Interrelation of Forest Structure and Variability of Ecosystem Functional Properties Derived from Eddy Covariance Flux Measurements

Climate change impacts European forests both directly, through shifts in temperature and precipitation, and indirectly, by increasing the frequency and intensity of extreme weather events such as droughts. As a result, understanding forests’ capacity to sustain their functions under stress has become a critical research area. This capacity is often represented by the variability of ecosystem functional properties (EFP), derived from eddy covariance flux measurements, with lower variability indicating higher resistance to stress and greater resilience to drought. EFP variability is hypothesized to be influenced by meteorology, soil conditions, and forest structure. Unravelling the specific role of forest structure in this variability could inform forest management strategies to enhance future resilience.

Previous studies have focused either on individual ecosystem functions or few site samples, not allowing to fully understand the drivers of EFP. Within the EU Horizon project CLIMB-FOREST, we collected data on CO₂, H₂O, and energy fluxes from 59 European forests to calculate key EFPs: underlying water use efficiency (uWUE), photosynthetic capacity (GPP_sat), Bowen ratio (β), canopy conductance (Gs), and albedo (α). These were analyzed for their distribution and variability, then correlated with forest structure metrics such as forest type, management regime, stand age, canopy height, and species diversity.

Our analysis revealed that deciduous broadleaf forests (DBF) exhibited higher uWUE, GPP_sat, and Gs compared to evergreen needleleaf forests (ENF) and mixed forests (MF), but also displayed the greatest variability in uWUE, GPP_sat, and β. Variability in GPP_sat decreased with increasing canopy height, with a slight upswing in stands exceeding 30 meters. Albedo variability was highest in young forests (0–49 years) and lowest in forests with an age between 100–149 years. However, no significant correlations emerged between forest structure variables and EFP variability. The limited availability of structural data likely constrained our correlation analysis, potentially masking significant trends.

To address these limitations, we aim to expand the dataset and apply advanced correlation techniques to better identify the drivers of EFP variability.