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Keywords:

  • Gossypium hirsutum;
  • abscisic acid;
  • carbon-13;
  • organic oxygen;
  • oxygen-18;
  • relative humidity

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Appendices

Cotton (Gossypium hirsutum L. cv. CS50) plants were grown at two levels of relative humidity (RH) and sprayed daily with abscisic acid (ABA) at four concentrations. Plants grown at lower humidity had higher transpiration rates, lower leaf temperatures and lower stomatal conductance. Plant biomass was also reduced at low humidity. Within each humidity environment, increasing ABA concentration generally reduced stomatal conductance, evaporation rates, superficial leaf density and plant biomass, and increased leaf temperature and specific leaf area. As expected, decreased stomatal conductance resulted in decreased carbon isotope discrimination in leaf material (Δ13Cl). Plants grown at low humidity were more enriched in 18O than those grown at high RH, as theory predicts. Within each humidity environment, increasing ABA concentration increased oxygen isotope enrichment of leaf cellulose (Δ18Oc) and whole-leaf tissue (Δ18Ol). Values of Δ13Cl and Δ18Ol predicted by theoretical models were close to those observed, accounting for 79% of the measured variation in Δ13Cl and 95% of the measured variation in Δ18Ol. Supporting theory, Δ13Cl and Δ18Ol in whole-leaf tissue were negatively related.


INTRODUCTION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Appendices

Carbon isotope discrimination (Δ13C) by plants has become an important tool for plant physiologists as a photosynthesis-weighted integrator of leaf transpiration efficiency. The strong negative correlation between Δ13C recorded in leaf tissue (Δ13Cl) and water-use efficiency found in many crop species (e.g. wheat; Farquhar & Richards 1984) has highlighted the utility of the technique in selecting for high water-use efficiency from low Δ13Cl plants. The first cultivars produced using Δ13Cl as a screening tool are due to be released shortly. Under well-watered conditions, wheat also shows a positive correlation between Δ13Cl and yield ( Condon et al. 1987 ).

The oxygen isotope ratio of plant organic material may also be useful to plant physiologists. Theory ( Craig & Gordon 1965; Dongmann et al. 1974 ; Farquhar & Lloyd 1993) and now data ( Farquhar et al., in press ) suggest that the oxygen isotope ratio of plant material may reflect leaf evaporative conditions (both humidity and transpiration rate). Recent work has shown a strong negative relationship between the oxygen isotope ratio of wheat leaves, stomatal conductance (gs) and grain yield ( Barbour et al., in press a ). Farquhar et al. (1994) and Yakir & Israeli (1995) suggested that the oxygen isotope ratio of leaf tissue (Δ18Ol) may also help to determine whether variation in Δ13Cl is a result of differences in photosynthetic capacity (Vl) or in gs, because Δ18Ol is not expected to reflect changes in Vl.

The preliminary study of oxygen isotopes in cotton reported by Farquhar et al. (in press) showed that variation in stomatal conductance (gs) produced by application of abscisic acid (ABA) was reflected in leaf Δ18O. The experiment described in this paper was designed to investigate the responses in greater detail, and in particular to apply ABA in such a way as to produce intermediate levels of stomatal closure. An assessment of the relationship between the oxygen isotope ratios of cellulose and whole-leaf tissue was also undertaken. Δ13Cl measurements were made on whole-leaf samples to investigate the suggestion that Δ18Ol may provide insight into whether variation in Δ13Cl is due to variation in gs or in Vl. This type of study, with enough replication for statistical treatment, is only possible with the development of a rapid new technique for measuring oxygen isotope ratios of both cellulose and nitrogen-containing organic matter ( Farquhar et al. 1997 ).

Carbon isotope theory

Discrimination against 13C recorded in leaf material (Δ13Cl) is defined ( Farquhar & Richards 1984) as:

  • image(1)

where Ra and Rl are the 13C/12C ratios of CO2 in the air and carbon in the leaf, respectively. Carbon isotope fractionation occurs in C3 plants via two processes. 13CO2 diffuses more slowly across the stomatal pathway, resulting in a fractionation (a) of 4·4‰. The second fractionation occurs during carboxylation by Rubisco (b), where 13C is effectively discriminated against by about 27‰. The fractionations are weighted by the CO2 partial pressure imposed at the step involved. Δ13Cl is modelled ( Farquhar et al. 1982 ; Farquhar & Richards 1984) by:

  • image(2)

where pi and pa are the CO2 partial pressures of intercellular and ambient air, respectively. Equation 2 predicts a positive linear relationship between pi/pa and Δ13Cl in C3 plants. Δ13Cl values typically lie between 17 and 23‰.

To predict Δ13Cl from Eqn 2, pi/pa must be known. In the present experiment, pi/pa was estimated using a rectangular hyperbolic fit of pi/pa to gs (mol m−2 s−1) in cotton plants, where variation in conductance was caused by changing vapour pressure deficit (J. Read and J. Yong, unpublished results), and yielded the curve:

  • image(3)

Oxygen isotope theory

Evaporation enriches water at the sites of evaporation in the leaf because the heavier H218O vapour has a lower vapour pressure than H216O and because H218O diffuses more slowly than H216O. The heavier molecule diffuses 1·028 times more slowly than the lighter molecule in air and through stomata, but 1·019 times more slowly through the laminar boundary layer, from Pohlhausen analysis ( Farquhar & Lloyd 1993). The kinetic fractionation factor (ɛk, in‰) becomes:

  • image(4)

where rs and rb are stomatal and boundary layer resistances to water flux, respectively.

The proportional depression of vapour pressure by the heavier water molecule (ɛ*) is related to temperature (T, in K) by ( Bottinga & Craig 1969):

  • image(5)

so ɛ* is 9·2‰ at 25°C and 9·0‰ at 20°C.

Following notation developed for carbon isotope discrimination, oxygen isotope enrichment at the sites of evaporation (Δ18Oe) is presented in relation to the isotope ratio of source water:

  • image(6)

where Re and RS are the 18O/16O ratios at the sites of evaporation and for source water, respectively. In steady-state conditions Δ18Oe is related to kinetic and vapour pressure fractionation factors and the leaf evaporative environment ( Craig & Gordon 1965; Dongmann et al. 1974 ; Farquhar & Lloyd 1993) by:

  • image(7)

where Δ18Ov is the isotopic composition of water vapour in the air, relative to source water, and ea and ei are ambient and intercellular vapour pressures, respectively.

A large discrepancy between Δ18Oe and measured leaf water is commonly found, and this discrepancy tends to increase with transpiration rate ( Flanagan et al. 1991 ; Walker & Lance 1991; Flanagan et al. 1994 ). Farquhar & Lloyd (1993) suggested that the discrepancy is due to gradients of isotopes within the leaf. These gradients are a result of diffusion of enriched water away from the sites of evaporation, opposed by convection of source water to those sites. In the steady state, the isotope ratio of water (Δ18Od) at some distance l from the evaporative site can be expressed as:

  • image(8)

where v is the velocity of water movement (m s−1) and D is the diffusivity of H218O in water (2·66 × 10−9 m2 s−1). The velocity of water movement is related to the evaporation rate (E) by a scaling factor (k; between 102 and 103), which converts the velocity of water moving through a leaf as a slab to that of movement through a porous medium, i.e.

  • image(9)

where C is the concentration of water (55·5 × 103 mol m−3). The dimension-less term EL/(CD) is the Péclet number, ℘, so that the average value of Δ18Od18OL) over the scaled effective length L (L = k× actual length) becomes ( Farquhar & Lloyd 1993):

  • image(10)

Evaporative enrichment of water in the leaf is passed on to organic material due to exchange of carbonyl oxygen with water, resulting in a 27‰ enrichment of the organic oxygen compared to water at equilibrium. The most important exchange occurs in triose phosphates, as two of the three oxygen are in carbonyl groups, and the half time to equilibration is known to be rapid ( Sternberg et al. 1986 ; Sternberg 1989; Farquhar et al. 1998 ). We expect sucrose to be in equilibrium with cytoplasmic water, which we take here to be Δ18OL ( Barbour et al., in press b ).

Sucrose is then transported from the source leaves to sink tissue in the phloem. To form cellulose from sucrose, cleavage to hexose phosphates occurs, allowing 20% oxygen to re-exchange with water. A proportion, y, of hexose phosphates also goes through a futile cycle to triose phosphates ( Hill et al. 1995 ), allowing further exchange of 60% of the oxygen. When the hexose phosphates are reformed, there is a 50% chance that the non-exchangeable oxygen in the previous hexose phosphate will be in the exchangeable position in the new hexose phosphate. The proportion of oxygen in cellulose that has exchanged with water in the developing cell (pex) may be expressed (corrected from that presented in Farquhar et al. 1998 ):

  • image(11)

and (1 –y) is the proportion of hexose phosphates that is immediately used to form cellulose without recycling through triose phosphates again. Hence, when all hexose phosphates are recycled through triose phosphates (i.e. if there is no cellulose synthesis), both y and pex are equal to 1. The derivation of Eqn 11 is presented in the Appendix.

The proportion of exchangeable oxygen in cellulose has been found to be 0·47 in carrot by Sternberg et al. (1986) and 0·38 in Lemna gibba ( Yakir & DeNiro 1990). Hill et al. (1995) present measurements of randomization of 14C-labelled hexose phosphates during cellulose synthesis in oak stem tissue. Recalculation of these figures gives a range in pex of between 0·49 and 0·57 (if one quarter of the label is found at the opposite end of the product, i.e. 25% randomization, pex is 0·5). pex may vary with rate of cellulose synthesis and sucrose import; if fluxes are slow, then pex may be high.

Water in the developing cell, with which oxygen exchange occurs, may be a mixture of both xylem water (un-enriched by exchange) and phloem water (initially, on leaving the source leaf, at Δ18OL). Bret-Harte & Silk (1994) suggest that the phloem could potentially supply 80% of the water required for cell expansion in root meristems of corn seedlings. This suggests that the proportion of xylem water in developing cells (px) could, in some conditions, be as low as 0·2. Phloem and xylem water may exchange during sucrose transport to the sink leaf, and water within the sink leaf may also be enriched by transpiration (assuming some cellulose is laid down after stomata become functional), and these processes are also included in the px term.

Assuming carbonyl oxygen have an average equilibrium fractionation factor with water (ɛwc) of 27‰, the oxygen isotope composition of cellulose (Δ18Oc) may be expressed ( Farquhar et al., in press ) as:

  • image(12)

Whole-leaf tissue is known to be less enriched than its cellulose by between 4·2 and 9·2‰ ( Farquhar et al., in press ), so Eqn 12 may be re-written to describe the oxygen isotope enrichment of whole tissue compared to source water (Δ18Ol) as:

  • image(13)

where ɛcp is the difference in enrichment between whole plant tissue and its cellulose (ɛcp = Δ18Ol–Δ18Oc, so that ɛcp is negative).

Using the models described above, both Δ13Cl and Δ18Ol may be predicted under the conditions applied in this experiment. Figure 1(b) shows predicted relationships between Δ13Cl and Δ18Ol at 43% and 76% relative humidity (RH) for a leaf with a boundary layer resistance (rb) of 0·2 m2 mol−1 s−1, when air temperature is 30°C and photosynthetically active radiation (PAR) is 1200 μmol m−2 s−1 for conductances ranging from 0·4 to 0·9 mol m−2 s−1. The effective scaled length for the Péclet effect is 8 mm, pex·px is set at 0·38 (anticipating the fitted value), and ɛcp is assumed to be − 8‰. In this model, photosynthetic capacity is held constant, but pi/pa is allowed to vary according to Eqn 3. The difference between leaf and air temperatures (ΔT) is given by:

image

Figure 1. The relationships between (a) modelled pi/pa and ea/ei, and (b) modelled Δ13Cl and Δ18Ol in two humidity environments and when Ta = 30 °C, PAR = 1200 μmol m−2 s−1, Δ18Ov = − 5·5‰, rb = 0·2 m2 mol−1 s−1, ɛwc = 27‰, pex·px = 0·38, L = 8 mm and ɛcp = − 7·5.

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  • image(14)

where r*bH is the combined resistance to sensible and radiative heat transfer in parallel, Q0 is the isothermal net radiation, L is the molar heat of vaporization, D is the water vapour concentration deficit, Cp is the molar specific heat constant of air at constant pressure, and ɛ is the change of latent heat content of saturated air with a change in sensible heat content (for details see Barbour et al., in press a ). The relationship between Δ13Cl and Δ18Ol is predicted to be negative and slightly curvilinear (see Fig. 1b). The curvilinear nature of the relationship is due to the non-linear relationship between pi/pa and ea/ei (see Fig. 1a). The slope of the relationship in Fig. 1(b) differs between the two environments, with a slope of − 0·68‰ change in Δ18Ol with a 1‰ change in Δ13Cl at low humidity, and a slope of − 0·54‰ at higher humidity.

MATERIALS AND METHODS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Appendices

Plant culture

Cotton plants (Gossypium hirsutum cv. CS50) were grown in 10 litre pots containing 6 parts sand and 1 part loam in two glasshouses with differing relative humidity. The glasshouses were set at 30/22 °C day/night temperature, one at an average humidity of 43% and the other at an average humidity of 76%. Pots were watered daily, and supplied with nutrient solution every third day. The nutrient solution provided macronutrient concentrations of (mmol l−1); Ca, 4; Mg, 1·1; S, 1·0; K, 4·0; P, 1·4; given as Ca(NO3)2·4H2O, MgSO4·7H2O, KNO3 and NaH2PO4·H2O, respectively. Concentrations of micronutrients were (μmol l−1); Fe, 54·0; Mn, 1·0; Cu, 1·0; Zn, 1·0; B, 50·0; Mo, 0·5; Cl, 100·0; Co, 0·2, given as Fe-EDTA, MnSO4·H2O, CuSO4·5H2O, ZnSO4·7H2O, H3BO3, Na2MoO4·2H2O, NaCl and Co(NO3)2·6H2O, respectively. A layer of vermiculite was added to the soil surface of each pot to reduce soil evaporation. Three pots without plants were included in each glasshouse for measurement of soil evaporation. Plants were thinned from three to one plant per pot 10 d after germination (d.a.g.), plants being chosen for uniformity of development.

When the plants were well established, 19 d.a.g., they were divided into five replicates of three abscisic acid (ABA) treatments and control plants in each glasshouse. ABA solutions (±cis-trans, Sigma Chemicals) (containing a little ‘Tween’ wetting agent) of 1 × 10−5, 1 × 10−4 and 1 × 10−3 mol l−1 were sprayed on both sides of all leaves, including cotyledons, every morning prior to watering. Plants were removed from the glasshouses during spraying to avoid the spray blowing onto other treatments. The soil surface was also shielded from spray. Control plants were sprayed daily with distilled water. From the start of ABA treatment, nutrient solution application was increased from every third day to daily, and pot position rotated both within each treatment and each treatment as a group after daily watering, to eliminate positional effects.

Measurements

Stomatal conductance (gs) and transpiration rate (E) were measured using a steady-state porometer (Li-Cor Li 1600). The porometer also measures leaf (Tl) and air (Ta) temperatures with two thermocouples, one pressed to the underside of the leaf and the other within the chamber. At 13·00 h on the day prior to the start of ABA treatment, we obtained measurements from the first leaf above the cotyledons (hereafter designated leaf 1) of eight randomly chosen plants from each glasshouse to establish pre-treatment gs. No significant differences were found between plants growing in different positions of the glasshouse. Plants from the low-humidity glasshouse had an average gs of 0·388 mol m−2 s−1 (standard error (SE) = 0·005), while those from the high-humidity glasshouse had an average of 0·422 mol m−2 s−1 (SE = 0·028). ea/ei calculated from porometry measurements of leaf and air temperatures differed considerably between the glasshouses; low-humidity plants having an average value of 0·29 (SE = 0·01), while the high-humidity plants had an average of 0·57 (SE = 0·01).

Stomatal conductance, leaf temperature and E were also measured during three time periods on three days after ABA treatment had started. Measurements were made on leaf 4 of every plant between 10·30 and 11·30 h, 13·00 and 14·00 h, and 15·00 and 16·00 h on each of the 26th, 27th and 28th d.a.g. (7, 8 and 9 d after the start of ABA treatment). Leaf 4 was used for measurements at this time because it was the youngest mature leaf at 26 d.a.g.

Leaf temperature (± 0·1 °C) was measured using a hand-held infra-red thermometer during the 31st and 35th d.a.g. Measurements were taken from leaf 5 (or leaf 4 if leaf 5 was shaded) of every plant on the hour, every hour between 10·00 and 17·00 h. Whole-plant water use was measured during the 29th and 35th d.a.g. by subtracting pot weight at 17·00 h from weight at 07·30 h. Correction for water loss via soil evaporation was made by subtracting the mean loss in weight from the three pots without plants in each glasshouse.

Harvest

Plants were harvested 36 d after germination. Leaf area of each leaf was measured using a portable area meter (Li-Cor Li 3000A). Leaves, flower buds, branches, stem and roots were dried to a constant weight in an 80 °C oven and weighed. Leaf 7 (the leaf expanding when porometry measurements were started) from each plant was ground for δ13C and δ18O analyses.

Cellulose extraction

Samples for cellulose extraction were placed in modified Pasteur pipettes between layers of glass wool, and solvent-extracted using a Soxhlet system; starting with 2/3 chloroform, 1/3 ethanol, then pure ethanol and finally distilled water. Samples were then bleached in acidified sodium chlorite in an ultrasonic bath. Holocellulose was reduced to α-cellulose in sodium hydroxide, again in an ultrasonic bath, following the procedure described by Loader et al. (1997) . Samples took about 14 d to bleach.

Source water and water vapour δ18O

Samples of nutrient solution were taken for δ18O analysis each time a new batch of solution was made, or about once a week during the establishment period, and every third day during ABA treatment. Analyses were performed by Dr A. Herczeg (CSIRO Land and Water, Urrbrae, Australia), and showed δ18Os to be − 6·3‰. Water vapour was sampled during the 3 days prior to harvest, but unfortunately the samples were lost during transit to Dr Herczeg. Accordingly, water vapour δ18O was assumed to be the same as that measured in these glasshouses by Farquhar et al., (in press) , when the temperature and humidity settings were the same, and about the same number of cotton plants were grown. δ18Ov was therefore taken to be − 12·3‰ for the low RH glasshouse and − 11·0‰ for the high RH glasshouse. Using the model described above, and anticipating our fitted values for pex·px of 0·38 and L of 8 mm, we find that if Δ18Ov is 1‰ more enriched than the value used, calculated Δ18Ol becomes 0·2‰ more enriched at 43% RH and 0·3‰ more enriched at 76% RH. With a measurement error of ± 0·2‰ for whole-leaf samples, these levels of uncertainty are acceptable.

δ13C analysis

δ13C was measured on dried, ground whole-leaf samples (leaf 7) by an Isomass Isochrom mass spectrometer coupled to a Carlo Erba preparation system. δ13C values were converted to Δ13C by ( Farquhar et al. 1989 ):

  • image(15)

where δ13Ca is the δ13C of air (taken to be − 7·8‰) and δ13Cl is δ13C of leaf material.

δ18O analysis

δ18O of cellulose and whole tissue was measured following the technique described by Farquhar et al. (1997) . The precision of the mass spectrometer for whole tissue samples was 0·12‰, taken as the standard deviation among 12 samples of a standard sugar with 2% nitrogen added. For the cellulose samples, a standard sugar without nitrogen was used, and gave a standard deviation of 0·19‰ among 12 samples. The whole tissue and cellulose samples were analysed 9 days apart and the difference in the precision of the mass spectrometer between the runs reflects a slight deterioration of the pyrolysis column as it aged. δ18O of leaf whole tissue and cellulose were recalculated as enrichment in 18O above source water (Δ18Ol and Δ18Oc, respectively) to a very good approximation ( Barbour et al., in press a ) by:

  • image(16)

and

  • image(17)

where δ18Ol, δ18Os and δ18OCELL are δ18O of leaf material, source water and cellulose, respectively.

Statistical analysis

Mean values and differences between treatments for balanced data (such as dry weight, leaf area, whole-plant water use and specific leaf area) were calculated using ANOVA in Microsoft Excel. When data were unbalanced (such as leaf temperature and porometry measurements), a mixed model analysis was required. Relative humidity, ABA and time and day of measurement were considered fixed and replicate random effects in the REML (restricted maximum likelihood) procedure in GenstatTM 5 ( Genstat-5 Committee 1993). Resulting values are best linear unbiased estimates (BLUES). Statistical differences among RH and ABA treatments, time and day of measurement were first established using Wald’s Z test. To avoid missing factor combinations in high-order interactions, significant day effects had to be ignored. The fixed model was therefore (RH/ABA) × time of measurement. This model allowed testing of the significance of RH and time of measurement effects, as well as second and third order interactions (RH × ABA, RH × time and RH × ABA × time). Relationships between the different characteristics were established from standard least squares regression and product–moment correlations conducted on treatment means. Within each RH environment, there are two degrees of freedom available, so that Pearson correlation coefficients greater than 0·90, 0·95 and 0·98 are statistically significant at the 10%, 5% and 1% levels, respectively. Over the whole experiment there are six degrees of freedom available, meaning that Pearson correlation coefficients greater than 0·62, 0·71 and 0·83 are statistically significant at the 10%, 5% and 1% levels, respectively.

RESULTS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Appendices

Biomass production

Control plants growing under high-humidity conditions were larger than those in the low-humidity environment, as found by Wong (1993). This was due to significant increases in all portions of total biomass except flower buds ( Fig. 2). Total leaf area was also larger at higher humidity. The humidity effects on area were greater than those on dry weight, so that higher humidity resulted in a lower superficial leaf density, ρl (48·1 compared to 52·3 g m−2).

image

Figure 2. Dry weights of various biomass components from plants grown at (a) 43% RH and (b) 76% RH, and sprayed daily with and without ABA.

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Within the low-humidity environment, spraying leaves with ABA significantly (P = 0·007) increased leaf dry weight at ABA concentrations of 1 × 10−5 and 1 × 10−4 mol l−1. At 1 × 10−3 mol l−1 ABA, a significant reduction in all biomass portions was found (see Fig. 2). No significant differences in leaf area were found between treatments until 1 × 10−3 mol l−1 ABA was applied. Plants treated with the highest concentration of ABA also had significantly (P < 0·05) lower ρl, although there was no significant difference between control plants and those treated with lower ABA concentrations.

At higher humidity, increasing ABA concentration resulted in significant decreases in all biomass portions, and also total biomass (P = 0·0002, see Fig. 2). ABA at 1 × 10−5 mol l−1 had no significant effect on leaf area at high humidity, but at higher concentrations increasing ABA caused decreasing leaf area. The responses of ρl to ABA followed a slightly different pattern, with the control plants having significantly (P < 0·01) higher ρl than the ABA-treated plants, but with no significant differences among ABA treatments.

Whole-plant water use

Average whole-plant water use (PW) also mirrored biomass trends. Plants grown at low humidity used more water than high-humidity plants treated with the same ABA concentration. In low RH conditions, 1 × 10−5 and 1 × 10−4 mol l−1 ABA produced no effect on water use, but PW was greatly reduced by application of 1 × 10−3 mol l−1 ABA. Under high-humidity conditions, increasing ABA concentrations significantly (P < 0·001) reduced PW (see Table 1).

Table 1.  Mean values (± standard error) for total leaf area, number of leaves, superficial leaf density (ρl) and whole plant water use (PW) from plants grown in two humidity environments (low (L), 43% RH; high (H), 76% RH) and treated with four ABA concentrations (C, control; 5, 1 × 10−5 mol l−1; 4, 1 × 10−4 mol l−1; 3, 1 × 10−3 mol l−1) (n = 5)
TreatmentTotal leaf area (m2) Number of leavesρl (g m−2) PW 29 d.a.g. (mmol s−1) PW 35 d.a.g. (mmol s−1)
C, L0·194 ± 0·00420·8 ± 0·453·5 ± 0·70·467 ± 0·0090·549 ± 0·017
5, L0·202 ± 0·00721·4 ± 0·254·2 ± 1·40·477 ± 0·0130·531 ± 0·010
4, L0·211 ± 0·00722·0 ± 0·352·0 ± 0·70·464 ± 0·0060·529 ± 0·008
3, L0·180 ± 0·00821·0 ± 0·449·3 ± 0·70·272 ± 0·0040·310 ± 0·006
C, H0·258 ± 0·01220·8 ± 0·453·2 ± 1·20·429 ± 0·0040·315 ± 0·006
5, H0·265 ± 0·03021·0 ± 0·547·3 ± 1·20·342 ± 0·0080·268 ± 0·005
4, H0·219 ± 0·00620·8 ± 0·546·6 ± 1·50·280 ± 0·0070·177 ± 0·005
3, H0·141 ± 0·01915·6 ± 1·445·1 ± 1·00·092 ± 0·0160·064 ± 0·009

Leaf temperature, stomatal conductance and transpiration rate

Plants grown in the high-humidity glasshouse had significantly (P < 0·001) higher leaf temperatures than those in the low-humidity glasshouse (as reported in cotton by Farquhar et al., in press ). Significant RH × ABA interaction was also found, showing that the different effects of ABA at the different humidities found for biomass and leaf area were also reflected in leaf temperature. At low humidity, control plants and those treated with 1 × 10−5 and 1 × 10−4 mol l−1 ABA did not have significantly different leaf temperatures, while increasing ABA progressively increased leaf temperature at high humidity.

As expected, a change in relative humidity from 76 to 43% resulted in a reduction in stomatal conductance for all ABA treatments (see Table 2). A significant (from REML analysis) RH × ABA interaction suggested that, like biomass and leaf temperature, gs responded differently to ABA at different humidities. Under low humidity, plants treated with 1 × 10−5 mol l−1 ABA showed a slight increase in gs compared to control plants under the same humidity conditions, although the differences were not significant. At ABA concentrations above 1 × 10−5 mol l−1, increasing ABA concentration caused significant reductions in gs. In the high-humidity environment, increasing ABA concentration resulted in significant and progressive reductions in conductance. As expected, leaf evaporation rate (E) was higher for the low-humidity plants. Like leaf temperature and gs, E also showed significant RH × ABA interaction.

Table 2.  Mean leaf temperature (from infra-red thermometer, n = 70), stomatal conductance and transpiration rate (from steady-state porometer, n = 35 or 45) measured on plants grown under four ABA treatments (C, control; 5, 1 × 10−5 mol l−1; 4, 1 × 10−4 mol l−1; 3, 1 × 10−3 mol l−1) and at two humidities (L = 43%, H = 76%). Mean Δ13Cl and Δ18Ol measured at harvest (n = 5) for each treatment are also presented. SED is the average standard error of the differences, from REML or ANOVA analysis
TreatmentTl (°C) gs (mol m−2 s−1) Leaf E (mmol m−2 s−1) Δ13Cl (‰) Δ18Ol (‰)
C, L26·70·5812·720·829·6
5, L27·10·6112·520·429·3
4, L27·20·4810·419·829·1
3, L29·40·40 9·718·930·7
C, H30·70·8510·321·126·2
5, H30·90·73 9·620·526·5
4, H31·70·63 8·020·327·0
3, H32·60·47 6·718·927·4
SED 0·20·03 0·2 0·2 0·2

Carbon isotope discrimination

At both relative humidities, increasing ABA concentration resulted in significant decreases in Δ13Cl (see Table 2). As stomata closed in response to the hormone, there was less discrimination against 13C, presumably because of lower pi. Differences in stomatal conductance in response to RH at the same ABA concentration resulted in significant changes in Δ13Cl only at 1 × 10−4 mol l−1 ABA. Δ13Cl was found to relate curvilinearly to gs as predicted by theory ( Eqns 2 and 3). A hyperbolic fit of the data reveals the expression ( Fig. 3):

image

Figure 3. The relationship between mean stomatal conductance and Δ13Cl for plants grown at 43% RH (circles) and 76% RH (squares). Δ13Cl = (22·31 gs− 4·22)/(gs− 0·15), χ2 = 0·158.

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  • image(18)

with gs having the units mol m−2 s−1.

Using Eqn 3 to estimate pi/pa from gs, Δ13Cl may be calculated for each treatment. Figure 4 shows that the model accurately predicts the response of Δ13Cl to ABA-induced changes in gs, but that the values are slightly offset by about 0·5‰. The model predicts 79% of variation in measured Δ13Cl.

image

Figure 4. The relationship between mean measured and modelled Δ13Cl for plants grown at 43% RH (circles) and 76% RH (squares). Measured Δ13Cl = − 0·53 + 1·00 modelled Δ13Cl; r = 0·90.

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Δ18O of leaf dry matter and cellulose

As theory predicts, plants grown at low humidity were more enriched in 18O than plants grown in more humid conditions (see Table 2). In the low-humidity glasshouse, applying ABA at increasing concentrations had little effect on Δ18O of either whole-leaf tissue or cellulose until the ABA concentration reached 1 × 10−3 mol l−1, when a sharp increase in enrichment was found. In contrast, when plants were grown at high humidity, increasing ABA concentrations resulted in progressive enrichment of both whole-leaf and cellulose Δ18O. These trends fit well with trends in plant dry matter, stomatal conductance and leaf temperature, which all suggest that at low humidity the lower ABA concentrations had little, or a slightly ‘positive’, effect on leaf characteristics. However, under humid conditions, increasing ABA progressively decreased stomatal conductance and increased leaf temperature, resulting in reduced plant productivity and more enriched 18O.

A strong correlation (r = 0·986) was found between Δ18O of whole-leaf material and of cellulose extracted from the leaves (see Fig. 5) suggesting that, at least in this experiment, analysing whole-leaf tissue will give similar information to that obtained by measuring extracted cellulose. Figure 5 shows that the line of best fit has a slope not significantly different from unity and an offset of 7·5‰ (i.e. ɛcp = − 7·5‰). The standard errors of the means from five plants per treatment were the same, or in some cases lower, when whole-tissue analyses were compared to cellulose analyses. Accordingly, relationships between Δ18O and other characteristics will only be assessed for Δ18Ol.

image

Figure 5. The relationship between mean Δ18Ol of cellulose and whole-leaf tissue for plants grown at 43% RH (circles) and 76% RH (squares). Error bars represent standard errors (n = 5). Δ18Ol = 1·08 Δ18Oc− 10·34; r = 0·98. A dotted line through the midpoint of the data with a slope of 1 is included to show that the line of best fit has a slope not significantly different from 1.

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As expected, Δ18Ol was higher at low humidity (see Fig. 6). However, within each humidity treatment, Δ18Ol was also found to be negatively related to stomatal conductance and transpiration rate and positively related to Tl. Stronger correlations were found at high humidity (r = − 0·96, r = 0·98 and r = − 0·99 for gs, Tl and E, respectively) compared to the low-humidity treatment (r = − 0·67, r = 0·90 and r = − 0·53 for gs, Tl and E, respectively) (see Fig. 6). Tl alone was significantly (P < 0·10) correlated with Δ18Ol at low humidity, whereas all leaf traits were significantly correlated at high humidity. The sensitivity of Δ18Ol to changes in gs was higher at low humidity, as predicted by Barbour et al. (in press a) , with a slope of –5·0‰ per mol m−2 s−1, while at high humidity Δ18Ol changed by − 3·3‰ per mol m−2 s−1 change in gs.

image

Figure 6. The relationships of mean Δ18Ol with (a) gs, (b) Tl and (c) E for plants grown at 43% RH (circles) and 76% RH (squares). All correlations are significant (P < 0·05) at 76% RH, but only Tl significantly (P < 0·10) correlates with Δ18Ol at 43% RH.

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Using Eqns 7, 10 and 13, Δ18Ol for each treatment is calculated, the model including both the stronger relative humidity effect and the more subtle effects of changing gs in response to the ABA treatment. Good agreement between modelled and measured values for Δ18Ol was found when pex·px = 0·38, L = 8 mm and ɛwc = − 7·5‰ ( Fig. 7). The slope of the fitted line is only 1% lower than 1:1, and the intercept is 0·33‰ above zero, with a highly significant (P < 0·001) least-squares fit. The model explains 95% of variation in measured Δ18Ol.

image

Figure 7. The relationship between mean measured and modelled Δ18Ol for plants grown at 43% RH (circles) and 76% RH (squares), when L = 8 mm, pex·px = 0·38 and ɛcp = − 7·5‰. Measured Δ18Ol = 0·33 + 1·01 modelled Δ18Ol; r = 0·976.

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Correlations between Δ13C and Δ18O

Δ18Ol was found to be negatively related to Δ13Cl, as predicted by theory ( Fig. 8). A strong correlation (r = − 0·95) was found between the two under high-humidity conditions, with a − 0·55‰ change in Δ18Ol for a 1‰ change in Δ13Cl. The correlation at low humidity was also negative, but not significant (r = − 0·69), with a slope of − 0·60. Figure 8 also shows the predicted relationships between Δ13Cl and Δ18Ol for plants grown in the two humidity environments. The predicted slopes are a little lower than measured, at − 0·56 and − 0·44 for 43 and 76% RH, respectively. The predicted Δ18Ol values at 76% RH are also slightly lower than measured. Both these effects are due to the leaf temperatures and evaporation rates predicted by Eqn 14 differing from measured values, probably due to slight errors in the input parameters (e.g. an average Ta was used, whereas Ta probably varied a little throughout both glasshouses).

image

Figure 8. The relationships between mean Δ13Cl and Δ18Ol for plants grown at 43% RH (circles) and 76% RH (squares). For low humidity, Δ18Ol = 41·6–0·60 Δ13Cl; r = − 0·69 (not significant), and at high humidity, Δ18Ol = 37·9–0·55 Δ13Cl; r = − 0·95 (P < 0·05). Relationships predicted by the model are also plotted for 43% RH (dotted line) and 76% RH (narrow solid line).

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DISCUSSION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Appendices

Physiological traits

The physiological traits measured in this experiment were generally consistent with each other. Plants in the high-humidity glasshouse had higher stomatal conductance (as reported by Farquhar et al., in press ) and leaf temperatures, and lower transpiration rates because the leaf-to-air vapour pressure gradient was smaller than that at low humidity, allowing less heat to be lost by evaporation. The higher humidity treatment presumably also improved water relations. Plants in the high-humidity glasshouse were larger (as reported by Wong 1993), with increases in the dry weight of all vegetative components and larger leaves with lower ρl. At high humidity, increasing ABA concentration resulted in progressive reductions in gs, E and plant biomass, and an increase in leaf temperature. However, at low humidity, no significant effect of ABA concentration on biomass, gs, E or Tl was measured except when plants were treated with 1 × 10−3 mol l−1 ABA.

It is unclear why the 1 × 10−5 and 1 × 10−4 mol l−1 ABA treatments in the low-humidity glasshouse produced non-significant, or slightly positive, effects on physiological characteristics compared to control plants. These responses compare to the typical ABA response of reduced gs and growth found for plants treated with the same ABA concentrations in the humid glasshouse. Perhaps leaves in the low-humidity glasshouse were wet for a shorter time, allowing less penetration of the hormone through the stomata into the leaf. Schönherr & Bukovac (1972) reported that the wetting agent used (Tween) did not allow spontaneous infiltration through Zebrina purpusii stomata regardless of the degree of stomatal opening. Significant penetration of ABA must have taken some time, and maybe the leaf dried before enough ABA had entered the leaf to produce a response, except at the highest concentration. Plants in the low-humidity glasshouse also had lower stomatal conductances, which may have further reduced ABA penetration.

A number of researchers have reported increased growth in response to low concentrations of ABA ( Milborrow 1974). For example, McWha & Jackson (1976) showed that when Lemna polyrhiza was grown in solution with varying ABA concentrations, the number of fronds formed was highest at 1 × 10−9 mol l−1 ABA compared to other concentrations, and to the control. The typical response of reduced growth compared to control plants was only observed when the ABA concentration was as high as 1 × 10−7 mol l−1 ABA. The growth responses demonstrated by McWha & Jackson (1976) could be occurring at the lowest two ABA concentrations in the low-humidity environment of this experiment, if less ABA entered the leaves of these plants compared to plants grown at high humidity and treated with the same ABA concentrations. If plants in the high-humidity glasshouse had been sprayed with very low ABA concentrations, a slightly positive growth effect may have been observed.

Comparison between predicted and observed isotope ratios

As predicted by theory ( Farquhar et al. 1982 ), carbon isotope discrimination increased with increasing gs. Much of the variation in Δ13Cl is explained by variation in gs, so photosynthetic capacity must have changed very little. The simplified model ( Eqn 2) used to predict Δ13Cl from estimated pi/pa predicts changes in Δ13Cl with changing gs with reasonable accuracy.

In agreement with theory ( Craig & Gordon 1965; Dongmann et al. 1974 ), and data from a number of experimental systems (e.g. Gray & Thompson 1977; Sternberg et al. 1989 ; Switsur et al. 1996 ), the enrichment of 18O in plant material was found to be higher when the growth humidity was lower. Also in agreement with theory ( Farquhar & Lloyd 1993), and data from wheat ( Barbour et al., in press a ) and cotton ( Farquhar et al., in press ), Δ18Ol was also found to increase as stomatal conductance and transpiration rates decrease and leaf temperature increases. Plants treated with ABA had lower gs and therefore high Tl, which decreased ea/ei, and increased ɛk and so increased Δ18Oe. If the Péclet effect is significant in practice, as suggested by Barbour et al. (in press b) , the decrease in E associated with lower gs would have also decreased ℘, so re-enforcing the increase in enrichment in plants treated with ABA.

The theory and models presented in the introduction explain 95% of measured variation in Δ18Ol, but a weakness of the model may lie in the expected variability in pex·px. The proportion of xylem water in the developing cell may be dependent on the development of phloem and xylem in the meristem, which may vary even within a plant. Further, the proportion of exchangeable oxygen during cellulose synthesis could vary, as sucrose import and cellulose synthesis rates change. If the sink strength is high, and sucrose supply is limiting, then pex should be low because sucrose is rapidly used for cellulose synthesis, so that a negative relationship between pex·px and growth rate may exist. Under mild water stress, when cell division and expansion are reduced but sucrose supply is not effected, pex could be very high. Yakir et al. (1990) reported that they found no significant difference in leaf cellulose Δ18O from droughted and well-watered cotton (Gossypium hirsutum L. cv. SJ-2), despite increased enrichment in the leaf water of the droughted plants. This could be explained if the pex·px product was close to 1, allowing complete equilibration of all oxygen with water of δ18Os composition during cellulose synthesis.

The data do show some evidence of a negative relationship between growth rate and pex·px. Taking the line of best fit through all data to predict Δ18Ol, a value of pex·px is fitted to force the values to fall exactly on the predicted line. In this way, fitted pex·px was found to vary between 0·345 and 0·426. A significant negative correlation was found between fitted pex·px and average leaf dry weight for each ABA treatment for plants grown in the high-humidity glasshouse (r = − 0·96, P = 0·04, see Fig. 9), but no significant relationship was found for plants grown at 43% RH.

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Figure 9. The relationship between leaf dry weight and fitted pex·px, when modelled Δ18Ol is forced to fit the line of best fit shown in Fig. 7, for plants grown at 43% RH (circles) and 76% RH (squares). The line represents a least-squares regression to the data from the high-humidity glasshouse, r = − 0·96, P = 0·04.

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Another weakness in the model is the unknown level of variability in ɛcp. In this experiment, a least-squares regression between Δ18Oc and Δ18Ol produced a value for ɛcp of – 7·5‰. However, this value could be expected to vary between species, and between plants of the same species grown in different environments, and has been shown to co-vary with Δ18Ol in field-grown wheat ( Barbour et al., in press a ). ɛcp could also vary if the relative proportions of different compounds in leaves were to vary. For example, Δ18Ol of leaves containing very high amounts of starch should not be compared to leaves with low starch content, as starch is expected to be enriched compared to cellulose ( Farquhar et al. 1998 ).

Relationships between Δ13Cl and Δ18Ol

The negative relationship between Δ13Cl and Δ18Ol found in both humidity environments supports theory presented in the introduction. A negative relationship between Δ13C and Δ18O represents a positive relationship between δ13C and δ18O, as δ13C values are negative. Such a positive relationship between δ13C and δ18O has been found for stem cellulose from three tree species by Saurer et al. (1997) and in leaf cellulose from different heights within a tropical forest by Sternberg et al. (1989) . Plants in both the Sternberg et al. (1989) and Saurer et al. (1997) experiments were field-grown, so that some of the variation in δ18O would have been due to variation in source water δ18O, water vapour δ18O, relative humidity and air temperature. As such, it is difficult to compare these relationships with relationships found in a controlled glasshouse experiment, such as reported here.

Farquhar et al. (1994) and Yakir & Israeli (1995) suggest that measuring both Δ13Cl and Δ18Ol may help separate gs and Vl effects on Δ13Cl, because if a negative relationship exists between the two isotopic discriminations (regardless of slope), part of the variation in Δ13Cl most likely arises from variation in gs as Vl is thought to have no effect on Δ18Ol. However, the system being investigated needs to be well understood to allow full interpretation of the results. A shallow slope for the relationship between Δ13Cl and Δ18Ol could indicate that either the sensitivity of Δ18Ol to changes in gs is low, or that part of the variation in Δ13Cl is caused by changes in Vl. Both ɛcp and the pex·px product must be known to allow these two effects to be separated.

CONCLUSIONS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Appendices

Despite the weaknesses in the model outlined above, measurement of Δ18Ol appears to be a promising tool for plant physiologists, as it seems to record the evaporative conditions of the leaf (in terms of both humidity and transpiration), and, with further research, may allow separation of stomatal and capacity effects on Δ13Cl.

ACKNOWLEDGMENTS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Appendices

We wish to acknowledge Dr J. Read for making photosynthetic data available and for valuable discussion, J. Yong for help with experimental set-up and access to photosynthetic data, Drs J. Maindonald and G. Rebetzke for help with statistical analysis, and Drs A. Herczeg and B. Henry and Ms S. Wood for assistance with mass spectrometry. We also acknowledge Drs C. Wong and J. Evans for valuable discussion and the Australian Rural Industries Research and Development Corporation and Micromass UK Ltd for their support.

REFERENCES

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Appendices
  • Barbour M.M., Fischer R.A., Sayre K.D., Farquhar G.D. (in press a) Oxygen isotope ratio of leaf and grain material correlates with stomatal conductance and yield in irrigated, field-grown wheat. Australian Journal of Plant Physiology in press.
  • Barbour M.M., Schurr U., Henry B.K., Wong S.C., Farquhar G.D. (in press b) Variation in the oxygen isotope ratio of phloem sap sucrose from castor bean: evidence in support of the Péclet effect.. Plant Physiology in press.
  • Bottinga Y. & Craig H. (1969) Oxygen isotope fractionation between CO2 and water, and the isotopic composition of marine atmospheric CO2. Earth and Planetary Science Letters 5, 285 295.
  • Bret-Harte M.S. & Silk W.K. (1994) Nonvascular, symplastic diffusion of sucrose cannot satisfy the carbon demands of growth in the primary root tip of Zea mays L. Plant Physiology 105, 19 33.
  • Condon A.G., Richards R.A., Farquhar G.D. (1987) Carbon isotope discrimination is positively correlated with grain yield and dry matter production in field-grown wheat. Crop Science 27, 996 1001.
  • Craig H. & Gordon L.I. (1965) Deuterium and oxygen-18 variations in the ocean and the marine atmosphere. In Proceedings of a Conference on Stable Isotopes in Oceanographic Studies and Palaeotemperatures (ed. E. Tongiorgi), pp. 9 130.Lischi and Figli, Pisa.
  • Dongmann G., Nurnberg H.W., Förstel H., Wagener K. (1974) On the enrichment of H218O in the leaves of transpiring plants. Radiation and Environmental Biophysics 11, 41 52.
  • Farquhar G.D. & Lloyd J. (1993) Carbon and oxygen isotope effects in the exchange of carbon dioxide between terrestrial plants and the atmosphere. In Stable Isotopes and Plant Carbon-Water Relations (eds Ehleringer J.R., Hall A.E. & Farquhar G.D.), pp. 47 70. Academic Press, San Diego.
  • Farquhar G.D. & Richards R.A. (1984) Isotopic composition of plant carbon correlates with water-use efficiency of wheat genotypes. Australian Journal of Plant Physiology 11, 539 552.
  • Farquhar G.D., Barbour M.M., Henry B.K. (1998) Interpretation of oxygen isotope composition of leaf material. In Stable Isotopes (ed. Griffiths H.), pp. 27 62.BIOS Scientific, Oxford.
  • Farquhar G.D., Barbour M.M., Styles J.M. et al. (in press) Oxygen isotope composition of plant matter reveals physiological and genetic effects on plant water use and crop yields. Proceedings of the National Academy of Sciences USA in press
  • Farquhar G.D., Condon A.G., Masle J. (1994) Use of carbon and oxygen isotope composition and mineral ash content in breeding for improved rice production under favorable, irrigated conditions. In Breaking the Yield Barrier. Proceedings of a Workshop on Rice Yield Potential in Favourable Environments. IRRA, 29 November – 4 December 1993 (ed. Cassman K.G.), pp. 95 101. International Rice Research Institute, Manila.
  • Farquhar G.D., Ehleringer J.R., Hubick K.T. (1989) Carbon isotope discrimination and photosynthesis. Annual Review of Plant Physiology and Plant Molecular Biology 40, 503 537.
  • Farquhar G.D., Henry B.K., Styles J.M. (1997) A rapid on-line technique for determination of oxygen isotope composition of nitrogen-containing organic matter and water. Rapid Communications in Mass Spectrometry 11, 1550 1560.
  • Farquhar G.D., O’Leary M.H., Berry J.A. (1982) On the relationship between carbon isotope discrimination and intercellular carbon dioxide concentration in leaves. Australian Journal of Plant Physiology 9, 121 137.
  • Flanagan L.B., Comstock J.P., Ehleringer J.R. (1991) Comparison of modeled and observed environmental influences on the stable oxygen and hydrogen isotope composition of leaf water in Phaseolus vulgaris L. Plant Physiology 96, 588 596.
  • Flanagan L.B., Philips S.L., Ehleringer J.R., Lloyd J., Farquhar G.D. (1994) Effects of changes in leaf water oxygen isotopic composition on discrimination against C18O16O during photosynthesis. Australian Journal of Plant Physiology 21, 221 234.
  • Genstat-5 Committee. (1993) GenstatTM 5 Release 3 Reference Manual. Oxford University Press, Oxford.
  • Gray J. & Thompson P. (1977) Climatic information from 18O/16O analysis of cellulose, lignin and whole wood from tree rings. Nature 270, 708 709.
  • Hill S.A., Waterhouse J.S., Field E.M., Switsur V.R., ApRees T. (1995) Rapid recycling of triose phosphates in oak stem tissue. Plant, Cell and Environment 18, 931 936.
  • Loader N.J., Robertson I., Barker A.C., Switsur V.R., Waterhouse J.S. (1997) An improved technique for the batch processing of small wholewood samples to α-cellulose. Chemical Geology 136, 313 317.
  • McWha J.A. & Jackson D.L. (1976) Some growth promotive effects of abscisic acid. Journal of Experimental Botany 27, 1004 1008.
  • Milborrow B.V. (1974) The chemistry and physiology of abscisic acid. Annual Review of Plant Physiology 25, 259 307.
  • Saurer M., Aellen K., Siegwolf R. (1997) Correlating δ13C and δ18O in cellulose of trees. Plant, Cell and Environment 20, 1543 1550.
  • Schönherr J. & Bukovac M.J. (1972) Penetration of stomata by liquids. Plant Physiology 49, 813 819.
  • Sternberg L. (1989) Oxygen and hydrogen isotope ratios in plant cellulose. mechanisms and applications. In Applications of Stable Isotopes in Ecological Research (eds Rundel P.W., Ehleringer J.R. & Nagy K.A.), pp. 124 141.Springer Verlag, New York.
  • Sternberg L., DeNiro M., Savidge R. (1986) Oxygen isotope exchange between metabolites and water during biochemical reactions leading to cellulose synthesis. Plant Physiology 82, 423 427.
  • Sternberg L., Mulkey S.S., Wright S.J. (1989) Oxygen isotope ratio stratification in a moist tropical forest. Oecologia 81, 51 56.
  • Switsur V.R., Waterhouse J.S., Field E.M., Carter A.H.C. (1996) Climatic signals from stable isotopes in oak tree rings from East Anglia, Great Britain. In Tree Rings, Environment and Humanity (eds Dean J.S., Meko D.M. & Swetnam T.W.), pp. 637 645. Radiocarbon, Dept. of Geosciences, University of Arizona.
  • Walker C.D. & Lance R.C.M. (1991) The fractionation of 2H and 18O in leaf water of barley. Australian Journal of Plant Physiology 18, 411 425.
  • Wong S.C. (1993) Interaction between elevated atmospheric concentration of CO2 and humidity on plant growth. Comparison between cotton and radish. Vegetatio 104/105, 211 221.
  • Yakir D. & DeNiro M.J. (1990) Oxygen and hydrogen isotope fractionation during cellulose metabolism in Lemna gibba L. Plant Physiology 93, 325 332.
  • Yakir D. & Israeli Y. (1995) Reduced solar irradiance effects on net primary productivity (NPP) and the δ13C and δ18O values in plantations of Musa sp. Musaceae. Geochimica et Cosmochimica Acta 59, 2149 2151.
  • Yakir D., DeNiro M.J., Ephrath J.E. (1990) Effects of water stress on oxygen, hydrogen and carbon isotope ratios in two species of cotton plants. Plant, Cell and Environment 13, 949 955.

Appendices

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Appendices

APPENDIX

Calculation of the proportion of exchangeable oxygen in cellulose

The repeating unit in a cellulose molecule has six carbon and five oxygen atoms. To calculate the proportion of exchangeable oxygen in this unit, we assume two possible pathways for the hexose phosphates formed from sucrose, namely immediate incorporation into the cellulose molecule (with a probability of 1 –y) or a futile cycle through triose phosphates (with a probability of y). The hexose phosphates that are used directly for cellulose synthesis have two exchangeable oxygens (assuming an equilibrium between fructose-6-phosphate and glucose-6-phosphate); those attached to carbons 1 and 2. The oxygen attached to carbon 1 is lost during cellulose synthesis because the β1–4 link is always formed from the oxygen attached to carbon 4 of the cellulose residue ( Farquhar et al. 1998 ), so that 0·2 oxygens in cellulose have exchanged if hexose phosphates do not recycle through triose phosphates.

If hexose phosphates are recycled through triose phosphates, then another 0·6 oxygens in cellulose are exchangeable. Two out of three oxygens in triose phosphates are in carbonyl groups, but one in every four of these oxygens have already exchanged while in fructose-6-phosphate.

When the triose phosphates are used to re-form hexose phosphate, in half the cases the hexose phosphate will be formed with the carbons in the same order as in the previous hexose phosphate, and in half the cases carbons 1, 2 and 3 in the previous hexose phosphate will form carbons 6, 5 and 4 of the new hexose phosphate (and vice versa). This means that there is a 50% chance that the oxygen attached to carbon 6 of the new hexose phosphate will have exchanged while in the previous hexose phosphate, after one passage through the triose phosphate cycle, allowing a further (0·2 × 0·5) oxygens in cellulose to exchange. After two turns of the cycle, the probability that the oxygen attached to carbon 6 in the cellulose unit has exchanged will be 0·2 (0·5 + 0·52), and the probability after n turns of the cycle will be 0·2 (0·5 + 0·5n).

The proportion of exchangeable oxygen in cellulose may be described by:

  • image

so that:

  • image
  • image((A1))

To simplify,

  • image

yA = A – y

  • image

and let

  • image((A2))
  • image

and let

  • image((A3))

Substituting Eqns A2 and A3 into A1:

  • image