期刊
JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES
卷 118, 期 4, 页码 1532-1545出版社
AMER GEOPHYSICAL UNION
DOI: 10.1002/2013JG002400
关键词
isotope; soil organic carbon; forests; microbial oxidation; fractionation; decomposition
资金
- National Science Foundation [0754888]
- Division Of Earth Sciences
- Directorate For Geosciences [0754888] Funding Source: National Science Foundation
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [0955141] Funding Source: National Science Foundation
Carbon isotopes are applied to estimate soil decomposition and physical mixing in well-drained forest soils by coupling new isotope and soil organic carbon (SOC) data with literature meta-analysis and carbon isotope mass balance modeling. New soil data results are presented for old- and second-growth forests in Southern Appalachia, USA and the Blue Mountains, Australia. The soils exhibit a SOC decrease and C-13 increase with depth. The regressed gradient, termed , of C-13 and the logarithm of SOC with depth in the soil column ranged from -1.09 to -1.65 for the measured soils. Twenty-four soils from 11 published studies across a range of cool temperate to tropical forest soils are used to show that is dependent upon mean annual temperature (MAT) alone as well as mean annual temperature, mean annual precipitation, and soil texture, thus connecting the natural (nonlabeled) carbon isotope signature to the soil factors controlling soil decomposition and physical mixing. Carbon elemental and isotopic mass balance modeling of multiple SOC pools and multiple soil depths suggest that rates of decomposition and mixing are of the same order of magnitude for turnover in the studied forest soils. The results support the hypothesis that a pronounced negative, regressed is indicative of isotopic fractionation during decomposition and physical mixing processes that occurs during soil turnover, and other hypotheses posed in the literature are marginalized using modeling and discussion. We discuss integration of the isotope method with existing SOC turnover models as a future research avenue.
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