PT - JOURNAL ARTICLE AU - Beane, Nathan R. AU - Rentch, James S. TI - Using Known Occurrences to Model Suitable Habitat for a Rare Forest Type in West Virginia Under Select Climate Change Scenarios AID - 10.3368/er.33.2.178 DP - 2015 Jun 01 TA - Ecological Restoration PG - 178--189 VI - 33 IP - 2 4099 - http://er.uwpress.org/content/33/2/178.short 4100 - http://er.uwpress.org/content/33/2/178.full AB - Many studies now identify the impacts of global climate change as a conservation threat to terrestrial ecosystems. In West Virginia, red spruce (Picea rubens) forests are a rare forest community of great ecological value, occurring in ‘island-like’ distributions among higher elevations. In this study, 168 red spruce presence localities and 24 environmental and sitespecific variables were used to model red spruce habitat using select climate change scenarios. Maximum entropy models were created for aggressive and conservative climate change scenarios, with each scenario model performed for three time periods (i.e., 2020, 2050, and 2080). Results for both model analyses identified three variables which contributed significantly to model performance: mean temperature of the coldest quarter of the year, elevation, and minimum temperature of the coldest month of the year. Changes in suitable habitat area were also assessed for both model scenarios at each time period examined. Approximately 6.2% of the land area in West Virginia was modeled under current habitat conditions as suitable red spruce habitat. However, by the time period 2020, a loss of 78% and 52% was identified for the aggressive and conservative climate change models, respectively. By the time period 2080, no suitable red spruce habitat was modeled using the aggressive climate change scenario with an 85% reduction in current modeled habitat identified using the conservative model. These findings indicate the potential impacts of climate change on red spruce forest habitat in West Virginia and provide valuable guidance for future restoration efforts by identifying areas most likely to succeed under altered climatic conditions.