Future climate change will drive expansion of suitable planting areas for Fructus Aurantii in Jiangxi Province, China

Fructus Aurantii (FA) is a valuable medicinal material used in traditional China medicine. Predicting the suitable distribution areas of FA and identifying its potential distribution patterns driven by various environmental factors are crucial for the selection of planting sites and maintenance of medicinal quality. Here, the maximum entropy model was used to predict the potential distribution of FA in Jiangxi Province, China under current and future climate conditions. A total of 105 geographical distribution data of FA were collected through field investigation and 32 environmental variables were obtained from public databases. The maximum entropy model showed high prediction accuracy when 16 environmental variables were selected (AUC = 0.932). The habitat suitability of FA was prominently affected by climate, which surpassed topography and soil factors. Maximum temperature of the warmest month, annual temperature range, precipitation of the wettest month, precipitation coefficient of variation, elevation, aspect, and soil organic carbon were the key factors shaping the geographic distribution of FA. Among them, maximum temperature of the warmest month (16.9%), followed by annual temperature range (16.1%), made the greatest contribution to model predictions. In the current climate background, the total potential suitable area for FA covered 6.30 × 10⁴ km² of garden land. Under future climate warming scenarios (shared socioeconomic pathways 245, 585), the potential suitable area was predicted to move southward and expand twice in 2040-2080, with notable increase in moderately and poorly suitable areas. Low hilly areas at higher elevations with moist cool conditions and gentle undulations would become more suitable for future introduction and planting of FA. Regionalized strategies for different suitable planting areas were proposed taking into account future climate change. All data are available in Mendeley Data (DOI: 10.17632/s9wsnn2xcn.1). Code is available at https://github.com/mrmaxent/Maxent.