An ecosystem resilience index that integrates measures of vegetation function, structure, and composition
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Abstract not provided.
Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ~5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. Further, we evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG imagery with a mean absolute error (MAE) <0.13, and 3) our PLSR models outperformed traditional, fully constrained least-squares (FCLS) linear mixture analysis and produced high-quality, spatially contiguous PFT FCover and PFT maps that captured vegetation spatial patterns with similar accuracy to those developed from UAS imagery. The developed PLSR models have the potential to be broadly applied for quantifying vegetation composition with AVIRIS-NG images to help monitor tundra vegetation dynamics and improve process-based modeling of tundra ecosystems.
Aryl diazonium electrografting is a powerful method for imparting molecular functionality onto various substrates by forming a stable carbon-surface covalent bond. While the high reactivity of the aryl radical intermediate makes this method fast and reliable, it can also lead to the formation of an insulating and disordered multilayer film. These thick films affect electrochemical performance, especially for semiconductor substrates used in photoelectrochemical applications. For this work we studied the effects of film thickness and composition by electrografting in situ-generated aminobenzene diazonium salts onto both n-type and p-type silicon electrodes at fixed potentials. Next, we attached ferrocene to the amine-terminated films and probed their (photo)electrochemical behavior. Cyclic voltammetry measurements showed decreased electrochemical reversibility with increasing diazonium film thickness; this reversibility was restored when ferrocene was incorporated throughout the film with a layer-by-layer deposition process. Finally, we compared the behavior of dark p-type electrodes to n-type photoelectrodes and observed differences in the electrochemical reversibility that we attribute to the change in potential drop across the two interfaces.
The invention relates to functionalized lignin, rubber compositions which contain functionalized lignin and to products which have at least one component comprised of such rubber composition.
Local lattice distortions (LLD) and structural stability of body-centered cubic (bcc) Nb–Ta–Ti–Hf high-entropy alloys (HEAs) are studied as functions of composition employing ab initio density-functional theory calculations, with specific focus on the role of the relative concentrations of group IV (Ti and Hf) versus group V (Nb and Ta) elements. Calculated results are presented as a function of composition x in Nb x Ta 0.25 Ti (0.75-x)/2 Hf (0.75-x)/2 alloys, for elastic moduli, phonon spectral functions, LLD and structural energy differences for the bcc and competing hexagonal close-packed (hcp) and ω phases. The results highlight the important role of group V elements and LLD in stabilizing the bcc structure. They further reveal how composition x can be tuned to alter both the magnitude of the LLD and structural energy differences. Specifically, the magnitude of the structural energy differences, and elastic and dynamic stability of the bcc phase, are enhanced with increasing x, while the LLD increase in magnitude as this concentration is decreased. The results also show evidence of correlated LLD at lower values of x, reflecting local structural distortions towards the ω phase, but not hcp. The degree of ω-collapse is nevertheless partial i.e., transformation towards this phase is not observed to be complete due to the presence of Ta and Nb. At lower values of x we further find an energy landscape characterized by multiple, nearly degenerate local energy minima for different values of the LLD.
During 1992, Magellan altimetry echoes were processed to yield scattering functions sigma(sub o)(phi) over near-nadir angles phi less than or equal to 10 deg from some 2000 Venus orbits. SAR image strips acquired simultaneously at oblique angles were also reduced to give scattering functions but over a few deg of incidence angle in the range 15 less than or equal to phi less than or equal to 45 deg. By sorting, the altimetry and SAR results can be combined to give composite scattering functions for much of Venus' surface. The data in hand should allow definition of such composite functions for 70-80 percent of Venus at resolutions of 20 km--approximately one million separate functions.
The hybrid stochastic deterministic transport code COMET, based on the incident response expansion theory, is used to model reactor cores with high fidelity and formidable computational speed. COMET models a reactor core using a library of incident flux response expansion coefficients that are pre computed for all the unique lattice cells (e.g., fuel assemblies, reflector blocks, etc.) in the core. In order to further improve its computational efficiency in pre-calculating the response library a new response function generation method is developed to compute the response functions for the composite coarse meshes made of a smaller set of unique lattices on the fly within the COMET's deterministic transport core sweep. The efficiency is achieved by eliminating a number of unique lattices that can be made up from the reduced set of unique meshes on the fly. The numerical process consists of the following steps. First, the boundary condition on composite coarse mesh boundaries is projected onto the expansion basis to compute the incident flux moments on external surfaces of all the basic (reduced set of unique) coarse meshes. Secondly, the deterministic sweeping solver in COMET is used to converge on the outgoing/incoming flux expansion moments crossing interfaces between the basic coarse meshes. Thirdly, the response functions for the composite coarse meshes are constructed as a superposition on the fly. The new response function generation method was tested on 88 composite coarse meshes consisting of CANDU fuel bundles and moderator blocks. It was found that response functions generated by the new method agree very well with those generated by direct Monte Carlo calculations. The average and maximum relative differences in the surface-to-surface response coefficients computed by the two methods are 0.10% and 0.20%, respectively. Similarly, the average and maximum relative differences in the response fission densities are 0.13% and 0.43%, respectively. These discrepancies are within one standard deviation of the stochastic uncertainties. The new method is five times faster than the original direct Monte Carlo method. The size of the response function library for the new method is five times smaller than that for the original method, leading to significantly less requirement for the computer hard drive space and memory. (authors)
As the temperature of glass melt increases, its structure approaches the state of a simple liquid while the configuration entropy approaches a maximum value. We describe this gradual change using a power law function of inverse temperature. The Adam-Gibbs model for glass viscosity as a function of temperature and glass composition augmented in this way is greatly simplified when applied to common glass families occupying moderate composition regions, such as float glass or nuclear waste glasses, on which properties can be approximated as linear functions of composition. The parsimonious model thus obtained is preferable for use in optimizing glass formulation and mathematical modeling of glass melting and forming. For multicomponent glasses with N viscosity-affecting components, the augmented Adam-Gibbs model requires 2N + 3 adjustable parameters. The model efficacy is demonstrated by fitting the model to a viscosity-temperature-composition dataset for low-activity nuclear waste glasses.
Highlights: • A functionally-graded NbC – white cast iron composite was centrifugally cast. • Primary NbC particle distribution follows Stokes' Law, with 30 vol% at the surface. • Presence of primary NbC particles refined eutectic M{sub 7}C{sub 3} morphology at the surface. • The surface layer was hard and wear resistant, while the interior was tougher. • NbC and M{sub 7}C{sub 3} carbides form independent eutectic structures within the interior. - Abstract: A functionally graded NbC – high chromium white cast iron composite was successfully fabricated by centrifugal casting for high wear applications. The migration of primary NbC particles to the outer periphery, as predicted by Stokes' law, resulted in an extremely hard outer layer at the surface of a softer bulk material. Microstructural characterisation showed that both the volume fraction and the morphology of the carbides varies along the thickness of the specimen. At the outer surface, the NbC was present as primary carbides while Chinese-script NbC was found in the bulk of the material. Hardness, sliding wear and three-point bending tests confirmed that the outer layer was harder and more wear resistant than the rest of the material, which was correspondingly tougher. The study of the fracture surface of the specimens showed that there is a strong bond between NbC and the matrix such that delamination between the reinforcements and the matrix did not occur during fracture. If Chinese-script NbC can be reduced or prevented in the bulk material, further improvements to toughness could be achieved.
Additive manufacturing (AM) techniques can efficiently and cost-effectively fabricate components with both geometrical and compositional complexities, where functionally-graded compositions in a component allow multiple service requirements to be simultaneously addressed, such as corrosion and high temperature structural requirements. High cost alloy compositions need only be deposited in local areas where they are needed. A multi-wire arc additive manufacturing (m-WAAM) system was developed to enable functionally-graded, additively manufactured complex structures. The m-WAAM system utilizes multiple filler wire alloys, allowing control of composition and composition gradients in the component during additive fabrication. Ultimately, this project successfully developed a high deposition rate, arc-based, additive manufacturing process with the truly novel capability of enabling spatial composition tailoring during the build of the component. System capabilities and process science will be studied in a follow-on LDRD starting next year.
The IsoGenie3 Project delivered new systems-level insights into carbon cycling in thawing permafrost landscapes, with an emphasis on methane and carbon dioxide emissions. From >200 samples from the site collected over a decade, co-analyzed for geochemistry and microbiology, the team recovered ~1,500 assembled microbial genomes and ~1,900 viral population genomes, revealing appreciable genetic novelty - from a new highly abundant bacterial phylum, to novel methane consumers and their activities, to rampant viral novelty. IsoGenie3 linked these organisms to carbon compound transformations (which define the cycling of organic matter in soils, and the loss of the greenhouse gases carbon dioxide and methane), and saw that the microbes at each stage of permafrost thaw had different genetic potential to degrade categories of compounds, expressed that genetic potential differently, and actually transformed carbon compounds into greenhouse gases in different ways. IsoGenie 3 identified that some of the thaw-stage differences were due to plant-microbiome relationships; the plant species across the thaw gradient contributed different carbon compounds into the soil, and hosted distinct microbiota (differing among parts of plants as well as species). Lastly, microbes in the saturated post-thaw conditions appeared likely to contribute to the mobilization and toxification of mercury released during thaw. In parallel with ongoing field sampling and analysis, hypotheses arising from field observations were tested via lab incubation experiments. When communities are taken out of their native habitats, they behave differently, and the team first rigorously quantified the magnitude of this effect on microbiome composition and functional capacity, organic matter composition, and gas production; overall the main system processes were maintained in the lab incubations under the conditions tested. Further, the microbial data could inform geochemical reaction network models of those processes. Then, the team ran experiments with additions of compounds, varying temperature, and “live” vs. “dead” peat (the latter having been gamma irradiated, with a few additional variants to control for methodological artifacts). From these, we (a) determined the importance of plant-derived soluble phenolic compounds in bogs’ extraordinary recalcitrance of organic matter, and carbon gas emissions skewed to carbon dioxide; (b) proposed an abiotic ‘tanning’ mechanism, which could contribute to Sphagnum’s inhibitory effect on anaerobic decomposition through alteration of N availability. IsoGenie3 illuminated longer-term and landscape-scale interactions of permafrost thaw and carbon cycling, advancing knowledge of the drivers of methane dynamics not only across in the permafrost-associated peatland (where hydrology and plant communities dictate microbiomes) but also their interconnected lakes (where sediment carbon quality and resident microbiota are determined by position within lake, and lake features). By leveraging observations of site methane dynamics extending well before this project, the team was able to construct a 44-year portrait of the interplay of permafrost thaw, hydrology, vegetation dynamics, and carbon gas emissions, and the doubling of the fully-thawed fens over this time. From the detailed study of this focal site, IsoGenie3 also aimed to improve model representation of these kinds of sites and processes. To improve predictions of methane transformations, we incorporated acetate and isotope dynamics into the ‘DNDC’ biogeochemistry model. In addition, recovered genomes were grouped into ‘functional groups’, i.e. the genomes that perform a specific function of interest, then used to parameterize maximum growth rate and optimum growth temperature (via signatures in their sequence composition) for the BioCrunch model. The BioCrunch model was then in turn used to test the impact of increasing functional resolution of the microbes, on the carbon gas emissions. Lastly for modeling, the ecosys model was parameterized from the microbial and other data, and used to evaluate drivers of e.g. change in methane emissions. Finally, this project also led to the development of a range of new methods and tools, a new metric of organic matter decomposability, as well as a graph-database solution to multidisciplinary data storage and querying. This project’s ongoing analyses at our focal site also contributed to broader advancements in understanding elements of genetic plasticity and methane metabolism, climate change microbiology and community assembly, global peatland geochemistry and Arctic lakes’ roles in climate feedbacks.
Abstract Vegetation distribution and composition are expected to change in northern high latitudes under rapid warming, which regulates ecosystem functions but remains challenging to predict. Vegetation change arises from the interplay of chronic climate trends such as warming and transient demographic processes of recruitment, growth, competition, and mortality. Most predictive models overlooked the role of demographic dynamics controlled by plant traits. Here, we simulate vegetation dynamics at the Kougarok Hillslope site in Alaska under historical and future climates using the E3SM Land Model coupled to the Functionally Assembled Terrestrial Simulator (ELM‐FATES). To evaluate the roles of plant traits, we parameterize the model with 5,265 trait configurations representing diverse physiological and demographic strategies. Results show current modeled biomass, composition, and productivity are most sensitive to traits controlling photosynthetic capacity, carbon allocation, allometry, and phenology. Among all trait configurations, ∼5% reproduce in situ biomass and plant functional type (PFT) composition measured in 2016, that are indistinguishable from these two observed ecosystem states. Notably, these same trait configurations produce diverging biomass, composition, and productivity under future climate, where the uncertainty attributable to traits is twice the change attributable to climate change. The variation of projected productivity arises from emerging PFT composition under novel climate regimes, primarily explained by traits controlling cold‐induced mortality, recruitment, and allometry. Our findings highlight the importance and uncertainty of demographic dynamics and its interaction with climate change in shaping Arctic vegetation change. Improved model predictions will likely benefit from explicit consideration of vegetation demography and better constraints of critical traits.
Understanding the diversity, composition, structure, function, and dynamics of human microbiomes in individual human hosts is crucial to reveal human-microbial interactions, especially for patients with microbially mediated disorders, but challenging due to the high diversity of the human microbiome. Here we have developed a functional gene-based microarray for profiling human microbiomes (HuMiChip) with 36,802 probes targeting 50,007 protein coding sequences for 139 key functional gene families. Computational evaluation suggested all probes included are highly specific to their target sequences. HuMiChip was used to analyze human oral and gut microbiomes, showing significantly different functional gene profiles between oral and gut microbiome. Obvious shifts of microbial functional structure and composition were observed for both patients with dental caries and periodontitis from moderate to advanced stages, suggesting a progressive change of microbial communities in response to the diseases. Consistent gene family profiles were observed by both HuMiChip and next generation sequencing technologies. Additionally, HuMiChip was able to detect gene families at as low as 0.001% relative abundance. The results indicate that the developed HuMiChip is a useful and effective tool for functional profiling of human microbiomes.
Abstract Restoring ecosystems requires the re-establishment of diverse soil microbial communities that drive critical ecosystem functions. In grasslands, restoration and management require the application of disturbances like fire and grazing. Disturbances can shape microbial taxonomic composition and potentially functional composition as well. We characterized taxonomic and functional gene composition of soil communities using whole genome shotgun metagenomic sequencing to determine how restored soil communities differed from pre-restoration agricultural soils and original remnant soils, how management affects soil microbes, and whether restoration and management affect the number of microbial genes associated with carbohydrate degradation. We found distinct differences in both taxonomic and functional diversity and composition among restored, remnant, and agricultural soils. Remnant soils had low taxonomic and functional richness and diversity, as well as distinct composition, indicating that restoration of agricultural soils does not re-create soil microbial communities that match remnants. Prescribed fire management increased functional diversity, which also was higher in more recently planted restorations. Finally, restored and post-fire soils included high abundances of genes encoding cellulose-degrading enzymes, so restorations and their ongoing management can potentially support functions important in carbon cycling.
Highlights: • CNTs–mullite composite was prepared via chemical vapor deposition (CVD) method. • The prepared composite was modified with concentrated nitric acid and chitosan. • The modified CNTs–mullite composites were used as novel adsorbents. • Copper ion removal from water by the prepared adsorbents was performed. • Langmuir and Freundlich isotherms and two kinetic models were applied to fit the experimental data. - Abstract: Carbon nanotubes–mullite composite was synthesized by direct growth of carbon nanotubes on mullite particles via chemical vapor deposition method using cyclohexanol and ferrocene as carbon precursor and catalyst, respectively. The carbon nanotubes–mullite composite was oxidized with concentrated nitric acid and functionalized with chitosan and then used as a novel adsorbent for copper ions removal from water. The results demonstrated that modification with concentrated nitric acid and chitosan improves copper ions adsorption capacity of the prepared composite, significantly. Langmuir and Freundlich isotherms and two kinetic models were applied to fit the experimental data. The carbon nanotubes growth on mullite particles to form the carbon nanotubes–mullite composite with further modification is an inherently safe approach for many promising environmental applications to avoid some concerns regarding environment, health and safety. It was found that the modified carbon nanotubes–mullite composite can be considered as an excellent adsorbent for copper ions removal from water.
In this work, we report the in-plane electron transport in the MXenes (i.e., within the MXene layers) as a function of composition using the density-functional tight-binding method, in conjunction with the non-equilibrium Green’s functions technique. Our study reveals that all MXene compositions have a linear relationship between current and voltage at lower potentials, indicating their metallic character. However, the magnitude of the current at a given voltage (conductivity) has different trends among different compositions. For example, MXenes without any surface terminations (Ti 3 C 2 ) exhibit higher conductivity compared to MXenes with surface functionalization. Among the MXenes with –O and –OH termination, those with –O surface termination have lower conductivity than the ones with –OH surface terminations. Interestingly, conductivity changes with the ratio of –O and –OH on the MXene surface. Our calculated I–V curves and their conductivities correlate well with transmission functions and the electronic density of states around the Fermi level. The surface composition-dependent conductivity of the MXenes provides a path to tune the in-plane conductivity for enhanced pseudocapacitive performance.
Containerless processing using electromagnetic levitation (EML) is a powerful technique in the investigation of reactive molten metal systems. On ground, the power required to overcome the weight of the sample is sufficient to cause significant heating and induce substantial melt convection. In microgravity, the heating and positioning fields may be decoupled and the field strength may be varied to achieve the desired level of convection within the limits set by the geometry of the levitation coil and the sample size. From high-speed digital images of the double recalescence behavior of Fe-Cr-Ni alloys in ground-based testing and in reduced-gravity aboard the NASA KC-135 parabolic aircraft, we have shown that phase selection can be predicted based on a growth competition model. An important parameter in this model is the delay time between primary nucleation and subsequent nucleation of the stable solid within the liquid/metastable solid array. This delay time is a strong function of composition and a weak function of the undercooling of the melt below the metastable liquidus. From the results obtained during the first Microgravity Sciences Laboratory (MSL-1) mission, we also know that convection may significantly influence the delay time, especially at low undercoolings. Currently, it is unclear what mechanism controls the formation of a heterogeneous site that allows nucleation of the austenitic phase on the pre-existing ferrite skeleton. By examining the behavior of the delay time under different convective conditions, we hypothesize that we can differentiate between several of these mechanisms to gain an understanding of how to control microstructural. evolution. We will anchor these predictions by examining samples quenched at different times following primary recalescence in microgravity. A second important parameter in the growth competition model is the identification of the growth rate of the stable phase into the semi-solid array that formed during primary recalescence. Current dendritic growth theory is inadequate in predicting solidification behavior under these conditions as metallographic analyses show that stable phase growth proceeds along the interface between the metastable solid and residual liquid. Since growth velocity is independent of the initial undercooling relative to the metastable liquidus, we hypothesize that purely thermal effects can be separated from other important growth model parameters by careful selection of the liquid composition in a ternary system.