SOILSOILSOILSOIL2199-398XCopernicus GmbHGöttingen, Germany10.5194/soil-1-313-2015Investigating microbial transformations of soil organic matter: synthesizing knowledge from disparate fields to guide new
experimentationBillingsS. A.sharon.billings@ku.eduTiemannL. K.Ballantyne IVF.LehmeierC. A.MinK.Department of Ecology and Evolutionary Biology and the Kansas
Biological Survey, University of Kansas, Lawrence, KS, USADepartment of Plant, Soil, and Microbial Sciences, Michigan State
University, East Lansing, MI, USAOdum School of Ecology, University of Georgia, Athens, GA, USAS. A. Billings (sharon.billings@ku.edu)9April20151131333030October20143December2014–15March2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://soil.copernicus.org/articles/1/313/2015/soil-1-313-2015.htmlThe full text article is available as a PDF file from https://soil.copernicus.org/articles/1/313/2015/soil-1-313-2015.pdf
Discerning why some soil organic matter (SOM) leaves soil profiles
relatively quickly while other compounds, especially at depth, can be
retained for decades to millennia is challenging for a multitude of
reasons. Simultaneous with soil-specific advances, multiple other
disciplines have enhanced their knowledge bases in ways potentially useful
for future investigations of SOM decay. In this article, we highlight
observations highly relevant for those investigating SOM decay and retention
but often emanating from disparate fields and residing in literature seldom
cited in SOM research. We focus on recent work in two key areas. First, we
turn to experimental approaches using natural and artificial aquatic
environments to investigate patterns of microbially mediated OM
transformations as environmental conditions change, and highlight how
aquatic microbial responses to environmental change can reveal processes
likely important to OM decay and retention in soils. Second, we emphasize
the importance of establishing intrinsic patterns of decay kinetics for
purified substrates commonly found in soils to develop baseline rates. These
decay kinetics – which represent the upper limit of the reaction rates –
can then be compared to substrate decay kinetics observed in natural
samples, which integrate intrinsic decay reaction rates and edaphic factors
essential to the site under study but absent in purified systems. That
comparison permits the site-specific factors to be parsed from the
fundamental decay kinetics, an important advance in our understanding of SOM
decay (and thus persistence) in natural systems. We then suggest ways in
which empirical observations from aquatic systems and purified
substrate–enzyme reaction kinetics can be used to advance recent theoretical
efforts in SOM-focused research. Finally, we suggest how the observations in
aquatic and purified substrate–enzyme systems could be used to help unravel
the puzzles presented by oft-observed patterns of SOM characteristics with
depth, as one example of the many perplexing SOM-related problems.
Introduction
In spite of a multitude of studies exploring the drivers of soil organic
matter (SOM) decay, investigators still struggle with a deceptively
simple-sounding question: why does some SOM leave the soil profile relatively quickly, while other compounds, especially those at depth, appear to be retained on timescales ranging from the decadal to the millennial?
This question is important on a practical as well as academic level:
understanding SOM retention over long time periods helps us predict soil
fluxes of carbon (C) and thus Earth's atmospheric CO2, as well as
fundamental features of ecosystem metabolism. However, addressing this
question is challenging for a multitude of reasons. Most of the
biogeochemical tools employed by those investigating SOM decay capture data
of a very integrated nature, as they are influenced by many processes. As a
result, such data are difficult to interpret. Respired CO2, activity
levels of exo-enzymes exuded by microbes, and changing availability of
dissolved organic carbon (DOC), for example, integrate fluxes driven by the
metabolically active subset of the whole living microbial community in a soil
sample, but how the active subset fits into the context of the greater
community is not known. Furthermore, the organic substrates that the active subset
transforms into energy, biomass, exo-enzymes, or waste are typically of
unknown identity. Of key interest for many scientists is how these fluxes
(and hence the size of the pools those fluxes drain or augment) are modified
with environmental factors such as temperature or moisture. Such knowledge
remains elusive while we still struggle with attempts to measure and
understand these processes in relatively stable environments. Further
complicating our efforts, soil profiles are heterogeneous environments.
Physical and chemical protection of SOM and microbial community composition
varies across spatial scales ranging from the molecular to the continental
(Schimel and Schaeffer, 2012). Thus, one soil sample's SOM decay response to
an environmental perturbation may not hold true for samples collected in
close proximity, much less for different depths at the same location, or for
soil types in distinct climate regimes.
Concerns about SOM destabilization with climate change have generated
increased urgency within the discipline in recent decades (Kirschbaum, 1995;
Bradford, 2013; Billings and Ballantyne, 2013). Soil-focused literature is
now replete with papers empirically describing temperature, moisture, or
nutrient concentration effects on different SOM decay processes (e.g., Craine
et al., 2010; Wagai et al., 2013; Manzoni et al., 2012b; Tiemann and
Billings, 2011a; Moyano et al., 2013). From these and related efforts, we
have gained an appreciation for the apparent relevance of the carbon (C)
quality hypothesis, which states that slowly decomposing SOM is more
sensitive, in a relative sense, to temperature changes than SOM that decays
more quickly (Bosatta and Ågren, 1999). However, this response is not
evident in some soils (Laganiere et al., 2015). We also have learned that
historic conditions serve as a meaningful driver of contemporary
biogeochemical responses to varying conditions in soils (Evans and
Wallenstein, 2012). We have appreciated the tremendous diversity of soil
microbial communities and their rapidly varying composition as environmental
conditions vary (Howe et al., 2014; Billings and Tiemann, 2014). There is
growing recognition of an apparent lack of inherent recalcitrance of many SOM
pools previously thought to be relatively stable, particularly those at depth
(Fontaine et al., 2007; Schmidt et al., 2011), prompting considerations that
temperature sensitivity may not vary with depth as much as previously
thought. Recent modeling efforts, particularly those focusing on temperature
and nutrient availability as drivers of microbial behavior, also have
enhanced our ability to identify key factors important to SOM fate in a
changing environment (e.g., Manzoni et al., 2012a).
Simultaneous with these soil-specific advances, other disciplines have
enhanced their knowledge bases in ways potentially useful for future
investigations of SOM decay. However, results of these efforts are reported
in a widely dispersed literature often not frequented by the SOM-focused
community of scholars. For example, microbiologists have demonstrated that
gene expression by heterotrophic bacteria in the oceans can exhibit diurnal
fluctuations (Ottesen et al., 2014). Such work highlights linkages between
heterotrophic activity and short-term fluctuations in resource availability,
a topic of central importance to OM decay. Though some of the principles of
OM decay in ocean systems clearly are relevant to soils (Jiao et al., 2010),
studies describing oceanic OM transformations are rarely cited in the soil
literature. Also rarely invoked by soil biogeochemists are laboratory
experiments that study soil-relevant processes using reductionist approaches.
For example, chemostat experiments are ideally suited to study fundamental
physiological functioning of microbes and can provide empirical data relevant
to recent advances in ecological stoichiometric theory (Elser et al., 2000;
Manzoni et al., 2012a). However, the relative paucity of linkages across
disciplines exploring aquatic and terrestrial OM and microbiology makes it
challenging to apply such results in a broader, ecological context.
In this article, we highlight observations highly relevant for those
investigating SOM decay and retention but often emanating from disparate
fields and residing in literature seldom cited in SOM research papers. We
focus on recent work in two key areas. First, we turn to experimental
approaches using natural and artificial aquatic environments to investigate
patterns of microbially mediated OM transformations as environmental
conditions change. In 1997, John Hedges and John Oades made an elegant plea
for investigators of OM decay in soils and aquatic environments to integrate
their approaches and ideas to elucidate patterns and mechanisms common to
both systems (Hedges and Oades, 1997). We echo this call by highlighting how
some of the microbial responses to environmental change in aquatic
environments can reveal processes likely important to OM decay and retention
in soils. Second, we emphasize the importance of establishing intrinsic
patterns of decay kinetics for purified substrates commonly found in soils to
develop baseline rates. These decay kinetics can then be compared to
substrate decay kinetics observed in natural samples, which integrate
intrinsic decay reaction rates and edaphic factors essential to the site
under study but absent in purified systems. That comparison permits the
site-specific factors to be parsed from the fundamental decay kinetics, an
important advance in our understanding of SOM decay (and thus persistence) in
natural systems. We then suggest ways in which empirical observations from
aquatic systems and purified substrate–enzyme reaction kinetics can be used
to advance recent theoretical efforts in SOM-focused research. Finally, we
suggest how the observations in aquatic and purified substrate–enzyme systems
could be used to help unravel the puzzles presented by oft-observed patterns
of SOM characteristics with depth, as one example of the many perplexing
SOM-related problems.
Using well-mixed natural and artificial systems to avoid challenges
present in soils
One potential means of addressing some of the challenges in SOM research
described above is to investigate the decay of organic substrates in the
absence of soils. Much ocean and freshwater OM decay proceeds via the same
fundamental processes present in soil, via microbially produced exo-enzymes,
and can be restricted via some of the same processes as well. For example,
aggregate formation can protect ocean OM from decay (Jiao et al., 2010), much
as it does in soils (Six and Paustian, 2013). As such, invoking knowledge
derived from ocean and freshwater systems about the microbial processes
relevant to aquatic OM decay, where substrate and enzymatic diffusion is far
less limiting than in typical soil profiles, can provide valuable insight into
the microbial processes driving SOM decay or retention.
Artificial aquatic systems in which environmental conditions and resident
microbes can be strictly controlled are also useful for those investigating
SOM decay and retention. Such systems represent conditions far removed from
soil profiles, and at first glance appear foreign to SOM studies. Chemostats
are well suited to support one, isolated microbial population (Monod, 1950),
in sharp contrast with the complex communities found in natural systems.
Chemostats also typically present the microorganisms they support with a
constant substrate supply, and are subjected to manipulation of just one
environmental parameter (Ferenci, 2008). As a result, we probably cannot
consider absolute values of the size or composition of any resource pool or
flux observed during such experiments as immediately comparable to those that
would occur in soils. However, by largely relieving diffusional constraints
on organic substrates, exo-enzymes, mineral nutrients, and the microorganisms
themselves, chemostat environments mitigate at least one concern present in
soil research: that results are relevant only for one particular soil profile
due to heterogeneous conditions. Furthermore, experiments in artificial
aquatic environments can offer proof of concept for physiological responses
of microbes to a varying environment (e.g., changing temperature or nutrient
availability), and as such provide those who venture into natural soil
environments with information about fundamental, baseline responses of
microbes to changing conditions. That information, in turn, can provide a
starting point for formulating predictions about how soil microorganisms may
respond to environmental change.
By turning to natural and artificial aquatic systems for guidance, we do not
mean to imply that diffusional constraints are not important. Indeed, they
may be the prominent feature driving SOM decay in many soils (Dungait et al.,
2012). However, by studying aquatic systems we gain insight into enzymatic and
microbial responses to changing environmental conditions in relative
isolation from such constraints, and that in turn allows us to assess the
relative importance of the very constraints we have eliminated. In the
following sections, we present advances from natural and artificial
environments relevant to research on microbially mediated SOM
transformations, beginning with oceanic and lacustrine systems and then
examining increasingly controlled environments.
Natural aquatic systems as well-mixed environments in which to
explore drivers of C fluxes and microbial elemental composition
Investigations of microbial transformations of OM in the oceans provide
important information for those interested in understanding SOM dynamics. For
example, organic geochemists working in the ocean have appreciated the role
of the “microbial loop” as a governing feature of ocean OM composition and
availability for decades (Pomeroy, 1974; Azam et al., 1983; Pomeroy et al.,
2007). Work in ocean waters has demonstrated the importance of microbial
byproducts as contributors to the ocean's reservoirs of OM (Kawasaki and
Benner, 2006; Kaiser and Benner, 2008) and, more specifically, to the ocean's
slow-turnover OM pools (Jiao et al., 2010). The call made by Hedges and
Oades (1997) to integrate aquatic and terrestrial studies is slowly being
heeded, as reflected in the soil literature acknowledging the important role
microorganisms appear to play as producers, not just consumers, of SOM
(Simpson et al., 2007; Liang et al., 2011;
Hobara et al., 2014), which has been elucidated in the ocean (Kawasaki and
Benner, 2006; Kaiser and Benner, 2008; Jiao et al., 2010). The composition
and transformations of aquatic C are increasingly being used to better
understand the terrestrial systems whence some fraction of aquatic C is
derived. Indeed, the Battin et al. “boundless C cycle” concept emphasizes the
importance of aquatic C flows as essential to quantify if we wish to
understand both terrestrial and aquatic C transformations (Battin et al.,
2009), and yet more recent work highlights how OM composition in aquatic
systems can help us understand both aquatic C fluxes and the terrestrial
systems upstream (Marín-Spiotta et al., 2014).
The stoichiometry of resources and of microbial resource demand are both
relevant to OM decay and retention because microbial stoichiometry governs
the resources that can be used effectively and thus the stocks of OM
(including microbial necromass) that are retained (Elser et al., 2000).
Adding C to lake water, for example, can induce greater bacterial biomass and
greater bacterial mass-specific uptake of phosphorus (P; Stets and Cotner,
2008). However, this effect is attenuated when grazing by organisms in higher
tropic levels limits the pool size of bacterial biomass (Stets and Cotner,
2008). Thus, it seems important to investigate the extent to which soil food
webs can provide a top-down limitation on the turnover of SOM after C
additions. Knowledge of bacterial responses to C additions from the aquatic
literature is also relevant to investigations of the distinctions between
bulk soil SOM transformations and those in the rhizosphere, where C
availability tends to be higher (Cheng et al., 2014), and can help us understand both lateral and
vertical patterns of nutrient demand in soils.
Indeed, experiments in freshwater lakes also reveal that changes in bacterial
stoichiometry with changing resource stoichiometry are dwarfed by the
responses of biomass stoichiometry to changing growth rates (Makino et al.,
2003). Stoichiometric plasticity of microorganisms, though acknowledged as a
potentially important way in which microbes may respond to environmental
change (Billings and Ballantyne, 2013), is rarely incorporated into
conceptual or quantitative models of SOM transformations, in stark contrast
to the aquatic literature (e.g., Klausmeier et al., 2007). The degree to which organisms exhibit
stoichiometric flexibility appears to vary widely (Geider and Laroche, 2002),
but in organisms exhibiting such plasticity, C : P can be many times more
variable than C : N (Hessen et al., 2013). It is unknown how such variation
may influence OM decay, whether in aquatic or soil environments, but because
one or multiple resources ultimately limit growth and rates of decomposition,
understanding the causes and consequences of microbial stoichiometry in soils
is important for modeling SOM degradation and associated respiratory C loss.
Aquatic scientists also have observed that increasing temperatures tend to
result in increasing C : P and N : P of bacterial biomass (Cotner et al.,
2006), and that some of these changes
are driven by changes in community composition (Hall et al., 2008). Bacterial
growth efficiency (production/(production + respiration); delGiorgio
and Cole, 1998) appears to decline with warming in aquatic systems (Hall and
Cotner, 2007) and to be lower in tropical compared to temperate lakes (Amado
et al., 2013), though this warming response is not ubiquitous (delGiorgio and
Cole, 1998). Lower respiratory C losses at a particular temperature from
bacteria sampled from warmer environments compared to those sampled from
colder environments are congruent with microbial acclimation to temperature
regimes (Hall and Cotner, 2007). Currently, the efficiency with which soil
microbes generate biomass relative to CO2 (often referred to as C use
efficiency, or CUE) is a key focus of SOM investigations, but aquatic
literature suggests that variables like biomass pool size (driven by both
bottom-up and top-down pressures, Amado et al., 2013) and biomass
stoichiometry (C : N : P) should be included in soil-focused studies of
microbial CUE.
Parameters frequently of interest for empirical and theoretical
investigations of SOM transformations (left column), typical challenges
encountered when interpreting data derived from soil studies (middle column),
and the benefits of employing chemostats (rows 1 through 3, last column) and purified
substrate–enzyme reactions (row 4, last column). Controlled environments
where microbial populations and environmental conditions can be strictly
monitored provide unique insights that can be used to develop hypotheses for
soil-based studies or parameterize models of SOM transformations. See
Sects. 2 and 3 for detailed explanation of all table cells.
Soil parameterof interestChallenges for soil based studiesBenefits of chemostat-based studies (rows 1–3) Benefits of purified, abiotic studies (row 4)Carbon useefficiency (CUE)– Recycling of isotopic label through microbial biomass is likely across diverse timescales. – Growth rate is unknown.– Growth rate is known. – Growth rate can be manipulated. – Isotopic fractionation can be quantified. – Fraction of dead cells is small.Microbial stoichiometric plasticity– Stoichiometric change may occur in extant populations, or from changing relative abundances of distinct populations. – Stoichiometric analyses of soil microbial biomass typically reflect total biomass, not just active biomass.– The identity, pool size, and growth rates of the active microbes are all known.Environmental controlson gene expression– Metatranscriptomes or functional gene transcription are dependent on growth rates, nutrient availability, and environmental controls on transcription rates that are unknown.– Growth rates are known, nutrient availability is constant, and gene expression can be monitored as individual environmental signals are manipulated.Ea and associatedtemperature sensitivityof SOM decay– Differences among soils in apparent Ea may result from different microbial physiology, microbial community structure, or substrate availability, and not from inherent differences in substrate Ea of decay.– Intrinsic kinetics of decay can be quantified in controlled conditions and under varying environmental parameters such as pH and temperature. – The C : N flow ratio can be computed as environmental conditions change, reflecting how C and N availability can change even in the absence of microbial adaptation.Chemostats as well-mixed, reductionist environments in which to
explore drivers of microbial elemental composition
Chemostat experiments enable almost complete control over microbial growth
dynamics, and thus are useful for exploring fundamental microbial responses
to environmental variation. Scientists have used chemostats for decades to
understand the determinants of microbial growth (Monod, 1950; Droop, 1974;
Rhee and Gotham, 1981) because microbial growth rate can be controlled via
dilution rate (Table 1; Monod, 1950, see Ferenci, 2008, for discussion).
Unfortunately we cannot know microbial growth rates in non-steady state
conditions. However, the benefits of exploring microbial behaviors in
continuous culture mode are great, given how difficult it is to know
microbial growth rates in soils and their importance for understanding
microbial responses to environmental cues.
In recent years, chemostat studies have enjoyed a resurgence in popularity
(e.g., Miller et al., 2013; Simonds et al., 2010), driven in part by
investigations of bacterial responses to environmental change and associated
patterns of gene expression (Ferenci, 2008). For example, components of
recent models of SOM transformations such as the stoichiometric constraints
on substrates, enzymes, and microbial biomass (Moorhead et al., 2012; Manzoni
et al., 2012a; Allison, 2012; Ballantyne IV and Billings, 2015) are frequently investigated in chemostat
studies. Though some models invoke plasticity of microbial stoichiometry as a
potential response to environmental change, the extent to which biomass
plasticity vs. homeostasis is realized, and under what conditions, remains
unclear. While total soil microbial biomass C : N : P appears
well-constrained to an average of 60 : 7 : 1 across multiple ecosystems
and a wide range of nutrient availabilities (Cleveland and Liptzin, 2007),
studies manipulating soil nutrients demonstrate that meaningful shifts in
microbial stoichiometry are sometimes realized (Tiemann and Billings, 2011b).
Where plastic biomass stoichiometry is observed, two key reasons make it
difficult to understand the mechanisms underlying the phenomenon: (1) it is
difficult to know if such shifts result from stoichiometric change in extant
populations or from changing relative abundances of distinct populations, and
(2) stoichiometric analyses of soil microbial biomass typically reflect total
biomass, not just the active biomass (Table 1). Chemostats allow us to
disentangle these competing mechanisms.
In a chemostat, changes in biomass stoichiometry provide evidence that
microbial stoichiometric plasticity can be a consequence of environmental
change, a conclusion difficult to formulate using soil in which we do not
know the identity or the abundance of the active microbial players.
Stoichiometric plasticity of microbes can vary to a much greater extent than
what is typically observed in SOM literature. For example,
Pseudomonas fluorescens biomass C : N : P showed variation from
52 : 8 : 1 to 163 : 25 : 1, depending on whether P was abundant or
scarce relative to N (Chrzanowski and Kyle, 1996). Chemostats also have
revealed that some stoichiometric ratios (e.g., C : N) of actively
metabolizing microorganisms can remain similar as nutrient availability
changes, while others (e.g., N : P) vary only when a substrate
stoichiometric threshold is surpassed (Chrzanowski and Kyle, 1996). It
remains unclear if stoichiometric plasticity represents opportunistic uptake
in response to changing nutrient availability, or if it is a reflection of a
microbial population's inability to regulate uptake and/or excretion.
Regardless of the mechanism, changing microbial stoichiometry can influence
both resource demand and, given the generation of microbial necromass, SOM
composition.
Chemostats are also a key means of advancing our knowledge about microbial
stoichiometry in different temperature regimes and at different growth rates.
Chemostats inform us, with great clarity, that growth rate and in some
circumstances temperature are key drivers of microbial stoichiometry. Growth
rate appears to be a dominant driver of stoichiometric patterns in
chemostat-raised organisms (Rhee and Gotham, 1981; Klausmeier et al.,
2007; Chrzanowski and Grover,
2008), consistent with observations from lakes (Makino et al., 2003).
Microbes growing at relatively fast rates tend to exhibit greater cellular P
concentrations across a range of P availabilities, consistent with
observations from natural waters (Elser et al., 2003) and the growth rate
hypothesis (GRH), which states that C : P and N : P ratios reflect
changing organismal allocation to ribosomal RNA, a P-rich molecule, as growth
rate varies (Elser et al., 2000). Bacterial stoichiometry (C : P, N : P)
also appears to vary with temperature in nutrient-limited (N, P)
environments, perhaps due to greater investment in P-rich RNA at cooler
temperatures (Cotner et al., 2006). Interestingly, the effects of temperature
and growth rate on cellular P content may cancel each other when cell growth
is not proceeding at the maximum rate as would be the case in batch culture
(Cotner et al., 2006), highlighting the complexity of the interactions
driving microbial stoichiometry.
Chemostats as well-mixed, reductionist environments in which to
explore C fluxes
Chemostats also allow us to study how the fate of C substrates changes with
changing environmental conditions in a manner impossible in soils. A flurry
of recent studies investigating microbial C flows with changing soil
conditions highlights how microbial C fate dictates the magnitude of soil
feedbacks to climate (Manzoni et al., 2012a; Wieder et al., 2013; Sinsabaugh
et al., 2013), but without knowing the rate at which soil microorganisms
are growing and what limits their growth, we cannot know the fraction of C
uptake allocated to growth vs. respired CO2 (typically expressed as the
CUE), and thus the gross CO2 flux from soil. It follows that it is
exceedingly difficult to assess how the propensity to generate biomass vs.
CO2 might change with environmental conditions (Table 1). Adding an
isotopically labeled substrate can help us understand microbial uptake of a
particular resource or suite of substrates (e.g., Ziegler et al., 2005; Li et
al., 2012; Frey et al., 2013), but we must interpret resultant data with the
knowledge that we have perturbed the natural system, and that recycling of
the isotopic label through the microbial biomass is likely to confound
inferences from such studies as the temporal extent of sampling increases.
Recently, Lehmeier et al. (2015) exploited the chemostat environment to
investigate the consequences of changing temperature regime on C flux from OM
substrate into microbial biomass, and into respired CO2. At a constant
rate of growth, microorganisms experienced an increase in specific
respiration rate and a corresponding decline in CUE with increasing
temperature. This work substantiates inferences from soil-based studies that CUE declines with temperature (e.g., Frey et al., 2013). The CUE
finding is critical for efforts to incorporate soil processes into Earth
system models used to predict future atmospheric CO2 concentrations
(Wieder et al., 2013).
Second, this study also highlighted strong isotopic fractionations among
substrate, biomass, and respired CO2 pools that vary with temperature
(Lehmeier et al., 2015). Apparent respiratory fractionation during fungal
(Henn and Chapela, 2000) and bacterial (Blair, 1985) respiratory losses of
CO2 has been observed, but is difficult to interpret when microbial
growth rate is not known and the system is not at steady state. Isotopic
fractionation during CO2-generating respiratory fluxes is rarely
considered in studies that use δ13C-CO2 to infer mesocosm or
ecosystem function, though the potential importance of this phenomenon in
plant respiration across diverse scales has been noted (Pataki, 2005).
Because of difficulties knowing which active microbial population produced
measured CO2, or the substrate from which it was derived, it is
difficult to quantify isotopic fractionation effects among organic and
inorganic C pools in soil-based studies. Lehmeier et al. (2015) demonstrate
the importance of chemostat studies for avoiding these soil-based challenges
and provide proof of concept for temperature dependence of a respiratory
fractionation factor. In contrast to studies in which soil temperature is
manipulated, chemostats demonstrate that isotopic variation in respired
CO2 can result even while holding constant substrate identity and
availability, active microorganism identity, and microbial growth rate.
Importantly, other chemostat studies have demonstrated that microbial growth
rate itself, in isolation from other conditions such as temperature or
nutrient availability, appears to influence specific respiration rates
(Larsson et al., 1993; Payot et al., 1998; Kayser et al., 2005). This is
consistent with the GRH (Elser et al., 2000). However, soil biogeochemists
and microbial ecologists typically presume that a combination of resource
availability and community composition determines the size and growth
efficiency of a microbial community, which in turn influences the respiratory
C efflux, and that changing environmental conditions (e.g., temperature) can
induce changes in specific respiration rate. Chemostat studies, however,
demonstrate that growth rate governs not only specific respiration (Kayser et
al., 2005) but also the relative dominance of respiratory pathways that
produce CO2 (Nanchen et al., 2006). If growth rate is a driver of
specific respiration in soil microbial communities, these data suggest an
important and underappreciated mechanism driving microbially mediated soil C
fluxes.
Chemostats as well-mixed, reductionist environments in which
to explore microbial gene expression
Chemostats present the ideal conditions for linking gene expression to
biogeochemically relevant fluxes, which are transferrable to soils. Patterns
of microbial gene expression are often considered the gold standard for
understanding microbial community function in a multitude of environments
(Ottesen et al., 2014; Ofek-Lalzar et al., 2014), and microbial gene
expression in soils is obviously of great relevance to questions of SOM decay
and soil microbial ecology more generally (Baldrian and
López-Mondéjar, 2014). However, as outlined by Schimel and
Schaeffer (2012), using modern molecular tools to better understand SOM decay
is challenging given the lack of specificity of decay-related genes; unlike
processes like methanogenesis and methanotrophy or denitrification, SOM decay
is governed by a relatively large number of genes residing in a greater
diversity of organisms. Despite the seemingly daunting level of microbial
genetic diversity, soil metagenomes can be mined for their annotated and
functionally assigned genes, and then used to assess how potential metabolic
pathways can shift with changes in the environment such as soil warming (Luo
et al., 2014). New tools such as Functional Ontology Assignments for
Metagenomes (FOAM, Prestat et al., 2014) are making it even easier to use
metagenomic data to group microbial communities based on broadly categorized
metabolic processes. This is an important step forward as it has been
recently demonstrated that even inclusion of coarse, physiologically defined
functional groupings, e.g., oligotrophs versus copiotrophs, can improve models
of litter and SOM decay (Wieder et al., 2014).
Understanding and predicting microbial gene expression is challenging, in
part because patterns of gene expression in soils are driven by both
bacterial growth rates (Ferenci, 1999) and the identity of any limiting
nutrient (Hua et al., 2004) (Table 1). Thus, changes we observe in soil
transcriptomes with environmental conditions may not be the direct result of,
for example, a temperature change, but instead may result from altered growth
rates and/or changes in relative nutrient availability as induced by the
change in temperature. These gaps in our knowledge can be filled through the
use of chemostats. In a controlled chemostat environment where nutrient
availability is constant and growth rates can be monitored, researchers can
measure gene expression in response to isolated environmental stressors such
as osmotic potential or temperature changes. For example, in a controlled
chemostat-like system, Gülez et al. (2012) examined gene expression in
relation to stress induced by manipulating matric potential. Hebly et
al. (2014) used a chemostat approach to quantify changes in gene
transcription and physiology of Saccharomyces cerevisiae during
cyclic 12 to 30 ∘C shifts in daily temperature, and demonstrate the
importance of microbial acclimation to temperature at these short timescales.
These studies are of direct relevance to SOM-related investigations of the
influence of soil water stress and temperature on SOM transformations. As we
increase our understanding of the environmental controls on gene expression
and transcription networks, we can begin to understand how the snapshot of
whole community gene transcription represented by a soil metatranscriptome is
linked to changes in the physiology of the community, and observed changes in
soil processes such as SOM decay. These research avenues are critical for
formulating and parameterizing SOM decay models, discussed in Sect. 3.
Both natural and artificial aquatic systems are increasingly viewed as
relevant to soil studies (e.g., Marín-Spiotta et al., 2014; Lehmeier et
al., 2015), and we applaud such efforts. However, though sometimes used in
conjunction with natural aquatic environments (Sterner et al., 2008),
chemostats are only just beginning to be explored in the context of
soil-specific questions, and can provide knowledge about OM decay not
feasible to obtain using natural soil profiles. In the next section, we
explore another underexploited concept relevant to SOM transformations –
that of intrinsic vs. apparent exo-enzyme kinetics. Though different
soils may exhibit different apparentEa, it is difficult
if not impossible to know the extent to which intrinsic properties
of a soil's substrates vs. other, soil-specific, features govern
apparent Ea.
Intrinsic decay rates as baseline values for comparison with observed
patterns of SOM decay
Multiple studies explore apparent activation energies (apparent
Ea; in kJ mol-1) required for SOM decay to proceed, often
in the context of investigating the temperature sensitivity of SOM decay. The
Ea is one way to quantify the ease with which decay of compounds
can proceed. A substrate with intrinsically higher Ea is more
difficult to decay than one with lower Ea at a given temperature
(Sierra, 2012) and,
accordingly, the C quality–temperature hypothesis suggests that OM more
resistant to decay should exhibit higher relative temperature sensitivity
(Bosatta and Ägren, 1999; Davidson and Janssens, 2006). Apparent
Ea thus represents one means of quantifying more qualitative
terms like “recalcitrance” and “quality” that are difficult to interpret
(Kleber, 2010; Kleber et al., 2010; Conant et al., 2011). Apparent
Ea is clearly an important feature to consider when investigating
soil feedbacks to climate because in a warmer environment SOM exhibiting
long residency times may exhibit greater relative increases in decay rates
than SOM that decays more rapidly. However, it is difficult to interpret why
one soil's apparent Ea may be different from another's, for we
cannot know if the substrates undergoing decay possessed different intrinsic
Ea of decay, or if soil-specific factors such as texture or the
identity of the active microbial community drove apparent Ea
differences. Selecting ubiquitous substrates and some of the key
biogeochemical reactions that induce their decay, and characterizing the
kinetics of these reactions when isolated from other substrates and microbes
themselves, represents an incremental movement towards addressing these
questions. This approach will provide estimates of reaction rates and
estimates of Ea that are as close to intrinsic values as is
feasible if they are conducted when neither enzyme nor substrate is limiting.
It is important to consider the drivers of differences among potential and
observed reaction rates, and apparent and intrinsic Ea, for a
specific decay reaction when interpreting decay reaction rates and apparent
Ea values derived from the soil environment. Recalling that the
slope of an Arrhenius plot is considered the Ea of a reaction, we
first must note that the line defining intrinsic Ea should, in
theory, always be above (have a higher y intercept than) any line defining apparent Ea. This follows from
the assumption that a decay reaction rate quantified in purified, abiotic
solutions when neither enzyme nor substrate is limiting represents the upper
limit for that reaction rate at that temperature. This is a difficult
hypothesis to test because the units in which purified substrate–enzyme
reaction rates are expressed must necessarily be different from the typical
units employed in studies of exo-enzyme reactions in soils and sediments
(e.g., Sinsabaugh et al., 2012), but its logic is difficult to challenge.
In spite of the difficulties associated with directly comparing the temperature sensitivities
of pure substrate–enzyme kinetics and actual SOM decomposition, it is
valuable to consider the multiple ways in which apparent Ea of
decay reactions in soils exposed to different temperatures may vary relative
to intrinsic Ea for those same reactions. Because the slope
estimates (Ea in kJ mol-1) are independent of the reaction
rate units, they can be compared and yield meaningful interpretations across
samples. In some soils, we may observe an apparent Ea greater
than intrinsic Ea for a particular substrate–enzyme reaction (a
steeper slope in an Arrhenius plot). However, it is feasible that some
environmental samples may exhibit lower apparent Ea (a
shallower slope), or equivalentEa (parallel slope; note
that y intercepts for Arrhenius plots depicting apparent Ea will
always be equal to or lower than those depicting intrinsic Ea as
discussed above). A lower apparent Ea may occur if, for example,
cooler temperatures promoted a competitive advantage for microbial
populations that preferentially produce the exo-enzyme that catalyzes the
reaction in question, boosting observed reaction rates to a greater extent
than the direct influence of temperature on the purified reaction rate would
predict. It remains unknown how changing temperature regimes may result in
changing competitive advantages for different microbial groups, however.
Alternatively, soil moisture may decrease with increasing temperature,
constraining diffusion (Wang et al., 2014), or warming could affect plant
inputs to soil in multiple ways (Flury and Gessner, 2014). Either of these
phenomena could alter microbial demand for substrates and thus modify
exo-enzyme production, pushing observed reaction rates away from intrinsic
reaction rates differentially across a temperature range.
Estimates of intrinsic (closed symbols) and apparent (open symbols)
Ea for the BGase/BG reaction (a) and the NAGase/NAG reaction (b).
The literature values for apparent Ea are shown at the pH at which the
reaction was actually observed, and does not necessarily correspond to the pH
of the soils from which the samples were taken. See Sect. 3 for interpretation.
Lehmeier et al. (2013) determined reaction rates of β-D-cellobioside
as catalyzed by β-glucosidase (BGase) and N-acetyl-β-D-glucosamine (NAG) as catalyzed by β-N-acetyl glucosaminidase
(NAGase) in purified (and therefore non-confounding, ideal conditions) at
temperatures between 5 and 25 ∘C and a pH of 6.5. These reactions are
proxies for the cleaving of monomers from cellulose and chitin, respectively.
Because they were conducted when neither enzyme nor substrate was limiting,
the study provided Ea values of these compounds
(31 kJ mol-1 for BG/BGase, 41 kJ mol-1 for NAG/NAGase), which
are as close to intrinsic values as is feasible. Expanding on this study, Min
et al. (2014) confirmed the values and explored how the Ea of these
reactions change when the pH was varied in a reasonable range for soil pH
around the world. They reported distinct pH optima for both BG/BGase (5.5–8.5)
and NAG/NAGase (5.5–6.5), and a significant effect of pH on the temperature
sensitivity of BGase but not NAGase (Fig. 1). Baseline, intrinsic properties
of these reactions in multiple pH regimes helps us to develop
biogeographically based predictions of the temperature response of cellulose
and chitin decay.
Such baseline values for intrinsic Ea only represent conditions
in which neither enzyme nor substrate is limiting, a scenario that is only
sometimes relevant to soils. However, baseline values are nonetheless
essential for comparisons with estimates of apparent Ea of
cellulose and chitin decay derived from soil samples. For example, estimates
for apparent Ea of the BGase/BG reaction derived from diverse
soils exhibit varying values compared to intrinsic Ea values
assessed in purified conditions (Fig. 1a). Though some papers present
apparent Ea values from soils for the NAGase/NAG reaction (e.g.,
German et al., 2012), it is difficult to find those that present units
comparable among studies. The few that do (Fig. 1b) suggest meaningful
variation in values (Fig. 1b). If apparent Ea values are greater
than intrinsic values, this suggests that soil-related factors confounding
the intrinsic temperature response of the NAGase/NAG reaction become
relatively more influential at lower temperatures. In contrast, soil-related
factors confounding intrinsic Ea for the BGase/BG reaction appear
to both increase and decrease apparent Ea relative to intrinsic
values. Assessing Ea values at the actual soil pH, not at an
arbitrary buffer pH, may offer important insights too. For instance, Barta et
al. (2014) demonstrated the BGase/BG reaction can proceed in soils at pH 3.5.
This is in apparent contrast to Min et al. (2014), where BGase/BG activity at
pH lower than 4.5 could not be detected in purified conditions. The reasons for
this discrepancy remain unclear, but one possible explanation is the microbial
generation of distinct isozymes capable of inducing catalysis in low pH
environments. This and related insights are impossible to generate without
developing baseline intrinsic Ea values. Similar work on a
diversity of substrate–enzyme pairings will provide an important knowledge
base for future SOM decay research.
Values of intrinsic Ea of decay reported thus far suggest that
the influence of temperature on exo-enzymes, even in isolation from all the
other changes that temperature can impart on soils, is important for the
relative availability of resources for microbial assimilation. Specifically,
studies indicate how temperature alone can alter the relative availability of
C and N liberated from substrates as they decay – the C : N flow ratio –
if those substrates have distinct C : N ratios and Ea of decay
(Billings and Ballantyne, 2013). Exo-enzyme age also appears to interact
with temperature to influence the relative availability of C and N released
during decay reactions; the catalytic rate of exo-enzymes and the temperature
at which the enzyme ages prior to catalyzing decay reactions can influence
the decay rate of BG and NAG differently
(S. Billings, unpublished data). The C : N flow ratio is important because
it represents the return on microbial investments in exo-enzymes, and how
that return on investment may change with temperature in ways that have
nothing to do with microbial responses to temperature per se.
Because changing relative availability of microbial resources may influence
microbial stoichiometry (see Sects. 2.1 and 2.2), and, in turn, decay of
additional substrates, exploring additional drivers of changing C : N flow
rates appears to be an important, complementary avenue of research.
Using experimental advances to enhance recent theoretical efforts to
model SOM decay
Investigators have modeled SOM decay for decades. Though an exhaustive review
of these advances is beyond the scope of this paper, we highlight recent
advances and elucidate how these advances could benefit from some of the
discoveries detailed above. Coarsely, models of SOM decay can be grouped into
two categories: those that are spatially explicit, and those that implicitly
treat the factors influencing SOM decay as spatially homogeneous. The first
category comprises models such as reactive transport models, often invoked by
engineers or hydrologists (Masse et al., 2007; Scheibe et al., 2009), while
the second category is more familiar to ecologists (Schimel and Weintraub,
2002; Allison, 2005; Allison et al., 2010; Davidson et al., 2012; Manzoni et
al., 2012a; Moorhead et al., 2012; Moyano et al., 2013; Ballantyne IV and
Billings, 2015). Recent work
begins to merge both abiotic properties of soils and plastic vs. homeostatic
microbes (Tang and Riley, 2015), and some efforts have incorporated space into ecologically
focused models by considering diffusional constraints on exo-enzymes within
the soil matrix (Allison, 2005; Allison et al., 2010; Manzoni et al., 2014).
However, realistic physics of diffusion are rarely incorporated into models
that explicitly consider microbes, and thus it is difficult to know if the
temporal and spatial scales invoked for modeled diffusion are appropriate.
Comparing substrate usage in chemostats or natural aquatic environments to
that in soils can be valuable for discerning the influence of diffusion
constraints on OM transformations, given minimal diffusion limitation in
liquid environments relative to that in soils. However, empirical
measurements of enzyme flow in soil (e.g., Vetter et al., 1998) highlight how
difficult it is to generate realistic enzyme movements in a
diffusion-constraining matrix, and the challenges of integrating spatially
distinct processes into ecologically focused process models. This category
distinction is important because processes relevant to SOM decay occur at the
fine scales typically envisioned by ecological modelers (Schimel and
Schaeffer, 2012), but key goals of the community are to predict SOM decay and
associated CO2 release at far coarser scales (e.g., Wieder et al., 2013).
Thus at its core, projecting decomposition of SOM processes relevant at the
Earth system scale is an exercise in accurate physiological and physical
modeling combined with scaling approaches.
Multiple modeling efforts have attempted to move us toward the goal of
projecting large-scale SOM transformations from physiologically based models,
and recent years have seen a proliferation of models describing SOM decay
(Manzoni and Porporato, 2009). Only rarely have investigators tried to
estimate both model parameter values and the variance in those estimates from
empirically derived data (Davidson et al., 2012), and quantitative results
are difficult to apply across diverse soil types, ecosystems, and climate
regimes. As a result, most of the insights provided by SOM decay models are
qualitative. These models attempt to model SOM transformations by
incorporating factors known or thought to govern SOM decay rates and
associated CO2 efflux, such as microbial growth rates, CUE, allocation
of C to enzyme production, and C uptake rates (Allison et al., 2010; Allison,
2012; Manzoni et al., 2014). However, many models assume fixed fractions of
microbial C allocated to processes such as enzyme production and maintenance
metabolism, contrasting with evidence from physiological experiments which
indicate that allocation patterns shift with the interplay between microbial
resource demand and availability (Larsson et al., 1993; Payot et al., 1998;
Dauner et al., 2001; Dijkstra et al., 2011).
The omission of microbial physiological plasticity in these and related
models is unfortunate because it is the fundamental microbial physiology
that shapes C flow through microbial biomass and associated CUE (Billings and
Ballantyne, 2013). An important advance relates aggregate C fluxes through
soil microbes to microbial CUE (Manzoni et al., 2012a), critical both because
this term governs the propensity of soil organic carbon (SOC) to remain in the soil profile vs.
leaving as CO2, and because CUE is a “tunable” parameter in multiple
other models (e.g., Wieder et al., 2013). Importantly, though, CUE is not a
parameter that microbes govern as an end goal; rather, CUE is a byproduct of
the changing relative importance of anabolism and catabolism as metabolic
resource demand and resource availability vary in response to environmental
conditions. An important step forward will be to develop models that do not
modify only CUE, but that reflect multiple changes in environmental
conditions influencing microbial stoichiometry and metabolism, with CUE
changing as a result. Chemostat data again become important for these
modeling efforts because they provide baseline values for biomass production
and specific respiration rates under varying environmental conditions which,
in turn, dictate CO2 efflux from soils.
Developing a theoretical scaffolding on which we can build physiologically
mechanistic models that ultimately can be made spatially explicit, and thus
useful for modeling at the scale of the Earth system, will require two key
advances. First, more physiological realism needs to be incorporated into our
modeling frameworks. Enhancing the physiological realism of existing
ecological models can take multiple forms. Regulatory–metabolic network
models that reflect microbial decision making and metabolic constraints can
be developed. Metabolic flux analysis can be an effective means of modeling
in situ metabolic transformations in soils (e.g., Scheibe et al., 2009), but
progress in this realm remains slow (but see Dijkstra et al., 2011).
Interdisciplinary studies such as Tang et al. (2009), who highlighted how
13C and multiple “-omics” fields can be effectively integrated,
represent large strides towards the development of this field. Importantly,
chemostats represent ideal experiments from which to build such models. Gene
expression and proteomics measured in chemostats under constant conditions
provide the best chance for matching expression and network state to putative
C transformations. Additionally, parameter values for microbial substrate
uptake, mass of C per unit dry mass of microbial biomass, dry weight per
cell, enzyme deactivation rate, and the microbial biomass fraction of N and P
(e.g., Allison, 2012; Manzoni et al., 2014) are available for changing
environmental conditions from chemostat studies (e.g., Chrzanowski and Kyle,
1996; Chrzanowski and Grover, 2008; Lehmeier et al., 2015). Though the
absolute values from reductionist laboratory experiments may not be directly
applied to soils, they are a great starting point for accurately
parameterizing models. Values of Ea for SOM decay are typically
treated as one aggregated value as a simplifying assumption (e.g., Allison et
al., 2010), though we know this to be false. Estimates of intrinsic
Ea values derived from purified, biogeochemically relevant
enzymes (Lehmeier et al., 2013; Min et al., 2014) are analogous starting
points for parameterizing decay kinetics, which result from
regulatory–metabolic network driven allocation and feedback upon
physiological state.
Second, we must accurately average SOM transformations and heterotrophic
respiration over heterogeneity in the soil matrix to extract responses at
reasonable scales for Earth system modeling. This exercise of “coarse
graining” will enable modelers to identify characteristic scales associated
with SOM transformations, and in the process improve our understanding of how
edaphic and biological features interact in generalizable ways. Once
characteristic scales have been identified, spatially explicit model dynamics
can then be compared to those of non-spatial ecological models. This will
enable ecological model dynamics to be applied at appropriate scales with
appropriate parameters. There are two approaches widely employed in other
fields that could be used for coarse graining SOM dynamics. One is to start
with individual dynamics, as in Masse et al. (2007), and then derive the
dynamics of the aggregate, in this case the entire soil profile, from the
individual level dynamics. Durrett and Levin (1994) refer to this as deriving
a hydrodynamic limit because of the analogous derivation of Navier–Stokes
equations from the mass transfer for individual parcels of liquid. From such
limits, characteristic length scales can often be inferred. Another approach
is to start again with individual-level dynamics, but with stochasticity, and
then derive mean dynamics for a profile or site in terms of higher-order
moments. This gives rise to the problem of moment closure, but moment closure
methods have been effectively applied to model the mean dynamics of spatially
explicit ecological dynamics (Bolker and Pacala, 1997). Successfully
averaging over the heterogeneity we know exists in soils will allow us to
capture the important governors of SOM transformations at scales relevant for
Earth systems models. By initially considering the full extent of
heterogeneity and then employing robust analytical methods to translate the
consequences of that heterogeneity for dynamics at larger scales, i.e., whole
soil profiles over reasonable spatial extents, we will obtain more realistic
projections of SOM dynamics as well as more meaningful measures of confidence
in those projections.
Applying these concepts to the puzzles presented by changing SOM
characteristics with depth
We can apply some of the empirical and theoretical concepts described above
to help address the question we posed in the introduction: why does some SOM leave the soil profile relatively quickly, while other compounds, especially those at depth, appear to be retained on timescales ranging from the decadal to the millennial? In recent years, the community of scholars
focused on SOM transformations has become increasingly appreciative of the
importance of relatively deep SOM. Indeed, investigators are establishing
Critical Zone Observatories around the globe to investigate whole-ecosystem
function down to bedrock (Jordan et al., 2001), and are developing an
increasing appreciation of the importance of deep metabolic processes for
ecosystem functioning (Richter and Billings, 2015). It is difficult to define
what is meant by “deep SOM”. Absolute depths are arbitrary, and using the
plant rooting zone as an indicator of “shallow” horizons is challenging
when we consider highly weathered profiles in which active plant roots can
function tens of meters below the surface (Stone and Kalisz, 1991), surrounded
by SOM we might otherwise consider to be “deep”. However, general trends in
SOM stability with depth are clear: with depth, SOM stability appears to
increase, with mean residence times of millennia not uncommon (Trumbore,
2009; Schmidt et al., 2011, Fig. 2). In this section, we briefly describe
some of the mysteries of deep SOM, and then depict how changes with depth in
microbial characteristics, the C to N ratio of SOM, and temperature regime
can be investigated using some of the ideas revealed by aquatic studies, and
by advancing microbial models.
Depiction of parameters describing drivers of SOM decay and
retention with depth. Salient physical and chemical features are described on
the left, and microbial features on the right. Key features both resulting
from and driving patterns of SOM decay are the mean age of SOM and its
associated degree of degradation and C : N ratio, and the degree to which it
forms organo-mineral complexes and micro- vs. macroaggregates. All of these
except bulk C : N are typically are enhanced with depth. A greater mean
residence time is often associated with a greater degree of microbial
processing of that material, hence the greater degree of degradation. When
coupled with the greater amount of organo-mineral complexes that form with
depth, these features drive more energy intensive SOM decay at depth,
increasing the activation energy (Ea) of decay and associated
temperature sensitivity of decay. In turn, these physical and chemical
changes with depth govern the diversity, physiology, and functional guild of
microbial groups in shallow vs. deep soil horizons. The thicker arrow at depth
represents likely greater interaction strength in deep soil horizons among
energy availability in substrates, temperature sensitivity, and microbial
physiology, given the generally greater Ea and lower energy
available at depth. Importantly, the microbial community can serve as both an
agent of decay and of production of SOM compounds with apparently long
residence times; this concept has only recently been explored in the soil
literature.
We understand very little about what controls the persistence or decay of
deep SOM in comparison with our understanding of more surficial processes
(Schmidt et al., 2011), though an estimated 21–46 % of global soil C
stocks reside at depths > 100 cm (Jobbágy and Jackson, 2000). Of
course, it is not depth per se that governs SOM persistence or
decay, but rather changes with depth in the relative dominance of variables
that influence decomposition rates. The predominant state factors (Jenny,
1941) influencing SOM dynamics appear to change below surface
horizons: climate becomes less dominant as an influence on SOM
transformations with depth, and soil texture appears to assume a greater role
(Jóbbagy and Jackson, 2000). In addition, the chemistry of deep SOM is
quite different than shallower SOM, with lower C : N ratios, a higher
abundance of lipids, polysaccharides and N-bearing compounds, enrichment in
13C and 15N, and a greater proportion of apparently slow-to-decay
compounds of microbial origin (e.g., Ehleringer et al., 2000; Billings and
Richter, 2006; Fröberg et al., 2007; Rumpel and Kögel-Knabner, 2011).
These changes in SOM chemistry and abiotic conditions with depth also alter
microbial communities, reducing microbial diversity and altering microbial
community structure and function (Agnelli et al., 2004; Goberna et al., 2005;
Fierer et al., 2003; Will et al., 2010; Gabor et al., 2014; Eilers et al.,
2012). Such changes are important not only because they affect SOM decay
rates, but also SOM formation; the byproducts of microbial communities appear
to comprise a meaningful fraction of OM reservoirs, ranging from 40 to
80 % (Liang et al., 2011; Simpson et al., 2007), and can persist over
long timescales (Voroney et al., 1989; Jiao et al., 2010; Six et al., 2006;
Miltner et al., 2011; Liang et al., 2011; Grandy and Neff, 2008; Simpson et
al., 2007; Hobara et al., 2014). Given that some microbial decomposition byproducts can exhibit
relatively slow decay rates and that compounds of microbial origin appear to
be preferentially retained in pools of long-lived SOM, we might expect SOM
persistence to increase with depth as the dominance of plant relative to
microbial inputs decreases (Grandy and Neff, 2008). Our growing appreciation
of microbial contributions to SOM and the persistence of some of this
material over relatively long timescales prompts calls for experiments
designed to reveal how different microbial byproducts from distinct community
compositions invite or resist decay (Throckmorton et al.,
2012), and for investigations
into the relative dominance of microbial vs. plant inputs to deep SOM
reservoirs.
Changes in the C : N of SOM and soil temperature regime with depth can be
connected to the knowledge obtained from aquatic environments about microbial
transformations of OM, particularly when we consider interactions between
substrate stoichiometry and temperature. For example, the observation that
the bioaccessibility of organic C (energy) can govern the ability of microbes
to induce decay of slow-turnover SOM (Fontaine et al., 2007) is directly
relevant to observations of substrate stoichiometry driving microbial
biomass, and thus resource requirements, in natural and artificial aquatic
environments. Furthermore, bacterial stoichiometry appears to vary in
meaningful ways with temperature when nutrients are limiting (Cotner et al.,
2006). We thus might predict that when energy (i.e., organic C) is more
limiting, as is likely the case deep in a soil profile, where SOM C : N
ratios and plant inputs are relatively low, temperature effects on microbial
stoichiometry may be minimal. This prediction, if realized, has important
implications for projecting the effect of temperature on deep SOM decay
because it suggests that an increase in deep soil temperatures may not induce
a large shift in the stoichiometry of resource demand of extant microbial
populations, and that microbial responses to temperature will vary with
substrate C : N, and thus with depth. The observed importance of substrate
and microbial C : P and N : P ratios as drivers of OM flow in chemostat
studies (Chrzanowski and Kyle, 1996) as temperature varies (Cotner et al.,
2006) can also be applied to questions of SOM decay at depth, reminding us
that the relative N vs. P limitation in terrestrial ecosystems likely will
have an influence on each ecosystem's microbial response to temperature.
Current models of SOM decay do not incorporate these ideas, but doing so will
inform us about an important driver of SOM composition changes with depth:
the composition of the material accessed by microbes and transformed into
CO2 and other, non-gaseous-phase, microbial byproducts.
We also can use purified substrate–enzyme reaction kinetics (Lehmeier et al.,
2013; Min et al., 2014) to formulate additional research questions about
increasing SOM persistence with depth, and how destabilization of deep SOM
stocks may proceed in a warmer world. For example, pH optima for
exo-enzymatic catalytic rates and well-characterized interactions between pH
and Ea of decay for specific decay reactions (Min et al., 2014)
are useful for predicting how these substrate–enzyme reactions may proceed
in different soil horizons, if we know how pH varies with depth in a soil of
interest. We also can use changing C : N flow ratios as temperature varies
(Lehmeier et al., 2013; Min et al., 2014) to predict how microbial resource
availability may change with depth. We are far from knowing how C : N flow
ratios change with temperature in natural environments at any depth, but we
at least have a starting point derived from some biogeochemically relevant
substrate–enzyme pairings investigated in these works. Examining how
divergence from purified reaction kinetics changes with depth in
substrate–enzyme reaction rates will provide insight into the varying degree to
which physical and chemical protection in the soil matrix, as well as
microbial adaptation to temperature, govern depth patterns of SOM decay and
retention. This research approach will permit us to address a critical
question for understanding deep SOM retention: do deep-profile environmental
factors drive greater divergence from intrinsic reaction kinetics than in
more shallow horizons, and if so, which ones?
Finally, if a negative relationship between the Ea of decay and
C : N ratio exists for many soil substrates, as has been hypothesized
(Rumpel and Kögel-Knabner, 2011; Billings and Ballantyne, 2013), we can
use purified substrate–enzyme reaction kinetics to develop concepts of how
microbially available C and N may change with depth through a soil profile in
a warming climate. This is feasible given known trends in C : N and
Ea of aggregated substrate decay, which decrease and increase
with depth, respectively. It is also feasible to incorporate these concepts
into current models of SOM decay: Ea of decay and C : N are key
features of multiple models currently invoked in the literature. If the
temperature sensitivity of decay is greater for many substrates at depth, and
many of these substrates possess low C : N, enzyme kinetics suggest that
the availability of C relative to N may decline with warming, particularly at
depth. Microbes must respond to any such change in resource availability, and
in so doing can shift community composition and resource allocation, which
may influence necromass formation and retention over relatively long time
periods (Throckmorton et al., 2012; Nemergut et al., 2014).
Modelers also can take advantage of our existing knowledge of deep SOM
characteristics such as low C : N ratios and apparently low energy-yielding
potential of deep SOC (Fig. 2). Deeper soils also are likely to exhibit
preferential sorption of compounds to mineral surfaces (Schrumpf et al.,
2013), generating
organo-mineral complexes almost impervious to enzymatic attack (Schrumpf et
al., 2013; Fontaine et al.,
2007; Kögel-Knabner et al., 2008). This, combined with the well-processed nature of deep SOM molecules,
results in deep SOM decay requiring a large energy investment by microbes to
obtain resources from that decay. Because it is this energy limitation that
may be largely responsible for the apparent stability and persistence of deep
SOM (Fontaine et al., 2007; Kuzyakov, 2010; Wang et al., 2014), it would be
fruitful to use potential energy supply to microbes in varying substrate
landscapes as a key feature of microbial models. Studies in controlled
aquatic environments where diffusion limitations are small can provide
maximum values of energy made available upon decay for such models. Given
recent advances in our understanding of linkages between iron reduction and
the mobilization of organic C in soils (Buettner et al., 2014) and a growing
understanding of redox features driving diffusive transport of metals (Fimmen
et al., 2008), the development of models that account for varying microbial
access to SOM given changing forms of soil minerals and diffusive constraints
appears to be another low-hanging fruit for the research community. These
advances would help us understand how added energy sources can promote
enhanced decay of deep SOM (Fontaine et al., 2007), a phenomenon that
suggests old SOM is not necessarily intrinsically “recalcitrant” (Kleber,
2010; Kleber et al., 2010).
Conclusions
There has been some effort in the literature to link research that
examines natural aquatic, sedimentary, and soil OM transformations (Hedges
and Oades, 1997; Billings et al., 2012; Marín-Spiotta et al., 2014). In spite of calls for
integration, these disciplines have remained relatively distinct. We
emphasize the great utility of employing knowledge from natural aquatic
systems to better predict how SOM decay and retention will proceed in the
future. Like soils, aquatic systems can reveal how both physical protection
and microbially mediated processes govern OM transformations in changing
environmental conditions. The concept of the microbial loop in the ocean
(Pomeroy, 1974; Azam et al., 1983; Pomeroy et al., 2007) and the observation
that microbial byproducts form a great fraction of oceanic OM (Kawasaki and
Benner, 2006; Kaiser and Benner, 2008; Jiao et al., 2010) pushes soil
scientists to test analogous hypotheses in terrestrial systems (Liang et al.,
2011). We encourage the further application of empirical observations in aquatic
systems in terrestrial soils. In this way, we can develop the nascent concept
of soil microbial communities functioning both as decomposers and generators
of byproducts with potentially long residence times.
With the exception of a few investigators who work in both chemostats
and natural aquatic environments (e.g., Elser, 2003), literature describing
chemostats is rarely invoked by SOM-focused investigators (Lehmeier et al.,
2015). However, chemostats have much to tell us about the influence of resource
availability and temperature, for example, on microbial resource demand,
resource allocation, and ultimately microbial growth. Understanding how C is
taken up and transformed will help us understand the characteristics of
substrates not accessed by microbes and thus features of SOM that
persists in soil profiles. This is especially relevant to questions of deep
SOM given the increase in SOM mean residence time deep in soil profiles.
Chemostats also tell us that microbial growth rate has a direct influence on
microbial stoichiometry and specific respiration rate, a phenomenon currently
not appreciated by the modeling community. This, in turn, can govern CUE and
resource demand – and thus the composition of substrates “left behind” and
thus retained in the profile. Chemostat experiments have great potential for
understanding SOM dynamics across depth, precisely because they permit
manipulation of the very environmental features known to vary with soil
depth, such as resource stoichiometry, Ea of decay, and
temperature.
Purified kinetics of biogeochemically relevant decay reactions provide
baseline values to use in models of SOM decay, and differences among known
biogeochemical reactions – their raw rates and Ea derived from
them – give us a sense of Ea values appropriate for model use.
Developing baseline upper values for substrate–exo-enzyme reaction kinetics
is another important avenue of research for those interested in OM decay and
retention. Baseline values derived from purified reaction kinetics allow for
the parsing of intrinsic responses to top-down drivers of decay such as
temperature from other soil-specific factors that may change with the
environment.
There are important and underexplored avenues for modelers who focus on
SOM transformations in response to changing climate, and within soil profiles
across depth. For example, modelers who attempt to use soil physics and
diffusive properties of enzymes and substrates to better predict OM
transformations can expand their efforts to explicitly model shallow versus
deep SOM. By altering diffusive parameters to better reflect the differences
in relative abundances of macro vs. microaggregate structure across
soil depth, and the different degrees of tortuosity throughout a soil
profile, we can gain a sense of the importance of these features as drivers
of SOM protection at depth. Scaling approaches will be critical for extending
profile-scale dynamics to scales relevant for Earth system models. Modelers
also can use information from some natural aquatic environments and
chemostats to better understand how microbial stoichiometry, resource access,
elemental cell content, and specific respiration rates change with
environmental conditions. Though absolute values of these parameters from
chemostats are likely not appropriate for use in modeling soil profiles,
chemostat values provide at least qualitative indications of how these
parameters may change with environmental conditions, including those that
vary with depth.
Acknowledgements
The authors thank Johan Six for his encouragement in writing this paper,
Mitch Sellers for assistance organizing the references, and two anonymous
reviewers, whose input improved the manuscript. This work was partially
supported via National Science Foundation grants DEB-0950095 and
EAR-1331846. We also are grateful for discussions among members of the KU Center for Metagenomic Microbial Community Analyses. Edited by: K. Denef
ReferencesAgnelli, A., Ascher, J., Corti, G., Ceccherini, M. T., Nannipieri, P., and
Pietramellara, J.: Distribution of microbial communities in a forest soil
profile investigated by microbial biomass, soil respiration and DGGE of total
and extracellular DNA, Soil Biol. Biochem., 36, 859–868, 2004.Allison, S. D.: Cheaters, diffusion and nutrients constrain decomposition by
microbial enzymes in spatially structured environments, Ecol. Lett., 8,
626–635, 2005.Allison, S. D.: A trait-based approach for modelling microbial litter
decomposition, Ecol. Lett., 15, 1058–1070, 2012.Allison, S. D., Wallenstein, M. D., and Bradford, M. A.: Soil-carbon response
to warming dependent on microbial physiology, Nat. Geosci., 3, 336–340,
2010.Amado, A. M., Meirelles-Pereira, F., Vidal, L. O., Sarmento, H., Suhett, A. L.,
Farjalla, V. F., Cotner, J., and Roland, F.: Tropical freshwater ecosystems
have lower bacterial growth efficiency than temperate ones, Front.
Microbiol., 4, 1–8, 2013.Amutha, B., Khire, J. M., and Khan, M. I.: Characterization of a novel
exo-N-acetyl-β-D-glucosaminidase from the thermotolerant
Bacillus sp. NCIM 5120, Biochim. Biophys. Acta, 1425, 300–310,
1998.Azam, F., Fenchel, T., Field, J. G., Gray, J. S., Meyer-Reil, L. A., and
Thingstad, F.: The ecological role of water-column microbes in the sea, Mar.
Ecol. Prog. Ser., 10, 257–263, 1983.Baldrian, P. and López-Mondéjar, R.: Microbial genomics,
transcriptomics and proteomics: new discoveries in decomposition research
using complementary methods, Appl. Microbiol. Biot., 98, 1531–1537, 2014.Ballantyne IV, F. and Billings, S. A.: Linking microbial resource allocation to
exoenzymes, biomass stoichiometry, and soil respiration, Ecol. Lett., in
revision, 2015.Barta, J., Slajsova, P., Tahovska, K., Picek, T., and Santruckova, H.: Different
temperature sensitivity and kinetics of soil enzymes indicate seasonal shifts
in C, N and P nutrient stoichiometry in acid forest soil, Biogeochemistry,
117, 525–537, 2014.Battin, T. J., Luyssaert, S., Kaplan, L. A., Aufdenkampe, A. K., Richter,
A., and Tranvik, L. J.: The boundless carbon cycle, Nat. Geosci., 2,
598–600, 2009.Billings, S. A. and Ballantyne, F.: How interactions between microbial
resource demands, soil organic matter stoichiometry, and substrate reactivity
determine the direction and magnitude of soil respiratory responses to
warming, Glob. Change Biol., 19, 90–102, 2013.Billings, S. A. and Richter, D. D.: Changes in stable isotopic signatures
of soil nitrogen and carbon during 40 years of forest development, Oecologia,
148, 325–333, 2006.Billings, S. A. and Tiemann, L. K.: Warming-induced enhancement of soil
N2O efflux linked to distinct response times of genes driving N2O
production and consumption, Biogeochemistry, 119, 371–386, 2014.Billings, S. A., Ziegler, S. E., Schlesinger, W. H., Benner, R., and
Richter, D. D.: Predicting carbon cycle feedbacks to climate: integrating the
right tools for the job, EOS, 93, 188–189, 2012.Blair, N., Leu, A., Muñoz, E., Olsen, J., Kwong, E., and Des Marals, D.:
Carbon isotopic fractionation in heterotrophic microbial metabolism, Appl.
Environ. Microb., 50, 996–1001, 1985.Bolker, B. and Pacala, S. W.: Using moment equations to understand
stochastically driven spatial pattern formation in ecological systems, Theor.
Popul. Biol., 52, 179–197, 1997.Bosatta, E. and Ågren, G. I.: Soil organic matter quality interpreted
thermodynamically, Soil Biol. Biochem., 31, 1889–1891, 1999.Bradford, M. A.: Thermal adaptation of decomposer communities in warming
soils, Front. Microbiol., 4, 1–16, 2013.Buettner, S. W., Kramer, M. G., Chadwick, O. A., and Thompson, A.:
Mobilization of colloidal carbon during iron reduction in basaltic soils,
Geoderma, 221, 139–145, 2014.Cheng, W., Parton, W. J., Gonzalez-Meler, M. A., Phillips, R., Asao, S.,
McNickle, G. G., Brzostek, E., and Jastrow, J. D.: Synthesis and modeling
perspectives of rhizosphere priming, New Phytol., 201, 31–44, 2014.Chrzanowski, T. H. and Kyle, M.: Ratios of carbon, nitrogen and phosphorus
in Pseudornonas fluorescens as a model for bacterial element ratios
and nutrient regeneration, Aquat. Microb. Ecol., 10, 115–122, 1996.Chrzanowski, T. H. and Grover, J. P.: Element content of Pseudomonas fluorescens varies with
growth rate and temperature: A replicated chemostat study addressing
ecological stoichiometry, Limnol. Oceanogr., 53, 1242–1251, 2008.
Cleveland, C. C. and Liptzin, D.: C : N : P Stoichiometry in Soil: Is
There a “Redfield Ratio: for the Microbial Biomass?, Biogeochemistry, 85,
235–252, 2007.Conant, R. T., Ryan, M. G., Ågren, G. I., Birge, H. E., Davidson, E. A.,
Eliasson, P. E., Evans, S. E., Frey, S. D., Giardina, C. P., Hopkins, F. M.,
Hyvönen, R., Kirschbaum, M. U. F., Lavalle, J. M., Leifeld, J., Parton,
W. J., Steinweg, J. M., Wallenstein, M. D., Martin Wetterstedt, J. Å.,
and Bradford, M. A.: Temperature and soil organic matter decomposition rates
– synthesis of current knowledge and a way forward, Glob. Change Biol., 7,
3392–3404, 2011.Cotner, J. B., Makino, W., and Biddanda, B. A.: Temperature Affects
Stoichiometry and Biochemical Composition of Escherichia coli,
Microbial Ecol., 52, 26–33, 2006.Craine, J. M., Fierer, N., and McLauchlan, K. K.: Widespread coupling
between the rate and temperature sensitivity of organic matter decay, Nat.
Geosci., 3, 854–857, 2010.Dauner, M., Storni, T., and Sauer, W.: Bacillus subtillis metabolism and energetics in
carbon-limited and excess-carbon chemostat culture, J. Bacteriol., 183,
7308–7317, 2001.Davidson, E. A. and Janssens, I. A.: Temperature sensitivity of soil carbon
decomposition and feedbacks to climate change, Nature, 440, 165–173, 2006.Davidson, E. A., Samanta, D., Caramori, S. S., and Savage, K.: The Dual
Arrhenius and Michaelis–Menten kinetics model for decomposition of soil
organic matter at hourly to seasonal time scales, Glob. Change Biol., 18,
371–384, 2012.Del Giorgio, P. A. and Cole, J. J.: Bacterial growth efficiency in natural
aquatic systems, Annu. Rev. Ecol. Syst., 29, 503–541, 1998.Dijkstra, P., Thomas, S. C., Heinrich, P. L., Koch, G. W., Schwartz, E., and
Hungate, B. A.: 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, 2011.Droop, M. R.: The nutrient status of algal cells in continuous culture, J. Mar. Biol. Assoc.,
54, 825–855, 1974.Dungait, J. A. J., Hopkins, D. W., and Gregory, A. S.: Soil organic matter turnover
is governed by accessibility not recalcitrance, Glob. Change Biol., 18,
1781–1796, 2012.Durrett, R. and Levin, S.: The importance of being discrete (and spatial),
Theor. Popul. Biol., 46, 363–394, 1994.Ehleringer, J. R., Buchmann, N., and Flanagan, L. B.: Carbon isotope ratios
in belowground carbon cycle processes, Ecol. Appl., 10, 412–422, 2000.Eilers, K. G., Debenport, S., Anderson, S., and Fierer, N.: Digging deeper
to find unique microbial communities: the strong effect of depth on the
structure of bacterial and archaeal communities in soil, Soil Biol. Biochem.,
50, 58–65, 2012.
Eivazi, F. and Tabatabai, M. A.: Glucosidases and galacotisdases in soils, Soil Biol. Biochem., 20, 601–606,
1988.Elser, J. J., Sterner, R. W., Gorokhova, E., Fagan, W. F., Markow, T. A.,
Cotner, J. B., Harrison, J. F., Hobbie, S. E., Odell, G. M., and Weider, L.
J.: Biological stoichiometry from genes to ecosystems, Ecol. Lett., 3,
540–550, 2000.Elser, J. J., Acharya, K., Kyle, M., Cotner, J., Makino, W., Markow, T.,
Watts, T., Hobbie, S., Fagan, W., Schade, J., Hood, J., and Sterner, R. W.:
Growth rate-stoichiometry couplings in diverse biota, Ecol. Lett., 6,
936–943, 2003.Evans, S. E. and Wallenstein, M. D.: Soil microbial community response to
drying and rewetting stress: does historical precipitation regime matter?,
Biogeochemistry, 109, 101–116, 2012.Ferenci, T.: Regulation by nutrient limitation, Curr. Opin. Microbiol., 2, 208–213, 1999.Ferenci, T.: Bacterial physiology, regulation and mutational adaptation in a
chemostat environment, Adv. Microb. Physiol., 53, 169–230, 2008.Fierer, N., Schimel, J. P., and Holden, P. A.: Variations in microbial
community composition through two soil depth profiles, Soil Biol. Biochem.,
35, 167–176, 2003.Fimmen, R. L., Richter, D. de B., Vasudevan, D., Williams, M. A., and West, L. T.:
Rhizogenic Fe-C redox cycling: a hypothetical biogeochemical mechanism that
drives crustal weathering in upland soils, Biogeochemistry., 87, 127–141,
2008.Flury, S. and Gessner, M. O.: Effects of experimental warming and nitrogen
enrichment onleaf and litter chemistry of a wetland grass, Phragmites australis, Basic Appl. Ecol., 15, 219–228, 2014.Fontaine, S., Barot, S., Barré, P., Bdioui, N., Mary, B., and Rumpel,
C.: Stability of organic carbon in deep soil layers controlled by fresh
carbon supply, Nature, 450, 277–281, 2007.Frey, S. D., Lee, J., Melillo, J. M., and Six, J.: The temperature response
of soil microbial efficiency and its feedback to climate, Nat. Clim. Change,
3, 395–398, 2013.Fröberg, M., Jardine, P. M., Hanson, P. J., Swanston, C. W., Todd, D. E.,
Tarver, J. R., and Garten Jr., C. T.: Low dissolved organic carbon input from
fresh litter to deep mineral soils, Soil Sci. Soc. Am. J., 71, 347–354,
2007.Gabor, R. S., Eilers, K., McKnight, D. M., Fierer, N., and Anderson, S. P.:
From the litter layer to the saprolite: Chemical changes in water-soluble
soil organic matter and their correlation to microbial community composition,
Soil Biol. Biochem., 68, 166–176, 2014.Geider, R. J. and La Roche, J.: Redfield revisited: variability of C : N : P in
marine microalgae and its biochemical basis, Eur. J. Phycol., 37, 1–17,
2002.German, D. P., Marcelo, K. R. B., Stone, M. M., Allison, S. D.: The
Michaelis-Menten kinetics of soil extracellular enzymes in response to
temperature: a cross-latitudinal study, Glob. Change Biol., 18, 1468–1479,
2012.Goberna, M., Insam, H., Klammer, S., Pascual, J. A., and Sanchez, J.: Microbial
community structure at different depths in disturbed and undisturbed semiarid
Mediterranean forest soils, Microb. Ecol., 50, 315–326, 2005.Grandy, A. S. and Neff, J. C.: Molecular C dynamics downstream: the
biochemical decomposition sequence and its impact on soil organic matter
structure and function, Sci. Total Environ., 404, 297–307, 2008.Gülez, G., Dechesne, A., Workman, C. T., and Smets, B. F.: Transcriptome
dynamics of Pseudomonas putida KT2440 under water stress, Appl.
Environ. Microb., 78, 676–683, 2012.Hall, E. K. and Cotner, J. B.: Interactive effect of temperature and
resources on carbon cycling by freshwater bacterioplankton communities,
Aquat. Microb. Ecol., 49, 35–45, 2007.Hall, E. K., Neuhauser, C., and Cotner, J. B.: Toward a mechanistic
understanding of how natural bacterial communities respond to changes in
temperature in aquatic ecosystems, ISME J., 2, 471–481, 2008.Hebly, M., de Ridder, D., de Hulster, E. A., de la Torre Cortes, P., Pronk,
J. T., and Daran-Lapujade, P.: Physiological and transcriptional responses of
anaerobic chemostat cultures of Saccharomyces cerevisiae subjected
to diurnal temperature cycles, Appl. Environ. Microb., 80, 4433–4449, 2014.Hedges, J. I. and Oades, J. M.: Comparative organic geochemistries of soils
and marine sediments, Org. Geochem., 27, 319–361, 1997.Henn, M. R. and Chapela, I. H.: Differential C isotope discrimination by
fungi during decomposition of C3- and C4-derived sucrose, Appl.
Environ. Microb., 66, 4180–4186, 2000.Hessen, D. O., Elser, J. J., Sterner, R. W., and Urabe, J.: Ecological
stoichiometry: An elementary approach using basic principles, Limnol.
Oceanogr., 58, 2219–2236, 2013.Hobara, S., Osono, T., Hirose, D., Noro, K., Mitsuru, H., and Benner, R.:
The roles of microorganisms in litter decomposition and soil formation,
Biogeochemistry, 118, 471–486, 2014.Howe, A. C., Jansson, J. K., Malfatti, S. A., Tringe, S. G., Tiedje, J. M.,
and Brown, C. T.: Tackling soil diversity with the assembly of large, complex
metagenomes, P. Natl. Acad. Sci., 111, 4904–4909, 2014.Hua, Q., Yang, C., Oshima, T., Mori, H., and Shimizu, K.: Analysis of gene
expression in Escherichia coli in response to changes of
growth-limiting nutrient in chemostat cultures, Appl. Environ. Microb., 70,
2354–2366, 2004.Jenny, H.: Factors of Soil Formation. A System of Quantitative
Pedology,
McGraw Hill Book Company, New York, NY, USA, 1941.Jiao, N., Herndl, G. H., Hansell, D. A., Benner, R., Kattner, G., Wilhelm,
S. W., Kirchman, D. L., Weinbauer, M. G., Luo, T., Chen, F., and Azam, F.:
Microbial production of recalcitrant dissolved organic matter: long-term
carbon storage in the global ocean, Nat. Rev. Microbiol., 8, 593–599, 2010.Jobbágy, E. and Jackson, R. B.: The vertical distribution of soil
organic carbon and its relation ot climate and vegetation, Ecol. Appl., 10,
423–436, 2000.Jordan, T., Ashley, G. M., Barton, M. D., Burges, S. J., Farley, K. A., Freeman,
K. H., Jeanloz, R., Marshall, C. R., Orcutt, J. A., Richter, F. M., Royden,
L. H., Scholz, C. H., Tyler, N., and Wilding, L. P.: Basic Research
Opportunities in Earth Science, National Academy Press, Washington, D.C.,
2001.Kaiser, K. and Benner, R.: Major bacterial contribution to the ocean
reservoir of detrital organic carbon and nitrogen, Limnol. Oceanogr., 53,
99–112, 2008.Kawasaki, N. and Benner, R.: Bacterial release of dissolved organic matter
during cell growth and decline: molecular origin and composition, Limnol.
Oceanogr., 51, 2170–2180, 2006.Kayser, A., Weber, J., Hecht, V., and Rinas, U.: Metabolic flux analysis of
Escherichia coli in glucose-limited continuous culture. I.
Growth-rate dependent metabolic efficiency at steady state, Microbiol-SGM,
151, 693–706, 2005.Khalili, B., Nourbakhsh, F., Nili, N., Khademi, H., and Sharifnabi, B.:
Diversity of soil cellulase isoenzymes is associated with soil cellulase
kinetic and thermodynamic parameters, Soil Biol. Biochem., 43, 1639–1648,
2011.Kirschbaum, M. U. F.: The temperature dependence of soil organic matter
decomposition, and the effect of global warming on soil organic storage, Soil
Biol. Biochem., 27, 753–760, 1995.Klausmeier, C. A., Litchman, E., and Levin, S. A.: A model of flexible
uptake of two essential resources, J. Theor. Biol., 246, 278–289,
2007.Kleber, M.: What is recalcitrant soil organic matter?, Environ. Chem., 7, 320–332, 2010.Kleber, M., Nico, P. S., Plante, A., Filley, T., Kramer, M., Swanston, C.,
and Sollins, P.: Old and stable soil organic matter is not necessarily
chemically recalcitrant: implications for modeling concepts and temperature
sensitivity, Glob. Change Biol., 17, 1097–1107,
10.1111/j.1365-2486.2010.02278.x, 2010.
Kögel-Knabner, I., Ekschmitt, K., Flessa, H., Guggenberger, G., Matzner, E., Marschner, B.,
and von Lutzow, M.: An integrative approach of organic matter stabilization in temperate soils: Linking chemistry, physics, and biology, J. Plant Nutr. Soil Sc., 171, 5–13, 2008.Kuzyakov, Y.: Priming effects: interactions between living and dead organic
matter, Soil Biol. Biochem., 42, 1363–1371, 2010.Laganiere, J., Podrebarac, F., Billings, S. A., Edwards, K. A., and Ziegler, S. E.:
A warmer climate reduces biological reactivity without increasing the
temperature sensitivity of CO2 losses in boreal forest soils, Soil Biol.
Biochem., 84, 177–188, 2015.Larsson, C., von Stockar, U., Marison, I., and Gustafsson, L.: Growth and
metabolism of Saccharomyces cerevisiae in chemostat cultures under
carbon-, nitrogen-, or carbon- and nitrogen-limiting conditions, J.
Bacteriol., 175, 4809–4816, 1993.Lehmeier, C. A., Min, K., Niehues, N. D., Ballantyne IV, F., and Billings, S.
A.: Temperature-mediated changes of exoenzyme-substrate reaction rates and
their consequences for the carbon to nitrogen flow ratio of liberated
resources, Soil Biol. Biochem., 57, 374–382, 2013.Lehmeier, C. A., Ballantyne IV, F., Min, K., and Billings, S. A.:
Temperature-mediated changes in microbial carbon use efficiency and 13C
discrimination, submitted, 2015.Li, J., Ziegler, S., Lane, C. S., and Billings, S. A.: Warming-enhanced
preferential microbial mineralization of humified boreal forest soil organic
matter: Interpretation of soil profiles along a climate transect using
laboratory incubations, J. Geophys. Res., 117, G02008,
10.1029/2011JG001769, 2012.Liang, C., Cheng, G., Wixon, D. L., and Balser, T. C.: An Absorbing Markov
Chain approach to understanding the microbial role in soil carbon
stabilization, Biogeochemistry, 106, 303–309, 2011.Luo, C., Rodriguez-R, L. M., Johnston, E. R., Wu, L., Cheng, L., Xue,
K., and Konstantinidis, K. T.: Soil microbial community responses to a decade
of warming as revealed by comparative metagenomics, Appl. Environ. Microb.,
80, 1777–1786, 2014.Makino, W., Cotner, J. B., Sterner, R. W., and Elser, J. J.: Are bacteria
more like plants or animals? Growth rate and resource dependence of bacterial
C : N : P stoichiometry, Funct. Ecol., 17, 121–130, 2003.Manzoni, S. and Porporato, A.: Soil carbon and nitrogen mineralization:
Theory and models across scales, Soil Biol. Biochem., 41, 1355–1379, 2009.Manzoni, S., Taylor, P., Richter, A., Porporato, A., and Ågren, G. I.:
Environmental and stoichiometric controls on microbial carbon-use efficiency
in soils, New Phytol., 196, 79–91, 10.1111/j.1469-8137.2012.04225.x,
2012a.Manzoni, S., Schimel, J. P., and Porporato, A.: Responses of soil microbial
communities to water stress: results from a meta-analysis, Ecology, 93,
930–938, 2012b.Manzoni, S., Schaeffer, S. M., Katul, G., Porporato, A., and Schimel, J. P.:
A theoretical analysis of microbial eco-physiological and diffusion
limitations to carbon cycling in drying soils, Soil Biol. Biochem., 73,
69–83, 2014.Marín-Spiotta, E., Gruley, K. E., Crawford, J., Atkinson, E. E.,
Miesel, J. R., Greene, S., Cardona-Correa, C., and Spencer, R. G. M.:
Paradigm shifts in soil organic matter research affect interpretations of
aquatic carbon cycling: transcending disciplinary and ecosystem boundaries,
Biogeochemistry, 117, 279–297, 2014.Masse, D., Cambier, C., Bauman, A., Sall, S., Assigbetse, K., and Chotte,
J.-L.: Mior: an individual-based model for simulating the spatial patterns of
soil organic matter microbial decomposition, Eur. J. Soil Sci., 58,
1127–1135, 2007.Miller, A. W., Belfort, C., Kerr, E. O., and Dunham, M. J.: Design and Use
of Multiplexed Chemostat Arrays, J. Visualized Exp., 72, 1–6, 2013.Miltner, A., Bombach, P., Schmidt-Brücken, B., and Kästner, M.: SOM
genesis: microbial biomass as a significant source, Biogeochemistry, 111,
41–55, 2011.Min, K., Lehmeier, C. A., Ballantyne, F., Tatarko, A., and Billings, S. A.:
Differential effects of pH on temperature sensitivity of organic carbon and
nitrogen decay, Soil Biol. Biochem., 76, 193–200, 2014.Moorhead, D. L., Lashermes, G., Sinsabaugh, R. L.: A theoretical model of C-
and N-acquiring exoenzyme activities, which balances microbial demands during
decomposition, Soil Biol. Biochem., 53, 133–141, 2012.Monod, J.: La technique de culture continue; theorie et application, Ann. Inst. Pasteur, 79,
390–410, 1950.Moyano, F. E., Manzoni, S., and Chenu, C.: Responses of soil heterotrophic
respiration to moisture availability: An exploration of processes and models,
Soil Biol. Biochem., 59, 72–85, 2013.Nanchen, A., Schicker, A., and Sauer, U.: Nonlinear dependency of
intracellular fluxes on growth rate in miniaturized continuous cultures of
Escherichia coli, Appl. Environ. Microb., 72, 1164–1172, 2006.Nemergut, D. R., Shade, A., and Violle, C.: When where, and how does
microbial community composition matter?, Front. Microbiol., 5, 1–3,
10.3389/fmicb.2014.00497, 2014.Ofek-Lalzar, M., Sela, N., Goldman-Voronov, M., Green, S. J., Hadar, Y.,
Minz, D.: Niche and host-associated functional signatures of the root surface
microbiome, Nat. Comm., 5, 4950, 10.1038/ncomms5950, 2014.Ottesen, E. A., Young, C. R., Gifford, S. M., Eppley, J. M., Marin III, R.,
Schuster, S. C., Scholin, C. A., and DeLong, E. F.: Multispecies diel
transcriptional oscillations in open ocean heterotrophic bacterial
assemblages, Science, 345, 207–212, 2014.Parham, J. A. and Deng, S. P.: Detection, quantification and
characterization of β-glucosaminidase activity in soil, Soil Biol.
Biochem., 33, 1183–1190, 2000.Pataki, D. E.: Emerging topics in stable isotope ecology: are there isotope
effects in plant respiration?, New Phytol., 167, 321–323, 2005.Payot, S., Guedon, E., Cailliez, C., Gelhaye, E., and Petitdemange, H.:
Metabolism of cellobiose by Clostribium celluloyticurn growing in
continuous culture: evidence for decreased nadh reoxiation as a factor
limiting growth, Microbiol., 144, 375–384, 1998.Pomeroy, L. R.: The ocean's food web, a changing paradigm, Bioscience, 24, 499–504,
1974.Pomeroy, L. R., Williams, P. J., Azam, F., and Hobbie, J. E.: The microbial
loop, Oceanography, 20, 28–33, 2007.Prestat, E., David, M. M., Tas, N., Lamendella, R., Dvornik, J., Mackelprang,
R., Myrold, D. D., Jumpponen, A., Tringe, S. C., Holman, E., Mavromatis, K.,
and Jansson, J. K.: FOAM (Functional Ontology Assignments for Metagenomes): a
Hidden Markov Model (HMM) database with environmental focus, Nucleic Acids
Res., 42, e145, 10.1093/nar/gku702, 2014.Rhee, G.-Y. and Gotham, I. J.: The effect of environmental factors on phytoplankton
growth: Temperature and the interactions of temperature with nutrient
limitation, Limnol. Oceanogr., 26, 635–648, 1981.Richter, D. and Billings, S. A.: “One Physical System”: Tansley's
Ecosystem as Earth's Critical Zone, New Phytol., online first, 10.1111/nph.13338, 2015.Rumpel, C. and Kögel-Knabner, I.: Deep soil organic matter-a key but
poorly understood component of terrestrial C cycle, Plant Soil, 338,
143–158, 2011.Scheibe, T. D., Mahadevan, R., Fang, Y., Garg, S., Lon, P. E., and Lovley,
D. R.: Coupling a genome-scale metabolic model with a reactive transport
model to describe in situ uranium bioremediation, Microb. Biotech., 2,
274–286, 2009.Schimel, J. P. and Schaeffer, S. M.: Microbial control over carbon cycling
in soil, Front. Microbiol., 3, 1–11, 2012.Schimel, J. P. and Weintraub, M. N.: The implication of exoenzyme activity
on microbial carbon and nitrogen limitation in soil: a theoretical model,
Soil Biol. Biochem., 35, 549–563, 2002.Schmidt, M. W. I., Torn, M. S., Abiven, S. A., Dittmar, T., Guggenberger,
G., Janssens, I. A., Kleber, M., Kögel-Knabner, I., Lehmann, J., Manning,
D. A. C., Nannipieri, P., Rasse, D. P., Weiner, S., and Trumbore, S. E.:
Persistence of soil organic matter as an ecosystem property, Nature, 478,
49–56, 2011.Schrumpf, M., Kaiser, K., Guggenberger, G., Persson, T., Kögel-Knabner, I., and Schulze, E.-D.: Storage and stability of organic carbon in soils as related to depth, occlusion within aggregates, and attachment to minerals, Biogeosciences, 10, 1675–1691, 10.5194/bg-10-1675-2013, 2013.Sierra, C.: Temperature sensitivity of organic matter decomposition in the
Arrhenius equation: some theoretical considerations, Biogeochemistry, 108,
1–15, 2012.Simonds, S., Grover, J. P., and Chrzanowski, T. H.: Element content of
Ochromonas danica: a replicated chemostat study controlling the
growth rate and temperature, FEMS Microb. Ecol., 74, 346–352, 2010.Simpson, A. J., Simpson, M. J., Smith, E., Kelleher, B. P.: Microbially
derived inputs to soil organic matter: are current estimates too low?,
Environ. Sci. Technol., 41, 8070–8076, 2007.Sinsabaugh, R. L., Follstad Shah, J. J., Hill, B. H., and Elonen, C. M.:
Ecoenzymatic stoichiometry of stream sediments with comparison to terrestrial
soils, Biogeochemistry, 111, 455–467, 2012.Sinsabaugh, R. L., Manzoni, S., Moorhead, D. L., and Richter, A.: Carbon use
efficiency of microbial communities: stoichiometry, methodology and
modelling, Ecol. Lett., 16, 930–939, 2013.Six, J. and Paustian, K.: Aggregate-associated soil organic matter as an
ecosystem property and a measurement tool, Soil Biol. Biochem., 68, A4–A9,
10.1016/j.soilbio.2013.06.014, 2013.Six, J., Frey, S. D., Thiet, R. K., and Batten, K. M.: Bacterial and fungal
contributions to carbon sequestration in agroecosystems, Soil Sci. Soc. Am.
J., 70, 555–569, 2006.Steinweg, J. M., Jagadamma, S., Frerichs, J., and Mayes, M. A.: Activation
energy of extracellular enzymes in soils from different biomes, PLos One, 8,
e59943, 10.1371/journal.pone.0059943, 2013.Sterner, R. W., Anderson, T., Elser, J. J., Hessen, D. O., Hood, J. M.,
McCauley, E., and Urabe, J.: Scale-dependent carbon : nitrogen : phosphorus
seston stoichiometry in marine and freshwaters, Limnol. Oceanogr., 53,
1169–1180, 2008.Stets, E. G. and Cotner, J. B.: The influence of dissolved organic carbon
on bacterial phosphorus uptake and bacteria-phytoplankton dynamics in two
Minnesota lakes, Limnol. Oceanogr., 53, 137–147, 2008.Stone, E. L. and Kalisz, P. J.: On the maximum extent of tree roots, Forest Ecol. Manag., 46,
59–102, 1991.Tang, J. and Riley, W. J.: Weaker soil carbon-climate feedbacks resulting from
microbial and abiotic interactions, Nat. Clim. Change, 5, 56–60,
10.1038/NCLIMATE2438, 2015.Tang, Y. J., Martin, H. G., Myers, S., Rodriguez, S., Baidoo, E. E. K., and
Keasling, J. D.: Advances in analysis of microbial metabolic fluxes via
13C isotopic labeling, Mass Spectrom. Rev., 28, 362–375, 2009.Throckmorton, H. M., Bird, J. A., Dane, L., Firestone, M. K., and Horwath,
W. R.: The source of microbial C has little impact on soil organic matter
stabilization in forest ecosystems, Ecol. Lett., 15, 1257–1265,
2012.Tiemann, L. K. and Billings, S. A.: Changes in variability of soil moisture
alter microbial community C and N resource use, Soil Biol. Biochem., 43,
1837–1847, 2011a.Tiemann, L. K. and Billings, S. A.: Indirect effects of nitrogen amendments
on organic substrate quality increase enzymatic activity driving
decomposition in a mesic grassland, Ecosystems, 14, 234–247, 2011b.Trasar-Cepeda, C., Gil-Sotres, F., and Leirós, M. C.: Thermodynamic
parameters of enzymes in grassland soils from Galicia, NW Spain, Soil Biol.
Biochem., 39, 311–319, 2007.Trumbore, S.: Radiocarbon and Soil Carbon Dynamics, Annu. Rev. Earth Planet. Sc., 37, 47–66, 2009.Vetter, Y. A., Deming, J. W., Jumars, P. A., and Krieger-Brockett, B. B.: A
predictive model of bacterial foraging by means of freely released
extracellular enzymes, Microbiol. Ecol., 36, 75–92, 1998.Voroney, R. P., Paul, E. A., and Anderson, D. W.: Decomposition of wheat
straw and stabilization of microbial products, Can. J. Soil Sci., 69, 63–77,
1989.Wagai, R., Kishimoto-mo, A. W., Yonemura, S., Shirato, Y., Hiradate, S., and
Yagasaki, Y.: Linking temperature sensitivity of soil organic matter
decomposition to its molecular structure, accessibility, and microbial
physiology, Glob. Change Biol., 19, 1114–1125, 2013.Wang, X., Liu, L., Piao, S., Janssens, I. A., Tang, J., Liu, W., Chi, Y.,
Wang, J., and Xu, S.: Soil respiration under climate warming: differential
response of heterotrophic and autotrophic respiration, Glob. Change Biol.,
20, 3229–3237, 2014.Wieder, W. R., Bonan, G. B., and Allison, S. D.: Global soil carbon
projections are improved by modelling microbial processes, Nat. Clim. Change,
3, 909–912, 2013.Wieder, W. R., Grandy, A. S., Kallenbach, C. M., and Bonan, G. B.:
Integrating microbial physiology and physio-chemical principles in soils with
the MIcrobial-MIneral Carbon Stabilization (MIMICS) model, Biogeosciences,
11, 3899–3917, 10.5194/bg-11-3899-2014, 2014.Will, C., Thürmer, A., Wollherr, A., Nacke, H., Herold, N., Schrumpf,
M., Gutknecht, J., Wubet, T., Buscot, F., and Daniel, R.: Horizon-specific
bacterial community composition of German grassland soils, as revealed by
pyrosequencing-based analysis of 16S rRNA genes, Appl. Environ. Microb., 76,
6751–6759, 2010.
Ziegler, S. E., White, P. M., Wolf, D. C., and Thoma, G. J.: Tracking the
fate and recycling of 13C-labeled glucose in soil, Soil Sci., 170,
767–778, 2005.