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At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Forest tree growth is linked to mycorrhizal fungal composition and function across Europe

Most trees form symbioses with ectomycorrhizal fungi (EMF) which influence access to growth-limiting soil resources. Mesocosm experiments repeatedly show that EMF species differentially affect plant development, yet whether these effects ripple up to influence the growth of entire forests remains unknown. Here we tested the effects of EMF composition and functional genes relative to variation in well-known drivers of tree growth by combining paired molecular EMF surveys with high-resolution forest inventory data across 15 European countries. We show that EMF composition was linked to a three-fold difference in tree growth rate even when controlling for the primary abiotic drivers of tree growth. Fast tree growth was associated with EMF communities harboring high inorganic but low organic nitrogen acquisition gene proportions and EMF which form contact versus medium-distance fringe exploration types. These findings suggest that EMF composition is a strong bio-indicator of underlying drivers of tree growth and/or that variation of forest EMF communities causes differences in tree growth. While it may be too early to assign causality or directionality, our study is one of the first to link fine-scale variation within a key component of the forest microbiome to ecosystem functioning at a continental scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The interplay of labile organic carbon, enzyme activities and microbial communities of two forest soils across seasons

Soil labile organic carbon (LOC) responds rapidly to environmental changes and plays an important role in carbon cycle. In this study, the seasonal fluctuations in LOC, the activities of carbon-cycle related enzymes, and the bacterial and fungal communities were analyzed for soils collected from two forests, namely Betula albosinensis (Ba) and Picea asperata Mast. (Pa), in the Qinling Mountains of China. Results revealed that the seasonal average contents of microbial biomass carbon (MBC), easily oxidized organic carbon (EOC), and dissolved organic carbon (DOC) of Pa forest soil were 13.5%, 30.0% and 15.7% less than those in Ba soil. The seasonal average enzyme activities of β-1,4-glucosidase (βG), and β-1,4-xylosidase (βX) of Ba forest soils were 30.0% and 32.3% higher than those of Pa soil while the enzyme activity of cellobiohydrolase (CBH) was 19.7% lower. Furthermore, the relative abundance of Acidobacteria was significantly higher in summer than in winter, whereas the relative abundance of Bacteroidetes was higher in winter. Regarding the fungal communities, the relative abundance of Basidiomycota was lowest in winter, whereas Ascomycota predominated in the same season. In addition, the soil LOC was significantly positively correlated with the CBH, βG and βX activities. Changes in LOC were significantly correlated with Acidobacteria, Bacteroidetes and Basidiomycota. We conclude that the seasonal fluctuations in forest soil LOC fractions relied on carbon cycle-associated enzymatic activities and microorganisms, which in turn were affected by climatic conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Low temperature growth of ultra-high mass density carbon nanotube forests on conductive supports

We grow ultra-high mass density carbon nanotube forests at 450 °C on Ti-coated Cu supports using Co-Mo co-catalyst. X-ray photoelectron spectroscopy shows Mo strongly interacts with Ti and Co, suppressing both aggregation and lifting off of Co particles and, thus, promoting the root growth mechanism. The forests average a height of 0.38 μm and a mass density of 1.6 g cm{sup −3}. This mass density is the highest reported so far, even at higher temperatures or on insulators. The forests and Cu supports show ohmic conductivity (lowest resistance ∼22 kΩ), suggesting Co-Mo is useful for applications requiring forest growth on conductors.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2016

Micro glow plasma for localized nanostructural modification of carbon nanotube forest

This paper reports the localized selective treatment of vertically aligned carbon nanotubes, or CNT forests, for radial size modification of the nanotubes through a micro-scale glow plasma established on the material. An atmospheric-pressure DC glow plasma is shown to be stably sustained on the surface of the CNT forest in argon using micromachined tungsten electrodes with diameters down to 100 μm. Experiments reveal thinning or thickening of the nanotubes under the micro glow depending on the process conditions including discharge current and process time. These thinning and thickening effects in the treated nanotubes are measured to be up to ∼30% and ∼300% in their diameter, respectively, under the tested conditions. The elemental and Raman analyses suggest that the treated region of the CNT forest is pure carbon and maintains a degree of crystallinity. The local plasma treatment process investigated may allow modification of material characteristics in different domains for targeted regions or patterns, potentially aiding custom design of micro-electro-mechanical systems and other emerging devices enabled by the CNT forest.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

No evidence of canopy-scale leaf thermoregulation to cool leaves below air temperature across a range of forest ecosystems

Understanding and predicting the relationship between leaf temperature (T leaf ) and air temperature (T air ) is essential for projecting responses to a warming climate, as studies suggest that many forests are near thermal thresholds for carbon uptake. Based on leaf measurements, the limited leaf homeothermy hypothesis argues that daytime T leaf is maintained near photosynthetic temperature optima and below damaging temperature thresholds. Specifically, leaves should cool below T air at higher temperatures (i.e., > ~25–30°C) leading to slopes <1 in T leaf /T air relationships and substantial carbon uptake when leaves are cooler than air. This hypothesis implies that climate warming will be mitigated by a compensatory leaf cooling response. A key uncertainty is understanding whether such thermoregulatory behavior occurs in natural forest canopies. We present an unprecedented set of growing season canopy-level leaf temperature (T can ) data measured with thermal imaging at multiple well-instrumented forest sites in North and Central America. Our data do not support the limited homeothermy hypothesis: canopy leaves are warmer than air during most of the day and only cool below air in mid to late afternoon, leading to T can /T air slopes >1 and hysteretic behavior. We find that the majority of ecosystem photosynthesis occurs when canopy leaves are warmer than air. Using energy balance and physiological modeling, we show that key leaf traits influence leaf-air coupling and ultimately the T can /T air relationship. Canopy structure also plays an important role in T can dynamics. Future climate warming is likely to lead to even greater T can , with attendant impacts on forest carbon cycling and mortality risk.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

A new approach to evaluate the MODIS annual NPP product (MOD17A3) using forest field data from Turkey

In this study we present the first evaluation of the MODIS (MODerate resolution Imaging Spectroradiometer) annual Net Primary Product (NPP) for Turkey's forest ecosystems using field measurements. Due to lack of country scale field measurements (i.e. flux tower for forest ecosystems), tree DBH (Diameter at Breast Height) dataset provided by Ministry of Forest and Water Affair (MFWA) of Turkey is used to calculate NPP of Turkey’s forest ecosystems. The lack of a reliable NPP dataset leads the researchers to use global NPP models such as MODIS annual NPP product. The MODIS MOD17A3 product of vegetation net primary production (NPP) is one of the most highly used data sources for studies of global carbon 25 cycle. However, it is still necessary to test its predictions in multiple biomes, especially for heterogeneous areas in terms of its accuracy and potential bias. Here, we studied a new approach to evaluate coarse scale NPP estimates from the MODIS NPP- MOD17A3 data product, using 2008-2013 field measurements of tree growth throughout Turkey. There different methods were used to calculate field NPP, including standardized growth coefficients (MC), growth coefficients from North America (JC) and annual expected increment (AEI). The average NPP values for all the country is calculated as 2.06 kgC m-2(5years)-1 (0.412 kgC m-2 year-1) (SD = 1.15 kgC m-2 (5years)-1) from MOD17A3, 0.90 kgC m-2(5years)-1 (0.18 kgC m-2 year-1) (SD = 0.57 kgC m-2(5years)-1) with MC, 0.63 kgC m-2(5years)-1 (0.126 kgC m-2 year-1) (SD = 0.37 kgC m-2(5years)-1) with JC and 0.58 kgC m-2 year-1 (SD = 0.29 kgC m-2(5years)-1) with AEI for the studied plots. We found that the MODIS NPP product has a clear relation with both the NPP estimates obtained by using MC (R36 2 = 0.34, RMSE=1.51 kgC m-2(5years)-1) and JC (R37 2 = 0.32, RMSE = 1.73 kgC m-2(5years)-1). In addition to that, the relation between MOD17A3 product and AEI-derived NPP is relatively strong (R39 2 = 0.48, RMSE = 0.26 kgC m-2 year-1). We discuss possible reasons for these trade-offs among different methods. This study lays out a new approach to validate coarse scale MODIS product using field data directly, including for highly heterogeneous areas.

biomass↗
DOE OSTI2014

LyMAS: Predicting large-scale Lyα forest statistics from the dark matter density field

We describe Lyα Mass Association Scheme (LyMAS), a method of predicting clustering statistics in the Lyα forest on large scales from moderate-resolution simulations of the dark matter (DM) distribution, with calibration from high-resolution hydrodynamic simulations of smaller volumes. We use the 'Horizon-MareNostrum' simulation, a 50 h {sup –1} Mpc comoving volume evolved with the adaptive mesh hydrodynamic code RAMSES, to compute the conditional probability distribution P(F{sub s} |δ {sub s}) of the transmitted flux F{sub s} , smoothed (one-dimensionally, 1D) over the spectral resolution scale, on the DM density contrast δ {sub s}, smoothed (three-dimensionally, 3D) over a similar scale. In this study we adopt the spectral resolution of the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS) at z = 2.5, and we find optimal results for a DM smoothing length σ = 0.3 h {sup –1} Mpc (comoving). In its simplest form, LyMAS draws randomly from the hydro-calibrated P(F{sub s} |δ {sub s}) to convert DM skewers into Lyα forest pseudo-spectra, which are then used to compute cross-sightline flux statistics. In extended form, LyMAS exactly reproduces both the 1D power spectrum and one-point flux distribution of the hydro simulation spectra. Applied to the MareNostrum DM field, LyMAS accurately predicts the two-point conditional flux distribution and flux correlation function of the full hydro simulation for transverse sightline separations as small as 1 h {sup –1} Mpc, including redshift-space distortion effects. It is substantially more accurate than a deterministic density-flux mapping ({sup F}luctuating Gunn-Peterson Approximation{sup )}, often used for large-volume simulations of the forest. With the MareNostrum calibration, we apply LyMAS to 1024{sup 3} N-body simulations of a 300 h {sup –1} Mpc and 1.0 h {sup –1} Gpc cube to produce large, publicly available catalogs of mock BOSS spectra that probe a large comoving volume. LyMAS will be a powerful tool for interpreting 3D Lyα forest data, thereby transforming measurements from BOSS and other massive quasar absorption surveys into constraints on dark energy, DM, space geometry, and intergalactic medium physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

The large-scale quasar-Lyman α forest cross-correlation from BOSS

We measure the large-scale cross-correlation of quasars with the Lyα forest absorption in redshift space, using ∼ 60000 quasar spectra from Data Release 9 (DR9) of the Baryon Oscillation Spectroscopic Survey (BOSS). The cross-correlation is detected over a wide range of scales, up to comoving separations r of 80 h{sup −1}Mpc. For r > 15 h{sup −1}Mpc, we show that the cross-correlation is well fitted by the linear theory prediction for the mean overdensity around a quasar host halo in the standard ΛCDM model, with the redshift distortions indicative of gravitational evolution detected at high confidence. Using previous determinations of the Lyα forest bias factor obtained from the Lyα autocorrelation, we infer the quasar bias factor to be b{sub q} = 3.64{sup +0.13}{sub −0.15} at a mean redshift z = 2.38, in agreement with previous measurements from the quasar auto-correlation. We also obtain a new estimate of the Lyα forest redshift distortion factor, β{sub F} = 1.1±0.15, slightly larger than but consistent with the previous measurement from the Lyα forest autocorrelation. The simple linear model we use fails at separations r < 15h{sup −1}Mpc, and we show that this may reasonably be due to the enhanced ionization due to radiation from the quasars. We also provide the expected correction that the mass overdensity around the quasar implies for measurements of the ionizing radiation background from the line-of-sight proximity effect.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Forests dominate the interannual variability of the North American carbon sink

Understanding what drives the interannual variability (IAV) of the land carbon sink is crucial for improving future predictions of this important, yet uncertain, component of the climate system. While drivers of global and hemispheric-scale net ecosystem exchange (NEE) IAV have been investigated, our understanding of the drivers of NEE IAV at regional scales (e.g. sub-continental, biome-level) is quite poor. Here we explore the biome-level attribution and drivers of North American NEE using inverse estimates derived from a dense network of atmospheric CO2 observations. We find that deciduous broadleaf and mixed forests are the primary regions responsible for North American NEE IAV, which differs from the ecoregions identified for the globe and Northern Hemisphere. We also find that a suite of terrestrial biosphere models (TBMs) do not agree on the dominant biome contributing to NEE IAV, with TBMs falling along an apparent spectrum ranging between those with IAV dominated primarily by forested ecosystems to those with IAV dominated by non-forested ecosystems. Furthermore, this regional trade-off in TBM NEE IAV is found to be linked to differing regional responses to environmental drivers among TBMs. This work displays the importance of extra-tropical forests in driving continental NEE IAV and also highlights the challenges and limitations of using TBMs to inform regional-scale carbon flux dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Mitigating drought mortality by incorporating topography into variable forest thinning strategies

Abstract Drought-induced productivity reductions and tree mortality have been increasing in recent decades in forests around the globe. Developing adaptation strategies hinges on an adequate understanding of the mechanisms governing the drought vulnerability of forest stands. Prescribed reduction in stand density has been used as a management tool to reduce water stress and wildfire risk, but the processes that modulate fine-scale variations in plant water supply and water demand are largely missing in ecosystem models. We used an ecohydrological model that couples plant hydraulics with groundwater hydrology to examine how within-stand variations in tree spatial arrangements and topography might mitigate forest vulnerability to drought at individual-tree and stand scales. Our results demonstrated thinning generally ameliorated plant hydraulic stress and improved carbon and water fluxes of the remaining trees, although the effectiveness varied by climate and topography. Variable thinning that adjusted thinning intensity based on topography-mediated water availability achieved higher stand productivity and lower mortality risk, compared to evenly-spaced thinning at comparable intensities. The results from numerical experiments provided mechanistic evidence that topography mediates the effectiveness of thinning and highlighted the need for an explicit consideration of within-stand heterogeneity in trees and abiotic environments when designing forest thinning to mitigate drought impacts.

Environmental Sciences & Ecology↗
DOE OSTI2020

Comparison of linear regression, k-nearest neighbour and random forest methods in airborne laser-scanning-based prediction of growing stock

Abstract In this study, for five sites around the world, we look at the effects of different model types and variable selection approaches on forest yield modelling performances in an area-based approach (ABA). We compared ordinary least squares regression (OLS), k-nearest neighbours (kNN) and random forest (RF). Our objective was to test if there are systematic differences in accuracy between OLS, kNN and RF in ABA predictions of growing stock volume. The analyses are based on a 5-fold cross-validation at five study sites: an eucalyptus plantation, a temperate forest and three different boreal forests. Two completely independent validation datasets were also available for two of the boreal sites. For the kNN, we evaluated multiple measures of distance including Euclidean, Mahalanobis, most similar neighbour (MSN) and an RF-based distance metric. The variable selection approaches we examined included a heuristic approach (for OLS, kNN and RF), exhaustive search among all combinations (OLS only) and all variables together (RF only). Performances varied by model type and variable selection approaches among sites. OLS and RF had similar accuracies and were more efficient than any of the kNN variants. Variable selection did not affect RF performance. Heuristic and exhaustive variable selection performed similarly for OLS. kNN fared the poorest amongst model types, and kNN with RF distance was prone to overfitting when compared with a validation dataset. Additional caution is therefore required when building kNN models for volume prediction though ABA, being preferable instead to opt for models based on OLS with some variable selection, or RF with all variables together.

Cosenza, Diogo N.↗
DOE OSTI2023

The Alcock–Paczynski effect from Lyman- α forest correlations: analysis validation with synthetic data

The three-dimensional distribution of the Ly α forest has been extensively used to constrain cosmology through measurements of the baryon acoustic oscillations (BAO) scale. However, more cosmological information could be extracted from the full shapes of the Ly α forest correlations through the Alcock–Paczynski (AP) effect. In this work, we prepare for a cosmological analysis of the full shape of the Ly α forest correlations by studying synthetic data of the extended Baryon Oscillation Spectroscopic Survey (eBOSS). We use a set of 100 eBOSS synthetic data sets in order to validate such an analysis. These mocks undergo the same analysis process as the real data. We perform a full-shape analysis on the mean of the correlation functions measured from the 100 eBOSS realizations, and find that our model of the Ly α correlations performs well on current data sets. We show that we are able to obtain an unbiased full-shape measurement of D M /D H (z eff ), where D M is the transverse comoving distance, D H is the Hubble distance, and z eff is the effective redshift of the measurement. We test the fit over a range of scales, and decide to use a minimum separation of r min = 25 h –1 Mpc. Here, we also study and discuss the impact of the main contaminants affecting Ly α forest correlations, and give recommendations on how to perform such analysis with real data. While the final eBOSS Ly α BAO analysis measured D M /D H (z eff = 2.33) with 4 per cent statistical precision, a full-shape fit of the same correlations could provide an $\sim 2~{{\ \rm per\ cent}}$ measurement.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Forecasting constraints on the mean free path of ionizing photons at z ≥ 5.4 from the Lyman-α forest flux autocorrelation function

ABSTRACT Fluctuations in Lyman-α (Ly α) forest transmission towards high-z quasars are partially sourced from spatial fluctuations in the ultraviolet background, the level of which are set by the mean free path of ionizing photons (λmfp). The autocorrelation function of Ly α forest flux characterizes the strength and scale of transmission fluctuations and, as we show, is thus sensitive to λmfp. Recent measurements at z ∼ 6 suggest a rapid evolution of λmfp at z > 5.0 which would leave a signature in the evolution of the autocorrelation function. For this forecast, we model mock Ly α forest data with properties similar to the XQR-30 extended data set at 5.4 ≤ z ≤ 6.0. At each z, we investigate 100 mock data sets and an ideal case where mock data matches model values of the autocorrelation function. For ideal data with λmfp = 9.0 cMpc at z = 6.0, we recover $\lambda _{\text{mfp}}=12^{+6}_{-3}$ cMpc. This precision is comparable to direct measurements of λmfp from the stacking of quasar spectra beyond the Lyman limit. Hypothetical high-resolution data leads to a $\sim 40~{{\ \rm per\ cent}}$ reduction in the error bars over all z. The distribution of mock values of the autocorrelation function in this work is highly non-Gaussian for high-z, which should caution work with other statistics of the high-z Ly α forest against making this assumption. We use a rigorous statistical method to pass an inference test, however future work on non-Gaussian methods will enable higher precision measurements.

Astronomy & Astrophysics↗
DOE OSTI2019

Impact of inhomogeneous reionization on the Lyman-α forest

The Lyman-α forest at high redshifts is a powerful probe of reionization. Modelling and observing this imprint comes with significant technical challenges: inhomogeneous reionization must be taken into account while simultaneously being able to resolve the web-like small-scale structure prior to reionization. In this work, we quantify the impact of inhomogeneous reionization on the Lyman-α forest at lower redshifts (2 < z < 4), where upcoming surveys such as Dark Energy Spectroscopic Instrument will enable precision measurements of the flux power spectrum. We use both small box simulations capable of handling the small-scale structure of the Lyman-α forest and seminumerical large box simulations capable of representing the effects of inhomogeneous reionization. We find that inhomogeneous reionization could produce a measurable effect on the Lyman-α forest power spectrum. The deviation in the 3D power spectrum at z obs = 4 and |$k = 0.14 \, {\rm Mpc}^{-1}$| ranges from 19 to 36 percent, with a larger effect for later reionization. The corrections decrease to 2.0–4.1 percent by z obs = 2. The impact on the 1D power spectrum is smaller, and ranges from 3.3–6.5 percent at z obs = 4 to 0.35–0.75 percent at z obs = 2, values which are comparable to the statistical uncertainties in current and upcoming surveys. Furthermore, we study how can this systematic be constrained with the help of the quadrupole of the 21 cm power spectrum.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Precision measurements of EFT parameters and BAO peak shifts for the Lyman- α forest

We present precision measurements of the bias parameters of the one-loop power spectrum model of the Lyman- α (Ly- α ) forest, derived within the effective field theory (EFT) of large-scale structure. We fit our model to the three-dimensional flux power spectrum measured from the ACCEL 2 hydrodynamic simulations. The EFT model fits the data with an accuracy of below 2% up to k = 2 h Mpc − 1 . Further, we analytically derive how nonlinearities in the three-dimensional clustering of the Ly- α forest introduce biases in measurements of the baryon acoustic oscillations (BAOs) scaling parameters in radial and transverse directions. From our EFT parameter measurements, we obtain a theoretical error budget of Δ α ∥ = − 0.2 % ( Δ α ⊥ = − 0.3 % ) for the radial (transverse) parameters at redshift z = 2.0 . This corresponds to a shift of − 0.3 % (0.1%) for the isotropic (anisotropic) distance measurements. We provide an estimate for the shift of the BAO peak for Ly- α -quasar cross-correlation measurements assuming analytical and simulation-based scaling relations for the nonlinear quasar bias parameters resulting in a shift of − 0.2 % ( − 0.1 % ) for the radial (transverse) dilation parameters, respectively. This analysis emphasizes the robustness of Ly- α forest BAO measurements to the theory modeling. We provide informative priors and an error budget for measuring the BAO feature—a key science driver of the currently observing Dark Energy Spectroscopic Instrument (DESI). Our work paves the way for full-shape cosmological analyses of Ly- α forest data from DESI and upcoming surveys such as the Prime Focus Spectrograph, WEAVE-QSO, and 4MOST. Published by the American Physical Society 2025

de Belsunce, Roger (ORCID:0000000336604028)↗
DOE OSTI2016

Marker-free registration of forest terrestrial laser scanner data pairs with embedded confidence metrics

Terrestrial laser scanning (TLS) has emerged as an effective tool for rapid comprehensive measurement of object structure. Registration of TLS data is an important prerequisite to overcome the limitations of occlusion. However, due to the high dissimilarity of point cloud data collected from disparate viewpoints in the forest environment, adequate marker-free registration approaches have not been developed. The majority of studies instead rely on the utilization of artificial tie points (e.g., reflective tooling balls) placed within a scene to aid in coordinate transformation. We present a technique for generating view-invariant feature descriptors that are intrinsic to the point cloud data and, thus, enable blind marker-free registration in forest environments. To overcome the limitation of initial pose estimation, we employ a voting method to blindly determine the optimal pairwise transformation parameters, without an a priori estimate of the initial sensor pose. To provide embedded error metrics, we developed a set theory framework in which a circular transformation is traversed between disjoint tie point subsets. This provides an upper estimate of the Root Mean Square Error (RMSE) confidence associated with each pairwise transformation. Output RMSE errors are commensurate with the RMSE of input tie points locations. Thus, while the mean output RMSE=16.3cm, improved results could be achieved with a more precise laser scanning system. This study 1) quantifies the RMSE of the proposed marker-free registration approach, 2) assesses the validity of embedded confidence metrics using receiver operator characteristic (ROC) curves, and 3) informs optimal sample spacing considerations for TLS data collection in New England forests. Furthermore, while the implications for rapid, accurate, and precise forest inventory are obvious, the conceptual framework outlined here could potentially be extended to built environments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Drought responses by individual tree species are not often correlated with tree species diversity in European forests

Drought frequency and intensity are predicted to increase in many parts of the Northern Hemisphere and the effects of such changes on forest growth and tree mortality are already evident in many regions around the world. Mixed-species forests and increasing tree species diversity have been put forward as important risk reduction and adaptation strategies in the face of climate change. But, little is known about whether the species interactions that occur in diverse forests will reduce drought susceptibility or water stress. In this study, we focused on the effect of drought on individual tree species (n = 16) within six regions of Europe and assessed whether this response was related to tree species diversity and stand density, and whether community-level responses resulted from many similar or contrasting species-level responses. For each species in each plot, we calculated the increase in carbon isotope composition of latewood from a wet to a dry year (Δδ13C) as an estimate of its drought stress level. Furthermore, when significant community-level relationships occurred (three of six regions), there was only one species within the given community that showed a significant relationship (three of 25 species–region combinations), showing that information about a single species can be a poor indicator of the response of other species or the whole community. There were many two-species mixtures in which both species were less water-stressed compared with their monocultures, but also many mixtures where both species were more stressed compared with their monocultures. Furthermore, a given species combination responded differently in different regions. Synthesis and applications. Our study shows that drought stress may sometimes be reduced in mixed-species forests, but this is not a general pattern, and even varies between sites for a given combination. The management or prediction of drought stress requires consideration of the physiological characteristics of the mixed species, and how this complements the water-related climatic and edaphic features of the site, rather than species richness.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Drought timing and local climate determine the sensitivity of eastern temperate forests to drought

Abstract Projected changes in temperature and drought regime are likely to reduce carbon (C) storage in forests, thereby amplifying rates of climate change. While such reductions are often presumed to be greatest in semi‐arid forests that experience widespread tree mortality, the consequences of drought may also be important in temperate mesic forests of Eastern North America ( ENA ) if tree growth is significantly curtailed by drought. Investigations of the environmental conditions that determine drought sensitivity are critically needed to accurately predict ecosystem feedbacks to climate change. We matched site factors with the growth responses to drought of 10,753 trees across mesic forests of ENA , representing 24 species and 346 stands, to determine the broad‐scale drivers of drought sensitivity for the dominant trees in ENA . Here we show that two factors—the timing of drought, and the atmospheric demand for water (i.e., local potential evapotranspiration; PET )—are stronger drivers of drought sensitivity than soil and stand characteristics. Drought‐induced reductions in tree growth were greatest when the droughts occurred during early‐season peaks in radial growth, especially for trees growing in the warmest, driest regions (i.e., highest PET ). Further, mean species trait values (rooting depth and ψ 50 ) were poor predictors of drought sensitivity, as intraspecific variation in sensitivity was equal to or greater than interspecific variation in 17 of 24 species. From a general circulation model ensemble, we find that future increases in early‐season PET may exacerbate these effects, and potentially offset gains in C uptake and storage in ENA owing to other global change factors.

D'Orangeville, Loïc↗