Carbon (C) metabolism is at the core of ecosystem function. Decomposers play a critical role in this metabolism as they drive soil C cycle by mineralizing organic matter to CO2. Their growth depends on the carbon-use efficiency (CUE), defined as the ratio of growth over C uptake. By definition, high CUE promotes growth and possibly C stabilization in soils, while low CUE favors respiration. Despite the importance of this variable, flexibility in CUE for terrestrial decomposers is still poorly characterized and is not represented in most biogeochemical models. Here, we synthesize the theoretical and empirical basis of changes in CUE across aquatic and terrestrial ecosystems, highlighting common patterns and hypothesizing changes in CUE under future climates. Both theoretical considerations and empirical evidence from aquatic organisms indicate that CUE decreases as temperature increases and nutrient availability decreases. More limited evidence shows a similar sensitivity of CUE to temperature and nutrient availability in terrestrial decomposers. Increasing CUE with improved nutrient availability might explain observed declines in respiration from fertilized stands, while decreased CUE with increasing temperature and plant C : N ratios might decrease soil C storage. Current biogeochemical models could be improved by accounting for these CUE responses along environmental and stoichiometric gradients.
While most of the global primary productivity enters the decomposition pathway and is returned to the atmosphere as CO2 on a yearly basis, some detrital carbon is transformed biologically into stable organic matter through a variety of stabilization pathways (Cebrian, 1999; Six et al., 2006). Understanding how soil microorganisms partition detrital carbon between respiration and biomass growth – and ultimately storage – is necessary to predict the responses of the global C cycle to climatic changes, and to design sustainable strategies for ecosystem management and agricultural development. The stabilization of plant-derived organic matter in soils is in fact gaining interest as a potential mechanism to buffer increasing atmospheric CO2 concentrations, as well as to improve soil quality (Lal, 2008). The amount of C that is ultimately stabilized depends on both organic matter protection (Sollins et al., 1996; Kleber et al., 2007) and how effectively the decomposer community converts carbon to biomass, relative to how much carbon is lost in gaseous (CO2 and CH4) or soluble form (Six et al., 2006). The biological processes regulating this conversion of substrate to biomass are the most sensitive to environmental conditions and are thus critical for our understanding of C fate in ecosystems, and for quantifying the global C cycle and feedbacks to climate.
A commonly used parameter to quantify how C is partitioned between growth and respiration is carbon-use efficiency (CUE), defined as the ratio of the amount of C employed in new biomass (excluding C excreted in form of metabolites and enzymes) relative to the amount of C that has been consumed (del Giorgio & Cole, 1998). The same ratio is also called gross growth efficiency or growth yield (Sterner & Elser, 2002); we use these terms synonymously throughout the paper. CUE is thus a synthetic representation of decomposer C metabolism, which defines how decomposers balance anabolic and catabolic reactions. High values of CUE indicate efficient growth and relatively low release of C to the atmosphere. By contrast, low CUE implies relatively large C losses (through respiration and exudation) and less C converted to biomass, ultimately reducing the potential for long-term sequestration.
Microbial CUE has been extensively studied under laboratory conditions (Payne, 1970; Panikov, 1995), showing that strong variation in CUE may occur depending on environmental factors (e.g. temperature), on the type of substrate (nutritional and elemental composition, as well as energy content), and on the biochemical pathway of degradation and assimilation (Roels, 1980; Russell & Cook, 1995; Keiblinger et al., 2010). The bacterial CUE also shows a strong variability in aquatic ecosystems, in part as a result of temperature differences and nutrient availability (del Giorgio & Cole, 1998; Rivkin & Legendre, 2001; Jansson et al., 2006). The CUE of microbial organisms and communities of terrestrial soils and litter also shows large variability (Fig. 1), the causes of which have only started being addressed recently. Despite the different approaches used to measure CUE in aquatic and terrestrial systems, there are striking similarities in the response of terrestrial and aquatic microbial CUE to important environmental factors. Similarly to aquatic systems, soil microbial CUE tends to increase with nutrient availability (Ågren et al., 2001) and decline with increasing temperature (Devevre & Horwath, 2000), pointing to some fundamental stoichiometric and environmental controls on decomposer metabolism and CUE (Sterner & Elser, 2002; Manzoni et al., 2008). While these controls on CUE have been considered in some theoretical models of microbial stoichiometry (Sterner, 1997; Schade et al., 2005; Manzoni & Porporato, 2009) and kinetics (Allison et al., 2010; Eliasson & Ågren, 2011; German et al., 2012), most biogeochemical models of terrestrial ecosystems typically assume a constant CUE that does not acclimate to altered conditions (Fig. 2), thus potentially missing critical feedbacks among environmental factors, resource composition, and biological activity. Furthermore, soil biogeochemical models assume widely different CUE values (Fig. 1), indicating a lack of consensus and a great deal of uncertainty regarding this parameter.
The aim of this contribution is to quantify and synthesize the role of different environmental factors and resource stoichiometry in decomposer CUE. We start with theoretical considerations of environmental and biochemical controls on CUE (‘Theory’ section) and then briefly discuss the experimental approaches to estimating this parameter (‘Measuring CUE’ section), and follow up by comparing patterns in CUE from aquatic to terrestrial systems (see the section on ‘Observed patterns in CUE’). Based on evidence from these systems and stoichiometric theory, we propose specific hypotheses on how CUE might respond to climatic and stoichiometric changes in terrestrial ecosystems (see the section on ‘Ecosystem-level implications of flexibility in CUE’). This set of hypotheses allows us to explore the expected responses of soil and litter respiration to major climatic and ecological changes – namely, increased temperature and expected shifts in detrital and soil nutrient availability that could decrease or increase with rising atmospheric CO2 concentrations and N deposition, respectively. We conclude with some directions on how to improve current models by including variability in CUE.
In this section we review the decomposer mass balance equations for C and a generic essential nutrient, denoted by E. These equations are interpreted at the scale of a typical soil sample, and thus encompass the soil microbial community as a whole. Adopting this scale of interpretation allows us to provide a theoretical framework to evaluate and interpret experimental estimates of CUE. The downside of this interpretation is the difficulty in linking processes known to occur at the microbial cell or colony scale to bulk soil dynamics.
where U is the microbial C uptake, EX represents the excretion of extracellular enzymes and metabolites, G is the net microbial growth (excluding EX ), BD is the microbial mortality, and RG, RM, RE, and RO are, respectively, respiration for growth, maintenance, extracellular enzyme production, and overflow respiration. Growth respiration is usually assumed to be proportional to the uptake flux, that is, RG = (1 − ε)U, where ε is the intrinsic growth efficiency (also called assimilation or C conversion efficiency; Gommers et al., 1988). By contrast, maintenance respiration is a function of the amount of microbial biomass (Chapman & Gray, 1986; Panikov, 1996; Pansu et al., 2004; Fontaine & Barot, 2005). Enzyme production requires both an energy (RE) and a C substrate investment, accounted for in the excretion flux, EX (Schimel & Weintraub, 2003; Allison, 2005), while overflow respiration is not related to maintenance or growth, but includes all respiratory mechanisms that arise in the presence of strong imbalances of resources (Larsson et al., 1995; Russell & Cook, 1995; Frost et al., 2005; Franklin et al., 2011). Excretion of metabolites as a result of strong C excess in the substrate or anaerobic metabolism is included in EX (Russell & Cook, 1995; Xu et al., 1999; Šantrůčkováet al., 2004). Similar mechanisms to reduce the assimilation of C have been hypothesized for consumers at higher trophic levels as well (Sterner, 1997).
Based on the definition of CUE and on the mass balance of Eqn 1, we obtain
where all the respiration terms appear explicitly. Note that this ‘effective’ CUE is lower than the intrinsic growth efficiency, ε, when any other respiration term is considered (as noted by Panikov, 1995). The choice of subtracting C excretion from biomass growth is motivated by two considerations. On the one hand, EX needs to be subtracted because the empirical estimates of CUE do not account for excretion and enzyme production (which are not measured through, for example, fumigation extraction). This definition thus provides a consistent theoretical framework to interpret the empirical estimates. On the other hand, excreted compounds (mainly organic acids, carbohydrates and other microbial metabolites) and enzymes have a faster turnover than biomass residues. Hence, subtracting EX yields a CUE that quantifies how much C is directed towards relatively stable pools in which C can ultimately be sequestered. Combining Eqns 1 and 2, the C balance of the microbial biomass becomes,
where all respiration terms are now lumped in the ‘effective’ parameter CUE. In most cases, CUE would be estimated from measurements based on Eqns 2 and 3 (see the section on ‘Measuring CUE’). Hence, measured CUE values may vary as a result of changes in any of the respiration terms or changes in the proportion of C lost by respiration vs losses of C in soluble form. Eqn 3 is also at a level of resolution consistent with most biogeochemical models, which avoid overparameterization by using ‘lumped’ representations of the respiration terms (Manzoni & Porporato, 2009). In these models, CUE is assumed constant, ranging from 0.15 (in the Rothamsted model, Jenkinson & Rayner, 1977) to 0.6, depending on the individual models (Figs 1, 2). The Verberne model (Verberne et al., 1990) and the Q-model (Ågren & Bosatta, 1998) assume CUE ≈ 0.25; in CENTURY, CUE = 0.45 and 0.55, respectively, for below-ground and surface organic matter decomposition (Parton et al., 1987), whereas in Daisy, NCSOIL, and ICBM, CUE = 0.6 for the labile pools and is lower for the most recalcitrant ones (Molina et al., 1983; Hansen et al., 1991; Kätterer & Andrén, 2001). Some models distinguish between growth and maintenance respiration, in particular microbiological models applied to soils and rhizosphere models (denoted as ‘detailed models’ in Fig. 2), whereas only a few consider other respiration terms or define CUE as a function of environmental and stoichiometric conditions. Separating the different respiration terms or even describing individual metabolic pathways that ultimately determine CUE (Dijkstra et al., 2011a) requires a more biochemically based approach, which results in complex models potentially difficult to parameterize.
where a is a scaling factor, E is an effective activation energy (J mol−1), R is the gas constant (8.314 J mol−1 K 1), and T the absolute temperature (K). The original Arrhenius equation was derived for single-step chemical reactions, while microbial growth is more complex and exhibits a lower sensitivity to temperature at high T (Ratkowsky et al., 1982; Rodrigo et al., 1997), partly as a result of increased respiratory costs and heat stress responses (Apple et al., 2006). Moreover, when substrate uptake can be described by Michaelis–Menten kinetics with a temperature-dependent maximum rate and a half-saturation constant, the overall temperature sensitivity of U may be lower than the sensitivities of the individual parameters (Ågren & Wetterstedt, 2007; German et al., 2012). These compound effects cause the function U(T) to diverge significantly from Eqn 4, resulting in a plateau or even a decline above an optimum temperature threshold, whereas Ri(T) generally maintains a monotonic response to T. As a consequence of these different responses, CUE tends to decline with increasing temperature, with a stronger effect above the optimum temperature threshold (Apple et al., 2006).
Soil moisture The effect of soil water availability on microbial metabolism and the resulting ecosystem-level dynamics are not yet fully understood (Schimel et al., 2007). On the one hand, reduced water availability limits substrate diffusivity and accessibility, albeit increasing the oxygen transport rate, thus lowering microbial growth (Or et al., 2007; Manzoni et al., 2012). On the other hand, declining water potentials may trigger a water-stress response, in the form of osmotic regulation and drought resistance, or drought avoidance through dormancy. Hence, by altering the balance of growth and maintenance, changes in soil water might affect the microbial CUE (Tiemann & Billings, 2011). Moreover, the release of osmolytes upon rewetting after a prolonged period in dry conditions (Fierer & Schimel, 2003) results in increased EX (Eqn 2), which in turn decreases CUE in the short term. However, if these osmotically active substances are rapidly recycled as new substrates, their impact on long-term CUE might be limited.
A special situation is microbial metabolism under water-saturated (anaerobic) conditions, which leads to considerable differences in metabolic pathways and metabolic end products, including acetate and CH4, rather than just CO2 (Burgin et al., 2011). In these conditions, the end products are not completely oxidized and hence CUE decreases, even though C uptake might remain stable, while C excretion increases (Šantrůčkováet al., 2004).
Substrate quality affects CUE in two different ways. On the one hand, compounds that require a large number of enzymatic steps to be degraded may result in lowered efficiency of conversion to new biomass (Ågren & Bosatta, 1987). Additionally, different substrates require different metabolic pathways to be completely assimilated, which also leads to a wide range of respiration rates per unit C assimilated (Gommers et al., 1988; van Hees et al., 2005). On the other hand, a low degree of reduction of C in the substrate (γS), a measure of the chemical energy per unit mole of C, decreases CUE (Payne, 1970; Roels, 1980; Gommers et al., 1988). The γS is calculated as the number of electron equivalents per mole of carbon and varies between 1 (e.g. for oxalate) and 8 (methane) or more in highly condensed black carbon; most of the substrates used by soil microorganisms have a γS of 3 (e.g. organic acids such as citrate or malate and neutral and basic amino acids), 4 (glucose and other carbohydrates, some acidic and aromatic amino acids and short-chain acids such as acetate and lactate), and rarely 5 or higher (e.g. leucine, polyhydroxyalkanoates or lipids). When γS is lower than the degree of reduction of C in the biomass (γB ≈ 4.2) (Roels, 1980), a unit of substrate does not contain sufficient energy to produce a unit of biomass, hence the assimilation efficiency (defined as the ratio of new biomass C to consumed C in the assimilatory pathways) cannot reach the theoretical limit of one. Accordingly, the CUE remains lower than a maximum value of c. 0.8 (i.e. 20% of C taken up enters the dissimilatory pathway; see Gommers et al., 1988). As γS becomes larger than γB, microbial growth becomes limited by the amount of available C in the substrate, whereas the energy content is now sufficient or in excess (Roels, 1980; Gommers et al., 1988).
Nutrient availability (N and P, in particular) affects growth and respiration because decomposer cells need to maintain a balanced composition of C and nutrients. To address these interactions, we write the mass balance equation for a generic essential element E in the microbial biomass (Fig. 3), as follows:
where the first term indicates the amount of nutrient taken up by the microbes (assuming that the nutrient uptake efficiency from the organic substrate is 1), Mnet is the net exchange of nutrient E between biomass and the inorganic pools (net mineralization), and the last terms account for nutrient losses through enzyme and metabolite excretion, and microbial mortality, respectively.
If the organism is strictly homeostatic, that is, its elemental composition does not change with varying substrate composition (d(C /E )B/dt = 0), and assuming, for illustrative purposes, that EX is negligible with respect to the other C fluxes, Eqns 1 and 5 can be combined to compute the nutrient mineralization flux (Manzoni & Porporato, 2009):
which takes positive values (net release of nutrient E) when the substrate C : E ratio is lower than the critical value (C /E )cr = (C /E )B/CUE, and negative values (net immobilization) when (C /E )S > (C /E )cr (Bosatta & Staaf, 1982). The hypothesis of homeostasis and the mass balance Eqns 1 and 5 thus provide the theoretical basis to define the critical substrate composition (C /E )cr (also referred to as the threshold element ratio; Frost et al., 2006; Sinsabaugh et al., 2009) that triggers net release or immobilization, based on the stoichiometric characteristics of the decomposers. The critical ratio increases with increasing decomposer C : E ratio across species or populations, whereas it decreases with increasing CUE. In terrestrial systems, variability in community-level microbial composition tends to be low, with (C /N )B ≈ 7 and (C /P )B ≈ 23 in mineral soils and slightly higher (and more variable) values in litter and organic soils (Cleveland & Liptzin, 2007; Manzoni et al., 2010). Accordingly, changes in (C /E )cr can be primarily attributed to variations in CUE.
As long as the substrate is relatively nutrient-rich, its C : E ratio remains lower than the critical ratio, (C /E )S < (C /E )cr, and the decomposer community is C-limited. By contrast, when net immobilization occurs, the inorganic nutrient availability may be too low to sustain the nutrient supply required to maintain a constant (C /E )B. Under such a condition, two alternative patterns may emerge (Manzoni & Porporato, 2009): nutrient limitation inhibits the uptake of both substrate C and nutrient; or no inhibition occurs, but excessive C is routed to overflow respiration or is excreted. In the former case, nutrient limitation does not have an impact on CUE, whereas in the latter case nutrient limitation causes a decline in CUE because of increased overflow respiration or C excretion (Eqn 2). This positive feedback between nutrient availability and CUE has been implemented in only a few biogeochemical models (Bosatta & Berendse, 1984; Hadas et al., 1998; Schimel & Weintraub, 2003; Raynaud et al., 2006; Eliasson & Ågren, 2011), although it is typically missing (Manzoni & Porporato, 2009). In general, a combination of the two responses (inhibition and decreased CUE) probably occurs, so that some degree of CUE decline under N limitation may be expected.
Nonhomeostatic decomposers may alter their cellular composition to limit the stoichiometric imbalance with their food source (Sterner & Elser, 2002). The effects described earlier are thus buffered, because (C /E )cr now changes with (C /E )B, and variability in CUE is not the only option to increase the critical ratio when facing low-nutrient substrates. Variability in cellular composition, however, is limited by physiological bounds, so that elemental imbalances are often likely to emerge, even in partially nonhomeostatic organisms. Because of this, even when (C /E )B varies, overflow respiration as a result of excessive C availability may occur, as implemented in a recent global C-N model (Esser et al., 2011). Nutrient availability may also affect enzyme production. For example, in the case of N, higher investment in extracellular enzymes drains the microbial N pool because enzymes have low C : N ratios ((C /N )EX ≈ 3), but may alleviate nutrient limitation by supplying more nutrient-rich substrates to microbes (Schimel & Weintraub, 2003; Allison, 2005; Sinsabaugh et al., 2009). Concurrently, however, higher enzyme investment is lowering CUE as RE increases and carbon is additionally needed as a building block for enzymatic proteins.
Because distinct microbial groups decompose and assimilate C compounds at different rates depending on their quality (Waldrop & Firestone, 2004), an effect of microbial community composition on CUE may also be expected (Ziegler & Billings, 2011). Early studies suggested that CUE is higher in fungal-dominated soil communities than in bacterial-dominated ones; however, recent estimates provide a more nuanced picture, with similar CUE across soil communities (Six et al., 2006; Thiet et al., 2006). A similar result has also been obtained using a metabolic network model parameterized for different bacterial-to-fungal biomass ratios (Dijkstra et al., 2011a). Despite the lack of effect of community composition, differences in CUE emerge between autochthonous (slow-growing and humus-degrading) and zymogenous (fast-growing and opportunistic) microbial communities. Populations dominated by zymogenous organisms develop in soils amended with abundant glucose, where they achieve a rapid growth, albeit at low CUE. By contrast, autochthonous populations are prevalent where C availability is low, allowing slow growth rates, but at high CUE (Shen & Bartha, 1996). As a consequence, high glucose additions often result in lower CUE than low-glucose treatments (Anderson & Domsch, 1986; Shen & Bartha, 1996); CUE values estimated by amendments with glucose or other carbon sources at high concentrations must therefore be used cautiously.
Recalling that CUE is a macroscopic characteristic of the whole soil decomposer community, an effect of soil fauna C metabolism and food web structure might also be expected. Similarly to microbes, the CUE of insects also increases as the nutrient concentration of their food improves (Pandian & Marian, 1986; Elser et al., 2000). Moreover, increased grazing by protozoa has been shown to stimulate microbial growth and hence enhance growth respiration relative to maintenance, thus increasing overall CUE (Frey et al., 2001). Environmental conditions that favor grazers are hence expected to improve CUE, although the enhanced C cycling will also likely lead to increased soil respiration.
Several approaches have been proposed to estimate CUE from observations of substrate consumption, respiration, and microbial biomass growth. Table 1 summarizes the different approaches and compares their advantages and potential issues. Most methods employ some variation of Eqn 2, depending on the quantity that is measured, often relying on labeled substrates to track the fate of C in microbial biomass and respired products. All methods employ respiration measurements, but some utilize changes in substrate concentration or substrate uptake rate to compute CUE (denoted as substrate-based and uptake-based methods in Table 1), whereas others utilize changes in microbial biomass (estimated using fumigation extraction) or microbial growth rate (biomass-based and growth-based methods).
Table 1. Overview of methods used to estimate carbon-use efficiency (CUE) in aquatic and terrestrial systems
bAs a variant of the uptake-based method, CUE can be calculated from gross N immobilization rates and C : N of the microbial biomass, as CUE = (C /N )BNimm/[(C /N )BNimm + Rcum] (Hart et al., 1994; Herron et al., 2009; Schimel, 1988). This approach assumes that all N used for growth is obtained from the inorganic pools, possibly underestimating CUE.
cΔCS, change in concentration of substrate carbon over time; Rcum, cumulative respiration rate; ΔCB, change in biomass carbon over time; U, gross uptake of substrate; G, microbial growth rates (estimated by the 3H-thymidine or 14C-leucine method); Nimm, gross N immobilization (pool-dilution method).
Process-based model for mineralization kinetics or stoichiometric relationships, where CUE appears as a parameter
Decrease in substrate concentration and cumulative respiration
Increase in microbial biomass and cumulative respiration
Substrate uptake rate and respiration
Microbial growth rate and respiration
Mineralization of substrate and kinetic analysis; C and nutrient evolution during decomposition
Usually long-term, nonlabelled substrates
Standard method, mostly with 14C- or 13C-labeled substrates
Based on gross substrate uptake (e.g. pool dilution); labeled substrates
Mainly used in aquatic systems; involves radiolabeled substrates
Based on kinetic models of amino acid half-life; radiolabeled substrates; C and nutrient mass loss. CUE is estimated from model fitting to the data
Accounts for losses of microbial production to grazing, etc.
Biomass incorporation can be measured even if no net change in biomass occurs
U accounts for losses of microbial production that cannot be estimated by ΔCB
G can be independently estimated in the presence of natural dissolved organic C
This approach does not employ direct biomass incorporation measurements
Adsorption of substrate needs to be measured; high substrate concentration needed
CB usually measured by fumigation-extraction; needs conversion to biomass
U difficult to measure (only short-term measurement possible)
Estimation of G strongly depends on conversion factors; only total respiration can be measured
Modeled CUE only, constrained by model assumptions; adsorption of amino acids is neglected
As an alternative to direct estimates of CUE, process-based mathematical models where CUE is an explicit parameter can be also employed (see ‘Model-based methods’ in Table 1). These models can be used to fit measured mineralization rates or stoichiometric relationships to obtain estimates of CUE. Model-based approaches are particularly useful when the model formulation allows an analytical solution that describes the observed quantities. In such a case, linear or nonlinear regression can be used to obtain CUE and its uncertainty. Both mineralization kinetic models (Farrell et al., 2011) and models describing stoichiometric relationships, for example, between C and nutrient concentrations during decomposition (Manzoni et al., 2008, 2010), can be employed. When the model does not have an analytical solution for the specific experimental conditions, more complex calibration techniques can also be used to estimate CUE (Wetterstedt & Ågren, 2011).
Observed patterns in CUE
This section illustrates the main patterns in CUE as a function of environmental and stoichiometric factors, in both aquatic and terrestrial systems. These observations are compared with predictions by kinetic and stoichiometric theories discussed in the section on ‘Theory’.
The temperature sensitivity of CUE is debated in the literature on aquatic systems, mainly because of difficulties in disentangling purely temperature-driven patterns from confounding effects of nutrient availability. A large body of evidence shows declining CUE with increasing temperature, as expected from the plateauing of the growth rate as temperature increases (Rivkin & Legendre, 2001; Apple et al., 2006). By contrast, other recent work has shown that activation energies for maintenance respiration and growth are comparable between −20 and 20°C (Price & Sowers, 2004; Lopez-Urrutia & Moran, 2007), and hence CUE is not expected to change strongly with temperature in this range.
Carbon-use efficiency has also been shown to decrease with increasing substrate C : E ratio (del Giorgio & Cole, 1998; Jansson et al., 2006) and with decreasing nutrient availability in general (Lopez-Urrutia & Moran, 2007). Our review of CUE estimates from a variety of ecosystems (Fig. 4) confirmed this decline, which also agrees with theoretical predictions of declining CUE as nutrient availability decreases (‘Nutrient limitation section’). Because low nutrient availability often covaries with high temperature, it has been argued that the observed negative temperature response of CUE is caused by reduced nutrient availability, and not temperature per se (Lopez-Urrutia & Moran, 2007). Studies assessing the CUE–T relationship under different nutrient conditions found that CUE declines consistently with T, but that at a given T, CUE increases with nutrient availability (Apple et al., 2006). These patterns suggest that, in terrestrial systems also, variability in CUE could be explained by environmental and stoichiometric factors, as described next.
Experimental estimates of CUE across environmental and stoichiometric gradients in soils and decomposing plant residues are fewer than for aquatic systems, but similarly suggest decreasing CUE values as temperature increases (Fig. 5) and nutrient availability decreases (Table 2). While negative effects have been found in most studies considering temperature gradients, a recent analysis showed little sensitivity (Dijkstra et al., 2011b), and indirect evidence from N-release curves (the shape of which depends on CUE; see Eqn 6) suggests limited climatic effects on CUE (Manzoni et al., 2008). Further studies on this relationship are needed to disentangle possible confounding factors such as nutrient availability and substrate quality.
Table 2. Changes of microbial-community carbon-use efficiency (CUE) in studies with strong environmental (temperature and soil moisture) and stoichiometric (available N) gradients in soils and decomposing plant residues
Soil moisture (%WHC)
Substrate C : N
−, negative effects; +, positive effect; ∼, neutral effects; NA, lack of data; WHC, water holding capacity; A, aerobic; AN, anaerobic conditions; SAT, saturated conditions.
CUE decreases with increasing drought duration; substrate C : N ratio computed for soil solution
Carbon-use efficiency of intact decomposer communities has been shown to increase with increasing soil organic matter N : C ratio (Devevre & Horwath, 2000) and inorganic N supply (Thiet et al., 2006; Ziegler & Billings, 2011). As a result, in soils incubated without nutrient additions, CUE declines significantly with the substrate C : N ratio, whereas in soils amended with inorganic nutrients, CUE tends to be higher and shows a moderate increase with substrate C : N ratio (Fig. 4c). Consistently with this pattern, increased respiration rates, corresponding to lowered CUE, have been found in nutrient-limited conditions across soil and litter types (Craine et al., 2007). However, these community-scale trends may mask more complex responses at the functional-group and species levels. Fungi have a higher (C /E )B than bacteria (Six et al., 2006) and thus require less N per unit new biomass. This might result in lower sensitivity of CUE to substrate nutrient status, or even declining CUE with increasing substrate nutrient : C ratio; by contrast, bacterial CUE tends to increase strongly with nutrient availability (Keiblinger et al., 2010). Estimates from process-based models confirm these general patterns. Declining CUE with increasing temperature allowed the dynamics of litter respiration to be better captured in incubation experiments with step changes in temperature (Wetterstedt & Ågren, 2011). Model estimates of long-term average CUE for litter decomposers indicate increased CUE with increasing litter N : C and P : C ratios (d’Annunzio et al., 2008; Manzoni et al., 2008, 2010), as well as inorganic N availability (Ågren et al., 2001; Franklin et al., 2003; Eliasson & Ågren, 2011).
Unlike aquatic systems, water availability is often an important constraint for microbes (Manzoni et al., 2012). Microbial CUE was found to increase as soil water availability decreased as a result of accumulation of C-rich solutes to withstand water stress (Herron et al., 2009), although others found that CUE actually declined in dry soils and with increasing length and severity of the water stress events (Tiemann & Billings, 2011). These contrasting results indicate that the response to soil water availability may strongly depend on the soil condition at the beginning of the incubation and on ecosystem or soil type. In contrast to dry soils, anaerobiosis affects CUE in saturated soils as well as in nonsaturated soils in microsites where O2 has been consumed. In studies comparing soils in anoxic and aerobic conditions, CUE was considerably lower under anoxic conditions, when including carbon losses by respiration and excreted metabolites (Parsons & Smith, 1989; Šantrůčkováet al., 2004).
There is experimental evidence that CUE of soil microbial communities may also vary with substrate quality (Hart et al., 1994; van Hees et al., 2005; Brant et al., 2006). van Hees et al. (2005) demonstrated that 60–90% of organic acids and 20–60% of monosaccharides, but only 10–30% of amino acids were respired in the short term, consistent with the overall degree of reduction of these compounds, but also with stoichiometric theory. The average degree of reduction of mixtures of low-molecular-weight compounds typically found in soils (van Hees et al., 2005, 2008) ranges between 3.6 and 4 (calculated as the weighted average of compound-specific γS), which is lower than the microbial value of γB ≈ 4.2. Hence, microbial growth in soils appears to be energy-limited and the corresponding CUE is lower than the maximum theoretical values (Fig. 6). Soil incubations with substrates of varying degree of reduction confirmed the general pattern observed in culture studies (closed symbols in Fig. 6), although chemical characteristics other than γS and microbial community structure also play a role (Brant et al., 2006), introducing additional variability in the CUE values. Furthermore, CUE changes through time as decomposition progresses and the fraction of complex compounds increases or the added labile material (e.g. glucose) is exhausted (Ladd et al., 1992; Hart et al., 1994; Ziegler et al., 2005). This decline in CUE can be ascribed to shifts from growth to maintenance respiration and to increased investment in enzyme production as decomposition proceeds. Changes in CUE through time may be confounded with changes as a result of temperature, because higher temperatures accelerate microbial metabolism and C cycling, thus causing the labile substrates to be exhausted earlier. As a result, the lower CUE values estimated at higher temperature may be caused, in part, by altered substrate quality through time.
Ecosystem-level implications of flexibility in CUE
Based on stoichiometric theory and empirical evidence reviewed in the preceding sections, CUE is expected to decrease in response to increasing temperatures and increase in response to improved N availability when the microbes are N-limited. By contrast, there is no consensus on CUE responses to water availability. Here we first discuss the role of CUE in driving ecosystem C and N cycles, and then address how CUE changes as a result of altered climatic conditions and how stoichiometric imbalances may affect these ecosystem dynamics.
Fig. 7 shows the consequences of changes in CUE on ecosystem-level processes. Decomposers with high CUE can convert more efficiently substrates to new biomass, reducing respiration per unit of C taken up. As a result, more C remains in the soil in the form of microbial biomass and byproducts, increasing C sequestration (Six et al., 2006). There are, however, also two major indirect effects of increased CUE. On the one hand, high CUE decreases the critical C : N and C : P ratios (Eqn 6), hence delaying net N mineralization from plant residues to the later stages of decomposition. This may result in accumulation of organic N in biomass and possibly more stable compounds, resulting in lower available N for plant uptake. Reduced net N mineralization may thus negatively impact productivity and litter input to the soil surface, in turn decreasing C storage in the soil pools. Alternatively, reduced nutrient mineralization and thus nutrient availability for plants could yield higher plant C : N ratios, which would in turn exacerbate nutrient limitation for residue decomposers, ultimately down-regulating CUE. An increase in CUE will also increase the resources microbes have for enzyme production (Allison et al., 2010), which should lead to increased rates of decomposition and decreased C storage. However, increased decomposition rates as a result of larger decomposer and extracellular enzyme pools might also have a positive impact on N mineralization if the litter C : N ratio is low, thus promoting productivity and ultimately C accumulation in the ecosystem. Hence, the consequences of changes in CUE are not easily predicted, as there are changes operating in opposite directions and that involve ecosystem-level equilibrations occurring over different timescales.
CUE under climate change and stoichiometric imbalance
Climatic changes will likely result in higher temperatures and more intermittent precipitation events, leading to longer periods of water stress (IPCC, 2007). Both changes are likely to reduce CUE, thus potentially decreasing C storage (Fig. 7). These shifts, however, might also have large direct effects on microbial activity and heterotrophic respiration, with increased rates in warmer soils and decreased rates if drier conditions become more frequent (Conant et al., 2011; Manzoni et al., 2012). In addition to effects of increasing temperature through lowered CUE, higher temperatures will raise enzyme activity and increase soil organic matter decomposition. However, lowered microbial growth as a result of declining CUE may negatively affect enzyme production, resulting in decreased decomposition and possibly improved C storage, as suggested by a recent modeling analysis (German et al., 2012). Increased C losses as a result of warming may increase net N mineralization (Eqn 6), thus improving N availability for plant production. If plants take up this extra N, the ecosystem C stores will increase in the short term, because in general more C is stored per unit N in vegetation than in soil (Rastetter et al., 1992). However, increased respiration losses will reduce soil C storage in the long term. Increasing N deposition and fertilization, especially under elevated atmospheric CO2 concentration, will in many cases increase plant production and hence ecosystem C stores (de Graaff et al., 2006).
As described in the ‘Terrestial systems’ section, the CUE of N-limited decomposers (e.g. litter and organic soil layers) also tends to increase in response to N addition (Fig. 4), as catabolic energy expenditure down-regulates relative to anabolic metabolism. N additions may be a result of fertilization or atmospheric N deposition – both of which are projected to increase as agricultural practices become more intense. The projected soil-level effect is a reduction in CO2 emissions, as commonly found in observational and experimental studies (Butnor et al., 2003; Janssens et al., 2010; Ramirez et al., 2010; Ziegler & Billings, 2011). To date, however, shifts in CUE have not been invoked as a likely mechanism for the concomitant increase in soil C stock following N enrichment, although our analysis here suggests that they may play a considerable role.
When elevated atmospheric CO2 concentration is also considered, predicting shifts in CUE becomes more complicated. Elevated CO2 increases the C : E ratios of plant tissues, leading to higher litter and soil C : E ratios (Rastetter et al., 1992; Norby et al., 2001; Yang et al., 2011). Higher substrate C : E may, in turn, result in nutrient limitation, which may reduce microbial activity and respiration rate (inhibition hypothesis, e.g. Recous et al., 1995), but it also lowers CUE because of both increased C expenses for N mining (Craine et al., 2007) and C overflow (Manzoni & Porporato, 2009). Overall, these effects may partly compensate each other, resulting in small changes in decomposition rate under elevated atmospheric CO2 (Norby et al., 2001). However, improved soil and litter N status as a result of deposition and fertilization might relieve decomposers from conditions of N limitation and increase their CUE (Ziegler & Billings, 2011), especially soon after leaf shedding or disturbance events. Hence, improving soil N status has two contrasting consequences, that is, increased respiration rate (according to the inhibition hypothesis) and improved CUE, the net effect of which on soil C and N cycling is not easy to assess. How the compound effects of elevated CO2 and nutrient availability on CUE and the resulting feedbacks at the ecosystem level balance each other remains an open question that can probably be addressed only with biogeochemical models that explicitly account for flexibility in CUE. Moreover, the CUE-mediated effects of climatic changes and stoichiometric imbalances on ecosystem dynamics may be less apparent than direct effects of climate and nutrient availability on productivity, making it more difficult to distinguish them in experimental studies.
A long history of research shows that soil carbon cycling is highly responsive to changes in temperature, soil moisture, and nutrient availability. Microbial CUE is similarly sensitive, although such sensitivity has not been fully assessed in soils despite strong theoretical and empirical evidence that CUE affects C storage and CO2 emissions. To advance our understanding of the role of CUE in C cycling, we suggest that current and future field experiments explicitly examine CUE when temperature, water, and nutrient controls on soil C cycling are being considered. Measuring CUE in addition to bulk CO2 fluxes could offer an additional insight into the microbial mechanisms underlying observed shifts in emissions.
The observed responses of CUE to temperature, soil moisture, and nutrients provide guidelines for model experiments. The patterns in CUE presented here provide some preliminary constraints and allow us to hypothesize possible directions of change of CUE under different climatic and stoichiometric scenarios. The CUE of soil decomposers is expected to decline with increasing temperature and lengthening of droughts, it will increase in response to increased N deposition and fertilization, and might be negatively affected by higher litter C : E ratios caused by elevated atmospheric CO2. The ecosystem-level consequences of these changes when all driving factors act in concert are, however, difficult to predict because of the complexities in the soil–plant interactions, thus requiring novel theoretical approaches. The current process-based models that incorporate microbial physiology could be improved to this end by building algorithms that allow for CUE flexibility as a function of environmental factors and stoichiometric constraints. At the most basic level, including different temperature sensitivities of growth and respiration to temperature and soil moisture, and accounting for C overflow mechanisms and enhanced C investment in enzymes under N limitation may provide the needed flexibility. More detailed models of microbial physiology (Tolla et al., 2007; Franklin et al., 2011; Klanjscek et al., 2012) and metabolic network models (Dijkstra et al., 2011a) will offer insights at a more fundamental level, because they allow changes in CUE to be attributed to specific physiological responses.
This work was initiated at the 27th New Phytologist Symposium, organized by the New Phytologist Trust. We acknowledge support from the US Department of Energy (DOE) through the Office of Biological and Environmental Research (BER) Terrestrial Carbon Processes (TCP) program (DE-SC0006967), the US Department of Agriculture (2011-67003-30222), the US National Science Foundation (CBET-1033467 and DEB-1145875/1145649), and the Austrian Science Fund (FWF – CLIMEX P22214). We are also grateful to Lisa Tiemann for sharing CUE data and to three anonymous reviewers for their constructive comments.