Authors: Christina Morrow*, , Chris P. S. Larsen, University at Buffalo, Stephen J. Tulowiecki, SUNY Geneseo
Topics: Biogeography, Physical Geography, Quantitative Methods
Keywords: quercus, oak, red oak, white oak, DBH, tree, statistics, geography, regression, regression model,
Session Type: Poster
Start / End Time: 3:05 PM / 4:45 PM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: No File Uploaded
Determining tree age is important in various research, such as for verifying historical information and conducting ecological research. While tree cores can accurately determine tree age, they are not always obtainable. As such, research has studied whether external predictors of tree age exist, such as tree diameter or environmental conditions, that would provide a non-invasive means of determining age. This purpose of this study was to create a regression model that predicts tree age based on external variables for red oak (Quercus rubra) and white oak (Quercus alba), two species of high economic and ecological value. Cores were obtained and counted from 62 trees in western New York State. Included in models as predictors were diameter at breast height (DBH), and environmental (e.g. soil, topographic) variables. Regression models were developed to predict tree age based on these diameter and environmental variables. Notable results include that Quercus rubra age is more correlated with diameter alone (R2 = 0.486, p <0.001) than Quercus alba (R2 = 0.785, p = 0.175). DBH was the most significant independent variable in models that included additional environmental variables. Other soil variables showed little to no significance in additional models. In conclusion, tree age remains difficult to predict using external variables but certain species (e.g. Quercus rubra) may show stronger relationships with DBH than others.