## Materials and Methods
### Model Evaluation  
The model was evaluated using measured GHG soil fluxes and EC data recorded between 2008 and 2014. In addition, soil temperature and soil moisture data that had been recorded continuously since 2008 near the tower at depths of 10 cm and 30 cm are compared with simulated output.  
Since EC data comprise both fluxes from the thinned and unthinned sectors, monthly mean measured fluxes were separated into wind direction sectors according to [PERSON] et al. [45]. Daily simulations were assigned to the appropriate sector depending on whether climate data showed wind predominantly from east or western sectors for that day, and these data were compared to simulations that assumed either no thinning (if the wind direction was from the western sector) or 30% (if wind direction was from the eastern sector) canopy tree removal. For the statistical analysis, we used the freely available statistical package ModEval v2.0, and methods outlined in [46] to quantify differences between simulated and measured daily averaged and annual ecosystem GPP, TER and NEP, and monthly and annual soil fluxes of CO\({}_{2}\) and N\({}_{2}\)O. Model outputs produced with optimised parameters derived from the same site cannot be considered as fully independent, but t-tests can still be used to examine if there are significant differences between simulated and measured values [46]. Subsequent standard errors were calculated using EC measurements between 2008 and 2014 and soil flux measurements between 2013 and 2014 only (with six replicate chambers, which were used to put 95% confidence limits on the Root Mean Squared Error (RMSE)). As described by [PERSON] et al. [46], this information is used to assess if simulated values were within the 95% confidence intervals of measurements. Modelling efficiency (ME), also known as Nash-Sutcliffe efficiency [47], was calculated to assess the predictive power of the model. A perfect simulation gives an ME value of 1.0, while values below zero (no limit on size) suggest the mean of observations would be a better predictor than the simulation. Thus, an 'efficient' model is one where ME is between zero and 1.0. The coefficient of determination (CD), which represents the proportion of total variance in observed data explained by simulated data, was also calculated, and bias assessed for the mean difference, M, and the relative error, E [46].  
In the case of GPP, TER and NEP, measurements were not replicated and therefore no statistical significance could be derived for differences from simulated values, but RMSE values could be calculated. However, [PERSON] et al. [48] have estimated that most EC data have an error of 10%-15%, when measurement and gap-filling errors and spatial variability are considered, an error of 15% was therefore used to estimate the statistical significance of RMSE.  
A sensitivity analysis was carried out to identify those input variables which had the largest influence on simulated GHG fluxes. The method for so-called simple models, as described by [PERSON] et al. [49], was used in which each set of input variables that describe soil characteristics, N deposition or climate were changed individually by \(\pm\) 10%. Input variables that produced differences of more than \(\pm\) 10% in annual total GHG fluxes averaged over 1999-2007 using PnET and PSIM were considered to be sensitive. The same method was used to quantify the contributions of selected vegetation module parameters.