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Scientia Silvae Sinicae ›› 2021, Vol. 57 ›› Issue (6): 158-168.doi: 10.11707/j.1001-7488.20210618

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Prediction of Potential Distribution of Ziziphus jujuba var. spinosa in China under Context of Climate Change

Guanghua Zhao1,3,Xinyue Cui2,Zhi Wang1,Hongli Jing2,Baoguo Fan1,*   

  1. 1. College of Life Science, Shanxi Normal University Linfen 041000
    2. Agriculture College, Guangxi University Nanning 530000
    3. Shanwei Middle School Shanwei 516600
  • Received:2020-01-16 Online:2021-06-25 Published:2021-08-06
  • Contact: Baoguo Fan

Abstract:

Objective: In order to provide a scientific basis for the development and utilization of Ziziphus jujuba var. spinosa germplasm resources,the suitable range for growing Z. jujuba var. spinosa was predicted under the context of climate change. Method: Based on the GIS technology and R language,data of 121 geographical concurrence points and 34 environmental factors were selected for the study. The default parameters of MaxEnt model were adjusted by using ENMeval package in R language,and the geographical distribution of suitable areas for Z. jujuba var. spinosa was predicted by using the optimized MaxEnt model. Person correlation analysis and VIF variance expansion factor were used to select the required factors for modeling,and the jackknife method was used to select the dominant environmental factors of the suitable area. According to the fifth climate model released by IPCC,the changes of future geographical distribution of Z. jujuba var. spinosa under different climate scenarios were discussed. Result: The model optimization showed that the MaxEnt model has the lowest complexity and the best when the feature combination is linear,quadratic and fragmented,and the regulation frequency multiplier is 3.5. The AUC of the working characteristic curve analysis method of the subjects was 0.946,indicating that the prediction model has high reliability and excellent accuracy. The jackknife test showed that the main environmental factors affecting the distribution of Z. jujuba var. Spinose were the annual mean temperature,the mean temperature of wettest quarter,the precipitation of wettest month,the precipitation seasonality,the elevation,and the base saturation of topsoil (0-30 cm). At present,the suitable growing areas for Z. jujuba var. spinosa in China are mainly concentrated in Shaanxi,northern Henan,central and southern Shanxi,Hebei,northern Ningxia and southeastern Inner Mongolia,northeastern Sichuan,western Liaoning and Shandong. Under the different climate scenarios in the future,the suitable areas for Z. jujuba var. spinosa will change in varying degrees. By the 2050s and the 2070s, the suitable areas for Z. jujuba var. spinosa will increase to a certain extent. The suitable areas under RCP 4.5 and RCP 8.5 scenarios respond sensitively,showing an increasing trend,and the increasing trend is weaker under RCP 2.6 scenario than under the other two scenarios. Conclusion: Climate,soil and terrain factors all affect the potential geographical distribution of Z. jujuba var. spinosa,among which climate factors account for the largest weight,which is most likely to cause the migration of geographical distribution of Z. jujuba var. spinosa. The optimized model can accurately simulate and predict the potential geographical distribution area of Z. jujuba var. spinosa. In the future,the climate change of global warming will change the distribution pattern of Z. jujuba var. spinose,the total suitable growing areas will increase,the expansion will be mainly in the middle and high latitudes,and the reduction will be mainly in the low latitudes. Under the three climate change scenarios,the center of suitable areas for Z. jujuba var. spinosa in the 2050s and the 2070s will migrate to high latitudes. The suitable area for Z. jujuba var. spinosa will migrate to the North China Plain and the Northeast Plain as a whole,and the priority should be given to arrange new planting areas in this region,so as to reduce the loss caused by climate change.

Key words: Ziziphus jujuba var. spinosa, MaxEnt model, ENMeval package, climate change, prediction of suitable growing areas

CLC Number: