Incorporating Functional Gene Quantification into Traditional Decomposition Models

Thursday, 18 December 2014
Katherine E Todd-Brown1, Jizhong Zhou1, Huaqun Yin1, LiYou Wu1, James M Tiedje2, Edward A G Schuur3, Kostas Konstantinidis4 and Yiqi Luo1, (1)Univ Oklahoma, Norman, OK, United States, (2)Michigan State University, East Lansing, MI, United States, (3)Univ Florida, Gainesville, FL, United States, (4)Georgia Institute of Technology Main Campus, Atlanta, GA, United States
Incorporating new genetic quantification measurements into traditional substrate pool models represents a substantial challenge. These decomposition models are built around the idea that substrate availablity, with environmental drivers, limit carbon dioxide respiration rates. In this paradigm, microbial communities optimally adapt to a given substrate and environment on much shorter time scales then the carbon flux of interest. By characterizing the relative shift in biomass of these microbial communities, we informed previously poorly constrained parameters in traditional decomposition models.

In this study we coupled a 9 month laboratory incubation study with quantitative gene measurements with traditional CO2 flux measurements plus initial soil organic carbon quantification. GeoChip 5.0 was used to quantify the functional genes associated with carbon cycling at 2 weeks, 3 months and 9 months. We then combined the genes which ‘collapsed’ over the experiment and assumed that this tracked the relative change in the biomass associated with the ‘fast’ pool. We further assumed that this biomass was proportional to the ‘fast’ SOC pool and thus were able to constrain the relative change in the fast SOC pool in our 3-pool decomposition model.

We found that biomass quantification described above, combined with traditional CO2 flux and SOC measurements, improve the transfer coefficient estimation in traditional decomposition models. Transfer coefficients are very difficult to characterized using traditional CO2 flux measurements, thus DNA quantification provides new and significant information about the system. Over a 100 year simulation, these new biologically informed parameters resulted in an additional 10% of SOC loss over the traditionally informed parameters.