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

Author:

Evans Andrew1ORCID,Odom Richard2,Resler Lynn3,Ford W. Mark4,Prisley Steve5

Affiliation:

1. Department of Geography, Texas A&M University, College Station, TX 77840, USA

2. Geospatial and Environmental Analysis Program, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA

3. Department of Geography, Virginia Polytechnic Institute and State University, Blacksburg, VA 24060, USA

4. Department of Fish and Wildlife Conservation, Virginia Cooperative Fish and Wildlife Research Unit, U.S. Geological Survey, Blacksburg, VA 24061, USA

5. Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA

Abstract

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.

Funder

U.S. Geological Survey

Publisher

Hindawi Limited

Subject

Nature and Landscape Conservation,Plant Science,Ecology, Evolution, Behavior and Systematics,Forestry

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