Prediction of the potentially suitable areas of Paeonia lactiflora in China based on Maxent and Marxan models - Wang, Yongji; Huo, Wentao; Wu, Kefan; Cao, Jiaying; Zhao, Guanghua; Zhang, Fenguo (Frontiers in Plant Science, 2025)
Paeonia lactiflora Pall. (P. lactiflora) is an important medicinal plant in China with high ornamental value. Predicting the potential habitat of P. lactiflora is crucial for identifying its geographic distribution characteristics and ensuring its ecological and economic importance. Therefore, we aimed to predict the potential geographic distribution of P. lactiflora in China under future climate change scenarios. To this end, we used an optimized Maxent model and ArcGIS software to analyze the influence of 12 environmental variables on P. lactiflora potential distribution in China based on 291 effective distribution records. The key factors limiting the potential geographic distribution of P. lactiflora were evaluated by combining the contribution rates of the environmental variables with the significance of their replacement. The jackknife method was employed to assess the importance of these factors. Response curves were used to determine the appropriate intervals for the environmental factor variables and to analyze the changes in spatial patterns. The Maxent model exhibited a low degree of overfitting and good prediction accuracy. The main variables influencing P. lactiflora distribution were precipitation in the wettest month and hottest quarter, lowest temperature in the coldest month, and highest temperature in the warmest month. Under current climatic conditions, P. lactiflora could theoretically grow across and area of 231.1 × 104 km2 in China. Under the six future climate change scenarios, the potential geographic distribution area was reduced compared with the current distribution area, and the potentially suitable areas shifted southwestward. The majority of priority conservation sites for P. lactiflora are located in northern and northeastern China, which align with the highly favorable areas predicted by the Maxent model. The findings of this investigation can guide the selection of future introductions as well as artificial cultivation and preservation of P. lactiflora resources.