Content uploaded by Gabriel Reuben Smith
Author content
All content in this area was uploaded by Gabriel Reuben Smith on Jul 15, 2019
Content may be subject to copyright.
Resource-ratio theory predicts mycorrhizal control of litter
decomposition
Gabriel R. Smith
1
*and Joe Wan
2
*
1
Department of Biology, Stanford University, 371 Serra Mall, Stanford, CA, USA;
2
Department of Environmental Systems Science, ETH Z€urich, 8092, Z€urich, Switzerland
Author for correspondence:
Gabriel R. Smith
Tel: +1 650 723 0552
Email: grsmith@stanford.edu
Received: 11 October 2018
Accepted: 24 February 2019
New Phytologist (2019) 223: 1595–1606
doi: 10.1111/nph.15884
Key words: biogeochemistry, forest ecology,
Gadgil effect, litter decomposition,
mycorrhizal fungi, nitrogen limitation,
resource-ratio theory.
Summary
Ecosystems with ectomycorrhizal plants have high soil carbon : nitrogen ratios, but it is not
clear why. The Gadgil effect, where competition between ectomycorrhizal and saprotrophic
fungi for nitrogen slows litter decomposition, may increase soil carbon. However, experimen-
tal evidence for the Gadgil effect is equivocal.
Here, we apply resource-ratio theory to assess whether interguild fungal competition for dif-
ferent forms of organic nitrogen can affect litter decomposition. We focus on variation in
resource input ratios and fungal resource use traits, and evaluate our model’s predictions by syn-
thesizing prior experimental literature examining ectomycorrhizal effects on litter decomposition.
In our model, resource input ratios determined whether ectomycorrhizal fungi suppressed
saprotrophic fungi. Recalcitrant litter inputs favored the former over the latter, allowing the
Gadgil effect only when such inputs predominated. Although ectomycorrhizal fungi did not
always hamper litter decomposition, ectomycorrhizal nitrogen uptake always increased car-
bon : nitrogen ratios in litter.
Our meta-analysis of empirical studies supports our theoretical results: ectomycorrhizal
fungi appear to slow decomposition of leaf litter only in forests where litter inputs are highly
recalcitrant. We thus find that the specific contribution of the Gadgil effect to high soil
carbon : nitrogen ratios in ectomycorrhizal ecosystems may be smaller than predicted previously.
Introduction
Soil holds more carbon than all plants and the atmosphere
together (Batjes, 1996) and is a major component of the land car-
bon sink, which absorbs almost a third of our annual CO
2
emis-
sions (Canadell et al., 2007). Terrestrial decomposition
influences the size of soil carbon stocks by facilitating the return
of carbon contained in organic matter to the atmosphere and also
is a fundamental process underlying ecosystem productivity
(Swift et al., 1979). Understanding the factors that determine
decomposition speed is therefore key to developing accurate
models of the Earth’s carbon cycle, and to predicting the
responses of soil carbon stocks to environmental change.
Nearly a century of research into plant litter decomposition
has established climate and substrate quality as major rate deter-
minants (Tenney & Waksman, 1929; Swift et al., 1979). Litter
chemistry, especially the concentration of lignin and nitrogen, is
said to govern decomposition speed within climatic constraints,
with high-lignin and/or low-nitrogen litters usually decomposing
most slowly (Meentemeyer, 1978; Aerts, 1997). Recent evidence
continues to support climate and chemistry as key controls
(Cornwell et al., 2008) but new research also challenges the com-
pleteness of this perspective, indicating greater potential for
community composition of microbes to affect decomposition
(Bradford et al., 2017; Glassman et al., 2018). This emerging
view is exemplified by findings suggesting that slowed decompo-
sition caused by interactions between two trophic guilds of soil
fungi, ectomycorrhizal and saprotrophic fungi may, in fact, be a
stronger determinant of soil carbon and nitrogen stocks than cli-
matic variables (Averill et al., 2014).
Even independently, ectomycorrhizal and saprotrophic fungi
powerfully influence the biogeochemical cycles of their environ-
ments. In return for up to 20% of their host’s net primary pro-
ductivity (Hobbie, 2006), ectomycorrhizal fungi help plants to
obtain crucial limiting nutrients such as nitrogen, mediating the
carbon and nitrogen cycles of boreal and temperate forests (Read
& Perez-Moreno, 2003; Zak et al., 2019). Free-living sapro-
trophic fungi consume abundant, recalcitrant plant polymers
such as cellulose (Cooke & Rayner, 1984; van der Wal et al.,
2013) and contribute to heterotrophic soil respiration, one of the
Earth’s largest carbon fluxes (Bond-Lamberty et al., 2018).
Although ectomycorrhizal and saprotrophic fungi rely on differ-
ent sources of carbon, they are closely related evolutionarily (Ted-
ersoo & Smith, 2013) and share aspects of their ecologies. In
fact, some ectomycorrhizal fungi oxidize organic matter as sapro-
trophic fungi do (Rineau et al., 2012; Shah et al., 2016; Op De
Beeck et al., 2018), probably not to obtain carbon but to acquire
nitrogen instead (Lindahl & Tunlid, 2015).
*These authors contributed equally to this work.
Ó2019 The Authors
New Phytologist Ó2019 New Phytologist Trust
New Phytologist (2019) 223: 1595–1606 1595
www.newphytologist.com
Research
Despite their trophic differences, ectomycorrhizal and sapro-
trophic fungi can thus both participate in enzymatic processing
of soil organic matter (B€odeker et al., 2016). This overlap in fun-
damental niche space creates opportunities for interaction
between the two trophic guilds. For example, the presence of
ectomycorrhizal fungi can suppress saprotrophic activity, slowing
decomposition of leaf litter (Gadgil & Gadgil, 1971). This phe-
nomenon is called the Gadgil effect, and could explain why
ecosystems characterized by ectomycorrhizal plants have high soil
carbon : nitrogen ratios relative to other ecosystem types (Averill
et al., 2014). Although there is not yet general consensus concern-
ing the main driver of the Gadgil effect (Fernandez & Kennedy,
2016), the ubiquity of terrestrial nitrogen limitation (LeBauer &
Treseder, 2008) and the important role that ectomycorrhizal
fungi play in plant nitrogen nutrition points to nitrogen competi-
tion between ectomycorrhizal and saprotrophic fungi as a likely
cause. This mechanism is further supported by evidence for ecto-
mycorrhizal utilization of organic nitrogen sources both in vitro
(Rineau et al., 2012; Shah et al., 2016; Op De Beeck et al., 2018)
and in situ (Talbot et al., 2013; B€odeker et al., 2014; Sterkenburg
et al., 2018), as well as ecosystem models showing that soil carbon
stocks can be influenced by ectomycorrhizal nitrogen uptake
(Orwin et al., 2011; Baskaran et al., 2017).
The Gadgil effect, mediated by nitrogen competition between
ectomycorrhizal and saprotrophic fungi, thus provides a parsimo-
nious explanation for the observed effects of ectomycorrhizal fungi
on soil corbon:nitrogen ratios (Averill et al., 2014). However,
empirical evidence for the Gadgil effect is uneven. Although ecto-
mycorrhizal fungi sometimes slow decomposition in experimental
trials (Gadgil & Gadgil, 1971; Sterkenburg et al., 2018), they also
frequently exert no observable effect (Mayor & Henkel, 2006;
Brzostek et al., 2015), and sometimes even accelerate decomposi-
tion (Zhu & Ehrenfeld, 1996; Subke et al., 2011). The dependence
of such findings on experimental context confounds our ability to
directly link the Gadgil effect to soil carbon dynamics. Here, we
seek to resolve this problematic uncertainty using theoretical com-
munity ecology. We expand upon prior efforts by incorporating
biogeochemical cycling into resource-ratio theory (Tilman, 1982),
explicitly linking fungal communities and ecosystem function.
Applying this analytically tractable and well-studied community
ecology framework, we derive the general mathematical conditions
required for nitrogen competition to cause the Gadgil effect. We
complement our analytical results with a meta-analysis of experi-
ments investigating the Gadgil effect. In doing so, we reveal which
fungal and plant traits control the effect of ectomycorrhizal fungi
on decomposition and show that inconsistent experimental evi-
dence for the Gadgil effect points to a simple, consistent root cause.
Materials and Methods
Theoretical model
Incorporating the effects of fungal stoichiometry, mycorrhizal
resource exchange and variation in substrate quality, we apply the
mechanistic consumer–resource framework of Tilman (1982)
and Chase & Leibold (2003) to link fungal nitrogen competition
within leaf litter to the dynamics of substrate carbon. Using this
approach, we derive analytical expressions for the conditions
which allow ectomycorrhizal fungi to suppress saprotrophic activ-
ity through competition for nitrogen, resulting in the Gadgil
effect.
Model structure
Our model is summarized visually in Fig. 1, and its state vari-
ables, trait parameters, and resource fluxes are defined in Table 1.
A full model description is given in Supporting information
Methods S1. Here, we describe the structure and biological moti-
vation of the model, outlining its relationship to the classic mod-
els of Tilman (1982) and Chase & Leibold (2003).
Following the two-resource competition models of Tilman
(1982), we model a saprotrophic fungus Sand an ectomycor-
rhizal fungus Mcompeting for two nutrient pools, labile nitrogen
N
‘
and recalcitrant nitrogen N
r
. Nitrogen from leaf litter enters
these two pools at the constant rates I
‘
and I
r
, respectively. The
saprotrophic fungus and ectomycorrhizal fungus take up N
r
at
per-capita rates R
S
,R
M
and N
‘
at per-capita rates L
S
,L
M
, resulting
in growth at per-capita rates G
S
,G
M
. As either form of nitrogen
may satisfy growth requirements, N
‘
and N
r
are substitutable
resources sensu Tilman (1982). Furthermore, the two species of
fungi experience mortality at a fixed per-capita rate, D. Although
dead fungal mycelium can make large contributions to soil car-
bon stocks (Clemmensen et al., 2013), Gadgil experiments usu-
ally focus on quantifying leaf litter decomposition without
considering necromass dynamics (Fernandez & Kennedy, 2016).
We thus consider removal of fungal necromass from the system
to be an appropriate simplification.
The two nitrogen pools, N
‘
and N
r
,areoperationalcategories
representing variation in nitrogen substrate quality. Rather than
focusing on particular chemical forms of nitrogen, we categorize
these resources according to the cost of their acquisition, thereby
L
L
C
R
SE
D
R
C
Fig. 1 Resource pools (circles), populations (squares), and fluxes (nitrogen,
blue; carbon, orange) in our theoretical model. Parameters are given in
Table 1.
New Phytologist (2019) 223: 1595–1606 Ó2019 The Authors
New Phytologist Ó2019 New Phytologist Trust
www.newphytologist.com
Research
New
Phytologist
1596
capturing the sum of physical and chemical factors. By this
approach N
r
thus refers to nitrogen protected by refractory plant
polymers (e.g. nitrogen physically protected within lignified plant
tissue), which is energetically costly to take up (e.g. due to the ther-
modynamic cost of oxidizing lignin). By contrast, N
‘
represents
forms of nitrogen whose uptake does not incur a cost (e.g. inorganic
nitrogen or physically unprotected organic nitrogen). These cate-
gories correspond to the litter quality distinction encoded by some
ecosystem models (e.g. the structural and metabolic litter pools of
Orwin et al., 2011), plant competition models (e.g. the readily and
slowly decomposable organic nutrient pools of Miki et al., 2010),
and plant population dynamic models (e.g. the available and non
available pools of Pastor et al., 2006).
We extend the classic two-resource framework to incorporate
carbon cycling by including a litter carbon pool C. Carbon from
litter enters this pool at a constant rate I
c
. Rather than enforcing
the coupling of the carbon pool to nitrogen dynamics, we allow
this coupling to dynamically emerge from the interaction of sub-
strate chemistry with the population dynamics of our stoichio-
metrically-constrained consumers. Saprotrophic fungus Stakes
up carbon from the environment at a per-capita rate K, whereas
ectomycorrhizal fungus Mobtains its carbon through biotrophic
exchange with an external plant host. Thus, G
S
is a function of
N
‘
,N
r
, and C, whereas G
M
is a function only of N
‘
and N
r
. Car-
bon is required for growth of both species, and also must be
respired to mine nitrogen protected by recalcitrant plant poly-
mers (e.g. lignocellulose). In either case, carbon may limit growth
when it is not sufficient to meet these demands, as discussed in
the ‘Linking nutrient fluxes and growth’ subsection below.
Our model isolates biotic control of substrate resources by
excluding leaching. This allows us to easily discern limiting
resources in the model system: all resources not under biotic con-
trol accumulate indefinitely rather than reaching equilibrium,
demonstrating a qualitative difference from those that are under
biotic control. Scenarios in which the presence of the ectomycor-
rhizal fungus releases Cfrom biotic control of the saprotrophic
fungus are considered to show evidence of the Gadgil effect.
Although these resource dynamics differ from those generally
observed in microbially-explicit carbon cycle models, we note
that many such models also omit leaching (e.g. Allison et al.,
2010) and maintain carbon equilibrium by enforcing biotic con-
trol of carbon. To this familar perspective, our model simply
adds the possibility for stoichiometry to dynamically affect such
biotic control. Nonetheless, we demonstrate that incorporating
leaching into the model makes biotic control less obvious, but
does not impact our qualitative predictions (Methods S6).
In summary, the dynamics of our model’s five state variables
(saprotrophic fungal population S, ectomycorrhizal fungal popu-
lation M, litter carbon C, labile nitrogen N
‘
, and recalcitrant
nitrogen N
r
) are described by the following system of ordinary
differential equations:
dS
dt¼GSSDS Eqn 1
dM
dt¼GMMDM Eqn 2
dC
dt¼IcKS Eqn 3
dN‘
dt¼I‘LSSLMMEqn 4
dNr
dt¼IrRSSRMMEqn 5
Consumer population dynamics are determined by the balance
between growth and death, whereas resource dynamics are deter-
mined by the balance between resource inputs (I
c
,I
‘
and I
r
) and
biotic uptake by consumers.
Linking nutrient fluxes and growth
We mechanistically link resource uptake to biomass production.
Biomass production in our model requires a constant stoichio-
metric ratio of carbon to nitrogen. Because C,N
‘
and N
r
are
Table 1 Description and dimensions of parameters and variables in the
model.
Symbol Description Dimensions
State variables
SBiomass of saprotrophic fungus biomass
MBiomass of ectomycorrhizal fungus biomass
N
‘
Labile nitrogen level N (as equivalent
biomass)
N
r
Recalcitrant nitrogen level N (as equivalent
biomass)
CCarbon level C (as equivalent
biomass)
Species traits
r
‘S
,r
‘M
Labile nitrogen uptake constant (time biomass)
1
r
rS
,r
rM
Recalcitrant nitrogen uptake constant (time biomass)
1
r
c
Carbon uptake constant (sap. only) (time biomass)
1
q
S
,q
M
Marginal carbon cost of recalcitrant
nitrogen uptake
C : N (ratio)
vEctomycorrhizal trading rate C : N (ratio)
Population rates
DPer-capita death rate (constant; identical
for both species)
time
1
G
S
,G
M
Per-capita growth rate time
1
Resource input rates
I
‘
Input rate of labile nitrogen (constant) N/time
I
r
Input rate of recalcitrant nitrogen
(constant)
N/time
I
c
Input rate of carbon (constant) C/time
Biotic resource fluxes
L
S
,L
M
Per-capita labile nitrogen use (maximum:
r
‘S
N
‘
,r
‘M
N
‘
)
N/(time biomass)
R
S
,R
M
Per-capita recalcitrant nitrogen use
(maximum: r
rS
N
r
,r
rM
N
r
)
N/(time biomass)
KPer-capita carbon use (sap. only;
maximum: r
c
C)
C/(time biomass)
XPer-capita nitrogen allocation to trade
(ecto. only)
N/(time biomass)
Carbon (C) and nitrogen (N) are measured as equivalent biomass, but are
distinguished for clarity when indicating dimensions.
Ó2019 The Authors
New Phytologist Ó2019 New Phytologist Trust
New Phytologist (2019) 223: 1595–1606
www.newphytologist.com
New
Phytologist Research 1597
measured in units of equivalent hyphal biomass, our model is
scaled such that this stoichiometric ratio is unity. This emphasizes
biotic controls on biogeochemistry by facilitating the interpreta-
tion of substrate stoichiometry and species traits relative to fungal
demand. Because Mdoes not access C, it must instead satisfy its
carbon requirement by trading nitrogen to its plant host at per-
capita rate X, receiving vunits of carbon for each unit of nitrogen
traded.
Both species can allocate carbon toward energetically expensive
N
r
uptake, at the species-specific costs of q
S
,q
M
units of Cper
unit of N
r
acquired. Accordingly, growth, nitrogen flux into
biomass, and carbon flux into biomass are related by Eqns 6 & 7:
GS¼LSþRS
|fflfflfflffl{zfflfflfflffl}
nitrogen flux
¼KqSRS
|fflfflfflfflfflffl{zfflfflfflfflfflffl}
carbon flux
Eqn 6
GM¼LMþRMX
|fflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflffl}
nitrogen flux
¼vXqMRM
|fflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflffl}
carbon flux
Eqn 7
In our model, resource uptake rates are constrained by species-
specific maximum physiological rates. Uptake abilities, a species-
specific trait measured by the rconstants (Table 1), determine
the strength of the linear relationship between resource availabil-
ity and maximum physiological uptake rate. That is, uptake of C,
N
r
and N
‘
by Scannot exceed r
c
C,r
rS
N
r
, and r
‘S
N
‘
, respectively,
whereas uptake of N
r
and N
‘
by Mcannot exceed r
rM
N
r
and
r
‘M
N
‘
.
Rather than treating nutrient acquisition strategy as a fixed
trait, we allow each fungus to maximize its growth rate by adjust-
ing resource uptake and allocation within the constraints
imposed by stoichiometry and physiology. Sachieves this by
modulating its N
r
uptake, R
S
, whereas Mmodulates both its N
r
uptake of, R
M
, and its plant-directed nitrogen transfer, X.
Whereas fungi will always find at least one resource limiting,
plasticity in resource allocation traits sometimes allows them to
escape the single-resource limitation expected under a strict inter-
pretation of Liebig’s law of the minimim (von Liebig, 1840).
After finding the optimal strategy, we solve for each of the
fluxes and growth functions. These expressions are piecewise con-
tinuous functions of resource levels, reflecting changes in nutrient
limitation according to resource availability. By substituting into
Eqns 1–5, we derive the fully-specified model equations, which
are given in Methods S1.
Parameter choices and model analysis
Following empirical evidence for resource partitioning between
saprotrophic and ectomycorrhizal fungi, we focused our analysis
on cases in which Mwas a superior competitor for N
r
, but not
for N
‘
. Although ectomycorrhizal fungi have reduced genetic
capacity for enzyme production compared to saprotrophic rela-
tives and enzymatic capacity varies among differing lineages
(Kohler et al., 2015; Pellitier & Zak, 2018), laboratory experi-
ments have found that examined ectomycorrhizal fungi produce
more peroxidase enzyme per unit fungal biomass in vitro than
saprotrophs (Talbot et al., 2015), and soil peroxidase activity in
nature correlates positively with greater ectomycorrhizal abun-
dance relative to saprotrophs (B€odeker et al., 2014; Talbot et al.,
2014; Sterkenburg et al., 2018).
Additionally, ectomycorrhizal fungi have access to large pools
of biotrophic carbon (Hobbie, 2006) and thus are well-equipped
to carry out degradation of refractory plant compounds such as
lignin, whose breakdown involves an energy-intensive,
cometabolic process (Kirk & Farrell, 1987). Finally, resource-ra-
tio theory requires that species specialize on (that is, have a lower
R*for) different resources in order to potentially coexist with one
another (Tilman, 1982). For these reasons, the results presented
in the main text refer to this scenario (but an overview of all cases
can be found in Methods S2).
We considered the effect of variation in resource input ratios
on the ability of the ectomycorrhizal fungus to induce or exacer-
bate nitrogen limitation in the saprotroph. This can be inferred
by accumulation of carbon because we exclude resource leaching.
When Sis limited by the availability of nitrogen more than by
that of carbon, accumulation of carbon occurs because the rate of
carbon use by Sis lower than the rate of carbon input. We there-
fore sought specifically to determine the necessary conditions for
Mto suppress growth of Sto a degree sufficient to cause carbon
accumulation, as is consistent with the Gadgil effect. Details of
our approach are summarized in Methods S2.
Meta-analysis
We coded empirical studies of ectomycorrhizal influence on litter
decomposition compiled by Fernandez & Kennedy (2016)
according to whether the Gadgil effect was observed. In these
studies, researchers examined decomposition, measured by mass
loss of a leaf litter test medium or of the forest litter layer, as a
function of ectomycorrhizal colonization. In most experiments,
ectomycorrhizal presence was manipulated by trenching or by
girdling of trees, whereas one used a correlational approach
(Koide & Wu, 2003). We excluded one study in which the test
medium was not a natural substrate, Fisher & Gosz (1986), and
added two further studies, Subke et al. (2011) and Sterkenburg
et al. (2018), for a total of 12 experiments.
Our model focuses on the potential for variation in resource
inputs to influence the Gadgil effect, so we collected data on leaf
litter traits of test species from prior published literature, using
the median values where we found multiple sources. We focused
on lignin and nitrogen because lignin can contribute to nitrogen
immobilization and is considered resistant to microbial decay
(Couteaux et al., 1995). Ratios of lignin : nitrogen in litter thus
correspond to I
r
/I
‘
in our model.
For Brzostek et al. (2015), in which a mixture of Q. rubra and
Q. alba litter was used, we averaged values for these two species.
For Subke et al. (2011), in which green Tsuga heterophylla needles
were used as a decomposition substrate rather than
T. heterophylla litter, we used foliar lignin and nitrogen measure-
ments rather than those of litter tissue. We were unable to locate
data for Dicymbe corymbosa, the test litter of two experiments
(Mayor & Henkel, 2006; McGuire et al., 2010), so samples of
mature D. corymbosa leaves were analyzed for nitrogen content
New Phytologist (2019) 223: 1595–1606 Ó2019 The Authors
New Phytologist Ó2019 New Phytologist Trust
www.newphytologist.com
Research
New
Phytologist
1598
using a Carlo-Erba NA 1500 elemental analyzer (Waltham, MA,
USA), and were sent to Cumberland Valley Analytical Services
(Waynesboro, PA, USA) for lignin analysis according to Goering
& van Soest (1970). We adjusted the measured nitrogen content
of D. corymbosa before statistical analysis to account for resorp-
tion, using the average resorption efficiency of tropical ectomyc-
orrhizal trees as calculated by Zhang et al. (2018). Data used for
the meta-analysis are available in Table S1.
We used a Welch’s t-test to determine whether the lignin : nitro-
gen ratios of litter were significantly greater in studies where the
Gadgil effect was observed. We further tested the robustness of our
result with a non parametric unpaired Wilcoxon rank sum test.
Data accessibility
All data used for this manuscript appear in the paper or in the
supporting information.
Results
In our model, resource input rates determine the impact of ecto-
mycorrhizal fungi on litter carbon dynamics. Here, we highlight
key analytical results describing this relationship. In brief, we find
that the ectomycorrhizal fungus is able to suppress saprotrophic
growth and promote carbon accumulation only when nitrogen
inputs to the system are predominantly recalcitrant. This is in
accordance with the predictions of resource-ratio theory, as recal-
citrant nitrogen is the form for which the ectomycorrhizal fungus
is a superior competitor. Our analysis shows, therefore, that the
Gadgil effect is not a universal consequence of nitrogen competi-
tion, but ectomycorrhizal fungi can alter substrate carbon : nitro-
gen ratios even when they do not suppress litter decomposition.
Model outcomes
The outcome of competition and resulting carbon dynamics
depends on resource input, as illustrated by the numerical simu-
lations in Fig. 2. Rather than representing a specific ecosystem,
we choose parameter values (Methods S7) that allow us to high-
light the full range of outcomes possible under the model. Our
conclusions are not based upon these particular parameter values
but rather on the general mathematical analysis presented in the
next section. For ease of interpretation, we shall nevertheless
begin by discussing these illustrative simulations because they
provide a useful way to visualize the outcomes of our model.
In Fig. 2, we vary resource input rates and introduce the ecto-
mycorrhizal fungus Mafter the saprotroph Sis near equilibrium
with its limiting resources. Successful invasion by the ectomycor-
rhizal fungus always increases recalcitrant nitrogen utilization
(Fig. 2a–d), but its effects on carbon vary. The ectomycorrhizal
fungus can exclude the saprotroph from the system, suppressing
all carbon utilization (Fig. 2a). Under other circumstances, the
ectomycorrhizal fungus coexists with the saprotroph and initiates
(Fig. 2a,b) or accelerates (Fig. 2c) carbon accumulation. We con-
sider all of these outcomes, including not only total exclusion of
the saprotroph but also initiation or acceleration of carbon
accumulation, to be representative of the Gadgil effect. At other
resource input rates, the ectomycorrhizal fungus has no long-term
impact on carbon dynamics (Fig. 2d) or cannot invade the sapro-
troph-only community (Fig. 2e).
These divergent outcomes are linked to the nutrient limitation
status of the saprotophic fungus. Because the saprotroph is the
sole consumer of litter carbon, carbon accumulates only when
saprotrophic growth is limited by nitrogen. This can occur even
in a saprotrophic monoculture, as evidenced by the two cases
where nitrogen was lowest relative to carbon (Fig. 2c,e). In cases
where N
r
is high relative to N
‘
, competition between the two
fungi for nitrogen induces nitrogen limitation where it does not
already occur (Fig. 2a,b) or exacerbates existing nitrogen limita-
tion (Fig. 2c). Nonetheless, in the two cases where recalcitrant N
r
input is lowest, the ectomycorrhizal fungus does not affect the
nutrient limitation status of the saprotroph (Fig. 2d,e).
The ectomycorrhizal fungus also can influence elemental
cycling in litter without inducing the Gadgil effect. For instance,
the illustrative simulations include cases where the ectomycor-
rhizal fungus suppresses the accumulation of N
‘
and N
r
without
affecting the nitrogen limitation status of the saprotroph (Figs 2d,
S2). Thus, in our model, it is possible for ectomycorrhizal nitro-
gen uptake to elevate of carbon : nitrogen ratios without inducing
the Gadgil effect.
Examination of model outcomes for a full range of resource
input ratios confirms the trends observed from individual simula-
tions: the ectomycorrhizal fungus promotes the Gadgil effect only
when N
r
is high relative to N
‘
. Noting that model outcomes can
be predicted by resource input ratios alone, we visualize competi-
tive outcomes and nutrient dynamics as a function of the labile
nitrogen-to-carbon input ratio, I
‘
:I
c
, and recalcitrant nitrogen-to-
carbon input ratio, I
r
:I
c
(Fig. 3); the remaining parameters have
values identical to those used in Fig. 2. For the saprotrophic fun-
gus growing in isolation, carbon only accumulates when both
forms of nitrogen input are low relative to carbon input (Fig. 3a,
orange zone). When both species are present, carbon accumulation
is possible under a greater range of conditions. If N
r
is sufficiently
high relative to N
‘
, the ectomycorrhizal fungus induces (Fig. 3b,
regions A and B) or increases (Fig. 3b, region C) carbon accumula-
tion. This change in carbon accumulation rate is quantitatively
shown in Fig. 3(c), where a nitrogen-mediated Gadgil effect occurs
at high N
r
input (above the gray and orange solid lines of Fig. 3b).
Analytical results
Here, we highlight key results from the analytical treatment of
our model, corresponding to each of the boundary lines of Fig. 3.
A full set of analytical expressions for the outcomes of our model
is presented in Methods S2, and detailed derivations are given in
Methods S3–S5.
In a saprotroph-only system (as in Fig. 3a), nitrogen and car-
bon cycling are tightly linked. Carbon accumulation is deter-
mined solely by the relative stoichiometry of nitrogen and carbon
inputs to the system. Analytically, we find that carbon accumu-
lates when the following condition, corresponding to the solid
orange boundary in Fig. 3(a), holds:
Ó2019 The Authors
New Phytologist Ó2019 New Phytologist Trust
New Phytologist (2019) 223: 1595–1606
www.newphytologist.com
New
Phytologist Research 1599
Ic[I‘þIrþqSIrEqn 8
Because our model is scaled to facilitate comparison of
resource inputs to consumer stoichiometric requirements, this
inequality demands that carbon input exceeds the rate needed to
make fungal biomass from nitrogen input (I
‘
+I
r
) after account-
ing for the cost of utilizing recalcitrant nitrogen (q
S
I
r
).
Two conditions determine whether the ectomycorrhizal fun-
gus can cause the Gadgil effect (Methods S2–S5). First, the ecto-
mycorrhizal fungus must be able to successfully compete with the
saprotroph for nitrogen. This condition allows the ectomycor-
rhizal fungus to persist if both fungi become nitrogen-limited
due to carbon accumulation. Here, we show this for the biologi-
cally realistic case in which the ectomycorrhizal fungus specializes
on N
r
, but an analysis of all possible R*values demonstrates that
this requirement is general: Mmust have a lower R*for at least
one resource in order for the Gadgil effect to occur (see analysis
for all cases in Methods S2). Second, carbon and nitrogen
dynamics must be decoupled. Stated simply, the ectomycorrhizal
fungus must suppress saprotrophic growth to the extent that the
saprotroph does not use all available carbon. Together, these con-
ditions ensure that carbon accumulates indefinitely.
Condition 1: ectomycorrhizal competition for nitrogen
When the Gadgil effect occurs in our model, both fungal species
are limited by nitrogen. Here, we can apply the classic resource-
ratio theory of Tilman (1982) for analysis. Resource require-
ments are thus summarized by R*, the level of resource Rat
which a species can maintain a constant population. A lower R*
(a) (b)
(d) (e)
(c)
Ti Ti Ti
Ti Ti
P
R
Fig. 2 Numerical model simulations. Species
populations and resources are plotted as a
function of time, excluding N
‘
, which is held
at so low a level as to not be visible.
Saprotrophic fungus Senters at time 0; entry
of ectomycorrhizal fungus Mis marked by a
vertical line. Resource input ratios I
‘
/I
c
,I
r
/I
c
vary across the simulations, creating different
outcomes (see Supporting Information
Methods S6 for parameters and simulation
details).
(a) (b) (c)
LLL
CS
S
Fig. 3 Equilibria and carbon dynamics of the model at different resource input ratios. As model behavior depends only on the ratio of resource inputs, the
axes show the ratio of each form of nitrogen to carbon, scaled so that a value of 1 is the stoichiometric ratio required to make fungal biomass. (a) Resource
limitation status and nutrient dynamics of a saprotrophic monoculture at steady state for different resource input ratios. (b) Competitive outcomes and
effects on nutrient dynamics for two-species system. Hatched regions indicate resource ratios at which the two species cannot coexist. Boundaries from (a)
are overlaid to highlight changes in resource limitation. Regions A–E correspond to the panels of Fig. 2(a–e). (c) The strength of the Gadgil effect, measured
as the difference in carbon accumulation rate between the saprotroph-only and coexistence (or ectomycorrhizal-only) equilibria. The scale expresses this
change as a percentage of the carbon input rate I
c
. The boundaries of (b) are overlaid for clarity.
New Phytologist (2019) 223: 1595–1606 Ó2019 The Authors
New Phytologist Ó2019 New Phytologist Trust
www.newphytologist.com
Research
New
Phytologist
1600
implies greater competitive ability for that resource. For the
saprotroph, these values are inversely proportional to uptake abil-
ity (r
‘S
and r
rS
, respectively):
ðN‘Þ
S¼D
r‘S
;ðNrÞ
S¼D
rrS
Eqn 9
Thus, competitive ability is determined by the ability to take
up enough nitrogen to make up for the rate of mortality, D. For
the ectomycorrhizal fungus, we find the following R*values:
ðN‘Þ
M¼D
r‘M
vþ1
v;ðNrÞ
M¼D
rrM
vþ1
vqM
Eqn 10
Ectomycorrhizal R*values are also inversely proportional to
uptake ability (r
‘M
,r
rM
), but each expression contains a further
term representing the additional nitrogen cost incurred by
resource trade with an external plant host.
Our focal scenario, where the ectomycorrhizal fungus is a
superior competitor for N
r
, corresponds to the assumption that
the ectomycorrhizal fungus has a lower R*for N
r
and a higher R*
for N
‘
than the saprotrophic fungus does: Nr
ðÞ
M\Nr
ðÞ
Sand
N‘
ðÞ
M[N‘
ðÞ
S. Applying the graphical zero net-growth isocline
approach of (Tilman, 1982) to this case illustrates the conditions
which determine competitive outcomes (Fig. 4a). Though the
zero net-growth isoclines differ from the model of (Tilman,
1982) when carbon is low enough to limit saprotrophic growth,
such conditions do not enable the Gadgil effect and are not
examined here (Fig. 4b).
Three conditions determine competitive outcome in the model
of Tilman (1982): the relative positions of the zero net-growth iso-
clines, the resource consumption of the competing species at equi-
librium, and the supply of resources. Under the case we consider
here, the first two conditions are fixed: the zero net-growth isolines
cross and each species consumes relatively more of the resource
that has a greater impact on its growth (Fig. 4a). By contrast, we
allow resource supply to vary. As such, depending on I
r
/I
‘
,the
ectomycorrhizal fungus may competitively exclude the saprotroph
(Fig. 4a, region I), the two species may coexist (region II), or the
saprotroph may exclude the ectomycorrhizal fungus (region III).
In particular, we find that if I
r
/I
‘
is above a critical threshold
Q
M
, the ectomycorrhizal fungus competitively excludes the
saprotroph:
Ir
I‘
[QM;where QM¼rrM
r‘M
1=ðN‘Þ
S1=ðN‘Þ
M
1=ðNrÞ
M1=ðNrÞ
S
Eqn 11
This inequality corresponds to the solid green line of Fig. 3(b).
Here, Q
M
is the product of two terms representing ectomycor-
rhizal specialization on N
r
and the ratio of ectomycorrhizal com-
petitive dominance for N
r
to saprotrophic competitive
dominance for N
‘
. When this condition holds, the saprotrophic
fungus is driven to extinction and carbon accumulates at the
maximum possible rate (as in Fig. 2a). It therefore represents the
strongest possible Gadgil effect.
If instead I
r
/I
‘
is below Q
M
but above the threshold for sapro-
trophic dominance Q
S
, the ectomycorrhizal fungus coexists with
the saprotroph:
QS\Ir
I‘
\QM;where QS¼rrS
r‘S
1=ðN‘Þ
S1=ðN‘Þ
M
1=ðNrÞ
M1=ðNrÞ
S
Eqn 12
This inequality represents the solid gray line of Fig. 3(b). The
two terms in the expression for Q
S
are analogous to those in the
expression for Q
M
. The first term represents the relative ability of
the saprotrophic fungus (instead of the ectomycorrhizal fungus) to
use N
r
.AsinQ
M
, the second term is the ratio of ectomycorrhizal
competitive dominance for N
r
to saprotrophic competitive domi-
nance for N
‘
.
Condition 2: decoupling of carbon and nitrogen dynamics
The second condition for the Gadgil effect is that the ectomycor-
rhizal fungus must decouple carbon and nitrogen dynamics by
suppressing saprotrophic growth. This is possible because the
ectomycorrhizal fungus obtains carbon from its plant host, allow-
ing it to suppress saprotrophic uptake of litter carbon while
remaining independent of the litter carbon pool.
Our analysis of nitrogen competition demonstrated that high
I
r
favor ectomycorrhizal competitive dominance. Considering the
second condition, we find that N
r
also determines the degree to
which the ectomycorrhizal fungus suppresses saprotrophic carbon
utilization. In order for carbon to accumulate, I
r
must be greater
than the following threshold:
Ir[QMI‘QMQS
1þðqSþ1ÞQS
IcEqn 13
This inequality defines the solid orange line in Fig. 3(b). The
first term in the right-hand expression (Q
M
I
‘
) requires that N
r
input be high enough to compensate for I
‘
. The second term (I
c
multiplied by a negative constant) indicates that as carbon input
increases, carbon has a greater tendency to accumulate, and thus
N
r
is required to induce carbon accumulation.
Finally, we note that nitrogen use by the ectomycorrhizal fun-
gus always draws nitrogen availability down to a stable equilib-
rium (given by the analytical expressions in Methods S3.4–S3.7),
invariably altering nitrogen cycling regardless of its ability to
induce the Gadgil effect. Fueled by plant photosynthate, the ecto-
mycorrhizal fungus can take up nitrogen at its maximum physio-
logical rate, which makes its nitrogen acquisition complementary
to that of the saprotroph. Because the ectomycorrhizal fungus
does not directly interact with litter carbon in our model, its pres-
ence always elevates carbon : nitrogen ratios.
Meta-analysis
We tested whether our model could adequately predict occur-
rence of the Gadgil effect in ectomycorrhizal forests by compar-
ing our findings to the results of prior empirical investigations.
Ó2019 The Authors
New Phytologist Ó2019 New Phytologist Trust
New Phytologist (2019) 223: 1595–1606
www.newphytologist.com
New
Phytologist Research 1601
Our analysis showed that higher I
r
/I
‘
promoted the Gadgil effect.
Because lignin : nitrogen ratios in decomposition test substrates
are functionally analogous to I
r
/I
‘
, we hypothesized that lignin :
nitrogen ratios would be significantly higher in experiments
where the Gadgil effect was observed than in those where it was
not. A Welch’s t-test showed that lignin : nitrogen ratios were
indeed significantly higher in studies showing the Gadgil effect,
supporting our theoretical results (Fig. 5; t=5.271, P=0.0002).
A non-parametric Wilcoxon rank sum test returned qualitatively
equivalent results (P=0.0028), underscoring the robustness of
this finding.
Discussion
Chemical recalcitrance has long been supported alongside climate
as a major factor affecting litter decomposition speed (Tenney &
Waksman, 1929; Swift et al., 1979; Cornwell et al., 2008). A
growing body of literature now shows that ecological factors such
as decomposer community composition may be of equal or
greater importance, however (Gadgil & Gadgil, 1971; Bradford
et al., 2017; Glassman et al., 2018). Here, we have found that
interactions between ecological and chemical factors jointly influ-
ence this important ecosystem function. Concisely, while climatic
conditions constrain decomposer physiology, substrate stoi-
chiometry selects for different decomposers.
Our model analysis showed that decomposition can indeed be
inhibited by ectomycorrhizal nitrogen uptake, but that this out-
come is not universal. The ability of ectomycorrhizal fungi to
induce nitrogen limitation in co-occurring saprotrophs is a func-
tion of the nitrogen use traits of the two fungal guilds and the
chemistry of their substrate. In accordance with empirical evi-
dence, ectomycorrhizal fungi in our model are superior competi-
tors for recalcitrant nitrogen sources, and were thus favored by
litter inputs with higher ratios of recalcitrant to labile nitrogen.
When ratios were sufficiently high, ectomycorrhizal fungi
excluded saprotrophic fungi and thereby hindered carbon uptake,
demonstrating the Gadgil effect. By contrast, lower input ratios
of recalcitrant to labile nitrogen benefited saprotrophic fungi,
which were then able to evade nitrogen limitation even when
ectomycorrhizal fungi were present. Substrate chemistry deter-
mined whether the Gadgil effect was possible, but ectomycor-
rhizal fungi always increased substrate carbon : nitrogen ratios,
even when the Gadgil effect did not occur.
We determined moreover that ectomycorrhizal nitrogen use
traits are key controllers of the Gadgil effect. Ectomycorrhizal
fungi benefit from reduced carbon limitation thanks to their pho-
tosynthetic host, but this alone cannot provide the advantage
required for them to limit saprotrophic activity. Fundamentally,
if nitrogen competition is to cause the Gadgil effect, this must be
a result not only of differences in carbon acquisition traits, but
also of differences in nitrogen acquisition traits. Specifically, ecto-
mycorrhizal fungi must be superior competitors sensu Tilman
(a) (b)
Species
Fig. 4 Graphical analysis of the resource competition model. We plot the zero net-growth isoclines (solid lines) and resource consumption vectors (solid
arrows) of both fungi at high and low levels of substrate carbon C. (a) When carbon is high and does not limit growth, the model represents the
substitutable resource case of Tilman (1982). The system’s equilibrium point is found where the isoclines cross and the consumption vectors ensure that
stable coexistence is possible. Here, the outcome of competition is determined by resource input relative to the inverse of the resource consumption vectors
(dashed lines). If recalcitrant inputs predominate (region I), the ectomycorrhizal fungus excludes the saprotroph. Coexistence occurs at intermediate ratios
of recalcitrant to labile nitrogen inputs (region II, highlighted). If labile inputs predominate (region III), the saprotroph excludes the ectomycorrhizal fungus.
(b) When saprotrophic growth is limited by carbon availability, the isoclines no longer conform to any of the classic cases outlined by Tilman (1982) and the
conditions for two-resource competition no longer apply. We therefore do not depict the outcome of competition for this case.
OU
L
Fig. 5 Lignin : nitrogen ratios predict the occurrence of the Gadgil effect in
12 empirical studies. See Supporting Information Table S1 for studies used
and trait data. Welch’s two sample t-test confirms the significance of the
difference (t= 5.271, P= 0.0002).
New Phytologist (2019) 223: 1595–1606 Ó2019 The Authors
New Phytologist Ó2019 New Phytologist Trust
www.newphytologist.com
Research
New
Phytologist
1602
(1982) for at least one form of nitrogen, enabling more efficient
growth at a given environmental nitrogen level compared to
saprotrophic fungi. In fact, they must acquire nitrogen far more
efficiently than saprotrophs because they must balance both their
own budget and that of their host.
As predicted by our model, recalcitrant litter appears to be a
prerequisite for the Gadgil effect in empirical trials. Consistent
with ectomycorrhizal specialization on recalcitrant nitrogen
sources, experiments using test litters with high lignin : nitrogen
ratios found ectomycorrhizal retardation of decomposition,
whereas those using more labile litters did not. Furthermore, all
experiments in the former category occurred in coniferous forests.
We have found no direct evidence for the Gadgil effect in tem-
perate or tropical broadleaf forests (Mayor & Henkel, 2006;
McGuire et al., 2010; Brzostek et al., 2015) despite development
of typical ectomycorrhizal nutrient economies in these environ-
ments (Phillips et al., 2013; Corrales et al., 2016). Taken
together, our results indicate that the specific influence of the
Gadgil effect may be restricted to ectomycorrhizal ecosystems
where recalcitrant litter already slows decomposition.
Like that of Tilman (1982), our model focuses on interactions
between ecological communities and resources. Although we cap-
ture interguild differences in resource-use traits between microbes
in greater detail than do many larger-scale ecosystem models
(Sulman et al., 2018), we do not explicitly represent a complete
carbon or nitrogen cycle. Omitting respiration and nitrogen min-
eralization, we do not track total ecosystem carbon and nitrogen
stocks. As such, we also omit direct coupling of these elemental
cycles. The model’s strong concordance with empirical evidence
indicates that it can successfully answer our specific question:
does uptake of nitrogen by ectomycorrhizal fungi cause sapro-
trophic fungi to become nitrogen-limited, and thereby slow the
decomposition of leaf litter? However, many further questions
remain.
For example, there is now wide recognition that a narrow focus
on litter decomposition excludes many other processes mediating
soil carbon storage (Jackson et al., 2017). Indeed, although his-
torical perspectives emphasize the accumulation of undecom-
posed plant polymers as a driver of soil carbon stocks (Lehmann
& Kleber, 2015), new conceptual and empirical discoveries high-
light the significant contributions of microbial residues and
necromass (Clemmensen et al., 2013; Cotrufo et al., 2013). As
our model does not include recycling of dead fungal mycelium,
we cannot here make predictions concerning the effects of ecto-
mycorrhizal nitrogen uptake on the formation and stability of
microbially-derived soil organic matter. In light of our findings,
we nevertheless speculate that this comparatively understudied
subject may be of greater relevance for total soil carbon stocks
than the Gadgil effect sensu stricto.
Community interactions not present in our model also may be
of major importance in assessing the total environmental effects
of ectomycorrhizal fungi. Enzyme production by ectomycorrhizal
mycelium could subsidize saprotrophic growth by increasing car-
bon availability (Baskaran et al., 2017), or even contribute
directly to carbon mineralization (Hofrichter et al., 1999). This
would likely slow carbon accumulation, even when the Gadgil
effect occurs. Differences in the modeled fate of carbon-rich ecto-
mycorrhizal decomposition products in prior work contribute to
opposite predictions for total soil carbon stocks (Orwin et al.,
2011; Baskaran et al., 2017), underscoring the significance of this
topic.
Finally, interactions between individual plants and fungi in
symbiosis may be consequential as well. We here used fixed trad-
ing rates of nitrogen and carbon between the ectomycorrhizal
fungus and its external host (v). Because the relative profitability
of the trade for the ectomycorrhizal partner in our model affects
its nitrogen uptake and thus the nitrogen nutrition of its host,
extensions including context-dependency and fluctuation of this
value could reveal interesting feedbacks between aboveground
and belowground compartments with potential impacts on
decomposition. Prior work incorporating flexible trading rates
and multiple potential fungal partners shows that plant carbon
allocation towards ectomycorrhizal fungi may be highly depen-
dent in particular on ecological marketplace dynamics (Franklin
et al., 2014), which do not generally appear in models addressing
effects of mycorrhizal fungi on decomposition (Orwin et al.,
2011; Baskaran et al., 2017), ours included.
We have not found evidence for a globally significant Gadgil
effect mediated by nitrogen competition, and thus conclude
that its contribution to high soil carbon : nitrogen ratios in
ectomycorrhizal soils may not be as large as anticipated (Averill
et al., 2014). We observe, however, that introducing the ecto-
mycorrhizal fungus to a saprotrophic monoculture in our
model always lowers nitrogen levels, regardless of its effect on
carbon, simply due to more efficient utilization of the total
nitrogen pool. Several studies show that differences in soil car-
bon : nitrogen ratios in forests with trees of different mycor-
rhizal type are better explained by variation in nitrogen stocks
than in carbon stocks (Craig et al., 2018; Zhu et al., 2018),
consistent with this simpler mechanism. We fully expect that a
shift in soil stoichiometry facilitated by ectomycorrhizal fungi is
likely to have important consequences for process rates in many
ecosystems, potentially resulting in decreased decomposition
and/or increased primary productivity. However, we caution
that inferring this from variation in stock sizes alone risks con-
flating cause and effect.
Evidence that mycorrhizal associations are perhaps the most
important plant traits controlling not only ecosystem biogeo-
chemistry (Phillips et al., 2013; Averill et al., 2014), but also
responses to global change pressures (Terrer et al., 2016; Averill
et al., 2018) continues to mount. Interest in the effects of ecto-
mycorrhizal fungi on soil carbon dynamics has grown accordingly
(Zak et al., 2019). Here, we have used theoretical community
ecology to show that a nitrogen-mediated Gadgil effect, hypothe-
sized to explain globally significant biogeochemical patterns, lacks
strong general support. We emphasize that this result does not
undermine findings of ectomycorrhizal importance in global car-
bon and nitrogen cycling (Averill et al., 2014). It does, however,
demonstrate that conclusions regarding the mechanisms driving
observed variation are likely premature and that there exists an
urgent need for further research clarifying the role of fungi in soil
carbon and nitrogen dynamics. We anticipate that many exciting
Ó2019 The Authors
New Phytologist Ó2019 New Phytologist Trust
New Phytologist (2019) 223: 1595–1606
www.newphytologist.com
New
Phytologist Research 1603
discoveries in this area are waiting to be made, and that the devel-
opment and application of ecological theory will prove invaluable
in revealing them.
Acknowledgements
We thank K. G. Peay, E. A. Mordecai, P-J. Ke, K. C. Abbott, C.
Averill, and members of the Peay and Abbott Labs for valuable
constructive comments. We thank B. D. Lindahl and three
anonymous reviewers for recommendations that led to the sub-
stantial improvement of this manuscript. Finally, we thank T. W.
Henkel for providing D. corymbosa samples for analysis. The
work of GRS is funded by a US NSF Graduate Research Fellow-
ship.
Author contributions
GRS and JW contributed equally to this work.
ORCID
Gabriel R. Smith https://orcid.org/0000-0003-3676-0821
Joe Wan https://orcid.org/0000-0001-5950-2353
References
Aerts R. 1997. Climate, leaf litter chemistry and leaf litter decomposition in
terrestrial ecosystems: a triangular relationship. Oikos 79: 439.
Allison SD, Wallenstein MD, Bradford MA. 2010. Soil-carbon response to
warming dependent on microbial physiology. Nature Geoscience 3: 336.
Averill C, Dietze MC, Bhatnagar JM. 2018. Continental-scale nitrogen pollution
is shifting forest mycorrhizal associations and soil carbon stocks. Global Change
Biology 24: 4544–4553.
Averill C, Turner BL, Finzi AC. 2014. Mycorrhiza-mediated competition between
plants and decomposers drives soil carbon storage. Nature 505: 543–545.
Baskaran P, Hyv€onen R, Linnea Berglund S, Clemmensen KE,
Agren GI,
Lindahl BD, Manzoni S. 2017. Modelling the influence of ectomycorrhizal
decomposition on plant nutrition and soil carbon sequestration in boreal forest
ecosystems. New Phytologist 213: 1452–1465.
Batjes N. 1996. Total carbon and nitrogen in the soils of the world. European
Journal of Soil Science 47: 151–163.
B€odeker ITM, Clemmensen KE, de Boer W, Martin F, Olson A, Lindahl BD.
2014. Ectomycorrhizal Cortinarius species participate in enzymatic oxidation of
humus in northern forest ecosystems. New Phytologist 203: 245–256.
B€odeker IT, Lindahl BD, Olson A, Clemmensen KE. 2016. Mycorrhizal and
saprotrophic fungal guilds compete for the same organic substrates but affect
decomposition differently. Functional Ecology 30: 1967–1978.
Bond-Lamberty B, Bailey VL, Chen M, Gough CM, Vargas R. 2018.
Globally rising soil heterotrophic respiration over recent decades. Nature
560:80–83.
Bradford MA, Ciska GF, Bonis A, Bradford EM, Classen AT, Cornelissen JHC,
Crowther TW, De Long JR, Freschet GT, Kardol P et al. 2017. A test of the
hierarchical model of litter decomposition. Nature Ecology and Evolution 1:
1836–1845.
Brzostek ER, Dragoni D, Brown ZA, Phillips RP. 2015. Mycorrhizal type
determines the magnitude and direction of root-induced changes in
decomposition in a temperate forest. New Phytologist 206: 1274–1282.
Canadell JG, Le Quere C, Raupach MR, Field CB, Buitenhuis ET, Ciais P,
Conway TJ, Gillett NP, Houghton RA, Marland G. 2007. Contributions to
accelerating atmospheric CO
2
growth from economic activity, carbon intensity,
and efficiency of natural sinks. Proceedings of the National Academy of Sciences,
USA 104: 18866–18870.
Chase JM, Leibold MA. 2003. Ecological niches: linking classical and contemporary
approaches. Chicago, IL, USA: University of Chicago Press.
Clemmensen KE, Bahr A, Ovaskainen O, Dahlberg A, Ekblad A, Wallander H,
Stenlid J, Finlay RD, Wardle DA, Lindahl BD. 2013. Roots and associated
fungi drive long-term carbon sequestration in boreal forest. Science 340: 1615–
1618.
Cooke RC, Rayner ADM. 1984. Ecology of saprotrophic fungi. New York, NY:
Longman.
Cornwell WK, Cornelissen JHC, Amatangelo K, Dorrepaal E, Eviner VT,
Godoy O, Hobbie SE, Hoorens B, Kurokawa H, Perez-Harguindeguy N et al.
2008. Plant species traits are the predominant control on litter decomposition
rates within biomes worldwide. Ecology Letters 11: 1065–1071.
Corrales A, Mangan SA, Turner BL, Dalling JW. 2016. An ectomycorrhizal
nitrogen economy facilitates monodominance in a neotropical forest. Ecology
Letters 19: 383–392.
Cotrufo MF, Wallenstein MD, Boot CM, Denef K, Paul E. 2013. The
Microbial Efficiency Matrix Stabilization (MEMS) framework integrates plant
litter decomposition with soil organic matter stabilization: do labile plant
inputs form stable soil organic matter? Global Change Biology 19: 988–995.
Couteaux MM, Bottner P, Berg B. 1995. Litter decomposition climate and litter
quality. Trends in Ecology and Evolution 10:63–66.
Craig ME, Turner BL, Liang C, Clay K, Johnson DJ, Phillips RP. 2018. Tree
mycorrhizal type predicts within-site variability in the storage and distribution
of soil organic matter. Global Change Biology 24: 3317–3330.
Fernandez CW, Kennedy PG. 2016. Revisiting the ‘Gadgil effect’: do interguild
fungal interactions control carbon cycling in forest soils? New Phytologist 209:
1382–1394.
Fisher FM, Gosz JR. 1986. Effects of trenching on soil processes and
properties in a New Mexico mixed-conifer forest. Biology and Fertility of
Soils 2:35–42.
Franklin O, N€asholm T, H€ogberg P, H€ogberg MN. 2014. Forests trapped in
nitrogen limitation –an ecological market perspective on ectomycorrhizal
symbiosis. New Phytologist 203: 657–666.
Gadgil RL, Gadgil PD. 1971. Mycorrhiza and litter decomposition. Nature 233:
133.
Glassman SI, Weihe C, Li J, Albright MBN, Looby CI, Martiny AC, Treseder
KK, Allison SD, Martiny JBH. 2018. Decomposition responses to climate
depend on microbial community composition. Proceedings of the National
Academy of Sciences, USA 115: 11994–11999.
Goering HK, van Soest PJ. 1970. Forage fiber analysis (apparatus reagents,
procedures and some applications). In: USDA Agriculture Handbook,379.
Washington, DC, USA: USDA-ARS, 1–19.
Hobbie E. 2006. Carbon allocation to ectomycorrhizal fungi correlates with
belowground allocation in culture studies. Ecology 87: 563–569.
Hofrichter M, Vares T, Kalsi M, Galkin S, Scheibner K, Fritsche W, Hatakka A.
1999. Production of manganese peroxidase and organic acids and
mineralization of
14
C-labelled lignin (
14
C-DHP) during solid-state
fermentation of wheat straw with the white rot fungus Nematoloma frowardii.
Applied and Environmental Microbiology 65: 1864–1870.
Jackson RB, Lajtha K, Crow S, Hugelius G, Kramer M, Pinero G. 2017. The
ecology of soil carbon: pools, vulnerabilities, and biotic and abiotic controls.
Annual Review of Ecology, Evolution, Systematics 48: 419–445.
Kirk TK, Farrell RL. 1987. Enzymatic “combustion”: the microbial degradation
of lignin. Annual Review of Microbiology 41: 465–501.
Kohler A, Kuo A, Nagy LG, Morin E, Barry KW, Buscot F, Canback B, Choi C,
Cichocki N, Clum A et al. 2015. Convergent losses of decay mechanisms and
rapid turnover of symbiosis genes in mycorrhizal mutualists. Nature Genetics
47: 410–415.
Koide RT, Wu T. 2003. Ectomycorrhizas and retarded decomposition in a Pinus
resinosa plantation. New Phytologist 158: 401–407.
LeBauer DS, Treseder KK. 2008. Nitrogen limitation of net primary productivity
in terrestrial ecosystems is globally distributed. Ecology 89:371–379.
Lehmann J, Kleber M. 2015. The contentious nature of soil organic matter.
Nature 528:60–68.
New Phytologist (2019) 223: 1595–1606 Ó2019 The Authors
New Phytologist Ó2019 New Phytologist Trust
www.newphytologist.com
Research
New
Phytologist
1604
von Liebig J. 1840. Die organische Chemie in ihrer Anwendung auf Agrikultur und
Physiologie. Braunschweig, Germany: Friedrich Vieweg und Sohn.
Lindahl BD, Tunlid A. 2015. Ectomycorrhizal fungi –potential organic matter
decomposers, yet not saprotrophs. New Phytologist 205: 1443–1447.
Mayor JR, Henkel TW. 2006. Do ectomycorrhizas alter leaf-litter
decomposition in monodominant tropical forests of Guyana? New
Phytologist 169: 579–588.
McGuire KL, Zak DR, Edwards IP, Blackwood CB, Upchurch R. 2010. Slowed
decomposition is biotically mediated in an ectomycorrhizal, tropical rain forest.
Oecologia 164: 785–795.
Meentemeyer V. 1978. Macroclimate and lignin control of litter decomposition
rates. Ecology 59: 465–472.
Miki T, Ushio M, Fukui S, Kondoh M. 2010. Functional diversity of microbial
decomposers facilitates plant coexistence in a plant–microbe–soil feedback
model. Proceedings of the National Academy of Sciences, USA 107: 14251–
14256.
Op De Beeck M, Troein C, Peterson C, Persson P, Tunlid A. 2018. Fenton
reaction facilitates organic nitrogen acquisition by an ectomycorrhizal fungus.
New Phytologist 218: 335–343.
Orwin KH, Kirschbaum MUF, St John MG, Dickie IA. 2011. Organic nutrient
uptake by mycorrhizal fungi enhances ecosystem carbon storage: a model-based
assessment. Ecology Letters 14: 493–502.
Pastor J, Durkee Walker R, Larsson S. 2006. Delays in nutrient cycling and
plant population oscillations. Oikos,112: 698–705.
Pellitier PT, Zak DR. 2018. Ectomycorrhizal fungi and the enzymatic liberation
of nitrogen from soil organic matter: why evolutionary history matters. New
Phytologist 217:68–73.
Phillips RP, Brzostek E, Midgley MG. 2013. The mycorrhizal-associated
nutrient economy: a new framework for predicting carbon-nutrient couplings
in temperate forests. New Phytologist 199:41–51.
Read DJ, Perez-Moreno J. 2003. Mycorrhizas and nutrient cycling in ecosystems
–a journey towards relevance? New Phytologist 157: 475–492.
Rineau F, Roth D, Shah F, Smits M, Johansson T, Canb€ack B, Olsen PB,
Persson P, Grell MN, Lindquist E et al. 2012. The ectomycorrhizal fungus
Paxillus involutus converts organic matter in plant litter using a trimmed
brown-rot mechanism involving Fenton chemistry. Environmental Microbiology
14: 1477–1487.
Shah F, Nicolas C, Bentzer J, Ellstr€om M, Smits M, Rineau F, Canb€ack B,
Floudas D, Carleer R, Lackner G et al. 2016. Ectomycorrhizal fungi
decompose soil organic matter using oxidative mechanisms adapted from
saprotrophic ancestors. New Phytologist,209: 1705–1719.
Sterkenburg E, Clemmensen KE, Ekblad A, Finlay RD, Lindahl BD. 2018.
Contrasting effects of ectomycorrhizal fungi on early and late stage
decomposition in a boreal forest. ISME Journal 12: 2187–2197.
Subke JA, Voke NR, Leronni V, Garnett MH, Ineson P. 2011. Dynamics and
pathways of autotrophic and heterotrophic soil CO
2
efflux revealed by forest
girdling. Journal of Ecology 99: 186–193.
Sulman BN, Moore JA, Abramo R, Averill C, Kivlin S, Georgiou K, Sridhar B,
Hartman MD, Wang G, Wieder WR et al. 2018. Multiple models and
experiments underscore large uncertainty in soil carbon dynamics.
Biogeochemistry 141: 109–123.
Swift MJ, Heal OW, Anderson JM. 1979. Decomposition in terrestrial ecosystems,
vol. 5. Berkeley, CA, USA: University of California Press.
Talbot JM, Bruns TD, Smith DP, Branco S, Glassman SI, Erlandson S, Vilgalys
R, Peay KG. 2013. Independent roles of ectomycorrhizal and saprotrophic
communities in soil organic matter decomposition. Soil Biology and
Biochemistry,57: 282–291.
Talbot JM, Bruns TD, Taylor JW, Smith DP, Branco S, Glassman SI,
Erlandson S, Vilgalys R, Liao H-L, Smith ME et al. 2014. Endemism and
functional convergence across the North American soil mycobiome. Proceedings
of the National Academy of Sciences, USA 111: 6341–6346.
Talbot JM, Martin F, Kohler A, Henrissat B, Peay KG. 2015. Functional guild
classification predicts the enzymatic role of fungi in litter and soil
biogeochemistry. Soil Biology and Biochemistry 88: 441–456.
Tedersoo L, Smith ME. 2013. Lineages of ectomycorrhizal fungi revisited:
foraging strategies and novel lineages revealed by sequences from belowground.
Fungal Biology Reviews 27:83–99.
Tenney F, Waksman SA. 1929. Composition of natural organic materials and
their decomposition in the soil: IV. The nature and rapidity of decomposition
of the various organic complexes in different plant materials, under aerobic
conditions. Soil Science 28:55–84.
Terrer C, Vicca S, Hungate BA, Phillips RP, Prentice IC. 2016. Mycorrhizal
association as a primary control of the CO
2
fertilization effect. Science 353:72–74.
Tilman D. 1982. Resource competition and community structure. Monographs in
Population Biology 17:1–296.
van der Wal A, Geydan TD, Kuyper TW, de Boer W. 2013. A thready affair:
linking fungal diversity and community dynamics to terrestrial decomposition
processes. FEMS Microbiology Reviews 37: 477–494.
Zak DR, Pellitier PT, Argiroff WA, Castillo B, James TY, Nave LE, Averill C,
Beidler K, Bhatnagar J, Blesh J et al. 2019. Exploring the role of
ectomycorrhizal fungi in soil carbon dynamics. New Phytologist 223:33–39.
Zhang HY, Xiao-Tao L, Hartmann H, Keller A, Han XG, Trumbore S, Phillips
RP. 2018. Foliar nutrient resorption differs between arbuscular mycorrhizal
and ectomycorrhizal trees at local and global scales. Global Ecology and
Biogeography 27: 875–885.
Zhu W, Ehrenfeld JG. 1996. The effects of mycorrhizal roots on litter
decomposition, soil biota, and nutrients in a spodosolic soil. Plant and Soil
179: 109–118.
Zhu K, McCormack ML, Lankau RA, Egan JF, Wurzburger N. 2018.
Association of ectomycorrhizal trees with high carbon-to-nitrogen ratio soils
across temperate forests is driven by smaller nitrogen not larger carbon stocks.
Journal of Ecology,106: 524–535.
Appendix A1
Citations used in the meta-analyses
Aber JD, Melillo JM, McClaugherty CA.1990. Predicting long-term patterns of
mass loss, nitrogen dynamics, and soil organic matter formation from initial
fine litter chemistry in temperate forest ecosystems. Botany 68: 2201–2208.
Berg B, Lindberg T. 1980. Is litter decomposition retarded in the presence of
mycorrhiza in forest soil? In: Swedish Coniferous Forest Project. Uppsala, Swe-
den.
Brzostek ER, Dragoni D, Brown ZA, Phillips RP.2015. Mycorrhizal type deter-
mines the magnitude and direction of root-induced changes in decomposition
in a temperate forest. New Phytologist 206: 1274–1282.
Gadgil RL, Gadgil PD.1971. Mycorrhiza and litter decomposition. Nature 233:
133.
Gadgil RL, Gadgil PD.1975. Suppression of litter decomposition by mycorrhizal
roots of Pinus radiata.New Zealand Journal of Forestry Science 5:33–41.
Girisha GK, Condron LM, Clinton PW, Davis MR.2003. Decomposition and
nutrient dynamics of green and freshly fallen radiata pine (Pinus radiata) nee-
dles. Forest Ecology and Management 179: 169–181.
Harmer R, Alexander IJ.1985. Effects of root exclusion on nitrogen transforma-
tions and decomposition processes in spruce humus. Ecological Interactions in
Soil: Plants, Microbes and Animals 4: 267–277.
Harmon ME, Baker GA, Spycher G, Greene SE.1990. Leaf-litter decomposition
in the Picea/Tsuga forests of Olympic National Park, Washington, USA. Forest
Ecology and Management 31:55–66.
Harmon ME, Silver WL, Fasth B, Chen H, Burke IC, Parton WJ, Hart SC,
Currie WS, Laundre J, Wright J et al. 2009. Long-term patterns of mass loss
during the decomposition of leaf and fine root litter: an intersite comparison.
Global Change Biology 15: 1320–1338.
Hobbie SE, Reich PB, Oleksyn J, Ogdahl M, Zytkowiak R, Hale C, Karolewski
P.2006. Tree species effects on decomposition and forest floor dynamics in a
common garden. Ecology 87: 2288–2297.
Johansson M-B, Berg B, Meentemeyer V.1995. Litter mass-loss rates in late
stages of decomposition in a climatic transect of pine forests. Long-term
decomposition in a Scots pine forest. IX. Canadian Journal of Botany 73: 1509–
1521.
Jonard M, Andre F, Ponette Q.2008. Tree species mediated effects on leaf litter
dynamics in pure and mixed stands of oak and beech. Canadian Journal of
Forest Research 38: 528–538.
Ó2019 The Authors
New Phytologist Ó2019 New Phytologist Trust
New Phytologist (2019) 223: 1595–1606
www.newphytologist.com
New
Phytologist Research 1605
Koide RT, Wu T.2003. Ectomycorrhizas and retarded decomposition in a Pinus
resinosa plantation. New Phytologist 158: 401–407.
Manzoni S, Trofymow JA, Jackson RB, Porporato A.2010. Stoichiometric con-
trols on carbon, nitrogen, and phosphorus dynamics in decomposing litter.
Ecological Monographs 80:89–106.
Mayor JR, Henkel TW.2006. Do ectomycorrhizas alter leaf-litter decomposition
in monodominant tropical forests of Guyana? New Phytologist 169: 579–588.
McGuire KL, Zak DR, Edwards IP, Blackwood CB, Upchurch R.2010. Slowed
decomposition is biotically mediated in an ectomycorrhizal, tropical rain forest.
Oecologia 164: 785–795.
Staaf H.1988. Litter decomposition in beech forests –effects of excluding tree
roots. Biology and Fertility of Soils 6: 302–305.
Sterkenburg E, Clemmensen KE, Ekblad A, Finlay RD, Lindahl BD.2018.
Contrasting effects of ectomycorrhizal fungi on early and late stage decomposi-
tion in a boreal forest. The ISME Journal 12: 2187–2197.
Subke JA, Voke NR, Leronni V, Garnett MH, Ineson P.2011. Dynamics and
pathways of autotrophic and heterotrophic soil CO
2
efflux revealed by forest
girdling. Journal of Ecology 99: 186–193.
Talhelm AF, Smith AMS.2018. Litter moisture adsorption is tied to tissue struc-
ture, chemistry, and energy concentration. Ecosphere 9: e02198.
White DL, Haines BL, Boring LR.1988. Litter decomposition in southern
Appalachian black locust and pine-hardwood stands: litter quality and nitrogen
dynamics. Canadian Journal of Forest Research 18:54–63.
White MA, Thornton PE, Running SW, Nemani RR.2000. Parameterization
and sensitivity analysis of the BIOME–BGC terrestrial ecosystem model: net
primary production controls. Earth Interactions 4:1–85.
Zukswert JM, Prescott CE.2017. Relationships among leaf functional traits, lit-
ter traits, and mass loss during early phases of leaf litter decomposition in 12
woody plant species. Oecologia 185: 305–316.
Supporting Information
Additional Supporting Information may be found online in the
Supporting Information section at the end of the article.
Fig. S1 Zero net-growth isoclines for saprotrophic fungus Sand
ectomycorrhizal fungus M.
Fig. S2 Full set of numerical model simulations.
Fig. S3 Equilibria of the model at different resource input ratios,
with limitation status indicated.
Methods S1 Full description of the model.
Methods S2 Overview of analytical results.
Methods S3 Feasibility and persistence of model equilibria.
Methods S4 Stability of model equilibria.
Methods S5 Invasibility analysis.
Methods S6 Numerical simulation of the model.
Methods S7 Model with leaching.
Table S1 Data used for the meta-analysis appearing in the main
text.
Please note: Wiley Blackwell are not responsible for the content
or functionality of any Supporting Information supplied by the
authors. Any queries (other than missing material) should be
directed to the New Phytologist Central Office.
New Phytologist is an electronic (online-only) journal owned by the New Phytologist Trust, a not-for-profit organization dedicated
to the promotion of plant science, facilitating projects from symposia to free access for our Tansley reviews and Tansley insights.
Regular papers, Letters, Research reviews, Rapid reports and both Modelling/Theory and Methods papers are encouraged.
We are committed to rapid processing, from online submission through to publication ‘as ready’ via Early View – our average time
to decision is <26 days. There are no page or colour charges and a PDF version will be provided for each article.
The journal is available online at Wiley Online Library. Visit www.newphytologist.com to search the articles and register for table
of contents email alerts.
If you have any questions, do get in touch with Central Office (np-centraloffice@lancaster.ac.uk) or, if it is more convenient,
our USA Office (np-usaoffice@lancaster.ac.uk)
For submission instructions, subscription and all the latest information visit www.newphytologist.com
New Phytologist (2019) 223: 1595–1606 Ó2019 The Authors
New Phytologist Ó2019 New Phytologist Trust
www.newphytologist.com
Research
New
Phytologist
1606
