## 3 Results
### Effects of N addition on C exchange Fluxes, ET, WUE and LUE in a Chronosequence
\begin{table}
\begin{tabular}{c c c|c c c c c|c c c} \hline \hline
**Flux** & **Stand** & **Method** & \multicolumn{4}{c|}{**Calibration**} & \multicolumn{3}{c}{**Prediction**} \\ \hline  & & & & & & & & & & & \\ \hline GPP & DF49 & MVMLR & \(y=0.97x+5.94\) & 21 & 0.97 & \(y=0.88x+18.18\) & 30 & 0.94 & \(y=1.01x+6.1\) & 22 & 0.97 \\  & MVANN & \(y=x+0.07\) & 2 & 0.99 & \(y=0.99x-0.35\) & 3 & 0.99 & \(y=0.86x+15.42\) & 27 & 0.97 \\  & HDF88 & MVMLR & \(y=0.92x+8.65\) & 21 & 0.92 & \(y=0.84x-2.36\) & 36 & 0.91 & \(y=0.82x+16.47\) & 33 & 0.88 \\  & MVANN & \(y=x+0.42\) & 5 & 0.99 & \(y=0.97x+2.22\) & 6 & 0.99 & \(y=0.78x+6.51\) & 40 & 0.90 \\  & HDF00 & MVMLR & \(y=0.82x+8.23\) & 24 & 0.82 & \(y=0.36x+48.16\) & 62 & 0.55 & \(y=0.43x+68.56\) & 59 & 0.61 \\  & MVANN & \(y=0.98x+0.24\) & 7 & 0.98 & \(y=0.98x-1.25\) & 6 & 0.99 & \(y=0.65x-0.42\) & 43 & 0.96 \\ \(R\) & DF49 & MVMLR & \(y=0.92x+11.45\) & 29 & 0.92 & \(y=0.89x+22.65\) & 34 & 0.90 & \(y=1.2x-17.4\) & 33 & 0.94 \\  & MVANN & \(y=0.99x+1.31\) & 8 & 0.99 & \(y=x+0.03\) & 6 & 0.99 & \(y=1.02x-5.39\) & 25 & 0.96 \\  & HDF88 & MVMLR & \(y=0.92x+9.71\) & 16 & 0.92 & \(y=0.73x+26.58\) & 25 & 0.92 & \(y=0.59x+43.8\) & 33 & 0.78 \\  & MVANN & \(y=x-0.07\) & 3 & 0.99 & \(y=0.98x+1.63\) & 2 & 0.99 & \(y=0.96x+9.18\) & 23 & 0.89 \\  & HDF00 & MVMLR & \(y=0.84x+14.57\) & 23 & 0.84 & \(y=0.66x+53.33\) & 40 & 0.80 & \(y=0.81x+59.39\) & 42 & 0.76 \\  & MVANN & \(y=0.99x+2.53\) & 3 & 0.99 & \(y=1.01x-0.98\) & 3 & 0.99 & \(y=0.89x-1.03\) & 28 & 0.91 \\ NEP & DF49 & MVMLR & \(y=0.71x+8.56\) & 19 & 0.76 & \(y=0.55x+4.65\) & 24 & 0.82 & \(y=0.59x-2.14\) & 35 & 0.64 \\  & MVANN & \(y=0.99x+0.63\) & 8 & 0.96 & \(y=0.99x-0.54\) & 6 & 0.98 & \(y=0.67x-9.36\) & 39 & 0.54 \\  & HDF88 & MVMLR & \(y=0.93x-0.65\) & 12 & 0.86 & \(y=1.01x-13.44\) & 23 & 0.81 & \(y=1.08x-5.52\) & 24 & 0.80 \\  & MVANN & \(y=0.99x-0.62\) & 4 & 0.98 & \(y=0.96x-1.33\) & 5 & 0.98 & \(y=0.53x-21\) & 41 & 0.51 \\  & HDF00 & MVMLR & \(y=0.67x-15.4\) & 17 & 0.34 & \(y=-0.04x-58.5\) & 42 & 0.01 & \(y=0.21x-55.3\) & 53 & 0.02 \\  & MVANN & \(y=0.99x+1.21\) & 8 & 0.82 & \(y=0.89x-6.47\) & 7 & 0.87 & \(y=0.31x-32.2\) & 42 & 0.06 \\ ET & DF49 & MVMLR & \(y=0.96x+1.26\) & 4 & 0.96 & \(y=0.91x-0.82\) & 6 & 0.94 & \(y=1.05x-10.3\) & 5 & 0.93 \\  & MVANN & \(y=0.99x+0.34\) & 1 & 0.99 & \(y=0.98x+0.36\) & 1 & 0.99 & \(y=1.04x-2.1\) & 6 & 0.9 \\ HDF88 & MVMLR & \(y=0.93x+2.06\) & 6 & 0.93 & \(y=1.24x-14.99\) & 13 & 0.90 & \(y=1.09x-20.64\) & 20 & 0.91 \\  & MVANN & \(y=0.99x+0.21\) & 1 & 0.99 & \(y=0.99x-0.08\) & 1 & 0.99 & \(y=x+0.56\) & 5 & 0.95 \\  & HDF00 & MVMLR & \(y=0.92x+1.78\) & 5 & 0.92 & \(y=0.91x-2.6\) & 9 & 0.88 & \(y=0.8x-5.49\) & 16 & 0.76 \\  & MVANN & \(y=0.99x-0.03\) & 1 & 0.99 & \(y=x+0.23\) & 1 & 0.99 & \(y=0.83x+3.62\) & 9 & 0.84 \\ \hline \hline \end{tabular} Note: The regressions among calibration, verification and prediction periods were obtained using the monthly EC-measured values for the C component fluxes as the independent variable and the monthly values estimated using the ANN and MLR models as the dependent variable. MVMLR indicates measured versus MLR modeled; MVANN indicates measured versus ANN modeled; three different-aged Douglas-fr stands: DF49, HDF88 and HDF00, established in 1949, 1988, and 2000, respectively.  
\end{table} 
Table 1: Comparisons of artificial neural network (ANN) and multiple linear regression (MLR) model performances among the calibration, verification and prediction periods for monthly gross primary productivity (GPP, g-C m\({}^{-2}\)-year\({}^{-1}\)), ecosystem respiration (\(R\), g C-m\({}^{-2}\)-year\({}^{-1}\)) and evapotranspiration (ET, mm-year\({}^{-1}\)) in the three Douglas-fr stands. Figure 5: Artificial neural network (ANN) simulated and eddy-covariance tower measured monthly gross primary productivity (GPP), ecosystem respiration (\(R\)) and net ecosystem productivity (NEP) in all three stands for the four post-fertilization years 2007–2010. (**a–c**) for GPP; (**d–f**) for \(R\); and (**g–i**) for NEP.