Search NASA⌕ Search

SEARCH · Search NASA

Results for “FORESTS”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A strong blend in the morning: studying the circumgalactic medium before cosmic noon with strong, blended Ly α forest systems

ABSTRACT We study of the properties of a new class of circumgalactic medium absorbers identified in the Ly α forest: ‘Strong, Blended Lyman-α’ (or SBLA) absorption systems. We study SBLAs at 2.4 < z < 3.1 in SDSS-IV/eBOSS spectra by their strong extended Ly α absorption complexes covering 138 $\, \, {\rm km}\, {\rm s}^{-1}$ with an integrated $\log (N_{\rm H\, {\small I}}/\mathrm{cm}^{-2}) =16.04$$\substack{+0.05 \\ -0.06}$ and Doppler parameter b = 18.1$\substack{+0.7 \\ -0.4}$$\, \, {\rm km}\, {\rm s}^{-1}$. Clustering with the Ly α forest provides a large-scale structure bias of b = 2.34 ± 0.06 and halo mass estimate of $M_h \approx 10^{12}\, h^{-1}\, {\rm M_{\odot }}$ for our SBLA sample. We measure the ensemble mean column densities of 22 metal features in the SBLA composite spectrum and find that no single-population multiphase model for them is viable. We therefore explore the underlying SBLA population by forward modelling the SBLA absorption distribution. Based on covariance measurements and favoured populations we find that ≈25 per cent of our SBLAs have stronger metals. Using silicon only we find that our strong metal SBLAs trace gas with a log (nH/cm−3) > −2.40 for T = 103.5 K and show gas clumping on <210 parsec scales. We fit multiphase models to this strong subpopulation and find a low ionization phase with nH = 1 cm−3, T = 103.5 K, and [X/H] = 0.8, an intermediate ionization phase with log (nH/cm−3) = −3.05, T = 103.5 K and [X/H] = −0.8, and a poorly constrained higher ionization phase. We find that the low ionization phase favours cold, dense super-solar metallicity gas with a clumping scale of just 0.009 parsecs.

Morrison, Sean (ORCID:0000000267702627)↗
DOE OSTI2024

The 3D Lyman- α forest power spectrum from eBOSS DR16

We measure the three-dimensional power spectrum (P3D) of the transmitted flux in the Lyman-α (Ly α) forest using the complete extended Baryon Oscillation Spectroscopic Survey data release 16 (eBOSS DR16). This sample consists of ~205 000 quasar spectra in the redshift range 2 ≤ z ≤ 4 at an effective redshift z = 2.334. We propose a pair-count spectral estimator in configuration space, weighting each pair by exp( i k ∙ r), for wave vector k and pixel pair separation r, effectively measuring the anisotropic power spectrum without the need for fast Fourier transforms. This accounts for the window matrix in a tractable way, avoiding artefacts found in Fourier-transform based power spectrum estimators due to the sparse sampling transverse to the line of sight of Ly α skewers. We extensively test our pipeline on two sets of mocks: (i) idealized Gaussian random fields with a sparse sampling of Ly α skewers, and (ii) log-normal LyaCoLoRe mocks including realistic noise levels, the eBOSS survey geometry and contaminants. On eBOSS DR16 data, the Kaiser formula with a non-linear correction term obtained from hydrodynamic simulations yields a good fit to the power spectrum data in the range $(0.02 ≤ k ≤ 0.35)$ h Mpc -1 at the 1–2σ level with a covariance matrix derived from LyaCoLoRe mocks. We demonstrate a promising new approach for full-shape cosmological analyses of Ly α forest data from cosmological surveys such as eBOSS, the currently observing Dark Energy Spectroscopic Instrument and future surveys such as the Prime Focus Spectrograph, WEAVE-QSO, and 4MOST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Modelling the impact of quasar redshift errors on the full-shape analysis of correlations in the Lyman-α forest.

In preparation for the first cosmological measurements from the full shape of the Lyman-α (Lyα) forest from DESI, we must carefully model all relevant systematics that might bias our analysis. It was shown in Youles et al. (2022) that random quasar redshift errors produce a smoothing effect on the mean quasar continuum in the Lyα forest region. This, in turn, gives rise to spurious features in the Lyα autocorrelation and its cross-correlation with quasars. Using synthetic data sets based on the DESI survey, we confirm that the impact on BAO measurements is small, but that a bias is introduced to parameters which depend on the full shape of our correlations. We combine a model of this contamination in the cross-correlation (Youles et al. 2022) with a new model we introduce here for the auto-correlation. These are parametrised by 3 parameters, which, when included in a joint fit to both correlation functions, successfully eliminate any impact of redshift errors on our full-shape constraints. We also present a strategy for removing this contamination from real data, by removing ∼0.3% of correlating pairs.

cosmology↗
DOE OSTI2025

Forecasting constraints on the high-z IGM thermal state from the Lyman-α forest flux autocorrelation function

ABSTRACT The autocorrelation function of the Lyman-$\alpha$ (Ly $\alpha$) forest flux from high-z quasars probes the small-scale structure of the intergalactic medium (IGM). The thermal state of the IGM, determined by the physics of reionization, sets the small-scale power observed in the Ly $\alpha$ forest. To explore the sensitivity of the autocorrelation function to the IGM’s thermal state, we compute the autocorrelation function from a cosmological hydrodynamical simulation with an instantaneous reionization model and 135 post-processed thermal states. Using mock data sets of 20 quasars, we forecast constraints on $T_0$ and $\gamma$, which characterize the post-processed IGM thermal state, at $5.4 \le z \le 6$. While this model simplifies the IGM’s thermal state, it serves as a key first step in assessing future observational prospects. We also perform an inference test on mocks and re-weight out posterior distributions to guarantee that they exhibit statistically correct behaviour. At $z = 5.4$, we find that an idealized data set constrains $T_0$ to 59 per cent and $\gamma$ to 16 per cent at the 1$\sigma$ equivalent confidence level. To explore more realistic, non-instantaneous reionization scenarios, we analyse four models combining temperature and ultraviolet background (UVB) fluctuations at $z = 5.8$. We find that mock data generated from a model with both temperature and UVB fluctuations can rule out a model with only temperature fluctuations at the $> 1\sigma$ level 73.9 per cent of the time.

Wolfson, Molly↗
DOE OSTI2014

The Lyman α forest in optically thin hydrodynamical simulations

We study the statistics of the Lyα forest in a flat Λ cold dark matter cosmology with the N-body + Eulerian hydrodynamics code NYX.We produce a suite of simulations, covering the observationally relevant redshift range 2 ≤ z ≤ 4. We find that a grid resolution of 20 h-1kpc is required to produce 1 per cent convergence of Lyα forest flux statistics, up to k = 10 h -1 Mpc. In addition to establishing resolution requirements, we study the effects of missing modes in these simulations, and find that box sizes of L > 40h -1 Mpc are needed to suppress numerical errors to a sub-per cent level. Our optically thin simulations with the ionizing background prescription of Haardt & Madau reproduce an intergalactic medium density-temperature relation with T 0 ≈ 10 4 K and γ ≈ 1.55 at z = 2, with a mean transmitted flux close to the observed values. When using the ionizing background prescription of Faucher-Giguère et al., the mean flux is 10-15 per cent below observed values at z = 2, and a factor of 2 too small at z = 4. We show the effects of the common practice of rescaling optical depths to the observed mean flux and how it affects convergence rates. We also investigate the practice of 'splicing' results from a number of different simulations to estimate the 1D flux power spectrum and show it is accurate at the 10 per cent level. Finally, we find that collisional heating of the gas from dark matter particles is negligible in modern cosmological simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Leaf surface traits and water storage retention affect photosynthetic responses to leaf surface wetness among wet tropical forest and semiarid savanna plants

While it is reasonable to predict that photosynthetic rates are inhibited while leaves are wet, leaf gas exchange measurements during wet conditions are challenging to obtain due to equipment limitations and the complexity of canopy-atmosphere interactions in forested environments. Thus, the objective of this study was to evaluate responses of seven tropical and three semiarid savanna plant species to simulated leaf wetness and test the hypotheses that (i) leaf wetness reduces photosynthetic rates (Anet), (ii) leaf traits explain different responses among species and (iii) leaves from wet environments are better adapted for wet leaf conditions than those from drier environments. The two sites were a tropical rainforest in northern Costa Rica with ~4200 mm annual rainfall and a savanna in central Texas with ~1100 mm. Gas exchange measurements were collected under dry and wet conditions on five sun-exposed leaf replicates from each species. Additional measurements included leaf wetness duration and stomatal density. We found that Anet responses varied greatly among species, but all plants maintained a baseline of activity under wet leaf conditions, suggesting that abaxial leaf Anet was a significant percentage of total leaf Anet for amphistomatous species. Among tropical species, Anet responses immediately after wetting ranged from -31% (Senna alata (L.) Roxb.) to +21% (Zamia skinneri Warsz. Ex. A. Dietr.), while all savanna species declined (up to -48%). After 10 min of drying, most species recovered Anet towards the observed status prior to wetting or surpassed it, with the exception of Quercus stellata Wangenh., a savanna species, which remained 13% below Anet dry. The combination of leaf wetness duration and leaf traits, such as stomatal density, trichomes or wax, most likely influenced Anet responses positively or negatively. There was also overlap between leaf traits and Anet responses of savanna and tropical plants. It is possible that these species converge on a relatively conservative response to wetness, each for divergent purposes (cooling, avoiding stomatal occlusion, or by several unique means of rapid drying). A better understanding of leaf wetness inhibiting photosynthesis is vital for accurate modeling of growth in forested environments; however, species adapted for wet environments may possess compensatory traits that mitigate these effects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Forecasting the Ly-𝛼 forest × CMB lensing bispectrum from ACT, SO, S4, and DESI

We forecast the sensitivity of future correlations between cosmic microwave background (CMB) lensing and the Lyman-𝛼 forest power spectrum. This squeezed-limit bispectrum probes the connection between the Lyman-𝛼 transmission and the underlying large-scale matter density field. We recover the measured signal-to-noise (SNR) ratio obtained with Planck × BOSS, and forecast a SNR of 10.3, 14.8 and 20.2 for DESI combined with ACT, SO, and CMB-S4, respectively. For DESI and SO/CMB-S4, the correlation should be detectable at SNR ≥ 5 in 5 redshift bins, useful for distinguishing our signal from several contaminants. Indeed, our forecast is affected by large theoretical uncertainties in the current modeling of the signal and its contaminants, such as continuum misestimation bias or damped-Lyman-𝛼 absorbers. Quantifying these more accurately will be crucial to enable a reliable cosmological interpretation of this observable in future measurements. We attempt to enumerate the features required in future Lyman-𝛼 forest simulations required to do so.

Cosmic microwave background↗
DOE OSTI2024

Searching for parity violation in SDSS DR16 Lyman-α forest data

The four-point correlation function is the lowest order correlation function for scalar fields that can be used to probe statistical parity invariance in an isotropic universe. There are intriguing claims of detection of parity violation in the 4-point function of BOSS galaxy clustering data. We apply the same estimator to the public SDSS Data Release 16 Lyman-α forest data. Lyman-α forest data probes a different redshift range and is sensitive to a different density regime using a completely different technique. A detection would therefore be a strong indication of new physics. We identify an accurate covariance matrix as a crucial impediment to performing this measurement accurately, consistent with existing literature on galaxy 4-point function. Here, we discuss several approaches to estimating the covariance matrix, several of which produce spurious detection. Using a robust, but very suboptimal, covariance matrix derived from subsample bootstrapping, we find no evidence for parity violation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Constraints on dark photon dark matter from Lyman- α forest simulations and an ultrahigh signal-to-noise quasar spectrum

The ultralight dark photon is a well-motivated, hypothetical dark matter candidate. In a dilute plasma, they can resonantly convert into photons, and heat up the intergalactic medium between galaxies. In this work, we explore the dark photon dark matter parameter space by comparing synthetic Lyman- α forest data from cosmological hydrodynamical simulations to observational data from VLT/UVES of the quasar HE0940-1050 ( z em = 3.09 ). We use a novel flux normalization technique that targets underdense gas, reshaping the flux probability distribution. Not only do we place robust constraints on the kinetic mixing parameter of dark photon dark matter, but notably our findings suggest that this model can still reconcile simulated and observed Doppler parameter distributions of z ∼ 0 Lyman- α lines, as seen by HST/COS. This work opens new pathways for the use of the Lyman- α forest to explore new physics, and can be extended to other scenarios such as primordial black hole evaporation, dark matter decay, and annihilation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Random forest prediction of crystal structure from electron diffraction patterns incorporating multiple scattering

Diffraction is the most common method to solve for unknown or partially known crystal structures. However, it remains a challenge to determine the crystal structure of a new material that may have nanoscale size or heterogeneities. Here, in this study, we train an architecture of hierarchical random forest models capable of predicting the crystal system, space group, and lattice parameters from one or more unknown two-dimensional electron diffraction patterns. Our initial model correctly identifies the crystal system of a simulated electron diffraction pattern from a 20-nm-thick specimen of arbitrary orientation 67% of the time. We achieve a topline accuracy of 79% when aggregating predictions from ten patterns of the same material but different zone axes. The space group and lattice predictions range from 70% to 90% accuracy and median errors of 0.01-0.5Å, respectively, for cubic, hexagonal, trigonal, and tetragonal crystal systems while being less reliable on orthorhombic and monoclinic systems. We apply this architecture to a four-dimensional scanning transmission electron microscopy scan of gold nanoparticles, where it accurately predicts the crystal structure and lattice constants. These random forest models can be used to significantly accelerate the analysis of electron diffraction patterns, particularly in the case of unknown crystal structures. Additionally, due to the speed of inference, these models could be integrated into live transmission electron microscopy experiments, allowing real-Time labeling of a specimen.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Improving the chances of successful protein structure determination with a random forest classifier

Using an extended set of protein features calculated separately for protein surface and interior, a new version of XtalPred based on a random forest classifier achieves a significant improvement in predicting the success of structure determination from the primary amino-acid sequence. Obtaining diffraction quality crystals remains one of the major bottlenecks in structural biology. The ability to predict the chances of crystallization from the amino-acid sequence of the protein can, at least partly, address this problem by allowing a crystallographer to select homologs that are more likely to succeed and/or to modify the sequence of the target to avoid features that are detrimental to successful crystallization. In 2007, the now widely used XtalPred algorithm [Slabinski et al. (2007 ▶), Protein Sci.16, 2472–2482] was developed. XtalPred classifies proteins into five ‘crystallization classes’ based on a simple statistical analysis of the physicochemical features of a protein. Here, towards the same goal, advanced machine-learning methods are applied and, in addition, the predictive potential of additional protein features such as predicted surface ruggedness, hydrophobicity, side-chain entropy of surface residues and amino-acid composition of the predicted protein surface are tested. The new XtalPred-RF (random forest) achieves significant improvement of the prediction of crystallization success over the original XtalPred. To illustrate this, XtalPred-RF was tested by revisiting target selection from 271 Pfam families targeted by the Joint Center for Structural Genomics (JCSG) in PSI-2, and it was estimated that the number of targets entered into the protein-production and crystallization pipeline could have been reduced by 30% without lowering the number of families for which the first structures were solved. The prediction improvement depends on the subset of targets used as a testing set and reaches 100% (i.e. twofold) for the top class of predicted targets.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Multiview marker-free registration of forest terrestrial laser scanner data with embedded confidence metrics

Terrestrial laser scanning has demonstrated increasing potential for rapid comprehensive measurement of forest structure, especially when multiple scans are spatially registered in order to reduce the limitations of occlusion. Although marker-based registration techniques (based on retro-reflective spherical targets) are commonly used in practice, a blind marker-free approach is preferable, insofar as it supports rapid operational data acquisition. To support these efforts, we extend the pairwise registration approach of our earlier work, and develop a graph-theoretical framework to perform blind marker-free global registration of multiple point cloud data sets. Pairwise pose estimates are weighted based on their estimated error, in order to overcome pose conflict while exploiting redundant information and improving precision. The proposed approach was tested for eight diverse New England forest sites, with 25 scans collected at each site. Quantitative assessment was provided via a novel embedded confidence metric, with a mean estimated root-mean-square error of 7.2 cm and 89% of scans connected to the reference node. Lastly, this paper assesses the validity of the embedded multiview registration confidence metric and evaluates the performance of the proposed registration algorithm.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Modeling Discrete Forest Anisotropic Reflectance Over a Sloped Surface With an Extended GOMS and SAIL Model

Topographic effects on canopy reflectance play a pivotal role in the retrieval of surface biophysical variables over rugged terrain. In this paper, we proposed a new canopy anisotropic reflectance model for discrete forests, Geometric Optical and Mutual Shadowing and Scattering-from-Arbitrarily-Inclined-Leaves model coupled with Topography (GOSAILT), which considers the effects of slope, aspect, geotropic nature of tree growth, multiple scattering, and diffuse skylight. GOSAILT-simulated areal proportions of four scene components (i.e., sunlit crown, shaded crown, sunlit background, and shaded background) were evaluated using the Geometric Optical model for Sloping Terrains (GOST) model. The canopy reflectances simulated by GOSAILT were validated against two reflectance data sets: Discrete anisotropic radiative transfer (DART) simulations and wide-angle infrared dual-model line/area array scanner (WIDAS) observations. Compared with a horizontal surface, the forest canopy reflectance over a steep slope (60°) is significantly distorted with absolute (relative) bias values of 0.048 (79.60%) and 0.056 (12.02%) for the red and near-infrared (NIR) bands, respectively. The GOSAILT-simulated component areal proportions show close agreements with GOST. Moreover, GOSAILT simulations have high overall accuracy (red band: coefficient of determination (R²) = 0.96; root-mean-square error (RMSE) = 0.003; and mean absolute percentage error (MAPE) = 3.91%; and NIR band: R² = 0.78, RMSE = 0.019; MAPE = 3.94%) when compared with the DART simulations. In conclusion, these extensive validations indicate good performances of GOSAILT in canopy reflectance simulations over sloped surfaces.

58 GEOSCIENCES↗
DOE OSTI2017

Nest survival modelling using a multi‐species approach in forests managed for timber and biofuel feedstock

Abstract Switchgrass ( Panicum virgatum ) intercropping is a novel forest management practice for biomass production intended to generate cellulosic feedstocks within intensively managed loblolly pine‐dominated landscapes. These pine plantations are important for early‐successional bird species, as short rotation times continually maintain early‐successional habitat. We tested the efficacy of using community models compared to individual surrogate species models in understanding influences on nest survival. We analysed nest data to test for differences in habitat use for 14 bird species in plots managed for switchgrass intercropping and controls within loblolly pine ( Pinus taeda ) plantations in Mississippi, USA. We adapted hierarchical models using hyper‐parameters to incorporate information from both common and rare species to understand community‐level nest survival. This approach incorporates rare species that are often discarded due to low sample sizes, but can inform community‐level demographic parameter estimates. We illustrate use of this approach in generating both species‐level and community‐wide estimates of daily survival rates for songbird nests. We were able to include rare species with low sample size (minimum n = 5) to inform a hyper‐prior, allowing us to estimate effects of covariates on daily survival at the community level, then compare this with a single‐species approach using surrogate species. Using single‐species models, we were unable to generate estimates below a sample size of 21 nests per species. Community model species‐level survival and parameter estimates were similar to those generated by five single‐species models, with improved precision in community model parameters. Covariates of nest placement indicated that switchgrass at the nest site (<4 m) reduced daily nest survival, although intercropping at the forest stand level increased daily nest survival. Synthesis and applications . Community models represent a viable method for estimating community nest survival rates and effects of covariates while incorporating limited data for rarely detected species. Intercropping switchgrass in loblolly pine plantations slightly increased daily nest survival at the research plot scale (0.1 km 2 ), although at a local scale (50 m 2 ) switchgrass negatively influenced nest survival. A likely explanation is intercropping shifted community composition, favouring species with greater disturbance tolerance.

Loman, Zachary G.↗
DOE OSTI2023

Climate, soil mineralogy and mycorrhizal fungi influence soil organic matter fractions in eastern US temperate forests

Identifying the primary controls of particulate (POM) and mineral-associated organic matter (MAOM) content in soils is critical for determining future stocks of soil carbon (C) and nitrogen (N) across the globe. However, drivers of these soil organic matter fractions are likely to vary among ecosystems in response to climate, soil type and the composition of local biological communities. We tested how soil factors, climate and plant–fungal associations influenced the distribution and concentrations of C and N in MAOM and POM in seven temperate forests in the National Ecological Observatory Network (NEON) across the eastern United States. Samples of upper mineral horizon soil within each forest were collected in plots representing a gradient of dominant tree–mycorrhizal association, allowing us to test how plant and microbial communities influenced POM and MAOM across sites differing in climate and soil conditions. We found that concentrations of C and N in soil organic matter were primarily driven by soil mineralogy, but the relative abundance of MAOM versus POM C was strongly linked to plot-level mycorrhizal dominance. Furthermore, the effect of dominant tree mycorrhizal type on the distribution of N among POM and MAOM fractions was sensitive to local climate: in cooler sites, an increasing proportion of ectomycorrhizal-associated trees was associated with lower proportions of N in MAOM, but in warmer sites, we found the reverse. As an indicator of soil carbon age, we measured radiocarbon in the MAOM fraction but found that within and across sites, Δ 14 C was unrelated to mycorrhizal dominance, climate, or soil factors, suggesting that additional site-specific factors may be primary determinants of long-term SOM persistence. Our results indicate that while soil mineralogy primarily controls SOM C and N concentrations, the distribution of SOM among density fractions depends on the composition of vegetation and microbial communities, with these effects varying across sites with distinct climates. Here we also suggest that within biomes, the age of mineral-associated soil carbon is not clearly linked to the factors that control concentrations of MAOM C and N.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Cryptic genetic structure and copy-number variation in the ubiquitous forest symbiotic fungus Cenococcum geophilum

Ectomycorrhizal (ECM) fungi associated with plants constitute one of the most successful symbiotic interactions in forest ecosystems. ECM support trophic exchanges with host plants and are important factors for the survival and stress resilience of trees. However, ECM clades often harbour morpho-species and cryptic lineages, with weak morphological differentiation. How this relates to intraspecific genome variability and ecological functioning is poorly known. Here, we analysed 16 European isolates of the ascomycete Cenococcum geophilum, an extremely ubiquitous forest symbiotic fungus with no known sexual or asexual spore-forming structures but with a massively enlarged genome. We carried out whole-genome sequencing to identify single-nucleotide polymorphisms. We found no geographic structure at the European scale but divergent lineages within sampling sites. Evidence for recombination was restricted to specific cryptic lineages. Lineage differentiation was supported by extensive copy-number variation. Finally, we confirmed heterothallism with a single MAT1 idiomorph per genome. Synteny analyses of the MAT1 locus revealed substantial rearrangements and a pseudogene of the opposite MAT1 idiomorph. Our study provides the first evidence for substantial genome-wide structural variation, lineage-specific recombination and low continent-wide genetic differentiation in C. geophilum. Our study provides a foundation for targeted analyses of intra-specific functional variation in this major symbiosis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Inhibition of nitrous oxide reduction in forest soil microcosms by different forms of methanobactin

Copper plays a critical role in controlling greenhouse gas emissions as it is a key component of the particulate methane monooxygenase and nitrous oxide reductase. Some methanotrophs excrete methanobactin (MB) that has an extremely high copper affinity. As a result, MB may limit the ability of other microbes to gather copper, thereby decreasing their activity as well as impacting microbial community composition. Here, we show using forest soil microcosms that multiple forms of MB; MB from Methylosinus trichosporium OB3b (MB-OB3b) and MB from Methylocystis sp. strain SB2 (MB-SB2) increased nitrous oxide (N 2 O) production as well caused significant shifts in microbial community composition. Such effects, however, were mediated by the amount of copper in the soils, with low-copper soil microcosms showing the strongest response to MB. Furthermore, MB-SB2 had a stronger effect, likely due to its higher affinity for copper. The presence of either form of MB also inhibited nitrite reduction and generally increased the presence of genes encoding for the iron-containing nitrite reductase (nirS) over the copper-dependent nitrite reductase (nirK). Lastly, these data indicate the methanotrophic-mediated production of MB can significantly impact multiple steps of denitrification, as well as have broad effects on microbial community composition of forest soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Variations of leaf longevity in tropical moist forests predicted by a trait-driven carbon optimality model

Leaf longevity (LL) varies more than 20-fold in tropical evergreen forests, but it remains unclear how to capture these variations using predictive models. Current theories of LL that are based on carbon optimisation principles are challenging to quantitatively assess because of uncertainty across species in the ‘ageing rate:’ the rate at which leaf photosynthetic capacity declines with age. Here in this paper, we present a meta-analysis of 49 species across temperate and tropical biomes, demonstrating that the ageing rate of photosynthetic capacity is positively correlated with the mass-based carboxylation rate of mature leaves. We assess an improved trait-driven carbon optimality model with in situLL data for 105 species in two Panamanian forests. Additionally, we show that our model explains over 40% of the cross-species variation in LL under contrasting light environment. Collectively, our results reveal how variation in LL emerges from carbon optimisation constrained by both leaf structural traits and abiotic environment.

54 ENVIRONMENTAL SCIENCES↗