Convergence in relationships between leaf traits, spectra and age across diverse canopy environments and two contrasting tropical forests

• Autor(a) principal: Wu, Jin
• Data de Publicação: 2017
• Outros Autores: Chavana-Bryant, Cecilia, Prohaska, Neill, Serbin, Shawn P., Guan, Kaiyu, Albert, Loren P., Yang, Xi, Van Leeuwen, Willem Jan Dirk, Garnello, Anthony John, Martins, Giordane Augusto, Malhi, Yadvinder Singh, Gerard, France F., Oliviera, Raimundo Cosme, Saleska, Scott Reid
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional do INPA
• Texto Completo: https://repositorio.inpa.gov.br/handle/1/15739
• topic: Canopy Reflectance; Convergence; Leaf; Least Squares Method; Life History Trait; Phenology; Tropical Forest; Understory; Vegetation Index; Water Content; Brasil; Peru; Anatomy And Histology; Brasil; Forest; Geography; Growth, Development And Aging; Light; Peru; Physiology; Plant Leaf; Quantitative Trait; Regression Analysis; Theoretical Model; Tree; Tropic Climate; Brasil; Forests; Geography; Light; Models, Theoretical; Peru; Plant Leaves; Quantitative Trait, Heritable; Regression Analysis; Trees; Tropical Climate

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. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust