13C and 15N allocations of two alpine species from early and late snowmelt locations reflect their different growth strategies

Intense efforts are currently devoted to disentangling the relationships between plant carbon (C) allocation patterns and soil nitrogen (N) availability because of their consequences for growth and more generally for C sequestration. In cold ecosystems, only a few studies have addressed whole-plant...

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Bibliographic Details
Published in:Journal of Experimental Botany
Main Authors: Baptist, Florence, Tcherkez, Guillaume, Aubert, Serge, Pontailler, Jean-Yves, Choler, Philippe, Nogués, Salvador
Format: Text
Language:English
Published: Oxford University Press 2009
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Online Access:http://jxb.oxfordjournals.org/cgi/content/short/erp128v1
https://doi.org/10.1093/jxb/erp128
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Summary:Intense efforts are currently devoted to disentangling the relationships between plant carbon (C) allocation patterns and soil nitrogen (N) availability because of their consequences for growth and more generally for C sequestration. In cold ecosystems, only a few studies have addressed whole-plant C and/or N allocation along natural elevational or topographical gradients. 12C/13C and 14N/15N isotope techniques have been used to elucidate C and N partitioning in two alpine graminoids characterized by contrasted nutrient economies: a slow-growing species, Kobresia myosuroides (KM), and a fast-growing species, Carex foetida (CF), located in early and late snowmelt habitats, respectively, within the alpine tundra (French Alps). CF allocated higher labelling-related 13C content belowground and produced more root biomass. Furthermore, assimilates transferred to the roots were preferentially used for growth rather than respiration and tended to favour N reduction in this compartment. Accordingly, this species had higher 15N uptake efficiency than KM and a higher translocation of reduced 15N to aboveground organs. These results suggest that at the whole-plant level, there is a compromise between N acquisition/reduction and C allocation patterns for optimized growth.