气候变化领域集成服务门户(CCPortal): Machine learning-based prediction for grassland degradation using geographic, meteorological, plant and microbial data

CCPortal

DOI	10.1016/j.ecolind.2022.108738
	Machine learning-based prediction for grassland degradation using geographic, meteorological, plant and microbial data
	Yan, Han; Ran, Qinwei; Hu, Ronghai; Xue, Kai; Zhang, Biao; Zhou, Shutong; Zhang, Zuopei; Tang, Li; Che, Rongxiao; Pang, Zhe; Wang, Fang; Wang, Di; Zhang, Jing; Jiang, Lili; Qian, Zhi; Zhang, Sanguo; Guo, Tiande; Du, Jianqing; Hao, Yanbin; Cui, Xiaoyong; Wang, Yanfen
通讯作者	Wang, YF (通讯作者)
发表日期	2022
ISSN	1470-160X
EISSN	1872-7034
卷号	137
英文摘要	Extensive grassland degradation under climate change and intensified human activities has threatened ecological security and caused a variety of environmental problems. However, it is still challenging to predict the grassland degradation status on a large scale because it is a multi-factorial phenomenon with complex changes in ecosystem structure and function, which is hard to be fully characterized through mechanism models. The emergence of machine learning algorithms provides a potential to model complex systems and mine information from multi-source data without elucidating underlying mechanisms. Here, we utilized random forest and neural network algorithms to predict the grassland degradation represented by the net primary productivity (NPP) changing rate based on multi-source data including geographic, meteorological, plant traits, land use type and microbial variables in the Chinese Northern grassland. Particularly, the microbial roles in determining the degradation status were concerned. Results show that a high prediction precision was achieved by random forest model, rather than by neural network model, with a mean relative error of 16.9% and a mean square error of 9.273e-05. Besides identified longitude, arid index and current NPP state, specific soil microbial groups, mainly Solirubrobacter, were screened as credible biomarkers. Regarding model fitting, geographic, meteorological and plant variables explained 61.8% of the total variance, which was enhanced up to 72.8% by the rest microbial markers. These findings provide a theoretical basis to establish a pre-warning system for grassland management and policy-making.
关键词	NET PRIMARY PRODUCTIVITY ALPINE GRASSLANDS TIBETAN PLATEAU INNER-MONGOLIA SOIL NITROGEN CARBON INDICATORS BIOMASS
英文关键词	Chinese northern grassland; Multi-source remote sensing data; Net primary productivity changing rate; Biomarkers; Random forest; Modeling and validation
语种	英语
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
WOS类目	Biodiversity Conservation ; Environmental Sciences
WOS记录号	WOS:000780227000005
来源期刊	ECOLOGICAL INDICATORS
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/260703
推荐引用方式 GB/T 7714	Yan, Han,Ran, Qinwei,Hu, Ronghai,et al. Machine learning-based prediction for grassland degradation using geographic, meteorological, plant and microbial data[J]. 中国科学院青藏高原研究所,2022,137.
APA	Yan, Han.,Ran, Qinwei.,Hu, Ronghai.,Xue, Kai.,Zhang, Biao.,...&Wang, Yanfen.(2022).Machine learning-based prediction for grassland degradation using geographic, meteorological, plant and microbial data.ECOLOGICAL INDICATORS,137.
MLA	Yan, Han,et al."Machine learning-based prediction for grassland degradation using geographic, meteorological, plant and microbial data".ECOLOGICAL INDICATORS 137(2022).