Chestnut, Castanea sativa Miller (Fagales: Fagaceae), is an ecologically and economically important tree species of the forest ecosystem in Southern Europe, North-Western Europe, Western Asia, North Africa, and the Caucasus. The distributional range of chestnut in Europe has been highly modified by humans since ancient times. Biotic and abiotic factors have dramatically changed its distribution. Historic anthropogenic range expansion makes it difficult to identify habitat requirements for natural stands of chestnut. In the Caucasus, natural stands of chestnut survived in glacial forest refugia and landscapes that have been difficult for humans to colonize. To identify the species reliable habitat requirements, we estimated the relationship between climatic variables and 620 occurrence locations of natural chestnut stands from the Caucasus and validated the model using GBIF data from outside the Caucasus. We found that our best model is reasonably accurate and the data from the Caucasus characterize chestnut stands throughout the species range well.

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.