Date Available
5-11-2026
Year of Publication
2025
Document Type
Master's Thesis
Degree Name
Master of Science (MS)
College
Agriculture, Food and Environment
Department/School/Program
Forestry and Natural Resources
Faculty
Lance A. Vickers
Faculty
Jian Yang
Abstract
Understanding site-species relationships is integral to effective forest management, as landscape factors influence both the presence of mature trees and the regeneration of future trees. This study examined the influence of topographic, soil, and canopy variables on species presence at both fine (i.e., stand-level) and broad (i.e., forest-wide) scales within Robinson Forest, Kentucky. At the fine-scale, using vegetation data from permanent sampling plots, LiDAR-derived topographic data, and historical soils data, the relationships between the species of interest and the landscape variables within a single watershed were analyzed using conditional inference tree (CTree) models. Elevation and slope aspect were the most significant predictors of overstory species presence, while conspecific overstory presence was a key predictor of reproduction presence within the watershed. At the broad-scale, continuous forest inventory (CFI) data, LiDAR-derived topographic data, and SSURGO soils data were used to analyze the relationships between the species of interest and the landscape variables within Robinson Forest. Topographic variables were the most significant predictors of species presence for overstory trees, though the broad-scale models were generally more complex than the fine-scale models. Applying broad-scale models to the fine-scale data yielded prediction accuracies comparable to the fine-scale models, indicating that broad-scale models can provide reliable insights at finer scales. Habitat suitability maps (HSM), created from the broad-scale model predictions, highlighted species-specific ecological niches and may help to identify potential targets for silvicultural intervention, particularly in the case of oak regeneration efforts. Overall, the findings of this study indicate that broad-scale datasets can effectively predict species presence at a finer scale and provide a possible framework to guide forest management in the Central Appalachian region.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2025.528
Recommended Citation
Shrout, Kyra L., "PREDICTING PATTERNS OF SPECIES PRESENCE IN EASTERN KENTUCKY USING FINE- AND BROAD-SCALE SITE-SPECIES RELATIONSHIPS" (2025). Theses and Dissertations--Forestry and Natural Resources. 79.
https://uknowledge.uky.edu/forestry_etds/79
