Date Thesis Awarded

7-2012

Access Type

Honors Thesis -- Access Restricted On-Campus Only

Degree Name

Bachelors of Science (BS)

Department

Biology

Advisor

Matthias Leu

Committee Members

Randolph M. Chambers

Martha Case

Stuart Hamilton

Abstract

Microstegium vimineum (Trin.) A. Camus (Japanese stiltgrass) is a widespread shade-tolerant exotic plant species throughout much of the eastern United States. Where it spreads, Microstegium profoundly affects ecological functions, altering soil chemistry and hydrology, displacing native flora, and thus reducing flora and fauna diversity. Successful control of this noxious species is highly contingent on early detection before large seed banks are established. As such, identifying areas at risk for invasion would allow conservation managers to better apply resources for maintenance and control. In this study, I developed a predictive species distribution model for Microstegium. Based on field surveys of 160 points throughout the Chesapeake Bay lowlands and Geographic Information System (GIS) analysis of landscape features, I developed a spatial model predicting patches likely to be currently or in the future invaded by Microstegium. I identified a suite of 11 landscape features and metrics that were important explaining the distribution of Microstegium. Habitat type variables (viz. proportion of clear-cut lands within a 2-km radius, Atlantic mesic forests within a 270-m radius, and dry-mesic oak forests within a 2-km radius) primarily had the strongest predictive value on Microstegium distribution. Additionally, several anthropogenic features, such as distance to various road types, and the distance to water bodies were identified as predictive of sites likely to be invaded by Microstegium. Ultimately, my model had high predictive success for sites unoccupied by Microstegium, but only low predictive success for invaded sites. While species distribution models are inherently limited by their inability to distinguish between current and future occupancy, this model can likely be improved through a more stratified sampling scheme and additional field surveys.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Public Domain Dedication 1.0 License.

Comments

Thesis is part of Honors ETD pilot project, 2008-2013. Migrated from Dspace in 2016.

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