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Earth Syst. Sci. Data, 14, 4339–4350, 2022
https://doi.org/10.5194/essd-14-4339-2022
© 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 (kai86liang@gmail.com) and Thomas W. Crowther
(tom.crowther@usys.ethz.ch)
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
https://doi.org/10.6084/m9.figshare.19556419 (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 https://doi.org/10.5194/essd-14-4339-2022
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 0.5◦for 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 =1−fungal
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:
//apps.webofknowledge.com, last access: 30 June 2020) and
the China National Knowledge Infrastructure Database (http:
//cnki.net, 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
https://doi.org/10.5194/essd-14-4339-2022 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 https://doi.org/10.5194/essd-14-4339-2022
K. Yu et al.: The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil 4343
unit (nmol g−1PLFA). 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
resolution.
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
pixel.
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.,
https://doi.org/10.5194/essd-14-4339-2022 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 https://doi.org/10.5194/essd-14-4339-2022
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
model
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-
ping.
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-
https://doi.org/10.5194/essd-14-4339-2022 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 https://doi.org/10.5194/essd-14-4339-2022
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
https://doi.org/10.6084/m9.figshare.19556419 (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: https://doi.org/10.5194/essd-14-4339-2022-supplement.
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,
RED, KMS, FM, MPW, YY, FTDV, RDB, PM, FB, SGB, EMB,
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.
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