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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Experimentally-validated correlation analysis reveals new anaerobic methane oxidation partnerships with consortium-level heterogeneity in diazotrophy

Abstract Archaeal anaerobic methanotrophs (“ANME”) and sulfate-reducing Deltaproteobacteria (“SRB”) form symbiotic multicellular consortia capable of anaerobic methane oxidation (AOM), and in so doing modulate methane flux from marine sediments. The specificity with which ANME associate with particular SRB partners in situ, however, is poorly understood. To characterize partnership specificity in ANME-SRB consortia, we applied the correlation inference technique SparCC to 310 16S rRNA amplicon libraries prepared from Costa Rica seep sediment samples, uncovering a strong positive correlation between ANME-2b and members of a clade of Deltaproteobacteria we termed SEEP-SRB1g. We confirmed this association by examining 16S rRNA diversity in individual ANME-SRB consortia sorted using flow cytometry and by imaging ANME-SRB consortia with fluorescence in situ hybridization (FISH) microscopy using newly-designed probes targeting the SEEP-SRB1g clade. Analysis of genome bins belonging to SEEP-SRB1g revealed the presence of a complete nifHDK operon required for diazotrophy, unusual in published genomes of ANME-associated SRB. Active expression of nifH in SEEP-SRB1g within ANME-2b—SEEP-SRB1g consortia was then demonstrated by microscopy using hybridization chain reaction (HCR-) FISH targeting nifH transcripts and diazotrophic activity was documented by FISH-nanoSIMS experiments. NanoSIMS analysis of ANME-2b—SEEP-SRB1g consortia incubated with a headspace containing CH4 and 15N2 revealed differences in cellular 15N-enrichment between the two partners that varied between individual consortia, with SEEP-SRB1g cells enriched in 15N relative to ANME-2b in one consortium and the opposite pattern observed in others, indicating both ANME-2b and SEEP-SRB1g are capable of nitrogen fixation, but with consortium-specific variation in whether the archaea or bacterial partner is the dominant diazotroph.

58 GEOSCIENCES↗
DOE OSTI2025

A machine learning approach to quantify degradation of nuclear fuels and the effects of fission products

Nuclear fuel performance is critically dependent on understanding the evolution of fuel properties under operational conditions, a complex challenge driven by chemical changes and substantial radiation damage during fission. Traditionally, property evolution has been determined via empirical data collected following irradiation. However, these empirical correlations are limited in their applicability beyond the specific conditions in which they were obtained. This study explores a novel approach to address this challenge by applying materials informatics to develop a machine learning random forest (ML-RF) model that captures the effects of fission products on fuel compounds. The model predicts formation enthalpy (ΔH f ) by leveraging extensive quantum materials property data and correlating it with material descriptors such as composition, atomic and site features, and crystal lattice properties. This ML-RF model enables rapid interpolation across the compositional and structural spaces covered by the training data, thus supporting high-throughput screening and energetic ranking of candidate phases. The model demonstrates the ability to predict ΔH f with a mean absolute error (MAE) of approximately 0.1 to 0.2 eV/atom across a wide range of compounds, including key nuclear fuel systems (U-O, U-N, U-C, U-Si, and U-Mo). For example, it was used to assess shifts in stoichiometry for UO 2 (O/M) and UN (N/M) fuels, revealing their distinct tendencies in chemical potential variation and enabling preliminary convex hull analyses. Furthermore, the model provides insights into how individual fission products affect fuel properties. Results indicate that larger fission products (e.g., Nd, Pu, Ce) have a more pronounced impact on UO 2 , while lighter ones (e.g., Zr) strongly influence UN. Here, the model developed in this work can be used to support the Accelerated Fuel Qualification approach by facilitating preliminary evaluations prior to extensive materials modeling and experimentation. To this end, the trained model has been made available to the fuel community to support ongoing fuel development efforts.

Accelerated fuel qualification↗
DOE OSTI2019

Future prospects for constraining nuclear matter parameters with gravitational waves

The gravitational wave emission from the merging binary neutron star system GW170817 arrived full of tidal information which can be used to probe the fundamental ultradense nuclear physics residing in these stars. In previous work, we used two-dimensional correlations between nuclear matter parameters and tidal deformabilities of neutron stars applying specifically to GW170817 to derive constraints on the former. Here, we extend this analysis by finding similar correlations for varying chirp masses, the dominant determining factor in the frequency evolution of the inspiral, such that one can apply the same method to future detections. We estimate how accurately one can measure nuclear parameters with future gravitational wave interferometers and show how such measurements can be improved by combining multiple events. We find that bounds on the nuclear parameters with future observations can improve from the current one with GW170817 only by ~30% due to the existence of systematic errors caused mainly by the remaining uncertainty in the equation of state near and just above the nuclear saturation density. As a result, we show that such systematic errors can be reduced by considering multidimensional correlations among nuclear parameters and tidal deformabilities with various neutron star masses.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Locally advanced rectal cancer: qualitative and quantitative evaluation of diffusion-weighted magnetic resonance imaging in restaging after neoadjuvant chemo-radiotherapy

Purpose: To determine the added value of qualitative and quantitative evaluation of diffusion-weighted magnetic resonance imaging (DWI) in locally advanced rectal cancer (LARC) restaging after neoadjuvant chemo-radiotherapy (CRT). Materials and Methods: A retrospective study was performed of 21 patients with LARC treated with CRT. All patients were evaluated with 1.5 T conventional magnetic resonance imaging (MRI) and DWI (0–1000 s/mm²) before starting therapy and after neoadjuvant CRT. All included patients underwent surgery after CRT: the histopathological evaluation of surgical specimens represented the reference standard for local staging after neoadjuvant therapy. The qualitative analysis was carried out by two operators in consensus, who reviewed the conventional MR image set [T1-weighted and T2-weighted morphological sequences + dynamic contrast-enhanced sequences (DCE)] and the combined set of conventional and DW images. For the quantitative analysis, the apparent diffusion coefficient (ADC) values were measured at each examination. For each lesion, the mean ADC value (ADCpre and ADCpost) and the ΔADC (ADCpost – ADCpre) were calculated, and values of the three groups of response [complete response (pCR), partial response (pPR), stable disease (pSD)] were compared. Results: In LARC restaging, conventional MRI showed a sensitivity of 80% and a specificity of 50%, with a total diagnostic capacity of 71.40%, while by adding DWI sensitivity increased to 100%, specificity to 67%, and total diagnostic capacity to 90.40%. ΔADC correlates with treatment response and a cutoff of 1.35 × 10{sup −3} mm²/s predicts the pCR with a sensitivity of 93.3% and a specificity of 83.3%. Conclusions: Adding DWI to conventional sequences may improve MRI capability to evaluate tumor response to CRT. The quantitative DWI assessment is promising, but larger studies are required.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Failure Analysis and Magnetic Evaluation of Tertiary Superheater Tube Used in Gas-Fired Boiler

Failure analysis was carried out on a prematurely failed tertiary superheater tube used in gas-fired boiler. The analysis includes a comparative study of visual examination, chemical composition, hardness and microstructure at failed region, adjacent and far to failure as well as on fresh tube. The chemistry was found matching to the standard specification, whereas the hardness was low in failed tube compared to the fish mouth opening region and the fresh tube. Microscopic examination of failed sample revealed the presence of spheroidal carbides of Cr and Mo predominantly along the grain boundaries. The primary cause of failure is found to be localized heating. Magnetic hysteresis loop (MHL) measurements were carried out to correlate the magnetic parameters with microstructure and mechanical properties to establish a possible non-destructive evaluation (NDE) for health monitoring of the tubes. The coercivity of the MHL showed a very good correlation with microstructure and mechanical properties deterioration enabling a possible NDE technique for the health monitoring of the tubes.

36 MATERIALS SCIENCE↗
NASA NTRS2014

Interannual Variation in Phytoplankton Class-Specific Primary Production at a Global Scale

We used the NASA Ocean Biogeochemical Model (NOBM) combined with remote sensing data via assimilation to evaluate the contribution of 4 phytoplankton groups to the total primary production. First we assessed the contribution of each phytoplankton groups to the total primary production at a global scale for the period 1998-2011. Globally, diatoms were the group that contributed the most to the total phytoplankton production (50, the equivalent of 20 PgC y-1. Coccolithophores and chlorophytes each contributed to 20 (7 PgC y-1 of the total primary production and cyanobacteria represented about 10 (4 PgC y(sub-1) of the total primary production. Primary production by diatoms was highest in high latitude (45) and in major upwelling systems (Equatorial Pacific and Benguela system). We then assessed interannual variability of this group-specific primary production over the period 1998-2011. Globally the annual relative contribution of each phytoplankton groups to the total primary production varied by maximum 4 (1-2 PgC y-1. We assessed the effects of climate variability on the class-specific primary production using global (i.e. Multivariate El Nio Index, MEI) and regional climate indices (e.g. Southern Annular Mode (SAM), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO)). Most interannual variability occurred in the Equatorial Pacific and was associated with climate variability as indicated by significant correlation (p 0.05) between the MEI and the class-specific primary production from all groups except coccolithophores. In the Atlantic, climate variability as indicated by NAO was significantly correlated to the primary production of 2 out of the 4 groups in the North Central Atlantic (diatomscyanobacteria) and in the North Atlantic (chlorophytes and coccolithophores). We found that climate variability as indicated by SAM had only a limited effect on the class-specific primary production in the Southern Ocean. These results provide a modeling and data assimilation perspective to phytoplankton partitioning of primary production and contribute to our understanding of the dynamics of the carbon cycle in the oceans at a global scale.

Chl-a↗
NASA NTRS2011

FDR doesn't Tell the Whole Story: Joint Influence of Effect Size and Covariance Structure on the Distribution of the False Discovery Proportions

As part of a 2009 Annals of Statistics paper, Gavrilov, Benjamini, and Sarkar report results of simulations that estimated the false discovery rate (FDR) for equally correlated test statistics using a well-known multiple-test procedure. In our study we estimate the distribution of the false discovery proportion (FDP) for the same procedure under a variety of correlation structures among multiple dependent variables in a MANOVA context. Specifically, we study the mean (the FDR), skewness, kurtosis, and percentiles of the FDP distribution in the case of multiple comparisons that give rise to correlated non-central t-statistics when results at several time periods are being compared to baseline. Even if the FDR achieves its nominal value, other aspects of the distribution of the FDP depend on the interaction between signed effect sizes and correlations among variables, proportion of true nulls, and number of dependent variables. We show examples where the mean FDP (the FDR) is 10% as designed, yet there is a surprising probability of having 30% or more false discoveries. Thus, in a real experiment, the proportion of false discoveries could be quite different from the stipulated FDR.

Feiveson, Alan H.↗
DOE OSTI2026

Explainable tokamak-agnostic forecasting of fusion plasma instability via megahertz turbulent fluctuations

Scientific applications of artificial intelligence (AI) often remain limited by device-specific training and unexplained “black-box” approaches, creating fundamental barriers to cross-system generalization. This challenge is critical for nuclear fusion, where future reactors will have limited operational data for AI training. Here, we demonstrate that our neural network, trained solely on megahertz-scale turbulence measurements from one machine (DIII-D), forecasts Type-I edge localized mode (ELM) onsets in a different tokamak (KSTAR) through zero-shot weight transfer following physics-consistent preprocessing without device-specific retraining. Through an explainable AI framework combining gradient-weighted class activation mapping with physics validation, we reveal that our network can internalize physics relationships governing the ELM instabilities rather than memorizing device-specific patterns. The network perceives spatiotemporal features that correlate consistently with independently calculated instability growth rates, magnetohydrodynamic stability limits, and pedestal structure dynamics. Statistical analyses of dimensionally-reduced saliency features reveal the identical triangular features between the saliency representations, instability growth rates, and prediction probability across tokamaks, providing evidence that our forecasting system can show tokamak-agnostic generalization. This work contributes to a foundation for explainable scientific AI systems, where cross-system developments are essential for transcending traditional domain-specific constraints.

AI↗
NASA NTRS2016

Assessing the Transferability of Statistical Predictive Models for Leaf Area Index Between Two Airborne Discrete Return LiDAR Sensor Designs Within Multiple Intensely Managed Loblolly Pine Forest Locations in the South-Eastern USA

Leaf area is an important forest structural variable which serves as the primary means of mass and energy exchange within vegetated ecosystems. The objective of the current study was to determine if leaf area index (LAI) could be estimated accurately and consistently in five intensively managed pine plantation forests using two multiple-return airborne LiDAR datasets. Field measurements of LAI were made using the LiCOR LAI2000 and LAI2200 instruments within 116 plots were established of varying size and within a variety of stand conditions (i.e. stand age, nutrient regime and stem density) in North Carolina and Virginia in 2008 and 2013. A number of common LiDAR return height and intensity distribution metrics were calculated (e.g. average return height), in addition to ten indices, with two additional variants, utilized in the surrounding literature which have been used to estimate LAI and fractional cover, were calculated from return heights and intensity, for each plot extent. Each of the indices was assessed for correlation with each other, and was used as independent variables in linear regression analysis with field LAI as the dependent variable. All LiDAR derived metrics were also entered into a forward stepwise linear regression. The results from each of the indices varied from an R2 of 0.33 (S.E. 0.87) to 0.89 (S.E. 0.36). Those indices calculated using ratios of all returns produced the strongest correlations, such as the Above and Below Ratio Index (ABRI) and Laser Penetration Index 1 (LPI1). The regression model produced from a combination of three metrics did not improve correlations greatly (R2 0.90; S.E. 0.35). The results indicate that LAI can be predicted over a range of intensively managed pine plantation forest environments accurately when using different LiDAR sensor designs. Those indices which incorporated counts of specific return numbers (e.g. first returns) or return intensity correlated poorly with field measurements. There were disparities between the number of different types of returns and intensity values when comparing the results from two LiDAR sensors, indicating that predictive models developed using such metrics are not transferable between datasets with different acquisition parameters. Each of the indices were significantly correlated with one another, with one exception (LAI proxy), in particular those indices calculated from all returns, which indicates similarities in information content for those indices. It can then be argued that LiDAR indices have reached a similar stage in development to those calculated from optical-spectral sensors, but which offer a number of advantages, such as the reduction or removal of saturation issues in areas of high biomass.

Sumnall, Matthew↗
DOE OSTI2017

Predicting Treatment Response of Colorectal Cancer Liver Metastases to Conventional Lipiodol-Based Transarterial Chemoembolization Using Diffusion-Weighted MR Imaging: Value of Pretreatment Apparent Diffusion Coefficients (ADC) and ADC Changes Under Therapy

PurposeTo use absolute pretreatment apparent diffusion coefficients (ADC) derived from diffusion-weighted MR imaging (DWI) to predict response to repetitive cTACE for unresectable liver metastases of colorectal carcinoma (CRLM) at 1 and 3 months after start of treatment.Materials and MethodsFifty-five metastases in 34 patients were examined with DWI prior to treatment and 1 month after initial cTACE. Treatment was performed in 4-week intervals. Response was evaluated at 1 and 3 months after start of therapy. Metastases showing a decrease of ≥30% in axial diameter were classified as responding lesions.ResultsOne month after initial cTACE, seven lesions showed early response. There was no significant difference in absolute pretreatment ADC values between responding and non-responding lesions (p = 0.94). Three months after initial cTACE, 17 metastases showed response. There was a significant difference (p = 0.021) between absolute pretreatment ADC values of lesions showing response (median 1.08 × 10{sup −3} mm{sup 2}/s) and no response (median 1.30 × 10{sup −3} mm{sup 2}/s). Pretreatment ADC showed fair diagnostic value to predict response (AUC 0.7). Lesions showing response at 3 months also revealed a significant increase in ADC between measurements before treatment and at one month after initial cTACE (p < 0.001). Applying an increase in ADC of 12.17%, response at 3 months after initial cTACE could be predicted with a sensitivity and specificity of 77 and 74%, respectively (AUC 0.817). Furthermore, there was a strong and significant correlation (r = 0.651, p < 0.001) between percentage change in size after third cTACE and percentage change in ADC.ConclusionIn patients with CRLM, ADC measurements are potential biomarkers for assessing response to cTACE.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials

Natural porous materials such as nanoporous clays are used as green and low-cost adsorbents and catalysts. The key factors determining their performance in these applications are the pore morphology and surface activity, which are typically represented by properties such as specific surface area, pore volume, micropore content and pH. The latter may be modified and tuned to specific applications through material processing and/or chemical treatment. Characterization of the material, raw or processed, is typically performed experimentally, which can become costly especially in the context of tuning of the properties towards specific application requirements and needing numerous experiments. In this work, we present an application of tree-based machine learning methods trained on experimental datasets to accelerate the characterization of natural porous materials. The resulting models allow reliable prediction of the outcomes of experimental characterization of processed materials (R2 from 0.78 to 0.99) as well as identification of key factors contributing to those properties through feature importance analysis. Furthermore, the high throughput of the models enables exploration of processing parameter–property correlations and multiobjective optimization of prototype materials towards specific applications. We have applied these methodologies to pinpoint and rationalize optimal processing conditions for clays exploitable in acid catalysis. One of such identified materials was synthesized and tested revealing appreciable acid character improvement with respect to the pristine material. Specifically, it achieved 79% removal of chlorophyll-a in acid catalyzed degradation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Holographic Quantum Simulation of Strongly Correlated Electron Systems

The project aimed to demonstrate a new holographic quantum simulation approach and co‐ designed quantum hardware to tackle three specific problems that fall within the broad umbrella of unraveling the physics of strongly correlated electron systems (SCES). These tasks were: (1) holographic preparation of ground‐ and thermal‐ states of correlated magnetic and electronic systems including quasi‐2d frustrated‐spin, Fermi‐Hubbard, and fractional quantum Hall (FQH) systems, (2) holographic‐simulation of long‐time out‐of‐equilibrium dynamics and (3) holographic analogs of embedding methods such as dynamical mean‐ field theory (DMFT) and density‐matrix embedding theory (DMET) to solve systems with complex structure or long‐range interactions. These tasks are prototypes for the kinds of material simulation problems of interest to BES, such as the simulation of multiferroic materials, perovskite photovoltaics and high‐temperature superconductors, that tax the capabilities of the most powerful classical supercomputers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1972

Analysis of flight test transition and turbulent heating data. Part 1: Boundary layer transition results

The objective of the study was to provide a detailed post flight evaluation of ballistic vehicle flight test boundary layer transition data. A total of fifty-five vehicles were selected for analysis. These vehicles were chosen from a data sampling of roughly two hundred flights and the criteria for vehicle selection is delineated herein. The results of the analysis indicate that frustum transition of re-entry vehicles appears to be nose tip dominated. Frustum related parameters and materials apparently have a second order effect on transition. This implies that local viscous parameters on the frustum should not correlate flight test transition data, and in fact they do not. Specific parameters relative to the nose tip have been identified as the apparent dominant factors that characterize the transition phenomena and a correlation of flight test data is presented.

Martellucci, A.↗
DOE OSTI2024

Active learning approach to simulations of strongly correlated matter with the ghost Gutzwiller approximation

Quantum embedding (QE) methods such as the ghost Gutzwiller approximation (gGA) offer a powerful approach to simulating strongly correlated systems, but come with the computational bottleneck of computing the ground state of an auxiliary embedding Hamiltonian (EH) iteratively. In this work, we introduce an active learning (AL) framework integrated within the gGA to address this challenge. The methodology is applied to the single-band Hubbard model and results in a significant reduction in the number of instances where the EH must be solved. Through a principal component analysis (PCA), we find that the EH parameters form a low-dimensional structure that is largely independent of the geometric specifics of the systems, especially in the strongly correlated regime. Our AL strategy enables us to discover this low-dimensionality structure on the fly, while leveraging it for reducing the computational cost of gGA, laying the groundwork for more efficient simulations of complex strongly correlated materials. Published by the American Physical Society 2024

36 MATERIALS SCIENCE↗
DOE OSTI2018

Hydroxide diffuses slower than hydronium in water because its solvated structure inhibits correlated proton transfer

Proton transfer via hydronium and hydroxide ions in water is ubiquitous. It underlies acid–base chemistry, certain enzyme reactions, and even infection by the flu. Despite two centuries of investigation, the mechanism underlying why hydroxide diffuses slower than hydronium in water is still not well understood. Herein, we employ state-of-the-art density-functional-theory-based molecular dynamics—with corrections for non-local van der Waals interactions, and self-interaction in the electronic ground state—to model water and hydrated water ions. At this level of theory, we show that structural diffusion of hydronium preserves the previously recognized concerted behaviour. However, by contrast, proton transfer via hydroxide is less temporally correlated, due to a stabilized hypercoordination solvation structure that discourages proton transfer. Specifically, the latter exhibits non-planar geometry, which agrees with neutron-scattering results. Asymmetry in the temporal correlation of proton transfer leads to hydroxide diffusing slower than hydronium.

Chen, Mohan↗
NASA NTRS1956

Theoretical performance of JP-4 fuel with a 70-30 mixture of fluorine and oxygen as a rocket propellant : equilibrium composition

Data were calculated for equivalence ratios of 1 to 4, chamber pressures of 300 and 600 pounds per square inch absolute, and pressure ratios of 1 to 1500. Parameters included are specific impulse, combustion and exit temperatures, molecular weight, characteristic velocity, coefficient of thrust, ratio of nozzle-exit area to throat area, specific heat at constant pressure, isentropic exponent, viscosity, and thermal conductivity. A correlation is given which permits determination of performance for a wide range of chamber pressures. A method for obtaining specific impulse of JP-4 fuel with OF2 and O3-F2 mixtures is given.

Gordon, Sanford↗
DOE OSTI2025

Compressing Hamiltonians with ab initio downfolding for simulating strongly-correlated materials on quantum computers

The accurate first-principles description of strongly correlated materials is an important and challenging problem in condensed matter physics. Ab initio downfolding has emerged as a way of deriving compressed many-body Hamiltonians that maintain the essential physics of strongly correlated materials. The solution of these material-specific models is still exponentially difficult to generate on classical computers, but quantum algorithms allow for a significant speed-up in obtaining the ground states of these compressed Hamiltonians. Here, we demonstrate that using quantum algorithms to obtain the properties of downfolded Hamiltonians can indeed yield high-fidelity solutions. By combining ab initio downfolding and variational quantum eigensolvers, we correctly predict the antiferromagnetic state of one-dimensional cuprate Ca 2 Cu O 3 , the excitonic ground state of monolayer W Te 2 , and the charge-ordered state of correlated metal Sr VO 3 . Numerical simulations using a classical tensor network implementation of variational quantum eigensolvers allow us to simulate large models with up to 54 qubits and encompassing up to four bands in the correlated subspace, which is indicative of the complexity that our framework can address. Through these methods we demonstrate the potential of classical preoptimization and downfolding techniques for enabling efficient materials simulation using quantum algorithms.

Alvertis, Antonios M. [NASA, Ames; LBNL, Berkeley]↗
DOE OSTI2017

Fungal Communities and Functional Guilds Shift Along an Elevational Gradient in the Southern Appalachian Mountains

Nitrogen deposition alters forest ecosystems particularly in high elevation, montane habitats where nitrogen deposition is greatest and continues to increase. We collected soils across an elevational (788–1940 m) gradient, encompassing both abiotic (soil chemistry) and biotic (vegetation community) gradients, at eight locations in the southern Appalachian Mountains of southwestern North Carolina and eastern Tennessee. We measured soil chemistry (total N, C, extractable PO 4 , soil pH, cation exchange capacity [ECEC], percent base saturation [% BS]) and dissected soil fungal communities using ITS2 metabarcode Illumina MiSeq sequencing. Total soil N, C, PO 4 , % BS, and pH increased with elevation and plateaued at approximately 1400 m, whereas ECEC linearly increased and C/N decreased with elevation. Fungal communities differed among locations and were correlated with all chemical variables, except PO 4 , whereas OTU richness increased with total N. Several ecological guilds (i.e., ectomycorrhizae, saprotrophs, plant pathogens) differed in abundance among locations; specifically, saprotroph abundance, primarily attributable to genus Mortierella, was positively correlated with elevation. Ectomycorrhizae declined with total N and soil pH and increased with total C and PO 4 where plant pathogens increased with total N and decreased with total C. Finally, our results demonstrate significant turnover in taxonomic and functional fungal groups across elevational gradients which facilitate future predictions on forest ecosystem change in the southern Appalachians as nitrogen deposition rates increase and regional temperature and precipitation regimes shift.

54 ENVIRONMENTAL SCIENCES↗