TY - JOUR
T1 - Can Leaf Spectroscopy Predict Leaf and Forest Traits Along a Peruvian Tropical Forest Elevation Gradient?
AU - Doughty, Christopher E.
AU - Santos-Andrade, P. E.
AU - Goldsmith, G. R.
AU - Blonder, B.
AU - Shenkin, A.
AU - Bentley, L. P.
AU - Chavana-Bryant, C.
AU - Huaraca-Huasco, W.
AU - Díaz, S.
AU - Salinas, N.
AU - Enquist, B. J.
AU - Martin, R.
AU - Asner, G. P.
AU - Malhi, Y.
N1 - Publisher Copyright: ©2017. American Geophysical Union. All Rights Reserved.
PY - 2017/11
Y1 - 2017/11
N2 - High-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 (400–1,075 nm) of sunlit and shaded leaves in ~150 dominant species across ten, 1 ha plots along a 3,300 m 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 (r2 > 30% and %RMSE < 30%), weakly predicted leaf venation, photosynthesis, and branch density (r2 between 10 and 35% and %RMSE between 10% and 65%), and did not predict leaf water repellency or woody growth rates (r2<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.
AB - High-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 (400–1,075 nm) of sunlit and shaded leaves in ~150 dominant species across ten, 1 ha plots along a 3,300 m 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 (r2 > 30% and %RMSE < 30%), weakly predicted leaf venation, photosynthesis, and branch density (r2 between 10 and 35% and %RMSE between 10% and 65%), and did not predict leaf water repellency or woody growth rates (r2<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.
KW - PLS regression
KW - spectroscopy
KW - tropical forests
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U2 - 10.1002/2017JG003883
DO - 10.1002/2017JG003883
M3 - Article
SN - 2169-8953
VL - 122
SP - 2952
EP - 2965
JO - Journal of Geophysical Research: Biogeosciences
JF - Journal of Geophysical Research: Biogeosciences
IS - 11
ER -