Xinling Fang, Yuxin Liang, Jinzhi Gu, Junwei Xuan
School of Resources and Environment, Xinjiang Agricultural University, Xinjiang Uygur Autonomous Region, Urumqi, 830052, China
Aconitum diphtheria is a kind of poisonous grass widely distributed in Xinjiang and northwest Gansu of China. In recent years, the propagation of poisonous grass has led to a sharp reduction of fine forage, which has caused great harm to animal husbandry and ecological environment. However, little is known about the habitat of Aconitum diphtheriae and the key environmental factors affecting its expansion. In this study, the maximum entropy (MaxEnt) model was used to assess the potential impact of climate change in 2040 and 2080 on the distribution of Aconitum diphtheriae under the two scenarios (SSP126 and SSP585) predicted under BCC-CSM2-MR mode analyzed by IPCC6, and the key environmental factors affecting the distribution of Aconitum diphtheriae were analyzed. A total of 90 species distribution points and 4 selected variables were used for modeling. The ROC curve method was used to evaluate the established model, and the results showed that it showed good performance (AUC>0.9).The experimental results are as follows: (1) Aconitum diphtheria tends to grow in areas with relatively small changes in environmental factors. Aconitum diphtheria is mainly distributed in the south and north slopes of Tianshan Mountains and grows intensively. (2) The environmental factors affecting the distribution of Aconitum diphtheria are: the wettest monthly rainfall (Bio13), the driest monthly rainfall (Bio14), the average rainfall in the coldest season (Bio19), and the variance of temperature change (Bio4).
Aconitum leucostomum Vorosch; MaxEnt Model; Climate Change; Geographical Distribution; Poisonous Grass
Xinling Fang, Yuxin Liang, Jinzhi Gu, Junwei Xuan. Study on Potential Geographical Distribution of Diphtheria Aconitum in Xinjiang under the Scenario of Future Climate Change. Academic Journal of Environment & Earth Science (2023) Vol. 5 Issue 5: 28-34. https://doi.org/10.25236/AJEE.2023.050505.
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