TY - JOUR
T1 - Convergence in relationships between leaf traits, spectra and age across diverse canopy environments and two contrasting tropical forests
AU - Wu, Jin
AU - Chavana-Bryant, Cecilia
AU - Prohaska, Neill
AU - Serbin, Shawn P.
AU - Guan, Kaiyu
AU - Albert, Loren P.
AU - Yang, Xi
AU - van Leeuwen, Willem J.D.
AU - Garnello, Anthony John
AU - Martins, Giordane
AU - Malhi, Yadvinder
AU - Gerard, France
AU - Oliviera, Raimundo Cosme
AU - Saleska, Scott R.
N1 - Funding Information:
Research in Brazil was supported by NSF (PIRE #0730305), NASA (#NNX11AH24G and NESSF to J.W.), and DOE (GoAmazon #DE-SC0008383). We thank LBA for support, and Dan Metcalfe, Ty Taylor, Kleber Campos and Katie Berg for assistance. Research in Peru was supported by NERC, via FSF equipment loans and grants to C.C-B. (TROBIT project #NE/D005469/1), and to F.G. via CEH. We thank Olivier Jaudoin, Michael Eltringham, Stefan Curtis, Ana Lombardero Morán, Valentine Alt and Italo Treviño Zeballos for assistance, and Eric Cosio for logistical support in Peru. S.P.S and J.W. were supported in part by the Next-Generation Ecosystem Experiment (NGEE-Tropics) project supported by the US DOE, Office of Science, Office of Biological and Environmental Research and through contract #DE-SC00112704 to Brookhaven National Laboratory.
Publisher Copyright:
© 2016 The Authors. New Phytologist © 2016 New Phytologist Trust
PY - 2017/5/1
Y1 - 2017/5/1
N2 - Leaf age structures the phenology and development of plants, as well as the evolution of leaf traits over life histories. However, a general method for efficiently estimating leaf age across forests and canopy environments is lacking. Here, we explored the potential for a statistical model, previously developed for Peruvian sunlit leaves, to consistently predict leaf ages from leaf reflectance spectra across two contrasting forests in Peru and Brazil and across diverse canopy environments. The model performed well for independent Brazilian sunlit and shade canopy leaves (R2 = 0.75–0.78), suggesting that canopy leaves (and their associated spectra) follow constrained developmental trajectories even in contrasting forests. The model did not perform as well for mid-canopy and understory leaves (R2 = 0.27–0.29), because leaves in different environments have distinct traits and trait developmental trajectories. When we accounted for distinct environment–trait linkages – either by explicitly including traits and environments in the model, or, even better, by re-parameterizing the spectra-only model to implicitly capture distinct trait-trajectories in different environments – we achieved a more general model that well-predicted leaf age across forests and environments (R2 = 0.79). Fundamental rules, linked to leaf environments, constrain the development of leaf traits and allow for general prediction of leaf age from spectra across species, sites and canopy environments.
AB - Leaf age structures the phenology and development of plants, as well as the evolution of leaf traits over life histories. However, a general method for efficiently estimating leaf age across forests and canopy environments is lacking. Here, we explored the potential for a statistical model, previously developed for Peruvian sunlit leaves, to consistently predict leaf ages from leaf reflectance spectra across two contrasting forests in Peru and Brazil and across diverse canopy environments. The model performed well for independent Brazilian sunlit and shade canopy leaves (R2 = 0.75–0.78), suggesting that canopy leaves (and their associated spectra) follow constrained developmental trajectories even in contrasting forests. The model did not perform as well for mid-canopy and understory leaves (R2 = 0.27–0.29), because leaves in different environments have distinct traits and trait developmental trajectories. When we accounted for distinct environment–trait linkages – either by explicitly including traits and environments in the model, or, even better, by re-parameterizing the spectra-only model to implicitly capture distinct trait-trajectories in different environments – we achieved a more general model that well-predicted leaf age across forests and environments (R2 = 0.79). Fundamental rules, linked to leaf environments, constrain the development of leaf traits and allow for general prediction of leaf age from spectra across species, sites and canopy environments.
KW - leaf mass per area (LMA)
KW - leaf water content (LWC)
KW - partial least-squares regression (PLSR)
KW - spectroscopy
KW - understory
KW - vegetation indices
KW - vertical canopy profiles
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U2 - 10.1111/nph.14051
DO - 10.1111/nph.14051
M3 - Article
C2 - 27381054
AN - SCOPUS:84978972818
VL - 214
SP - 1033
EP - 1048
JO - New Phytologist
JF - New Phytologist
SN - 0028-646X
IS - 3
ER -