International Journal of Forestry Research
| Autor: | W. Mark Ford, Stephen P. Prisley, Andrew M. Evans, Richard H. Odom, Lynn M. Resler |
|---|---|
| Přispěvatelé: | Fish and Wildlife Conservation, Forest Resources and Environmental Conservation, Geography |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
biology
Topographic model Article Subject lcsh:QH1-199.5 Ecology Foraging Elevation Endangered species Forestry Terrain Plant Science lcsh:General. Including nature conservation geographical distribution biology.organism_classification Hardwood forest Geography Habitat lcsh:SD1-669.5 Physical geography lcsh:Forestry Ecology Evolution Behavior and Systematics Northern flying squirrel Nature and Landscape Conservation |
| Zdroj: | International Journal of Forestry Research, Vol 2014 (2014) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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