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Prediction of the potentially suitable areas of Leonurus japonicus in China based on future climate change using the
Yongji Wang1, Liyuan Xie1, Xueyong Zhou1
1School of Life Science Shanxi Normal University Taiyuan China.
Abstract:
Leonurus japonicus Houtt. is a traditional Chinese medicinal plant with high medicinal and edible value. Wild L. japonicus resources have reduced dramatically in recent years. This study predicted the response of distribution range of L. japonicus to climate change in China, which provided scientific basis for the conservation and utilization. In this study, 489 occurrence points of L. japonicus were selected based on GIS technology and spThin package. The default parameters of MaxEnt model were adjusted by using ENMeva1 package of R environment, and the optimized MaxEnt model was used to analyze the distribution of L. japonicus. When the feature combination in the model parameters is hing and the regularization multiplier is 1.5, the MaxEnt model has a higher degree of optimization. With the AUC of 0.830, our model showed a good predictive performance. The results showed that L. japonicus were widely distributed in the current period. The maximum temperature of warmest month, the min temperature of coldest month, the precipitation of wettest month, the precipitation of driest month, and altitude were the main environmental factors affecting the distribution of L. japonicus. Under the three climate change scenarios, the suitable distribution area of L. japonicus will range shift to high latitudes, indicating that the distribution of L. japonicus has a strong response to climate change. The regional change rate is the lowest under the SSP126-2090s scenario and the highest under the SSP585-2090s scenario.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.