Modeling the Distribution of the Northern Hardwood Forest Type in Carolina Northern Flying Squirrel (Glaucomys sabrinus coloratus) Recovery Areas of the Southern Appalachians
| dc.contributor.author | Evans, Andrew M. | en |
| dc.contributor.committeechair | Resler, Lynn M. | en |
| dc.contributor.committeemember | Ford, W. Mark | en |
| dc.contributor.committeemember | Prisley, Stephen P. | en |
| dc.contributor.department | Geography | en |
| dc.date.accessioned | 2013-06-26T08:00:11Z | en |
| dc.date.available | 2013-06-26T08:00:11Z | en |
| dc.date.issued | 2013-06-25 | en |
| dc.description.abstract | The northern hardwood forest type is a critical habitat component for the endangered Carolina northern flying squirrel (CNFS; Glaucomys sabrinus coloratus) for denning sites and corridor habitats between montane conifer patches where the squirrel forages. This study examined terrain data, and patterns of occurrence for the northern hardwood forest type in the recovery areas of CNFS in western North Carolina and southwestern Virginia with the purpose of creating a more robust predictive model of this forest type for spatial delineation. I recorded overstory species composition as well as terrain variables at 338 points throughout the study area in order to quantitatively define the northern hardwood forest type. These data were used in conjunction with digital terrain data for creation of the predictive model. Terrain variables we examined to attempt to differentiate northern hardwoods from other forest types included elevation, aspect, slope gradient, curvature, and landform index. I used an information-theoretic approach to assess six models based on existing literature and a global model. My results indicate that on a regional, multi-state scale, latitude, elevation, aspect, and landform index (LFI) of an area are significant predictors of the presence of the northern hardwood forest type in the southern Appalachians. My model consisting of Elevation + LFI was the best approximating model based on lowest AICc score. Our Elevation + LFI model correctly predicted northern hardwood presence at 78.2% of our sample points observed to be northern hardwoods. I then used this model to create a predictive map of the distribution of the northern hardwood forest type in CNFS recovery areas. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:1323 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/23269 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | GIS | en |
| dc.subject | Predictive Forest Modeling | en |
| dc.subject | Southern Appalachians | en |
| dc.title | Modeling the Distribution of the Northern Hardwood Forest Type in Carolina Northern Flying Squirrel (Glaucomys sabrinus coloratus) Recovery Areas of the Southern Appalachians | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Geography | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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