###### Abstract
This method is based on the continuous measurement of CO\({}_{2}\) fluxes between ecosystems and the atmosphere, without segregating and quantifying the fluxes within the different C reservoirs of the ecosystem, for example the living biomass, the dead biomass and the soil biomass [36]. Although this method gives accurate estimations of net ecosystem productivity (NEP), i.e., the amount of C fixed by GPP after removing losses from autotrophic and heterotrophic respiration, it does so without the information of the components' contribution. Therefore the estimation of GPP from eddy-flux data is made under a set of assumptions regarding the autotrophic and heterotrophic respiration rates [6; 35]. Moreover, in cases that eddy-covariance flux towers operate in complex terrains and vegetation sites, additional approaches and data, such as mesoscale modelling, remote sensing and/or ecophysiological measurements, are used to accurately partition C fluxes [37]. The \"bottom-up\" or ecological approach consists in using finer spatial scale data, including biometric measurements, such as foliage, branch, stem and root growth and litter and root turnover rates, as well as gas exchanges measurements (photosynthesis and respiration) to understand C fluxes within an ecosystem [34; 38]. Biometric data can additionally provide information about the structure and the properties of the canopy and the stand. Detailed biometric data can be used to estimate Net Primary Productivity (NPP), i.e., the amount of C fixed by GPP after excluding autotrophic respiration [39]. Such data can also be integrated with photosynthetic and respiration measurements, to ultimately infer GPP and NEP [40]. In this perspective the ecological approach is more complex but does provide the means for upscaling from the leaf to the ecosystem scale [36; 41]. In contrast with the \"top-down\" approach, \"bottom-up\" studies necessitate the frequent physical presence of the researcher in the field and the associated difficulty to deliver continuous data [42]. By combining the \"top-down\" and the \"bottom-up\" approaches, more accurate estimations of ecosystem productivity can be obtained [43], although an agreement between the two methods is generally achieved after comparing multiple years of concurrent measurements [34]. Estimation of Mediterranean forests GPP has been primarily pursued through eddy-flux measurements and modelling studies [44; 45; 46; 47; 48; 49]. Based on eddy-flux measurements, estimation of GPP ranges from 2.49 to 4.60 g C m\({}^{-2}\) day\({}^{-1}\)[45; 47; 49] throughout the year. On the other hand, model simulations yield relatively lower GPP from 1.15 to 2.66 g C m\({}^{-2}\) day\({}^{-1}\)[44; 46; 48] for typical Mediterranean forests. The range of above-ground NPP in Mediterranean forests has been estimated through biometric studies (0.13-0.55 g C m\({}^{-2}\) day\({}^{-1}\)) [50] while annual NEP estimates from forest inventories can reach a rate of 0.25 g C m\({}^{-2}\) day\({}^{-1}\)[51]. Based on inferences from litterfall data, some studies estimate the annual NPP of Mediterranean pine forests at 1.64 Mg C\(\cdot\)ha\({}^{-1}\cdot\)yr\({}^{-1}\)[50]. However, to our knowledge a systematic implementation of the \"bottom up\" approach integrated with ecophysiological measurements has not been implemented in Mediterranean pine forests. To understand the interplay between tree morphological and physiological processes with the biotic and abiotic conditions of the stand and ultimately quantify C fluxes, process-based forest models can be used as a framework to integrate information on different ecosystem processes [52]. In this study we integrated the \"bottom-up\" approach with simple canopy photosynthesis models to estimate GPP variation in Mediterranean Pine forests (_Pinus brutia_ Ten.) across a post-fire chronosequence on Lesvos Island, Greece.