similar chemical image accuracy), thus reducing stress on plants during in situ LMJ-SSP-MS analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Moisture Effect on Chitin Decomposition Biogeochemistry

This dataset contains data files for multiple measurements of sample biogeochemistry and function collected for the Soils SFA Chitin Decomposition project in task 2.2. Samples were generated from soil incubated under different moisture levels, with and without chitin. Each sheet in the file refers to the preprocessed data collected. Sheet 1 "Respiration" measures CO2 production daily for the course of the incubation. Sheet 2 "Biomass" contains the microbial biomass and salt extractable measurements for carbon and nitrogen. Sheet 3 "Chitin" is for HPLC measured chitin from each sample. Sheet 4 "Extracellular Enzyme Assays" records the level of activity for several enzyme assays. Sheet 5 "Enzyme Kinetics" measures degradation of substrate over time for all samples.

Reichart, Nicholas J [Pacific Northwest National L↗

DOE OSTI2022

Image Analysis for Rapid Assessment and Quality-Based Sorting of Corn Stover

Imaging in the visible spectrum is a low-cost tool that can be readily deployed for in-field or over-belt monitoring of biomass quality for bio-refining operations. Rapid image analysis coupled with innovative preprocessing may reduce the impacts of feedstock variability through identification of contaminants or other material attributes to guide selective sorting and quality management. Image analysis was employed to evaluate the quality of corn stover in red-green-blue (RGB) chromatic space. This study used controlled, bench-scale imaging as a proof-of-concept for rapid quality assessment of corn stover based on variations in material attributes, including chemical and physical attributes, that relate to biological degradation and soil contamination. Additionally, logistic regression-based classification algorithms were used to develop a method for biomass screening as a function of biological degradation or soil contamination. This study demonstrated the use of image analysis to extract features from RGB color space to investigate variations in critical material attributes from chemical composition of corn stover. Fourier transform infrared (FT-IR) suggested a correlation between red band intensity and biological degradation, while detailed surface texture analysis was found to distinguish among variations in ash. These insights offer promise for development of a rapid screening tool that could be deployed by farmers for in-field assessment of biomass quality or biorefinery operators for in-line sorting and process optimization.

09 BIOMASS FUELS↗

NASA NTRS1970

Apollo 13 lunar heat flow experiment

Apollo 13 lunar surface heat flow experiment to measure vertical temperature gradients as function of time and soil thermal conductivity

Chute, J., Jr.↗

NASA NTRS1972

The lunar disturbance effect.

The observed phenomenon of darkening of the lunar surface when gently disturbed is investigated. It is suggested that this disturbance effect is due to changes in the photometric function of the lunar soil caused by rearrangement of the soil particles, rather than to any physical or chemical differences between the uppermost layer and the underlying materials.

Hapke, B. W.↗

NASA NTRS1975

Electrical methods of determining soil moisture content

The electrical permittivity of soils is a useful indicator of soil moisture content. Two methods of determining the permittivity profile in soils are examined. A method due to Becher is found to be inapplicable to this situation. A method of Slichter, however, appears to be feasible. The results of Slichter's method are extended to the proposal of an instrument design that could measure available soil moisture profile (percent available soil moisture as a function of depth) from a surface measurement to an expected resolution of 10 to 20 cm.

Silva, L. F.↗

NASA NTRS1984

Biogeochemical cycling and remote sensing

The present investigation is concerned with the role of remote sensing in the analysis of biochemical cycling. A general review is provided of the interest of NASA in biochemical cycling, taking into account an assessment of the state and dynamics of the pools and fluxes of four major elements (carbon, nitrogen, phosphorus, sulfur), an understanding of the coupling and interaction of the biosphere and the atmosphere, and an understanding of the biosphere and the oceans. Attention is given to biogeochemical cycling science issues, the potential remote sensing role, the vegetation type, aspects of vegetation structure, the leaf area index, the canopy height, functional relationships, environmental and soil variables, questions of experimental design, sampling sites and ground data, and radiometric data and analysis.

Peterson, D. L.↗

NASA NTRS1986

The effect of leaf size on the microwave backscattering by corn

Attema and Ulaby (1978) proposed the cloud model to predict the microwave backscattering properties of vegetation. This paper describes a modification in which the biophysical properties and microwave properties of vegetation are related at the level of the individual scatterer (e.g., the leaf or the stalk) rather than at the level of the aggregated canopy (e.g., the green leaf area index). Assuming that the extinction cross section of an average leaf was proportional to its water content, that a power law relationship existed between the backscattering cross section of an average green corn leaf and its area, and that the backscattering coefficient of the surface was a linear function of its volumetric soil moisture content, it is found that the explicit inclusion of the effects of corn leaf size in the model led to an excellent fit between the observed and predicted backscattering coefficients. Also, an excellent power law relationship existed between the backscattering cross section of a corn leaf and its area.

Paris, J. F.↗

NASA NTRS1991

Determination of lunar ilmenite abundances from remotely sensed data

The mineral ilmenite (FeTiO3) was found in abundance in lunar mare soils returned during the Apollo project. Lunar ilmenite often contains greater than 50 weight-percent titanium dioxide (TiO2), and is a primary potential resource for oxygen and other raw materials to supply future lunar bases. Chemical and spectroscopic analysis of the returned lunar soils produced an empirical function that relates the spectral reflectance ratio at 400 and 560 nm to the weight percent abundance of TiO2. This allowed mapping of the lunar TiO2 distribution using telescopic vidicon multispectral imaging from the ground; however, the time variant photometric response of the vidicon detectors produced abundance uncertainties of at least 2 to 5 percent. Since that time, solid-state charge-coupled device (CCD) detector technology capable of much improved photometric response has become available. An investigation of the lunar TiO2 distribution was carried out utilizing groundbased telescopic CCD multispectral imagery and spectroscopy. The work was approached in phases to develop optimum technique based upon initial results. The goal is to achieve the best possible TiO2 abundance maps from the ground as a precursor to lunar orbiter and robotic sample return missions, and to produce a better idea of the peak abundances of TiO2 for benefaction studies. These phases and the results are summarized.

Larson, Stephen M.↗

NASA NTRS1992

Modeling of SAR returns from a red pine stand

Bright P-band radar returns from red pine forests have been observed on synthetic aperture radar (SAR) images in Bangor, Maine. A plot of red pine trees was selected for the characterization and modeling to understand the cause of the high P-band returns. The red pine stand under study consisted of mature trees. Diameter at breast height (DBH) measurements were made to determine stand density as a function of tree diameter. Soil moisture and bulk density measurements were taken along with ground rough surface profiles. Detailed biomass measurements of the needles, shoots, branches, and trunks were also taken. These site statistics have been used in a distorted Born approximation model of the forest. Computations indicate that the direct-reflected or the double-bounce contributions from the ground are responsible for the high observed P-band returns for HH polarization.

Lang, R. H.↗

NASA NTRS2007

Estimating Irrigation Water Requirements using MODIS Vegetation Indices and Inverse Biophysical Modeling

An inverse process approach using satellite-driven (MODIS) biophysical modeling was used to quantitatively assess water resource demand in semi-arid and arid agricultural lands by comparing the carbon and water flux modeled under both equilibrium (in balance with prevailing climate) and non-equilibrium (irrigated) conditions. Since satellite observations of irrigated areas show higher leaf area indices (LAI) than is supportable by local precipitation, we postulate that the degree to which irrigated lands vary from equilibrium conditions is related to the amount of irrigation water used. For an observation year we used MODIS vegetation indices, local climate data, and the SiB2 photosynthesis-conductance model to examine the relationship between climate and the water stress function for a given grid-cell and observed leaf area. To estimate the minimum amount of supplemental water required for an observed cell, we added enough precipitation to the prevailing climatology at each time step to minimize the water stress function and bring the soil to field capacity. The experiment was conducted on irrigated lands on the U.S. Mexico border and Central Asia and compared to estimates of irrigation water used.

Imhoff, Marc L.↗

NASA NTRSDate not supplied

Parametric Optimization of Rigid Wheels for Planetary Surface Mobility Applications

Design-Build-Test approaches for spaceflight hardware are time and cost intensive, which can result in suboptimal mechanism designs. Optimization-based approaches that utilize high-fidelity models and physics simulation could overcome these limitations while simultaneously speeding up the mechanical design process and reducing cost. In this work, we present a toolchain that enables the multi-objective optimization of rigid rover wheels for planetary surface mobility applications. The toolchain uses Chrono’s Continuous Representation Model (CRM) functionality to simulate granular soil and performs multi-objective parametric optimization on candidate rover wheels to meet a desired performance criterion. The resulting wheel design is then evaluated experimentally using a single-wheel testbed. We end with a discussion of how the toolchain can be extended to simultaneously co-optimize other system parameters, such as system power consumption and feedback control gains.

Optimization↗

DOE OSTI2022

Improvement of eukaryotic protein predictions from soil metagenomes

During the last decades, metagenomics has highlighted the diversity of microorganisms from environmental or host-associated samples. Most metagenomics public repositories use annotation pipelines tailored for prokaryotes regardless of the taxonomic origin of contigs. Consequently, eukaryotic contigs with intrinsically different gene features, are not optimally annotated. Using a bioinformatics pipeline, we have filtered 7.9 billion contigs from 6,872 soil metagenomes in the JGI’s IMG/M database to identify eukaryotic contigs. We have re-annotated genes using eukaryote-tailored methods, yielding 8 million eukaryotic proteins and over 300,000 orphan proteins lacking homology in public databases. Comparing the gene predictions we made with initial JGI ones on the same contigs, we confirmed our pipeline improves eukaryotic proteins completeness and contiguity in soil metagenomes. The improved quality of eukaryotic proteins combined with a more comprehensive assignment method yielded more reliable taxonomic annotation. This dataset of eukaryotic soil proteins with improved completeness, quality and taxonomic annotation reliability is of interest for any scientist aiming at studying the composition, biological functions and gene flux in soil communities involving eukaryotes.

54 ENVIRONMENTAL SCIENCES↗