Predicting the potential suitable areas of Platycodon grandiflorum in China using the optimized Maxent model
Platycodon grandiflorum(Jacq.)A.DC is one of the most commonly used bulk medicinal herbs.It has important value in the fields of medicine,food and cosmetics,and its market demand is increasing year by year,and it has a good development prospect.In this study,based on 403 distribution records and 8 environmental variables,we used Maxent model to predict the potential distribution of P.grandiflorum under climate change.The results showed that the model simulation effect was the best when the Maxent parameter was FC(feature combination)=LQPH(L:linear features;Q:quadratic features;P:product features;H:hinge features)and RM(regularization multiplier)=2.1,AUC(area under curve)=0.901,and the model prediction showed that P.grandiflorum was widely distributed in 28 provinces of China under the current climate conditions,with a suitable area of 2 337 419.98 km2.Under the influence of climate change in the future,the area of suitable habitat of P.grandiflorum showed a decreasing trend,and the distribution center as a whole showed a trend of northward and eastward shift.The distribution area of P.grandiflorum in the three main producing areas is affected by climate change,and the overall trend is that the future suitable distribution area of Chifeng in Inner Mongolia increases,while the future suitable distribution area of Zibo in Shandong and Taihe,Bozhou in Anhui decreases or even disappears under the SSP5-8.5 scenario(shared socioeconomic pathways).It is suggested to maintain and protect the ecological environment and germplasm resources of the reserved area suitable for the growth of P.grandiflorum,and increase the cultivation research in the expansion area of P.grandiflorum,so as to lay a foundation for the subsequent expansion of P.grandiflorum planting and to promote the protection and sustainable development of P.grandiflorum resources.
Platycodon grandiflorumMaxentoptimizing parameterclimate changesuitable areas change
万方数据
维普
