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International Journal of Forestry Research
Volume 2014 (2014), Article ID 179415, 11 pages
Research Article

Developing a Topographic Model to Predict the Northern Hardwood Forest Type within Carolina Northern Flying Squirrel (Glaucomys sabrinus coloratus) Recovery Areas of the Southern Appalachians

1Department of Geography, Texas A&M University, College Station, TX 77840, USA
2Geospatial and Environmental Analysis Program, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
3Department of Geography, Virginia Polytechnic Institute and State University, Blacksburg, VA 24060, USA
4Department of Fish and Wildlife Conservation, Virginia Cooperative Fish and Wildlife Research Unit, U.S. Geological Survey, Blacksburg, VA 24061, USA
5Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA

Received 12 May 2014; Revised 14 July 2014; Accepted 18 July 2014; Published 28 August 2014

Academic Editor: Piermaria Corona

Copyright © 2014 Andrew Evans et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The northern hardwood forest type is an important habitat component for the endangered Carolina northern flying squirrel (CNFS; Glaucomys sabrinus coloratus) for den sites and corridor habitats between boreo-montane conifer patches foraging areas. Our study related terrain data to presence of northern hardwood forest type in the recovery areas of CNFS in the southern Appalachian Mountains of western North Carolina, eastern Tennessee, and southwestern Virginia. We recorded overstory species composition and terrain variables at 338 points, to construct a robust, spatially predictive model. Terrain variables analyzed included elevation, aspect, slope gradient, site curvature, and topographic exposure. We used an information-theoretic approach to assess seven models based on associations noted in existing literature as well as an inclusive global model. Our results indicate that, on a regional scale, elevation, aspect, and topographic exposure index (TEI) are significant predictors of the presence of the northern hardwood forest type in the southern Appalachians. Our elevation + TEI model was the best approximating model (the lowest AICc score) for predicting northern hardwood forest type correctly classifying approximately 78% of our sample points. We then used these data to create region-wide predictive maps of the distribution of the northern hardwood forest type within CNFS recovery areas.