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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Plant responses to rising vapor pressure deficit

Recent decades have been characterized by increasing temperatures worldwide, resulting in an exponential climb in vapor pressure deficit (VPD). VPD has been identified as an increasingly important driver of plant functioning in terrestrial biomes and has been established as a major contributor in recent drought‐induced plant mortality independent of other drivers associated with climate change. Despite this, few studies have isolated the physiological response of plant functioning to high VPD, thus limiting our understanding and ability to predict future impacts on terrestrial ecosystems. An abundance of evidence suggests that stomatal conductance declines under high VPD and transpiration increases in most species up until a given VPD threshold, leading to a cascade of subsequent impacts including reduced photosynthesis and growth, and higher risks of carbon starvation and hydraulic failure. Incorporation of photosynthetic and hydraulic traits in ‘next‐generation’ land‐surface models has the greatest potential for improved prediction of VPD responses at the plant‐ and global‐scale, and will yield more mechanistic simulations of plant responses to a changing climate. By providing a fully integrated framework and evaluation of the impacts of high VPD on plant function, improvements in forecasting and long‐term projections of climate impacts can be made.

mortality↗
DOE OSTI2022

Linking remotely sensed ecosystem resilience with forest mortality across the continental United States

Episodes of forest mortality have been observed worldwide associated with climate change, impacting species composition and ecosystem services such as water resources and carbon sequestration. Yet our ability to predict forest mortality remains limited, especially across large scales. Time series of satellite imagery has been used to document ecosystem resilience globally, but it is not clear how well remotely sensed resilience can inform the prediction of forest mortality across continental, multi-biome scales. Here, we leverage forest inventories across the continental United States to systematically assess the potential of ecosystem resilience derived using different data sets and methods to predict forest mortality. Here we found high resilience was associated with low mortality in eastern forests but was associated with high mortality in western regions. The unexpected resilience–mortality relation in western United States may be due to several factors including plant trait acclimation, insect population dynamics, or resource competition. Overall, our results not only supported the opportunity to use remotely sensed ecosystem resilience to predict forest mortality but also highlighted that ecological factors may have crucial influences because they can reverse the sign of the resilience–mortality relationships.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Expression quantitative trait loci mapping identified PtrXB38 as a key hub gene in adventitious root development in Populus

Summary Plant establishment requires the formation and development of an extensive root system with architecture modulated by complex genetic networks. Here, we report the identification of the PtrXB38 gene as an expression quantitative trait loci (eQTL) hotspot, mapped using 390 leaf and 444 xylem Populus trichocarpa transcriptomes. Among predicted targets of this trans ‐eQTL were genes involved in plant hormone responses and root development. Overexpression of PtrXB38 in Populus led to significant increases in callusing and formation of both stem‐born roots and base‐born adventitious roots. Omics studies revealed that genes and proteins controlling auxin transport and signaling were involved in PtrXB38‐mediated adventitious root formation. Protein–protein interaction assays indicated that PtrXB38 interacts with components of endosomal sorting complexes required for transport machinery, implying that PtrXB38‐regulated root development may be mediated by regulating endocytosis pathway. Taken together, this work identified a crucial root development regulator and sheds light on the discovery of other plant developmental regulators through combining eQTL mapping and omics approaches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Plant responses to rising vapor pressure deficit

Recent decades have been characterized by increasing temperatures worldwide, resulting in an exponential climb in vapor pressure de?cit (VPD). VPD has been identi?ed as an increasingly important driver of plant functioning in terrestrial biomes and has been established as a major contributor in recent drought-induced plant mortality independent of other drivers associated with climate change. Despite this, few studies have isolated the physiological response of plant functioning to high VPD, thus limiting our understanding and ability to predict future impacts on terrestrial ecosystems. An abundance of evidence suggests that stomatal conductance declines under high VPD and transpiration increases in most species up until a given VPD threshold, leading to a cascade of subsequent impacts including reduced photosynthesis and growth, and higher risks of carbon starvation and hydraulic failure. Incorporation of photosynthetic and hydraulic traits in ‘next-generation’ land-surface models has the greatest potential for improved prediction of VPD responses at the plant- and global-scale, and will yield more mechanistic simulations of plant responses to a changing climate. By providing a fully integrated framework and evaluation of the impacts of high VPD on plant function, improvements in forecasting and long-term projections of clNovickimate impacts can be made.

Grossiord, Charlotte↗
DOE OSTI2026

Data for Impact of Vertical and Seasonal Variation in Leaf Traits on Simulating Soybean Canopy Photosynthesis via 1D and 3D Modeling

Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax25), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax25 profile achieved comparable performance to the scheme with the measured Vcmax25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.

Photosynthesis↗
DOE OSTI2025

Tracking seasonal variability in plant traits from spaceborne PRISMA and NEON AOP across forest types and ecoregions

Plant traits serve as critical indicators of how plants adapt to environmental changes and influence ecosystem functions. While airborne hyperspectral remote sensing effectively maps plant traits through detailed reflectance properties, it is limited by cost and scale, making large-scale and temporal studies challenging. The recently launched spaceborne hyperspectral imager, PRecursore IperSpettrale della Missione Applicativa (PRISMA), offers frequent, large scale and high-fidelity observations on a spatial resolution of 30 m and a revisit time of around 29 days, making it suitable for large-scale seasonal trait mapping. However, their potential remains largely unexplored. This study developed a multi-stage framework by leveraging the PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits — chlorophyll content, carotenoid content, equivalent water thickness, and nitrogen content — across eleven NEON sites representing diverse forest types and ecoregions in the contiguous U.S. Our results demonstrated that PRISMA hyperspectral data can reliably track seasonal variability in plant traits, achieving overall R 2 values ranging from 0.78 to 0.88 and normalized root mean square error (NRMSE) values ranging from 5.4% to 8.4% for the four traits. Seasonal patterns revealed bell-shaped trajectories for chlorophyll and carotenoids, while equivalent water thickness decreased steadily across most sites, driven by structural changes during leaf maturation and senescence. Nitrogen content exhibited less pronounced seasonal variation but followed expected nutrient resorption patterns. Analysis of environmental drivers showed that seasonal variability is primarily controlled by solar radiation and day length in northern sites, vapor pressure in semi-arid regions, and temperature in mid-southeastern sites. Spatial variability, meanwhile, was primarily driven by soil properties, particularly during the peak growing season. However, the influence of soil variables slightly declines toward the end of the season at several sites, as climatic factors become more prominent. This study highlights the capability of PRISMA, and potentially other similar spaceborne hyperspectral data for large-scale, time-series plant trait mapping and provides valuable insights into the interactions between plant traits and environmental factors. In conclusion, these findings contribute to advancing our understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.

Environmental drivers↗
DOE OSTI2020

Organizing principles for vegetation dynamics

Understanding vegetation dynamics is very challenging because of the multitude of contributing processes at 64widely different spatial and temporal scales. In this Perspective we propose that understanding of vegetation dynamics can be improved, permitting better predictions, based on organizing principles that constrain plant and ecosystem be haviour: natural selection, self-organization, and entropy maximization. Although these ideas are increasingly used,a limited common understanding of their theoretical basis has prevented their full potential to be realized. We explain the power of natural selection-based optimality to predict photosynthesis and carbon allocation responses to multiple environmental drivers, and how individual plasticity leads to the predictable self-organization of forest canopies. We show how models of natural selection acting on a few key traits can generate realistic plant communities, and how entropy maximization can distinguish between stochastic and deterministic drivers of vegetation patterns. In combination with empirical exploration of patterns in plant functional variation, these principles can accelerate the development of dynamic vegetation models as well as trait-based ecology resting on strengthened theoretical and empirical foundations.

Geosciences↗
DOE OSTI2020

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

09 BIOMASS FUELS↗
DOE OSTI2023

Codon Optimization Improves the Prediction of Xylose Metabolism from Gene Content in Budding Yeasts

Xylose is the second most abundant monomeric sugar in plant biomass. Consequently, xylose catabolism is an ecologically important trait for saprotrophic organisms, as well as a fundamentally important trait for industries that hope to convert plant mass to renewable fuels and other bioproducts using microbial metabolism. Although common across fungi, xylose catabolism is rare within Saccharomycotina, the subphylum that contains most industrially relevant fermentative yeast species. The genomes of several yeasts unable to consume xylose have been previously reported to contain the full set of genes in the XYL pathway, suggesting the absence of a gene–trait correlation for xylose metabolism. Here, we measured growth on xylose and systematically identified XYL pathway orthologs across the genomes of 332 budding yeast species. Although the XYL pathway coevolved with xylose metabolism, we found that pathway presence only predicted xylose catabolism about half of the time, demonstrating that a complete XYL pathway is necessary, but not sufficient, for xylose catabolism. We also found that XYL1 copy number was positively correlated, after phylogenetic correction, with xylose utilization. We then quantified codon usage bias of XYL genes and found that XYL3 codon optimization was significantly higher, after phylogenetic correction, in species able to consume xylose. Finally, we showed that codon optimization of XYL2 was positively correlated, after phylogenetic correction, with growth rates in xylose medium. We conclude that gene content alone is a weak predictor of xylose metabolism and that using codon optimization enhances the prediction of xylose metabolism from yeast genome sequence data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Genomic prediction of hybrid performance for agronomic traits in sorghum

Hybrid breeding in sorghum [Sorghum bicolor (L.) Moench] utilizes the cytoplasmic-nuclear male sterility (CMS) system for seed production and subsequently harnesses heterosis. Since the cost of developing and evaluating inbred and hybrid lines in the CMS system is costly and time-consuming, genomic prediction of parental lines and hybrids is based on genetic data genotype. We generated 602 hybrids by crossing two female (A) lines with 301 diverse and elite male (R) lines from the sorghum association panel and collected phenotypic data for agronomic traits over two years. We genotyped the inbred parents using whole genome resequencing and used 2,687,342 high quality (minor allele frequency > 2%) single nucleotide polymorphisms for genomic prediction. For grain yield, the experimental hybrids exhibited an average mid-parent heterosis of 40%. Genomic best linear unbiased prediction (GBLUP) for hybrid performance yielded an average prediction accuracy of 0.76–0.93 under the prediction scenario where both parental lines in validation sets were included in the training sets (T2). However, when only female tester was shared between training and validation sets (T1F), prediction accuracies declined by 12–90%, with plant height showing the greatest decline. Mean accuracies for predicting the general combining ability of male parents ranged from 0.33 to 0.62 for all traits. Our results showed hybrid performance for agronomic traits can be predicted with high accuracy, and optimizing genomic relationship is essential for optimal training population design for genomic selection in sorghum breeding.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2024

Effects of Water Limitation and Competition on Tree Carbon Allocation in an Earth System Modeling Framework

Earth system models (ESMs) have a limited capacity to represent plant functional diversity and shifts in trait distributions. Approaches to improving the representation of this complexity in ESMs include (i) optimality-based approaches that predict trait–environment responses and (ii) explicitly modelling coexistence and community assembly. These approaches are expected to converge only when optimality-based approaches identify competitively dominant strategies, which often differ from strategies that maximize ecosystem functioning or fitness components in monoculture. We used two models, LM3-PPA (a vegetation demographic model designed as an ESM component) and BiomeE (a computationally efficient analog for LM3-PPA), to explore how water limitation affects carbon allocation strategies of canopy trees. We compared competitive allocation strategies and those that maximize biomass or productivity in monoculture. We did not explicitly model coexistence or community assembly. Rather, we used model experiments to identify competitive and maximizing strategies in a two-dimensional trait space under different precipitation and mortality scenarios. At 10 eastern US locations, we simulated historical, wet and dry climate scenarios, novel drought and three different mortality scenarios (low, medium or high sensitivity to water deficit). For each site and scenario, we identified the competitive strategy and three maximizing strategies (maximum biomass, productivity or drought-tolerance). Root: leaf ratios tended to increase and leaf area tended to decrease with increasing water stress (increasing water limitation and its effects on mortality). However, relative to maximizing strategies, competitive strategies shifted towards greater allocation to roots and leaves with increasing water stress. Competitive overinvestments (greater allocation to roots and leaves by competitive strategies compared with maximizing strategies) were robust across different modelling contexts, including vegetation parameter sets (Acer vs. Populus), models (LM3-PPA vs. BiomeE) and uncalibrated vs. calibrated BiomeE versions. Synthesis: The theoretical prediction that competitive and maximizing allocation strategies differ under water limitation is confirmed for a demographic model designed as an ESM component. Optimality-based trait predictions can simplify representing trait diversity in ESMs but do not always correspond to competitive outcomes. Explicitly modelling coexistence and community assembly in ESMs is challenging but is likely the most general approach to representing trait diversity.

vegetation demographic model↗
DOE OSTI2025

Remote Sensing of Live Fuel Moisture for Wildfires Using SMAP Satellite Observations

Live Fuel Moisture (LFM) is a critical parameter for wildfire risk assessment, traditionally measured by labor-intensive field sampling. However, sampled LFM data are influenced by site-specific factors, such as local vegetation types and plant traits, and are often collected retrospectively after wildfire events, making it difficult to obtain pre-fire data for predictive applications. Here, we evaluate the relationship between LFM and Vegetation Water Content (VWC) and Soil Moisture (SM) retrieved from SMAP L-band brightness temperature using the Maximum Entropy Production (MEP) approach. The MEP-retrieved VWC exhibited strong correlation with in situ measurements of LFM ( r > 0.6) in the Western U.S. The integration of high-resolution vegetation coverage data enhances the detection of sub-grid vegetation heterogeneity. This study demonstrates the operational potential of remote sensing derived VWC as a scalable proxy of LFM, supporting its application in regional assessment of wildfire risk.

Cho, Kyeungwoo [Georgia Institute of Technology, A↗
DOE OSTI2022

Representing plant diversity in land models: An evolutionary approach to make “Functional Types” more functional

Plants are critical mediators of terrestrial mass and energy fluxes, and their structural and functional traits have profound impacts on local and global climate, biogeochemistry, biodiversity, and hydrology. Yet, Earth System Models (ESMs), our most powerful tools for predicting the effects of humans on the coupled biosphere-atmosphere system, simplify the incredible diversity of land plants into a handful of coarse categories of "Plant Functional Types" (PFTs) that often fail to capture ecological dynamics such as biome distributions. The inclusion of more realistic functional diversity is a recognized goal for ESMs, yet there is currently no consistent, widely accepted way to add diversity to models, that is, to determine what new PFTs to add and with what data to constrain their parameters. Here we review approaches to representing plant diversity in ESMs and draw on recent ecological and evolutionary findings to present an evolution-based functional type approach for further disaggregating functional diversity. Specifically, the prevalence of niche conservatism, or the tendency of closely related taxa to retain similar ecological and functional attributes through evolutionary time, reveals that evolutionary relatedness is a powerful framework for summarizing functional similarities and differences among plant types. We advocate that Plant Functional Types based on dominant evolutionary lineages ("Lineage Functional Types") will provide an ecologically defensible, tractable, and scalable framework for representing plant diversity in next-generation ESMs, with the potential to improve parameterization, process representation, and model benchmarking. We highlight how the importance of evolutionary history for plant function can unify the work of disparate fields to improve predictive modeling of the Earth system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Tropical root responses to global changes: A synthesis

Tropical ecosystems face escalating global change. These shifts can disrupt tropical forests' carbon (C) balance and impact root dynamics. Since roots perform essential functions such as resource acquisition and tissue protection, root responses can inform about the strategies and vulnerabilities of ecosystems facing present and future global changes. However, root trait dynamics are poorly understood, especially in tropical ecosystems. We analyzed existing research on tropical root responses to key global change drivers: warming, drought, flooding, cyclones, nitrogen (N) deposition, elevated (e) CO 2 , and fires. Based on tree species- and community-level literature, we obtained 266 root trait observations from 93 studies across 24 tropical countries. We found differences in the proportion of root responsiveness to global change among different global change drivers but not among root categories. In particular, we observed that tropical root systems responded to warming and eCO 2 by increasing root biomass in species-scale studies. Drought increased the root: shoot ratio with no change in root biomass, indicating a decline in aboveground biomass. Despite N deposition being the most studied global change driver, it had some of the most variable effects on root characteristics, with few predictable responses. Episodic disturbances such as cyclones, fires, and flooding consistently resulted in a change in root trait expressions, with cyclones and fires increasing root production, potentially due to shifts in plant community and nutrient inputs, while flooding changed plant regulatory metabolisms due to low oxygen conditions. The data available to date clearly show that tropical forest root characteristics and dynamics are responding to global change, although in ways that are not always predictable. This synthesis indicates the need for replicated studies across root characteristics at species and community scales under different global change factors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Ponderosa pine hydraulic stress predicts more extreme wildfire behavior under future conditions in Bandelier National Monument, New Mexico

Background Live fuel moisture contributes to wildfire spread and reflects plant stress and physiological traits. The anticipated change in live fuel moisture under future conditions is likely non-linear, owing to physiological plant thresholds in water hydraulics. We constructed a mechanistic model of live fuel moisture’s response to water stress to understand the impact of future climate on live fuel moisture. We first gathered data on plant physiology and live fuel moisture for Pinus ponderosa at Bandelier National Monument, NM, USA, and modeled their relationship. We then parameterized a mechanistic plant hydrodynamics model (FATES-HYDRO) to simulate changes in plant stress and a statistical model to simulate the resulting impact on live fuel moisture. We then simulated FATES-HYDRO under future climate anomalies (SSP2-4.5 and SSP5-8.5: 2080–2100) to understand the change in plant stress and estimate its impact on live fuel moisture. Results We found that the number of days below crucial thresholds of live fuel moisture (100% and 79%) increased from contemporary levels (< 100%: 72 days, < 79%: 1.4 days) under SSP2-4.5 (< 100%: 185 days, < 79%: 10.2 day) and increased exponentially under SSP5-8.5 (< 100%: 215 days, < 79%: 65 days). We found that gross primary productivity decreased under both future climate scenarios (contemporary: 336 g C m −2 , SSP2-4.5: 203 g C m −2 , SSP5-8.5: 243 g C m −2 ); however, spring productivity increased under SSP5-8.5, potentially altering fuel loading. We additionally see a potentially lethal loss of conductivity in hydraulic P. ponderosa under SSP5-8.5. Conclusions The overall increase in plant water stress (as represented by loss of hydraulic conductivity and leaf water potential) and lower live fuel moisture appear to be driven by reduced precipitation during late summer monsoons typical of the region, extending the fire season later in the year. We predict increasing variability in the P. ponderosa wildfire regime under both climate projections driven by changing productivity, rising mortality, and an overall decrease in live fuel moisture.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Quantitative and Predictive Genetic Parts for Plant Synthetic Biology

Plant synthetic biology aims to harness the natural abilities of plants and to turn them to new purposes. A primary goal of plant synthetic biology is to produce predictable and programmable genetic circuits from simple regulatory elements and well-characterized genetic components. The number of available DNA parts for plants is increasing, and the methods for rapid quantitative characterization are being developed, but the field of plant synthetic biology is still in its early stages. We here describe methods used to describe the quantitative properties of genetic components needed for plant synthetic biology. Once the quantitative properties and transfer function of a variety of genetic parts are known, computers can select the optimal components to assemble into functional devices, such as toggle switches and positive feedback circuits. However, while the variety of circuits and traits that can be put into plants are limitless, doing synthetic biology in plants poses unique challenges. Plants are composed of differentiated cells and tissues, each representing potentially unique regulatory or developmental contexts to introduced synthetic genetic circuits. Further, plants have evolved to be highly sensitive to environmental influences, such as light or temperature, any of which can affect the quantitative function of individual parts or whole circuits. Measuring the function of plant components within the context of a plant cell and, ideally, in a living plant, will be essential to using these components in gene circuits with predictable function. Mathematical modeling will be needed to account for the variety of contexts a genetic part will experience in different plant tissues or environments. With such understanding in hand, it may be possible to redesign plant traits to serve human and environmental needs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Detection of the Metabolic Response to Drought Stress Using Hyperspectral Reflectance

Drought is the most important limitation on crop yield. Understanding and detecting drought stress in crops is vital for improving water use efficiency through effective breeding and management. Leaf reflectance spectroscopy offers a rapid, non-destructive alternative to traditional techniques for measuring plant traits involved in a drought response. We measured drought stress in six glasshouse-grown agronomic species using physiological, biochemical and spectral data. In contrast to physiological traits, leaf metabolite concentrations revealed drought stress before it was visible to the naked eye. We used full-spectrum leaf reflectance data to predict metabolite concentrations using partial least-squares regression, with validation R2 values of 0.49 to 0.87. We show for the first time that spectroscopy may be used for the quantitative estimation of proline and abscisic acid, demonstrating the first use of hyperspectral data to detect a phytohormone. We used linear discriminant analysis and partial least squares discriminant analysis to differentiate between droughted and watered plants based on measured traits (accuracy: 71%) and raw spectral data (66%). Finally, we validated our glasshouse-developed models in an independent field trial. We demonstrate that spectroscopy can detect drought stress via underlying biochemical changes, before visual differences occur, representing a powerful advance for measuring limitations on yield.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Ecosystem as Super-Organ/ism, Revisited: Scaling Hydraulics to Forests under Climate Change

Synopsis Classic debates in community ecology focused on the complexities of considering an ecosystem as a super-organ or organism. New consideration of such perspectives could clarify mechanisms underlying the dynamics of forest carbon dioxide (CO2) uptake and water vapor loss, important for predicting and managing the future of Earth’s ecosystems and climate system. Here, we provide a rubric for considering ecosystem traits as aggregated, systemic, or emergent, i.e., representing the ecosystem as an aggregate of its individuals or as a metaphorical or literal super-organ or organism. We review recent approaches to scaling-up plant water relations (hydraulics) concepts developed for organs and organisms to enable and interpret measurements at ecosystem-level. We focus on three community-scale versions of water relations traits that have potential to provide mechanistic insight into climate change responses of forest CO2 and H2O gas exchange and productivity: leaf water potential (Ψcanopy), pressure volume curves (eco-PV), and hydraulic conductance (Keco). These analyses can reveal additional ecosystem-scale parameters analogous to those typically quantified for leaves or plants (e.g., wilting point and hydraulic vulnerability) that may act as thresholds in forest responses to drought, including growth cessation, mortality, and flammability. We unite these concepts in a novel framework to predict Ψcanopy and its approaching of critical thresholds during drought, using measurements of Keco and eco-PV curves. We thus delineate how the extension of water relations concepts from organ- and organism-scales can reveal the hydraulic constraints on the interaction of vegetation and climate and provide new mechanistic understanding and prediction of forest water use and productivity.

Zoology↗