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Duan Yi-Zhong, Yu Hui, Wang Hai-Tao, Du Zhong-Yu. Geographical distribution and prediction of potentially suitable regions of endangered relict plant Tetraena mongolica[J]. Plant Science Journal, 2019, 37(3): 337-347. DOI: 10.11913/PSJ.2095-0837.2019.30337
Citation: Duan Yi-Zhong, Yu Hui, Wang Hai-Tao, Du Zhong-Yu. Geographical distribution and prediction of potentially suitable regions of endangered relict plant Tetraena mongolica[J]. Plant Science Journal, 2019, 37(3): 337-347. DOI: 10.11913/PSJ.2095-0837.2019.30337

Geographical distribution and prediction of potentially suitable regions of endangered relict plant Tetraena mongolica

  • The endangered relict plant Tetraena mongolica Maxim was studied, with the MaxEnt and Bioclim models used to predict potentially suitable regions. We used the knife-cutting method and environmental variable response curves to evaluate the dominant environmental factors affecting the distribution of T. mongolica. Furthermore, we used the ArcGIS natural discontinuity method to divide fitness levels. Results showed that T. mongolica was mainly distributed in the Xinjiang Uygur Autonomous Region, Tibet Region, Ningxia Hui Autonomous Region, Inner Mongolia, and the Gansu, Qinghai, Shaanxi, Shanxi, Hebei, Liaoning, Jilin, and Heilongjiang provinces in China, with a total area of 1.49×106 km2. Highly suitable zones were found in the Maowusu Sandy Land of Wuhai city, the Tengger Desert in the Alxa Left Banner, the southeastern part of Yinshan Mountain, and mountains in the Helan range. The potential distribution area of T. mongolica will be reduced to north of Inner Mongolia and western Northeast China by 2050. The Area Under Curve (AUC) average values of the two models were all above 0.8, justifying their application for predicting potential areas of T. mongolica. Among the 19 environmental variables, the main factors affecting the potential distribution of T. mongolica were average precipitation of the coldest quarter and temperature annual range, followed by the coefficient of variation of precipitation seasonality and standard deviation (SD) of temperature seasonality.
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