Productivity-environment models for Scots pine plantations in Bulgaria: an interaction of anthropogenic origin peculiarities and climate change

The consequences of climate change on forest growth can be exacerbated for the forest ecosystems of anthropogenic origin, especially for the populations at the margins of the species range. The productivity-environment relationships are a methodological approach for modeling growth at whole-stand level, which although empirical is applicable under changing climatic conditions. Some peculiarities of the Scots pine plantations in Bulgaria, and namely a distribution range spreading outside the species areal and high stocking rates, made it challenging to derive an adequate productivity-environment model for this type of stands, which was the main objective of our study. Dynamic phytocentric model, based on the function by Gompertz and fitted in a generalized algebraic difference equation form was derived at the first step of the analyses. The model is polymorphic, with variable, welldifferentiated among the site quality classes asymptotes, a reflection of the large variety of sites of different carrying capacity where the man-made stands of Scots pine are grown in Bulgaria. Two classifications of the Scots pine plantations, based on assessment of stand fit to its environment, were tested to specify the site quality model at a lower hierarchical level. The random parameter component was better localized at stand level, rather than at ecosystem fit category or stand-within-ecosystem fit category level and three groups of environmental factors, were examined to calibrate the mixed-effects model parameter. The most adequate climate-sensitive dynamic growth model considered the level of site illumination and the annual heat-moisture index as geocentric predictors. In order to assess how successfully the derived site-productivity relationship makes realistic prediction of the species productivity levels under the changing climate, the data were divided into 20th and 21st century subsets for validation. The validation statistics revealed unbiased estimates of small relative error magnitude, with the predictions being more adequate when the 20th century data were used for parameterization.