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Common structure in the heterogeneity of plant-matter decay.

Forney DC, Rothman DH - J R Soc Interface (2012)

Bottom Line: Changes in temperature and precipitation scale all rates similarly, whereas the initial substrate composition sets the time scale of faster rates.These findings probably result from the interplay of stochastic processes and biochemical kinetics, suggesting that the intrinsic variability of decomposers, substrate and environment results in a predictable distribution of rates.Within this framework, turnover times increase exponentially with the kinetic heterogeneity of rates, thereby providing a theoretical expression for the persistence of recalcitrant organic carbon in the natural environment.

View Article: PubMed Central - PubMed

Affiliation: Lorenz Center and Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. dforney@mit.edu

ABSTRACT
Carbon removed from the atmosphere by photosynthesis is released back by respiration. Although some organic carbon is degraded quickly, older carbon persists; consequently carbon stocks are much larger than predicted by initial decomposition rates. This disparity can be traced to a wide range of first-order decay-rate constants, but the rate distributions and the mechanisms that determine them are unknown. Here, we pose and solve an inverse problem to find the rate distributions corresponding to the decomposition of plant matter throughout North America. We find that rate distributions are lognormal, with a mean and variance that depend on climatic conditions and substrate. Changes in temperature and precipitation scale all rates similarly, whereas the initial substrate composition sets the time scale of faster rates. These findings probably result from the interplay of stochastic processes and biochemical kinetics, suggesting that the intrinsic variability of decomposers, substrate and environment results in a predictable distribution of rates. Within this framework, turnover times increase exponentially with the kinetic heterogeneity of rates, thereby providing a theoretical expression for the persistence of recalcitrant organic carbon in the natural environment.

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Rate distributions of plant-matter decay. (a) Litter decay from a LIDET dataset. Circles are data points. The curve is the predicted decay corresponding to the forward Laplace transform of the solid (blue) curve in (b). (b) Solid curve (blue) is the solution ρ(ln k) to the regularized inverse problem. Dashed curve (red) is a Gaussian distribution fit to ρ(ln k). σ2 is the variance of the Gaussian and μ is its mean. (c) (b) shows just one inversion, whereas the solid curve (blue) is the average of the 182 solutions ρ(ln k) having non-zero variance, each rescaled by the dataset-dependent parameters μ and σ. Dashed curve (red) is a Gaussian with zero mean and unit variance. The shaded area contains the middle 68% of the numerical inversion results. (d) Logarithmic transformation of the results of (c), where the dashed (red) straight lines indicate an exact lognormal distribution.
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RSIF20120122F1: Rate distributions of plant-matter decay. (a) Litter decay from a LIDET dataset. Circles are data points. The curve is the predicted decay corresponding to the forward Laplace transform of the solid (blue) curve in (b). (b) Solid curve (blue) is the solution ρ(ln k) to the regularized inverse problem. Dashed curve (red) is a Gaussian distribution fit to ρ(ln k). σ2 is the variance of the Gaussian and μ is its mean. (c) (b) shows just one inversion, whereas the solid curve (blue) is the average of the 182 solutions ρ(ln k) having non-zero variance, each rescaled by the dataset-dependent parameters μ and σ. Dashed curve (red) is a Gaussian with zero mean and unit variance. The shaded area contains the middle 68% of the numerical inversion results. (d) Logarithmic transformation of the results of (c), where the dashed (red) straight lines indicate an exact lognormal distribution.

Mentions: We apply this procedure to litter decomposition data from the LIDET study. An example of decay from an LIDET dataset is shown in figure 1a. The corresponding estimate of the rate distribution in logarithmic space, expressed as , where x = ln k, is shown in figure 1b. The rate k is rescaled by the period of seasonal forcing (1 year) and is therefore non-dimensional. The good fit of ρ(ln k) to a Gaussian indicates that the distribution of rates is lognormal, characterized by the parameters μ and σ, where μ is the mean of ln k and σ2 is the variance of ln k.Figure 1.


Common structure in the heterogeneity of plant-matter decay.

Forney DC, Rothman DH - J R Soc Interface (2012)

Rate distributions of plant-matter decay. (a) Litter decay from a LIDET dataset. Circles are data points. The curve is the predicted decay corresponding to the forward Laplace transform of the solid (blue) curve in (b). (b) Solid curve (blue) is the solution ρ(ln k) to the regularized inverse problem. Dashed curve (red) is a Gaussian distribution fit to ρ(ln k). σ2 is the variance of the Gaussian and μ is its mean. (c) (b) shows just one inversion, whereas the solid curve (blue) is the average of the 182 solutions ρ(ln k) having non-zero variance, each rescaled by the dataset-dependent parameters μ and σ. Dashed curve (red) is a Gaussian with zero mean and unit variance. The shaded area contains the middle 68% of the numerical inversion results. (d) Logarithmic transformation of the results of (c), where the dashed (red) straight lines indicate an exact lognormal distribution.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3405759&req=5

RSIF20120122F1: Rate distributions of plant-matter decay. (a) Litter decay from a LIDET dataset. Circles are data points. The curve is the predicted decay corresponding to the forward Laplace transform of the solid (blue) curve in (b). (b) Solid curve (blue) is the solution ρ(ln k) to the regularized inverse problem. Dashed curve (red) is a Gaussian distribution fit to ρ(ln k). σ2 is the variance of the Gaussian and μ is its mean. (c) (b) shows just one inversion, whereas the solid curve (blue) is the average of the 182 solutions ρ(ln k) having non-zero variance, each rescaled by the dataset-dependent parameters μ and σ. Dashed curve (red) is a Gaussian with zero mean and unit variance. The shaded area contains the middle 68% of the numerical inversion results. (d) Logarithmic transformation of the results of (c), where the dashed (red) straight lines indicate an exact lognormal distribution.
Mentions: We apply this procedure to litter decomposition data from the LIDET study. An example of decay from an LIDET dataset is shown in figure 1a. The corresponding estimate of the rate distribution in logarithmic space, expressed as , where x = ln k, is shown in figure 1b. The rate k is rescaled by the period of seasonal forcing (1 year) and is therefore non-dimensional. The good fit of ρ(ln k) to a Gaussian indicates that the distribution of rates is lognormal, characterized by the parameters μ and σ, where μ is the mean of ln k and σ2 is the variance of ln k.Figure 1.

Bottom Line: Changes in temperature and precipitation scale all rates similarly, whereas the initial substrate composition sets the time scale of faster rates.These findings probably result from the interplay of stochastic processes and biochemical kinetics, suggesting that the intrinsic variability of decomposers, substrate and environment results in a predictable distribution of rates.Within this framework, turnover times increase exponentially with the kinetic heterogeneity of rates, thereby providing a theoretical expression for the persistence of recalcitrant organic carbon in the natural environment.

View Article: PubMed Central - PubMed

Affiliation: Lorenz Center and Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. dforney@mit.edu

ABSTRACT
Carbon removed from the atmosphere by photosynthesis is released back by respiration. Although some organic carbon is degraded quickly, older carbon persists; consequently carbon stocks are much larger than predicted by initial decomposition rates. This disparity can be traced to a wide range of first-order decay-rate constants, but the rate distributions and the mechanisms that determine them are unknown. Here, we pose and solve an inverse problem to find the rate distributions corresponding to the decomposition of plant matter throughout North America. We find that rate distributions are lognormal, with a mean and variance that depend on climatic conditions and substrate. Changes in temperature and precipitation scale all rates similarly, whereas the initial substrate composition sets the time scale of faster rates. These findings probably result from the interplay of stochastic processes and biochemical kinetics, suggesting that the intrinsic variability of decomposers, substrate and environment results in a predictable distribution of rates. Within this framework, turnover times increase exponentially with the kinetic heterogeneity of rates, thereby providing a theoretical expression for the persistence of recalcitrant organic carbon in the natural environment.

Show MeSH
Related in: MedlinePlus