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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Understanding processes that control dust spatial distributions with global climate models and satellite observations

Dust aerosol is important in modulating the climate system at local and global scales, yet its spatiotemporal distributions simulated by global climate models (GCMs) are highly uncertain. In this study, we evaluate the spatiotemporal variations of dust extinction profiles and dust optical depth (DOD) simulated by the Community Earth System Model version 1 (CESM1) and version 2 (CESM2), the Energy Exascale Earth System Model version 1 (E3SMv1), and the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) against satellite retrievals from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer (MODIS), and Multi-angle Imaging SpectroRadiometer (MISR). We find that CESM1, CESM2, and E3SMv1 underestimate dust transport to remote regions. E3SMv1 performs better than CESM1 and CESM2 in simulating dust transport and the northern hemispheric DOD due to its higher mass fraction of fine dust. CESM2 performs the worst in the Northern Hemisphere due to its lower dust emission than in the other two models but has a better dust simulation over the Southern Ocean due to the overestimation of dust emission in the Southern Hemisphere. DOD from MERRA-2 agrees well with CALIOP DOD in remote regions due to its higher mass fraction of fine dust and the assimilation of aerosol optical depth. The large disagreements in the dust extinction profiles and DOD among CALIOP, MODIS, and MISR retrievals make the model evaluation of dust spatial distributions challenging. Our study indicates the importance of representing dust emission, dry/wet deposition, and size distribution in GCMs in correctly simulating dust spatiotemporal distributions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Memory forensic analysis of a programmable logic controller in industrial control systems

In industrial control systems (ICS), programmable logic controllers (PLCs) are used to automate physical processes such as nuclear plants and power grid stations, and are often subject to cyber attacks. As in conventional IT domain, the memory analysis of the PLCs can help answer important forensic questions about the attack, such as the presence of malicious firmware, injection of modified control logic (the program running on the PLC), and manipulation of I/O devices (e.g., sensors and actuators). Unlike conventional IT domain, PLCs have heterogeneous hardware architecture, proprietary firmware and control software, making it challenging to employ a unified framework for their memory forensics. For merely extracting artifacts of forensic importance, reverse-engineering the firmware is a tedious task, and the effort needs to be repeated for every PLC model. As a community, a step-wise approach to tackle this challenge is to analyze the memory of specific PLCs, and subsequently find a generic framework applicable to all PLCs. Our work is a step forward in this direction. By following a methodology that focuses on the functional layer of PLCs instead of reverse engineering the firmware, we analyze the digital forensic artifacts available in a common PLC, Allen-Bradley ControlLogix 1756-L61. Before diving into the memory dump, we analyze the PLC control software to create a list of important artifacts that are sure to exist in the PLC memory dump. The approach employs a setup where PLC control software RSLogix-5000 is connected to the PLC, and the memory dump can be obtained as and when needed. We create test cases that sequentially highlight each category of artifacts, followed by an examination of the resultant impact on memory. After attaining the listed artifacts, we employ conventional string and known data searches to extract interesting information present in this PLC's memory. The memory analysis profile, presented as a Python library and shared with the community, can help a forensic investigator to readily extract forensic artifacts from the same model's controller. The adopted approach may help researchers in creating memory profile of other PLCs, and ultimately formulating a generic PLC memory analysis framework.

Rais, Muhammad Haris↗
DOE OSTI2026

Single nucleotide variants drive evolutionary phage-host arms race in anaerobic carbon dioxide-converting microbiome

Microbial bioconversions are shaped by environmental perturbations and the adaptation of resident microbiomes. Prokaryotes coexist with bacteriophages, yet their coevolutionary trajectories remain underexplored. Here, we investigate the effects of a cultivation vessel leak on an anaerobic consortium performing carbon dioxide reduction. Using time-series shotgun metagenomic sequencing, we reconstruct microbial and viral genomes to track community shifts. We further apply single-nucleotide variant profiling and CRISPR array analysis to monitor viral microdiversity and host defense mechanisms. After bioaugmentation restores bioconversion efficiency, the consortium undergoes pronounced restructuring, with new dominant taxa emerging from the rare biosphere. We identify patterns consistent with phage predation selectively removing certain species, while others exhibit resilience to infection. This shift aligns with a widespread viral outbreak and a transient increased frequency of single nucleotide variants in bacterial CRISPR–Cas defense genes. Expansion of CRISPR spacers further supports that CRISPR-mediated processes influence microbial resilience. Concurrently, phages infecting resilient hosts exhibited adaptive evolution, marked by high genetic heterogeneity. Selective pressure varies across their genomes, targeting infectivity genes and protospacer-adjacent motifs. These findings highlight a dynamic evolutionary arms race driven by the selection of beneficial genetic variants, providing a mechanistic framework for multi-omics investigations, and informing biotechnological applications, including phage-based microbiome manipulation.

Ghiotto, G↗
NASA NTRS1986

Continuous wind measurement in the tropical Pacific using VHF radars

Very High Frequency (VHF) Radar Wind Profilers are being installed on Ponape, East Caroline Islands and Christmas Island, Republic of Kiribati to continuously monitor winds aloft. The purpose of this experiment is to study wind fluctuations on time scales between minutes and days, to determine the longitudinal character of these fluctuations, and to examine their relationship to climate variability. Six-hourly wind profiles will be provided via satellite to the scientific community for Project TOGA (Tropical Ocean Global Atmosphere).

Balsley, B. B.↗
DOE OSTI2022

Disease resistance in coral is mediated by distinct adaptive and plastic gene expression profiles

Infectious diseases are an increasing threat to coral reefs, resulting in altered community structure and hindering the functional contributions of disease-susceptible species. We exposed seven reef-building coral species from the Caribbean to white plague disease and determined processes involved in (i) lesion progression, (ii) within-species gene expression plasticity, and (iii) expression-level adaptation among species that lead to differences in disease risk. Gene expression networks enriched in immune genes and cytoskeletal arrangement processes were correlated to lesion progression rates. Whether or not a coral developed a lesion was mediated by plasticity in genes involved in extracellular matrix maintenance, autophagy, and apoptosis, while resistant coral species had constitutively higher expression of intracellular protein trafficking. This study offers insight into the process involved in lesion progression and within- and between-species dynamics that lead to differences in disease risk that is evident on current Caribbean reefs.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2015

A Module for Assimilating Hyperspectral Infrared Retrieved Profiles into the Gridpoint Statistical Interpolation System for Unique Forecasting Applications

Hyperspectral infrared sounder radiance data are assimilated into operational modeling systems however the process is computationally expensive and only approximately 1% of available data are assimilated due to data thinning as well as the fact that radiances are restricted to cloud-free fields of view. In contrast, the number of hyperspectral infrared profiles assimilated is much higher since the retrieved profiles can be assimilated in some partly cloudy scenes due to profile coupling other data, such as microwave or neural networks, as first guesses to the retrieval process. As the operational data assimilation community attempts to assimilate cloud-affected radiances, it is possible that the use of retrieved profiles might offer an alternative methodology that is less complex and more computationally efficient to solve this problem. The NASA Short-term Prediction Research and Transition (SPoRT) Center has assimilated hyperspectral infrared retrieved profiles into Weather Research and Forecasting Model (WRF) simulations using the Gridpoint Statistical Interpolation (GSI) System. Early research at SPoRT demonstrated improved initial conditions when assimilating Atmospheric Infrared Sounder (AIRS) thermodynamic profiles into WRF (using WRF-Var and assigning more appropriate error weighting to the profiles) to improve regional analysis and heavy precipitation forecasts. Successful early work has led to more recent research utilizing WRF and GSI for applications including the assimilation of AIRS profiles to improve WRF forecasts of atmospheric rivers and assimilation of AIRS, Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI) profiles to improve model representation of tropopause folds and associated non-convective wind events. Although more hyperspectral infrared retrieved profiles can be assimilated into model forecasts, one disadvantage is the retrieved profiles have traditionally been assigned the same error values as the rawinsonde observations when assimilated with GSI. Typically, satellitederived profile errors are larger and more difficult to quantify than traditional rawinsonde observations (especially in the boundary layer), so it is important to appropriately assign observation errors within GSI to eliminate potential spurious innovations and analysis increments that can sometimes arise when using retrieved profiles. The goal of this study is to describe modifications to the GSI source code to more appropriately assimilate hyperspectral infrared retrieved profiles and outline preliminary results that show the differences between a model simulation that assimilated the profiles as rawinsonde observations and one that assimilated the profiles in a module with the appropriate error values.

Berndt, Emily↗
DOE OSTI2025

Collaborative Proposal: Improving understanding of the internal structure and dynamics of deep convection using ARM observations and large eddy simulations

Recent observational and large eddy simulation (LES) modeling studies have nearly unanimously supported the view of deep cumulus convection being composed of a series of quasi-spherical bubbles of buoyant air, known as moist thermals. Despite the prevalence of moist thermals in deep convection, a comprehensive theory for the dynamics of these structures is lacking. Most current conceptual models for cumulus convection are based on canonical scaling theories for dry thermals or plumes; however, there is considerable evidence that the behavior of moist thermals differs markedly from these theories. Furthermore, the theoretical basis for most cumulus parameterizations originates from the plume conceptual model, and therefore these parameterizations are inconsistent with the real structure of moist convection. Motivated by the aforementioned knowledge gaps, this “end-to-end” research effort use theory, observations, numerical simulations, and direct improvements to the Zhang-McFarlane (ZM) convection scheme in the global climate Community Atmosphere Model (CAM) to address the following research questions: What key environmental parameters determine whether or not shallow convection will transition into deep convection, in the context of thermal-like updrafts? What factors regulate the size of thermals within cumulus updrafts? How does vertical wind shear influence thermal behavior, and as a consequence, vertical velocity and mass flux profiles and the shallow-to-deep convective transition? What are the critical processes that determine updraft vertical velocities and their connection to the vertical mass flux profile for thermal-like updrafts? Idealized LES modeling will be used in conjunction with theoretical models for the core properties of thermal-like updrafts to better understand key processes that regulate thermal ascent rates and entrainment properties. Thermal-tracking procedures will be used to characterize the behavior of thermals within the LES, and recently developed direct measures of entrainment and detrainment will be used to quantify entrainment/detrainment rates. Building from these results, we will analyze the structure of moist thermals from hemispheric range-height indicator scans taken during the Atmospheric Radiation Measurement Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign, and from “real case” LES of CACTI events. This combined modeling and observational analysis will provide essential validation for the existing body of research on moist thermal dynamics, which is based primarily on modeling studies. With the insight gained from the aforementioned activities, we will modify the Zhang-McFarlane convection scheme to improve its representation of updraft vertical velocity and entrainment rate profiles. These process-level changes will be tested in the Community Atmosphere Model to assess the impact on global climate simulations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

A Tool for Model-Based Generation of Scenario-driven Electric Power Load Profiles

Power consumption during all phases of spacecraft flight is of great interest to the aerospace community. As a result, significant analysis effort is exerted to understand the rates of electrical energy generation and consumption under many operational scenarios of the system. Previously, no standard tool existed for creating and maintaining a power equipment list (PEL) of spacecraft components that consume power, and no standard tool existed for generating power load profiles based on this PEL information during mission design phases. This paper presents the Scenario Power Load Analysis Tool (SPLAT) as a model-based systems engineering tool aiming to solve those problems. SPLAT is a plugin for MagicDraw (No Magic, Inc.) that aids in creating and maintaining a PEL, and also generates a power and temporal variable constraint set, in Maple language syntax, based on specified operational scenarios. The constraint set can be solved in Maple to show electric load profiles (i.e. power consumption from loads over time). SPLAT creates these load profiles from three modeled inputs: 1) a list of system components and their respective power modes, 2) a decomposition hierarchy of the system into these components, and 3) the specification of at least one scenario, which consists of temporal constraints on component power modes. In order to demonstrate how this information is represented in a system model, a notional example of a spacecraft planetary flyby is introduced. This example is also used to explain the overall functionality of SPLAT, and how this is used to generate electric power load profiles. Lastly, a cursory review of the usage of SPLAT on the Cold Atom Laboratory project is presented to show how the tool was used in an actual space hardware design application.

power load profile↗
DOE OSTI2022

Short Chain Fatty Acids and Bacterial Taxa Associated with Reduced Salmonella enterica serovar I 4,[5],12:i:- Shedding in Swine Fed a Diet Supplemented with Resistant Potato Starch

Salmonella enterica serovar I 4,[5],12:i:- is a foodborne pathogen of concern because many isolates are multidrug-resistant (resistant to ≥3 antimicrobial classes) and metal tolerant. In this study, three in-feed additives were individually tested for their ability to reduce Salmonella I 4,[5],12:i:- shedding in swine: resistant potato starch (RPS), high amylose corn starch, and a fatty acid blend, compared with a standard control diet over 21 days. Only RPS-fed pigs exhibited a reduction in Salmonella fecal shedding, different bacterial community compositions, and different cecal short chain fatty acid (SCFA) profiles relative to control animals. Within the RPS treatment group, pigs shedding the least Salmonella tended to have greater cecal concentrations of butyrate, valerate, caproate, and succinate. Additionally, among RPS-fed pigs, several bacterial taxa (Prevotella_7, Olsenella, and Bifidobacterium, and others) exhibited negative relationships between their abundances of and the amount of Salmonella in the feces of their hosts. Many of these same taxa also had significant positive associations with cecal concentrations of butyrate, valerate, caproate, even though they are not known to produce these SCFAs. Together, these data suggest the RPS-associated reduction in Salmonella shedding may be dependent on the establishment of bacterial cross feeding interactions that result in the production of certain SCFAs. However, directly feeding a fatty acid mix did not replicate the effect. RPS supplementation could be an effective means to reduce multidrug-resistant (MDR) S. enterica serovar I 4,[5],12:i:- in swine, provided appropriate bacterial communities are present in the gut.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Approximating accelerator impedances with resonator networks

It is common in the accelerator community to use the impedance of accelerator components to describe wake interactions in the frequency domain. However, it is often desirable to understand such wake interactions in the time domain in a general manner for excitations that are not necessarily Gaussian in nature. Here, the conventional method for doing this involves taking the inverse Fourier Transform of the component impedance, obtaining the Green's Function, and then convolving it with the desired excitation distribution. This method can prove numerically cumbersome, for a convolution integral must be evaluated for each individual point in time when the wake function is desired. An alternative to this method would be to compute the wake function analytically, which would sidestep the need for repetitive integration. Only a handful of cases, however, are simple enough for this method to be tenable. One of these cases is the case where the component in question is an RLC resonator, which has a closed-form analytical wake function solution. This means that a component which can be represented in terms of resonators can leverage this solution. As it happens, common network synthesis techniques may be used to map arbitrary impedance profiles to RLC resonator networks in a manner the accelerator community has yet to take advantage of. In this work, we will use Foster Canonical Resonator Networks and partial derivative descent optimization to develop a technique for synthesizing resonator networks that well approximate the impedances of real-world accelerator components. We will link this synthesis to the closed-form resonator wake function solution, giving rise to a powerful workflow that may be used to streamline beam dynamics simulations.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Removal of primary nutrient degrading members severely reduces growth of soil microbial communities even when additional degraders are present

Understanding how microorganisms within a soil community interact to support collective respiration and growth remains challenging. Here we used a model substrate, chitin, and a Model Soil Consortium, MSC-2, to investigate how individual members of a microbial community contribute to decomposition and community growth. While MSC-2 can grow using chitin as the sole carbon source, we do not yet know how the growth kinetics or final biomass yields of MSC-2 vary when certain chitin degraders, or other important members, are absent. To characterize specific roles within this representative community, we carried out experiments leaving out members of MSC-2 and measuring biomass yields and CO2 production. We chose two members to iteratively leave out (referred to by genus name): Streptomyces, as it is predicted via gene expression analysis to be a major chitin degrader in the community, and Rhodococcus as it is predicted via species co-abundance analysis to interact with several other members. Our results showed that when MSC-2 lacked Streptomyces, growth and respiration of the community was severely reduced. Removal of either Streptomyces or Rhodococcus led to major changes in abundance for several other species, pointing to a comprehensive shifting of the microbial community when important members are removed as well as alterations in the metabolic profile, especially when Streptomyces was removed. These results show that when keystone, chitin degrading members are removed, other members, even those with the potential to degrade chitin, do not fill the same metabolic niche to promote community growth. In addition, highly connected members may be removed with similar or even increased levels of growth and respiration. Our findings are critical to a better understanding of soil microbiology, specifically in how communities maintain activity when biotic or abiotic factors lead to changes in biodiversity in soil systems.

McClure, Ryan S↗
DOE OSTI2023

Removal of primary nutrient degraders reduces growth of soil microbial communities with genomic redundancy

Understanding how microorganisms within a soil community interact to support collective respiration and growth remains challenging. Here, we used a model substrate, chitin, and a synthetic Model Soil Consortium (MSC-2) to investigate how individual members of a microbial community contribute to decomposition and community growth. While MSC-2 can grow using chitin as the sole carbon source, we do not yet know how the growth kinetics or final biomass yields of MSC-2 vary when certain chitin degraders, or other important members, are absent. To characterize specific roles within this synthetic community, we carried out experiments leaving out members of MSC-2 and measuring biomass yields and CO 2 production. We chose two members to iteratively leave out (referred to by genus name): Streptomyces, as it is predicted via gene expression analysis to be a major chitin degrader in the community, and Rhodococcus as it is predicted via species co-abundance analysis to interact with several other members. Our results showed that when MSC-2 lacked Streptomyces, growth and respiration of the community was severely reduced. Removal of either Streptomyces or Rhodococcus led to major changes in abundance for several other species, pointing to a comprehensive shifting of the microbial community when important members are removed, as well as alterations in the metabolic profile, especially when Streptomyces was lacking. These results show that when keystone, chitin degrading members are removed, other members, even those with the potential to degrade chitin, do not fill the same metabolic niche to promote community growth. In addition, highly connected members may be removed with similar or even increased levels of growth and respiration. Our findings are critical to a better understanding of soil microbiology, specifically in how communities maintain activity when biotic or abiotic factors lead to changes in biodiversity in soil systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Geochemistry and metagenomics analyses of bacterial community structure in selected waste dumpsites in Lagos Metropolis, Nigeria

Dumpsites are reservoirs of persistent organic pollutants (POPs) and heavy metals (HMs), constituting environmental hazards to humanity. Autochthonous microorganisms in dumpsites exhibit various degrees of responses to contaminants. Unfortunately, there is a dearth of information on the types and concentration of pollutants and the array of microorganisms in these dumpsites which may play important roles in the metabolism of such pollutants or other community processes. Therefore, determining the microbial community structure in such contaminated sites across a municipality is essential for profiling the taxa that would serve as consensus degraders of the pollutants. In this study, soil samples from three dumpsites (Cele, CS; Solous, SS; and Computer Village, CVS) were characterized for geochemical properties using GC-MS, MP-AES, and other analytical protocols, while the dynamics of bacterial communities were evaluated based on their 16S rRNA gene barcodes. A significant difference in the bacterial communities was observed among the dumpsites in relation to the extent of pollution caused by POPs and HMs. CVS, with the highest HM contamination, was rich in Actinobacteria (41.7%) and Acidobacteria (10.2%), in contrast to CS and SS. Proteobacteria (34.1%) and Firmicutes (20%) were the dominant phyla in CS (highest POP contamination), while Bacteroidetes (45.5%) and Proteobacteria (39.9%) were dominant in SS soil.Bacilluswas the dominant genus in the most polluted dumpsite. Canonical correspondence analysis revealed that polycyclic aromatic hydrocarbons (PAHs) and HMs shaped the structure of the bacterial operational taxonomic units (OTUs) in the most polluted dumpsite. Out of a total of 706 OTUs, 628 OTUs exhibited a significant correlation (>50%) with benzo(b)fluoranthene, azobenzene, dibenzofurans, pyrene, dibenzo(a,l)pyrene, Cu, and Zn. In particular, Proteobacteria (Achromobactersp. andSerratiasp.), Bacteroidetes (Zhouiasp.), and Firmicutes (Bacillussp.) were suggested to be pivotal to the ecophysiology of dumpsite soils contaminated with POPs and HMs. The results generally underscored the importance of metagenomic and physicochemical analyses of polluted systems in enabling correlations for useful prediction of drivers of such ecosystems. This will further improve our understanding of the metabolic potential and adaptation of organisms in such systems.

Environmental Sciences & Ecology↗
NASA NTRS2013

Characterization of Microbial Communities Found in Bioreactor Effluent

The purpose of this investigation was to examine microbial communities of simulated wastewater effluent from hollow fiber membrane bioreactors collected from the Space Life Science Laboratory and Texas Technical University. Microbes were characterized using quantitative polymerase chain reaction where a total count of bacteria and fungi were determined. The primers that were used to determine the total count of bacteria and fungi were targeted for 16S rDNA genes and the internal transcribed spacer, respectively. PCR products were detected with SYBR Green I fluorescent dye and a melting curve analysis was performed to identify unique melt profiles resulting from DNA sequence variations from each species of the community. Results from both the total bacteria and total fungi count assays showed that distinct populations were present in isolates from these bioreactors. This was exhibited by variation in the number of peaks observed on the melting curve analysis graph. Further analysis of these results using species-specific primers will shed light on exactly which microbes are present in these effluents. Information gained from this study will enable the design of a system that can efficiently monitor microbes that play a role in the biogeochemical cycling of nitrogen in wastewater on the International Space Station to assist in the design of a sustainable system capable of converting this nutrient.

Flowe, Candice↗
DOE OSTI2022

The impact of energy-efficiency upgrades and other distributed energy resources on a residential neighborhood-scale electrification retrofit

We report ambitious targets for carbon emissions reductions are highlighting new challenges for electrification strategies, leading to an increased focus on building load flexibility and energy management to complement the variability inherent in renewable energy generation. Over the next decade millions of existing homes could undergo electrification retrofits, and there is an urgent need to understand the potential impacts of electrifying major residential loads such as water and space heating on community load characteristics, resident energy bills, and the utility's distribution system. Behind-the-meter distributed energy resources (DERs), including efficiency measures, photovoltaics (PV), battery storage, managed electric vehicle (EV) charging, and controls such as home energy management systems (HEMS), can significantly alter a neighborhood's load profile and provide benefits to both the residents and the grid. We present a novel approach to characterizing the impact of a hypothetical neighborhood-scale residential retrofit program on individual homes' energy use profiles, associated utility bills, and the local distribution system. We modeled a mixed-fuel community of 30 single-family homes in Denver, Colorado, and compared the effects of retrofit scenarios ranging from conventional energy-efficiency upgrades to full electrification with and without more advanced DER technologies. We analyzed which packages of DERs most reliably enable demand flexibility in response to a time-of-use (TOU) rate for this and similar neighborhoods. Our buildings-to-grid co-simulation framework includes a generic secondary distribution feeder model to capture voltage profiles, transformer loading, and other grid impacts in each case. We also calculated the carbon emissions associated with energy use in the community. The methodology developed here can be broadly applied to community-scale beneficial electrification studies in other regions, climates, utility infrastructures, and building typologies to make specific, targeted recommendations based on quantified projections of energy demand in any given community. Our findings indicate that residential electrification can be achieved without negatively impacting the monthly utility bill, and that a combination of conventional energy-efficiency measures, PV, battery, controls, and managed EV charging to maximize a community's demand flexibility is a promising strategy. Adding DERs (especially PV) as part of efficient electrification produces much bigger savings than efficient electrification without DERs. A key barrier is that upgrades require upfront costs, and modest utility bill savings result in long payback periods.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Active microbial biomass decreases, but microbial growth potential remains similar across soil depth profiles under deeply-vs. shallow-rooted plants

Climate-smart land management practices that replace shallow-rooted annual crop systems with deeply-rooted perennial plants can contribute to soil carbon sequestration. However, deep soil carbon accrual may be influenced by active microbial biomass and their capacity to assimilate fresh carbon at depth. Incorporating active microbial biomass, dormancy, and growth in microbially-explicit models can improve our ability to predict soil's capacity to store carbon. But, so far, the microbial parameters that are needed for such modeling are poorly constrained, especially in deep soil layers. Here, we used a lab incubation experiment and growth kinetics model to estimate how microbial parameters vary along 240 cm of soil depth in profiles under shallow- (soy) and deeply-rooted (switchgrass) plants 11 years after plant cover conversion. We also assessed resource origin and availability (total organic carbon, 14 C, extractable organic carbon, specific UV absorbance of K 2 SO 4 extractable organic C, total nitrogen, total dissolved nitrogen) along the soil profiles to examine associations between soil chemical and biological parameters. Even though root biomass was greater and rooting depth was deeper under switchgrass than soy, resource availability and microbial growth parameters were generally similar between vegetation types. Instead, depth significantly influenced soil chemical and biological parameters. For example, resource availability and total and relative active microbial biomass decreased with soil depth. Decreases in the relative active microbial biomass coincided with increased lag time (response time to external carbon inputs) along the soil profiles. Even at a depth of 210–240 cm, microbial communities were activated to grow by added resources within a day. Maximum specific growth rate decreased to a depth of 90 cm and then remained consistent in deeper layers. Our findings show that >10 years of vegetation and rooting depth changes may not be long enough to alter microbial growth parameters, and suggest that at least a portion of the microbial community in deep soils can grow rapidly in response to added resources. Our study determined microbial growth parameters that can be used in microbially-explicit models to simulate carbon dynamics in deep soil layers.

14C↗
DOE OSTI2026

Mobile genetic elements shape microbial diversity and functions in thawing permafrost soils

Ecosystems are shaped by communities of microorganisms whose niches and impacts depend on functional profiles influenced by gene gains and losses. Culture-based experiments demonstrate that mobile genetic elements (MGEs) can mediate gene flux, but quantitative understanding of these dynamics in natural systems remains limited. Here we develop and apply a systematic, meta-omic framework to investigate MGEs in a complex natural system using an 8-year soil time series collected at Stordalen Mire, in Sweden’s thawing permafrost margin. In this climate-critical peatland, we identify ~2.1 million MGE recombinases across 89 microbial phyla and assess ecological distributions, affected functions, past mobility and current activity. This revealed an active mobilome that shapes natural genetic diversity via differential impacts on major phyla and affects a wide range of functions, including metabolic genes involved in carbon flux and nutrient cycling. These findings and this analytic framework suggest avenues towards a better understanding of MGE diversity, activity, mobility and impacts across ecosystems.

Biological and medical sciences↗