Stand structural differences along a degradation gradient in the National forest inventory of Georgia

Abstract

Forest degradation assessments grounded in measurable structural indicators provide a more reproducible basis for management decisions than qualitative field classifications alone, particularly in ecologically diverse forest systems. Based on data from the first National Forest Inventory of Georgia (2019–2021), we clustered samples into eight forest formations using a phylogenetically informed approach and compared stand structural indicators across field-assigned degradation severity classes within each formation. Indicators quantifying stand stocking, structure, and productivity were compared across severity classes using non-parametric tests with post-hoc pairwise comparisons. Across formations, significant structural differences relative to undisturbed conditions were detected in 52%, 69%, and 83% of tested indicator-formation combinations at low, moderate, and high severity, respectively. Field-assigned severity classes were associated with consistent structural differences, but the direction and magnitude of those differences varied substantially among formations and were often non-monotonic across severity levels. Stocking indicators showed the most consistent and significant differences across formations and severity classes. Productivity indicators were more formation-specific, with volume increment declining across most formations while diameter increment increased, particularly at lower severity levels, reflecting reduced competition following partial canopy opening. Regeneration indicators showed the highest formation-dependence, with significant differences confined to two formations, reflecting the high within-formation variability of regeneration density. These findings support formation-specific interpretation of degradation severity classes and demonstrate that quantitative inventory analysis captures structural dynamics beyond the resolution of field-based severity assessment. Grounding severity assessment in ecologically coherent forest units provides a consistent basis for condition monitoring and ecologically informed management decisions.

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