On the relationship between environment and growth of Sweet chestnut (Castanea sativa) in the Caucasus

Quantifying the environmental factors influencing growth dynamics is essential for predicting ecosystem responses, especially under global climate change. However, achieving comprehensive, long-term tree-growth monitoring across extensive regions can be resource-intensive. Ideally, dendrochronological measurements are complemented by models capable of efficiently estimating growth patterns, particularly in under-sampled regions. We applied a modeling approach combining generalized additive models (GAMs) and extensive dendrochronological data from 258 Sweet chestnut ( Castanea sativa Miller) cores collected across Georgia and eastern Turkey. Our models identified stand age, minimum temperature of coldest month, precipitation during the driest quarter, soil nitrogen content, and soil pH as significant predictors, explaining substantial variability in Ca. sativa growth rates. Younger stands (<50 years) in regions characterized by mild winter temperatures, moderate precipitation in the late winter and early spring, acidic soils, and elevated nitrogen content exhibited optimal growth conditions. Future scenario analyses (SSP126, SSP370, and SSP585) revealed regionally variable impacts, highlighting areas vulnerable to climate-induced stress or benefiting from warmer and drier conditions. Although the predictive validity of our model is most reliable within the observed distribution range of Ca. sativa , extrapolations to additional regions are reasonable, provided that environmental conditions fall within the range of the training data.