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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Climate-Driven Legacies in Simulated Microbial Communities Alter Litter Decomposition Rates

The mechanisms underlying diversity-functioning relationships have been a consistent area of inquiry in biogeochemistry since the 1950s. Though these mechanisms remain unresolved in soil microbiomes, many approaches at varying scales have pointed to the same notion—composition matters. Confronting the methodological challenge arising from the complexity of microbiomes, this study used the model DEMENTpy, a trait-based modeling framework, to explore trait-based drivers of microbiome-dependent litter decomposition. We parameterized DEMENTpy for five sites along a climate gradient in Southern California, United States, and conducted reciprocal transplant simulations analogous to a prior empirical study. The simulations demonstrated climate-dependent legacy effects of microbial communities on plant litter decomposition across the gradient. This result is consistent with the previous empirical study across the same gradient. An analysis of community-level traits further suggests that a 3-way tradeoff among resource acquisition, stress tolerance, and yield strategies influences community assembly. Simulated litter decomposition was predictable with two community traits (indicative of two of the three strategies) plus local environment, regardless of the system state (transient vs. equilibrium). Although more empirical confirmation is still needed, community traits plus local environmental factors (e.g., environment and litter chemistry) may robustly predict litter decomposition across spatial-temporal scales. In conclusion, this study offers a potential trait-based explanation for climate-dependent community effects on litter decomposition with implications for improved understanding of whole-ecosystem functioning across scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean ( Glycine max ) nested association mapping population

We report photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500–2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R 2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data for Impact of Vertical and Seasonal Variation in Leaf Traits on Simulating Soybean Canopy Photosynthesis via 1D and 3D Modeling

Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax25), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax25 profile achieved comparable performance to the scheme with the measured Vcmax25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.

Photosynthesis↗
DOE OSTI2021

Disentangling the Effects of Vapor Pressure Deficit and Soil Water Availability on Canopy Conductance in a Seasonal Tropical Forest During the 2015 El Niño Drought

In land models used in Earth system models, vegetation water stress is heuristically related to soil water matric potential, and root water uptake in soil layers is simply proportional to root density to meet the transpiration demand. The efficiency of roots utilizing deeper soil water under water stress cannot be well represented, but it is of critical importance for transpiration regulation and carbon flux. To improve modeling of tropical forest response to drought, a plant hydrodynamic scheme (HYDRO), adapted from a trait-based model in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), has been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to represent the hydraulic dynamics and root water uptake strategy. The adapted model conforms to the big-leaf photosynthesis model in ELM. Vegetation water stress in the model is a function of prognostic leaf water potential and is used to regulate stomatal conductance. Point simulations were performed at two tropical forest sites in the Amazon and Panama. Model sensitivity to the numerical solver, plant hydraulic traits, soil properties, and atmospheric forcing has been evaluated. Our results showed that, even though water stress factors calculated at our study sites using HYDRO are comparable to that simulated by the heuristic water stress model, soil moisture prediction is improved with soil water redistributions through roots. Simulated results using HYDRO is not as sensitive to plant hydraulic traits measured for different tree species as to the soil properties and atmospheric forcing. Simplified numerical solver may cause inaccuracy as to where roots uptake water. This study suggests that plant hydrodynamic representation is critical for improved simulations of soil moisture under water stresses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Plant litter traits control microbial decomposition and drive soil carbon stabilization

Efforts to manage soils for carbon (C) sequestration remain limited by our understanding of how differences in plant traits and microbial traits mechanistically drive soil organic C (SOC) storage. Addressing this uncertainty is particularly critical in bioenergy agriculture, due to its potential to enhance soil C and provide a C neutral fuel. As such, we examined differences between two contrasting feedstocks, Zea mays (corn) and Miscanthus x giganteus (miscanthus), in the ability of their litter to form new chemically resistant particulate SOC vs. physically protected mineral associated SOC and used this data to improve the parameterization of a microbial SOC model. We tested a hypothesized conceptual model whereby easy to decompose corn litters drive greater microbial carbon use efficiency (CUE) and the formation of more mineral associated SOC over particulate SOC than more complex miscanthus litters. To do this, we performed a soil microcosm experiment where we added 13 C enriched aboveground and belowground litters to soils and traced the fate of the 13 C into microbial respiration and SOC pools. We found that corn litters promoted higher microbial CUE (0.37) than miscanthus litters (0.24). In turn, corn litter formed approximately 50% more mineral associated SOC than miscanthus litters. Similarly, structurally complex root litters promoted a lower CUE and formed less mineral associated SOC than leaf and shoot litters for both crops. When we used our data to parameterize the SOC model, we found that modelling microbial trait differences uniquely allowed the model to capture the fate of litter C in SOC. Collectively, we found a robust link between litter quality, microbial efficiency, and the formation of SOC. This link bridges the empirical uncertainty in how different crops can form new soil C and provides an empirical basis for modelling SOC transformations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

BrzostekEcologyLab/CORPSE-soil-jars

Efforts to manage soils for carbon (C) sequestration remain limited by our understanding of how differences in plant traits and microbial traits mechanistically drive soil organic C (SOC) storage. Addressing this uncertainty is particularly critical in bioenergy agriculture, due to its potential to enhance soil C and provide a C neutral fuel. As such, we examined differences between two contrasting feedstocks, Zea mays (corn) and Miscanthus x giganteus (miscanthus), in the ability of their litter to form new chemically resistant particulate SOC vs. physically protected mineral associated SOC and used this data to improve the parameterization of a microbial SOC model. We tested a hypothesized conceptual model whereby easy to decompose corn litters drive greater microbial carbon use efficiency (CUE) and the formation of more mineral associated SOC over particulate SOC than more complex miscanthus litters. To do this, we performed a soil microcosm experiment where we added 13C enriched aboveground and belowground litters to soils and traced the fate of the 13C into microbial respiration and SOC pools. We found that corn litters promoted higher microbial CUE (0.37) than miscanthus litters (0.24). In turn, corn litter formed approximately 50% more mineral associated SOC than miscanthus litters. Similarly, structurally complex root litters promoted a lower CUE and formed less mineral associated SOC than leaf and shoot litters for both crops. When we used our data to parameterize the SOC model, we found that modeling microbial trait differences uniquely allowed the model to capture the fate of litter C in SOC. Collectively, we found a robust link between litter quality, microbial efficiency, and the formation of SOC. This link bridges the empirical uncertainty in how different crops can form new soil C and provides an empirical basis for modeling SOC transformations.

Ridgeway, Joanna↗
NASA NTRS2017

Inter-Model Warming Projection Spread: Inherited Traits from Control Climate Diversity

Since Chaney’s report, the range of global warming projections in response to a doubling of CO2—from 1.5 °C to 4.5 °C or greater—remains largely unscathed by the onslaught of new scientific insights. Conventional thinking regards inter-model differences in climate feedbacks as the sole cause of the warming projection spread (WPS). Our findings shed new light on this issue indicating that climate feedbacks inherit diversity from the model control climate, besides the models’ intrinsic climate feedback diversity that is independent of the control climate state. Regulated by the control climate ice coverage, models with greater (lesser) ice coverage generally possess a colder (warmer) and drier (moister) climate, exhibit a stronger (weaker) ice-albedo feedback, and experience greater (weaker) warming. The water vapor feedback also inherits diversity from the control climate but in an opposite way: a colder (warmer) climate generally possesses a weaker (stronger) water vapor feedback, yielding a weaker (stronger) warming. These inherited traits influence the warming response in opposing manners, resulting in a weaker correlation between the WPS and control climate diversity. Our study indicates that a better understanding of the diversity amongst climate model mean states may help to narrow down the range of global warming projections.

Climate feedbacks↗
DOE OSTI2023

Denitrification and the challenge of scaling microsite knowledge to the globe

Here, our knowledge of microbial processes—who is responsible for what, the rates at which they occur, and the substrates consumed and products produced—is imperfect for many if not most taxa, but even less is known about how microsite processes scale to the ecosystem and thence the globe. In both natural and managed environments, scaling links fundamental knowledge to application and also allows for global assessments of the importance of microbial processes. But rarely is scaling straightforward: More often than not, process rates in situ are distributed in a highly skewed fashion, under the influence of multiple interacting controls, and thus often difficult to sample, quantify, and predict. To date, quantitative models of many important processes fail to capture daily, seasonal, and annual fluxes with the precision needed to effect meaningful management outcomes. Nitrogen cycle processes are a case in point, and denitrification is a prime example. Statistical models based on machine learning can improve predictability and identify the best environmental predictors but are—by themselves—insufficient for revealing process-level knowledge gaps or predicting outcomes under novel environmental conditions. Hybrid models that incorporate well-calibrated process models as predictors for machine learning algorithms can provide both improved understanding and more reliable forecasts under environmental conditions not yet experienced. Incorporating trait-based models into such efforts promises to improve predictions and understanding still further, but much more development is needed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Macroevolutionary diversity of traits and genomes in the model yeast genus Saccharomyces

Species is the fundamental unit to quantify biodiversity. In recent years, the model yeast Saccharomyces cerevisiae has seen an increased number of studies related to its geographical distribution, population structure, and phenotypic diversity. However, seven additional species from the same genus have been less thoroughly studied, which has limited our understanding of the macroevolutionary events leading to the diversification of this genus over the last 20 million years. Here, we show the geographies, hosts, substrates, and phylogenetic relationships for approximately 1,800 Saccharomyces strains, covering the complete genus with unprecedented breadth and depth. We generated and analyzed complete genome sequences of 163 strains and phenotyped 128 phylogenetically diverse strains. This dataset provides insights about genetic and phenotypic diversity within and between species and populations, quantifies reticulation and incomplete lineage sorting, and demonstrates how gene flow and selection have affected traits, such as galactose metabolism. These findings elevate the genus Saccharomyces as a model to understand biodiversity and evolution in microbial eukaryotes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Drought legacies mediated by trait trade‐offs in soil microbiomes

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

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Whole plant traits of coastal dune vegetation and implications for interactions with dune dynamics

Abstract Coastal dunes are important protective features against sea level rise and coastal storms. Interactions between dune plant aboveground structures and sediment trapping that allow for dune building and maintenance are well established. More recently, studies documenting belowground biomass for promoting erosion resistance in dominant dune species have been conducted, yet a knowledge gap remains regarding species‐specific characterization of whole plants, specifically with respect to roots, rhizomes, and belowground stems. Our objective was to quantify above‐ and belowground traits of four dominant dune grasses to document the potential for species‐specific effects on dune growth, maintenance, and erosion resistance. We examined above‐ and belowground traits among four prominent dune grasses of the Atlantic and Gulf Coasts of North America: Ammophila breviligulata , Panicum amarum , Spartina patens , and Uniola paniculata . Whole plant samples of each species were collected from the foredune at the US Army Engineer Research and Development Center's Field Research Facility in Duck, North Carolina, USA, and quantified for several above‐ and belowground traits (e.g., stem height, rhizome number and length, root surface area by diameter class, root tensile strength, and mycorrhizal percent infection). Belowground factors known to impact important dune processes, such as rhizome length, mycorrhizal percent infection, and root traits, differed substantially among species. When visualized in multivariate space, all species significantly differed in suites of above‐ and belowground traits. When considering belowground only, Ammophila and Spartina were similar, despite differences in biomass allocation. Species separated along axes related to mycorrhizal association, biomass allocation, and root construction. The four co‐occurring dune grass species were dissimilar in suites of plant traits. Belowground trait differences were driven by those describing root construction, biomass allocation, and mycorrhizal infection. Dissimilarity in above‐ and belowground suites of traits may demonstrate different approaches for surviving the dune environment. Incorporating belowground traits into modeling will enhance predictions of dune response to climate change through interactions between vegetation and dune dynamics that facilitate coastal resistance and resilience.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Ecological and genomic variation in ectomycorrhizal fungal exploration types

Ectomycorrhizal fungi (EMF) produce mycelia with variable extension and complexity, which can be classified according to soil ‘exploration types’ (ETs). ETs have received attention as one of the few mycorrhizal trait frameworks, but without an empirical classification of ET functional diversity and environmental preferences, understanding and interpreting EMF biogeographic patterns has been difficult. We conducted a synthesis combining: comparative EMF genomics to describe functional divergence in decomposition and nutrient cycling genes across ETs; and EMF trait distribution modeling across continental Europe, pairing soil and root EMF surveys to establish biogeographic ET niche profiles. We demonstrate a signature of ETs encoded in EMF genomes, which is independent from phylogeny and linked to biomass production strategies. EMF ET relative abundances were separated by soil, root, and dominant tree leaf type habitats and exhibited unique correlations with forest biotic (e.g. plant productivity and plant pathogen densities) and abiotic (e.g. nitrogen deposition and soil pH) conditions. These findings support a theory that EMF niche partitioning can be partially explained by extraradical mycelial traits, with underlying variation in ET biogeography likely arising from distinct decomposition and nutrient cycling potentials. We also identify important limitations to this trait framework and provide a guided outlook for future research.

biogeography↗
DOE OSTI2024

Invasions eliminate the legacy effects of substrate history on microbial nitrogen cycling

Abstract Changes in substrate quality driven by climate, land use, or other forms of global change may represent a strong selective force on microbial communities. Invasion of new taxa into a community through dispersal, evolution, or recolonization could impact the outcome of this environmental selection. Here, we simulated substrate change with a trait‐based model of microbial litter decomposition (DEMENTpy) to assess the legacy effects of past substrate quality and the impact of selection by a new substrate on community decomposition activity. Simulations were run with different levels of invasion, including invasion from communities long‐adapted to the new substrate. Legacy effects were evident with substrate change for native communities differing in composition. Protein was the only substrate that exerted a strong enough selective force to affect community composition. Legacy effects disappeared when invaders came from substrates similar to the new substrate. Together, our simulations demonstrate that substrate quality changes associated with global change can lead to legacy effects on substrate degradation. In decomposing plant litter, such legacy effects can occur if substrate inputs shift to higher protein content and if invasion is low.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Salinity exposure affects lower-canopy specific leaf area of upland trees in a coastal deciduous forest

Sea level rise and increasing storm surges are likely to affect the canopy physiology, ecology, and structure of coastal forests, even well in advance of tree mortality. Laboratory and greenhouse studies have documented that saltwater exposure can trigger changes in leaf-level physiology and morphology, but few in situ studies have examined how tree-specific leaf area (SLA), the ratio of leaf area to mass and a crucial trait and model parameter, is affected by saline soils. We conducted an observational study of SLA in a mid-Atlantic (USA) coastal deciduous forest, taking advantage of a natural gradient in salinity along a tidal creek. Measured SLA of the 239 trees and seven species sampled ranged from Carya glabra (N = 6 trees, mean SLA = 277.9±36.3cm 2 /g) to Fagus grandifolia (N=60, 321.9±62.9cm 2 g); as expected, trees species and canopy position (sun versus shade) significantly affected SLA. For trees (N=100) directly exposed to the tidal creek, salinity was highly significant after accounting for species (P<0.001), with trees in the lower reaches of the creek having lower SLA. Leaf area index (LAI), computed from SLA and litter traps, ranged from 4.8 to 15.8 and was inversely related to salinity exposure; the spatial variability in leaf litter production contributed much more to LAI uncertainty than did SLA variability. These in situ results are correlative but consistent with the hypothesis, based on previous greenhouse studies, that the stress of chronic salinity exposure changes species’ leaf morphology. Our findings are useful for understanding the growing effects of saltwater intrusion into upland forests, as well as parameterizing and testing ecosystem-scale models simulating forest stressors and disturbances at the terrestrial-aquatic interface.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Stressors Reveal Ecosystems' Hidden Characteristics

Abstract Vegetation responds dynamically to local microclimates at both short and long time scales via mechanisms ranging from physiological behaviors, such as stomatal closure, to acclimation and adaptation. These responses influence the carbon, water, and energy cycles directly and are therefore crucial to understanding and predicting Earth system responses to a changing climate. Several recent studies have demonstrated that differences in microclimate can induce structural and functional acclimations, and potentially adaptations, within the same ecosystem. Such microclimate divergence can be caused by variability in slopes, disturbance history, or even localized resource availability. Ecosystem stressors such as low soil water availability, limited photoperiod, or high vapor pressure deficit have been shown to reveal the large impact of the subtle differences within these systems such as the number of sun versus shade leaves or differences in whole‐plant water acquisition and use. These findings highlight the linkages between plant canopy structure and ecosystem function, alongside the need for comprehensive analyses of vegetation within the broader context of its environment. This commentary addresses some of the key implications of ecosystem stress responses and accompanying acclimations across three ecosystem types for ecosystem ecology, plant physiology, ecohydrology and trait‐based modeling of vegetation‐climate dynamics.

Matheny, Ashley M.↗