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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 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Biodiversity and ecosystem functioning relations in European forests depend on environmental context

The importance of biodiversity in supporting ecosystem functioning is generally well accepted. However, most evidence comes from small-scale studies, and scaling-up patterns of biodiversity–ecosystem functioning (B-EF) remains challenging, in part because the importance of environmental factors in shaping B-EF relations is poorly understood. In this study, using a forest research platform in which 26 ecosystem functions were measured along gradients of tree species richness in six regions across Europe, we investigated the extent and the potential drivers of context dependency of B-EF relations. Despite considerable variation in species richness effects across the continent, we found a tendency for stronger B-EF relations in drier climates as well as in areas with longer growing seasons and more functionally diverse tree species. Finally, the importance of water availability in driving context dependency suggests that as water limitation increases under climate change, biodiversity may become even more important to support high levels of functioning in European forests.

58 GEOSCIENCES↗
DOE OSTI2021

Root-derived inputs are major contributors to soil carbon in temperate forests, but vary by mycorrhizal type

Roots promote the formation of slow-cycling soil carbon (C), yet we have a limited understanding of the magnitude and controls on this flux. We hypothesised arbuscular mycorrhizal (AM)- and ectomycorrhizal (ECM)-associated trees would exhibit differences in root-derived C accumulation in the soil, and that much of this C would be transferred into mineral-associated pools. We installed δ 13 C-enriched ingrowth cores across mycorrhizal gradients in six Eastern U.S. forests (n = 54 plots). Overall, root-derived C was 54% greater in AM versus ECM-dominated plots. This resulted in nearly twice as much root-derived C in putatively slow-cycling mineral-associated pools in AM compared to ECM plots. Finally, given that our estimates of root-derived inputs were often equal to or greater than leaf litter inputs, our results suggest that variation in root-derived soil C accumulation due to tree mycorrhizal dominance may be a key control of soil C dynamics in forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The forest, the cicadas and the holey fluxes: Periodical cicada impacts on soil respiration depends on tree mycorrhizal type

Abstract The emergence of billions of periodical cicadas affects plant and animal communities profoundly, yet little is known about cicada impacts on soil carbon fluxes. We investigated the effects of Brood X cicadas (Magicicada septendecim, M. cassinii and M. septendeculain) on soil CO 2 fluxes (R S ) in three Indiana forests. We hypothesizedR S would be sensitive to emergence hole density, with the greatest effects occurring in soils with the lowest ambient fluxes. In support of our hypothesis,R S increased with increasing hole density and greater effects were observed near AM‐associating trees (which expressed lower ambient fluxes) than near EcM‐associating trees. Additionally,R S from emergence holes increased the temperature sensitivity (Q 10 ) ofR S by 13%, elevating the Q 10 of ecosystem respiration. Brood X cicadas increased annualR S by ca. 2.5%, translating to an additional 717 Gg of CO 2 across forested areas. As such, periodical cicadas can have substantial effects on soil processes and biogeochemistry.

Environmental Sciences & Ecology↗
DOE OSTI2014

Regional analysis of drought and heat impacts on forests: current and future science directions

Abstract Accurate assessments of forest response to current and future climate and human actions are needed at regional scales. Predicting future impacts on forests will require improved analysis of species‐level adaptation, resilience, and vulnerability to mortality. Land system models can be enhanced by creating trait‐based groupings of species that better represent climate sensitivity, such as risk of hydraulic failure from drought. This emphasizes the need for more coordinated in situ and remote sensing observations to track changes in ecosystem function, and to improve model inputs, spatio‐temporal diagnosis, and predictions of future conditions, including implications of actions to mitigate climate change.

Law, Beverly E.↗
DOE OSTI2014

Litter type control on soil C and N stabilization dynamics in a temperate forest

Abstract While plant litters are the main source of soil organic matter ( SOM ) in forests, the controllers and pathways to stable SOM formation remain unclear. Here, we address how litter type ( 13 C/ 15 N‐labeled needles vs. fine roots) and placement‐depth (O vs. A horizon) affect in situ C and N dynamics in a temperate forest soil after 5 years. Litter type rather than placement‐depth controlled soil C and N retention after 5 years in situ , with belowground fine root inputs greatly enhancing soil C (x1.4) and N (x1.2) retention compared with aboveground needles. While the proportions of added needle and fine root‐derived C and N recovered into stable SOM fractions were similar, they followed different transformation pathways into stable SOM fractions: fine root transfer was slower than for needles, but proportionally more of the remaining needle‐derived C and N was transferred into stable SOM fractions. The stoichiometry of litter‐derived C vs. N within individual SOM fractions revealed the presence at least two pools of different turnover times (per SOM fraction) and emphasized the role of N‐rich compounds for long‐term persistence. Finally, a regression approach suggested that models may underestimate soil C retention from litter with fast decomposition rates.

Hatton, Pierre‐Joseph↗
DOE OSTI2016

Response to CO 2 enrichment of understory vegetation in the shade of forests

Abstract Responses of forest ecosystems to increased atmospheric CO 2 concentration have been studied in few free‐air CO 2 enrichment ( FACE ) experiments during last two decades. Most studies focused principally on the overstory trees with little attention given to understory vegetation. Despite its small contribution to total productivity of an ecosystem, understory vegetation plays an important role in predicting successional dynamics and future plant community composition. Thus, the response of understory vegetation in Pinus taeda plantation at the Duke Forest FACE site after 15–17 years of exposure to elevated CO 2 , 6–13 of which with nitrogen (N) amendment, was examined. Aboveground biomass and density of the understory decreased across all treatments with increasing overstory leaf area index ( LAI ). However, the CO 2 and N treatments had no effect on aboveground biomass, tree density, community composition, and the fraction of shade‐tolerant species. The increases of overstory LAI (~28%) under elevated CO 2 resulted in a reduction of light available to the understory (~18%) sufficient to nullify the expected growth‐enhancing effect of elevated CO 2 on understory vegetation.

Kim, Dohyoung↗
DOE OSTI2016

Ecosystem fluxes of hydrogen in a mid-latitude forest driven by soil microorganisms and plants

Molecular hydrogen (H 2 ) is an atmospheric trace gas with a large microbe-mediated soil sink, yet cycling of this compound throughout ecosystems is poorly understood. Measurements of the sources and sinks of H 2 in various ecosystems are sparse, resulting in large uncertainties in the global H 2 budget. Constraining the H 2 cycle is critical to understanding its role in atmospheric chemistry and climate. We measured H 2 fluxes at high frequency in a temperate mixed deciduous forest for 15 months using a tower-based flux-gradient approach to determine both the soil-atmosphere and the net ecosystem flux of H 2 . We also found that Harvard Forest is a net H 2 sink (-1.4 ± 1.1 kg H 2 ha -1 ) with soils as the dominant H 2 sink (-2.0 ± 1.0 kg H 2 ha -1 ) and aboveground canopy emissions as the dominant H 2 source (+0.6 ± 0.8 kg H 2 ha -1 ). Aboveground emissions of H 2 were an unexpected and substantial component of the ecosystem H 2 flux, reducing net ecosystem uptake by 30% of that calculated from soil uptake alone. Soil uptake was highly seasonal (July maximum, February minimum), positively correlated with soil temperature and negatively correlated with environmental variables relevant to diffusion into soils (i.e., soil moisture, snow depth, snow density). Soil microbial H 2 uptake was correlated with rhizosphere respiration rates (r = 0.8, P < 0.001), and H 2 metabolism yielded up to 2% of the energy gleaned by microbes from carbon substrate respiration. Here, we elucidate key processes controlling the biosphere–atmosphere exchange of H 2 and raise new questions regarding the role of aboveground biomass as a source of atmospheric H 2 and mechanisms linking soil H 2 and carbon cycling. Our results should be incorporated into modeling efforts to predict the response of the H 2 soil sink to changes in anthropogenic H 2 emissions and shifting soil conditions with climate and land-use change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Living on the edge: A continental-scale assessment of forest vulnerability to drought

Globally, forests are facing an increasing risk of mass tree mortality events associated with extreme droughts and higher temperatures. Hydraulic dysfunction is considered a key mechanism of drought-triggered dieback. By leveraging the climate breadth of the Australian landscape and a national network of research sites (Terrestrial Ecosystem Research Network), we conducted a continental-scale study of physiological and hydraulic traits of 33 native tree species from contrasting environments to disentangle the complexities of plant response to drought across communities. Here, we found strong relationships between key plant hydraulic traits and site aridity. Leaf turgor loss point and xylem embolism resistance were correlated with minimum water potential experienced by each species. Across the data set, there was a strong coordination between hydraulic traits, including those linked to hydraulic safety, stomatal regulation and the cost of carbon investment into woody tissue. These results illustrate that aridity has acted as a strong selective pressure, shaping hydraulic traits of tree species across the Australian landscape. Hydraulic safety margins were constrained across sites, with species from wetter sites tending to have smaller safety margin compared with species at drier sites, suggesting trees are operating close to their hydraulic thresholds and forest biomes across the spectrum may be susceptible to shifts in climate that result in the intensification of drought.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Aggregated and complementary: symmetric proliferation, overyielding, and mass effects explain fine‐root biomass in soil patches in a diverse temperate deciduous forest landscape

Summary Few studies describe root distributions at the species level in diverse forests, although belowground species interactions and traits are often assumed to affect fine‐root biomass ( FRB ). We used molecular barcoding to study how FRB of trees relates to soil characteristics, species identity, root diversity, and root traits, and how these relationships are affected by proximity to ecotones in a temperate forest landscape. We found that soil patch root biomass increased in response to soil resources across all species, and there was little belowground vertical or horizontal spatial segregation among species. Root traits and species relative abundance did not explain significant variation in FRB after correcting for soil fertility. A positive relationship between phylogenetic diversity and FRB indicated significant belowground overyielding attributable to local root diversity. Finally, variation in FRB explained by soil fertility and diversity was reduced near ecotones, but only because of a reduction in biomass in periodically anoxic areas. These results suggest that symmetric responses to soil properties are coupled with complementary species traits and interactions to explain variation in FRB among soil patches. In addition, landscape‐level dispersal among habitats and across ecotones helps explain variation in the strength of these relationships in complex landscapes.

Valverde‐Barrantes, Oscar J.↗
DOE OSTI2015

Phosphorus cycling in deciduous forest soil differs between stands dominated by ecto‐ and arbuscular mycorrhizal trees

Summary Although much is known about how trees and their associated microbes influence nitrogen cycling in temperate forest soils, less is known about biotic controls over phosphorus (P) cycling. Given that mycorrhizal fungi are instrumental for P acquisition and that the two dominant associations – arbuscular mycorrhizal ( AM ) and ectomycorrhizal ( ECM ) fungi – possess different strategies for acquiring P, we hypothesized that P cycling would differ in stands dominated by trees associated with AM vs ECM fungi. We quantified soil solution P, microbial biomass P, and sequentially extracted inorganic and organic P pools from May to November in plots dominated by trees forming either AM or ECM associations in south‐central Indiana, USA . Overall, fungal communities in AM and ECM plots were functionally different and soils exhibited fundamental differences in P cycling. Organic forms of P were more available in ECM plots than in AM plots. Yet inorganic P decreased and organic P accumulated over the growing season in both ECM and AM plots, resulting in increasingly P‐limited microbial biomass. Collectively, our results suggest that P cycling in hardwood forests is strongly influenced by biotic processes in soil and that these are driven by plant‐associated fungal communities.

Rosling, Anna↗
DOE OSTI2021

Beyond leaf habit: generalities in plant function across 97 tropical dry forest tree species

Leaf habit has been hypothesized to define a linkage between the slow-fast plant economic spectrum and the drought resistance-avoidance trade-off in tropical forests (‘slow-safe vs fast-risky’). However, variation in hydraulic traits as a function of leaf habit has rarely been explored for a large number of species. We sampled leaf and branch functional traits of 97 tropical dry forest tree species from four sites to investigate whether patterns of trait variation varied consistently in relation to leaf habit along the ‘slow-safe vs fast-risky’ trade-off. Leaf habit explained from 0% to 43.69% of individual trait variation. We found that evergreen and semi-deciduous species differed in their location along the multivariate trait ordination when compared to deciduous species. While deciduous species showed consistent trait values, evergreen species trait values varied as a function of the site. Last, trait values varied in relation to the proportion of deciduous species in the plant community. Here We found that leaf habit describes the strategies that define drought avoidance and plant economics in tropical trees. However, leaf habit alone does not explain patterns of trait variation, which suggests quantifying site-specific or species-specific uncertainty in trait variation as the way forward.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Wood‐density has no effect on stomatal control of leaf‐level water use efficiency in an Amazonian forest

Forest disturbances increase the proportion of fast-growing tree species compared to slow-growing ones. To understand their relative capacity for carbon uptake and their vulnerability to climate change, and to represent those differences in Earth system models, it is necessary to characterise the physiological differences in their leaf-level control of water use efficiency and carbon assimilation. We used wood density as a proxy for the fast-slow growth spectrum and tested the assumption that trees with a low wood density (LWD) have a lower water-use efficiency than trees with a high wood density (HWD). We selected 5 LWD tree species and 5 HWD tree species growing in the same location in an Amazonian tropical forest and measured in situ steady-state gas exchange on top-of-canopy leaves with parallel sampling and measurement of leaf mass area and leaf nitrogen content. We found that LWD species invested more nitrogen in photosynthetic capacity than HWD species, had higher photosynthetic rates and higher stomatal conductance. Furthermore, contrary to expectations, we showed that the stomatal control of the balance between transpiration and carbon assimilation was similar in LWD and HWD species and that they had the same dark respiration rates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Multiwalled Carbon Nanotube Forest Grown via Chemical Vapor Deposition from Iron Catalyst Nanoparticles, by XPS

Carbon nanotubes (CNTs) have unique chemical and physical properties. Herein, we report an XPS analysis of a forest of multiwalled CNTs using monochromatic Al Kα radiation. Survey scans show only one element: carbon. The carbon 1s peak is centered 284.5 eV. The C 1s envelope also shows the expected π → π* shake-up peak at ca. 291 eV. The valence band and carbon KVV Auger signals are presented. When patterned, the CNT forests can be used as a template for subsequent deposition of metal oxides to make thin layer chromatography plates.1-3

Jensen, David S.↗
DOE OSTI2016

SU-C-207B-05: Tissue Segmentation of Computed Tomography Images Using a Random Forest Algorithm: A Feasibility Study

Purpose: Develop an automated Random Forest algorithm for tissue segmentation of CT examinations. Methods: Seven materials were classified for segmentation: background, lung/internal gas, fat, muscle, solid organ parenchyma, blood/contrast, and bone using Matlab and the Trainable Weka Segmentation (TWS) plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance each evaluated over a pixel radius of 2n, (n = 0–4). Also noise reduction and edge preserving filters, Gaussian, bilateral, Kuwahara, and anisotropic diffusion, were evaluated. The algorithm used 200 trees with 2 features per node. A training data set was established using an anonymized patient’s (male, 20 yr, 72 kg) chest-abdomen-pelvis CT examination. To establish segmentation ground truth, the training data were manually segmented using Eclipse planning software, and an intra-observer reproducibility test was conducted. Six additional patient data sets were segmented based on classifier data generated from the training data. Accuracy of segmentation was determined by calculating the Dice similarity coefficient (DSC) between manual and auto segmented images. Results: The optimized autosegmentation algorithm resulted in 16 features calculated using maximum, mean, variance, and Gaussian blur filters with kernel radii of 1, 2, and 4 pixels, in addition to the original CT number, and Kuwahara filter (linear kernel of 19 pixels). Ground truth had a DSC of 0.94 (range: 0.90–0.99) for adult and 0.92 (range: 0.85–0.99) for pediatric data sets across all seven segmentation classes. The automated algorithm produced segmentation with an average DSC of 0.85 ± 0.04 (range: 0.81–1.00) for the adult patients, and 0.86 ± 0.03 (range: 0.80–0.99) for the pediatric patients. Conclusion: The TWS Random Forest auto-segmentation algorithm was optimized for CT environment, and able to segment seven material classes over a range of body habitus and CT protocol parameters with an average DSC of 0.86 ± 0.04 (range: 0.80–0.99).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Metagenomic Characterization of Soil Microbial Communities in the Luquillo Experimental Forest (Puerto Rico) and Implications for Nitrogen Cycling

The phylogenetic and functional diversities of microbial communities in tropical rainforests and how these differ from those of temperate communities remain poorly described but are directly related to the increased fluxes of greenhouse gases such as nitrous oxide (N 2 O) from the tropics. Toward closing these knowledge gaps, in this study we analyzed replicated shotgun metagenomes representing distinct life zones and an elevation gradient from four locations in the Luquillo Experimental Forest (LEF), Puerto Rico. These soils had a distinct microbial community composition and lower species diversity compared to those of temperate grasslands or agricultural soils. In contrast to the overall distinct community composition, the relative abundances and nucleotide sequences of N 2 O reductases (nosZ) were highly similar between tropical forest and temperate soils. However, respiratory NO reductase (norB) was 2-fold more abundant in the tropical soils, which might be relatable to their greater N 2 O emissions. Nitrogen fixation (nifH) also showed higher relative abundance in rainforest than in temperate soils, i.e., 20% versus 0.1 to 0.3% of bacterial genomes in each soil type harbored the gene, respectively. Finally, unlike temperate soils, LEF soils showed little stratification with depth in the first 0 to 30 cm, with ~45% of community composition differences explained solely by location. Collectively, these results advance our understanding of spatial diversity and metabolic repertoire of tropical rainforest soil communities and should facilitate future ecological studies of these ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Patchy burn severity explains heterogeneous soil viral and prokaryotic responses to fire in a mixed conifer forest

ABSTRACT Effects of fire on soil viruses and virus–host dynamics are largely unexplored, despite known microbial contributions to biogeochemical processes and ecosystem recovery. Here, we assessed how viral and prokaryotic communities responded to a prescribed burn in a mixed conifer forest. We sequenced 91 viral-size fraction metagenomes (viromes) and 115 16S rRNA gene amplicon libraries from 120 samples: four samples at five timepoints (two before fire and three after fire) at six sites (four treatment, two control). We hypothesized that compositional differences would be most significant between burned and unburned soils, but instead, plot location best distinguished viral communities, more than treatment (burned or not), depth (0–3 or 3–6 cm), or timepoint. For both viruses and prokaryotes, some burned communities resembled unburned controls, while others were significantly different, revealing heterogeneous responses to fire. These patterns were explained by burn severity, here defined by soil chemistry. Viral but not prokaryotic richness decreased significantly with burn severity, and low viromic DNA yields indicated substantial loss of viral biomass at higher severity. The relative abundances of Firmicutes, Actinobacteriota, and the viruses predicted to infect them increased significantly with burn severity, suggesting survival and viral infection of these fire-responsive and potentially spore-forming taxa. The degree of burn severity experienced by each patch of soil, rather than burn status alone, differed over mere meters in the same fire. Therefore, our analyses highlight the importance of high-resolution, paired biogeochemical data to explain soil community responses to fire. IMPORTANCE The impact of fire on the soil microbiome, particularly on understudied soil viral communities, warrants investigation, given known microbial contributions to biogeochemical processes and ecosystem recovery. Here, we collected 120 soil samples before and after a prescribed burn in a mixed conifer forest to assess the impacts of this disturbance on soil viral and prokaryotic communities. We show that simple categorical comparisons of burned and unburned areas were insufficient to reveal the underlying community response patterns. The patchy nature of the fire (indicated by soil chemistry data) led to significant changes in viral and prokaryotic community composition in areas of high burn severity, while communities that experienced lower burn severity were indistinguishable from those in unburned controls. Our results highlight the importance of considering highly resolved burn severity and biogeochemical measurements, even in nearby soils after the same fire, in order to understand soil microbial responses to prescribed burns.

Microbiology↗
DOE OSTI2025

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds—five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.

Arasteh, Kourosh [Lawrence Livermore National Labo↗
DOE OSTI2021

Development of a Random-Forest Cloud-Regime Classification Model Based on Surface Radiation and Cloud Products

Various methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each method has inherent advantages and disadvantages, none of these cloud-typing methods actually includes measurements of surface shortwave or longwave radiative fluxes. Here, a method that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random-forest machine-learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud-type accuracy of approximately 80% using the random-forest classifier reveals that the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud-type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert that the cloud-regime model has the capacity to successfully differentiate clouds with comparable cloud–radiative interactions. Therefore, we conclude that the model can provide useful cloud-property information for fundamental cloud studies, inform renewable energy studies, and be a tool for numerical model evaluation and parameterization improvement, among many other applications.

54 ENVIRONMENTAL SCIENCES↗