Hyperspectral imaging for chloroplast movement detection.

We employed hyperspectral imaging to detect chloroplast positioning and assess its influence on common vegetation indices. In low blue light, chloroplasts move to cell walls perpendicular to the direction of the incident light. In high blue light, chloroplasts exhibit the avoidance response, moving to cell walls parallel to the light direction. Irradiation with high light results in significant changes in leaf reflectance and the shape of the reflectance spectrum. Using mutants with disrupted chloroplast movements, we found that blue-light-induced changes in the reflectance spectrum are mostly due to chloroplast relocations. We trained machine learning methods in the classification of leaves according to the chloroplast positioning, based on the reflectance spectra. The convolutional network showed low levels of misclassification of leaves irradiated with high light even when different species were used for training and testing, suggesting that reflectance spectra may be used to detect chloroplast avoidance in heterogeneous vegetation. We also examined the correlation between chloroplast positioning and values of indices of normalized-difference type for various combinations of wavelengths and identified an index sensitive to chloroplast positioning. We found that values of some of the vegetation indices, including those sensitive to the carotenoid levels, may be altered due to chloroplast rearrangements.

Cold Acclimation and Deacclimation of Winter Oilseed Rape, with Special Attention Being Paid to the Role of Brassinosteroids.

Winter plants acclimate to frost mainly during the autumn months, through the process of cold acclimation. Global climate change is causing changes in weather patterns such as the occurrence of warmer periods during late autumn or in winter. An increase in temperature after cold acclimation can decrease frost tolerance, which is particularly dangerous for winter crops. The aim of this study was to investigate the role of brassinosteroids (BRs) and BR analogues as protective agents against the negative results of deacclimation. Plants were cold-acclimated (3 weeks, 4 °C) and deacclimated (1 week, 16/9 °C d/n). Deacclimation generally reversed the cold-induced changes in the level of the putative brassinosteroid receptor protein (BRI1), the expression of BR-induced COR, and the expression of SERK1, which is involved in BR signal transduction. The deacclimation-induced decrease in frost tolerance in oilseed rape could to some extent be limited by applying steroid regulators. The deacclimation in plants could be detected using non-invasive measurements such as leaf reflectance, chlorophyll a fluorescence, and gas exchange monitoring.

Excised leaves show limited and species-specific effects on photosynthetic parameters across crop functional types.

Photosynthesis is increasingly becoming a recognized target for crop improvement. Phenotyping photosynthesis-related traits on field-grown material is a key bottleneck to progress here due to logistical barriers and short measurement days. Many studies attempt to overcome these challenges by phenotyping excised leaf material in the laboratory. To date there are no demonstrated examples of the representative nature of photosynthesis measurements performed on excised leaves relative to attached leaves in crops. Here, we tested whether standardized leaf excision on the day prior to phenotyping affected a range of common photosynthesis-related traits across crop functional types using tomato (C3 dicot), barley (C3 monocot), and maize (C4 monocot). Potentially constraining aspects of leaf physiology that could be predicted to impair photosynthesis in excised leaves, namely leaf water potential and abscisic acid accumulation, were not different between attached and excised leaves. We also observed non-significant differences in spectral reflectance and chlorophyll fluorescence traits between the treatments across the three species. However, we did observe some significant differences between traits associated with gas exchange and photosynthetic capacity across all three species. This study represents a useful reference for those who perform measurements of this nature and the differences reported should be considered in associated experimental design and statistical analyses.

Water-Deficit Stress in the Epiphytic Elkhorn Fern: Insight into Photosynthetic Response.

Progressive climate changes cause disturbance of water relations in tropical rainforests, where epiphytic ferns are an important element of biodiversity. In these plants, the efficiency of photosynthesis is closely related to the efficiency of water transport. In addition, due to the lack of contact with the soil, epiphytes are extremely susceptible to water-deficit stress. The aim of this experiment was to determine the response of the photosynthetic apparatus of Platycerium bifurcatum to a 6-week water deficit. The hydration and pigment composition of leaves were determined using reflectance spectroscopy and epifluorescence microscopy. Chlorophyll a fluorescence kinetics parameters, fluorescence induction curves (OJIP), low-temperature fluorescence curves at 77 K and proline concentration were analyzed at seven time points. After a decrease in leaf hydration by 10-15%, there were disturbances in the oxidation-reduction balance, especially in the initial photochemical reactions, a rapid decrease in plant vitality (PI) and significant fluctuations in chlorophyll a fluorescence parameters. The relative size of PSI antenna structures compared to PSII decreased in the following weeks of water deficit. Changes in photochemical reactions were accompanied by a decrease in gross photosynthesis and an increase in proline concentration. Changes in the functioning of photosynthesis light phase and the pigment composition of leaves are related to the resistance of elkhorn fern to long-term water deficit.

Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models.

MAIN CONCLUSION: By combining hyperspectral signatures of peanut and soybean, we predicted Vcmax and Jmax with 70 and 50% accuracy. The PLS was the model that better predicted these photosynthetic parameters. One proposed key strategy for increasing potential crop stability and yield centers on exploitation of genotypic variability in photosynthetic capacity through precise high-throughput phenotyping techniques. Photosynthetic parameters, such as the maximum rate of Rubisco catalyzed carboxylation (Vc,max) and maximum electron transport rate supporting RuBP regeneration (Jmax), have been identified as key targets for improvement. The primary techniques for measuring these physiological parameters are very time-consuming. However, these parameters could be estimated using rapid and non-destructive leaf spectroscopy techniques. This study compared four different advanced regression models (PLS, BR, ARDR, and LASSO) to estimate Vc,max and Jmax based on leaf reflectance spectra measured with an ASD FieldSpec4. Two leguminous species were tested under different controlled environmental conditions: (1) peanut under different water regimes at normal atmospheric conditions and (2) soybean under high [CO2] and high night temperature. Model sensitivities were assessed for each crop and treatment separately and in combination to identify strengths and weaknesses of each modeling approach. Regardless of regression model, robust predictions were achieved for Vc,max (R2 = 0.70) and Jmax (R2 = 0.50). Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral properties.

Leaf reflectance and functional traits as environmental indicators of urban dust deposition.

BACKGROUND: How to quickly predict and evaluate urban dust deposition is the key to the control of urban atmospheric environment. Here, we focus on changes of plant reflectance and plant functional traits due to dust deposition, and develop a prediction model of dust deposition based on these traits. RESULTS: The results showed that (1) The average dust deposition per unit area of Ligustrum quihoui leaves was significantly different among urban environments (street (18.1001 g/m2), community (14.5597 g/m2) and park (9.7661 g/m2)). Among different urban environments, leaf reflectance curves tends to be consistent, but there were significant differences in leaf reflectance values (park (0.052-0.585) > community (0.028-0.477) > street (0.025-0.203)). (2) There were five major reflection peaks and five major absorption valleys. (3) The spectral reflectances before and after dust removal were significantly different (clean leaves > dust-stagnant leaves). 695 ~ 1400 nm was the sensitive range of spectral response. (4) Dust deposition has significant influence on slope and position of red edge. Red edge slope was park > community > street. After dust deposition, the red edge position has obviously "blue shift". The moving distance of the red edge position increases with the increase of dust deposition. The forecast model of dust deposition amount established by simple ratio index (y = 2.517x + 0.381, R2 = 0.787, RMSE (root-mean-square error) = 0.187. In the model, y refers to dust retention, x refers to simple ratio index.) has an average accuracy of 99.98%. (5) With the increase of dust deposition, the specific leaf area and chlorophyll content index decreased gradually. The leaf dry matter content, leaf tissue density and leaf thickness increased gradually. CONCLUSION: In the dust-polluted environment, L. quihoui generally presents a combination of characters with lower specific leaf area, chlorophyll content index, and higher leaf dry matter content, leaf tissue density and leaf thickness. Leaf reflectance spectroscopy and functional traits have been proved to be effective in evaluating the changes of urban dust deposition.

Reading light: leaf spectra capture fine-scale diversity of closely related, hybridizing arctic shrubs.

Leaf reflectance spectroscopy is emerging as an effective tool for assessing plant diversity and function. However, the ability of leaf spectra to detect fine-scale plant evolutionary diversity in complicated biological scenarios is not well understood. We test if reflectance spectra (400-2400 nm) can distinguish species and detect fine-scale population structure and phylogenetic divergence - estimated from genomic data - in two co-occurring, hybridizing, ecotypically differentiated species of Dryas. We also analyze the correlation among taxonomically diagnostic leaf traits to understand the challenges hybrids pose to classification models based on leaf spectra. Classification models based on leaf spectra identified two species of Dryas with 99.7% overall accuracy and genetic populations with 98.9% overall accuracy. All regions of the spectrum carried significant phylogenetic signal. Hybrids were classified with an average overall accuracy of 80%, and our morphological analysis revealed weak trait correlations within hybrids compared to parent species. Reflectance spectra captured genetic variation and accurately distinguished fine-scale population structure and hybrids of morphologically similar, closely related species growing in their home environment. Our findings suggest that fine-scale evolutionary diversity is captured by reflectance spectra and should be considered as spectrally-based biodiversity assessments become more prevalent.

Detection of the metabolic response to drought stress using hyperspectral reflectance.

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

Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy.

The photosynthetic capacity or the CO2-saturated photosynthetic rate (Vmax), chlorophyll, and nitrogen are closely linked leaf traits that determine C4 crop photosynthesis and yield. Accurate, timely, rapid, and non-destructive approaches to predict leaf photosynthetic traits from hyperspectral reflectance are urgently needed for high-throughput crop monitoring to ensure food and bioenergy security. Therefore, this study thoroughly evaluated the state-of-the-art physically based radiative transfer models (RTMs), data-driven partial least squares regression (PLSR), and generalized PLSR (gPLSR) models to estimate leaf traits from leaf-clip hyperspectral reflectance, which was collected from maize (Zea mays L.) bioenergy plots with diverse genotypes, growth stages, treatments with nitrogen fertilizers, and ozone stresses in three growing seasons. The results show that leaf RTMs considering bidirectional effects can give accurate estimates of chlorophyll content (Pearson correlation r=0.95), while gPLSR enabled retrieval of leaf nitrogen concentration (r=0.85). Using PLSR with field measurements for training, the cross-validation indicates that Vmax can be well predicted from spectra (r=0.81). The integration of chlorophyll content (strongly related to visible spectra) and nitrogen concentration (linked to shortwave infrared signals) can provide better predictions of Vmax (r=0.71) than only using either chlorophyll or nitrogen individually. This study highlights that leaf chlorophyll content and nitrogen concentration have key and unique contributions to Vmax prediction.

Corrected photochemical reflectance index (PRI) is an effective tool for detecting environmental stresses in agricultural crops under light conditions.

High-throughput detection of plant environmental stresses is required for minimizing the reduction in crop yield. Environmental stresses in plants have primarily been validated by the measurements of photosynthesis with gas exchange and chlorophyll fluorescence, which involve complicated procedures. Remote sensing technologies that monitor leaf reflectance in intact plants enable real-time visualization of plant responses to environmental fluctuations. The photochemical reflectance index (PRI), one of the vegetation indices of spectral leaf reflectance, is related to changes in xanthophyll pigment composition. Xanthophyll dynamics are strongly correlated with plant stress because they contribute to the thermal dissipation of excess energy. However, an accurate assessment of plant stress based on PRI requires correction by baseline PRI (PRIo) in the dark, which is difficult to obtain in the field. In this study, we propose a method to correct the PRI using NPQT, which can be measured under light. By this method, we evaluated responses of excess light energy stress under drought in wild watermelon (Citrullus lanatus L.), a xerophyte. Demonstration on the farm, the stress behaviors were observed in maize (Zea mays L.). Furthermore, the stress status of plants and their recovery following re-watering were captured as visual information. These results suggest that the PRI is an excellent indicator of environmental stress and recovery in plants and could be used as a high-throughput stress detection tool in agriculture.

Powdery Mildew-Induced Hormonal and Photosynthetic Changes in Barley Near Isogenic Lines Carrying Various Resistant Genes.

The present work focused on the characterization of some physiological mechanisms activated upon powdery mildew inoculation of the susceptible barley cultivar Ingrid and its near-isogenic lines (NILs) carrying various resistant genes (Mla, Mlg and mlo). After inoculation with Blumeria graminis f. sp. hordei (Bgh), measurements of leaf reflectance and chlorophyll a fluorescence were performed 3 and 7 day post-inoculation (dpi), while hormone assays were made 7 dpi. Bgh-inoculated resistant genotypes were characterized by lowered leaf reflectance parameters that correlated with carotenoids (CRI) and water content (WBI) in comparison to inoculated Ingrid. The PSII activity (i.e., Fv/Fm, ETo/CSm and P.I.ABS) strongly decreased in susceptible Ingrid leaves when the disease symptoms became visible 7 dpi. In Mla plants with visible hypersensitive spots the PSII activity decreased to a lesser extent. Inoculation resulted in a very slight decrease of photosynthesis at later stage of infection in Mlg plants, whereas in resistant mlo plants the PSII activity did not change. Chlorophyll a fluorescence measurements allowed presymptomatic detection of infection in Ingrid and Mla. Changes in the homeostasis of 22 phytohormones (cytokinins, auxins, gibberellins and the stress hormones JA, SA and ABA) in powdery mildew inoculated barley are discussed in relation to resistance against this biotrophic pathogen.

Predicting dark respiration rates of wheat leaves from hyperspectral reflectance.

Greater availability of leaf dark respiration (Rdark ) data could facilitate breeding efforts to raise crop yield and improve global carbon cycle modelling. However, the availability of Rdark data is limited because it is cumbersome, time consuming, or destructive to measure. We report a non-destructive and high-throughput method of estimating Rdark from leaf hyperspectral reflectance data that was derived from leaf Rdark measured by a destructive high-throughput oxygen consumption technique. We generated a large dataset of leaf Rdark for wheat (1380 samples) from 90 genotypes, multiple growth stages, and growth conditions to generate models for Rdark . Leaf Rdark (per unit leaf area, fresh mass, dry mass or nitrogen, N) varied 7- to 15-fold among individual plants, whereas traits known to scale with Rdark , leaf N, and leaf mass per area (LMA) only varied twofold to fivefold. Our models predicted leaf Rdark , N, and LMA with r2 values of 0.50-0.63, 0.91, and 0.75, respectively, and relative bias of 17-18% for Rdark and 7-12% for N and LMA. Our results suggest that hyperspectral model prediction of wheat leaf Rdark is largely independent of leaf N and LMA. Potential drivers of hyperspectral signatures of Rdark are discussed.

Ecophysiological differentiation between two invasive species of Carpobrotus competing under different nutrient conditions.

PREMISE: Hybridization between the South African invasive species Carpobrotus edulis and C. acinaciformis in Europe has led to the formation of highly aggressive morphotypes referred to in the scientific literature as the new large "hybrid swarm" C. aff. acinaciformis. In the present study, we aimed to determine whether the taxonomic differentiation between taxa coincides with ecophysiological differentiation. With this aim, we tested for differences between both morphotypes in functional traits related to competitive ability and resource-use efficiency. Assuming that the complex hybrid C. aff. acinaciformis is more vigorous, depends more strongly on vegetative reproduction, and invests less in sexual reproduction than C. edulis, we predicted that the hybrid would show higher competitive ability and better physiological performance compared with the species. METHODS: We used a comparative ecophysiological approach to assess the extent to which two Carpobrotus morphotypes coexisting in northwestern Spain differ in physiological, reproductive, and growth traits when competing under different soil nutrients in controlled greenhouse conditions. RESULTS: C. aff. acinaciformis had a greater relative growth rate and water-use and photochemical efficiencies compared to C. edulis. However, C. edulis appeared to be more responsive to incremental change in soil nutrients than C. aff. acinaciformis. They also differed in the amount of resources invested in reproduction. CONCLUSIONS: The study findings demonstrate that the taxonomic differentiation between taxa corresponds to ecophysiological differentiation, warranting a detailed examination of all existing trades-offs to predict the long-term outcomes of the interaction between these taxa.

Species-specific functional trait responses in two species coexisting along a shore-to-inland dune gradient.

Coastal dunes are characterised by strong gradients of abiotic stress, typically increasing in severity from inland areas towards the shoreline. Thus, dune gradients represent unique opportunities to study intraspecific responses to environmental changes and to investigate which factors drive community change. This study aims to examine functional trait variation in two coexisting species in response to environmental changes along a dune gradient in NW Spain. Trait convergence was also investigated and compared between both ends of the gradient. We measured functional leaf traits related to plant efficiency in the use of light, water and nutrients, also possible stressors (salt content and pH) and availability of limiting resources (water and nutrients) in the soil. Most soil variables showed changes following a non-directional gradient. Differences in soil variables were site specific and depended on growth of the study species. Structural and functional traits depended on species and/or plant position on the gradient, except for effective quantum yield of PSII and leaf δ15N. The pattern of variation was mostly directional for reflectance indices related to leaf physiology. Multivariate analyses showed significant interspecific differences in the set of traits they exhibited along positions in the gradient. Species also differed in the combination of traits selected under given environmental conditions. Coexisting species display a specific set of traits that reflects different strategies to environmental stress. Our study highlights the overly simplistic nature of some previous studies that assume dune gradients are monotonically directional, without considering that these gradients may be differentially modified by species activity.

UV light and adaptive divergence of leaf physiology, anatomy, and ultrastructure drive heat stress tolerance in genetically distant grapevines.

The genetic basis of plant response to light and heat stresses had been unveiled, and different molecular mechanisms of leaf cell homeostasis to keep high physiological performances were recognized in grapevine varieties. However, the ability to develop heat stress tolerance strategies must be further elucidated since the morpho-anatomical and physiological traits involved may vary with genotype × environment combination, stress intensity, and duration. A 3-year experiment was conducted on potted plants of Sardinian red grapevine cultivars Cannonau (syn. Grenache) and Carignano (syn. Carignan), exposed to prolonged heat stress inside a UV-blocking greenhouse, either submitted to low daily UV-B doses of 4.63 kJ m-2 d-1 (+UV) or to 0 kJ m-2 d-1 (-UV), and compared to a control (C) exposed to solar radiation (4.05 kJ m-2 d-1 average UV-B dose). Irrigation was supplied to avoid water stress, and canopy light and thermal microclimate were monitored continuously. Heat stress exceeded one-third of the duration inside the greenhouse and 6% in C. In vivo spectroscopy, including leaf reflectance and fluorescence, allowed for characterizing different patterns of leaf traits and metabolites involved in oxidative stress protection. Cannonau showed lower stomatal conductance under C (200 mmol m-2 s-1) but more than twice the values inside the greenhouse (400 to 900 mmol m-2 s-1), where water use efficiency was reduced similarly in both varieties. Under severe heat stress and -UV, Cannonau showed a sharper decrease in primary photochemical activity and higher leaf pigment reflectance indexes and leaf mass area. UV-B increased the leaf pigments, especially in Carignano, and different leaf cell regulatory traits to prevent oxidative damage were observed in leaf cross-sections. Heat stress induced chloroplast swelling, plastoglobule diffusion, and the accumulation of secretion deposits in both varieties, aggravated in Cannonau -UV by cell vacuolation, membrane dilation, and diffused leaf blade spot swelling. Conversely, in Carignano UV-B, cell wall barriers and calcium oxalate crystals proliferated in mesophyll cells. These responses suggest an adaptive divergence among cultivars to prolonged heat stress and UV-B light. Further research on grapevine biodiversity, heat, and UV-B light interactions may give new insights on the extent of stress tolerance to improve viticulture adaptive strategies in climate change hotspots.

Chlorophyll Meter: A Precision Agricultural Decision-Making Tool for Nutrient Supply in Durum Wheat (Triticum turgidum L.) Cultivation under Drought Conditions.

How crop biodiversity adapts to drought conditions and enhances grain yield became the most important issue facing agronomists and plant breeders at the turn of the century. Variations in genetic response, inadequacy of nutrients in the soil, and insufficient access to nutrients are factors that aggravate drought stressors. The development of screening tools for identifying drought tolerance is important in the deployment of durum wheat varieties suited to drought-prone environments. An experiment was conducted to evaluate durum wheat varieties under a range of nutrient supplies in naturally imposed drought conditions. The treatments consisting of two nitrogen regimes (i.e., control and 60 kg ha-1), four durum wheat varieties, and three types of nutrients (control, sulfur, and zinc) that were arranged in a split-split plot design with three replications. Both foliar-based sulfur and zinc fertilization were employed at the flag leaf stage, at a rate of 4 and 3-L ha-1, respectively. The results showed a significant (p < 0.05) genetic variation in chlorophyll concentration, grain protein content, tillering potential, and leaf area index. Varieties that contained better leaf chlorophyll content had improved grain yield by about 8.33% under 60 kg/ha nitrogen. A combined application of nitrogen and zinc at flag leaf stage significantly improved grain yield of Duragold by about 21.3%. Leaf chlorophyll content was found to be a more important trait than spikes per m2 to discriminate durum wheat varieties. Foliar application of sulfur increased the grain yield of drought-stressed plants by about 12.23%. Grain yield and protein content were strongly correlated with late-season SPAD readings. Significant (p < 0.05) correlation coefficients were obtained between normalized difference vegetation index, leaf area index, grain yield, and protein content with late-season chlorophyll content, revealing the importance of chlorophyll content in studying and identifying drought-tolerant varieties.

Leaf morphology, optical characteristics and phytochemical traits of butterhead lettuce affected by increasing the far-red photon flux.

Light and its spectral characteristics are crucial for plant growth and development. The far-red photon flux mediates many plant processes through the action of phytochrome and also accelerates the photosynthetic electron transfer rate. In this study, we assessed the effects of far-red addition on butterhead lettuce morphology, light use efficiency, optical properties, and phytochemical characteristics. Three-week-old lettuce plants (Lactuca sativa L. cv. Alyssa) were grown for up to 28 days under a 10% blue and 90% red light spectrum (200 µmol m-2 s-1, 16 h photoperiod) to which five different intensities of far-red light (peak at 735 nm) were added (0-9-18-36-72 µmol m-2 s-1). White light-emitting diodes were included as a proxy for sunlight. Increasing supplemental far-red photon flux from zero to 21% increased the light use efficiency (g per mol) by 37% on day 14; 43% on day 21; and 39% on day 28. Measurements of projected head area suggest that this was associated with an increase in leaf expansion and photon capture and not necessarily a direct effect on photosynthesis. Moreover, vegetation indices based on leaf reflectance showed a decrease in chlorophyll-related indices under a high far-red photon flux. This decrease in pigment content was confirmed by chemical analyses, suggesting that the plants may not reach their full potential in terms of photon capture, limiting the overall photosynthetic performance. Furthermore, the stress-related Carter 1 index increased in plants grown under a high far-red photon flux, indicating early plant stress. Far-red tended to decrease the content of total phenolics and increase soluble sugars. The higher sugar levels can be attributed to an improved photochemical efficiency due to photosystem I excitation by far-red wavelengths, also known as the Emerson Enhancement effect. Despite these higher sugar levels, no effect on foliar nitrate content was observed. Our results show that far-red supplementation has the potential to enhance light interception at the early growth stages, although higher intensities of far-red may cause plant stress.

Evaluating potential of leaf reflectance spectra to monitor plant genetic variation.

Remote sensing of vegetation by spectroscopy is increasingly used to characterize trait distributions in plant communities. How leaves interact with electromagnetic radiation is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution ("hyperspectral") spectroscopy can characterize trait variation at finer scales, and may help to reveal underlying genetic variation-information important for assessing the potential of populations to adapt to global change. Here, we use a set of 360 inbred genotypes of the wild coyote tobacco Nicotiana attenuata: wild accessions, recombinant inbred lines (RILs), and transgenic lines (TLs) with targeted changes to gene expression, to dissect genetic versus non-genetic influences on variation in leaf spectra across three experiments. We calculated leaf reflectance from hand-held field spectroradiometer measurements covering visible to short-wave infrared wavelengths of electromagnetic radiation (400-2500 nm) using a standard radiation source and backgrounds, resulting in a small and quantifiable measurement uncertainty. Plants were grown in more controlled (glasshouse) or more natural (field) environments, and leaves were measured both on- and off-plant with the measurement set-up thus also in more to less controlled environmental conditions. Entire spectra varied across genotypes and environments. We found that the greatest variance in leaf reflectance was explained by between-experiment and non-genetic between-sample differences, with subtler and more specific variation distinguishing groups of genotypes. The visible spectral region was most variable, distinguishing experimental settings as well as groups of genotypes within experiments, whereas parts of the short-wave infrared may vary more specifically with genotype. Overall, more genetically variable plant populations also showed more varied leaf spectra. We highlight key considerations for the application of field spectroscopy to assess genetic variation in plant populations.

Effects of Water Availability on Leaf Trichome Density and Plant Growth and Development of Shepherdia ×utahensis.

Many arid lands across the globe are experiencing more frequent and extreme droughts due to warmer temperatures resulting from climate change, less predictable precipitation patterns, and decreased soil moisture. Approximately 60-90% of household water is used for urban landscape irrigation in the western United States, necessitating the establishment of landscapes using drought-tolerant plants that conserve water. Shepherdia ×utahensis (hybrid buffaloberry) is a drought-tolerant plant with dense leaf trichomes (epidermal appendages) that may limit excessive water loss by transpiration. However, little is known about how S. ×utahensis regulates leaf heat balance when transpirational cooling is limited. The objective of this research was to investigate the effects of substrate water availability on plant growth and development and trichome density of S. ×utahensis. Ninety-six clonally propagated plants were grown using an automated irrigation system, and their substrate volumetric water contents were controlled at 0.05-0.40 m3·m-3 for 2 months. Results showed that water stress impaired plant growth and increased the proportion of visibly wilted leaves. Shepherdia ×utahensis acclimates to drought by reducing cell dehydration and canopy overheating, which may be accomplished through decreased stomatal conductance, smaller leaf development, leaf curling, increased leaf thickness, and greater root-to-shoot ratio. Leaf trichome density increased when stem water potential decreased, resulting in greater leaf reflectance of visible light. Cell and leaf expansion were restricted under water stress, and negative correlations were exhibited between epidermal cell size and trichome density. According to our results, plasticity in leaves and roots aids plants in tolerating abiotic stresses associated with drought. Acclimation of S. ×utahensis to water stress was associated with increased trichome density due to plasticity in cell size. Dense trichomes on leaves reflected more lights which appeared to facilitate leaf temperature regulation.

Spectral differentiation of oak wilt from foliar fungal disease and drought is correlated with physiological changes.

Hyperspectral reflectance tools have been used to detect multiple pathogens in agricultural settings and single sources of infection or broad declines in forest stands. However, differentiation of any one disease from other sources of tree stress is integral for stand and landscape-level applications in mixed species systems. We tested the ability of spectral models to differentiate oak wilt, a fatal disease in oaks caused by Bretziella fagacearum ``Bretz'', from among other mechanisms of decline. We subjected greenhouse-grown oak seedlings (Quercus ellipsoidalis ``E.J. Hill'' and Quercus macrocarpa ``Michx.'') to chronic drought or inoculation with the oak wilt fungus or bur oak blight fungus (Tubakia iowensis ``T.C. Harr. & D. McNew''). We measured leaf and canopy spectroscopic reflectance (400-2400 nm) and instantaneous photosynthetic and stomatal conductance rates, then used partial least-squares discriminant analysis to predict treatment from hyperspectral data. We detected oak wilt before symptom appearance, and classified the disease with high accuracy in symptomatic leaves. Classification accuracy from spectra increased with declines in photosynthetic function in oak wilt-inoculated plants. Wavelengths diagnostic of oak wilt were only found in non-visible spectral regions and are associated with water status, non-structural carbohydrates and photosynthetic mechanisms. We show that hyperspectral models can differentiate oak wilt from other causes of tree decline and that detection is correlated with biological mechanisms of oak wilt infection and disease progression. We also show that within the canopy, symptom heterogeneity can reduce detection, but that symptomatic leaves and tree canopies are suitable for highly accurate diagnosis. Remote application of hyperspectral tools can be used for specific detection of disease across a multi-species forest stand exhibiting multiple stress symptoms.