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Functional linkages between leaf traits and net photosynthetic rate: reconciling empirical and mechanistic models

  1. B. SHIPLEY1,*,
  2. D. VILE1,2,
  3. E. GARNIER2,
  4. I. J. WRIGHT3,
  5. H. POORTER4

Article first published online: 24 JUN 2005

DOI: 10.1111/j.1365-2435.2005.01008.x

Issue

Functional Ecology

Functional Ecology

Volume 19, Issue 4, pages 602–615, August 2005

Additional Information

How to Cite

SHIPLEY, B., VILE, D., GARNIER, E., WRIGHT, I. J. and POORTER, H. (2005), Functional linkages between leaf traits and net photosynthetic rate: reconciling empirical and mechanistic models. Functional Ecology, 19: 602–615. doi: 10.1111/j.1365-2435.2005.01008.x

Author Information

  1. 1

    Département de Biologie, Université de Sherbrooke, Sherbrooke (QC), Canada J1K 2R1,

  2. 2

    Centre d’Ecologie Fonctionnelle et Evolutive (CNRS), Montpellier Cedex 1, France,

  3. 3

    Department of Biological Sciences, Macquarie University, Sydney 2109, Australia,

  4. 4

    Plant Ecophysiology, Utrecht University, PO Box 800·84, 3508 TB Utrecht, the Netherlands

* †Author to whom correspondence should be addressed. E-mail: Bill.Shipley@Usherbrooke.ca

Publication History

  1. Issue published online: 19 AUG 2005
  2. Article first published online: 24 JUN 2005
  3. Received 27 May 2004; revised 29 September 2004; accepted 1 November 2004

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Keywords:

  • assimilation rate;
  • leaf nitrogen mass fraction;
  • leaf thickness;
  • leaf tissue concentration;
  • leaf tissue density;
  • LDMR;
  • path analysis;
  • photosynthesis;
  • SLA;
  • structural equation models

Summary

  • 1
    We had two objectives: (i) to determine the generality of, and extend the applicability of, a previously reported empirical relationship between leaf-level net photosynthetic rate (AM, nmol g−1 s−1), specific leaf area (SLA, m2 kg−1) and leaf nitrogen mass fraction (NM, mmol g−1); and (ii) to compare these empirical results with a mechanistic model of photosynthesis in order to provide a mechanistic justification for the empirical pattern.
  • 2
    Our results were based on both literature and original data. There were a total of 160 and 87 data points for the leaf-level and whole-plant data, respectively.
  • 3
    Our multiple regression for single leaves was ln(AM) = 0·66 + 0·71 ln(SLA) + 0·79 ln(NM), r2 = −0·80; only the intercept (0·11) differed for the whole-plant data. These results are not significantly different from previously published relationships.
  • 4
    We then converted the mechanistic model of Evans and Poorter, and a modified version which includes leaf lamina thickness (T) and leaf dry matter (tissue) concentration (CM), into directed acyclic graphs. We then derived reduced graphs that involved only T, CM, SLA, NM and AM. These were tested using structural equation modelling, with measured lamina thickness (T′) and leaf dry matter ratio (LDMR, g dry mass g−1 fresh mass) as indicators of T and CM. The original Evans–Poorter model was rejected, but the modified version fitted the structural relationships well. The same qualitative models also applied to the whole-plant data, although the path coefficients sometimes differed.
  • 5
    Using simulations, we show that the original Evans–Poorter model predicts a positive correlation between SLA and NM that maximizes AM. The data closely follow this predicted relationship. The correlation between the actual values of AM (standardized units) and the predicted values obtained from the modified Evans–Poorter model was 0·74 and increased to 0·82 once three outlier points were removed.
  • 6
    These results provide a mechanistic explanation for the empirical trends relating leaf form and carbon fixation, and predict that SLA and leaf N must be quantitatively co-ordinated to maximize C fixation.
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