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Academic Journal of Environment & Earth Science, 2023, 5(5); doi: 10.25236/AJEE.2023.050505.

Study on Potential Geographical Distribution of Diphtheria Aconitum in Xinjiang under the Scenario of Future Climate Change

Author(s)

Xinling Fang, Yuxin Liang, Jinzhi Gu, Junwei Xuan

Corresponding Author:
Junwei Xuan
Affiliation(s)

School of Resources and Environment, Xinjiang Agricultural University, Xinjiang Uygur Autonomous Region, Urumqi, 830052, China

Abstract

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).

Keywords

Aconitum leucostomum Vorosch; MaxEnt Model; Climate Change; Geographical Distribution; Poisonous Grass

Cite This Paper

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.

References

[1] Bosso L, Luchi N, Maresi G, et al. Predicting Current and Future Disease Outbreaks of Diplodia Sapinea Shoot Blight in Italy: Species Distribution Models as a Tool for Forest Management Planning[J]. Forest Ecology and Management, 2017, 400:655–664. 

[2] Sillero N. What Does Ecological Modelling Model? A Proposed Classification of Ecological Niche Models Based on Their Underlying Methods[J]. Ecological Modelling, 2011, 222(8):1343–1346. 

[3] Lawler J J, Shafer S L, White D, Et Al. Projected Climate-Induced Faunal Change in the Western Hemisphere [J]. ECOLOGY, 2009, 90(3):588–597. 

[4] Pramanik M, Paudel U, Mondal B, et al. Predicting climate change impacts on the distribution of the threatened Garcinia indica in the Western Ghats, India[J]. Climate Risk Management, 2017: S2212096317300931. 

[5] Yang Huifeng, Zheng Jianghua, Wu Xiulan, et al. Prediction of potential distribution area of Aconitum diphtheriae in China based on MaxEnt model and ArcGIS [J]. China Plant Protection Guide, 2015, 35 (5): 50-54, 85

[6] Lan Xiaoning, Chen Lili. Analysis of Xinjiang grassland eco economic development. Journal of Tarim University, 2005, (03): 110-114

[7] Wang Fang, Zhao Jun, Zhao Feicui, et al. Studies on the chemical constituents of Aconitum diphtheriae [J]. China Pharmacy, 2015 (9): 1233-1235

[8] Zhang Jie, Cao Lige, Li Xiucang, et al. The latest progress of research on new socio-economic scenarios (SSPs) in IPC AR5 [J]. Progress in climate change research, 2013, 9 (3): 225-228

[9] Wang Yanjun, Jing Cheng, Cao Lige, et al. China's provincial population pattern when global warming is controlled at 1. 5 ℃ and 2. 0 ℃ [J]. Progress in climate change research, 2017, 13 (4): 327-336

[10] Xu Zhonglin, Peng Huanhua, Peng Shouzhang. Development and evaluation method of species distribution model [J]. Journal of Ecology, 2015, 32 (5): 557-567

[11] Hu Xiu, Wu Fuchuan, Guo Wei. Prediction of potential planting areas of sandalwood in China based on MaxEnt ecological model [J]. Forestry Science, 2014, 50 (5): 27-33