Date of Award

5-2018

Culminating Project Type

Thesis

Degree Name

Geography - Geographic Information Science: M.S.

Department

Geography and Planning

College

School of Public Affairs

First Advisor

Mikhail Blinnikov

Second Advisor

Jeffrey Torguson

Third Advisor

Jorge Arriagada

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Keywords and Subject Headings

oak wilt, oak, forest pathogen, forest disease, Minnesota, species distribution model, SDM, Maxent, maximum entropy, Ceratocystis fagacearum

Abstract

Forest diseases and pathogens can cause significant damage to an ecosystem. Understanding where they are going to occur and what variables are important in their distribution can stave off the detrimental effects they have on established and at risk ecosystems. With the advancement of spatial analysis and remote sensing technology, these diseases can now be managed through modeling. Modeling allows researchers to determine the extent of the disease, which variables lead to the increase in infection centers, and predict the distribution of the disease. This study used Maxent, a presence-only species distribution model (SDM), to map the potential probability distribution of the invasive forest pathogen oak wilt (Ceratocystis fagacearum) in eastern and southeastern Minnesota. The model related oak wilt occurrence data with environmental variables including climate, topography, land cover, soil, and population density. Results showed areas with the highest probability of oak wilt occur within and surrounding the Minneapolis/St. Paul metropolitan area. The jackknife test of variable importance indicated land cover and soil type as the most important variables contributing to the prediction of the distribution. Multiple methods of analysis showed the model performed better than random at predicting the occurrence of oak wilt. This study shows Maxent has the potential to be an accurate tool in the early detection and management of forest diseases.

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