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At least 73 records · Page 4

Plant litter traits control microbial decomposition and drive soil carbon stabilization

Efforts to manage soils for carbon (C) sequestration remain limited by our understanding of how differences in plant traits and microbial traits mechanistically drive soil organic C (SOC) storage. Addressing this uncertainty is particularly critical in bioenergy agriculture, due to its potential to enhance soil C and provide a C neutral fuel. As such, we examined differences between two contrasting feedstocks, Zea mays (corn) and Miscanthus x giganteus (miscanthus), in the ability of their litter to form new chemically resistant particulate SOC vs. physically protected mineral associated SOC and used this data to improve the parameterization of a microbial SOC model. We tested a hypothesized conceptual model whereby easy to decompose corn litters drive greater microbial carbon use efficiency (CUE) and the formation of more mineral associated SOC over particulate SOC than more complex miscanthus litters. To do this, we performed a soil microcosm experiment where we added 13 C enriched aboveground and belowground litters to soils and traced the fate of the 13 C into microbial respiration and SOC pools. We found that corn litters promoted higher microbial CUE (0.37) than miscanthus litters (0.24). In turn, corn litter formed approximately 50% more mineral associated SOC than miscanthus litters. Similarly, structurally complex root litters promoted a lower CUE and formed less mineral associated SOC than leaf and shoot litters for both crops. When we used our data to parameterize the SOC model, we found that modelling microbial trait differences uniquely allowed the model to capture the fate of litter C in SOC. Collectively, we found a robust link between litter quality, microbial efficiency, and the formation of SOC. This link bridges the empirical uncertainty in how different crops can form new soil C and provides an empirical basis for modelling SOC transformations.

59 BASIC BIOLOGICAL SCIENCES↗

BrzostekEcologyLab/CORPSE-soil-jars

Efforts to manage soils for carbon (C) sequestration remain limited by our understanding of how differences in plant traits and microbial traits mechanistically drive soil organic C (SOC) storage. Addressing this uncertainty is particularly critical in bioenergy agriculture, due to its potential to enhance soil C and provide a C neutral fuel. As such, we examined differences between two contrasting feedstocks, Zea mays (corn) and Miscanthus x giganteus (miscanthus), in the ability of their litter to form new chemically resistant particulate SOC vs. physically protected mineral associated SOC and used this data to improve the parameterization of a microbial SOC model. We tested a hypothesized conceptual model whereby easy to decompose corn litters drive greater microbial carbon use efficiency (CUE) and the formation of more mineral associated SOC over particulate SOC than more complex miscanthus litters. To do this, we performed a soil microcosm experiment where we added 13C enriched aboveground and belowground litters to soils and traced the fate of the 13C into microbial respiration and SOC pools. We found that corn litters promoted higher microbial CUE (0.37) than miscanthus litters (0.24). In turn, corn litter formed approximately 50% more mineral associated SOC than miscanthus litters. Similarly, structurally complex root litters promoted a lower CUE and formed less mineral associated SOC than leaf and shoot litters for both crops. When we used our data to parameterize the SOC model, we found that modeling microbial trait differences uniquely allowed the model to capture the fate of litter C in SOC. Collectively, we found a robust link between litter quality, microbial efficiency, and the formation of SOC. This link bridges the empirical uncertainty in how different crops can form new soil C and provides an empirical basis for modeling SOC transformations.

Ridgeway, Joanna↗

SPRUCE Diurnal and Seasonal Patterns of Water Potential in S1 Bog and SPRUCE Experimental Plot Vegetation beginning in 2010

This data set reports on water potential from excised terminal shoots or leaves (Mainthemum only) of vegetation at the SPRUCE site located in the S1 bog. Pretreatment measurements were collected from outside plots within the S1 bog. Post treatments were collected from inside SPRUCE plots. Water potential was measured on the overstory canopy species, consisting of the evergreen Picea mariana (Mill.) B.S.P. (black spruce) and deciduous Larix laricina (Du Roi) K. Koch (tamarack) trees, and ericaceous shrubs < 1 m tall, consisting of the evergreen Rhododendron groenlandicum (Oeder) Kron & Judd (Labrador tea) and Chamaedaphne calyculata (L.) Moench. (leatherleaf). Some measurement campaigns also included the evergreen Kalmia polifolia, Wangenh. (bog laurel), the deciduous Vaccinium angustifolium (Aiton) (blueberry), and the herbaceous Maianthemum trifolium (L.) Sloboda (three-leaf false Solomon’s seal). This dataset contains one data file in comma-separated (*.csv) format. This is the second release of water potential data from the site with additional data from 2018 and 2019. These data cover 2010-06-22 to 2019-07-16. Further results will be added to this data set and released to the public periodically as quality assurance and publication of results are accomplished. There are 10 experimental plots in SPRUCE: five temperature treatments (+0, +2.25, +4.5, +6.75, +9°C) at ambient CO2, and the same five temperature treatments at elevated CO2 (+500 ppm). These data span the pre- and post-treatment periods when enclosed plots were exposed to warming and elevated carbon dioxide (CO2) within the SPRUCE experiment. The pretreatment samples were collected from south end of the S1 bog prior to SPRUCE boardwalk construction, then throughout the bog once the boardwalks were completed.

54 ENVIRONMENTAL SCIENCES↗

Effects of radiation quality, intensity, and duration on photosynthesis and growth

Differences in radiation quality from the six most common electric lamps have little effect on photosynthetic rate. Radiation quality primarily alters growth because of changes in branching or internode elongation, which change radiation absorption. Growth and yield in wheat appear to be insensitive to radiation quality. Growth and yield in soybeans can be slightly increased under high pressure sodium (HPS) lamps compared to metal halide lamps, in spite of greatly reduced chlorophyll concentrations under HPS lamps. Daily integrated photosynthetic photon flux (mol m(exp -2)d(exp -1)) most directly determines leaf anatomy and growth. Photosynthetic photon flux (PPF) levels of 800 (mu)mol m(exp -2)s(exp -1) are adequate to simulate field daily-integrated PPF levels for both short and long day plants, but plant canopies can benefit from much higher PPF levels.

Bugbee, Bruce↗

AVIRIS performance during the 1987 flight season: An AVIRIS project assessment and summary of the NASA-sponsored performance evaluation

Results are presented of the assessment of AVIRIS performance during the 1987 flight season by the AVIRIS project and the earth scientists who were chartered by NASA to conduct an independent data quality and sensor performance evaluation. The AVIRIS evaluation program began in late June 1987 with the sensor meeting most of its design requirements except for signal-to-noise ratio in the fourth spectrometer, which was about half of the required level. Several events related to parts failures and design flaws further reduced sensor performance over the flight season. Substantial agreement was found between the assessments by the project and the independent investigators of the effects of these various factors. A summary of the engineering work that is being done to raise AVIRIS performance to its required level is given. In spite of degrading data quality over the flight season, several exciting scientific results were obtained from the data. These include the mapping of the spatial variation of atmospheric precipitable water, detection of environmentally-induced shifts in the spectral red edge of stressed vegetation, detection of spectral features related to pigment, leaf water and ligno-cellulose absorptions in plants, and the identification of many diagnostic mineral absorption features in a variety of geological settings.

Vane, Gregg↗

How Universal is the Relationship Between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment

Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes. In this study, we assessed the universality of the relationships between crop LAI and remotely sensed Vegetation Indices (VIs). We first compiled a global dataset of 1459 in situ quality-controlled crop LAI measurements and collected Landsat satellite images to derive five different VIs including Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), two versions of the Enhanced Vegetation Index (EVI and EVI2), and Green Chlorophyll Index (CI(sub Green)). Based on this dataset, we developed global LAI-VI relationships for each crop type and VI using symbolic regression and Theil-Sen (TS) robust estimator. Results suggest that the global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. These relationships explain more than half of the total variance in ground LAI observations (R2 greater than 0.5), and provide LAI estimates with RMSE below 1.2 m2/m2. Among the five VIs, EVI/EVI2 are the most effective, and the crop-specific LAI-EVI and LAI-EVI2 relationships constructed by TS, are robust when tested by three independent validation datasets of varied spatial scales. While the heterogeneity of agricultural landscapes leads to a diverse set of local LAI-VI relationships, the relationships provided here represent global universality on an average basis, allowing the generation of large-scale spatial-explicit LAI maps. This study contributes to the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research.

Vegetation Index↗

Determining Trends in Impervious Cover for the Mobile Bay, AL Region for 1974-2008, Based on a Landsat Time Series

This presentation will discuss the development and use of Landsat-based impervious cover products in conjunction with land use land cover change products to assess multi-decadal urbanization across the Mobile Bay region at regional and watershed scales. This nationally important coastal region has undergone a variety of ephemeral and permanent land use land cover change since the mid-1970s, including gradual but consequential increases in urban surface cover. This urban sprawl corresponds with increased regional percent impervious cover. The region s coastal zone managers are concerned about the increasing percent impervious cover, since it can negatively influence water quality and is an important consideration for coastal conservation and restoration work. In response, we processed multi-temporal Landsat data to compute maps of percent impervious cover for multiple dates from 1974 through 2008, roughly at 5-year intervals. Each year of product was classified using one single date of leaf-on and leaf-off Landsat data in conjunction with Cubist software. We are assessing Landsat impervious cover product accuracy through comparisons to available reference data, including available NLCD impervious cover products from the USGS, raw Landsat data, plus higher spatial resolution aerial and satellite data. In particular, we are quantitatively comparing the 2008 Landsat impervious cover products to those from QuickBird 2.4-meter multispectral data. Initial visual comparisons with the QuickBird impervious cover product suggest that the 2008 Landsat product tends to underestimate impervious cover for high density urban areas and to overestimate impervious cover in established residential subdivisions mixed with forested cover. Landsat TM and ETM data appears to produce more accurate impervious cover products compared to those using lower resolution Landsat MSS data. Although imperfect, these Landsat impervious cover products have helped the Mobile Bay National Estuary Program visualize basic urbanization trends for multiple HUC-12 watersheds of concern to them and their constituents

Spruce, Joseph P.↗

Improving the MODIS LAI Compositing Using Prior Time-Series Information

The Moderate Resolution Imaging Spectroradiometer (MODIS) long-term leaf area index (LAI) products have significantly contributed to global energy fluxes, climate change, and biogeochemistry research. However, the maximum fraction of photosynthetically active radiation absorbed by vegetation (Max-FPAR) compositing strategy of the Collection 6 (C6) products dictates that the main or backup algorithm is always triggered by observations of different quality, which indirectly causes the observed instability in the LAI time-series. Based on MODIS daily LAI retrievals, this study develops a prior knowledge time-series compositing algorithm (PKA) using a linear kernel driven (LKD) model. Our results show that the newly proposed PKA can significantly improve the LAI composites compared to the Max-FPAR strategy using ground-based observations for validation. We found that the PKA performs better than Max-FPAR in various aspects (different sites, seasons, and retrieval index (RI) ranges), with R2 increasing from 0.69 to 0.76 and root means square error (RMSE) decreasing from 1.01 to 0.84 compared to GBOV ground truth. The same improvement was shown for the ground truth LAIs measured at the Honghe and Hailun sites in northeastern China, with R2 increasing from 0.23 to 0.41 and RMSE decreasing from 1.27 to 1.25. In addition, three newly proposed temporal uncertainty metrics (time-series stability, TSS and time-series anomaly, TSA and reconstruction error metric, RE (the proximity to the main RT-based retrievals)) were applied to compare the stability of LAI time-series before and after PKA implementation. We found that the time series stability of PKA LAI was improved, the time series anomalies were reduced, and the retrieval rates of the main algorithm were also greatly enhanced compared to Max-FPAR LAI. A case intercomparison for Max-FPAR-MODIS, Max-FPAR-VIIRS (Visible Infrared Imager Radiometer Suite), and PKA-MODIS LAIs in the Amazon Forest region showed that the PKA is also effective in improving the LAI retrieval over large regions with few qualified observations due to poor atmospheric conditions (RE decreased from 2.37/2.35 (Max-FPAR-MODIS/Max-FPAR-VIIRS) to 2.25 (PKA-MODIS) and RI increased from 61.94%/59.62% to 66.88%). The same improvement was seen in the BELMANIP 2.1 sites for almost all biomes except deciduous broadleaf forest, where the RE decreased from 1.85/2.13 to 1.15 overall. We note that the PKA has the potential to be easily implemented in the operational algorithms of subsequent MODIS and MODIS-like LAI Collections.

MODIS↗

Population Genomic Analyses Reveal Connectivity via Human-Mediated Transport across Populus Plantations in North America and an Undescribed Subpopulation of Sphaerulina musiva

Domestication of plant species has affected the evolutionary dynamics of plant pathogens in agriculture and forestry. A model system for studying the consequences of plant domestication on the evolution of an emergent plant disease is the fungal pathogen Sphaerulina musiva. This ascomycete causes leaf spot and stem canker disease of Populus spp. and their hybrids. A population genomics approach was used to determine the degree of population structure and evidence for selection on the North American population of S. musiva. In total, 122 samples of the fungus were genotyped identifying 120,016 single-nucleotide polymorphisms after quality filtering. In North America, S. musiva has low to moderate degrees of differentiation among locations. Three main genetic clusters were detected: southeastern United States, midwestern United States and Canada, and a new British Columbia cluster (BC2). Population genomics suggest that BC2 is a novel genetic cluster from central British Columbia, clearly differentiated from previously reported S. musiva from coastal British Columbia, and the product of a single migration event. Phenotypic measurements from greenhouse experiments indicate lower aggressiveness of BC2 on Populus trichocarpa. In summary, S. musiva has geographic structure across broad regions indicative of gene flow among clusters. The interconnectedness of the North American S. musiva populations across large geographic distances further supports the hypothesis of anthropogenic-facilitated transport of the pathogen.

Biochemistry & Molecular Biology↗

Soil microbial function and fungal community data along an ectomycorrhizal basal area gradient, Vermont, May 2019

Mycorrhizal fungi can affect soil organic matter cycling through several mechanisms including priming, nutrient competition, and direct enzyme production. Differences in nutrient foraging strategies between ectomycorrhizal (EcM) and arbuscular mycorrhizal (AM) fungi produce divergent belowground dynamics: where EcM can take up organic nitrogen and directly break down SOM by producing enzymes, AM fungi are limited to scavenging mineral N. EcM-associated tree species also have leaf litter with relatively higher ratios of carbon to nitrogen (C:N), and belowground saprotrophic communities more dominated by fungi. Consequently, free-living microbes in EcM-dominated soils should experience nitrogen limitation, with subsequent increases in enzyme production and decreased carbon use efficiency (CUE). However, the relative importance of the effects of substrate quality and fungal community composition on enzyme production and CUE are unclear. To assess this distinction, we sampled the organic horizon and 10 cm of the mineral horizon in northern temperate forest soils along a gradient of EcM dominance (see SCxTS_DBH.csv for full tree species information). We characterized fungal community composition by measuring EcM relative abundances from extracted fungal DNA (To run raw ASV data through R code, use SCxTS_ASV_taxonomy.csv, and R code ITS_processing.Rmd; to see processed data with EcM relative abundance, use file SCxTS_sum_afitch_Feb18.20.csv) and the fungal to bacterial ratios (see data file SCxTS_plfa.csv) from phospholipid fatty acid analysis. Soil microbial functions were measured as potential activities of five hydrolytic and two oxidative enzymes ( To run raw data through R code us SCxTS_metadata.csv, hydrolytic_oxidative_raw.csv, MUBraw.csv, Enzyme_soilweight.csv, and incubationtime.csv, and R code SCxTS_enzymes.Rmd; to use Enzyme data ready to run through statistics code, use file Enzyme.data.csv) and microbial carbon use efficiency (see file SCxTS_CUE.csv and for full calculations file 18O_CUEcalcs_SCxTS_2019.xlsx). We assessed soil substrate quality as the soil carbon:nitrogen ratio (See file Organic_CN.csv). Statistical analysis can be run using R code from file SCxTS_analysis.Rmd.

54 ENVIRONMENTAL SCIENCES↗

Scaling photosynthetic function and CO2 dynamics from leaf to canopy level for maize – dataset combining diurnal and seasonal measurements of vegetation fluorescence, reflectance and vegetation indices with canopy gross ecosystem productivity

Recent advances in leaf fluorescence measurements and canopy proximal remote sensing currently enable the non-destructive collection of rich diurnal and seasonal time series, which are required for monitoring vegetation function at the temporal and spatial scales relevant to the natural dynamics of photosynthesis. Remote sensing assessments of vegetation function have traditionally used actively excited foliar chlorophyll fluorescence measurements, canopy optical reflectance data and vegetation indices (VIs), and only recently passive solar induced chlorophyll fluorescence (SIF) measurements. In general, reflectance data are more sensitive to the seasonal variations in canopy chlorophyll content and foliar biomass, while fluorescence observations more closely relate to the dynamic changes in plant photosynthetic function. With this dataset we link leaf level actively excited chlorophyll fluorescence, canopy proximal reflectance and SIF, with eddy covariance measurements of gross ecosystem productivity (GEP). The dataset was collected during the 2017 growing season on maize, using three automated systems (i.e., Monitoring Pulse-Amplitude-Modulation fluorimeter, Moni-PAM; Fluorescence Box, FloX; and from eddy covariance tower). The data were quality checked, filtered and collated to a common 30 minutes timestep. We derived vegetation indices related to canopy functioning (e.g., Photochemical Reflectance Index, PRI; Normalized Difference Vegetation Index, NDVI; Chlorophyll Red-edge, Clre) to investigate how SIF and VIs can be coupled for monitoring vegetation photosynthesis. The raw datasets and the filtered and collated data are provided to enable new processing and analyses.

Petya Campbell↗

Assessing Alaskan boreal forest landcover affected by climate-wildfire interactions from ground truth surveys and NASA airborne remote sensing

Alaska’s boreal forest is facing unprecedented challenges under rapid climate warming (increasingly severe fires, droughts, pest/disease outbreaks) that may destabilize its function as a global carbon sink. Forests near Fairbanks may be especially vulnerable, impacting air quality and ecosystem services. We combined GT (ground truthing) with Airborne Visible InfraRed Imaging Spectrometer (AVIRIS-NG) images collected by the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) program (2017-2019) to assess landcover change at five recently burned sites (2001-2019) of different fire severities and moisture regimes within 30 miles of Fairbanks. GT included tree seedling counts, understory % cover and >50% leaf canopy color assessment. 36 circular plots (1/30 ha radius) including 6 moderate to severely burned plots were selected across sites. 31 additional sites including 12 burned sites were geotagged in photos. AVIRIS images were processed from 29 spectral bands selected to identify changes in chlorophyll and water content. Images were segmented into natural boundaries (polygons) using ENVI 5.5 software. A spectral library of 8 AVIRIS bands with high between-class/low within-class variation was used in two random forest models to predict vegetation classes (model 1: 12 classes, model 2: 14 classes) in each AVIRIS scene, using 20% of the data as training data. Model 2 classified 20% more polygons overall, but only 42% of GT/geotagged polygons were correctly classified by both models. More forest sites were correctly classified (63%) than open vegetation (32%) or post-fire sites (46%). 50% of aspen forest and post-fire polygons were misclassified as shrubland. GT revealed that post-fire plots supported 134,000 (± 48,000) tree seedlings and saplings ha-1 (0.2 - 4 m height, 64% deciduous) versus 2500 (± 2100) shrubs ha-1 (1-6 m height). > 50% canopy browning was observed in conifer forest (8 plots) with no signs of insect infestation. Canopy herbivory > 50% (leaf miner, leaf beetle) and moose herbivory of tree bark was seen across aspen sites. Our study suggests: 1) low canopy vegetation presents challenges for improved landcover classification, and 2) aspen forest should be differentiated in vegetation maps which would aid in tracking herbivory.

Alaska↗

On the use of air temperature and precipitation as surrogate predictors in soil respiration modelling

Soil respiration (R S ), the soil-to-atmosphere CO 2 flux that is a major component of the global carbon cycle, is strongly influenced by local soil temperature (T soil ) and water content (SWC). Regional to global-scale R S modelling thus requires this information at local scales, but few high-quality, wall-to-wall (global) T soil and SWC data exist. As a result, such modelling efforts commonly use air temperature (T air ) and monthly precipitation (P m ) as surrogate predictors, but their site-scale accuracy and potential bias are unknown. In this report we used monthly data from 880 sites across a wide variety of different environmental conditions (i.e., climate, ecosystem type, elevation, vegetation leaf habit and drainage conditions) to determine the suitability of T air as a surrogate for T soil , and data from 507 sites to examine the suitability of P m as a surrogate for SWC. Site-specific linear and second-order exponential non-linear models were compared using model evaluation metrics (i.e., slope, p-value of slope, root mean square error [RMSE], index of agreement and model efficiency). We found that T soil and T air are highly correlated and explain similar R S variability. In contrast, P m is not a good surrogate for SWC, even though P m explains a similar amount of R S variability to SWC. The wide variability in the site-specific relationships between R S and SWC means that no single relationship can be used for large-scale modelling. The results from this study support the use of T air in continental-to-global scale R S models, and highlight the urgent need for continental-to-global scale SWC datasets for the modelling and evaluation of future soil carbon dynamics under global climate change.

54 ENVIRONMENTAL SCIENCES↗

Nitrogen Fertilizer, Arbuscular Mycorrhizal Fungi, and Soil Nematodes Affect Lignin Quality and Quantity in Switchgrass (Panicum virgatum L.)

Plant lignin content and composition, which limit cell wall digestibility and efficiency of cellulose conversion to bioethanol, can be influenced by belowground biotic and abiotic factors. Switchgrass (Panicum virgatum L.) is a leading lignocellulosic biofuel crop and forms strong belowground associations with arbuscular mycorrhizal fungi (AMF), is susceptible to belowground plant-parasitic nematodes (PPN), and when grown in monoculture generally requires nitrogen (N) fertilization. The main objectives of the study were to investigate the effects of N fertilizer and belowground organisms on lignin content and composition of switchgrass. Here, leaf, stem, and root tissues were evaluated separately to test whether these factors had varying belowground (local) or aboveground (systemic) effects on plants. These factors were manipulated in a field study in 2017 using biocide applications to reduce soil fungi and nematodes. Combined biocide application reduced p-hydroxyphenyl (H) unit abundance in the leaves by 14% and increased the syringyl:guaiacyl (S:G) ratio in stems by 2%. Application of fungicide alone increased stem syringyl (S) unit by 12.4% as compared with control plots, and 11.1% as compared with nematicide plots. Overall, fertilizer increased total stem lignin by 3%, stem S unit by 6.7%, and stem S:G ratio by 10%, whereas it reduced the amount of H-unit in the roots by 11%. While the effects of N fertilizer were more pronounced in this study, changes to soil organisms had similar magnitudes of effect for some measures of lignin, indicating that these belowground interactions may be important for growers to consider in the future.

59 BASIC BIOLOGICAL SCIENCES↗

Evaluation of VEGETATION and PROBA-V Phenology Using PhenoCam and Eddy Covariance Data

High-quality retrieval of land surface phenology (LSP) is increasingly important for understanding the effects of climate change on ecosystem function and biosphere–atmosphere interactions. We analyzed four state-of-the-art phenology methods: threshold, logistic-function, moving-average and first derivative based approaches, and retrieved LSP in the North Hemisphere for the period 1999–2017 from Copernicus Global Land Service (CGLS) SPOT-VEGETATION and PROBA-V leaf area index (LAI) 1 km V2.0 time series. We validated the LSP estimates with near-surface PhenoCam and eddy covariance FLUXNET data over 80 sites of deciduous forests. Results showed a strong correlation (R 2 > 0.7) between the satellite LSP and ground-based observations from both PhenoCam and FLUXNET for the timing of the start (SoS) and R 2 > 0.5 for the end of season (EoS). The threshold-based method performed the best with a root mean square error of ~9 d with PhenoCam and ~7 d with FLUXNET for the timing of SoS (30th percentile of the annual amplitude), and ~12 d and ~10 d, respectively, for the timing of EoS (40th percentile).

47 OTHER INSTRUMENTATION↗

Estimating Major Ion and Nutrient Concentrations in Mangrove Estuaries in Everglades National Park Using Leaf and Satellite Reflectance

Coastal mangrove ecosystems are under duress worldwide because of urban development, sea-level rise, and climate change, processes that are capable of changing the salinity and nutrient concentration of the water utilized by the mangroves. This study correlates long-term water chemistry in mangrove environments, located in Everglades National Park,with mangrove spectral reflectance measurements made at both the leaf and canopy scales. Spectral reflectance measurements were collected using a handheld spectrometer for leaf-level measurements and Landsat 5TMdata for regional coverage. Leaf-level reflectance data were collected from three mangrove species (i.e., red, black and white mangroves) across two regions; a tall mangrove (approximately 18m) and dwarf mangrove (1- 2 m) region. The reflectance data were then used to calculate a wide variety of biophysical reflectance indices (e.g., NDVI, EVI, SAVI) to determine signs of stress. Discrete, quarterly water samples from the surface water, groundwater, and pore water (20 and 85 cm depths) and daily autonomous surface water samples were collected at each site and analyzed for major anions (Cl(−) and SO(2(-)/4)), cations (Na(+), K(+), Mg(sup 2(+)), and Ca(sup 2(+)), total nitrogen (TN) and total phosphorus (TP).Mangrove sites that exhibited the highest salinity and ionic concentrations in the surface and subsurface water also had the lowest near-infrared reflectance at both the leaf and satellite levels. Seasonal reflectance responses were measured in the near-infrared (NIR) wavelengths at both the leaf and canopy scales and were strongly correlated with nutrient and ionic concentrations in the surface and subsurface water, even though there was no significant separability between the three mangrove species. Study sites that experienced the greatest variability in surface and subsurface water ionic concentrations also exhibited the greatest fluctuations in NIR spectral reflectance. Landsat 5TM images were able to detect tall and dwarf mangroves by the differences in spectral indices (e.g., NDVI, NDWI, and EVI) because of the variability in the background conditions amongst the environments. In addition, Landsat 5TM images spanning 16 years (1993- 2009) were successfully used to estimate the seasonal variability in ionic concentrations in the surface water across the Florida Coastal mangrove ecotone. This study has shown that water chemistry can be estimated indirectly by measuring the change in spectral response at the leaf- or satellite-scale. Furthermore, the results of this research may be extrapolated to similar coastal mangrove systems throughout the Caribbean and world-wide wherever red, black, and white mangroves occur.

Mangroves↗

High Dose Rate Irradiations of Enduray Vision System for Nuclear Inspections

Higher radiation-hardened video cameras are needed in the operation and remote handling of equipment in nuclear reactor inspection and refueling applications. Vega Wave Systems, Inc. has developed a radiation-hardened vision system for nuclear energy applications. This vision system has been developed under several small business innovative research programs (SBIRs) from the U.S. Department of Energy, and the previous GAIN voucher program at Argonne National Laboratory (ANL) was successful in demonstrating that a prototype of the camera is radiation-hard up to at least 525 kGy (5.25 × 106 rad, Si equivalent) at dose rates of 10 kGy/hr with no measurable degradation in image quality and no measurable radiation-induced noise (RIN). Vega Wave Systems has developed a new version of the camera with more than 3X the resolution using a new design and new components, and this new design requires radiation-hardness qualification for marketplace acceptance. The program described in this report provided high radiation-hardness testing of this redesigned high-resolution vision system using the Argonne Low-Energy Accelerator Facility (LEAF). This report presents the results of irradiation tests performed on Vega Wave System’s redesigned high resolution vision system at a dose rate of 9.3 kGy/hr and up to total doses of 1823 kGy. This was an accumulation of 197 hours of irradiation. The results were excellent, providing proof of the redesigned high-resolution vision system’s immunity to high levels of radiation.

42 ENGINEERING↗

The effects of expression of a hyperthermophilic endoglucanase and nutrient conditions on poplar growth and physiology

Abstract Efficient production and processing of poplar biomass feedstock requires costly pretreatments and enzyme additives. Transgenic alterations of poplar can reduce the need for these inputs by increasing biomass, improving lignocellulose quality and enhancing nutrient uptake. Previously, a transgenic line of poplar expressing a bacterial hyperthermophilic endoglucanase (TnCelB) in Populus alba × grandidentata (P39) was developed and characterized. This study reports the effects on the TnCelB transgenic poplar line under a reduced nutrient treatment. Overall, the nutrient treatment was the source of more observed significant differences than the genotype. Wild type and TnCelB poplar had similar responses in biomass allocation and net photosynthesis. TnCelB trees had a wrinkled leaf phenotype and relative to wild type, had reduced total biomass, reduced water-use efficiency, and a decreased proportion of cellulose to hemicellulose and lignin. In low nutrient conditions, TnCelB trees had increased structural carbohydrates with stable lignin values. The TnCelB line presents a viable option for poplar biomass feedstock, offering biomass comparable to wild type poplar and more efficient processing, with only mild negative phenotypes.

Natalie, Bethanie M. (ORCID:0009000843856585)↗