Search

Soils store more carbon than other terrestrial ecosystems1,2. How soil organic carbon (SOC) forms and persists remains uncertain1,3, which makes it challenging to understand how it will respond to climatic change3,4. It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss5-7. Although microorganisms affect the accumulation and loss of soil organic matter through many pathways4,6,8-11, microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes12,13. Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved7,14,15. Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate.

Describing the coupling of nitrogen (N), phosphorus (P), and carbon (C) cycles of land ecosystems requires understanding microbial element use efficiencies of soil organic matter (SOM) decomposition. These efficiencies are studied by the Soil Enzyme Steady Allocation Model (SESAM) at the decadal scale. The model assumes that the soil microbial communities and their element use efficiencies develop towards an optimum where the growth of the entire community is maximized. Specifically, SESAM approximated this growth optimization by allocating resources to several SOM-degrading enzymes proportional to the revenue of these enzymes, called the Relative approach. However, a rigorous mathematical treatment of this approximation has been lacking so far. Therefore, in this study we derive explicit formulas of enzyme allocation that maximize the total return from enzymatic processing, called the Optimal approach. Further, we derive another heuristic approach that prescribes the change of allocation without the need of deriving a formulation for the optimal allocation, called the Derivative approach. When comparing predictions across these approaches, we found that the Relative approach was a special case of the Optimal approach valid at sufficiently high microbial biomass. However, at low microbial biomass, it overestimated allocation to the enzymes having lower revenues compared to the Optimal approach. The Derivative-based allocation closely tracked the Optimal allocation. These findings increase our confidence in conclusions drawn from SESAM studies. Moreover, the new developments extend the range of conditions at which valid conclusions can be drawn. Further, based on these findings we formulated the constrained enzyme hypothesis. This hypothesis provides a complementary explanation why some substrates in soil are preserved over decades, although they are often decomposed within a few years in incubation experiments. This study shows how optimality considerations lead to simplified models, new insights, and new hypotheses. This is another step in deriving a simple representation of an adaptive microbial community, which is required for coupled stoichiometric C–N–P dynamic models that are aimed to study decadal processes beyond the ecosystem scale. [ABSTRACT FROM AUTHOR]

The response of the terrestrial carbon cycle to climate change is among the largest uncertainties affecting future climate change projections. The feedback between the terrestrial carbon cycle and climate is partly determined by changes in the turnover time of carbon in land ecosystems, which in turn is an ecosystem property that emerges from the interplay between climate, soil and vegetation type. Here we present a global, spatially explicit and observation-based assessment of whole-ecosystem carbon turnover times that combines new estimates of vegetation and soil organic carbon stocks and fluxes. We find that the overall mean global carbon turnover time is 23(+7)(-4) years (95 per cent confidence interval). On average, carbon resides in the vegetation and soil near the Equator for a shorter time than at latitudes north of 75° north (mean turnover times of 15 and 255 years, respectively). We identify a clear dependence of the turnover time on temperature, as expected from our present understanding of temperature controls on ecosystem dynamics. Surprisingly, our analysis also reveals a similarly strong association between turnover time and precipitation. Moreover, we find that the ecosystem carbon turnover times simulated by state-of-the-art coupled climate/carbon-cycle models vary widely and that numerical simulations, on average, tend to underestimate the global carbon turnover time by 36 per cent. The models show stronger spatial relationships with temperature than do observation-based estimates, but generally do not reproduce the strong relationships with precipitation and predict faster carbon turnover in many semi-arid regions. Our findings suggest that future climate/carbon-cycle feedbacks may depend more strongly on changes in the hydrological cycle than is expected at present and is considered in Earth system models.

Soil organic carbon (SOC) is the largest terrestrial carbon pool, but it is still uncertain how it will respond to climate change. Especially the fate of SOC due to concurrent changes in soil temperature and moisture is uncertain. It is generally accepted that microbially driven SOC decomposition will increase with warming, provided that sufficient soil moisture, and hence enough C substrate, is available for microbial decomposition. We use a mechanistic, microbially explicit SOC decomposition model, the Jena Soil Model (JSM), and focus on the depolymerisation of litter and microbial residues by microbes at different soil depths, and its sensitivities to soil warming and different drought intensities. In a series of model experiments we test the effects of soil warming and droughts on SOC stocks, in combination with different temperature sensitivities (Q 10 values) for the half-saturation constant Km (Q 10, Km ) associated with the breakdown of litter or microbial residues. Microbial depolymerisation rates of litter and residues are proportional to microbial biomass (reverse kinetics), so that at low microbial biomass, the temperature sensitivity of Km plays a more prominent role. We find that soil warming leads to long-term SOC losses, but depending on SOC composition and its associated Q 10, Km values, these losses can be either reduced or further accelerated, especially in the subsoil where microbial biomass is low. Droughts can alleviate the effects of soil warming and reduce SOC losses, and even lead to SOC gains, provided unchanged litter inputs. Furthermore, a combination of drought and different Q 10, Km values associated with the breakdown of litter or microbial residues can have counteracting effects on the overall decomposition rates. In this study, we show that while absolute SOC changes driven by soil warming and drought are highest in the topsoil, SOC in the subsoil is more sensitive to the (sometimes counteracting) interplay between Km , temperature and soil moisture changes, and mineral-associated SOC. [ABSTRACT FROM AUTHOR]

Describing the coupling of nitrogen (N), phosphorus (P), and carbon (C) cycles of land ecosystems requires understanding microbial element use efficiencies of soil organic matter (SOM) decomposition. These efficiencies are studied by the soil enzyme steady allocation model (SESAM) at decadal scale. The model assumes that the soil microbial communities and their element use efficiencies develop towards an optimum where the growth of the entire community is maximized. Specifically, SESAM approximated this growth optimization by allocating resources to several SOM degrading enzymes proportional to the revenue of these enzymes, called the Relative approach. However, a rigorous mathematical treatment of this approximation has been lacking so far. Therefore, in this study we derive explicit formulas of enzyme allocation that maximize total return from enzymatic processing, called the Optimal approach. Further, we derive another heuristic approach that prescribes the change of allocation without the need of deriving a formulation for the optimal allocation, called the Derivative approach. When comparing predictions across these approaches, we found that the Relative approach was a special case of the Optimal approach valid at sufficiently high microbial biomass. However, at low microbial biomass, it overestimated allocation to the enzymes having lower revenues compared to the Optimal approach. The Derivative-based allocation closely tracked the Optimal allocation. The model finding that the Relative approach was a special case of the more rigorous Optimal approach together with observing the same patterns across optimization approaches increases our confidence into conclusions drawn from SESAM studies. Moreover, the new developments extend the range of conditions at which valid conclusions can be drawn. The new model finding that a smaller set of enzyme types was expressed at low microbial biomass led us to formulate the constrained enzyme hypothesis, which provides a complementary explanation why some substrates in soil are preserved over decades although often being decomposed within a few years in incubation experiments. This study shows how optimality considerations lead to simplified models, new insights and new hypotheses. It is another step in deriving a simple representation of an adaptive microbial community, which is required for coupled stoichiometric CNP dynamic models that are aimed to study decadal processes beyond ecosystem scale. [ABSTRACT FROM AUTHOR]

Phosphorus (P) availability affects the response of terrestrial ecosystems to environmental and climate change (e.g. elevated CO2), yet the magnitude of this effect remains uncertain. This uncertainty arises mainly from a lack of quantitative understanding of the soil biological and geochemical P cycling processes, particularly the P exchange with soil mineral surfaces, which is often described by a Langmuir sorption isotherm. We first conducted a literature review on P sorption experiments and terrestrial biosphere models (TBMs) using Langmuir isotherm. We then developed a new algorithm to describe the inorganic P exchange between soil solution and soil matrix based on the double-surface Langmuir isotherm and extracted empirical equations to calculate the sorption capacity and Langmuir coefficient. We finally tested the conventional and new models of P sorption at five beech forest sites in Germany along a soil P stock gradient using the QUINCY (Quantifying Interactions between terrestrial Nutrient CYcles and the climate system) TBM. We found that the conventional (single-surface) Langmuir isotherm approach in most TBMs largely differed from P sorption experiments regarding the sorption capacities and Langmuir coefficients, and simulated a too low soil P buffering capacity. Conversely, the double-surface Langmuir isotherm approach adequately reproduced the observed patterns of soil inorganic P pools. The better representation of inorganic P cycling using the double Langmuir approach also improved simulated foliar N and P concentrations, and the patterns of gross primary production and vegetation carbon across the soil P gradient. The novel model generally reduces the estimates of P limitation compared to the conventional model, particularly at the low-P site, as the model constraint of slow inorganic P exchange on plant productivity is reduced. [ABSTRACT FROM AUTHOR]