SEARCH · Search NASA

Results for “Leaf nitrogen”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Soil microenvironmental variation drives below‐ground trait variation and interacts with macroclimate to structure above‐ground trait variation of arctic shrubs

Abstract Intraspecific trait variation can influence plant performance in different environments and may thereby determine the ability of individual plants to respond to climate change. However, our understanding of its patterns and environmental drivers across different spatial scales is incomplete, especially in understudied regions like the Arctic. To fill this knowledge gap, we examined above‐ground and below‐ground traits from three shrub taxa expanding across the tundra biome and evaluated their relationships with multiple microenvironmental and macroclimatic factors. The traits reflected plant size and structure (plant height, leaf area and root to shoot ratio), leaf economics (specific leaf area, nitrogen content), and root economics and collaboration with mycorrhizal fungi (specific root length, root tissue density, nitrogen content, and ectomycorrhizal colonisation intensity). We also measured leaf and root δ 15 N and leaf δ 13 C to characterise nitrogen source and acquisition pathways and plant water stress. Traits were measured in replicated plots ( N = 135) varying in soil microclimate, thaw depth and organic layer thickness established across five sites spanning a macroclimate gradient in northern Alaska. This hierarchical design allowed us to disentangle the independent and combined effects of fine‐scale and broad‐scale factors on intraspecific trait variation. We found substantial intraspecific variation at fine spatial scales for most traits and less variation along the macroclimate gradient and between shrub taxa. Consistent with these patterns, microenvironmental factors, mainly soil moisture and thaw depth, interacted with macroclimate, mainly climatic water deficit, to structure size‐structural and leaf trait variation. In contrast, most root traits responded additively to thaw depth and macroclimate. Synthesis . Our results demonstrate that above‐ground and below‐ground tundra shrub traits respond differently to microenvironmental and macroclimatic variation. These differing responses contribute to substantial trait variation at fine spatial scales and may decouple above‐ground and below‐ground trait responses to climate change.

Environmental Sciences & Ecology↗

DOE OSTI2025

SPRUCE Photosynthesis and Respiration of Picea mariana and Larix laricina in SPRUCE Experimental Plots, 2019

This dataset contains physiological, morphological, and chemical measurements of the two dominant coniferous species, Picea mariana and Larix laricina, in August 2019 (2019-08-20 to 2019-08-22) at the SPRUCE (Spruce and Peatland Responses under Changing Environments) experiment site in the Marcell Experimental Forest in northern Minnesota, USA. These observations help to assess the effects of whole ecosystem scale warming and elevated carbon dioxide (CO2) concentrations on peatland ecosystems. Measurements include light-saturated photosynthesis and foliar dark respiration measurements under standard conditions and growth conditions involving varying temperatures and atmospheric CO2 concentrations, as well as leaf morphology measurements (leaf mass per unit leaf area) and nitrogen content based on mass and leaf area. Net photosynthesis and dark respiration measurements were taken using portable photosynthesis systems (LI6400XT, LI6800, LI-COR Biosciences, USA). This dataset contains one data file in comma-separate values (*.csv) format. Additional metadata are provided: a data dictionary and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

SPRUCE Photosynthesis and Respiration of Picea mariana and Larix laricina in SPRUCE Experimental Plots, 2016-2017

This dataset contains empirical physiological, morphological, and chemical data collected on two dominant conifer species, Picea mariana and Larix laricina, between May 2016 and August 2017 at the SPRUCE (Spruce and Peatland Responses Under Changing Environments) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. Analyses assessed the response of conifers to whole-ecosystem warming and elevated atmospheric carbon dioxide (CO2) concentrations. Measurements include light-saturated net photosynthesis and dark respiration at ambient atmospheric carbon dioxide (CO2) concentration (2016), CO2 response curves of light-saturated net photosynthesis (2017), leaf morphology (leaf mass per unit leaf area) and nitrogen content (on both mass and area basis). Leaf photosynthesis and respiration were measured with portable photosynthesis systems (LI6400XT, LI6800, LI-COR Biosciences, USA). In 2016, photosynthesis and respiration were measured at a common temperature of 25 ˚C to assess thermal acclimation, and at growth temperatures (for each of the five treatment temperatures) to assess field performance. In 2017, CO2 response curves of net photosynthesis (so-called A-Ci curves) were conducted at different leaf temperatures (15, 25, 32.5, 40 and 45 ˚C) for assessment of biochemical parameters and their temperature sensitivity. In addition, dark respiration measurements at different leaf temperatures (10, 15, 20, 25, 30, 35, 40, and 45 ºC) were also taken. This dataset contains one data file in comma-separate values (*.csv) and Microsoft Excel (*.xlsx) format. Additional metadata are provided: a data dictionary and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Leaf spectral reflectance and transmittance, San Lorenzo, Panama, 2020

This data package contains full-range (350 – 2500 nm) leaf reflectance and transmittance spectra of leaves collected at the San Lorenzo forest canopy crane site in Panama (PA-SLZ) from January to March 2020. Around 1000 leaves were measured from 50 species and various leaf phenological stages. Leaves were sampled from both the top of the canopy and multiple heights within the canopy from 10 vertical profiles and for six core species, from the top of the canopy. This data package includes processed leaf spectra (*.csv), and raw data from the spectroradiometer (compressed as .zip). Metadata files include data descriptions (_dd.csv) for tabular data and sample information and a description of the field campaign protocol (PA-SLZ_2020_Protocol.pdf). In addition to the leaf spectral measurements included here, these samples were also used for measurement of leaf gas exchange, carbon and nitrogen content and leaf mass per unit leaf area (LMA) contained in related data packages.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Leaf gas exchange and fitted parameters, San Lorenzo, Panama, 2020

This data package contains photosynthetic CO2 response curves (ACi curves), light response curves (AQ curves), dark adapted dark respiration, conductance curves, and survey measurements for leaves measured at the San Lorenzo forest canopy crane site, Panama (PA-SLZ) from January to March 2020. Leaves were sampled from both the top of the canopy and multiple heights within the canopy from 10 vertical profiles and included around 50 species. In addition, six core species were also measured, from the top of the canopy. Fitted maximum carboxylation rate (Vcmax), maximum electron transport rate (Jmax), and triose phosphate utilization rate (TPU) were derived from measured data as well as the convexity, light use efficiency, apparent quantum yield, and Rdark. The measurements can also be used to derive the conductance parameters g0 and g1 of stomatal model. All the gas exchange data and metadata are presented in .csv files and complete instrument output are included in .zip folders. Data and metadata meet the ESS-DIVE leaf-level gas exchange reporting format requirements. The protocol details are provided as pdf documents. In addition to gas exchange data described here these samples were also used for measurement of leaf optical properties, carbon and nitrogen content, and leaf mass per unit leaf area (LMA) these data can be cross linked using the unique sample ID and are provided in separate related data packages. Sample information including canopy elevation and leaf area index (LAI) can be found in the related “Leaf and canopy traits” data package.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Leaf gas exchange and fitted parameters, two sites in Panama, 2022

Photosynthetic CO2 response curves (ACi curves), light response curves (AQ curves), dark adapted dark respiration, conductance curves, and survey measurements for leaves measured in the Parque Natural Metropolitano (PNM) and Gamboa, Panama, from January to April 2022 are presented. Measurements were made on leaves from 46 different tree, shrub and liana species, from sunlit canopy and understory locations. Leaf area index from 8 vertical profiles at PNM are also included. The aim of this measurement campaign was three-fold: (i) to improve our understanding of the vertical variation in leaf-level water use efficiency; (ii) to develop an understanding of the sensitivity of stomata to changes in environmental conditions, especially light, humidity, and temperature; (iii) to improve models which can predict leaf traits from leaf contact spectral measurements. All the gas exchange data and metadata are presented in .csv files and complete instrument output are included in .zip folders. Data and metadata meet the ESS-DIVE leaf-level gas exchange reporting format requirements. The protocol details are provided as pdf documents. In addition to gas exchange data reported here these samples were also used for measurement of leaf optical properties, carbon and nitrogen content, and leaf mass per unit leaf area (LMA). These data can be cross linked using the unique sample ID and are provided in separate data packages.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Leaf reflectance, leaf transmittance and sky irradiance, at four sites in Panama, 2022

Paired leaf reflectance and transmittance data measured in Panama, at the Parque Natural Metropolitano (PNM), on Barro Colorado Island (BCI), at the San Lorenzo canopy crane site (SLZ), and in Gamboa, Panama from January to April 2022. Measurements were made on leaves from 57 different tree, shrub and liana species, from sunlit canopy and understory locations. Bark reflectance was also measured on the trunks of common tree species at the PNM and SLZ sites. Also included are irradiance measurements and leaf area index (LAI) from eight vertical profiles at PNM. The aim of these data was to refine our understanding of vertical trait and environmental variation in tropical forests, and to improve models which can predict leaf traits from leaf contact spectral measurements. All spectral data and metadata are presented in .csv files and complete instrument output are included in .zip folders. Protocol details are provided as pdf documents. In addition to spectral data described here, some of these samples were also used for measurement of leaf gas exchange, carbon and nitrogen content, and leaf mass per unit leaf area (LMA) these data can be cross linked using the unique sample ID and are provided in separate related data packages.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling

Nitrogen is an essential nutrient that directly affects plant photosynthesis, crop yield, and biomass production for bioenergy crops, but excessive application of nitrogen fertilizers can cause environmental degradation. To achieve sustainable nitrogen fertilizer management for precision agriculture, there is an urgent need for nondestructive and high spatial resolution monitoring of crop nitrogen and its allocation to photosynthetic proteins as that changes over time. Here, we used visible to shortwave infrared (400–2400 nm) airborne hyperspectral imaging with high spatial (0.5 m) and spectral (3–5 nm) resolutions to accurately estimate critical crop traits, i.e., nitrogen, chlorophyll, and photosynthetic capacity (CO 2 -saturated photosynthesis rate, V max,27 ), at leaf and canopy scales, and to assess nitrogen deficiency on crop yield. We conducted three airborne campaigns over a maize (Zea mays L.) field during the growing season of 2019. Physically based soil-canopy Radiative Transfer Modeling (RTM) and data-driven approaches i.e. Partial-Least Squares Regression (PLSR) were used to retrieve crop traits from hyperspectral reflectance, with ground truth of leaf nitrogen, chlorophyll, V max,27 , Leaf Area Index (LAI), and harvested grain yield. To improve computational efficiency of RTMs, Random Forest (RF) was used to mimic RTM simulations to generate machine learning surrogate models RTM-RF. The results show that prior knowledge of soil background and leaf angle distribution can significantly reduce the ill-posed RTM retrieval. RTM-RF achieved a high accuracy to predict leaf chlorophyll content (R 2 = 0.73) and LAI (R 2 = 0.75). Meanwhile, PLSR exhibited better accuracy to predict leaf chlorophyll content (R 2 = 0.79), nitrogen concentration (R 2 = 0.83), nitrogen content (R 2 = 0.77), and V max,27 (R 2 = 0.69) but required measured traits for model training. We also found that canopy structure signals can enhance the use of spectral data to predict nitrogen related photosynthetic traits, as combining RTM-RF LAI and PLSR leaf traits well predicted canopy-level traits (leaf traits × LAI) including canopy chlorophyll (R 2 = 0.80), nitrogen (R 2 = 0.85) and V max,27 (R 2 = 0.82). Compared to leaf traits, we further found that canopy-level photosynthetic traits, particularly canopy V max,27 , have higher correlation with maize grain yield. This study highlights the potential for synergistic use of process-based and data-driven approaches of hyperspectral imaging to quantify crop traits that facilitate precision agricultural management to secure food and bioenergy production.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Singlet Oxygen Leads to Structural Changes to Chloroplasts during their Degradation in the Arabidopsis thaliana plastid ferrochelatase two Mutant

Abstract During stress, chloroplasts produce large amounts of reactive oxygen species (ROS). Chloroplasts also contain many nutrients, including 80% of a leaf’s nitrogen supply. Therefore, to protect cells from photo-oxidative damage and to redistribute nutrients to sink tissues, chloroplasts are prime targets for degradation. Multiple chloroplast degradation pathways are induced by photo-oxidative stress or nutrient starvation, but the mechanisms by which damaged or senescing chloroplasts are identified, transported to the central vacuole and degraded are poorly defined. Here, we investigated the structures involved with degrading chloroplasts induced by the ROS singlet oxygen (1O2) in the Arabidopsis thaliana plastid ferrochelatase two (fc2) mutant. Under mild 1O2 stress, most fc2 chloroplasts appeared normal, but had reduced starch content. A subset of chloroplasts was degrading, and some protruded into the central vacuole via ‘blebbing’ structures. A 3D electron microscopy analysis demonstrated that up to 35% of degrading chloroplasts contained such structures. While the location of a chloroplast within a cell did not affect the likelihood of its degradation, chloroplasts in spongy mesophyll cells were degraded at a higher rate than those in palisade mesophyll cells. To determine if degrading chloroplasts have unique structural characteristics, allowing them to be distinguished from healthy chloroplasts, we analyzed fc2 seedlings grown under different levels of photo-oxidative stress. A correlation was observed among chloroplast swelling, 1O2 signaling and the state of degradation. Finally, plastoglobule (PG) enzymes involved in chloroplast disassembly were upregulated while PGs increased their association with the thylakoid grana, implicating an interaction between 1O2-induced chloroplast degradation and senescence pathways.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery

CBI↗

DOE OSTI2021

Sustainability Trait Modeling of Field-Grown Switchgrass ( Panicum virgatum ) Using UAV-Based Imagery

Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Long-term leaf C:N ratio change under elevated CO 2 and nitrogen deposition in China: Evidence from observations and process-based modeling

Climate change, elevating atmosphere CO 2 (eCO 2 ) and increased nitrogen deposition (iNDEP) are altering the biogeochemical interactions between plants, microbes and soils, which further modify plant leaf carbon-nitrogen (C:N) stoichiometry and their carbon assimilation capability. Many field experiments have observed large sensitivity of leaf C:N ratio to eCO 2 and iNDEP. However, the large-scale pattern of this sensitivity is still unclear, because eCO 2 and iNDEP drive leaf C:N ratio toward opposite directions, which are further compounded by the complex processes of nitrogen acquisition and plant-and-microbial nitrogen competition. Here, we attempt to map the leaf C:N ratio spatial variation in the past 5 decades in China with a combination of data-driven model and process-based modeling. These two approaches showed consistent results. Over different regions, we found that leaf C:N ratio had significant but uneven changes between 2 time periods (1960-1989 and 1990-2015): a 5% ± 8% increase for temperate grasslands in northern China, a 3% ± 6% increase for boreal grasslands in western China, and by contrast, a 7% ± 6% decrease for temperate forests in southern China, and a 3% ± 5% decrease for boreal forests in northeastern China. Additionally, the structural equation models indicated that the leaf C:N change was sensitive to ΔNDEP, ΔCO 2 and ΔMAT rather than ΔMAP and ecosystem types. In this work, process-based modeling suggested that iNDEP was the main source of soil mineral nitrogen change, dominating leaf C:N ratio change in most areas in China, while eCO 2 led to leaf C:N ratio increase in low iNDEP area. This study also indicates that the long-term leaf C:N ratio acclimation was dominated by climate constraint, especially temperature, but was constrained by soil N availability over decade scale.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Full spectrum (350-2500 nm) leaf and canopy spectral reflectance, Seward Peninsula, Alaska, 2019

Full-range (350 - 2500 nm) leaf and canopy reflectance spectra of various Arctic tundra ecosystem endmembers, including species-level leaf reflectance, canopy-scale species endmember spectra, plot-scale spectra, and transect spectra. These data were collected in support of ongoing NASA ABoVE AVIRIS data synthesis work. The datasets were collected at the NGEE-Arctic watershed sites, Kougarok MM64 and MM80, and Teller MM27 on the Seward Peninsula, Alaska. The data were collected in July 2019 using a full-range Spectra Vista Corporation (SVC) HR-1024i spectroradiometer. Leaf-level spectra were collected with the original SVC leaf clip/plant probe connected to the spectrometer through a 1.15 meter long fiber optic cable, while canopy-scale reflectance was collected with an 8-degree field-of-view (FOV) foreoptic lens. All spectral measurements were collected as calibrated surface radiance and converted to surface reflectance using a 99.99% reflective Spectralon white reference standard. For those canopy spectra collected with associated functional trait data, the FOV of the instrument was positioned to include the same leaves harvested for functional trait measurements, including leaf mass per area (LMA) and foliar carbon and nitrogen content (see leaf trait data package for these data). Canopy spectra were obtained for 23 different species, and leaf spectra for 8 species. This data package includes processed canopy and leaf spectra (*.csv), processed dGPS locations (*.csv and *.kmz), digital photographs of spectral targets (*.jpg) and raw data from spectroradiometer and dGPS instruments (compressed as tar.gz). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA212_flmd.csv.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty

The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time-evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.

58 GEOSCIENCES↗

DOE OSTI2021

ORNL FACE Final Harvest, 2009, with data on leaf characteristics and tree mass

During the final year of the Oak Ridge National Laboratory (ORNL) free-air CO2 enrichment (FACE) experiment, 16 trees were felled and processed during July 2009. The trees had been grown for 12 years in ambient or elevated CO2. Two data sets are reported. Leaf characteristics (leaf mass per unit area, leaf thickness, average leaf size, carbon and nitrogen concentrations) are reported for each 1-meter section of the tree crowns in data set "Canopy_subsamples.csv". The measurements were made on approximately 20 subsamples collected from each 1-meter section of the canopy. Wood dry mass, total tree height, basal area, total leaf mass and leaf area are reported in "Tree_harvest.csv", based on field measurements of fresh mass and dry mass to fresh mass ratio of subsamples. The ORNL FACE experiment, including photos of the harvesting activity, is described at: https://richnorby.org/home/face-home/.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Leaf structural and chemical traits, and BNL field campaign sample details, San Lorenzo, Panama, 2020

This data package includes leaf traits, canopy traits and sample details for leaves from 71 species sampled from the San Lorenzo forest canopy crane site, Panama (PA-SLZ) during the BNL field campaign in January to March 2020. Each leaf sample is described with species, phenological stage and location within vertical canopy profiles. Leaf area index (LAI) and height is presented for each canopy profile location. Leaf mass per area (LMA), leaf water content (LWC) and leaf carbon and nitrogen content are included for a subset of the samples. This data package includes sample details, processed data for leaf traits and LAI (*.csv), LAI raw data (compressed as *.zip) and digital camera images (*.jpg, compressed as *.zip) of the leaf samples. Metadata files include data descriptions (_dd.csv) for tabular data, a list of all species sampled during the campaign (*.csv) and a detailed description of the field campaign protocol and methods (*.pdf). See related datasets for leaf gas exchange, leaf water potential and leaf spectral measurements made on the samples described here.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Leaf Trait Plasticity Alters Competitive Ability and Functioning of Simulated Tropical Trees in Response to Elevated Carbon Dioxide

The response of tropical ecosystems to elevated carbon dioxide (CO 2 ) remains a critical uncertainty in projections of future climate. Here, we investigate how leaf trait plasticity in response to elevated CO 2 alters projections of tropical forest competitive dynamics and functioning. We use vegetation demographic model simulations to quantify how plasticity in leaf mass per area and leaf carbon to nitrogen ratio alter the responses of carbon uptake, evapotranspiration, and competitive ability to a doubling of CO 2 in a tropical forest. Observationally constrained leaf trait plasticity in response to CO 2 fertilization reduces the degree to which tropical tree carbon uptake is affected by a doubling of CO 2 (up to -14.7% as compared to a case with no plasticity; 95% confidence interval [CI95%] -14.4 to -15.0). It also diminishes evapotranspiration (up to -7.0%, CI95% -6.4 to -7.7), and lowers competitive ability in comparison to a tree with no plasticity. Consideration of leaf trait plasticity to elevated CO 2 lowers tropical ecosystem carbon uptake and evapotranspirative cooling in the absence of changes in plant-type abundance. However, “plastic” responses to high CO 2 which maintain higher levels of plant productivity, many of which fall outside of the observed range of response, are potentially more competitively advantageous, thus, including changes in plant type abundance may mitigate these decreases in ecosystem functioning. Models that explicitly represent competition between plants with alternative leaf trait plasticity in response to elevated CO 2 are needed to capture these influences on tropical forest functioning and large-scale climate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Leaf traits and canopy structure together explain canopy functional diversity: an airborne remote sensing approach

Plant functional diversity is strongly connected to photosynthetic carbon assimilation in terrestrial ecosystems. However, many of the plant functional traits that regulate photosynthetic capacity, including foliar nitrogen concentration and leaf mass per area, vary significantly between and within plant functional types and vertically through forest canopies, resulting in considerable landscape-scale heterogeneity in three dimensions. Hyperspectral imagery has been used extensively to quantify functional traits across a range of ecosystems but is generally limited to providing information for top-of-canopy leaves only. On the other hand, lidar data can be used to retrieve the vertical structure of forest canopies. Because these data are rarely collected at the same time, there are unanswered questions about the effect of forest structure on the 3-D spatial patterns of functional traits across ecosystems. In the United States, the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) provides an opportunity to address this structure-function relationship by collecting lidar and hyperspectral data together across a variety of ecoregions. With a fusion of hyperspectral and lidar data from the NEON AOP and field-collected foliar trait data, we assessed the impacts of forest structure on spatial patterns of N. In addition, we examine the influence of abiotic gradients and management regimes on top of canopy %N and total canopy N (i.e. the total amount of N (g/m 2 ) within a forest canopy) at a NEON site consisting of a mosaic of open longleaf pine and dense broadleaf deciduous forests. Here, our resulting maps suggest that in contrast with top-of-canopy values, total canopy N variation is dampened across this landscape resulting in relatively homogeneous spatial patterns. At the same time, we found that leaf functional diversity and canopy structural diversity showed distinct dendritic patterns related to the spatial distribution of plant functional types.

54 ENVIRONMENTAL SCIENCES↗