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Using maximum entropy modeling to identify and prioritize red spruce forest habitat in West VirginiaAuthor(s): Nathan R. Beane; James S. Rentch; Thomas M. Schuler
Source: Research Paper NRS-23. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 16 p.
Publication Series: Research Paper (RP)
Station: Northern Research Station
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DescriptionRed spruce forests in West Virginia are found in island-like distributions at high elevations and provide essential habitat for the endangered Cheat Mountain salamander and the recently delisted Virginia northern flying squirrel. Therefore, it is important to identify restoration priorities of red spruce forests. Maximum entropy modeling was used to identify areas of suitable red spruce habitat, with a total of 32 variables analyzed. Maximum temperature of the warmest month and minimum temperature of the coldest month were identified as variables explaining the most information about red spruce forest habitat. In addition, habitat maps identifying areas of high, medium, and low suitability were created and quantified at the county level. These results will benefit current and future conservation and restoration management activities as they identify core areas that possess the necessary environmental conditions for supporting future complex red spruce communities. Restoration efforts focused in areas possessing high suitability ensure peak potential of success and will ultimately give red spruce forests in West Virginia the greatest resilience to future climatic conditions by establishing connectivity between red spruce forests and increasing genetic diversity.
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CitationBeane, Nathan R.; Rentch, James S.; Schuler, Thomas M. 2013. Using maximum entropy modeling to identify and prioritize red spruce forest habitat in West Virginia. Research Paper NRS-23. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 16 p.
Keywordsred spruce, habitat suitability, maximum entropy, distribution modeling, forest ecology
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