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Creators/Authors contains: "Ely, Kim S."

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  1. ; ; ; ; ; ; ; ; ; ; et al ( , Plant, Cell & Environment)
    Abstract

    Within vascular plants, the partitioning of hydraulic resistance along the soil‐to‐leaf continuum affects transpiration and its response to environmental conditions. In trees, the fractional contribution of leaf hydraulic resistance (Rleaf) to total soil‐to‐leaf hydraulic resistance (Rtotal), or fRleaf(=Rleaf/Rtotal), is thought to be large, but this has not been tested comprehensively. We compiled a multibiome data set of fRleafusing new and previously published measurements of pressure differences within trees in situ. Across 80 samples, fRleafaveraged 0.51 (95% confidence interval [CI] = 0.46−0.57) and it declined with tree height. We also used the allometric relationship between field‐based measurements of soil‐to‐leaf hydraulic conductance and laboratory‐based measurements of leaf hydraulic conductance to compute the average fRleaffor 19 tree samples, which was 0.40 (95% CI = 0.29−0.56). The in situ technique produces a more accurate descriptor of fRleafbecause it accounts for dynamic leaf hydraulic conductance. Both approaches demonstrate the outsized role of leaves in controlling tree hydrodynamics. A larger fRleafmay help stems from loss of hydraulic conductance. Thus, the decline in fRleafwith tree height would contribute to greater drought vulnerability in taller trees and potentially to their observed disproportionate drought mortality.

     
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  2. ; ; ; ; ; ; ; ; ; ; et al ( , Ecological Informatics)
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  3. ; ; ; ; ; ; ; ; ; ( , New Phytologist)
    Summary

    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.

     
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