气候变化领域集成服务门户(CCPortal): Predicting the Potential Distribution of Haloxylon ammodendron under Climate Change Scenarios Using Machine Learning of a Maximum Entropy Model

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DOI	10.3390/biology13010003
	Predicting the Potential Distribution of Haloxylon ammodendron under Climate Change Scenarios Using Machine Learning of a Maximum Entropy Model
	Xiao, Fengjin; Liu, Qiufeng; Qin, Yun
发表日期	2024
EISSN	2079-7737
起始页码	13
结束页码	1
卷号	13 期号:1
英文摘要	Haloxylon ammodendron (H. ammodendron) is a second-class protected plant of national significance in China that is known for its growth in desert and semidesert regions, where it serves as a desert ecosystem guardian by playing a substantial role in maintaining ecosystem structure and function. The changing global climate has substantially altered the growth conditions for H. ammodendron. This study focuses on identifying the key variables influencing the distribution of H. ammodendron and determining their potential impact on future distribution. We employed the Maxent model to evaluate the current climate suitability for H. ammodendron distribution and to project its future changes across various shared socioeconomic pathway (SSP) scenarios. Our findings indicate that precipitation during the warmest quarter and precipitation during the wettest month are the most influential variables affecting the potentially suitable habitats of H. ammodendron. The highly suitable habitat area for H. ammodendron currently covers approximately 489,800 km2. The Maxent model forecasts an expansion of highly suitable H. ammodendron habitat under all future SSP scenarios, with the extent of unsuitable areas increasing with greater global warming. The increased highly suitable habitats range from 40% (SSP585) to 80% (SSP126) by the 2070s (2060-2080). Furthermore, our results indicate a continued expansion of desertification areas due to global warming, highlighting the significant role of H. ammodendron in maintaining desert ecosystem stability. This study offers valuable insights into biodiversity preservation and ecological protection in the context of future climate change scenarios.
英文关键词	climate change; H. ammodendron; Maxent model; potential distribution; prediction
语种	英语
WOS研究方向	Life Sciences & Biomedicine - Other Topics
WOS类目	Biology
WOS记录号	WOS:001151826000001
来源期刊	BIOLOGY-BASEL
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/309895
作者单位	China Meteorological Administration
推荐引用方式 GB/T 7714	Xiao, Fengjin,Liu, Qiufeng,Qin, Yun. Predicting the Potential Distribution of Haloxylon ammodendron under Climate Change Scenarios Using Machine Learning of a Maximum Entropy Model[J],2024,13(1).
APA	Xiao, Fengjin,Liu, Qiufeng,&Qin, Yun.(2024).Predicting the Potential Distribution of Haloxylon ammodendron under Climate Change Scenarios Using Machine Learning of a Maximum Entropy Model.BIOLOGY-BASEL,13(1).
MLA	Xiao, Fengjin,et al."Predicting the Potential Distribution of Haloxylon ammodendron under Climate Change Scenarios Using Machine Learning of a Maximum Entropy Model".BIOLOGY-BASEL 13.1(2024).