The role of spatial scale in imaging spectroscopy of plant traits and plant functional diversity
No abstract provided
Dechant, Benjamin↗
SEARCH · Engineering Papers
Results for “plant traits”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Game-Theoretic Modeling of Vegetation Composition, Structure, and Dynamics: Physical Constraints, Fundamental Processes, and Emergent Properties
Vegetation structural and compositional dynamics emerge from plant physiological and demographic processes, individual-based competition, vegetation-soil feedbacks, and environmental variations and disturbance events. Predicting long-term changes in vegetation requires scaling plant individual behavior to large scale ecosystem processes. In this presentation, we summarize our studies in the modeling of vegetation demographic processes, competitively dominant plant traits, plant hydraulic processes, and stochastic disturbance effects on ecosystems, and illustrate the roles of the underlying ecological processes and eco-evolutionary optimization in vegetation modeling. With the case studies of evolutionarily stable strategy of allocation, leaf traits, and plant hydraulic processes, we show how the ecosystem processes and vegetation dynamics are determined by the individual-based plant competition and variations of soil and climate conditions. The predictions of ecosystem carbon dynamics can be greatly different with those from the traditional “single-tree” models. We also discuss the tradeoffs of plant traits and evolutionarily optimal strategies in the modeling of terrestrial ecosystem dynamics in an Earth system model.
vegetation models↗
Imaging Spectroscopy for Biodiversity Science: Plant Spectra, Traits, Diversity and Function Across Spatial Scales
No abstract provided
Schneider, Fabian D.↗
The Impact of Alternative Trait-Scaling Hypotheses for the Maximum Photosynthetic Carboxylation Rate (V (sub cmax)) on Global Gross Primary Production
The maximum photosynthetic carboxylation rate (V (sub cmax)) is an influential plant trait that has multiple scaling hypotheses, which is a source of uncertainty in predictive understanding of global gross primary production (GPP). Four trait-scaling hypotheses (plant functional type, nutrient limitation, environmental filtering, and plant plasticity) with nine specific implementations were used to predict global V(sub cmax) distributions and their impact on global GPP in the Sheffield Dynamic Global Vegetation Model (SDGVM). Global GPP varied from 108.1 to 128.2 petagrams of Carbon (PgC) per year, 65 percent of the range of a recent model intercomparison of global GPP. The variation in GPP propagated through to a 27percent coefficient of variation in net biome productivity (NBP). All hypotheses produced global GPP that was highly correlated (r equals 0.85-0.91) with three proxies of global GPP. Plant functional type-based nutrient limitation, underpinned by a core SDGVM hypothesis that plant nitrogen (N) status is inversely related to increasing costs of N acquisition with increasing soil carbon, adequately reproduced global GPP distributions. Further improvement could be achieved with accurate representation of water sensitivity and agriculture in SDGVM. Mismatch between environmental filtering (the most data-driven hypothesis) and GPP suggested that greater effort is needed understand V(sub cmax) variation in the field, particularly in northern latitudes.
chlorophyll fluorescence↗
Remote Sensing of Lineage Functional Types for Modeling and Monitoring Biodiversity
Hyperspectral remote sensing has the potential to continuously scale plant function and plant diversity information from landscape to global extents. Numerous studies have indicated that VSWIR (400-2500 nm) reflectance properties of vegetation capture evolutionarily conserved biochemical, structural, and other functional attributes of plant species. Spectral properties conserved in plants provide the opportunity to both 1) aggregate species into lineages with improved classification accuracy and 2) link those lineages directly to plant traits. Full realization of this goal will enable parameterization of Land Surface Models (LSMs) with remotely sensed information, e.g., canopy nitrogen, and better representations of biodiversity and functional diversity in biogeographic studies. In this study, we use hyperspectral AVIRIS data from the 2013 HyspIRI campaign over the Southern Sierra Nevada, California flight box to investigate the potential for incorporating evolutionary thinking into landcover classification. We link the airborne hyperspectral data with vegetation plot data from roughly 1372 surveys and a phylogeny representing 1361 species. We aggregate species into lineages ranging from species level groups down to similar number of Plant Functional Types as often used in LSMs. We assessed the ability of Random Forest and Partial Least Squares Discriminant Analysis to discriminate across these different phylogenetic scales and determine the optimal number of lineages to classify. Although there are some temporal and spatial differences in our training data, our best approaches achieved moderate classification accuracy (Kappa > 0.65). Given an optimal number of lineages, we explored approaches to improve classifications including machine learning and unmixing approaches. This work suggests that lineage-based methods may be a promising way to leverage the huge amounts of data that will come from high resolution and high return interval hyperspectral data planned for the Surface Biology and Geology mission with sparsely sampled existing ground-based ecological data.
Hyperspectral↗
How Accurately Do Maize Crop Models Simulate the Interactions of Atmospheric CO2 Concentration Levels With Limited Water Supply on Water Use and Yield?
This study assesses the ability of 21 crop models to capture the impact of elevated CO2 concentration [CO2] on maize yield and water use as measured in a 2-year Free Air Carbon dioxide Enrichment experiment conducted at the Thunen Institute in Braunschweig, Germany (Manderscheid et al. 2014). Data for ambient [CO2] and irrigated treatments were provided to the 21 models for calibrating plant traits, including weather, soil and management data as well as yield, grain number, above ground biomass, leaf area index, nitrogen concentration in biomass and grain, water use and soil water content. Models differed in their representation of carbon assimilation and evapotranspiration processes. The models reproduced the absence of yield response to elevated [CO2] under well-watered conditions, as well as the impact of water deficit at ambient [CO2], with 50 percent of models within a range of plus/minus 1 Mg ha(exp. -1) around the mean. The bias of the median of the 21 models was less than 1 Mg ha(exp. -1). However under water deficit in one of the two years, the models captured only 30 percent of the exceptionally high [CO2] enhancement on yield observed. Furthermore the ensemble of models was unable to simulate the very low soil water content at anthesis and the increase of soil water and grain number brought about by the elevated [CO2] under dry conditions. Overall, we found models with explicit stomatal control on transpiration tended to perform better. Our results highlight the need for model improvement with respect to simulating transpirational water use and its impact on water status during the kernel-set phase.
grain number↗
Assessing Vegetation Function with Imaging Spectroscopy
Healthy vegetation function supports diverse biological communities and ecosystem processes, and provides crops, forest products, forage, and countless other benefits. Vegetation function can be assessed by examining dynamic processes and by evaluating plant traits, which themselves are dynamic. Using both trait-based and process-based approaches, spectroscopy can assess vegetation function at multiple scales using a variety of sensors and platforms ranging from proximal to airborne and satellite measurements. Since spectroscopic data are defined by the instruments and platforms available, along with their corresponding spatial, temporal and spectral scales, and since these scales may not always match those of the function of interest, consideration of scale is a necessary focus. For a full understanding of vegetation processes, combined (multi-scale) sampling methodsusing empirical and theoretical approaches are required, along with improved informatics.
Gamon, J. A.↗
Continuity of A Global, Satellite-Based Terrestrial Primary Productivity Dataset in the VIIRS Era Achieved With Model-Data Fusion
The NASA Terra and Aqua satellites have been successfully operating for over two decades and have far exceeded their original 5‐year design life. However, the era of NASA’s Earth Observing System (EOS) may be coming to a close as early as 2023. We conducted a comprehensive calibration and validation of the MODIS MOD17 product [1,2] and the potential for continuity of multi‐decadal ecosystem gross primary productivity (GPP) and annual net primary productivity (NPP), using data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors aboard Suomi NPP and NOAA‐20. We combined an 18‐year record of eddy covariance flux tower measurements with hundreds of field measurements of NPP from the Oak Ridge National Laboratories Multi‐Biome collection to benchmark MODIS MOD17 Collection 6.1 (C61) and to develop the first terrestrial productivity estimates from VIIRS. Plant traits from the literature and the global TRY database [3,4] provide strong priors for identifying model parameters in a Bayesian model‐data fusion. As MODIS‐like observations are still needed for global environmental applications, the new VIIRS VNP17 product has the potential to extend these continuous estimates of global, terrestrial primary productivity beyond 2030.
K. Arthur Endsley↗
Hydraulic Diversity of Forests Regulates Ecosystem Resilience During Drought
Plants influence the atmosphere through fluxes of carbon, water and energy, and can intensify drought through land–atmosphere feedback effects. The diversity of plant functional traits in forests, especially physiological traits related to water (hydraulic) transport, may have a critical role in land–atmosphere feedback, particularly during drought. Here we combine 352 site-years of eddy covariance measurements from 40 forest sites, remote-sensing observations of plant water content and plant functional-trait data to test whether the diversity in plant traits affects the response of the ecosystem to drought. We find evidence that higher hydraulic diversity buffers variation in ecosystem flux during dry periods across temperate and boreal forests. Hydraulic traits were the predominant significant predictors of cross-site patterns in drought response. By contrast, standard leaf and wood traits, such as specific leaf area and wood density, had little explanatory power. Our results demonstrate that diversity in the hydraulic traits of trees mediates ecosystem resilience to drought and is likely to have an important role in future ecosystem–atmosphere feedback effects in a changing climate.
Climate and Earth system modelling↗
Observing Changing Ecological Diversity in the Anthropocene
As the world enters the Anthropocene, the planet's environment is changing rapidly, putting critical ecosystem services at risk. Understanding and forecasting how ecosystems will change over the coming decades requires understanding the sensitivity of species to environmental change. The extant distribution of species and functional groups contains valuable information about the performance of different species in different environments. However, with high rates of environmental change, information inherent in ranges of many species will disappear, since that information exists only under quasi-equilibrium conditions. The information content of distributional data obtained now is greater than data obtained in the future. New remote sensing technologies can map chemical and structural traits of plant canopies and allow inference of trait and in many cases, species ranges. Current satellite remote sensing data can only produce relatively simple classifications, but new techniques have dramatically higher biological information content.
environmental changes↗
Diverse Photosynthetic Capacity of Global Ecosystems Mapped by Satellite Chlorophyll Fluorescence Measurements
Photosynthetic capacity is often quantified by the Rubisco-limited photosynthetic capacity (i.e. maximum carboxylation rate, V(sub cmax)). It is a key plant functional trait that is widely used in Earth System Models for simulation of the global carbon and water cycles. Measuring V(sub cmax) is time-consuming and laborious; therefore, the spatiotemporal distribution of V(sub cmax) is still poorly understood due to limited measurements of V(sub cmax). In this study, we used a data assimilation approach to map the spatial variation of V(sub cmax) for global terrestrial ecosystems from a 11-year-long satellite-observed solar-induced chlorophyll fluorescence (SIF) record. In this SIF-derived V(sub cmax) map, the mean V(sub cmax) value for each plant function type (PFT) is found to be comparable to a widely used N-derived V(sub cmax) dataset by Kattge et al. (2009). The gradient of V(sub cmax) along PFTs is clearly revealed even without land cover information as an input. Large seasonal and spatial variations of V(sub cmax) are found within each PFT, especially for diverse crop rotation systems. The distribution of major crop belts, characterized with high V(sub cmax) values, is highlighted in this V(sub cmax) map. Legume plants are characterized with high V(sub cmax) values. This V(sub cmax) map also clearly illustrates the emerging soybean revolution in South America where V(sub cmax) is the highest among the world. The gradient of V(sub cmax) in Amazon is found to follow the transition of soil types with different soil N and P contents. This study suggests that satellite-observed SIF is powerful in deriving the important plant functional trait, i.e. V(sub cmax), for global climate change studies.
Liming He↗
Spatially-Explicit Monitoring of Crop Photosynthetic Capacity Through the Use of Space-Based Chlorophyll Fluorescence Data
Plant functional traits such as photosynthetic capacity are critical parameters for terrestrial biosphere models. However, their spatial and temporal characteristics are still poorly represented. In this study, we used satellite observations of sun-induced fluorescence (SIF) to estimate top-of-canopy photosynthetic capacity (maximum carboxylation rate, V(sub cmax) at a reference temperature of 25 deg C) for crops, which was in turn utilized to simulate regional gross primary production (GPP). We first estimate the key parameter, V(sub cmax), in the widely-used FvCB photosynthesis model using field measurements of CO2 and water fluxes during 2007-2012 at seven crop eddy covariance flux sites over the US Corn Belt. The results showed that satellite far-red SIF retrievals have a stronger link to V(sub cmax) at the seasonal scale (R(squared)=0.70 for C4 and R(squared)=0.63 for C3 crop) as compared with widely-used vegetation indices. We calibrate an empirical model linking V(sub cmax) with SIF that was used to estimate spatially and temporally varying crop V(sub cmax) for the US Corn Belt region. The resulting V(sub cmax) maps are used together with meteorological data from MERRA reanalysis data and vegetation structural parameters derived from the satellite- based spectral reflectance data to constrain the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model in order to estimate regional crop GPP. Our results show a substantial improvement in the seasonal and spatial patterns of cropland GPP when compared with crop yield inventory data. The evaluation with tall tower atmospheric CO2 measurements further supports our estimation of spatiotemporal V(sub cmax) from space-borne SIF. Considering that SIF has a direct link to photosynthetic activity, our findings highlight the potential to infer regional V(sub cmax) using remotely sensed SIF data and to use this information for a better quantification of regional cropland carbon cycles.
Drought↗
Storm Surge and Ponding Explain Mangrove Dieback in Southwest Florida Following Hurricane Irma
Mangroves buffer inland ecosystems from hurricane winds and storm surge. However, their ability to withstand harsh cyclone conditions depends on plant resilience traits and geomorphology. Using airborne lidar and satellite imagery collected before and after Hurricane Irma, we estimated that 62% of mangroves in southwest Florida suffered canopy damage, with largest impacts in tall forests (>10 m). Mangroves on well-drained sites (83%) resprouted new leaves within one year after the storm. By contrast, in poorly-drained inland sites, we detected one of the largest mangrove diebacks on record (10,760 ha), triggered by Irma. We found evidence that the combination of low elevation (median = 9.4 cm asl), storm surge water levels (>1.4 m above the ground surface), and hydrologic isolation drove coastal forest vulnerability and were independent of tree height or wind exposure. Our results indicated that storm surge and ponding caused dieback, not wind. Tidal restoration and hydrologic management in these vulnerable, low-lying coastal areas can reduce mangrove mortality and improve resilience to future cyclones.
David Lagomasino↗
Modeling Demographic-Driven Vegetation Dynamics and Ecosystem Biogeochemical Cycling in NASA GISS’s Earth System Model (ModelE-BiomeE v.1.0)
We developed a demographic vegetation model, BiomeE, to improve the modeling of vegetation dynamics and ecosystem biogeochemical cycles in the NASA Goddard Institute of Space Studies' ModelE Earth system model. This model includes the processes of plant growth, mortality, reproduction, vegetation structural dynamics, and soil carbon and nitrogen storage and transformations. The model combines the plant physiological processes of ModelE's original vegetation model, Ent, with the plant demographic and ecosystem nitrogen processes that have been represented in the Geophysical Fluid Dynamics Laboratory's LM3-PPA. We used nine plant functional types to represent global natural vegetation functional diversity, including trees, shrubs, and grasses, and a new phenology model to simulate vegetation seasonal changes with temperature and precipitation fluctuations. Competition for light and soil resources is individual based, which makes the modeling of transient compositional dynamics and vegetation succession possible. Overall, the BiomeE model simulates, with fidelity comparable to other models, the dynamics of vegetation and soil biogeochemistry, including leaf area index, vegetation structure (e.g., height, tree density, size distribution, and crown organization), and ecosystem carbon and nitrogen storage and fluxes. This model allows ModelE to simulate transient and long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems. Furthermore, BiomeE also allows for the eco-evolutionary modeling of community assemblage in response to past and future climate changes with its individual-based competition and demographic processes.
Biogeochemical cycles↗
A Heritage BioSensor for Lunar Biology Experiments
Introduction: Automated biological experiments on small spacecraft missions have gained prominence over the past decade due to their simplicity, accessibility, and small mass, volume, and power needs. Most recently, the BioSensor microfluidic CubeSat payload aboard BioSentinel used an automated microfluidic cell culture system to study the effects of environmental stressors like deep space radiation and microgravity on yeast growth and metabolism. BioSentinel’s successor, the Lunar Explorer Instrument for space biology Applications (LEIA), will study the effects of lunar gravity and radiation using an improved version of the BioSensor microfluidic platform. The BioSensor payload has great adaptability to host a diverse range of biological experiments with single- and multi-celled organisms in both crewed and uncrewed missions, making it a compelling candidate for future space biology studies in a lunar surface environment. BioSensor Instrumentation on BioSentinel: The first spaceflight mission with the BioSensor, BioSentinel’s biology experiments occurred at three locations -- deep space, ISS and ground. The payload contained 18 microfluidic cards, each featuring 16 growth wells (a total of 288 growth wells). Each well was loaded before launch with desiccated yeast. In space, liquid culture medium (nutrients) was automatically introduced to batches of wells at a time to initiate a series of biology experiments. Temperature was maintained by thin film heaters on both sides of each card. Each well was equipped with three LEDs emitting at 570 nm, 630 nm, and 850 nm, paired with photodetectors to measure cell concentration and the alamarBlue (metabolic indicator dye) color transition from blue to pink. Phenotypic parameters like cell viability, metabolic rate, and generation time can be derived from these measurements. The sequence and timing of fluid fills, optical measurements, and thermal control were stored onboard, but could be updated asynchronously via ground communication. LEIA: LEIA is slated for launch no earlier than 2026 on a CLPS lander. BioSentinel’s BioSensor has been modified for use in LEIA. These improvements include: (a) storage for multiple culture medium types, (b) additional LED color (465 nm) for a new biological assay for antioxidant (carotenoid) production, (c) housing modifications for later biology load before launch, (d) improved isolation between electronic and fluidic components, and (e) improved humidity control for prolonged organism viability in case of post-load launch delay. Future Prospects: The consistent and successful demonstration of complex fluidics platforms alongside reliable instrument operations in a space environment is poised to create strong momentum for BioSensor-based biological experiment payloads. Planned future developments with the BioSensor include extending compatibility to a broader range of organisms and assays. Preliminary work has already demonstrated successful growth of Arabidopsis seedlings in fluidic cards. With a few modifications to the optical assembly, the setup could easily measure photosynthetic traits in plants and cyanobacteria. The addition of fluorescence measurements and generation of novel luminescent assays will elevate BioSensor’s functionality further. Beyond the BioSensor’s potential uses on free-flyer missions, ISS and Gateway, and CLPS landers, deploying the BioSensor to the lunar surface or in an artificial habitat on crewed missions could enable pioneering research on both how life responds to lunar conditions and future bioproduction capabilities making the BioSensor an indispensable tool for future space biology research.
Chinmayee Govinda Raj↗
Transgenic plants with increased calcium stores
The present invention provides transgenic plants over-expressing a transgene encoding a calcium-binding protein or peptide (CaBP). Preferably, the CaBP is a calcium storage protein and over-expression thereof does not have undue adverse effects on calcium homeostasis or biochemical pathways that are regulated by calcium. In preferred embodiments, the CaBP is calreticulin (CRT) or calsequestrin. In more preferred embodiments, the CaBP is the C-domain of CRT, a fragment of the C-domain, or multimers of the foregoing. In other preferred embodiments, the CaBP is localized to the endoplasmic reticulum by operatively associating the transgene encoding the CaBP with an endoplasmic reticulum localization peptide. Alternatively, the CaBP is targeted to any other sub-cellular compartment that permits the calcium to be stored in a form that is biologically available to the plant. Also provided are methods of producing plants with desirable phenotypic traits by transformation of the plant with a transgene encoding a CaBP. Such phenotypic traits include increased calcium storage, enhanced resistance to calcium-limiting conditions, enhanced growth and viability, increased disease and stress resistance, enhanced flower and fruit production, reduced senescence, and a decreased need for fertilizer production. Further provided are plants with enhanced nutritional value as human food or animal feed.
Wyatt, Sarah↗
Phylogenetic Conservatism in Plant Phenology
Phenological events defined points in the life cycle of a plant or animal have been regarded as highly plastic traits, reflecting flexible responses to various environmental cues. The ability of a species to track, via shifts in phenological events, the abiotic environment through time might dictate its vulnerability to future climate change. Understanding the predictors and drivers of phenological change is therefore critical. Here, we evaluated evidence for phylogenetic conservatism the tendency for closely related species to share similar ecological and biological attributes in phenological traits across flowering plants. We aggregated published and unpublished data on timing of first flower and first leaf, encompassing 4000 species at 23 sites across the Northern Hemisphere. We reconstructed the phylogeny for the set of included species, first, using the software program Phylomatic, and second, from DNA data. We then quantified phylogenetic conservatism in plant phenology within and across sites. We show that more closely related species tend to flower and leaf at similar times. By contrasting mean flowering times within and across sites, however, we illustrate that it is not the time of year that is conserved, but rather the phenological responses to a common set of abiotic cues. Our findings suggest that species cannot be treated as statistically independent when modelling phenological responses.Closely related species tend to resemble each other in the timing of their life-history events, a likely product of evolutionarily conserved responses to environmental cues. The search for the underlying drivers of phenology must therefore account for species' shared evolutionary histories.
phylogenetic conservatism↗
Phytometric intelligence sensors
Methods and apparatus for determining when plants require watering, and methods of attending to the watering of plants including signaling the grower that the plants are in need of hydration are provided. The novel methods include real-time measurement of plant metabolics and phytometric physiology changes of intrinsic physical or behavioral traits within the plant such as determining physiological flux measurement of enzyme flux due to environmental changes such as the wind and drought stress, soil and plant mineral deficiencies, or the interaction with a bio-control for organic disease control including, cell movement, signal transduction, internal chemical processes and external environmental processes including when plants require watering, and methods of attending to the watering of plants including signaling the grower that the plants are in need of hydration.
Seelig, Hans-Dieter↗