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.

Species and phylogenetic lineages have evolved to differ in the way that they acquire and deploy resources, with consequences for their physiological, chemical and structural attributes, many of which can be detected using spectral reflectance from leaves. Recent technological advances for assessing optical properties of plants offer opportunities to detect functional traits of organisms and differentiate levels of biological organization across the tree of life. Here, we connect leaf-level full range spectral data (400-2400 nm) of leaves to the hierarchical organization of plant diversity within the oak genus (Quercus) using field and greenhouse experiments in which environmental factors and plant age are controlled. We show that spectral data significantly differentiate populations within a species and that spectral similarity is significantly associated with phylogenetic similarity among species. We further show that hyperspectral information allows more accurate classification of taxa than spectrally-derived traits, which by definition are of lower dimensionality. Finally, model accuracy increases at higher levels in the hierarchical organization of plant diversity, such that we are able to better distinguish clades than species or populations. This pattern supports an evolutionary explanation for the degree of optical differentiation among plants and demonstrates potential for remote detection of genetic and phylogenetic diversity.

Summary Spectroscopy has recently emerged as an effective method to accurately characterize leaf biochemistry in living tissue through the application of chemometric approaches to foliar optical data, but this approach has not been widely used for plant secondary metabolites. Here, we examine the ability of reflectance spectroscopy to quantify specific phenolic compounds in trembling aspen (Populus tremuloides) and paper birch (Betula papyrifera) that play influential roles in ecosystem functioning related to trophic‐level interactions and nutrient cycling. Spectral measurements on live aspen and birch leaves were collected, after which concentrations of condensed tannins (aspen and birch) and salicinoids (aspen only) were determined using standard analytical approaches in the laboratory. Predictive models were then constructed using jackknifed, partial least squares regression (PLSR). Model performance was evaluated using coefficient of determination (R2), root‐mean‐square error (RMSE) and the per cent RMSE of the data range (%RMSE). Condensed tannins of aspen and birch were well predicted from both combined (R2 = 0·86, RMSE = 2·4, %RMSE = 7%)‐ and individual‐species models (aspen: R2 = 0·86, RMSE = 2·4, %RMSE = 6%; birch: R2 = 0·81, RMSE = 1·9, %RMSE = 10%). Aspen total salicinoids were better predicted than individual salicinoids (total: R2 = 0·76, RMSE = 2·4, %RMSE = 8%; salicortin: R2 = 0·57, RMSE = 1·9, %RMSE = 11%; tremulacin: R2 = 0·72, RMSE = 1·1, %RMSE = 11%), and spectra collected from dry leaves produced better models for both aspen tannins (R2 = 0·92, RMSE = 1·7, %RMSE = 5%) and salicinoids (R2 = 0·84, RMSE = 1·4, %RMSE = 5%) compared with spectra from fresh leaves. The decline in prediction performance from total to individual salicinoids and from dry to fresh measurements was marginal, however, given the increase in detailed salicinoid information acquired and the time saved by avoiding drying and grinding leaf samples. Reflectance spectroscopy can successfully characterize specific secondary metabolites in living plant tissue and provide detailed information on individual compounds within a constituent group. The ability to simultaneously measure multiple plant traits is a powerful attribute of reflectance spectroscopy because of its potential for in situ–in vivo field deployment using portable spectrometers. The suite of traits currently estimable, however, needs to expand to include specific secondary metabolites that play influential roles in ecosystem functioning if we are to advance the integration of chemical, landscape and ecosystem ecology.

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Phytophagous insects must contend with numerous secondary defense compounds that can adversely affect their growth and development. The gypsy moth (Lymantria dispar) is a polyphagous herbivore that encounters an extensive range of hosts and chemicals. We used this folivore and a primary component of aspen chemical defenses, namely, phenolic glycosides, to investigate if bacteria detoxify phytochemicals and benefit larvae. We conducted insect bioassays using bacteria enriched from environmental samples, analyses of the microbial community in the midguts of bioassay larvae, and in vitro phenolic glycoside metabolism assays. Inoculation with bacteria enhanced larval growth in the presence, but not absence, of phenolic glycosides in the artificial diet. This effect of bacteria on growth was observed only in larvae administered bacteria from aspen foliage. The resulting midgut community composition varied among the bacterial treatments. When phenolic glycosides were included in diet, the composition of midguts in larvae fed aspen bacteria was significantly altered. Phenolic glycosides increased population responses by bacteria that we found able to metabolize these compounds in liquid growth cultures. Several aspects of these results suggest that vectoring or pairwise symbiosis models are inadequate for understanding microbial mediation of plant-herbivore interactions in some systems. First, bacteria that most benefitted larvae were initially foliar residents, suggesting that toxin-degrading abilities of phyllosphere inhabitants indirectly benefit herbivores upon ingestion. Second, assays with single bacteria did not confer the benefits to larvae obtained with consortia, suggesting multi- and inter-microbial interactions are also involved. Our results show that bacteria mediate insect interactions with plant defenses but that these interactions are community specific and highly complex.