# Environmental Effects on Normalized Gross Primary Productivity in Beech and Norway Spruce Forests
## 1 Introduction  
Gross primary productivity (GPP) constitutes an essential part of net ecosystem CO2 exchange (NEE) between the atmosphere and the forest ecosystems [1, 2, 3]. GPP providesimportant information about the photosynthetic capacity of the ecosystem and is modulated by numerous environmental factors leading to changes in forest growth [4; 5; 6; 7]. Nonetheless, the accurate quantification of forest GPP on a global scale remains challenging due to the spatio-temporal heterogeneity of forest ecosystems [8]. At the plot scale (within the area of the flux-tower footprint), the eddy covariance (EC) technique has emerged as a direct state-of-the-art approach to study the seasonality of carbon fluxes between the atmosphere and forest ecosystems, and thus represents an indirect yet reliable measure of GPP [9; 10; 11; 12]. Seasonal trends of GPP demonstrate the forest response to variations in environmental factors. However, these forest responses vary from site to site depending on the species, age, local climate, and additional factors. For instance, rising temperatures within the optimum range and water availability in most temperate forest ecosystems is positive to reduce the freezing stresses and increase ecosystem productivity by enhancing the rate of ecosystem photosynthesis. However, higher temperatures (beyond the optimum level) may induce varying physiological and biochemical responses to heat stress at different forest ecosystems based on the acclimation processes of specific plant species to changes in their environment [13; 14]. Such contrasting GPP responses (due to the shift in plant functional traits) of specific species to different environmental stress conditions at different sites are poorly understood [15]. Therefore, through ecosystem carbon flux measurements, physiological responses of individual plant species to certain climatic anomalies such as heat and water stress can be studied [16; 17]. Furthermore, as multiple climatic factors affect forest carbon uptake, there is the need to identify near-optimal environmental conditions for different species, under which GPP can reach maximal theoretically attainable values termed potential GPP (GPP\({}_{\text{pot}}\)). GPP is primarily driven by available photosynthetically active radiation (PAR) coupled with other physiological processes like stomatal conductance that control carbon fluxes between the ecosystem and the atmosphere [18; 19]. Moreover, the physiological processes that affect carbon uptake are also affected by complex interactions between a set of environmental drivers, including the quality of incoming solar radiation, temperature, atmospheric humidity, and soil moisture [18; 20]. For example, the work in [19] has shown that photosynthesis of the upper spruce canopy could be severely depressed during clear sunny days due to the abrupt closure of stomata under high values of vapour pressure deficit. However, under cloudy conditions, the rate of photosynthesis of the lower spruce canopy increases due to the effective penetration of anisotropic diffuse radiation into the canopy, thus leading to an enhanced carbon gain and light use efficiency [21]. The main goal of this study is to determine the main environmental variables influencing the ratio between observed GPP and GPP\({}_{\text{pot}}\) of three forest ecosystems with contrasting plant species using two types of regression models. Forests dominated by Norway spruce (_Picea abies_ (L.) [PERSON]) and European beech (_Fagus sylvatica_ L.) were selected for this study due to their sensitivity to climate and high commercial and ecological importance in Central Europe [22; 23]. Furthermore, the current distribution of spruce species at often unsuitable locations with sub-optimal climate conditions make them vulnerable to both biotic and abiotic stresses [24; 25; 26; 27]. Thus, it is important to study the physiological responses of different plant species to the local environmental conditions across different forest sites.