## 3 Results
### HF-EMS  
For the HF-EMS site, PPA-SiBGC showed higher \(R^{2}\) values and lower RMSE, MAE, and ME values compared to LANDIS-II NECN across the range of metrics. While PPA-SiBGC predicted NEE and species relative abundance showed weaker correlations with observed values compared to LANDIS-II NECN, the magnitude of error was lower, as evidenced by lower RMSE, MAE, and ME values. While LANDIS-II NECN showed a lower magnitude of error for belowground N, this is the only metric where this is the case, while the correlation of this metric to observed values was also lower than that of PPA-SiBGC. Overall results for the HF-EMS site model intercomparison are shown in Table 7. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{4}{c}{**PPA-SiBGC**} & \multicolumn{4}{c}{**LANDIS-II NECN**} \\ \cline{2-9}  & \(\mathbf{R^{2}}\) & **RMSE** & **MAE** & **ME** & \(\mathbf{R^{2}}\) & **RMSE** & **MAE** & **ME** \\ \hline Mean & 0.73 & 3.43 & 3.24 & 0.24 & 0.69 & 9.23 & 8.36 & 2.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Overall mean values across each of the sites and metrics tested. Figure 5: Overall model performance (\(R^{2}\)) for both models and sites; left = HF-EMS; right = JERC-RD; periwinkle = PPA-SiBGC; pink = LANDIS-II NECN; violet = intersection. Time-series figures allow a visual analysis of the temporal dynamics between observations and model predictions in order to assess the ability of models to capture interannual variability in carbon exchange. Both models effectively captured integrals of dynamics in biomass, C, and, species biomass and abundance. In Figure 6, the temporal differences in modeled NEE, aboveground C, ANPP, and soil respiration are shown for the two models in comparison to observations for the HF-EMS site. LANDIS-II NECN predicted NEE showed a higher correlation with observations while the magnitude of error and bias were lower. Furthermore, LANDIS-II NECN predicted that the HF-EMS site is a net C source, rather than sink, in contrary to observations. Meanwhile, PPA-SiBGC outperformed LANDIS-II NECN in aboveground C per both \(R^{2}\) and RMSE. Both models overpredicted species cohort biomass, while LANDIS-II NECN underpredicted total aboveground C. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**PPA-SiBGC**} & \multicolumn{4}{c}{**LANDIS-II NECN**} \\ \cline{2-9}
**Metric** & \(\mathbf{R^{2}}\) & **RMSE** & **MAE** & **ME** & \(\mathbf{R^{2}}\) & **RMSE** & **MAE** & **ME** \\ \hline NEE & 0.05 & 0.78 & 0.76 & 0.76 & 0.44 & 0.49 & 0.44 & 0.44 \\ \(B_{AG}\) & 1.00 & 10.31 & 10.30 & 10.30 & 0.98 & 2.48 & 2.48 & \(-\)2.48 \\ \(C_{AG}\) & 1.00 & 0.05 & 0.05 & 0.05 & 0.98 & 1.24 & 1.24 & \(-\)1.24 \\ \(N_{AG}\) & 1.00 & 1.44 & 1.44 & \(-\)1.44 & 0.12 & 1.99 & 1.99 & \(-\)1.99 \\ \(B_{BG}\) & 1.00 & 9.25 & 9.25 & 9.25 & 0.97 & 2.82 & 2.82 & \(-\)2.82 \\ \(C_{BG}\) & 1.00 & 4.92 & 4.92 & \(-\)4.92 & 0.94 & 6.99 & 6.99 & \(-\)6.99 \\ \(N_{BG}\) & 1.00 & 0.56 & 0.56 & 0.56 & 0.78 & 0.12 & 0.12 & \(-\)0.12 \\ \(r_{soil}\) & 0.17 & 0.63 & 0.62 & \(-\)0.62 & 0.06 & 1.10 & 1.10 & \(-\)1.10 \\ ANPP & 0.03 & 0.01 & 0.01 & \(-\)0.01 & 0.0002 & 0.97 & 0.93 & 0.90 \\ \(C_{SO}\) &... & 26.49 & 26.49 & \(-\)26.49 &... & 36.63 & 36.63 & \(-\)36.63 \\ \(N_{SO}\) &... & 1.33 & 1.33 & \(-\)1.33 &... & 1.60 & 1.60 & \(-\)1.60 \\ \(B_{Sp}\) & 1.00 & 5.07 & 2.92 & 2.92 & 0.97 & 133.70 & 119.87 & 119.87 \\ \(n_{Sp}\) & 0.82 & 0.05 & 0.03 & 0 & 0.99 & 0.29 & 0.22 & 0.22 \\ \hline Mean & 0.73 & 4.68 & 4.51 & \(-\)0.84 & 0.66 & 14.65 & 13.57 & 5.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Model fitness for HF-EMS. An analysis of simulated species biomass and abundance also shows greater fidelity of the PPA-SiBGC model to data, as shown in Figure 7. As LANDIS-II NECN does not contain data on individual trees, species relative abundance is calculated based on the number of cohorts of each species.