UMBS GIS Work

At UMBS, students over the years conducting projects on White Pine trees noticed that a small proportion of the trees had a distinct chemical fingerprint, referred to in the below presentation as High Limonene Trees. In the summer of 2020, I utilized QGIS and primarily the spatial join tool to determine if the any landscape factors correlated with the anomolous trees and did not indentify any varaibles that correlated. In summer of 2021, I was awarded an indepedent researcher grant to conduct field work at this site and collected over 300 samples of white pine trees and tested them for the anomolous feature in an experimental design used as a proxy for genetic inherentance of the trait. My hypothesis of chemical anomalies being a genetic trait was incorrect, and no other metric correlated with the anomalous trees; similar to Thomas Edison’s famed 10,000 ways not to make a lightbulb quote, we now know 10 variables that don’t explain this trend! I am still fascinated by the project and in the spirit of collaboration and open-source data, I remain in touch with Dr. Bertman who is interested in having a future student analyze these samples in new ways.