Maxent modeling for predicting the potential distribution of Sanghuang, an important group of medicinal fungi in China

• We use Maxent niche modeling to map potential distributions of three fungal species.
• 19 bioclimatic variables, altitude and 89 presence data were used in modeling.
• High training, test and mean AUC values were obtained for all three models.
• The precipitation and mean temperature have the highest predictive power.

The ability to identify the spatial distribution of economically important fungal species is crucial for understanding the environmental factors that affect them and for conservation management. A potentially valuable approach for this is maximum entropy (Maxent) spatial distribution modeling, which was applied here to map the potential distribution of three “Sanghuang” mushrooms in China, which include Phellinus baumii, Phellinus igniarius and Phellinus vaninii. Nineteen WorldClim bioclimatic variables, with corresponding altitude data, and 89 spatially well-dispersed species occurrence records were used in the modeling. The relative importance of the environmental variables was evaluated by Jackknife tests in the modeling analysis. The maximum entropy models obtained have high Area Under Receiver Operating Characteristic Curve (AUC) values: 0.956, 0.967 and 0.960, for P. baumii, P. igniarius and P. vaninii, respectively. The bioclimatic variable that most strongly affected distributions of P. baumii and P. vaninii was precipitation in the warmest quarter, while the mean temperature in the warmest quarter affected the distribution of P. igniarius most strongly. Overall, these models could provide valuable help in searching for the target species in areas where it is hitherto unknown, and be the reference of conservation measures for these medicinal fungal species.