## 4 Discussion  
In this study we provided a semi-empirical framework to quantify GPP variation across Mediterranean pine forests of different post-fire age. In accordance with forest dynamics theory, we simulated an increase in GPP with stand age until a maturity stage, followed by a small decrease in older stands. The shape of the GPP curve with time did not significantly change with the method used to upscale CO\({}_{2}\) fluxes from the leaf to the stand level. The Big Leaf model setups systematically underestimated GPP compared to the Sun/Shade model setups. Differences in the method used to parametrise our framework yielded an average plot GPP between 844 and 1166 gC m\({}^{-2}\) y\({}^{-1}\) across different LAI estimation methods, and an average GPP range from 934 to 1010 gC m\({}^{-2}\) y\({}^{-1}\) between constant and variant photosynthetic capacity setups. Overall, our simulations are within the range of GPP suggested for Mediterranean pine forests, namely from 816 to 1600 gC m\({}^{-2}\) y\({}^{-1}\)[48; 66; 84]. The trajectory of gross primary productivity with time since disturbance in unmanaged forests is frequently reported to present a parabolic shape [7] following the development of stand structure and LAI. After a wildfire, GPP immediately decreases, due to reduction in the available photosynthetic tissue biomass. Recovery of GPP can begin from the first year after the fire [85] following a steadily increasing trajectory. The degree of GPP rebound during the first few years is affected by the severity of the disturbance [14; 86] as well as by water and nutrient availability [85; 87]. A decade after the fire, when newly established trees take over, strong associations between GPP and canopy structure have been observed [88]. From this stage onwards, the recovery of GPP can be mostly explained by the recovery of LAI [89]. Across our post-fire chronosequence, LAI increased from younger to mature stands and then decreased at the 92 year-old stand (Table 3), probably due to competition between trees for light, water and nutrients [20; 90]. Optical methods, such as CMM and HPM, generally underestimate LAI values when compared to direct and/or semi-direct methods, such as LFM, especially at forest stands [17; 91]. The trend of LAI with time since the last stand-replacing fire controlled variation in GPP and shaped the typical parabolic curve with a peak in productivity at mature stands (Figure 3). This GPP trend was found in CMM and LFM model parameterisations, where stand-level LAI estimates were higher. In the HPM parameterisation, the lower LAI field estimates used to force the models yielded a continuously increasing GPP with stand age, as a result of carbon assimilation rate being away from the asymptote expected for relatively dense forest canopies with high LAI values. The results from the comparison of the variation related to the method used to estimate LAI illustrated that LAI measured using the LFM has the best agreement with the independent GPP dataset predictions. Overall, the selection of the LAI parameterisation method can have important effects on the simulation of GPP in studies that couple biometric and gas exchange data to estimate forest productivity, as has also been highlighted in satellite-data driven GPP simulation studies [92]. The control of climate, canopy structure and leaf physiology in controlling GPP has gained a lot of attention over the last decades. Many studies have explored the effect of climate [93; 94], leaf traits [95; 96] and canopy structure [97; 98] in determining GPP across different ecosystem types. For many ecosystems, GPP increases until a peak during the growing period when solar radiation, temperature and water availability are optimum and then gradually decreases, with the contribution of the individual climate factors shifting with latitude [99; 100].