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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Enriched Background Isotope Study (EBIS): Analysis of 14C-Enriched Carbon Cycle in Soils and Litter at Forested Oak Ridge and AmeriFlux Sites, 2001-2011

These data provide a record of the multi-year, multi-institutional Enriched Background Isotope Studies (EBIS) projects that ran from 2000 through 2011. Elevated levels of 14C enriched CO2 in the air and soil atmosphere as well as leaf, stem, and root tissues were observed on the Oak Ridge Reservations (ORR) during the summer of 1999, and were attributed to local incinerator activities on and/or near the ORR (Trumbore et al. 2002). The isolated enrichment of the background levels of 14C in local forest ecosystem represented a unique opportunity to study unresolved carbon cycling processes such as the contribution of leaf versus root litter contributions to soil carbon accumulation, the rate of vertical transport of carbon into deep soil storage pools, and the differential contribution of physicochemical versus faunal driven processes to soil carbon cycling and sequestration. Leaf litter from the local enriched forest was transplanted to selected sites on the ORR and to selected AmeriFlux study sites to study soil C cycling across a range of soils and climates. The EBIS research projects provide data on C flux from litter sources to mineral soil sinks for United States eastern hardwood forests necessary for testing process hypotheses and judging efficacy of soil C cycling models. Experimental results from this study are being used to parameterize and refine existing carbon dynamics models, the quantification of the long-term fate of ecosystem carbon inputs and as a means to judge the potential for ecosystem carbon sequestration via enhance litter inputs to soil. EBIS observations support conclusions that intra- and inter-annual soil carbon cycling in hardwood forest soils should be characterized as a least a two-compartment system where surface leaf-litter and belowground root turnover represent primary carbon sources for organic-layer and mineral-soil carbon cycles, respectively. EBIS experiments were conducted to complete enriched litterfall maniplations in upland forests on Ultisol and Inceptisol soils of the Oak Ridge Reservation, Oak Ridge, Tennessee. We also collected additional14C-enriched materials for new experimental applications, and applied those materials to multiple AmeriFlux sites over a range of climatic, edaphic and biological conditions. The research provided data for addressing DOE's goal of understanding mechanisms controlling C flux, and for the improvement of models to be applied to policy discussions regarding the safe levels of greenhouse gases for the earth's system. There are 5 data files provided in comma separated (*.csv) format for vegetation, field litter, soil and air [C] and C isotope data from the EBIS studies and associated environmental data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Demand for the development of an automated and integrated refining process for biofuels has increased in recent years due to the lack of generalized process inspection tools. In bio-oil upgrading processes, all process variables are maintained based on the offline specification of intermediates and products. A lack of real-time product specifications in batch-wise monitoring can cause process failure and wasted resources. Therefore, there is a need for a fast and accurate intermediates/product specification tool that can be used for real-time specification to reduce waste and mitigate the risk of process failure. Here, to address this gap, we developed a machine learning (ML) model for predicting speciated bio-oil composition, including paraffin, iso-paraffins, olefins, naphthene, and aromatics. The model is trained using the mass spectra from upgraded products collected in the vapor phase before condensation and predicts the composition of the condensed product. Training ML models using raw mass spectra is challenging due to numerous overlapped peaks originating from different parent compounds. With this in mind, we propose a protocol that (i) transforms raw mass spectra to chemistry-inspired predefined features and (ii) trains decision tree-based models using these features. Our results show that the random forest model was robust against overfitting and had the highest accuracy compared to other models. Moreover, a stochastic ablation method determined the eight most significant features while maximizing the accuracy. Our protocol facilitates real-time compositional analysis of upgraded bio-oils and thus real-time process monitoring. Additionally, this protocol enables the rational design of efficient catalysts and the determination of optimal process conditions.

09 BIOMASS FUELS↗
DOE OSTI2024

Machine learning techniques for intermediate mass gap lepton partner searches at the large hadron collider

We consider machine learning techniques associated with the application of a boosted decision tree (BDT) to searches at the Large Hadron Collider (LHC) for pair-produced lepton partners which decay to leptons and invisible particles. This scenario can arise in the minimal supersymmetric Standard Model (MSSM), but can be realized in many other extensions of the Standard Model (SM). We focus on the case of intermediate mass splitting ( ∼ 30 GeV ) between the dark matter (DM) and the scalar. For these mass splittings, the LHC has made little improvement over LEP due to large electroweak backgrounds. We find that the use of machine learning techniques can push the LHC well past discovery sensitivity for a benchmark model with a lepton partner mass of ∼ 110 GeV , for an integrated luminosity of 300 fb − 1 , with a signal-to-background ratio of ∼ 0.3 . The LHC could exclude models with a lepton partner mass as large as ∼ 160 GeV with the same luminosity. The use of machine learning techniques in searches for scalar lepton partners at the LHC could thus definitively probe the parameter space of the MSSM in which scalar muon mediated interactions between SM muons and Majorana singlet DM can both deplete the relic density through dark matter annihilation and satisfy the recently measured anomalous magnetic moment of the muon. We identify several machine learning techniques which can be useful in other LHC searches involving large and complex backgrounds. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2021

The inflationary wavefunction from analyticity and factorization

Here, we study the analytic properties of tree-level wavefunction coefficients in quasi-de Sitter space. We focus on theories which spontaneously break dS boost symmetries and can produce significant non-Gaussianities. The corresponding inflationary correlators are (approximately) scale invariant, but are not invariant under the full conformal group. We derive cutting rules and dispersion formulas for the late-time wavefunction coefficients by using factorization and analyticity properties of the dS bulk-to-bulk propagator. This gives a unitarity method which is valid at tree-level for general n-point functions and for fields of arbitrary mass. Using the cutting rules and dispersion formulas, we are able to compute n-point functions by gluing together lower-point functions. As an application, we study general four-point, scalar exchange diagrams in the EFT of inflation. We show that exchange diagrams constructed from boost-breaking interactions can be written as a finite sum over residues. Finally, we explain how the dS identities used in this work are related by analytic continuation to analogous identities in Anti-de Sitter space.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Custodial symmetry breaking and Higgs boson signatures at the LHC

We discuss the simplest model that predicts a tree-level modification of the ρ parameter from a shift in the W -mass without changing the prediction for the Z mass. This model predicts a new neutral Higgs and two charged Higgses, with very similar masses and suppressed couplings to the Standard Model fermions. We discuss the decay properties of these new scalar bosons, and the main signatures at the Large Hadron Collider. Comparing these signatures for the first time to the latest measurements, we show that while masses around 200 GeV are excluded for some scenarios, over a wide range of model parameter space the new bosons can have a mass close to the electroweak scale without violating existing limits from experimental searches or destroying the agreement with measurements. We investigate the scenario where the new neutral Higgs is fermiophobic and has a large branching ratio into W gauge bosons and/or photons, and show that this could lead to a signal in the diphoton mass spectrum at low Higgs masses. We discuss the different signatures that can motivate new measurements and searches at the Large Hadron Collider. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2025

Increasing the Scale of the Mass Spectrometry Query Language Compendium with Explainable AI

A significant bottleneck in metabolomics data interpretation is the effective use of domain knowledge to assign structural information based on fragmentation patterns. The mass spectrometry query language (MassQL) aims to make this process accessible and applicable across multiple analysis platforms. While advanced computational methods are capable of predicting compound structures from fragmentation data, AI/ML approaches often rely on complex, opaque criteria that are difficult to interpret or modify. As a result, their predictive patterns cannot be readily translated into human-readable rules, such as those used in MassQL. Here, in this study, we introduce ChemEcho, a machine learning embedding method that converts tandem mass spectrometry data into sparse feature vectors containing peak and neutral mass subformulae to enhance explainable AI/ML-based methods. An advantage of this approach is that decision trees trained using these feature vectors can be directly translated to MassQL. Using a battery of decision trees trained using ChemEcho embeddings to predict molecular attributes, we generated over 1500 MassQL queries for 765 molecular features and evaluated their precision and recall. From these queries, the 50 highest-performing queries were integrated into the MassQL compendium. This set of generated MassQL queries included environmentally and biologically relevant classes such as PFAS and molecules containing phosphate or sulfate substructures. To illustrate the impact these queries would have on a typical metabolomics experiment, these MassQL queries were applied to a public metabolomics data set─resulting in a marked increase in the structural information derived from tandem mass spectra. Access and reuse of these queries is expected to enhance structural annotation in untargeted experiments, leading to more specific claims and advancing many applications in metabolomics.

Harwood, Thomas V. [USDOE Joint Genome Institute (↗
DOE OSTI2020

Non-standard interactions in radiative neutrino mass models

Models of radiative Majorana neutrino masses require new scalars and/or fermions to induce lepton-number-violating interactions. We show that these new particles also generate observable neutrino non-standard interactions (NSI) with matter. We classify radiative models as type-I or II, with type-I models containing at least one Standard Model (SM) particle inside the loop diagram generating neutrino mass, and type-II models having no SM particle inside the loop. While type-II radiative models do not generate NSI at tree-level, popular models which fall under the type-I category are shown, somewhat surprisingly, to generate observable NSI at tree-level, while being consistent with direct and indirect constraints from colliders, electroweak precision data and charged-lepton flavor violation (cLFV). We survey such models where neutrino masses arise at one, two and three loops. In the prototypical Zee model which generates neutrino masses via one-loop diagrams involving charged scalars, we find that diagonal NSI can be as large as (8%, 3.8%, 9.3%) for (ε$_{ee}$, ε$_{μμ}$, ε$_{ττ}$), while off-diagonal NSI can be at most (10$^{–3}$%, 0.56%, 0.34%) for (ε$_{eμ}$, ε$_{eτ}$, ε$_{μτ}$). In one-loop neutrino mass models using leptoquarks (LQs), (ε$_{μμ}$, ε$_{ττ}$) can be as large as (21.6%, 51.7%), while ε$_{ee}$ and (ε$_{eμ}$, ε$_{eτ}$, ε$_{μτ}$) can at most be 0.6%. Other two- and three-loop LQ models are found to give NSI of similar strength. The most stringent constraints on the diagonal NSI are found to come from neutrino oscillation and scattering experiments, while the off-diagonal NSI are mostly constrained by low-energy processes, such as atomic parity violation and cLFV. We also comment on the future sensitivity of these radiative models in long-baseline neutrino experiments, such as DUNE. While our analysis is focused on radiative neutrino mass models, it essentially covers all NSI possibilities with heavy mediators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Training toward significance with the decorrelated event classifier transformer neural network

Experimental particle physics uses machine learning for many tasks, where one application is to classify signal and background events. This classification can be used to bin an analysis region to enhance the expected significance for a mass resonance search. In natural language processing, one of the leading neural network architectures is the transformer. In this work, an event classifier transformer is proposed to bin an analysis region, in which the network is trained with special techniques. The techniques developed here can enhance the significance and reduce the correlation between the network’s output and the reconstructed mass. It is found that this trained network can perform better than boosted decision trees and feed-forward networks. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2025

Towards smarter green infrastructure: Fusing bark ecology and stemflow hydrodynamics on tree stems

A wide array of bark surfaces sheath wooded plants in rural and urban areas alike. Much work has examined the function and role of bark in different contexts and different environs, including urban areas, finding that it is rich in life and can play a role in the transfer of water and matter to the ground surface. Accordingly, this paper presents a first step to weld and fuse bark ecology and stemflow hydrodynamics. It is an effort to develop a physically-based understanding of the transport of water and matter (e.g., solutes, particulates, microorganisms) along tree stems using relevant equations to allow a more informed consideration of bark in green infrastructure initiatives. In particular, the hydrodynamical equations are based on the conservation of water mass, conservation of momentum, and conservation of scalar mass. These equations, coupled with contemplation of corticular life, underpin and substantiate bark’s unifying role as a modulator and cultivator. By elucidating the ‘black box’ of the tree stem and utilizing the formulations set forth in this paper, urban foresters and planners can develop green infrastructure to help advance ecosystem services and sustainability development goals (SDG), especially SDG 11 and SDG 15.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Belowground respiration, root traits, and soil characteristics of an East Tennessee deciduous forest, 2019-2020

This dataset contains empirical physiological, morphological, and chemical data of root systems, and elemental, nutrient content for soils collected on forty individuals of eight temperate tree species, between June 2019 and July 2020 at The University of Tennessee Forest Research Center and Arboretum in Oak Ridge, Tennessee. The project used a novel methodology to empirically derive estimates of the autotrophic and heterotrophic components of soil respiration in-situ. The project consists of two measurement approaches. The first set of measurements uses a standard approach for measuring specific root respiration on excised root systems. The second used “in-situ root trays” This dataset includes 10 data files in comma separate (*.csv) ASCII format. Data include measurements of leaf and root functional traits for excised root systems and for living root systems housed within in-situ root trays, data on soil carbon and nitrogen pools, in-situ measurements of soil moisture and temperature, data on soil respiration rates for in-situ root trays (both as soil mass-based fluxes, and soil-area based fluxes), and data on the geographic coordinates and tree sizes of study trees. Forty study trees of eight temperate tree species were studied (five individuals per species). Two in-situ root trays were installed per species, each housing one entire root system comprising < 3 root orders, and still being attached to the tree via transportive root. All respiration measurements were conducted with the Li-6800 portable photosynthesis system (Li-COR, Lincoln, NE, USA). Root respiration measurements of excised root tissues were made using the Li-6800 and the Walz 3010-GWK1 gas exchange chamber (Heinz Walz GmbH, Effeltrich, Germany). Root scan images were analyzed using WinRHIZO. These images are companion files to this dataset and are contained in two compressed (*.zip) folders. Additional metadata are provided: 10 data dictionaries and a file-level metadata file in comma separate (*.csv) format and a user guide in PDF (*.pdf) format.

EARTH SCIENCE > BIOSPHERE > VEGETATION > LEAF CHAR↗
DOE OSTI2020

Spatial Description of Lithium Plating

Fast charging lithium ion batteries is a critical obstacle to overcome before widespread uptake of electric vehicles. During fast charging, lithium ion batteries in electric vehicles degrade at an accelerated rate, which is frequently caused by lithium plating. In this presentation, three different methods to spatially and temporally detect and quantify lithium plating are presented; notably, mass spectroscopy titration, operando depth profiling X-ray diffraction, and in-situ X-ray diffraction mapping of pouch cells. Each of the tree techniques successfully quantify lithium plating and provide insight into its spatial occurrence within lithium ion cells.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Mapping foliar photosynthetic capacity in sub-tropical and tropical forests with UAS-based imaging spectroscopy: Scaling from leaf to canopy

Accurate understanding of the variability in foliar physiological traits across landscapes is critical to improve parameterization and evaluation of terrestrial biosphere models (TBMs) that seek to represent the response of terrestrial ecosystems to a changing climate. Numerous studies suggest imaging spectroscopy can characterize foliar biochemical and morphological traits at the canopy scale, but there is only limited evidence for retrieving canopy photosynthetic capacity (e.g., maximum carboxylation rate, V c,max and maximum electron transport rate, J max ). Moreover, the effect of canopy structure within forest communities on scaling up spectra-trait relationships from leaf to canopy level is not well known. To advance the spectra-trait approach and enable the estimation of key traits using remote sensing, we collected imaging spectroscopy data from an Unoccupied Aerial System (UAS) platform over two forest sites in China (a subtropical forest in Mt. Dinghu and a tropical rainforest in Xishuangbanna). At these sites, we also collected ground measurements of leaf spectra and traits, including biochemical (leaf nitrogen, phosphorus, chlorophyll, and water content), morphological (leaf mass per area, LMA) and physiological (V c,max25 and J max25 ) traits (n=135 tree-crowns from 42 species across two sites). Using a partial least-squares regression (PLSR) approach, we built and tested spectra-trait models with repeated cross-validation. The spectral models developed with leaf spectra were directly transferred to canopy spectra to evaluate the effect of canopy structure. Here we further applied canopy spectral models to map these traits at individual tree-crown scale. The results demonstrate that (1) UAS-based canopy spectra can be used to estimate V c,max (R 2 =0.55, nRMSE=11.79%), Jmax (R 2 =0.54, nRMSE=12.34%), and five additional foliar traits (R 2 =0.38-0.60, nRMSE=10.11-13.56%) at the tree-crown scale with demonstrated generalizability across two sites; (2) canopy structure strongly affects the spectratrait relationships from leaf to canopy level, but the effects vary considerably across foliar traits and cannot be well captured by the 4SAIL canopy radiative transfer model. UAS-based imaging spectroscopy maps large variability in all foliar traits (including physiological traits) with spatially explicit information, reproducing the field-observed inter- and intra-specific variations. These results demonstrate the capability of using UAS-based imaging spectroscopy for characterizing the variability of foliar physiological traits at individual tree-crown scale over forest landscapes and highlight the similar generalizability but different biophysical mechanisms underlying spectra-trait relationships at leaf and canopy levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Modeling starch dynamics from seasonal variations of photosynthesis, growth and respiration

Abstract Nonstructural carbohydrates (NSCs) buffer differences in plant carbon supply (photosynthesis) and demand (respiration, growth, etc.), but the regulation of their dynamics remains unresolved. Seasonal variations in NSCs are well-documented, but differences in the time-average, amplitude, phase and other characteristics across ecosystems and functional types lack explanation; furthermore, observed dynamics do not always match expectations. The failure to match observed and expected dynamics has stimulated debate on whether carbon supply or demand drives NSC dynamics. To gain insight into how carbon supply and demand drive seasonal NSC dynamics, we derive a simple model of NSC dynamics based on carbon mass balance and linearizing the NSC demand to determine how supply-driven and demand-driven seasonal NSC dynamics differ. We find that supply-driven and demand-driven dynamics yield distinct timings of seasonal extrema, and supply overrides demand when carbon supply is low in winter (e.g., at high latitudes). Our results also suggest that NSC dynamics often lag changes carbon mass balance. We also predict differences in NSC dynamics across mass, suggesting that saplings are more dynamic and respond more quickly to the environment than mature trees. Our findings suggest that substrate-dependent regulation with environmental variation is sufficient to generate complex NSC dynamics.

Forestry↗
DOE OSTI2024

Update on the gradient flow scale on the 2+1+1 HISQ ensembles

We report on the ongoing effort of improving the determination of the gradient flow scale on the (2+1+1)-flavor HISQ ensembles generated by the MILC collaboration. We compute the scales $\sqrt{t_0}/a$ and $w_0/a$ with the Wilson and Symanzik flow using three discretizations for the action density: clover, Wilson and tree-level Symanzik improved. For the absolute scale setting, we intend to employ the $\Omega$-baryon mass, but are also using the pion decay constant while the $\Omega$-mass calculations are in progress.

Bazavov, Alexei↗
DOE OSTI2021

Cloud drop number concentrations over the western North Atlantic Ocean: seasonal cycle, aerosol interrelationships, and other influential factors

Cloud drop number concentrations (N d ) over the western North Atlantic Ocean (WNAO) are generally highest during the winter (DJF) and lowest in summer (JJA), in contrast to aerosol proxy variables (aerosoloptical depth, aerosol index, surface aerosol mass concentrations, surface cloud condensation nuclei (CCN) concentrations) that generally peak inspring (MAM) and JJA with minima in DJF. Using aircraft, satellite remote sensing, ground-based in situ measurement data, and reanalysis data, we characterize factors explaining the divergent seasonal cycles and furthermore probe into factors influencing N d on seasonal timescales. The results can be summarized well by features most pronounced in DJF, including features associated with cold-air outbreak (CAO) conditions such as enhanced values of CAO index, planetary boundary layer height (PBLH),low-level liquid cloud fraction, and cloud-top height, in addition to winds aligned with continental outflow. Data sorted into high- and low-N d days in each season, especially in DJF, revealed that all of these conditions were enhanced on the high-N d days, including reduced sea level pressure and stronger wind speeds. Although aerosols may be more abundant in MAM and JJA, the conditions needed to activate those particles into cloud droplets are weaker than in colder months, which is demonstrated by calculations of the strongest (weakest) aerosol indirect effects in DJF (JJA) based on comparing N d to perturbations in four different aerosol proxy variables (total and sulfate aerosol optical depth, aerosol index, surface mass concentration of sulfate). We used three machine learning models and up to 14 input variables to infer about most influential factors related to N d for DJF and JJA, with the best performance obtained with gradient-boosted regression tree (GBRT) analysis. The model results indicated that cloud fraction was the most important input variable, followed by some combination (depending on season) of CAO index and surface mass concentrations of sulfate and organic carbon. Future work is recommended to further understand aspects uncovered here such as impacts of free tropospheric aerosol entrainment on clouds, degree of boundary layer coupling, wet scavenging, and giant CCN effects on aerosol–N d relationships, updraft velocity, and vertical structure of cloud properties such as adiabaticity that impact the satellite estimation of N d .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Photosynthetic parameters and nutrient content of trees at the Panama crane sites.

This data package contains leaf chemistry and photosynthetic traits data from trees within the NGEE Tropics Parque Natural Metropolitano (PA-PNM) and Bosque Protector San Lorenzo (PA-SLZ) sites in Panama. Photosynthetic parameters including maximum photosynthesis, Vcmax, Jmax, and TPU, were calculated from A/Ci curves. Foliar nitrogen and phosphorus concentrations and leaf mass per unit leaf area of the leaves also were determined. The plants that were sampled were classified as to species, family, order, clade, and growth form (tree, liana, perennial herb, or other). Wood density of the tree and the source of that data also are included. Included in this data package is a zip file with a single Excel data file and CSV copies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Comparing galaxy formation in the L-GALAXIES semi-analytical model and the IllustrisTNG simulations

ABSTRACT We perform a comparison, object by object and statistically, between the Munich semi-analytical model, L-GALAXIES, and the IllustrisTNG hydrodynamical simulations. By running L-GALAXIES on the IllustrisTNG dark matter-only merger trees, we identify the same galaxies in the two models. This allows us to compare the stellar mass, star formation rate, and gas content of galaxies, as well as the baryonic content of subhaloes and haloes in the two models. We find that both the stellar mass functions and the stellar masses of individual galaxies agree to better than ${\sim} 0.2\,$dex. On the other hand, specific star formation rates and gas contents can differ more substantially. At z = 0, the transition between low-mass star-forming galaxies and high-mass quenched galaxies occurs at a stellar mass scale ${\sim} 0.5\,$dex lower in IllustrisTNG than that in L-GALAXIES. IllustrisTNG also produces substantially more quenched galaxies at higher redshifts. Both models predict a halo baryon fraction close to the cosmic value for clusters, but IllustrisTNG predicts lower baryon fractions in group environments. These differences are primarily due to differences in modelling feedback from stars and supermassive black holes. The gas content and star formation rates of galaxies in and around clusters and groups differ substantially, with IllustrisTNG satellites less star forming and less gas rich. We show that environmental processes such as ram-pressure stripping are stronger and operate to larger distances and for a broader host mass range in IllustrisTNG. We suggest that the treatment of galaxy evolution in the semi-analytic model needs to be improved by prescriptions that capture local environmental effects more accurately.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Leaf-level physiological strategies related to productivity and plasticity of Populus in the Southeastern United States

Introduction: Populus and its hybrids are attractive bioenergy crops and the southeastern United States has broad ability to supply bioenergy markets with woody biomass. Breeding and hybridization have led to superior eastern cottonwood (Populus deltoides W. Bartram ex Marshall) and hybrid poplars adapted to a wide variety of site types not suited for agricultural production. In order to maximize productivity and minimize inputs, genotypes need to efficiently use available site resources and tolerate environmental stresses. In addition, we need to determine plasticity of traits and their coordination across sites to select traits that will broadly characterize genotypes. Therefore, our study objectives were to determine (1) which leaf traits were correlated with growth, (2) if traits and genotypes exhibited significant plasticity across sites, and (3) how traits were coordinated within and across sites and Populus taxa. Methods: We measured trees at two sites in northeastern Mississippi, United States: one upland and one alluvial terrace site. Genotypes included eastern cottonwoods as well as F 1 crosses of eastern cottonwood and P. maximowiczii (Henry), P. nigra (L.) and P. trichocarpa (Torr. & Gray). Results: We found that sites differed in which leaf traits were correlated with productivity; with water use efficiency specifically being positively correlated with growth at an alluvial terrace site, but negatively correlated with growth at an upland site. Tree height growth, leaf isotope composition (δ 13 C and δ 15 N), as well as leaf mass per area (LMA) exhibited the least plasticity across sites, while physiological gas exchange parameters and leaf nitrogen concentration exhibited the highest plasticity. Broadly across taxa, leaf carbon isotope ratios were correlated with intrinsic water use efficiency, and stomatal conductance was positively correlated with photosynthetic nitrogen use efficiency across sites, while leaf nitrogen isotope ratios exhibited contrasting relationships with leaf nitrogen concentration. Discussion: Overall, these results allow us to refine selections of productive genotypes based on site conditions and site-specific relationships with physiological parameters to better match Populus taxa with sites and landowner objectives.

bioenergy feedstocks↗