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 - Funding Information:
This work is a product of the Global Ecosystems Monitoring (GEM) network (gem.tropicalforests.ox.ac.uk), the Andes Biodiversity and Ecosystems Research Group ABERG (andesresearch. org), the Amazon Forest Inventory Network RAINFOR (www.rainfor.org), and the Carnegie Spectranomics Project (spectranomics.carnegiescience.edu) research consortia. The field campaign was funded a grant to Y. M. from the UK Natural Environment Research Council (grant NE/J023418/1), with additional support from European Research Council advanced investigator grants GEM -TRAITS (321131), T -FORCES (291585), and a John D. and Catherine T. MacArthur Foundation grant to G. P. A. We thank the Servicio Nacional de Áreas Naturales Protegidas por el Estado (SERNANP) and personnel of Manu and Tambopata National Parks for logistical assistance and permission to work in the protected areas. We also thank the Explorers’ Inn and the Pontifical Catholic University of Peru, as well as ACCA. We thank Eric Cosio (Pontifical Catholic University of Peru) for his assistance with research permissions and sample analysis and storage. Taxonomic work at Carnegie Institution was helped by Raul Tupayachi, Felipe Sinca, and Nestor Jaramillo. B. B. was supported by a U.S. National Science Foundation graduate research fellowship and doctoral dissertation improvement grant DEB - 1209287, as well as a UK Natural Environment Research Council independent research fellowship NE/M019160/1. G. P. A. and the Spectranomics team were supported by the endowment of the Carnegie Institution for Science and a grant from the National Science Foundation (DEB - 1146206). S. D. was partially supported by a Visiting Professorship Grant from the Leverhulme Trust, UK. Y. M. was also supported by the Jackson Foundation. G. R. G. was supported by funding from the European Community’s Seventh Framework Program (FP7/2007-2013) under grant agreement number 290605 (COFUND: PSI-FELLOW). C. E. D. received funding from the John Fell Fund and a Google Earth Engine award. All data in this paper can be found in a data repository with the following DOI: https://ora. ox.ac.uk/objects/uuid:4101e249-3cf5-443f-9c29-9204604c667b. Parts of the data are under embargo through January 2018. Code is available at https://github.com/cdoughty99/JGR_ Spectroscopy. C. E. D. wrote the paper with contributions from G. P. A., B. B., P. E. S. A., G. R. G., and C. C. B. PESA and C. E. D. collected the spectral data. P. E. S. A., A. S., L. B., G. G., B. B., W. H. H., N. S., B. E., R. M., G. P. A., and Y. M. provided data. C. E. D. analyzed the data. The field study was funded by grants to Y. M. and G. P. A.
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
AN - SCOPUS:85034249824
SN - 2169-8953
VL - 122
SP - 2952
EP - 2965
JO - Journal of Geophysical Research: Biogeosciences
JF - Journal of Geophysical Research: Biogeosciences
IS - 11
ER -