Applying Hotspot Detection Methods in Forestry: A Case Study of Chestnut Oak Regeneration

摘要

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.