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	Bayesian method predicts belowground biomass of natural grasslands
	Tang, Zhuangsheng1; An, Hui1; Shangguan, Zhouping1; Shangguan, ZP (reprint author), Inst Soil & Water Conservat, Xinong Rd 26, Yangling 712100, Shaanxi, Peoples R China.; Deng, Lei1
刊名	ECOSCIENCE
	2017
卷号	24期号:3-4页码:127-136
关键词	Bayesian Analysis Regression Belowground Biomass Richness
ISSN号	1195-6860
DOI	10.1080/11956860.2017.1376262
文献子类	Article
英文摘要	Belowground biomass accounts for most of the carbon fluxes between biosphere and atmosphere. However, the relative importance of geographical, climatic, vegetation, and soil factors to belowground biomass at the regional scale is not well understood. To improve our understanding and estimations of belowground biomass, we used multilevel regression modeling to estimate the primary productivity of natural grasslands and determine the effects of the above-mentioned factors on belowground biomass. Mean annual precipitation (MAP), longitude, soil bulk density (SB), and soil moisture content (SMC) explained 22.4% (highest density interval, HDI: 12.6-32.5%), 10.5% (HDI: 0.6-20.6%), 10.2% (HDI: 1.9-18.8%), and 13.1% (HDI: 1.5-25.2%) of the variation in regional belowground biomass, respectively. Our results clearly demonstrate that belowground biomass values of ecological communities exhibited the pattern meadow > steppe > desert steppe. MAP was the most important driver of productivity, and SMC was a goodpredictor of variations in productivity at the regional scale. Our results show that multifunctionality indices that appropriately account for the comprehensive responses of the multiple drivers of grassland ecosystems are important at the regional scale.
学科主题	Environmental Sciences & Ecology
URL标识	查看原文
出版地	PHILADELPHIA
语种	英语
出版者	TAYLOR & FRANCIS INC
WOS记录号	WOS:000414401900005
资助机构	National Natural Science Foundation of China [41390463, 41501094, 31260125]; Science and Technology Service Network Initiative of the Chinese Academy of Sciences [KFJ-EW-STS-005]; National Sci-Tech Basic Program of China [2014FY210100]; Open Project Program of Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration of North-western China/ Key Laboratory for Restoration and Reconstruction of Degraded Ecosystem in North-western China of Ministry of Education [2017KF007]; National Sci-Tech Support Program of China [2015BAC01B03] ; National Natural Science Foundation of China [41390463, 41501094, 31260125]; Science and Technology Service Network Initiative of the Chinese Academy of Sciences [KFJ-EW-STS-005]; National Sci-Tech Basic Program of China [2014FY210100]; Open Project Program of Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration of North-western China/ Key Laboratory for Restoration and Reconstruction of Degraded Ecosystem in North-western China of Ministry of Education [2017KF007]; National Sci-Tech Support Program of China [2015BAC01B03] ; National Natural Science Foundation of China [41390463, 41501094, 31260125]; Science and Technology Service Network Initiative of the Chinese Academy of Sciences [KFJ-EW-STS-005]; National Sci-Tech Basic Program of China [2014FY210100]; Open Project Program of Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration of North-western China/ Key Laboratory for Restoration and Reconstruction of Degraded Ecosystem in North-western China of Ministry of Education [2017KF007]; National Sci-Tech Support Program of China [2015BAC01B03] ; National Natural Science Foundation of China [41390463, 41501094, 31260125]; Science and Technology Service Network Initiative of the Chinese Academy of Sciences [KFJ-EW-STS-005]; National Sci-Tech Basic Program of China [2014FY210100]; Open Project Program of Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration of North-western China/ Key Laboratory for Restoration and Reconstruction of Degraded Ecosystem in North-western China of Ministry of Education [2017KF007]; National Sci-Tech Support Program of China [2015BAC01B03]
内容类型	期刊论文
源URL	[http://ir.iswc.ac.cn/handle/361005/8057]
专题	水保所科研产出--SCI_2017--SCI
通讯作者	Shangguan, ZP (reprint author), Inst Soil & Water Conservat, Xinong Rd 26, Yangling 712100, Shaanxi, Peoples R China.
作者单位	1.Northwest A&F Univ, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling, Shaanxi, Peoples R China 2.Ningxia Univ, United Ctr Ecol Res & Bioresource Exploitat Weste, Minist Educ, Key Lab Restorat & Reconstruct Degraded Ecosyst N, Yinchuan, Peoples R China
推荐引用方式 GB/T 7714	Tang, Zhuangsheng,An, Hui,Shangguan, Zhouping,et al. Bayesian method predicts belowground biomass of natural grasslands[J]. ECOSCIENCE,2017,24(3-4):127-136.
APA	Tang, Zhuangsheng,An, Hui,Shangguan, Zhouping,Shangguan, ZP ,&Deng, Lei.(2017).Bayesian method predicts belowground biomass of natural grasslands.ECOSCIENCE,24(3-4),127-136.
MLA	Tang, Zhuangsheng,et al."Bayesian method predicts belowground biomass of natural grasslands".ECOSCIENCE 24.3-4(2017):127-136.

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