Abstract
Hotspot detection has been widely adopted in health sciences for disease surveillance, but rarely in natural resource disciplines. In this paper, two spatial scan statistics (SaTScan and ClusterSeer) and a nonspatial classification and regression trees method were evaluated as techniques for identifying chestnut oak (Quercus Montana) regeneration hotspots among 50 mixed-oak stands in the central Appalachian region of the eastern United States. Hotspots defined by the three methods had a moderate level of conformity and revealed similar chestnut oak regeneration site affinity. Chestnut oak regeneration hotspots were positively associated with the abundance of chestnut oak trees in the overstory and a moderate cover of heather species (VacciniumandGaylussaciaspp.) but were negatively associated with the abundance of hayscented fern (Dennstaedtia punctilobula) and mountain laurel (Kalmia latiforia). In general, hotspot detection is a viable tool for assisting natural resource managers with identifying areas possessing significantly high or low tree regeneration.
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