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dc.contributor.authorDoughty, Christopher E.
dc.contributor.authorSantos-Andrade, P. E.
dc.contributor.authorGoldsmith, G. R.
dc.contributor.authorBlonder, B.
dc.contributor.authorShenkin, A.
dc.contributor.authorBentley, L. P.
dc.contributor.authorChavana-Bryant, C.
dc.contributor.authorHuaraca-Huasco, W.
dc.contributor.authorDíaz, S.
dc.contributor.authorSalinas, N.
dc.contributor.authorEnquist, B. J.
dc.contributor.authorMartin, R.
dc.contributor.authorAsner, G. P.
dc.contributor.authorMalhi, Y.
dc.date.accessioned2018-01-31T15:54:39Z
dc.date.available2018-01-31T15:54:39Z
dc.date.issued2017-11
dc.identifier.citationCan Leaf Spectroscopy Predict Leaf and Forest Traits Along a Peruvian Tropical Forest Elevation Gradient? 2017, 122 (11):2952 Journal of Geophysical Research: Biogeosciencesen
dc.identifier.issn21698953
dc.identifier.doi10.1002/2017JG003883
dc.identifier.urihttp://hdl.handle.net/10150/626445
dc.description.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.
dc.description.sponsorshipUK 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 [290605]; John Fell Fund; Google Earth Engine awarden
dc.language.isoenen
dc.publisherAMER GEOPHYSICAL UNIONen
dc.relation.urlhttp://doi.wiley.com/10.1002/2017JG003883en
dc.rights© 2017. American Geophysical Union. All Rights Reserved.en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectPLS regressionen
dc.subjectspectroscopyen
dc.subjecttropical forestsen
dc.titleCan Leaf Spectroscopy Predict Leaf and Forest Traits Along a Peruvian Tropical Forest Elevation Gradient?en
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Ecol & Evolutionary Biolen
dc.identifier.journalJournal of Geophysical Research: Biogeosciencesen
dc.description.note6 month embargo; published online: 18 November 2017en
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
dc.contributor.institutionSICCS; Northern Arizona University; Flagstaff AZ USA
dc.contributor.institutionDepartamento de Ecologia; Universidad Nacional San Antonio Abad del Cusco; Cusco Perú
dc.contributor.institutionEcosystem Fluxes Group, Laboratory for Atmospheric Chemistry; Paul Scherrer Institute; Villigen Switzerland
dc.contributor.institutionEnvironmental Change Institute, School of Geography and the Environment; University of Oxford; Oxford UK
dc.contributor.institutionEnvironmental Change Institute, School of Geography and the Environment; University of Oxford; Oxford UK
dc.contributor.institutionDepartment of Biology; Sonoma State University; Rohnert Park CA USA
dc.contributor.institutionEnvironmental Change Institute, School of Geography and the Environment; University of Oxford; Oxford UK
dc.contributor.institutionDepartamento de Ecologia; Universidad Nacional San Antonio Abad del Cusco; Cusco Perú
dc.contributor.institutionInstituto Multidisciplinario de Biología Vegetal (IMBIV); CONICET and Universidad Nacional de Córdoba; Córdoba Argentina
dc.contributor.institutionDepartamento de Ecologia; Universidad Nacional San Antonio Abad del Cusco; Cusco Perú
dc.contributor.institutionDepartment of Ecology and Evolutionary Biology; University of Arizona; Tucson AZ USA
dc.contributor.institutionDepartment of Global Ecology; Carnegie Institution for Science; Stanford CA USA
dc.contributor.institutionDepartment of Global Ecology; Carnegie Institution for Science; Stanford CA USA
dc.contributor.institutionEnvironmental Change Institute, School of Geography and the Environment; University of Oxford; Oxford UK
refterms.dateFOA2018-05-18T00:00:00Z
html.description.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.


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