干旱区生态环境知识资源中心(Arid): Biogeomorphological niche of a landform: Machine learning approaches reveal controls on the geographical distribution of Nitraria tangutorum nebkhas

Arid

DOI	10.1002/esp.5783
	Biogeomorphological niche of a landform: Machine learning approaches reveal controls on the geographical distribution of Nitraria tangutorum nebkhas
	Zhang, Haochen; Li, Shihan; Mason, Joseph A.; Yizhaq, Hezi; Gui, Dongwei; Xu, Zhiwei
通讯作者	Xu, ZW
来源期刊	EARTH SURFACE PROCESSES AND LANDFORMS
ISSN	0197-9337
EISSN	1096-9837
出版年	2024
卷号	49 期号:5 页码:1515-1529
英文摘要	Nebkhas are distinctive biogeomorphological landforms prevalent in global drylands and coastal environments. They play a crucial role in supporting local biodiversity and preventing land desertification and often serve as an indicator of local environmental change. Despite their significance, the environmental factors that affect their geographical distribution and how they respond to climate change have not been fully explored. This study represents a novel application of machine learning models to quantifying the biogeomorphological niche of Nitraria nebkhas in northern China and simulating their geographical distribution under future climate change conditions. Findings underscore that climatic variables influence the growth of formative shrub species on nebkhas, whereas climate, soil and geomorphological conditions, along with their spatial configuration, determine the probability of nebkha occurrence. Predictions under medium and high greenhouse gas emission scenarios indicate a northward shift in the potential distribution of nebkhas in northern China by the end of the century, accompanied by a decrease in the south due to rising temperatures. Given the potential impact of nebkha field degradation on biodiversity and soil hydrological conditions, adaptive land use strategies should be designed to protect nebkhas and mitigate the impact of climate change. Our study not only provides valuable insights for informing policy-making and conservation initiatives but also serves as an example for quantifying the niche of biogeomorphological landforms and simulating their dynamics by integrating machine learning approaches into empirical geomorphological studies.
英文关键词	biogeomorphological niche climate change machine learning nebkha potential distribution
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001157176800001
WOS关键词	SPECIES DISTRIBUTION MODELS ; RANDOM FOREST ; WIND EROSION ; SOUTH-AFRICA ; VEGETATION ; DEGRADATION ; INDICATORS ; PREDICTION ; MORPHOLOGY ; MAXENT
WOS类目	Geography, Physical ; Geosciences, Multidisciplinary
WOS研究方向	Physical Geography ; Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403318
推荐引用方式 GB/T 7714	Zhang, Haochen,Li, Shihan,Mason, Joseph A.,et al. Biogeomorphological niche of a landform: Machine learning approaches reveal controls on the geographical distribution of Nitraria tangutorum nebkhas[J],2024,49(5):1515-1529.
APA	Zhang, Haochen,Li, Shihan,Mason, Joseph A.,Yizhaq, Hezi,Gui, Dongwei,&Xu, Zhiwei.(2024).Biogeomorphological niche of a landform: Machine learning approaches reveal controls on the geographical distribution of Nitraria tangutorum nebkhas.EARTH SURFACE PROCESSES AND LANDFORMS,49(5),1515-1529.
MLA	Zhang, Haochen,et al."Biogeomorphological niche of a landform: Machine learning approaches reveal controls on the geographical distribution of Nitraria tangutorum nebkhas".EARTH SURFACE PROCESSES AND LANDFORMS 49.5(2024):1515-1529.