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  1. Cavaleri, Molly (Ed.)
    Abstract

    Leaf trait variation enables plants to utilize large gradients of light availability that exist across canopies of high leaf area index (LAI), allowing for greater net carbon gain while reducing light availability for understory competitors. While these canopy dynamics are well understood in forest ecosystems, studies of canopy structure of woody shrubs in grasslands are lacking. To evaluate the investment strategy used by these shrubs, we investigated the vertical distribution of leaf traits and physiology across canopies of Cornus drummondii, the predominant woody encroaching shrub in the Kansas tallgrass prairie. We also examined the impact of disturbance by browsing and grazing on these factors. Our results reveal that leaf mass per area (LMA) and leaf nitrogen per area (Na) varied approximately threefold across canopies of C. drummondii, resulting in major differences in the physiological functioning of leaves. High LMA leaves had high photosynthetic capacity, while low LMA leaves had a novel strategy for maintaining light compensation points below ambient light levels. The vertical allocation of leaf traits in C. drummondii canopies was also modified in response to browsing, which increased light availability at deeper canopy depths. As a result, LMA and Na increased at lower canopy depths, leading to a greater photosynthetic capacity deeper in browsed canopies compared to control canopies. This response, along with increased light availability, facilitated greater photosynthesis and resource-use efficiency deeper in browsed canopies compared to control canopies. Our results illustrate how C. drummondii facilitates high LAI canopies and a compensatory growth response to browsing—both of which are key factors contributing to the success of C. drummondii and other species responsible for grassland woody encroachment.

     
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  2. Abstract

    Grassland ecosystems are historically shaped by climate, fire, and grazing which are essential ecological drivers. These grassland drivers influence morphology and productivity of grasses via physiological processes, resulting in unique water and carbon-use strategies among species and populations. Leaf-level physiological responses in plants are constrained by the underlying anatomy, previously shown to reflect patterns of carbon assimilation and water-use in leaf tissues. However, the magnitude to which anatomy and physiology are impacted by grassland drivers remains unstudied. To address this knowledge gap, we sampled from three locations along a latitudinal gradient in the mesic grassland region of the central Great Plains, USA during the 2018 (drier) and 2019 (wetter) growing seasons. We measured annual biomass and forage quality at the plot level, while collecting physiological and anatomical traits at the leaf-level in cattle grazed and ungrazed locations at each site. Effects of ambient drought conditions superseded local grazing treatments and reduced carbon assimilation and total productivity inA. gerardii. Leaf-level anatomical traits, particularly those associated with water-use, varied within and across locations and between years. Specifically, xylem area increased when water was more available (2019), while xylem resistance to cavitation was observed to increase in the drier growing season (2018). Our results highlight the importance of multi-year studies in natural systems and how trait plasticity can serve as vital tool and offer insight to understanding future grassland responses from climate change as climate played a stronger role than grazing in shaping leaf physiology and anatomy.

     
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  3. null (Ed.)
    Abstract Background and Aims Andropogon gerardii is a highly productive C4 grass species with a large geographic range throughout the North American Great Plains, a biome characterized by a variable temperate climate. Plant traits are often invoked to explain growth rates and competitive abilities within broad climate gradients. For example, plant competition models typically predict that species with large geographic ranges benefit from variation in traits underlying high growth potential. Here, we examined the relationship between climate variability and leaf-level traits in A. gerardii, emphasizing how leaf-level microanatomical traits serve as a mechanism that may underlie variation in commonly measured traits, such as specific leaf area (SLA). Methods Andropogon gerardii leaves were collected in August 2017 from Cedar Creek Ecosystem Science Reserve (MN), Konza Prairie Biological Station (KS), Platte River Prairie (NE) and Rocky Mountain Research Station (SD). Leaves from ten individuals from each site were trimmed, stained and prepared for fluorescent confocal microscopy to analyse internal leaf anatomy. Leaf microanatomical data were compared with historical and growing season climate data extracted from PRISM spatial climate models. Key Results Microanatomical traits displayed large variation within and across sites. According to AICc (Akaike’s information criterion adjusted for small sample sizes) selection scores, the interaction of mean precipitation and temperature for the 2017 growing season was the best predictor of variability for the anatomical and morphological traits measured here. Mesophyll area and bundle sheath thickness were directly correlated with mean temperature (annual and growing season). Tissues related to water-use strategies, such as bulliform cell and xylem area, were significantly correlated with one another. Conclusions The results indicate that (1) microanatomical trait variation exists within this broadly distributed grass species, (2) microanatomical trait variability appears likely to impact leaf-level carbon and water use strategies, and (3) microanatomical trait values vary across climate gradients, and may underlie variation in traits measured at larger ecological scales. 
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  4. Abstract

    Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Ecological Observatory Network’s (NEON’s) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high‐resolution trait mapping. We tested the accuracy of readily available data products of NEON’s AOP, such as Leaf Area Index (LAI), Total Biomass, Ecosystem Structure (Canopy height model [CHM]), and Canopy Nitrogen, by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The strongest relationships were between AOP LAI and ground‐measured LAI (r = 0.32) and AOP Total Biomass and ground‐measured biomass (r = 0.23). We also examined how well the full reflectance spectra (380–2,500 nm), as opposed to derived products, could predict vegetation traits using partial least‐squares regression (PLSR) models. Among all the eight traits examined, only Nitrogen had a validation of more than 0.25. For all vegetation traits, validation ranged from 0.08 to 0.29 and the range of the root mean square error of prediction (RMSEP) was 14–64%. Our results suggest that currently available AOP‐derived data products should not be used without extensive ground‐based validation. Relationships using the full reflectance spectra may be more promising, although careful consideration of field and AOP data mismatches in space and/or time, biases in field‐based measurements or AOP algorithms, and model uncertainty are needed. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area, mixed functional types of plant communities, and heterogeneous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time. But the opportunity to engage a diverse community of NEON data users will depend on establishing rigorous links with in‐situ field measurements across a diversity of sites.

     
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  5. Summary

    Process‐based vegetation models attempt to represent the wide range of trait variation in biomes by grouping ecologically similar species into plant functional types (PFTs). This approach has been successful in representing many aspects of plant physiology and biophysics but struggles to capture biogeographic history and ecological dynamics that determine biome boundaries and plant distributions. Grass‐dominated ecosystems are broadly distributed across all vegetated continents and harbour large functional diversity, yet most Land Surface Models (LSMs) summarise grasses into two generic PFTs based primarily on differences between temperate C3grasses and (sub)tropical C4grasses. Incorporation of species‐level trait variation is an active area of research to enhance the ecological realism of PFTs, which form the basis for vegetation processes and dynamics in LSMs. Using reported measurements, we developed grass functional trait values (physiological, structural, biochemical, anatomical, phenological, and disturbance‐related) of dominant lineages to improve LSM representations. Our method is fundamentally different from previous efforts, as it uses phylogenetic relatedness to create lineage‐based functional types (LFTs), situated between species‐level trait data and PFT‐level abstractions, thus providing a realistic representation of functional diversity and opening the door to the development of new vegetation models.

     
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