Growth losses in Swiss forests caused by ozone: Epidemiological data analysis of stem increment of Fagus sylvatica L. and Picea abies Karst.

Environmental Pollution 192 (2014) 129e138

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Growth losses in Swiss forests caused by ozone: Epidemiological data analysis of stem increment of Fagus sylvatica L. and Picea abies Karst. Sabine Braun a, *, Christian Schindler b, Beat Rihm c €nenbuch, Switzerland Institute for Applied Plant Biology, Sandgrubenstrasse 25, CH-4124 Scho Swiss Tropical and Public Health Institute, Socinstrasse 57, CH-4002 Basel, Switzerland c Meteotest, Fabrikstrasse 14, CH-3012 Bern, Switzerland a

b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 18 August 2013 Received in revised form 28 March 2014 Accepted 5 May 2014 Available online xxx

The estimate of growth losses by ozone exposure of forest trees is a significant part in current C sequestration calculations and will also be important in future modeling. It is therefore important to know if the relationship between ozone flux and growth reduction of young trees, used to derive a Critical Level for ozone, is also valid for mature trees. Epidemiological analysis of stem increment data from Fagus sylvatica L. and Picea abies Karst. observed in Swiss forest plots was used to test this hypothesis. The results confirm the validity of the flux-response relationship at least for beech and therefore enable estimating forest growth losses by ozone on a country-wide scale. For Switzerland, these estimates amount to 19.5% growth reduction for deciduous forests, 6.6% for coniferous forests and 11.0% for all forested areas based on annual ozone stomatal uptake during the time period 1991e2011. © 2014 Elsevier Ltd. All rights reserved.

Keywords: Ozone flux Fagus sylvatica Picea abies Growth reduction Critical level

1. Introduction Ozone (O3) is a highly phytotoxic air pollutant (Fuhrer et al., 1997; Matyssek et al., 2010a). Apart from growth loss (Karlsson et al., 2007; Wittig et al., 2009) and decreasing carbon sequestration (Büker et al., 2012), harmful effects of ozone on vegetation include also visible injury on leaves (Hicks, 1978; Günthardt-Goerg et al., 1993; VanderHeyden et al., 2001; Paoletti et al., 2009; Sicard et al., 2011), decreasing foliar chlorophyll content (Dalstein and Vas, 2005), inhibited carbon allocation to the roots (Coleman et al., 1995; Samuelson and Kelly, 1996; Lux, 1997), premature leaf€a €kko € nen et al., 1997; Gielen et al., 2007), a senescence (Pa changed susceptibility to abiotic and biotic stress factors (Braun and Flückiger, 1989; Wellburn and Wellburn, 1994; Manning and von Tiedemann, 1995; Karnosky et al., 2002, 2007) and sluggish or impaired response of stomata to drought (Maier-Maercker and Koch, 1991; Pearson and Mansfield, 1993; Paoletti, 2005). The United Nations Economic Commission for Europe (UNECE) has defined critical levels for external ozone concentration and stomatal ozone uptake to protect from negative impacts (UNECE, 2010).

* Corresponding author. E-mail address: [email protected] (S. Braun). http://dx.doi.org/10.1016/j.envpol.2014.05.016 0269-7491/© 2014 Elsevier Ltd. All rights reserved.

Air pollution control measures have led to a reduction of peak ozone concentrations in Switzerland but the average and the lower quantiles have been increasing in rural stations between 1990 and 2012 (BAFU, 2013, Bundesamt für Umwelt). In addition, an increase of ground-level ozone is expected due to climate change at global scale (Fowler et al., 1999; Sitch et al., 2007). Wittig et al. (2009) estimate that growth reduction in trees may reach 17% by 2100 relative to preindustrial O3 and thus a further 10% reduction relative to today. Ozone has also the ability to counteract growth stimulations by increased CO2 (King et al., 2005) and thus plays an important role in C sequestration (Harmens and Mills, 2012). For the estimate of C sequestration in climate models and for forestry management plans, a realistic estimate of growth loss by ozone is essential, since biomass in forests represent a significant amount of C stock. Dose-response relationships for growth of forest trees have been established using the “Accumulated Ozone over a Threshold of 40 ppb” (AOT40; Fuhrer and Achermann, 1994). However, accumulating evidence suggests that the responses of vegetation to O3 related to the absorbed dose through stomata are better (Reich, 1987; Paoletti and Manning, 2007). Therefore, the DO3SE (Deposition of Ozone for Stomatal Exchange) model has been developed to calculate a stomatal ozone flux (Emberson et al., 2000; Büker et al., 2007). Old fumigation experiments with forest trees have been recalculated to establish a flux based doseeresponse relationship

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S. Braun et al. / Environmental Pollution 192 (2014) 129e138

Fig. 1. Location of the forest observation plots used in this study.

(Karlsson et al., 2007; Mills et al., 2012). These fumigation experiments were, however, conducted with seedlings or saplings in chambers, and it has been questioned if these results can be transferred to mature forest trees (Kolb and Matyssek, 2001). Young and mature trees differ in various aspects (Fredericksen et al., 1995). Differences between young and mature trees in stomatal conductance or ozone sensitivity have been reported for several tree species but the pattern is not consistent (Kolb and Matyssek, 2001). Grulke and Miller (1994) stated a decreasing stomatal conductance with increasing tree age in giant sequoia which affected O3 sensitivity. Based on photosynthesis measurements in twig chambers, Wieser et al. (2002a,b) concluded that mature trees of Norway spruce were more than 4 times less sensitive to O3 than seedlings, one of the reasons being a higher amount of ascorbate per unit surface area of the needles. Greater sensitivity of older trees have been reported for black cherry (Fredericksen et al., 1995, 1996) and for red oaks (Samuelson and Edwards, 1993; Hanson et al., 1994; Samuelson and Kelly, 1996). Even the differentiation between shade and light leaves within the crown may affect ozone sensitivity in a contrasting way (e.g. Nunn et al., 2005). In shade-tolerant tree species (Fagus sylvatica, Acer saccharum) shade leaves were more sensitive to ozone as assessed by measurements of photosynthesis (Tjoelker et al., 1993; Kitao et al., 2009) whereas in light demanding tree species (Populus sp.) the opposite was demonstrated (Tjoelker et al., 1993, 1995). Pathogen or pest interactions may differ between natural field conditions and chambers because of the diverse microclimate (McLeod and Long, 1999; Karnosky et al., 2001). In the Aspen FACE (Free-Air Carbon Dioxide Enrichment) experiment, ozone fumigated aspen leaves were preferred by the common leaf weevil (Freiwald et al., 2008). Free air fumigation experiments with mature trees (Karnosky et al., 2007; Matyssek et al., 2010b) may help to answer this question, but this expensive approach cannot be used for a large

number of ecosystems. Epidemiological data analysis may help to fill this gap. It needs a quantitative response measure within a wide range of ozone load, ozone maps with a high spatial resolution and good information on possible confounding factors such as drought stress. Visible injury (Ferretti et al., 2007) or growth data (Braun et al., 1999; Karlsson et al., 2006) may fulfill these requirements. The aim of the present study was (1) to analyze stem increment data from permanent forest observation plots in Switzerland for beech (Fagus sylvatica L.) and Norway spruce (Picea abies Karst.) and (2) to estimate forest growth losses by ozone for Switzerland. 2. Materials and methods 2.1. Description of forest plots and increment measurement The study was performed in a network of permanent forest observation plots which was initiated in 1984 to study forest dieback. They consist of either pure Fagus sylvatica, pure Picea abies or a mixture of both and include 30e60 individuals of each species. Fig. 1 and Table 1 give an overview of the plots included in this study. Stem diameter was measured every 4 years at marked points at breast height, with the last period ending in 2010. Basal area increment was calculated from the difference in diameter. Felled trees and trees with implausible measurements were removed from the dataset. The basal area increment was taken as a surrogate for biomass increment in the epidemiological analysis because a transformation to volume would have added more uncertainty.

Table 1 Description of observation plots. Age is given for 2010. Species

Fagus sylvatica

Picea abies

Number of plots Number of trees Altitude range (m. a.s.l.¼ Tree age range (years) Number of increment measuring periods

62 3304 260e1220 76e186 5

42 1522 290e1870 70e321 4

Table 2 Annual ozone fluxes (POD1, mmol m2) for beech (excluding soil water) calculated at 38 ozone monitoring stations between 1991 and 2011. The average number of available stations (n) during the whole period is 24. Station

1991

6

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

14

6 21 14

5 23 13

8 23 13

5 20 15

4 20 15

4 21 15

6 20 17

5 20 17

7 21 17

6 20 6

4 20 17

24

25

13 23 11

15 26 13

22 14 13 32 13

24 14 15 24 13

23 14 13 17 15

24 18 14 39 16

27 17 15 10 15

26 11 14 21 16

28 19 15 18 15

26 17 14 17 13

26 19 14 5 14

17

2003

14

2004

2005

2006

10

6 21 14

7 19 17

10 19 16

26 16 15 17 15

25 8 16 11 11

26 13 15 17 16

25 11 14 17 15

24 13 14 16 14

18

19

12

17

14

14

16

16

18

18

17

18

10

19

17

16

12

13

16

12

16

18

15 12

17 17

17 13

16 16

14 15

16 13

13 11

14 18

15 16

14 15

23 24

22 25

21 22

21 25

21 23

22 23

23 25 29

16 25 34

25 26 37

13 26 22

24 25 29

25 24 35

16 27 25

23 25 22

23 24 24

22 25 24

22

25

21

24

23

18

19 11

15 13

16 12

16 15

12 15

20 17

22 30 22 13

16 24 21 15

12 23 25 16

11 24 22 15

16 27 23 17

7 16 24 9

10 24 25 14

12 18 24 15

12 19 22 14

13

14

13

14

14

14

15

15

15

13

13

14

14

12

12

18

18

18

19

20

21

22

20

21

20

20

18

21

19

18

15

18

12

11

11

15

13

12

25 22

26 22

22 23

26 23

29 23

18 22

23 23

23 23

17

17

17

14 11 19 16 20 17 22

15 22 17 24

15 20 18 22

13 18 15 23

14 10 15 8 18 16 17 17 13 14 11 12 22 21 20 14

12 10 14 13 19 18 15 15 14 11 15 16 20 17 21 16 17 20 15 17 11 11 17 9 16 7 19 23 13 36

12 10 14 4 16 14 15 12 14 9 11 13 23 24 19 12

8 12 8 20 14 20 17 25 14 14 17 14 10 17 15 16 16 15 14 14 20 24 26 21 16 21 20 14 17 13 11 19 7 17 12 20 23 22 24

23 17 36

16

17

Mean

5 11 12

21 21 33

15

16

2011

11 14 11

25 23

15

13

2010

6 9 13

14

15

22 20

2009

12 14 12 20 15 21 17 23

20 15 17 11 11 19 11 20 7

16

18

2008

13 12 15 12 18 16 16 20 15 13 15 18 22 23 22 12 11 19 14 17 11 12 19 7 19 10

14

22 10

2007

13 11 15 15 19 12 16 13 14 16 19 21 21 21 13

20 16 18 13 11 19 5 20 10 21 25 19 35

23 11 16 11 13 19 4 12 8 20 21 21 35

S. Braun et al. / Environmental Pollution 192 (2014) 129e138

Aigle res Anie Arosa Bachtel Basel Brunnersberg Castaneda Chaumont Chablais Davos Dornach Dübendorf Eggerberg Etzelkofen Les-Giettes €geren La Le Landeron Magadino Muri Passeiry Payerne Rigi Sagno Saignelegier €nenbuch Scho Sciss St. Gallen-S Sisseln Soglio St. Moritz Schwyz €nikon Ta Turtmann Weerswilen Wengernalp Wald Zimmerwald Zugerberg n

131

132

S. Braun et al. / Environmental Pollution 192 (2014) 129e138

2.2. Meteorological data and calculation of drought Drought was used as a covariate in the data analysis of stem increment and to model the soil water effect on ozone uptake. It was estimated using a hydrological model (Wasim-ETH version 9.05.03; Schulla and Jasper, 2013) for each observation plot in daily resolution. Meteorological data were available from Swissmetnet (www.meteoschweiz.ch) in hourly resolution. They were interpolated using the “Shepard's Gravity Interpolation” method (Zelenka et al., 1992) from the nearest eight monitoring stations. This method considers the three dimensional distance between stations, taking into account the altitude. The spatial resolution of the data is given by the resolution of the topographical dataset which is 250  250 m. The interpolation resulted in daily averages of temperature, relative humidity, precipitation amounts, irradiation and wind speed which were used as input to the hydrological model. The soil properties which were necessary for model input (texture, density, carbon concentration, stones) were assessed in each plot with a soil pit according to the classification system of Benzler et al. (1982). From the model outputs, soil water potential, soil moisture deficit, relative available soil water or the ratio between actual and potential evapotranspiration were extracted in daily resolution. These parameters were averaged either for the whole season or parts of it (spring months) to get annual indicators. The hydrological model was validated with measurements of soil water content and soil water potential. Both were continuously monitored in 27 of the plots using Decagon 10HS soil water sensors and Delmhorst GB2 gypsum blocks, respectively, in depths of 20, 40 and 60 cm. The probes were connected to V3 and SP3 loggers, respectively (EMS Brno, CZ), and logged in hourly resolution. 2.3. Maps of ozone flux (POD1) 2.3.1. Ozone monitoring network For flux calculation ozone and meteorological data from 38 rural air pollution monitoring stations throughout Switzerland were available (Table 2, Figs. 2 and 3). They belong to the National Air Pollution Monitoring Network (www.bafu.admin.ch/ luft/luftbelastung), cantonal stations and own monitoring stations. Quality control was performed according to the national guidelines (BUWAL, 2003). Data gaps were filled using correlations with neighboring stations. 2.3.2. Flux calculation POD1 (Phytotoxic Ozone Dose POD1, mmol m2) is the stomatal ozone uptake calculated with a threshold of 1 nmol m2 PLA s1 and cumulated over one growing season for beech and over one year for Norway spruce as recommended by UNECE

(2010). It was calculated with the model DO3SE (Deposition of Ozone for Stomatal Exchange; Büker et al., 2011) using ozone concentrations extrapolated to the top of the canopy. The parameterizations for beech and Norway spruce for Continental Central Europe (Table 3) were applied, including the effect of soil moisture on ozone uptake. For reasons of comparability, medium water storage capacity was used uniformly. However, soil water limitation rarely added to the limitations by VPD. The average difference of the DO3SE outputs for the monitoring stations between flux with and without soil water was 4.6% for beech and 1.2% for Norway spruce. The phenological part of the DO3SE model was replaced by annual observation data from the Swiss Meteorological Network (Defila and Clot, 2005) including an altitude correction. The dates for budbreak and leaf fall of beech were used for start and end of the growing season (SGS and EGS, respectively). 2.3.3. Mapping procedure The annual cumulative stomatal ozone flux values obtained from the DO3SE calculations of the rural monitoring stations were subjected to mapping. A direct spatial interpolation (e.g. kriging) of the station data was not possible as the number of stations was too small in relation to the complexity of topographical and climatic zonation. In a first step, average flux values for each station were regressed against maps of long-term ozone concentrations, NO2 concentrations, air humidity, temperature, absolute and relative altitude, precipitation, wind speed and irradiation to find predictors for the spatial distribution, avoiding combinations of highly correlated variables such as temperature and altitude. The resulting significant predictors were then used on a grid basis to produce nationwide maps. All maps were calculated on a raster with a cell size of 250 m. In a second step, annual maps of fluxes were produced by calculating ratios for fluxes at the monitoring stations by dividing the flux of the specific year by the mean flux calculated over the period. These ratios were spatially interpolated from the 12 nearest stations using an IDW-approach (weight of a station is inverse to its distance), resulting in a ratio map. The ratio map was then multiplied with the map of the mean flux of the time period. Procedures based on more theoretical considerations (e.g. estimates of stomatal flux from meteorology) produced maps with larger error. Flux maps were produced for ozone uptake without limitation of soil water. In a second step, regression analysis was used to derive a soil water factor from the site specific Eta/Etp ratio and the flux without limitation. In the case of flux for beech this soil water limiting flux showed, however, serious collinearity with ETa/ETp as a drought indicator, making it necessary to map the soil water limited flux in a similar way as the non limited flux. On an average of all beech plots, these two methods gave

Fig. 2. Map of interpolated ozone fluxes for beech, average 1991e2011, shown for forest areas with more than 10% deciduous trees according to LFI/WSL (1990/92). Triangles are the 38 monitoring stations used for flux calculation.

S. Braun et al. / Environmental Pollution 192 (2014) 129e138

133

Fig. 3. Map of interpolated ozone fluxes for Norway spruce, average 1991e2011, shown for forest areas with more than 10% coniferous trees according to LFI/WSL (1990/92). Triangles are the 38 monitoring stations used for flux calculation.

rather similar estimates. The first procedure resulted in a mean ozone uptake of 16.4, the second in an estimate of 17.3 mmol m2 a1, whereas the average of the ozone uptake without considering soil water was 18.5 mmol m2 a1.

2.4. Epidemiological statistics Basal area increment over the 4 year increment period was used as a dependent variable. It was averaged for each individual tree over the whole period and the ratio to this long term average was used in the statistical analysis. The explanatory variables such as ozone flux and climatic variables were averaged over the 4-year increment periods and the ratio to the long term average calculated as well. The

Table 3 Parameters used for the ozone flux calculation (UNECE, 2010 parameterization for Continental Central Europe). Parameter

Fagus sylvatica

Picea abies

Canopy height Root depth Leaf dimension Albedo (fraction) gmax (mmol O3/m2 PLA/s) fmin (fraction) SGS EGS Fphen e Fphen 1 Fphen 2 Threshold Y for PODy (nmol m2 s1) Lighta Minimum temperature (Tmin,  C) Optimum temperature (Topt,  C) Maximum temperature (Tmax,  C) VPD for max g (VPDmax, kPa) VPD for min g (VPDmin, kPa) SWP for min g (SWPmin, MPa) SWP for max g (SWPmax, MPa)

30 1.0 0.070 0.16 150 0.13 Phenological observation 0.4 20 20 1 0.006 5 16 33 1.0 3.1 1.25 0.05

30 1.0 0.008 0.12 125 0.16 e e

use of ratios instead of absolute values removed the effect of confounding factors which are constant over time such as altitude or site fertility. The data analysis was performed using a linear mixed-effects model (function lme in R; Pinheiro and Bates, 2000), with backward selection of parameters. The Akaike Information Criterion (AIC) was used to compare different models, aiming at minimizing it. Ozone concentrations and ozone flux are correlated with various climate variables. Various regression models were therefore run with ozone and different combinations of climate predictors, and the output of these models was compared. Ozone coefficients varying from one model to the other suggest a problem with confounding factors, whereas stable coefficients support the result (Kleinbaum et al., 2008). Various climate variables were tested as direct predictors in a backward procedure. Predictors which lead to an increase of AIC were removed. To address the problem of collinearity, two multivariable procedures were used. 1) Factor analysis: The set of climate parameters was subjected to a factor analysis and the resulting factor scores included in the mixed regression analysis. 2) Lasso (Elfron et al., 2004): Growth and ozone were separately regressed against a set of climate variables, using the function l1ce (packages lasso2 and lars in R). Then the residuals of growth were regressed against the residuals of ozone. Residuals were analyzed for normal distribution, heteroscedasticity and for outliers using diagnostic plots and checked for spatial and temporal homogeneity.

3. Results 3.1. Maps of phytotoxic ozone dose (POD1)

1 0.010 0 14 35 0.5 3.0 0.50 0.05

Fig. 2 shows the ozone flux map for beech in deciduous forest areas of Switzerland (fraction of deciduous trees 10% according to LFI/WSL 1990/92). The mapped flux values extend from 9.8 to 23.1 mmol m2 year1, with an average of 17.7. The map for Norway spruce flux in spruce in areas with a coniferous coverage of 10% is shown in Fig. 3. The mapped values range from 15.7 to 33.8 mmol m2 year1, with an average of 27.3. Both maps show the highest flux values at medium altitudes (800e1200 m a.s.l.), where

134

S. Braun et al. / Environmental Pollution 192 (2014) 129e138

Table 4 Correlation matrix of the variables used in the regression analysis. n ¼ 12743

1 2 3 4 5 6 7 8

Beech data set (n ¼ 12743)

Growth POD1 without soil water POD1 with soil water Irradiation Temperature ETa/ETp ETa/ETp early season Soil water potential

1

2

1.00 0.06 0.01 0.10 0.16 0.05 0.03

1.00 0.96 0.45 0.67 0.39 0.34

3

1.00 0.60 0.59 0.61 0.55

Spruce data set (n ¼ 5310) 4

5

1.00 0.01 0.61 0.56

1.00 0.20 0.17

the air humidity and ozone levels are relatively high. The high ozone concentrations in Southern Switzerland (Ticino; BAFU, 2013) are hardly reflected in higher ozone uptake rates. 3.2. Model development and confounding factors Growth was regressed against mapped ozone flux, various drought indicators and other climate predictors. Table 4 shows a correlation matrix of the input variables used. Drought parameters, irradiation and air temperature are strongly correlated with ozone flux which suggests possible collinearity problems. The influence of confounding factors was checked by comparing the output of models with different covariates as well as of models including a multivariable aggregation of the meteorological data. The result of this comparison is shown in Table 5 for beech and in Table 6 for Norway spruce. In beech, the coefficient of the ozone regression was significantly negative across all models tested. The low variation of the estimates for growth reduction suggests a rather robust relationship. The best single drought variable for beech was the evapotranspiration ratio averaged over the early season (from 5 days before to 80 days after budbreak) as suggested by the lowest AIC among the models with single predictors. As a multivariable procedure, lasso yielded the lowest AIC but its results are more difficult to interpret because of the lack of identifiable predictors. In the case of Norway spruce, the ozone correlations were mostly not significant, and the variations between the estimates for growth reduction are much larger suggesting a problem with confounding factors. Air temperature and irradiation were not included in the analyses with single predictors because unrealistic coefficients were obtained: a strong negative correlation of stem increment with air temperature and a positive correlation with

6

1.00 0.93

1

2

3

4

5

1.00 0.00 0.03 0.01 0.07 0.13

1.00 0.97 0.36 0.52 0.15

1.00 0.48 0.47 0.39

1.00 0.02 0.50

0.113

0.085

0.221

0.471

6

1.00 0.05

1.00

0.044

0.562

irradiation which was beyond experienced relationships as e.g. published by Aber and Federer (1992). They are, however, included in the two multivariable procedures as potential confounders of ozone. Foliar N concentration was also confounding. It was omitted as well as it is a vitality indicator which may be affected by ozone itself making it a likely intermediate endpoint. The best single drought predictor was soil water potential averaged over the uppermost 60 cm. 3.3. Quantitative estimates of stem increment reduction From the coefficients for POD1 in Tables 5 and 6 quantitative estimates for growth reduction were derived. The coefficients of all the other predictors were multiplied with their averages in the dataset. These estimates and the intercept of the equation were summed up to yield an intercept for the calculation of the ozone effect. In the case of beech, the resulting estimate for growth reduction at 4 mmol m2 yr1, the current critical flux, was 4.1 ± 1.9% (confidence interval) with flux data without soil water and 4.3 ± 1.9% with flux including soil water. This is in excellent agreement with the 4.4% growth reduction expected from the fluxresponse relationship from fumigation experiments with seedlings or saplings (Table 7). The estimate of the multivariable procedure is somewhat higher (6.1 ± 2.8 and 6.6 ± 2.8%, respectively, for the flux data with and without soil water). In the case of Norway spruce, the ozone correlations were not significant and the uncertainty resulting from confounding factors was large. The best model with single parameters yielded estimates for growth reductions at the critical level of 5.9 ± 6.5 and 4.2 ± 7.1%, respectively, for the flux calculated without and with soil water. The estimates from the multivariable procedure represent the lower and the upper end of the estimates (for flux including soil water

Table 5 Output of different regression models for beech with various drought variables and two multivariable procedures (“Lasso” and “Factor analysis”). Dependent variable: basal area growth relative to a long-term average. The table lists the AIC (Akaike Information Criterion), the regression coefficient for ozone and the standard error as well as the estimated percentage growth reduction at the Critical Level and at average ozone flux. ETa/ETp is the ratio between actual and potential evapotranspiration, averaged either for the whole season or over the early season (from 5 days before budbreak to 80 days after budbreak). Flux without soil water AIC

Without soil water Eta/Etp whole season ETa/Etp early season Relative saturation Soil moisture deficit Soil water potential 40 cm Soil water potential 60 cm Lasso Factor analysis

10,711.3 10,622.5 10,610.3 10,745.1 10,702.9 10,677.5 10,687.3 10,462.6 10,594.1

Coeff POD1

0.213 0.369 0.295 0.286 0.243 0.247 0.253 0.358 0.350

Flux with soil water SE POD1

0.071 0.072 0.071 0.070 0.071 0.071 0.071 0.084 0.072

p POD1

0.0027 <0.001 0.0052 <0.0001 0.0006 0.0005 0.0004 <0.0001 <0.0001

Growth reduction at Critical level (4)

Average flux (18.5)

3.1 4.9 4.1 4 3.5 3.5 3.6 6.1 4.7

14.4 22.7 19 18.5 16.2 16.4 16.7 28.3 21.8

AIC

10,715.0 10,619.1 10,608.6 10,748.3 10,707.0 10,681.2 10,691.1 10,458.4 10,591.6

Coeff POD1

0.151 0.368 0.287 0.239 0.180 0.190 0.195 0.367 0.341

SE POD1

0.065 0.068 0.066 0.065 0.065 0.065 0.065 0.078 0.066

p POD1

0.0189 <0.0001 <0.0001 0.0002 0.0056 0.0033 0.0027 0.0005 0.0083

Growth reduction at Critical level (4)

Average flux (17.4)

2.5 5.5 4.3 3.7 2.9 3 3.1 6.6 4.4

10.7 22.7 18.5 16 12.5 13.1 13.4 29.1 19.1

S. Braun et al. / Environmental Pollution 192 (2014) 129e138

135

Table 6 Output of different regression models for Norway spruce with various drought variables and two multivariable procedures (“Lasso” and “Factor analysis”). Dependent variable: growth relative to a long-term average. The table lists the AIC, the regression coefficient for POD1 and the standard error as well as the estimated growth reduction at the critical level and at average ozone flux. Flux without soil water AIC

Without drought covariate ETa/ETp Soil water potential 40 cm Soil water potential 60 cm Soil moisture deficit Lasso Factor analysis

6975 6964 6976 6972 6974 6732 6864

Coeff POD1

0.356 0.286 0.345 0.318 0.336 0.103 0.493

Flux with soil water

SE POD1

0.178 0.179 0.178 0.179 0.178 0.180 0.186

p POD1

0.046 n.s. 0.053 0.075 0.059 n.s. 0.008

Growth reduction at Critical level (8)

Average flux (27.1)

6.4 6.6 6.2 5.8 6.1 2.4 8.2

21.6 22.4 21.2 19.9 20.8 8 27.8

2.2% and 8.1%, respectively, for lasso and factor analysis). The large scatter suggests that the data analysis for Norway spruce cannot be used to derive an estimate for growth reduction. 3.4. Country-wide estimates of growth reductions The excellent agreement between doseeresponse curve and regression result for the beech data enables to estimate growth reductions caused by ozone. Because of the uncertainties associated with the Norway spruce data it was decided to base this estimate on the experimental doseeresponse function published by Mills et al. (2011) rather than on the epidemiological results. Thus, the flux values for beech were multiplied with a factor of 1.1 and those for Norway spruce with a factor of 0.24 to get growth reduction estimates. These values were then multiplied with the proportion of deciduous and of coniferous forests, respectively, for the forested areas in Switzerland according to LFI/WSL (1992). The resulting estimate of the reduction in annual growth rate was 19.5% for deciduous and 6.6% for coniferous forests. The area-weighted average for all forests was 11.0% reduction in annual growth rate during the period 1991e2011 (Table 7, Fig. 4). 4. Discussion The results suggest that the doseeresponse curve from the fumigation experiments with seedlings or saplings is also valid for mature trees although the data from Norway spruce are not as clear as the beech data. There may be various reasons for the inferior

Table 7 Growth reduction resulting from the POD1 coefficient in Tables 5 and 6 (including 95% confidence intervals, C.I.) in comparison to the flux-response relationship from experiments used to derive the critical level (Mills et al., 2010), average ozone flux for forest areas in Switzerland and resulting estimate for annual growth reduction. Fagus sylvatica

Picea abies C.I.

Critical Level (mmol m2) Growth reduction at CL (%) Growth reduction from epidemiology (%) (flux including soil water)

Average ozone flux for forest areas (mmol m2 year1) Estimated growth reduction (%) Weighted average of growth reduction (%)

4 4.4 4.3

±1.9

C.I. 8 1.9 4.2

±7.1

Deciduous forest area 17.7

Coniferous forest area 27.3

19.5 11.0

6.6

AIC

6977 6965 6978 6974 6976 6732 6864

Coeff POD1

0.229 0.278 0.229 0.206 0.217 0.096 0.467

SE POD1

0.178 0.178 0.177 0.178 0.177 0.176 0.183

p POD1

n.s. n.s. n.s. n.s. n.s. n.s. 0.011

Growth reduction at Critical level (8)

Average flux (26.5)

4.6 5.4 4.6 4.2 4.4 2.2 8.1

15.3 17.8 15.1 13.9 14.4 7.4 26.8

result in Norway spruce. First, there were more confounding factors, and the test of different drought variables yielded less clear results than in beech. Even foliar N concentration was confounding. This was omitted in the analysis as foliar N is a vitality parameter €ki and Wang, 1998; which itself may be reduced by O3 (Kelloma Kopper et al., 2001), making it an intermediate endpoint which should be avoided in epidemiological data analysis. Second, the improvement of the experimental doseeresponse relationship when changing from AOT40 to flux was not as good in Norway spruce as for the deciduous trees indicating a possible problem with ozone uptake calculations (UNECE, 2010). Unpublished data analysis from sap flow data in Norway spruce suggest, too, that the uptake models may have to be improved. The present study confirms results from an earlier analysis of beech shoot growth data in relation to AOT40 (Braun et al., 2007) which also found very good agreement between mature trees and experimental data. Karlsson et al. (2006) observed a negative correlation between stem increment of Norway spruce and AOT40 in Sweden without, however, quantifying the relationship. The 19.5% and 6.6% estimate of average annual growth loss for deciduous and coniferous forests (Table 7) in Switzerland, respectively, agree well with a European wide estimate of forest growth loss based also on ozone flux maps (Harmens and Mills, 2012). For Central and Eastern Europe their estimate of average ozone flux was 23.2 mmol m2 year1 and the resulting annual growth loss 12.0%. These growth losses are calculated in reference to zero ozone flux which is not equivalent to preindustrial conditions. However, test calculations based on historic ozone concentrations published by Volz and Kley (1988) revealed that the preindustrial ozone flux is close to zero and therefore negligible. The two open air fumigation experiments with large trees conducted so far came both to the conclusion that the seedling response in chambers is certainly not higher than the response of mature trees at least for deciduous trees. In the Aspen FACE experiment, King et al. (2005) found a growth reduction similar to what was expected from fumigation experiments with small trees as reported in the review of Pye (1988). 1.5x elevated O3 (average concentrations of 49.0 in the fumigated plots and 35.9 ppb in the controls) reduced biomass of pure aspen, aspen-birch and aspenemaple communities, respectively, by 22, 12 and 16%. The sensitivity of the trees remained also constant throughout the first seven years of exponential growth. Uddling et al. (2010) calculated the stomatal ozone uptake in this experiment (POD1.6) for the time between mid-June and end of August. For the pure aspen stands the difference between controls and O3 treated plots was 9.1 and for the mixed aspenebirch stands it was it was 8.6 mmol m2. In the Kranzberg free ozone fumigation experiment, the sensitivity of beech was even higher. Pretzsch et al. (2010) reported a 43.5%

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Fig. 4. Map of growth reduction in Swiss forests according to the procedure outlined in Table 7.

reduction in volume growth of Fagus sylvatica at 2 ambient ozone exposure (POD1 difference to control 15.1 mmol m2 year1 according to Grünhage et al. (2012) whereas the diameter increment at breast height was reduced by 11.5%. It has therefore been argued that increment measurements at breast height may underestimate the ozone effect (Grünhage et al., 2013). The difference between diameter and volume increment was explained by a slimmer form of the ozone treated Fagus trees. These results suggest that total volume growth may be more sensitive to ozone than growth estimated from diameter measurements at breast height, but the resulting ozone sensitivity is unrealistically high. It would mean that the estimated average ozone flux for Swiss deciduous forests (17.7 mmol m2) would result in a 51% growth reduction. The 11.5% reduction in increment at breast height reported from the Kranzberg experiment for beech, although not significant, is much more realistic and within the results of the study presented here. King et al. (2005) emphasize also the possible relevance of species mixtures for ozone response. The proportion of deciduous and coniferous trees in the stands was therefore also included in the data analysis. Whereas the ozone response of Fagus sylvatica was not at all correlated with the species composition there was a slight trend that the ozone coefficient of Picea abies was more negative in stands with more than 80% coniferous trees. These results were, however, not included in the final estimate of the ozone coefficient as the significance was small and quantitative assessments may be difficult when interactions are involved. The direction of the response was opposite to the results from Liu et al. (2004) that O3 treated Norway spruce benefited in a mixture with beech compared to monoculture. In beech, the ratio between actual and potential evapotranspiration was the best drought indicator, especially when averaged over the first 80 days after budbreak. Evapotranspiration is reduced in the hydrological model when the water potential within the

rooting zone is decreased. The root depth was set to the depth observed in the soil profile (usually between 0.8 and 1 m). In Norway spruce the water potential averaged over the uppermost 60 cm was a better predictor. This suggests that the thickness of the rooting zone in the hydrological model may have been overestimated for Norway spruce. It would be possible to adjust this for the pure spruce stands but not for the mixed stands. 5. Conclusions Epidemiological analysis of stem increment data is a useful tool to quantify growth losses by ozone, allowing to test results obtained from experiments under field conditions. Precautions have, however, to be taken for confounding factors by comparing the outputs of different regression models. The estimates for ozone flux of beech seem quite robust whereas for Norway spruce temperature, irradiation and foliar N were confounding. The doseeresponse curve validated with this study and the flux maps allow an estimate of growth losses in Swiss forests by ozone. These estimates are substantial and may be of significance in C sequestration calculations, with increasing importance in the future. However, more studies are needed with better data for Norway spruce and data for other important tree species such as Quercus sp. Acknowledgments The data analysis was financed by the Federal Office for the Environment (FOEN), Air Pollution Control and Chemicals Division, the forest observation plots by the cantonal forest authorities of the cantons AG, BL, BE, BS, FR, SO, TG, ZH and ZG. We thank Beat Achermann (FOEN) for his interest in the work, Karsten Jasper for help in using the hydrological model WASIM-ETH, Jan Remund (Meteotest) for the meteorological interpolation, MeteoSchweiz for

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phenological observation data, the National Air Pollution Monitoring Network and various cantonal air pollution authorities for supplying ozone data and the team of the Institute for Applied Plant Biology for field and lab work. References Aber, J.D., Federer, A., 1992. A generalized, lumped-parameter model of photosynthesis, evapotranspiration and net primary production in temperate and boreal forest ecosystems. Oecologia 92, 463e474. BAFU, 2013. Luftbelastung 2012. Messresultate des Nationalen Beobachtungsnetzes für Luftfremdstoffe (NABEL). Umwelt-Zustand UZ-1324-D. Bundesamt für Umwelt, Bern, 128pp. Benzler, J.H., Finnern, H., Müller, W., Roeschmann, G., Will, K.H., Wittmann, O., 1982. Bodenkundliche Kartieranleitung. E. Schweizerbart'sche Verlagsbuchhandlung, Stuttgart, 331pp. Braun, S., Flückiger, W., 1989. Effect of ambient ozone and acid mist on aphid development. Environ. Pollut. 56, 177e187. Braun, S., Rihm, B., Schindler, C., Flückiger, W., 1999. Growth of mature beech in relation to ozone and nitrogen deposition: an epidemiological approach. Water Air Soil Pollut. 116, 357e364. Braun, S., Schindler, C., Rihm, B., Flückiger, W., 2007. Shoot growth of mature Fagus sylvatica and Picea abies in relation to ozone. Environ. Pollut. 146, 624e628. Büker, P., Emberson, L.D., Ashmore, M.R., Cambridge, H.M., Jacobs, C.M.J., Massman, W.J., Müller, J., Nikolov, N., Novak, K., Oksanen, E., Schaub, M., de la Torre, D., 2007. Comparison of different stomatal conductance algorithms for ozone flux modelling. Environ. Pollut. 146, 726e735. Büker, P., Morissey, T., Briolat, A., Falk, R., Simpson, D., Tuovinen, J.-P., Alonso, R., Barth, S., Baumgarten, M., Grulke, N.E., Karlsson, P.E., King, J., Lagergren, F., Matyssek, R., Nunn, A., Ogaya, R., Penuelas, J., Rhea, L., Schaub, M., Uddling, J., Werner, W., Emberson, L.D., 2011. DO3SE modelling of soil moisture to determine ozone flux to European forest trees. Atmos. Chem. Phys. Discuss. 11, 33583e33650. Büker, P., Morissey, T., Briolat, A., Falk, R., Simpson, D., Tuovinen, J.-P., Alonso, R., Barth, S., Baumgarten, M., Grulke, N.E., Karlsson, P.E., King, J., Lagergren, F., Matyssek, R., Nunn, A., Ogaya, R., Penuelas, J., Rhea, L., Schaub, M., Uddling, J., Werner, W., Emberson, L.D., 2012. DO3SE modelling of soil moisture to determine ozone flux to European forest trees. Atmos. Chem. Phys. 12, 5537e5562. BUWAL, 2003. Empfehlungen über die Immissionsmessung von Luftfremdstoffen. Bundesamt für Umwelt, Wald und Landschaft (BUWAL), Bern, 41pp. Coleman, M.D., Dickson, R.E., Isebrands, J.G., Karnosky, D.F., 1995. Carbon allocation and partitioning in aspen clones varying in sensitivity to tropospheric ozone. Tree Physiol. 15, 593e604. Dalstein, L., Vas, N., 2005. Ozone concentrations and ozone-induced symptoms on coastal and alpine mediterranean pines in southern France. Water Air Soil Pollut. 160, 181e195. Defila, C., Clot, B., 2005. Phytophenological trends in the Swiss Alps, 1951e2002. Meteorol. Z. 14, 191e196. Elfron, B., Hastie, R., Johnstone, I., Tibshirani, R., 2004. Least angle regression. Ann. Stat. 32, 407e499. Emberson, L.D., Ashmore, M.R., Cambridge, H.M., Simpson, D., Tuovinen, J.-P., 2000. Modelling stomatal ozone flux across Europe. Environ. Pollut. 109, 403e414. Ferretti, M., Calderini, M., Bussotti, F., 2007. Ozone exposure, defoliation of beech (Fagus sylvatica L.) and visible foliar symptoms on native plants in selected plots of South-Western Europe. Environ. Pollut. 145, 644e651. Fowler, D., Cape, J.N., Coyle, M., Flechard, C., Kuylenstierna, J., Hicks, K., Derwent, D., Johnson, C., Stevenson, D., 1999. The global exposure of forests to air pollutants. Water Air Soil Pollut. 116, 5e32. Fredericksen, T.S., Joyce, B.J., Skelly, J.M., Steiner, K.C., Kolb, T.E., Kouterick, K.B., Savage, J.E., Snyder, K.R., 1995. Physiology, morphology, and ozone uptake of leaves of black cherry seedlings, saplings, and canopy trees. Environ. Pollut. 89, 273e283. Fredericksen, T.S., Skelly, J.M., Steiner, K.C., Kolb, T.E., Kouterick, K.B., 1996. Sizemediated foliar response to ozone in black cherry trees. Environ. Pollut. 91, 53e63. Freiwald, V., Haikio, E., Julkunen-Tiitto, R., Holopainen, J.K., Oksanen, E., 2008. Elevated ozone modifies the feeding behaviour of the common leaf weevil on hybrid aspen through shifts in developmental, chemical, and structural properties of leaves. Entomol. Exp. Appl. 128, 66e72. Fuhrer, J., Achermann, B., 1994. Critical Levels for Ozone. A UN-ECE Workshop Report, 328pp. Eidg. Forschungsanstalt für Agrikulturchemie und Umwelthygiene, Liebefeld-Bern. ISSN-1013e1154X. €rby, L., Ashmore, M.R., 1997. Critical levels for ozone effects on vegeFuhrer, J., Ska tation in Europe. Environ. Pollut. 97, 91e106. Gielen, B., Low, M., Deckmyn, G., Metzger, U., Franck, F., Heerdt, C., Matyssek, R., Valcke, R., Ceulemans, R., 2007. Chronic ozone exposure affects leaf senescence of adult beech trees: a chlorophyll fluorescence approach. J. Exp. Bot. 58, 785e795. Grulke, N.E., Miller, P.R., 1994. Changes in gas exchange characteristics during the lifespan of giant sequoia: implications for response to current and future concentrations of atmospheric ozone. Tree Physiol. 14, 659e668. €berle, K.H., Wieser, G., Metzger, U., Leuchner, M., Grünhage, L., Matyssek, R., Ha Menzel, A., Dieler, J., Pretzsch, H., Grimmeisen, W., Zimmermann, L., Raspe, S.,

137

2012. Flux-based ozone risk assessment for adult beech forests. Trees Struct. Funct. 26, 1713e1721. Grünhage, L., Matyssek, R., Wieser, G., H€ aberle, K.H., Leuchner, M., Menzel, A., Dieler, J., Pretzsch, H., Grimmeisen, W., Zimmermann, L., Raspe, S., Schreiner, M., 2013. Flux-Based ozone risk assessment for adult beech and Spruce forests. In: Matyssek, R., Clarke, N., Cudlin, P., Mikkelsen, T.N., Tuovinen, J.-P., Wieser, G., Paoletti, E. (Eds.), Climate Change, Air Pollution and Global Challenges. Elsevier, Amsterdam, pp. 251e266. Günthardt-Goerg, M., Matyssek, R., Scheidegger, C., Keller, T., 1993. Differentiation and structural decline in the leaves and bark of birch (Betula pendula) under low ozone concentrations. Trees 7, 104e114. Hanson, P.J., Samuelson, L.J., Wullschleger, S.D., Tabberer, T.A., Edwards, G.S., 1994. Seasonal patterns of light-saturated photosynthesis and leaf conductance for mature and seedling Quercus rubra L. foliage: differential sensitivity to ozone exposure. Tree Physiol. 14, 1351e1366. Harmens, H., Mills, G., 2012. Ozone Pollution: Impacts on Carbon Sequestration in Europe. Report prepared by the ICP Vegetation. Working Group on Effects of the Convention on Long-range Transboundary Air Pollution. Centre for Ecology & Hydrology, Bangor, 88pp. Hicks, D.A., 1978. Diagnosing Vegetation Injury Caused by Air Pollution. U.S. Department of Commerce, National Technical Information Center, Springfield. € Karlsson, P.E., Orlander, G., Langvall, O., Uddling, J., Hjorth, U., Wiklander, K., Areskough, B., Grennfelt, P., 2006. Negative impact of ozone on the stem basal area increment of Norway spruce in south Sweden. For. Ecol. Manag. 232, 146e151. Karlsson, P.E., Braun, S., Broadmeadow, M., Elvira, S., Emberson, L.D., Gimeno, B., LeThiec, D., Novak, K., Oksanen, E., Schaub, M., Uddling, J., Wilkinson, M., 2007. Risk assessment for forest trees: the performance of the ozone flux versus the AOT concepts. Environ. Pollut. 146, 608e616. Karnosky, D.F., Gielen, G., Ceulemans, R., Schlesinger, W.H., Norby, R.J., Oksanen, E., Matyssek, R., Hendrey, G.R., 2001. Face systems for studying the impacts of greenhouse gases on forest ecosystems. In: Karnosky, D.F., ScarasciaMugnozza, G., Ceulemans, R., Innes, J. (Eds.), The Impact of Carbon Dioxide and Other Greenhouse Gases on Forest Ecosystems. CABI Publishing, New York, USA, pp. 297e324. Karnosky, D.F., Percy, K., Xiang, B., Callan, B., Noormets, A., Mankovska, B., Hopkin, A., Sober, J., Jones, W., Dickson, R.E., Isebrands, J.G., 2002. Interacting elevated CO2 and tropospheric O3 predisposes aspen (Populus tremuloides Michx.) to infection by rust (Melampsora medusae f. sp. tremuloidae). Glob. Change Biol. 8, 329e338. Karnosky, D.F., Werner, H., Holopainen, T., Percy, K., Oksanen, T., Oksanen, E., Heerdt, C., Fabian, P., Nagy, J., Heilman, W., Cox, R., Nelson, N., Matyssek, R., 2007. Free-air exposure systems to scale up ozone research to mature trees. Plant Biol. 9, 181e190. Kellom€ aki, S., Wang, K.-Y., 1998. Growth, respiration and nitrogen content in needles of Scots pine exposed to elevated ozone and carbon dioxide in the field. Environ. Pollut. 101, 263e274. King, J.S., Kubiske, M.E., Pregitzer, K.S., Hendrey, G.R., McDonald, E.P., Giardina, C.P., Quinn, V.S., Karnosky, D.F., 2005. Tropospheric O3 compromises net primary production in young stands of trembling aspen, paper birch and sugar maple in response to elevated atmospheric CO2. New Phytol. 168, 623e636. €w, M., Heerdt, C., Grams, T.E.E., Ha €berle, K.H., Matyssek, R., 2009. Effects Kitao, M., Lo of chronic elevated ozone exposure on gas exchange responses of adult beech trees (Fagus sylvatica) as related to the within-canopy light gradient. Environ. Pollut. 157, 537e544. Kleinbaum, D.G., Kupper, L.L., Nizam, A., Muller, K.E., 2008. Applied Regression Analysis and Other Multivariable Methods. Thomson Higher Education, Belmont USA, 978e0495384960. Kolb, T.E., Matyssek, R., 2001. Limitations and perspectives about scaling ozone impacts in trees. Environ. Pollut. 115, 373e393. Kopper, B.J., Lindroth, R.L., Nordheim, E.,V., 2001. CO2 and O3 effects on paper birch (Betulaceae: Betula papyrifera) phytochemistry and whitemarked tussock moth (Lymantriidae: Orgyia leucostigma) performance. Environ. Entomol. 30, 1119e1126. LFI/WSL, 1992. Schweizerisches Landesforstinventar (LFI), Eidg. Forschungsanstalt für Wald, Schnee und Landschaft (WSL), Birmensdorf. Liu, X., Kozovits, A.R., Grams, T.E.E., Blaschke, H., Rennenberg, H., Matyssek, R., 2004. Competition modifies effects of enhanced ozone/carbon dioxide concentrations on carbohydrate and biomass accumulation in juvenile Norway spruce and European beech. Tree Physiol. 24, 1045e1055. Lux, D., 1997. Die Wirkung umgebungsluft-relevanter Ozonimmissionen auf Kohlenhydrathaushalt und Wachstum von Forstpflanzen. Wissenschafts-Verlag Dr. Wigbert Maraun, Frankfurt/M. Maier-Maercker, U., Koch, W., 1991. Experiments on the control capacity of stomata of Picea abies (L.) Karst. after fumigation with ozone and in environmentally damaged material. Plant Cell Environ. 14, 175e184. Manning, W.J., von Tiedemann, A., 1995. Climate change: potential effects of increased atmospheric carbon dioxice (CO2), ozone (O3) and ultraviolet-B (UVB) radiation on plant diseases. Environ. Pollut. 88, 219e245. Matyssek, R., Karnosky, D.F., Wieser, G., Percy, K., Oksanen, E., Grams, T.E.E., Kubiske, M., Hanke, D., Pretzsch, H., 2010a. Advances in understanding ozone impact on forest trees: messages from novel phytotron and free-air fumigation studies. Environ. Pollut. 158, 1990e2006. Matyssek, R., Wieser, G., Ceulemans, R., Rennenberg, H., Pretzsch, H., Haberer, K., € w, M., Nunn, A.J., Werner, H., Wipfler, P., Oßwald, W., Nikolova, P., Hanke, D.E., Lo

138

S. Braun et al. / Environmental Pollution 192 (2014) 129e138

Kraigher, H., Tausz, M., Bahnweg, G., Kitao, M., Dieler, J., Sandermann, H., €ther, M., Deckmyn, G., Grams, T.E.E., Herbinger, K., Grebenc, T., Blumenro €berle, K.H., 2010b. Enhanced ozone strongly Heerdt, C., Leuchner, M., Fabian, P., Ha reduces carbon sink strength of adult beech (Fagus sylvatica) e resume from the free-air fumigation study at Kranzberg Forest. Environ. Pollut. 158, 2527e2532. McLeod, A.R., Long, S.P., 1999. Free-air carbon dioxide enrichment (FACE) in global change research: a review. Adv. Ecol. Res. 28, 1e56. Mills, G., Pleijel, H., Büker, P., Braun, S., Emberson, L.D., Harmer, R., Hayes, F., Simpson, D., Grünhage, L., Karlsson, P.E., Danielsson, H., Bermejo, V., Gonzalez Fernandez, I., 2010. Mapping critical levels for vegetation. Revision undertaken in Summer 2010 to include new flux-based critical levels and response functions for ozone. In: Mapping Manual 2004. International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops, 112 pp. Mills, G., Pleijel, H., Braun, S., Büker, P., Bermejo, V., Calvo, E., Danielsson, H., Emberson, L.D., Gonzalez Fernandez, I., Grünhage, L., Harmens, H., Hayes, F., Karlsson, P.E., Simpson, D., 2011. New stomatal flux-based critical levels for ozone effects on vegetation. Atmos. Environ. 45, 5064e5068. Mills, G., Pleijel, H., Braun, S., Büker, P., Bermejo, V., Calvo, E., Danielsson, H., Emberson, L.D., Gonzalez Fernandez, I., Grünhage, L., Harmens, H., Hayes, F., Karlsson, P.E., Simpson, D., 2012. New stomatal flux-based critical levels for ozone effects on vegetation. Atmos. Environ. 45, 5064e5068. Nunn, A.J., Kozovits, A.R., Reiter, I.M., Heerdt, C., Leuchner, M., Lütz, C., Liu, X., €w, M., Winkler, J.B., Grams, T.E.E., Ha €berle, K.H., Werner, H., Fabian, P., Lo Rennenberg, H., Matyssek, R., 2005. Comparison of ozone uptake and sensitivity between a phytotron study with young beech and a field experiment with adult beech (Fagus sylvatica). Environ. Pollut. 137, 494e506. €kko €nen, E., Holopainen, T., Ka €renlampi, L., 1997. Differences in growth, leaf P€ aa senescence and injury, and stomatal density in birch (Betula pendula Roth.) in relation to ambient levels of ozone in finland. Environ. Pollut. 96, 117e127. Paoletti, E., 2005. Ozone slows stomatal response to light and leaf wounding in a Mediterranean evergreen broadleaf, Arbutus unedo. Environ. Pollut.134, 439e445. Paoletti, E., Manning, W.J., 2007. Toward a biologically significant and usable standard for ozone that will also protect plants. Environ. Pollut. 150, 85e95. Paoletti, E., Contran, N., Bernasconi, P., Gunthardt-Goerg, M.S., Vollenweider, P., 2009. Structural and physiological responses to ozone in Manna ash (Fraxinus ornus L.) leaves of seedlings and mature trees under controlled and ambient conditions. Sci. Total Environ. 407, 1631e1643. Pearson, M., Mansfield, T.A., 1993. Interacting effects of ozone and water stress on the stomatal resistance of beech (Fagus sylvatica L.). New Phytol. 123, 351e358. Pinheiro, J.C., Bates, D.M., 2000. Mixed-Effects Models in S and S-PLUS. Statistics and Computing. Springer-Verlag, New York, 0-387-98957-9. Pretzsch, H., Dieler, J., Matyssek, R., Wipfler, P., 2010. Tree and stand growth of mature Norway spruce and European beech under long-term ozone fumigation. Environ. Pollut. 158, 1061e1070. Pye, J.M., 1988. Impact of ozone on the growth and yield of trees: a review. J. Environ. Qual. 17, 346e360. Reich, P.B., 1987. Quantifying plant response to ozone: a unifying theory. Tree Physiol. 3, 63e91.

Samuelson, L.J., Edwards, G.S., 1993. A comparison of sensitivity to ozone in seedlings and trees of Quercus rubra L. New Phytol. 125, 373e379. Samuelson, L.J., Kelly, J.M., 1996. Carbon partitioning and allocation in northern red oak seedlings and mature trees in response to ozone. Tree Physiol. 16, 853e858. Schulla, J., Jasper, K., 2013. Model Description Wasim-ETH (Zürich). http://www. wasim.ch/downloads/doku/wasim/wasim_2013_en.pdf. Sicard, P., Dalstein-Richier, L., Vas, N., 2011. Annual and seasonal trends of ambient ozone concentration and its impact on forest vegetation in Mercantour National Park (South-eastern France) over the 2000e2008 period. Environ. Pollut. 159, 351e362. Sitch, S., Cox, P.M., Collins, W.J., Huntingford, C., 2007. Indirect radiative forcing of climate change through ozone effects on the land-carbon sink. Nature 448, 791e794. Tjoelker, M.G., Volin, J.C., Oleksyn, J., Reich, P.B., 1993. Light environment alters response to ozone stress in seedlings of Acer saccharum Marsh. and hybrid Populus L. I. In situ net photosynthesis, dark respiration and growth. New Phytol. 124, 627e636. Tjoelker, M.G., Volin, J.C., Oleksyn, J., Reich, P.B., 1995. Interaction of ozone pollution and light effects on photosynthesis in a forest canopy experiment. Plant, Cell Environ. 18, 895e905. Uddling, J., Hogg, A.J., Teclaw, R.M., Carroll, M.A., Ellsworth, D.S., 2010. Stomatal uptake of O3 in aspen and aspen-birch forests under free-air CO2 and O3 enrichment. Environ. Pollut. 158, 2023e2031. UNECE, 2010. Mapping Critical Levels for Vegetation, International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops. United Nations Economic Commission for Europe, Bangor, UK, 114 pp. VanderHeyden, D., Skelly, J., Innes, J., Hug, C., Zhang, J., Landolt, W., Bleuler, P., 2001. Ozone exposure thresholds and foliar injury on forest plants in Switzerland. Environ. Pollut. 111, 321e331. Volz, A., Kley, D., 1988. Evaluation of the Montsouris series of ozone measurements made in the 19th-century. Nature 332, 240e242. Wellburn, F.A.M., Wellburn, A.R., 1994. Atmospheric ozone affects carbohydrate allocation and winter hardiness of Pinus halepensis Mill. J. Exp. Bot. 45, 607e614. Wieser, G., Hecke, K., Tausz, M., Haberle, K.H., Grams, T.E.E., Matyssek, R., 2002a. The role of antioxidative defense in determining ozone sensitivity of Norway spruce (Picea abies (L.) karst.) across tree age: implications for the sun- and shadecrown. Phyt. Ann. Rei Bot. 42, 245e253. Wieser, G., Tegischer, K., Tausz, M., Haeberle, K.H., Grams-Thorsten, E.E., Matyssek, R., 2002b. Age effects on Norway spruce (Picea abies) susceptibility to ozone uptake: a novel approach relating stress avoidance to defense. Tree Physiol. 22, 583e590. Wittig, V.E., Ainsworth, E.A., Naidu, S.L., Karnosky, D.F., Long, S.P., 2009. Quantifying the impact of current and future tropospheric ozone on tree biomass, growth, physiology and biochemistry: a quantitative meta-analysis. Glob. Change Biol. 15, 396e424. Zelenka, A., Czeplak, G., D'Agostino, V., Weine, J., Maxwell, E., Perez, R., Noia, M., Ratto, C., Festa, R., 1992. Techniques for Supplementing Solar Radiation Network Data, vol. 1e3. IEA-SHCP-9D-1. IEA Report.