Biogeosciences Needle age-related and seasonal photosynthetic capacity variation is negligible for modelling yearly gas exchange of a temperate Scots pine forest

Needle age-related and seasonal photosynthetic capacity variation is negligible for modelling yearly gas exchange of a temperate Scots pine forest M. Op de Beeck, B. Gielen, I. Jonckheere, R. Samson, I. A. Janssens, and R. Ceulemans Research Group of Plant and Vegetation Ecology, Department of Biology, University of Antwerp, Wilrijk, Belgium Biosystems Department, Geomatics Group, Katholieke Universiteit Leuven, Leuven, Belgium Department of Bioscience Engineering, University of Antwerp, Antwerpen, Belgium these authors contributed equally to this manuscript Received: 22 September 2009 – Accepted: 24 September 2009 – Published: 9 October 2009 Correspondence to: M. Op de Beeck (maarten.opdebeeck@ua.ac.be) Published by Copernicus Publications on behalf of the European Geosciences Union.


Introduction
Coniferous canopies have a complex heterogeneous structure, both in terms of foliage architecture and physiology.Needles are unevenly distributed in the canopy through aggregation into whorls and clumps (e.g.Čerm ák et al., 1998), while needle physiological properties vary with canopy position (Peters et al., 2008), needle age (Wang et al., 1995), and time of growing season (Misson et al., 2006).Reliable estimates of conifer canopy gas exchange therefore require an accurate characterization of the canopy structure in space and time (Monteith, 1975;Stenberg et al., 1994).In process-based multi-layer canopy models this condition is typically met.Their multilayered scheme allows for a detailed description of foliage distribution, physiological gradients, and radiation transfer within the canopy.Moreover, they often include needle age-related and seasonal photosynthetic capacity and leaf area changes (e.g.Mohren and van de Veen, 1995;Tingey et al., 2001;Weiskittel, 2006).Due to the large input parameter requirement and number of calculations involved, it is not feasible to represent canopy complexity in such detail in models simulating canopy gas exchange at the larger scale such as land surface schemes used in general circulation models (e.g.Kowalczyk et al., 2006;Verseghy, 2000).Here, the canopy scheme is reduced to one sun/shade layer and needle age-related and seasonal photosynthetic capacity variation is mostly not taken into account.Whereas the canopy scheme reduction has been shown not to induce significant accuracy loss (Wang and Leuning, 1998;Dai et al., 2004), the omittance of needle age-related and seasonal photosynthetic capacity variation could lead to considerably less accurate estimations of conifer canopy gas exchange.
Additionally, the general relationship between photosynthetic capacity and needle Introduction

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Full Screen / Esc Printer-friendly Version Interactive Discussion nitrogen (N) content (Field and Mooney, 1986) is not clear along different needle ages in conifers (Vapaavuori et al., 1995;Warren et al., 2003), though leaf N content is commonly used as an indicator for photosynthetic capacity because of its close association with the amounts of photosynthesis-related N compounds such as chlorophyll and Rubisco (Evans, 1989).
In this study, we quantified the predictive accuracy loss involved with omitting photosynthetic capacity variation for a Scots pine (Pinus sylvestris L.) stand in Flanders, Belgium.Over the course of one phenological year, we measured the photosynthetic capacity parameters maximum carboxylation capacity at 25 • C (V m25 ) and maximum electron transport capacity at 25 • C (J m25 ) and the Leaf Area Index (LAI) of differentaged needles in the upper and lower canopy.We used these measurements as input for a process-based multi-layer canopy model with the objective to quantify the difference in yearly Gross Ecosystem Productivity (GEP) and canopy transpiration (E can ) simulated under scenarios in which the observed needle age-related and/or seasonal variation of V m25 and J m25 was omitted.We compared simulated GEP with estimations obtained from eddy covariance measurements.Additionally, we measured summer needle N content to investigate the relationship between photosynthetic capacity parameters and needle N content along different needle ages.
2 Materials and methods

Experimental site
The experimental site is an even-aged, 2 ha Scots pine stand, representing a portion of the 150 ha mixed coniferous/deciduous De Inslag forest.The forest is located in Brasschaat, in the Campine region of the province of Antwerpen, Belgium (51 ders, Belgium).Ten-year mean annual and growing season (April-October) temperature at the site are 11.8 and 14.9 • C, respectively.Mean annual and growing season precipitation are 824 and 505 mm, respectively.Rainfall is fairly evenly distributed throughout the year.The study site has a flat topography (slope less than 0.3%).The upper soil layer is ca.1.8 m thick.The soil has been described as a moderately wet sandy soil with a distinct humus and/or iron B-horizon (Baeyens et al., 1993).Due to a clay layer at a depth of 1.5 to 2 m the site has poor drainage.The soil is moist and often saturated, with a high hydraulic conductivity in the upper soil layer.The Scots pine stand was planted in 1929 and was 78 years old at the time of the present study (2007)(2008).The present stock density is 374 trees ha −1 (Xiao et al., 2003).Average diameter at breast height is 0.3 m and average tree height is 21.4 m.The stand canopy is sparse, with a peak projected LAI of 1.31 m 2 m −2 in 2007 (this study) and a mean canopy gap fraction of 42%.The canopy has a mean depth of 8.3 m (Xiao et al., 2003).The pine trees only bear two needle age classes (currentyear needles and one-year-old needles), as nearly all needles older than two years are dropped in winter (Janssens et al., 1999).Needle analysis has shown the stand to be low in magnesium and phosphorus (Van den Berge et al., 1992;Roskams et al., 1997).However, needle N content was optimal (>2% in current-year needles; Roskams and Neirynck, 1999), most probably because the pine stand is located in an area with high NO x and ammonia deposition (30-40 kg ha −1 y −1 ; Neirynck et al., 2005Neirynck et al., , 2007)), with high NO − 3 leaching to the ground water (Neirynck et al., 2008).

Photosynthetic parameter measurements and needle N analysis
The photosynthetic parameters V m25 and J m25 were derived from in situ gas exchange measurements.Platforms on a 41 m high flux tower positioned in the middle of the stand gave access to the crown of two pine trees growing near the tower.Gas exchange was measured on attached current-year and one-year-old needles in the upper and lower crown of these two pines with a portable open-path gas exchange measurement system (LI-6400, Li-COR, Lincoln, NE, USA Values for the photosynthetic parameters V m25 and J m25 were derived from the A n /C i curves by fitting the biochemical photosynthesis model of Farquhar (Farquhar et al., 1980) with the method of least squares.
After gas exchange measurements, needles were harvested and projected needle area was estimated using a binocular microscope (M5 Wild, Wild Heerbrugg, Gais, Switzerland) in combination with an ocular equipped with a reticule (Leitz, Wetzlar, Germany, periplan, GW 10xm).Needles sampled in June, July, and August 2007 were subsequently dried in a dry oven (70  1.

Leaf Area Index measurements
Effective LAI was determined by an optical close-range remote sensing method, using hemispherical canopy photographs as described by Jonckheere et al. (2005a,b).The photographs were taken at 30 points in a systematic sampling grid within the experi-9742 Introduction

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Full Screen / Esc Printer-friendly Version Interactive Discussion mental plot at a biweekly interval from 28 March 2007 till 31 January 2008.Effective LAI measures were calculated from binarized photographs resulting from an automatic global thresholding algorithm combined with a local thresholding algorithm in order to correct for local light anomalies (e.g., sun flecks, underexposure) in the photographs (Jonckheere et al., 2005b(Jonckheere et al., , 2006)).Post correction for clumping on branch-and treelevel was done by dividing the measured effective LAI by a clumping factor for Scots pine (0.83) (Jonckheere et al., 2005a).Daily values were obtained through linear interpolation.The LAI pattern for the different age classes was reconstructed using litter fall data.Litter fall was measured at a biweekly interval between May 2007 and December 2007 on 10 places within the experimental plot with litter collectors (surface area of 0.3 m 2 ) made from nylon-mesh netting.All litter was oven-dried (48 h, 75 • C), sorted into branches, needles and reproductive organs, and weighed.Current-year needle LAI was calculated as the sum of needle litter fall and the increase of total LAI until it reached a maximum (early September 2007).One-year-old needle LAI was calculated as the difference between total and current-year needle LAI.A relative distribution of current-year and one-year-old needle LAI between the upper and lower canopy was estimated from destructive sampling in August 2007.This was done by measuring current-year and one-year-old needle dry weight for four harvested branches from the upper and lower crown of five trees surrounding the flux tower.These dry weight values were averaged and converted to LAI values by multiplication with specific leaf area values from a previous site study (Xiao et al., 2006).

Gross Ecosystem Productivity measurements
Gross Ecosystem Productivity (GEP) was estimated from vertical CO 2 flux measurements above the canopy using the eddy covariance technique (Baldocchi and Meyers, 1998).The measurements were conducted at the top of the tower at a height of 41 m, circa 18 m above the canopy.The eddy covariance system consisted of a sonic anemometer (Model SOLENT 1012R2, Gill Instruments, Lymington, UK) for wind speed and an infrared gas analyser (IRGA) (Model LI-6262, LI-COR Inc., Lincoln, 9743 Introduction

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Full Screen / Esc Printer-friendly Version Interactive Discussion NE, USA) to measure the CO 2 concentrations.A detailed description of the experimental setup can be found in Kowalski et al. (2000) and Carrara et al. (2003).Half-hourly net ecosystem exchange fluxes were calculated following the recommendations of the Euroflux network (Aubinet et al., 2000;Papale et al., 2006;Reichstein et al., 2005).Gap-filling and separation of net ecosystem exchange fluxes into total ecosystem respiration and GEP was done as described by Reichstein et al. (2005).

Meteorology
On top of the flux tower, 41 m above ground level, the following meteorological variables were measured at 0.1 Hz: incoming solar irradiance (I) (Kipp and Zonen CM6B, the Netherlands), air temperature (T ) and relative humidity (RH) (DTS-5A, Didcot Instrument Co Ltd, Abingdon, UK), atmospheric pressure (p a ) (SETRA Barometric Pressure transducer Model 278, Setra systems, Boxborough, MA, USA), and wind speed (v) (Didcot DWR-205G).Measurement data were converted to half-hourly means and stored on a data logger (Campbell CR10, UK).Data gaps were filled with data from nearby weather stations.Air vapour pressure deficit (VPD) was derived from measured RH and T , following Jones (1992).Half-hourly atmospheric CO 2 concentration (C a ) was obtained by averaging 20.8 Hz measurements that were conducted on top of the tower with an Infra Red Gas Analyser (IRGA) (Model LI-6262, LI-COR Inc., Lincoln, NE, USA).These meteorological data were used as input for the canopy model.Precipitation was measured with a rain gauge (Didcot DRG-51) and recorded half-hourly.

Canopy model: description
The process-based multi-layer canopy model applied in this study is a generic model and is described in detail in Appendix A. The model includes a radiation submodel (Goudriaan, 1977) and a leaf physiological submodel (Farquhar et al., 1980;Leuning, 1995)

Canopy model: parameterization and validation
The parameterization of the canopy model was partially based on previous site study results and on values from the literature (see Table 2).The stomatal model (Eqs.A15-16) making part of the leaf physiological submodel (Eqs.A7-20) was parameterized to new site-specific gas exchange measurements in order to obtain reliable model results.Therefore, needle-level gas exchange diurnals and responses to VPD were assessed.On nine occasions throughout the summer 2007, after a morning A n /C i curve assessment, needles were held in the LI-6400 chamber and diurnal gas exchange courses were tracked under ambient conditions.Chamber CO 2 concentration was set to 360 ppm.Three out of the nine diurnals included night-time measurements and on two occasions a biurnal, a day-night-day period, was covered.Needle gas exchange responses to VPD were measured on three needle samples in September 2007.Leaf chamber VPD was varied within a range of 0.5 up to 4.0 kPa at saturating PAR (1000 µmol m −2 s −1 ), chamber air temperature between 20 and 25 • C, and chamber CO 2 concentration of 360 ppm.With these measurements, the stomatal model was parameterized.An average input value for the night-time conductance to CO 2 (g 0 ) was directly obtained from the night-time measurements.An average input value for the empirical parameters a 1 and VPD 0 was obtained by fitting the stomatal model to the gas exchange diurnals and the measured VPD responses, respectively, through minimization of the sum of squared differences between simulated and measured g st .
The full leaf physiological submodel was validated to the two biurnals.First, the empirical parameter a 1 was optimized again to the first day of each biurnal by fitting the stomatal model through minimization of the sum of squared differences between simu-9745 Introduction

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Full Screen / Esc Printer-friendly Version Interactive Discussion lated and measured g st .The leaf physiological submodel was subsequently validated to the second day of each biurnal using this a 1 value, the V m25 and J m25 values derived from the A n /C i curves assessed on the needles before the biurnal was started, and the other parameter values as given in Table 2.The submodel's performance was judged by evaluating simulated versus measured half-hourly averaged net photosynthesis (A n ) and E .

Photosynthetic parameter input scenarios
The canopy model was run for one phenological year (1 May 2007 to 30 April 2008) with the half-hourly meteorological input and daily LAI input provided in Fig. 1, and the parameterization given in Table 2. Yearly GEP and E can were simulated under four V m25 −J m25 input scenarios in which the measured needle age-related and seasonal variation of V m25 and J m25 were included or omitted.In the first scenario (scen-AS), which was the reference scenario, both seasonal and needle age-related variation were included.In the second scenario (scen-A), only needle age-related variation was included.In the third scenario (scen-S), only seasonal variation was included.In the fourth scenario (scen-B), which was the basic scenario, both needle age-related and seasonal variation were omitted.For the scenarios in which seasonal variation was included (scen-AS, scen-S), V m25 and J m25 measurements from consecutive sampling dates were pooled when statistically not significantly different, as indicated by an analysis of variance (ANOVA) post hoc comparison test.Continuous time courses of V m25 and J m25 were obtained by linear interpolation.For the scenarios in which seasonal variation was omitted (scen-A, scen-B), V m25 and J m25 input values were based on the July and August 2007 measurements only, as photosynthetic capacity measurements for model parameterization are typically done in summer.Values of the two dates were pooled.For the scenarios in which needle-age related variation was omitted (scen-S, scen-B), V m25 and J m25 measurements from current-year and one-year-old needles were pooled and the weighted average was calculated with current-year and one-yearold needle LAI as weighting factor.In all four V m25 −J m25 input scenarios, current-year 9746 Introduction

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Full and one-year-old needles were given the same values for the other parameters in the leaf physiological submodel with the exception of the R d 25 /V m25 ratio (see Table 2).

Statistics
All statistical analyses were performed using the statistical package of the ORIGIN ® software (Version 7, OriginLab Corporation, Northampton, MA, USA) and SAS (version 9.1, SAS Institute Inc., Cary, NC, USA).To test for significant differences between two or more means, a two-tailed Student's t-test or a one-way ANOVA was applied.
To unravel the effect of needle age and seasonality on V m25 , J m25 , and the J m25 /V m25 ratio, we performed an analysis of covariance (ANCOVA) with needle age as treatment and the day of the phenological year (1 May 2007=1) as the covariate.In case of a statistically significant difference (p<0.05), the analyses were followed by post hoc comparisons of all means by the Tukey-Kramer HSD test.A Monte Carlo technique was used to estimate the uncertainty on simulated yearly GEP and E can from the uncertainty distributions of the input parameters V m25 and J m25 .The number of Monte Carlo model runs under each scenario was set to 500, the minimum number after which the standard deviation on simulated yearly GEP and E can converged.We assumed normality of the probability density function of V m25 and J m25 , which was tested for with a Shapiro-Wilk test.The performance of the leaf physiological submodel was evaluated by the coefficient of determination (R 2 ), the slope and the intercept of the linear regression of simulated versus measured A n and E , the root-mean-square-error (RMSE), and Willmott's index of agreement (d ) (Willmott, 1981).This index ranges from 0 to 1, 1 indicating perfect agreement.Statistical significance for all tests was set at the 0.05 level.In text and tables, given errors on means are standard errors (SE).Introduction

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Meteorological site conditions
Phenological year and growing season mean air temperature during the study period (May 2007-April 2008) were 10.4 and 13.9 • C, respectively, which are 1.4 and 1.0 • C below the ten-year mean.Phenological year and growing season total precipitation mounted to 903 and 502 mm, respectively, the former of which is about 10% above the long-term mean.Growing season total precipitation followed the long term mean of 505 mm.The study period was characterized by the absence of extreme air temperatures and dry atmospheric conditions: T virtually never exceeded 25 • C and VPD hardly exceeded 1.5 kPa (Fig. 1b,c).

Leaf Area Index
Total LAI during the study period varied between 0.94 and 1.31 m 2 m −2 (Fig. 1d).Total LAI was minimal just before bud burst in spring 2007 and peaked after full expansion of current-year needles in late summer.By this time, current-year needle LAI had increased to 0.55 m 2 m −2 , contributing to 42% of total LAI.By the end of autumnal needle shed, one-year-old needle projected LAI had dropped to 0.39 m 2 m −2 and total LAI reached a minimum again.In winter 2007 and spring 2008, current-year needles and one-year-old needles made up 60% and 40% of total canopy LAI, respectively.Destructive sampling in August 2007 showed a slightly uneven upper/lower canopy distribution of current-year and one-year-old needle LAI (58/42% and 47/53%, respectively).

Upper versus lower canopy
Photosynthetic capacity and needle N content differences between the upper and the lower canopy were specifically examined during the first sampling (June 2007), in order to confirm the findings of a previous site study, which reported a non-significant canopy Introduction

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Full effect on V m25 and J m25 (Janssens et al., 1998) and so to possibly reduce the number of measurements during the following samplings.As current-year needles were still too small to be sampled, all measurements were made on one-year-old needles.Maximum carboxylation capacity at 25 • C was not significantly different between the upper canopy (67.8±2.3 µmol m −2 s −1 ; n=12) and the lower canopy (71.3±2.5 µmol m −2 s −1 ; n=9) (p=0.47).Also J m25 was not significantly different between the upper canopy (147.2±2.1 µmol m −2 s −1 ) and the lower canopy (157.4±7.9 µmol m −2 s −1 ) (p=0.41).
In line with the photosynthetic parameters, N a did not significantly differ between the upper canopy (648.9±50.6 mmol m −2 ) and the lower canopy (573.6±41.2mmol m −2 ) (p=0.13).On the basis of these results, which were confirmed a posteriori when considering all data (results not shown), we decided to pool measurements from the upper and lower canopy.

Needle-age related and seasonal photosynthetic parameter variation
Seasonal variations in V m25 , J m25 , and the J m25 /V m25 ratio are depicted in Fig. 2 for current-year needles (white bars) and one-year-old needles (grey bars).An ANCOVA was performed to unravel the effect of needle age and seasonality on V m25 , J m25 , and the J m25 /V m25 ratio.The analysis revealed a significant effect of needle age on V m25 after controlling for the seasonality effect (p<0.0001).Maximum carboxylation capacity at 25 • C was significantly higher in current-year than in one-year-old needles.Adjusted V m25 means were 81.3±2.5 and 63.1±1.9 µmol m −2 s −1 , respectively.Moreover, the seasonality effect was significant (p< 0.0001), with V m25 decreasing with day of the phenological year (slope=−0.0762±0.115µmol m −2 s −1 d −1 ).The analysis also revealed a significant effect of needle age on J m25 after controlling for the effect of seasonality (p<0.05).Maximum electron transport capacity at 25 • C was significantly higher in current-year than in one-year-old needles.Adjusted J m25 means were 163.3±5.8 and 144.9±4.6 µmol m −2 s −1 , respectively.In contrast with V m25 , the seasonality effect on J m25 was not significant (p=0.79).Furthermore, also a significant needle age effect on Introduction

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Full Screen / Esc Printer-friendly Version Interactive Discussion the J m25 /V m25 ratio was detected, after controlling for the seasonality effect (p<0.001).
The ratio was significantly higher in one-year-old than in current-year needles.The adjusted ratio means were 2.05±0.08 and 2.34±0.07,respectively.The effect of seasonality on the ratio was significant (p<0.0001), with a positive relation between the ratio and day of the phenological year (slope=0.0028±0.0004d −1 ).

Photosynthetic parameters versus needle N content
Based on the measurements in July and August 2007, the summer relationship between photosynthetic parameters and needle N content was quantified along the two needle ages (Table 3).The parameters V m25 and J m25 were significantly higher in current-year than in one-year-old needles, while at the same time biomass-and areabased N content (N b and N a ) were significantly higher in one-year-old needles.As a result, the V m25 /N a ratio and the J m25 /N a ratio were much higher in current-year than in one-year-old needles.Maximum carboxylation capacity at 25 • C was even negatively correlated with N a when considering the data of both needle ages together (r=−0.61,p<0.001;Fig. 3a).The correlation between J m25 and N a was not significant (r=−0.11,p=0.53;Fig. 3b).

Canopy gas exchange simulations
The leaf physiological submodel of the canopy model reproduced half-hourly averaged net photosynthesis (A n ) and transpiration (E ) of the second day of the two validation biurnals to a more than satisfying degree, as indicated by the R 2 and d values, being close to 1, and the low RMSE values (Fig. 4).Predictions of E were slightly less accurate than predictions of A n .This is most probably due to the fact that, contrary to A n , g st and, hence, E respond very slowly to light changes.These dynamics could not be fully captured by the submodel assuming steady state conditions.It should be noted that the submodel was tested with a 1 values optimized to the first day of each biurnal.
If the submodel was tested with the average a 1 value used in the canopy model runs Introduction

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Full (see Table 2), it would have been less accurate.Nonetheless, Fig. 4 shows how well the submodel could behave on the needle-level.
After validating the submodel, the canopy model was run under the four V m25 − J m25 input scenarios, for which the V m25 and J m25 input values are given in Table 4.Under the reference scenario, including both needle age-related and seasonal variation (scen-AS), simulated yearly GEP and E can amounted to 1.561±0.004kg C m −2 y −1 and 201.8±0.5 kg H 2 O m −2 y −1 , respectively (Table 5).Yearly GEP and E can simulated under the scenario including needle age-related variation only (scen-A) were not significantly different from these values.Relative to scen-AS, yearly GEP and E can were significantly underestimated under the scenario including seasonal variation only (scen-S), and significantly overestimated under the basic scenario, omitting both needle agerelated and seasonal variation (scen-B).The percentagewise differences with the scen-AS results, however, were small (within 2.5%).Measured yearly GEP, which amounted to 1.352 kg C m −2 y −1 , was considerably overestimated by the canopy model under all scenario's (+13.0% to +17.5%).
Daily GEP simulated under scen-AS clearly followed the seasonal course of measured GEP (Fig. 5a), yet simulated GEP was slightly lower in spring 2007 and higher from August 2007 on (Fig. 5b).Daily GEP courses were very similar under all scenarios.For reasons of clarity, we depicted the simulated daily GEP differences with scen-AS instead of absolute daily GEP values (Fig. 5c-e).The seasonal courses of daily GEP simulated under scen-A and scen-AS were virtually equal (Fig. 5c).Under scen-S, the model simulated slightly lower daily GEP in summer 2007, relative to scen-AS (Fig. 5d).Under scen-B, daily GEP values were slightly higher in early summer (Fig. 5e).The differences in simulated daily GEP were all within 0.5 g C m −2 d −1 .Daily E can simulations showed relative patterns analogous to daily GEP and were not presented to avoid redundancy.Introduction

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Upper versus lower canopy
We did not observe a significant difference between the photosynthetic parameters V m25 and J m25 or the N content of upper and lower canopy needles.These findings are corroborated by a previous site study (Janssens et al., 1998).Though we only sampled one-year-old needles, we have no reasons to assume results would be different for current-year needles.We believe the absence of any canopy position effect is a consequence of the canopy sparsity (maximal LAI=1.31m 2 m −2 , Fig. 1d).In the virtual absence of a light gradient within the sparse canopy, no vertical canopy N or photosynthetic capacity profile is (or better, has to be) developed within the needle age class to optimize canopy photosynthesis.

Needle-age related and seasonal photosynthetic parameter variation
We found significantly lower V m25 and J m25 values in one-year-old needles than in current-year needles, following the general trend of decreasing photosynthetic capacity with needle age (Rundel and Yoder, 2000;Niinemets, 2002).The effect of needle age on J m25 was smaller than the effect on V m25 .In addition to the effect of needle age, we found a seasonality effect on V m25 , but not on J m25 .The seasonal variation of V m25 , however, was weaker than reported in other Pinus studies (Misson et al., 2006;Han et al., 2008;Ellsworth, 2000).Overall, the observed V m25 and J m25 values are in agreement with previous observations at the site (Janssens et al., 1998) and with literature values for P. sylvestris (Wang et al., 1995;Kellom äki and Wang, 1997;Jach and Ceulemans, 2000;Niinemets et al., 2001).The V m25 values do fall within the higher range of reported values, summarized by Niinemets (2002) and Katge et al. ( 2009), but are typical for N-rich sites.
In general, the needle age-related decline of photosynthetic capacity in conifers is assumed to be caused by (1) decreasing needle N content (Field, 1983) and (2) de-Introduction

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Full creasing CO 2 concentration at the carboxylation sites through declining internal conductance to CO 2 as needles become denser and cell walls thicken when needles age (Warren, 2006;Niinemets et al., 2009).Although we measured increasing needle N content with needle age (Table 3), our results do certainly not give unequivocal proof against the first assumption, since our needle samples were not specifically analyzed for photosynthesis-related N and might have been "contaminated" (see next paragraph).We could not verify the second assumption, a declining internal conductance to CO 2 with needle age, with measurements.Yet, such a decline might explain the observed smaller effect of needle age on J m25 than on V m25 .Both photosynthetic parameters were obtained by optimizing the biochemical photosynthesis model of Farquhar (Farquhar et al., 1980) to A n /C i curves.Here, internal conductance was ignored in the calculation of leaf intercellular CO 2 concentration (C i ), which we assume also to be the CO 2 concentration at the carboxylation sites.As a consequence, our calculation would yield a relative overestimation of C i in one-year-old needles if internal conductance to CO 2 was really lower in one-year-old needles.This would lead to a relative underestimation of optimized V m25 but not of J m25 , as the optimization of J m25 but not of V m25 to an A n /C i curve is largely independent of C i .These assumed underlying physiological causes of the effect of needle age might provide an explanation for the observed seasonality effect as well, as seasonal variation mainly results from needle ageing within the growing season.

Photosynthetic parameters versus needle N content
We found that the V m25 /N a ratio and the J m25 /N a ratio were much higher in current-year than in one-year-old needles.When considering the data of both needle ages together, a negative correlation between V m25 and N a and a non-significant correlation between J m25 and N a was observed.These rather unexpected trends should be ascribed to the observed needle N contents, which were lower in current-year needles than in one-year-old needles.Our observations deviate from the general finding that needle N content tends to decrease with needle age (Field, 1983;Helmisaari, 1990 2002), but are not unprecedented for P. sylvestris (Gielen et al., 2000).We bring forward two explanations for the observed lower needle N contents in current-year needles as compared to one-year-old needles.First, we hypothesize that under the conditions of high N availability prevailing at the site (Neirynck et al., 2008) the N demand of expanding current-year-needles and shoots is partially met by supply of N taken up at high rates by the roots.This supply of soil-borne N might partially inhibit or render superfluous the commonly observed translocation of N stored in now-one-year-old needles the previous autumn to the expanding current-year needles (Vapaavuori et al., 1995;Warren et al., 2003).Furthermore, storage of excess N in one-year-old needles, which is usually limited to autumn (e.g.N äsholm and Ericsson, 1990;Vapaavuori et al., 1995), might already occur in summer under conditions of high N availability.As a result, one-year-old needles still or already contain significant amounts of N not associated with photosynthesis in summer.Second, we do not rule out the possibility that our needle samples have been "contaminated" with extracuticular N originating from epiphytic nitrophylic microflora occurring on the needle surfaces.
The abundance of epiphytic microflora on conifer needles has been shown to positively correlate with the amount of nitrogen deposition (Br åkenhielm and Qinghong, 1995;Poikolainen et al., 1998).As nitrogen deposition rates in the experimental stand are very high (30-40 kg ha −1 y −1 ; Neirynck et al., 2007) and epiphytic microfloral biomass has been shown to accumulate with needle age (Søchting, 1997;G öransson, 1992), we believe this could contribute to some extent to the observed higher measured N content in one-year-old needles.

Canopy gas exchange simulations
With an average GEP of 1.56 kg C m −2 y −1 , our canopy model produced values very close to the mean GEP reported for temperate humid evergreen forests (1.76±0.06kg C m −2 y −1 ; the 25 to 75 percentiles lying at 1.39 and 2.13 kg C m −2 y −1 ; Luyssaert et al., 2007).Simulated GEP showed a very similar seasonal pattern as the eddy covariance-based estimates of GEP made at the site, yet were higher during 9754 Introduction

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Interactive Discussion most of the year.This was not really surprising.Eddy covariance-based estimates of GEP are obtained by subtracting modeled ecosystem respiration estimates from the measured net ecosystem exchange.The respiration model is based on night-time CO 2 flux measurements that are subsequently extrapolated to day time using temperature response functions.Night-time CO 2 fluxes are typically underestimated during low turbulent conditions, but this problem is mitigated by excluding all wind still hours from the data series and subsequent gap filling.A potentially larger problem that may contribute to underestimated daytime respiration and subsequently also to underestimated GEP is that the footprint of the eddy covariance system is much larger during night time than during day time.This implies that GEP for the area contributing most to the daytime CO 2 fluxes is estimated by subtracting respiration estimates from a largely different area.Because the forest site in this study is located in a highly heterogeneous, mixed forest, with a low-productive heath land in the night-time fetch, the error associated herewith is potentially very large.Nonetheless, overall, our simulated GEP and the eddy covariance-based GEP estimates agreed well.
Because the objective of this study was not to simulate the absolute GEP, but rather to study the effect of more versus less detailed parameterization on simulated GEP, the simulated GEP differences between the scenarios are more relevant than the absolute differences between measured and simulated GEP are.Omitting seasonal photosynthetic parameter variation only (scen-A) did not result in significant differences in simulated yearly GEP and E can , relative the reference scenario in which both needle-age related and seasonal photosynthetic parameter variation were considered.Omitting needle age-related photosynthetic parameter variation (scen-S, scen-B) led to significant differences in yearly GEP and E can , relative to the reference scenario.These differences were small (within 2.5%) and, hence, rather trivial from an ecological point of view.The small differences in yearly GEP and E can resulted from small differences in simulated daily GEP and E can in spring and summer when climatic conditions favoured gas exchange (Fig. 5c,d we doubt that differences in yearly GEP and E can between the scenarios would become ecologically relevant if simulations were done under the long-term site meteorological conditions. In a study similar to ours, Bernier et al. (2001) also found canopy gas exchange differences of less than 3%, simulated under a scenario considering age-related variation (scen-A) and a scenario with a needle age-averaged photosynthetic capacity input (scen-B), for an Abies balsamea (L.) stand in Canada.In another analogous study, however, Og ée et al. ( 2003) used eddy covariance measurements to validate fluxes for a Pinus pinaster (Ait.)stand in France, simulated under a scenario including both seasonal and needle age-related photosynthetic capacity variation (scen-AS) and a scenario including seasonality only (scen-S).They found that omitting needle age-related variation resulted in considerable loss in predictive quality.Their study differed from the study of Bernier et al. (2001) and the present study in that the differences in photosynthetic parameter input between the needle age classes were much higher.For example, in Og ée et al. 's study (2003), V m25 input values for one-year-old and two-yearold needles were about 80% and 60% of the current-year needle value, respectively.Likewise, the J m25 input values only amounted to about 60% and 35% of the currentyear-needle value.In our study, the V m25 and J m25 input value for one-year-old needles were 79% and 89% of the current-year-needle input value.
Generally, the effect of omitting age-related variation on simulated canopy gas exchange depends on the magnitude of the input photosynthetic capacity differences between the needle age classes and the steepness of the vertical distribution profile of different-aged needles in the canopy (along the light gradient).The effect of omitting seasonal photosynthetic capacity variation, i.e. applying summer parameter values instead of full seasonal courses, will obviously depend on the magnitude of the seasonal variation.As canopy gas exchange rates are highest in summer, omitting seasonality could result in significant overestimations only when needle photosynthetic capacity is considerably lower in spring and autumn than in summer.Because this was not really the case at our temperate study site, it is only logical that we did not find significant Introduction

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Full Screen / Esc Printer-friendly Version Interactive Discussion differences with the reference scenario in the present study.To the best of the authors' knowledge, the literature does not report other studies in which direct leaf-level photosynthetic capacity measurements are used in a process-based model to study the effect of seasonal photosynthetic capacity variation on coniferous canopy gas exchange simulations.Yet, Santaren et al. (2007) indirectly showed the importance of including seasonal photosynthetic parameter variation in a study in which a processbased model was optimized to eddy covariance flux data for the abovementioned P. pinaster stand in France.

Conclusions
From our results, we conclude that summer sampling of the different needle age classes would suffice to provide photosynthetic parameter input for accurate simulations of yearly canopy gas exchange.Furthermore, we reckon caution is required when assessing relationships between photosynthetic parameters and needle N content from measurements on different needle age classes.These conclusions are valid, at least, for the Scots pine stand under study.We recognize the studied stand is, through the high nitrogen deposition rates and its sparse canopy, not fully representative for temperate Scots pine stands in general.Nevertheless, we believe well-parameterized process-based canopy models -as applied in this study -are a useful tool to quantify losses of predictive accuracy involved with canopy simplification.As they provide a fast means to estimate and rank sources of canopy gas exchange variation, they might even be helpful in guiding experimental design.Introduction

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Appendix A

Canopy model description
The process-based multi-layer canopy model applied in this study is a generic model and simulates Gross Ecosystem Productivity (GEP) and canopy transpiration (E can ) on a half-hourly time resolution.It is driven by half-hourly input of incoming solar radiation (I), air temperature (T ), atmospheric vapour pressure deficit (VPD), air CO 2 concentration (C a ), atmospheric pressure (p a ), and wind speed (v), as well as by daily LAI input of current-year and one-year-old needles.The model includes a radiation submodel (Goudriaan, 1977) and a leaf physiological submodel which combines the biochemical photosynthesis model of Farquhar (Farquhar et al., 1980) with a Ball-Berry-Leuning type stomatal model (Leuning, 1995).The canopy is treated as a horizontally multilayered structure with a canopy layer depth of 1 m.

The radiation submodel
At each time step, the radiation submodel splits up incoming irradiance at the canopy top (I) into direct beam irradiance (I b0 ) and diffuse irradiance (I d 0 ).The sunlit LAI fraction of each layer i (f sun(i ) ) is calculated with Beer's law: Here, k b is the beam radiation extinction coefficient and LAI c(i ) is the cumulative LAI above a canopy layer i from the canopy top.For a uniform needle angle distributionwhich we assume in this study -k b is given by: Here, β is solar elevation angle, which is calculated from day of the year, time of day, and latitude (Campbell and Norman, 1998 layer i (f shad(i ) ) is given by: Beam radiation intensity does not decline with canopy depth.Diffuse irradiance declines with canopy depth and is calculated for every layer i with Beer's law: Here, k d is the diffuse radiation extinction coefficient.The total received irradiance by a sunlit fraction (I sun(i ) ) is the sum of beam irradiance and diffuse irradiance.Shaded leaves only receive diffuse radiation: Here, Π/3 is the averaged leaf angle for a uniform needle angle distribution.Total received irradiance is converted to total received PAR.From total received PAR, the leaf physiological submodel simulates leaf-level photosynthesis and transpiration for current-year and one-year-old needles in each canopy layer fraction.
The effect of inter-and intra-crown needle foliage clumping on light distribution in the canopy is accounted for following Sampson et al. (2001) and Sinclair and Knoerr (1982).

The leaf physiological submodel
The leaf physiological submodel combines the biochemical photosynthesis model of Farquhar (Farquhar et al., 1980) with a Ball-Berry-Leuning type stomatal model (Leuning, 1995).The biochemical photosynthesis model of Farquhar simulates gross photosynthesis under both nitrogen limiting conditions (A v ) and light limiting conditions (A j ).where VPD 0 is an empirical parameter.The supply formula is introduced to calculate C s and to link the photosynthesis model with the stomatal conductance model: Here, C a is the atmospheric CO 2 concentration and g bl is the leaf boundary layer conductance to CO 2 .The latter is calculated from wind speed (v) and the characteristic needle dimension (d ; needle diameter) (Jones, 1992): The system of equations is solved to obtain a steady-state solution for A b and g st .
Needle transpiration rate (E ) is then calculated by: where g tot is total leaf conductance and p a is atmospheric pressure.Total leaf conductance is obtained by summing g st and g bl , following the rules of adding conductance.
The factor 1.56 converts conductance to CO 2 to conductance to H 2 O. Calculated gross photosynthesis values for j -aged needles in the sunlit and shaded fractions of canopy layers i (A bsun(i ,j ) , A bshad(i ,j ) ) are multiplied with the respective LAI values (LAI sun(i ,j ) , LAI shad(i ,j ) ) and summed to obtain instant Gross Ecosystem Productivity (GEP).Similarly, calculated transpiration for j -aged needles in the sunlit and shaded fractions of canopy layers i (E sun(i ,j ) , E shad(i ,j ) ) are integrated to obtain instant A bsun(i ,j ) LAI sun(i ,j ) + A bshad(i ,j ) LAI shad(i ,j ) , (A21) E sun(i ,j ) LAI sun(i ,j ) + E shad(i ,j ) LAI shad(i ,j ) .(A22)   Leaf Area Index of j -aged needles in the sunlit and shaded fraction of a canopy layer i LAI shad(i ,j ) m 2 m −2 Leaf Area Index of j -aged needles in the sunlit and shaded fraction of a canopy layer i N b g g  Full ), which was somewhat expected.Even though the phenological year May 2007-April 2008 was slightly colder and wetter than the long-term mean, irradiance at the canopy top I d 0 W m −2 diffuse irradiance at the canopy top I d (i ) W m −2 diffuse irradiance in a canopy layeri I sun(i ) W m −2 total received irradiance by the sunlit fraction of a canopy layer i I shad(i ) W m −2 total received irradiance by the shaded fraction of a canopy layer i m −2 s −1 photosynthetically active radiation effectively absorbed by photosystem II R J mol −1 K −1 universal gas constant R d , R d 25 µmol m −2 s −1 leaf respiration rate per unit leaf area, at prevailing temperature and at 25 • C R d 25 /V m25 dimensionless ratio of leaf respiration rate to maximum carboxylation rate under RuBP saturation at 25 • C RH % relative humidity S J mol −1 K −1 electron-transport temperature response parameter T • C air temperature v m s −1 wind speed V m , V m25 µmol m −2 s −1 maximum carboxylation rate per unit leaf area under RuBP saturation, at prevailing temperature and at 25 • C point in the absence of mitochondrial respiration, at prevailing temperature and at 25 • C θ dimensionless curvature of leaf response of electron transport to PAR Input values of carboxylation capacity at 25• C (V m25 ; µmol m −2 s −1 ) and electron transport capacity at 25 • C (J m25 ; µmol m −2 s −1 ) (value±SE) for the four scenarios.For the scenario including both seasonal and needle age-related variation (scen-AS) and the scenario including needle age-related variation only (scen-S), values between solid lines are obtained from pooling the measurements from the consecutive sampling dates underscored by the solid lines and are constant over period between the consecutive sampling dates.Values between non-pooled consecutive sampling dates are linearly intrapolated.For the scenario including needle agerelated variation only (scen-A) and the scenario omitting both seasonal and needle age-related variation (scen-B), values are based on measurements from July and August 2007 only.--------163.1±3.3 -------------161.6±4.5 one-year-old V m25 ---------68.4±1.7 -------

Fig. 1 .
Fig. 1.Time courses of the half-hourly meteorological variables (a) incoming solar irradiance (I), (b) air temperature (T ), (c) air vapour pressure deficit (VPD), and of (d) Leaf Area Index (LAI) of current-year needles (black line), one-year-old needles (grey line) and all needles (dotted line), over the phenological year May 2007-April 2008.

Fig. 2 .
Fig. 2. Seasonal variation of (a) maximum carboxylation capacity at 25• C (V m25 ), (b) maximum electron transport capacity at 25 • C (J m25 ), and (c) the J m25 /V m25 ratio, for current-year needles (white bars) and one-year-old needles (grey bars).Error bars represent SE.In June 2007, current-year needles were too small to be sampled.
May 2007-30 April 2008, on a monthly basis (June, July, August, and September 2007; April 2008).At each sampling, within the first week of the month, between 15 and 30 needle samples (each 6 to 8 needles, i.e. 3 to 4 fascicles) were placed into the LI-6400 leaf chamber.Foam mounting paths held the needles in the chamber, preventing self-shading.Response curves of ). Measurements were carried out 9741 n /C i curve assessment.During measurements leaf chamber humidity varied between 50 and 80%.
, and v, and daily input of current-year and one-year-old needle LAI.At each time step, the model calculates leaf-level gross photosynthesis (A b ) and E for current-year and one-year-old sunlit and shaded needles in each canopy layer.These values are integrated over the canopy and over time to obtain instant, daily, and yearly GEP and E can .Table 2 lists all constant model parameters with their references.
. It simulates Gross Ecosystem Productivity (GEP) and canopy transpiration (E can ) on a half-hourly time resolution.It is driven by half-hourly input of I, T , VPD, C a , Introduction PAR is the needle PAR absorptivity and f is a spectral correction factor.The parameters V m , R d , Γ , K c , and K o are temperature-dependent and calculated from reference values at 25• C, applying an Arrhenius equation: Introduction

Table 2 .
List of input parameter constants, with their reference.

Table 5 .
Measured and simulated yearly Gross Ecosystem Productivity (GEP; kg C m −2 y −1 ) and simulated canopy transpiration (E can ; kg H 2 O m −2 y −1 ) under the four scenarios (mean ±SE), with the percentagewise difference and p-values of t-test comparison with scen-AS results.Also given are the percentagewise difference and the p-values of One Sample t-test comparison of simulated GEP with measured GEP.n=500.