1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Regulation of stomatal (gs) and mesophyll conductance (gm) is an efficient means for optimizing the relationship between water loss and carbon uptake in plants. We assessed water-use efficiency (WUE)-based drought adaptation strategies with respect to mesophyll conductance of different functional plant groups of the forest understory. Moreover we aimed at assessing the mechanisms of and interactions between water and CO2 conductance in the mesophyll. The facts that an increase in WUE was observed only in the two species that increased gm in response to moderate drought, and that over all five species examined, changes in mesophyll conductance were significantly correlated with the drought-induced change in WUE, proves the importance of gm in optimizing resource use under water restriction. There was no clear correlation of mesophyll CO2 conductance and the tortuosity of water movement in the leaf across the five species in the control and drought treatments. This points either to different main pathways for CO2 and water in the mesophyll either to different regulation of a common pathway.


electron transport rate


photosynthetic photon flux density


ribulose 1,5-bisphosphate carboxylase/oxygenase


water content


water-soluble organic matter


water-use efficiency


intrinsic water-use efficiency


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Currently climate models predict that many important forest regions in Central Europe will experience increased frequency and severity of drought periods (Ciais et al. 2005, IPCC 2007). Water restriction intensifies the general predicament of plant gas exchange, i.e. the loss of water to gain carbon (Chaves et al. 2003) and thus the optimization between CO2 uptake (A) and water loss (E) to increase water-use efficiency (WUE = A/E) is often an important adaptive advantage under drought conditions (Heschel et al. 2002, Aranda et al. 2012, Flexas et al. 2013). Improved WUE allows greater biomass production per unit of rainfall (Condon et al. 2002 Condon et al. 2004) thus offering a competitive advantage when water is limiting (Tsialtas et al. 2001).

Stomatal regulation in vascular plants is an efficient means for adjusting water use to changes in plant water supply and demand and this fine-tuned mechanism allows a rapid reaction to altered water availability, while attempting to maximize carbon uptake, thus optimizing the relationship between water loss and carbon uptake. At the leaf level the balance between CO2 uptake and water loss is reasonably well understood (Farquhar and Richards 1984, Warren et al. 2001, Chaves et al. 2003). However, there is an increasing awareness that particular drivers of this balance such as mesophyll conductance (gm) might vary more strongly than previously assumed (Warren and Adams 2006, Vrabl et al. 2009, Douthe et al. 2011, 2012, Evans and von Caemmerer 2013).

Different species, provenances and cultivars differ in their ability to adapt stomatal conductance or leaf biochemical capacity for carbon fixation to optimize carbon gain with respect to water loss. Such differences in optimization strategies can strongly affect synecological interactions especially at sites exposed to periodic drought (Grams et al. 2007, Niinemets et al. 2009). Against this background, the importance of gm to the relationship between E and A was brought back to general attention (Warren and Adams 2006, Aranda et al. 2007, Barbour et al. 2010). It is still under fierce debate whether/how drought-related changes in gm affect short- and longer-term WUE strategies of different species (Flexas et al. 2008, Flexas et al. 2013). Closing the stomata and thus reducing stomatal conductance (gs) in response to drought increases WUE but also decreases net photosynthesis. In theory the decrease in A could be compensated by means of increasing gm with the result of increasing WUE not at the expense of A (Aranda et al. 2007). It is hypothesized that aquaporins, which facilitate the diffusion of CO2 through cell membranes, are involved in short-term changes in gm (Flexas et al. 2006, Miyazawa et al. 2008, Uehlein and Kaldenhoff 2008, Evans et al. 2009). On the other hand, anatomical changes might be important for medium- to long-term acclimation to drought (Tosens et al. 2011). Changes in mesophyll conductance to CO2 might also be coupled to changes in leaf hydraulic conductance (Ferrio et al. 2012), which in turn can affect transpiration, mainly via stomatal regulation of leaf water potential (Sack and Holbrook 2006). In particular, several studies suggest that water movement through the mesophyll is not only apoplastic but can also be mediated by aquaporins via cell vacuoles (transcellular pathway) to the sites of evaporation (Steudle and Frensch 1996, Martre et al. 2002, Sack et al. 2004, Cochard et al. 2007, Kaldenhoff et al. 2008, Heinen et al. 2009, Pou et al. 2013). However, direct evidence of changes in mesophyll hydraulic conductance in response to drought is scarce, particularly due to the confounding effect of vein xylem embolism (Johnson et al. 2009). In this context, stable isotopes in leaf water offer an alternative approach to get insight into mesophyll limitations for water flow (Barbour and Farquhar 2003, Ferrio et al. 2009).

Drought-induced changes in leaf hydraulic properties, e.g. a restriction of aquaporin-mediated transcellular pathways (Miyazawa et al. 2008, Pou et al. 2013), are expected to increase the tortuosity for water movement through the mesophyll (Ferrio et al. 2009, Pou et al. 2013), which in turn determines a key parameter in the models describing isotopic enrichment of leaf water, the effective pathlength of water movement from the xylem to the sites of evaporation (L) (Farquhar and Lloyd 1993, Barbour and Farquhar 2003). Taking advantage of this, Ferrio et al. (2012) showed that L, as a measure of tortuosity, was inversely related to leaf hydraulic conductance and gm in grapevine. As a consequence they assumed that water and CO2 could share, under certain circumstances, an important part of their respective diffusion pathways in the mesophyll, leading to coupled changes in water and CO2 transport. In the same study, the tight inverse link between L and hydraulic conductance was further supported by a survey on literature data of 16 genera. Flexas et al. (2012) also reported a positive trend when comparing literature data on hydraulic conductance and gm, but only in 20 out of the 28 genera studied.

Although in some cases gm and hydraulic conductance have shown to be similarly restricted by aquaporin suppression (Miyazawa et al. 2008), recent results indicate that the aquaporin-mediated regulation of diffusion can be different for CO2 and H2O (Otto et al. 2010, Kaldenhoff 2012). Briefly, this model proposes that aquaporin subunits with different affinities to CO2 and H2O compete in the formation of aquaporin complexes. In this regard, Flexas et al. (2012) showed evidence from tobacco mutants that higher expression of a CO2-aquaporin increased gm for CO2 and at the same time increased L, thus breaking the correlation discussed above between gm and L, and pointing to a negative relationship between mesophyll hydraulic conductance and gm. Moreover, if the relative contributions of apoplastic vs symplastic pathways of water movement differ among species and/or environmental conditions we might expect more (relatively higher apoplastic contribution) or less (relatively higher symplastic contribution) common regulation of CO2 and H2O movement in the mesophyll.

Beyond the mechanisms controlling CO2 and liquid water conductance, it is still an open question whether drought adaptation strategies involving mesophyll conductance are of general importance. Whereas comparable declines in gm and gs in response to drought have been observed for several species (Flexas et al. 2009, Galle et al. 2009, Galle et al. 2011), under certain conditions gm can be less sensitive than gs (Duan et al. 2009, Galmés et al. 2011, Cano et al. 2013), or even remain unaffected, despite strong reductions in gs (Bunce 2009). On the other hand, we lack information about gm-related strategies for drought adaptation of potentially competing or at least co-occurring species. Only recently it was observed that variations in gm between functional groups can be small (Warren 2008) in comparison with variations within these groups and even within cultivars of a particular species exposed to different environmental conditions (Flexas et al. 2008). In contrast, Peguero-Pina et al. (2012) showed that higher mesophyll conductance in the drought-adapted Abies pinsapo compared to the more mesic Abies alba resulted in higher A and WUE in the former species. In our study we assessed drought adaptation strategies with respect to mesophyll conductance of different functional plant groups of the forest understory. We exposed seedlings of the tree species Acer platanoides and Fraxinus excelsior as well as the herbaceous forest understory species Impatiens noli tangere, Allium ursinum and Mercurialis annua to moderate drought as normally expected in the forest understory, where extreme conditions (particularly humidity and temperature) are normally buffered by the overstory (Fotelli et al. 2003), and monitored transpiration rate (E), assimilation rate (A), stomatal and mesophyll conductance, WUE and intrinsic water-use efficiency (WUEi = A/gs).

Concerning the mechanisms of and interactions between water and CO2 conductance in the mesophyll we took as our working hypothesis, based on the work of Ferrio et al. (2012), that the tortuosity for leaf water movement scales inversely with gm in the different tree and herbaceous species indicating similar pathways and similar regulation for CO2 and water in the mesophyll. Any drought-induced change in gm would thus not only affect carbon assimilation directly but also leaf hydraulic properties.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Plant material and experimental set-up

Two different tree and three herbaceous species growing in the understory of mixed deciduous forests in Central Europe were selected for the experiments. Fraxinus excelsior (common ash) is known to be drought tolerant (Peltier and Marigo 1999) and A. platanoides (Norway maple) was described as having a high WUE (Kloeppel and Abrams 1995). For the herbaceous species we have chosen two different life form types: I. noli tangere and M. annua as therophytes and A. ursinum as a geophyte. The tree species were obtained from a tree nursery (Handel, Metzingen, Germany). The 2-year-old F. excelsior seedlings were planted with soil container substrate (Kausek, Mittenwalde, Germany) in 5.5 l pots and the A. platanoides seedlings in 4 l pots. 9–12 g lime (Rüdersdorf, Germany) was applied per pot. The I. noli tangere and M. annua seeds were sourced from the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) and bulbs of A. ursinum were collected from a natural field site close to Müncheberg, Germany. The herbs grew in 15 l plant tubes per six individuals in the greenhouse. 45 g lime was applied for I. noli tangere and Mercurialis annua.

The plants were grown in a greenhouse at an average air temperature of 20°C. The photosynthetic photon flux density (PPFD) at the canopy level was kept at approximately 600 µmol m−2 s−1 by applying supplemental illumination (bulb type NARVA NC 1000–00). The light period was adjusted to 16 h for the two tree species and to 14 h for the herbs. The bulk density of the mainly organic soil substrate was 0.12 ± 0.01 g cm−3. Volumetric soil water content (θs) was detected continuously with EC-5 soil moisture sensors (UMS Analytic systems, Munich, Germany) at a depth of 5–10 cm for herbs and at a depth 10–15 cm for the trees (Table 1). During the acclimation period in the greenhouse the pots containing the plants were watered every second day to field capacity.

Table 1. Characterization of the species and the experimental conditions. The plant age is given in years and for the two tree species the code for German provenance is given (811 07 and 800 04). Tree height before the treatment and number of individuals tested (N) in each treatment (drought and control) is also given. Θs is the average volumetric water content (±sd) during the drought treatment. The duration of the drought treatment is given in units of days (d)
Species parameterFraxinus excelsiorAcer platanoidesMercurialis annuaImpatiens noli tangereAllium ursinum
Age (years)22115
Origin811 07800 04Wild plantWild plantWild plant
Height (cm)60–12040–6030–5030–505–15
Duration (d)2114171710
θs (%)48.1 ± 4.144.2 ± 1.446.7 ± 1.345.9 ± 3.549.4 ± 1.9
Soil water restriction
θs (%)20.8 ± 2.718.9 ± 1.724.3 ± 0.411.3 ± 0.233.8 ± 1.0

The total sample size was 24 individuals for each tree species and 60 for each herb species. After 4–5 weeks of acclimation in the greenhouse half of the individuals of each species were exposed to soil water restriction for 10–21 days (see Table 1) whereas the other half (control) were well watered by keeping the soil moisture at field capacity.

Soil water potential (Ψs) was derived from θs according to Schindler et al. (2010). The thresholds of soil moisture tension for the forest species (Bittner et al. 2010) were used and pots were carefully watered to reach these target values if Ψs fell below the threshold. The thresholds in soil moisture imply still a minimal root water uptake as defined in Bittner et al. (2010); below the threshold value root water uptake is assumed to be zero. By doing so we exposed plants to moderate drought stress but avoided damage or death due to severe drought conditions. For the herbaceous species there are no threshold values given in the literature. Water supply was lowered until first clear signs of drought (wilting at noon) were visible.

Gas-exchange measurements and mesophyll conductance

A, E, gs at different leaf intercellular CO2 concentrations (Ci) and different PPFD were recorded in all species, during the experiment using a GFS-3000 (Walz Measurement Instrumentation, Effeltrich, Germany). The measurements were carried out twice for each treatment, once before lowering soil moisture and once within the last three days before the end of the experiment. Simultaneously electron transport rate (ETR) of the leaves was estimated as follows for each CO2 concentration (Eqn (1))

  • display math(1)

where the effective quantum yield of PSII (ΔF/Fm′) was calculated as (Fm′−F)/Fm′. F is the fluorescence yield of the light-adapted sample and Fm′ is the maximum fluorescence obtained when a saturating light pulse is superimposed on the prevailing environmental light levels (Schreiber and Bilger 1993). The intensity and duration of the saturating light pulse was adjusted according to the fluorescence kinetics ( The pulse duration was 800 ms, and the pulse intensity was set to maximum, which equals < 4500 µmol m−2 s−1. ( We acknowledge that recent results from Loriaux et al. (2013) have shown that a single multiphase flash of sub-saturating intensity might further improve the accuracy of fluorescence measurements. This procedure was not possible with the PAM fluorometer of the GFS-3000, which however has the advantage compared to other commercially available systems of integrating both gas exchange and fluorescence on an 8 cm2 area. The factor of 0.84 represents the total light absorption of the leaf. This empirical factor takes into account that only a fraction of the incident light is absorbed (Rascher et al. 2000). Under stress, leaf absorbance might, however, decrease or increase – depending on the species – and might reach values below 0.7 (Delfine et al. 1999) or above 0.9 (Loriaux et al. 2013). In order to take such potential variation of the light absorption factor into account we included variations of ETR in a sensitivity analysis for gm (see below).

For the calculation of CO2 concentration at the sites of carbon fixation (Cc) according to Eqn (2) the ribulose 1,5-bisphosphate carboxylase/oxygenase (RubisCO) specificity factor (SΓ) was estimated. To derive this parameter we computed Γ*, the CO2 compensation point in the absence of day respiration for all plants and treatments. In other words it is the CO2 concentration where CO2 uptake by carboxylation is matched by photorespiratory CO2 release, and where the rate of CO2 release is day respiration (Laisk 1977, Atkin et al. 2000). Γ* was calculated from A/Ci curves (Farquhar et al. 1980, Brooks and Farquhar 1985, Long and Bernacchi 2003) separately for plants from the drought treatment and the controls and applied to quantify the SΓ. Five A/Ci curves were measured at different light intensities (50, 100, 200, 400 and 700 µmol m−2 s−1) with eight different CO2 concentrations per curve, ranging from 25 to 400 ppm. We also compared the initial slopes of the A/Ci curves between drought treatment and control since changes of this slope are assumed to be a symptom of stomatal patchiness (Grassi and Magnani 2005). The slopes did not vary between treatments with the exception of I. noli tangere and A. platanoides, where slopes slightly diverged but only at the lowest light intensity, pointing to the fact that calculation of Ci was not affected by patchy stomatal closure for the vast majority of the conditions applied.

The calculation of Cc was carried out as follows:

  • display math(2)

We assumed O the O2 concentration in the chloroplast to be 0.21 mol mol−1. Rl is the mitochondrial respiration rate in the light on a leaf area basis. gm was calculated as follows according to Terashima and Ono (2002) (Eqn (3)).

  • display math(3)

Where mi is the slope of A/Ci curves and mc is the slope of A/Cc curves at a defined light intensity at low absolute CO2 concentrations. mc and mi were calculated from the linear regression between A and the corresponding Cc and Ci values below 150 µmol mol−1, respectively. The method of Terashima and Ono (2002), which is a modified version of the approach described by Epron et al. (1995) and where Ci is varied in a range with RubisCO limitation, compares well with other variable or constant J methods commonly applied to determine gm (Pons et al. 2009).

Diel course and water extraction

After the A/Ci curves, diel courses of gas exchange combined with plant tissue sampling for isotope analyses were carried out for each species. Measurements for A, E, gs, PPFD, leaf temperature, specific leaf area, leaf length, ambient temperature and relative humidity were carried out at 03:00 h in the dark and during the light period at 09:00 h, 15:00 h and at 21:00 h. At the same time points five leaves per plant and stem wood (trees) or root crowns (herbs) of three individuals per species were harvested.

Two leaves were used to analyze the carbon isotopic composition (δ13C) of leaf water-soluble organic matter (WSOM). With one leaf we estimated leaf water content (WC). Extraction of WSOM and determination of WC have been described in detail by Barnard et al. (2007). The last two leaves were placed together in glass tubes (Schott-Duran, Mainz, Germany), immediately frozen in liquid nitrogen and stored at −30°C. These leaves and the stem wood (with the bark removed) and the root crown (hypocotyl) were used for tissue water extraction by cryogenic vacuum distillation (Ferrio et al. 2009). For A. ursinum only the middle part of the long monocotyledon leaf was sampled and also gas exchange was performed with this part of the leaf.

Isotopic measurements and calculations

δ18O in water extracted from leaves, root crowns and stem wood was determined using a TC/EA (high temperature conversion/elemental analyzer; ThermoFinnigan, Bremen, Germany) coupled with a DeltaPlus XP mass spectrometer via a ConFlo III interface. The precision of the measurement was <0.15‰. Values are given in delta (δ) notation (in ‰) relative to the standard VSMOW (Vienna Standard Mean Ocean Water).

The extracted water of the root crown or the stem wood was considered as source water and its δ18O value was applied to calculate the observed evaporative enrichment of leaf water Δ18OL.

  • display math(4)

Where δ18OL is the oxygen isotopic composition of leaf water and δ18Osource is the isotopic composition of the source water.

Leaf water enrichment during the diel courses was also modeled as described in detail by Barnard et al. (2007) to obtain the scaled effective pathlength L.

First, steady-state isotopic enrichment of 18O over source water at the site of evaporation in the leaf (Δe) under steady state conditions was calculated as follows (Craig and Gordon 1965, Dongmann et al. 1974):

  • display math(5)

Where ε+ is the equilibrium fractionation between liquid water and water vapor; εκ is the kinetic fractionation as vapor diffuses from leaf intercellular spaces to the atmosphere (Farquhar et al. 1989), Δ18Ov is the isotopic enrichment of water vapor relative to the source water taken up by the plant and ea/ei is the ratio of ambient to intercellular vapor pressures. At each time point during the diel course the δ18O of the atmospheric water vapor in the greenhouse was analyzed by using a Picarro Isotopic Water Analyzer L2120-i (Sunnyvale, CA).

Average lamina mesophyll water is less enriched than the water at the evaporative sites, resulting in an isotopic gradient between the leaf vein and the evaporative sites. The steady-state isotopic enrichment of mean lamina mesophyll water (ΔLsP) can be described by correcting Eqn (5) for the Péclet effect (Farquhar and Lloyd 1993), as shown in Eqn (6). The Péclet effect is the net effect of the advection of unenriched source water to the leaf evaporative sites via the transpiration stream as opposed by the diffusion of evaporatively enriched water away from the sites of evaporation.

  • display math(6)

℘ is the Péclet number, E the leaf transpiration rate (mol m−2 s−1), L is the scaled effective path length (m) for water movement from the xylem to the site of evaporation, C the molar concentration of water (mol m−3), and D the diffusivity (m2 s−1) of the H218O isotopologue in ‘normal’ water. The scaled effective path length as a measure for tortuosity was estimated by fitting the non-steady state model to the observed Δ18OL at 15:00 h under expected steady state conditions that typically occur in the afternoon.

Under non steady-state conditions, the enrichment of mean lamina mesophyll water above source water (ΔLnP) was calculated following Farquhar and Cernusak (2005):

  • display math(7)

where α+ = 1 + ε+ and αk = 1 + εk, W is the lamina leaf water concentration (mol m−2), t is time (s), g is the total conductance to water vapor of stomata and boundary layer (mol m−2 s−1), and wi is the mole fraction of water vapor in the leaf intercellular air spaces (mol mol−1). An iterative solution was calculated with the ‘Solver’ function in Excel. The model requires initial values for ΔLnP and W for a time point (t0–1) preceding the first observation. To initialize the model we took the values from the last measurement.

In WSOM extracts, δ13C was determined by combusting the samples in a Flash HT elemental analyzer (ThermoFinnigan) coupled via a ConFlo III interface to a Delta V advantage isotope ratio mass spectrometer. The precision of the measurement was <0.10‰. Small delta values are given relative to the standard VPDB.

We determined δ13C of atmospheric CO213CCO2) and CO2 concentration (ca) during the diel courses with a G2101-i Picarro Isotopic CO2 Analyzer to calculate photosynthetic carbon isotope discrimination Δ13C.

  • display math(8)

where δ13CL is the carbon isotopic composition of the leaf WSOM. WUEi describes the ratio between A and gs and was calculated from Δ13C according to Eqn (9) (Farquhar et al. 1982, Seibt et al. 2008).

  • display math(9)

Where a is the carbon isotope fractionation during diffusion through the stomata. b is normally defined as the discrimination during carboxylation of RubisCO which amounts to 30.5‰ in the relevant (gas) phase (i.e. to 29.5‰ in the liquid phase; Tcherkez et al. 2013). We, however, took a value that takes into account the typical drop in pCO2 from the intercellular spaces to the sites of carboxylation, and b = 26‰ as we used it is a reasonable such number empirically. We averaged the WUEi values of the leaves sampled at 09:00 h, 15:00 h and at 21:00 h (i.e. during the light period).

Statistical analyses

All measured variables (under dry conditions and in control treatment) were first characterized by descriptive statistics (means and standard deviations of the means).

The error bars in the figures represent the standard deviation (sd). Statistical analyses and fittings were carried out with r 2.8.0 (R Development Core Team 2010) with the nlme package for linear mixed effect models (Pinheiro et al. 2008) and Sigma-Plot 12.0. Exponential curves were fitted by using Sigma-Plot 12.0. Student's t-test and Wilcox rank test were utilized to determine significance of difference between the two treatments.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Gas-exchange and mesophyll conductance

The light-response curves of A, E and gs at 400 ppm CO2 for the five species under control and reduced water supply conditions are given in Fig. 1, the maximum values (Amax, Emax and gs max) calculated from the light-response curves are shown in Fig. 2. The water restriction treatment for I. noli tangere caused a clear decrease in A (Fig. 1A), E (Fig. 1B) and gs (Fig. 1C) at all light levels measured as well as a significant decrease in the calculated maximum values Amax, Emax and gs max (Fig. 2).


Figure 1. Light response of photosynthesis (A), transpiration (E) and stomatal conductance for H2O (gs) in control and drought treatments. In (A)–(C) the herbaceous species (n = 4–5) and in (D)–(F) the trees (n = 10) are displayed. The regression lines in (A) and (D) are based on Schulte et al. (2003), the regression lines in (B), (C), (E) and (F) are based on exponential fitting. Open symbols: well watered controls (ww), black symbols: dry conditions (ds), solid line: regressions line of well watered, dotted line: regressions line of dry conditions. Data shown are means ± standard deviation. All measurements were performed at 400 ppm CO2 with a leaf temperature of 24 ± 1°C and at 60 ± 3% relative humidity.

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Figure 2. Cardinal points of the light response curves in the drought and control treatments. The figure shows the calculated Amax (µmol m−2 s−1), Emax (mmol m−2 s−1), gs max (mmol m−2 s−1) from the light response curves in Fig. 1. In addition gm calculated for an A of 2 µmol m−2 s−1 (gm norm, mmol m−2 s−1) and at Amax (gm max, mmol m−2 s−1) are shown. Significant differences (P < 0.05) are marked with*.

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In contrast, we did not observe clear drought effects on E and gs for A. ursinum (Fig. 1B, C) but Amax was slightly lower than the control under the drought treatment (Fig. 2). No significant effect of drought on A, Amax, E, Emax, gs and gs max were recorded for M. annua (Figs 1A–C and 2). The two tree species clearly differed in their reaction to the drought treatment. Whereas only the light-response of A was slightly affected by drought in F. excelsior causing a significant reduction of Amax (Fig. 2), A, E and gs decreased strongly in A. platanoides at all light intensities measured (Fig 1D–F). Consequently, the maximum values Amax, Emax and gs max were also clearly and significantly reduced by the drought treatment for A. platanoides (Fig. 2).

Since gm is known to scale more (Evans and von Caemmerer 1996) or less (Warren and Adams 2006) clearly with A, we plotted A vs gm for the light-response experiment. In general we observed a linear relationship between A and gm for a given species (Fig. S1A–E) but in two species (I. noli tangere and A. platanoides) the slopes of the regression lines were lower in the drought treatment than in the control. In A. ursinum and M. annua the A vs gm relationship was very similar between the treatment and the control and for F. excelsior a clear regression could not be obtained for the drought conditions due to the low assimilation rates. Especially at lower gs, and A under drought, the accuracy and precision of the measurements and estimates of the input parameters as given in the Eqns (1)-(3) might strongly influence the calculation of gm. To assess this influence we performed sensitivity analyses and show as examples the results of a deviation in A, ci and ETR as well as a variation in the estimate of Γ* on gm at Amax for I. noli tangere in the drought treatment and the control (Fig. S2). The effect of %-deviations of the input parameters were more pronounced in the drought treatment and the strongest effect was observed for deviations of A where a variation of ±5% caused a change in gm of ca. 60 mmol m−2 s−1 (49%). gm was less sensitive to deviations of ETR (which also includes deviations from the estimated leaf absorbance) and a variation of ±5% caused gm to change by 39 mmol m−2 s−1 (i.e. 31%).

The relationship between stomatal and mesophyll conductance for the different species under drought and control conditions (with the variation of both parameters driven by varying light intensity) is shown in Fig. S1F–J. In A. ursinum, M. annua and – at least hinted at – in F. excelsior, gs and gm were regulated more or less concertedly independent of the drought or control treatment. For I. noli tangere reduced water supply led only to a slight increase of gs with light intensity as shown in Fig. S1F, but in contrast, gm increased strongly reaching values up to 135 mmol m−2 s−1. In A. platanoides gm showed a comparable range along the PPFD gradient in the drought and in the control treatment but at very different gs ranges (control: >100 mmol m−2 s−1; drought between 15 and 30 mmol m−2 s−1).

gm at Amax (gm max) was affected by the drought treatment in some species. It significantly decreased by approximately 50% under dry conditions for A. ursinum (Fig. 2). In M. annua and F. excelsior a slight albeit insignificant decrease of gm max upon drought was observed, whereas in I. noli tangere gm max increased under drought by 45%. In A. platanoides gm max was not clearly influenced by the treatment. Since gm scales with A and Amax was significantly reduced upon drought in this species we additionally calculated a normalized gm norm for a given A of 2 µmol m−2 s−1 (from the linear relationship between A and gm as shown in Fig. S1) in Fig. 2. It becomes obvious here that for a given A, reduced water supply caused a significant increase in gm for A. platanoides and I. noli tangere.

Oxygen isotope enrichment and tortuosity of water movement in the leaf

Measured leaf water Δ18OL showed maximum values during the light period in all species (Fig. 3) with more or less clear diel courses of leaf water enrichment. There was no clear difference in the absolute values and in the diel patterns between the drought and control treatments. Predicted Δe clearly overestimated measured Δ18OL in all species in both treatments with the exception of A. platanoides. In the other four species the consideration of the Péclet effect (ΔLsP) greatly improved the predictions during the light period. Taking into account the isotopic non-steady state of leaf water (ΔLnP) improved the predictions especially in the night and directly before dusk but only slightly improved the estimates during the rest of the light period. The effective pathlength L obtained from fitting the steady state ΔLsP model to Δ18OL at 15:00 significantly increased under reduced water supply in I. noli tangere, A. ursinum and A. platanoides and stayed constant in M. annua and F. excelsior (Fig. 4).


Figure 3. Diel course of observed mean lamina leaf water enrichment (Δ18OL) and enrichment predicted from models of different complexity. Δ18OL is denoted by the white circles connected by black lines. Enrichment at the site of evaporation (Δe) is shown as black dotted line, modeled leaf water enrichment under steady state (ΔLsP) by a gray dashed line and under non-steady state (ΔLnP) by a gray bold line. Gray shadowed areas denote the dark periods.

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Figure 4. Calculated effective pathlength for water movement in the leaf L (cm), maximum water-use efficiency [WUEmax (µmol mmol−1)] and intrinsic WUE [WUEi max (µmol mmol−1)], as derived from the light curves and intrinsic WUE derived from the Δ13C of leaf water soluble organic matter [WUEi (µmol mmol−1)] as determined as daytime averages during the diel courses. Error bars denote standard deviation. Significant differences (P < 0.05) are marked with*.

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When plotted against E for all species and treatments, it is visible that L increases with decreasing E below a threshold of approximately 0.5 mmol m−2 s−1 (Fig. 5). At higher transpiration rates this relationship was not present. The exponential fit displayed in Fig. 5 resulted in an R2 of 0.98. In contrast, there was no clear relationship between gm max and L.


Figure 5. Relationship between the scaled effective pathlength L and (A) transpiration E at 15:00 h and (B) gm max. Data shown are mean values for species in a given treatment (n = 3). White symbols denote the control, black symbols the drought treatment. In (A) a first order exponential decay function (gray line) has been fitted to the data (R2 = 0.98). A.u., Allium ursinum; A.p., Acer platanoides; I.n., Impatiens noli tangere; M.a., Mercurialis annua; F.e., Fraxinus excelsior.

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Water-use efficiency

Gas-exchange derived WUE (as calculated from Amax/Emax) and WUEi (Amax/gs max) showed comparable patterns among species. In I. noli-tangere and A. platanoides both parameters tended to increase under reduced water supply, whereas a tendency for a decrease was observed in A. ursinum and no change in the other two species (Fig. 4). WUEi derived from δ13C of WSOM corroborate the findings from the gas exchange measurements and indicates that the short-term integrating values (gas exchange measurements) are representative for the longer term (δ13C of leaf WSOM). A correlation analysis between the change in WUEmax as a consequence of soil water restriction on the one hand and the related change in L, gs max or gm max on the other hand for all five species, produced significant results only for gm max (R2 = 0.77 P = 0.04). The positive slope of the regression line in Fig. 6 indicates that across all species tested an increase in mesophyll conductance goes along with an increase in WUE.


Figure 6. Change in gm max as a response to the drought treatment plotted against the change in water-use efficiency (WUEmax) for the five species examined. The regression line is significant at P = 0.04 (R2 = 0.77). A.u., Allium ursinum; A.p., Acer platanoides; I.n., Impatiens noli tangere; M.a., Mercurialis annua; F.e., Fraxinus excelsior.

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  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

The ability to increase WUE is a competitive advantage for plants under water limiting conditions (Richards et al. 2002). Reduced soil water availability resulting from the drought treatment had effects both on A and E in some of the species examined in this study. In all species with the exception of M. annua maximum photosynthesis rate decreased as a consequence of drought but only in I. noli tangere and A. platanoides were Emax and gs max decreased significantly during the water restriction regimes applied. Generally, it is assumed that stomatal closure is the first response to drought. However, Flexas and Medrano (2002) suggested that ribulose bisphosphate regeneration and ATP synthesis and thus photosynthesis rate might be impaired as an early response to water restriction when gs is still high. Such a response of A matches our observations. The aim of our experiment was to apply a moderate drought treatment without irreversibly damaging plants and plant function and in a range realistic for the plants in their natural habitat. Climate models project higher variability of rainfall in future (IPCC 2007, but refer Sun et al. 2012) causing periods with higher rainfall alternating with drier periods. Even though intensive summer droughts similar to the drought in 2003 (Breda et al. 2006) might occur more frequently in the future, mainly moderate droughts are to be expected for forest understory species as the herbs and tree seedlings examined here. This is because the forest understory experiences damped environmental fluctuations, due to shelter by the overstory tree canopy (Fotelli et al. 2003). Temperatures at the forest floor show lower amplitude compared to open vegetation and humidity levels are higher, reducing evapotranspiration and thus maintaining relatively high soil moisture compared to grassland or arable fields (Prescott 2002). These rather mild drought conditions obviously caused impairment in the functioning of photosynthesis in A. ursinum and F. excelsior before any stomatal response could be observed. As a consequence of the different A and E responses, WUE increased upon drought in I. noli tangere and A. platanoides whereas it stayed constant in M. annua and even decreased in A. ursinum and F. excelsior.

In two species we observed a clear drought effect on the A vs gm relationship: for both I. noli tangere and A. platanoides gm increased more strongly with A in the drought treatment as compared to the control. Moreover, in these two species the coordinated adjustment of gm and gs differed between the two treatments. As a consequence of these altered relationships, gm max (i.e. gm at light saturation and thus maximum A) increased (I. noli tangere) or remained constant (A. platanoides) in the drought treatment even though Amax and gs max decreased. In these two species WUE (WUEmax in Fig. 4) also increased upon drought, whereas it either remained constant or even decreased for the other species tested.

The fact that an increase in WUE was observed only in the two species that increased gm (either gmax or at least gm norm at a given A) in response to drought and that the change in gm max (but not in gs max) was significantly correlated with the change in WUE due to the drought treatment (Fig. 6) proves the importance of gm in optimizing resource use under water restriction. Indeed, a recovery in gm (but not gs) after prolonged drought conditions has been reported for some species (Galle et al. 2009, 2011), as a mechanism to restore assimilation rates after long water restriction.

When assessing mesophyll conductance, especially under conditions where gs and A are rather low such as in drought experiments errors in the measurement and estimates of the input parameters can significantly affect the calculation of gm (Pons et al. 2009). Our sensitivity analysis showed that deviations in single input parameters from −5% to +5% of the measured mean values can cause changes in gm of up to 49% (for A), which is in agreement with the studies of Pons et al. (2009). This sensitivity of gm needs to be taken into account when interpreting our data (and gm data in general) and we need to be cautious not to overemphasize small differences as a result of a treatment. We, however, consider the difference in the relationship of A vs gm between the two treatments for I. noli tangere and A. platanoides clearly large enough to be taken as a drought effect. The strong sensitivity of gm to variations in A calls for the use of gas exchange cuvettes as large as possible (Pons et al. 2009) and thus justifies the application of our system, which provided a chamber area of eight cm2. In addition, stomatal patchiness might invalidate calculation of Ci (Grassi and Magnani 2005) and thus gm. Our analysis of A/Ci curves showed no symptoms of stomatal patchiness for almost all conditions, which is in agreement with findings that mainly a rapid strong dehydration causes patchy stomatal closure (Gunasekera and Berkowitz 1992, Kubiske and Abrams 1993).

It might be additionally assumed that under low light only the mesophyll surface in the top part of the leaf is taken into account for mesophyll conductance since under these conditions photosynthesis mainly occurs in that part of the leaf and thus gm values cannot be compared with those under high light conditions. We, however argue that such a pattern occurs under all light intensities. In leaves that receive light from above, the chloroplasts at the bottom of the leaf are “shade” chloroplasts (compared to those at the top of the leaf), so that even in high light applied to the upper surface these bottom chloroplasts still only do a low portion of the leaf's photosynthesis. Indeed it has been shown (Farquhar 1989) that the optimal performance of such a leaf will be to have the photosynthetic capacity (Rubisco activity and electron transport activity per unit leaf area) vary through the leaf in the same proportion as the light absorbed. This is regardless of the amount of chlorophyll, which determines the absorptance of the leaf layer. Such ideas are supported by experimental studies as summarized in the review of Terashima and Hikosaka (1995). So again, the upper cells do most photosynthesis and the lower cells the least. This is so regardless of whether the overall intensity is high or low. What this means is that as the light intensity increases the photosynthesis of all the layers goes up in parallel, and in proportion, so that they hit saturation at the same external light intensity. So while it should be the case that, in low light, the surface areas of the chloroplasts in the upper part of the leaf preferentially contribute to gm, the same result should also be true at high, saturating light intensity and thus gm should be comparable with changing A.

In order to explore the interaction between mesophyll conductance for CO2 and water properties, we used the effective pathlength L as a proxy for changes in mesophyll water pathways (Ferrio et al. 2012). L was obtained from fitting a steady state evaporative enrichment model to measured lamina leaf water Δ18OL in the afternoon when steady state conditions were assumed. The comparison between the steady state (ΔLsP) and the non-steady state model (ΔLnP) indicates that isotopic non-steady state only needs to be considered directly before and during the night. This finding is in good agreement with recent observations (Cernusak et al. 2005, Farquhar and Cernusak 2005, Cuntz et al. 2007, Gessler et al. 2013) and corroborates our postulate that L can be fitted at 15:00 h under steady state assumptions. The values obtained for L are within or close to the range given usually in the literature [0.4–17 cm according to Wang et al. (1998)], with the exception of A. ursinum (but see below).

L increased clearly in the drought treatment in I. noli tangere, A. ursinum and A. platanoides but was not significantly affected in M. annua and F. excelsior. Observations of the response to reduced water supply among different species have included both an increase (Ferrio et al. 2009, 2012) and no significant change of L (Kahmen et al. 2009). Only recently it was found that L is strongly dependent on transpiration at low E (below approximately 0.5–3 mmol m−2 s−1) but not at E above this threshold value (Ferrio et al. 2012, Song et al. 2013). Consequently, the different response of L toward drought in different species can be explained by the extent of drought-induced changes in E. As pointed out by Ferrio et al. (2009), the underlying mechanism for such relationships could be a shift in water pathways (apoplastic/symplastic/transcellular), but also could be a reduction in the number of functional pathways, affecting both hydraulic conductivity and path tortuosity (Morillon and Chrispeels 2001, Pou et al. 2013). Indeed, the lack of differences in L found by Kahmen et al. (2009) coincided with no drought response in leaf hydraulic conductivity, despite significant changes in gs and E. In contrast, in Ferrio et al. (2012) L was better correlated to leaf hydraulic conductivity than to E. Nevertheless, due to uncertainties associated with the calculation of L, together with covariation of many variables in drought response, a mechanistic explanation for changes in L is still a controversial issue (Zhou et al. 2011, Ferrio et al. 2012, Cernusak and Kahmen 2013, Song et al. 2013).

In our study we also observed an exponential relationship between E and L with a strong increase in L for E < 0.5 mmol m−2 s−1 comparable to the observations of Ferrio et al. (2012) and Song et al. (2013). This finding can also explain the generally high L values observed in A. ursinum as in this species transpiration rates during the diel course experiments were low (Fig. 5). Recently Song et al. (2013) observed comparably high values for L at very low transpiration rates in pine species. We should note in this context that understory species such as the ones examined in our study generally show low transpiration rates (Givnish 1988). Moreover, understory species might behave similarly to shade-leaves, which appear to have stronger limitations from gm (Tosens et al. 2011, Cano et al. 2013).

In contrast to Ferrio et al. (2012), we found no clear relationship between gm and L. This might be attributed to the fact that Ferrio et al. (2012) examined only the effects of drought on one species (Vitis vinifera), which is rather adapted to high light whereas we assessed the variations across shade-tolerant understory species. According to Tosens et al. (2011), leaves developing under low light show most CO2 limitations at the stroma, a compartment that is not shared by water movement, and show less contribution of diffusion limitations at the cell wall and across membranes than light-adapted leaves. Thus, potentially shade-leaves would tend to have less common pathways for CO2 and water through the mesophyll. Moreover, the drought conditions applied by Ferrio et al. (2012) were more severe than the ones applied here, as indicated by the much stronger reduction of gs.

Our results indicate that an increase in gm does not automatically imply a reduction in L and, subsequently, not in the tortuosity of the water movement in the mesophyll either. On the basis of the results presented here we have to reject our working hypothesis that the tortuosity for leaf water movement (L) in general scales inversely with gm. We might, however, speculate that more severe drought stress as applied by Ferrio et al. (2012) might cause a stronger interference between the pathways of water and CO2 movement and also that inter-specific difference or differences between functional plant groups (e.g. light- vs dark-adapted species) might define the degree of interference. There are two main explanations for the lack of a clear correlation of mesophyll CO2 and water conductance across the five species under our experimental conditions: (1) Under the rather mild drought we applied, trans-membrane diffusion of water does not play a large role for water flux in the five species selected. If water is mainly transported via the apoplastic pathway any change in the permeability of membranes will thus neither affect the effective pathlength L nor hydraulic conductivity. (2) Aquaporin-mediated CO2 and H2O transport are independent or even competing. As suggested by Otto et al. (2010), aquaporins form heterotetramers in the membranes, and depending on the monomer composition a pore either facilitates water or CO2 transport. Flexas et al. (2012) gave a mechanistic explanation for the fact that both (1) increasing CO2 conductance and declining water conductance (high L) and (2) increasing CO2 and water conductance have been seen in experiments. The authors assumed that positive correlations between gm and the path length of mesophyll water transfer were due to altered expression of a distinct aquaporin class changing the proportion of the monomer types in the heterotetramers. In other words, an increase in the relative proportion of the PIP1 aquaporin facilitating CO2 transport would increase gm and at the same time reduce the membrane permeability for water. Negative correlations between gm and L might be due to variations in total aquaporin expression with no changes in the composition of the heterotetramers. For the species we tested such a joint increase in gm and L upon drought was observed in A. ursinum and I. noli tangere. In the other species either a (slightly) negative relationship could be observed (F. excelsior, M. annua) or an increase in L with no change in gm (A. platanoides) was observed. We might thus speculate that different regulation of aquaporin expression and tetramer composition among species leads to the lack of a consistent trend between gm and L.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

We acknowledge support by the Deutsche Forschungsgemeinschaft (DFG) under contract numbers GE 1090/8-1 and 9–1. J. P. F was supported by the Ramón y Cajal programme (RYC-2008-02050). G. D. F acknowledges support by an Alexander-von-Humboldt award.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
ppl12160-sup-0001-FigureS1.docWord document482KFig. S1. Relationships between net photosynthesis A and mesophyll conductance gm (A–E) and between stomatal conductance gs and gm (F–J) under drought and control conditions. Data are derived from the light curve experiments for each species at 400 ppm CO2. Open circles: well watered, black circles: dry conditions. Solid lines represent the regression lines for the well watered treatments, dotted lines are the regressions for the drought treatments, data shown are mean values (herbaceous species n = 5, trees n =10) ± standard deviation.
ppl12160-sup-0002-FigureS2.docWord document70KFig. S2. Sensitivity analysis for the calculation of gm for Impatiens noli tangere. The figure shows the sensitivity of gm for a deviation (in %) of the measured values of Ci, ETR and A and for a variation (in %) of the estimated values of Γ* for the plants from the control (solid lines) and the drought treatment (dotted line). The sensitivity analysis was performed for gm at Amax.

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