COS 114-4 - The diel variation of leaf water 18O enrichment in Amazonian forests and pastures

Thursday, August 9, 2007: 9:00 AM
Almaden Blrm II, San Jose Hilton
Chun-Ta Lai, Department of Biology, San Diego State University, San Diego, CA, Jean Ometto, Centro de Energia Nuclear na Agricultura, Brazil, Luiz A. Martinelli, Center for Nuclear Energy in Agriculture (CENA), University of Sao Paulo, Brazil, Joe Berry, Stanford University, Tomas Domingues, School of Geosciences, University of Edinburgh, Edinburgh, United Kingdom and James R. Ehleringer, Department of Biology, University of Utah, Salt Lake City, UT

We measured oxygen isotope ratios (d18O) of leaf and stem water from a suite of major functional types in Amazonian forests and pastures to investigate diel patterns of isotopic leaf water enrichment (Do) in March and September 2004 as part of the Large-Scale Biosphere Atmosphere Project (LBA), an international effort to better understand ecosystem processes at regional scales in the Amazon Basin. Two experiments were conducted separately: one in the wet season (March) and the other in the dry season (September). During each field experiment, leaf and stem samples were collected on two-hour intervals at night and hourly during the day for a total of 50 hours from six species in the forest and two species in a pasture, representing four major functional types in the Amazonia, including overstory trees and lianas, lower canopy trees and lianas in the forest and C4 grasses and C3 shrubs in the pasture. Measured Do showed similar diel variations between the two species in the pasture while considerable variations were observed among the six species in the forest. Physiological and gas exchange properties, including leaf water content and stomatal conductance, also were measured at times representing the two seasons in order to predict isotopic leaf water enrichment using three different models that considered either steady state or non-steady state description. Species-specific and environmental factors that influence predictions of Do with respect to these models are discussed.

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