Bayesian method predicts belowground biomass of natural grasslands

doi:10.1080/11956860.2017.1376262

ISWC OpenIR > 水保所科研产出--SCI > 2017--SCI

	Bayesian method predicts belowground biomass of natural grasslands
	Tang, Zhuangsheng 1; Deng, Lei 1; An, Hui 1; Shangguan, Zhouping 1; Shangguan, ZP (reprint author), Inst Soil & Water Conservat, Xinong Rd 26, Yangling 712100, Shaanxi, Peoples R China.
文章类型	Article
	2017
发表期刊	ECOSCIENCE
ISSN	1195-6860
通讯作者邮箱	shangguan@ms.iswc.ac
卷号	24 期号:3-4 页码:127-136
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.
关键词	Bayesian Analysis Regression Belowground Biomass Richness
学科领域	Environmental Sciences & Ecology
DOI	10.1080/11956860.2017.1376262
URL	查看原文
收录类别	SCI
出版地	PHILADELPHIA
语种	英语
WOS记录号	WOS:000414401900005
出版者	TAYLOR & FRANCIS INC
项目资助者	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]
引用统计
文献类型	期刊论文
条目标识符	sbir.nwafu.edu.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,Deng, Lei,An, Hui,et al. Bayesian method predicts belowground biomass of natural grasslands[J]. ECOSCIENCE,2017,24(3-4):127-136.
APA	Tang, Zhuangsheng,Deng, Lei,An, Hui,Shangguan, Zhouping,&Shangguan, ZP .(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.