Can leaf spectroscopy predict leaf and forest traits along a peruvian tropical forest elevation gradient?

Autores
Doughty, Christopher E.; Santos-Andrade, P. E.; Goldsmith, G. R.; Blonder, B.; Shenkin, A.; Bentley. L. P.; Chavana-Bryant, C.; Huaraca-Huasco, W.; Díaz, Sandra Myrna; Salinas, N.; Enquist, B. J.; Martin, R.; Asner, G. P.; Malh, Y.
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Doughty, Christopher E.. University of Arizona; Estados Unidos
Fil: Santos-Andrade, P. E.. Universidad San Antonio Abad, Cusco; Perú
Fil: Goldsmith, G. R.. Paul Scherrer Institute, Villigen, Switzerland; Suiza
Fil: Blonder, B.. University of Oxford; Reino Unido
Fil: Shenkin, A.. University of Oxford; Reino Unido
Fil: Bentley. L. P.. Sonoma State University; Estados Unidos. University of Oxford; Reino Unido
Fil: Chavana-Bryant, C.. University of Oxford; Reino Unido
Fil: Huaraca-Huasco, W.. University of Oxford; Reino Unido. Universidad San Antonio Abad, Cusco; Perú
Fil: Díaz, Sandra Myrna. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; Argentina
Fil: Salinas, N.. Universidad San Antonio Abad, Cusco; Perú. Pontificia Universidad Católica de Perú; Perú
Fil: Enquist, B. J.. Arizona State University; Estados Unidos. Santa Fe Institute; Estados Unidos
Fil: Martin, R.. Carnegie Institution for Science; Estados Unidos
Fil: Asner, G. P.. Carnegie Institution for Science; Estados Unidos
Fil: Malh, Y.. University of Oxford; Reino Unido
Materia
LEAF REFLECTANCE
LEAF PROPERTIES
HIGH-RESOLUTION SPECTROSCOPY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/45989
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/45989
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Can leaf spectroscopy predict leaf and forest traits along a peruvian tropical forest elevation gradient?Doughty, Christopher E.Santos-Andrade, P. E.Goldsmith, G. R.Blonder, B.Shenkin, A.Bentley. L. P.Chavana-Bryant, C.Huaraca-Huasco, W.Díaz, Sandra MyrnaSalinas, N.Enquist, B. J.Martin, R.Asner, G. P.Malh, Y.LEAF REFLECTANCELEAF PROPERTIESHIGH-RESOLUTION SPECTROSCOPYhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1High‐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.Fil: Doughty, Christopher E.. University of Arizona; Estados UnidosFil: Santos-Andrade, P. E.. Universidad San Antonio Abad, Cusco; PerúFil: Goldsmith, G. R.. Paul Scherrer Institute, Villigen, Switzerland; SuizaFil: Blonder, B.. University of Oxford; Reino UnidoFil: Shenkin, A.. University of Oxford; Reino UnidoFil: Bentley. L. P.. Sonoma State University; Estados Unidos. University of Oxford; Reino UnidoFil: Chavana-Bryant, C.. University of Oxford; Reino UnidoFil: Huaraca-Huasco, W.. University of Oxford; Reino Unido. Universidad San Antonio Abad, Cusco; PerúFil: Díaz, Sandra Myrna. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Salinas, N.. Universidad San Antonio Abad, Cusco; Perú. Pontificia Universidad Católica de Perú; PerúFil: Enquist, B. J.. Arizona State University; Estados Unidos. Santa Fe Institute; Estados UnidosFil: Martin, R.. Carnegie Institution for Science; Estados UnidosFil: Asner, G. P.. Carnegie Institution for Science; Estados UnidosFil: Malh, Y.. University of Oxford; Reino UnidoAgu Publications2017-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/45989Doughty, Christopher E.; Santos-Andrade, P. E.; Goldsmith, G. R.; Blonder, B.; Shenkin, A.; et al.; Can leaf spectroscopy predict leaf and forest traits along a peruvian tropical forest elevation gradient?; Agu Publications; Journal of Geophysical Research; 122; 11; 11-2017; 2952-29652169-89532169-8961CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://bit.ly/2Lo5i5ninfo:eu-repo/semantics/altIdentifier/doi/10.1002/2017JG003883info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:20:59Zoai:ri.conicet.gov.ar:11336/45989instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:20:59.582CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Can leaf spectroscopy predict leaf and forest traits along a peruvian tropical forest elevation gradient?
title Can leaf spectroscopy predict leaf and forest traits along a peruvian tropical forest elevation gradient?
spellingShingle Can leaf spectroscopy predict leaf and forest traits along a peruvian tropical forest elevation gradient?
Doughty, Christopher E.
LEAF REFLECTANCE
LEAF PROPERTIES
HIGH-RESOLUTION SPECTROSCOPY
title_short Can leaf spectroscopy predict leaf and forest traits along a peruvian tropical forest elevation gradient?
title_full Can leaf spectroscopy predict leaf and forest traits along a peruvian tropical forest elevation gradient?
title_fullStr Can leaf spectroscopy predict leaf and forest traits along a peruvian tropical forest elevation gradient?
title_full_unstemmed Can leaf spectroscopy predict leaf and forest traits along a peruvian tropical forest elevation gradient?
title_sort Can leaf spectroscopy predict leaf and forest traits along a peruvian tropical forest elevation gradient?
dc.creator.none.fl_str_mv Doughty, Christopher E.
Santos-Andrade, P. E.
Goldsmith, G. R.
Blonder, B.
Shenkin, A.
Bentley. L. P.
Chavana-Bryant, C.
Huaraca-Huasco, W.
Díaz, Sandra Myrna
Salinas, N.
Enquist, B. J.
Martin, R.
Asner, G. P.
Malh, Y.
author Doughty, Christopher E.
author_facet Doughty, Christopher E.
Santos-Andrade, P. E.
Goldsmith, G. R.
Blonder, B.
Shenkin, A.
Bentley. L. P.
Chavana-Bryant, C.
Huaraca-Huasco, W.
Díaz, Sandra Myrna
Salinas, N.
Enquist, B. J.
Martin, R.
Asner, G. P.
Malh, Y.
author_role author
author2 Santos-Andrade, P. E.
Goldsmith, G. R.
Blonder, B.
Shenkin, A.
Bentley. L. P.
Chavana-Bryant, C.
Huaraca-Huasco, W.
Díaz, Sandra Myrna
Salinas, N.
Enquist, B. J.
Martin, R.
Asner, G. P.
Malh, Y.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv LEAF REFLECTANCE
LEAF PROPERTIES
HIGH-RESOLUTION SPECTROSCOPY
topic LEAF REFLECTANCE
LEAF PROPERTIES
HIGH-RESOLUTION SPECTROSCOPY
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv 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.
Fil: Doughty, Christopher E.. University of Arizona; Estados Unidos
Fil: Santos-Andrade, P. E.. Universidad San Antonio Abad, Cusco; Perú
Fil: Goldsmith, G. R.. Paul Scherrer Institute, Villigen, Switzerland; Suiza
Fil: Blonder, B.. University of Oxford; Reino Unido
Fil: Shenkin, A.. University of Oxford; Reino Unido
Fil: Bentley. L. P.. Sonoma State University; Estados Unidos. University of Oxford; Reino Unido
Fil: Chavana-Bryant, C.. University of Oxford; Reino Unido
Fil: Huaraca-Huasco, W.. University of Oxford; Reino Unido. Universidad San Antonio Abad, Cusco; Perú
Fil: Díaz, Sandra Myrna. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; Argentina
Fil: Salinas, N.. Universidad San Antonio Abad, Cusco; Perú. Pontificia Universidad Católica de Perú; Perú
Fil: Enquist, B. J.. Arizona State University; Estados Unidos. Santa Fe Institute; Estados Unidos
Fil: Martin, R.. Carnegie Institution for Science; Estados Unidos
Fil: Asner, G. P.. Carnegie Institution for Science; Estados Unidos
Fil: Malh, Y.. University of Oxford; Reino Unido
description 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.
publishDate 2017
dc.date.none.fl_str_mv 2017-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/45989
Doughty, Christopher E.; Santos-Andrade, P. E.; Goldsmith, G. R.; Blonder, B.; Shenkin, A.; et al.; Can leaf spectroscopy predict leaf and forest traits along a peruvian tropical forest elevation gradient?; Agu Publications; Journal of Geophysical Research; 122; 11; 11-2017; 2952-2965
2169-8953
2169-8961
CONICET Digital
CONICET
url http://hdl.handle.net/11336/45989
identifier_str_mv Doughty, Christopher E.; Santos-Andrade, P. E.; Goldsmith, G. R.; Blonder, B.; Shenkin, A.; et al.; Can leaf spectroscopy predict leaf and forest traits along a peruvian tropical forest elevation gradient?; Agu Publications; Journal of Geophysical Research; 122; 11; 11-2017; 2952-2965
2169-8953
2169-8961
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://bit.ly/2Lo5i5n
info:eu-repo/semantics/altIdentifier/doi/10.1002/2017JG003883
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Agu Publications
publisher.none.fl_str_mv Agu Publications
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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