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At least 1,423 records · Page 79

Expansion of high-latitude deciduous forests driven by interactions between climate warming and fire

High-latitude regions have experienced rapid warming in recent decades, and this trend is projected to continue over the twenty-first century. Fire is also projected to increase with warming. We show here, consistent with changes during the Holocene, that changes in twenty-first century climate and fire are likely to alter the composition of Alaskan boreal forests. We hypothesize that competition for nutrients after fire in early succession and for light in late succession in a warmer climate will cause shifts in plant functional type. Consistent with observations, our ecosystem model predicts evergreen conifers to be the current dominant tree type in Alaska. However, under future climate and fire, our analysis suggests the relative dominance of deciduous broadleaf trees nearly doubles, accounting for 58% of the Alaska ecosystem's net primary productivity by 2100, with commensurate declines in contributions from evergreen conifer trees and herbaceous plants. Post-fire deciduous broadleaf tree growth under a future climate is sustained from enhanced microbial nitrogen mineralization caused by warmer soils and deeper active layers, resulting in taller trees that compete more effectively for light. Finally, the expansion of deciduous broadleaf forests will affect the carbon cycle, surface energy fluxes and ecosystem function, thereby modifying important feedbacks with the climate system.

54 ENVIRONMENTAL SCIENCES↗

Contrasting age-dependent leaf acclimation strategies drive vegetation greening across deciduous broadleaf forests in mid- to high latitudes

Increasing leaf area and extending vegetation growing seasons are two primary drivers of global greening, which has emerged as one of the most significant responses to climate change. However, it remains unclear how these two leaf acclimation strategies would vary across forests at a large spatial scale. Here, in this study, using multiple satellite-based datasets and field measurements, we analysed the temporal changes (Δ) in maximal leaf area index (LAI max ) and length of the growing season (LOS) from 2002 to 2021 across deciduous broadleaf forests (DBFs) in the middle to high latitudes. Contrary to the widely held assumption of coordination, our results revealed a negative correlation between ΔLAI max and ΔLOS. Notably, the trade-offs between ΔLAImax and ΔLOS were strongly explained by stand age. Younger DBFs, with lower baseline LAI max , predominantly located in eastern Asia, displayed an increase in LAI max with small changes in LOS. This acquisitive strategy facilitated younger DBFs to grow more photosynthetically efficient leaves with low leaf mass per area, enhancing their light use efficiency. Conversely, older DBFs with a higher baseline LAI max , primarily located in North America and Europe, extended their LOS by increasing leaf mass per area. This conservative strategy facilitated older DBFs to produce thicker, but less photosynthetically efficient leaves, resulting in decreased light use efficiency. Our findings offer new insights into the contrasting changes in leaf area and growing season length and highlight their divergent impacts on ecosystem functioning.

Wang, Fangyi [Sun Yat-Sen Univ., Zhuhai (China)]↗

Continental scale structuring of forest and soil diversity via functional traits

Trait-based ecology claims to offer a mechanistic approach for explaining the drivers that structure biological diversity and predicting the responses of species, trophic interactions and ecosystems to environmental change. However, support for this claim is lacking across broad taxonomic groups. A framework for defining ecosystem processes in terms of the functional traits of their constituent taxa across large spatial scales is needed. Here, we provide a comprehensive assessment of the linkages between climate, plant traits and soil microbial traits at many sites spanning a broad latitudinal temperature gradient from tropical to subalpine forests. Our results show that temperature drives coordinated shifts in most plant and soil bacterial traits but these relationships are not observed for most fungal traits. Shifts in plant traits are mechanistically associated with soil bacterial functional traits related to carbon (C), nitrogen (N) and phosphorus (P) cycling, indicating that microbial processes are tightly linked to variation in plant traits that influence rates of ecosystem decomposition and nutrient cycling. In conclusion, our results are consistent with hypotheses that diversity gradients reflect shifts in phenotypic optima signifying local temperature adaptation mediated by soil nutrient availability and metabolism. They underscore the importance of temperature in structuring the functional diversity of plants and soil microbes in forest ecosystems and how this is coupled to biogeochemical processes via functional traits.

54 ENVIRONMENTAL SCIENCES↗

Large CO 2 removal potential of woody debris preservation in managed forests

Limiting climate warming to 1.5 °C requires reductions in greenhouse gas emissions and CO 2 removal. While various CO 2 removal strategies have been explored to achieve global net-zero greenhouse gas emissions and account for legacy emissions, additional exploration is warranted to examine more durable, scalable and sustainable approaches to achieve climate targets. Here, in this study, we show that preserving woody debris in managed forests can remove gigatonnes of CO 2 from the atmosphere sustainably based on a carbon cycle analysis using three Earth system models. Woody debris is produced from logging, sawmill wastes and abandoned woody products, and can be preserved in deep soil to lengthen its residence time (a measure of durability) by thousands of years. Preserving annual woody debris production in managed forests has the capacity to remove 769–937 GtCO 2 from the atmosphere cumulatively (10.1–12.4 GtCO 2 yr −1 on average) from 2025 to 2100, if its residence time is lengthened for 100–2,000 years and after 5% CO 2 removal is discounted to account for CO 2 emission due to machine operation for wood debris preservation. This translates to a reduction in global temperatures of 0.35–0.42 °C. Given the large potential, relatively low cost and long durability, future efforts should be focused on establishing large-scale demonstration projects for this technology in a variety of contexts, with rigorous monitoring of CO 2 removal, its co-benefits and side-effects.

Luo, Yiqi [Cornell Univ., Ithaca, NY (United State↗

Reevaluating growing season length controls on net ecosystem production in evergreen conifer forests

Abstract Growing season length (GSL) is a key unifying concept in ecology that can be estimated from eddy covariance-derived estimates of net ecosystem production (NEP). Previous studies disagree on how increasing GSLs may affect NEP in evergreen coniferous forests, potentially due to the variety of methods used to quantify GSL from NEP. We calculated GSL and GSL-NEP regressions at eleven evergreen conifer sites across a broad climatic gradient in western North America using three common approaches: (1) variable length (3–7 days) regressions of day of year versus NEP, (2) a smoothed threshold approach, and (3) the carbon uptake period, followed by a new approach of a method-averaged ensemble. The GSL and the GSL-NEP relationship differed among methods, resulting in linear relationships with variable sign, slope, and statistical significance. For all combinations of sites and methods, the GSL explained between 6% and 82% of NEP with p -values ranging from 0.45 to < 0.01. These results demonstrate the variability among GSL methods and the importance of selecting an appropriate method to accurately project the ecosystem carbon cycling response to longer growing seasons in the future. To encourage this approach in future studies, we outline a series of best practices for GSL method selection depending on research goals and the annual NEP dynamics of the study site(s). These results contribute to understanding growing season dynamics at ecosystem and continental scales and underscore the potential for methodological variability to influence forecasts of the evergreen conifer forest response to climate variability.

54 ENVIRONMENTAL SCIENCES↗

North American boreal forests are a large carbon source due to wildfires from 1986 to 2016

Wildfires are a major disturbance to forest carbon (C) balance through both immediate combustion emissions and post-fire ecosystem dynamics. Here we used a process-based biogeochemistry model, the Terrestrial Ecosystem Model (TEM), to simulate C budget in Alaska and Canada during 1986–2016, as impacted by fire disturbances. We extracted the data of difference Normalized Burn Ratio (dNBR) for fires from Landsat TM/ETM imagery and estimated the proportion of vegetation and soil C combustion. We observed that the region was a C source of 2.74 Pg C during the 31-year period. The observed C loss, 57.1 Tg C year -1 , was attributed to fire emissions, overwhelming the net ecosystem production (1.9 Tg C year -1 ) in the region. Our simulated direct emissions for Alaska and Canada are within the range of field measurements and other model estimates. As burn severity increased, combustion emission tended to switch from vegetation origin towards soil origin. When dNBR is below 300, fires increase soil temperature and decrease soil moisture and thus, enhance soil respiration. However, the post-fire soil respiration decreases for moderate or high burn severity. The proportion of post-fire soil emission in total emissions increased with burn severity. Net nitrogen mineralization gradually recovered after fire, enhancing net primary production. Net ecosystem production recovered fast under higher burn severities. The impact of fire disturbance on the C balance of northern ecosystems and the associated uncertainties can be better characterized with long-term, prior-, during- and post-disturbance data across the geospatial spectrum. Our findings suggest that the regional source of carbon to the atmosphere will persist if the observed forest wildfire occurrence and severity continues into the future.

54 ENVIRONMENTAL SCIENCES↗

Plant traits, productivity, biomass and soil properties from forest sites in the Pacific Northwest, 1999–2014

Plant trait measurements are needed for evaluating ecological responses to environmental conditions and for ecosystem process model development, parameterization, and testing. Here, we present a standardized dataset integrating measurements from projects conducted by the Terrestrial Ecosystem Research and Regional Analysis- Pacific Northwest (TERRA-PNW) research group between 1999 and 2014 across Oregon and Northern California, where measurements were collected for scaling and modeling regional terrestrial carbon processes with models such as Biome-BGC and the Community Land Model. The dataset contains measurements of specific leaf area, leaf longevity, leaf carbon and nitrogen for 35 tree and shrub species derived from more than 1,200 branch samples collected from over 200 forest plots, including several AmeriFlux sites. The dataset also contains plot-level measurements of forest composition, structure (e.g., tree biomass), and productivity, as well as measurements of soil structure (e.g., bulk density) and chemistry (e.g., carbon). Publically-archiving regional datasets of standardized, co-located, and geo-referenced plant trait measurements will advance the ability of earth system models to capture species-level climate sensitivity at regional to global scales.

09 BIOMASS FUELS↗

Assimilation and transport of organic bound tritium in an irrigated pine forest

The speciation of radioactive tritium (T) in a naturally-established subtropical loblolly pine forest that has been irrigated with highly-contaminated pond water for the last 20 years is reported. This irrigation project was created to limit the underground transport of a tritium-rich plume which also contains low levels of toxic organics, metals and radionuclides such as carbon-14 ( 14 C) from a nearby low-level waste burial ground. The levels of tritiated water (HTO) in the wood cores were not influenced by recent irrigation activities. However, the tritium levels in the last 20 years of tree growth were more than 3-fold higher than that of tritium in the older growth. This was due to recent irrigation with organic-bound tritium (OBT)-rich water and subsequent accumulation of high levels tritium as OBT relative to tissue HTO. High levels of pond irrigation water OBT resulted from biogenic processes that converted HTO to OBT. Data for 14 C that were acquired for some of the forest materials indicated that the processes controlling the movement and accumulation of 14 C in this system are somewhat different than that of tritium. Spectroscopic characterization of tree core tissue of <20 years in age found no explanation for the unusually wide dark growth rings. Here, it was concluded that the trees were over-irrigated based on results from other published studies with wood from severely-flooded areas. Although HTO is indeed toxic to biota, OBT represents a relatively greater hazard to biota because it can be bioaccumulated and retained for long periods of time in living tissues.

54 ENVIRONMENTAL SCIENCES↗

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds—five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.

Chemistry↗

ForestFlow: predicting the Lyman-α forest clustering from linear to nonlinear scales

On large scales, the Lyman-α forest provides insights into the expansion history of the Universe, while on small scales, it imposes strict constraints on the growth history, the nature of dark matter, and the sum of neutrino masses. This work introduces ForestFlow, a novel framework that bridges the gap between large- and small-scale analyses, which have traditionally relied on distinct modeling approaches. Using conditional normalizing flows, ForestFlow predicts the two Lyman-α linear biases (b δ and b η ) and six parameters describing small-scale deviations of the three-dimensional flux power spectrum (P 3D ) from linear theory as a function of cosmology and intergalactic medium physics. These are then combined with a Boltzmann solver to make consistent predictions, from arbitrarily large scales down to the nonlinear regime, for P 3D and any other statistics derived from it. Trained on a suite of 30 fixed-and-paired cosmological hydrodynamical simulations spanning redshifts from z = 2 to 4.5, ForestFlow achieves 3 and 1.5% precision in describing P 3D and the one-dimensional flux power spectrum (P 1D ) from linear scales to k = 5 Mpc −1 and k ∥ = 4 Mpc −1 , respectively. Thanks to its conditional parameterization, ForestFlow shows similar performance for ionization histories and two ΛCDM model extensions – massive neutrinos and curvature – even though none of these are included in the training set. This framework will enable full-scale cosmological analyses of Lyman-α forest measurements from the DESI survey.

79 ASTRONOMY AND ASTROPHYSICS↗

Significant thermal conductivity reduction of silicon nanowire forests through discrete surface doping of germanium

Silicon nanowires (SiNWs) are promising materials for the realization of highly-efficient and cost effective thermoelectric devices. Reduction of the thermal conductivity of such materials is a necessary and viable pathway to achieve sufficiently high thermoelectric efficiencies, which are inversely proportional to the thermal conductivity. In this article, vertically aligned forests of SiNW and germanium (Ge)-doped SiNW with diameters around 100 nm have been fabricated, and their thermal conductivity has been measured. The results show that discrete surface doping of Ge on SiNW arrays can lead to 23% reduction in thermal conductivity at room temperature compared to uncoated SiNWs. Such reduction can be further enhanced to 44% following a thermal annealing step. By analyzing the binding energy changes of Ge-3d and Si-2p using X-ray photoelectron spectroscopy, we demonstrate that surface doped Ge interacts strongly with Si, enhancing phonon scattering at the Si-Ge interface as has also been shown in non-equilibrium molecular dynamics studies of single nanowires. Overall, our results suggest a viable pathway to improve the energy conversion efficiency of nanowire-forest thermoelectric nanomaterials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Vapor-liquid-solid epitaxial growth of Si 1-x Ge x alloy nanowires. Composition dependence on precursor reactivity and morphology control for vertical forests

The growth of high-density group IV alloy nanowire forests is critical for exploiting their unique functionalities in many applications. Here, the compositional dependence on precursor reactivity and optimized conditions for vertical growth are studied for Si 1- x Ge x alloy nanowires grown by the vapor-liquid-solid method. The nanowire composition versus gas partial-pressure ratio for germane-silane and germane-disilane precursor combinations is obtained at 350°C over a wide composition range (0.05 ≤ x ≤ 0.98) and a generalized model to predict composition for alloy nanowires is developed based on the relative precursor partial pressures and reactivity ratio. In combination with germane, silane provides more precise compositional control at high Ge concentrations (x > 0.7), whereas disilane greatly increases the Si concentration for a given gas ratio and enables more precise alloy compositional control at small Ge concentrations (x < 0.3). Vertically oriented, non-kinking nanowire forest growth on Si (111) substrates is then discussed for silane/germane over a wide range of compositions, with temperature and precursor partial pressure optimized by monitoring the nanowire growth front using in-situ optical reflectance. For high Ge compositions (x ≈ 0.9), a “two-step” growth approach with nucleation at higher temperatures results in nanowires with high-density and uniform vertical orientation. Furthermore, increasing Si content (x ≈ 0.8), the optimal growth window is shifted to higher temperatures, which minimizes nanowire kinking morphologies. For Si-rich Si 1- x Ge x alloys (x ≈ 0.25), vertical nanowire growth is enhanced by single-step, higher-temperature growth at reduced pressures.

36 MATERIALS SCIENCE↗

Stabilization of a locally minimal forest

The method of partial stabilization of locally minimal networks, which was invented by Ivanov and Tuzhilin to construct examples of shortest trees with given topology, is developed. According to this method, boundary vertices of degree 2 are not added to all edges of the original locally minimal tree, but only to some of them. The problem of partial stabilization of locally minimal trees in a finite-dimensional Euclidean space is solved completely in the paper, that is, without any restrictions imposed on the number of edges remaining free of subdivision. A criterion for the realizability of such stabilization is established. In addition, the general problem of searching for the shortest forest connecting a finite family of boundary compact sets in an arbitrary metric space is formalized; it is shown that such forests exist for any family of compact sets if and only if for any finite subset of the ambient space there exists a shortest tree connecting it. The theory developed here allows us to establish further generalizations of the stabilization theorem both for arbitrary metric spaces and for metric spaces with some special properties. Bibliography: 10 titles.

97 MATHEMATICS AND COMPUTING↗

Estimating canopy fuel characteristics for predicting crown fire potential in common forest types of the Atlantic Coastal Plain, USA

We computed four stand-level canopy stratum variables important for crown fire modelling – canopy cover, stand height, canopy base height and canopy bulk density – from forest inventory data. We modelled the relationship between the canopy variables and a set of common inventory parameters – site index, stem density, basal area, stand age or stand height – and number of prescribed burns. We used a logistic model to estimate canopy cover, a linear model to estimate the other canopy variables, and the information theoretic approach for model selection. Coefficients of determination across five forest groups were 0.72–0.91 for stand height, 0.36–0.83 for canopy base height, 0.39–0.80 for canopy cover, and 0.63–0.78 for canopy bulk density. We assessed crown fire potential (1) for several sets of environmental conditions in all seasons, and (2) with increasing age, density and number of prescribed burns using our modelled canopy bulk density and canopy base height variables and local weather data to populate the Crown Fire Initiation and Spread model. Results indicated that passive crown fire is possible in any season in Atlantic coastal plain pine stands with heavy surface fuel loads and active crown fire is most probable in infrequently burned, dense stands at low fuel moistures.

Forestry↗

Iterative random forests to discover predictive and stable high-order interactions

Significance We developed a predictive, stable, and interpretable tool: the iterative random forest algorithm (iRF). iRF discovers high-order interactions among biomolecules with the same order of computational cost as random forests. We demonstrate the efficacy of iRF by finding known and promising interactions among biomolecules, of up to fifth and sixth order, in two data examples in transcriptional regulation and alternative splicing.

Science & Technology - Other Topics↗

IGM CONSTRAINTS FROM THE SDSS-III/BOSS DR9 Lyα FOREST TRANSMISSION PROBABILITY DISTRIBUTION FUNCTION

The Lyα forest transmission probability distribution function (PDF) is an established probe of the intergalactic medium (IGM) astrophysics, especially the temperature-density relationship of the IGM. We measure the transmission PDF from 3393 Baryon Oscillations Spectroscopic Survey (BOSS) quasars from Sloan Digital Sky Survey Data Release 9, and compare with mock spectra that include careful modeling of the noise, continuum, and astrophysical uncertainties. The BOSS transmission PDFs, measured at (z) = [2.3, 2.6, 3.0], are compared with PDFs created from mock spectra drawn from a suite of hydrodynamical simulations that sample the IGM temperature-density relationship, γ, and temperature at mean density, T {sub 0}, where T(Δ) = T {sub 0}Δ{sup γ} {sup –} {sup 1}. We find that a significant population of partial Lyman-limit systems (LLSs) with a column-density distribution slope of β{sub pLLS} ∼ – 2 are required to explain the data at the low-transmission end of transmission PDF, while uncertainties in the mean Lyα forest transmission affect the high-transmission end. After modeling the LLSs and marginalizing over mean transmission uncertainties, we find that γ = 1.6 best describes the data over our entire redshift range, although constraints on T {sub 0} are affected by systematic uncertainties. Within our model framework, isothermal or inverted temperature-density relationships (γ ≤ 1) are disfavored at a significance of over 4σ, although this could be somewhat weakened by cosmological and astrophysical uncertainties that we did not model.

79 ASTRONOMY AND ASTROPHYSICS↗

AUTOCLASSIFICATION OF THE VARIABLE 3XMM SOURCES USING THE RANDOM FOREST MACHINE LEARNING ALGORITHM

In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ∼92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ∼95%. Manual investigation of a random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid.

79 ASTRONOMY AND ASTROPHYSICS↗

High redshift signatures in the 21 cm forest due to cosmic string wakes

Cosmic strings induce minihalo formation in the early universe. The resultant minihalos cluster in string wakes and create a ''21 cm forest'' against the cosmic microwave background (CMB) spectrum. Such a 21 cm forest can contribute to angular fluctuations of redshifted 21 cm signals integrated along the line of sight. We calculate the root-mean-square amplitude of the 21 cm fluctuations due to strings and show that these fluctuations can dominate signals from minihalos due to primordial density fluctuations at high redshift (z∼>10), even if the string tension is below the current upper bound, Gμ < 1.5 × 10{sup −7}. Our results also predict that the Square Kilometre Array (SKA) can potentially detect the 21 cm fluctuations due to strings with Gμ ≈ 7.5 × 10{sup −8} for the single frequency band case and 4.0 × 10{sup −8} for the multi-frequency band case.

79 ASTRONOMY AND ASTROPHYSICS↗