Ecology Letters, (2013)
doi: 10.1111/ele.12113
REVIEW AND
SYNTHESIS
Robert L. Sinsabaugh,1* Stefano
Manzoni,2 Daryl L. Moorhead3 and
Andreas Richter4
Carbon use efficiency of microbial communities: stoichiometry,
methodology and modelling
Abstract
Carbon use efficiency (CUE) is a fundamental parameter for ecological models based on the physiology of
microorganisms. CUE determines energy and material flows to higher trophic levels, conversion of plantproduced carbon into microbial products and rates of ecosystem carbon storage. Thermodynamic
calculations support a maximum CUE value of ~ 0.60 (CUEmax). Kinetic and stoichiometric constraints on
microbial growth suggest that CUE in multi-resource limited natural systems should approach ~ 0.3
(CUEmax/2). However, the mean CUE values reported for aquatic and terrestrial ecosystems differ by twofold (~ 0.26 vs. ~ 0.55) because the methods used to estimate CUE in aquatic and terrestrial systems generally differ and soil estimates are less likely to capture the full maintenance costs of community metabolism
given the difficulty of measurements in water-limited environments. Moreover, many simulation models
lack adequate representation of energy spilling pathways and stoichiometric constraints on metabolism,
which can also lead to overestimates of CUE. We recommend that broad-scale models use a CUE value of
0.30, unless there is evidence for lower values as a result of pervasive nutrient limitations. Ecosystem models operating at finer scales should consider resource composition, stoichiometric constraints and biomass
composition, as well as environmental drivers, to predict the CUE of microbial communities.
Keywords
Carbon use efficiency, ecoenzymatic activity, ecological stoichiometry, microbial production, nutrient limitation, threshold element ratio.
Ecology Letters (2013)
INTRODUCTION
Most of the net primary production of the biosphere is mineralised
through decomposer food webs (Cebrian & Lartigue 2004). The
trophic base of these food webs is the production of microbial biomass from the catabolism of detrital organic matter. The efficiency
of this conversion, often termed carbon use efficiency (CUE), controls the conversion of plant-produced carbon into microbial products, rates of ecosystem carbon storage, and energy and material
flows to higher trophic levels (Six et al. 2006; Miltner et al. 2012).
The terms growth yield, growth efficiency, metabolic efficiency
and CUE are variously defined and sometimes used interchangeably.
Adding to the confusion, these ratios are estimated using a variety
of methods that differ in their capacity to represent the metabolism
of microbial communities. These difficulties complicate comparisons
between systems and impede the development of predictive models
(Manzoni et al. 2012). Herein, we address these issues by considering
the thermodynamic, physiological and ecological constraints on
microbial growth; the role of multi-resource stoichiometry in regulating the growth of microbial communities; the limitations of existing
methodologies for assessing microbial community growth; and the
options for representing microbial community growth in ecological
models. Our goals are to clarify apparent inconsistencies in the literature, emphasise the commonalities of microbial community metab1
Biology Department, University of New Mexico, Albuquerque, NM, 87131,
olism across systems and recommend strategies for representing the
CUE of microbial communities in ecological process models.
MICROBIAL CUE: DEFINITIONS AND CONTROLS
The thermodynamics of microbial growth can be measured in multiple currencies. Analyses that calculate growth yields (Y) as cells
per mole of adenosine triphosphate (ATP) formed during growth
(YATP), yields per substrate electron available for respiration or
incorporation into cellular material (Yav e), and yields per kilocalorie of total energy consumed from the medium by both assimilation
and dissimilation (Ykcal) converge on a maximum value of approximately 0.60 (Payne 1970; Payne & Wiebe 1978; Roels 1980; Von
Stockar & Marison 1993; Lettau & Kuzyakov 1999).
In ecological studies, growth yields are generally calculated in
terms of carbon, rather than energy. In this paper, we refer to
growth yields calculated from rates of carbon transformation as
CUE, typically (though not always) defined as the ratio of growth
(l) to assimilation, that is, CUE = l/(l + R), where R includes any
C losses to respiration (Sterner & Elser 2002; Manzoni et al. 2012).
Using this definition, CUE is generally greater than zero and limited
to a maximum value set by thermodynamic constraints. However,
CUE can be negative if some metabolic costs are considered external to the assimilation of carbon leading to l < 0 (Wang et al.
3
Department of Environmental Science, University of Toledo, Toledo, OH,
USA
USA
2
4
Civil and Environmental Engineering Department, Duke University, Durham,
NC, USA
Department of Terrestrial Ecosystem Research, University of Vienna, Austria
*Correspondence: E-mail: [email protected]
© 2013 John Wiley & Sons Ltd/CNRS
2 R. L. Sinsabaugh et al.
2012a,b), as when biomass declines because losses through respiration and/or mortality exceed C income. The definition of CUE as
growth-to-assimilation ratio is consistent with most ecological models of microbial metabolism, and is thus useful for comparing
empirical and modelling studies.
Microbial growth rates, biomass composition and environmental
C and nutrient availability vary across systems, but these parameters
are interrelated through evolutionary legacies such that growth rate
(l) and CUE can be represented as functions of environmental
resource supply in relation to biomass composition (Elser et al.
2003; Gillooly et al. 2005; Frost et al. 2006; DeLong et al. 2010; Doi
et al. 2010; Franklin et al. 2011). Thus, CUE is a measure of the
energetic and material costs of sustaining an autocatalytic organism
in a particular environment. The maximal value of CUE (CUEmax)
is fixed by thermodynamic constraints (Roels 1980), but the realised
CUE of microbial communities varies with environmental conditions, substrate availability, stoichiometry, and the physiological state
and composition of the community (Manzoni et al. 2012).
CUE tends to increase with growth rate because total maintenance costs, included in R, often decrease relative to assimilation
(del Giorgio & Cole 1998; Van Bodegom 2007; Robinson 2008).
Some CUE estimates exceed thermodynamic maxima for growth
yields because the measurements do not capture the full maintenance costs of metabolism, and catabolic and anabolic processes
can be temporally displaced (Gommers et al. 1988; Russell & Cook
1995), as discussed in section 4. CUE can be relatively high when C
availability (i.e. energy) is limiting, which couples catabolic and anabolic metabolism. In contrast, carbon sources that are recalcitrant to
decomposition, as the result of stochastic oxidation and condensation reactions in the environment, may reduce CUE by increasing
the cost of extracellular and intracellular catabolism (Blagodatskaya
& Kuzyakov 2008). Nutrient limitation can also reduce CUE by
uncoupling catabolism and anabolism through energy spilling pathways and increased extracellular production of enzymes and polysaccharides (Larsson et al. 1995; Russell & Cook 1995). In addition,
environmental variables such as temperature and soil moisture alter
microbial metabolism, shifting the balance of l and R, and thereby
CUE (Allison et al. 2010; Manzoni et al. 2012). This variability of
CUE with respect to metabolic, environmental and stoichiometric
factors is largely neglected in ecosystem models. In the sections that
follow, we consider these constraints in a unifying framework that
bridges terrestrial and aquatic systems, based on the premise that
the metabolic and stoichiometric constraints on microbial metabolism are broadly similar across ecosystems.
MICROBIAL COMMUNITY METABOLISM AND STOICHIOMETRY
Respiration and growth
The relationship of CUE to growth and respiration rates is nonlinear. On broad scales, faster biomass-specific growth rates generally
increase CUE in a saturating fashion as it approaches the thermodynamic limit (CUEmax). This large-scale pattern masks several confounded factors. For example, productivity may be greater in
relatively cold, nutrient-rich water bodies than in warmer oligotrophic ones. As a result, CUE decreases as temperature increases,
but the trend can result from declining nutrient availability rather
than a different temperature sensitivity for growth and respiration
(Lopez-Urrutia & Moran 2007). This example indicates that both
© 2013 John Wiley & Sons Ltd/CNRS
Review and Synthesis
environmental constraints and resource supply need to be considered in interpreting CUE patterns and representing CUE in models.
For organisms, communities and ecosystems, the temperature
sensitivity of respiration is largely determined by the activation
energy of the respiratory electron transport system (~ 0.62 eV)
(Brown et al. 2004; Yvon-Durocher et al. 2012). Within this pattern,
the ‘apparent’ activation energy for microbial respiration may vary
with local conditions because resource supply and other environmental factors affect responses to temperature (e.g. Agren & Wetterstedt 2007; Wagai et al. 2013). For example, activation energies
for the mineralisation of recalcitrant organic matter by microbial
communities can be greater than 0.62 eV because more enzymatic
steps are needed to transform organic carbon into CO2 (Sierra
2012). Ramirez et al. (2012) reported a value of ~ 0.85 eV for
microbial respiration from 28 soils in unamended microcosms maintained for 1 year.
Because CUE represents the ratio of growth to assimilation rates,
differences in the temperature sensitivity of these two components
causes variations in CUE as a function of temperature. Generally,
respiration increases more than growth as a function of temperature, so that CUE tends to decrease with temperature, in both soils
and aquatic systems (Rivkin & Legendre 2001; Allison et al. 2010;
Wetterstedt & Agren 2011).
Other environmental drivers such as water availability in soils
may also uncouple growth and respiration, causing shifts in CUE.
In a short-term water stress event, for example, CUE increases as
osmoregulatory solutes and storage compounds are accumulated
(Uhlırova et al. 2005; Herron et al. 2009). However, in the long term
CUE is reduced by repeated stress events, as the cumulative effects
of the C costs for water stress responses become apparent
(Tiemann & Billings 2011). Aquatic organisms may experience similar effects in response to fluctuating salinity conditions.
As resource supply or composition shifts, microorganisms, as
individuals or communities, respond to changes in resource availability by altering the kinetics of enzyme-mediated assimilation pathways (Button 1993; Narang 1998; Hobbie & Hobbie 2012). Monod
models describe enzyme-mediated uptake (I) as a saturating function
of substrate or nutrient concentration (C), I = Imax C/(C + Ks),
where Imax is the maximum uptake rate and the Ks is the half-saturation constant. In natural systems, the availability of substrates for
uptake is generally linked to the activities of extracellular enzymes
that deconstruct macromolecules. These activities are represented by
Michaelis–Menten models, V = Vmax S/(S + Km), where Vmax is
the maximum reaction rate and the Km is the half-saturation constant. Selective pressures to optimise uptake rate in relation to the
resource costs of sustaining the uptake system are such that the
Monod parameters C, Imax and Ks and the Michaelis–Menten
parameters S, Vmax and Km are correlated, with C Ks and
S Km (Williams 1973; Lobry et al. 1992; Sinsabaugh & Follstad
Shah 2010; Hobbie & Hobbie 2012). As a result, equilibrium values
of I and V approach Imax/2 and Vmax/2 and the ratio of growth
rate (l) to Ks remains relatively constant. A pulse of substrate that
exceeds the concentration to which the community has adapted will
result in an increase in uptake, which transiently uncouples catabolism and anabolism. An analogous effect is expected if there is a
transient loss of a key substrate. As assays of microbial growth are
often conducted over short-time intervals (one-to-few hours), a
dynamic system with respect to substrate availability may show considerable variance in l (and CUE). As the temporal scale expands
Review and Synthesis
Microbial carbon use efficiency 3
to ecosystem models, CUE variation will attenuate, approaching the
value of a steady-state system.
The need to allocate cellular resources to optimise the acquisition
of multiple essential nutrients imposes additional limits on microbial
growth and growth efficiency (Chen & Christensen 1985; Zinn et al.
2004; Cherif & Loreau 2007; Danger et al. 2008; Franklin et al.
2011). Sinsabaugh & Follstad Shah (2012) proposed a community
growth model that incorporates the co-limiting effects of multiple
resource acquisition:
l ¼ lmax fðS1 S2 Sn Þ=½ðKS1 þ S1 Þ ðKS2 þ S2 Þ ðK Sn þ Sn Þg1=n
ð1Þ
where Si and KSi are, respectively, the concentration and half-saturation constant of resource i. The premise of the model is that the
acquisition of multiple resources by a microbial community is neither wholly independent nor fully integrated across the constituent
populations. Because nutrient assimilation is a saturating function,
growth increases sublinearly with environmental nutrient concentration, approaching an asymptote (lmax) that represents the maximum
capacity of a cell. At the community scale, biomass increases may
allow growth to continue rising until pressed by another limit. CUE
may increase with growth rate, if fixed maintenance and respiratory
costs per unit biomass decline as a fraction of energy and material
income. Alternatively, growth limitation caused by limited availability of an essential non-carbon element may decouple growth from
respiration, decreasing CUE.
Stoichiometry
The elemental C, N and P composition of microbial biomass varies
narrowly relative to environmental variation in C, N and P availability. The mean C : N ratios for microbial biomass in soils, plankton
and aquatic ecosystems are 8.6, 6.6 and 8.3 respectively; C : P ratios
are more variable with means of 60, 106 and 166 (Cleveland &
Liptzin 2007; Sterner et al. 2008; Manzoni et al. 2010; Sistla &
Schimel 2012). Variation within and across systems is about twofold
for C : N and threefold for C : P (Sardens et al. 2012). These stoichiometric requirements for biomass production force microbial
communities to adapt their foraging strategies to the available
substrates, which affects rates of growth and respiration.
In stoichiometric theory, nutrient-limited growth occurs when the
availability of an essential element (E) relative to carbon (C : E)
falls below the critical ratio or threshold element ratio (TER)
required for optimum growth. The relationship between TERC : E
and CUE is commonly defined by
TERC:E ¼ AE ½BC:E =CUE
ð2Þ
where AE is the assimilation efficiency of element E and BC : E is
the C : E ratio of biomass (Frost et al. 2006; Manzoni & Porporato
2009). This definition is typically adopted in litter and soil biogeochemical models with the assumption that AE = 1 (Bosatta & Staaf
1982). In contrast, Doi et al. (2010) define TERC : E as
TERC:E ¼ ½GEmax E =GEmax C BC=E
max
max
ð3Þ
where GE E and GE C are maximum growth efficiencies with
respect to C and E. These definitions highlight a confusing issue in
the literature regarding the interpretation of TER. Studies of ectothermic animals focus on variation in assimilation efficiency, which
is calculated as a fraction of ingestion, and biomass composition as
the principal determinants of TER because CUE is relatively constant. For osmotrophic microbial communities, assimilation efficiency is a problematic concept, given that ingestion and
assimilation are not distinct processes, and CUE can vary considerably, implying that TER has a similar variance.
Moreover, AE may also vary with the physical structure of the
environment. In soils, for example, pore-scale spatial heterogeneities
in substrate availability and stoichiometry may cause transfer of
nutrients between microbial populations in different patches. At larger scales, that is, soil core, these transfers may manifest as lower
nutrient assimilation efficiency (AE < 1) (Manzoni et al. 2008).
Traditionally, the TERC : N for terrestrial microbial communities
is considered something close to a constant with a value of 20–25,
based on empirical studies that measure the critical transition in
organic matter decomposition from net N immobilisation to net N
mineralisation (Berg & McClaugherty 2003). However, these studies
focused on the mineralisation of plant residues with relatively low
C : N ratios. An expanded analysis that includes litter types with
C : N ratios ranging from 10 to 1000 (i.e. including conifer litter
and wood) suggests that TERC : N does scale with the initial litter
C : N ratio, implying that CUE decreases with increasing litter
C : N (Fig. 1b). This analysis is based on the assumptions that the
CUE and microbial composition are constant through time, and
that BC : N does not depend on substrate quality. As a result, a single value of TER is obtained for each substrate type. A more recent
study including variability in BC : N and thus TER, however, shows
similar (albeit weaker) patterns (
Agren et al. 2013).
It is important to emphasise that the scaling of TERC : E and
C : E is sublinear, implying (1) that AN (eqn 2) may not be a constant and (2) changes in CUE do not fully compensate for the stoichiometric imbalance between litter and decomposer biomass. This
gap leads to N immobilisation when litter C : N ratio is high. In
other terms, CUE can be greater than predicted by the bulk C : N
ratio of the litter either because nutrients are translocated from the
surrounding environment (net immobilisation) or opportunistic
microorganisms are selectively targeting low C : E substrates within
the litter matrix (Allison 2005; Bastian et al. 2009). Nonetheless, as
litter decomposition progresses N immobilisation and community
CUE generally increase, and TER declines until the critical value
20–25 is reached. What remains unclear is whether microbial community CUE continues to increase (and TERC : N decrease) as the
C : N ratio of soil organic matter moves below the N immobilisation-mineralisation threshold. If TERC : N does not decrease, CUE
is expected to decline as C increasingly becomes the limiting
resource. Complicating this issue is the increasing recalcitrance of
the residual organic matter to decomposition, which is associated
with slower microbial growth, and presumably lower CUE.
The Doi et al. (2010) formulation, which defines TER as the element ratio corresponding to maximum growth efficiencies (eqn 3),
circumvents the problem of defining assimilation efficiency for osmotrophs. The ratio GEmaxN/GEmaxC appears to have a narrow
range of variation with a mean value of approximately 1.4 (Herron
et al. 2009; Jones et al. 2009; Doi et al. 2010; Zeglin et al. 2012). If
so, eqn 3 predicts that TERC : N is directly proportional to BC : N,
rather than a function of CUE as represented in eqn 2. As a result,
the TERC : N values predicted by eqn 3 vary more narrowly than
those predicted by eqn 2. The Doi et al. (2010) formulation more
closely approximates traditional ecological conceptions of the role
of nutrient availability in regulating microbial community metabo© 2013 John Wiley & Sons Ltd/CNRS
4 R. L. Sinsabaugh et al.
Review and Synthesis
substrate stoichiometry to CUE. This declining pattern also leads to
increasing TER values as the C : N and C : P of the litter widens.
For litter with an initial C : N ratio of 50–70 (global averages for
initial litter C : N and C : P ratios are 57 and 1217 on a mass basis,
McGroddy et al. 2004), the CUE for decomposition is predicted to
be about half the maximum CUE achieved with high-nutrient substrates, approximately 0.3 (Fig. 1a).
Sinsabaugh & Follstad Shah (2012) presented a stoichiometric
model that relates ecoenzymatic activities (EEA), biomass composition and environmental nutrient concentrations to the CUE of heterotrophic microbial communities.
CUE
(a) 100
10–1
Wood residues
Conifer leaves
Angiosperm leaves
Soil organic matter
Equation (4)
Equation (5)
10–2
100
10
1
2
10
10
3
10
4
C:N
1:1
TERC:N(C:N) − closed symbols
TERC:P(C:P) − open symbols
TERC:E
10
TERC:E = BC:E/CUE = C:E
103
102
101
101
102
103
104
105
C:E
Figure 1 Relationships between CUE, threshold element ratios (TER) and
organic matter C : N ratio for terrestrial decomposers. (a) CUE as a function of
soil organic matter or initial litter C : N ratio; CUE for litter decomposers is
estimated using a mass-balance approach (Manzoni et al. 2010); soil microbial
CUE values are obtained from published sources (Manzoni et al. 2012). The
solid line is the least square regression for eqn 4, assuming only N is limiting
(CUE/CUEmax = 1/(1 + 0.015 C : N); the dashed line is based on eqn 5
(CUE/CUEmax = min[1, BC : N/(LC : NCUEmax)], where BC:N = 10 and
CUEmax = 0.6). (b) TER as a function of soil or litter C : E ratio, where E
indicates either nitrogen (closed symbols and solid line) or phosphorus (open
symbols and dashed line). TER is estimated as the ratio of microbial C : E ratio
(BC : E) and CUE [from panel (a)]. Lines are nonlinear least square regressions
[TERC:N = 2.33(C : N)0.78 and TERC : P = 2.91(C : P)0.83].
lism. From this perspective, maximal growth efficiency is predicted
when the environmental availabilities of all essential elements are at
their TER, or when nutrient concentrations are great enough to saturate available uptake capacity, conditions that are rare in natural
environments.
While TER values are useful indicators of relative nutrient
limitation, CUE can be more directly modelled as a function of
nutrient and substrate availabilities. To evaluate the relationship
between CUE and substrate C : E, Manzoni et al. (2010) developed
a model describing remaining C and N during litter decomposition.
The model explicitly accounts for CUE (e in their notation) and can
be used to estimate the value of CUE through nonlinear fitting of
the C and N data. The estimated CUE, averaged over the course of
decomposition, declined with increasing initial litter C : N and
C : P ratios (Fig. 1a), providing a scaling relationship linking
© 2013 John Wiley & Sons Ltd/CNRS
¼ f½AN BC=N =TERC=N ½AP BC=P =TERC=P g0:5
ð4Þ
(b) 105
4
CUE ¼ CUEmax fðSC=N SC=P Þ=½ðKC=N þ SC=N Þ ðKC=P þ SC=P Þg0:5
where CUE max is set at 0.60; SC : N = BC : N/LC :N 1/EEAC : N
and SC : P = BC : P/LC : P1/EEAC : P; LC : N and LC : P are the
elemental C : N and C : P ratios of labile organic matter; KC : N
and KC : P are half-saturation constants; EEAC : N = BG/
(LAP + NAG); EEAC : P = BG/AP where BG, AP, NAG and
LAP are the potential activities of b-1,4-glucosidase, acid (alkaline)
phosphatase, b-1,4-N-acetylglucosaminidase and leucine aminopeptidase respectively. These indicator enzymes generate assimilable
nutrients from the principal organic sources of C, N and P (blinked glucans, protein and aminopolysaccharides, and phosphoesters respectively). AP and AN are assimilation efficiencies for P and
N. BC : P and BC : N are the elemental C : P and C : N ratios of
microbial biomass. The parameters SC : N and SC : P are scalar measures of resource availability for microbial growth based on the
composition of available organic matter and the relative distribution
of EEA. The model is a saturating function that predicts community CUE as a geometric mean of N and P supply relative to C.
Using global EEA data sets, the model yields similar estimates of
mean CUE for terrestrial soils, freshwater sediments and plankton
(0.29, 0.27, 0.28, respectively, approximately CUEmax/2) even
though relative nutrient availabilities, EEA and biomass composition vary across these systems (Sinsabaugh & Follstad Shah 2012).
Assuming for simplicity that only N limits decomposition, eqn 4
can be reduced to CUE/CUEmax = (1 + KC : NEEAC : NLC : N/
BC : N)1. Fitting this simplified expression to the CUE measured
in soils and estimated from the stoichiometric model by Manzoni
et al. (2010) yields a numerical value for the term
KC : NEEAC : N/BC : N = 0.0155 (solid line in Fig. 1), which corresponds to a CUE/CUEmax value of 0.82 for soils and litter. Using
the mean soil values for these parameters given by Sinsabaugh &
EEAC : N = 1.434,
Follstad
Shah
(2012),
KC : N = 0.5,
BC : N = 8.6, their predicted value for CUE/CUEmax is 0.46. The
latter estimate implies that the mean value of AN for soils (eqn 2) is
approximately 0.5, rather than the generally assumed value of 1.
This lower estimate may be reasonable for soils considering the net
mineralisation of N and the competition for mineral nitrogen by
plants and dissimilatory microbial processes. If AN decreases as the
C : N ratio of organic matter narrows, then TERC : N is less variable than eqn 2 otherwise predicts, bringing the formulations of
TERC : N in eqns 2 and 3 into congruity. Indeed, the equations
yield similar values for the mean TERC : N of soils (14.3 for eqn 2,
12.1 for eqn 3), assuming mean BC : N = 8.6, AN = 0.5,
CUE = 0.3, and GEmaxE/GEmaxC = 1.4. These TERC : N estimates
Review and Synthesis
approximate the mean C : N ratio of soil organic matter
(14.3 0.5 SE, Cleveland & Liptzin 2007).
A simpler model of CUE can be constructed by assuming that all
the carbon taken up by microbes that cannot be used for growth at
a given BC : N due to limited N availability is mineralised through
overflow respiration (Moorhead et al. 2012; Manzoni & Porporato
2009; Schimel & Weintraub 2003). Assuming AN = 1 and neglecting
maintenance respiration, CUE is equal to CUEmax when
(~ 14.3
for
BC : N = 8.6,
LC : N < ANBC : N/CUEmax
CUEmax = 0.6, AN = 1) and equal to ANBC : N/LC : N when
LC : N > ANBC : N/CUEmax:
CUE
AN BC:N
;
ð5Þ
¼ min 1;
CUEmax
CUEmax LC:N
where LC : N is the substrate C : N ratio as in eqn 4. If, for example, AN is 0.5 as eqn 4 implies, eqn 5 predicts that CUE at
LC : N = 14.3 (the mean C : N ratio of SOM) equals CUEmax/2
with CUE = CUEmax at LC : N < 7.1. If AN remains constant at
1.0, CUEmax/2 occurs at LC : N ~ 28, which approximately corresponds to the ecological transition from net N immobilisation to
net N mineralisation. This minimal model does not consider net immobilisation of mineral N as a mechanism to compensate for large
substrate C : N ratios, which increase the sensitivity of CUE to
changes in organic matter C : N. Because it neglects N immobilisation, this minimal model underestimates CUE when BC : N is
assumed equal to a reasonable value of 10 for litter (note the bias
in the dashed line in Fig. 1a). Nevertheless, it predicts that CUE is
inversely related to LC : N when the C : N ratio is wide, consistent
with empirical data. Fitting eqn 5 to the estimated CUE (dashed
line Fig. 1) yields a value for the term ANBC : N (the only fitting
parameter) near 15. If TERC : N is also approximately 15, then
CUE by soil microbial communities should approach CUEmax,
which is consistent with empirical estimates. If TERC : N is 28, then
the CUE of soil microbial communities approximates CUEmax/2.
All three stoichiometric models (eqns 2, 4 and 5) highlight the
key problem of resolving the relationship between AN, TERC : N
and CUE. If AN for soil microbial communities averages 0.5, as
eqn 4 predicts based on EEA, then the mean CUE for soils is
approximately CUEmax/2, consistent with predictions and measurements for aquatic ecosystems. If AN = 1, then the mean CUE of
soil microbial communities approaches CUEmax, consistent with
measurements for soil ecosystems. In the next section, we argue
that the apparent discrepancy between model predictions and experimental measurements is likely the result of differences in the methodologies used to estimate CUE in aquatic and terrestrial
ecosystems, rather than a fundamental difference in microbial community metabolism.
MEASURING THE CUE OF MICROBIAL COMMUNITIES
For aquatic ecosystems, the CUE predictions of stoichiometric
models are consistent with empirical estimates. del Giorgio & Cole
(1998) compiled data on bacterial metabolism in aquatic environments. For rivers, oceans, lakes and estuaries, mean CUE ranged
from 0.22 to 0.32. Bacterial respiration (R) increased sublinearly
with production (standardised major axis regression: R = 3.42 l0.61,
units = lgC l1 h1). A more recent analysis by Robinson (2008)
yielded R = 3.69 l0.58 (lgC l1 h1, ordinary least squares regression). Consequently, CUE generally increases with production,
Microbial carbon use efficiency 5
approaching an asymptote of approximately 0.50, with a global average of 0.26 (~ CUEmax/2) (del Giorgio & Cole 1998).
A more recent compilation by Manzoni et al. (2012) reported
mean CUE values of 0.20–0.30 for lacustrine, estuarine, coastal and
riverine systems, as well as microbial cultures. The mean CUE for
mid-ocean bacterioplankton was 0.12; similar values were calculated
by del Giorgio & Cole (1998) and Robinson (2008). Presumably,
the low CUE of marine bacterioplankton reflects the low availability
of essential nutrients and labile carbon.
For terrestrial ecosystems, the average microbial CUE for litter
decomposition is approximately 0.3 (CUEmax/2, Fig. 1). The mean
measured CUE for soil microbial communities is 0.55 (Fig. 1, see
also Manzoni et al. 2012), a value that approaches the thermodynamic limits of metabolic efficiency. This value is at odds with
the lower CUE prediction of 0.29 from eqn 4, but consistent with
the prediction of eqn 5, assuming AN = 1. This twofold discrepancy in CUE estimates impedes development of simulation models
for soil processes that incorporate microbial community growth.
We argue that the resolution of this issue lies in considering the
methodological limitations inherent to measurements of microbial
community growth.
There are several methods for estimating CUE. For cultures,
direct measurements of mass balance can be made. In natural systems, measurements typically rely on quantifying the transformation
of labelled compounds. In aquatic ecosystems, bacterial growth rates
are most commonly estimated by measuring rates of biosynthesis
using 3H-thymidine (TdR) incorporation into DNA or 3H-leucine
(Leu) incorporation into protein (Bell 1993; Findlay 1993; Kirchman
1993). Following a short incubation (~ 1 h), unincorporated label is
rinsed away, samples are digested in trichloroacetic acid, and the
nucleic acid or protein fractions are isolated for radioisotopic analysis. Production rates expressed in units of carbon are calculated
using conversion factors determined from mass-balance calibrations;
estimated values for these conversion factors vary about threefold
(Kirchman & Ducklow 1993). Concurrent measurements of 3HTdR and 3H-Leu incorporation show that rates of DNA and protein biosynthesis may differ by up to tenfold. Fungal growth rates
can be measured similarly by quantifying 14C-acetate incorporation
into ergosterol (Newell & Fallon 1991). To calculate CUE as l/
(l + R), these production assays must be accompanied by respiration measurements as either oxygen consumption or carbon dioxide
production. Because respiration measurements are generally not as
sensitive as the production assays, measurements are often made
over longer time intervals, which may introduce additional variation
into CUE estimates.
Estimating microbial growth rates in soils is more difficult. The
medium is a heterogeneous mix of organic and mineral particles of
diverse composition, size and aggregation. Pore volume is generally
not water saturated and the kinetics of nutrient consumption are
conflated with the kinetics of sorption and diffusion (Resat et al.
2012). The 3H-TdR, 3H-Leu and 14C-acetate production assays are
not widely used for soils for methodological reasons, but there are
protocols available (Rousk & B
a
ath 2011). Applying these assays,
the turnover rates (l/B) for bacterial and fungal biomass in soils
are similar to those for aquatic ecosystems (Su et al. 2007; Rousk &
B
a
ath 2011). More commonly, growth rates in soils are estimated
from the rate of incorporation of labelled labile carbon substrates
into biomass, rather than as rates of community biosynthesis. However, short-term incorporation of labelled substrates into biomass is
© 2013 John Wiley & Sons Ltd/CNRS
6 R. L. Sinsabaugh et al.
not necessarily a measure for growth, but rather an estimate of
community uptake rate. In addition, most approaches to measure
soil CUE quantify respiration in terms of the labelled substrate and
therefore do not capture maintenance and growth respiration, leading to an overestimate of CUE. Consequently, this approach may
be better described as measure of instantaneous substrate use efficiency than a community CUE. Conceptually, these values may be
more closely related to the A terms in eqns 4 and 5, than to CUE.
If considered as such, eqns 4 and 5 predict that CUE for soil
microbial communities is similar to that of aquatic ecosystems.
Other limitations of this approach that tend to inflate CUE have
been extensively discussed (e.g. del Giorgio & Cole 1998; Frey et al.
2001; Herron et al. 2009). To summarise, most CUE estimates for
soil are based on short-term incubations during which microbial
uptake rates (and eventually biomass growth) are stimulated by relatively large substrate additions, yielding high apparent CUE. Over
time, the initial CUE associated with a labile substrate pulse declines
as energy generation and maintenance processes progress (Ladd
et al. 1992; Hart et al. 1994; Ziegler et al. 2005). Such a decline is
illustrated in Fig. 2 using the soil biogeochemical model developed
by Schimel & Weintraub (2003).
The root of the pulse problem is the difficulty of determining the
size of bioavailable substrate pools. Microbial communities exhibit
multiphasic kinetics. Empirical estimates of the kinetic parameters
Km, Ks, Vmax, l increase as substrate concentrations increase. When
‘tracers’ are added in quantities that exceed ambient bioavailable substrate concentration, the rates are not representative of in situ metabolism. In a review of amino acid cycling in planktonic and soil
systems, Hobbie & Hobbie (2012) conclude that uptake of labelled
amino acids in soils does not follow Michaelis–Menten kinetics.
They attribute this finding in part to the use of substrate concentrations well in excess of ambient bioavailable concentrations, which
may be well below extractable concentrations. They also note that
nutrient delivery in soils tends to follow a pulse pattern tied to environmental fluctuations such as wet-dry and freeze-thaw cycles. These
dynamics generate large stocks of microbial biomass with a high
Review and Synthesis
latent capacity to rapidly consume and store nutrient pulses (e.g.
Boot et al. 2013). These difficulties are compounded if CUE is calculated using the rate of respiration of the labelled substrate because
the respiratory rate of individual compounds is typically a function
of the length of their catabolic pathway (Hobbie & Hobbie 2012).
For example, Herron et al. (2009) used vapour phase additions of
13
C-acetic acid and 15N-ammonia to measure CUE in relation to soil
water content. The uptake and respiration of label were relatively
insensitive to soil water content, except for very dry soils, but total
soil respiration rate increased steadily with soil water content.
This observation highlights another issue: the consumption and
fate of a single labile substrate may not reflect microbial community
metabolism in toto, because (1) simple substrates can be consumed
without preliminary solubilisation or depolymerisation steps (Shen
& Bartha 1996); (2) consumption of a particular substrate may be
restricted to a subset of the microbial community; and (3) fastgrowing opportunist organisms may have higher CUE than slowgrowing decomposers adapted to low substrate concentration (Shen
& Bartha 1996). For example, Stursova et al. (2012) studied the
incorporation of 13C-cellulose into the microbial biomass of litter
and soil from a Picea abies forest over a 20 day period. Cellulose
decomposition in the fungal-dominated litter was ten times faster
than rates in soil but 13C accumulated largely in bacteria. In bacterial-dominated mineral soil, 13C accumulated largely in fungi. Stable
isotope probing of community DNA showed that only 10–20% of
the taxa present accumulated carbon from cellulolysis. Fungi tend
to have wider C : N : P ratios than bacteria and larger C demands
(Keiblinger et al. 2010). As a consequence, increasing substrate
C : nutrient ratios might result in increased CUE in fungal communities, but decreased CUE in bacterial-dominated ones (Keiblinger
et al. 2010).
Collectively, these problems suggest that CUE measurements in
soil are inflated relative to those for aquatic ecosystems because the
most widely used methodologies do not adequately represent microbial community growth and its associated maintenance costs, given
the difficulties imposed by discontinuous water availability. Given
the general correspondence between biosynthesis measurements and
the predictions of metabolic and stoichiometric theories in other
systems, we propose that these models are applicable to microbial
community metabolism in soils as well and should be the basis for
calibrating simulation models.
CUE REPRESENTATION IN MICROBIAL PROCESS MODELS
Figure 2 Temporal changes in CUE after a pulsed addition (a) of different levels
of labile substrate (indicated by increasing line thickness). Simulations are
performed using the model and parameters by Schimel & Weintraub (2003),
except for a growth efficiency set to 0.6, and a continuous input of organic
matter of 0.5 mgC gC1 d1 to drive the soil to equilibrium before the C
amendment at day 5. C taken up by microbes is preferentially used for enzyme
production and maintenance; residual carbon is used for biomass growth.
© 2013 John Wiley & Sons Ltd/CNRS
The CUE of microbial communities is a major driver of C dynamics and C sequestration potential in simulation models. One example is the widely used CENTURY model (Parton et al. 1988), which
describes the dynamics of organic matter decomposition and stabilisation into soil organic matter pools in relation to climatic variables
and soil characteristics. Figure 3 illustrates the sensitivity of the
mean residence time of soil C from the CENTURY model as the
efficiency of C transfer among pools (conceptually similar to CUE)
is altered from its baseline value. Decreasing CUE from the default
value of 0.55 decreases the mean residence time of soil C, implying
less C sequestration. Clearly, not all soil models are structured like
CENTURY, and changing mass loss rates together with CUE may
buffer the sensitivity illustrated in Fig. 3. For example, Allison et al.
(2010) showed that the interplay between increased decay rates and
decreasing CUE as temperature increased may favour C storage.
Review and Synthesis
Figure 3 Sensitivity of mean soil carbon residence time (T) to changes in CUE
and decay rates in the CENTURY model (Parton et al. 1987). Sensitivities are
computed with respect to baseline parameter values (subscript ‘0’). CUE0 is set
at 0.55, corresponding to the mean value calculated for soils by Manzoni et al.
(2012). Residence times for CENTURY (T0) are calculated for the C pools only,
using the approach described by Manzoni et al. (2009). T/T0 are computed using
default (black solid line) and altered decay rates (grey lines): in the ‘higher k’
scenario default decay rates are multiplied by 1.5 (grey dashed line), in the ‘lower
k’ scenario by 0.6 (dotted grey line). At CUE = 0.275, which approximates the
mean value for predicted and measured CUE in most ecosystems, the predicted
C residence times are much lower than using the default CUE values.
Nevertheless, these modelling exercises show that accurate estimates
of CUE in soils, and elsewhere, are critical for grounding simulation
models that use microbial metabolism to drive carbon dynamics.
Most models that include an explicit microbial pool usually
include only the C costs associated with the production of biomass
(Manzoni et al. 2012). Fewer also include a basic maintenance cost
(e.g. Schimel & Weintraub 2003; Parnas 1975). These growth and
maintenance costs are not easy to separate experimentally, but are
convenient to model separately. Typically, a fixed fraction of standing biomass defines maintenance respiration (Rm) (e.g. Manzoni &
Porporato 2009) and fractions of the leftover C-uptake in excess of
this maintenance cost are allocated to a respiration component
accompanying biosynthesis (growth-respiration, Rg) and biomass
production (l). Thus most models effectively define CUE as the
ratio of l/(l + Rg) whereas in the broader literature, CUE is often
(implicitly) considered the ratio of l/(l + Rg + Rm). These differing conceptions add confusion about CUE values generated by
models. For comparison purposes, values of Rm from simulations
should be included in estimates of CUE, which can add considerable variation to these estimates.
Variations in model estimates of CUE directly result from separating respiration into the two components, Rg and Rm. First, consistent with empirical data, CUE increases with growth rate (l) if
Rm remains a fixed fraction of biomass while l becomes a larger
component of the ratio l/(l + Rg + Rm). Second, CUE will vary
with the composition of the substrate consumed because the ratio
of l/(l + Rg) varies among substrates. Finally, CUE may also vary
with temperature if Rg and Rm differ in their temperature sensitivities (Allison et al. 2010; Dijkstra et al. 2011; Wetterstedt & Agren
2011). Consequently, many variations in CUE can emerge from simple model formulations.
Microbial carbon use efficiency 7
Wang et al. (2012a,b) recently linked maintenance respiration (Rm)
to C-uptake rate, rather than holding it at a constant fraction of
standing biomass. Although this alternative approach did not have a
large impact on the overall model behaviour unless the microbial
community was C-limited, it raises questions about other factors
affecting Rm. For example, if enzyme production is constitutive and
has priority over Rm as claimed by Schimel & Weintraub (2003),
then the relationship between Rm and Rg is not as simple as most
models assume. In fact, Van Bodegom (2007) summarised eight
non-growth components for microbial maintenance. Regardless of
how many respiratory terms are included in decomposition models,
any model that has at least one that is independent of carbon
uptake has the potential to generate a negative CUE when carbon
uptake is insufficient to meet maintenance demand.
More complex decomposition models separate respiration into as
many as four components (Manzoni et al. 2012). Schimel &
Weintraub (2003) and Moorhead et al. (2012) explicitly allocate (1)
Rm, (2) Rg for both biomass and enzymes, separately, and (3) ‘overflow metabolism’ (Ro), which represents excess carbon released
from litter under N-limitation. They define Rm and Rg, as described
above, but calculate Ro by comparing the C : N ratios of substrates
consumed with the C : N ratio of biomass and assuming that the
excess carbon is respired. Equation 5 uses this approach. Changes
in maintenance respiration or enzyme investment may thus alter
CUE, as illustrated in Fig. 4, where steady-state CUE is shown as a
function of both substrate C : N ratio and the maintenance respiration coefficient. Increasing maintenance respiration decreases CUE
for any substrate C : N, but at high C : N ratios overflow respiration also contributes to the decline.
In most biogeochemical models, respiration is linked to microbial
C balance, but not to the N balance, thus missing stoichiometric
feedbacks on CUE, which are particularly important in the case of
litter decomposition. Overflow respiration, represented as a consequence of C : N stoichiometry (e.g. Russell & Cook 1995), is seldom
included in decomposition models (Manzoni & Porporato 2009).
Alternatively, when C-uptake is in excess of the stoichiometric
requirements, some models include an excretion mechanism that
transfers these products into a soluble pool that may not be imme-
Figure 4 Effect of substrate C : N ratio and maintenance respiration coefficient
(kM) on steady-state CUE. Maintenance respiration is described as RM = kMBC,
where BC is microbial carbon (Schimel & Weintraub 2003); same model and
parameterisation as in Fig. 2. The dashed line indicates the divide between
C-limited and N-limited growth.
© 2013 John Wiley & Sons Ltd/CNRS
8 R. L. Sinsabaugh et al.
diately used by microorganisms (Hunt et al. 1983; Hadas et al.
1998). Similarly, if the uptake kinetics of the soluble pool are modelled separately from the hydrolysis of insoluble substrates then the
two processes are partly decoupled. Spatially explicit models that
include diffusion of enzyme, substrate and/or degradation products
are a good example (Vetter et al. 1998; Allison 2005; Allison et al.
2010; Resat et al. 2012). These modelling approaches could affect
estimates of CUE based on the mass balance of substrate, microbial
and respiratory carbon flows. C and N investment in enzymes is
also rarely considered (e.g. Runyan & D’Odorico 2012; Franklin
et al. 2011; Schimel & Weintraub 2003), despite its potential role in
the overall microbial C balance and stoichiometry.
Phosphorus dynamics are largely neglected in soil biogeochemical
models (Manzoni & Porporato 2009), with a few exceptions (e.g.
Hunt et al. 1983; Parton et al. 1988; Runyan & D’Odorico 2012).
Resource allocation to P acquisition leads to reductions in CUE just
as N-limitation. Because the C : N and C : P ratios of plant litter
and soil organic matter are generally correlated (Cleveland & Liptzin
2007; Manzoni et al. 2010), decreasing CUE as nutrient availability
decreases might be the result of co-limitation by both elements. In
highly weathered low latitude soils, P availability is considered the
primary limitation on microbial growth (Waring et al. 2013). The
stoichiometric model proposed by Sinsabaugh & Follstad Shah
(2012) (eqn 4) represents N and P interaction by calculating CUE
as a geometric mean of relative N and P availabilities. In this model,
the need to allocate resources to acquisition of multiple resources
both reduces and buffers CUE.
Another source of variation in model estimates of CUE is biomass stoichiometry, which may vary as a result of changing community composition, for example, the ratio of fungi: bacteria, or
through changes in cellular metabolism, for example, production of
storage compounds. In addition, the growth rate hypothesis states
that growth rates are correlated with the density of P-rich ribosomes (Sterner & Elser 2002), suggesting that biomass P : C ratio
may vary with growth rate. As biomass carbon:element ratios
increase, so would the apparent CUE, thereby buffering the relationships between litter and microbial stoichiometry and CUE. Such
relationships are intrinsic to stoichiometric models (eqns 2, 3 and 4)
and empirical studies show that biomass C : P ratio varies much
more than biomass C : N ratio (Cleveland & Liptzin 2007; Doi
et al. 2010; Franklin et al. 2011; Sardens et al. 2012). A similar effect
may result from transient accumulation of osmolytes during
drought, which increase microbial C : N ratio and hence the apparent CUE (Uhlırova et al. 2005; Herron et al. 2009). Incorporating
greater stoichiometric flexibility into simulation models should lead
to more realistic, and more buffered, CUE values.
In conclusion, most modelling assumptions are at least partly constrained by the stoichiometric relationships between biomass composition and detrital chemistry, thereby allowing CUE to vary.
However, few empirical studies have sufficient resolution to provide
a clear set of model assumptions needed to generate predictions of
CUE with a high level of confidence, especially in terrestrial systems.
CONCLUSIONS AND RECOMMENDATIONS
CUE is an ambiguous term. Even its simplest definition, as the
ratio of microbial biomass production to material uptake from available substrates, entrains much uncertainty as a result of differences
in the temporal and spatial resolution of the estimates and the met© 2013 John Wiley & Sons Ltd/CNRS
Review and Synthesis
abolic and taxonomic characteristics of the microbial community.
As an emergent property of the system, CUE is unlikely to be constant either within or between systems. It is responsive to differences in state and driving variables, yet constrained by multiple
biochemical and biophysical limits on metabolism. We argue based
on theoretical considerations and the preponderance of empirical
observations that CUE over a wide range of field conditions converges on ~ 0.30 or about half the thermodynamic maximum of
~ 0.60 for microbial growth. For the reasons explained herein, we
propose that many of the reported values of apparent CUE for terrestrial ecosystems are inflated and should not be used as a fixed
parameter or target for simulation models.
We recommend that estimates of CUE within modelling frameworks reflect the relevant characteristics of the study system and
goals. For example, broad spatial scale models operating at yearly
or longer time steps could use a constant value for CUE of 0.30
unless there is evidence for lower values as a result of pervasive
nutrient limitations. Models operating on finer time scales (days to
seasons) should consider the effects of changing resource composition, multi-resource stoichiometric constraints, and microbial community physiology, as well as environmental drivers. Operationally,
such models could either calculate CUE as a function of nutrient
availability (e.g. Eliasson & Agren 2011), or incorporate stoichiometric models (e.g. eqns 4 and 5, as in Moorhead et al. 2012; Schimel & Weintraub 2003 and Touratier et al. 1999) to predict CUE
responses as a function of other modelled quantities and
processes.
ACKNOWLEDGEMENTS
S.M. acknowledges the support of the Agriculture and Food
Research Initiative from the USDA National Institute of Food and
Agriculture (2011-67003-30222), and the National Science Foundation (DEB-1145875 ⁄ 1145649). D.L.M. and R.L.S. acknowledge
support from the NSF Ecosystem Sciences programme (DEB0918718). A.R. acknowledges the support of the Austrian Science
Fund (FWF- I 370-B17).
AUTHORSHIP
This study was a collaborative effort; all authors made substantial
contributions to the review of literature, synthesis of ideas and
development of the article.
REFERENCES
Agren, G.I., Hyv€
onen, R., Berglund, S.L., & Hobbie, S.E. (2013). The critical N:
C ratio as a predictor of litter decomposition and soil organic matter
stoichiometry, Soil Biol. Biochem., Submitted.
Agren, G.I. & Wetterstedt, J.A.M. (2007). What determines the temperature
response of soil organic matter decomposition? Soil Biol. Biochem., 39(7), 1794–
1798.
Allison, S.D. (2005). Cheaters, diffusion and nutrients constrain decomposition
by microbial enzymes in spatially structured environments. Ecol. Lett., 8, 626–
635.
Allison, S.D., Wallenstein, M.D. & Bradford, M.A. (2010). Soil-carbon response
to warming dependent on microbial physiology. Nat. Geosci., 3, 336–340.
Bastian, F., Bouziri, L., Nicolardot, B. & Ranjard, L. (2009). Impact of wheat
straw decomposition on successional patterns of soil microbial community
structure. Soil Biol. Biochem., 41, 363–275.
Review and Synthesis
Bell, R.T. (1993). Estimating production of heterotrophic bacterioplankton via
incorporation of tritiated thymidine. In: Handbook of Methods in Aquatic
Microbial Ecology (eds Kemp, P.F., Sherr, B.F., Sherr, E.B. & Cole, J.J.). Lewis
Publishers, Ann Arbor, pp. 495–504.
Berg, B. & McClaugherty, C. (2003). Plant Litter: Decomposition, Humus Formation,
Carbon Sequestration. Springer, London.
Blagodatskaya, E. & Kuzyakov, Y. (2008). Mechanisms of real and apparent
priming effects and their dependence on soil microbial biomass and
community structure: critical review. Biol. Fertil. Soils, 45, 115–131.
Boot, C.M., Schaeffer, S.M. & Schimel, J.P. (2013). Static osmolyte
concentrations in microbial biomass during seasonal drought in a California
grassland. Soil Biol. Biochem., 57, 356–361.
Bosatta, E. & Staaf, H. (1982). The control of nitrogen turn-over in forest litter.
Oikos, 39, 143–151.
Brown, J.H., Gillooly, J.F., Allen, A.P., Savage, V.M. & West, G.B. (2004).
Toward a metabolic theory of ecology. Ecology, 85, 1771–1789.
Button, D.K. (1993). Nutrient-limited microbial-growth kinetics - Overview and
recent advances. Antonie Van Leeuwenhoek, 63, 225–235.
Cebrian, J. & Lartigue, J. (2004). Patterns of herbivory and decomposition in
aquatic and terrestrial ecosystems. Ecol. Monogr., 74, 237–259.
Chen, C.Y. & Christensen, E.R. (1985). A unified theory for microbial growth
under multiple nutrient limitation. Water Res., 19, 791–798.
Cherif, M. & Loreau, M. (2007). Stoichiometric constraints on resource use,
competitive interactions, and elemental cycling in microbial decomposers. Am.
Nat., 169, 709–724.
Cleveland, C.C. & Liptzin, D. (2007). C : N : P stoichiometry in soil: is there a
“Redfield ratio” for the microbial biomass? Biogeochemistry, 85, 235–252.
Danger, M., Daufresne, T., Lucas, F., Pissard, S. & Lacroix, G. (2008). Does
Liebig’s law of the minimum scale up from species to communities? Oikos,
117, 1741–1751.
DeLong, J.P., Okie, J.G., Moses, M.E., Sibly, R.M. & Brown, J.H. (2010). Shifts
in metabolic scaling, production and efficiency across major evolutionary
transitions of life. Proc. Natl Acad. Sci., 107, 12941–12945.
Dijkstra, P., Thomas, S.C., Heinrich, P.L., Koch, G.W., Schwartz, E. & Hungate,
B.A. (2011). Effect of temperature on metabolic activity of intact microbial
communities: evidence for altered metabolic pathway activity but not for
increased maintenance respiration and reduced carbon use efficiency. Soil Biol.
Biochem., 43, 2023–2031.
Doi, H., Cherif, M., Iwabuchi, T., Katano, I., Stegen, J.C. & Striebel, M. (2010).
Integrating elements and energy through the metabolic dependencies of gross
growth efficiency and the threshold elemental ratio. Oikos, 119, 752–765.
Eliasson, P.E. & Agren, G.I. (2011). Feedback from soil inorganic nitrogen on
soil organic matter mineralisation and growth in a boreal forest ecosystem.
Plant Soil, 338, 193–203.
Elser, J.J., Acharya, K., Kyle, M., Cotner, J., Makino, W., Markow, T. et al. (2003).
Growth rate-stoichiometry couplings in diverse biota. Ecol. Lett., 6, 936–943.
Findlay, S. (1993). Thymidine incorporation into DNA as an estimate of
sediment bacterial production. In: Handbook of Methods in Aquatic Microbial
Ecology (eds Kemp, P.F., Sherr, B.F., Sherr, E.B. & Cole, J.J.). Lewis
Publishers, Ann Arbor, pp. 505–508.
Franklin, O., Hall, E.K., Kaiser, C., Battin, T.J. & Richter, A. (2011).
Optimization of biomass composition explains microbial growth-stoichiometry
relationships. Am. Nat., 177, E29–E42.
Frey, S.D., Gupta, V.V.S.R., Elliott, E.T. & Paustian, K. (2001). Protozoan
grazing affects estimates of carbon utilization efficiency of the soil microbial
community. Soil Biol. Biochem., 33, 1759–1768.
Frost, P.C., Benstead, J.P., Cross, W.F., Hillebrand, H., Larson, J.H.,
Xenopoulos, M.A. et al. (2006). Threshold elemental ratios of carbon and
phosphorus in aquatic consumers. Ecol. Lett., 9, 774–779.1148
Gillooly, J.F., Allen, A.P., Brown, J.H., Elser, J.J., del Rio, C.M., Savage, V.M.
et al. (2005). The metabolic basis of whole-organism RNA and phosphorus
content. Proc. Natl Acad. Sci., 102, 11923–11927.
del Giorgio, P.A. & Cole, J.J. (1998). Bacterial growth efficiency in natural
aquatic ecosystems. Annu. Rev. Ecol. Syst., 29, 503–541.
Gommers, P.J.F., van Schie, B.J., van Dijken, J.P. & Kuenen, J.G. (1988).
Biochemical limits to microbial-growth yields: an analysis of mixed substrate
utilization. Biotechnol. Bioeng., 32, 86–94.
Microbial carbon use efficiency 9
Hadas, A., Parkin, T.B. & Stahl, P.D. (1998). Reduced CO2 release from
decomposing wheat straw under N-limiting conditions: simulation of carbon
turnover. Eur. J. Soil Sci., 49, 487–494.
Hart, S.C., Nason, G.E., Myrold, D.D. & Perry, D.A. (1994). Dynamics of gross
nitrogen transformations in an old-growth forest - the carbon connection.
Ecology, 75, 880–891.
Herron, P.M., Stark, J.M., Holt, C., Hooker, T. & Cardon, Z.G. (2009).
Microbial growth efficiencies across a soil moisture gradient assessed using
13
C-acetic acid vapor and 15N-ammonia gas. Soil Biol. Biochem., 41, 1262–1269.
Hobbie, J.E. & Hobbie, E.A. (2012). Amino acid cycling in planktonic and soil
microbes studied with radioisotopes: measured amino acids in soil do not
reflect bioavailability. Biogeochemistry, 107, 339–360.
Hunt, H.W., Stewart, J.W.B. & Cole, C.V. (1983). A conceptual model for
interactions among carbon, nitrogen, sulphur, and phosphorus in grasslands.
In The Major Biogeochemical Cycles and Their Interactions. (eds Bolin, B., Cook,
R.B.). SCOPE, John Wiley & Sons, New York, pp. 303–325.
Jones, D.L., Kielland, K., Sinclair, F.L., Dahlgren, R.A., Newsham, K.K., Farrar,
J.F. et al. (2009). Soil organic nitrogen mineralization across a global latitudinal
gradient. Global Biogeochem. Cycles, 23, GB1016. doi:10.1029/2008GB003250.
Keiblinger, K.M., Hall, E.K., Wanek, W., Szukics, U., Hammerle, I., Ellersdorfer,
G. et al. (2010). The effect of resource quantity and resource stoichiometry on
microbial carbon-use-efficiency. FEMS Microbiol. Ecol., 73, 430–440.
Kirchman, D.L. (1993). Leucine incorporation as a measure of biomass
production by heterotrophic bacteria. In: Handbook of Methods in Aquatic
Microbial Ecology (eds Kemp, PF, Sherr, B.F., Sherr, E.B. & Cole, J.J.). Lewis
Publishers, Ann Arbor, pp. 509–512.
Kirchman, D.L. & Ducklow, H.W. (1993). Estimating conversion factors for the
thymidine and leucine methods for measuring bacterial production. In:
Handbook of Methods in Aquatic Microbial Ecology (eds Kemp, P.F., Sherr, B.F.,
Sherr, E.B. & Cole, J.J.). Lewis Publishers, Ann Arbor, pp. 513–518.
Ladd, J.N., Jocteurmonrozier, L. & Amato, M. (1992). Carbon turnover and
nitrogen transformations in an alfisol and vertisol amended with 14C[U]
glucose and 15N ammonium sulfate. Soil Biol. Biochem., 24, 359–371.
Larsson, C., Vonstockar, U., Marison, I. & Gustafsson, L. (1995). Metabolic
Uncoupling in Saccharomyces-Cerevisiae. Thermochim. Acta, 251, 99–110.
Lettau, T. & Kuzyakov, Y. (1999). Carbon use efficiency of organic substances
by soil microbial biomass as a function of chemical and thermodynamical
parameters. J. Plant Nutr. Soil Sci., 162, 171–177.
Lobry, J.R., Flandrois, J.P., Carret, G. & Pave, A. (1992). Monod’s microbial
growth model revisited. Bull. Math. Biol., 54, 117–121.
Lopez-Urrutia, A. & Moran, X.A.G. (2007). Resource limitation of bacterial
production distorts the temperature dependence of oceanic carbon cycling.
Ecology, 88, 817–822.
Manzoni, S. & Porporato, A. (2009). Soil carbon and nitrogen mineralization:
theory and models across scales. Soil Biol. Biochem., 41, 1355–1379.
Manzoni, S., Porporato, A. & Schimel, J. P. (2008). Soil heterogeneity in lumped
mineralization-immobilization models. Soil Biol. Biochem., 40, 1137–1148.
Manzoni, S., Katul, G.G. & Porporato, A. (2009). Analysis of soil carbon transit
times and age distributions using network theories. J. Geophys. Res.-Biogeosci.,
114, G04025.
Manzoni, S., Trofymow, J.A., Jackson, R.B. & Porporato, A. (2010).
Stoichiometric controls on carbon, nitrogen, and phosphorus dynamics in
decomposing litter. Ecol. Monogr., 80, 89–106.
Manzoni, S., Taylor, P., Richter, A., Porporato, A. & Agren, G.I. (2012). Soil
carbon and nitrogen mineralization: theory and models across scales. New
Phytol., 196, 79–91.
McGroddy, M.E., Daufresne, T. & Hedin, L.O. (2004). Scaling of C : N : P
stoichiometry in forests worldwide: implications of terrestrial Redfield-type
ratios. Ecology, 85, 2390–240.
Miltner, A., Bombach, P., Schmidt-Br€ucken, B. & K€astner, M. (2012).
SOM genesis: microbial biomass as a significant source. Biogeochemistry, 111,
45–55.
Moorhead, D.L., Lashermes, G. & Sinsabaugh, R.L. (2012). A theoretical model
of C- and N-acquiring exoenzyme activities balancing microbial demands
during decomposition. Soil Biol. Biochem., 53, 131–141.
Narang, A. (1998). The steady states of microbial growth on mixtures of
substitutable substrates in a chemostat. J. Theor. Biol., 190, 241–261.
© 2013 John Wiley & Sons Ltd/CNRS
10 R. L. Sinsabaugh et al.
Newell, S.Y. & Fallon, R.D. (1991). Toward a method for measuring
instantaneous fungal growth rates in field samples. Ecology, 72, 1547–1559.
Parnas, H. (1975). Model for decomposition of organic material by
microorganisms. Soil Biol. Biochem., 7, 161–169.
Parton, W.J., Stewart, J.W.B. & Cole, C.V. (1988). Dynamics of C, N, P and S in
Grassland Soils - a Model. Biogeochemistry, 5, 109–131.
Payne, W.J. (1970). Energy yields and growth of heterotrophs. Annu. Rev.
Microbiol., 24, 17–52.
Payne, W.J. & Wiebe, W.J. (1978). Growth yield and efficiency in
chemosynthetic microorganisms. Annu. Rev. Microbiol., 32, 155–183.
Ramirez, K.S., Craine, J.M. & Fierer, N. (2012). Consistent effects of nitrogen
on soil microbial communities and processes across biomes. Glob. Change Biol.,
18, 1918–1927.
Resat, H., Bailey, V., McCue, L.A. & Konopka, A. (2012). Modeling microbial
dynamics in heterogeneous environments: growth on soil carbon sources.
Microb. Ecol., 63, 883–897.
Rivkin, R.B. & Legendre, L. (2001). Biogenic carbon cycling in the upper ocean:
effects of microbial respiration. Science, 291, 2398–2400.
Robinson, C. (2008). Heterotrophic bacterial respiration. In: Microbial Ecology of
the Oceans. 2nd edn. (ed Kirchman, D.L.). John Wiley & Sons Inc, New York,
pp. 299–334.
Roels, J.A. (1980). Application of Macroscopic Principles to MicrobialMetabolism. Biotechnol. Bioeng., 22, 2457–2514.
Rousk, J. & B
a
ath, E. (2011). Growth of saprotrophic fungi and bacteria in soil.
FEMS Microbiol. Ecol., 78, 17–30.
Runyan, C.W. & D’Odorico, P. (2012). Hydrologic controls on phosphorus
dynamics: a modeling framework. Adv. Water Resour., 35, 94–109.
Russell, J.B. & Cook, G.M. (1995). Energetics of bacterial-growth - Balance of
anabolic and catabolic reactions. Microbiol. Rev., 59, 48–62.
Sardens, J., Rivas-Ubach, A. & Pe~
nuelas, J. (2012). The elemental stoichiometry
of aquatic and terrestrial ecosystems and its relationships with organismic
lifestyle and ecosystem structure and function: a review and perspectives.
Biogeochemistry, 111, 1–39.
Schimel, J.P. & Weintraub, M.N. (2003). The implications of exoenzyme activity
on microbial carbon and nitrogen limitation in soil: a theoretical model. Soil
Biol. Biochem., 35, 549–563.
Shen, J. & Bartha, R. (1996). Metabolic efficiency and turnover of soil microbial
communities in biodegradation tests. Appl. Environ. Microbiol., 62, 2411–2415.
Sierra, C.A. (2012). Temperature sensitivity of organic matter decomposition in the
Arrhenius equation: some theoretical considerations. Biogeochemistry, 108, 1–15.
Sinsabaugh, R.L. & Follstad Shah, J.J. (2010). Integrating resource utilization and
temperature in metabolic scaling of riverine bacterial production. Ecology, 91,
1455–1465.
Sinsabaugh, R.L. & Follstad Shah, J.J. (2012). Ecoenzymatic stoichiometry and
ecological theory. Annu. Rev. Ecol. Evol. Syst., 43, 313–342.
Sistla, S.A. & Schimel, J.P. (2012). Stoichiometric flexibility as a regulator of
carbon and nutrient cycling in terrestrial ecosystems under change. New Phytol.,
196, 68–78.
Six, J., Frey, S.D., Thiet, R.K. & Batten, K.M. (2006). Bacterial and fungal
contribution to carbon sequestration in agroecosystems. Soil Sci. Soc. Am. J., 70,
555–569.
Sterner, R.W. & Elser, J.J. (2002). Ecological Stoichiometry: The Biology of Elements
from Molecules to The Biosphere. Princeton University, Princeton.
Sterner, R.W., Andersen, T., Elser, J.J., Hessen, D.O., Hood, J.M., McCauley, E.
et al. (2008). Scale-dependent carbon:nitrogen:phosphorus seston stoichiometry
in marine and freshwater. Limnol. Oceanogr., 5, 1169–1180.
Stursova, M., Zifcakova, L., Leigh, M.B., Burgess, R. & Baldrian, P. (2012).
Cellulose utilization in forest litters and soils: identification of bacterial and
fungal decomposers. FEMS Microbiol. Ecol., 80, 735–746.
© 2013 John Wiley & Sons Ltd/CNRS
Review and Synthesis
Su, R., Amonette, R., Kuehn, K.A., Sinsabaugh, R.L. & Neely, R.K. (2007).
Microbial dynamics associated with decaying Typha angustifolia litter in two
contrasting Lake Erie coastal wetlands. Aquat. Microb. Ecol., 46, 295–307.
Tiemann, L.K. & Billings, S.A. (2011). Changes in variability of soil moisture
alter microbial community C and N resource use. Soil Biol. Biochem., 43,
1837–1847.
Touratier, F., Legendre, L. & Vezina, A. (1999). Model of bacterial growth
influenced by substrate C : N ratio and concentration. Aquat. Microb. Ecol., 19,
105–118.
Uhlırova, E., Elhottova, D., Trıska, J. & Santr
uckova, H. (2005). Physiology and
microbial community structure in soil at extreme water content. Folia
Microbiol., 50, 161–166.
Van Bodegom, P. (2007). Microbial maintenance: a critical review on its
quantification. Microb. Ecol., 53, 513–523.
Vetter, Y.A., Deming, J.W., Jumars, P.A. & Krieger-Grockett, B.B. (1998). A
predictive model of bacterial foraging by means of freely released extracellular
enzymes. Microb. Ecol., 36, 75–92.
Von Stockar, U. & Marison, I.W. (1993). The definition of energetic growth
efficiencies for aerobic and anerobic microbial growth and their determination
by calorimetry and other means. Thermochim. Acta, 229, 157–172.
Wagai, R., Kishimoto-Mo, A.W., Yonemura, S., Shirato, Y., Hiradate, S. &
Yagasaki, Y. (2013). Linking temperature sensitivity of soil organic matter
decomposition to its molecular structure, accessibility, and microbial
physiology. Glob. Change Biol., 19, 1114–1125.
Wang, G.S. & Post, W.M. (2012). A theoretical reassessment of microbial
maintenance and implications for microbial ecology modeling. FEMS Microbiol.
Ecol., 81, 610–617.
Wang, G., Post, W.M. & Mayes, M.A. (2012a). Development of microbialenzyme-mediated decomposition model parameters through steady-state and
dynamic analyses. Ecol. Appl., 23, 255–272.
Wang, G., Post, W.M., Mayes, M.A., Frerichs, J.T. & Sindhu, J. (2012b).
Parameter estimation for models of ligninolytic and cellulolytic enzyme
kinetics. Soil Biol. Biochem., 48, 28–38.
Waring, B.G., Weintraub, S.R. & Sinsabaugh, R.L. (2013). Ecoenzymatic
stoichiometry of microbial nutrient acquisition in tropical soils. Biogeochemistry,
(in press).
Wetterstedt, J.A.M. & Agren, G.I. (2011). Quality or decomposer efficiency which is most important in the temperature response of litter
decomposition? A modelling study using the GLUE methodology.
Biogeosciences, 8, 477–487.
Williams, P.J.I. (1973). The validity of the application of simple kinetic analysis
to heterogeneous microbial populations. Limnol. Oceanogr., 18, 159–165.
Yvon-Durocher, G., Caffrey, J.M., Cescatti, A., Dossena, M., del Giorgio, P.,
Gasol, J.M. et al. (2012). Reconciling the temperature dependence of
respiration across timescales and ecosystem types. Nature, 487, 472–476.
Zeglin, L.H., Kluber, L.A. & Myrold, D.D. (2012). The importance of amino
sugar turnover to C and N cycling in organic horizons of old growth
Douglas-fir forest soils colonized by ectomycorrhizal mats. Biogeochemistry, 112,
679–693.
Ziegler, S.E., White, P.M., Wolf, D.C. & Thoma, G.J. (2005). Tracking the fate
and recycling of C-13-labeled glucose in soil. Soil Sci., 170, 767–778.
Zinn, M., Witholt, B. & Egli, T. (2004). Dual nutrient limited growth: models,
experimental observations, and applications. J. Biotechnol., 113, 263–279.
Editor, James Elser
Manuscript received 9 January 2013
First decision made 20 February 2013
Manuscript accepted 14 March 2013