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DOE OSTI2021

Beyond the usual suspects: methanogenic communities in eastern North American peatlands are also influenced by nickel and copper concentrations

Peatlands both accumulate carbon and release methane, but their broad range in environmental conditions means that the diversity of microorganisms responsible for carbon cycling is still uncertain. Here, we describe a community analysis of methanogenic archaea responsible for methane production in 17 peatlands from 36 to 53 N latitude across the eastern half of North America, including three metal-contaminated sites. Methanogenic community structure was analysed through Illumina amplicon sequencing of the mcrA gene. Whether metal-contaminated sites were included or not, metal concentrations in peat were a primary driver of methanogenic community composition, particularly nickel, a trace element required in the F 430 cofactor in methyl-coenzyme M reductase that is also toxic at high concentrations. Copper was also a strong predictor, likely due to inhibition at toxic levels and/or to cooccurrence with nickel, since copper enzymes are not known to be present in anaerobic archaea. Here, the methanogenic groups Methanocellales and Methanosarcinales were prevalent in peatlands with low nickel concentrations, while Methanomicrobiales and Methanomassiliicoccales were abundant in peatlands with higher nickel concentrations. Results suggest that peat-associated trace metals are predictors of methanogenic communities in peatlands.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Parameter inference with non-linear galaxy clustering: accounting for theoretical uncertainties

ABSTRACT We implement euclidemulator (version 1), an emulator for the non-linear correction of the matter power spectrum, into the Markov chain Monte Carlo forecasting code montepython. We compare the performance of halofit, hmcode, and euclidemulator1, both at the level of power spectrum prediction and at the level of posterior probability distributions of the cosmological parameters, for different cosmological models and different galaxy power spectrum wavenumber cut-offs. We confirm that the choice of the power spectrum predictor has a non-negligible effect on the computed sensitivities when doing cosmological parameter forecasting, even for a conservative wavenumber cut-off of $0.2\, h\, {\rm Mpc}^{-1}$. We find that euclidemulator1 is on average up to 17 per cent more sensitive to the cosmological parameters than the other two codes, with the most significant improvements being for the Hubble parameter of up to 42 per cent and the equation of state of dark energy of up to 26 per cent, depending on the case. In addition, we point out that the choice of the power spectrum predictor contributes to the risk of computing a significantly biased mean cosmology when doing parameter estimations. For the four tested scenarios we find biases, averaged over the cosmological parameters, of between 0.5σ and 2σ (from below 1σ up to 6σ for individual parameters). This paper provides a proof of concept that this risk can be mitigated by taking a well-tailored theoretical uncertainty into account as this allows to reduce the bias by a factor of 2 to 5, depending on the case under consideration, while keeping posterior credibility contours small: the standard deviations are amplified by a factor of ≤1.4 in all cases.

Astronomy & Astrophysics↗

DOE OSTI2023

SDSS-IV MaNGA: The incidence of major mergers in type I and II AGN host galaxies in the DR15 sample

We present a study on the incidence of major mergers and their impact on the triggering of nuclear activity in 47 type I and 236 type II optically selected AGN from the MaNGA DR15 sample. From an estimate of non-parametric image predictors (Gini, M_20, concentration (C), asymmetry (A), clumpiness (S), Sérsic index (n), and shape asymmetry ()) using the SDSS images, in combination with a Linear Discriminant Analysis Method, we identified major mergers and merger stages. We reinforced our results by looking for bright tidal features in our post-processed SDSS and DESI legacy images. We find a statistically significant higher incidence of major mergers of 29 per cent ± 3 per cent in our type I+II AGN sample compared to 22 per cent ± 0.8 per cent for a non-AGN sample matched in redshift, stellar mass, colour, and morphological type, finding also a prevalence of post-coalescence (51 per cent ± 5 per cent) over pre-coalescence (23 per cent ± 6 per cent) merger stages. The levels of AGN activity among our massive major mergers are similar to those reported in other works using [O iii] tracers. However, similar levels are produced by our AGN-galaxies hosting stellar bars, suggesting that major mergers are important promoters of nuclear activity but are not the main nor the only mechanism behind the AGN triggering. The tidal strength parameter Q was considered at various scales looking for environmental differences that could affect our results on the merger incidence, finding non-significant differences. Finally, the H-H β diagram could be used as an empirical predictor for the flux coming from an AGN source, useful to correct photometric quantities in large AGN samples emerging from surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Reinterpreting fundamental plane correlations with machine learning

ABSTRACT This work explores the relationships between galaxy sizes and related observable galaxy properties in a large volume cosmological hydrodynamical simulation. The objectives of this work are to develop a better understanding of both the correlations between galaxy properties and the influence of environment on galaxy physics in order to build an improved model for the galaxy sizes, building off of the fundamental plane. With an accurate intrinsic galaxy size predictor, the residuals in the observed galaxy sizes can potentially be used for multiple cosmological applications, including making measurements of galaxy velocities in spectroscopic samples, estimating the rate of cosmic expansion, and constraining the uncertainties in the photometric redshifts of galaxies. Using projection pursuit regression, the model accurately predicts intrinsic galaxy sizes and have residuals which have limited correlation with galaxy properties. The model decreases the spatial correlation of galaxy size residuals by a factor of ∼5 at small scales compared to the baseline correlation when the mean size is used as a predictor.

Astronomy & Astrophysics↗

DOE OSTI2022

Adrenal Stress Hormone Regulation of Hepatic Homeostatic Function After an Acute Ozone Exposure in Wistar-Kyoto Male Rats

Ozone-induced lung injury, inflammation, and pulmonary/hypothalamus gene expression changes are diminished in adrenalectomized (AD) rats. Acute ozone exposure induces metabolic alterations concomitant with increases in epinephrine and corticosterone. We hypothesized that adrenal hormones are responsible for observed hepatic ozone effects, and in AD rats, these changes would be diminished. In total, 5–7 days after sham (SH) or AD surgeries, male Wistar-Kyoto rats were exposed to air or 0.8-ppm ozone for 4 h. Serum samples were analyzed for metabolites and liver for transcriptional changes immediately post-exposure. Ozone increased circulating triglycerides, cholesterol, free fatty-acids, and leptin in SH but not AD rats. Ozone-induced inhibition of glucose-mediated insulin release was absent in AD rats. Unlike diminution of ozone-induced hypothalamus and lung mRNA expression changes, AD in air-exposed rats (AD-air/SH-air) caused differential hepatic expression of ~1000 genes. Likewise, ozone in AD rats caused differential expression of ~1000 genes (AD-ozone/AD-air). Ozone-induced hepatic changes in SH rats reflected enrichment for pathways involving metabolic processes, including acetyl-CoA biosynthesis, TCA cycle, and sirtuins. Upstream predictor analysis identified similarity to responses produced by glucocorticoids and pathways involving forskolin. These changes were absent in AD rats exposed to ozone. However, ozone caused unique changes in AD liver mRNA reflecting activation of synaptogenesis, neurovascular coupling, neuroinflammation, and insulin signaling with inhibition of senescence pathways. In these rats, upstream predictor analysis identified numerous microRNAs likely involved in glucocorticoid insufficiency. These data demonstrate the critical role of adrenal stress hormones in ozone-induced hepatic homeostasis and necessitate further research elucidating their role in propagating environmentally driven diseases.

Toxicology↗

DOE OSTI2016

The changing role of mammal life histories in Late Quaternary extinction vulnerability on continents and islands

Understanding the drivers of extinction in a human dominated world is important if we are to preserve the remaining biodiversity. Here we compared extinction events on continents and islands after human arrival in prehistoric and historic times. We used regression trees to examine the role of life history in these extinctions. We found that all extinctions were significantly size-biased except for events on small islands in historic times. Further we found that life history traits associated with a lack of ability to respond quickly to perturbations were good predictors of prehistoric extinctions. In general life history traits were poor predictors of historic extinctions. Lastly, we conclude that these extinctions are part of one continuous extinction event, but that historic extinctions have multiple drivers, whereas prehistoric extinctions had fewer.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2012

Modeling the SHG activities of diverse protein crystals

The origins of the diversity in the SHG signal from protein crystals are investigated and potential protein-crystal coverage by SHG microscopy is assessed. A symmetry-additive ab initio model for second-harmonic generation (SHG) activity of protein crystals was applied to assess the likely protein-crystal coverage of SHG microscopy. Calculations were performed for 250 proteins in nine point-group symmetries: a total of 2250 crystals. The model suggests that the crystal symmetry and the limit of detection of the instrument are expected to be the strongest predictors of coverage of the factors considered, which also included secondary-structural content and protein size. Much of the diversity in SHG activity is expected to arise primarily from the variability in the intrinsic protein response as well as the orientation within the crystal lattice. Two or more orders-of-magnitude variation in intensity are expected even within protein crystals of the same symmetry. SHG measurements of tetragonal lysozyme crystals confirmed detection, from which a protein coverage of ∼84% was estimated based on the proportion of proteins calculated to produce SHG responses greater than that of tetragonal lysozyme. Good agreement was observed between the measured and calculated ratios of the SHG intensity from lysozyme in tetragonal and monoclinic lattices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2011

Charge Prediction of Lipid Fragments in Mass Spectrometry

An artificial neural network is developed for predicting which fragment is charged and which fragment is neutral for lipid fragment pairs produced from a liquid chromatography tandem mass spectrometry simulation process. This charge predictor is integrated into software developed at PNNL for in silico spectra generation and identification of metabolites known as Met ISIS. To test the effect of including charge prediction in Met ISIS, 46 lipids are used which show a reduction in false positive identifications when the charge predictor is utilized.

Met ISIS↗

DOE OSTI2021

Spatio-Temporal Modeling of Field Surveyed Backsheet Degradation

Assessing photovoltaic module backsheet durability is critical to increasing module lifetime. Lab based accelerating testing has recently failed to predict large scale failures of widely adopted polymeric materials. Field surveyed data is critical to assess the performance of component lifetime. Using a documented field survey protocol, 13 field surveys where conducted. Each measurement is encoded with it's spatial location in respect to the other modules. By combining field survey data on degradation predictors with real time satellite weather data, data-driven predictive models of backsheet degradation were trained. LOESS models were constructed to investigate the spatial dependence of measurements. It was found that micro-climatic effects like tree-lines, ground surface changes, and elevation changes effected the magnitude and variance of the measurements. A GAM model was created to predict the value of degradation based on measured predictors. The model includes variables on the climate of the system and the location of each measurement in the PV mounting structure. The model performed well with an adj.R 2 of 0.95 for yellowness index prediction. The model was cross-validated using k-folds.

backsheet↗

DOE OSTI2021

Decentralized Filtering Adaptive Neural Network Control for Uncertain Switched Interconnected Nonlinear Systems

This article presents a novel decentralized filtering adaptive neural network control framework for uncertain switched interconnected nonlinear systems. Each subsystem has its own decentralized controller based on the established decentralized state predictor. For each subsystem, the nonlinear uncertainties are approximated by a Gaussian radial basis function (GRBF) neural network incorporated with a piecewise constant adaptive law, where the adaptive law will update adaptive parameters from the error dynamics between the host system and the decentralized state predictor by discarding the unknowns, whereas a decentralized filtering control law is derived to cancel both local and mismatched uncertainties from other subsystems, as well as achieve the local objective tracking of the host system. The achievement of global objective depends on the achievement of local objective for each subsystem. The matched uncertainties are canceled directly by adopting their opposite in the control signal, whereas a dynamic inversion of the system is required to eliminate the effect of the mismatched uncertainties on the output. By exploiting the average dwell time principle, the error bounds between the real system and the virtual reference system, which defines the best performance that can be achieved by the closed-loop system, are derived. A numerical example is given to illustrate the effectiveness of the decentralized filtering adaptive neural network control architecture by comparing against the model reference adaptive control (MRAC).

Average dwell time, decentralized, filtering adapt↗

DOE OSTI2019

Plant phylogenetic history explains in-stream decomposition at a global scale

1. Evolutionary history and adaptation to climate shape plant traits. Some include leaf traits that influence litter quality. As a result, evolutionary history should affect litter decomposition, a crucial ecosystem process. In addition, litter decomposition is directly influenced by climate. Similarly, we expect plant phylogeny, adaptation and climate to jointly influence litter decomposition. These effects and their interactions have yet to be untangled at a global scale. 2. Here we introduce an analysis of variation in litter decomposition rates in rivers and streams across 285 published studies for 239 species (from ferns to angiosperms) distributed at 494 locations world-wide. We estimated the relative contributions of climatic conditions and phylogenetic heritage on litter decomposition rates, partitioning phylogenetic from climatic effects at the site and species levels using phylogenetic eigenvector analysis and phylogenetic linear mixed models. In addition, we modelled transitions in decomposition rates under a suite of multiple adaptive-regime Ornstein–Uhlenbeck models to test the hypothesis that natural selection has shaped clade-level litter decomposition rates. 3. Leaf litter decomposition rate exhibited a significant phylogenetic signal. Modelling decomposition rate as a function of location, climatic niche and phylogeny consistently recovered phylogeny alone as one of the top models in species-level analyses. Since many previous studies have focused on the same species across many locations, we also conducted analyses at the species × site level. Both phylogenetic and climatic factors were favoured in models of this analysis, but the single most important predictor for angiosperms and for all taxa analysed together was phylogeny alone. 4. Synthesis. For plant species distributed globally at nearly 500 locations we found that plant phylogenetic history is a critically important predictor of litter decomposition rate in rivers and streams, explaining more of the variance in decomposition than site or climatic regime. Thus, our study demonstrates the influence of evolutionary history on suites of plant traits that shape a key ecosystem process.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Changes in microbial communities along redox gradients in polygonized Arctic wet tundra soils

Summary This study investigated how microbial community structure and diversity varied with depth and topography in ice wedge polygons of wet tundra of the Arctic Coastal Plain in northern Alaska and what soil variables explain these patterns. We observed strong changes in community structure and diversity with depth, and more subtle changes between areas of high and low topography, with the largest differences apparent near the soil surface. These patterns are most strongly correlated with redox gradients (measured using the ratio of reduced Fe to total Fe in acid extracts as a proxy): conditions grew more reducing with depth and were most oxidized in shallow regions of polygon rims. Organic matter and pH also changed with depth and topography but were less effective predictors of the microbial community structure and relative abundance of specific taxa. Of all other measured variables, lactic acid concentration was the best, in combination with redox, for describing the microbial community. We conclude that redox conditions are the dominant force in shaping microbial communities in this landscape. Oxygen and other electron acceptors allowed for the greatest diversity of microbes: at depth the community was reduced to a simpler core of anaerobes,

Lipson, David A.↗

DOE OSTI2021

Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distributions

Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSFs) or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from golden eagles Aquila chrysaetos from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During the summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Genotype–environment associations across spatial scales reveal the importance of putative adaptive genetic variation in divergence

Abstract Identifying areas of high evolutionary potential is a judicious strategy for developing conservation priorities in the face of environmental change. For wide‐ranging species occupying heterogeneous environments, the evolutionary forces that shape distinct populations can vary spatially. Here, we investigate patterns of genomic variation and genotype–environment associations in the hermit thrush ( Catharus guttatus ), a North American songbird, at broad (across the breeding range) and narrow spatial scales (at a hybrid zone). We begin by building a genoscape or map of genetic variation across the breeding range and find five distinct genetic clusters within the species, with the greatest variation occurring in the western portion of the range. Genotype–environment association analyses indicate higher allelic turnover in the west than in the east, with measures of temperature surfacing as key predictors of putative adaptive genomic variation rangewide. Since broad patterns detected across a species' range represent the aggregate of many locally adapted populations, we investigate whether our broadscale analysis is consistent with a finer scale analysis. We find that top rangewide temperature‐associated loci vary in their clinal patterns (e.g., steep clines vs. fixed allele frequencies) across a hybrid zone in British Columbia, suggesting that the environmental predictors and the associated candidate loci identified in the rangewide analysis are of variable importance in this particular region. However, two candidate loci exhibit strong concordance with the temperature gradient in British Columbia, suggesting a potential role for temperature‐related barriers to gene flow and/or temperature‐driven ecological selection in maintaining putative local adaptation. This study demonstrates how patterns identified at the broad (macrogeographic) scale can be validated by investigating genotype–environment correlations at the local (microgeographic) scale. Furthermore, our results highlight the importance of considering the spatial distribution of putative adaptive variation when assessing population‐level sensitivity to climate change and other stressors.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

The response of stomatal conductance to seasonal drought in tropical forests

Stomata regulate CO2 uptake for photosynthesis and water loss through transpiration. The approaches used to represent stomatal conductance (gs) in models vary. In particular, current understanding of drivers of the variation in a key parameter in those models, the slope parameter (i.e. a measure of intrinsic plant water-use-efficiency), is still limited, particularly in the tropics. Here we collected diurnal measurements of leaf gas exchange and water potential (?leaf), and a suite of plant traits from the upper canopy of 15 tropical trees in two contrasting Panamanian forests throughout the dry season of the 2016 El Niño. The plant traits included wood density, leaf-mass-per-area (LMA), leaf carboxylation capacity (Vc,max25), leaf water content, the degree of isohydry, and predawn ?leaf. We first investigated how the choice of four commonly used leaf-level gs models with and without the inclusion of ?leaf as an additional predictor variable influence the ability to predict gs, and then explored the abiotic (i.e. month, site-month interaction) and biotic (i.e. tree-species-specific characteristics) drivers of slope parameter variation. Our results show that the inclusion of ?leaf did not improve model performance and that the models that represent the response of gs to vapor pressure deficit performed better than corresponding models that respond to relative humidity. Within each gs model, we found large variation in the slope parameter, and this variation was attributable to the biotic driver, rather than abiotic drivers. We further investigated potential relationships between the slope parameter and the six available plant traits mentioned above, and found that only one trait, LMA, had a significant correlation with the slope parameter (R2=0.66, n=15), highlighting a potential path towards improved model parameterization. This study advances understanding of gs dynamics over seasonal drought, and identifies a practical, trait-based approach to improve modeling of carbon and water exchange in tropical forests.

carbon and water exchange, stomatal conductance mo↗

DOE OSTI2024

Nitrogen fertilization effects on aged Miscanthus × giganteus stands: Exploring biomass yield, yield components, and biomass prediction using in–season morphological traits

For sustainable biomass production of Miscanthus × giganteus (hereafter miscanthus), understanding the impact of stand age and nitrogen (N) fertilization on biomass yield is crucial. This study investigated the effects of varying N fertilization rates (0, 56, 112, and 168 kg N ha –1 ) on yield components (tiller height, density, and weight) and their correlations with end-of-season biomass yield in miscanthus. We also explored end-of-season biomass yield prediction using in-season traits (canopy height, leaf area index, and leaf chlorophyll content [LCC]). The study was conducted at two sites in Illinois: a previously unfertilized 10-year-old miscanthus research stand at Urbana and a 16-year-old commercial stand at Pesotum with a history of annual 56N application. Results from 2018 to 2021 in Urbana and 2020 to 2021 in Pesotum showed increased biomass yields with N fertilization, varying by rate, year, and location. Biomass yield in Pesotum peaked at 56N, while in Urbana, it increased significantly at 112 kg N ha –1 . Biomass yield was strongly correlated with tiller height and weight measured at Urbana across N rates. Morphological traits measured every 2–3 weeks during the 2020 and 2021 growing seasons showed that canopy height was the strongest single predictor of miscanthus biomass yield, followed by LCC. Mid-August to September measurements of these traits were the best predictors of biomass yield. Multiple regressions involving the canopy height and LCC further improved yield predictions. We conclude that while N enhances biomass yields of aging miscanthus, the optimum rate depends on the site, environmental conditions, and management history.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Morphological traits for allometric scaling of the European Sea Bass Dicentrarchus labrax (Linnaeus, 1758) from Southern Portugal population

Abstract The present study aimed to determine the allometric scaling among a selection of morphological traits in European sea bass ( Dicentrarchus labrax ) to estimate fish body weight. A set of morphological traits (fish body weight, length, height, and width) were directly measured in 146 fish of a recirculating aquaculture system, with body weights ranging from 17.11 to 652.21 g. In addition, a collection of digital imagery of each anesthetized fish from the side and top views were used to estimate other traits (indirect measures). Multiple regression analysis and regression coefficients were calculated using all possible combinations of biometric data (predictors) to estimate fish body weight, applying different numerical fitting models (linear, log‐linear, quadratic, exponential). The results showed that the best combination of traits for estimating fish body weight were fish body width, length and height, collected from direct measure ( R 2 = 0.995), for a log‐linear model fitting, which revealed more accurate determinations than the most commonly used length–weight relationship. Nevertheless, other combinations of morphological traits and fitting models were also found to be suitable in successfully predict fish body weight, with variability ranging between 92.5% and 98.5%. For indirect measures, the best predictor was a combination of traits from top view (width, eye distance and area without fins) fitted with a log‐linear function. These results comprise a relevant baseline in supporting the high potential of noninvasive methods to accurately follow the growth of European sea bass juveniles, recurring to imagery analysis of anesthetized fish. It has major potential applications in feeding consumption trials and fish growth models, as it allows for continuously following up fish growth under different experimental conditions without therein distress derived from manipulation.

Azevedo, Ana↗

DOE OSTI2017

Using traits to uncover tropical forest function

Plant traits reflect their evolutionary history and influence physiological processes (Reich, 2014). For example, the embolism risk taken by plants, called the embolism safety margin, is a good predictor of stomatal conductance, and hence photosynthesis (Skelton et al., 2015). Trait-science has grown dramatically in the last decade as we have found niversal patterns governing the carbon and nutrient economies of plants (Bloom et al., 1985). Perhaps the greatest value of studying plant functional traits is that they yield understanding of plant functional processes.

Amazon forest function↗