Carbon Isotopes and Water Use Efficiency - Sense and Sensitivity

Supplementary Material

for

Carbon isotopes and water use efficiency - Sense and sensitivity

Ulli Seibt 1,2

Abazar Rajabi 1,3

Howard Griffiths 1

Joe Berry 2

1 Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK

2 Department of Global Ecology, Carnegie Institution of Washington, 260 Panama Street, Stanford CA 94305, USA

3 now at Sugar Beet Seed Institute (SBSI), P.O. Box 31585-4114, Karaj, Iran

Appendix: Trends in Wt, Wg, Wg,c, Wg,lin, and  for the Table 3 scenarios

For comparison, we present the results of model simulations ("WanD_trends" version) for the scenarios listed in Table 3 of the main text. Long-term trends are shown for:

(a) 13C discrimination,  ("classical" equation, Farquhar et al. 1982):

(Eq 13, main text)

(b) instantaneous water use efficiency, Wt = A/E, calculated from leaf gas exchange

(d) intrinsic water use efficiency, calculated from the classical equation for :

(Eq 8, main text)

(e) intrinsic water use efficiency, calculated from the linear equation for :

(Eq 4, main text)

The model is available at The site includes a description of how to download and run the model, example model output, and a list of model input parameters. Users can define constants (such as fractionations and light extinction coefficient), and specify trends over the period of interest. The latter fall in two categories: (1) trends in environmental conditions (wa, incoming radiation, air temperature), and (2) trends in plant physiological properties (Vmax, mBB, λ) directly related to plant carbon-water regulation. In addition, users can choose how gs is calculated (Ball-Berry (Ball et al. 1987) or optimal assumption λ (Lloyd 1991)), whether day respiration (Rd) is light-inhibited, and mesophyll conductance (gi) set to a constant value (Warren & Adams 2006) or related to Rubisco content: gi = 4800 Vmax (Aranibar et al. 2006). There are two model versions: for temporal trends ("WanD_trends"), and site or species comparison ("WanD_compare"). The trend version can be used, for example, to examine 13Cplant time series. For any period between 1850 and 2005, the model computes annual results from user defined parameters as described above, and the respective Ca and 13Ca for each year (see Table 2 in McCarroll & Loader 2004, using data from Robertson et al. 2001, Francey et al. 1999). The comparison version simply computes two sets of results from user defined parameters.

Figures S1 to S5 illustrate the model output for the five scenarios listed in Table 3 of the main text. The resulting trends in Wt, Wg, Wg,lin, Wg,c and  are expressed relative to the start year of the calculation, so that they can be compared directly. As in Table 3, an increase in atmospheric CO2 mole fraction (Ca, mol mol-1) combined with changes in air temperature (Tair, °C), relative humidity (h, %), leaf-to-air vapour mole fraction deficit (Da, mmol mol-1), stomatal conductance (gs, mol m-2 s-1), and net assimilation rate (A, mol m-2 s-1) can result in comparable trends in , and hence Wg,c and Wg,lin, but different trends inWt and Wg (Figures S2 to S5). In addition to the values listed for each of the scenarios, the fractionation factors ab = 2.9 ‰, a = 4.4 ‰, and am = 1.8 ‰ were used in all calculations. The plots and text files (input and output) are also shown on the website (

References

Aranibar J.N., J.A. Berry, W.J. Riley, D.E. Pataki, B.E. Law, J.R. Ehleringer (2006) Combining meteorology, eddy fluxes, isotope measurements, and modeling to understand environmental controls of carbon isotope discrimination at the canopy scale. Global Change Biology 12, 710-730.

Ball J.T., I.E. Woodrow, J.A. Berry (1987) A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. In: Biggins, I. (Ed.) Progress in Photosynthesis Research, Vol. IV. Proceedings of the International Congress on Photosynthesis. Martinus Nihjoff, Dordrecht, pp. 221-224.

Farquhar G.D., M.H. O'Leary, J.A. Berry (1982) On the relationship between carbon isotope discrimination and the intercellular carbon dioxide concentration in leaves. Australian Journal of Plant Physiology 9, 121-137.

Francey R.J., C.E. Allison, D.M. Etheridge, C.M. Trudinger, I.G. Enting, M. Leuenberger, R.L. Langenfelds, E. Michel, L.P. Steele (1999) A 1000-year high precision record of 13C in atmospheric CO2. Tellus 51B, 170–193.

Lloyd J. (1991) Modelling stomatal responses to environment in Macadamia integrifolia. Australian Journal of Plant Physiology 18, 649-660.

McCarroll D., N.J. Loader (2004) Stable isotopes in tree rings. Quaternary Science Reviews 23, 771–801.

Robertson, A., J. Overpeck, D. Rind, E. Mosley-Thompson, G. Zielinski, J. Lean, D. Koch, J. Penner, I. Tegen, R. Healy (2001) Hypothesized climate forcing time series for the last 500 years. Journal of Geophysical Research 106, 14783–14803.

Warren C.R., M.A. Adams (2006) Internal conductance does not scale with photosynthetic capacity: implications for carbon isotope discrimination and the economics of water and nitrogen use in photosynthesis. Plant Cell and Environment 29, 192-201.

Figure legends

Figure S1: Model output for relative trends from 1877 to 1977 (relative to first year) resulting from an increase in Ca from 290 to 333 mol mol-1, everything else is kept constant (baseline scenario). S1 baseline input parameters (as listed in the downloadable input file) are:

15. 15. ;; min air temperature (degC) at start & end

20. 20. ;; max (mid-day) air temperature (degC) at start & end

60. 60. ;; min (mid-day) rel humidity (%) at start & end

80. 80. ;; max rel humidity (%) at start & end

9. 9. ;; slope of stomatal conductance (Ball-Berry model) at start & end

40. 40. ;; Vmax at top of canopy at start & end

Figure S2: Model output for scenario 2, input parameters different from those in S1 are:

9. 8. ;; slope of stomatal conductance (Ball-Berry model) at start & end

Figure S3: Model output for scenario 3, input parameters different from those in S1 are:

15. 18. ;; min air temperature (degC) at start & end

20. 23. ;; max (mid-day) air temperature (degC) at start & end

40. 45. ;; Vmax at top of canopy at start & end

Figure S4: Model output for scenario 4, input parameters different from those in S1 are:

60. 62. ;; min (mid-day) rel humidity (%) at start & end

80. 82. ;; max rel humidity (%) at start & end

9. 8. ;; slope of stomatal conductance (Ball-Berry model) at start & end

40. 44. ;; Vmax at top of canopy at start & end

Figure S5: Model output for scenario 5, input parameters different from those in S1 are:

60. 70. ;; min (mid-day) rel humidity (%) at start & end

80. 90. ;; max rel humidity (%) at start & end

9. 9.1 ;; slope of stomatal conductance (Ball-Berry model) at start & end

40. 50. ;; Vmax at top of canopy at start & end

Figure S1

Figure S2

Figure S3

Figure S4

Figure S5