Can Leaf Spectroscopy Predict Leaf and Forest Traits Along a Peruvian Tropical Forest Elevation Gradient?
AuthorDoughty, Christopher E.
Santos-Andrade, P. E.
Goldsmith, G. R.
Bentley, L. P.
Enquist, B. J.
Asner, G. P.
AffiliationUniv Arizona, Dept Ecol & Evolutionary Biol
MetadataShow full item record
PublisherAMER GEOPHYSICAL UNION
CitationCan Leaf Spectroscopy Predict Leaf and Forest Traits Along a Peruvian Tropical Forest Elevation Gradient? 2017, 122 (11):2952 Journal of Geophysical Research: Biogeosciences
Rights©2017. American Geophysical Union. All Rights Reserved.
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AbstractHigh-resolution spectroscopy can be used to measure leaf chemical and structural traits. Such leaf traits are often highly correlated to other traits, such as photosynthesis, through the leaf economics spectrum. We measured VNIR (visible-near infrared) leaf reflectance (400-1,075nm) of sunlit and shaded leaves in similar to 150 dominant species across ten, 1ha plots along a 3,300m elevation gradient in Peru (on 4,284 individual leaves). We used partial least squares (PLS) regression to compare leaf reflectance to chemical traits, such as nitrogen and phosphorus, structural traits, including leaf mass per area (LMA), branch wood density and leaf venation, and higher-level traits such as leaf photosynthetic capacity, leaf water repellency, and woody growth rates. Empirical models using leaf reflectance predicted leaf N and LMA (r(2)>30% and %RMSE<30%), weakly predicted leaf venation, photosynthesis, and branch density (r(2) between 10 and 35% and %RMSE between 10% and 65%), and did not predict leaf water repellency or woody growth rates (r(2)<5%). Prediction of higher-level traits such as photosynthesis and branch density is likely due to these traits correlations with LMA, a trait readily predicted with leaf spectroscopy.
Note6 month embargo; published online: 18 November 2017
VersionFinal published version
SponsorsUK Natural Environment Research Council [NE/J023418/1, NE/M019160/1]; European Research Council [321131, 291585]; John D. and Catherine T. MacArthur Foundation; U.S. National Science Foundation [DEB - 1209287]; Carnegie Institution for Science; National Science Foundation [DEB - 1146206]; Leverhulme Trust, UK; Jackson Foundation; European Community ; John Fell Fund; Google Earth Engine award