2019 UND Graduate Research Achievement Day
Morgen Burke, Earth System Science & Policy Ph.D. student, is an award-winner at the 2019 UND Graduate Research Achievement Day. Morgen receive 1st place in the Natural Science category for his research on "Detection of Shelterbelt Density Change Using Historic APFO and NAIP Aerial Imagery." In the same category, ESSP M.S. student Shelby Osborne finished in 4th place with her research on "Predictive Ecology: Simulating Long-Term Changes in Forest Populations, with Applications for 15/16 American-Chinese Chestnut Tree Reintroduction."
Morgen's Abstract: Grand Forks County boasts the highest concentration of shelterbelts in the World. However, in recent years, the rate of tree removal is thought to exceed the rate of replanting. Through manual digitization and geographic object-based image analysis (GEOBIA), we mapped shelterbelt densities in the county using historical and contemporary aerial photography and estimated changes in density over 54 years. We found a doubling in shelterbelt densities from 1962 to 2014, and a density loss from 2014 to 2016. The reduction of shelterbelt density is likely related to changes in farming practices and a decline in the Conservation Reserve Program, resulting from the increased returns of growing other row crops. To encourage shelterbelt planting as a conservation practice, additional guidelines and financial support should be considered to balance the trade-off between soil erosion and agricultural intensification.
Shelby's Abstract: We propose a multi-scale study of 15/16 American-Chinese Chestnut reintroduction, since these hybrids are the most Blight resistant American Chestnut trees available. Vida, previously known as SERA, is a simulation-based, biometric tool that has previously been shown to accurately forecast forest structure of an Abies alba and Pinus ponderosa environment. The replication results were evaluated and confirmed on the basis of the numerical agreement between observed and predicted scaling exponents. Using individual tree-growth characteristics, and accurate world parameterization, these simulations can determine the best methods of large-scale species reintroduction and interaction dynamics. Vida is written in Python and archived on GitHub. Vida serves as a null hypothesis by demonstrating that biologically complex phenomena, including widely observed scaling relationships, can emerge from the operation of simple and transparent rules governing competition for space and light.