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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Global photosynthetic capacity is optimized to the environment.

Earth system models (ESMs) use photosynthetic capacity, indexed by the maximum Rubisco carboxylation rate (Vcmax ), to simulate carbon assimilation and typically rely on empirical estimates, including an assumed dependence on leaf nitrogen determined from soil fertility. In contrast, new theory, based on biochemical coordination and co-optimization of carboxylation and water costs for photosynthesis, suggests that optimal Vcmax can be predicted from climate alone, irrespective of soil fertility. Here, we develop this theory and find it captures 64% of observed variability in a global, field-measured Vcmax dataset for C3 plants. Soil fertility indices explained substantially less variation (32%). These results indicate that environmentally regulated biophysical constraints and light availability are the first-order drivers of global photosynthetic capacity. Through acclimation and adaptation, plants efficiently utilize resources at the leaf level, thus maximizing potential resource use for growth and reproduction. Our theory offers a robust strategy for dynamically predicting photosynthetic capacity in ESMs.

From the Arctic to the tropics: multibiome prediction of leaf mass per area using leaf reflectance.

Leaf mass per area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long-standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking. We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 g m-2 . Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad- and needleleaf species, and upper- and lower-canopy (i.e. sun and shade) growth environments. Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age and canopy position from diverse biomes. Our model captures LMA variability with high accuracy and low error (R2  = 0.89; root mean square error (RMSE) = 15.45 g m-2 ). Our finding highlights the fact that the leaf economics spectrum is mirrored by the leaf optical spectrum, paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.

Anatomical defences against bark beetles relate to degree of historical exposure between species and are allocated independently of chemical defences within trees.

Conifers possess chemical and anatomical defences against tree-killing bark beetles that feed in their phloem. Resins accumulating at attack sites can delay and entomb beetles while toxins reach lethal levels. Trees with high concentrations of metabolites active against bark beetle-microbial complexes, and more extensive resin ducts, achieve greater survival. It is unknown if and how conifers integrate chemical and anatomical components of defence or how these capabilities vary with historical exposure. We compared linkages between phloem chemistry and tree ring anatomy of two mountain pine beetle hosts. Lodgepole pine, a mid-elevation species, has had extensive, continual contact with this herbivore, whereas high-elevation whitebark pines have historically had intermittent exposure that is increasing with warming climate. Lodgepole pine had more and larger resin ducts. In both species, anatomical defences were positively related to tree growth and nutrients. Within-tree constitutive and induced concentrations of compounds bioactive against bark beetles and symbionts were largely unrelated to resin duct abundance and size. Fewer anatomical defences in the semi-naïve compared with the continually exposed host concurs with directional differences in chemical defences. Partially uncoupling chemical and morphological antiherbivore traits may enable trees to confront beetles with more diverse defence permutations that interact to resist attack.

Making inference with messy (citizen science) data: when are data accurate enough and how can they be improved?

Measurement or observation error is common in ecological data: as citizen scientists and automated algorithms play larger roles processing growing volumes of data to address problems at large scales, concerns about data quality and strategies for improving it have received greater focus. However, practical guidance pertaining to fundamental data quality questions for data users or managers-how accurate do data need to be and what is the best or most efficient way to improve it?-remains limited. We present a generalizable framework for evaluating data quality and identifying remediation practices, and demonstrate the framework using trail camera images classified using crowdsourcing to determine acceptable rates of misclassification and identify optimal remediation strategies for analysis using occupancy models. We used expert validation to estimate baseline classification accuracy and simulation to determine the sensitivity of two occupancy estimators (standard and false-positive extensions) to different empirical misclassification rates. We used regression techniques to identify important predictors of misclassification and prioritize remediation strategies. More than 93% of images were accurately classified, but simulation results suggested that most species were not identified accurately enough to permit distribution estimation at our predefined threshold for accuracy (<5% absolute bias). A model developed to screen incorrect classifications predicted misclassified images with >97% accuracy: enough to meet our accuracy threshold. Occupancy models that accounted for false-positive error provided even more accurate inference even at high rates of misclassification (30%). As simulation suggested occupancy models were less sensitive to additional false-negative error, screening models or fitting occupancy models accounting for false-positive error emerged as efficient data remediation solutions. Combining simulation-based sensitivity analysis with empirical estimation of baseline error and its variability allows users and managers of potentially error-prone data to identify and fix problematic data more efficiently. It may be particularly helpful for "big data" efforts dependent upon citizen scientists or automated classification algorithms with many downstream users, but given the ubiquity of observation or measurement error, even conventional studies may benefit from focusing more attention upon data quality.

Spectral Data Collection by Dual Field-Of-View System Under Changing Atmospheric Conditions-A Case Study of Estimating Early Season Soybean Populations.

There is an increasing interest in using hyperspectral data for phenotyping and crop management while overcoming the challenge of changing atmospheric conditions. The Piccolo dual field-of-view system collects up- and downwelling radiation nearly simultaneously with one spectrometer. Such systems offer great promise for crop monitoring under highly variable atmospheric conditions. Here, the system's utility from a tractor-mounted boom was demonstrated for a case study of estimating soybean plant populations in early vegetative stages. The Piccolo system is described and its performance under changing sky conditions are assessed for two replicates of the same experiment. Plant population assessment was estimated by partial least squares regression (PLSR) resulting in stable estimations by models calibrated and validated under sunny and cloudy or cloudy and sunny conditions, respectively. We conclude that the Piccolo system is effective for data collection under variable atmospheric conditions, and we show its feasibility of operation for precision agriculture research and potential commercial applications.

Forecasting effects of tree species reintroduction strategies on carbon stocks in a future without historical analog.

American chestnut (Castanea dentata) was once an important component forests in the central Appalachians (USA), but it was functionally extirpated nearly a century ago. Attempts are underway to reintroduce blight-resistant chestnut to its former range, but it is uncertain how current forest composition, climate, and atmospheric changes and disturbance regimes will interact to determine future forest dynamics and ecosystem services. The combination of novel environmental conditions (e.g. climate change), a reintroduced tree species and new disturbance regimes (e.g. exotic insect pests, fire suppression) have no analog in the past that can be used to parameterize phenomenological models. We therefore used a mechanistic approach within the LANDIS-II forest landscape model that relies on physiological first principles to project forest dynamics as the outcome of competition of tree cohorts for light and water as a function of temperature, precipitation, CO2 concentration, and life history traits. We conducted a factorial landscape simulation experiment to evaluate specific hypotheses about future forest dynamics in two study sites in the center of the former range of chestnut. Our results supported the hypotheses that climate change would favor chestnut because of its optimal temperature range and relative drought resistance, and that chestnut would be less competitive in the more mesic Appalachian Plateau province because competitors will be less stressed. The hypothesis that chestnut will increase carbon stocks was supported, although the increase was modest. Our results confirm that aggressive restoration is needed regardless of climate and soils, and that increased aggressiveness of chestnut restoration increased biomass accumulation. The hypothesis that chestnut restoration will increase both compositional and structural richness was not supported because chestnut displaced some species and age cohorts. Although chestnut restoration did not markedly enhance carbon stocks, our findings provide hope that this formerly important species can be successfully reintroduced and associated ecosystem services recovered.

The spatial sensitivity of the spectral diversity-biodiversity relationship: an experimental test in a prairie grassland.

Remote sensing has been used to detect plant biodiversity in a range of ecosystems based on the varying spectral properties of different species or functional groups. However, the most appropriate spatial resolution necessary to detect diversity remains unclear. At coarse resolution, differences among spectral patterns may be too weak to detect. In contrast, at fine resolution, redundant information may be introduced. To explore the effect of spatial resolution, we studied the scale dependence of spectral diversity in a prairie ecosystem experiment at Cedar Creek Ecosystem Science Reserve, Minnesota, USA. Our study involved a scaling exercise comparing synthetic pixels resampled from high-resolution images within manipulated diversity treatments. Hyperspectral data were collected using several instruments on both ground and airborne platforms. We used the coefficient of variation (CV) of spectral reflectance in space as the indicator of spectral diversity and then compared CV at different scales ranging from 1 mm2 to 1 m2 to conventional biodiversity metrics, including species richness, Shannon's index, Simpson's index, phylogenetic species variation, and phylogenetic species evenness. In this study, higher species richness plots generally had higher CV. CV showed higher correlations with Shannon's index and Simpson's index than did species richness alone, indicating evenness contributed to the spectral diversity. Correlations with species richness and Simpson's index were generally higher than with phylogenetic species variation and evenness measured at comparable spatial scales, indicating weaker relationships between spectral diversity and phylogenetic diversity metrics than with species diversity metrics. High resolution imaging spectrometer data (1 mm2 pixels) showed the highest sensitivity to diversity level. With decreasing spatial resolution, the difference in CV between diversity levels decreased and greatly reduced the optical detectability of biodiversity. The optimal pixel size for distinguishing α diversity in these prairie plots appeared to be around 1 mm to 10 cm, a spatial scale similar to the size of an individual herbaceous plant. These results indicate a strong scale-dependence of the spectral diversity-biodiversity relationships, with spectral diversity best able to detect a combination of species richness and evenness, and more weakly detecting phylogenetic diversity. These findings can be used to guide airborne studies of biodiversity and develop more effective large-scale biodiversity sampling methods.

Using imaging spectroscopy to detect variation in terrestrial ecosystem productivity across a water-stressed landscape.

A central challenge to understanding how climate anomalies, such as drought and heatwaves, impact the terrestrial carbon cycle, is quantification and scaling of spatial and temporal variation in ecosystem gross primary productivity (GPP). Existing empirical and model-based satellite broadband spectra-based products have been shown to miss critical variation in GPP. Here, we evaluate the potential of high spectral resolution (10 nm) shortwave (400-2,500 nm) imagery to better detect spatial and temporal variations in GPP across a range of ecosystems, including forests, grassland-savannas, wetlands, and shrublands in a water-stressed region. Estimates of GPP from eddy covariance observations were compared against airborne hyperspectral imagery, collected across California during the 2013-2014 HyspIRI airborne preparatory campaign. Observations from 19 flux towers across 23 flight campaigns (102 total image-flux tower pairs) showed GPP to be strongly correlated to a suite of spectral wavelengths and band ratios associated with foliar physiology and chemistry. A partial least squares regression (PLSR) modeling approach was then used to predict GPP with higher validation accuracy (adjusted R2  = 0.71) and low bias (0.04) compared to existing broadband approaches (e.g., adjusted R2  = 0.68 and bias = -5.71 with the Sims et al. model). Significant wavelengths contributing to the PLSR include those previously shown to coincide with Rubisco (wavelengths 1,680, 1,740, and 2,290 nm) and Vcmax (wavelengths 1,680, 1,722, 1,732, 1,760, and 2,300 nm). These results provide strong evidence that advances in satellite spectral resolution offer significant promise for improved satellite-based monitoring of GPP variability across a diverse range of terrestrial ecosystems.

Vernal freeze damage and genetic variation alter tree growth, chemistry, and insect interactions.

Anticipated consequences of climate change in temperate regions include early spring warmup punctuated by intermittent hard freezes. Warm weather accelerates leaf flush in perennial woody species, potentially exposing vulnerable young tissues to damaging frosts. We employed a 2 × 6 randomized factorial design to examine how the interplay of vernal (springtime) freeze damage and genetic variation in a hardwood species (Populus tremuloides) influences tree growth, phytochemistry, and interactions with an insect herbivore (Chaitophorus stevensis). Acute effects of freezing included defoliation and mortality. Surviving trees exhibited reduced growth and altered biomass distribution. Reflushed leaves on these trees had lower mass per area, lower lignin concentrations, and higher nitrogen concentrations, altered chemical defence profiles, and supported faster aphid population growth. Many effects varied among plant genotypes and were related with herbivore performance. This study suggests that a single damaging vernal freeze event can alter tree-insect interactions through effects on plant growth and chemistry. Differential responses of various genotypes to freeze damage suggest that more frequent vernal freeze events could also influence natural selection, favouring trees with greater freeze hardiness, and more resistance or tolerance to herbivores following damage.

Defence syndromes in lodgepole - whitebark pine ecosystems relate to degree of historical exposure to mountain pine beetles.

Warming climate is allowing tree-killing bark beetles to expand their ranges and access naïve and semi-naïve conifers. Conifers respond to attack using complex mixtures of chemical defences that can impede beetle success, but beetles exploit some compounds for host location and communication. Outcomes of changing relationships will depend on concentrations and compositions of multiple host compounds, which are largely unknown. We analysed constitutive and induced chemistries of Dendroctonus ponderosae's primary historical host, Pinus contorta, and Pinus albicaulis, a high-elevation species whose encounters with this beetle are transitioning from intermittent to continuous. We quantified multiple classes of terpenes, phenolics, carbohydrates and minerals. Pinus contorta had higher constitutive allocation to, and generally stronger inducibility of, compounds that resist these beetle-fungal complexes. Pinus albicaulis contained higher proportions of specific monoterpenes that enhance pheromone communication, and lower induction of pheromone inhibitors. Induced P. contorta increased insecticidal and fungicidal compounds simultaneously, whereas P. albicaulis responses against these agents were inverse. Induced terpene accumulation was accompanied by decreased non-structural carbohydrates, primarily sugars, in P. contorta, but not P. albicaulis, which contained primarily starches. These results show some host species with continuous exposure to bark beetles have more thoroughly integrated defence syndromes than less-continuously exposed host species.