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1 October 2017 Bayesian Method Predicts Belowground Biomass of Natural Grasslands
Zhuangsheng Tang, Lei Deng, Hui An, Zhouping Shangguan
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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.

2017 Université Laval
Zhuangsheng Tang, Lei Deng, Hui An, and Zhouping Shangguan "Bayesian Method Predicts Belowground Biomass of Natural Grasslands," Ecoscience 24(3-4), 127-136, (1 October 2017).
Received: 29 June 2017; Accepted: 1 September 2017; Published: 1 October 2017
Analyse bayésienne
Bayesian analysis
belowground biomass
biomasse racinaire
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