ArticlePDF Available

The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil

  • Southwest Minzu University


Fungi and bacteria are the two dominant groups of soil microbial communities worldwide. By controlling the turnover of soil organic matter, these organisms directly regulate the cycling of carbon between the soil and the atmosphere. Fundamental differences in the physiology and life history of bacteria and fungi suggest that variation in the biogeography of relative abundance of soil fungi versus bacteria could drive striking differences in carbon decomposition and soil organic matter formation between different biomes. However, a lack of global and predictive information on the distribution of these organisms in terrestrial ecosystems has prevented the inclusion of relative abundance of soil fungi versus bacteria and the associated processes in global biogeochemical models. Here, we used a global-scale dataset of >3000 distinct observations of abundance of soil fungi versus bacteria in the surface topsoil (up to 15 cm) to generate the first quantitative and high-spatial-resolution (1 km2) explicit map of soil fungal proportion, defined as fungi/fungi + bacteria, across terrestrial ecosystems. We reveal striking latitudinal trends where fungal dominance increases in cold and high-latitude environments with large soil carbon stocks. There was a strong nonlinear response of fungal dominance to the environmental gradient, i.e., mean annual temperature (MAT) and net primary productivity (NPP). Fungi dominated in regions with low MAT and NPP and bacteria dominated in regions with high MAT and NPP, thus representing slow vs. fast soil energy channels, respectively, a concept with a long history in soil ecology. These high-resolution models provide the first steps towards representing the major soil microbial groups and their functional differences in global biogeochemical models to improve predictions of soil organic matter turnover under current and future climate scenarios. Raw datasets and global maps generated in this study are available at (Yu, 2022).
Earth Syst. Sci. Data, 14, 4339–4350, 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
The biogeography of relative abundance of
soil fungi versus bacteria in surface topsoil
Kailiang Yu1, Johan van den Hoogen1, Zhiqiang Wang2, Colin Averill1, Devin Routh1,
Gabriel Reuben Smith3,1, Rebecca E. Drenovsky4, Kate M. Scow5, Fei Mo6, Mark P. Waldrop7,
Yuanhe Yang8, Weize Tang9,10, Franciska T. De Vries11, Richard D. Bardgett12, Peter Manning13,
Felipe Bastida14, Sara G. Baer15, Elizabeth M. Bach16 , Carlos García14, Qingkui Wang17, Linna Ma8,
Baodong Chen18,19, Xianjing He20, Sven Teurlincx21, Amber Heijboer22,23, James A. Bradley24, and
Thomas W. Crowther1
1Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland
2Sichuan Zoige Alpine Wetland Ecosystem National Observation and Research Station, Institute of
Qinghai-Tibetan Plateau, Southwest Minzu University, Chengdu, China
3Department of Biology, Stanford University, Stanford, California, USA
4Biology Department, John Carroll University, University Heights, Ohio, USA
5Department of Land, Air and Water Resources, University of California, Davis, California, USA
6College of Agronomy, Northwest A&F University, Shaanxi, China
7U.S. Geological Survey, Geology, Minerals, Energy, and Geophysics Science Center,
Menlo Park, California, USA
8State Key Laboratory of Vegetation and Environmental Change, Institute of Botany,
Chinese Academy of Sciences, Beijing, China
9South China Botanical Garden, University of Chinese Academy of Sciences, Beijing, China
10Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems,
South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
11Institute of Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, the Netherlands
12Department of Earth and Environmental Sciences, University of Manchester, Oxford Road, Manchester, UK
13Department of Biological Sciences, University of Bergen, Bergen, Norway
14CEBAS-CSIC, Department of Soil and Water Conservation,
Campus Universitario de Espinardo, Murcia, Spain
15Kansas Biological Survey and Department of Ecology & Evolutionary Biology,
University of Kansas, Lawrence, Kansas, USA
16The Nature Conservancy, Nachusa Grasslands, Franklin Grove, IL, USA
17Huitong Experimental Station of Forest Ecology, CAS Key Laboratory of Forest Ecology and Management,
Institute of Applied Ecology, Shenyang, China
18State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences,
Chinese Academy of Sciences, Beijing, China
19College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
20Key Laboratory of the Three Gorges Reservoir Region’s Eco-Environment, Ministry of Education,
Chongqing University, Chongqing, China
21Department of Aquatic Ecology, Netherlands Institute of Ecology (NIOO-KNAW),
Wageningen, the Netherlands
22Biometris, Wageningen University & Research, Wageningen, the Netherlands
23Ecology and Biodiversity Group, Department of Biology, Institute of Environmental Biology,
Utrecht University, Padualaan, the Netherlands
24School of Geography, Queen Mary University of London, London, E1 4NS, UK
Published by Copernicus Publications.
4340 K. Yu et al.: The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil
Correspondence: Kailiang Yu ( and Thomas W. Crowther
Received: 12 April 2022 Discussion started: 19 April 2022
Revised: 17 August 2022 Accepted: 20 August 2022 Published: 22 September 2022
Abstract. Fungi and bacteria are the two dominant groups of soil microbial communities worldwide. By con-
trolling the turnover of soil organic matter, these organisms directly regulate the cycling of carbon between the
soil and the atmosphere. Fundamental differences in the physiology and life history of bacteria and fungi sug-
gest that variation in the biogeography of relative abundance of soil fungi versus bacteria could drive striking
differences in carbon decomposition and soil organic matter formation between different biomes. However, a
lack of global and predictive information on the distribution of these organisms in terrestrial ecosystems has
prevented the inclusion of relative abundance of soil fungi versus bacteria and the associated processes in global
biogeochemical models. Here, we used a global-scale dataset of >3000 distinct observations of abundance of
soil fungi versus bacteria in the surface topsoil (up to 15cm) to generate the first quantitative and high-spatial-
resolution (1 km2) explicit map of soil fungal proportion, defined as fungi/fungi +bacteria, across terrestrial
ecosystems. We reveal striking latitudinal trends where fungal dominance increases in cold and high-latitude
environments with large soil carbon stocks. There was a strong nonlinear response of fungal dominance to the
environmental gradient, i.e., mean annual temperature (MAT) and net primary productivity (NPP). Fungi dom-
inated in regions with low MAT and NPP and bacteria dominated in regions with high MAT and NPP, thus
representing slow vs. fast soil energy channels, respectively, a concept with a long history in soil ecology. These
high-resolution models provide the first steps towards representing the major soil microbial groups and their
functional differences in global biogeochemical models to improve predictions of soil organic matter turnover
under current and future climate scenarios. Raw datasets and global maps generated in this study are available at (Yu, 2022).
1 Introduction
Fungi and bacteria are the dominant members of soil micro-
bial communities worldwide in terms of diversity, abundance
and biomass (Bahram et al., 2018). Representing distinct
kingdoms of life, bacteria and fungi systematically differ in a
multitude of physiological and life-history traits with direct
implications for global soil biogeochemical cycles (Waring
et al., 2013; Wieder et al., 2015), including the decomposi-
tion of organic matter, which contributes to one of the largest
fluxes of CO2on earth (Glassman et al., 2018). Compared
to bacteria, fungi generally have slower growth and turnover
rates (Rousk and Bååth, 2007), a greater carbon (C) to nu-
trient stoichiometry (Waring et al., 2013), a greater capacity
to degrade a wider and more recalcitrant range of substrates
(Strickland and Rousk, 2010) and, potentially, a higher C
use efficiency (Soares and Rousk, 2019). For these reasons,
a new generation of soil and ecosystem models have begun
to explicitly represent these fundamentally distinct fast and
slow cycling microbial groups, suggesting that spatially ex-
plicit information about the relative abundance of fungi ver-
sus bacteria in a region can dramatically improve the accu-
racy of global carbon cycling model predictions (Shi et al.,
2018; Sulman et al., 2014; Wieder et al., 2013, 2015). Gener-
ating an understanding of the factors affecting the biogeogra-
phy of the relative abundance of fungi versus bacteria in soil,
and its connection to the global carbon cycle, would repre-
sent a breakthrough step forward in our general understand-
ing of the natural history of soil microbial life.
Temperature, precipitation, soil pH and soil C :N have all
been linked to the balance of fungi vs. bacteria within soil
communities across different spatial scales (Bahram et al.,
2018; Strickland and Rousk, 2010; Tedersoo et al., 2014).
Relative to fungi, bacteria tend to dominate in locations with
high soil nutrient contents or in frequently disturbed soils that
limit the growth of fungal hyphae or make N more available
(Fierer et al., 2009; Van Der Heijden et al., 2008; Strickland
and Rousk, 2010). However, until now, the relative impor-
tance of these different environmental drivers at the global
scale remains relatively unclear, and the biogeography of
these major functional groups (fungi vs. bacteria) has only
been demonstrated at local and regional scales. A recent anal-
ysis suggested that the relative soil bacterial abundance is
high in tropical latitudes and decreases in abundance towards
the high-latitude boreal regions, where fungi tend to dom-
inate (Bahram et al., 2018). Translating these broad-scale
trends into quantitative, spatially explicit information will be
necessary if we intend to represent regional variations in soil
community functioning (Wieder et al., 2013, 2015) or predict
future changes in terrestrial carbon and nutrient cycling.
Some progress was made in the quantitative and spa-
tially explicit understanding of global biogeographic pat-
terns of fungal and bacterial biomass and their biomass ra-
Earth Syst. Sci. Data, 14, 4339–4350, 2022
K. Yu et al.: The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil 4341
tio. By synthesizing phospholipid-derived fatty acid data
from 1323 locations across the globe and extrapolating lin-
ear relationships with environmental factors, a recent study
generated global maps of fungal and bacterial biomass and
their biomass ratio at a resolution of 0.5for topsoil (0–
30 cm) (He et al., 2020). This approach provided support for
the broad-scale latitudinal trends, with a high fungal domi-
nance in high-latitude regions. Yet, there are still three cru-
cial knowledge gaps to address. First, we still lack a high-
resolution evaluation of the spatial patterns and regional con-
tingencies in fungal :bacterial ratios, which would allow the
representation of microbe-mediated mechanisms that oper-
ate within and/or across ecosystems at fine scales (Frindte et
al., 2019; Zhu et al., 2017). Second, the response of soil mi-
crobial community composition across environmental gradi-
ents is expected to be nonlinear, with strong interactive ef-
fects of different environmental characteristics that give rise
to thresholds that diverge from the global latitudinal trends
(Sengupta et al., 2021; Wang et al., 2018; Waring et al.,
2013). This nonlinear linkage of soil microbial communi-
ties with the environmental resource gradient has not been
assessed, even though it has fundamental implications for
ecosystem functions and management solutions (Sengupta et
al., 2021; Wang et al., 2018). Third, there are distinct dif-
ferences in soil nutrients, the soil microbial community and
the associated biogeochemical processes across soil depths,
i.e., from surface topsoil (i.e., 0–10 cm) to subsurface topsoil
(i.e., 0–30 cm) (Lavahun et al., 1996; Yue et al., 2015). A
continental-scale empirical study further showed that strong
positive associations among the soil microbial community,
fertility and plant productivity are limited to the surface top-
soil (Delgado-Baquerizo et al., 2017), thus highlighting the
potentially dominant role of surface topsoil microbial com-
munities in regulating ecosystem functions and the need for
research that can provide a global spatially explicit under-
standing of soil fungi versus bacteria in surface topsoil.
Here, we present a global analysis of the total and rel-
ative abundances of soil fungi versus bacteria in soil sur-
faces (defined as the top 10–15 cm) informed by over 3000
spatially distinct surface soil observations of phospholipid-
derived fatty acids (PLFAs) (Fig. 1a). The use of PLFA data
provides an opportunity to provide quantitative insights into
the abundances of the major functional groups. We con-
ducted the analysis of the abundances in view of the uncer-
tainty in conversion factors used to convert the abundance de-
rived from PLFA to biomass (Frostegård et al., 2011; Klamer
and Bååth, 2004). We used machine learning to link the vari-
ation in soil fungi versus bacteria abundances to the global
variation in 95 climate, vegetation and soil variables. This
allowed us to (1) explore the environmental drivers of fun-
gal and bacterial dominance, defined as fungal proportion,
i.e., fungi /(fungi +bacteria), where values closer to 1 in-
dicate a higher fungal dominance and values closer to zero
indicate a greater bacterial dominance (see the “Methods”
section), and (2) examine the nonlinear response or pat-
tern of fungal proportion across environmental gradients, i.e.,
mean annual temperature (MAT) and net primary productiv-
ity (NPP). Based on the observed relationships (accounting
for the nonlinearity), we generated a quantitative spatially ex-
plicit global map (1 km2) of fungal proportion and assessed
how soil fungal and bacterial dominance varies with key cli-
mate, soil, vegetation and geographic drivers.
2 Material and methods
2.1 Data acquisition of soil microbe composition
We compiled data on the abundance of soil fungi
versus bacteria and the fungal proportion, defined as
fungi /(fungi +bacteria). We focused on phospholipid-
derived fatty acids (PLFAs), and the data derived from
PLFAs regarding the balance between fungal and bacte-
rial PLFAs (Frostegård et al., 2011) can provide a valu-
able estimation of the comparative dominance of both func-
tional groups. Data based on qPCR were not included be-
cause of the difference in units with PLFAs. With nonsignif-
icant difference of the general pattern and conclusion us-
ing data on fungal proportion and the fungi :bacteria ratio,
we focused on and reported the results on the fungal pro-
portion rather than the fungi :bacteria ratio because (1) the
fungal proportion is insensitive to whether fungi or bacte-
ria are the numerator (i.e., bacterial proportion =1fungal
proportion) and (2) the spread of the frequency distribu-
tion of the fungal proportion was greater and thus led to
better machine learning predictions (Fig. S1 in the Supple-
ment). The data were compiled in a primary literature review
performed through Google Scholar, Web of Science (http:
//, last access: 30 June 2020) and
the China National Knowledge Infrastructure Database (http:
//, last access: 30 June 2020) up to 30 June 2020
using the keywords “fungi”, “bacteria”, “abundance” and
“PLFA”. To be included in our data analysis, the study had
to at least have the following metadata: longitude and lat-
itude, sampling date, sampling depth, information on land
use (agriculture, tree plantations, or natural sites), units and
the methods used. In total, this led to 319 references. We fur-
ther used the following criteria to select eligible references
and datasets. (1) When the studies were manipulative experi-
ments, we only included the data from “control” plots (Chen
et al., 2016). (2) We standardized our efforts by focusing on
all samples that were collected from surface topsoils (0–
10/15 cm) because this layer contains the greatest biomass
and the majority of samples were taken from it. (3) We used
the datasets that reported abundance in units of nmol, µmol,
or mol %, since the majority (>90 %) of the datasets re-
ported abundance. Thus, we excluded all datasets that re-
ported biomass instead of abundance. (4) We excluded ob-
servations located in the sea, since our study focuses on ter-
restrial ecosystems. (5) We only included the datasets on soil
samples derived from field experiments and thus excluded Earth Syst. Sci. Data, 14, 4339–4350, 2022
4342 K. Yu et al.: The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil
Figure 1. (a) Map of sample locations and fungal proportion data. A total of 3224 samples were collected and aggregated into 943 1 km2
pixels that were used for geospatial modeling. (b) The medians and interquartile ranges of fungal abundance, bacterial abundance and fungal
proportion in various vegetation biomes. Since they had low sample sizes (<25), tundra and boreal forest data were combined, as were
Mediterranean and desert data.
the datasets from incubation experiments. (6) Some datasets
reported in the original references as averages across sam-
pling sites or sampling dates were included.
The criteria were carefully scrutinized by three indepen-
dent researchers, and this ultimately led to the use of 179
eligible references (see the references for PLFAs in the Sup-
plement) in this study. In total, we compiled a dataset of fun-
gal proportion (n=3224) at a global scale. The subset of
data (n=1795) on only natural ecosystems (Fig. S2a in the
Supplement) were used to examine the potential role of land-
use change (see the “Methods” section in the Supplement).
The results showed a minimal difference between the two
scenarios of including all data and including only data on
natural ecosystems. All data points falling within the same
30 arcsec (1 km2) pixel were aggregated via an average.
The aggregated data on fungal proportion (n=946 for all
data; n=716 for natural ecosystems) were used to examine
its environmental controls and in geospatial modeling to cre-
ate the global map (Figs. 1a and S2a).
The spatial variations of the fungi to bacteria ratio or fun-
gal proportion across latitude could be influenced by changes
(increases or decreases) in either the abundance of fungi, the
abundance of bacteria, or both. Thus, to better understand
the biogeographic pattern of fungal and bacterial composi-
tion, we also analyzed the spatial patterns of abundance of
fungi and bacteria by using abundance data with the same
Earth Syst. Sci. Data, 14, 4339–4350, 2022
K. Yu et al.: The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil 4343
unit (nmol g1PLFA). In total, compiling our data led to fi-
nal subsets of 2753 and 2759 samples which were used for
further analyses of the abundances of fungi and bacteria, re-
spectively (Fig. S3). As compared to the larger sample size
of fungal proportion (n=946 for all data), data on the abun-
dances of fungi (n=646 for all data) and bacteria (n=647
for all data) aggregated within a 30 arcsec (1 km2) pixel via
an average were used for the analysis of their spatial trends
across vegetation biomes, vegetation types and latitudes (see
the “Methods” section in the Supplement).
2.2 Geospatial modeling
A stack (n=95) of ecologically relevant global map lay-
ers, including soil physical, chemical and nutrient proper-
ties, climate conditions, vegetative indices, radiation and to-
pographic variables, and anthropogenic covariates (Table S1
in the Supplement), were used to derive the environmental
factors which could affect fungal proportion. All of these
covariate map layers were standardized at 30arcsec resolu-
tion (1 km2at the Equator) (van den Hoogen et al., 2019).
These covariates were then derived based on the georefer-
enced coordinates of the soil samples aggregated at 30 arcsec
We used a random forest machine learning algorithm (see
the “Methods” section in the Supplement) with the derived
95 covariates to extrapolate these relationships between fun-
gal proportion and environmental conditions across the globe
and generated the first spatially explicit quantitative map of
fungal proportion at a global scale. The strength of prediction
was evaluated using k-fold cross-validation (with k=10)
and the best models with a high coefficient of determination
and a low standard deviation in the mean cross-validation
were used to generate the global map of fungal proportion.
The standard error sharply decreased with increasing sample
size across all vegetation biomes, and the analysis showed
that an efficient prediction required a large sample size (n >
500) (Fig. S4). To evaluate the sensitivity, we also generated
a map of uncertainty (standard deviation as a fraction of the
mean predicted value) of fungal proportion by using a strati-
fied bootstrapping procedure (van den Hoogen et al., 2019).
The fungal proportion data were stratified by biome to avoid
biases. In total, 100 bootstrap iterations were used, thus gen-
erating 100 global maps of fungal proportion that were used
to quantify statistically robust 95 % confidence intervals per
2.3 Environmental drivers and statistic analysis
To examine the environmental controls of soil microbial
composition at a global scale, we chose the top drivers (Chen
et al., 2016; Drenovsky et al., 2010a; de Vries et al., 2012),
which include soil properties, climate conditions, vegetation
index and human activities (see the “Methods” section in the
Supplement). These variables were examined to avoid mul-
ticollinearity: a matrix of pairwise correlations was used to
remove any variable with high correlations (R > 0.7) with
other predictor variables (Anderegg et al., 2013). A random
forest machine learning algorithm was then used to deter-
mine the importance of each variable (Breiman, 2001). The
mean decrease in accuracy (% IncMSE) and the mean de-
crease in the Gini coefficient (IncNodePurity) were reported,
and the variables with greater values of %IncMSE and Inc-
NodePurity are more important in influencing fungal propor-
tion. Partial functions of the most important variables (MAT
and NPP) were plotted using the forestFloor package to ex-
amine their influences on fungal proportion.
3 Results and discussion
3.1 Raw data patterns of fungal proportion
Globally, we observed a greater than 10-fold variation in soil
fungal proportion across all sites, ranging from 0.01 to 0.6
(Fig. 1b). At a global scale, we found clear latitudinal trends,
with both the abundance of fungi and the abundance of bacte-
ria increasing in high-latitude regions. Yet, the abundance of
fungi increased with latitude at a greater rate than the abun-
dance of bacteria (Fig. S5), resulting in a higher proportion
of fungi in the cold, high-latitude regions. These latitudinal
trends lend support to the general global patterns detected in
a previous broad-scale analysis (Bahram et al., 2018) and in
a recent meta-data analysis (He et al., 2020). As such, the
highest fungal dominance was observed in tundra and bo-
real forest ecosystems (mean ±1 SE: 0.23±0.02; Fig. 1b). In
addition, highly elevated and cold grasslands (i.e., montane
grasslands) with a large soil organic C (SOC) content gener-
ally harbor a higher proportion of fungi relative to bacteria
(Fig. 1b).
In similar climates, the abundances of soil fungi versus
bacteria as well as the fungal proportion were greatest in
ecosystems harboring woody vegetation compared to grass-
lands and managed (agricultural) ecosystems (Fig. S6). This
finding is consistent with the idea that ecosystems domi-
nated by woody plants generate lignified, more recalcitrant
and nutrient-poor soil C inputs that characteristically favor
fungal dominance (Fierer et al., 2009; Strickland and Rousk,
2010) and have a biomass stoichiometry that is better suited
to low-nutrient environments (Waring et al., 2013). But we
stress that this link of belowground soil microbial compo-
sition (fungi vs. bacteria) with aboveground plant commu-
nity composition (woody plants vs. grasses) can be complex,
nonlinear and even divergent, as demonstrated by the well-
mixed fungi vs. bacteria abundances in both grasslands and
forests but the nonexistence of woody plants in grasslands
and scarcity of grasses in forests. This raises our curiosity
as to whether the interactions, associations, or couplings of
belowground soil microbial composition with aboveground
plant community composition are stronger in ecosystems
where woody plants and grasses interact or coexist (i.e., Earth Syst. Sci. Data, 14, 4339–4350, 2022
4344 K. Yu et al.: The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil
savannas) (Yu and D’Odorico, 2015). It also remains un-
clear how this coupling could improve our understanding of
ecosystem carbon cycling and other services.
The management of agricultural ecosystems often disrupts
soil fungal networks (i.e., tillage, frequent dry/wet cycles due
to irrigation, machine operations, etc.), which decreases the
abundance of fungi relative to bacteria in agricultural soils
(Fig. S6) (Drenovsky et al., 2010b; Jangid et al., 2011; Wal-
drop et al., 2017). A central concern in agricultural ecosys-
tems is the tradeoff of increased food production to feed the
increasing population vs. the decreased soil carbon storage,
which accelerates global climate change (Sanderman et al.,
2017). This study shows a higher bacterial abundance rela-
tive to fungal abundance in soils of agricultural lands where
soil carbon storage is low; this corresponds with the global
trend for bacterial dominance at low latitudes where soil car-
bon storage is low. These results suggest potential strong
but complex interactions and feedbacks between soil micro-
bial composition and soil functions (i.e., soil carbon storage)
(Bardgett et al., 2008), while the mechanistic links need fur-
ther studies.
3.2 Drivers of fungal proportion
Globally, the fungal proportion in soil can be predicted by a
few primary environmental drivers (Figs. 2 and S7). Specifi-
cally, mean annual temperature (MAT) and primary produc-
tivity (NPP) were found to be strong determinants of fun-
gal dominance. The responses of fungal proportion to both
MAT and NPP were strongly nonlinear, with warmer, more
productive regions of the world (i.e., tropical forest biomes)
showing a lower dominance of fungi as compared to colder,
less productive ecosystems (i.e., boreal forest and tundra
biomes, Figs. 3 and S8). This pattern is consistent with the
idea that fungi and bacteria represent slow vs. fast soil en-
ergy channels, respectively (Crowther et al., 2019; Malik
et al., 2016), a concept with a long history in soil ecology
(Moore et al., 2003; Moore and William Hunt, 1988). This
finding is important because it could potentially link the be-
lowground slow (fungi) vs. fast (bacteria) energy channels
with aboveground slow plant growth rates (woody plants)
vs. fast growth rates (grasses), while the linkage could be
complex, nonlinear or even divergent. The fast vs. slow con-
cept or spectrum has fundamentally improved the under-
standing and prediction of land carbon storage across re-
source gradients or under global change. There could typi-
cally be a tradeoff between faster growth and higher mor-
tality or heterotrophic respiration with resource (i.e., CO2)-
enriched conditions (Jiang et al., 2020; Terrer et al., 2021;
Yu et al., 2019), thus constraining land carbon storage. This
raises the question of how the belowground fast vs. slow en-
ergy channels and the aboveground fast vs. slow growth spec-
trum could potentially be linked or integrated to assess land
carbon storage.
Figure 2. Mean decrease in accuracy (% IncMSE, mean and SD;
a) and mean decrease in the Gini coefficient (IncNodePurity, mean
and SD; b) estimated from 1000 simulations of random forests. This
was used to evaluate the importance of top environmental drivers in
the proportion of fungi derived from the “all” dataset.
Temperature can affect soil microbial composition in com-
plex ways: directly via physiology or indirectly via the soil
substrate (Romero-Olivares et al., 2017). Previous studies
have shown a nonlinear response of the soil fungal and bac-
terial ratio to soil substrate nutrient availability (Waring et
al., 2013). A nonlinear trend in the temperature sensitiv-
ity (Q10) of soil organic C decomposition, as regulated by
the soil fungal and bacterial ratio, was also found with lati-
tude (Wang et al., 2018). Other environmental variables such
as the soil C to nitrogen ratio (C :N) have previously been
found to be important drivers of fungal proportion within
local- and regional-scale analyses (Fierer et al., 2009; Waring
et al., 2013). Our results suggest a more complicated rela-
tionship between fungal proportion and the soil C :N. In the
Earth Syst. Sci. Data, 14, 4339–4350, 2022
K. Yu et al.: The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil 4345
Figure 3. Fungal proportion is primarily associated with climate:
mean annual temperature (MAT) and net primary productivity
(NPP). (a, b) Partial feature contributions of the primary environ-
mental variables (a) MAT and (b) NPP to fungal proportion. (c) Par-
tial feature contributions of primary environmental variable interac-
tions (MAT vs. NPP) to fungal proportion.
low range of soil C :N values, fungal proportion decreased
with soil C :N (Fig. S9a), suggesting that the role of site-
specific differences (i.e., climate or plant community) likely
outweighs the influence of N availability (Soares and Rousk,
2019). Aside from these ecosystems, we observed a positive
relationship between fungal proportion and soil C :N at a
global scale, consistent with previous work at local and re-
gional scales (Strickland and Rousk, 2010; Waring et al.,
2013). Additionally, pH has been thought of as a critical
driver of microbial diversity and biomass in soils. At local
scales, previous studies have reported either no relationship,
a negative correlation or a convex curve between the fungal
and bacterial ratio and soil pH (Rousk et al., 2009, 2010;
de Vries et al., 2012). Our global-scale analysis suggests a
convex relationship between fungal proportion and soil pH,
with fungi dominating only within a narrow pH range (<5–
6) (Fig. S9b).
3.3 Biogeographic pattern from the machine learning
Across all samples, the machine learning model was able to
predict the variation in fungal and bacterial dominance with
high predictive accuracy (R2=0.43/0.35 in 10-fold cross-
validation, R2=0.92/0.91 in the final model; Fig. S10a
and b). By extrapolating these relationships across terrestrial
ecosystems, we could identify clear global trends in fungal
dominance. Despite these general global-scale patterns of an
increase in fungi dominance with latitude, our models also
revealed regions that diverge from the global trends (Figs. 4a
and S11a). For instance, northeastern Europe is dominated
by woody vegetation and exhibits a high fungal proportion,
while the United Kingdom and northern Kazakhstan have
much lower fungal proportions despite being at comparable
latitudes, likely because these areas are dominated by herba-
ceous vegetation with lower lignin contents than in woody
tissues. Tibetan alpine grasslands are at much lower lati-
tudes but have high values of fungal proportion, in part due
to very high SOC stocks and cold temperatures. Model pre-
dictions of fungal proportion had high uncertainty in dry re-
gions (i.e., northern and southern Africa, Australia, western
USA and eastern Mongolia) (Figs. 4b and S11b), presum-
ably because of the low sample size from drylands and/or the
complex responses of fungi and bacteria to water availability
(Fierer et al., 2009; Strickland and Rousk, 2010). Indeed, our
datasets are mostly concentrated in the US, Europe and East
Asia, thus highlighting the data gaps for tropical and boreal
biomes. Even for the temperate biome, there were data gaps
in West Australia and central Asia. Because of the unbal-
anced sample distribution, we also used a bootstrapping strat-
egy (100 iterations) by randomly sampling 90 % of the data
with replacement. The results showed similar spatial patterns
of fungal proportion (Fig. S12a) and uncertainty (Fig. S12b)
to those obtained using the full dataset without bootstrap-
Our study differs from a previous study (He et al., 2020)
in several aspects, including sample size (n > 3000), spatial
resolution (1 km2), consideration of nonlinearity (through
random forest analysis) and soil depth (soil surface 0–
10/15 cm). We also note that our analysis sticks to the origi-
nal data on abundance derived from PLFA instead of convert-
ing abundance to biomass. The conversion of abundance to
biomass needs the conversion factor, which has large uncer-
tainty (Frostegård et al., 2011; Klamer and Bååth, 2004). Our
high-resolution map allows the representation of microbe- Earth Syst. Sci. Data, 14, 4339–4350, 2022
4346 K. Yu et al.: The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil
Figure 4. Global maps of fungal proportion (a) and bootstrapped (100 iterations) coefficient of variation (b) at the 30arcsec (approximately
1 km2) pixel scale. The bootstrapped coefficient of variation is the standard deviation divided by the mean predicted value and is a measure
of prediction accuracy. Sampling was stratified by biome.
mediated mechanisms at fine scales, and therefore allows us
to link these mechanisms with ecosystem functions. For in-
stance, the significant functional differences between fungi
and bacteria mean that the relative dominance of fungi
vs. bacteria is likely to influence a wide range of ecosys-
tem functions such as C use efficiency (CUE) of the decom-
poser community (Six et al., 2006; Soares and Rousk, 2019)
and enzymatic activity in soil N vs. P acquisition (Caldwell,
2005; Crowther et al., 2019). At fine, local or even regional
scales, these relationships between soil microbial composi-
tion and ecosystem functions could only be identified well
using fine-scale maps of soil microbial composition.
3.4 Implications and limitations of this study
It is generally accepted that the soil microbiome exerts ma-
jor control over soil processes and in turn ecosystem func-
tioning, and by extension the global biogeochemical cycles
(Bahram et al., 2018; Crowther et al., 2019; Van Der Heij-
den et al., 2008; Jenny, 1941). Fungi and bacteria represent
most of the diversity of life on earth (Bardgett and van der
Putten, 2014; Locey and Lennon, 2016). Yet, the inclusion
of fungal and bacterial abundances in quantitative ecosystem
and earth system models has been hindered by the paucity
of information about organisms at appropriate spatial scales.
Here, we impose a global top-down constraint on the broad
composition of soil microbial life. By doing so, we hope to
empower microbial, ecosystem and earth-system scientists to
consider how this broad constraint on the soil biodiversity
may inform and transform our understanding of terrestrial
ecosystem functioning. As we develop a spatially explicit un-
derstanding of the global soil community, we will be able to
better parameterize and benchmark our predictions about the
rate and efficiency of carbon turnover in soil and the feed-
backs to ongoing climate change.
Despite the progress made in this study, we must clar-
ify two limitations of this study. First, our study highlights
the data gaps in fungal proportion prediction at low latitudes
(tropical biome) and high latitudes (boreal biome, i.e., boreal
forests and tundra). Tropical and boreal biomes are hotspots
Earth Syst. Sci. Data, 14, 4339–4350, 2022
K. Yu et al.: The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil 4347
or debated regions in terms of their relative capacity and ca-
pability to sequestrate atmospheric CO2and mitigate climate
change in an increasingly changing climate (Schimel et al.,
2015; Tagesson et al., 2020). They are also regions with strik-
ing differences in soil microbial composition (fungal propor-
tion), plant communities and soil carbon storage, thus sug-
gesting that there are potentially strong interactions and feed-
backs between these factors in these regions (Bardgett et al.,
2008). The boreal biome contains a large amount of soil or-
ganic carbon, which could be sensitive to global change (i.e.,
warming), wherein the soil microbial community (i.e., the to-
tal biomass or the relative abundance of soil fungi versus bac-
teria) could play an essential role. Second, microbial biomass
(C) is more relevant to the soil carbon cycling and carbon
stock due to its contribution to living carbon pools and the
impacts of its microbial necromass (Liang et al., 2019), while
the conversion factor for the conversion of abundance into
biomass is currently not available. To mechanistically and ex-
plicitly incorporate soil microbial composition into biogeo-
chemical models, the biogeographic pattern of abundance or
biomass of each major group (fungi vs. bacteria) and the rel-
ative ratios of categories of fungi (i.e., saprotrophic fungi, ar-
buscular mycorrhizal fungi vs. ectomycorrhizal fungi) and/or
bacteria (i.e., gram-positive bacteria vs. gram-negative bacte-
ria) would also be critical, in view of their striking functional
difference (Averill et al., 2014; Crowther et al., 2019). These
research gaps highlight the urgent need for new research that
utilizes the increasing availability of datasets.
4 Data availability
The map and data for this manuscript are available at (Yu, 2022).
5 Conclusions
This study used a global-scale dataset of >3000 distinct ob-
servations of abundance of soil fungi versus bacteria in the
surface topsoil (up to 15 cm) to generate the first quantita-
tive and high-spatial-resolution (1km2) explicit maps of rel-
ative abundance of soil fungi versus bacteria across global
terrestrial ecosystems. Our machine learning approach (ran-
dom forest) enabled us to link the variation in fungal pro-
portion to global variations in climate, soil, vegetation and
other environmental drivers whilst accounting for the inter-
actions and nonlinearities among them. We found striking
latitudinal trends where fungal dominance increases in cold
and high-latitude environments with large soil carbon stocks.
The fungal proportion in soil can be predicted by a few pri-
mary environmental drivers: temperature and NPP, which
show strong nonlinear effects. We demonstrated that fungi
and bacteria represent slow vs. fast energy channels, whereby
they dominate in regions of low MAT and NPP vs. high MAT
and NPP, respectively. Overall, our spatially explicit model
would enable us to explicitly represent the different contri-
butions of fast (bacterial) vs. slow (fungal) energy channels
in spatially explicit biogeochemical models, with the poten-
tial to enhance the accuracy of soil carbon turnover and car-
bon storage predictions. We further highlight the data gaps in
tropical and boreal regions and the need for future research
endeavors to generate high-resolution biogeographic patterns
of the biomass of each major microbial group and the relative
biomass ratios between and within major microbial groups.
Supplement. The supplement related to this article is available
online at:
Author contributions. KY and TWC designed the project. KY
built the PLFA datasets with help from JvdH and ZW. KY per-
formed the analysis with inputs from DR and CA. KY, CA and
TWC wrote the paper, with revisions from all other coauthors. GRS,
CG, QW, LM, BC, XH, WT, ST, AH and JAB contributed to the
PLFA datasets.
Competing interests. The contact author has declared that none
of the authors have any competing interests.
Disclaimer. Publisher’s note: Copernicus Publications remains
neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Acknowledgements. We are grateful for the data contributors
and the scientific community which made the data accessible and
useful for our study. Zhiqiang Wang would like to acknowledge the
funding support by International Science and Technology Coopera-
tion Project of Qinghai province of China (2022-HZ-817).
Financial support. This research has been supported by T.W.C.
from DOB ecology.
Review statement. This paper was edited by David Carlson and
reviewed by two anonymous referees.
Anderegg, L. D. L., Anderegg, W. R. L., Abatzoglou, J., Haus-
laden, A. M., and Berry, J. A.: Drought characteristics’ role in
widespread aspen forest mortality across Colorado, USA, Glob.
Chang. Biol., 19, 1526–1537,,
Averill, C., Turner, B. L., and Finzi, A. C.: Mycorrhiza-
mediated competition between plants and decomposers Earth Syst. Sci. Data, 14, 4339–4350, 2022
4348 K. Yu et al.: The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil
drives soil carbon storage, Nature, 505, 543–545,, 2014.
Bahram, M., Hildebrand, F., Forslund, S. K., Anderson, J. L.,
Soudzilovskaia, N. A., Bodegom, P. M., Bengtsson-Palme, J.,
Anslan, S., Coelho, L. P., Harend, H., Huerta-Cepas, J., Medema,
M. H., Maltz, M. R., Mundra, S., Olsson, P. A., Pent, M., Põlme,
S., Sunagawa, S., Ryberg, M., Tedersoo, L., and Bork, P.: Struc-
ture and function of the global topsoil microbiome, Nature, 560,
233–237,, 2018.
Bardgett, R. D. and van der Putten, W. H.: Belowground bio-
diversity and ecosystem functioning., Nature, 515, 505–511,, 2014.
Bardgett, R. D., Freeman, C., and Ostle, N. J.: Microbial contribu-
tions to climate change through carbon cycle feedbacks, ISME
J., 2, 805–814,, 2008.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,, 2001.
Caldwell, B. A.: Enzyme activities as a component of
soil biodiversity: A review, Pedobiologia, 49, 637–644,, 2005.
Chen, Y. L., Ding, J. Z., Peng, Y. F., Li, F., Yang, G. B.,
Liu, L., Qin, S. Q., Fang, K., and Yang, Y. H.: Patterns
and drivers of soil microbial communities in Tibetan alpine
and global terrestrial ecosystems, J. Biogeogr., 43, 2027–2039,, 2016.
Crowther, T. W., van den Hoogen, J., Wan, J., Mayes, M. A., Keiser,
A. D., Mo, L., Averill, C., and Maynard, D. S.: The global soil
community and its influence on biogeochemistry, Science, 365,
eaav0550,, 2019.
Delgado-Baquerizo, M., Powell, J. R., Hamonts, K., Reith, F., Mele,
P., Brown, M. V., Dennis, P. G., Ferrari, B. C., Fitzgerald, A.,
Young, A., Singh, B. K., and Bissett, A.: Circular linkages be-
tween soil biodiversity, fertility and plant productivity are limited
to topsoil at the continental scale, New Phytol., 215, 1186–1196,, 2017.
de Vries, F. T., Manning, P., Tallowin, J. R. B., Mortimer, S.
R., Pilgrim, E. S., Harrison, K. A., Hobbs, P. J., Quirk, H.,
Shipley, B., Cornelissen, J. H. C., Kattge, J., and Bardgett, R.
D.: Abiotic drivers and plant traits explain landscape-scale pat-
terns in soil microbial communities, Ecol. Lett., 15, 1230–1239,, 2012.
Drenovsky, R. E., Steenwerth, K. L., Jackson, L. E., and Scow, K.
M.: Land use and climatic factors structure regional patterns in
soil microbial communities, Glob. Ecol. Biogeogr., 19, 27–39,, 2010a.
Drenovsky, R. E., Steenwerth, K. L., Jackson, L. E., and Scow, K.
M.: Land use and climatic factors structure regional patterns in
soil microbial communities, Glob. Ecol. Biogeogr., 19, 27–39,, 2010b.
Fierer, N., Strickland, M. S., Liptzin, D., Bradford, M. A., and
Cleveland, C. C.: Global patterns in belowground communi-
ties, Ecol. Lett., 12, 1238–1249,
0248.2009.01360.x, 2009.
Frindte, K., Pape, R., Werner, K., Löffler, J., and Knief, C.:
Temperature and soil moisture control microbial community
composition in an arctic–alpine ecosystem along elevational
and micro-topographic gradients, ISME J., 13, 2031–2043,, 2019.
Frostegård, Å., Tunlid, A., and Bååth, E.: Use and misuse of PLFA
measurements in soils, Soil Biol. Biochem., 43, 1621–1625,, 2011.
Glassman, S. I., Weihe, C., Li, J., Albright, M. B. N., Looby, C. I.,
Martiny, A. C., Treseder, K. K., Allison, S. D., and Martiny, J.
B. H.: Decomposition responses to climate depend on microbial
community composition, P. Natl. Acad. Sci. USA, 115, 11994–
11999,, 2018.
He, L., Mazza Rodrigues, J. L., Soudzilovskaia, N. A., Barceló, M.,
Olsson, P. A., Song, C., Tedersoo, L., Yuan, F., Yuan, F., Lipson,
D. A., and Xu, X.: Global biogeography of fungal and bacte-
rial biomass carbon in topsoil, Soil Biol. Biochem., 151, 108024,, 2020.
Jangid, K., Williams, M. A., Franzluebbers, A. J., Schmidt, T. M.,
Coleman, D. C., and Whitman, W. B.: Land-use history has a
stronger impact on soil microbial community composition than
aboveground vegetation and soil properties, Soil Biol. Biochem.,
43, 2184–2193,,
Jenny, H.: Factors of Soil Formation, Soil Sci., 52, 415,, 1941.
Jiang, M., Medlyn, B. E., Drake, J. E., Duursma, R. A., Anderson,
I. C., Barton, C. V. M., Boer, M. M., Carrillo, Y., Castañeda-
Gómez, L., Collins, L., Crous, K. Y., De Kauwe, M. G., dos San-
tos, B. M., Emmerson, K. M., Facey, S. L., Gherlenda, A. N.,
Gimeno, T. E., Hasegawa, S., Johnson, S. N., Kännaste, A., Mac-
donald, C. A., Mahmud, K., Moore, B. D., Nazaries, L., Neilson,
E. H. J., Nielsen, U. N., Niinemets, Ü., Noh, N. J., Ochoa-Hueso,
R., Pathare, V. S., Pendall, E., Pihlblad, J., Piñeiro, J., Powell, J.
R., Power, S. A., Reich, P. B., Renchon, A. A., Riegler, M., Rin-
nan, R., Rymer, P. D., Salomón, R. L., Singh, B. K., Smith, B.,
Tjoelker, M. G., Walker, J. K. M., Wujeska-Klause, A., Yang, J.,
Zaehle, S., and Ellsworth, D. S.: The fate of carbon in a mature
forest under carbon dioxide enrichment, Nature, 580, 227–231,, 2020.
Klamer, M. and Bååth, E.: Estimation of conversion factors
for fungal biomass determination in compost using ergos-
terol and PLFA 18 :2ω6,9, Soil Biol. Biochem., 36, 57–65,, 2004.
Lavahun, M. F. E., Joergensen, R. G., and Meyer, B.: Activity and
biomass of soil microorganisms at different depths, Biol. Fertil.
Soils, 23, 38–42,, 1996.
Liang, C., Amelung, W., Lehmann, J., and Kästner, M.: Quan-
titative assessment of microbial necromass contribution to
soil organic matter, Glob. Chang. Biol., 25, 3578–3590,, 2019.
Locey, K. J. and Lennon, J. T.: Scaling laws predict global mi-
crobial diversity, P. Natl. Acad. Sci. USA, 113, 5970–5975,, 2016.
Malik, A. A., Chowdhury, S., Schlager, V., Oliver, A., Puissant,
J., Vazquez, P. G. M., Jehmlich, N., von Bergen, M., Grif-
fiths, R. I., and Gleixner, G.: Soil fungal: Bacterial ratios are
linked to altered carbon cycling, Front. Microbiol., 7, 1247,, 2016.
Moore, J. C. and William Hunt, H.: Resource compartmentation
and the stability of real ecosystems, Nature, 333, 261–263,, 1988.
Moore, J. C., McCann, K., Setälä, H., and De Ruiter,
P. C.: Top-down is bottom-up: Does predation in
Earth Syst. Sci. Data, 14, 4339–4350, 2022
K. Yu et al.: The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil 4349
the rhizosphere regulate aboveground dynamics?,
Ecology, 84, 846–857,
9658(2003)084[0846:TIBDPI]2.0.CO;2, 2003.
Romero-Olivares, A. L., Allison, S. D., and Treseder, K. K.: Soil
microbes and their response to experimental warming over time:
A meta-analysis of field studies, Soil Biol. Biochem., 107, 32–
40,, 2017.
Rousk, J. and Bååth, E.: Fungal biomass production
and turnover in soil estimated using the acetate-in-
ergosterol technique, Soil Biol. Biochem., 39, 2173–2177,, 2007.
Rousk, J., Brookes, P. C., and Bååth, E.: Contrasting soil pH ef-
fects on fungal and bacterial growth suggest functional redun-
dancy in carbon mineralization, Appl. Environ. Microbiol., 75,
1589–1596,, 2009.
Rousk, J., Bååth, E., Brookes, P. C., Lauber, C. L., Lozupone, C.,
Caporaso, J. G., Knight, R., and Fierer, N.: Soil bacterial and
fungal communities across a pH gradient in an arable soil, ISME
J., 4, 1340–1351,, 2010.
Sanderman, J., Hengl, T., and Fiske, G. J.: Soil carbon debt of
12,000 years of human land use, P. Natl. Acad. Sci. USA, 114,
9575–9580,, 2017.
Schimel, D., Stephens, B. B., and Fisher, J. B.: Effect of increasing
CO2on the terrestrial carbon cycle, P. Natl. Acad. Sci. USA, 112,
436–441,, 2015.
Sengupta, A., Fansler, S. J., Chu, R. K., Danczak, R. E.,
Garayburu-Caruso, V. A., Renteria, L., Song, H.-S., Toyoda,
J., Wells, J., and Stegen, J. C.: Disturbance triggers non-linear
microbe–environment feedbacks, Biogeosciences, 18, 4773–
4789,, 2021.
Shi, Z., Crowell, S., Luo, Y., and Moore, B.: Model structures am-
plify uncertainty in predicted soil carbon responses to climate
change, Nat. Commun., 9, 2171,
018-04526-9, 2018.
Six, J., Frey, S. D., Thiet, R. K., and Batten, K. M.:
Bacterial and Fungal Contributions to Carbon Sequestra-
tion in Agroecosystems, Soil Sci. Soc. Am. J., 70, 555,, 2006.
Soares, M. and Rousk, J.: Microbial growth and carbon use ef-
ficiency in soil: Links to fungal-bacterial dominance, SOC-
quality and stoichiometry, Soil Biol. Biochem., 131, 195–205,, 2019.
Strickland, M. S. and Rousk, J.: Considering fungal: Bacte-
rial dominance in soils Methods, controls, and ecosys-
tem implications, Soil Biol. Biochem., 42, 1385–1395,, 2010.
Sulman, B. N., Phillips, R. P., Oishi, A. C., Shevliakova, E., and
Pacala, S. W.: Microbe-driven turnover offsets mineral-mediated
storage of soil carbon under elevated CO2, Nat. Clim. Chang., 4,
1385–1395,, 2014.
Tagesson, T., Schurgers, G., Horion, S., Ciais, P., Tian, F., Brandt,
M., Ahlström, A., Wigneron, J. P., Ardö, J., Olin, S., Fan, L.,
Wu, Z., and Fensholt, R.: Recent divergence in the contribu-
tions of tropical and boreal forests to the terrestrial carbon sink,
Nat. Ecol. Evol., 4, 202–209,
019-1090-0, 2020.
Tedersoo, L., Bahram, M., Põlme, S., Kõljalg, U., Yorou, N. S.,
Wijesundera, R., Ruiz, L. V., Vasco-Palacios, A. M., Thu, P. Q.,
Suija, A., Smith, M. E., Sharp, C., Saluveer, E., Saitta, A., Rosas,
M., Riit, T., Ratkowsky, D., Pritsch, K., Põldmaa, K., Piepen-
bring, M., Phosri, C., Peterson, M., Parts, K., Pärtel, K., Ots-
ing, E., Nouhra, E., Njouonkou, A. L., Nilsson, R. H., Morgado,
L. N., Mayor, J., May, T. W., Majuakim, L., Lodge, D. J., Lee,
S., Larsson, K. H., Kohout, P., Hosaka, K., Hiiesalu, I., Henkel,
T. W., Harend, H., Guo, L. D., Greslebin, A., Grelet, G., Geml,
J., Gates, G., Dunstan, W., Dunk, C., Drenkhan, R., Dearnaley,
J., De Kesel, A., Dang, T., Chen, X., Buegger, F., Brearley, F.
Q., Bonito, G., Anslan, S., Abell, S., and Abarenkov, K.: Global
diversity and geography of soil fungi, Science, 346, 1256688,, 2014.
Terrer, C., Phillips, R. P., Hungate, B. A., Rosende, J., Pett-Ridge,
J., Craig, M. E., van Groenigen, K. J., Keenan, T. F., Sulman, B.
N., Stocker, B. D., Reich, P. B., Pellegrini, A. F. A., Pendall, E.,
Zhang, H., Evans, R. D., Carrillo, Y., Fisher, J. B., Van Sundert,
K., Vicca, S., and Jackson, R. B.: A trade-off between plant and
soil carbon storage under elevated CO2, Nature, 591, 599–603,, 2021.
van den Hoogen, J., Geisen, S., Routh, D., Ferris, H., Traunspurger,
W., Wardle, D. A., de Goede, R. G. M., Adams, B. J., Ahmad,
W., Andriuzzi, W. S., Bardgett, R. D., Bonkowski, M., Campos-
Herrera, R., Cares, J. E., Caruso, T., de Brito Caixeta, L., Chen,
X., Costa, S. R., Creamer, R., Mauro da Cunha Castro, J., Dam,
M., Djigal, D., Escuer, M., Griffiths, B. S., Gutiérrez, C., Ho-
hberg, K., Kalinkina, D., Kardol, P., Kergunteuil, A., Korthals,
G., Krashevska, V., Kudrin, A. A., Li, Q., Liang, W., Mag-
ilton, M., Marais, M., Martín, J. A. R., Matveeva, E., Mayad,
E. H., Mulder, C., Mullin, P., Neilson, R., Nguyen, T. A. D.,
Nielsen, U. N., Okada, H., Rius, J. E. P., Pan, K., Peneva, V.,
Pellissier, L., Carlos Pereira da Silva, J., Pitteloud, C., Pow-
ers, T. O., Powers, K., Quist, C. W., Rasmann, S., Moreno, S.
S., Scheu, S., Setälä, H., Sushchuk, A., Tiunov, A. V., Trap,
J., van der Putten, W., Vestergård, M., Villenave, C., Waeyen-
berge, L., Wall, D. H., Wilschut, R., Wright, D. G., Yang, J.,
and Crowther, T. W.: Soil nematode abundance and functional
group composition at a global scale, Nature, 572, 194–198,, 2019.
Van Der Heijden, M. G. A., Bardgett, R. D., and Van Straalen,
N. M.: The unseen majority: Soil microbes as drivers of plant
diversity and productivity in terrestrial ecosystems, Ecol. Lett.,
11, 296–310,,
Waldrop, M. P., Holloway, J. M., Smith, D. B., Goldhaber,
M. B., Drenovsky, R. E., Scow, K. M., Dick, R., Howard,
D., Wylie, B., and Grace, J. B.: The interacting roles of
climate, soils, and plant production on soil microbial com-
munities at a continental scale, Ecology, 98, 1957–1967,, 2017.
Wang, Q., Liu, S., and Tian, P.: Carbon quality and soil mi-
crobial property control the latitudinal pattern in temper-
ature sensitivity of soil microbial respiration across Chi-
nese forest ecosystems, Glob. Chang. Biol., 24, 2841–2849,, 2018.
Waring, B. G., Averill, C., and Hawkes, C. V.: Differences in fungal
and bacterial physiology alter soil carbon and nitrogen cycling:
Insights from meta-analysis and theoretical models, Ecol. Lett.,
16, 887–894,, 2013.
Wieder, W. R., Bonan, G. B., and Allison, S. D.: Global soil carbon
projections are improved by modelling microbial processes, Nat. Earth Syst. Sci. Data, 14, 4339–4350, 2022
4350 K. Yu et al.: The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil
Clim. Chang., 3, 909–912,,
Wieder, W. R., Allison, S. D., Davidson, E. A., Georgiou, K.,
Hararuk, O., He, Y., Hopkins, F., Luo, Y., Smith, M. J., Sul-
man, B., Todd-Brown, K., Wang, Y. P., Xia, J., and Xu,
X.: Explicitly representing soil microbial processes in Earth
system models, Global Biogeochem. Cycles, 29, 1782–1800,, 2015.
Yu, K.: Biogeography-of-soil-microbes, figshare [data set],, 2022.
Yu, K. and D’Odorico, P.: Hydraulic lift as a determinant of tree-
grass coexistence on savannas, New Phytol., 207, 1038–1051,, 2015.
Yu, K., Smith, W. K., Trugman, A. T., Condit, R., Hubbell, S. P.,
Sardans, J., Peng, C., Zhu, K., Peñuelas, J., Cailleret, M., Lev-
anic, T., Gessler, A., Schaub, M., Ferretti, M., and Anderegg, W.
R. L.: Pervasive decreases in living vegetation carbon turnover
time across forest climate zones, P. Natl. Acad. Sci. USA, 116,
24662–24667,, 2019.
Yue, H., Wang, M., Wang, S., Gilbert, J. A., Sun, X., Wu,
L., Lin, Q., Hu, Y., Li, X., He, Z., Zhou, J., and Yang,
Y.: The microbe-mediated mechanisms affecting topsoil car-
bon stock in Tibetan grasslands, ISME J., 9, 2012–2020,, 2015.
Zhu, Q., Riley, W. J., and Tang, J.: A new theory of plant-
microbe nutrient competition resolves inconsistencies between
observations and model predictions, Ecol. Appl., 27, 875–886,, 2017.
Earth Syst. Sci. Data, 14, 4339–4350, 2022
... Waring et al. (2013) have discussed how changes in soil conditions across biomes and land use affect the ratio of fungal to bacterial biomass, as differences in physiology affect the biogeographic distributions of these two groups. A recent study (Yu et al., 2022) showed that fungi with their generally slower growth and turnover rates (Rousk & Bååth, 2007), greater carbon to nutrient stoichiometry (Waring et al., 2013), and greater capacity to degrade more recalcitrant substrates (Strickland & Rousk, 2010) dominate in high latitudes with low mean annual temperature and high net primary productivity relative to soil bacteria that dominate in the tropical regions and in arable lands with frequent tillage disturbances. ...
Full-text available
Soil water status, which refers to the wetness or dryness of soils, is crucial for the productivity of agroecosystems, as it determines nutrient cycling and uptake physically via transport, biologically via the moisture-dependent activity of soil flora, fauna and plants, and chemically via specific hydrolysis and redox reactions. Here, we focus on the dynamics of nitrogen (N), phosphorus (P), and sulfur (S), and review how soil water is coupled to the cycling of these elements and related stoichiometric controls across different scales within agroecosystems. These scales span processes at the molecular level, where nutrients and water are consumed, to processes in the soil pore system, within a soil profile and across the landscape. We highlight that with increasing mobility of the nutrients in water, water-based nutrient flux may alleviate or even exacerbate imbalances in nutrient supply within soils, for example, based on the different mobility of individual nutrients, by transport of mobile nutrients towards previously depleted microsites (alleviating imbalances), or by selective loss of mobile nutrients from microsites (increasing imbalances). These imbalances can be modulated by biological activity, especially of fungal hyphae and roots, which contribute to nutrient redistribution within soils and which are themselves dependent on specific, optimal water availability. At larger scales, such small-scale effects converge with nutrient inputs from atmospheric (wet deposition) or non-local sources and nutrient losses from the soil system towards aquifers. Hence, water acts as a major control in nutrient cycling across scales in agroecosystems, and may either exacerbate or remove spatial disparities in the availability of the individual nutrients (N, P, S) required for biological activity.
Full-text available
Conceptual frameworks linking microbial community membership, properties, and processes with the environment and emergent function have been proposed but remain untested. Here we refine and test a recent conceptual framework using hyporheic zone sediments exposed to wetting–drying transitions. Our refined framework includes relationships between cumulative properties of a microbial community (e.g., microbial membership, community assembly properties, and biogeochemical rates), environmental features (e.g., organic matter thermodynamics), and emergent ecosystem function. Our primary aim was to evaluate the hypothesized relationships that comprise the conceptual framework and contrast outcomes from the whole and putatively active bacterial and archaeal communities. Throughout the system we found threshold-like responses to the duration of desiccation. Membership of the putatively active community – but not the whole bacterial and archaeal community – responded due to enhanced deterministic selection (an emergent community property). Concurrently, the thermodynamic properties of organic matter (OM) became less favorable for oxidation (an environmental component), and respiration decreased (a microbial process). While these responses were step functions of desiccation, we found that in deterministically assembled active communities, respiration was lower and thermodynamic properties of OM were less favorable. Placing the results in context of our conceptual framework points to previously unrecognized internal feedbacks that are initiated by disturbance and mediated by thermodynamics and that cause the impacts of disturbance to be dependent on the history of disturbance.
Full-text available
Terrestrial ecosystems remove about 30 per cent of the carbon dioxide (CO2) emitted by human activities each year¹, yet the persistence of this carbon sink depends partly on how plant biomass and soil organic carbon (SOC) stocks respond to future increases in atmospheric CO2 (refs. 2,3). Although plant biomass often increases in elevated CO2 (eCO2) experiments4,5,6, SOC has been observed to increase, remain unchanged or even decline⁷. The mechanisms that drive this variation across experiments remain poorly understood, creating uncertainty in climate projections8,9. Here we synthesized data from 108 eCO2 experiments and found that the effect of eCO2 on SOC stocks is best explained by a negative relationship with plant biomass: when plant biomass is strongly stimulated by eCO2, SOC storage declines; conversely, when biomass is weakly stimulated, SOC storage increases. This trade-off appears to be related to plant nutrient acquisition, in which plants increase their biomass by mining the soil for nutrients, which decreases SOC storage. We found that, overall, SOC stocks increase with eCO2 in grasslands (8 ± 2 per cent) but not in forests (0 ± 2 per cent), even though plant biomass in grasslands increase less (9 ± 3 per cent) than in forests (23 ± 2 per cent). Ecosystem models do not reproduce this trade-off, which implies that projections of SOC may need to be revised.
Full-text available
Bacteria and fungi, representing two major soil microorganism groups, play an important role in global nutrient biogeochemistry. Biogeographic patterns of bacterial and fungal biomass are of fundamental importance for mechanistically understanding nutrient cycling. We synthesized 1323 data points of phospholipid fatty acid-derived fungal biomass C (FBC), bacterial biomass C (BBC), and fungi:bacteria (F:B) ratio in topsoil, spanning 11 major biomes. The FBC, BBC, and F:B ratio display clear biogeographic patterns along latitude and environmental gradients including mean annual temperature, mean annual precipitation, net primary productivity, root C density, soil temperature, soil moisture, and edaphic factors. At the biome level, tundra has the highest FBC and BBC densities at 3684 (95% confidence interval: 1678–8084) mg kg−1 and 428 (237–774) mg kg−1, respectively; desert has the lowest FBC and BBC densities at 16.92 (14.4–19.89) mg kg−1 and 6.83 (6.1–7.65) mg kg−1, respectively. The F:B ratio varies dramatically, ranging from 1.8 (1.6–2.1) in savanna to 8.6 (6.7–11.0) in tundra. An empirical model was developed for the F:B ratio and it is combined with a global dataset of soil microbial biomass C to produce global maps for FBC and BBC in 0–30 cm topsoil. Across the globe, the highest FBC is found in boreal forest and tundra while the highest BBC is in boreal forest and tropical/subtropical forest, the lowest FBC and BBC are in shrub and desert. Global stocks of living microbial biomass C were estimated to be 12.6 (6.6–16.4) Pg C for FBC and 4.3 (0.5–10.3) Pg C for BBC in topsoil. These findings advance our understanding of the global distribution of fungal and bacterial biomass, which facilitates the incorporation of fungi and bacteria into Earth system models. The global maps of bacterial and fungal biomass serve as a benchmark for validating microbial models in simulating the global C cycle under a changing climate.
Full-text available
Anthropogenic land use and land cover changes (LULCC) have a large impact on the global terrestrial carbon sink, but this effect is not well characterized according to biogeographical region. Here, using state-of-the-art Earth observation data and a dynamic global vegetation model, we estimate the impact of LULCC on the contribution of biomes to the terrestrial carbon sink between 1992 and 2015. Tropical and boreal forests contributed equally, and with the largest share of the mean global terrestrial carbon sink. CO2 fertilization was found to be the main driver increasing the terrestrial carbon sink from 1992 to 2015, but the net effect of all drivers (CO2 fertilization and nitrogen deposition, LULCC and meteorological forcing) caused a reduction and an increase, respectively, in the terrestrial carbon sink for tropical and boreal forests. These diverging trends were not observed when applying a conventional LULCC dataset, but were also evident in satellite passive microwave estimates of aboveground biomass. These datasets thereby converge on the conclusion that LULCC have had a greater impact on tropical forests than previously estimated, causing an increase and decrease of the contributions of boreal and tropical forests, respectively, to the growing terrestrial carbon sink.
Full-text available
Forests play a major role in the global carbon cycle. Previous studies on the capacity of forests to sequester atmospheric CO 2 have mostly focused on carbon uptake, but the roles of carbon turnover time and its spatiotemporal changes remain poorly understood. Here, we used long-term inventory data (1955 to 2018) from 695 mature forest plots to quantify temporal trends in living vegetation carbon turnover time across tropical, temperate, and cold climate zones, and compared plot data to 8 Earth system models (ESMs). Long-term plots consistently showed decreases in living vegetation carbon turnover time, likely driven by increased tree mortality across all major climate zones. Changes in living vegetation carbon turnover time were negatively correlated with CO 2 enrichment in both forest plot data and ESM simulations. However, plot-based correlations between living vegetation carbon turnover time and climate drivers such as precipitation and temperature diverged from those of ESM simulations. Our analyses suggest that forest carbon sinks are likely to be constrained by a decrease in living vegetation carbon turnover time, and accurate projections of forest carbon sink dynamics will require an improved representation of tree mortality processes and their sensitivity to climate in ESMs.
Full-text available
Microbes' role in soil decomposition Soils harbor a rich diversity of invertebrate and microbial life, which drives biogeochemical processes from local to global scales. Relating the biodiversity patterns of soil ecological communities to soil biogeochemistry remains an important challenge for ecologists and earth system modelers. Crowther et al. review the state of science relating soil organisms to biogeochemical processes, focusing particularly on the importance of microbial community variation on decomposition and turnover of soil organic matter. Although there is variation in soil communities across the globe, ecologists are beginning to identify general patterns that may contribute to predicting biogeochemical dynamics under future climate change. Science , this issue p. eaav0550
Full-text available
Soil carbon transformation and sequestration have received significant interest in recent years due to a growing need for quantifying its role in mitigating climate change. Even though our understanding of the nature of soil organic matter has recently been substantially revised, fundamental uncertainty remains about the quantitative importance of microbial necromass as part of persistent organic matter. Addressing this uncertainty has been hampered by the absence of quantitative assessments whether microbial matter makes up the majority of the persistent carbon in soil. Direct quantification of microbial necromass in soil is very challenging because of overlapping molecular signature with non‐microbial organic carbon. Here we use a comprehensive analysis of existing biomarker amino sugar data published between 1996‐2018, combined with novel appropriation using ecological systems approach, elemental carbon‐nitrogen stoichiometry, and biomarker scaling, to demonstrate a suit of strategies for quantifying the contribution of microbe‐derived carbon to the topsoil organic carbon reservoir in global temperate agricultural, grassland, and forest ecosystems. We show that microbial necromass can make up more than half of soil organic carbon. Hence, we suggest next‐generation field management requires promoting microbial biomass formation and necromass preservation to maintain healthy soils, ecosystems, and climate. Our analyses have important implications for improving current climate and carbon models, and helping develop management practices and policies. This article is protected by copyright. All rights reserved.
Full-text available
Soil organisms are a crucial part of the terrestrial biosphere. Despite their importance for ecosystem functioning, few quantitative, spatially explicit models of the active belowground community currently exist. In particular, nematodes are the most abundant animals on Earth, filling all trophic levels in the soil food web. Here we use 6,759 georeferenced samples to generate a mechanistic understanding of the patterns of the global abundance of nematodes in the soil and the composition of their functional groups. The resulting maps show that 4.4 ± 0.64 × 10²⁰ nematodes (with a total biomass of approximately 0.3 gigatonnes) inhabit surface soils across the world, with higher abundances in sub-Arctic regions (38% of total) than in temperate (24%) or tropical (21%) regions. Regional variations in these global trends also provide insights into local patterns of soil fertility and functioning. These high-resolution models provide the first steps towards representing soil ecological processes in global biogeochemical models and will enable the prediction of elemental cycling under current and future climate scenarios.
Full-text available
Atmospheric carbon dioxide enrichment (eCO2) can enhance plant carbon uptake and growth, thereby providing an important negative feedback to climate change by slowing the rate of increase of the atmospheric CO2 concentration. While evidence gathered from young aggrading forests has generally indicated a strong CO2 fertilization effect on biomass growth, it is unclear whether mature forests respond to eCO2 in a similar way. In mature trees and forest stands, photosynthetic uptake has been found to increase under eCO2 without any apparent accompanying growth response, leaving an open question about the fate of additional carbon fixed under eCO2. Here, using data from the first ecosystem-scale Free-Air CO2 Enrichment (FACE) experiment in a mature forest, we constructed a comprehensive ecosystem carbon budget to track the fate of carbon as the forest responds to four years of eCO2 exposure. We show that, although the eCO2 treatment of ambient +150 ppm (+38%) induced a 12% (+247 gCm-2yr-1) increase in carbon uptake through gross primary production, this additional carbon uptake did not lead to increased carbon sequestration at the ecosystem level. Instead, the majority of the extra carbon was emitted back into the atmosphere via several respiratory fluxes, with increased soil respiration alone contributing ~50% of the total uptake surplus. Our results call into question the predominant thinking that the capacity of forests to act as carbon sinks will be generally enhanced under eCO2, and challenge the efficacy of climate mitigation strategies that rely on CO2 fertilization as a driver of increased carbon sinks in standing forests and afforestation projects.
Microbial communities in arctic–alpine soils show biogeographic patterns related to elevation, but the effect of fine-scale heterogeneity and possibly related temperature and soil moisture regimes remains unclear. We collected soil samples from different micro-topographic positions and elevational levels in two mountain regions of the Scandes, Central Norway. Microbial community composition was characterized by 16S rRNA gene amplicon sequencing and was dependent on micro-topography and elevation. Underlying environmental drivers were identified by integration of microbial community data with a comprehensive set of site-specific long-term recorded temperature and soil moisture data. Partial least square regression analysis allowed the description of ecological response patterns and the identification of the important environmental drivers for each taxonomic group. This demonstrated for the first time that taxa responding to elevation were indeed most strongly defined by temperature, rather than by other environmental factors. Micro-topography affected taxa were primarily controlled by temperature and soil moisture. In general, 5-year datasets had higher explanatory power than 1-year datasets, indicating that the microbial community composition is dependent on long-term developments of near-ground temperature and soil moisture regimes and possesses a certain resilience, which is in agreement with an often observed delayed response in global warming studies in arctic–alpine regions.