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Physical Model for the Decay and Preservation of Marine Organic Carbon

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Science  01 Jun 2007:
Vol. 316, Issue 5829, pp. 1325-1328
DOI: 10.1126/science.1138211

Abstract

Degradation of marine organic carbon provides a major source of atmospheric carbon dioxide, whereas preservation in sediments results in accumulation of oxygen. These processes involve the slow decay of chemically recalcitrant compounds and physical protection. To assess the importance of physical protection, we constructed a reaction-diffusion model in which organic matter differs only in its accessibility to microbial degradation but not its intrinsic reactivity. The model predicts that organic matter decays logarithmically with time t and that decay rates decrease approximately as 0.2 × t–1 until burial. Analyses of sediment-core data are consistent with these predictions.

Nearly half of Earth's primary production of organic carbon occurs in the oceans (1). Once fixed, this reduced carbon immediately enters the marine food chain, where nearly all of it—about 99.9%—is eventually oxidized back to CO2 via heterotrophic metabolism (2). The remainder escapes degradation and is immobilized in sediments (24). Whereas degradation rates influence atmospheric and oceanic concentrations of CO2 at short time scales, burial rates influence O2 concentrations at long (geologic) time scales (3, 4). Moreover, the impact of this slow leak from the biological to the geological carbon cycle extends well beyond O2. For example, the burial of organic carbon is a natural form of carbon sequestration (5) that can lead to the formation of petroleum (6), and O2 accumulated as a consequence of burial must have preceded the evolution of complex life dependent on aerobic metabolism (7).

A fundamental understanding of the rates of decay and burial has been elusive, however. A central problem is that there appears to be no single rate that characterizes the microbial decay of organic matter (8, 9). Indeed, a pronounced slowdown in respiration rates, as organic matter falls through the water column and degrades in sediments, has been unambiguously documented at time scales ranging from days to millions of years (10, 11). However, the mechanisms responsible for this slowdown and their relation, if any, to the small fraction of organic matter that is preserved in sediments remain unresolved.

Many hypotheses have been proposed. Roughly speaking, these appeal to either chemistry or physics. The chemical scenarios typically ascribe the slowdown to changes in the chemical composition of organic matter. In probably the simplest such formulation, intrinsically highly reactive “labile” organic carbon is consumed first and then followed by less reactive compounds. The most recalcitrant organic compounds—for example, algaenans—are then “selectively preserved” (12, 13). An alternative hypothesis suggests that recalcitrance is ultimately not traceable to biosynthesis. It instead results from random repolymerization and condensation of a small nonselective set of incompletely degraded but enzymatically depolymerized compounds, which, over time, constitute an increasingly large fraction of the remaining organic matter (6).

The physical scenarios attribute the slowdown and burial to some form of physical protection. This reasoning follows in part from observations showing a strong correlation between the concentrations of organic carbon and clay particles (14) or mineral surface area (1517) in sediments, suggesting that some physical property of the sediments themselves is promoting the preservation of organic matter. Further evidence of physical protection comes from observations indicating that the chemical composition of organic matter changes little as it sinks to the sea floor (18), and from experiments suggesting that when apparently recalcitrant organic matter is physically separated from its mineral matrix, it becomes “labile” and is rapidly consumed (19).

Both chemical and physical mechanisms must play a role, but they have not received similar attention in quantitative models. Whereas intrinsic reactivity is commonly invoked (8, 9, 20), explicit consideration of physical mechanisms has been rare (21). To address this, we construct a theoretical model that purposely assumes an extreme case in which the chemical composition of organic matter is uniform. Degradation and preservation rates instead derive only from the physics of diffusion-limited reactions in porous media. As we show below, this simple theory provides predictions that are quantitatively consistent with a variety of observations, thereby suggesting that physical mechanisms play an important role in setting both degradation and preservation rates.

Our model assumes that organic matter is randomly distributed on the mineral surfaces of a porous medium populated by randomly distributed heterotrophic bacteria (Fig. 1). We assume that the typical bacterial spacing rbn–1/3 associated with the bacterial number density n is much greater than the typical pore size of the porous medium, consistent with images obtained by microscopy (22). Spatial averaging of the porous medium at a scale greater than the pore size but less than rb then provides a continuum approximation in which each microbe is embedded in a smooth porous medium characterized by its void fraction (porosity) ϕ.

Fig. 1.

Schematic illustration of the main features of our model. In a porous medium characterized by its porosity ϕ, organic carbon (oc) is associated with the surface area of clay (c) and other minerals. Extracellular enzymes are emitted from randomly distributed bacteria (b), diffuse through the medium, and hydrolyze organic matter upon contact. For simplicity, diffusive paths show only first encounters of enzymes with solid surfaces; later encounters occur until enzymes become inactive, at a specified rate α. Except for the random distribution of bacteria, each of these features is contained in a more detailed model proposed by Vetter et al. (21).

The decay of a particular parcel of organic matter is assumed to be limited by its accessibility to hydrolytic enzymes. “Accessibility” is associated with grain boundaries. It is interpreted physically as the frequency f with which a mineral surface encounters a diffusing enzyme. We assume that f is proportional to the concentration c of functional enzymes in the pore fluid near the mineral surface, and that enzymes lose their functionality at rate α. Otherwise, c is conserved and diffuses within pores with diffusivity D. Averaged over lengths much greater than a pore size, the volume-averaged concentration = ϕc of active enzymes therefore evolves according to the reaction-diffusion equation Math(1) where is the effective diffusivity in the porous aggregate.

A complete treatment of this problem would require coupling the diffusion of c to the diffusion of organic matter hydrolyzed by its contact with c along with the consumption of hydrolysate by bacteria (21). It would also include coupling the production of enzymes to energy gained from hydrolysate. We simplify our analysis, however, by assuming that the decay of organic matter is rate-limited by hydrolysis, and that the bacterial population density and the flux of enzymes emanating from each microbe is constant. We then set local decay rates kf and assume that the appropriate solution of Eq. 1, c(r) ∝ e–βr/r, where r is the distance from the nearest microbe and β = (α/)1/2, suffices to determine f from the relation fc. These assumptions correspond to a quasistatic limit in which enzymes diffuse much faster than microbes, hydrolysis is diffusion-limited, and the porosity ϕ is constant. They also imply that decreases in hydrolysate production represent a decreasing surplus. Although such excess solubilization suggests inefficiency at early times, it is consistent with previous model calculations (21) and observations of effluxes of dissolved organic matter from marine sediments (23) and sinking organic aggregates (24).

These simplifications allow us to express the temporal decay of the total concentration of organic matter, g(t), as a continuous (20) superposition of first-order reactions (8, 9) weighted by the concentration ρ(k,t) dk of organic matter associated with rate k at time t. Our formulation depends on two phenomenological parameters: a characteristic concentration g0g(0) arising from the requirement that g(0) = ∫ ρ(k, 0) dk, and a minimum reaction rate kmin associated with the typical distance (rb) between microbes. Assuming that kmin is much smaller than the (maximum) reaction rate adjacent to microbes, we obtain the approximation (supporting online text) Math(2) where τ = kmint, k*= k/kmin, g*(kmint)= g(t)/g0, and τmin = kmintmin is the dimensionless finite time associated with the initiation of observable decay at dimensional time t = tmin (i.e., in a core-top sample). Equation 2 is a special case of the reactive continuum models introduced by Boudreau and Ruddick (20). Its particular form, which defines the exponential integral E1(τ)(25), derives from its connections to the reaction-diffusion dynamics of Eq. 1.

For τ ≪ 1, an asymptotic expansion of Eq. 2 predicts that g*(τ) decays logarithmically (25): Math(3) where γ = 0.5772... is Euler's constant. The characteristic time τ ∼1(i.e., Math) marks the termination of the logarithmic decay. Because subsequent decay proceeds slowly, as e–τ/τ (25), we interpret Math as the approximate time of burial and the effective cessation of degradation. Consequently Math approximates the burial concentration and g0E1(1)/gmax, where gmax is the observed concentration at t = tmin, approximates the burial efficiency. Assuming ln τmin ≪ –γ, Eq. 3 implies g*(τmin)∼ –ln τmin and therefore (supporting online text) Math(4) The second relation derives from kmine–βrb/rb and the assumption that βrb ≫ 1. It approximates burial efficiency [neglecting the factor of E1(1) ≅ 0.22] by the ratio of the diffusion length β–1 to the bacterial spacing rb. In conjunction with the first relation, it also shows that as rb increases (with β and tmin constant), kmin decreases exponentially—reflecting the longer degradation times associated with lower microbial population densities and less efficient burial.

To test these predictions, we assembled a database of 23 published analyses of dated sediment cores that span a wide range of environmental settings, from the deep ocean to shallow waters, and from aerobic to anoxic conditions at the sediment-water interface (table S1). Each data set provides measurements of the bulk concentration gi of particulate organic carbon at time ti, where ti is obtained by dividing depth by the average accumulation rate Vi of the overlying sediment. A least-squares fit of each core to Eq. 2 provides a comparison between theory and observation along with estimates of the parameters kmin and g0 (supporting online text).

Figure 2A shows data from a single core beneath oxygenated waters. In this case the fit, which is generally good, yields a value of kmin that is much less than the inverse of the time associated with the base of the core. Thus, the resulting theoretical curve is nearly identical to the asymptotic logarithmic decay of Eq. 3. Figure 2B shows the data from all 23 cores, after rescaling the g and t axes by the estimates of g0 and kmin, respectively, that derived from each fit. The data generally show a reasonable fit to Eq. 2, including some evidence of diminishing decay rates near Math.

Fig. 2.

(A) Concentration (weight %) of organic carbon (filled circles) from Pacific–Antarctic Ridge sediment core 7812-05 of Reimers and Suess (32). The theoretical curve (smooth line) corresponds to Eq. 2 with log10 kmin ≅ –5.1 (year–1) and g0 = 0.08 (%Corg). (B) Rescaling of 23 cores (287 points total) with respect to dimensionless axes ln kmint and g/g0. Each core corresponds to a different kmin, g0 pair. Different colors and symbols correspond to different cores. Red corresponds to cores underlying anoxic environments.

Figure 3A displays the 23 estimates of the paired parameters kmin and g0. One sees that the cores underlying anoxic waters are associated with higher burial concentrations (∝ g0) and earlier terminations (i.e., higher kmin). The otherwise apparent disarray of the data, however, masks the relation between kmin and g0 predicted by Eq. 4. Figure 3B compares this prediction to each of the g0, kmin pairs of Fig. 3A. The good fit derives from the consistency of the degradation data with the theoretical model of Eq. 2.

Fig. 3.

(A) The 23 kmin, g0 pairs used to rescale the data of Fig. 2B. The cores underlying anoxic waters (red squares) are associated with higher kmin and g0. (B) The same kmin, g0 pairs plotted with dimensionless rescaled axes g0/gmax and ln kmintmin. The vertical axis is proportional to burial efficiency. The smooth curve is the theoretical prediction of Eq. 4, which contains no free parameters. (C) Log-log plot of the sediment fluxes Vg0 versus Vgmax, where Vg0 is proportional to the burial flux of organic carbon and Vgmax is an estimate of the flux to the sea floor. The straight line corresponds to g0 = 〈g0/gmaxgmax, where 〈g0/gmax 〉 ≅ 0.18 is the mean burial efficiency multiplied by E1(1) ≅ 0.22. The correlation between log Vg0 and log Vgmax is only marginally improved (from r = 0.97 to r = 0.99) by the common factor of the sedimentation rate V. See fig. S1 for a plot of g0 versus gmax.

Figure 3B also shows that the variation of g0/gmax is not large and that there is a tendency for both oxic and anoxic cores to cluster around the mean 〈g0/gmax 〉. Thus, in Fig. 3C, wherewe plot the burial flux Vg0 versus the flux to the sea floor Vgmax, we find a good fit to a straight line with a slope of unity in logarithmic coordinates. Although anoxia appears to have little influence on burial efficiency (26) (Fig. 3B), it is associated with a higher burial flux (Fig. 3C), which is in turn proportional, on average, to the flux to the sea floor. Insertion of the constant of proportionality, 〈g0/gmax 〉 ≅ 0.18, into Eq. 4 then yields the mean 〈βrb 〉 ≅ 5.6, with all values of βrb between 2.2 and 11.5. The narrow range of βrb = (α/)1/2rb might be related to the apparent constancy of bacterial abundance with respect to fluid volume (27). If we assume D ∼ ϕD (28), this range is also consistent with the natural variation of the dimensional constants α, rb, D, and ϕ (21), but it suggests that 〈βrb 〉 ≅ 5.6 is atypically large. We therefore hypothesize that the effective diffusivity D ≪ ϕD, because of sorption of enzymes to mineral surfaces (9, 29), shielding of organic matter (16) in clay-rich microenvironments (22), or both.

Finally, we return to the slowdown of respiration rates discussed in the introduction. Middelburg et al. (10, 11) have shown that the phenomenological effective rate K(t) ≡–dln g/dt is well fit by the power law K = 0.21× t–0.99 (11). Their fit holds over nearly 10 orders of magnitude and includes water-column sediment-flux data in addition to analyses of sea-floor sediments. Figure 4 shows a similar analysis with the 23 cores in our database. Here, rather than numerically estimating dlng/dt from noisy data, we calculate it analytically from Eq. 2. Conversion back to dimensional variables then yields Math(5) The approximation is obtained by averaging the logarithm of the slowly varying factor in parentheses over ln t, between the average minimum and maximum values of ln kmint (–6.95 and –3.19, respectively). Its excellent correspondence to Middelburg's expression is notable, not only because it is derived without any free parameters, but also because it indicates that our analysis may also apply to the decay of organic matter as it sinks, accompanied by ballast minerals (30), to the sea floor.

Fig. 4.

The effective time-dependent reactivity K(t) = –dlng/dt. Symbols: K computed from Eq. 5, with estimates of kmin obtained from the fits of Fig. 2. Symbols and colors in both figures correspond to the same cores. Straight line: the theoretical approximation K ≅ 0.23 × t–1 of Eq. 5, obtained without any free parameters. The rightward trends away from the line represent the slowing of rates as t approaches Embedded Image.

The picture that emerges is one in which the decay and preservation of marine organic matter are strongly influenced by physical constraints. In our model, physical protection (2, 1519) manifests itself as spatially varying reactivity that decays rapidly with distance from the nearest microbe, with a characteristic length scale (β–1) that depends on the active lifetime of enzymes and their effective diffusivity. Because this mechanism appears to be general, it seems likely that it should apply to other contexts, such as soils (31), where finely divided media restrict microbial motility.

Supporting Online Material

www.sciencemag.org/cgi/content/full/316/5829/1325/DC1

SOM Text

Fig. S1

Table S1

References

References and Notes

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