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. 2012 Sep 7;9(74):2255-67.
doi: 10.1098/rsif.2012.0122. Epub 2012 Apr 25.

Common structure in the heterogeneity of plant-matter decay

David C Forney  1 , Daniel H Rothman
Affiliations

Common structure in the heterogeneity of plant-matter decay

David C Forney et al. J R Soc Interface. .

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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Figures

Figure 1.
Figure 1.
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.
Figure 2.
Figure 2.
Plots of the lognormal parameters [Image: see text] and σ versus experimental variables. (a) [Image: see text] versus mean annual temperature. The Spearman rank-correlation coefficient rs indicates a significant positive trend (rs = 0.62, p = 0.002, n = 22). (b) [Image: see text] versus the initial litter lignin-to-nitrogen ratio ℓ/N (rs = 0.89, p = 0.004, n = 11). (c) [Image: see text] versus mean annual temperature shows no significant relation (rs = −0.13, p = 0.56, n = 22). (d) [Image: see text] versus ℓ/N (rs = 0.92, p < 10−5, n = 11). The colour of data points in panels (b,d) indicates tissue type: roots (blue), leaves (red), needles (green), wood (black) and wheat (cyan). The data in (a,c) represent 22 sites containing at least six different litters each, while the data in (b,d) represent 11 different litter types planted in at least four different locations. Error bars represent one s.d. of the mean.
Figure 3.
Figure 3.
The effect of composition on the initial decomposition rate [Image: see text] and the turnover time τ. The colour of data points indicates the tissue type: roots (blue), leaves (red), needles (green), wood (black) and wheat (cyan). (a) [Image: see text] versus the initial lignin-to-nitrogen ratio ℓ/N exhibits a strong negative correlation (rs = −0.85, p = 0.002, n = 11). (b) Turnover time [Image: see text] versus ℓ/N shows no significant correlation (rs = 0.36, p = 0.27, n = 11). (c) [Image: see text] and [Image: see text] for each litter type are significantly correlated (rs = 0.85, p = 0.002, n = 11) The dashed line represents both a constant turnover time [Image: see text] and, by inspection of figure 2b,d, the direction of changing ℓ/N. Data points represent 11 different litter types averaged over at least four different locations.
Figure 4.
Figure 4.
Lognormal distributions ρ(ln k) associated with different climates and plant-matter compositions. (a) Environmental differences tend to shift the distribution along the ln k axis. Both distributions have a value of σ corresponding to the mean of the data in figure 2c. The lower value of μ of the (blue) dashed distribution is consistent with values found in colder, drier climates; the higher value of μ (solid red distribution) is characteristic of warmer, wetter climates. (b) Faster rates are more sensitive to compositional change, e.g. changing the lignin-to-nitrogen ratio ℓ/N, than slower rates. The dashed blue distribution has values of μ and σ consistent with distributions associated on average with needles or high ℓ/N; the solid red distribution is characteristic on average of leaves or litters with lower ℓ/N. Values of μ and σ are taken from the dashed line in figure 3c; thus both distributions result in the same turnover time τ.
Figure 5.
Figure 5.
A Histogram of turnover times of 215 LIDET datasets. The vertical black line has a turnover time of 1000 years and indicates a clear separation between a main cluster of datasets, and the beginning of a tail that contains extremely long turnover times. We eliminate those datasets to the right of the black line.
Figure 6.
Figure 6.
Plots of the unaveraged lognormal parameters μ and σ versus experimental variables temperature and ℓ/N. All 191 datasets are shown in each figure. Colours indicate tissue types. Roots (blue), leaves (red), needles (green), wood (black) and wheat (cyan). (a) μ versus mean annual temperature. (b) μ versus ℓ/N. (c) σ versus mean annual temperature. (d) σ versus ℓ/N. Comparison with figure 2 of the main text shows similar trends.
Figure 7.
Figure 7.
Effect of composition on the unaveraged mean decomposition rate 〈k〉 and turnover time τ. All 191 datasets are shown in each figure. Colours indicate tissue types. Roots (blue), leaves (red), needles (green), wood (black) and wheat (cyan). (a) [Image: see text] versus ℓ/N. (b) τ versus ℓ/N. (c) σ2 versus μ. Comparison with figure 3 of the main text shows similar trends.
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