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The leaf economic spectrum is a widely studied axis of plant trait variability that defines a trade-off between leaf longevity and productivity. While this has been investigated at the global scale, where it is robust, and at local scales, where deviations from it are common, it has received less attention at the intermediate scale of plant functional types (PFTs). We investigated whether global leaf economic relationships are also present within the scale of plant functional types (PFTs) commonly used by Earth System models, and the extent to which this global-PFT hierarchy can be used to constrain trait estimates. We developed a hierarchical multivariate Bayesian model that assumes separate means and covariance structures within and across PFTs and fit this model to seven leaf traits from the TRY database related to leaf longevity, morphology, biochemistry, and photosynthetic metabolism. Although patterns of trait covariation were generally consistent with the leaf economic spectrum, we found three approximate tiers to this consistency. Relationships among morphological and biochemical traits (specific leaf area [SLA], N, P) were the most robust within and across PFTs, suggesting that covariation in these traits is driven by universal leaf construction trade-offs and stoichiometry. Relationships among metabolic traits (dark respiration [Rd], maximum RuBisCo carboxylation rate [Vc,max], maximum electron transport rate [Jmax]) were slightly less consistent, reflecting in part their much sparser sampling (especially for high-latitude PFTs), but also pointing to more flexible plasticity in plant metabolistm. Finally, relationships involving leaf lifespan were the least consistent, indicating that leaf economic relationships related to leaf lifespan are dominated by across-PFT differences and that within-PFT variation in leaf lifespan is more complex and idiosyncratic. Across all traits, this covariance was an important source of information, as evidenced by the improved imputation accuracy and reduced predictive uncertainty in multivariate models compared to univariate models. Ultimately, our study reaffirms the value of studying not just individual traits but the multivariate trait space and the utility of hierarchical modeling for studying the scale dependence of trait relationships.

Flammability is an important plant trait, relevant to plant function, wildfire behaviour and plant evolution. However, systematic comparison of plant flammability across ecosystems has proved difficult because of varying methodologies and assessment of different fuels comprising different plant parts. We compared the flammability of plant species at the leaf‐level (most commonly used in flammability studies) and shoot‐level (which retains aspects of plant architecture). Furthermore, we examined relationships between leaf functional traits and flammability to identify key leaf traits determining shoot‐level flammability. We collated and analysed existing leaf‐ and shoot‐level flammability data from 43 common indigenous perennial New Zealand plant species, along with existing data on leaf morphological and chemical traits. Shoot‐level flammability was decoupled from leaf‐level flammability. Moreover, leaf‐level rankings of flammability were not correlated with rankings of flammability of plants derived from expert opinion based on field observations, while shoot‐level rankings had a significant positive relationship. Shoot‐level flammability was positively correlated with leaf dry matter content (LDMC), phenolics and lignin, and negatively correlated with leaf thickness. Synthesis. Our study suggests that shoot‐level measurements of flammability are a useful and easily replicable way of characterizing the flammability of plants, particularly canopy flammability. With many parts of the world becoming more fire‐prone, due to anthropogenic activities, such as land‐use change and global warming, this finding will help forest and fire managers to make informed decisions about fuel management, and improve modelling of fire‐vegetation‐climate feedbacks under global climate change. Additionally, we identified some key, widely measured leaf traits, such as leaf dry matter content (LDMC), that may be useful surrogates for plant flammability in global dynamic vegetation models. Shoot‐ and leaf‐level flammability were decoupled, and shoot flammability corresponded to rankings based on expert opinion, suggesting that shoot‐level tests are a useful way to characterize the flammability of canopy fuels. Furthermore, we identified some widely measured leaf traits, such as leaf dry matter content, that were highly correlated to shoot flammability and can be useful surrogates for measuring plant flammability.

Plant functional traits provide information about adaptations to climate and environmental conditions, and can be used to explore the existence of alternative plant strategies within ecosystems. Trait data are also increasingly being used to provide parameter estimates for vegetation models. Here we present a new database of plant functional traits from China. Most global climate and vegetation types can be found in China, and thus the database is relevant for global modeling. The China Plant Trait Database contains information on morphometric, physical, chemical, and photosynthetic traits from 122 sites spanning the range from boreal to tropical, and from deserts and steppes through woodlands and forests, including montane vegetation. Data collection at each site was based either on sampling the dominant species or on a stratified sampling of each ecosystem layer. The database contains information on 1,215 unique species, though many species have been sampled at multiple sites. The original field identifications have been taxonomically standardized to the Flora of China. Similarly, derived photosynthetic traits, such as electron-transport and carboxylation capacities, were calculated using a standardized method. To facilitate trait–environment analyses, the database also contains detailed climate and vegetation information for each site. The data set is released under a Creative Commons BY license. When using the data set, we kindly request that you cite this article, recognizing the hard work that went into collecting the data and the authors’ willingness to make it publicly available.

A major goal of remote sensing is the development of generalizable algorithms to repeatedly and accurately map ecosystem properties across space and time. Imaging spectroscopy has great potential to map vegetation traits that cannot be retrieved from broadband spectral data, but rarely have such methods been tested across broad regions. Here we illustrate a general approach for estimating key foliar chemical and morphological traits through space and time using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-Classic). We apply partial least squares regression (PLSR) to data from 237 field plots within 51 images acquired between 2008 and 2011. Using a series of 500 randomized 50/50 subsets of the original data, we generated spatially explicit maps of seven traits (leaf mass per area ( M area ), percentage nitrogen, carbon, fiber, lignin, and cellulose, and isotopic nitrogen concentration, δ 15 N) as well as pixel-wise uncertainties in their estimates based on error propagation in the analytical methods. Both M area and %N PLSR models had a R 2 > 0.85. Root mean square errors (RMSEs) for both variables were less than 9% of the range of data. Fiber and lignin were predicted with R 2 > 0.65 and carbon and cellulose with R 2 > 0.45. Although R 2 of %C and cellulose were lower than M area and %N, the measured variability of these constituents (especially %C) was also lower, and their RMSE values were beneath 12% of the range in overall variability. Model performance for δ 15 N was the lowest ( R 2 = 0.48, RMSE = 0.95‰), but within 15% of the observed range. The resulting maps of chemical and morphological traits, together with their overall uncertainties, represent a first-of-its-kind approach for examining the spatiotemporal patterns of forest functioning and nutrient cycling across a broad range of temperate and sub-boreal ecosystems. These results offer an alternative to categorical maps of functional or physiognomic types by providing non-discrete maps (i.e., on a continuum) of traits that define those functional types. A key contribution of this work is the ability to assign retrieval uncertainties by pixel, a requirement to enable assimilation of these data products into ecosystem modeling frameworks to constrain carbon and nutrient cycling projections.

Leaf litter and its breakdown products represent an important input of organic matter and nutrients to mangrove sediments and adjacent coastal ecosystems. It is commonly assumed that old-grown stands with mature trees contribute more to the permanent sediment organic matter pool than younger stands. However, neither are interspecific differences in leaf decay rates taken into account in this assumption nor is our understanding of the underlying mechanisms or drivers of differences in leaf chemistry sufficient. This study examines the influence of different plant species and ontogenetic stage on the microbial decay of mangrove leaf litter. A litterbag experiment was conducted in the Matang Mangrove Forest Reserve, Malaysia, to monitor leaf litter mass loss, and changes in leaf litter chemistry and microbial enzyme activity. Four mangrove species of different morphologies were selected, namely the trees Rhizophora apiculata and Bruguiera parviflora, the fern Acrostichum aureum and the shrub Acanthus ilicifolius. Decay rates of mangrove leaf litter decreased from A. ilicifolius to R. apiculata to B. parviflora to A. aureum. Leaf litter mass, total phenolic content, protein precipitation capacity and phenol oxidase activity were found to decline rapidly during the early stage of decay. Leaf litter from immature plants differed from that of mature plants in total phenolic content, phenolic signature, protein precipitating capacity and protease activity. For R. apiculata, but not of the other species, leaf litter from immature plants decayed faster than the litter of mature plants. The findings of this study advance our understanding of the organic matter dynamics in mangrove stands of different compositions and ages and will, thus, prove useful in mangrove forest management.

1. Studying the effects of climate or weather extremes such as drought and heat waves on biodiversity and ecosystem functions is one of the most important facets of climate change research. In particular, primary production is amounting to the common currency in field experiments world-wide. Rarely, however, are multiple ecosystem functions measured in a single study in order to address general patterns across different categories of responses and to analyse effects of climate extremes on various ecosystem functions. 2. We set up a long-term field experiment, where we applied recurrent severe drought events annually for five consecutive years to constructed grassland communities in central Europe. The 32 response parameters studied were closely related to ecosystem functions such as primary production, nutrient cycling, carbon fixation, water regulation and community stability. 3. Surprisingly, in the face of severe drought, above- and below-ground primary production of plants remained stable across all years of the drought manipulation. 4. Yet, severe drought significantly reduced below-ground performance of microbes in soil indicated by reduced soil respiration, microbial biomass and cellulose decomposition rates as well as mycorrhization rates. Furthermore, drought reduced leaf water potential, leaf gas exchange and leaf protein content, while increasing maximum uptake capacity, leaf carbon isotope signature and leaf carbohydrate content. With regard to community stability, drought induced complementary plant-plant interactions and shifts in flower phenology, and decreased invasibility of plant communities and primary consumer abundance. 5. Synthesis. Our results provide the first field-based experimental evidence that climate extremes initiate plant physiological processes, which may serve to regulate ecosystem productivity. A potential reason for different dynamics in various ecosystem services facing extreme climatic events may lie in the temporal hierarchy of patterns of fast versus slow response. Such data on multiple response parameters within climate change experiments foster the understanding of mechanisms of resilience, of synergisms or decoupling of biogeochemical processes, and of fundamental response dynamics to drought at the ecosystem level including potential tipping points and thresholds of regime shift. Future work is needed to elucidate the role of biodiversity and of biotic interactions in modulating ecosystem response to climate extremes.

• Canopy chemistry and spectroscopy offer insight into community assembly and ecosystem processes in high-diversity tropical forests, but phylogenetic and environmental factors controlling chemical traits underpinning spectral signatures remain poorly understood. • We measured 21 leaf chemical traits and spectroscopic signatures of 594 canopy individuals on high-fertility Inceptisols and low-fertility Ultisols in a lowland Amazonian forest. The spectranomics approach, which explicitly connects phylogenetic, chemical and spectral patterns in tropical canopies, provided the basis for analysis. • Intracrown and intraspecific variation in chemical traits varied from 1.4 to 36.7% (median 9.3%), depending upon the chemical constituent. Principal components analysis showed that 14 orthogonal combinations were required to explain 95% of the variation among 21 traits, indicating the high dimensionality of canopy chemical signatures among taxa. Inceptisols and lianas were associated with high leaf nutrient concentrations and low concentrations of defense compounds. Independent of soils or plant habit, an average 70% (maximum 89%) of chemical trait variation was explained by taxonomy. At least 10 traits were quantitatively linked to remotely sensed signatures, which provided highly accurate species classification. • The results suggest that taxa found on fertile soils carry chemical portfolios with a deep evolutionary history, whereas taxa found on low-fertility soils have undergone trait evolution at the species level. Spectranomics provides a new connection between remote sensing and community assembly theory in high-diversity tropical canopies.

More than ever, ecologists seek to employ herbarium collections to estimate plant functional traits from the past and across biomes. However, many trait measurements are destructive, which may preclude their use on valuable specimens. Researchers increasingly use reflectance spectroscopy to estimate traits from fresh or ground leaves, and to delimit or identify taxa. Here, we extend this body of work to non‐destructive measurements on pressed, intact leaves, like those in herbarium collections. Using 618 samples from 68 species, we used partial least‐squares regression to build models linking pressed‐leaf reflectance spectra to a broad suite of traits, including leaf mass per area (LMA), leaf dry matter content (LDMC), equivalent water thickness, carbon fractions, pigments, and twelve elements. We compared these models to those trained on fresh‐ or ground‐leaf spectra of the same samples. The traits our pressed‐leaf models could estimate best were LMA (R2 = 0.932; %RMSE = 6.56), C (R2 = 0.855; %RMSE = 9.03), and cellulose (R2 = 0.803; %RMSE = 12.2), followed by water‐related traits, certain nutrients (Ca, Mg, N, and P), other carbon fractions, and pigments (all R2 = 0.514–0.790; %RMSE = 12.8–19.6). Remaining elements were predicted poorly (R2 < 0.5, %RMSE > 20). For most chemical traits, pressed‐leaf models performed better than fresh‐leaf models, but worse than ground‐leaf models. Pressed‐leaf models were worse than fresh‐leaf models for estimating LMA and LDMC, but better than ground‐leaf models for LMA. Finally, in a subset of samples, we used partial least‐squares discriminant analysis to classify specimens among 10 species with near‐perfect accuracy (>97%) from pressed‐ and ground‐leaf spectra, and slightly lower accuracy (>93%) from fresh‐leaf spectra. These results show that applying spectroscopy to pressed leaves is a promising way to estimate leaf functional traits and identify species without destructive analysis. Pressed‐leaf spectra might combine advantages of fresh and ground leaves: like fresh leaves, they retain some of the spectral expression of leaf structure; but like ground leaves, they circumvent the masking effect of water absorption. Our study has far‐reaching implications for capturing the wide range of functional and taxonomic information in the world’s preserved plant collections. Résumé Plus que jamais, les écologistes cherchent à utiliser des collections d'herbiers pour estimer les traits fonctionnels des plantes dans le passé et à travers des biomes. Cependant, plusieurs mesures de traits sont destructives et pourraient ne pas être effectuées sur des spécimens de grande valeur. De plus en plus, les chercheuses et chercheurs utilisent la spectroscopie de réflectance pour estimer des traits des feuilles fraîches ou broyées, et pour délimiter ou identifier les espèces. Nous étendons ici ces travaux en réalisant des mesures non‐destructives avec des feuilles entières et pressées. À partir de 618 échantillons provenant de 68 espèces, nous avons utilisé la régression des moindres carrés partiels pour construire des modèles liant les spectres de réflectance des feuilles pressées avec un large ensemble de traits, incluant la masse foliaire spécifique (‘leaf mass per area,’ LMA), la teneur en matière sèche des feuilles (‘leaf dry matter content,’ LDMC), l'épaisseur d'eau équivalente, les fractions de carbone, des pigments et douze éléments. Nous avons comparé ces modèles à ceux entraînés sur les spectres des feuilles fraîches ou broyées provenant des mêmes échantillons. Les traits les mieux estimés par nos modèles sur des feuilles pressées étaient la LMA (R2 = 0.932; %REQM = 6.56), le carbone (R2 = 0.855; %REQM = 9.03) et la cellulose (R2 = 0.803; %REQM = 12.2), suivis des traits liés à l'eau, de certains éléments nutritifs (Ca, Mg, N et P), des autres fractions de carbone et des pigments (tous les R2 = 0.514–0.790; %REQM = 12.8–19.6). Les autres éléments nutritifs ne pouvaient pas être bien estimés (R2 < 0.5, %RMSE >20). Pour la plupart des traits chimiques, les modèles sur des feuilles pressées étaient plus performants que ceux de feuilles fraîches, mais moins performants que ceux à partir de feuilles broyées. Les modèles sur des feuilles pressées performaient moins bien que ceux sur des feuilles fraîches pour estimer la LMA et la LDMC, mais performaient mieux que ceux sur des feuilles broyées pour la LMA. Finalement, pour un sous‐ensemble d'échantillons, nous avons utilisé l'analyse discriminante des moindres carrés partiels et réussi à classifier les spécimens parmi 10 espèces avec une précision presque parfaite (>97%) à partir des spectres des feuilles pressées ou broyées, et avec une précision légèrement plus basse (>93%) à partir de feuilles fraîches. Nos résultats démontrent que l'application de la spectroscopie sur des feuilles pressées est une approche non‐destructive prometteuse pour estimer des traits fonctionnels et pour identifier des espèces. Les spectres des feuilles pressées semblent combiner les avantages des feuilles fraîches et de celles broyées: comme les feuilles fraîches, elles conservent une partie de l'expression spectrale de la structure foliaire; comme les feuilles broyées, elles contournent l'effet masquant de l'absorption par l'eau. Notre étude a des implications importantes pour l'acquisition de données fonctionnelles et taxonomiques à partir des collections de plantes préservées à travers le monde.

Key message Austropuccinia psidii infection and increase in diseased leaf area resulted in a reduction of photosynthesis, an upregulation of stomatal conductance, and an increase in leaf starch and sucrose content. Austropuccinia psidii is a biotrophic rust pathogen that causes myrtle rust, affecting over 480 species in the Myrtaceae family. The development of chlorotic and necrotic leaf areas following A. psidii infection has been shown to affect leaf gas exchange. In this study, we quantified photosynthesis, stomatal conductance, and non-structural carbohydrates in seedlings of a long-lived tree, Metrosideros excelsa (pōhutukawa), following A. psidii infection in a glasshouse experiment (infected and control seedlings) conducted over 20 weeks. The diseased leaf area rose from 8% in week 2 to 95% in week 20 after A. psidii inoculation. The photosynthetic rate declined by over 90% within 6 weeks after inoculation and was associated with biochemical damage in CO 2 fixation. Stomatal conductance decreased over the first 4 weeks after inoculation and then increased. An increase in lesions and necrotic cells may inhibit stomatal regulation. Starch content was threefold higher in infected than control leaves 20 weeks after inoculation. Increased starch accumulation in the infected leaf area could be due to reduced export of newly fixed carbon from the infected leaves. Meanwhile, glucose + fructose content was 31% lower in infected leaves at the experiment’s end, likely because of leaf necrosis. If the pathogen-induced damage and loss of leaves, reduction in photosynthesis and changes in non-structural carbohydrates shown in this study also occur in wild M. excelsa seedlings and reduces their biomass, this may in turn reduce their competitive ability in the primary successions that they currently often dominate.

While both plant–soil feedbacks and allelochemical interactions are key drivers of plant community dynamics, the potential for these two drivers to interact with each other remains largely unexplored. If soil microbes influence allelochemical production, this would represent a novel dimension of heterogeneity in plant–soil feedbacks. To explore the linkage between soil microbial communities and plant chemistry, we experimentally generated soil microbial communities and evaluated their impact on leaf chemical composition and allelopathic potential. Four native perennial old-field species (two each of Aster and Solidago) were grown in pairwise combination with each species’ soil microbial community as well as a sterilized inoculum. We demonstrated unequivocally that variation in soil microbial communities altered leaf chemical fingerprints for all focal plant species and also changed their allelopathic potential. Soil microbes reduced allelopathic potential in bioassays by increasing germination 25–54% relative to sterile control soils in all four species. Plants grown with their own microbial communities had the lowest allelopathic potential, suggesting that allelochemical production may be lessened when growing with microbes from conspecifics. The allelopathic potential of plants grown in congener and confamilial soils was indistinguishable from each other, indicating an equivalent response to all non-conspecific microbial communities within these closely related genera. Our results clearly demonstrated that soil microbial communities cause changes in leaf tissue chemistry that altered their allelopathic properties. These findings represent a new mechanism of plant–soil feedbacks that may structure perennial plant communities over very small spatial scales that must be explored in much more detail.