## 3 Results
### Foliage Properties and Their Seasonal Variation  
Outputs from the mixed effect model indicated that the average values of both LMA and the photosynthetic MM model parameters were not different between the study plots, except for higher \(A_{sat}\) in the 13 year-old plot, suggesting the potential for using a common (average) light response curve across all study sites (Table 4). However, significant variation in the random (month) component was also identified (Table 4), suggesting that seasonal variation should be considered (monthly photosynthetic light response model setup). Among all foliar properties studied, LMA showed the lowest seasonal variation (Figure 2A-D, Table A3). Monthly \(A_{sat}\) variability followed the seasonal patterns of temperature and precipitation variation (Figure 2E-H) with the highest values observed in spring. Between plots the highest seasonal variation was found in the youngest and less dense plot (Table A3). For all plots, lower \(A_{sat}\) values were observed in the summer of 2020 (Figure 2E-H), while \(K_{m}\) covaried with \(A_{sat}\) (Figure 2I-L). Leaf dark respiration showed the highest seasonal variation, with middle age stands having the higher CV. The saturated net photosynthetic rate and the dark respiration were not statistically different between sunlit and shaded needles (\(A_{sat,sun}=A_{sat,sh}\), \(R_{d,sun}=R_{d,sh}\)) as inferred from the Wilcoxon tests applied within and across all plots (Table A4). However, the half saturation coefficient was different between sunlit (\(K_{m,sun}\)) and shaded needles (\(K_{m,sh}\)). In order to simplify our model parameterisation, a correction for \(K_{m,sh}\) was implemented by multiplying the value of \(K_{m,sun}\) by 0.76, estimated from the ratio of \(K_{m,sh}/K_{m,sun}\) across all monthly estimates. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Parameter** & **13 Year-Old** & **40 Year-Old** & **72 Year-Old** & **92 Year-Old** & **AIC\({}_{plot}\)** & **AIC\({}_{plot+month}\)** & **AAIC** \\ \hline LMA & 139.3 (\(\pm\)4.5) & 146.4 (\(\pm\)4.4) & 142.5 (\(\pm\)4.4) & 139.0 (\(\pm\)3.3) & 2253 & 2231 & \(-\)22 \\ \(A_{sat}\) & 10.9 (\(\pm\)1.3) & 5.9 (\(\pm\)1.3) & 8 (\(\pm\)1.4) & 7.9 (\(\pm\)1.0) & 1395 & 1207 & \(-\)188 \\ \(K_{m}\) & 303.4 (\(\pm\)37.4) & 223.3 (\(\pm\)36.9) & 235.6 (\(\pm\)38.2) & 239.4 (\(\pm\)27.9) & 3235 & 3121 & \(-\)114 \\ \(R_{d}\) & 0.53 (\(\pm\)0.1) & 0.48 (\(\pm\)0.1) & 0.45 (\(\pm\)0.1) & 0.49 (\(\pm\)0.07) & 158 & 111 & \(-\)47 \\ \hline \end{tabular}
\end{table} 
Table 4: Plot-specific fixed effect estimates (\(\pm\)1 standard error) for Leaf Mass per Area (LMA, g m\({}^{-2}\)), saturated net photosynthetic rate (\(A_{sat}\), \(\mu\)mole m\({}^{-2}\) s\({}^{-1}\)), half saturation coefficient (\(K_{m}\), \(\mu\)mole quanta m\({}^{-2}\) s\({}^{-1}\)) and dark respiration rate (\(R_{d}\), \(\mu\)mole m\({}^{-2}\) s\({}^{-1}\)). AIC\({}_{plot}\) indicates the AIC for a simple linear regression with plot, while AIC\({}_{plot+month}\) indicates the AIC when seasonal variation was taken into account by including a monthly random component. \(\Delta\)AIC indicates the difference between AIC\({}_{plot+month}\) and AIC\({}_{plot}\). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Stand Age** & **Mean Tree** & **Trees/Plot** & **Mean dbh** & **Basal Area** & \multicolumn{2}{c}{**LAI Method**} \\ **(Years)** & **Height (m)** & **(n\({}_{o}\)/900 m\({}^{2}\))** & **(cm)** & **(m\({}^{2}\) ha\({}^{-1}\))** & **HPM** & **LFM** & **CMM** \\ \hline
13 & 1.93 & 99 & 1.98 & 0.59 & 0.90 & 1.47 & 0.66 \\
40 & 5.46 & 300 & 7.46 & 22.87 & 1.48 & 2.66 & 1.77 \\
72 & 9.28 & 62 & 20.72 & 32.92 & 1.45 & 3.88 & 2.13 \\
92 & 12.87 & 21 & 43.36 & 35.79 & 1.47 & 3.36 & 1.79 \\ \hline \end{tabular}
\end{table} 
Table 3: Variation in stand structure across the study plots. Leaf Area Index (LAI) was estimated with three different methods (see text for details). Diameter at breast height (dbh) measured for trees with a diameter \(>\) 1 cm.