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Grounds and Justification To elucidate the microbial contribution to stable soil C, it is necessary to differentiate the living and dead microbial biomass-C. Due to species-specific effects of plants on their associated microbial communities (Priha et al., 2001; Grayston and Prescott, 2005), we focused our efforts on three dominant tree species site: Sugar Maple (SM), Basswood (BA) and Hemlock (HL) in an upper Michigan old growth forest, USA (Fujinuma et al., 2005). Just recently, we directly evaluated the combined use of PLFA and AS for microbial analysis (Liang et al., in prep). Microbial biomass-C indicated by PLFA and AS differed in each site; total PLFA amount among sites were not significantly different, while bulk AS differed (p < 0.05) (Fig. 1). As expected, total PLFA and AS do not correlate well, supporting past hypotheses that each biomarker indicates a different fraction of the community (e.g. live versus senesced). The conversion factor between PLFA and AS is reasonable to obtain from microbial culture or history literature. After quantitative standardization, measurement of the living microbial biomass at present and the necromass in history, coupled with the transformation rate among, can build up a model to simulate process of microbial C transformation. Introduction Humification is an important mechanism for carbon (C) stabilization in terrestrial ecosystems. Application of modern techniques indicates a far greater role for incorporation of microbial biomass into soil stable C pools (via microbial byproducts and senesced microbial biomass) than previously believed (Haider, 1992; Kogel- Knabner, 1997). Although the C in their biomass is only a small fraction of total, with their relatively rapid turnover in soil, microorganisms continuously add to soil C (Balser, 2005). To date, the microbial role in soil C dynamics is still poorly understood. Phospholipid fatty acids (PLFA) and amino sugars (AS) are considered biomarkers for soil microbial living biomass and microbial necromass (including minor living cells), respectively (Zelles, 1999; Amelung, 2001). In this study, we evaluate the microbial role in soil C cycling using an Absorbing Markov Chain (AMC) model, with the two biomarkers combined. Challenge 1. Theoretical Concern: Robust absorbing; Biomass accuracy using biomarkers; Restrictive case; 2. Technical Concern: Using fixed transition matrix to simulate non-stationary AMC; Measurement Error; Missing Value; Unequal Sampling Interval; Tentative Design In our research, we define C transformation as a function of three microbial states: living biomass, necromass, and removed (from the microbial processing regime). In order to get the transformation rate among the three states for AMC buildup and application, we conducted a soil microcosm with 13 C- glucose as substrate. Samples were collected in a time-series for PLFA, AS, CO 2 analysis. Using the isotopic tracer, we can quantify C present in the three states at different sampling times. We then construct the transition matrix based on the collected data, assuming AMC is stationary. Specifically speaking, the estimated fixed transition matrix can be assumed to be the weighted average of the matrix estimated for each transition. Each weight function can be constructed in order to minimize the errors between the actual values and predicted values. Significance Using the transition matrix in AMC system, we can then predict the situation after fixed steps and calculate expected number of steps to absorption. This can tell us how initial C will be distributed among the three states after the specific time, and also how long it will take for the entire amount of initial C to move out of the microbial processing regime. Also with constant input in AMC system, we can predict the stable situation after sufficient transition. This can tell us how C distributes ideally in future. This AMC portrayal is a unique approach that can substantially increase our understanding of microbial role in C dynamics in soils. Evaluating the microbial role in soil carbon dynamics using Markov Chain analysis Chao Liang 1, Guang Cheng 2, *Teri C. Balser 1 1 Department of Soil Science, University of Wisconsin, Madison WI 53706 2 Department of Statistics, University of Wisconsin, Madison WI 53706 Reference Amelung, W. 2001. Methods using amino sugars as markers for microbial residues in soil. In Lal, R., Kimble, J.M., Follett, R.F., Stewart, B.A. (Eds.), Assessment methods for soil carbon. Adv. Soil Sci., CRC/Lewis Publishers, Boca Raton, FL, pp. 233-270. Balser, T.C., 2005. Humification, In: Hillel, D. et al., (Eds.), Encyclopedia of Soils in the Environment. Vol. 2. Elsevier, Oxford, UK, pp 195-207. Fujinuma, R., Bockheim, J., Balster, N. 2005. Base-cation cycling by individual tree species in old-growth forests of Upper Michigan, USA. Biogeochemistry 74, 357-376. Grayston, S.J., Prescott, C.E. 2005. Microbial communities in forest floors under four tree species in coastal British Columbia. Soil Biology and Biochemistry 37, 1157-1167. Haider, K. 1992. Problems related to the humification processes in soils of the temperate climate. In: Bollag, J.-M., Stotzky, G. (Eds.). Soil Biochemistry, Vol. 7. Marcel Dekker, New York, pp.55-94. Kogel-Knabner, I. 1997. 13C and 15N NMR spectroscopy as a tool in soil organic matter studies. Geoderma 80:243-270. Liang, C., Fujinuma, R., Balser, T.C. (In prep.) Comparing PLFA and amino sugars for microbial analysis in an upper Michigan old growth forest. Priha, O., Grayston, S.J., Hiukka, R., Pennanen, T., Smolander, A. 2001. Microbial community structure and characteristics of the organic matter in soils under Pinus sylvestris, Picea abies and Betula pendula seedlings at two forest sites. Biology and Fertility of Soils 33, 17-24. Zelles, L. 1999. Fatty acid patterns of phospholipids and lipopolysaccharides in the characterisation of microbial communities in soil: a review. Biology and Fertility of Soils 29, 111-129. DxDx DyDy LxLx LyLy N = (E-Q) -1 = Living Part Dead Part t = 0 P t t+, all C enter into air Air Living Part Dead Part Air 0 a%a%1-a%-a % b% 1-b%-b % b% Living Part Dead Part 1 a% 0 Canonical form : Transition Matrix = E 0 RQ P P t = RtRt QtQt E0 Fundamental Matrix Lx+Ly: how long in living part Dx+Dy: how long in dead part If the initial distribution ratio in two states known, we might expect how long the material persist in the system e.g. initial carbon ratio of L/D is p:q, then the all carbon will lose after time unit of (Lx+Ly)*p+(Dx+Dy)*q Matrix Pace (time interval) F = [f 0] F*N = [f*L x f*L y ] DxDx DyDy LxLx LyLy N =N = With constant input, the materials will be stable in the living part and dead part in future at f*Lx and f*Ly, which might be marked by PLFA and AS. With Constant C Input --- Constant microbial population --- Stable PLFA amount ID: 13517 Chao Liang (C2966) UW-Madison 608-265-4850 (O) chaoliang@wisc.edu Living Dead Air