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Keywords:

  • elevated carbon dioxide (CO2);
  • elevated ozone (O3);
  • Fagus sylvatica;
  • Picea abies;
  • cellulose;
  • photosynthetic capacity (Amax);
  • semi-quantitative model approach;
  • stable isotope ratios;
  • stomatal conductance for water vapour (gl)

ABSTRACT

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

Combined δ13C and δ18O analyses of leaf material were used to infer changes in photosynthetic capacity (Amax) and stomatal conductance (gl) in Fagus sylvatica and Picea abies trees growing under natural and controlled conditions. Correlation between gl and δ18O in leaf cellulose (δ18Ocel) allowed us to apply a semi-quantitative model to infer gl from δ18Ocel and also interpret variation in δ13C as reflecting variation in Amax. Extraction of leaf cellulose was necessary, because δ18O from leaf organic matter (δ18OLOM) and δ18Ocel was not reliably correlated.

In juvenile trees, the model predicted elevated carbon dioxide (CO2) to reduce Amax in both species, whereas ozone (O3) only affected beech by reducing CO2 uptake via lowered gl. In adult trees, Amax declined with decreasing light level as gl was unchanged. O3 did not significantly affect isotopic signatures in leaves of adult trees, reflecting the higher O3 susceptibility of juvenile trees under controlled conditions. The isotopic analysis compared favourably to the performance of leaf gas exchange, underlining that the semi-quantitative model approach provides a robust way to gather time-integrated information on photosynthetic performance of trees under multi-faced ecological scenarios, in particular when information needed for quantitative modelling is only scarcely available.


INTRODUCTION

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

Over two decades of intensive research have shown that the proportion of 13C to 12C in plant organic matter (i.e. δ13Cp) is often negatively correlated with the long-term mean of leaf internal to external CO2 concentrations (ci/ca) (Farquhar, O'Leary & Berry 1982) in C3 plants as:

  • image(1)

where δ13Ca is the carbon isotope ratio of atmospheric CO2 (c. −8‰ under natural conditions), a is the fractionation caused by differential gaseous diffusion of 13CO2 and 12CO2 through the stomatal pore (4.4‰) and b is the net fractionation (c. 27‰) caused by the leaf which is dominated by the fractionation of the CO2-fixing enzyme ribulose-1·5-bisphosphate carboxylase/oxygenase (Rubisco) and to a lesser extent by other carboxylases present in the leaves of C3 plants. Equation 1 is a useful but simplified version of the full equation proposed by Farquhar et al. (1982), and neglects smaller fractionation factors such as diffusion through the leaf boundary layer, dissolution and liquid phase diffusion within the leaf as well as fractionation by dark and photorespiration (Farquhar, Ehleringer & Hubick 1989a; Brugnoli & Farquhar 2000).

According to Eqn 1, a lowered leaf internal CO2 concentration (ci) results in an increased δ13Cp by a lower discrimination against 13CO213C). CO2 diffusion into the leaf depends on stomatal aperture and ci, resulting from the balance between CO2 influx (supply) and carboxylation (CO2 consumption) within the leaf. Hence, the overall activity of the CO2-fixing enzymes, in addition, directly affects ci and δ13Cp (Farquhar et al. 1989a).

More recently, several authors reported δ18O of leaf organic matter (δ18OLOM) can be negatively correlated with mean stomatal conductance (gl) (Barbour & Farquhar 2000; Barbour et al. 2000; Siegwolf et al. 2001). This is especially true when plants have access to the same source water δ18O (δ18Os) and occupy the same aerial environment so that they are exposed to the same δ18O in water vapour (δ18Ov) and to the same leaf external water vapour pressure (ea). Such prerequisites are typically fulfilled when plants are grown in controlled environments (e.g. in glasshouses, phytotrons). In the field, maintaining specific growth conditions is more challenging, although not impossible, to achieve.

The relationship between gl and δ18Op has a mechanistic basis which differs from that which determines δ13Cp (Farquhar & Lloyd 1993; Barbour et al. 2000). Firstly, the O-isotope composition of leaf water (δ18Ol) is determinedby a combination of factors such as the δ18O at the sites of evaporation (e.g., the water ‘film’ covering the cells just inside the stomatal cavity of the leaf; δ18Oe), the δ18Os and δ18Ov as well as the evaporative gradient ea/ei, as originally formulated by Craig & Gordon (1965) and modified by Farquhar & Lloyd (1993) as:

  • image(2)

where ε* is the equilibrium fractionation factor for water exchange between the liquid and vapour phase which depends on leaf temperature (Bottinga & Craig 1969). εk is the kinetic fractionation occurring during diffusion through the stomata and leaf boundary layer; εk can be calculated as:

  • image(3)

where gb and gl are the boundary and stomatal conductance of water vapour (Cernusak, Farquhar & Pate 2005). The evaporative surface inside the leaf may not always be representative in determining the δ18O of the bulk leaf water (δ18Ol). In fact, δ18Oe is linked to δ18Ol via the Péclet effect (Farquhar & Lloyd 1993) as follows:

  • image(4)

where ℘ is a Péclet number, which is calculated as EL/(CD), with E being the transpiration rate, L is a scaled effective path length, C is the molar concentration of water and D is the diffusivity of H218O in water.

The exchange of oxygen atoms with local water in the meristematic tissues where cellulose is synthesized enhances the impact of δ18Os on δ18Ocel as modelled by Barbour & Farquhar (2000):

  • image(5)

where εwc is the equilibrium fractionation between water and carbonyl groups (C=O) (Sternberg & Deniro 1983), while Pex and Px are the proportion of exchangeable oxygen in cellulose and the proportion of xylem water in the meristematic tissue where cellulose is synthesized, respectively. Recently, Pex was estimated to be close to 0.4 (Cernusak et al. 2005), while the degree of variability in Px is not as clear and creates a weakness in the model (Barbour 2007). Equations 2–5 reflect increasing gl to be associated with decrease in δ18Ocel via three different effects: (1) decreased εk associated with increased gl; (2) increased ea/ei caused by lowered leaf temperature (resulting from increased transpiration cooling); and (3) an increase of ℘ upon increasing E.

Recently, the combined analysis of δ13C and δ18O has provided insights into a plant's long-term photosynthetic performance (given as Amax) in relation to its water use (given as gl) (Farquhar et al. 1989b; Sternberg, Mulkey & Wright 1989; Farquhar & Lloyd 1993; Yakir & Israeli 1995; Saurer, Aellen & Siegwolf 1997; Scheidegger et al. 2000; Sullivan & Welker 2007). These studies have embraced the original qualitative nature of the 13C/18O relationships outlined in a paper by Scheidegger et al. (2000). Here, we present a semi-quantitative modification to this earlier model (Scheidegger et al. 2000) and test it for forest trees of contrasting habit (i.e. Norway spruce versus European beech) and ontogenetic stages (juvenile versus adult trees). We explore the possibility of inferring gl directly from δ18O and subsequently test the advanced model under a variety of ecological scenarios. To induce changes in Amax and gl, and therefore in δ13C and δ18O of leaf material, we exposed plants to elevated CO2 and O3 concentrations as well as different light environments. Furthermore, we tested for the impact of intra- and interspecific competition on δ13Cp and δ18Op under controlled chamber conditions. This set of contrasting natural and controlled growth scenarios provided a rewarding way to explore the robustness of the advanced model. Our broader goal from these studies was to provide a generalized approach for using δ13Cp and δ18Op in combination to infer physiological performance under a wide range of environmental conditions.

MATERIALS AND METHODS

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

Phytotron experiment

In spring 1998, 2- and 3-year-old seedlings of European beech (Fagus sylvatica L.) and Norway spruce [Picea abies (L.) Karst.], respectively, were planted in containers (0.7 × 0.4 m, and 0.3 m in depth), which had been filled with untreated forest soil (dystric cambisol, Ah-B horizon). Twenty trees (arranged in rows of 4 × 5 individuals) were planted into each of 32 containers such that 16 containers had only one species (eight each of spruce or beech) and 16 others were one-to-one beech/spruce mixtures. To minimize potential edge effects, measurements were only taken on the six central individuals in each container.

Being preadapted to ambient and elevated (ambient + 300 µL L−1) CO2 concentrations in two climate-controlled greenhouse chambers, the containers were transferred into four walk-in phytotrons (size c. 2.8 × 3.4 m) maintained by the GSF–National Research Center for Environment and Health in Neuherberg near Munich, Germany. Details on the phytotrons can be found in Payer et al. (1993) and Thiel et al. (1996). During the subsequent two growing seasons in the phytotrons, plants were, in addition to the two CO2 concentrations, exposed in each phytotron either to ambient or twice-ambient O3 concentrations (restricted to < 150 nL L−1) using Plexiglas subchambers (Röhm GmbH, Darmstadt, Germany) Kozovits et al. 2005a). The result was establishing four CO2/O3 treatments: (1) ambient CO2/ambient O3, hereafter referred to as ‘control’; (2) ambient CO2/elevated O3 = ‘+ O3’; (3) elevated CO2/ambient O3 = ‘+ CO2’; and (4) elevated CO2/elevated O3 = ‘+ CO2/ +O3’. The climatic conditions and O3 concentrations were adopted from the study site ‘Kranzberg Forest’ (Germany, 490 m a.s.l.) (Nunn et al. 2002) and reproduced in the phytotrons on an hourly basis throughout the seasonal courses (Kozovits et al. 2005a). The external, cumulative O3 exposure under the ambient O3 concentration resulted in an AOT40 (calculated for daylight hours, according to Fuhrer, Skärby & Ashmore 1997) of 10.5 and 9.2 µL L−1 h−1 and in a SUM0 of 89.5 and 82.1 µL L−1 h−1 for the first and second growing season, respectively. In the corresponding twice-ambient O3 treatment, AOT40 and SUM0 were c. 6.5 and 2.0 times higher, respectively (Kozovits et al. 2005a).

Three tensiometers (model T5, UMS, Munich, Germany) per container continuously monitored soil moisture at a depth of 7 cm and were set to trigger irrigation with deionized water whenever soil water tension reached 350 hPa. Irrigation water with a δ18O of c. −11.3‰ was supplied from a common tank. At any irrigation event, the containers were supplied with 0.5 L (April, May) or 1.0 L (June to September) of deionized water. Liquid fertilizer (1 L of double-concentrated Hoaglands solution) (Hoagland & Arnon 1950) was applied four and six times during the first and second growing season, respectively, to maintain nutrient levels similar to those found in natural soils of Bavarian forests (Kreutzer et al. 1991).

During the winter months of 1998/1999 and 1999/2000, plants were placed into open-top chambers outdoors where corresponding CO2 concentrations were maintained. Leaves were harvested at the end of the second growing season in the phytotrons and dried for 3 d at 65 °C. For details on the phytotron experiment, see Kozovits et al. (2005a,b).

Plants grown under ambient CO2 concentrations were exposed to CO2 from outside air. Mean ambient CO2 concentration was 397.0 ± 0.1 µL L−1 (mean ± SE). The supplementary CO2 added in the elevated CO2 treatment was supplied from a large tank, which was refilled once or twice a year with CO2 of known δ13C (in the range of −4.0 to −4.4‰; mean value of −4.2‰). Mean CO2 concentration in the elevated CO2 treatment was 690.0 ± 0.7 µL L−1 and resulted in a mean air −δ13C (δ13Ca) of −6.4‰. Because of the different δ13Ca in the two CO2 treatments, δ13C of leaf material could not be compared directly. Therefore, comparison was made on the basis of discrimination against 13CO213C), calculated as follows (Farquhar et al. 1989a):

  • image(6)

where δ13Ca is the carbon isotope ratio of atmospheric CO2 (here: −8‰ and −6.4‰ in the case of ambient and elevated CO2 concentrations, respectively) and δ13Ccel is the carbon isotope ratio of leaf cellulose. We related Δ13C or δ13Ccel to δ18Ocel to evaluate effects of gaseous treatments and types of competition on the trees grown in the phytotrons. To facilitate the comparison between the experiments in phytotrons and in the field, we inverted the orientation of the Δ13C-axis (ordinate) in Figs 2 and 4.

image

Figure 2. Correlation between 13C-discrimination (Δ13C) and the ratio of leaf-internal to external CO2 partial pressure (ci/ca) of juvenile beech (a) and spruce (b) trees grown in phytotrons. Monocultures are given as solid and mixed cultures as open symbols. Circles denote gaseous control (ambient CO2 and O3 concentrations), triangles +O3, squares +CO2 and rhomboids +CO2/+O3. Data are means ± SE (n = 4–12). Solid lines represent linear data fits. Dotted lines give model outputs (see Eqn 1) using different input values for the net fractionation caused by the leaf (parameter b).

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image

Figure 4. Discrimination against 13C (Δ13C) versus δ18O in leaf cellulose (δ18Ocel) of juvenile beech (a) and spruce (b) trees grown in phytotrons. Monocultures are given as solid and mixed cultures as open symbols. Circles denote gaseous control (ambient CO2 and O3 concentrations), triangles +O3, squares +CO2 and rhomboids +CO2/+O3. Inserts indicate significant main effects by gaseous treatments (elevated O3, elevated CO2) and by competition in mixed culture. Data are means ± SE (n = 4–12). Please note inverted ordinates.

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Leaf gas exchange was measured with an open-flow, steady-state porometer (CQP130, Walz, Effeltrich, Germany) (Schulze et al. 1982), which was equipped with a differential infrared CO2/H2O gas analyser. Measurements on phenologically representative leaves (n = 12 per atmospheric treatment and type of competition) were performed under growth conditions by approximate monthly intervals throughout the growing seasons of 1999 (1 June, 26 June, 15 July, 27 August, 17 September) and 2000 (7 June, 6 July, 3 August, 24 August). Leaf internal to external CO2 partial pressure (ci/ca) and leaf stomatal conductance for water vapour (gl) were calculated according to the equations of von Caemmerer & Farquhar (1981), based on one-sided leaf area for beech and projected leaf area for spruce.

Field experiment

In the field, leaf material was sampled from 55- to 60-year-old Norway spruce and European beech trees grown in a forest stand in southern Bavaria, Germany (‘Kranzberg Forest’, for details see Pretzsch, Kahn & Grote 1998). In May 2000, a ‘free-air’ O3 fumigation was set into operation in the canopy, and five entire crowns of beech and spruce each were exposed to twice-ambient O3 concentrations (2× O3, being restricted to < 150 nL L−1) (Nunn et al. 2002; Werner & Fabian 2002). Leaves of trees grown under ambient O3 (1× O3 = control) and 2× O3 were accessible via scaffolding and canopy crane and were sampled at four dates throughout the year 2001 (10 June, 5 July, 2 August and 30 September). For spruce, leaf sampling was concentrated on 1-year-old needles. We sampled at three different crown positions: sun, intermediate and shade crown corresponding to specific leaf area (SLA) of 11.9 ± 0.7, 21.8 ± 2.0 and 40.0 ± 1.4 m2 kg−1 for beech and 3.1 ± 0.4, 4.9 ± 0.5 and 6.0 ± 0.7 m2 kg−1 for spruce, respectively (means ± SE). SLA significantly differed between the three crown positions (P < 0.001 and P = 0.003 for beech and spruce, respectively).

Because of several nearby canopy gaps, the stand was well ventilated, and CO2 gradients in concentration and δ13C were unlikely to be large (Werner, Ecoclimatology, Technische Universität München, Germany, personal communication).

Cellulose extraction

Cellulose was extracted from homogenized leaf material using a modified method first published by Brendel, Iannetta & Stewart (2000) that adds an extraction step using 17% w/v NaOH and subsequent rinsing steps with water (three times) which enhanced the purity of the extracted cellulose used for isotope analysis. The modified Brendel method produces reliable cellulose extracts for 13C and 18O analyses (for full details on these modifications and comparison to other methods of cellulose extraction, see Gaudinski et al. (2005).

Hot-water extraction of water-soluble carbohydrates

During 2 days of high O3 levels in the field experiment, on 26 July and 1 August 2001, leaves were sampled for the analysis of hot-water extractable carbohydrates. The 24 h means of O3 concentrations at 1× O3 and 2× O3 were 41.8 and 82.6 nL L−1, respectively, on 26 July, and 60.6 and 120.7 nL L−1 on 1 August, respectively. Leaves were sampled during afternoon hours (1415–1745 h), killed immediately in a microwave oven (Popp et al. 1996) and subsequently dried for 3 d at 65 °C. Hot-water extracts (1 h at 95 °C) were prepared from milled plant material. Non-soluble material was centrifuged (5 min at 10.000 g), and the supernatant was used for carbon isotope analysis (δ13C) after drying in an oven overnight at 65 °C.

Analysis for stable isotopes 13C and 18O

Analysis of carbon isotope ratio (δ13C) was performed with a PDZ Europa 20/20 isotope ratio mass spectrometer (Manchester, UK), while oxygen isotope analysis was conducted in a Finnigan MAT Delta PlusXL (Finnigan MAT, Bremen, Germany) following the method of Farquhar, Henry & Styles (1997) housed at the Center for Isotope Biogeochemistry, U.C. Berkeley, USA. All isotope ratios are expressed in δ-notation using PeeDee Belemnite (PDB) as the standard for carbon and Vienna-standard mean ocean water (V-SMOW) as the standard for oxygen (see Dawson et al. 2002). Long-term (3+ year) external precisions for carbon and oxygen isotope analyses are 0.17‰ and 0.23‰, respectively.

Statistics

Statistical analysis was performed using SPSS 12.0 for Windows (SPSS, GmbH, Munich, Germany). In the phytotron experiment, main effects of O3, CO2 and type of competition (intra- versus interspecific) were analysed using a three-way analysis of variance (anova). In the following, we refer to main effects of the applied gaseous treatments using the terms ‘elevated CO2’ and ‘elevated O3’, while effects of individual treatments are denoted using the aforementioned terms ‘control’, ‘+O3’, ‘+CO2’ and ‘+CO2/+O3’. Data from the field site (Kranzberg Forest) were analysed in a two-way anova with the independent variables ‘O3 treatment’ and ‘irradiance level’ (i.e. crown position). Because in the field experiment, crown positions of sampled leaves were located within the O3 treatments, a nested design (crown position nested into O3 treatment) was employed. Replicated measures in time were taken into account when present (Fig. 5 and Table 1).

image

Figure 5. Time course of δ13C in LOM (δ13CLOM) during the growing season of 2001 of adult beech (a) and spruce (b) trees at Kranzberg Forest. Open symbols denote sunlit, gray intermediate (half-shaded) and black shaded leaves. Circles and triangles denote ambient and twice-ambient O3 concentrations, respectively. Inserts indicate significant main effects by decreasing light level. Data are means ± SE, n = 5 (except for Julian day 212 where n = 3).

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Table 1. δ13C of hot water extractable carbohydrates (δ13CHWC) from leaves of adult beech and spruce trees grown in Kranzberg Forest under ambient (1× O3) and twice-ambient O3 (2× O3) concentrations
 Beech 26 July 1× O32× O31 August 1× O32× O3Spruce 1 August 1× O32× O3
  1. Leaves from sun, intermediate (half-shade) and shade crown positions were sampled during 2 days with high O3 concentrations (26 July and 1 August 2001). Ozone concentrations in 1× O3 and 2× O3-plots were 41.8 and 82.6 nL L−1 on 26 July and 60.6 and 120.7 nL L−1 on 1 August, respectively (above canopy, 24 h means). Data are means ± SE (n = 3–5).

Sun−28.2 ± 0.6−27.2 ± 0.4−27.1 ± 0.3−27.5 ± 0.3−28.0 ± 0.6−28.6 ± 1.0
Intermediate−29.6 ± 0.3−29.7 ± 0.7
Shade−31.0 ± 0.7−31.3 ± 0.5−31.4 ± 0.3−31.3 ± 0.3−30.8 ± 0.7−30.3 ± 1.1

RESULTS

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

The correlations between δ13C of leaf organic matter (δ13CLOM) and leaf cellulose (δ13Ccel) were highly significant in both species, irrespective of their growing environment in the phytotron (Fig. 1a) or field (Fig. 1b). In all cases, δ13Ccel was higher (more enriched in 13C) than in δ13CLOM. These differences were smaller in juvenile spruce (0.5–1.5‰) than in juvenile beech trees (1.5–3.5‰). The correlation between δ18OLOM and δ18Ocel was only significant in needles from adult spruce trees (small symbols in Fig. 1d) although the variance was high (e.g., r2 = 0.28). In all cases, leaf cellulose was enriched in 18O relative to LOM (i.e. δ18Ocel > δ18OLOM). This offset was highest in adult trees growing in the forest (c. 5.5‰ on average, Fig. 1d) compared with juvenile trees of beech (1 to 4‰) and spruce (2–4‰) grown in the phytotrons (Fig. 1c). Given the lack of a reliable correlation between δ18OLOM and δ18Ocel, subsequent analyses were based on δ18O from purified leaf cellulose.

image

Figure 1. Correlation between δ13CLOM (a,b) and δ13Ccel of beech and spruce trees. Corresponding correlations for δ18O are shown in (c,d). Data represent either juvenile trees grown in phytotrons (a,c) or adult trees from Kranzberg Forest (b,d). Large symbols are data from beech and small symbols from spruce trees. Monocultures are given as solid and mixed cultures as open symbols. Circles denote gaseous control (ambient CO2 and O3 concentrations), triangles +O3, squares +CO2 and rhomboids +CO2/+O3. Correlations were calculated across all treatments and are given by thick and thin lines for beech and spruce, respectively.

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Discrimination against 13C (Δ13C) by juvenile trees growing in the phytotrons was calculated from δ13Ccel and δ13Ca (see Eqn 6). Δ13C was significantly correlated with ci/ca (Fig. 2) as derived from leaf gas-exchange assessments. In the evergreen spruce trees, Δ13C was correlated with ci/ca in both years 1999 and 2000; this most likely occurred because more than 85% of total needle biomass was constructed during these 2 years (Fig. 2b). Correspondingly, in the deciduous beech trees, we elected to use the leaves for 2000 in the correlation shown (Fig. 2a).

In juvenile beech, atmospheric treatments and types of competition (intra- versus interspecific) resulted in ci/ca varying between 0.74 and 0.86 as averaged across four sampling dates throughout the different growing seasons (Fig. 2a). Variation in ci/ca within a treatment (e.g. mixed culture under +CO2; open squares: 0.78 ± 0.06), was the result of seasonal changes. Mean ci/ca correlated linearly with Δ13C (solid line, r2 = 0.86); however, in the modelled relationship (see Eqn 1, dotted lines) variation in ci/ca only accounts for a 3‰ shift as opposed to the observed 6‰ shift in Δ13C. The discrepancy between the modelled and observed variation in Δ13C could be accounted for via the reduction in the parameter, b, the net fractionation caused by the leaf, at lowered ci/ca (Fig. 2a). A value of 21 for b was needed to reproduce the low Δ13C of c. 17‰ at the ci/ca shown. In spruce, the data were also linearly correlated and in accordance with the model at standard parameterization (b = 27, Fig. 2b).

In juvenile beech, gl correlated negatively with δ18Ocel (Fig. 3a) but not with δ18OLOM (data not shown). Beech saplings grown in mixture with spruce, and particularly under enhanced CO2 or O3 concentrations, had reduced gl and, thus, their δ18Ocel increased. The highest gl in beech was measured under the gaseous control in monoculture. The model output (Eqn 5) fits well with the observed values if one assumes that leaf temperature increased above air temperature by 2 and 4K at high and low gl (i.e. 120 and 40 mmol m−2 s−1), respectively. In general, gl and δ18Ocel obtained from juvenile spruce trees were less affected by the gaseous treatments and types of competition than in beech (Fig. 3b). As gl of spruce varied between 85 and 130 mmol m−2 s−1, the δ18Ocel fell between 30.0 and 31.5‰. In spruce, a significant negative correlation was only found when one outlier (monoculture under +O3: closed triangle) was excluded from the analysis (Fig. 3b, r2 = 0.67, P = 0.024). At an increased leaf temperature of c. 3K above air temperature, the model output (Eqn 5) fits well with the observed values.

image

Figure 3. Correlation between δ18O of leaf cellulose (δ18Ocel) and stomatal conductance for water vapour (gl) in juvenile beech (a) and spruce (b) trees grown in phytotrons. The linear data fit (solid line) shown in (b) is only significant (r2 = 0.67, P = 0.024) if one outlier (close triangle) is excluded from the regression. Monocultures are given as solid and mixed cultures as open symbols. Circles denote gaseous control (ambient CO2 and O3 concentrations), triangles +O3, squares +CO2 and rhomboids +CO2/+O3. Data are means ± SE (n = 4–9). Dotted lines give model outputs (see Eqn 5) with different input values for leaf temperature, i.e. +2 to +4K above air temperature. The following additional input values were applied: boundary layer conductance (gb) of 3.0 mmol m−2 s−1, scaled effective path length for Péclet effect (L) of 0.018 and 0.01 m as well as a proportion of xylem water in meristematic tissue of 0.8 and 0.6 for beech and spruce, respectively.

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Discrimination against 13C (Δ13C) was then related to δ18Ocel so as to evaluate the effects of the gaseous treatments and types of competition on tree performance in the phytotron experiment (Fig. 4). In beech trees grown under elevated O3, Δ13C was significantly reduced by up to 2.5‰ (main effect, P < 0.033, Fig. 4a), while δ18Ocel increased (in particular, under +O3). Plant response to elevated O3 can be visualized in the uppermost right-hand insert ‘O3’ in Fig. 4a with the arrow pointing up and to the right reflecting the O3-induced shift in Δ13C and δ18Ocel. Conversely, elevated CO2 led to an increase in Δ13C by up to 4.0‰ (main effect, P < 0.001), while δ18Ocel remained largely unchanged (see right-hand ‘CO2’ insert with downward arrow). When concentrations of both gases were increased (+CO2/+O3), no significant change in the isotope ratios compared with the gaseous control treatment were found in beech leaves. Δ13C and δ18Ocel were most strongly affected by the different types of competition. In mixed culture, beech showed a decrease in Δ13C by up to 4.5‰, while δ18Ocel was increased by up to 2.0‰ compared with beech grown in the corresponding gaseous treatments in monoculture (P < 0.001 for both isotopes, visualized in the insert ‘Comp’ by the arrow pointing upward-right). In the less responsive species, spruce, only elevated CO2 affected the isotope ratios of C and O, increasing both Δ13C and δ18Ocel (P < 0.001 and P < 0.01, respectively), as visualized by the arrow pointing downward-right in the ‘CO2’ insert of Fig. 4b.

Crown position had a marked effect (main effect, P < 0.001) on δ13CLOM from adult beech trees growing in Kranzberg Forest at the four sampling dates during the growing season of 2001 (Fig. 5a). The highest δ13CLOM occurred in the sunlit leaves (approximately −28.0‰) and lowest in the lowermost and shaded crown parts (approximately −32.2‰, Fig. 5a). At all crown positions, δ13CLOM decreased slightly throughout the growing season. No significant effect of elevated O3 concentrations was found. Needles of adult spruce trees had changes in δ13CLOM of similar spatio-temporal pattern as encountered in beech (Fig. 5b). However, the effect by crown position was only marginally significant (P = 0.051). The elevated O3 treatment tended to increase δ13CLOM in spruce needles (P = 0.085), in particular, early in the season.

Cellulose was extracted from leaves sampled at the end of September, analysed for δ18O and plotted in relation to δ13CLOM (Fig. 6). In addition to the mentioned decline of δ13CLOM with decreasing light level (see also insert in Fig. 6a), δ18Ocel in beech leaves was also significantly affected by crown position (P < 0.001, Fig. 6a). However, this response was complex, because δ18Ocel increased from shade to half-shade crown position, but decreased from half-shade to sun-lit positions. In contrast, δ18Ocel of spruce needles was not affected by canopy position. Overall, no significant O3 effect was found on δ13CLOM and δ18Ocel of leaves from adult beech and spruce trees at the end of the growing season.

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Figure 6. Correlations between δ13C of LOM (δ13CLOM) and δ18O in cellulose (δ18Ocel) of leaves harvested in late September from adult beech (a) and spruce (b) trees at Kranzberg Forest. Open symbols represent sunlit, gray intermediate (half-shaded) and black shaded leaves. Circles and triangles denote ambient and twice-ambient O3 concentrations, respectively. The insert indicates a significant main effect by decreasing light level. Data are means ± SE (n = 5).

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δ13C of hot-water extractable carbohydrates (δ13CHWC) were analysed in leaves from adult trees sampled on 26 July and 1 August, 2001, two days with high O3 concentrations, to investigate short-term O3 effects on δ13CHWC (Table 1). Crown position significantly influenced the δ13CHWC for both species (P < 0.001 and P = 0.027 in beech and spruce, respectively). No significant ozone effect was found however in δ13CHWC of either species. In general, findings in δ13CHWC were similar to δ13CLOM (cf. Fig. 5).

DISCUSSION

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

Stable isotopes are well-established integrators of physiological plant responses to abiotic and biotic factors (Dawson et al. 2002). In this regard, the proportion of 13C to 12C in plant organic matter (i.e. δ13Cp) is known to be negatively correlated with the leaf internal to external CO2 concentration (ci/ca) (Farquhar et al., 1982) and has been used in numerous investigations of crop and wild plant species (e.g. Farquhar et al. 1989a; Dawson et al. 2002). More recently, several authors reported that δ18OLOM can serve as a time-integrated estimate of relative humidity (Yakir 1992; Roden & Ehleringer 1999) or gl (Barbour & Farquhar 2000; Barbour et al. 2000; Siegwolf et al. 2001) although the nature of the latter correlation is currently under debate (Sheshshayee et al. 2005; Farquhar, Cernusak & Barnes 2007). In the data presented here, variation in transpiration is under control of stomatal aperture and not driven by variation in evaporative demand. In such a case, theory predicts reductions in gl to be associated with increasing Δ18O (Farquhar et al. 2007); this prediction is supported by our data (Fig. 3).

Changes in ci (and therefore in ci/ca) typically result from variation in stomatal aperture and thereby impact CO2 diffusion into the leaf, changes to the CO2 demand by chloroplasts the leaf mesophyll (i.e. photosynthetic capacity, Amax) or some proportion of both. The combination of C and O isotope analysis can indicate if observed changes in δ13CLOM were caused by modified stomatal aperture and/or Amax (Farquhar et al. 1989b; Sternberg et al. 1989; Yakir & Israeli 1995; Saurer et al. 1997). To this end, Scheidegger et al. (2000) introduced a conceptual model to infer photosynthetic performance of herbaceous plants from δ13CLOM and δ18OLOM. They estimated changes in relative humidity from δ18OLOM and used this information to predict changes that would have likely occurred in gl. In the expansion of the original Scheidegger-model presented here (see Fig. 7), we infer gl directly from δ18Ocel bypassing the previously used relative humidity/δ18OLOM relationship. The applied correlation between gl and δ18O of leaf material has been presented previously by Barbour et al. (2000) as well as others cited above and was confirmed in our study as shown in Fig. 3. A prerequisite for using this relationship is establishing similar δ18Os among the study plants and their exposure to similar δ18Ov. This can be accomplished very well in phytotrons or even in the field if the trees form joined canopies or live in close proximity. Otherwise, δ18Os and δ18Ov may vary between plants, and thus, would need to be corrected for in applying the model (e.g. by determination of δ18Os in xylem water of individual plants). In addition to changes in gl, a potential change in ci/ca is derived from Δ13C or δ13CLOM (Fig. 2). Combining information on gl and ci/ca therefore allows one to draw conclusions about Amax as shown in Fig. 7. For example, in scenario ‘A’ in Fig. 7, δ18O and thus gl remains unchanged (see ‘o’ in the gl row), and therefore a reduction in ci/ca (indicated by ‘–’ in the ci/ca row) results from increasing Amax (indicated by ‘+’ in the Amax row). The resulting model output is an upward arrow, indicating a higher Amax, while gl remains unchanged. Following this rationale, all possible δ13C/δ18O combinations (scenarios shown in boxes A to H in the top row of the figure) are exemplified and converted to changes in Amax versus gl (very bottom row of Fig. 7).

image

Figure 7. Conceptual model (adapted from Scheidegger et al. 2000) with further development discussed in the text). All possible δ13C/δ18O combinations are given in the boxes at the top of the figure as scenarios A to H (model input). Arrows represent changes between two treatments (e.g. increase of CO2 from ambient to elevated concentrations). Stomatal conductance (gl) is derived directly from δ18Ocel (see Fig. 3) and ci/ca from δ13Ccel (see Fig. 2). ‘+’, ‘○’ and ‘–’ represent increase, no response or decrease in levels, respectively, of gl, ci/ca and Amax. Information derived from δ18Ocel and δ13Ccel about gl and ci/ca lead to subsequent interpretation of photosynthetic capacity (Amax). The model output in the bottom line of boxes gives relative changes of Amax versus gl caused by treatments (e.g., increase of CO2 from ambient to the elevated concentrations). The cases B, D and E are highlighted, as they represent plant responses to treatments applied in this study (cf. Figs 4 & 6).

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To predict ci/ca, we relied on Δ13C calculated from δ13Ccel (Fig. 2) or on δ13CLOM on the basis that a satisfactory correlation between δ13CLOM and δ13Ccel could be established (Fig. 1a,b). However, a reliable correlation between δ18OLOM and δ18Ocel was not found (Fig. 1c,d). Although earlier studies had presented such correlations for herbaceous and other woody plants (Barbour et al. 2000; Barbour, Andrews & Farquhar 2001; Cernusak, Pate & Farquhar 2004), our data from central European trees suggest that caution is needed when using δ18OLOM to infer physiological performance. In contrast to the recent findings shown by Sullivan & Welker (2007), our study was unable to find a reliable correlation between mean seasonal gl and δ18OLOM. We did, however, find a correlation between gl and δ18Ocel. This correlation is based on changes in leaf temperature and transpiration-driven Péclet effect (Cernusak et al. 2003; Farquhar et al. 2007), which are the direct consequences of variation in gl. These findings are substantiated because irrigation water and climatic conditions were identical for all plants (Kozovits et al. 2005a,b). However, a variety of leaf secondary metabolites such as lignin and fatty acids have a lower δ18O compared with that of cellulose (Gray & Thompson 1977; Bricout 1979; Schmidt, Werner & Rossmann 2001) and may be responsible for the observed differences between the δ18Ocel and δ18OLOM. On average, δ18OLOM was lowered by c. 5.5‰ compared with δ18Ocel (Fig. 1; cf. Barbour & Farquhar 2000; Barbour et al. 2000). In addition, secondary metabolites such as lignin and phenolics may vary in concentration with light levels and plant exposure to elevated O3 (Lange, Lapierre & Sandermann 1995; Sandermann 1996; Zinser, Ernst & Sandermann 1998). Such abiotic interferences may also have contributed to the poor correlation between δ18OLOM and δ18Ocel (Fig. 1c,d). For these reasons, we relied on using δ18Ocel instead of δ18OLOM.

In juvenile beech trees, exposure to the enhanced O3 concentrations resulted in an increase in both δ18Ocel and δ13Ccel (Fig. 4a), which represents scenario ‘B’ in the extended model proposed in Fig. 7. In this case, the model output predicts a reduction of gl, while Amax remains unchanged. This interpretation has been confirmed by assessments of Amax using A/ci-curves (Winkler, GSF-National Research Center for Environment and Health, Germany, personal communication) as well as the analysis of CO2 assimilation shown by Kozovits et al. (2005a). However, applying the model described by Eqn 1, one realizes that only about half of the increase in δ13Ccel is explained by the variation in ci/ca (Fig. 2a) with discrepancies being most pronounced under treatments resulting in low gl (e.g. +O3). With regard to understanding what may have led to O3-induced increase of δ13Cp, previous studies have reported an up-regulation of the CO2-fixing enzyme PEP-C under enhanced O3 concentrations (cf. Saurer et al. 1995; Landolt et al. 1997), which reduces total net fractionation by the leaf (parameter b in Eqn 1) because of the lower Δ13C of PEP-C (Farquhar et al. 1989a). Hence, increased PEP-C activity might explain, at least partially, the low Δ13C at narrowed stomatal aperture (e.g. under +O3, Fig. 2a). In addition, other factors such as a lowered leaf internal conductance (gi) might have contributed to the low net fractionation by the leaf (value of 21 for the parameter b, cf. Brugnoli & Farquhar 2000). However, a recent study showed gi to be unaffected by enhanced O3 concentrations (Warren et al. 2007), and therefore it appears unlikely that changes in gi are involved. Overall, the application of Eqn 1 to the data shown in Fig. 2a demonstrates the additional potential of a quantitative modelling approach compared with a semi-quantitative assessment to provide further mechanistic insight. Likewise, in Fig. 3, variability in leaf temperature was indicated by the application of the model Eqn 5. On the other hand, those modelling approaches require additional data, such as relative air humidity, air temperature, isotopic signatures of air and water, etc. which might not always be readily accessible. In those cases, the application of the semi-quantitative approach of Fig. 7 appears most rewarding.

Both species responded to enhanced CO2 concentrations in a similar way with decreasing δ13Ccel and in spruce trees in combination with increasing δ18Ocel. This corresponds to scenarios ‘D’ and ‘E’ in our model for spruce and beech, respectively (Fig. 7). In both cases, the model output predicts a reduction in the photosynthetic capacity (Amax), a common response for trees when exposed to enhanced CO2 concentrations (Curtis 1996), and is confirmed by previous findings for both juvenile beech and spruce trees grown under similar conditions (Lippert et al. 1997; Grams et al. 1999). Interestingly, the strongest effect we observed was caused by interspecific competition. Both δ13Ccel and δ18Ocel of beech were increased in mixed culture, whereas these isotope ratios remained unchanged in spruce grown in mixed culture compared with monoculture. The response of beech trees to competition with spruce represents case ‘B’ in the model (Fig. 7), which results in a reduced gl (cf. Fig. 3). Such a competitive effect we interpret as being caused by water limitation in beech brought about by the interspecific resource competition with spruce, which is currently under investigation. In addition, this result reflects the higher overall responsiveness of juvenile beech compared with juvenile spruce to the competition treatments used (Grams et al. 2002; Grams and Andersen 2007; Luedemann et al. 2005). In the field, the model reflected the well-documented natural gradient in ci/ca with canopy height and light level in both species (Waring & Silvester 1994; Buchmann, Brooks & Ehleringer 2002). Effects of O3 on adult trees were less clear, which is in accordance with recent reports on lower O3 susceptibility of adult trees relative to juvenile beech and spruce trees grown in phytotrons (Wieser et al. 2002; Nunn et al. 2005).

In conclusion, the correlation between average gl and δ18O from leaf cellulose (δ18Ocel) as presented here (Fig. 3) and by others (Barbour & Farquhar 2000; Barbour et al. 2000; Siegwolf et al. 2001) permits us to extend the model originally proposed by Scheidegger et al. (2000) and directly estimate changes in gl from δ18Ocel. The δ18Ocel data, when coupled with δ13Ccel, can be used further to infer how Amax in turn has responded in trees of different sizes and in relation to competition, O3 and CO2 treatments. In view of the analyses presented here for trees grown under controlled chamber and field scenarios, the extended semi-quantitative model proposed here offers a straightforward approach for gathering time-integrated information on the photosynthetic and stomatal performance for plants experiencing a host of different growing conditions. The application of the semi-quantitative model proposed here (Fig. 7) appears to be most revealing if data needed for a fully quantitative modelling approach are not available. The combined analysis of δ13C and δ18O in leaf material provides a robust way to investigate the impacts of elevated CO2 and O3 concentrations, light limitation and competition on trees, and advocates its use also in other multi-faceted ecophysiological investigations.

ACKNOWLEDGMENTS

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

We thank Drs. H.-D. Payer, H.K. Seidlitz (GSF–National Research Center for Environment and Health) and their team for excellent and unstinting cooperation and support during the experiment in the phytotrons. Drs. P. Fabian and H. Werner are gratefully acknowledged for the O3 fumigation and data analysis at Kranzberg Forest. The expertise and assistance of Drs. P. Brooks and S. Mambelli in performing the stable isotope analyses was very much appreciated. We also thank T. Feuerbach, J. Hu, A. Jungermann and I. Süß for skilful technical assistance. Discussions with Drs. A. Nunn, I. Reiter and J.B. Winkler, and in particular comments of Drs. K. Tu and G.D. Farquhar on an earlier version of the manuscript were extremely useful, and we thank them all. The investigation was supported by Deutsche Forschungsgemeinschaft (DFG) through SFB 607 ‘Growth and Parasite Defense’ and a faculty core grant awarded to T.E.D. from the University of California. A.K. was sponsored by a fellowship from DAAD/CAPES, and T.E.E.G. by a research grant from DFG.

REFERENCES

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES
  • Barbour M.M. (2007) Stable oxygen isotope composition of plant tissue: a review. Functional Plant Biology 34, 8394.
  • Barbour M.M. & Farquhar G.D. (2000) Relative humidity- and ABA-induced variation in carbon and oxygen isotope ratios of cotton leaves. Plant, Cell & Environment 23, 473485.
  • Barbour M.M., Fischer R.A., Sayre K.D. & Farquhar G.D. (2000) Oxygen isotope ratio of leaf and grain material correlates with stomatal conductance and grain yield in irrigated wheat. Australian Journal of Plant Physiology 27, 625637.
  • Barbour M.M., Andrews T.J. & Farquhar G.D. (2001) Correlations between oxygen isotope ratios of wood constituents of Quercus and Pinus samples from around the world. Australian Journal of Plant Physiology 28, 335348.
  • Bottinga Y. & Craig H. (1969) Oxygen isotope fractionation between CO2 and water, and the isotopic composition of marine atmospheric CO2. Earth and Planetary Science Letters 5, 285295.
  • Brendel O., Iannetta P.P.M. & Stewart D. (2000) A rapid and simple method to isolate pure alpha-cellulose. Phytochemical Analysis 11, 710.
  • Bricout J. (1979) Natural abundance levels of 2H and 18O in plant organic matter. In Stable Isotopes, Proceedings of the 3rd International Conference (eds E.R.Klein & P.D.Klein), pp. 215222. Academic Press, New York, NY, USA.
  • Brugnoli E. & Farquhar G.D. (2000) Photosynthetic fractionation of carbon isotopes. In Photosynthesis: Physiology and Metabolism (eds R.C.Leegood, T.D.Sharkey & S.Von Caemmerer),pp. 399434. Kluwer Academic Publishers, Dordrecht, The Netherlands.
  • Buchmann N., Brooks J.R. & Ehleringer J.R. (2002) Predicting daytime carbon isotope ratios of atmospheric CO2 within forest canopies. Functional Ecology 16, 4957.
  • Von Caemmerer S. & Farquhar G.D. (1981) Some relationships between the biochemistry of photosynthesis and the gas exchange of leaves. Planta 153, 376387.
  • Cernusak L.A., Arthur D.J., Pate J.S. & Farquhar G.D. (2003) Water relations link carbon and oxygen isotope discrimination to phloem sap sugar concentration in Eucalyptus globulus. Plant Physiology 131, 15441554.
  • Cernusak L.A., Pate J.S. & Farquhar G.D. (2004) Oxygen and carbon isotope composition of parasitic plants and their hosts in southwestern Australia. Oecologia 139, 199213.
  • Cernusak L.A., Farquhar G.D. & Pate J.S. (2005) Environmental and physiological controls over oxygen and carbon isotope composition of Tasmanian blue gum, Eucalyptus globulus. Tree Physiology 25, 129146.
  • Craig H. & Gordon L.I. (1965) Deuterium and oxygen-18 variations in the ocean and the marine atmosphere. In Proceedings of Conference on Stable Isotopes in Oceanographic Studies and Palaeotemperatures (ed. E.Tongiorgi), pp. 9130. Lischi and Figli, Pisa, Italy.
  • Curtis P.S. (1996) A meta-analysis of leaf gas exchange and nitrogen in trees grown under elevated carbon dioxide. Plant, Cell & Environment 19, 127137.
  • Dawson T.E., Mambelli S., Plamboeck A.H., Templer P.H. & Tu K.P. (2002) Stable isotopes in plant ecology. Annual Review of Ecology and Systematics 33, 507559.
  • Farquhar G.D. & Lloyd J. (1993) Carbon and oxygen isotope effects in the exchange of carbon dioxide between terrestrial plants and the atmosphere. In Stable Isotopes and Plant Carbon–Water Relations (eds J.R.Ehleringer, A.E.Hall & G.D.Farquhar), pp. 4770. Academic Press, San Diego, CA, USA.
  • Farquhar G.D., O'Leary M.H. & Berry J.A. (1982) On the relationship between carbon isotope discrimination and the intercellular carbon dioxide concentration in leaves. Australian Journal of Plant Physiology 9, 121137.
  • Farquhar G.D., Ehleringer J.R. & Hubick K.T. (1989a) Carbon isotope discrimination and photosynthesis. Annual Review of Plant Physiology and Plant Molecular Biology 40, 503537.
  • Farquhar G.D., Hubick K.T., Condon A.G. & Richards R.A. (1989b) Carbon isotope fractionation and plant water-use efficiency. In Stable Isotopes in Ecological Research (eds P.W.Rundel, J.R.Ehleringer & K.A.Nagy), pp. 2146. Springer, Heidelberg, Germany/New York, NY, USA.
  • Farquhar G.D., Henry B.K. & Styles J.M. (1997) A rapid on-line technique for determination of oxygen isotope composition of nitrogen-containing organic matter and water. Rapid Communications in Mass Spectrometry 11, 15541560.
  • Farquhar G.D., Cernusak L.A. & Barnes B. (2007) Heavy water fractionation during transpiration. Plant Physiology 143, 1118.
  • Fuhrer J., Skärby L. & Ashmore M.R. (1997) Critical levels for ozone effects on vegetation in Europe. Environmental Pollution 97, 91106.
  • Gaudinski J.B., Dawson T.E., Quideau S., Schuur E.A.G., Roden J.S., Trumbore S.E., Sandquist D.R., Oh S.W. & Wasylishen R.E. (2005) Comparative analysis of cellulose preparation techniques for use with 13C, 14C and 18O isotopic measurements. Analytical Chemistry 77, 72127224.
  • Grams T.E.E. & Andersen C.P. (2007) Competition for resources in trees: physiological versus morphological plasticity. In Progress in Botany (eds K.Esser, U.Lüttge, W.Beyschlag & J.Murata), pp. 356381. Springer-Verlag, Berlin, Heidelberg, Germany.
  • Grams T.E.E., Anegg S., Häberle K.H., Langebartels C. & Matyssek R. (1999) Interactions of chronic exposure to elevated CO2 and O3 levels in the photosynthetic light and dark reactions of European beech (Fagus sylvatica). New Phytologist 144, 95107.
  • Grams T.E.E., Kozovits A.R., Reiter I.M., Winkler J.B., Sommerkorn M., Blaschke H., Häberle K.H. & Matyssek R. (2002) Quantifying competitiveness in woody plants. Plant Biology 4, 153158.
  • Gray J. & Thompson P. (1977) Climatic information from 18O/16O analysis of cellulose, lignin and whole wood from tree rings. Nature 270, 708709.
  • Hoagland D.R. & Arnon D.I. (1950) The Water Culture Method for Growing Plants without Soil (vol. Circular 374). California Agricultural Experimental Station, Berkeley, CA, USA.
  • Kozovits A.R., Matyssek R., Blaschke H., Göttlein A. & Grams T.E.E. (2005a) Competition increasingly dominates the responsiveness of juvenile beech and spruce to elevated CO2 and/or O3 concentration throughout two subsequent growing seasons. Global Change Biology 11, 13871401.
  • Kozovits A.R., Matyssek R., Winkler J.B., Göttlein A., Blaschke H. & Grams T.E.E. (2005b) Above-ground space sequestration determines competitive success in juvenile beech and spruce trees. New Phytologist 167, 181196.
  • Kreutzer K., Göttlein A., Pröbstle P. & Zuleger M. (1991)Höglwaldforschung 1982–1989. Zielstand, Versuchskonzept, Basisdaten. In Forstwissenschaftliche Forschungen, Ökosystemforschung Höglwald: Auswirkungen von saurer Beregnung und Kalkung in einem Fichtenaltbestand (eds K.Kreutzer & A.Göttlein), pp. 1122. Paul Parey Verlag, Hamburg, Germany.
  • Landolt W., Günthardt-Goerg M.S., Pfenninger I., Einig W., Hampp R., Maurer S. & Matyssek R. (1997) Effect of fertilization on ozone-induced changes in the metabolism of birch (Betula pendula) leaves. New Phytologist 137, 389397.
  • Lange B.M., Lapierre C. & Sandermann H. (1995) Elicitor-induced spruce stress lignin – structural similarity to early developmental lignins. Plant Physiology 108, 12771287.
  • Lippert M., Steiner K., Pfirrmann T. & Payer H.D. (1997) Assessing the impact of elevated O3 and CO2 on gas exchange characteristics of differently K supplied clonal Norway spruce trees during exposure and the following season. Trees – Structure and Function 11, 306315.
  • Luedemann G., Matyssek R., Fleischmann F. & Grams T.E.E. (2005) Acclimation to ozone affects host/pathogen interaction and competitiveness for nitrogen in juvenile Fagus sylvatica and Picea abies trees infected with Phytophthora citricola. Plant Biology 7, 640649.
  • Nunn A.J., Reiter I.M., Häberle K.H., Werner H., Langebartels C., Sandermann H., Heerdt C., Fabian P. & Matyssek R. (2002) ‘Free-air’ ozone canopy fumigation in an old-growth mixed forest: concept and observations in beech. Phyton – Annales Rei Botanicae 42, 105119.
  • Nunn A.J., Kozovits A.R., Reiter I.M., et al. (2005) Comparison of ozone uptake and sensitivity between a phytotron study with young beech and a field experiment with adult beech (Fagus sylvatica). Environmental Pollution 137, 494406.
  • Payer H.D., Blodow P., Köfferlein M., Lippert M., Schmolke W., Seckmeyer G., Seidlitz H.K., Strube D. & Thiel S. (1993) Controlled environment chambers for experimental studies on plant responses to CO2 and interactions with pollutants. In Ecosystems Research Report Nr. 6: Design and Execution of Experiments on CO2 Enrichment (eds E.D.Schulze & H.A.Mooney), pp. 127145. Commission European Communities, Brussels, Belgium.
  • Popp M., Lied W., Meyer A.J., Richter A., Schiller P. & Schwitte H. (1996) Sample preservation for determination of organic compounds: microwave versus freeze-drying. Journal of Experimental Botany 47, 14691473.
  • Pretzsch H., Kahn M. & Grote R. (1998) The mixed spruce–beech forest stands of the ‘Sonderforschungsbereich’‘growth or parasite defence?’ in the forest district Kranzberger Forst. Forstwissenschaftliches Centralblatt 117, 241257.
  • Roden J.S. & Ehleringer J.R. (1999) Observations of hydrogen and oxygen isotopes in leaf water confirm the Craig–Gordon model under wide-ranging environmental conditions. Plant Physiology 120, 11651173.
  • Sandermann H. (1996) Ozone and plant health. Annual Review of Phytopathology 34, 347366.
  • Saurer M., Maurer S., Matyssek R., Landolt W., Günthardt-Goerg M.S. & Siegenthaler U. (1995) The influence of ozone and nutrition on δ13C in Betula pendula. Oecologia 103, 397406.
  • Saurer M., Aellen K. & Siegwolf R. (1997) Correlating δ13C and δ18O in cellulose of trees. Plant, Cell & Environment 20, 15431550.
  • Scheidegger Y., Saurer M., Bahn M. & Siegwolf R. (2000) Linking stable oxygen and carbon isotopes with stomatal conductance and photosynthetic capacity: a conceptual model. Oecologia 125, 350357.
  • Schmidt H.L., Werner R.A. & Rossmann A. (2001) 18O pattern and biosynthesis of natural plant products. Phytochemistry 58, 932.
  • Schulze E.D., Hall A.E., Lange O.L. & Walz H. (1982) A portable steady-state porometer for measuring the carbon-dioxide and water-vapor exchanges of leaves under natural conditions. Oecologia 53, 141145.
  • Sheshshayee M.S., Bindumadhava H., Ramesh R., Prasad T.G., Lakshminarayana M.R. & Udayakumar M. (2005) Oxygen isotope enrichment (Δ18O) as a measure of time-averaged transpiration rate. Journal of Experimental Botany 56, 30333039.
  • Siegwolf R.T.W., Matyssek R., Saurer M., Maurer S., Günthardt-Goerg M.S., Schmutz P. & Bucher J.B. (2001) Stable isotope analysis reveals differential effects of soil nitrogen and nitrogen dioxide on the water use efficiency in hybrid poplar leaves. New Phytologist 149, 233246.
  • Sternberg L. & Deniro M.J.D. (1983) Biogeochemical implications of the isotopic equilibrium fractionation factor between the oxygen-atmos of acetone and water. Geochimica et Cosmochimica Acta 47, 22712274.
  • Sternberg L.D.L., Mulkey S.S. & Wright S.J. (1989) Oxygen isotope ratio stratification in a tropical moist forest. Oecologia 81, 5156.
  • Sullivan P.F. & Welker J.M. (2007) Variation in leaf physiology of Salix arctica within and across ecosystems in the High Arctic: test of a dual Delta C-13 and Delta O-18 conceptual model. Oecologia 151, 372386.
  • Thiel S., Döhring T., Köfferlein M., Kosak A., Martin P. & Seidlitz H.K. (1996) A phytotron for plant stress research: how far can artificial lighting compare to natural sunlight? Journal of Plant Physiology 148, 456463.
  • Waring R.H. & Silvester W.B. (1994) Variation in foliar δ13C values within the crowns of Pinus radiata trees. Tree Physiology 14, 12031213.
  • Warren C.R., Löw M., Matyssek R. & Tausz M. (2007)Internal conductance to CO2 transfer of adult Fagus sylvatica: variation between sun and shade leaves and due to free-air ozone fumigation. Environmental and Experimental Botany 59, 130138.
  • Werner H. & Fabian P. (2002) Free-air fumigation of mature trees – a novel system for controlled ozone enrichment in grown-up beech and spruce canopies. Environmental Science and Pollution Research 9, 117121.
  • Wieser G., Tegischer K., Tausz M., Häberle K.H., Grams T.E.E. & Matyssek R. (2002) Age effects on Norway spruce (Picea abies) susceptibility to ozone uptake: a novel approach relating stress avoidance to defense. Tree Physiology 22, 583590.
  • Yakir D. (1992) Variations in the natural abundance of oxygen-18 and deuterium in plant carbohydrates. Plant, Cell & Environment 15, 10051020.
  • Yakir D. & Israeli Y. (1995) Reduced solar irradiance effects on net primary productivity (Npp) and the δ13C and δ18O values in plantations of Musa sp., Musaceae. Geochimica et Cosmochimica Acta 59, 21492151.
  • Zinser C., Ernst D. & Sandermann H. (1998) Induction of stilbene synthase and cinnamyl alcohol dehydrogenase mRNAs in Scots pine (Pinus sylvestris L.) seedlings. Planta 204, 169176.