Species Distribution Modeling of Sassafras Tzumu and Implications for Forest Management

Abstract

Sassafras tzumu (Chinese sassafras) is an economically and ecologically important deciduous tree species. Over the past few decades, increasing market demands and unprecedented human activity in its natural habitat have created new threats to this species. Nonetheless, the distribution of its habitat and the crucial environmental parameters that determine the habitat suitability remain largely unclear. The present study modeled the current and future geographical distribution of S. tzumu by maximum entropy (MAXENT) and genetic algorithm for rule set prediction (GARP). The value of area under the receiver operating characteristic curve (AUC), Kappa, and true skill statistic (TSS) of MAXENT was significantly higher than that of GARP, indicating that MAXENT performed better. Temperate and subtropical regions of eastern China where the species had been recorded was suitable for growth of S. tzumu. Relative humidity (26.2% of permutation importance), average temperature during the driest quarter (16.6%), annual precipitation (12.6%), and mean diurnal temperature range (10.3%) were identified as the primary factors that accounted for the present distribution of S. tzumu in China. Under the climate change scenario, both algorithms predicted that range of suitable habitat will expand geographically to northwest. Our results may be adopted for guiding the preservation of S. tzumu through identifying the habitats susceptible to climate change.