4.8 Article

Responses of leaf nitrogen concentration and specific leaf area to atmospheric CO2 enrichment:: a retrospective synthesis across 62 species

期刊

GLOBAL CHANGE BIOLOGY
卷 8, 期 7, 页码 631-642

出版社

WILEY
DOI: 10.1046/j.1365-2486.2002.00497.x

关键词

atmospheric CO2 enrichment; ecological modelling; growth environment; leaf nitrogen concentration; plant functional group; specific leaf area

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Knowledge of leaf responses to elevated atmospheric [CO2 ] (CO2 concentration) is integral to understanding interactions between vegetation and global change. This work deals with responses of leaf mass-based nitrogen concentration (N (m) ) and specific leaf area (SLA). It assesses the statistical significance of factors perceived as influential on the responses, and quantifies how the responses vary with the significant factors identified, based on 170 data cases of 62 species compiled from the literature. Resultant equations capture about 41% of the variance in the data for percent responses of N (m) and SLA, or about 95% of the variance for N (m) and SLA at 57-320% normal [CO2 ]; these performance statistics also hold for leaf area-based N concentration and specific leaf weight. The equations generalize that: (i) both N (m) and SLA decline as [CO2 ] increases; (ii) proportional decline of N (m) is greater with deciduous woody species and with plants of normally low N (m) , increases with pot size in growth chamber and greenhouse settings and with temperature and photosynthetic photon flux density (PPFD), and is mitigated by N fertilization; and (iii) proportional decline of SLA depends on pot size and PPFD similarly to N (m) , increases with leaf life span and water vapour pressure deficit in enclosed experiments, and decreases with prolonged exposure to elevated [CO2 ] among broadleaf woody species in field conditions. The results highlight great uncertainty in the percent-response data and reveal the potential feasibility to estimate N (m) and SLA at various magnitudes of elevated [CO2 ] from a few key plant and environmental factors of broad data bases.

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