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Chamber. Mean del. del_std. Mean flux. flux_std. autotrophic fraction. partition_ stddv. 1. -29.56 b. 4.36. 2.63 d. 0.4. 0.5. 0.2. 2. -30.44 bc. 3.15. 3.4 c. 0.7. 0.39. 0.18. 3. -28.25 a. 2.79. 5.46 a. 1.1. 0.66. 0.16. 4. -31.66 c. 4.87. 2.6 d. 0.6. 0.22. 0.17. 5.

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INTRODUCTION.

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Introduction

Chamber

Mean del

del_std

Mean flux

flux_std

autotrophic fraction

partition_ stddv

1

-29.56b

4.36

2.63d

0.4

0.5

0.2

2

-30.44bc

3.15

3.4c

0.7

0.39

0.18

3

-28.25a

2.79

5.46a

1.1

0.66

0.16

4

-31.66c

4.87

2.6d

0.6

0.22

0.17

5

-27.29a

3.03

4.05b

0.64

0.78

0.16

6

-30.35bc

5.64

1.93e

0.4

0.39

0.23

Partitioning soil respiration between auto- and heterotrophs using stable carbon-13 isotope discrimination

David Y. Hollinger1, D. Bryan Dail2, Eric A. Davidson3, Holly Hughes3, Andrew D. Richardson4

1.USDA Forest Service, Durham, NH ([email protected]), 2. University of Maine, 3. The Woods Hole Research Center, 4. Harvard University

INTRODUCTION.

Virtually all major terrestrial carbon cycle models postulate a decreasing future C sink and, in fact, a positive feedback to increasing atmospheric CO2 concentration as the climate warms (Friedlingstein et al. 2006). A particularly striking example of this behavior is the coupled carbon-climate HadCM3 model results (Cox et al. 2000) that showed a runaway increase of 250 ppm CO2 between 2050 and 2100. The mechanism for such future releases of CO2 from terrestrial systems in these models is the loss of soil carbon via enhanced respiration.

In order to have confidence in these future predictions, models must be able to duplicate present patterns of soil respiration in natural stands and experimental manipulations. Estimating the contributions of autotrophic respiration from roots and the rhizosphere (RA) and heterotrophic respiration (RH) to total soil respiration (RS) can provide powerful constraints to ecosystem carbon models (Smith and Fang 2010, Trumbore 2006).

Using an inverse modeling analysis at the Howland forest, we have demonstrated that both annual woody biomass increment and soil respiration measurements provide valuable constraints to eddy covariance NEE measurements and contribute to marked reductions in uncertainties in parameter estimates and model predictions (Richardson et al. 2010). However, although model skill for predicting total ecosystem respiration improved when soil respiration constraints were added, root and soil C pool dynamics and fluxes remained poorly constrained in the DALEC ecosystem carbon model, suggesting that these should be targets for future measurement efforts.

RESULTS

Chambers differed significantly in their mean flux and d13C (Fig. 3). They also differed in the amplitude of diurnal variation. We hypothesized that these variations were associated with differences in the relative contribution of autotrophic and heterotrophic respiration in each chamber.

Solving for the best combination of RA and RH for each chamber yields a range of solutions that result from the uncertainty in flux and isotope measurements (Fig. 4). A relatively limited range in the dA and dH, source signatures for RA and RH, provide the best overall fit:

dA = -25.2 ± 1.5

dH = -34.0 ± 1.7

For the site (all chambers, total flux weighted) the mean partitioning observed was 53 ± 1% autotrophic and 47± 1% heterotrophic respiration.

These results suggest this approach may be useful for partitioning soil respiration and further constraining ecosystem carbon model fluxes.

Fig. 4. MCMC parameter estimates for autotropic and heterotrophic d13C end-members and fraction of total respiration from autotrophs for 3 chambers.

  • METHOD.

  • Autotrophic and heterotrophic respiration can be partitioned by a variety of techniques including root exclusion or trenching, separating roots from soil and integrating the respiratory contribution of each, and various techniques utilizing carbon isotopes (Hanson et al. 2000). The isotopic methods are potentially attractive as they minimize disturbance of the soil and roots. We assess here a separation method based on measuring the flux and 13C isotopic signal of soil respiration via cavity ringdown spectroscopy (CRDS) from a series of automatic chambers at the Howland Forest in Maine, USA. In this system chambers are closed sequentially for 10 minutes and the airstream circulated through the analyzer and back to the chamber. The analyzer monitors the increasing CO2 concentration and changing isotopic d13C of the airstream (Fig. 1) to estimate the isotopic discrimination of soil respiration via the Keeling plot approach (Fig. 2).

  • There are a number of technological and theoretical issues that may impede the interpretation of these data:

  • 1)     Instrumentation issues – (air leakage in the instrument pump, transient response errors, dilution and band broadening effects of water vapor, cross sensitivity of d13C to CO2).

  • 2)     Disturbance of the depth profile of δ13CO2 caused by soil chambers could produce artifacts in δ13CRS measurements (Bowling et al., 2003).

  • 3)     δ13C enrichment of SOM often increases with soil depth, and this depth distribution must be measured and/or the depth from which most CO2 production occurs must be known.

  • 4)     Variation in canopy conditions throughout the day, with passing synoptic weather patterns, and as seasons change, affects the isotopic discrimination of photosynthesis (Farquhar et al. 1989, Ekblad and Hogberg 2001; Ekblad et al. 2005), which can cause variation in the isotopic signature of RA (Gessler et al. 2007).

  • 5)     Isotopic fractionation in non-equilibrium diffusive environments can obscure (or mimic) a true time-varying signal (Nickerson and Risk 2009, Moyes et al. 2010).

  • If we assume each chamber measures a combination of RA and RH, for each chamber i, we have the total CO2 flux (RSi) and a flux-weighted d13Ci (di );

  • RSi = RAi + RHi

  • di = (dA RAi + dH RHi) / RSi

  • Although this system is underdetermined (fewer equations than unknowns), because we have a variety of constraints (all RAi and RHi >=0) and differences between chambers, if the d13C of RA and RH are distinct then the system can be ‘solved’ (or more correctly, a range of solutions obtained, here using the Metropolis algorithm) for dA, dH, the isotopic discrimination of auto- and heterotrophs and the partitioning of each chamber.

Fig. 1 (above). Raw [CO2] and del value of recirculating airstream. Fig. 2 (right). Example inverse (Keeling) plot from one 10-min series of chamber measurements.

Fig. 2

  • REFERENCES.

  • Bowling DR, Burns SP, Conway TJ, Monson RK, White JWC. 2005. Extensive observations of CO2 carbon isotope content in and above a high-elevation subalpine forest. Global Biogeochemical Cycles 19:GB3023. doi: 10.1029/2004GB002394

  • Bowling DR, Pataki DE, Randerson JT. 2008. Carbon isotopes in terrestrial ecosystem pools and

  • CO2 fluxes. New Phytologist 178: 24–40.

  • Cox, P.M., R.A. Betts, C.D. Jones, S.A. Spall, and I.J. Totterdell. 2000. Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408:184-187.

  • Ekblad A, Högberg P. 2001. Natural abundance of 13C in CO2 respired from forest soils reveals

  • speed of link between tree photosynthesis and root respiration. Oecologia 127: 305–308.

  • Ekblad A, Boström B, Holm A, Comstedt D. 2005. Forest soil respiration rate and d13C is

  • regulated by recent above ground weather conditions. Oecologia 143: 136–142.

  • Farquhar, G.D., M.H. O'Leary, J.A. Berry. 1982. On the Relationship between Carbon Isotope

  • Discrimination and the Intercellular Carbon Dioxide Concentration in Leaves. Aust J Plant Physiol 9:121-37.

  • Friedlingstein, P., P. Cox, R. Betts, L. Bopp et al. 2006. Climate-Carbon cycle feedback analysis: Results from the C4MIP model intercomparison. 2006. Journal of Climate 19:3337-3353.

  • Gessler A, Keitel C, Kodama N, Weston C, Winters AJ, Keith H, Grice K, Leuning R, Farquhar

  • GD. 2007. δ13C of organic matter transported from the leaves to the roots in Eucalyptus

  • delegatensis: short-term variations and relation to respired CO2. Functional Plant

  • Biology 34: 692–706.

  • Hanson, P.J., Edwards N.T., Garten C.T., Andrews J.A. 2000. Separating root and soil microbial

  • contributions to soil respiration: a review of methods and observations. Biogeochemistry

  • 48: 115–146.

  • Moyes, A.B., S.J. Gaines, R.T. Siegwolf, D.R. Bowling. 2010. Diffusive fractionation complicates isotopic partitioning of autotrophic and heterotrophic sources of soil respiration. Plant, Cell & Environment 33:1804-19.

  • Nickerson, N., and D. Risk. (2009) Physical Controls on the Isotopic Composition of Soil Respired CO2. Journal of Geophysical Research-Biogeosciences, 114, G01016, doi:10.1029/2008JG000844

  • Richardson, A.D., M. Williams, D.Y. Hollinger, D.J. P. Moore, D.B. Dail, E.A. Davidson, N.A. Scott, R.S. Evans, H. Hughes, J.T. Lee, C. Rodrigues, and K. Savage. 2010. Estimating parameters of a forest ecosystem C model with measurements of stocks and fluxes as joint constraints. Oecologia 164:25-40.

  • Risk, D., Kellman, L. (2008) Isotopic Fractionation in Non-Equilibrium Diffusive Environments. Geophysical Research Letters, 35, L02403, doi:10.1029/2007GL032374

  • Smith, P. and C. Fang. 2010. Carbon cycle: A warm response by soils. Nature 464:499-500.

  • Trumbore S. 2006. Carbon respired by terrestrial systems – recent progress and challenges.

  • Global Change Biology 12: 141-153.

  •  This work was supported by the USDA Forest Service and Office of Science (BER), U.S. Department of Energy, through the Terrestrial Carbon Program under Interagency Agreement No. DE-AI02-07ER64355.

Fig 3. Diurnal pattern of soil respiration and source del13C.


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