Relationship of leaf oxygen and carbon isotopic composition with transpiration efficiency in the C₄ grasses Setaria viridis and Setaria italica

Leaf carbon isotopic composition correlated well with transpiration efficiency, while leaf oxygen isotopic composition was confounded by leaf length in the C₄ grasses Setaria viridis and Setaria italica.

Abstract

Leaf carbon and oxygen isotope ratios can potentially provide a time-integrated proxy for stomatal conductance (g_s) and transpiration rate (E), and can be used to estimate transpiration efficiency (TE). In this study, we found significant relationships of bulk leaf carbon isotopic signature (δ¹³C_BL) and bulk leaf oxygen enrichment above source water (Δ¹⁸O_BL) with gas exchange and TE in the model C₄ grasses Setaria viridis and S. italica. Leaf δ¹³C had strong relationships with E, g_s, water use, biomass, and TE. Additionally, the consistent difference in δ¹³C_BL between well-watered and water-limited plants suggests that δ¹³C_BL is effective in separating C₄ plants with different availability of water. Alternatively, the use of Δ¹⁸O_BL as a proxy for E and TE in S. viridis and S. italica was problematic. First, the oxygen isotopic composition of source water, used to calculate leaf water enrichment (Δ¹⁸O_LW), was variable with time and differed across water treatments. Second, water limitations changed leaf size and masked the relationship of Δ¹⁸O_LW and Δ¹⁸O_BL with E. Therefore, the data collected here suggest that δ¹³C_BL but not Δ¹⁸O_BL may be an effective proxy for TE in C₄ grasses.

Introduction

The bulk leaf carbon isotopic signature (δ¹³C_BL) can potentially provide time-integrated proxies of stomatal conductance and transpiration efficiency (TE), where TE is defined as the quantity of carbon fixed per unit water lost through transpiration (for a glossary of terms, see Table 1). For example, δ¹³C_BL has been successfully used in wheat breeding programs to screen for TE (Farquhar and Richards, 1984; Cabrera-Bosquet et al., 2009a, 2011; Yousfi et al., 2012; Araus et al., 2013) and has been studied in C₄ species such as maize (Monneveux et al., 2007; Cabrera-Bosquet et al., 2009b,c), sorghum (Henderson et al., 1998), sugarcane (Saliendra et al., 1996), and pearl millet (Brück et al., 2000). Additionally, bulk leaf oxygen enrichment above source water (S) (Δ¹⁸O_BL=δ¹⁸O_BL−δ¹⁸O_S) has been proposed as a proxy for transpiration rate (E) when comparing plants grown together under the same atmospheric and climatic conditions (Barbour et al., 2000a; Condon et al., 2004; Barbour, 2007). For example, Δ¹⁸O_BL has been shown to vary with E in several crop species such as tea (Sheshshayee et al., 2010), sunflower (Sheshshayee et al., 2005), cowpea (Bindu Madhava et al., 1999), and wheat (Cabrera-Bosquet et al., 2009a). However, to date C₄ plant-breeding programs have not generally used stable isotopes to phenotype or select for TE.

Table 1.

Glossary of terms

Term	Definition
Δ13C	Photosynthetic carbon discrimination (δ13Cambient−δ13CBL)
δ13CBL	Leaf carbon isotopic composition
δ18OLW	Oxygen isotopic composition of leaf water
δ18OSW	Oxygen isotopic composition of soil water
δ18ORC	Oxygen isotopic composition of root crown water
δ18OS	Oxygen isotopic composition of source water
δ18OI	Oxygen isotopic composition of irrigation water
Δ18OLW	Leaf water enrichment (δ18OLW−δ18OS)
Δ18OBL	Bulk leaf enrichment (δ18OBL−δ18OS)
Δ18Ov	Enrichment of water vapor above source water (δ18Ov−δ18OS)
C i/Ca	Intercellular to ambient CO2 concentration
g s	Stomatal conductance (mol m−2 s−1)
A net	Net photosynthetic rate (µmol m−2 s−1)
E	Transpiration rate (mmol m−2 s−1)
TEintrinsic	Intrinsic transpiration efficiency (Anet/gs)
TEinstantaneous	Instantaneous transpiration efficiency (Anet/E)
TEplant	Plant level transpiration efficiency (total aboveground biomass/water transpired)
TEw	δ13CBL-derived transpiration efficiency
e a/ei	Molar ratio of ambient to intercellular vapor
ε+	Equilibrium fractionation
εk	Kinetic fractionation
ϕw	Ratio of night-time and non-stomatal water loss to daytime transpiration
ϕr	Ratio of CO2 respiration occurring at night and in non- photosynthetic tissue during the day to assimilation rate
A	Fractionation during diffusion of CO2 in air through stomata (4.4‰)
b 3	Fractionation by Rubisco (30‰)
b 4	Fractionation of PEP carboxylation and isotopic equilibrium during dissolution and hydration of CO2 (–5.2‰ at a leaf temperature of 30 °C)
S	Fractionation during the CO2 leakage from the bundle sheath cells (1.8‰)
ϕ	Leakiness of CO2 from the bundle sheath

Variation in δ¹³C_BL in plants grown under the same climatic conditions is primarily determined by leaf photosynthetic CO₂ isotope discrimination:

$${\Delta }^{13}{\mathrm{C}}_{\text{BL}}= \frac{{\delta }^{13}{\mathrm{C}}_{\text{ambient}}-{\delta }^{13}{\mathrm{C}}_{\text{BL}}}{1+\frac{{\delta }^{13}{\mathrm{C}}_{\text{BL}}}{1000}}$$ (1)

where δ values are in ‰ notation and δ¹³C_ambient is the signature of available atmospheric CO₂. In C₄ plants, Δ¹³C is influenced by fractionations associated with diffusion of CO₂, carboxylation reactions, and the ratio of bundle sheath CO₂ leak rate to PEP carboxylase rate (leakiness, ϕ), and it is proportional to the partial pressure of intercellular to ambient CO₂ (C_i/C_a). C_i/C_a is a measure of the supply of CO₂ to photosynthesis, and as C_i/C_a increases, discrimination decreases (for the simplified model; von Caemmerer et al., 2014):

$${\Delta }^{13}{\mathrm{C}}_{\text{BL}}=a+\left(b_4+\varphi \left(b_3-s\right)-a\right)\frac{C_{\mathrm{i}}}{C_{\mathrm{a}}}$$ (2)

where a is the fractionation during diffusion of CO₂ in air through stomata (4.4‰), b₄ is the combined fractionation of PEP carboxylation and the preceding isotopic equilibrium during dissolution and hydration of CO₂ (–5.2‰ at a leaf temperature of 30 °C) as described in Henderson et al. (1992), b₃ is the fractionation by Rubisco (30‰), s is the fractionation during the leakage of CO₂ out of the bundle sheath cells (1.8‰), and ϕ is the leakiness of CO₂ from the bundle sheath (Henderson et al., 1992, 1998).

The CO₂ concentrating mechanism in C₄ plants minimizes Rubisco fractionation, so the relationship between Δ¹³C and C_i/C_a in C₄ plants is dampened compared with C₃ plants and is less variable across growth conditions and genotypes (Henderson et al., 1998). Leakiness (ϕ) determines the slope of the relationship between Δ¹³C and C_i/C_a, controlling the directionality of this relationship from positive to negative. However, ϕ has been shown to be relatively constant in many C₄ species, varying little across light intensities, temperatures, and CO₂ partial pressures (Ubierna et al., 2011; Sun et al., 2012; Kromdijk et al., 2014; Sage, 2014). Therefore, if ϕ is relatively robust and constant across different growth conditions, then changes in Δ¹³C are primarily driven by variation in C_i/C_a, which is influenced by both the net rates of CO₂ fixation (A_net) and stomatal conductance (g_s). For example, increasing A_net can draw down C_i relative to C_a and a reduction in g_s can decrease the supply of atmospheric CO₂ to the intercellular air space for photosynthetic assimilation. Since TE is also related to A_net and g_s, this means that TE and Δ¹³C are linked through their relationship with C_i/C_a, which makes δ¹³C_BL a potential proxy for TE (Farquhar et al., 1989; Henderson et al., 1998).

Alternatively, oxygen isotopic enrichment above source water in leaf tissue (Δ¹⁸O_BL) comes partly from oxygen isotopic enrichment in leaf lamina water, a component of leaf water enrichment (Δ¹⁸O_LW), and where organic compounds are synthesized within the leaf. At these sites of carbonyl oxygen isotope exchange, the leaf water oxygen isotope signal is passed on to photosynthetic intermediates and consequently passed on to bulk leaf tissue (Barbour et al., 2000a, b; Gan et al., 2003; Barbour and Farquhar, 2004; Barbour, 2007). The model of Craig and Gordon (1965), which describes the evaporative isotopic enrichment of water from the surface of a water body, was modified to explain how oxygen isotopes in leaf water are enriched:

$${\Delta }^{18}{\mathrm{O}}_{\mathrm{e}}={\epsilon }^{+}+{\epsilon }_{\mathrm{k}}+\frac{e_{\mathrm{a}}}{e_{\mathrm{i}}}\left({\Delta }^{18}{\mathrm{O}}_{\mathrm{v}}-{\epsilon }_{\mathrm{k}}\right)$$ (3)

where isotopic enrichment of water at the evaporation sites (Δ¹⁸O_e) is influenced by the isotopic enrichment above source water of ambient vapor (Δ¹⁸O_v), temperature-dependent gradient in the molar ratio of ambient to intercellular water vapor (e_a/e_i), temperature-dependent equilibrium fractionation (ε⁺), and kinetic fractionation (ε_k), which is a function of stomatal and boundary layer conductances (Eq. 3 and more in-depth explanation in Appendix).

The Craig–Gordon model describes Δ¹⁸O_e, but tends to overestimate Δ¹⁸O_LW. To account for this overestimation, the Craig–Gordon model was modified to account for unenriched leaf xylem water and the mixing behavior between xylem and lamina water pools (Péclet and two-pool models; Farquhar et al., 1998; Roden and Ehleringer, 1999). The Péclet model suggests that δ¹⁸O_LW reflects the relative isotopic contributions of advection of source water in the xylem and back diffusion of water from the sites of evaporation. The proportional mixing of source water and water from the evaporation sites is primarily determined by the transpiration rate (E) and the mean effective path length (L) through which water passes from the xylem to the stomates (Farquhar and Lloyd, 1993; Barbour and Farquhar, 2004). Therefore, if L remains constant, Δ¹⁸O_LW decreases as E increases by decreasing the influence of Δ¹⁸O_e on Δ¹⁸O_LW. Because Δ¹⁸O_BL partly reflects Δ¹⁸O_LW, in the Péclet model where Δ¹⁸O_LW is related to E, Δ¹⁸O_BL can potentially provide an integrated proxy of E over the life of the leaf (Barbour et al., 2000b; Barbour, 2007), and when coupled with biomass measurements can be a proxy for TE (Barbour et al., 2000a). However, support for the Péclet effect has not been found in many instances (Song et al., 2013; Roden et al., 2015; Song et al., 2015; Cernusak et al., 2016; Holloway‐Phillips et al., 2016). Therefore, it is uncertain if Δ¹⁸O_BL can be used as a proxy for E in C₄ grasses.

In this study we tested the relationship between δ¹³C_BL and Δ¹⁸O_BL with TE and E, respectively, in the model C₄ grasses Setaria viridis (L.) P. Beauv. and S. italica (L.) P. Beauv. These species are part of the C₄ panicoid grass clade and are closely related to important food and biofuel crops, such as sugar cane, maize, miscanthus, and sorghum (Brutnell et al., 2010; Li and Brutnell, 2011). S. viridis is a unique model organism for this clade because it has a short lifespan, a sequenced genome and a single nucleotide polymorphism (SNP) map for quantitative trait locus analysis (Doust et al., 2009; Bennetzen et al., 2012). Additionally, S. viridis and S. italica are drought-resistant species, growing in areas that cannot support sorghum, sugar cane, or maize production (Li and Wu, 1996). In the current study, both species were grown under well-watered and water-limited conditions to determine the effect that water limitations had on δ¹³C_BL and Δ¹⁸O_BL to evaluate their use as proxies for g_s, E, and TE.

Material and methods

Growth and greenhouse conditions

Experiment with Setaria viridis

Setaria viridis (L.) P. Beauv. (accession A-10) was grown in the greenhouses at Washington State University, Pullman, WA, USA between June and July of 2013. Day and night temperatures were 26–30 and 21–25 °C, respectively. Daytime and night-time relative humidity were 30–57% and 59–89%, respectively. Plants received 500–1500 µmol m⁻² s⁻¹ photosynthetic photon flux density (PPFD) over 14 h. Pot distribution in the greenhouse was randomized every day to minimize the influence of lighting heterogeneity. Fifteen S. viridis seedlings per treatment with one seedling per pot were transplanted into 11.3-liter pots using a commercial potting soil mix (Sunshine LC 1). Plants received 20-20-20 NPK with micronutrients (JR Peters Inc., Allentown, PA 18106) twice weekly at 2.8 g l⁻¹ water.

After the initial watering at transplanting, pots in the well-watered and water-limited treatments were maintained at a gravimetric water content (GWC) of ~4 and 1 g water g soil⁻¹, respectively. In GWC calculations, fresh biomass would be considered soil water weight, causing GWC to be overestimated, so throughout the experiment daily transpiration was used as a proxy for changes in fresh weight over time to accurately calculate the soil water volume and estimates of GWC. All pots and lateral holes were covered with plastic sheeting to minimize soil evaporation. Daily transpiration was measured as the difference in pot weight between dawn and dusk minus soil evaporation (difference in a covered pot weight without a plant). All of the plants could not feasibly be measured and harvested in one day, so the plants were randomly divided into three collection groups of five plants from each treatment. Gas exchange measurements and harvesting of plant material of collections 1, 2, and 3 took place on 39, 43, and 44 d after germination. At all collection times, panicles had begun to emerge.

Experiment with Setaria italica

Setaria italica (L.) P. Beauv. (accession B-100; (Li and Brutnell, 2011) was grown in a controlled-environment growth cabinet (Enconair Ecological GC-16). Growth conditions were set at 16 h photoperiod including a 2 h ramp at dawn and dusk and maximum PPFD of 1000 µmol quanta m⁻² s⁻¹. Day and night temperatures were maintained at 28 ± 1 and 18 ± 1 °C, respectively and a mean relative humidity of 59 ± 6%. Pot location was randomized every day. A total of 33 S. italica seedlings (11 plants per treatment with one seedling per pot) were transplanted into 7.5-liter pots at 15 d after germination. The potting soil was the same as was used with S. viridis. Plants received 15-5-15 CalMag (JR Peters Inc., Allentown, PA, USA) twice weekly at a rate of 2.5 g l⁻¹ water, Sprint 330 iron chelate (0.25 g l⁻¹) weekly, and Scott-Peters Soluble Trace Element Mix, 10.0 mg l⁻¹ biweekly (The Scotts Co., OH, USA).

After initial watering after transplanting, the GWC of the well-watered, moderately and severely water-limited treatments was maintained at 4.0, 0.9, and 0.5 g water g soil⁻¹, respectively. Pots were covered with plastic similar to S. viridis. Six and five plants from each treatment were randomly selected to be harvested in the first and second collections, respectively. Leaf gas exchange measurements were made at six time points (31, 34, 40, 43, 53, and 54 d after germination). Plant material for stable isotope analysis was only collected at the final harvest, immediately following gas exchange measurements. At all collection times, panicles had begun to emerge in all plants.

Gas exchange measurements

Measurements were made on the uppermost fully expanded leaf between 11:00 h and 15:00 h. Leaves were placed in a 2 × 3 chamber of an LI-6400XT open gas exchange system (Li-COR Biosciences, Inc., Lincoln, NE, USA). The leaf was allowed to acclimate at 1500 µmol m⁻² s⁻¹ PPFD, leaf temperature of 29 °C, flow rate of 300 µmol s⁻¹, 21% O₂, 35 Pa CO₂ for S. viridis. The same conditions were used for S. italica except that light intensity was 900 µmol m⁻² s⁻¹ PPFD to reflect the light intensity of their growing conditions. Relative humidity (RH) in the LI-COR chamber was within 10% of the RH under growth conditions, and the implications of this difference in RH is explained in the next section.

Sample collection for stable isotope analysis

To obtain sufficient leaf water to measure δ¹⁸O of leaf water (δ¹⁸O_LW) from S. viridis, an aggregate of five to eight leaves (including the leaf used to measure gas exchange) was collected from each plant at the time of harvest. However, in S. italica the leaf used for gas exchange measurements was sufficient to analyse δ¹⁸O_LW. The same leaf samples that were analysed for δ¹⁸O_LW were also analysed for leaf carbon isotopic composition (δ¹³C_leaf) and oxygen isotopic composition of bulk leaf tissue (δ¹⁸O_BL). All leaves collected for stable isotope analysis were the youngest, fully expanded leaves, which developed 15–20 d after soil water content reached the treatment set point. For both species, leaves were removed from the plant and photographed to measure leaf area using ImageJ software (Schneider et al., 2012). Photographing each leaf only took approximately 20 s, and then the leaf was stored in sealed glass tubes awaiting water extraction.

In S. italica only, using the same leaf for gas exchange measurements and stable isotope analysis could have an effect on δ¹⁸O_LW because RH and the oxygen isotope ratio in the growth cabinet air (δ¹⁸O_v) could differ between growth and gas exchange chambers (Loucos et al., 2015). However, gas exchange measurements were conducted within the growth chamber, and the mean±SE proportion of the total leaf area continuously exposed to growth cabinet conditions during gas exchange measurements was 94.0 ± 0.6%, 91.0 ± 0.8%, and 89.5 ± 0.8% for well-watered, moderately water-limited, and severely water-limited treatments, respectively. Therefore, the δ¹⁸O of the growth cabinet air was the most appropriate measure of δ¹⁸O_v. The difference in RH between the growth cabinet and the gas exchange chamber could contribute a 1.9–5.5‰ shift in δ¹⁸O_e for the leaf section in the gas exchange chamber, assuming, however unlikely, that 10–20 min was adequate time for the leaf section to acclimate. This would represent a shift in δ¹⁸O_e for the entire leaf of 0.24–0.43‰, and Δ¹⁸O_LW would shift by a fraction of this. Nonetheless, taking into account this potential shift did not significantly influence observed Δ¹⁸O_LW values across treatments. Differences between greenhouse and LI-COR chamber conditions for S. viridis were irrelevant because leaf water was extracted from an aggregate of five to eight leaves.

The δ¹⁸O of water vapor in the greenhouse and growth chambers was measured every 30 min during gas exchange measurements by collecting air in 5-liter Supel inert foil gas sampling bags (Supelco, Bellefonte, PA, USA). Bags were flushed several times with air before filling, and δ¹⁸O of water vapor was immediately measured on the cavity ringdown spectrometer (L1102-i water analyser, Picarro Inc, Santa Clara, CA, USA).

Following the gas exchange measurements, root crowns were cleaned of soil and stored at −20 °C in air-tight tubes. Root crowns are considered the bottom 1 cm of the culm or tiller, but no actual roots were collected. Additionally, two soil samples were collected at the top and bottom of each pot, and stored using the same method as with the root crowns. Leaves, soils, and root crowns were distilled using a cryogenic vacuum distillation method (Vendramini and Sternberg, 2007). Additionally, daily samples of irrigation water (IW) were collected to measure δ¹⁸O_IW.

Biomass measurements

After collection of plant tissue for stable isotope analysis, the entire aboveground biomass was collected and weighed for fresh weight. Samples were dried at 65 °C for 3 d before weighing dry biomass.

Stable isotope analysis

The stable isotope composition of carbon and oxygen (δ¹⁸O and δ¹³C, respectively) were reported in δ notation in parts per thousand (‰),

$$\delta = \left(\frac{R_{\text{sample}}}{R_{\text{standard}}}-1\right)$$ (4)

where R_sample and R_standard are the molar ratios of heavy to light isotope (¹⁸O/¹⁶O and ¹³C/¹²C) of the sample and international standard, respectively. The international standard used for oxygen was Vienna Standard Mean Ocean Water (VSMOW) and for carbon was Vienna Pee Dee Belemnite (VPDB).

Stable isotope analysis by isotope ratio mass spectrometer

Leaf tissue was analysed for oxygen isotopic analysis by converting to CO with a pyrolysis elemental analyser (TC/EA, Thermo Finnigan, Bremen, Germany) and analysed with a continuous flow isotope ratio mass spectrometer (Delta PlusXP, Thermo Finnigan; Brenna et al., 1997; Gehre and Strauch, 2003). For carbon isotopic analysis, leaf tissue was converted to CO₂ with an elemental analyser (ECS 4010, Costech Analytical, Valencia, CA, USA) and analysed with a continuous flow isotope ratio mass spectrometer (Delta PlusXP; Brenna et al., 1997; Qi et al., 2003). Isotopic standards, Standard Light Antarctica Precipitation (SLAP; Gonfiantini, 1978) and Puerto Rico water (Qi et al., 2014), were analysed alongside samples to calculate δ¹⁸O based on the VSMOW scale. Lab standards, calibrated to international standards, were used to calculate δ¹³C relative to VPDB. Standard error for δ¹³C values for the S. viridis and S. italica experiments was 0.11 and 0.05‰, respectively. The standard error for δ¹⁸O was 0.2‰ for both experiments.

Leaf and root crown water were analysed for oxygen isotope composition by equilibrating 0.5 ml of water at room temperature with 0.3% CO₂:He mixture for 48 h on a ThermoFinnigan GasBench II (Thermo Electron Corp., Bremen, Germany). CO₂ was analysed with a continuous flow isotope ratio mass spectrometer (Delta PlusXP; Brenna et al., 1997; Qi et al., 2003).

Stable isotope analysis by isotope ratio infrared spectroscopy

Soil and irrigation water were measured by isotope ratio infrared spectroscopy (model L1102-i, Picarro, Sunnyvale, CA, USA) connected to a vaporization chamber (V1102-i). The mean δ¹⁸O was calculated of the last three of six consecutive analyses on each sample. Three laboratory standards, calibrated to the VSMOW scale, were interspersed among the samples and were used to correct the sample δ¹⁸O values to the VSMOW scale. Water vapor was analysed for at least 15 min, but the mean of the last 5 min was used for δ¹⁸O and corrected to the VSMOW scale.

Calculations of transpiration efficiency

TE_{instantaneous} (A_net/E) and TE_intrinsic (A_net/g_s) are derived from gas exchange measurements, but they are independent of both TE_plant and TE_w, which are independent of each other. TE_plant was derived from whole-plant measures of biomass and transpiration. TE_w was calculated using C_i/C_a derived from δ¹³C_BL, and the calculations are independent of the gas exchange measurements (described below).

Discrimination (Δ¹³C_BL) and C_i/C_a are related in Eq. 2 and were used to calculate the integrated C_i/C_a over the life of the leaf (Henderson et al., 1992, 1998). A constant leakiness (ϕ) of 0.21 was assumed for all plants. The δ¹³C of ambient CO₂ in the greenhouse and growth chamber that was used to calculate Δ¹³C_BL was −10.7 ± 0.8‰, which was collected various times over a period of several months that included the time that the experiment was conducted. Air samples were collected in 5-liter Tedlar gas sampling bags using the same collection procedure used to collect air vapor. The gas was analysed by introducing air directly into either the isotope ratio mass spectrometer or the tunable diode laser absorption spectroscope (model TGA 200A, Campbell Scientific, Inc., Logan, UT, USA; Ubierna et al., 2013). Transpiration efficiency (TE_w), defined as the ratio of dry matter produced per unit of water transpired, was calculated from the δ¹³C_BL as described in Henderson et al. (1998) as:

$${\text{TE}}_{\mathrm{w}}=\left(1-\frac{C_{\mathrm{i}}}{C_{\mathrm{a}}}\right)\times \frac{C_{\mathrm{a}}}{1.6v}\times \frac{\left(1-{\varphi }_{\mathrm{r}}\right)}{\left(1-{\varphi }_{\mathrm{w}}\right)}$$ (5)

where v is the leaf to air vapor pressure difference and ϕ_r was calculated as the measured ratio of respiration and photosynthetic rate (0.08 for all plants). This parameter was calculated from gas exchange measurements made during the experiment. The parameter ϕ_w was calculated as the measured ratio of whole-plant night to day transpiration (0.30 and 0.23 for water-limited and well-watered plants, respectively) measured in this experiment. Both night-time and daytime whole-plant transpiration were measured on S. viridis. Both ϕ_r and ϕ_w were measured for S. viridis and assumed to be the same for S. italica. C_i/C_a was calculated from δ¹³C_BL using Eq. 2.

Model calculations to determine validity of the Péclet model

To test the applicability of the Péclet model and its relationship to E, we used the method described by Holloway-Phillips et al. (2016) and Song et al. (2015) of determining the proportional deviation (f) of Δ¹⁸O_LW from Δ¹⁸O_e plotted against E where f is calculated as:

$$f=1- \frac{{\Delta }^{18}{\mathrm{O}}_{\text{LW}}}{{\Delta }^{18}{\mathrm{O}}_{\mathrm{e}}}$$ (6)

Effective path length (L) was calculated using the equations described in Song et al. (2013).

Statistical analysis

In both experiments statistical analyses were conducted in R v. 3.3.0 (R Core Team, 2013), using car (v. 2.0-26) and agricolae (version 1.2-2) packages for statistical tests. Model II regressions (standard major axis regression) were calculated, using the lmodel2 (v. 1.7-2) package, because neither variable was controlled, both varied naturally with their own associated error, and the physical units of both variables were not the same. Homogeneity was tested based on plotting predicted fit vs residuals. Using the extRemes package (v. 2.0-8), normality was tested by plotting residuals on quantiles–quantiles plots. In all cases, where normality was questionable, transforming the data did not change the statistical results, so the data were not transformed. One- and two-way analysis of variance (ANOVA) was used to determine differences across treatments in the experiment with S. italica and between treatments and collection periods for S. viridis. Two-sample Student’s t-tests were performed to determine the difference between δ¹⁸O_SW and δ¹⁸O_RC values. One-sample t-tests were performed to determine if the difference of both δ¹⁸O_SW and δ¹⁸O_RC with δ¹⁸O_IW was significantly different from 0. Repeated measures ANOVAs were conducted on the following parameters: total plant water use and GWC for S. viridis and on daily water use, GWC, A_net, E, and g_s for S. italica.

Results

Plant growth and treatment effect

Maintaining water-limited plants at a GWC 71% lower than the well-watered plants significantly reduced total water use by 59% (Table 2 and Fig. 1). Additionally, at all collection times fresh and dry aboveground biomass were lower in the water-limited treatment relative to the well-watered treatment (Table 3). The results for S. italica were similar to S. viridis in that the GWC was reduced by 72% and 82%, and total water use by 70% and 80% in the moderately and severely water-limited treatments, respectively (Table 2; Fig. 1). In S. italica, fresh and dry aboveground biomass were reduced in the moderately water-limited, 71% and 68%, respectively, and in the severely water-limited treatment, 79% and 74%, respectively (Table 4; Fig. 1). Additionally, leaf length in the water-limited treatments was shorter compared with the well-watered leaves by 18% in S. viridis and in S. italica by 32% and 57% in the moderately and severely water-limited treatments, respectively (Tables 3 and 4). The number of tillers per plant in the well-watered treatment was 44% greater than the water-limited treatment in S. viridis (Table 3); however, the number of tillers per S. italica plant did not differ across treatments (Table 4).

Table 2.

Statistical summary for repeated-measures ANOVA of variables measured throughout the experiments

Levels of significance were calculated from two-factor repeated measures ANOVA described in ‘Materials and methods’, *P<0.05, **P<0.01, and ***P<0.001, ns not significant (P>0.05).

Level	Variables	Species	Between effects		Within effects
			Treatment		Date		Date × Treatment
			F ndf,ddf	P	F ndf,ddf	P	F ndf,ddf	P
Plant	Water use (l H2O plant−1)	S. viridis	180.81,28	***	325.816,448	***	16.7316,448	***
		S. italica	187.02,29	***	119.318,522	***	69.0136,522	***
	GWC (g water g dry soil−1)	S. viridis	205.91,28	***	97.616,448	***	30.51,448	ns
		S. italica	312.32,29	***	45.12,29	***	5.23536,522	***
Leaf	C i/Ca	S. italica	33.672,16	***	0.4434,64	ns	1.4648,64	ns
	E (mmol−1 H2O m−2 s−1)	S. italica	73.52,18	***	4.075,98	ns	1.00810,98	ns
	g s (mmol−1 H2O m−2 s−1)	S. italica	60.82,18	***	1.8695,98	ns	1.64410,98	ns
	A net (µmol−1 CO2 m−2 s−1)	S. italica	9.8342,18	**	2.155,98	ns	0.98810,98	ns

Fig. 1.

Gravimetric water content (GWC; A, B) and daily water use (C, D) over the course of the experiment. Circles, triangles, and squares represent well-watered, moderately water-limited and severely water-limited plants, respectively. Solid and dashed lines represent well-watered and water-limited treatments, respectively. Error bars represent standard error.

Table 3.

Parameters of plant water relations and isotopic composition of S. viridis

Each variable was analysed with a separate two-factor ANOVA. The Δ¹⁸O_LW and Δ¹⁸O_BL values were calculated using each of the possible water sources (source water in parentheses). P-values for the ANOVAs are located on the right side (*P<0.05, **P<0.01, and ***P<0.001, ns not significant). Columns 1, 2 and ‘I’ are the factors ‘differential irrigation’ (1), ‘collection time’ (2), and the differential irrigation × collection time interaction (I). Means±SE within a row, followed by the same superscripted letters, are not significantly different. Other factors are given in Supplementary Tables S1 and S2 at JXB online.

Parameters	Collection 1		Collection 2		Collection 3		ANOVA
	Well-watered	Water-limited	Well-watered	Water-limited	Well-watered	Water-limited	1	2	I
Total water use (l)	5.33 ± 0.38	2.13 ± 0.06	6.97 ± 0.29	2.86 ± 0.06	7.94 ± 0.56	3.24 ± 0.2	***	***	ns
Fresh aboveground biomass (g)	192.3 ± 10.7	74.7 ± 2.1	229.4 ± 5.1	87.8 ± 1.3	234.5 ± 13.8	77.0 ± 1.6	***	**	ns
Dry aboveground biomass (g)	31.8 ± 2.1	16.1 ± 0.4	41.6 ± 2.2	21.0 ± 0.3	45.4 ± 3.3	22.6 ± 0.6	***	***	ns
Number of tillers	125 ± 6	74 ± 3	142 ± 4	78 ± 4	145 ± 17	77 ± 1	***	ns	ns
Δ18OLW (root crown)	21.0 ± 0.3c	21.8 ± 0.7bc	19.5 ± 0.4c	21.7 ± 0.6bc	23.8 ± 0.5b	29.4 ± 0.7a	***	***	**
Δ18OLW (soil)	20.4 ± 0.3	22.4 ± 0.6	19.4 ± 0.4	21.7 ± 0.6	24.2 ± 0.6	28.8 ± 0.5	***	***	ns
Δ18OLW (irrigation)	21.4 ± 0.3cd	24.0 ± 0.8b	20.1 ± 0.5d	23.3 ± 0.5bc	25.0 ± 0.5b	31.7 ± 0.6a	***	***	**
Δ18OBL (root crown)	43.5 ± 1.1	45.2 ± 0.8	43.6 ± 0.5	44.2 ± 0.6	44.7 ± 0.8	45.4 ± 0.7	ns	ns	ns
Δ18OBL (soil)	43.2 ± 0.9	45.8 ± 0.8	43.6 ± 0.6	44.2 ± 0.6	45.1 ± 0.8	44.9 ± 0.5	ns	ns	ns
Δ18OBL (irrigation)	44.2 ± 0.9	47.3 ± 0.7	44.2 ± 0.7	45.7 ± 0.6	45.7 ± 0.8	47.7 ± 0.6	**	ns	ns
δ13CBL	−13.4 ± 0.1	−14.4 ± 0.1	−13.4 ± 0.1	−14.5 ± 0.1	−13.5 ± 0.04	−14.6 ± 0.1	***	ns	ns
δ18ORC	−16.5 ± 0.2	−14.9 ± 0.2	−16.5 ± 0.2	−15.5 ± 0.2	−15.9 ± 0.1	−14.7 ± 0.2	***	**	ns
δ18OSW	−16.0 ± 0.1b	−15.5 ± 0.2b	−16.4 ± 0.1b	−16.3 ± 0.3b	−16.3 ± 0.2b	−14.1 ± 0.2a	***	*	**
C i/Ca	0.41 ± 0.03	0.26 ± 0.06	0.37 ± 0.02	0.18 ± 0.02	0.36 ± 0.02	0.13 ± 0.02	***	*	ns
g s (mol H2O m−2 s−1)	0.26 ± 0.01a	0.22 ± 0.02a	0.26 ± 0.01a	0.15 ± 0.02b	0.26 ± 0.02a	0.06 ± 0.01c	***	***	***
E (mmol H2O m−2 s−1)	5.03 ± 0.23ab	4.57 ± 0.19b	4.88 ± 0.14ab	3.27 ± 0.36c	5.6 ± 0.16a	1.87 ± 0.19d	***	***	***
A net (µmol CO2 m−2s−1)	28.3 ± 0.8ab	32.3 ± 1.2a	31.0 ± 0.6ab	25.4 ± 2.2b	31.2 ± 0.6a	12.1 ± 1.5c	***	***	***
Water/leaf area (l H2O m dry leaf−2)	0.14 ± 0.01	0.13 ± 0.01	0.12 ± 0.003	0.11 ± 0.002	0.11 ± 0.001	0.10 ± 0.01	ns	***	ns
TEinstantaneous (Anet/E)	5.68 ± 0.31	7.13 ± 0.47	6.36 ± 0.12	7.86 ± 0.24	5.59 ± 0.11	6.44 ± 0.31	***	**	ns
TEintrinsic (Anet/gs)	109.0 ± 5.6	151.6 ± 12.8	121.1 ± 3.8	170.1 ± 5.3	123.5 ± 5.4	193.4 ± 5.1	***	**	ns
TEplant (g biomass l H2O−1)	5.98 ± 0.09	7.57 ± 0.10	5.96 ± 0.09	7.35 ± 0.09	5.69 ± 0.08	7.06 ± 0.16	***	ns	ns
TEw (derived from δ13CBL)	5.47 ± 0.22	7.25 ± 0.13	5.49 ± 0.10	7.43 ± 0.22	5.64 ± 0.07	7.61 ± 0.09	***	ns	ns
Effective leaf length (mm)	13.58 ± 1.9a	10.84 ± 3.9ab	16.88 ± 2.1a	11.92 ± 2.2a	9.16 ± 4.1ab	1.76 ± 1b	*	**	ns
Leaf length (cm)	19.2 ± 0.2	17.5 ± 0.1	19.9 ± 0.8	16.7 ± 0.4	19.6 ± 0.7	15.7 ± 0.2	***	ns	ns

Table 4.

Plant water relations, growth, and stable isotopes of S. italica grown under well-watered, moderately and severely water-limited treatments and harvested during two collection periods

One-way ANOVAs were conducted on each variable as described in ‘Materials and methods’. The source water used to calculate Δ¹⁸O_LW and Δ¹⁸O_BL was root crown water. P-values are reported in the right column (***P<0.001, **P<0.01, *P<0.05, ns P>0.05). Means±SE were calculated for each variable, and within the same row, means followed by the same letter were not significantly different. Other factors also were analysed and are given in Supplementary Tables S3 and S4.

Variables	Well-watered	Water limitation		ANOVA
		Moderate	Severe	P
Total water use (l)	12.65 ± 0.88a	3.77 ± 0.07b	2.57 ± 0.06c	***
Fresh aboveground biomass (g)	486.1 ± 36.4a	140.7 ± 6.3b	100.4 ± 3.3c	***
Dry aboveground biomass (g)	104.7 ± 8.0a	33.4 ± 1.3b	24.9 ± 1.1c	***
Number of tillers	9.2 ± 1.6a	6.6 ± 1.0a	7.9 ± 0.9a	ns
Δ18OLW	21.5 ± 0.8a	20.8 ± 1.0a	20.6 ± 1.3a	ns
Δ18OBL	42.1 ± 0.5a	40.8 ± 0.4ab	40.1 ± 0.5b	*
δ13CBL	−12.96 ± 0.06a	−13.88 ± 0.10b	−13.81 ± 0.11b	***
δ18OSW	−16.2 ± 0.1c	−14.2 ± 0.2b	−13.0 ± 0.1a	***
δ18ORC	−15.9 ± 0.1c	−13.9 ± 0.2b	−12.7 ± 0.3a	***
C i/Ca	0.44 ± 0.01a	0.30 ± 0.01b	0.27 ± 0.01b	***
g s (mmol−1 H2O m−2 s−1)	0.173 ± 0.019a	0.119 ± 0.019b	0.110 ± 0.009b	***
E (mmol−1 H2O m−2 s−1)	2.13 ± 0.16a	1.59 ± 0.19b	1.48 ± 0.12b	***
A net (µmol−1 CO2 m−2 s−1)	20.3 ± 1.4a	18.1 ± 2.1b	17.4 ± 0.8b	***
Water per leaf area (l H2O m−2 dry leaf)	0.19 ± 0.008a	0.17 ± 0.013ab	0.15 ± 0.009b	*
TEinstantaneous (Anet/E)	9.6 ± 0.2b	11.4 ± 0.3a	11.8 ± 0.2a	***
TEintrinsic (Anet/gs)	118.4 ± 3.39b	153.2 ± 3.25a	158.1 ± 3.08a	***
TEplant (g biomass l H2O transpired−1)	8.25 ± 0.22b	8.89 ± 0.34ab	9.69 ± 0.36a	*
TEw (mmol C mol H2O−1; derived from δ13CBL)	4.94 ± 0.09b	6.49 ± 0.19a	6.37 ± 0.17a	***
Leaf length (cm)	43.9 ± 1.9a	33.2 ± 1.4b	28.0 ± 1.0c	***

For both species, stomatal conductance (g_s), rates of transpiration (E), and the net rate of CO₂ assimilation (A_net) were generally higher in the well-watered treatment compared with the water-limited treatment. Gas exchange measurements of S. viridis were made during the three biomass collections, and E was 33% and 67% greater in the well-watered treatment in collections 2 and 3, respectively, but did not differ between treatments in collection 1 (Table 3). Additionally, g_s was 41% and 76% greater in the well-watered treatment during collection 2 and 3, respectively, but did not differ between treatments in collection 1. However, A_net in S. viridis was different between treatments only in collection 3 when A_net of water-limited plants was 61% lower than that of the well-watered treatment (Table 3). In S. italica, E, g_s, and A_net were not different between the severely and moderately water-limited treatments, but both treatments were on average 27%, 32%, and 16% lower, respectively, than the well-watered treatment (Table 4).

Leaf carbon isotopic composition

The response in leaf carbon isotopic signature (δ¹³C) to water limitations was similar for both species. For example, δ¹³C values in S. viridis were consistently lower in the water-limited treatment for all collections by 1.1‰ (Table 3) and in S. italica the δ¹³C values were 0.9‰ lower in the two water-limited treatments compared with the well-watered treatments (Table 4). The δ¹³C values of both species were positively correlated with A_net, E, and g_s, leaf water content, Δ¹⁸O_LW, Δ¹⁸O_BL, and all measurements of TE and plant growth and had stronger correlations with these parameters than either Δ¹⁸O_LW or Δ¹⁸O_BL (Table 5 and Figs 2 and 3).

Table 5.

Correlations between measured variables of both well-watered and water-limited plants and leaf water enrichment (Δ ¹⁸ O _LW ), bulk leaf enrichment (Δ ¹⁸ O _BL ), and δ ¹³ C _BL for S. viridis and δ¹³C_BLfor S. italica

The source water used to calculate Δ¹⁸O_LW and Δ¹⁸O_BL is listed in the header of each column. Significant correlations (r) are in bold, and the level of significance is given. Levels of significance are *P<0.05, **P<0.01, ***P<0.001. Other factors also were analysed and are given in Supplementary Tables S5 and S6. Correlations between Δ¹⁸O_LW and Δ¹⁸O_BL were only calculated where the same source water was used to calculate both

Parameter	S. viridis							S. italica
	Δ18OLW			Δ18OBL			δ13CBL	δ13CBL
	Irrigation water	Soil	Root crown	Irrigation water	Soil	Root crown
g s	−0.75***	−0.57**	—0.71***	−0.50**	—0.26	−0.29	0.65***	0.77***
E	−0.66***	−0.46*	—0.61***	−0.41*	—0.22	−0.26	0.59***	0.77***
A net	−0.77***	—0.73***	—0.76***	−0.33	—0.06	−0.17	0.56**	0.56**
Fresh aboveground biomass	−0.47*	−0.44*	−0.36	−0.45*	—0.22	−0.19	0.87***	0.77***
Dry aboveground biomass	−0.26	−0.21	−0.15	−0.32	—0.13	−0.09	0.74***	0.76***
Total water transpired	−0.28	−0.23	−0.17	−0.33	—0.14	−0.10	0.85***	0.77***
Leaf water per area (l m−2)	−0.57**	−0.66**	—0.59**	−0.33	—0.32	−0.33	0.33	−0.02
TEplant	0.30	0.25	0.17	0.42*	0.29	0.18	—0.87***	— 0.39*
TEinstantaneous	−0.05	−0.04	−0.16	0.26	0.36	0.16	—0.55**	—0.72***
TEintrinsic	0.64***	0.64***	0.56**	0.56**	0.35	0.35	—0.81***	—0.81***
δ13CBL	−0.60***	−0.54**	—0.50**	−0.59***	—0.36	−0.35
Δ18OBL	0.63***	0.31	0.45*

Fig. 2.

Linear relationship between δ¹³C_BL and transpiration rate (A, D), stomatal conductance (B, E), and photosynthetic rate (C, F) as measured at time of plant harvest. In the top panel, circles represent the water-limited treatment and squares represent well-watered plants, and open, gray-filled and black-filled symbols represent collection 1, 2, and 3, respectively. In the bottom panel, squares represent the well-watered treatment, circles represent the moderately water-limited treatment, and triangles represent the severely water-limited treatment. Lines represent Model II regression.

Fig. 3.

Intrinsic TE (A, D; A_net/g_s), instantaneous TE (B, E; A_net/E) and long term TE (C, F; g aboveground biomass l H₂O transpired⁻¹) regressed on bulk leaf carbon isotopic composition (δ¹³C_BL). Lines represent Model II regression. (A–C) are S. viridis and (D–F) are S. italica.

Using the simplified equation (Eq. 2) of Δ¹³C versus C_i/C_a, where Δ¹³C was calculated from δ¹³C_BL and C_i/C_a was measured, the mean leakiness of 0.21 ± 0.02 and 0.19 ± 0.01 was calculated for S. viridis in the well-watered and water-limited treatments, respectively. In S. italica leakiness was 0.17 ± 0.01 for well-watered and severely water-limited treatments and 0.20 ± 0.01 in the moderately water-limited. Overall, leakiness did not significantly differ between species or across treatments (0.17–0.21). The difference in leakiness would account for 0.22 ± 0.03‰ and 0.24 ± 0.01‰ of the observed difference in Δ¹³C_BL between treatments over the observed range of C_i/C_a in S. italica and S. viridis, respectively.

Transpiration efficiency (TE) and leaf δ ¹³ C-derived transpiration efficiency (TE _w)

Four different methods were used to calculate transpiration efficiency: (i) long term TE (TE_plant; grams of aboveground dry biomass per liter water transpired); (ii) instantaneous TE (TE_{instantaneous}; A_net/E); (iii) intrinsic TE (TE_intrinsic; A_net/g_s); and (iv) δ¹³C_BL-derived TE (TE_w; mmoles carbon fixed per mole H₂O transpired). For S. viridis the water-limited plants had higher TE regardless of how it was estimated, except in collection 3 where there was no difference in TE_{instantaneous} between treatments (Table 3). In S. italica, all four estimates of TE were higher in both water-limited treatments than the well-watered treatment, but the difference was not significant between the moderately and severely water-limited treatments. In both species, the TE_intrinsic had the largest differences between treatments (45% and 32% greater in S. viridis and S. italica, respectively; Tables 3 and 4). Additionally, TE_intrinsic had the strongest relationship with Δ¹⁸O_LW, Δ¹⁸O_BL, and δ¹³C (Table 5).

Leaf oxygen isotopic composition

The gas exchange measurements and leaf samples were collected between 11:00 and 15:00 h. During this time the vapor oxygen isotope ratios (δ¹⁸O_v) in both the greenhouse for S. viridis and in the growth chambers for S. italica were relatively stable. For S. viridis, δ¹⁸O_v values (mean±SE) were −17.4 ± 0.2, −21.9 ± 0.4, and −24.2 ± 0.3 for the three collections, respectively. For S. italica, δ¹⁸O_v values were −25.8 ± 0.1 and −20.1 ± 0.8 for the two collections, respectively.

Plants were top irrigated in covered pots with irrigation water (−17.0 ± 0.1‰ and −16.9 ± 0.1‰ for S. viridis and S. italica, respectively). The mean δ¹⁸O_SW values (average of δ¹⁸O of soil samples from the top and bottom of the pot) of the well-watered and water-limited treatments were 0.8 ± 0.1‰ and 1.9 ± 0.2‰ higher than irrigation water for S. viridis (P<0.0001), respectively. For S. italica, δ¹⁸O_SW values were 0.8 ± 0.2‰, 2.7 ± 0.2‰, and 3.9 ± 0.4‰ higher than irrigation water in the well-watered, moderately and severely water-limited treatments, respectively (P<0.0001). In S. viridis, the δ¹⁸O_RC values were 0.6 ± 0.1‰ and 2.0 ± 0.1‰ higher than δ¹⁸O_IR in the well-watered and water-limited plants (P=0.0002 and P<0.0001), respectively. In S. italica, δ¹⁸O values were 1.1 ± 0.1‰, 3.1 ± 0.2‰, and 4.3 ± 0.3‰ higher than the irrigation water in the well-watered, moderately and severely water-limited treatments (P<0.0001), respectively. The δ¹⁸O of root crown and soil water was not significantly different in either S. viridis or S. italica (P>0.05).

Leaf water enrichment (Δ¹⁸O_LW) showed a significant treatment effect in S. viridis independent of which water was considered source water (irrigation, soil, or root crown water), but the treatment effect on Δ¹⁸O_LW was greatest with irrigation water. Likewise the strength of the correlation between parameters of gas exchange, water use, and growth depended on which source water was used to calculate Δ¹⁸O_LW. However, independent of the source water, Δ¹⁸O_LW negatively correlated with A_net, E, and g_s and positively correlated with TE_intrinsic and δ¹³C_BL (Table 5 and Fig. 4). In S. italica, Δ¹⁸O_LW did not have a significant treatment effect or correlate with growth, gas exchange, or TE variables, regardless of source water used (Table 4).

Fig. 4.

Relationship between Δ¹⁸O_LW (A, B) and Δ¹⁸O_BL (C, D) and transpiration rate (A, C) and stomatal conductance (B, D) in S. viridis. Gas exchange measurements were made at time of plant harvest. Circles represent the water-limited treatment and squares represent well-watered plants. Open, gray-filled and black-filled symbols represent collection 1, 2, and 3, respectively. The points represent Δ¹⁸O_LW and Δ¹⁸O_BL when they were calculated using δ¹⁸O_RC as source water (δ¹⁸O_LW or δ¹⁸O_BL minus δ¹⁸O_S). The dashed regression line represents the regression when δ¹⁸O_I was used to calculate Δ¹⁸O_LW and Δ¹⁸O_BL. The solid regression line represents the regression when δ¹⁸O_SW or δ¹⁸O_RC were used to calculate Δ¹⁸O_LW and Δ¹⁸O_BL. The resulting regression line between Δ¹⁸O_LW or Δ¹⁸O_BL with transpiration rate (E) and stomatal conductance (g_s) did not differ when δ¹⁸O_SW or δ¹⁸O_RC was used to calculate Δ¹⁸O_LW and Δ¹⁸O_BL. Therefore, the shaded region represents the variation associated with which source water was used. Lines represent Model II regressions.

In S. viridis, a significant treatment effect in Δ¹⁸O_BL was only found and Δ¹⁸O_BL only correlated with g_s, E, fresh aboveground biomass, TE_plant, and TE_intrinsic, Δ¹⁸O_LW, and δ¹³C_BL when irrigation water was used as source water (Table 5 and Fig. 4). The only parameter that correlated significantly with Δ¹⁸O_BL when root crown water was considered the source water was Δ¹⁸O_LW. For S. italica, the Δ¹⁸O_BL in the well-watered treatment was 2.0‰ greater than the severely water-limited treatment, but Δ¹⁸O_BL of the moderately water-limited treatment was not different from either treatment (Table 4). This difference resulted in significant positive correlations with E and g_s (0.60 and 0.59, respectively) when negative correlations were expected.

The Péclet model was tested by comparing the proportional deviation of Δ¹⁸O_LW from Δ¹⁸O_e (f; Eq. 6) with E (see Supplementary Fig. S1 at JXB online). This relationship was significant for S. viridis (f=0.086E–0.221, R²=0.36, P=0.0005) but not for S. italica (P=0.93) suggesting that in S. viridis Δ¹⁸O_LW deviated more from Δ¹⁸O_e as E increased. For S. italica, the Δ¹⁸O_LW was larger than Δ¹⁸O_e (negative f values), causing Δ¹⁸O_LW to be more enriched than would be expected based on the evaporative environment. For S. viridis the estimated effective leaf length (L) was small but significantly different between treatments (13.2 ± 2.7 and 8.2 ± 2.4 mm in well-watered and water-limited treatments, respectively; Supplementary Tables S1 and S2).

Discussion

Leaf carbon isotopic composition

Water-limited C₄ plants consistently have lower δ¹³C_BL values than well-watered plants (Henderson et al., 1998; Ghannoum et al., 2002). Water limitations also typically reduce g_s, and in C₄ plants low g_s results in depleted δ¹³C_BL. This is primarily because Δ¹³C_BL decreases with C_i/C_a when ϕ is generally below 0.37, and low g_s tends to decrease C_i/C_a (Cernusak et al., 2013; von Caemmerer et al., 2014). Alternatively, a decreased photosynthetic capacity in the water-limited plants could increase C_i/C_a, which would decrease Δ¹³C_BL and lead to an increase in δ¹³C_BL values. Therefore, the decrease in δ¹³C_BL observed in the water-limited plants is mostly due to changes in g_s and its influence on C_i/C_a. However, it is possible that water limitations increase ϕ, and lower δ¹³C_BL values.

Models of C₄ isotope exchange suggest that a decrease in the capacity of the CO₂ concentrating mechanism, as would occur with a stomatal limitation in CO₂ supply, could decrease ϕ (Ellsworth and Cousins, 2016). Additionally, leaf level measurements of CO₂ exchange have demonstrated that ϕ remains fairly constant under various environmental conditions (Henderson et al., 1998; Ubierna et al., 2011, 2013; Sun et al., 2012; Cernusak et al., 2013; Kromdijk et al., 2014; von Caemmerer et al., 2014). The ability of C₄ plants to maintain and minimize ϕ in response to long-term changes in growth conditions is not surprising as the C₄ and C₃ cycles are metabolically coordinated between the mesophyll and bundle sheath cells, causing them to function as integrated and not independent cycles (Furbank et al., 2013).

Using the simplified model of Δ¹³C (Eq. 2), the calculated ϕ from δ¹³C_BL and C_i/C_a produced similar values (0.17–0.21) to what has been published previously (see list of studies in Kromdijk et al., 2014). This difference in ϕ could account for a mean 22% and 24% of the measured difference in δ¹³C_BL across treatments in S. italica and S. viridis, respectively. In a separate study, under well-watered conditions Δ¹³C_{instantaneous} at similar C_i/C_a was approximately 4.4 ± 0.2‰ for both S. viridis and S. italica, giving evidence that ϕ is not inherently different between these species (Ellsworth et al., unpublished data). Additionally, the relationship between Δ¹³C_BL and C_i/C_a was similar for both species under all treatments, potentially falling on the same line where ϕ controls the slope (see Fig 1; Ellsworth and Cousins, 2016). Granted these calculations of ϕ provide only an approximation because instantaneous measures of Δ¹³C and δ¹³C_BL are known to differ because of post-photosynthetic fractionations, as discussed below (von Caemmerer et al., 2014; Ellsworth and Cousins, 2016).

Post-photosynthetic fractionation

Post-photosynthetic fractionations of carbon compounds could influence δ¹³C_BL; however, to influence δ¹³C_BL there must be a change in the leaf carbon mass balance by isotopic flux into or out of the leaf (von Caemmerer et al., 2014). Potentially, water-limited plants differ in which carbon pools are exported from the leaf. For example, if enriched amino acids are transported from the leaf at a greater rate than in well-watered plants (Melzer and O’Leary, 1987), then δ¹³C_BL could decrease. However, enriched amino acids are a small pool of carbon compared with sucrose and cellulose, so their export would have to be extremely large to account for the observed depletion in δ¹³C_BL. Additionally, the export or consumption by respiration of sucrose from starch degradation instead of from triose phosphate synthesis could also affect δ¹³C_BL because sucrose from starch degradation is more enriched in ¹³C (Hobbie and Werner, 2004; Tcherkez and Farquhar, 2005; von Caemmerer et al., 2014). However, the respiratory carbon flux out of the leaf is relatively small compared with photosynthetic flux into the leaf, so water-limited plants would need an unrealistic shift in the isotopic signature of enriched respiratory carbon source (~12‰) to account for the −1‰ shift observed in δ¹³C_BL. In water-limited C₃ bean plants, sucrose was the primary carbon pool for respiration, suggesting that a dramatic shift in Setaria is unlikely (Duranceau et al., 1999; Tcherkez et al., 2003). Therefore, as discussed above, the shift in δ¹³C_BL is most likely not due to post-photosynthetic fractionations but rather to the treatment effect on leaf gas exchange and TE.

Leaf carbon isotopic composition across drought experiments

In both species, δ¹³C_BL in water-limited plants consistently showed lower values by 0.9–1.1‰ than in the well-watered plants. Previous studies also have found a difference in δ¹³C_BL values between well-watered and water-limited plants of 0.2–0.6‰, which probably depended on the type, severity, or duration of the reduction in water availability (Saliendra et al., 1996; Henderson et al., 1998; Brück et al., 2000; Monneveux et al., 2007; Cabrera-Bosquet et al., 2009a). Nonetheless, the consistent depletion in ¹³C in response to water limitations persisted across C₄ species and experiments, lending further evidence that decreased g_s is driving the response in δ¹³C_BL. Additionally, Gresset et al. (2014) found that δ¹³C_BL in C₄ maize was under genetic control, giving support to the potential use of δ¹³C_BL as a genetic screen for TE. However, further research is needed to determine the degree to which δ¹³C_BL can be used to detect subtle differences in TE in C₄ plants, if δ¹³C_BL is under similar genetic control as TE, and if it can be used to screen for TE across genotypes.

Transpiration efficiency estimated from leaf carbon isotopic composition

Transpiration efficiency (TE_w) calculated from δ¹³C_BL correlated strongly with leaf-level gas exchange measurements of g_s and TE_intrinsic (A_net/g_s). Therefore, calculating TE_w based on C_i/C_a estimated from δ¹³C_BL accurately reflected differences in g_s between treatments. However, in S. viridis, TE_plant was more highly correlated with TE_intrinsic and TE_{instantaneous} than in S. italica. This may be because the short and bushy S. viridis has proportionally more leaf biomass than the upright S. italica, so leaf characteristics would have a greater influence on plant-level estimates of TE (e.g. TE_plant) in S. viridis than in S. italica. Nonetheless, for both species, δ¹³C_BL reflected differences in both whole-plant and leaf-level estimates of TE.

Leaf water enrichment

In Setaria viridis, Δ¹⁸O_LW formed a negative relationship with E as expected based on the Péclet model. In the Péclet model, the Péclet number is proportional to E and L, so a positive relationship between Δ¹⁸O_LW and E requires L to remain constant across individuals or treatments. L differed only slightly between treatments, and this did not remove the relationship between Δ¹⁸O_LW and f (1−Δ¹⁸O_LW/Δ¹⁸O_e) with E, showing evidence that the Péclet model best describes leaf water isotopic composition (Supplementary Fig. S1 and Fig. 4). The expected relationship existed for S. viridis, lending strength to the possible use of leaf oxygen isotopic composition as a proxy of E.

Contrary to S. viridis, Δ¹⁸O_LW and E did not form a significant relationship in S. italica, and Δ¹⁸O_LW values were more enriched than Δ¹⁸O_e. One potential reason for this disparity in results between the two species is that leaf temperature differed between treatments and across the range of leaf temperature. High transpiration rate can change Δ¹⁸O_LW and subsequently Δ¹⁸O_BL by decreasing leaf temperature through evaporative cooling. Both ε⁺ and e_i (and therefore e_a/e_i) are temperature-dependent (Barbour, 2007; Barbour et al., 2004). In the experiment with S. italica, the maximum difference in leaf temperature across the treatments was less than 2 °C (Ellsworth et al., 2016); Δ¹⁸O_e of leaves of water-limited plants would only increase by ~0.6‰, and Δ¹⁸O_LW, being partly composed of Δ¹⁸O_e, would change by a fraction of 0.6‰. If leaf temperature in S. viridis differed less than 2 °C, as previously observed with S. italica (Ellsworth et al., 2016), then the temperature difference between treatments would be insufficient to explain the difference in Δ¹⁸O_LW observed in S. viridis. As for S. italica, Δ¹⁸O_LW values showed the opposite trend as would be expected if the differences were based on leaf temperature. Therefore, the relationship between Δ¹⁸O_LW and E in S. viridis suggests a Péclet effect in S. viridis but not in S. italica.

A possible reason why E does not affect the mixing of source water with water from the sites of evaporation in S. italica (as described in the Péclet model and observed in S. viridis) may be because Δ¹⁸O_LW increases with leaf length in C₄ grasses (Helliker and Ehleringer, 2000). In S. italica, longer leaf length in well-watered plants than in water-limited plants apparently increased Δ¹⁸O_LW values sufficiently to mask the expected relationship between Δ¹⁸O_LW and E. This effect of leaf length was strong enough that Δ¹⁸O_BL was positively correlated with E and g_s. Enrichment up the leaf blade, described as the longitudinal Péclet effect, occurs because the xylem water being supplied to the sites of evaporation becomes progressively more enriched from the base to the tip of the leaf blade (Gan et al., 2003). Therefore, relative to source water entering the leaf base, the water at the sites of evaporation is enriched above what can be attributed to E. In contrast, the treatment difference in leaf length in S. viridis was minimal and insufficient to mask the transpiration-derived differences in Δ¹⁸O_LW.

Bulk leaf enrichment and E

According to theory, bulk leaf enrichment (Δ¹⁸O_BL) should reflect the isotopic signature of leaf water in which bulk tissue is synthesized (Farquhar and Lloyd, 1993; Sternberg et al., 1986). As expected, a weak but significant positive relationship between Δ¹⁸O_LW and Δ¹⁸O_BL was found in S. viridis, but this relationship did not translate into a significant difference in Δ¹⁸O_BL between treatments or significant correlations of Δ¹⁸O_BL with measures of gas exchange or growth. Three possibilities exist that may explain this pattern. First, the relationship between Δ¹⁸O_LW and E primarily exists because of the longitudinal or xylem Péclet effect and not the mesophyll/lamina or radial Péclet effect. Therefore, the oxygen isotope signature of lamina water that is passed onto organic molecules may have little E-related enrichment, so the relationship between Δ¹⁸O_LW and E would not be passed onto the bulk leaf tissue (Holloway‐Phillips et al., 2016). Second, Δ¹⁸O_LW was measured once, and a single measurement may not capture all variation in E and climatic conditions such as δ¹⁸O_v, relative humidity, and leaf temperature over the leaf lifespan, which can be difficult to control or account for precisely even in a controlled environment setting (Roden and Siegwolf, 2012; Roden and Farquhar, 2012). Third, leaf length would not affect Δ¹⁸O_BL as much as Δ¹⁸O_LW because Δ¹⁸O_BL would be driven principally by Δ¹⁸O_LW early in leaf construction when oxygen isotope exchange between water and sucrose takes place. Nevertheless, the magnitude of this non-significant difference between treatments was similar to what has been reported in other studies (Cabrera‐Bosquet et al., 2009c; Sánchez-Bragado et al., 2016). The oxygen isotopic composition of other plant organs has been proposed as proxies because they produced stronger correlations with grain yield than δ¹⁸O_BL (Sánchez-Bragado et al., 2016). However, it is necessary to understand how the leaf oxygen isotope enrichment that is related to E is passed onto these organs before their δ¹⁸O can be used an effective proxy for E.

Another problem that can obscure the relationship that Δ¹⁸O_LW and Δ¹⁸O_BL have with E is misidentifying source water (δ¹⁸O_S) used to calculate Δ¹⁸O_LW and Δ¹⁸O_BL. In this study, we measured the isotopic signature of three possible source waters: (i) irrigation water, (ii) mean soil water, and (iii) root crown water. Isotopically soil and root crown water were statistically indistinguishable, confirming previous studies that there is little fractionation of oxygen isotopes upon uptake by roots, so root crown water is a good representation of δ¹⁸O_S at the time of water collection (Ellsworth and Williams, 2007). Irrigation water does not reflect source water in water limitation studies because it undergoes evaporative enrichment, creating isotopically distinct soil water pools for each treatment. As a result, differences in Δ¹⁸O_LW and Δ¹⁸O_BL between treatments could simply be an artefact of incorrectly identifying δ¹⁸O_S, and not because of other physiological traits or environmental factors. Therefore, care must be taken to define the real source water of leaves.

Conclusion

Leaf δ¹³C had strong relationships with E, g_s, water use, aboveground biomass production, and all measures of TE. Although the variation in δ¹³C_BL was less than that in C₃ species, the overall consistency of the signal between well-watered and water-limited plants suggests that δ¹³C_BL may be an effective tool for distinguishing between well-watered and water-limited plants. However, more research is needed to determine if δ¹³C_BL can be used to detect differences in TE and g_s across more similar genotypes and serve as an effective proxy of TE in high throughput phenotyping across a range of field growth conditions. Alternatively, the use of Δ¹⁸O_BL as a proxy for transpiration rate in the C₄ grass Setaria is problematic for three reasons. First, source water can be isotopically variable across time and different between treatment conditions, making accurate calculations of Δ¹⁸O_LW and Δ¹⁸O_BL difficult. Furthermore, assuming that both well-watered and water-limited plants have the same δ¹⁸O_S may lead to erroneous implications for differences in E. Second, either a small mesophyll Péclet effect where organic molecules are synthesized or leaf water oxygen exchange with lamina water in sucrose synthesis was not sufficient to pass the leaf water isotopic signature on to that of bulk leaf tissue, so that the subtle differences in Δ¹⁸O_BL across a gradient of E were weak. Finally, changes in leaf size in response to water limitations appeared to mask the expected relationship of Δ¹⁸O_LW and Δ¹⁸O_BL with E.

Supplementary data

Supplementary data are available at JXB online.

Fig. S1. The relationship between proportional deviation of leaf water (Δ¹⁸O_LW) from evaporative site water (Δ¹⁸O_e) oxygen isotopic enrichment (f) and transpiration rate (E).

Table S1. F values, numerator degrees of freedom (ndf), denominator degrees of freedom (ddf) and P values from two-way ANOVA of the effects of a differential irrigation treatment and collection period on plant water use and growth, leaf water relations, and isotopic composition for S. viridis.

Table S2. Plant water relations, growth, and isotopic composition of S. viridis grown under well-watered and water-limited conditions and harvested during three collection periods.

Table S3. F values, numerator degrees of freedom (ndf), denominator degrees of freedom (ddf) and P values from one-way ANOVA of the effects of a differential irrigation treatment on plant water use and growth, leaf water relations, and isotopic composition for S. italica.

Table S4. Plant water relations, growth, and stable isotopes of S. italica grown under well-watered, moderately and severely water-limited treatments and harvested during two collection periods.

Table S5. Correlations between measured parameters of both well-watered and water-limited plants and leaf water enrichment (Δ¹⁸O_LW), bulk leaf enrichment (Δ¹⁸O_BL), and δ¹³C for S. viridis.

Table S6. Correlations of measured parameters with δ¹³C, Δ¹⁸O_LW, and Δ¹⁸O_BL for S. italica.

Appendix: Theory behind the relationship of δ¹⁸O_LW, δ¹⁸O_BL, and transpiration

Transpiration has been shown to affect the leaf water oxygen isotope composition (δ¹⁸O_LW) in three different ways. The first two deal with the role that transpiration plays in modifying the evaporative environment under which leaf water becomes enriched. At the sites of evaporation near the stomates, the water undergoing evaporation becomes enriched in ¹⁸O relative to the source water entering the leaf via the xylem. The third way considers the effect that transpiration has on the quantitative contribution of enriched water at the sites of evaporation (δ¹⁸O_e) to δ¹⁸O_LW.

Leaf water isotopic composition (δ¹⁸O_LW) can be considered a mixture of source water in the xylem (δ¹⁸O_S) and water from the sites of evaporation and mesophyll (δ¹⁸O_e):

$${\delta }^{18}{\mathrm{O}}_{\text{LW}}=f\times {\delta }^{18}{\mathrm{O}}_{\mathrm{S}}+\left(1-f\right){\delta }^{18}{\mathrm{O}}_{\mathrm{e}}$$ (A1)

where f is the fraction of unenriched water in leaf veins. By expressing the oxygen isotopic composition in leaf water as enrichment above source water (Δ¹⁸O_LW), any differences between in source water between leaves is accounted for and only changes in enrichment of ¹⁸O within the leaf are considered:

$${\Delta }^{18}{\mathrm{O}}_{\text{LW}}= \frac{{\delta }^{18}{\mathrm{O}}_{\text{LW}}-{\delta }^{18}{\mathrm{O}}_{\mathrm{S}}}{1-{\delta }^{18}{\mathrm{O}}_{\text{LW}}}$$ (A2)

Equation S1 can be expressed in terms of enrichments of ¹⁸O, where Δ¹⁸O_LW is equal to the proportional contribution of oxygen isotope enrichment in water from the sites of evaporation (Δ¹⁸O_e) to Δ¹⁸O_LW (Barbour, 2007; Cernusak et al., 2016):

$${\Delta }^{18}{\mathrm{O}}_{\text{LW}}= \left(1-f\right){\Delta }^{18}{\mathrm{O}}_e$$ (A3)

Considering Eq. A3, Δ¹⁸O_LW can vary across a gradient of E if either Δ¹⁸O_e or the proportional contribution of Δ¹⁸O_e (1−f) represented in the leaf changes with E.

The two ways in which Δ¹⁸O_e itself can be influenced by E are based on the effect that E has on leaf temperature and the resulting effect that changing leaf temperature has on the equilibrium fractionation (ε⁺) and intercellular air vapor mole fraction (e_i) when e_a remains constant:

$${\Delta }^{18}{\mathrm{O}}_{\mathrm{e}}={\epsilon }^{+}+{\epsilon }_{\mathrm{k}}+\left({\Delta }^{18}{\mathrm{O}}_{\mathrm{v}}-{\epsilon }_{\mathrm{k}}\right)e_{\mathrm{a}}/e_{\mathrm{i}}$$ (A4)

This, in turn, influences Δ¹⁸O_LW (Eq S3). Δ¹⁸O_e is assumed to follow the Craig–Gordon model and can be derived from ε⁺, the ambient to intercellular air vapor mole fraction (e_a/e_i), the kinetic fractionation (ε_k), and air vapor enrichment (Δ¹⁸O_v) (Craig & Gordon, 1965; Dongmann et al., 1974; Flanagan et al., 1991). Based on this equation, increasing g_s and subsequently E can either dry the intercellular area or increase ε⁺ by decreasing leaf temperature (T_L), both of which increase Δ¹⁸O_e. This because ε⁺ is inversely related to T_L:

$${\epsilon }^{+}=2.644-3.206\left(\frac{{10}^3}{T_{\mathrm{L}}}\right)+1.534\left(\frac{{10}^6}{T_{\mathrm{L}}^2}\right)$$ (A5)

E can decrease leaf temperature by increasing evaporative cooling and consequently increase ε⁺ (Gates, 1968). Ascribing changes in Δ¹⁸O_e to variation in E assumes that Δ¹⁸O_v and e_a remain constant.

Contrastingly, ε_k is inversely proportional to the stomatal (g_s) and boundary layer (g_bl) conductances, so increasing g_s decreases ε_k:

$${\epsilon }_{\mathrm{k}}= \frac{32 g_s^{-1}+21g_{\text{bl}}^{-1}}{g_{\mathrm{s}}^{-1}+ g_{\text{bl}}^{-1}}$$ (A6)

The third way in which E can change Δ¹⁸O_e and consequently Δ¹⁸O_LW is by modifying the contribution (1−f in Eq. A3) of Δ¹⁸O_e to Δ¹⁸O_LW. The Péclet model was developed to explain how Δ¹⁸O_LW values were often lower than Δ¹⁸O_e values, meaning that something is influencing Δ¹⁸O_LW other than the evaporative enrichment (Farquhar et al., 1998). It describes the extent to which the advection of relatively unenriched xylem water mixes with enriched water from the sites of evaporation. This depends on the Péclet number (ρ), which is proportional to E and the effective path length (L) that water takes to move from the veins to the sites of evaporation, and inversely proportional to the molar density of water (C) and the diffusivity of H₂¹⁸O in water (D) (Farquhar et al., 1998; Barbour & Farquhar, 2000; Barbour et al., 2000b; Gan et al., 2003; Barbour & Farquhar, 2004; Barbour, 2007):

$$\rho =\frac{LE}{CD}$$ (A7)

ρ is then related to Δ¹⁸O_LW according to:

$${\Delta }^{18}{\mathrm{O}}_{\text{LW}}= \frac{{\Delta }^{18}{\mathrm{O}}_{\mathrm{e}} \left(1-e^{-\rho }\right)}{\rho }$$ (A8)

If L remains constant, then ρ increases with E, meaning that back diffusion of water from sites of evaporation decreases relative to the advection of unenriched water from the xylem to the sites of evaporation. Simply stated, the relative contribution of Δ¹⁸O_e to Δ¹⁸O_LW decreases, and Δ¹⁸O_LW decreases with increasing E, assuming that L remains constant.

Bulk leaf enrichment above source water (Δ¹⁸O_BL) reflects both Δ¹⁸O_LW, the biochemical fractionation during the synthesis of organic compounds, and the degree of oxygen exchange during synthesis of cellulose (Craig & Gordon, 1965; Dongmann et al., 1974; Yakir, 1992; Barbour, 2007):

$${\Delta }^{18}{\mathrm{O}}_{\text{BL}}= {\Delta }^{18}{\mathrm{O}}_{\text{LW}}\left(1-p_{\text{ex}}{p}_{\mathrm{x}}\right)+ {\epsilon }_{\text{wc}}+ {\epsilon }_{\text{cp}}$$ (A9)

Where p_ex is the proportion of exchangeable oxygen in cellulose being formed from glucose, p_x is the proportion of source water at the site of cellulose/tissue synthesis, ε_wc is the biochemical fractionation factor during the carbonyl oxygen exchange with water during cellulose synthesis, and ε_cp is the isotopic difference between cellulose and whole tissue. This equation assumes a constant ε_cp, although this factor may vary among leaves and with leaf age Cernusak et al., 2005. Therefore, in the scenario where Δ¹⁸O_LW varies across a gradient of E, Δ¹⁸O_BL integrates this variation over the period of leaf construction. In this case, Δ¹⁸O_BL may be used as a proxy for E during leaf growth (Barbour et al., 2000a; Barbour, 2007).

The concept behind the Péclet effect theory that the proportional deviation of Δ¹⁸O_LW from Δ¹⁸O_e (f in Eq. A3) increases along a gradient of E has failed to be observed in several studies (Roden & Ehleringer, 1999; Cernusak et al., 2003; Loucos et al., 2015; Song et al., 2015; Holloway‐Phillips et al., 2016). The failure to find this relationship in many instances has been explained by changes in factors such as T_L and e_i with E, as discussed above and (Barbour et al., 2000a,b, 2004; Barbour & Farquhar, 2004). Song et al. (2015) found that the two-pool model proposed by Roden and Ehleringer (1999) better described the response in Δ¹⁸O_LW to environmental conditions in upland cotton (Gossypium hirsutum) than the Péclet model. Additionally, the assumption that L is constant for species and across a gradient of transpiration rates has not always been supported (Song et al., 2013; Roden et al., 2015). If L does not remain constant across a range of E, then it obscures any relationship that may exist between E and Δ¹⁸O_LW or Δ¹⁸O_BL.

Vein density and vein volume fraction affect the enrichment of leaf water because of the strong Péclet effect within the xylem (Helliker & Ehleringer, 2000; Farquhar & Gan, 2003; Gan et al., 2003; Holloway‐Phillips et al., 2016). This Péclet has been called the longitudinal or xylem Péclet and is greater in magnitude than the radial or mesophyll Péclet (Farquhar & Gan, 2003; Gan et al., 2003; Holloway‐Phillips et al., 2016). Vein volume fraction increases the relative importance of xylem water in Δ¹⁸O_LW, which makes the influence of the xylem Péclet increasingly measureable in Δ¹⁸O_LW. This means that the xylem and not the mesophyll may be the location of the Péclet effect (Holloway‐Phillips et al., 2016). Since the mesophyll is where sugars are synthesized, this may explain why the oxygen isotope signature of leaf water is not always reflected in bulk leaf tissue.