| Literature DB >> 27381054 |
Jin Wu1, Cecilia Chavana-Bryant2, Neill Prohaska1, Shawn P Serbin3, Kaiyu Guan4, Loren P Albert1, Xi Yang5, Willem J D van Leeuwen6, Anthony John Garnello1, Giordane Martins7, Yadvinder Malhi2, France Gerard8, Raimundo Cosme Oliviera9, Scott R Saleska1.
Abstract
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.Keywords: leaf mass per area (LMA); leaf water content (LWC); partial least-squares regression (PLSR); spectroscopy; understory; vegetation indices; vertical canopy profiles
Mesh:
Year: 2016 PMID: 27381054 DOI: 10.1111/nph.14051
Source DB: PubMed Journal: New Phytol ISSN: 0028-646X Impact factor: 10.151