Prediction of potential distribution of <i>Lycium chinense</i> based on MaxEnt model and analysis of its important influencing factors

Tang Yan; Zhao Runan; Ren Gang; Cao Fuliang; Zhu Zunling

doi:10.12171/j.1000-1522.20200103

Journal of Beijing Forestry University > 2021 > 43(6): 23-32. > DOI: 10.12171/j.1000-1522.20200103

Tang Yan, Zhao Runan, Ren Gang, Cao Fuliang, Zhu Zunling. Prediction of potential distribution of Lycium chinense based on MaxEnt model and analysis of its important influencing factors[J]. Journal of Beijing Forestry University, 2021, 43(6): 23-32. DOI: 10.12171/j.1000-1522.20200103

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Prediction of potential distribution of Lycium chinense based on MaxEnt model and analysis of its important influencing factors

Tang Yan^{1, 2,},
Zhao Runan^{1, 2},
Ren Gang³,
Cao Fuliang^{2, 4},
Zhu Zunling^{1, 2, 5, ,}

1.
College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, Jiangsu, China
2.
Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, Jiangsu, China
3.
Institute of Biology of Haixi State, Qinghai Province, Golmud 816000, Qinghai, China
4.
College of Forestry, Nanjing Forestry University, Nanjing 210037, Jiangsu, China
5.
College of Art and Design, Nanjing Forestry University, Nanjing 210037, Jiangsu, China

More Information

Received Date: April 08, 2020
Revised Date: May 21, 2020
Available Online: April 13, 2021
Published Date: June 29, 2021

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Abstract

Abstract

Objective In most areas of China, Lycium chinense is in a wild state and its application is limited. In the context of global climate change, the prediction of current and future suitable distribution areas of L. chinense is of great significance for the protection of its germplasm resources, rational introduction and cultivation, and large-scale promotion.
Method Based on ArcGIS and MaxEnt models, we used 124 distribution data of L. chinense and 8 climatic variables to evaluate the main climatic factors restricting its geographical distribution and discuss its current and future potential distribution areas.
Result At present, the total suitable area of L. chinense occupied about 36.73% of the national land area, and the horizontal distribution interval was about 18°−45°N and 90°−123°E. Core suitable areas were mainly located in the Qinling Mountains, Taihang Mountains of northern China; and Zhejiang, Jiangsu and Anhui in eastern China; Yunnan, Guizhou, Chongqing and Sichuan in the southwestern China; Gansu, Ningxia in the northwestern China and their neighboring areas. The main climatic factors affecting its distribution were the Min. temperature of the coldest month, the precipitation of the wettest month, the precipitation of the driest month, and the Max. temperature of the warmest month. With the future climate warming, the total suitable area of L. chinense was not much different from the current one, but its main core suitable area tended to “expansion at high altitudes”, “migration in coastal areas” and “central gathering”. The specific manifestation was the expansion to high-altitude areas in Shaanxi, Gansu and other Qinling areas of northwestern China; the eastern coastal areas gradually formed a narrow strip core suitable area; the central Hunan and Hubei areas gradually formed a large area of flaky core suitable area.
Conclusion The suitable areas of L. chinense is mainly wide and continuous, while core suitable area is narrow and discontinuous. The climatic factors affecting its geographical distribution are mainly temperature and rainfall, and the Min. temperature of the coldest month is the most important climatic factor limiting its distribution. In the future, the eastern coastal areas of China, Dabie Mountains, Qinling Mountains and Daba Mountains in central China, southwestern regions such as Sichuan, Chongqing and their adjacent areas are suitable for the introduction, cultivation and promotion of L. chinense.
- Lycium chinense,
- MaxEnt model,
- climatic factor,
- distribution area prediction

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References (51)

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Prediction of potential distribution of Lycium chinense based on MaxEnt model and analysis of its important influencing factors

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