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Tropospheric concentrations of ozone (O3), a secondary agent in global climate change, have been rising since the industrial revolution and are predicted to rise globally in the near future (IPCC, 2007). Because of its phytotoxicity, O3 may limit the carbon (C) sink strength of plants (Ashmore, 2005; Giles, 2005; Sitch et al., 2007). Such a limitation decreases or even negates the expected stimulation of plant growth by elevated atmospheric carbon dioxide concentrations (Karnosky et al., 2003; Oksanen et al., 2007; Matyssek et al., 2010a). Because C sequestration in forests is particularly relevant in global C budgets, O3-induced reduction in the C sink strength of forest trees will potentially exacerbate radiative forcing (Sitch et al., 2007). Reductions in stem growth as an endpoint of O3 impact on trees have been reported (Bortier et al., 2000; Matyssek & Sandermann, 2003; Vollenweider et al., 2003; Karlsson et al., 2004; Wittig et al., 2009; Pretzsch et al., 2010). With a few exceptions that involved adult Fagus sylvatica and Picea abies trees (Matyssek et al., 2010b; Pretzsch et al., 2010), these results are derived from chamber studies on small trees or free-air O3 exposure experiments on fast-growing young trees of pioneer species (Karnosky et al., 2003; Kontunen-Soppela et al., 2007; Matyssek et al., 2010a).
Direct assessment of O3 impact on growth and carbon sink strength of large, adult forest trees is difficult and involves long-term observations (Manning, 2003). For example, an effect of twice-ambient O3 concentrations (2 × O3) on the growth of adult trees was only detectable after eight growing seasons, and even then only on one of the two species involved (on Fagus sylvatica, Pretzsch et al., 2010). It is therefore important to identify stages of O3 impact, that is, effects at the molecular and biochemical levels that precede potential visible foliar symptoms and growth decline (Wild & Schmitt, 1995). Despite a large number of papers (for reviews see, e.g., Matyssek & Sandermann, 2003; Wittig et al., 2009) and some good theoretical understanding of aspects of the mode of action (Heath, 2008; Matyssek et al., 2008), identification of O3-specific effects on leaves of adult forest trees is surprisingly elusive under field conditions. A number of studies have summarized that many tree physiology-related parameters are affected by O3, and theoretical frameworks to interpret such multiple and often transient responses have been proposed (Karnosky et al., 2003; Nunn et al., 2005; Matyssek et al., 2007a; Wittig et al., 2009; Leisner & Ainsworth, 2011). Empirical and statistical evidence in support of such theoretical frameworks is often poor, because physiological and biochemical responses to O3 can apparently be inconsistent or conflicting (Bortier et al., 2000; Nunn et al., 2005; cf. Matyssek et al., 2007a). These studies provide an insight into the O3 response of individual parameters, but do not provide a comprehensive simultaneous multivariate analysis of the overall leaf-level response to O3.
Most reviews on the topic postulate consistent metabolic patterns characterizing the response to O3, linking responses within and across organizational levels from molecules to the whole plant (Matyssek & Sandermann, 2003; Sandermann & Matyssek, 2004; Heath, 2008; Matyssek et al., 2008; Heath et al., 2009). Such patterns would be detectable as concerted changes in single biochemical and physiological parameters. Despite the high variability in the response to O3 of most of the biochemical and physiological parameters in question (Nunn et al., 2005), underlying patterns composed from several measured parameters may become accessible using multivariate explorative statistical methods. One study by Nali et al. (2005) utilized a combination of multivariate variance analysis and canonical discriminant analysis characterizing the overall response of clover to O3, with the aim of analysing the biochemical and physiological bases of O3 tolerance and identifying potential biomarkers for O3. Kontunen-Soppela et al. (2007) used principal component analysis (PCA) on metabolomic profiles of two clones of O3-exposed birch to identify genotypic and O3 effects on leaf metabolites such as phenolics, and lipophilic and polar compounds. PCA, a common multivariate method, was successfully employed more generally to identify patterns in physiological datasets (García-Plazaola et al., 2000; Tausz et al., 2001; Wright et al., 2004; Warren et al., 2005). Such an approach allows one to test whether one or more response patterns identified by PCA are related to O3 exposure (Kontunen-Soppela et al., 2007).
In the present study, we exploited an extensive database of leaf-level physiological and biochemical parameters of c. 60-yr-old Fagus sylvatica (European beech) trees exposed to experimental 2 × O3 under open field conditions (Matyssek et al., 2007a). The database integrates data collected during the years 2003 and 2004. The parameters in the database cover multiple aspects of leaf-level physiology and biological pathways (including photosynthesis, C metabolism, phytohormones, antioxidants, pigments, and stomatal O3 uptake) and were selected to represent biochemical and physiological leaf functions that are known to be affected by O3 (Matyssek & Sandermann, 2003). Owing to the inclusion of the extraordinary European drought season of 2003 (Ciais et al., 2005), two climatically contrasting growing seasons were captured in the database. This allows the analysis of O3 impact on tree functioning in interaction with drought conditions. A range of significant leaf-level responses to O3 treatment have been reported previously for F. sylvatica, but they were highly variable in extent and time (Nunn et al., 2005; Matyssek et al., 2007a, 2010b). As in many other studies, few of the selected leaf parameters available in the database were consistently responsive to O3 stress. Nevertheless, after 8 yr of 2 × O3 exposure, growth of F. sylvatica was significantly impaired (Pretzsch et al., 2010).
We applied PCA to explore higher-order variables derived from the original database with the aim of testing the hypothesis that one or several of these extracted variables (principal components, PCs) represent multivariate metabolic processes that are responsive to O3, that is, they respond significantly and with consistent patterns to 2 × O3. Our second aim was to examine further whether a relationship exists between such PCs (if any) and the widely used O3 exposure index AOT40 (accumulated O3 exposure over a threshold of 40 nl O3 l−1) and/or the physiologically relevant O3 dose (i.e. the cumulative stomatal O3 uptake, COU), which has been identified as the preferable metric for correlating O3 impact with plant responses (Matyssek et al., 2007b, 2008).
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The main aim of this study was to identify associations between changes in leaf-level physiological processes of adult F. sylvatica upon exposure to O3 under field conditions. O3 is known to affect many leaf-internal processes (Matyssek & Sandermann, 2003; Matyssek et al., 2007a; Heath, 2008; Heath et al., 2009). Because each effect on individual parameters can be minor, variable, or only transient (Nunn et al., 2005), it is difficult to assess effects of O3 on trees before growth impairment can be measured. This is especially relevant for adult trees where O3 impact might be masked by large C storage available to repair and defence processes (Matyssek et al., 2008).
We employed PCA on a large multivariate physiological data set with the aim of identifying underlying response patterns related to O3. We extracted five PCs explaining c. 60% of the original variance in the dataset. In some other studies, fewer PCs were sufficient to explain a similar proportion of data variance in physiological or morphological leaf parameters (Tausz et al., 2001; Wright et al., 2004). In contrast to those studies, our data covered seasonal courses, strong interannual climate variations, sun and shade crown positions, as well as the experimental O3 regimes, which can all be expected to affect any response variable or metabolic pattern (Karnosky et al., 2005; Matyssek et al., 2010b). Furthermore, we used more input variables, covering a wider range of physiological leaf functions than the cited studies. Such studies, using a greater number of input parameters, commonly report smaller proportions of variance explained by relevant PCs (e.g. Kontunen-Soppela et al., 2007). Therefore PCs extracted in our analyses, for example, PC1 covering 19% of the overall variation (Table 2), are meaningful representations of underlying response patterns.
Only PC1 was significantly affected by the experimental O3 regime and appears suitable for identifying physiological leaf-level response patterns to O3 (Tables 2, 3). As only five of the originally measured 27 parameters were affected by O3, and only two of these five are represented in PC1, this indicates an otherwise unrecognized multivariate pattern.
In our ANOVA (Table 3), the responses of the PCs to the experimentally imposed increase in O3 over ambient O3 were tested under simultaneous consideration of sampling date and sampling year, factors that incorporate variability in environmental factors such as temperature or irradiance, which are often correlated to ambient O3. As this study focused on O3 effects, we will henceforth only discuss PC1, as the only PC significantly affected by O3, in detail. Scores of PC1 were significantly higher under 2 × O3 than under 1 × O3 (Table 3, Fig. 2), even under extraordinarily dry conditions, when most O3 effects on physiological parameters appeared to be overruled by drought (Löw et al., 2006). The physiological interpretation of PC1 can be derived from the loadings contributed by the original variables (Table 2). PC1 is mainly indicative of changes in defence mechanisms and carbon metabolism. High scores, as observed under 2 × O3, are related to high cellulose concentration but low sucrose (Blumenröther et al., 2007) and glucose concentrations – that is, structural rather than soluble carbohydrates. High scores of PC1 are also related to low activities of the carboxylating enzymes Rubisco and PEPc, consistent with negative O3 effects on carbon fixation reactions (Dizengremel et al., 1994), even though the instantaneous measurement of carbon fixation, Asat or Jmax, was not relevant for this PC (Table 2). Enzyme activity and external N sources and concentration can also be related to N metabolism, as indicated by δ15N (Högberg, 1997; Kolb & Evans, 2003). According to Haberer et al. (2007a), δ15N was decreased in leaves under 2 × O3, a response reflected in PC1 as well.
High scores of PC1 were furthermore associated with more negative δ13C (cf. Gessler et al., 2009). Being a measure of C isotope discrimination of assimilated C, δ13C is directly dependent on ci/ca ([CO2] in the sub-stomatal cavity ci / [CO2] in ambient air ca), with high ci related to more negative δ13C (Cernusak et al., 2005). Declining δ13C (i.e. towards ‘more negative’ concentrations) may be caused by a decrease in photosynthetic carboxylation, a reduced proportion of carbon fixed via PEPc, or high stomatal conductance (reducing resistance to CO2 influx), or any combination of these factors (Cernusak et al., 2005). In adult beech trees, stomatal narrowing (decrease in gs) rather than opening was observed under 2 × O3 (Löw et al., 2006). In addition, stomatal opening would lead to greater transpiration, which may be reflected by Δ18O signatures (Adams & Grierson, 2001) – an effect apparently not related to PC1 (Table 2). Therefore, the association of δ13C with PC1 seems to reflect changes in carboxylation, also indicated by enzyme activities (Table 2), rather than increased transpiration.
Some parameters related to the photosynthetic light reactions, defence and the antioxidative systems (DEEPS, GSSG, ascorbate, and Chla + b) also showed considerable loadings on PC1, indicating some oxidative stress signalling upon O3 exposure (an increase in GSSG has been ascribed to initial stress response; Tausz et al., 2004), maybe related to a weakening of antioxidative defence (indicated by negative loadings of ascorbate). Oxidative stress, however, did not seem to originate within the chloroplast (‘photo-oxidative stress’). Under photo-oxidative stress, DEEPS, as an indicator of protective thermal energy dissipation, is expected to increase (Demmig-Adams & Adams, 2006), and Chl content should decline. By contrast, in our study, high scores on PC1 are related to a decrease in DEEPS and an increase in Chl; hence the pattern reflected by PC1 appears consistent with oxidative stress initiated by O3 in the apoplast or at the plasmalemma (Matyssek & Sandermann, 2003).
High PC1 scores during the dry 2003 season (Fig. 2) may reflect stress from extraordinary drought on the leaf, eliciting responses similar to O3 impact. Some previous studies suggested that O3 effects were less severe under drought conditions (see summaries by Matyssek et al., 2007a, 2010b). However, exacerbation of stress under high O3 impact and drought as a result of synergetic effects has also been suggested, because both stress factors impose pressure on defence systems, thus perhaps shifting the ‘effective O3 dose’ towards increased responsiveness (Löw et al., 2006; Matyssek et al., 2006, 2008; Musselman et al., 2006; Tausz et al., 2007). The dependence of PC1 scores on month are in agreement with the seasonal variability of many leaf-level parameters (e.g. Löw et al., 2006; Table S2, cf. references in Table 2). PC1 scores were also greater in shade crown leaves, suggesting potentially lowered protection against stress (as suggested for drought stress effects, e.g., by Valladares & Pearcy, 2002), maybe because shade leaves have fewer defence compounds per unit COU (Wieser et al., 2002; Wieser & Matyssek, 2007). Inherently weaker defence in shade than in sun leaves coincides with an apparently stronger response of PC1 in the shade relative to the sun crown. Although the interaction between O3 and crown position was not statistically significant, there is a trend towards differences in slopes between sun and shade crown when PC1 scores are plotted against quantitative exposure (AOT40) and uptake (COU) data (Fig. 2). Low COU in the shade crown (as compared with the sun crown) may already trigger O3-driven responses, perhaps because of light limitation on photosynthate supply for repair and detoxification (Fig. 2).
The assessment of O3 impact on plants has moved towards O3 uptake-based thresholds and indicators to replace O3 exposure-based indices such as AOT40 (Ashmore et al., 2004; Matyssek et al., 2007b, 2008). The relationship of PC1 with AOT40 and COU was similar in the dry and the average year, and was similar in the sun and shade crown (Fig. 2b,c). In our study, the covariates AOT40 and COU both had a statistically significant effect on PC1 at P < 0.001. However, COU contributed relatively more to the R2 of the GLM (comparable to the factor year), whereas the relative contribution of AOT40 was smaller. This result supports COU as a better index to potentially predict multivariate plant responses to O3.
Our study shows that multivariate analysis is able to detect O3-related changes in leaf physiology (PC1) in the absence of consistent O3 effects on individual parameters. As PC1 showed a consistent O3-related effect over two growing seasons, which was even found during the severe drought of 2003, it underlines the susceptibility of adult trees growing in a forest environment to elevated O3.