| Literature DB >> 33875648 |
Caroline Signori-Müller1,2, Rafael S Oliveira3, Fernanda de Vasconcellos Barros4,5, Julia Valentim Tavares6, Martin Gilpin6, Francisco Carvalho Diniz6, Manuel J Marca Zevallos7,8, Carlos A Salas Yupayccana8, Martin Acosta9, Jean Bacca7, Rudi S Cruz Chino8, Gina M Aramayo Cuellar10, Edwin R M Cumapa7, Franklin Martinez10, Flor M Pérez Mullisaca7, Alex Nina8, Jesus M Bañon Sanchez7, Leticia Fernandes da Silva9, Ligia Tello7, José Sanchez Tintaya7, Maira T Martinez Ugarteche10, Timothy R Baker6, Paulo R L Bittencourt4,5, Laura S Borma11, Mauro Brum5,12, Wendeson Castro9, Eurídice N Honorio Coronado13, Eric G Cosio14, Ted R Feldpausch4, Letícia d'Agosto Miguel Fonseca11, Emanuel Gloor6, Gerardo Flores Llampazo15, Yadvinder Malhi16, Abel Monteagudo Mendoza7, Victor Chama Moscoso7, Alejandro Araujo-Murakami10, Oliver L Phillips6, Norma Salinas14,16, Marcos Silveira9, Joey Talbot17, Rodolfo Vasquez18, Maurizio Mencuccini19,20, David Galbraith6.
Abstract
Non-structural carbohydrates (NSC) are major substrates for plant metabolism and have been implicated in mediating drought-induced tree mortality. Despite their significance, NSC dynamics in tropical forests remain little studied. We present leaf and branch NSC data for 82 Amazon canopy tree species in six sites spanning a broad precipitation gradient. During the wet season, total NSC (NSCT) concentrations in both organs were remarkably similar across communities. However, NSCT and its soluble sugar (SS) and starch components varied much more across sites during the dry season. Notably, the proportion of leaf NSCT in the form of SS (SS:NSCT) increased greatly in the dry season in almost all species in the driest sites, implying an important role of SS in mediating water stress in these sites. This adjustment of leaf NSC balance was not observed in tree species less-adapted to water deficit, even under exceptionally dry conditions. Thus, leaf carbon metabolism may help to explain floristic sorting across water availability gradients in Amazonia and enable better prediction of forest responses to future climate change.Entities:
Year: 2021 PMID: 33875648 DOI: 10.1038/s41467-021-22378-8
Source DB: PubMed Journal: Nat Commun ISSN: 2041-1723 Impact factor: 14.919