Combining δ¹³C and δ¹⁸O analyses to unravel competition, CO₂ and O₃ effects on the physiological performance of different-aged trees

Combined δ¹³C and δ¹⁸O analyses of leaf material were used to infer changes in photosynthetic capacity (Amax) and stomatal conductance (gl) in Fagus sylvatica and Picea abies trees growing under natural and controlled conditions. Correlation between gl and δ¹⁸O in leaf cellulose (δ¹⁸Ocel) allowed us...

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Published inPlant, cell and environment Vol. 30; no. 8; pp. 1023 - 1034
Main Authors GRAMS, THORSTEN E.E, KOZOVITS, ALESSANDRA R, HÄBERLE, KARL-HEINZ, MATYSSEK, RAINER, DAWSON, TODD E
Format Journal Article
LanguageEnglish
Published Oxford, UK Oxford, UK : Blackwell Publishing Ltd 01.08.2007
Blackwell Publishing Ltd
Blackwell
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Summary:Combined δ¹³C and δ¹⁸O analyses of leaf material were used to infer changes in photosynthetic capacity (Amax) and stomatal conductance (gl) in Fagus sylvatica and Picea abies trees growing under natural and controlled conditions. Correlation between gl and δ¹⁸O in leaf cellulose (δ¹⁸Ocel) allowed us to apply a semi-quantitative model to infer gl from δ¹⁸Ocel and also interpret variation in δ¹³C as reflecting variation in Amax. Extraction of leaf cellulose was necessary, because δ¹⁸O from leaf organic matter (δ¹⁸OLOM) and δ¹⁸Ocel was not reliably correlated. In juvenile trees, the model predicted elevated carbon dioxide (CO₂) to reduce Amax in both species, whereas ozone (O₃) only affected beech by reducing CO₂ uptake via lowered gl. In adult trees, Amax declined with decreasing light level as gl was unchanged. O₃ did not significantly affect isotopic signatures in leaves of adult trees, reflecting the higher O₃ susceptibility of juvenile trees under controlled conditions. The isotopic analysis compared favourably to the performance of leaf gas exchange, underlining that the semi-quantitative model approach provides a robust way to gather time-integrated information on photosynthetic performance of trees under multi-faced ecological scenarios, in particular when information needed for quantitative modelling is only scarcely available.
Bibliography:http://dx.doi.org/10.1111/j.1365-3040.2007.01696.x
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ISSN:0140-7791
1365-3040
DOI:10.1111/j.1365-3040.2007.01696.x