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Date of Award


Document Type

Campus Access Master's Thesis

Degree Name

Master of Science in Applied Ecology (MS)

Administrative Home Department

College of Forest Resources and Environmental Science

Advisor 1

Blair Orr

Advisor 2

Erik Lilleskov

Committee Member 1

Ann Maclean

Committee Member 2

Kate Heckman


Our study applies the machine learning method, Random Forest (RF), to understand distribution patterns and predictive powers of environmental variables determining earthworm occurrence in northern hardwood forests of the Great Lakes region. In our study we found earthworm species: Dendrobaena octaedra (Savigny), Lumbricus rubellus (Hoffmeister), Lumbricus terrestris (L.), Aporrectodea rosea (Saigny), Aporrectodea calignosa (Saigny), and Aporrectodea tuberculata (Eisen). Presence/absence data of L. terrestris were used in predictive distribution modeling for the Ottawa National Forest in the Upper Peninsula of Michigan based on the following Geographic Information Systems (GIS) variables: forest cover type, soil texture, soil pH, and distance from roads. Random Forest results were successful in producing models with high predictive accuracies and stable environmental variables when predicting L. terrestris occurrence. Deciduous cover type contributed the most to the outcome of the RF models, followed by soil texture, distance from roads and soil pH. The effectiveness of this approach in modeling earthworm distribution could be the first step in leading a large-scale predictive modeling effort to determine earthworm distribution for all of the Great Lakes region and other northern hardwood forest ecosystems. Having this insight would advance forest management efforts and regional studies addressing earthworm ecological effects.