Remotely sensed crown nutrient concentrations modulate forest reproduction across the contiguous United States.

Global forests are increasingly lost to climate change, disturbance, and human management. Evaluating forests' capacities to regenerate and colonize new habitats has to start with the seed production of individual trees and how it depends on nutrient access. Studies on the linkage between reproduction and foliar nutrients are limited to a few locations and few species, due to the large investment needed for field measurements on both variables. We synthesized tree fecundity estimates from the Masting Inference and Forecasting (MASTIF) network with foliar nutrient concentrations from hyperspectral remote sensing at the National Ecological Observatory Network (NEON) across the contiguous United States. We evaluated the relationships between seed production and foliar nutrients for 56,544 tree-years from 26 species at individual and community scales. We found a prevalent association between high foliar phosphorous (P) concentration and low individual seed production (ISP) across the continent. Within-species coefficients to nitrogen (N), potassium (K), calcium (Ca), and magnesium (Mg) are related to species differences in nutrient demand, with distinct biogeographic patterns. Community seed production (CSP) decreased four orders of magnitude from the lowest to the highest foliar P. This first continental-scale study sheds light on the relationship between seed production and foliar nutrients, highlighting the potential of using combined Light Detection And Ranging (LiDAR) and hyperspectral remote sensing to evaluate forest regeneration. The fact that both ISP and CSP decline in the presence of high foliar P levels has immediate application in improving forest demographic and regeneration models by providing more realistic nutrient effects at multiple scales.

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset.

Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55%, and 7.0-33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.

'rtry': An R package to support plant trait data preprocessing.

Plant trait data are used to quantify how plants respond to environmental factors and can act as indicators of ecosystem function. Measured trait values are influenced by genetics, trade-offs, competition, environmental conditions, and phenology. These interacting effects on traits are poorly characterized across taxa, and for many traits, measurement protocols are not standardized. As a result, ancillary information about growth and measurement conditions can be highly variable, requiring a flexible data structure. In 2007, the TRY initiative was founded as an integrated database of plant trait data, including ancillary attributes relevant to understanding and interpreting the trait values. The TRY database now integrates around 700 original and collective datasets and has become a central resource of plant trait data. These data are provided in a generic long-table format, where a unique identifier links different trait records and ancillary data measured on the same entity. Due to the high number of trait records, plant taxa, and types of traits and ancillary data released from the TRY database, data preprocessing is necessary but not straightforward. Here, we present the 'rtry' R package, specifically designed to support plant trait data exploration and filtering. By integrating a subset of existing R functions essential for preprocessing, 'rtry' avoids the need for users to navigate the extensive R ecosystem and provides the functions under a consistent syntax. 'rtry' is therefore easy to use even for beginners in R. Notably, 'rtry' does not support data retrieval or analysis; rather, it focuses on the preprocessing tasks to optimize data quality. While 'rtry' primarily targets TRY data, its utility extends to data from other sources, such as the National Ecological Observatory Network (NEON). The 'rtry' package is available on the Comprehensive R Archive Network (CRAN; https://cran.r-project.org/package=rtry) and the GitHub Wiki (https://github.com/MPI-BGC-Functional-Biogeography/rtry/wiki) along with comprehensive documentation and vignettes describing detailed data preprocessing workflows.

Designing an Observing System to Study the Surface Biology and Geology (SBG) of the Earth in the 2020s.

Observations of planet Earth from space are a critical resource for science and society. Satellite measurements represent very large investments and United States (US) agencies organize their effort to maximize the return on that investment. The US National Research Council conducts a survey of Earth science and applications to prioritize observations for the coming decade. The most recent survey prioritized a visible to shortwave infrared imaging spectrometer and a multispectral thermal infrared imager to meet a range of needs for studying Surface Biology and Geology (SBG). SBG will be the premier integrated observatory for observing the emerging impacts of climate change by characterizing the diversity of plant life and resolving chemical and physiological signatures. It will address wildfire risk, behavior, and recovery as well as responses to hazards such as oil spills, toxic minerals in minelands, harmful algal blooms, landslides, and other geological hazards. The SBG team analyzed needed instrument characteristics (spatial, temporal, and spectral resolutions, measurement uncertainty) and assessed the cost, mass, power, volume, and risk of different architectures. We present an overview of the Research and Applications trade-study analysis of algorithms, calibration and validation needs, and societal applications with specifics of substudies detailed in other articles in this special collection. We provide a value framework to converge from hundreds down to three candidate architectures recommended for development. The analysis identified valuable opportunities for international collaboration to increase the revisit frequency, adding value for all partners, leading to a clear measurement strategy for an observing system architecture.

A phenology of fear: Investigating scale and seasonality in predator-prey games between wolves and white-tailed deer.

Predators and prey engage in games where each player must counter the moves of the other, and these games include multiple phases operating at different spatiotemporal scales. Recent work has highlighted potential issues related to scale-sensitive inferences in predator-prey interactions, and there is growing appreciation that these may exhibit pronounced but predictable dynamics. Motivated by previous assertions about effects arising from foraging games between white-tailed deer and canid predators (coyotes and wolves), we used a large and year-round network of trail cameras to characterize deer and predator foraging games, with a particular focus on clarifying its temporal scale and seasonal variation. Linear features were strongly associated with predator detection rates, suggesting these play a central role in canid foraging tactics by expediting movement. Consistent with expectations for prey contending with highly mobile predators, deer responses were more sensitive to proximal risk metrics at finer spatiotemporal scales, suggesting that coarser but more commonly used scales of analysis may miss useful insights into prey risk-response. Time allocation appears to be a key tactic for deer risk management and was more strongly moderated by factors associated with forage or evasion heterogeneity (forest cover, snow and plant phenology) than factors associated with the likelihood of predator encounter (linear features). Trade-offs between food and safety appeared to vary as much seasonally as spatially, with snow and vegetation phenology giving rise to a "phenology of fear." Deer appear free to counter predators during milder times of year, but a combination of poor foraging state, reduced forage availability, greater movements costs, and reproductive state dampen responsiveness during winter. Pronounced intra-annual variation in predator-prey interactions may be common in seasonal environments.

The Spectral Species Concept in Living Color.

Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the 'spectral species' concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.

Remotely detected aboveground plant function predicts belowground processes in two prairie diversity experiments.

Imaging spectroscopy provides the opportunity to incorporate leaf and canopy optical data into ecological studies, but the extent to which remote sensing of vegetation can enhance the study of belowground processes is not well understood. In terrestrial systems, aboveground and belowground vegetation quantity and quality are coupled, and both influence belowground microbial processes and nutrient cycling. We hypothesized that ecosystem productivity, and the chemical, structural and phylogenetic-functional composition of plant communities would be detectable with remote sensing and could be used to predict belowground plant and soil processes in two grassland biodiversity experiments: the BioDIV experiment at Cedar Creek Ecosystem Science Reserve in Minnesota and the Wood River Nature Conservancy experiment in Nebraska. We tested whether aboveground vegetation chemistry and productivity, as detected from airborne sensors, predict soil properties, microbial processes and community composition. Imaging spectroscopy data were used to map aboveground biomass, green vegetation cover, functional traits and phylogenetic-functional community composition of vegetation. We examined the relationships between the image-derived variables and soil carbon and nitrogen concentration, microbial community composition, biomass and extracellular enzyme activity, and soil processes, including net nitrogen mineralization. In the BioDIV experiment-which has low overall diversity and productivity despite high variation in each-belowground processes were driven mainly by variation in the amount of organic matter inputs to soils. As a consequence, soil respiration, microbial biomass and enzyme activity, and fungal and bacterial composition and diversity were significantly predicted by remotely sensed vegetation cover and biomass. In contrast, at Wood River-where plant diversity and productivity were consistently higher-belowground processes were driven mainly by variation in the quality of aboveground inputs to soils. Consequently, remotely sensed functional, chemical and phylogenetic composition of vegetation predicted belowground extracellular enzyme activity, microbial biomass, and net nitrogen mineralization rates but aboveground biomass (or cover) did not. The contrasting associations between the quantity (productivity) and quality (composition) of aboveground inputs with belowground soil attributes provide a basis for using imaging spectroscopy to understand belowground processes across productivity gradients in grassland systems. However, a mechanistic understanding of how above and belowground components interact among different ecosystems remains critical to extending these results broadly.

FlexBRDF: A Flexible BRDF Correction for Grouped Processing of Airborne Imaging Spectroscopy Flightlines.

Bidirectional reflectance distribution function (BRDF) effects are a persistent issue for the analysis of vegetation in airborne imaging spectroscopy data, especially when mosaicking results from adjacent flightlines. With the advent of large airborne imaging efforts from NASA and the U.S. National Ecological Observatory Network (NEON), there is increasing need for methods that are flexible and automatable across images with diverse land cover. Flexible bidirectional reflectance distribution function (FlexBRDF) is built upon the widely used kernel method, with additional features including stratified random sampling across flightline groups, dynamic land cover stratification by normalized difference vegetation index (NDVI), interpolation of correction coefficients across NDVI bins, and the use of a reference solar zenith angle. We demonstrate FlexBRDF using nine long (150-400 km) airborne visible/infrared imaging spectrometer (AVIRIS)-Classic flightlines collected on 22 May 2013 over Southern California, where diverse land cover and a wide range of solar illumination yield significant BRDF effects. We further test the approach on additional AVIRIS-Classic data from California, AVIRIS-Next Generation data from the Arctic and India, and NEON imagery from Wisconsin. Comparison of overlapping areas of flightlines show that models built from multiple flightlines performed better than those built for single images (root mean square error improved up to 2.3% and mean absolute deviation 2.5%). Standardization to a common solar zenith angle among a flightline group improved performance, and interpolation across bins minimized between-bin boundaries. While BRDF corrections for individual sites suffice for local studies, FlexBRDF is an open source option that is compatible with bulk processing of large airborne data sets covering diverse land cover needed for calibration/validation of forthcoming spaceborne imaging spectroscopy missions.

Leaf spectroscopy reveals divergent inter- and intra-species foliar trait covariation and trait-environment relationships across NEON domains.

Concurrent measurement of multiple foliar traits to assess the full range of trade-offs among and within taxa and across broad environmental gradients is limited. Leaf spectroscopy can quantify a wide range of foliar functional traits, enabling assessment of interrelationships among traits and with the environment. We analyzed leaf trait measurements from 32 sites along the wide eco-climatic gradient encompassed by the US National Ecological Observatory Network (NEON). We explored the relationships among 14 foliar traits of 1103 individuals across and within species, and with environmental factors. Across all species pooled, the relationships between leaf economic traits (leaf mass per area, nitrogen) and traits indicative of defense and stress tolerance (phenolics, nonstructural carbohydrates) were weak, but became strong within certain species. Elevation, mean annual temperature and precipitation weakly predicted trait variation across species, although some traits exhibited species-specific significant relationships with environmental factors. Foliar functional traits vary idiosyncratically and species express diverse combinations of leaf traits to achieve fitness. Leaf spectroscopy offers an effective approach to quantify intra-species trait variation and covariation, and potentially could be used to improve the characterization of vegetation in Earth system models.

Integrating remote sensing with ecology and evolution to advance biodiversity conservation.

Remote sensing has transformed the monitoring of life on Earth by revealing spatial and temporal dimensions of biological diversity through structural, compositional and functional measurements of ecosystems. Yet, many aspects of Earth's biodiversity are not directly quantified by reflected or emitted photons. Inclusive integration of remote sensing with field-based ecology and evolution is needed to fully understand and preserve Earth's biodiversity. In this Perspective, we argue that multiple data types are necessary for almost all draft targets set by the Convention on Biological Diversity. We examine five key topics in biodiversity science that can be advanced by integrating remote sensing with in situ data collection from field sampling, experiments and laboratory studies to benefit conservation. Lowering the barriers for bringing these approaches together will require global-scale collaboration.

Coupling spectral and resource-use complementarity in experimental grassland and forest communities.

Reflectance spectra provide integrative measures of plant phenotypes by capturing chemical, morphological, anatomical and architectural trait information. Here, we investigate the linkages between plant spectral variation, and spectral and resource-use complementarity that contribute to ecosystem productivity. In both a forest and prairie grassland diversity experiment, we delineated n-dimensional hypervolumes using wavelength bands of reflectance spectra to test the association between the spectral space occupied by individual plants and their growth, as well as between the spectral space occupied by plant communities and ecosystem productivity. We show that the spectral space occupied by individuals increased with their growth, and the spectral space occupied by plant communities increased with ecosystem productivity. Furthermore, ecosystem productivity was better explained by inter-individual spectral complementarity than by the large spectral space occupied by productive individuals. Our results indicate that spectral hypervolumes of plants can reflect ecological strategies that shape community composition and ecosystem function, and that spectral complementarity can reveal resource-use complementarity.

Snapshot Wisconsin: networking community scientists and remote sensing to improve ecological monitoring and management.

Biological data collection is entering a new era. Community science, satellite remote sensing (SRS), and local forms of remote sensing (e.g., camera traps and acoustic recordings) have enabled biological data to be collected at unprecedented spatial and temporal scales and resolution. There is growing interest in developing observation networks to collect and synthesize data to improve broad-scale ecological monitoring, but no examples of such networks have emerged to inform decision-making by agencies. Here, we present the implementation of one such jurisdictional observation network (JON), Snapshot Wisconsin, which links synoptic environmental data derived from SRS to biodiversity observations collected continuously from a trail camera network to support management decision-making. We use several examples to illustrate that Snapshot Wisconsin improves the spatial, temporal, and biological resolution and extent of information available to support management, filling gaps associated with traditional monitoring and enabling consideration of new management strategies. JONs like Snapshot Wisconsin further strengthen monitoring inference by contributing novel lines of evidence useful for corroboration or integration. SRS provides environmental context that facilitates inference, prediction, and forecasting, and ultimately helps managers formulate, test, and refine conceptual models for the monitored systems. Although these approaches pose challenges, Snapshot Wisconsin demonstrates that expansive observation networks can be tractably managed by agencies to support decision making, providing a powerful new tool for agencies to better achieve their missions and reshape the nature of environmental decision-making.

Remote spectral detection of biodiversity effects on forest biomass.

Quantifying how biodiversity affects ecosystem functions through time over large spatial extents is needed for meeting global biodiversity goals yet is infeasible with field-based approaches alone. Imaging spectroscopy is a tool with potential to help address this challenge. Here, we demonstrate a spectral approach to assess biodiversity effects in young forests that provides insight into its underlying drivers. Using airborne imaging of a tree-diversity experiment, spectral differences among stands enabled us to quantify net biodiversity effects on stem biomass and canopy nitrogen. By subsequently partitioning these effects, we reveal how distinct processes contribute to diversity-induced differences in stand-level spectra, chemistry and biomass. Across stands, biomass overyielding was best explained by species with greater leaf nitrogen dominating upper canopies in mixtures, rather than intraspecific shifts in canopy structure or chemistry. Remote imaging spectroscopy may help to detect the form and drivers of biodiversity-ecosystem function relationships across space and time, advancing the capacity to monitor and manage Earth's ecosystems.

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.

Generalized model-based solutions to false-positive error in species detection/nondetection data.

Detection/nondetection data are widely collected by ecologists interested in estimating species distributions, abundances, and phenology, and are often imperfect. Recent model development has focused on accounting for both false-positive and false-negative errors given evidence that misclassification is common across many sampling protocols. To date, however, model-based solutions to false-positive error have largely addressed occupancy estimation. We describe a generalized model structure that allows investigators to account for false-positive error in detection/nondetection data across a broad range of ecological parameters and model classes, and demonstrate that previously developed model-based solutions are special cases of the generalized model. Simulation results demonstrate that estimators for abundance and migratory arrival time ignoring false-positive error exhibit severe (20-70%) relative bias even when only 5-10% of detections are false positives. Bias increased when false-positive detections were more likely to occur at sites or within occasions in which true positive detections were unlikely to occur. Models accounting for false-positive error following the site-confirmation or observation-confirmation designs generally reduced bias substantially, even when few detections were confirmed as true or false positives or when the process model for false-positive error was misspecified. Results from an empirical example focusing on gray fox (Urocyon cinereoargenteus) abundance in Wisconsin, USA reinforce concerns that biases induced by false-positive error can also distort spatial predictions often used to guide decision making. Model sensitivity to false-positive error extends well beyond occupancy estimation, but encouragingly, model-based solutions developed for occupancy estimators are generalizable and effective across a range of models widely used in ecological research.

Reflectance Spectroscopy for Non-Destructive Measurement and Genetic Analysis of Amounts and Types of Epicuticular Waxes on Onion Leaves.

Epicuticular waxes on the surface of plant leaves are important for the tolerance to abiotic stresses and plant-parasite interactions. In the onion (Allium cepa L.), the variation for the amounts and types of epicuticular waxes is significantly associated with less feeding damage by the insect Thrips tabaci (thrips). Epicuticular wax profiles are measured using used gas chromatography mass spectrometry (GCMS), which is a labor intensive and relatively expensive approach. Biochemical spectroscopy is a non-destructive tool for measurement and analysis of physiological and chemical features of plants. This study used GCMS and full-range biochemical spectroscopy to characterize epicuticular waxes on seven onion accessions with visually glossy (low wax), semi-glossy (intermediate wax), or waxy (copious wax) foliage, as well as a segregating family from the cross of glossy and waxy onions. In agreement with previous studies, GCMS revealed that the three main waxes on the leaves of a wild type waxy onion were the ketone hentriacontanone-16 (H16) and fatty alcohols octacosanol-1 (Oct) and triacontanol-1 (Tri). The glossy cultivar "Odourless Greenleaf" had a unique phenotype with essentially no H16 and Tri and higher amounts of Oct and the fatty alcohol hexacosanol-1 (Hex). Hyperspectral reflectance profiles were measured on leaves of the onion accessions and segregating family, and partial least-squares regression (PLSR) was utilized to generate a spectral coefficient for every wavelength and prediction models for the amounts of the three major wax components. PLSR predictions were robust with independent validation coefficients of determination at 0.72, 0.70, and 0.42 for H16, Oct, and Tri, respectively. The predicted amounts of H16, Oct, and Tri are the result of an additive effect of multiple spectral features of different intensities. The variation of reflectance for H16, Oct, and Tri revealed unique spectral features at 2259 nm, 645 nm, and 730 nm, respectively. Reflectance spectroscopy successfully revealed a major quantitative trait locus (QTL) for amounts of H16, Oct, and Tri in the segregating family, agreeing with previous genetic studies. This study demonstrates that hyperspectral signatures can be used for non-destructive measurement of major waxes on onion leaves as a basis for rapid plant assessment in support of developing thrips-resistant onions.

Investigating potato late blight physiological differences across potato cultivars with spectroscopy and machine learning.

Understanding plant disease resistance is important in the integrated management of Phytophthora infestans, causal agent of potato late blight. Advanced field-based methods of disease detection that can identify infection before the onset of visual symptoms would improve management by greatly reducing disease potential and spread as well as improve both the financial and environmental sustainability of potato farms. In-vivo foliar spectroscopy offers the capacity to rapidly and non-destructively characterize plant physiological status, which can be used to detect the effects of necrotizing pathogens on plant condition prior to the appearance of visual symptoms. Here, we tested differences in spectral response of four potato cultivars, including two cultivars with a shared genotypic background except for a single copy of a resistance gene, to inoculation with Phytophthora infestans clonal lineage US-23 using three statistical approaches: random forest discrimination (RF), partial least squares discrimination analysis (PLS-DA), and normalized difference spectral index (NDSI). We find that cultivar, or plant genotype, has a significant impact on spectral reflectance of plants undergoing P. infestans infection. The spectral response of four potato cultivars to infection by Phytophthora infestans clonal lineage US-23 was highly variable, yet with important shared characteristics that facilitated discrimination. Early disease physiology was found to be variable across cultivars as well using non-destructively derived PLS-regression trait models. This work lays the foundation to better understand host-pathogen interactions across a variety of genotypic backgrounds, and establishes that host genotype has a significant impact on spectral reflectance, and hence on biochemical and physiological traits, of plants undergoing pathogen infection.

Leaf reflectance spectra capture the evolutionary history of seed plants.

Leaf reflectance spectra have been increasingly used to assess plant diversity. However, we do not yet understand how spectra vary across the tree of life or how the evolution of leaf traits affects the differentiation of spectra among species and lineages. Here we describe a framework that integrates spectra with phylogenies and apply it to a global dataset of over 16 000 leaf-level spectra (400-2400 nm) for 544 seed plant species. We test for phylogenetic signal in spectra, evaluate their ability to classify lineages, and characterize their evolutionary dynamics. We show that phylogenetic signal is present in leaf spectra but that the spectral regions most strongly associated with the phylogeny vary among lineages. Despite among-lineage heterogeneity, broad plant groups, orders, and families can be identified from reflectance spectra. Evolutionary models also reveal that different spectral regions evolve at different rates and under different constraint levels, mirroring the evolution of their underlying traits. Leaf spectra capture the phylogenetic history of seed plants and the evolutionary dynamics of leaf chemistry and structure. Consequently, spectra have the potential to provide breakthrough assessments of leaf evolution and plant phylogenetic diversity at global scales.

Foliar functional traits from imaging spectroscopy across biomes in eastern North America.

Foliar functional traits are widely used to characterize leaf and canopy properties that drive ecosystem processes and to infer physiological processes in Earth system models. Imaging spectroscopy provides great potential to map foliar traits to characterize continuous functional variation and diversity, but few studies have demonstrated consistent methods for mapping multiple traits across biomes. With airborne imaging spectroscopy data and field data from 19 sites, we developed trait models using partial least squares regression, and mapped 26 foliar traits in seven NEON (National Ecological Observatory Network) ecoregions (domains) including temperate and subtropical forests and grasslands of eastern North America. Model validation accuracy varied among traits (normalized root mean squared error, 9.1-19.4%; coefficient of determination, 0.28-0.82), with phenolic concentration, leaf mass per area and equivalent water thickness performing best across domains. Across all trait maps, 90% of vegetated pixels had reasonable values for one trait, and 28-81% provided high confidence for multiple traits concurrently. Maps of 26 traits and their uncertainties for eastern US NEON sites are available for download, and are being expanded to the western United States and tundra/boreal zone. These data enable better understanding of trait variations and relationships over large areas, calibration of ecosystem models, and assessment of continental-scale functional diversity.

Contact Reflectance Spectroscopy for Rapid, Accurate, and Nondestructive Phytophthora infestans Clonal Lineage Discrimination.

Populations of Phytophthora infestans, the oomycete causal agent of potato late blight in the United States, are predominantly asexual, and isolates are characterized by clonal lineage or asexual descendants of a single genotype. Current tools for clonal lineage identification are time consuming and require laboratory equipment. We previously found that foliar spectroscopy can be used for high-accuracy pre- and postsymptomatic detection of P. infestans infections caused by clonal lineages US-08 and US-23. In this work, we found subtle but distinct differences in spectral responses of potato foliage infected by these clonal lineages in both growth-chamber time-course experiments (12- to 24-h intervals over 5 days) and naturally infected samples from commercial production fields. In both settings, we measured continuous visible to shortwave infrared reflectance (400 to 2,500 nm) on leaves using a portable spectrometer with contact probe. We consistently discriminated between infections caused by the two clonal lineages across all stages of disease progression using partial least squares (PLS) discriminant analysis, with total accuracies ranging from 88 to 98%. Three-class random forest differentiation between control, US-08, and US-23 yielded total discrimination accuracy ranging from 68 to 76%. Differences were greatest during presymptomatic infection stages and progressed toward uniformity as symptoms advanced. Using PLS-regression trait models, we found that total phenolics, sugar, and leaf mass per area were different between lineages. Shortwave infrared wavelengths (>1,100 nm) were important for clonal lineage differentiation. This work provides a foundation for future use of hyperspectral sensing as a nondestructive tool for pathovar differentiation.