ISWC OpenIR  > 水保所科研产出--SCI  > 2017--SCI
Bayesian method predicts belowground biomass of natural grasslands
Tang, Zhuangsheng1; Deng, Lei1; An, Hui1; Shangguan, Zhouping1; Shangguan, ZP (reprint author), Inst Soil & Water Conservat, Xinong Rd 26, Yangling 712100, Shaanxi, Peoples R China.
SubtypeArticle
2017
Source PublicationECOSCIENCE
ISSN1195-6860
description.correspondentemailshangguan@ms.iswc.ac
Volume24Issue:3-4Pages:127-136
AbstractBelowground 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.
KeywordBayesian Analysis Regression Belowground Biomass Richness
Subject AreaEnvironmental Sciences & Ecology
DOI10.1080/11956860.2017.1376262
URL查看原文
Indexed BySCI
Publication PlacePHILADELPHIA
Language英语
WOS IDWOS:000414401900005
PublisherTAYLOR & FRANCIS INC
Funding OrganizationNational 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]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iswc.ac.cn/handle/361005/8057
Collection水保所科研产出--SCI_2017--SCI
Corresponding AuthorShangguan, ZP (reprint author), Inst Soil & Water Conservat, Xinong Rd 26, Yangling 712100, Shaanxi, Peoples R China.
Affiliation1.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
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Tang, Zhuangsheng]'s Articles
[Deng, Lei]'s Articles
[An, Hui]'s Articles
Baidu academic
Similar articles in Baidu academic
[Tang, Zhuangsheng]'s Articles
[Deng, Lei]'s Articles
[An, Hui]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Tang, Zhuangsheng]'s Articles
[Deng, Lei]'s Articles
[An, Hui]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.