Katarína Střelcová

Transpiration and sap flow are physiologically interconnected processes that regulate nutrient and water uptake, controlling major aspects of tree life. They hold special relevance during drought, where wrecked sap flow can undermine overall tree growth and development. The present study encompasses five-year (2012–2015 and 2017) sap flow datasets on European beech (Fagus sylvatica). Four different techniques were used for sap flow modeling, namely, a linear model (LM), random forest (RF), extreme gradient boosting machine (XGBM), and neural networks (NN). We used six variants (Variants 1–6) differing in the captured conditions and the dataset size. The ‘prediction power’ was the ratio of the predicted and observed sap flow. We found the LM had the maximum prediction power for the overall sap flow in beech trees with 1 h shift of global radiation. In the reaming variants, the LM provided comparable prediction power to RF and XGBM. At the same time, NN exhibited relatively poor prediction power over other machine learning models. The study supports an easier-to-apply and computationally simpler approach (LM) to assess sap flow over more sophisticated machine learning approaches (RF, XGBM, and NN).

The current projections of climate change might exceed the ability of European forest trees to adapt to upcoming environmental conditions. However, stomatal and leaf morphological traits could greatly influence the acclimation potential of forest tree species subjected to global warming, including the single most important forestry species in Europe, European beech. We analysed stomatal (guard cell length, stomatal density and potential conductance index) and leaf (leaf area, leaf dry weight and leaf mass per area) morphological traits of ten provenances from two provenance trials with contrasting climates between 2016 and 2020. The impact of meteorological conditions of the current and preceding year on stomatal and leaf traits was tested by linear and quadratic regressions. Ecodistance was used to capture the impact of adaptation after the transfer of provenances to new environments. Interactions of trial–provenance and trial–year factors were significant for all measured traits. Guard cell length was lowest and stomatal density was highest across beech provenances in the driest year, 2018. Adaptation was also reflected in a significant relationship between aridity ecodistance and measured traits. Moreover, the meteorological conditions of the preceding year affected the interannual variability of stomatal and leaf traits more than the meteorological conditions of the spring of the current year, suggesting the existence of plant stress memory. High intraspecific variability of stomatal and leaf traits controlled by the interaction of adaptation, acclimation and plant memory suggests a high acclimation potential of European beech provenances under future conditions of global climate change.