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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Chapter 14: Sustainable Biomass Conversion Process Assessment

Process intensification and integration for sustainable design play an essential role in addressing and alleviating severe sustainable development challenges. It is prudent to evaluate a process design in its early stage development to ensure the process can achieve sustainability goals. The primary goal of a sustainability evaluation is to assess the impact of systems on areas sought to be protected and maintained over time, such as human well-being and ecosystems (Schaubroeck, T. and Rugani, B. (2017). A revision of what life cycle sustainability assessment should entail: towards modeling the net impact on human well-being. Journal of Industrial Ecology 21 (6): 1464-1477). Parenthetically, sustainable process design to convert biomass to fuels and chemicals plays an integral part in the bioeconomy sustainability. It is recognized that the integration of sustainability in process design is core to the mission in developing renewable fuels and should be considered a necessary practice in biorefinery design. This study demonstrates the use of Gauging Reaction Effectiveness for ENvironmental Sustainability of Chemistries with a multi-Objective Process Evaluator (GREENSCOPE) as a tool to help track progress on process sustainability performance attributed to conversion improvement. With the incorporation of a wide range of indicators, GREENSCOPE captures the multi-dimensional aspect of the process design and operation. As opposed to conventional techno-economic analysis and life cycle assessment (in which the focuses are limited to economic and environmental aspects), GREENSCOPE enables the consideration of process intensification and integration for sustainable design. A high-octane gasoline production from woody biomass feedstock is used as a case study to demonstrate the approach.

biomass↗
DOE OSTI2021

Linking habitat suitability with a longleaf pine-hardwood model: Building a species-predictive fire-land management framework

Active management of fire-dependent ecosystems for specific species leads to complex tradeoffs, which affect conservation outcomes to other species. Therefore a multi-species evaluation of management actions is required. Habitat Suitability Models (HSMs) can help in predicting the likelihood of species occurrence using corresponding environmental variables and empirical relationships that link occurrence with specific environmental conditions. Incorporating multiple species into HSMs and relating them to habitat dynamics is crucial for ecosystems that require active management with prescribed fire. To address this issue, we developed multi-species HSM driven within an existing population model of the longleaf pine-hardwood ecosystem to assess the suitability of an ecosystem given different fire management strategies and environmental conditions. The population model used in this study provides spatial and temporal changes of longleaf pine-hardwood habitat structure in response to fire. These habitat values are used by the HSM to calculate habitat suitability for three threatened and endangered faunal species of this ecosystem, which all thrive with frequent fire, but have unique habitat requirements. Transient habitat conditions are traced to predict longleaf pine ecosystem trajectories under various management strategies, thereby evaluating current land management actions, such as thinning or prescribed fire frequencies. We tested a suite of environmental conditions to emphasize the sensitivity of the species to different fire management actions. The results of our modeling suggest that maximum suitable habitat for all three species can be achieved with fire frequency occurring at approximately once every three years. The modeling results support current management actions and provide a new habitat assessment tool that incorporates ecological factors for multiple species, thus providing for habitat optimization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Test of Functional Balance Theory for Wetland Biomass Allocation in a Global Change Experiment

Abstract Forecasts of root growth and carbon sequestration under global change are compromised by uncertainty in how plants will allocate biomass between above and belowground pools. Here, we develop a simple model to assess whether functional balance theory can explain a complex biomass allocation response observed in a brackish marsh under experimental warming and elevated CO 2 . Our model shows how treatment‐driven changes in nitrogen supply and demand can explain divergent observations of root growth (i.e., maximum responses under intermediate warming and elevated CO 2 ). The model also reveals a surprising interaction between warming and eutrophication, where enhanced N loading to coastal marshes may reduce adverse impacts of warming on root growth. Our findings provide a mechanistic basis for incorporating biomass allocation into forecast models of marsh evolution. They also provide a general example of using ecological theory to decompose complex net responses observed in multi‐factor global change experiments into constituent processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Expression of macromolecular organic nitrogen degrading enzymes identifies potential mediators of soil organic N availability to an annual grass

Abstract Nitrogen (N) is frequently limiting to plant growth, in part because most soil N is present as polymeric organic compounds that are not readily taken up by plants. Microbial depolymerization of these large macromolecular N-substrates gradually releases available inorganic N. While many studies have researched and modeled controls on soil organic matter formation and bulk N mineralization, the ecological—spatial, temporal and phylogenetic—patterns underlying organic N degradation remain unclear. We analyzed 48 time-resolved metatranscriptomes and quantified N-depolymerization gene expression to resolve differential expression by soil habitat and time in specific taxonomic groups and gene-based guilds. We observed much higher expression of extracellular serine-type proteases than other extracellular N-degrading enzymes, with protease expression of predatory bacteria declining with time and other taxonomic patterns driven by the presence (Gammaproteobacteria) or absence (Thermoproteota) of live roots and root detritus (Deltaproteobacteria and Fungi). The primary chitinase chit1 gene was more highly expressed by eukaryotes near root detritus, suggesting predation of fungi. In some lineages, increased gene expression over time suggests increased competitiveness with rhizosphere age (Chloroflexi). Phylotypes from some genera had protease expression patterns that could benefit plant N nutrition, for example, we identified a Janthinobacterium phylotype and two Burkholderiales that depolymerize organic N near young roots and a Rhizobacter with elevated protease levels near mature roots. These taxon-resolved gene expression results provide an ecological read-out of microbial interactions and controls on N dynamics in specific soil microhabitats and could be used to target potential plant N bioaugmentation strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Geographic patterns of genomic diversity and structure in the C4 grass Panicum hallii across its natural distribution

Abstract Geographic patterns of within-species genomic diversity are shaped by evolutionary processes, life history and historical and contemporary factors. New genomic approaches can be used to infer the influence of such factors on the current distribution of infraspecific lineages. In this study, we evaluated the genomic and morphological diversity as well as the genetic structure of the C4 grass Panicum hallii across its complex natural distribution in North America. We sampled extensively across the natural range of P. hallii in Mexico and the USA to generate double-digestion restriction-associated DNA (ddRAD) sequence data for 423 individuals from 118 localities. We used these individuals to study the divergence between the two varieties of P. hallii, P. hallii var. filipes and P. hallii var. hallii as well as the genetic diversity and structure within these groups. We also examined the possibility of admixture in the geographically sympatric zone shared by both varieties, and assessed distribution shifts related with past climatic fluctuations. There is strong genetic and morphological divergence between the varieties and consistent genetic structure defining seven genetic clusters that follow major ecoregions across the range. South Texas constitutes a hotspot of genetic diversity with the co-occurrence of all genetic clusters and admixture between the two varieties. It is likely a recolonization and convergence point of populations that previously diverged in isolation during fragmentation events following glaciation periods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The transition from resistance to acceptance: Managing a marine invasive species in a changing world

Abstract Marine invasive species can transform coastal ecosystems, yet mitigating their effects can be difficult, and even impractical. Often, marine invasive species are managed at poorly matched spatial scales, and at the same time, rates of spread and establishment are increasing under climate change and can outpace resources available for population suppression. These circumstances challenge traditional conservation goals of maintaining a historic environmental state, especially for a species like the European green crab ( Carcinus maenas ), a formidable invader with few examples of successful long‐term removal programs. A management paradigm where decision alternatives include resisting or accepting a new ecological trajectory may be needed. We apply mathematical concepts from decision theory to develop a quantitative framework for navigating management decisions in this new resist‐accept paradigm. We develop a model of European green crab growth, removal and colonization, and we find optimal levels of removal effort that minimize both ecological change and removal cost. We establish a benchmark of colonization pressure at which green crab density becomes decoupled from a decision maker's actions, such that population control can no longer shape the invasion trajectory. For informing the decision boundary between resistance and acceptance, our results highlight that a decision maker's understanding of how removal cost scales with removal effort is more important than understanding the density‐impact relationship. We show that assuming stationary system dynamics can result in sub‐optimal levels of species removal effort, highlighting the importance of developing anticipatory management strategies by accounting for non‐stationary dynamics. Policy implications . For marine invasive species that can disperse across long distances and recolonize rapidly after removal, the focus of conservation policy should shift away from understanding how to resist change to understanding when to stop resisting change. Navigating this decision problem involves trade‐offs among competing objectives, highlighting the need for structured approaches to elicit objective weights that reflect the values of the decision maker. For natural resource managers facing possible ecosystem transformation, this decision framework can enable proactive and strategic decisions made under uncertainty in a changing world.

Keller, Abigail G. [Department of Environment Scie↗
DOE OSTI2024

Variation in forest root image annotation by experts, novices, and AI

Abstract Background The manual study of root dynamics using images requires huge investments of time and resources and is prone to previously poorly quantified annotator bias. Artificial intelligence (AI) image-processing tools have been successful in overcoming limitations of manual annotation in homogeneous soils, but their efficiency and accuracy is yet to be widely tested on less homogenous, non-agricultural soil profiles, e.g., that of forests, from which data on root dynamics are key to understanding the carbon cycle. Here, we quantify variance in root length measured by human annotators with varying experience levels. We evaluate the application of a convolutional neural network (CNN) model, trained on a software accessible to researchers without a machine learning background, on a heterogeneous minirhizotron image dataset taken in a multispecies, mature, deciduous temperate forest. Results Less experienced annotators consistently identified more root length than experienced annotators. Root length annotation also varied between experienced annotators. The CNN root length results were neither precise nor accurate, taking ~ 10% of the time but significantly overestimating root length compared to expert manual annotation ( p = 0.01). The CNN net root length change results were closer to manual ( p = 0.08) but there remained substantial variation. Conclusions Manual root length annotation is contingent on the individual annotator. The only accessible CNN model cannot yet produce root data of sufficient accuracy and precision for ecological applications when applied to a complex, heterogeneous forest image dataset. A continuing evaluation and development of accessible CNNs for natural ecosystems is required.

Handy, Grace↗
NASA NTRS2000

BOREAS TE-21 SSA Site Characteristics Data

The Boreal Ecosystem-Atmospheric Study (BOREAS) TE-20 (Terrestrial Ecology) team collected several data sets for use in developing and testing models of forest ecosystem dynamics. This data set contains measurements of site characteristics conducted in the Southern Study Area (SSA) from 18 Jul 1994 to 30 Jul 1994. The data are stored in CSV files. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Knox, Robert↗
NASA NTRS2017

A Probabilistic Method of Assessing Carbon Accumulation Rate at Imnavait Creek Peatland, Arctic Long Term Ecological Research Station, Alaska

Arctic peatlands are an important part of the global carbon cycle, accumulating atmospheric carbon as organic matter since the Late glacial. Current methods for understanding the changing efficiency of the peatland carbon sink rely on peatlands with an undisturbed stratigraphy. Here we present a method of estimating primary carbon accumulation rate from a site where permafrost processes have either vertically or horizontally translocated nearby carbon-rich sediment out of stratigraphic order. Briefly, our new algorithm estimates the probability of the age of deposition of a random increment of sediment in the core. The method assumes that if sediment age is measured at even depth increments, dates are more likely to occur during intervals of higher accumulation rate and vice versa. Multiplying estimated sedimentation rate by measured carbon density yields carbon accumulation rate. We perform this analysis at the Imnavait Creek Peatland, near the Arctic Long Term Ecological Research network site at Toolik Lake, Alaska. Using classical radiocarbon age modeling, we find unreasonably high rates of carbon accumulation at various Holocene intervals. With our new method, we find accumulation rate changes that are in improved agreement within the context of other sites throughout Alaska and the rest of the Circum-Arctic region.

carbon accumulation;Imnavait;peatlands;permafrost;↗
NASA NTRS2024

Development of A Composite Drought Indicator for Operational Drought Monitoring in the MENA Region

This paper presents the composite drought indicator (CDI) that Jordanian, Lebanese, Moroccan, and Tunisian government agencies now produce monthly to support operational drought management decision making, and it describes their iterative co-development processes. The CDI is primarily intended to monitor agricultural and ecological drought on a seasonal time scale. It uses remote sensing and modelled data inputs, and it reflects anomalies in precipitation, vegetation, soil moisture, and evapotranspiration. Following quantitative and qualitative validation assessments, engagements with policymakers, and consideration of agencies’ technical and institutional capabilities and constraints, we made changes to CDI input data, modelling procedures, and integration to tailor the system for each national context. We summarize validation results, drought modelling challenges and how we overcame them through CDI improvements, and we describe the monthly CDI production process and outputs. Finally, we synthesize procedural and technical aspects of CDI development and reflect on the constraints we faced as well as trade-offs made to optimize the CDI for operational monitoring to support policy decision-making—including aspects of salience, credibility, and legitimacy—within each national context.

Karim Bergaoui↗
NASA NTRS2020

Neural Network Reflectance Prediction Model for Both Open Ocean and Coastal Waters

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

radiative transfer↗
DOE OSTI2022

Non-growing Season Plant Nutrient Uptake Controls Arctic Tundra Vegetation Composition: Modeling Archive

This Modeling Archive is in support of a NGEE-Arctic publication: Riley et al. (2021) “Non-growing season plant nutrient uptake controls Arctic tundra vegetation composition under future climate” (Riley et al., 2021). The dataset contains “ecosys” model outputs reported in Riley et al. (2021) that examines the role of non-growing season (NGS) nutrient dynamics on 21st century vegetation composition. The study highlighted the importance of these nutrient dynamics on the emergent plant functional type distributions, focusing on their role in shrub expansion. The study showed that ignoring NGS nutrient dynamics led to a dramatic under-prediction of shrub expansion by year 2100, motivating the need to include these processes in Earth System Models. Included are modeled net primary production (NPP) and nitrogen (N) uptake for two scenarios: (1) baseline and (2) no NGS N uptake. The output is provided (1) at 25 km resolution across the North American tundra; (2) for years 2012 through 2100; and (3) PFT-specific. Dataset contains 20 NetCDF files in a zip file and one pdf.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Leptothrix ochracea genomes reveal potential for mixotrophic growth on Fe(II) and organic carbon

ABSTRACT Leptothrix ochracea creates distinctive iron-mineralized mats that carpet streams and wetlands. Easily recognized by its iron-mineralized sheaths, L. ochracea was one of the first microorganisms described in the 1800s. Yet it has never been isolated and does not have a complete genome sequence available, so key questions about its physiology remain unresolved. It is debated whether iron oxidation can be used for energy or growth and if L. ochracea is an autotroph, heterotroph, or mixotroph. To address these issues, we sampled L. ochracea -rich mats from three of its typical environments (a stream, wetlands, and a drainage channel) and reconstructed nine high-quality genomes of L. ochracea from metagenomes. These genomes contain iron oxidase genes cyc2 and mtoA, showing that L. ochracea has the potential to conserve energy from iron oxidation. Sox genes confer potential to oxidize sulfur for energy. There are genes for both carbon fixation (RuBisCO) and utilization of sugars and organic acids (acetate, lactate, and formate). In silico stoichiometric metabolic models further demonstrated the potential for growth using sugars and organic acids. Metatranscriptomes showed a high expression of genes for iron oxidation; aerobic respiration; and utilization of lactate, acetate, and sugars, as well as RuBisCO, supporting mixotrophic growth in the environment. In summary, our results suggest that L. ochracea has substantial metabolic flexibility. It is adapted to iron-rich, organic carbon-containing wetland niches, where it can thrive as a mixotrophic iron oxidizer by utilizing both iron oxidation and organics for energy generation and both inorganic and organic carbon for cell and sheath production. IMPORTANCE Winogradsky's observations of L. ochracea led him to propose autotrophic iron oxidation as a new microbial metabolism, following his work on autotrophic sulfur-oxidizers. While much culture-based research has ensued, isolation proved elusive, so most work on L. ochracea has been based in the environment and in microcosms. Meanwhile, the autotrophic Gallionella became the model for freshwater microbial iron oxidation, while heterotrophic and mixotrophic iron oxidation is not well-studied. Ecological studies have shown that Leptothrix overtakes Gallionella when dissolved organic carbon content increases, demonstrating distinct niches. This study presents the first near-complete genomes of L. ochracea , which share some features with autotrophic iron oxidizers, while also incorporating heterotrophic metabolisms. These genome, metabolic modeling, and transcriptome results give us a detailed metabolic picture of how the organism may combine lithoautotrophy with organoheterotrophy to promote Fe oxidation and C cycling and drive many biogeochemical processes resulting from microbial growth and iron oxyhydroxide formation in wetlands.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Impact of Changing Winds on the Mauna Loa CO 2 Seasonal Cycle in Relation to the Pacific Decadal Oscillation

Abstract Long‐term measurements at the Mauna Loa Observatory (MLO) show that the CO 2 seasonal cycle amplitude (SCA) increased from 1959 to 2019 at an overall rate of 0.22 0.034 ppm decade −1 while also varying on interannual to decadal time scales. These SCA changes are a signature of changes in land ecological CO 2 fluxes as well as shifting winds. Simulations with the TM3 tracer transport model and CO 2 fluxes from the Jena CarboScope CO 2 Inversion suggest that shifting winds alone have contributed to a decrease in SCA of −0.10 0.022 ppm decade −1 from 1959 to 2019, partly offsetting the observed long‐term SCA increase associated with enhanced ecosystem net primary production. According to these simulations and MIROC‐ACTM simulations, the shorter‐term variability of MLO SCA is nearly equally driven by varying ecological CO 2 fluxes (49%) and varying winds (51%). We also show that the MLO SCA is strongly correlated with the Pacific Decadal Oscillation (PDO) due to varying winds, as well as with a closely related wind index (U‐PDO). Since 1980, 44% of the wind‐driven SCA decrease has been tied to a secular trend in the U‐PDO, which is associated with a progressive weakening of westerly winds at 700 mbar over the central Pacific from 20°N to 40°N. Similar impacts of varying winds on the SCA are seen in simulations at other low‐latitude Pacific stations, illustrating the difficulty of constraining trend and variability of land CO 2 fluxes using observations from low latitudes due to the complexity of circulation changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Fungal Spore Seasons Advanced Across the US Over Two Decades of Climate Change

Abstract Phenological shifts due to climate change have been extensively studied in plants and animals. Yet, the responses of fungal spores—organisms important to ecosystems and major airborne allergens—remain understudied. This knowledge gap limits our understanding of their ecological and public health implications. To address this, we analyzed a long‐term (2003–2022), large‐scale (the continental US) data set of airborne fungal spores collected by the US National Allergy Bureau. We first pre‐processed the spore data by gap‐filling and smoothing. Afterward, we extracted 10 metrics describing the phenology (e.g., start and end of season) and intensity (e.g., peak concentration and integral) of fungal spore seasons. These metrics were derived using two complementary but not mutually exclusive approaches—ecological and public health approaches, defined as percentiles of total spore concentration and allergenic thresholds of spore concentration, respectively. Using linear mixed‐effects models, we quantified annual shifts in these metrics across the continental US. We revealed a significant advancement in the onset of the spore seasons defined in both ecological (11 days, 95% confidence interval: 0.4–23 days) and public health (22 days, 6–38 days) approaches over two decades. Meanwhile, total spore concentrations in an annual cycle and in a spore allergy season tended to decrease over time. The earlier start of the spore season was significantly correlated with climatic variables, such as warmer temperatures and altered precipitations. Overall, our findings suggest possible climate‐driven advanced fungal spore seasons, highlighting the importance of climate change mitigation and adaptation in public health decision‐making.

Environmental Sciences & Ecology↗
NASA NTRS1982

Ecological optimality in water-limited natural soil-vegetation systems. I - Theory and hypothesis

The solution space of an approximate statistical-dynamic model of the average annual water balance is explored with respect to the hydrologic parameters of both soil and vegetation. Within the accuracy of this model it is shown that water-limited natural vegetation systems are in stable equilibrium with their climatic and pedologic environments when the canopy density and species act to minimize average water demand stress. Theory shows a climatic limit to this equilibrium above which it is hypothesized that ecological pressure is toward maximization of biomass productivity. It is further hypothesized that natural soil-vegetation systems will develop gradually and synergistically, through vegetation-induced changes in soil structure, toward a set of hydraulic soil properties for which the minimum stress canopy density of a given species is maximum in a given climate. Using these hypotheses, only the soil effective porosity need be known to determine the optimum soil and vegetation parameters in a given climate.

Eagleson, P. S.↗
NASA NTRS1996

Considerations in miniaturizing simplified agro-ecosystems for advanced life support

Miniaturizing the Earth's biogeochemical cycles to support human life during future space missions is the goal of the NASA research and engineering program in advanced life support. Mission requirements to reduce mass, volume, and power have focused efforts on (1) a maximally simplified agro-ecosystem of humans, food crops, and microbes; and, (2) a design for optimized productivity of food crops with high light levels over long days, with hydroponics, with elevated carbon dioxide and other controlled environmental factors, as well as with genetic selection for desirable crop properties. Mathematical modeling contributes to the goals by establishing trade-offs, by analyzing the growth and development of experimental crops, and by pointing to the possibilities of directed phasic control using modified field crop models to increase the harvest index.

Non-NASA Center↗