Jukuri, open repository of the Natural Resources Institute Finland (Luke) 
   
 
   
All material supplied via Jukuri is protected by copyright and other intellectual property rights. Duplication 
or sale, in electronic or print form, of any part of the repository collections is prohibited. Making electronic 
or print copies of the material is permitted only for your own personal use or for educational purposes.  For 
other purposes, this article may be used in accordance with the publisher’s terms. There may be 
differences between this version and the publisher’s version. You are advised to cite the publisher’s 
version. 
 
This is an electronic reprint of the original article.  
This reprint may differ from the original in pagination and typographic detail. 
 
Author(s): Mark A. Anthony, Leho Tedersoo, Bruno De Vos, Luc Croisé, Henning Meesenburg, 
Markus Wagner, Henning Andreae, Frank Jacob, Paweł Lech, Anna Kowalska, Martin 
Greve, Genoveva Popova, Beat Frey, Arthur Gessler, Marcus Schaub, Marco Ferretti, 
Peter Waldner, Vicent Calatayud, Roberto Canullo, Giancarlo Papitto, Aleksander 
Marinšek, Morten Ingerslev, Lars Vesterdal, Pasi Rautio, Helge Meissner, Volkmar 
Timmermann, Mike Dettwiler, Nadine Eickenscheidt, Andreas Schmitz, Nina Van 
Tiel, Thomas W. Crowther & Colin Averill 
 
 
Title: Fungal community composition predicts forest carbon storage at a continental scale. 
Year: 2024 
Version: Published version 
Copyright:   The Author(s) 2024 
Rights: CC BY 4.0 
Rights url: http://creativecommons.org/licenses/by/4.0/ 
 
Please cite the original version: 
Anthony, M.A., Tedersoo, L., De Vos, B. et al. Fungal community composition predicts forest carbon 
storage at a continental scale. Nat Commun 15, 2385 (2024). https://doi.org/10.1038/s41467-024-
46792-w. 
Article https://doi.org/10.1038/s41467-024-46792-w
Fungal community composition predicts
forest carbon storage at a continental scale
Mark A. Anthony 1,2,3 , Leho Tedersoo 4, Bruno De Vos5, Luc Croisé6,
Henning Meesenburg 7, Markus Wagner7, Henning Andreae8, Frank Jacob8,
Paweł Lech 9, Anna Kowalska9, Martin Greve10, Genoveva Popova11,
Beat Frey 2, Arthur Gessler 1,2, Marcus Schaub 2, Marco Ferretti2,
Peter Waldner2, Vicent Calatayud 12, Roberto Canullo 13, Giancarlo Papitto14,
Aleksander Marinšek15, Morten Ingerslev16, Lars Vesterdal 16, Pasi Rautio 17,
Helge Meissner18, Volkmar Timmermann19, Mike Dettwiler1,
Nadine Eickenscheidt20, Andreas Schmitz 20,21, Nina Van Tiel 1,22,
Thomas W. Crowther 1 & Colin Averill1
Forest soils harbor hyper-diverse microbial communities which fundamentally
regulate carbon and nutrient cycling across the globe. Directly testing hypoth-
eses on how microbiome diversity is linked to forest carbon storage has been
difficult, due to a lack of paired data on microbiome diversity and in situ
observations of forest carbon accumulation and storage. Here, we investigated
the relationship between soil microbiomes and forest carbon across 238 forest
inventory plots spanning 15 European countries. We show that the composition
anddiversity of fungal, but not bacterial, species is tightly coupled toboth forest
biotic conditions and a seven-fold variation in tree growth rates and biomass
carbon stocks when controlling for the effects of dominant tree type, climate,
andother environmental factors. This linkage isparticularly strong for symbiotic
endophytic and ectomycorrhizal fungi known to directly facilitate tree growth.
Since tree growth rates in this system are closely and positively correlated with
belowground soil carbon stocks, we conclude that fungal composition is a
strongpredictor of overall forest carbon storage across the European continent.
Forests are home to roughly 80% of terrestrial biodiversity1 and
represent one of the world’s largest carbon sinks2–4. Perhaps the least
understood andmost complex component of forest biodiversity is the
soil microbiome. With a growing need to offset the effects of climate
change, there is a rising interest to discover how the biodiversity of
Earth’s most diverse lifeforms – microbes5 – affects terrestrial carbon
storage6–10. Soil microbes mediate unique aspects of the forest carbon
cycle. Microbial life is responsible for over 50% of soil respiration11,
most plant litter decomposition8, and steers tree growth and death via
mutualisms and pathogen infections12–14. Soil microbial community
composition is a measure of the identity and relative abundance of
microbial species within communities. While it is well known that the
composition of tree species strongly impacts forest processes such as
growth15, albedo16, and carbon sequestration17, a comparable effort to
understandhowsoilmicrobial community composition impactswhole
forest-scale processes is urgently needed.
A growing body of experimental and observational studies sug-
gest thatmicrobial composition can affect entire forest functioning by
influencing key forest carbon pools, fluxes, and process efficiencies.
For example, dark septate fungal endophytes, an ubiquitous group of
biotrophic plant root symbionts, stimulate plant growth 52-138%
depending onplant and fungal species18, and similar observations have
been made for different rhizosphere bacterial19, endophytic
bacterial20, and ecto- and arbuscular mycorrhizal fungal species21.
Received: 1 December 2023
Accepted: 11 March 2024
Check for updates
A full list of affiliations appears at the end of the paper. e-mail: manthony5955@gmail.com
Nature Communications |         (2024) 15:2385 1
12
34
56
78
9
0
()
:,;
12
34
56
78
9
0
()
:,;
Belowground, bacterial, and fungal diversity promotes soil
respiration10, microbial carbon use efficiency22, and overall decom-
position rates23. Some of these experimental discoveries also seem to
generalize to carbon cycle outcomes in actual forest systems. For
example, differences in ectomycorrhizal fungal composition have
been linked to a three-fold variation in tree growth rates across
Europe7, an observation consistent with decades of mesocosm
experiments24–26. Studies on soil biogeochemistry have observed that
variation in decomposition rates are linked to differences in bacterial
composition23,27,28 and fungal richness29, and that these different
community types likely explain important variation in soil organic
carbon storage30,31. All these signatures highlight the potential impor-
tance of microbial biodiversity, but how they translate to total forest
carbon storage remains unknown.
Globally, forest tree biomass and soil organic carbon represent
most of the total forest carbon stock2, and these two pools may
positively32,33 or negatively34,35 interact. Soil organic carbon storage is
balanced by inputs and outputs, withmost carbon inputs derived from
net primary production36. For this reason, plant growth and soil
organic carbon stocks are positively linked in commonly used carbon
models such as RothC37 and CENTURY38. Yet, experimental research
suggests that this connection is often more complex due to priming39,
mineralogy40, forest management and disturbance32, soil carbon
saturation41, andmycorrhizal symbiosis42. A recentmeta-analysis found
that the positive effects of elevated CO2 onplant growth are negatively
correlated with changes in soil organic carbon stocks across the
globe35. This was attributed to enhanced nutrient scavenging from
organic matter by ectomycorrhizal fungi that can boost plant growth.
Many ectomycorrhizal fungi, but not all, also decay soil organicmatter
to mine for nitrogen43, a function that is expected to increase soil
organic carbon stocks under nitrogen-limiting conditions42. Therefore,
due to the context-dependency of plant biomass-soil carbon storage
relationships, the link between forest soil microbiomes (i.e., a com-
munity of microorganisms) and total forest carbon storage requires
explicit examination of both above- and belowground carbon pools.
In this study, we explored how in situ forest properties and pro-
cesses are linked to soil microbiome composition at a large spatial
scale across Europe. We then modeled the extent to which features of
the soil microbiome are correlated to forest carbon accumulation and
storage, both above- and belowground, and we identified which con-
stituents of the microbiome explain these patterns. Until now, efforts
to link soil microbial composition to the major components of forest
carbon storage have been limited by a lack of paired data onmicrobial
composition, tree biomass carbon stocks, tree growth, and soil organic
carbon stocks. We used DNA sequencing to generate soil microbiome
profiles of bacteria and fungi across 238 forest monitoring plots
spanning 15 European countries (Fig. 1a). All forest monitoring plots
are part of the International Cooperative Programme on Assessment
and Monitoring of Air Pollution Effects on Forests (ICP Forests) net-
work and have extensive data on forest carbon cycling and storage
above and belowground. This microbiome survey allowed us to gen-
erate a unique analysis between forestmicrobiomeprofiles andpaired,
co-located observations of total forest carbon balance signatures at a
continental scale. We show that soil fungal communities, especially
tree-associated ectomycorrhizal and endophytic guilds, are strong
predictors of forest tree growth and biomass. Because tree growth is
also positively correlated with soil carbon stocks in this system, fungal
composition and diversity are prominent bioindicators of overall for-
est carbon storage.
Results and discussion
Variation in forest biotic conditions is linked to fungal versus
bacterial composition
Forest biotic variables were correlated with fungal versus bacterial
community composition (Fig. 1b–e). Fungal composition was
specifically correlatedwith the dominant tree type, forest age, and tree
growth rate (Fig. 1b, d, f). Since tree growth and tree biomass stocks
were also positively correlated themselves (r =0.7, P < 0.001), we only
included tree growth to avoid issues of co-linearity and redundancy.
Conversely, neither forest age nor tree growth rate were correlated
with bacterial community composition (Fig. 1f). While patterns were
similar between soil horizons for both groups, there was a stronger
effect of dominant tree type and forest age on fungal composition in
the organic versus mineral soil horizon, while variation in tree growth
rate was more tightly linked to mineral than organic horizon fungal
community composition. Fungal communities typically differ between
broadleaves and conifers, especially in the organic horizon44,45, and
fungal composition specifically varies with forest age46–48. While we
observed the same dissimilarly patterns in fungal communities with
forest type and age, fungal composition varied even more strongly
with tree growth rate. It is possible that variation across specific tree
species would reveal even finer-scale differences, but exploring this
was beyond the goal of our study sincemost sites weredominated by a
single species. Fungal, not bacterial, composition is therefore a
uniquely informative marker of forest productivity in addition to for-
est type and age.
Soil and remaining geographic variables captured similar varia-
tion in bacterial and fungal community compositions. Soil clay con-
tent, soil pH, and soil carbon stocks were significantly correlated with
both fungal and bacterial community compositions, though correla-
tions to soil pHandcarbon stockswere stronger for bacteria than fungi
(Fig. 1f). For both groups, correlations with soil pH and carbon stocks
were twice as strong in the organic compared to mineral soil horizon.
These are expected results since soil pH and soil carbon content often
co-vary with microbial composition46,49–55. However, few microbiome
studies measure soil carbon stocks - an actual metric of forest carbon
storage because carbon content alone does not account for the
quantity of soil in a system. We suggest that previous focuses on car-
bon content have obscured the link between microbial composition
and soil carbon storage because carbon content was only weakly
correlatedwithmicrobiome composition in our study (Supplementary
Fig. 1). The remaining geographic characteristics were not tightly
linked tomicrobial composition, except formean annual precipitation
and geographic space. Total variation explained in microbiome com-
position by all environmental variables based on distance-based
redundancy analysis ranged between 22.8–28.2%, the typical amount
of variation explained in microbiome composition at large spatial
scales56–58.
Digging deeper: usingmicrobiomemetrics to predict forest tree
growth and biomass
Our analyses ofmicrobial community composition indicate that fungal
and bacterial communities are differentially linked to multiplemetrics
of forest carbon storage. Since forest carbon pools and microbial
communities interact, this cause-and-effect conundrum could not be
resolved in our observational study. But we could investigate these
linkages deeper to evaluate which components of the microbial com-
munity best captured variation in above and belowground carbon
storage. To do this, we also took into consideration known factors
affecting forest tree growth andbiomass across the ICP Forest network
where this work was conducted. Earlier research showed that the best
non-microbial predictors of tree growth include nitrogen deposition,
stand age, and multiple aspects of climate (see ref. 59). Here, we show
that none of these non-microbial predictor variables are strongly
multicollinear with microbiome composition and diversity (variance
inflation values ≤ 5 in all models sensu60), but they are important pre-
dictors of tree growth in our study (see supplementary tables and
datasets referenced throughout the results section). Our analyses of
the environmental predictors of microbial composition also demon-
strate that non-microbial predictors of tree growth are only partially
Article https://doi.org/10.1038/s41467-024-46792-w
Nature Communications |         (2024) 15:2385 2
linked to microbial composition, which highlights that there is unique
variation in soil microbiomes that could contribute to variation in
carbon storage outcomes that cannot be explained by the environ-
ment alone. We therefore built and compared statistical models to
explore which dimensions of microbiome biodiversity are most
strongly linked to forest tree growth and biomass carbon stocks after
accounting for other important co-variables.
We demonstrate that both fungal community composition
(principal coordinate analysis axis 1; PCoA1; Fig. 2a, e), and now fungal
richness (Fig. 2c, g) are correlated with tree biomass and rates of tree
growth, even after statistically controlling and accounting for the
influence of other important environmental co-variables (Supple-
mentary Data 1, Supplementary Table 1). Tree growth was more
strongly linked to fungal composition compared to fungal richness,
indicating that which species are present could have larger impacts on
tree growth than the overall number of species in a community. These
links were also stronger in conifer versus broadleaf forests, but com-
parable correlations were observed in both stand types (Supplemen-
tary Fig. 2). Tree growthwas not correlated with bacterial composition
(Fig. 1b, f) nor richness (Fig. 1d, h). Tree growth was alsomore strongly
correlated with fungal composition in the mineral versus organic
horizon (Supplementary Data 1 and Supplementary Table 2), poten-
tially because most tree roots grow in the mineral horizon61 and sym-
biotic, ectomycorrhizal fungal relative abundances were higher in
mineral compared to organic horizon soils (Supplementary Table 3).
To that end, the composition of ectomycorrhizal fungi followed by
endophytes was most tightly linked to tree growth rates (Fig. 3a). This
is consistent with an earlier root tip survey of ectomycorrhizal fungal
communities7 based on an entirely independent sampling effort and
medium (individual root-tip sequencing rather than whole soil DNA
sequencing). We also discovered that fungal endophyte richness was
strongly and positively linked to tree growth rates (Fig. 3a, b), followed
by richness of saprotrophs, wood-decomposing fungi (a subgroup
within the saprotroph community), plant pathogens, and ericoid but
not ectomycorrhizal fungi. The endophyte richness effect size was
approximately one third higher than all other groups, even when
accounting for other co-variables (Supplementary Table 4). Unlike
other fungal guilds, endophytic and ectomycorrhizal fungi are both
mutualistic, tree-biotrophic groups in these forests, which might
explain why both groups were more connected to forest tree growth
compared to other fungi.
All plants in the environment associate with fungal endophytes
that can profoundly impact plant fitness. Yet, the ecological sig-
nificance of fungal endophytes in forests is surprisingly understudied
compared to pathogens andmycorrhizae. Most endophyte research is
conducted in grasslands62,63, but it is possible that some of these
findings can be generalized to forest systems. Endophytes can pro-
mote plant growth via phytohormone production, protection against
Fig. 1 | Relationships between the soilmicrobiome and forest abiotic and biotic
conditions. Soil was collected from285 forestmonitoring plots across 15 European
countries participating in the International Co-operative Programme on Assess-
ment and Monitoring of Air Pollution Effects on Forests (ICP Forests) network (a).
Forestswereclassified as either broadleaves or conifersbasedon thedominant tree
types at each site (≥50% cover). Fungal (b, d) and bacterial (c, e) community
compositions in the organic (b; n = 209; c; n = 255) and mineral (d; n = 195, e;
n = 266) soil horizons. Sample size variations result from samples unable to be
amplified for fungal and/or bacterial profiling or those not meeting quality control
standards of sequencing depth. Vectors show correlations with forest abiotic and
biotic variables with principal coordinate analysis (PCoA) axes 1 and 2 where each
environmental variable is predicted by PCoA axes 1 and 2 usingmultiple regression.
Solid lines with black labels show significant correlations (P ≤ 0.05) while dashed
arrows with gray text show non-significant correlations. The significance of fitted
vectors and factors was tested using 999 permutations of environmental variables.
Squared correlation coefficients for each variablewith respect to PCoA axes 1 and 2
for fungi (panel with left-orientation bars) and bacteria (panel with right-
orientation bars) (f). N dep. nitrogen deposition, MAT mean annual temperature,
MAPmean annual precipitation, C stocks soil carbon stocks.
Article https://doi.org/10.1038/s41467-024-46792-w
Nature Communications |         (2024) 15:2385 3
pathogens, increased nutrient uptake, and abiotic stress alleviation64.
Nevertheless, the benefits provided by endophytes to plantsmight not
always be reciprocal with plant investments, especially for some
groups such as dark-septate endophytes which were common in our
study system65. However, it is particularly interesting that the endo-
phytic indicator species we identified in our study (Fig. 3c, d) include
Trichoderma citrinoviridie and T. koningiiwhich produce high levels of
the plant growth promoting hormone, indole acetic acid, compared to
other fungi with known endophytic life cycles66, and their relative
abundances were positively correlated with broadleaf tree growth in
our study (Supplementary Data 2). We also identified four putatively
endophytic Mortierella indicators of fast conifer and broadleaf tree
growth. In agricultural systems, many Mortierella stimulate indole
acetic acid production, reduce abiotic stress levels, and improve
access to phosphorus and iron67, with similar positive effects recently
observed in tree seedlings68. This highlights these taxa as important for
future research in forestry applications. Our results suggest that forest
fungal endophyte richness and species identity may be key compo-
nents of forest biodiversity-ecosystem function relationships.
It is important to note that most endophytic fungi, including
Mortierella, can live saprotrophically, which is one reason we detect
them in soil samples where roots were removed. However, much like
ectomycorrhizal fungi, some dark-septate endophytes also grow
hundreds ofmeters of extraradical hyphaeper gramof soil69, including
endophytes present inour soil samples. Taxa annotated as endophytes
in our study most likely have mixed ecological strategies and were
detected inboth biotrophic and saprotrophic states, a limitationof our
study since we cannot identify the precise trophic strategy employed
by fungi with endophytic capacities in our samples. This is why we did
not separate endophytes into “pure” and “mixed” ecological groups, as
we did for ectomycorrhizal fungi where there is clearer albeit still
ambiguous trophic division. However, the distinction between soil and
roots is constantly obscured as roots modify nearby soil, giving rise to
conditions where similar endophyte communities may reside inside
roots and the surrounding soil70. This suggests that even though some
of the endophytic fungi we observed were probably living sapro-
trophically, they are still indicators of taxa that form symbioses
with trees.
While every significant endophyte indicator was positively corre-
lated with tree growth, the other ubiquitous biotrophic group –
ectomycorrhizal fungi – included species linked to both slow and fast
tree growth (Fig. 3c, d; Supplementary DatV). This is consistent with
our earlier root tip survey of ectomycorrhizal fungi7. We identified
numerous Russula and Cortinariaceae (including the entire genus
Cortinarius) taxa significantly linked to variation in tree growth. In
general, the most indicative species of fast tree growth were Russula
species (Fig. 3c) whereas Cortinarius and Inocybe indicator species
were the topmost negatively correlated OTUs with tree growth.
Cortinarius are energy demanding species that produce extensive
biomass71, fungal rhizomorphs72, and extracellular enzymes73. In con-
trast to some Russula, both Cortinarius and Inocybe also actively
assimilate nitrogen from organic sources as deep as 30 cm
belowground74, which not only requires producing exploratory
mycelium to vertically tunnel deeper into denser soil but also requires
costly oxidases and proteases to access organically bound nitrogen.
Because mycorrhizal fungi obtain their energy from host-trees, these
“costly” traits may constrain tree growth compared to certain, less
energy-demanding Russula species. Russula itself is a large genus
containing nitrophobic and nitrophilic species75, which could explain
why we detected Russula indicators of both slow and fast tree growth
rates for both conifer and broadleaf forests in our study. Importantly,
local environmental conditions such as forest succession76, soil pH77,
drought78, and nitrogen deposition79 also select for these particular
ectomycorrhizal taxa, which in turn shapes their distributions and
potential impacts on forest tree growth. Thus, any potential effects of
these fungi on tree growth are likely modulated by environmental
Fig. 2 | Correlations between microbiome community composition and rich-
ness and forest tree growth and biomass. Panels showing that total fungal
composition (principal coordinate analysis axis 1; PCoA1; a, e; n = 112) and richness
(c, g; n = 112), but not bacterial composition (b, f) and richness (d,h), are correlated
with tree biomass and tree growth rates. Plotted lines show linear correlations,
shaded areas around each line are 95% confidence intervals, and r values are
Pearson correlation coefficients. Communities from themineral soil are shownhere
because they were more tightly correlated than organic horizon communities. The
full statistical models, including all co-variates, for each correlation are shown in
Supplementary Data 1, Supplementary Table 1. Only significant correlation coeffi-
cients are shown (P ≤ 0.05).
Article https://doi.org/10.1038/s41467-024-46792-w
Nature Communications |         (2024) 15:2385 4
conditions. Our results provide support for the idea that ectomycor-
rhizal symbiosis spans a large spectrum of outcomes for plant growth
in forest ecosystems80.
Which microbiome metrics predict soil organic carbon stocks?
Fungal (Fig. 4a) and bacterial (Fig. 4c) community compositions were
correlatedwith organic horizon carbon stocks, though this correlation
was more than two times stronger for bacteria compared to fungi,
even after accounting for other co-variables of known importance (see
Supplementary Data 3), including soil clay content81, climate80, forest
type82, and nitrogen deposition83. Like microbiome composition, both
fungal (Fig. 4b) and bacterial (Fig. 4d) richness were also negatively
correlated with organic horizon carbon stocks, and this effect was
relatively larger for bacteria compared to fungi and less driven by
dominant tree type differences (i.e., broadleaf forests possessing
higher soil microbial diversity and lower organic horizon carbon
stocks than coniferous forests). Like tree growth, organic horizon
carbon stocks were more tightly linked to microbiome composition
than richness. Bacterial, but not fungal, composition was also corre-
lated with mineral horizon carbon stocks (P = 0.006), but this effect
size was approximately one sixth of that observed in the organic
horizon (Supplementary Fig. 3). Neither bacterial richness nor fungal
composition/richness were correlated with mineral horizon carbon
stocks. This shows that while fungal composition is correlated with
mineral horizon carbon stocks alone (Fig. 1f), this correlation is not
robust when we account for other co-variables. Thus, in contrast to
Fig. 3 | Fungal functional and taxonomic components of species richness and
composition significantly linked to tree growth. Generalized additive model
coefficients showing the slope of the linear relationship between tree growth and
fungal richness or fungal composition that is independent of all other co-variables
(a). The standardized model coefficient (bars) and standard error (error bars) are
shown so each predictor is on the same scale but note that the effect size of
richness and composition are on different scales and should not be directly com-
pared. Separatemodelsweremade for each fungal functional group. ‘Pure’ refers to
analyses with fungi only identified to one versusmultiple functional categories and
asterisks (*) indicates a significant effect (*P ≤ 0.05, **P <0.01; see Supplementary
Table 4 for full statistical model summaries, including exact P-values). Correlation
between endophyte fungal richness and tree growth rate (b; n = 112). The plotted
line shows the linear correlation, shaded area around the line shows the 95% con-
fidence interval, and the r value is the Pearson correlation coefficient. The top
biotrophic fungal indicator species of variation in tree growth rates (c). The top five
positively and negatively correlated ectomycorrhizal fungi and all endophytic
fungal indicators are visualized. We only show the top ten ectomycorrhizal fungi
because there were too many ectomycorrhizal indicator species to fit in one gra-
phic (see Supplementary Data 2 for a complete list). And we only show ectomy-
corrhizal fungi and endophytes because both are the major biotrophic fungal
group in European forest soils, and their compositions both predicted tree growth
rates. Bars with a genus level designation are OTUs that could not be identified at
the species level. For example, there are two Russula OTUs with opposite direc-
tional effect sizes distinguished as species (spp) 1 and 2. Indicator species were
identified as those having significant differential relative abundances based on the
negative binomial distribution. Values are reported on a logarithmic scale to base 2
and represent changes in relative abundance for a unit change in tree growth (bars)
and their standard error (error bars). Volcano plot showing the strength of all
endophytic and ectomycorrhizal fungal OTUs significantly correlated to tree
growth (d). Values less andmore than 0 indicate negative and positive correlations
with tree growth, respectively.
Article https://doi.org/10.1038/s41467-024-46792-w
Nature Communications |         (2024) 15:2385 5
tree growth, bacterial communities were more tightly correlated with
carbon stocks in the organic horizon compared to fungal commu-
nities, and bacteria were the only group correlated with mineral hor-
izon carbon stocks.
The relationship between biodiversity and ecosystem functioning
is of long-standing ecological and conservation interest. An open
question is why fungal and bacterial richness is negatively correlated
with organic horizon carbon stocks in our study system. On one hand,
this is surprising because the species-energy hypothesis predicts that
increasing carbon inputs to a system should boost biodiversity84, and
thus, we should expect microbial richness to positively co-vary with
carbon stocks. We would also expect this based on the species-area
relationship85, where less organic horizon carbon means less habitat
for species co-existence. In contrast, the biodiversity-ecosystem
function concept challenges the idea that environmental conditions
alone determine species diversity86 and argues that higher levels of
diversity increase rates of emergent biological processes such as
productivity and decomposition. A major soil carbon loss pathway is
decomposition, which can be enhanced by microbial richness in Eur-
opean forests under certain scenarios87. A recent meta-analysis also
shows that experimental reduction of bacterial and fungal diversity
decreases soil respiration10, and microbial richness is positively cor-
related with decomposition rate in multiple observational studies23,29.
We will not disentangle the directionality or causality of these corre-
lations in our study, we can raise this as a subject for future
investigation. A notable starting point would be to explore the links
between microbial richness and soil carbon storage while experimen-
tally removing confounding effects of soil pH. Microbial richness is
often linked to soil pH46,49, and soil pH is correlated with soil carbon
stocks in our study (Supplementary Data 4). While soil pH was not so
strongly correlated with microbial richness in our study system, and
the statistical effects of richness and soil pH on soil carbon stocks were
independent, we cannot disentangle the possibility that microbial
richness is largely structured by soil pH and thus, only indirectly linked
to soil carbon stocks.
While the link between microbial richness and carbon storage is
particularly interesting in the context of conflicting ecological the-
ories, it is important to emphasize that bacterial composition was
more strongly linked to carbon stocks than species richness. We
therefore explored which bacterial lineages were positively and
negatively linked to organic horizon carbon stocks. At the OTU-level,
there were both positive and negative indicators of organic horizon
carbon stocks within most major phyla. Eighty percent of the Proteo-
bacteria [Pseudomonadota] indicatorOTUswerenegatively correlated
with carbon stocks in conifer forests (Supplementary Fig. 4). Relative
abundances of Proteobacteria in conifer stands was also positively
correlatedwith soil pH (r = 0.24, P =0.02; Supplementary Fig. 5), which
is negatively correlated with organic horizon carbon stocks indepen-
dent of the microbiome (Supplementary Data 4). An indirect tie to soil
pH could further explain this lineage’s link to organic horizon carbon
Fig. 4 | Correlations between carbon (C) stocks and the soil microbiome in the
organic horizon. Panels showing that total fungal composition (principal coordi-
nate analysis axis 2; PCoA2; a; n = 106) and richness (b; n = 106) as well as bacterial
composition (PCoA1; c; n = 104) and richness (d; n = 104) are correlated with tree
growth rates. Because therewere no correlations in themineral horizon (except for
a weak correlation between bacterial composition and soil organic carbon stocks,
see Supplementary Fig. 3), communities and carbon stocks from the organic hor-
izon alone are shown. Plotted lines show linear correlations, shaded areas around
each line are 95% confidence intervals, and r values are Pearson correlation coef-
ficients. The full statisticalmodels, including all co-variates, for each correlation are
shown in Supplementary Data 3. Only significant correlation coefficients are shown
(P ≤ 0.05).
Article https://doi.org/10.1038/s41467-024-46792-w
Nature Communications |         (2024) 15:2385 6
stocks. For fungi, we identified two ectomycorrhizal fungal Russula-
ceaeOTUs (Lactifluus vellereus and Russula rhodopus) positively linked
to organic horizon carbon stocks in conifer stands (Supplementary
Fig. 6). These ectomycorrhizal fungi might slow decomposition via the
Gadgill effect88, an expected outcome of some ectomycorrhizal fungi
in conifer forests89. Though other ectomycorrhizal lineages with
stronger decomposing potentials, such as Cortinarius or Piloderma,
would be themore anticipated fungi responsible for the Gadgill effect,
ectomycorrhizal genera with weaker decomposing potentials have
been recently linked to slower carbon cycling90. Manymore fungi were
indicators of organic horizon carbon stocks in broadleaf versus conifer
forests (120 versus 11 OTUs). Most top positive indicators were ecto-
mycorrhizal fungi, whereas the top negative indicators were mostly
saprotrophs, with or lacking mixed trophic assignments (e.g., sapro-
troph-pathogen), implying that ectomycorrhizal fungal indicators are
more strongly tied to higher organic horizon carbon stocks. Among
the top indicators were various Inocybe, Sebacina, and Russula OTUs
(Supplementary Data 5), all ectomycorrhizal fungi that we also found
to be strongly linked to variation in tree growth (Fig. 2c). These OTUs,
many of which we classified at the species-level so they can be inves-
tigated more directly, are unique since they are indicators of carbon
cycling both above- and belowground.
Conclusion and limitations
In this study, we linked soil microbiome composition and diversity to
threemajor forest carbon storagemetrics across Europe (tree growth,
tree biomass, and soil organic carbon stocks).We show that fungal, but
not bacterial, composition and richness are correlated with tree
growth rates and tree biomass carbon stocks, when controlling for the
effects of climate, dominant tree type, and other important co-
variables. We suspect a major reason for opposing fungal and bac-
terial signals aboveground is the ubiquity of biotrophic fungal
groups in the forest mycobiome and key groups of symbiotic endo-
phytic and ectomycorrhizal fungi. Fungal endophyte biodiversitywas
positively linked to tree growth rates above and beyond any other
microbial group we studied, a surprising discovery since most work
in forests highlights mycorrhizal fungi7,12,30,31,91,92. It is important to
note that there are alsomanybiotrophic bacteria in forests, including
endophytes93, but these are especially difficult to identify based on
DNA sequencing alone and could not be confidently separated from
the entire bacterial community in our study. Future work will need to
further investigate our fungal endophyte-tree growth results to
explore any causal relationship between the two. That said, the bio-
technology sector has already begun widely capitalizing on fungal
endophytes for applied tree growth promotion94. By sourcing spe-
cific endophytes or communities from forested areas in the wild,
locatable using observational studies like ours, it would be possible
to test whether even more powerful plant biostimulants can be
developed.
Even though tree growth is a major component of forest carbon
cycling, an equal or even greater quantity of carbon is stored
belowground2. Both fungal and bacterial composition and richness
were negatively correlated with organic horizon carbon stocks,
implying a potential direct link between the two that will need to be
disentangled further with experimental studies to investigate any
cause-effect interactions. In the mineral soil horizon, where most car-
bon is stored, the microbiome was not tightly linked to soil organic
carbon stocks. However, tree growth and biomass were tightly linked
to mineral horizon organic carbon stocks in our study system (Fig. 5)
even after controlling for other co-variables (Supplementary Data 4).
Since the mycobiome was tightly correlated with tree growth/tree
biomass, it is therefore indirectly linked to mineral horizon organic
carbon stocks and in turn total ecosystem carbon storage. This
establishes that the soil mycobiome is a unique biological indicator of
forest carbon storage across Europe.
Methods
Study sites
This work was conducted across the ICP Forests network which has
been monitoring hundreds of permanent forest plots across Europe
since the 1990’s95. We sampled level II plots that are intensively mon-
itored, at least 0.25 ha, and where almost all trees with a > 5 cm dia-
meter at breast height (DBH) are measured approximately every five
years, a common interval for estimating tree growth. At each plot, we
measured tree species membership and whether the plot was domi-
nated by conifer versus broadleaf trees (>50% cover). There were 21
tree species included in the survey, and tree richness ranged from 1 to
9 tree species.Most plots were between 1 and 5 tree species. Forest age
ranged from <30 years old to >120 years old with an average age of 90
years. The locations spanned a -2.5 to 15.5 °Cmean annual temperature
range, a 443 to 2,082mmyear-1 mean annual precipitation range, and a
0.10 to 50.11 t C ha-1 year-1 productivity range.
Fig. 5 | Correlation between tree growth and biomass and soil organic carbon
(C) stocks. Panels show the correlation between tree biomass (a) and growth (b)
with soil organic carbon stocks in the mineral horizon (0−10 cm depth; n = 88).
Plotted lines show linear correlations, shaded areas around each line are 95%
confidence intervals, and r values are Pearson correlation coefficients. The full
statistical models, including all co-variates, for each correlation are shown in Sup-
plementary Data 4. Only significant correlation coefficients are shown (P ≤ 0.05).
Article https://doi.org/10.1038/s41467-024-46792-w
Nature Communications |         (2024) 15:2385 7
Soil sampling
Soil was sampled between July-August in 2019 and 2020 from 285 level
II plots across 18 European countries.Wewere only able to include 238
plots from 15 countries in our study due to issues extracting DNA and
amplifying microbial marker genes. A 30 ×30m subplot was estab-
lished inside the plot or in the buffer zone, and nine samples were
collected in a grid-design (Supplementary Fig. 7). The organic horizon
was first removed using a serrated knife and spatula (only at sites
where it was formed and separable), andmineral soil was collected to a
10 cm depth using a soil corer (5 cm diameter). Soil samples were
pooled within horizon, homogenized, and dried in an oven at 40 °C or
air-dried for at least 48 hours, dependingonwhether a dryingovenwas
available. Fully dried samples were then shipped to ETH Zürich and
stored at -20 °C prior to analysis.
Carbon cycling and meta-data measurements
Each level II monitoring plot collects in situ tree, vegetation, soil, cli-
mate, and atmospheric chemistry data. Tree growth was calculated
using periodic DBH measurements and allometric equations for each
tree species and DBH size range. In short, we removed any dead trees,
trees with <5 cm DBH, trees that shrank over the growth period, and
then used the first and last DBH measurement to calculate diameter
growth increment. The mean growth interval was 5.5 years, the mean
initial year was 2005, and the mean final year was 2008. While this
varies marginally from the time of soil sampling, previous work has
demonstrated that year-to-year variation in microbiome composition
is rather low96,97. Next, we used species specific allometric equations
from publications made studying trees in Europe within the size range
of those observed in our dataset to compute tree mass at the first and
final census (see7), computed tree growth mass, and assumed a 50% C
content across all species98. Because every tree in a plot is not mea-
sured forDBH,wecould not strictly sum themassof allmeasured trees
to go from the tree to stand level.We therefore randomly sampledwith
replacement trees which are periodically measured until reaching
in situ stem density 1,000 times and used the mean value to estimate
stand-level tree growth rates (tonnes C ha-1 yr-1) and live tree biomass
(tonnes C ha−1).
Soil carbon and nitrogen stocks were calculated using measure-
ments of elemental content (%), bulk density, and sampling depth
(tonnes C ha-1) determined from field-based measurements. Soil car-
bon and nitrogen contents were measured using dry combustion on
finely ground soil samples. Soil pHwasmeasured in soil slurrieswithDI
water (10 g soil: 20mL DI water) using a pH probe. Soil clay content
was measured in situ and also estimated using SoilGrids at a 250m
resolution99. However, this data-product-derived estimate was only
used after we assessed the accuracy of these estimates using in situ
data. We compared estimated values to those collected in the lab for a
subset of the plots where data was available across the entire ICP level
II network. The two values were strongly correlated (r =0.51,
P <0.0001, n = 321), so we used data from SoilGrids to have more
complete observations except whenmodeling soil carbon storage. For
soil carbon storage, we used in situ clay data due to the importance of
clay in stabilizing soil carbon. Finally, we obtained mean annual tem-
perature and precipitation measurements from WorldClim100, and N
deposition predictions for 2019 at a 1 km resolution from EMEP101.
Molecular analyses
DNA was extracted from frozen soil (250mg) using the DNeasy Pow-
erSoil Pro kit (Qiagen, Hilden, Germany). Template DNAwas then used
to amplify the variable regions 4 and 5 of the 16S rRNA gene using the
primers 515F (GTGYCAGCMGCCGCGGTAA) + 926R (GGCCGYCAATT
YMTTTRAGTTT)102 to study prokaryotes and the entire ITS region
using the primers ITS9munngs (GTACAC ACCGCCCGTCG)+ ITS4ng
(CGCCTSCSCTTANTDATATGC)103 to study fungi. The 16S primers
were selected because they offer improved phylogenetic resolution
compared to the use of alternative reverse primers104, and the ITS
primers were selected because they also span a wide phylogenetic
range of fungi and best recapitulate mock communities compared to
other primer combinations103. Each primer contained a 12 bp index
sequence in the 5’ position. PCR reactions were performed induplicate
25μL reactions (13μL of PCR gradewater, 10μL of Phusion Flash High-
Fidelity PCR Master Mix, 1μL 12.5μM forward primer, 1μL 12.5μM
reverse primer, and 1μL of template DNA). 16S amplicon thermocycler
conditions were 94 °C for 3min followed by 30 cycles of 94 °C for 45 s,
50 °C for 60 s, and 72 °C for 90 s, then 72 °C for 10min, and finally a
4 °C hold. ITS amplicon thermocycler conditions were 95 °C for 15min
followedby30cycles of 95 °C for 30 s, 57 °C for 30 s, and 72 °C for60 s,
then 72 °C for 10min, and finally a 4 °C hold.
The success and relative quantity of PCR product was assessed
using agarose gel electrophoresis. We then pooled samples based on
band intensity and removed remaining PCR reagents, short DNA and
PCR products, and PCR primer dimers using AMPure beads for specific
size selection. The ITS amplicons averaged ca. 750bp whereas the 16S
amplicons averaged 300bp. Pooled products were then quantified on
a Qubit using the dsDNA BR Assay Kit (Invitrogen, Waltham, Massa-
chusetts, USA) and sent for library preparation and sequencing at the
Functional Genomics Center Zürich. 16S libraries were sequenced
using four Illumina MiSeq Runs with v3 chemistry (2 × 300bp). ITS
libraries were sequenced using four PacBio Sequel IIe SMRT Cell 8M
(15 h movie lengths).
Bioinformatics
Raw sequences werefirst demultiplexed using Cutadapt105 allowing for
0.10% mismatch, no insertions or deletions, and using the –pair-
adapters function. 16S reads included (forward) F and (reverse) R reads
whereas ITS sequences were HiFi reads produced using the circular
consensus sequencing mode. The accuracy of HiFi reads provides a
base-level resolution of 99.9% accuracy. Demultiplexed sequences
were then imported into QIIME2 (v2021.8) for downstream
processing106. However, prior to importing the ITS sequences, we first
extracted the complete ITS region using ITSx (v1.1.3)107. 16SF and R
reads were first merged using the vsearch join-pairs plug-in derived
from USEARCH108. Because the ITS reads are single end, there was no
need for pairing.We thenQC filtered all reads using the quality-filter q-
score command removing reads with average PHRED scores <4,
truncating reads if >3 successive base call PHRED scores were <3, and
removing all sequences with ambiguous base calls. We then derepli-
cated sequences and clustered de novo operational taxonomic units
(OTUs) at 97% sequence similarity for 16S sequences and 98%
sequence similarity for ITS sequences to account for variation in
sequenceconservationandbetter capture species identities compared
to computing amplicon sequence variants or ASVs109 using the
dereplicate-sequences and cluster-features-de-novo functions,
respectively. Previous research also shows that computing ASVs or
OTUs makes little difference when detecting patterns in community
composition, species richness, and relative abundances of taxa for
both 16 S and ITS DNA metabarcoding110. Singletons were later
removed from the dataset in R. Finally, we assigned taxonomy to
representative 16 S and ITSOTUs usingGreengenes111 (2019-05 release)
and UNITE112 (v8, 2021-10 release), respectively. We used the naïve
Bayesmachine-learning classifier and the feature-classifierfit-classifier-
I-bayes function to train the classifier. We then assigned taxonomy
using the classify-sklearn function and used the default confidence
parameter of 0.7. ITS OTUs were also assigned functional guild anno-
tations at the genus level using FUNGuild113, accepting all ‘probable’ or
higher level annotations. Where multiple functional annotations were
assigned, we grouped them together (e.g., ectomycorrhizal-sapro-
troph). We also calculated metrics for taxa strictly identified to one
Article https://doi.org/10.1038/s41467-024-46792-w
Nature Communications |         (2024) 15:2385 8
trophic group for ectomycorrhizal fungi and saprotrophic fungi as
‘pure’ ectomycorrhizal fungi and ‘pure’ saprotrophic fungi. We did not
do this for pathogens or endophytes because these groups are gen-
erally expected to harbor >1 trophic strategy. See Supplementary
Table 3 for a summary of the relative abundance of different fungal
functional group annotations.
Statistical analyses
Microbiome diversity and composition. Samples with low sequen-
cing depth were first removed from the dataset (<5000 sequences for
16S analysis; <500 sequences for ITS analysis). Because PacBio Sequel
IIe sequencing is much shallower than Illumina MiSeq (2 × 300bp)
sequencing, we rarified the fungal dataset to a lower depth than the
bacterial dataset. Although, sequencing depth in the ITS dataset is
relatively low, we find similar correlations with environmental vari-
ables when removing low-depth samples and rarefying to
3000 sequences. We therefore opted to retain more samples and
rarefied to <500 sequences (see Supplementary Data 6 for raw
sequence counts). For estimating alpha and beta diversity, we rarified
the datasets to the lowest sequencing depth using the rrarefy function
in vegan (2.6-4)114. We then calculated relative abundance of OTUs and
measured the correlation between microbiome composition and
environmental variables used to predict tree growth and soil organic
carbon stocks (in addition to latitude referred to as ‘geographic space’
in Fig. 1) using distance-based redundancy analysis and the capscale
function in the veganpackage. Analyseswereperformed separately for
bacteria and fungi using Bray–Curtis dissimilarities, and predictor
variables were scaled to directly compare effect sizes. We also esti-
mated species richness and Shannon Diversity using the specnumber
and diversity functions in vegan, respectively. We then estimated
community composition (i.e., beta diversity) based on OTU relative
abundances converted to Bray–Curtis dissimilarities to have a metric
of community composition for our statistical models of tree growth
and SOC stocks. Bray–Curtis dissimilarities were analyzed using prin-
cipal coordinate analysis (PCoA) and the pcoa function in the ape
package (5.6-2)90 (see Supplementary Table 5 for eigenvalues).We also
fit environmental variables to PCoA1 and 2 using the envfit function in
the vegan package. Analyses were conducted separately for bacteria
and fungi, and then the fungal dataset was split into functional guilds
and analyses were repeated separately for each guild. Microbiome
alpha and beta diversity (PCoA1 and 2) were then used to predict tree
growth, tree biomass, and soil carbon stocks in subsequent regression
analyses (see below).
Indicator species analysis of discrete and continuous variables. We
identified which taxa were linked to continuous variables of C cycling
(tree growth rate and soil organic carbon stocks stocks) using analysis
of differential relative abundances and negative binomial models. We
used the non-rarified OTU table and DESeq2 package (1.34.0)115. We
used the estimateSizeFactors function with type = ‘poscounts’ and
then the DESeq function with test = ‘Wald’ and fitType = ‘parametric’.
Estimated values from this analysis represent a log change in sequence
abundance for a unit change in the response variable. Taxa with sig-
nificant correlations were identified as “indicators species”, defined as
those with significant p-values and log two-fold change >0.6 or <-0.6,
consistent with most RNA sequencing workflows. The direction of the
model coefficient was used to assess whether they were linked to low
or high values of each response variable. Although we use the term
“indicator species”, this is distinct from those species identified using
traditional indicator species analysis116 which only identifies indicator
species linked to discrete groups versus continuous variables.
Regression analyses. All statistical analyses were conducted in R117
and significance was set to P ≤ 0.05. We used generalized additive
modeling (GAMs) to account for the linear and non-linear effects of
predictors on tree growth and soil carbon stocks. We used the gam
function from the mgcv package (1.8–38)118 and used REML estima-
tion of the smoothing parameters. We predicted tree growth, tree
biomass carbon stock, and soil carbon stocks using nitrogen
deposition, soil nitrogen stocks (for tree growth only), mean annual
temperature, mean annual precipitation, soil pH, soil clay content,
stem density, forest age, and a categorical predictor of broadleaf
versus conifer stand type. Each model also contained one micro-
biome predictor (e.g., PCoA1, PCoA2, species richness) to maintain
independence and facilitate model comparisons across different
microbiome predictors. We diagnosed model fit based on the dis-
tribution of the residuals and confirmed that predictors were not too
strongly multi-collinear based on variance inflation factors ≤560 (see
Supplementary Data 7 for results). To emphasize the correlational
nature of our work in the display items, we computed Pearson cor-
relation coefficients (r) versus the coefficient of determination from
regression analysis (r2).
Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.
Data availability
Full access to raw ICP Forest datasets is available via the ICP Forests
network upon request (http://icp-forests.net/page/data-requests).
Restrictions apply to the availability of thesedatawithout a formaldata
request. Raw microbiome datasets can be downloaded from the NCBI
SRA using accession numbers PRJNA1068067, PRJNA639984,
PRJNA644776, and PRJNA1068308. Microbiome and other data pro-
ducts can be downloaded in the following repository https://gitlab.
com/fungalecology/icpf.micro. The fungal taxonomic database UNITE
can be accessed here: https://unite.ut.ee/index.php; the bacterial
taxonomic database Greengenes can be accessed here: https://
greengenes.secondgenome.com/. The fungal functional group data-
base FUNGuild can be accessed here: http://www.funguild.org/.
Code availability
All scripts used for the analysis are openly available at the following
repository: https://gitlab.com/fungalecology/icpf.micro.
References
1. Watch, Global Forest. “Global forest watch.” World Resources
Institute, Washington, DC Available from http://www.
globalforestwatch.org (2002).
2. Pan, Y. et al. A large and persistent carbon sink in the world’s
forests. Science 333, 988–993 (2011).
3. Crowther, T. W. et al. Mapping tree density at a global scale.
Nature 525, 201–205 (2015).
4. McNeely, J. A. Lessons from the past: forests and biodiversity.
Biodivers. Conserv. 3, 3–20 (1994).
5. Anthony, M. A., Bender, S. F. & van der Heijden, M. G. A. Enu-
merating soil biodiversity. Proc. Natl Acad. Sci. 120,
e2304663120 (2023).
6. Schimel, J. &Schaeffer, S.Microbial control over carboncycling in
soil. Front. Microbiol. 3, 348 (2012).
7. Anthony, M. A. et al. Forest tree growth is linked to mycorrhizal
fungal composition and function across Europe. ISME J. 16,
1327–1336 (2022).
8. Schneider, T. et al. Who is who in litter decomposition? Metapro-
teomics revealsmajormicrobial players and their biogeochemical
functions. ISME J. 6, 1749–1762 (2012).
9. Averill, C. et al. Defending Earth’s terrestrial microbiome. Nat.
Microbiol. 7, 1–9 (2022).
Article https://doi.org/10.1038/s41467-024-46792-w
Nature Communications |         (2024) 15:2385 9
10. DeGraaff, M.-A., Adkins, J., Kardol, P. & Throop, H. Ameta-analysis
of soil biodiversity impacts on the carbon cycle. Soil 1,
257–271 (2015).
11. Bond-Lamberty, B., Wang, C. & Gower, S. T. A global
relationship between the heterotrophic and autotrophic com-
ponents of soil respiration? Glob. Change Biol. 10,
1756–1766 (2004).
12. Read, D. & Perez‐Moreno, J. Mycorrhizas and nutrient cycling in
ecosystems–a journey towards relevance? N. Phytologist 157,
475–492 (2003).
13. Brundrett, M. C. & Tedersoo, L. Evolutionary history ofmycorrhizal
symbioses and global host plant diversity. N. Phytologist 220,
1108–1115 (2018).
14. Bennett, J. A. et al. Plant-soil feedbacks and mycorrhizal type
influence temperate forest population dynamics. Science 355,
181–184 (2017).
15. Chamagne, J. et al. Forest diversity promotes individual tree
growth in central European forest stands. J. Appl. Ecol. 54,
71–79 (2017).
16. Halim,M. A., Chen, H. Y. H. & Thomas, S. C. Stand age and species
composition effects on surface albedo in a mixedwood boreal
forest. Biogeosciences 16, 4357–4375 (2019).
17. Vesterdal, L., Clarke, N., Sigurdsson, B. D. & Gundersen, P. Do tree
species influence soil carbon stocks in temperate and boreal
forests? For. Ecol. Manag. 309, 4–18 (2013).
18. Newsham, K. K. Ameta-analysis of plant responses to dark septate
root endophytes. N. Phytologist 190, 783–793 (2011).
19. Singh, M. et al. Complementarity among plant growth promoting
traits in rhizospheric bacterial communities promotes plant
growth. Sci. Rep. 5, 1–8 (2015).
20. Hannula, S. E. et al. Persistence of plant-mediated microbial soil
legacy effects in soil and inside roots. Nat. Commun. 12,
5686 (2021).
21. Hoeksema, J. D. et al. A meta‐analysis of context‐dependency in
plant response to inoculationwithmycorrhizal fungi. Ecol. Lett. 13,
394–407 (2010).
22. Domeignoz-Horta, L. A. et al. Microbial diversity drives carbon use
efficiency in a model soil. Nat. Commun. 11, 3684 (2020).
23. Wagg, C., Schlaeppi, K., Banerjee, S., Kuramae, E. E. & van der
Heijden, M. G. A. Fungal-bacterial diversity and microbiome
complexity predict ecosystem functioning. Nat. Commun. 10,
4841 (2019).
24. Alberton,O., Kuyper, T.W. &Gorissen, A. Competition for nitrogen
between Pinus sylvestris and ectomycorrhizal fungi generates
potential for negative feedback under elevated CO2. Plant Soil
296, 159–172 (2007).
25. Sim, M.-Y. & Eom, A.-H. Effects of ectomycorrhizal fungi on
growth of seedlings of Pinus densiflora. Mycobiology 34,
191–195 (2006).
26. Jonsson, L. M., Nilsson, M., Wardle, D. A. & Zackrisson, O. Context
dependent effects of ectomycorrhizal species richness on tree
seedling productivity. Oikos 93, 353–364 (2001).
27. Glassman, S. I. et al. Decomposition responses to climate depend
on microbial community composition. Proc. Natl Acad. Sci. 115,
11994–11999 (2018).
28. Chiba, A. et al. Soil bacterial diversity is positively correlated with
decomposition rates during early phases of maize litter decom-
position. Microorganisms 9, 357 (2021).
29. van der Wal, A., Ottosson, E. & de Boer, W. Neglected role of
fungal community composition in explaining variation in wood
decay rates. Ecology 96, 124–133 (2015).
30. Jörgensen, K., Granath, G., Strengbom, J. & Lindahl, B. D. Links
between boreal forest management, soil fungal communities and
below-ground carbon sequestration. Funct. Ecol. 36,
392–405 (2022).
31. Lindahl, B. D. et al. A group of ectomycorrhizal fungi restricts
organic matter accumulation in boreal forest. Ecol. Lett. 24,
1341–1351 (2021).
32. Jandl, R. et al. How strongly can forestmanagement influence soil
carbon sequestration? Geoderma 137, 253–268 (2007).
33. Liski, J., Perruchoud, D. & Karjalainen, T. Increasing carbon stocks
in the forest soils of western Europe. For. Ecol. Manag. 169,
159–175 (2002).
34. Chen, G. et al. Accelerated soil carbon turnover under tree plan-
tations limits soil carbon storage. Sci. Rep. 6, 19693 (2016).
35. Terrer, C. et al. A trade-off between plant and soil carbon storage
under elevated CO2. Nature 591, 599–603 (2021).
36. Powlson, D., Smith, P. & Nobili, M. D. in Soil Conditions and Plant
Growth (eds Gregory, P. J. & Nortcliff, S.) 86–131 (Blackwell Pub-
lishing Ltd. 2013).
37. Jenkinson, D. S. The turnover of organic carbon and nitrogen in
soil. Philos. Trans. R. Soc. Lond. Ser. B: Biol. Sci. 329,
361–368 (1990).
38. Parton, W. J., Schimel, D. S., Cole, C. V. & Ojima, D. S. Analysis of
factors controlling soil organic matter levels in Great Plains
grasslands. Soil Sci. Soc. Am. J. 51, 1173–1179 (1987).
39. Kuzyakov, Y., Friedel, J. K. & Stahr, K. Review of mechanisms and
quantification of priming effects. Soil Biol. Biochem. 32,
1485–1498 (2000).
40. Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E.
The M icrobial E fficiency‐M atrix S tabilization (MEMS) framework
integrates plant litter decomposition with soil organic matter
stabilization: do labile plant inputs formstable soil organicmatter?
Glob. Change Biol. 19, 988–995 (2013).
41. Stewart, C. E., Paustian, K., Conant, R. T., Plante, A. F. & Six, J. Soil
carbon saturation: evaluation and corroboration by long-term
incubations. Soil Biol. Biochem. 40, 1741–1750 (2008).
42. Baskaran, P. et al. Modelling the influence of ectomycorrhizal
decomposition on plant nutrition and soil carbon sequestration in
boreal forest ecosystems. N. Phytologist 213, 1452–1465 (2017).
43. Frey, S. D. Mycorrhizal fungi as mediators of soil organic matter
dynamics. Annu. Rev. Ecol. Evol. Syst. 50, 237–259 (2019).
44. Prescott,C. E. &Grayston, S. J. Tree species influenceonmicrobial
communities in litter and soil: Current knowledge and research
needs. For. Ecol. Manag. 309, 19–27 (2013).
45. Urbanová, M., Šnajdr, J. & Baldrian, P. Composition of fungal and
bacterial communities in forest litter and soil is largely determined
by dominant trees. Soil Biol. Biochem. 84, 53–64 (2015).
46. Tedersoo, L. et al. Regional-scale in-depth analysis of soil fungal
diversity reveals strong pH and plant species effects in Northern
Europe. Front. Microbiol. 11, 1953 (2020).
47. Tedersoo, L. et al. Towards global patterns in the diversity and
community structure of ectomycorrhizal fungi. Mol. Ecol. 21,
4160–4170 (2012).
48. Wallander,H., Johansson,U., Sterkenburg,E., BrandströmDurling,
M. & Lindahl, B. D. Production of ectomycorrhizal mycelium peaks
during canopy closure in Norway spruce forests. N. Phytologist
187, 1124–1134 (2010).
49. Glassman, S. I., Wang, I. J. & Bruns, T. D. Environmental filtering by
pH and soil nutrients drives community assembly in fungi at fine
spatial scales. Mol. Ecol. 26, 6960–6973 (2017).
50. Lauber, C. L., Hamady, M., Knight, R. & Fierer, N. Pyrosequencing-
based assessment of soil pH as a predictor of soil bacterial com-
munity structure at the continental scale. Appl. Environ. Microbiol.
75, 5111–5120 (2009).
51. Liu, S. et al. Phylotype diversity within soil fungal functional
groups drives ecosystem stability. Na. Ecol. Evol. 6,
900–909 (2022).
52. Feng, X., Simpson, A. J., Schlesinger, W. H. & Simpson, M. J.
Altered microbial community structure and organic matter
Article https://doi.org/10.1038/s41467-024-46792-w
Nature Communications |         (2024) 15:2385 10
composition under elevated CO2 and N fertilization in the duke
forest. Glob. Change Biol. 16, 2104–2116 (2010).
53. Tripathi, B. M. et al. Soil pH mediates the balance between sto-
chastic and deterministic assembly of bacteria. ISME J. 12,
1072–1083 (2018).
54. Anthony, M. A., Stinson, K. A., Trautwig, A. N., Coates-Connor, E. &
Frey, S. D. Fungal communities do not recover after removing
invasive Alliaria petiolata (garlic mustard). Biol. Invasions 21,
3085–3099 (2019).
55. Delgado-Baquerizo, M. et al. Carbon content and climate varia-
bility drive global soil bacterial diversity patterns. Ecol. Monogr.
86, 373–390 (2016).
56. van der Linde, S. et al. Environment and host as large-scale con-
trols of ectomycorrhizal fungi. Nature 558, 243–248 (2018).
57. Tedersoo, L. et al. Global diversity and geography of soil fungi.
Science 346, 1256688 (2014).
58. Davison, J. et al. Global assessment of arbuscular mycorrhizal
fungus diversity reveals very low endemism. Science 349,
970–973 (2015).
59. Etzold, S. et al. Nitrogen deposition is the most important envir-
onmental driver of growth of pure, even-aged and managed
European forests. For. Ecol. Manag. 458, 117762 (2020).
60. O’brien, R. M. A caution regarding rules of thumb for variance
inflation factors. Qual. Quant. 41, 673–690 (2007).
61. Claus, A. &George, E. Effect of stand age onfine-root biomass and
biomass distribution in three European forest chronosequences.
Can. J. For. Res. 35, 1617–1625 (2005).
62. Rodriguez, R. J.,White, J. F. Jr, Arnold, A. E. & Redman, R. S. Fungal
endophytes: diversity and functional roles. N. Phytologist 182,
314–330 (2009).
63. Moller, L., Lerm, B. & Botha, A. Interactions of arboreal yeast
endophytes: an unexplored discipline. Fungal Ecol. 22,
73–82 (2016).
64. Joubert, P. M. & Doty, S. L. in Endophytes of Forest Trees. Forestry
Sciences (eds. Pirttilä, A. & Frank, A.) 3–14 (Springer, 2018).
65. Ruotsalainen, A. L. et al. Dark septate endophytes:mutualism from
by-products? Trends Plant Sci. 27, 247–254 (2022).
66. Chen, L. et al. Towards the biological control of devastating forest
pathogens from the genus Armillaria. Forests 10, 1013 (2019).
67. Ozimek, E. & Hanaka, A. Mortierella species as the plant growth-
promoting fungi present in the agricultural soils. Agriculture 11,
7 (2020).
68. Moreno, J., León, J. D. & Osorio, N. W. Tree seedling growth pro-
motion by dual inoculation with Rhizoglomus fasciculatum
(Thaxt.) Sieverding, Silva & Oehl and Mortierella sp., rhizosphere
fungi for reforestation purposes, to promote plant P uptake and
growth at the nursery state.Acta Agron.ómica65, 239–247 (2016).
69. Alberton, O., Kuyper, T. W. & Summerbell, R. C. Dark septate root
endophytic fungi increase growth of Scots pine seedlings under
elevated CO2 through enhanced nitrogen use efficiency. Plant
Soil 328, 459–470 (2010).
70. Sieber, T. N. inPlant Roots (edsWaisel, Y., Eshel, A., Beeckman, T.&
Kafkafi, U.) 1369−1418 (CRC Press, 2002).
71. Agerer, R. Exploration types of ectomycorrhizae. Mycorrhiza 11,
107–114 (2001).
72. Deslippe, J. R., Hartmann, M., Grayston, S. J., Simard, S. W. &
Mohn, W. W. Stable isotope probing implicates a species of Cor-
tinarius in carbon transfer through ectomycorrhizal fungal myce-
lial networks in Arctic tundra. N. Phytologist 210, 383–390 (2016).
73. Bödeker, I. T. et al. Ectomycorrhizal C ortinarius species partici-
pate in enzymatic oxidation of humus in northern forest ecosys-
tems. N. Phytologist 203, 245–256 (2014).
74. Hobbie, E. A. et al. Fungal carbon sources in a pine forest: evi-
dence froma 13C-labeled global change experiment. Fungal Ecol.
10, 91–100 (2014).
75. Looney, B. P. et al. Russulaceae: a new genomic dataset to study
ecosystem function and evolutionary diversification of ectomy-
corrhizal fungi with their tree associates. N. Phytologist 218,
54–65 (2018).
76. Gao,C. et al. Community assembly of ectomycorrhizal fungi along
a subtropical secondary forest succession. N. Phytologist 205,
771–785 (2015).
77. Ge, Z.-W., Brenneman, T., Bonito, G. & Smith, M. E. Soil pH and
mineral nutrients strongly influence truffles and other ectomy-
corrhizal fungi associated with commercial pecans (Carya illinoi-
nensis). Plant Soil 418, 493–505 (2017).
78. Long, D., Liu, J., Han, Q., Wang, X. & Huang, J. Ectomycorrhizal
fungal communities associated with Populus simonii and Pinus
tabuliformis in the hilly-gully region of the Loess Plateau, China.
Sci. Rep. 6, 24336 (2016).
79. Moore, J. A. M. et al. Fungal community structure and function
shifts with atmospheric nitrogen deposition. Glob. Change Biol.
27, 1349–1364 (2021).
80. Jones, M. D. & Smith, S. E. Exploring functional definitions of
mycorrhizas: Are mycorrhizas always mutualisms? Can. J. Bot. 82,
1089–1109 (2004).
81. Georgiou, K. et al. Global stocks and capacity of mineral-
associated soil organic carbon. Nat. Commun. 13, 3797 (2022).
82. De Vos, B. et al. Benchmark values for forest soil carbon stocks in
Europe: Results from a large scale forest soil survey. Geoderma
251, 33–46 (2015).
83. Frey, S. D. et al. Chronic nitrogen additions suppress decom-
position and sequester soil carbon in temperate forests. Bio-
geochemistry 121, 305–316 (2014).
84. Kriegel, P. et al. Ambient and substrate energy influence decom-
poser diversity differentially across trophic levels. Ecol. Lett. 26,
1157–1173 (2023).
85. MacArthur, R. H., Wilson, E. O. & Wilson, E. O. in The Theory of
Island Biogeography 8–18 (Princeton University Press, 1967).
86. Picasso, V. D. The “biodiversity–ecosystem function debate”: an
interdisciplinary dialogue between ecology, agricultural science,
and agroecology. Agroecology Sustain. Food Syst. 42,
264–273 (2018).
87. Purahong, W., Kahl, T., Krüger, D., Buscot, F. & Hoppe, B. Home-
field advantage in wood decomposition is mainly mediated by
fungal community shifts at “home” versus “away”. Microb. Ecol.
78, 725–736 (2019).
88. Fernandez, C.W. &Kennedy, P. G. Revisiting the ‘Gadgil effect’: do
interguild fungal interactions control carbon cycling in forest
soils? N. Phytologist 209, 1382–1394 (2016).
89. Smith, G. R. & Wan, J. Resource-ratio theory predicts mycorrhizal
control of litter decomposition. N. Phytologist 223,
1595–1606 (2019).
90. Fernandez, C.W., See, C. R. & Kennedy, P. G. Decelerated carbon
cycling by ectomycorrhizal fungi is controlled by substrate
quality and community composition. N. Phytologist 226,
569–582 (2020).
91. Hupperts, S. F. & Lilleskov, E. A. Predictors of taxonomic and
functional composition of black spruce seedling ectomycorrhizal
fungal communities along peatland drainage gradients. Mycor-
rhiza 32, 67–81 (2022).
92. Clemmensen, K. E. et al. Carbon sequestration is related to
mycorrhizal fungal community shifts during long‐term succession
in boreal forests. N. Phytologist 205, 1525–1536 (2015).
93. Pirttilä, A. M. in Endophytes of Forest Trees: Biology and Applica-
tions (eds Pirttilä, A. M. & Caroline Frank, A.) 139–149
(Springer, 2011).
94. Ortega, H. E., Torres-Mendoza, D. & Cubilla-Rios, L. Patents on
endophytic fungi for agriculture and bio- and phytoremediation
applications. Microorganisms 8, 1237 (2020).
Article https://doi.org/10.1038/s41467-024-46792-w
Nature Communications |         (2024) 15:2385 11
95. Ferretti, M. & Fischer, R. Forest Monitoring: Methods for Terrestrial
Investigations in Europe with an Overview of North America and
Asia 12 (Elsevier, 2013).
96. Averill, C., Cates, L. L., Dietze, M. C. & Bhatnagar, J. M.
Spatial vs. temporal controls over soil fungal community
similarity at continental and global scales. ISME J. 13,
2082–2093 (2019).
97. Pellitier, P. T., Ibáñez, I., Zak, D. R., Argiroff, W. A. & Acharya, K.
Ectomycorrhizal access to organic nitrogen mediates CO2 fertili-
zation response in a dominant temperate tree. Nat. Commun. 12,
1–10 (2021).
98. Penman, J. et al. Good practice guidance for land use, land-use
change and forestry. Good practice guidance for land use, land-
use change and forestry. Institute for Global Environmental Stra-
tegies (IGES) for the IPCC (2003).
99. Hengl, T. et al. SoilGrids250m: Global gridded soil information
based on machine learning. PLoS ONE 12, e0169748 (2017).
100. Fick, S. E. &Hijmans, R. J.WorldClim 2: new 1‐kmspatial resolution
climate surfaces for global land areas. Int. J. Climatol. 37,
4302–4315 (2017).
101. Tørseth, K. et al. Introduction to the European Monitoring and
Evaluation Programme (EMEP) and observed atmospheric com-
position change during 1972–2009. Atmos. Chem. Phys. 12,
5447–5481 (2012).
102. Walters, W. et al. Improved bacterial 16S rRNA gene (V4 and V4-5)
and fungal internal transcribed spacer marker gene primers for
microbial community surveys. Msystems 1,
e00009–e00015 (2016).
103. Tedersoo, L. & Anslan, S. Towards PacBio‐based pan‐eukaryote
metabarcoding using full‐length ITS sequences. Environ. Micro-
biol. Rep. 11, 659–668 (2019).
104. Parada, A. E., Needham, D.M. & Fuhrman, J. A. Every basematters:
assessing small subunit rRNA primers for marine microbiomes
with mock communities, time series and global field samples.
Environ. Microbiol. 18, 1403–1414 (2016).
105. Martin, M. Cutadapt removes adapter sequences from high-
throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
106. Bolyen, E. et al. Reproducible, interactive, scalable and extensible
microbiome data science using QIIME 2. Nat. Biotechnol. 37,
852–857 (2019).
107. Bengtsson‐Palme, J. et al. Improved software detection and
extraction of ITS1 and ITS 2 from ribosomal ITS sequences of fungi
and other eukaryotes for analysis of environmental sequencing
data. Methods Ecol. Evol. 4, 914–919 (2013).
108. Edgar, R.C. Search and clustering orders ofmagnitude faster than
BLAST. Bioinformatics 26, 2460–2461 (2010).
109. Tedersoo, L. et al. Best practices in metabarcoding of fungi:
From experimental design to results. Mol. Ecol. 31,
2769–2795 (2022).
110. Glassman, S. I. & Martiny, J. B. H. Broadscale ecological patterns
are robust to use of exact sequence variants versus operational
taxonomic units.mSphere 3, e00148–18 (2018).
111. DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA
gene database and workbench compatible with ARB. Appl.
Environ. Microbiol. 72, 5069–5072 (2006).
112. Abarenkov, K. et al. The UNITE database for molecular identifica-
tion of fungi–recent updates and future perspectives. N. Phytolo-
gist 186, 281–285 (2010).
113. Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing
fungal community datasets by ecological guild. Fungal Ecol. 20,
241–248 (2016).
114. Dixon, P. VEGAN, a package of R functions for community ecol-
ogy. J. Vegetation Sci. 14, 927–930 (2003).
115. Love, M., Anders, S. & Huber, W. Differential analysis of count
data–the DESeq2 package. Genome Biol. 15, 10–1186 (2014).
116. De Caceres, M., Jansen, F. & De Caceres, M. M. Package ‘indic-
species’. indicators 8 (2016).
117. Team, R. C. R.: A Language and Environment for Statistical Com-
puting (R Foundation for Statistical Computing, 2013).
118. Wood, S. & Wood, M. S. Package ‘mgcv’. R. Package Version 1,
729 (2015).
Acknowledgements
This work was supported by an Ambizione grant (PZ00P3_179900
awarded to C.A.) from the Swiss National Science Foundation (SNSF).
M.A.A. was also partially supported by an Ambizione grant
(PZ00P3_208648 awarded toM.A.A.) from the SNSF. The evaluationwas
based on data (Data Request No 168) that was collected by partners of
the official UNECE ICP Forests Network (http://icp-forests.net/
contributors). Part of the data was co-financed by the European Com-
mission (Data achieved at “15.02.2022”).
Author contributions
Conceptualization, M.A.A. and C.A.; Formal Analysis, M.A.A.; Investiga-
tion, M.A.A., C.A.; Resources, C.A., T.W.C., M.A.A.; Writing—Original
Draft, M.A.A.; Review & Editing, M.A.A., L.T., B.D.V, L.C., H.Mee., M.W.,
H.A., F.J., P.L., A.K., M.G., G.Pop., B.F., A.G., M.S., M.F., P.W., V.C., R.C.,
G.Pap., A.M.,M.I., L.V., P.R., H.Mei., V.T.,M.D., N.E., A.S., N.v.T., T.C., C.A.;
Supervision, C.A., T.W.C.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains
supplementary material available at
https://doi.org/10.1038/s41467-024-46792-w.
Correspondence and requests for materials should be addressed to
Mark A. Anthony.
Peer review information Nature Communications thanks the anon-
ymous reviewers for their contribution to the peer review of this work. A
peer review file is available.
Reprints and permissions information is available at
http://www.nature.com/reprints
Publisher’s note Springer Nature remains neutral with regard to jur-
isdictional claims in published maps and institutional affiliations.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as
long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons licence, and indicate if
changes were made. The images or other third party material in this
article are included in the article’s Creative Commons licence, unless
indicated otherwise in a credit line to the material. If material is not
included in the article’s Creative Commons licence and your intended
use is not permitted by statutory regulation or exceeds the permitted
use, you will need to obtain permission directly from the copyright
holder. To view a copy of this licence, visit http://creativecommons.org/
licenses/by/4.0/.
© The Author(s) 2024
Article https://doi.org/10.1038/s41467-024-46792-w
Nature Communications |         (2024) 15:2385 12
1Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland. 2Swiss Federal Institute for Forests, Snow, and the Landscape Research
(WSL), Birmensdorf, Switzerland. 3Center for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria. 4Mycology and
Microbiology Center, University of Tartu, Tartu, Estonia. 5Environment & Climate Unit, Research Institute for Nature and Forest, Geraardsbergen, Belgium.
6French National Forest Office, Fontainebleau, France. 7Northwest German Forest Research Institute, Göttingen, Germany. 8Sachsenforst State Forest, Pirna
OTGraupa,Germany. 9Forest Research Institute, SękocinStary, Poland. 10Research Institute for Forest Ecologyand Forestry, Trippstadt, Germany. 11Executive
Environmental Agency at the Ministry of Environment and Water, Sofia, Bulgaria. 12Mediterranean Center for Environmental Studies, Paterna, Spain.
13Department of Plant Diversity and EcosystemManagement, University of Camerino, Camerino, Italy. 14Armadei Carabinieri Forestry Environmental andAgri-
food protection Units, Rome, Italy. 15Slovenian Forestry Institute, Ljubljana, Slovenia. 16Department of Geosciences and Natural Resource Management,
University of Copenhagen, Frederiksberg C, Denmark. 17Natural Resources Institute Finland, Rovaniemi, Finland. 18Division of Forest and Forest Resources,
Norwegian Institute of Bioeconomy Research, Ås, Norway. 19Division of Biotechnology and Plant Health, Norwegian Institute of Bioeconomy Research,
Ås, Norway. 20State Agency for Nature, Environment and Consumer Protection of North Rhine-Westphalia, Recklinghausen, Germany. 21Thuenen Institut of
Forest Ecosystems, 16225 Eberswalde, Germany. 22Environmetnal Computational Science and Earth Observation Laboratory, EPFL, Lausanne, Switzerland.
e-mail: manthony5955@gmail.com
Article https://doi.org/10.1038/s41467-024-46792-w
Nature Communications |         (2024) 15:2385 13