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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Defining the Sphagnum Core Microbiome across the North American Continent Reveals a Central Role for Diazotrophic Methanotrophs in the Nitrogen and Carbon Cycles of Boreal Peatland Ecosystems

Peat mosses of the genus Sphagnum are ecosystem engineers that frequently predominate over photosynthetic production in boreal peatlands. Sphagnum spp. host diverse microbial communities capable of nitrogen fixation (diazotrophy) and methane oxidation (methanotrophy), thereby potentially supporting plant growth under severely nutrient-limited conditions. Moreover, diazotrophic methanotrophs represent a possible “missing link” between the carbon and nitrogen cycles, but the functional contributions of the Sphagnum-associated microbiome remain in question. A combination of metagenomics, metatranscriptomics, and dual-isotope incorporation assays was applied to investigate Sphagnum microbiome community composition across the North American continent and provide empirical evidence for diazotrophic methanotrophy in Sphagnum-dominated ecosystems. Remarkably consistent prokaryotic communities were detected in over 250 Sphagnum SSU rRNA libraries from peatlands across the United States (5 states, 17 bog/fen sites, 18 Sphagnum species), with 12 genera of the core microbiome comprising 60% of the relative microbial abundance. Additionally, nitrogenase (nifH) and SSU rRNA gene amplicon analysis revealed that nitrogen-fixing populations made up nearly 15% of the prokaryotic communities, predominated by Nostocales cyanobacteria and Rhizobiales methanotrophs. While cyanobacteria comprised the vast majority (>95%) of diazotrophs detected in amplicon and metagenome analyses, obligate methanotrophs of the genus Methyloferula (order Rhizobiales) accounted for one-quarter of transcribed nifH genes. Furthermore, in dual-isotope tracer experiments, members of the Rhizobiales showed substantial incorporation of 13 CH 4 and 15 N 2 isotopes into their rRNA. Our study characterizes the core Sphagnum microbiome across large spatial scales and indicates that diazotrophic methanotrophs, here defined as obligate methanotrophs of the rare biosphere (Methyloferula spp. of the Rhizobiales) that also carry out diazotrophy, play a keystone role in coupling of the carbon and nitrogen cycles in nutrient-poor peatlands.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Metagenomic clustering links specific metabolic functions to globally relevant ecosystems

ABSTRACT Metagenomic sequencing has advanced our understanding of biogeochemical processes by providing an unprecedented view into the microbial composition of different ecosystems. While the amount of metagenomic data has grown rapidly, simple-to-use methods to analyze and compare across studies have lagged behind. Thus, tools expressing the metabolic traits of a community are needed to broaden the utility of existing data. Gene abundance profiles are a relatively low-dimensional embedding of a metagenome’s functional potential and are, thus, tractable for comparison across many samples. Here, we compare the abundance of KEGG Ortholog Groups (KOs) from 6,539 metagenomes from the Joint Genome Institute’s Integrated Microbial Genomes and Metagenomes (JGI IMG/M) database. We find that samples cluster into terrestrial, aquatic, and anaerobic ecosystems with marker KOs reflecting adaptations to these environments. For instance, functional clusters were differentiated by the metabolism of antibiotics, photosynthesis, methanogenesis, and surprisingly GC content. Using this functional gene approach, we reveal the broad-scale patterns shaping microbial communities and demonstrate the utility of ortholog abundance profiles for representing a rapidly expanding body of metagenomic data. IMPORTANCE Metagenomics, or the sequencing of DNA from complex microbiomes, provides a view into the microbial composition of different environments. Metagenome databases were created to compile sequencing data across studies, but it remains challenging to compare and gain insight from these large data sets. Consequently, there is a need to develop accessible approaches to extract knowledge across metagenomes. The abundance of different orthologs (i.e., genes that perform a similar function across species) provides a simplified representation of a metagenome’s metabolic potential that can easily be compared with others. In this study, we cluster the ortholog abundance profiles of thousands of metagenomes from diverse environments and uncover the traits that distinguish them. This work provides a simple to use framework for functional comparison and advances our understanding of how the environment shapes microbial communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

ChatHPC: Building the Foundations for a Productive and Trustworthy AI-Assisted HPC Ecosystem

ChatHPC democratizes large language models for the high-performance computing (HPC) community by providing the infrastructure, ecosystem, and knowledge needed to apply modern generative AI technologies to rapidly create specific capabilities for critical HPC components while using relatively modest computational resources. Our divide-and-conquer approach focuses on creating a collection of reliable, highly specialized, and optimized AI assistants for HPC based on the cost-effective and fast Code Llama fine-tuning processes and expert supervision. We target major components of the HPC software stack, including programming models, runtimes, I/O, tooling, and math libraries. Thanks to AI, ChatHPC provides a more productive HPC ecosystem by boosting important tasks related to portability, parallelization, optimization, scalability, and instrumentation, among others. With relatively small datasets (on the order of KB), the AI assistants, which are created in a few minutes by using one node with two NVIDIA H100 GPUs and the ChatHPC library, can create new capabilities with Meta’s 7-billion parameter Code Llama base model to produce high-quality software with a level of trustworthiness of up to 90% higher than the 1.8-trillion parameter OpenAI ChatGPT-4o model for critical programming tasks in the HPC software stack.

Young, Aaron [ORNL] (ORCID:0000000254484667)↗
DOE OSTI2017

The Functionally-Assembled Terrestrial Ecosystem Simulator Version 1

The Functionally-Assembled Terrestrial Ecosystem Simulator (FATES) is a vegetation model for use in Earth system models (ESMs). The model includes a size- and age-structured representation of tree dynamics, competition between functionally diverse plant functional types, and the biophysics underpinning plant growth, competition, mortality, as well as the carbon, water, and energy exchange with the atmosphere. The FATES model is designed as a modular vegetation model that can be integrated within a host land model for inclusion in ESMs. The model is designed for use in global change studies to understand and project the responses and feedbacks between terrestrial ecosystems and the Earth system under changing climate and other forcings.

Xu, Chonggang↗
DOE OSTI2021

NRIC Digital Engineering Ecosystem

The NRIC Digital Engineering Ecosystem is a software suite designed to eliminate procedural failures in the engineering lifecycle of complex projects. It focuses on improving the flow of scheduling data, safety information, engineering requirements, and CAD files among stakeholders of complex engineering projects. The project comprises software adapters that move engineering data from disparate software tools in and out of a central database. The ecosystem is designed to efficiently incorporate data sources from many different engineering toolsets, making it software agnostic, and adaptable to many different engineering applications.

Harris, BrennanK↗
DOE OSTI2023

Virus diversity and activity is driven by snowmelt and host dynamics in a high-altitude watershed soil ecosystem

Background: Viruses impact nearly all organisms on Earth, including microbial communities and their associated biogeochemical processes. In soils, highly diverse viral communities have been identified, with a global distribution seemingly driven by multiple biotic and abiotic factors, especially soil temperature and moisture. However, our current understanding of the stability of soil viral communities across time and their response to strong seasonal changes in environmental parameters remains limited. Here, we investigated the diversity and activity of environmental soil DNA and RNA viruses, focusing especially on bacteriophages, across dynamics’ seasonal changes in a snow-dominated mountainous watershed by examining paired metagenomes and metatranscriptomes. Results: We identified a large number of DNA and RNA viruses taxonomically divergent from existing environmental viruses, including a significant proportion of fungal RNA viruses, and a large and unsuspected diversity of positive single-stranded RNA phages ( Leviviricetes ), highlighting the under-characterization of the global soil virosphere. Among these, we were able to distinguish subsets of active DNA and RNA phages that changed across seasons, consistent with a “seed-bank” viral community structure in which new phage activity, for example, replication and host lysis, is sequentially triggered by changes in environmental conditions. At the population level, we further identified virus-host dynamics matching two existing ecological models: “Kill-The-Winner” which proposes that lytic phages are actively infecting abundant bacteria, and “Piggyback-The-Persistent” which argues that when the host is growing slowly, it is more beneficial to remain in a dormant state. The former was associated with summer months of high and rapid microbial activity, and the latter with winter months of limited and slow host growth. Conclusion: Taken together, these results suggest that the high diversity of viruses in soils is likely associated with a broad range of host interaction types each adapted to specific host ecological strategies and environmental conditions. As our understanding of how environmental and host factors drive viral activity in soil ecosystems progresses, integrating these viral impacts in complex natural microbiome models will be key to accurately predict ecosystem biogeochemistry.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Modeling and validating tritium transfer in a grassland ecosystem in response to {sup 3}H releases

In this paper a radioecological model (TOCATTA) for tritium transfer in a grassland ecosystem developed on an hourly time-step basis is proposed and compared with the first data set obtained in the vicinity of the AREVA-NC reprocessing plant of La Hague (France). The TOCATTA model aims at simulating dynamics of tritium transfer in agricultural soil and plant ecosystems exposed to time-varying HTO concentrations in air water vapour and possibly in irrigation and rain water. In the present study, gaseous releases of tritium from the AREVA NC nuclear reprocessing plant in normal operation can be intense and intermittent over a period of less than 24 hours. A first comparison of the model predictions with the field data has shown that TOCATTA should be improved in terms of kinetics of tritium transfer.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

Assembly-Driven Community Genomics of a Hypersaline Microbial Ecosystem

Microbial populations inhabiting a natural hypersaline lake ecosystem in Lake Tyrrell, Victoria, Australia, have been characterized using deep metagenomic sampling, iterative de novo assembly, and multidimensional phylogenetic binning. Composite genomes representing habitat-specific microbial populations were reconstructed for eleven different archaea and one bacterium, comprising between 0.6 and 14.1% of the planktonic community. Eight of the eleven archaeal genomes were from microbial species without previously cultured representatives. These new genomes provide habitat-specific reference sequences enabling detailed, lineage-specific compartmentalization of predicted functional capabilities and cellular properties associated with both dominant and less abundant community members, including organisms previously known only by their 16S rRNA sequences. Together, these data provide a comprehensive, culture-independent genomic blueprint for ecosystem-wide analysis of protein functions, population structure, and lifestyles of co-existing, co-evolving microbial groups within the same natural habitat. The ‘‘assembly-driven’’ community genomic approach demonstrated in this study advances our ability to push beyond single gene investigations, and promotes genome-scale reconstructions as a tangible goal in the quest to define the metabolic, ecological, and evolutionary dynamics that underpin environmental microbial diversity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Representation of Dormant and Active Microbial Dynamics for Ecosystem Modeling

Dormancy is an essential strategy for microorganisms to cope with environmental stress. However, global ecosystem models typically ignore microbial dormancy, resulting in notable model uncertainties. To facilitate the consideration of dormancy in these large-scale models, we propose a new microbial physiology component that works for a wide range of substrate availabilities. This new model is based on microbial physiological states and the major parameters are the maximum specific growth and maintenance rates of active microbes and the ratio of dormant to active maintenance rates. A major improvement of our model over extant models is that it can explain the low active microbial fractions commonly observed in undisturbed soils. Our new model shows that the exponentially-increasing respiration from substrate-induced respiration experiments can only be used to determine the maximum specific growth rate and initial active microbial biomass, while the respiration data representing both exponentially-increasing and non-exponentially-increasing phases can robustly determine a range of key parameters including the initial total live biomass, initial active fraction, the maximum specific growth and maintenance rates, and the half-saturation constant. Our new model can be incorporated into existing ecosystem models to account for dormancy in microbially-driven processes and to provide improved estimates of microbial activities.

Wang, Gangsheng↗
DOE OSTI2016

Diagnosing the Dynamics of Observed and Simulated Ecosystem Gross Primary Productivity with Time Causal Information Theory Quantifiers

Data analysis and model-data comparisons in the environmental sciences require diagnostic measures that quantify time series dynamics and structure, and are robust to noise in observational data. This paper investigates the temporal dynamics of environmental time series using measures quantifying their information content and complexity. The measures are used to classify natural processes on one hand, and to compare models with observations on the other. The present analysis focuses on the global carbon cycle as an area of research in which model-data integration and comparisons are key to improving our understanding of natural phenomena. We investigate the dynamics of observed and simulated time series of Gross Primary Productivity (GPP), a key variable in terrestrial ecosystems that quantifies ecosystem carbon uptake. However, the dynamics, patterns and magnitudes of GPP time series, both observed and simulated, vary substantially on different temporal and spatial scales. Here we demonstrate that information content and complexity, or Information Theory Quantifiers (ITQ) for short, serve as robust and efficient data-analytical and model benchmarking tools for evaluating the temporal structure and dynamical properties of simulated or observed time series at various spatial scales. At continental scale, we compare GPP time series simulated with two models and an observations-based product. This analysis reveals qualitative differences between model evaluation based on ITQ compared to traditional model performance metrics, indicating that good model performance in terms of absolute or relative error does not imply that the dynamics of the observations is captured well. Furthermore, we show, using an ensemble of site-scale measurements obtained from the FLUXNET archive in the Mediterranean, that model-data or model-model mismatches as indicated by ITQ can be attributed to and interpreted as differences in the temporal structure of the respective ecological time series. At global scale, our understanding of C fluxes relies on the use of consistently applied land models. Here, we use ITQ to evaluate model structure: The measures are largely insensitive to climatic scenarios, land use and atmospheric gas concentrations used to drive them, but clearly separate the structure of 13 different land models taken from the CMIP5 archive and an observations-based product. In conclusion, diagnostic measures of this kind provide data-analytical tools that distinguish different types of natural processes based solely on their dynamics, and are thus highly suitable for environmental science applications such as model structural diagnostics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Files and scripts to support manuscript Shuman et al 2023 FATES-SPITFIRE ecosystem assembly across tropics

The dataset includes the parameter and domain files, relevant output files, and scripts to generate simulations and perform analysis with Jupyter notebooks that support the manuscript Shuman, JK et al 2023 “Dynamic ecosystem assembly and escaping the “fire-trap” in the tropics: Insights from FATES_15.0.0”. We have adapted the fire-behavior and effects module, SPITFIRE, for use with the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a size-structured vegetation demographic model. We test how climate, fire regime and fire-tolerance plant traits interact to determine the biogeography of tropical forests and grasslands. We assign different fire-tolerance strategies based on crown, leaf and bark characteristics, which are key observed fire-tolerance traits across woody plants. For these simulations, three types of vegetation compete for resources: a fire-vulnerable tree with thin bark, a vulnerable deep crown and fire-intolerant foliage; a fire-tolerant tree with thick bark, a thin crown and fire-tolerant foliage; and a fire-promoting C4 grass. We explore the model sensitivity to a critical parameter governing fuel moisture, and show that drier fuels promote increased burning, an expansion of area for grass and fire-tolerant trees and a reduction of area for fire-vulnerable trees. This conversion to lower biomass or grass areas with increased fuel drying results in increased fire burned area and its effects, which could fee back to local climate variables. Simulated size-based fire mortality for trees less than 20 cm in diameter and those with fire-vulnerable traits is higher than that for larger and/or fire-tolerant trees, in agreement with observations. Fire-disturbed forests demonstrate reasonable productivity and capture observed patterns of aboveground biomass in areas dominated by natural vegetation for the recent historical period, but have a large bias in less disturbed areas. Though the model predicts a greater extent of burned fraction than observed in areas with grass dominance, the resulting biogeography of fire-tolerant, thick-bark trees and fire-vulnerable, thin-bark trees corresponds to observations across the tropics. In areas with more than 2500 mm of precipitation, simulated fire frequency and burned area are low, with fire intensities below 150 kW m-1, consistent with observed understory fire behavior across the Amazon. Areas drier than this demonstrate fire intensities consistent with those measured in savannas and grasslands, with high values up to 4000 kW m-1. The results support a positive grass-fire feedback across the region, and suggest that forests which have existed without frequent burning may be vulnerable at higher fire intensities, which is of greater concern under intensifying climate and land use pressures. The ability of FATES to capture the connection between fire disturbance and plant fire-tolerance strategies in determining biogeography provides a useful tool for assessing the vulnerability and resilience of these critical carbon storage areas under changing conditions across the tropics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

A data ecosystem to support machine learning in materials science

Facilitating the application of machine learning to materials science problems requires enhancing the data ecosystem to enable discovery and collection of data from many sources, automated dissemination of new data across the ecosystem, and the connecting of data with materialsspecific machine learning models. Here, we present two projects, the Materials Data Facility (MDF) and the Data and Learning Hub for Science (DLHub), that address these needs. We use examples to show how MDF and DLHub capabilities can be leveraged to link data with machine learning models and how users can access those capabilities through web and programmatic interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Business Models for Scaling Demand Flexibility Volume III – Stakeholder ecosystem management challenges, and lessons learned from U.S. programs

Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on demand flexibility program stakeholder ecosystem management strategies, which provide information on value delivery and describe how program implementers leverage partner capabilities and collaborate to deliver customer and grid benefits. This report discusses the role of stakeholder ecosystem management in demand flexibility programs, identifies existing challenges to effective stakeholder management, and describes lessons learned. This report is part of a series that includes reports on value propositions, customer relationship management strategies, and program life cycle.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Soil Carbon Accumulation and Nutrient Availability in Managed and Unmanaged Ecosystems of East Tennessee

In the last two decades, urban and suburban lands grew by 34% in the United States, with 16 to 20 million ha maintained under turfgrass systems. Side-by-side comparison of turfgrass systems with other managed and unmanaged ecosystems have not been comprehensively conducted in the climate transition zone of the United States, despite the environmental significance of land use changes. Our objective was to determine the relative effects of C 3 and C 4 turfgrasses on soil organic carbon (SOC) accumulation and nutrient availability, in comparison with managed row crop and unmanaged woodlot and grassland systems. Soil samples from depths of 0 to 5, 5 to 15, and 15 to 30 cm were collected during March 2017 at the University of Tennessee’s East Tennessee Research and Education Center from seven ecosystems: (i) corn ( Zea mays L.)–soybean ( Glycine max L.) rotation, (ii) continuous soybean, (iii) tall fescue ( Festuca arundinacea L.), (iv) Kentucky bluegrass ( Poa pratensis L.), (v) bermudagrass ( Cynodon dactylon L.), (vi) unmanaged grassland, and (vii) unmanaged woodlot. All turfgrass species were established in 2012 and were managed like low-input residential lawns. Within the 30-cm soil profile, turfgrass systems contained 4.2 kg m –2 SOC at the time of sampling, which was 33% more than croplands and 34% less than unmanaged systems, and the C 4 turfgrass system contained 2.3 kg m –2 SOC and 0.4 mg kg –1 inorganic N, which were 56 and 57% lower than C 3 systems, respectively. Here, the results from this study highlight that low-input lawns with suitable grass species can offer higher SOC stock and nutrient availability than conventional cropland systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Next-Generation Ecosystem Experiment Arctic Rainfall Simulator: a tool to understand the effects of changing rainfall patterns in the Arctic

Rainfall frequency and intensity are expected to increase in the Arctic, with potential detrimental impacts on permafrost, leading to enhanced thawing and carbon release to the atmosphere. However, there have been very few studies on the effect of discrete rain events on permafrost in the Arctic and sub-Arctic. Conducting controlled rainfall experiments within permafrost landscapes can provide an improved understanding of the effect of changing intensity, duration, and timing of rain events on permafrost tundra ecosystems. Here, we describe the design and implementation of the Next-Generation Ecosystem Experiment Arctic Rainfall Simulator (NARS), a variable intensity (4–82 mm/h) rainfall simulator that can be used to study the effects of rainfall on permafrost stability. The NARS design includes a 3D-printed 4 cm H-flume and uses an eTape resistivity sensor that was calibrated (R 2 = 0.9–0.96) to measure discharge from the system. NARS is designed to be lightweight, simple to construct, and can be easily deployed in remote locations. As a field validation of updated rainfall simulator design and modernized controls, NARS was tested on the Seward Peninsula, AK. Because of its portability, versatility in deployment, dimensions, and rainfall intensity, NARS represents a methodological innovation for researching the impacts of rainfall on permafrost environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Ecosystem Controls on C & N Sequestration Following Afforestation of Agricultural Lands

The controls affecting soil organic carbon (SOC) and soil organic nitrogen (SON) dynamics and their sequestration are receiving increasing attention from both scientists and the public as impacts of global change start to affect our environment and natural resources. Soil organic matter (SOM) studies are conducted not only for sequestration and ecosystem sustainability, but also for food security and global change effects. The need to understand the role of soils in ecosystem functioning for 1) soil nutrient storage in a changing climate, 2) increased, organic agriculture production, and 3) increased food needs, even as biofuel production utilizes an expanded acreage of our soil resources, all point to the need for a better knowledge of the controls on SOM dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

The Effects of Iron Complexing Ligands on the Long Term Ecosystem Response to Iron Enrichment of HNLC waters (Final Activity Report)

Substantial increases in the concentrations of the stronger of two Fe(III) complexing organic ligand classes measured during the mesoscale Fe enrichment studies IronEx II and SOIREE appeared to sharply curtailed Fe availability to diatoms and thus limited the efficiency of carbon sequestration to the deep. Detailed observations during IronEx II (equatorial Pacific Ocean) and SOIREE (Southern Ocean –Pacific sector) indicate that the diatoms began re-experiencing Fe stress even though dissolved Fe concentrations remained elevated in the patch. This surprising outcome likely is related to the observed increased concentrations of strong Fe(III)-complexing ligands in seawater. Preliminary findings from other studies indicate that diatoms may not readily obtain Fe from these chemical species whereas Fe bound by strong ligands appears to support growth of cyanobacteria and nanoflagellates. The difficulty in assessing the likelihood of these changes with in-situ mesoscale experiments is the extended monitoring period needed to capture the long-term trajectory of the carbon cycle. A more detailed understanding of Fe complexing ligand effects on long-term ecosystem structure and carbon cycling is essential to ascertain not only the effect of Fe enrichment on short-term carbon sequestration in the oceans, but also the potential effect of Fe enrichment in modifying ecosystem structure and trajectory.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

The Oncor Geodatabase for the Columbia Estuary Ecosystem Restoration Program: Handbook of Data Reduction Procedures, Workbooks, and Exchange Templates

This Handbook of Data Reduction Procedures, Workbooks, and Exchange Templates is designed to support the Oncor geodatabase for the Columbia Estuary Ecosystem Restoration Program (CEERP). The following data categories are covered: water-surface elevation and temperature, sediment accretion rate, photo points, herbaceous wetland vegetation cover, tree plots and site summaries, fish catch and density, fish size, fish diet, fish prey, and Chinook salmon genetic stock identification. The handbook is intended for use by scientists collecting monitoring and research data for the CEERP. The ultimate goal of Oncor is to provide quality, easily accessible, geospatial data for synthesis and evaluation of the collective performance of CEERP ecosystem restoration actions at a program scale.

54 ENVIRONMENTAL SCIENCES↗