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Inverse analysis of coupled carbon-nitrogen cycles against multiple datasets at ambient and elevated CO_(2)

Journal of Plant Ecology 2016年第3期9卷 285-295页

作者： Zheng Shi Yuanhe Yang Xuhui Zhou Ensheng Weng Adrien C.Finzi Yiqi Luo Department of Botany and Microbiology University of OklahomaNormanOK 73019USA State Key Laboratory of Vegetation and Environmental Change Institute of BotanyChinese Academy of SciencesBeijing 100093China Department of Biology Boston UniversityBostonMA 02215USA

Aims Carbon(C)sequestration in terrestrial ecosystems is strongly regulated by nitrogen(N)***,key parameters that determine the degree of N regulation on terrestrial C sequestration have not been well *** Here,we used a Bayesian probabilistic inversion approach to estimate 14 target parameters related to ecosystem C and N interactions from 19 datasets obtained from duke Forests under ambient and elevated carbon dioxide(CO_(2)).Important FindingsOur results indicated that 8 of the 14 target parameters,such as C:N ratios in most ecosystem compartments,plant N uptake and external N input,were well constrained by available datasets whereas the others,such as N allocation coefficients,N loss and the initial value of mineral N pool were poorly *** analysis showed that elevated CO_(2)led to the increases in C:N ratios in foliage,fine roots and ***,elevated CO_(2)stimulated plant N uptake and increased ecosystem N capital in duke Forests by 25.2 and 8.5%,*** addition,elevated CO_(2)resulted in the decrease of C exit rates(*** in C residence times)in foliage,woody biomass,structural litter and passive soil organic matter,but the increase of C exit rate in fine *** results demonstrated that CO_(2)enrichment substantially altered key parameters in determining terrestrial C and N interactions,which have profound implications for model improvement and predictions of future C sequestration in terrestrial ecosystems in response to global change.

关键词： Bayesian probabilistic inversion carbon-nitrogen interactions carbon-nitrogen coupled model duke face.

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