Can Leaf Spectroscopy Predict Leaf and Forest Traits Along a Peruvian Tropical Forest Elevation Gradient?

Christopher E. Doughty, P. E. Santos-Andrade, Gregory R. Goldsmith, Benjamin Blonder, Alexander Shenkin, Lisa Patrick Bentley, C. Chavana-Bryant, W. Huaraca-Huasco, Sandra Díaz, Norma Salinas, Brian J. Enquist, Roberta E. Martin, Gregory P. Asner, Yadvinder Malhi

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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 
Idioma originalEspañol
Páginas (desde-hasta)2952-2965
Número de páginas14
PublicaciónJournal of Geophysical Research: Biogeosciences
EstadoPublicada - 1 nov. 2017

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