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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Detecting ecological signatures of long-term human activity across an elevational gradient in the Šumava Mountains, Central Europe

Central European mountains, including the Šumava Mountains located along the Czechia/Germany border, have a long and rich anthropogenic history. Yet, documenting prehistoric human impact in Central European mountain environments remains a challenge because of the need to disentangle climate and human-caused responses in terrestrial systems. Here, we present the first reconstructed water table depths (WTDs) from two sites, Pěkná and Blatenská slať, located in the Šumava Mountains. We compare these local WTD records with new and published pollen, non-pollen palynomorphs (NPPs), plant macrofossils, geochemistry and archeological records to investigate how changes in local hydrology and human activities impacted forest succession and fire activity throughout the Holocene across an elevational gradient. Using a generalized additive model, our results suggest that changes in forest succession and fire activity have been primarily caused by climate throughout the Holocene. However, humans have been utilizing mountain environments and their resources continuously since ∼4600 cal yr BP, thus playing a secondary role in modifying forest succession to increase resources beneficial to both humans and grazers. Over the last 1000 years, we provide evidence of directly observed human-caused modifications to the landscape. These results contribute to a growing body of literature illustrating human activities and landscape modifications in Central European mountains.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Global-Scale Convergence Obscures Inconsistencies in Soil Carbon Change Predicted by Earth System Models

Soil carbon (C) responses to environmental change represent a major source of uncertainty in the global C cycle. Feedbacks between soil C stocks and climate drivers could impact atmospheric CO 2 levels, further altering the climate. Here, we assessed the reliability of Earth system model (ESM) predictions of soil C change using the Coupled Model Intercomparison Project phases 5 and 6 (CMIP5 and CMIP6). ESMs predicted global soil C gains under the high emission scenario, with soils taking up 43.9 Pg (95% CI: 9.2–78.5 Pg) C on average during the 21st century. The variation in global soil C change declined significantly from CMIP5 (with average of 48.4 Pg [95% CI: 2.0–94.9 Pg] C) to CMIP6 models (with average of 39.3 Pg [95% CI: 23.9–54.7 Pg] C). For some models, a small C increase in all biomes contributed to this convergence. For other models, offsetting responses between cold and warm biomes contributed to convergence. Although soil C predictions appeared to converge in CMIP6, the dominant processes driving soil C change at global or biome scales differed among models and in many cases between earlier and later versions of the same model. Random Forest models, for soil carbon dynamics, accounted for more than 63% variation of the global soil C change predicted by CMIP5 ESMs, but only 36% for CMIP6 models. Although most CMIP6 models apparently agree on increased soil C storage during the 21st century, this consensus obscures substantial model disagreement on the mechanisms underlying soil C response, calling into question the reliability of model predictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Metal-induced oxidative stress and human plasma protein oxidation after SARS-CoV-2 infection

Pathogenesis of COVID-19 by SARS-CoV-2 resulted in a global pandemic and public health emergency in 2020. Viral infection can induce oxidative stress through reactive oxygen species (ROS). Inflammation and environmental stress are major sources of oxidative stress after infection. Micronutrients such as iron, copper, zinc, and manganese play various roles in human tissues and their imbalance in blood can impact immune responses against pathogens including SARS CoV-2. We hypothesized that alteration of free metal ions during infection and metal-catalyzed oxidation plays a critical role towards pathogenesis after infection. We analyzed convalescent and hospitalized COVID-19 patient plasma using orthogonal analytical techniques to determine redox active metal concentrations, overall protein oxidation, oxidative modifications, and protein levels via proteomics to understand the consequences of metal-induced oxidative stress in COVID-19 plasma proteins. Metal analysis using ICP-MS showed significantly greater concentrations of copper in COVID-19 plasma compared to healthy controls. We demonstrate significantly greater total protein carbonylation, other oxidative modifications, and deamidation of plasma proteins in COVID-19 plasma compared to healthy controls. Proteomics analysis showed that levels of redox active proteins including hemoglobulin were elevated in COVID-19 plasma. Molecular modeling concurred with potential interactions between iron binding proteins and SARS CoV-2 surface proteins. Overall, increased levels of redox active metals and protein oxidation indicate that oxidative stress-induced protein oxidation in COVID-19 may be a consequence of the interactions of SARS-CoV-2 proteins with host cell metal binding proteins resulting in altered cellular homeostasis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Widespread misregulation of inter-species hybrid transcriptomes due to sex-specific and sex-chromosome regulatory evolution

When gene regulatory networks diverge between species, their dysfunctional expression in inter-species hybrid individuals can create genetic incompatibilities that generate the developmental defects responsible for intrinsic post-zygotic reproductive isolation. Both cis- and trans-acting regulatory divergence can be hastened by directional selection through adaptation, sexual selection, and inter-sexual conflict, in addition to cryptic evolution under stabilizing selection. Dysfunctional sex-biased gene expression, in particular, may provide an important source of sexually-dimorphic genetic incompatibilities. Here, we characterize and compare male and female/hermaphrodite transcriptome profiles for sibling nematode species Caenorhabditis briggsae and C. nigoni, along with allele-specific expression in their F 1 hybrids, to deconvolve features of expression divergence and regulatory dysfunction. Despite evidence of widespread stabilizing selection on gene expression, misexpression of sex-biased genes pervades F 1 hybrids of both sexes. This finding implicates greater fragility of male genetic networks to produce dysfunctional organismal phenotypes. Spermatogenesis genes are especially prone to high divergence in both expression and coding sequences, consistent with a “faster male” model for Haldane’s rule and elevated sterility of hybrid males. Moreover, underdominant expression pervades male-biased genes compared to female-biased and sex-neutral genes and an excess of cis-trans compensatory regulatory divergence for X-linked genes underscores a “large-X effect” for hybrid male expression dysfunction. Extensive regulatory divergence in sex determination pathway genes likely contributes to demasculinization of XX hybrids. The evolution of genetic incompatibilities due to regulatory versus coding sequence divergence, however, are expected to arise in an uncorrelated fashion. This study identifies important differences between the sexes in how regulatory networks diverge to contribute to sex-biases in how genetic incompatibilities manifest during the speciation process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Data used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”

The package contains the data layers used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”. Spatial data layers include: topography, wetland vegetation cover, time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. The study aims to characterize coastal wetlands with particular focus on the co-variability between plant dynamics, topography, soil, and other environmental factors. We proposed a functional zonation approach based on machine learning clustering to identify the spatial regions, i.e., zones that capture these co-varied properties. This approach was applied to publicly available datasets along Lake Erie, in the Great Lakes Region

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Mauka Energy FEVER Tool DOE SBIR Phase 1 Final Scientific/Technical Report

This report is on the Forestry Electric Vehicle Energy Routing (FEVER) Tool, a novel software system developed to support heavy-duty electric vehicle (EV) operations in remote, forested, and mountainous regions. The Phase I project aimed to demonstrate the feasibility of modeling EV energy consumption using terrain elevation, road conditions, and route features specific to forestry logistics. The tool combines geographic information systems (GIS), electric motor physics, and vehicle-specific data to calculate feasible, energy-efficient routes. Collaborations with Oregon State University’s Research Forests and Titan Freight Systems enabled collection and validation of GPS and elevation-based trip data. The FEVER Tool offers substantial opportunities for the efficient management of medium- and heavy-duty electric vehicles in sectors like forestry, agriculture, mining, defense and waste management—areas which are beginning to adopt HDEVs. The project demonstrated technical feasibility and lays the groundwork for commercial development and deployment in other industries and environmental conditions in Phase II.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Stochastic agent-based model for predicting turbine-scale raptor movements during updraft-subsidized directional flights

Rapid expansion of wind energy development across the world has highlighted the need to better understand turbine-caused avian mortality. The risk to golden eagles (Aquila chrysaetos) is of particular concern due to their small population size and conservation status. Golden eagles subsidize their flight in part by soaring in orographic updrafts, which can place them in conflict with wind turbines utilizing the same low-altitude wind resource. Understanding the behavior of soaring raptors in varying atmospheric conditions can therefore be relevant to predicting and mitigating their risk of collision. We present a predictive movement model that simulates individual paths of golden eagles during directional flight (such as migration) that is subsidized by orographic updraft. We modeled eagles in a 50 km by 50 km study area in Wyoming containing three wind power plants with documented golden eagle collisions with turbines. The movement model is applicable to any region where ground elevation is known at turbine scale (50 m) and wind conditions are known at facility scale (3 km). For a given set of atmospheric conditions, the model simulates movements of thousands of orographic soaring eagles to produce a density map quantifying the relative probability of eagle presence. We validated the simulated tracks with GPS telemetry data showing four directional tracks made by golden eagles transiting through the area in 2019 and 2020. For each eagle track, validation was performed using the ratio of the model-simulated eagle presence likelihood with uniform eagle presence and the presence computed using directed random-walk movements. We found that the predictive performance of the model was significantly better (likelihood ratio 1) for low-altitude movements than high-altitude movements that can involve thermal-soaring. We employed the model to produce seasonal presence maps for migrating golden eagles. We found significant turbine-level variations in eagle presence between northerly and southerly migration routes through the study area. Overall, the proposed model offers a generalizable, probabilistic, and predictive tool to assist wind energy developers, ecologists, wildlife managers, and industry consultants in estimating the potential for conflict between soaring birds and wind turbines, thereby reducing the need for site-specific data on golden eagle movements.

17 WIND ENERGY↗
DOE OSTI2022

Atomic-scale mechanism of carbon nucleation from a deep crustal fluid by replica exchange reactive molecular dynamics simulation

Here we present a mechanistic model of carbon nucleation and growth from a fluid at elevated temperature (T) and pressure conditions, typical of those found in the shallow Earth’s lithosphere. Our model uses a replica exchange reactive molecular dynamics framework in which molecular configurations are swapped between adjacent T replica at regular intervals according to underlying statistical mechanics. This framework allows predicting complex molecular structures and thermodynamics while remaining computationally efficient. Here we simulate the reactivity of an unstable mixture of CO 2 and CH 4 at 1000 K and 1 GPa. We find that the path to thermodynamic equilibrium is initially entropy-driven, producing a diversity of short-lived species, including various alcohols with intermediate carbon oxidation states. Cyclic and polycyclic radicals that are sometimes resonance-stabilized form next and set the stage for carbon nucleation. The carbon exsolution process releases abundant water, is exothermic and starts with the nucleation of a large aggregate of hydrogenated graphene flakes from covalently bonded polycyclic units. The carbon backbone of this nucleus subsequently grows into a hydrogen-depleted fullerene-like structure, before evolving toward a partially bilayered graphene layer. Overall, our results show that the mechanism of graphitic C formation is certainly not bimolecular, and that it may involve a combination of key condensation and radical chain reactions. This will help understand the isotopic, and reactive characteristics of carbon-bearing fluids during their upward transit through the Earth’s mantle and crust. Moreover, the mechanistic insights outlined here present intriguing similarities with the process of soot and interstellar dust formation, which suggests that the widespread distribution of abiotic polyaromatic and graphitic material on Earth and beyond may reflect the prevalence of a fundamental chemical pathway.

58 GEOSCIENCES↗
DOE OSTI2024

Photosynthetic capacity in middle-aged larch and spruce acclimates independently to experimental warming and elevated CO 2

Photosynthetic acclimation to both warming and elevated CO 2 of boreal trees remains a key uncertainty in modelling the response of photosynthesis to future climates. We investigated the impact of increased growth temperature and elevated CO 2 on photosynthetic capacity (V cmax and J max ) in mature trees of two North American boreal conifers, tamarack and black spruce. We show that V cmax and J max at a standard temperature of 25°C did not change with warming, while V cmax and J max at their thermal optima (T opt ) and growth temperature (T g ) increased. Moreover, V cmax and J max at either 25°C, T opt or T g decreased with elevated CO 2 . The J max /V cmax ratio decreased with warming when assessed at both T opt and T g but did not significantly vary at 25°C. The J max /V cmax increased with elevated CO 2 at either reference temperature. We found no significant interaction between warming and elevated CO 2 on all traits. If this lack of interaction between warming and elevated CO 2 on the V cmax , J max and J max /V cmax ratio is a general trend, it would have significant implications for improving photosynthesis representation in vegetation models. However, future research is required to investigate the widespread nature of this response in a larger number of species and biomes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Flood Susceptibility Mapping Using Machine Learning and Geospatial-Sentinel-1 SAR Integration for Enhanced Early Warning Systems

This study presents a comprehensive framework for flood susceptibility mapping by integrating geospatial factors with both statistical and machine learning models. Thirteen Flood-related factors, including DEM, slope, TWI, NDVI, etc., are extracted as features of models, and historical flood data derived from Sentinel-1 SAR from 2018 to 2023 are used as the target variables of the models. These datasets are analyzed using a frequency-based statistical model and three machine learning models, including Random Forest, XGBoost, and CNN, to generate flood susceptibility maps. The performance of each model is evaluated through AUC; and SHAP scores are separately generated for Machine learning (ML) models to explain each feature contribution in the ML model. The generated susceptibility maps are validated by high-flood-risk locations monitored by flood sensors, BLE inundation models, and flood-prone areas suggested by the Local Community Task Force. The results indicate that the XGBoost model outperforms all other models, with an AUC of 0.92 and demonstrates the highest alignment with recommended high-flood-risk locations, while the frequency-based statistical model showed the weakest performance with an AUC of 0.65. SHAP value graphs highlight the elevation, slope, and TWI as the most influential features across all models. The susceptibility maps generated by the machine learning model show strong agreement with the BLE map and high-flood-risk areas identified by the local Community Task Force.

Google Engine↗
DOE OSTI2025

Cerebellar dysfunction in a mouse model of childhood-onset manganese-induced dystonia parkinsonism

Humans with pathogenic variants of the manganese (Mn) transporter gene SLC39A14 exhibit highly elevated brain Mn concentrations and childhood-onset dystonia-parkinsonism. Here we show that Slc39a14-knockout (KO) mice, a preclinical model of the disease with elevated Mn concentrations in the CB, express deficits in physiological tremor implicating cerebellar (CB) dysfunction. Imaging of intracellular Mn in Purkinje cells (PCs) using synchrotron-based X-ray fluorescence microscopy confirmed highly elevated Mn concentrations in the PCs of Slc39a14-KO mice. To determine biological pathways altered in the CB of Slc39a14-KO mice relative to wildtype (WT), we performed RNA sequencing and discovered significant upregulation of pathways and genes regulating immune response and cell death. To substantiate these findings, we performed quantitative autoradiography of the neuroinflammation biomarker Translocator Protein 18 kDa (TSPO) which was significantly increased in the CB of Slc39a14-KO mice relative to WT. The latter findings were confirmed via immunostaining with the microglial marker Iba-1, revealing widespread microglia activation and clustering in the CB cortex. Immunostaining for cleaved caspase-3 (cCASP3), a marker of apoptosis, showed increased number of PCs with positive immunolabeling for cCASP3 in Slc39a14-KO mice relative to WT. Degeneration of PCs was confirmed by Hematoxylin and Eosin (H&E) staining. Lastly, functional electrophysiological assessment of CB neurocircuitry revealed a marked decrease in firing rates of cerebellar nuclei (CN) neurons and increased variability of PC simple spikes firing. Collectively, these findings show, for the first time, Mn-induced PC degeneration and dysfunctional CB circuitry in Slc39a14-KO mice providing additional evidence for the pathological underpinnings of the dystonia-like movements, balance, and gait abnormalities in SLC39A14 mutation carriers.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantifying precursors to void nucleation and coalescence in aluminum

Ductile rupture is a common failure mode for engineering alloys. It is generally comprised of three mechanisms: void nucleation at secondary particles, void growth, and coalescence of voids. Conventional models for ductile rupture have limited precision for predicting macroscopic failure. This is partially because they have been corroborated using ex-situ observations of these three mechanisms and calibrated by macroscopic strain or stress metrics. In this study, in-situ high-energy X-ray characterization was conducted to correlate sites of void nucleation via particle cracking and sites of void growth with grain-scale metrics. Here it is shown that particle cracking is not predicated by elevated stress metrics, which disagrees with classical models. Instead, particle cracking tended to occur for the largest, least spherical particles. This trend persisted on the aggregate-scale and within individual neighborhoods around particles. Furthermore, an Eshelby analysis illuminated a statistically significant increase in maximum principal stress within a cracked particle, compared to an uncracked particle. In-situ observations also revealed two separate examples of intragranular void growth comprised of flat, crack-like features that grew showing alignment with slip systems of the highest resolved shear stress. Post-mortem fractography revealed a secondary population of dimples on these crack-like features, implying that void sheeting may be occurring during this crack-like void growth. Furthermore, these in-situ observations imply that classical models for void growth, that assume a homogenous medium, should be extended to account for the anisotropic grain-level behavior to correctly capture distinct features of void nucleation and growth.

Al-2219↗
DOE OSTI2025

Observed and Imputed Volumetric Soil Water Content Timeseries for the New Mexico Elevation Gradient

Reliable soil water content (SWC) data are essential for understanding dryland ecosystem dynamics, but high-frequency SWC sensors often fail, creating gaps in critical datasets. To address this, we developed a Bayesian mixture model that imputes missing SWC using both linear interpolation and an ecosystem water balance model (SOILWAT2), tested across six AmeriFlux eddy covariance tower sites in the New Mexico Elevation Gradient, demonstrating its effectiveness in reconstructing SWC patterns while providing insights into the factors driving SWC variability. Daily volumetric soil water content (SWC) data are provided as csv-formatted spreadsheets for the six AmeriFlux sites (US-Seg, US-Ses, US-Wjs, US-Mpi, US-Vcp, and US-Vcs). For each site there is an observed SWC file (site_SWC_gapfill.csv) and a file that contains imputed SWC (imputed_SWC_site.csv). The observed SWC files contain temperature corrected sensor values, tower precipitation data, as well as outputs from SOILWAT2 simulations that were used to impute SWC. The imputed files contain the original observed SWC values and the imputed missing SWC values. When SWC was missing from the original data, the missing value was imputed based on the Bayesian imputation mixture model. The posterior mean of all imputed values is reported as "mean_X". When the observed SWC was NOT missing, mean_X = observed SWC value (original data). The standard deviation, 2.5th percentile and the 97.5th percentile for the imputed values are also reported in the imputed files. There are readme text files for each file type explaining the contents of each column.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Volatilization Behavior of Supported Au Nanoparticle Arrays under H 2 at High Temperature

In this work, the fabrication and experimental study of model Au nanoparticle arrays (nanodots) was used to allow a direct comparison of reactive evaporation at elevated temperatures and nonambient H 2 -containing gas atmospheres with theoretical models. Strong temperature and H 2 -concentration dependencies were confirmed as expected, and the observed dependence in particle diameter with time was effectively described using a reactive evaporation and gas phase diffusion based quantitative model.

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

ADVANCED THERMAL-HYDRAULIC MODEL OF HEAT RECOVERY STEAM GENERATORS

Steam-side oxidation at elevated temperatures in heat recovery steam generators (HRSGs) can lead to overheating of local tubes, potential oxide scale exfoliation, and damage to critical equipment. Predicting the oxide growth distribution requires detailed knowledge of the distribution of tube metal temperatures. To address these challenges, a computational fluid dynamics (CFD) model of a high-pressure superheater (HPSH) was developed within the Siemens’ STAR-CCM+ CFD commercial software. The HPSH section of the steam generator was selected for modeling because of its high oxidation rates.The model development included several steps. In the first step, an oxidation model based on a parabolic oxidation relationship was implemented. The oxidation growth predicted by the model was then verified for a full-load boiler operation case. In the second step, a three-dimensional CFD model was developed to account for the thermal and flow physics involved in HRSGs. The CFD model was then verified and validated on a simplified geometry that contained six row-tubes which mimicked the tube arrangements of an HPSH section. Numerical results compared well with published experimental results. In the third step, the oxidation model was coupled to the HRSG’s CFD model, and a comparison study was performed for two cases: (a) regular finless tubes and (b) finned tubes. Preliminary results showed that the oxide thickness predicted by the case with tube fins was higher compared to that of the finless tube case, thus demonstrating the need to model the effects of the tube fins on oxide thickness.

Panicker, Nithin↗
DOE OSTI2020

Optimizing long-term monitoring of radiation air-dose rates after the Fukushima Daiichi Nuclear Power Plant

Radiation air dose rates near the Fukushima Daiichi Nuclear Power Plant (FDNPP) have been steadily decreasing over the past eight years since the release of radioactive elements in March 2011. Currently, the radiation monitoring program is expected to transition to long-term monitoring after most of the remediation activities are completed. The main long-term monitoring objectives are to (1) confirm the continuing reduction of contaminant and hazard levels, (2) provide assurance for the public, (3) accumulate the basic datasets for scientific knowledge and future preparation, and (4) detect changes or anomalies in contaminant mobility (if they occur), or any unexpected processes or events. In this work, we have developed a methodology for optimizing the monitoring locations of radiation air dose-rate monitoring. Our approach consists of three steps in order to determine monitoring locations in a systematic manner: (1) prioritizing the critical locations, such as schools or regulatory requirement locations, (2) diversifying locations that cover the key environmental controls that are known to influence contaminant mobility and distributions, and (3) capturing the heterogeneity of radiation air-dose rates across the domain. Therefore, for the second step, we use a Gaussian mixture model to identify the representative locations among multiple environmental variables, such as elevation and land-cover types. For the third step, we use a Gaussian process model to capture and estimate the heterogeneity of air-dose rates across the domain. Employing an integrated dose-rate map derived from Bayesian geostatistical methods as a reference map, we distribute the monitoring locations in such a way as to capture the heterogeneity of the reference map. Our results have shown that this approach allows us to select monitoring locations in a systematic manner such that the heterogeneity of air dose rates is captured by the minimal number of monitoring locations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Crystal plasticity modeling and analysis for the transition from intergranular to transgranular failure in nickel-based alloy Inconel 740H at elevated temperature

The precipitation-strengthened Nickel alloy Inconel® 740H® (IN740H) exhibits increased ductility at higher applied strain rates during quasi-static tensile tests at an elevated temperature of 760°C. The examination of fracture surfaces in this context reveals a noteworthy transition of underlying fracture mechanisms from transgranular to intergranular fracture as the applied strain rate decreases from 1×10 -3 / s to 0.83×10 -4 / s . To thoroughly understand the mechanical response of IN740H under these conditions, this study develops a crystal plasticity finite element (CPFE) model. Further, this model incorporates various deformation mechanisms including dislocation slips, climb, and grain boundary sliding, which are relevant to the test conditions. The model is calibrated using data from both tensile tests at different strain rates and creep tests across a broad stress range at 760°C, enabling the accurate determination of model parameters for each mechanism. Simulation results well captured the experimental observations of different failure modes. At higher strain rates, the model shows a dominance of dislocation slip leading to heterogeneous plastic deformation and formation of transgranular shear bands causing the failure, while at lower strain rates, an increased activity of grain boundary sliding causes grain boundaries crack leading to intergranular failure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Characterizing the vertical structure of forests in the Brazilian Amazon

Little is known about the structure of tropical forests despite its critical role in the provisioning of ecosystem services. Here we assess the vertical structure of forests in the Brazilian Amazon with a large-scale airborne LiDAR dataset. We show that fire has greater impact in the lowest forest strata, differently from selective logging and windthrow. We also find that secondary forests quickly recover or even exceed reference areas at the 1-10 m height stratum but that full recovery for the 20-30 m height stratum has not been achieved even after 35 years. Our modeling results suggest that proximity to roads, elevation, precipitation, soil pH, and proportion of sand in the soil are the most important predictors of forest structure. Finally, we identify 5 forest structural types (FSTs) and use them to visualize the spatial distribution of forest structure. This study provides important information for forest monitoring, management, and conservation.

Biogeography↗