The spatial distributions of four plant species native to Asia, yet considered invasive in southwestern Virginia, were studied in order to produce predictive habitat maps. The study took place in the mountains to the north of Blacksburg, VA, on National Forest lands. A random GPS survey of each of the four species, Microstegium vimineum, Lonicera japonica, Rosa multiflora and Elaeagnus umbellate, was used in combination with a series of Geographic Information System (GIS) layers representing environmental variables (Elevation, Aspect, Roads, Trails, Streams, & Normalized Difference Moisture Index) to produce logistic regression models. After field- validating the models, the models were ranked according to usefulness, with the E. umbellate model proving most accurate. It is hoped that such GIS models will allow forest managers to more productively search for invasive species within their jurisdiction, by indicating sites more likely to provide habitat suitable to the invasive species described by the model.
A non-GIS search for correlations between the study species' presence and field-collected discrete environmental variables was also included. Both Disturbance and Canopy Cover were considered for their effect upon Microstegium vimineum, Lonicera japonica, Rosa multiflora and Elaeagnus umbellate presence. Using Pearson's Correlation with the Canopy Cover data, and Chi-squared Correlation with the Disturbance data, only R. multiflora and E. umbellate showed significant correlation to decreasing canopy cover. / Master of Science
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/31281 |
| Date | 05 March 2009 |
| Creators | Murray, David Patrick |
| Contributors | Geography, Resler, Lynn M., Kolivras, Korine N., Carstensen, Laurence W. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | Final-2.2.pdf |
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