## Results
### Mechanisms behind the Treatment Effect on GPP and F\({}_{760}\) at Leaf and Canopy Scale  
Figure 6 shows the output of the path analysis. The results of the final models are displayed as graphs. The overall model fit is evaluated: \(\chi^{2}=129\pm 23\), CFI = 0.901 \(\pm\) 0.03, SRMR = 0.07 \(\pm\) 0.02 and RMSEA= 0.19 \(\pm\) 0.02. CFI and SRMR show excellent fit according to [PERSON] & Bentler (2016). In contrast, the RMSEA is higher than expected. RMSEA is part of the parsimony-adjusted fit indexes, which reward low model complexity. Our goal is however to represent a holistic model that includes all the relevant processes and we do not use the path analysis a posteriori as a mean of model selection. Additionally, according to ([PERSON] et al., 2017), \"RMSEA over-rejects true models for'small' \(n\) (\(n<250\))\", which might be the cause of our RMSEA value, as our sample size is 133. Figure 6: Path analysis displays the role of canopy nitrogen content (Canopy N) and relative graminoids abundance (%graminoids) on the energy partitioning at the leaf and canopy level. Photosynthetic active radiation (PAR); absorbed by vegetation photosynthetic active radiation (APAR); fluorescence emission by all leaves at 760 nm calculated by forward runs of SCOPE (F\({}_{760\text{leaf,fw}}\)); gross primary production (GPP); surface temperature (Ts); and observed fluorescence at 760 nm (F\({}_{760}\)). The strength of the relationship among variables is expressed by the standardized coefficient (\(\beta\)) of the path analysis. Each standardized coefficient has a standard error obtained from bootstrapping (\(n=100\) times). The width of the arrows is proportional to their standardized coefficient (\(\beta\)). Colored lines (both solid or dotted) represent direct relationships between variables, whereas gray double-headed arrows represent the covariance among variables. Solid and dotted lines indicate significant (\(p<0.05\)) and non-significant relationships, respectively. The width of the arrows is proportional to their standardized coefficient (\(\beta\)). The different colors are introduced to increase readability of the standardized path coefficients. The fit by the overall model is measured by means of Chi-squared (\(\chi\)2), comparative fit index (CFI) and standardized root mean square of residual (SRMR). Figure 6 shows the clear effect of the %graminoids on F\({}_{760}\). The N and NP treatments significantly affect N% with \(\beta\) of \(0.44\pm 0.07\) and \(0.47\pm 0.08\), respectively. N and NP treatments also affect significantly %graminoids with \(\beta\) of \(-0.27\pm 0.1\) and \(-0.21\pm 0.09\), respectively. N% has a significant relationship with four variables: APAR, Ts, GPP, and F\({}_{760\text{leaf,fw}}\) with \(\beta\) of \(0.37\pm 0.05\), \(-0.37\pm 0.06\), \(0.12\pm 0.03\) and \(0.10\pm 0.04\), respectively. %graminoids significantly affects APAR and F\({}_{760}\) with \(\beta\) of \(0.27\pm 0.09\) and \(-0.17\pm 0.02\), respectively. The path between %graminoids and Ts is however not significant. APAR significantly influences GPP, F\({}_{760\text{leaf,fw}}\) and Ts with \(\beta\) of \(0.87\pm 0.02\), \(0.77\pm 0.03\) and \(-0.25\pm 0.06\). Finally, F\({}_{760\text{leaf,fw}}\) and Ts have a significant covariance with \(\beta\) of \(-0.17\pm 0.04\). F\({}_{760\text{leaf,fw}}\) and GPP have a significant covariance with \(\beta\) of \(0.07\pm 0.02\) and so do GPP and Ts with \(\beta\) of \(-0.18\pm 0.03\). Alternative models using different estimates of F\({}_{760\text{leaf}}\) were tested and we found that the same paths are selected as significant, and the magnitude of the \(\beta\) coefficients are almost unchanged (Figure S16). This suggests that the path analysis model is not strongly dependent by the estimation type of the fluorescence emission. The results of the intervention removing treatments show that the vast majority of the paths remain constant and significant.