Ecology Letters, (2009) 12: 1-12
doi: 10.1111/j.1461-0248.2009.01360.x
REVIEW AND
SYNTHESIS
Global patterns in belowground communities
A b s tr a c t
Noah Fiererd’^* M ichael S.
A lthough below ground ecosystem s have b een studied extensively an d soil b io ta play
Stricklandd Daniel Liptzind
integral roles in biogeochem icai processes, surprisingly w e have a lim ited u nderstanding
M a rk A. Bradford^ and
o f global p attern s in below ground biom ass an d co m m unity structure. T o address this
Cory C. Cleveland®
critical gap, w e co n d u cted a meta-analysis o f p ublished data (> 1300 datapoints) to
’D epartm ent o f Ecology and
Evolutionary Biology, University
o f Colorado, Boulder, CO 80309,
USA
^Cooperative Institute for
Research in Environm ental
Sciences, University o f Colorado,
Boulder, CO 80309, USA
^O dum School o f Ecology,
University o f Georgia, A thens,
GA 30602, USA
"'Department o f Environm ental
Science, Policy, and
M anagem ent, University o f
California, Berkeley, CA 94720,
USA
^School o f Forestry and
Environm ental Studies, Yale
University, N ew Haven, CT
06511, USA
^D epartm ent o f Ecosystem and
Conservation Sciences,
University o f M ontana,
Missoula, M T 59812, USA
*Correspondence: E-mail:
noah.fierer@ colorado.edu
com pare below ground plant, m icrobial an d faunal biom ass across seven o f th e m ajor
biom es o n E arth. W e also assem bled data to assess biom e-level p attern s in below ground
m icrobial com m unity com position. O u r analysis suggests th a t variation in m icrobial
biom ass is predictable across biom es, w ith m icrobial biom ass carbon representing 0.6—
1.1% o f soil organic carb o n ( / — 0.91) an d 1 -2 0 % o f to tal p lan t biom ass carbon
( / — 0.42). A pproxim ately 50% o f to tal anim al biom ass can b e fo u n d below ground and
soil faunal biom ass represents < 4% o f m icrobial biom ass across aU biom es. T h e
structure o f below ground m icrobial com m unities is also predictable: bacterial co m m u ­
nity com position an d fungal : bacterial gene ratios can be p red icted reasonably well from
soil p H and soil C : N ratios respectively. T o g eth er these results identify ro b u st p attern s
in th e structure o f below ground m icrobial an d faunal com m unities at b ro ad scales w hich
m ay be explained by universal m echanism s th a t regulate below ground b io ta across
biom es.
K ey w o rd s
Acari, CoUembola, n em atodes, 16S rR N A , soil bacteria, soil faunal biom ass, soil fo o d
w ebs, soil fungi, soil m icrobial biom ass.
Ecology Letters (2009) 12: 1—12
INTRODUCTION
F or centuries ecologists have m apped the distribution and
abundance o f plants and animals across terrestrial biomes.
This w ork has provided key insights into the factors that
structure plant and animal communities across the globe and
such surveys have been critical to advances in a wide range
o f disciplines, including biogeochemistry, global change
ecology and biogeography. In contrast, we lack a similar
global perspective on the distribution o f non-plant biomass
in soil and the distribution o f individual soil taxa across
biomes.
A lthough the long history o f soil ecological research has
revealed variations in belowground fauna and microbes
across sites, there have been few com prehensive crossbiome com parisons. O f course, there are some notable
exceptions, including w ork by Petersen & Luxton (1982)
(a product o f the International Biosphere Project) as well as
a num ber o f other studies that have com pared soil biota and
com m unity characteristics across a broad range o f ecosys­
tem types (e.g. Wardle 1992; Z ak et al. 1994; Boag & Yeates
1998; Cleveland & Liptzin 2007). Likewise, global-scale
variations in belowground biota have been synthesized in
several textbooks (Lee 1985; LaveUe & Spain 2001; Wardle
2002; Coleman et al. 2004; Bardgett 2005), but such
© 2009 BlackweU PubHshing L td/C N R S
2 N. Fierer e t al.
summaries were generally generated by extrapolating data
from studies w hich focused on a few sites or a few select
taxa. It is thus reasonable to conclude that a comprehensive,
cross-blome analysis o f soil microbial and faunal community
com position and abundance has n o t been conducted. As a
result, a num ber o f fundamental questions In soil ecology
remain unanswered. Such questions include: (a) W hich
microbial taxa are m ost abundant in soils from different
biomes? (b) H ow does the ‘average’ belowground microbial
and faunal biomass vary across biomes? (c) Are there
predictable changes In the structure o f belowground
communities across biomes? A lthough such broad-scale
blogeographical questions have long been examined by plant
and animal biologists, similar inform ation describing global
patterns o f soil faunal communities Is less com prehensive
or, in the case o f soil microbes, essentially non-existent.
Several factors have im peded our ability to generate
blome-scale com parisons o f belowground, non-plant bio­
mass and com m unity com position. For example, logistical
and financial constraints often force ecologists to focus on
single sites or groups o f organisms. T here are also a num ber
o f m ethodological issues associated with obtaining accurate
estimates o f soil faunal and microbial biomass (Martens
1995; A ndre et al. 2002; Jenklnson et al 2004; Coleman &
Wall 2007), with Httie agreement on the suitability o f various
m ethods. likew ise, only In recent years have researchers
developed the high-throughput molecular techniques that
perm it the characterization and com parison o f a sufficientiy
large num ber o f soil microbial communities w ithout
introducing the biases associated with cultivation-dependent
approaches (Kirk et al 2004; Thles 2007). D espite these
limitations, the ability to compare the structure o f
belowground communities across broad scales would allow
us to describe general patterns and test paradigms In soil
ecology that are widely accepted but often supported by
only limited direct evidence. F or example, one reasonable
hypothesis Is that belowground faunal and microbial
biomass, like the biomass o f aboveground herbivores
(M cNaughton et al. 1989), Is strongly correlated w ith net
primary production. Likewise, one might predict that the
overall com position o f soil communities — Hke plant
communities — should be m ore variable between distinct
biomes than within a given blome.
In this study, we synthesized published data to generate
estimates o f total belowground biomass, and used that
inform ation to com pare soil microbial and faunal biomass
to estimates o f plant biomass, plant productivity and soil
organic carbon (SOC) concentrations across biomes. In
addition, because there have been few attem pts to analyse
broad-scale patterns In microbial com m unity com position
we present data describing changes In soil bacterial
com m unity stm cture and bacterial : fungal ratios across
biomes. Finally, we highlight the major uncertainties In our
© 2009 Blackwell Pubkshing L td/C N R S
Review a n d Synthesis
extrapolations and discuss the predictability o f patterns In
soil microbial biomass and com m unity structure at the
global scale.
ME T HO DS
E stim ates o f soil m icrobial b io m ass
Microbial biomass was estimated from data collected on c.
400 individual soils (see references In Appendix SI).
Individual soils were grouped Into one o f seven general
blome categories: boreal forest, desert, tem perate coniferous
forest, tem perate deciduous forest, tem perate grassland,
tropical m oist forest or tundra. We excluded biomes from
which there were insufficient data and cultivated soils (or
soils that were intensively managed). We excluded cultivated
soils because they do n ot represent a unique blome
classification, they can represent diverse plant cover types
(e.g. pine plantations or annual crops), and m anagem ent
practices can vary widely with large anticipated effects on
belowground communities (e.g. H endrix et al. 1986). O n a
per-blom e basis, the resulting database Included a minimum
o f 38 (boreal forest) and a maximum o f 91 (temperate
grassland) individual soil microbial biomass estimates. We
only used microbial biomass estimates obtained using the
chloroform fumigation—extraction (CFE) technique (Vance
et al. 1987; Tate et al. 1988) as this Is the m ost com monly
used m ethod and It should provide an Index o f total
microbial biomass In soil (including b oth bacteria and fungi).
The CFE technique Is subject to some biases and may
under- or over-estimate microbial biomass C (Mlc,.) In soils
with low porosity and high organic m atter concentrations
respectively (Badalucco et al. 1997; Jenklnson et al. 2004).
H owever, no microbial biomass m easurem ent approach Is
error-free and other techniques have n o t been used as
ftequentiy across a wide range o f soil types. We excluded
biomass estimates that were exclusively obtained from litter
o r organic horizons In the database. Limiting our analysis to
mineral soils led to an underestim ation o f microbial
biomass, b u t because m ost data are for mineral soil
horizons alone, this was necessary to have consistent
com parisons between biomes. Finally, where microbial
biomass was estimated at multiple time points at the same
location, we calculated an arithmetic m ean o f reported
values and If experimental treatm ents were Included In a
study, we only used those data from the ‘control’ (unmanlpulated) plots.
M icrobial biomass C values are usually calculated using
the following equation:
MlCc = K c / E c
( 1)
where Aj. represents the difference between fumigated and
unfumlgated extractable C concentrations and E ^ represents
Review an d Synthesis
Global p attern s in below gro un d com m unities 3
the extraction efficiency (Vance et al. 1987). T o determine
for each soil, we either used the reported
value or we
calculated
from the reported biomass C value and the
coefficient that was applied. We then used a constant E ^
value o f 0.4 to calculate Mic,. concentrations for aU soils as
this E ^ value is within the range o f E ^ values (0.35—0.45)
m ost com monly reported in the literature (fenkinson et al.
2004; Kandeler 2007). A lthough different soils or m ethod­
ologies result in variable extraction efficiencies, E ^ values
are rarely determ ined experimentally for individual soils and
using a standard E ^ value allowed us to com pare estimates
across studies.
Microbial biomass is m ost com m only reported as units o f
microbial C per gram dry soil. T o facilitate comparisons
across biomes and between different taxa, we converted all
microbial biomass to units o f Mic,. m ^ using bulk density
values (where reported) or estimating bulk density values for
individual soils by m atching the soil description (or site
location if no soil description was provided) to those soils
included in the global soil profile database (Batjes 1995).
Because m ost studies estimate microbial biomass in only the
top 15—25 cm o f the soil profile, we used the following
equation to estimate the total am ount o f biomass found in
the top m eter o f the soil profile for each soil included in the
database:
where x — the depth interval sampled and B — measured
microbial biomass. This equation is based on the biomass
depth distributions reported in the few studies that have
measured microbial biomass to a depths o f at least 1 m
(Dictor et al 1998; Blume et al 2002; Fierer et al 2003;
Agnelli et al. 2004; Castellazzi et al. 2004) and it assumes that
c. 60+O o f the microbial biomass is in the top 5 cm, 70®/o in
the top 10 cm and 80®/o in the top 20 cm, dow n to 1 m.
This extrapolation o f microbial biomass estimates through
the soil profile is likely to lead to an over- and under-estim ation o f total microbial biomass in very shallow and deep
soils respectively. However, we extrapolated to 1 m depth to
facilitate com parisons across studies and to compare
microbial biomass estimates to published estimates o f root
biomass and SOC concentrations.
across biomes. T ogether the faunal biomass database
consisted o f c. 930 individual datapoints (see references in
A ppendix 82) b ut there were m ore data for certain biomes
and taxa than others leading to variability in the
confidence o f our estimates (Table 1). As with the
microbial biomass estimates, all data were converted to
units o f biomass C m ^ and we excluded data obtained
strictiy from litter material b ut many o f the biomass
estimates may have included litter material. In m ost cases,
faunal biomass data were reported as num bers o f
individuals or mg o f faunal biomass m ^ (dry weight
basis), b ut if biomass data were reported on a per gram
soil basis, estimates were converted using a bulk density
value appropriate to each soil type. T o convert numbers o f
individuals to biomass we used estimates from Petersen &
Luxton (1982) w ith average dry weights per individual as
follows: nem atodes (0.1 pg), Acari (5 pg), CoUembola
(5 pg), enchytraeids (50 pg) and earthworms (10 mg). We
assumed that aU organisms have a carbon content o f 50®/o
C (Sohlenius 1979; Ferris et al 1997; Elser et al 2000).
N ote that using average dry weights per individual per taxa
may introduce error given variation in some taxa across
biomes (see Petersen & L uxton 1982). We did not
extrapolate faunal biomass through the entire soil profile
as sampling depths were n ot recorded for > 30®/o o f the
samples (most biomass estimates were only reported as
individuals or unit o f faunal biomass m ^) and because
biomass depth distributions are likely to vary considerably
across faunal taxa and within individual taxa across
biomes. Since the majority o f soil faunal biomass is likely
to be restricted to the top 10—15 cm o f the soil profile
(LaveUe & Spain 2001), this is utUikely to lead to a
significant underestim ation o f faunal biomass in m ost soils
(although desert soUs may be the exception, see Results
and discussion). Likewise, because we did n ot adjust the
faunal biomass estimates to account for efficiencies o f
faunal extraction, our estimates are likely conservative.
There is a high degree o f variability in the efficiency o f
extracting faunal biomass from soil (Andre et al. 2002;
Coleman & WaU 2007) and correcting for differences in
extraction efficiencies between studies would be very
difficult given that efficiencies are highly dependent on the
taxon in question, soU characteristics and the specific
extraction technique utiUzed.
E stim ates o f fa u n a l b io m ass
O th er e s tim a te s o f bio m e-lev el c h a ra c te ristic s
We collected biomass estimates for five major groups o f
soU fauna (Acari, CoUembola, Enchytraeids, N em atoda and
earthworms) across aU seven biomes. O ther taxa that may
also contribute significantiy to soil faunal biomass (e.g.
termites, ants and isopods) were excluded from the
analysis due to insufficient data on their biomass levels
Biome-level estimates o f aboveground and belowground net
primary productivity were based on estimates pubUshed in
Saugier et al. (2001), assuming tissue is 49®/o C by mass
(Jackson et al. 1997). Plant root and shoot biomass values
were obtained from Jackson et al. (1996). T he biome-level
estimates o f average SOC concentrations follow those
Microbial biomass C (to 1 m) = (—0.132)
+ 0.605)
X
X
log(x)
B
(2)
© 2009 BlackweU PubUshing L td/C N R S
4 N. Fierer e t al.
Review a n d Synthesis
G to u p o f
N o. data
points
Biome
otganism s
B oteal fotest
Acati
CoUembola
Enchyttaeids
N em atoda
35
33
21
14
E atthw otm s
M ictobes
Acati
CoUembola
Enchyttaeids
N em atoda
5
38
12
12
ND
105
ND
40
12
14
48
D esett
T em petate
deciduous fotest
E atthw otm s
M ictobes
Acati
CoUembola
Enchyttaeids
N em atoda
E atthw otm s
M ictobes
Acati
CoUembola
T em petate
gtassland
Enchyttaeids
N em atoda
E atthw otm s
M ictobes
Acati
CoUembola
Tem pem te
conifetous fotest
Ttopical Fotest
T undta
Enchyttaeids
N em atoda
E atthw otm s
M ictobes
Acati
CoUembola
Enchyttaeids
N em atoda
E atthw otm s
M ictobes
Acati
CoUembola
Enchyttaeids
N em atoda
E atthw otm s
M ictobes
100
12
49
29
36
26
44
28
75
37
38
18
44
22
91
16
13
ND
5
22
59
41
34
27
17
8
40
M ean
(1 SEM)
0.20
0.06
0.32
0.08
0.28
57
(0.02)
(0.01)
(0.06)
(0.02)
(0.18)
(6)
0.01
0.01
ND
0.01
ND
43
0.35
0.24
0.80
0.10
1.2
175
0.23
0.12
0.64
(0.00)
(0.00)
(0.00)
(8)
(0.12)
(0.06)
(0.10)
(0.01)
(0.6)
(26)
(0.03)
(0.03)
(0.16)
M edian
0.14
0.05
0.28
0.06
0.10
51
0.01
0.01
ND
0.03
19
0.15
0.17
0.56
0.06
0.13
89
0.16
0.06
0.25
2.0
116
0.18
0.16
0.31
0.36
3.8
131
0.16
0.02
ND
(0.11)
(0.4)
(9.4)
(0.03)
(0.05)
(0.07)
(0.08)
(1.9)
(10)
(0.05)
(0.01)
0.30
0.05
1.19
82
0.09
0.05
0.26
0.17
0.79
114
0.13
0.01
ND
0.01
4.9
203
0.13
0.10
0.99
0.18
1.4
136
(0.00)
(1.78)
(20.84)
(0.02)
(0.02)
(0.16)
(0.05)
(0.80)
(20)
0.01
0.48
167
0.07
0.05
0.83
0.11
0.09
74
Confidence
in estim ate
Table 1 Estim ated
m ean
faunal biom ass by biom e
m icrobial
and
M edium
M edium
M edium
L ow
L ow
H igh
L ow
L ow
H igh
H igh
M edium
M edium
H igh
H igh
L ow
H igh
M edium
M edium
M edium
H igh
L ow
H igh
H igh
H igh
M edium
H igh
M edium
H igh
M edium
M edium
L ow
L ow
H igh
M edium
M edium
M edium
M edium
L ow
M edium
All m ean and m edian biom ass values in units o f g biomass C m w ith one standard error o f
the m ean indicated in parentheses. Confidence in the estim ate is a qualitative description o f
our confidence in the reported m ean value for each taxon and is based o n the num ber o f data
points that go into each estimate, the geographic breadth o f the samples used for the biomelevel biom ass estim ates, the range in the biom ass values, and the difficulties associated w ith
obtaining accurate biom ass estim ates for the group in question. N D , no data or insufficient
data to w arrant reporting any estimates; either the group is largely absent from that biom e or
the group has been insufficiently studied.
reported in Jobbagy & Jackson (2000) and those soil
respiration rates reported in Raich & Schlesinger (1992). We
used estimates o f average total faunal biomass (sum o f
© 2009 Blackwell PubHshing L td/C N R S
aboveground plus belowground animal biomass) provided
in W hittaker (1975). These estimates o f total faunal biomass
across biomes are similar to those reported elsewhere
Review an d Synthesis
(M cN aughton et al. 1991) but provide estimates for a
broader range o f biomes.
D eterm in atio n of soil b a c te ria l co m m u n ity stru c tu re an d
fu n g al : b a c te ria l ra tio s
T o com plem ent the estimates o f microbial biomass across
the biomes, we also examined biome-level variability in soil
bacterial com m unity com position and soil fungal : bacterial
ratios. We used soil bacterial com m unity com position data
from Lauber et al. (2009), w ho analysed 88 soils collected
from across N o rth and South America using a barcoded
pyrosequencing approach. Fungal : bacterial ratios were
estimated across a subset o f these 88 soils using the
quantitative PCR technique described in Fierer et al. (2005).
Briefly, copy num bers o f bacterial 168 rRN A genes and
fungal 188 rRN A genes were determ ined in separate assays
for each individual soil (78 soils in total) with each assay
conducted in quadruplicate. We report the average ratio o f
fungal to bacterial copy num bers for each soil because using
a ratio reduces the biases associated with different soil D N A
samples having different amplification efficiencies (Fierer
et al. 2005). F or each o f these soils analysed, we collected a
wide range o f soil and site characteristics (see Fierer et al.
(2007) and Fierer & Jackson (2006) for details on the
soil/site characteristics that were measured and how they
were measured). We used regression models im plem ented in
8 y s t a t [8ystat 8oftware Inc. (2004)] to test relationships
between fungal : bacterial ratios and individual soil
parameters.
RESULTS AND DI S C US S I O N
G lobal p a tte rn s in b e lo w g ro u n d m icrobial b io m ass
Across all soils included in our database, estimates o f
microbial biomass ranged from 2 to 806 g C m ^ with some
o f the highest concentrations from those soils in tem perate
coniferous and tropical forests. O n a per gram soil basis, the
highest microbial biomass concentrations were in tundra
soils, but soil bulk density was often lower in tundra soils
than in soils from other biomes. A lthough microbial
biomass concentrations were highly variable across soils
within a given biome (Table 1), there were distinct
differences in average microbial biomass values across
biomes. F or example, we found that desert and boreal forest
soils harboured the lowest average microbial biomass per
area (43 and 57 g C m ^ respectively) and that tropical and
tem perate coniferous forest soils had the highest average
biomass (203 and 175 g C m ^ respectively).
Variation in microbial biomass across biomes was m ost
strongly related to 8 0 C concentrations (Fig. la; r ' — 0.91)
confirming the results o f a previous analysis using > 100
Global p attern s in below gro un d com m unities 5
individual paired microbial C and 8 0 C concentration data
obtained from multiple biomes (Cleveland & Liptzin 2007).
In our analysis we used biome-level means o f M ic„ not
individual datapoints per site, because this allowed us to
assess the potential role o f other variables in explaining
variation in microbial C across biomes. These other
variables are n ot typically available from the same study
(hence the use o f biome-level means). 8pecifically, we also
found significant, though weaker, relationships between
microbial biomass and soil respiration rates (Fig. lb ), total
plant productivity (Fig. Ic), belowground plant productivity
(Fig. Id), total plant biomass (Fig. le) and belowground
plant (root) biomass (Fig. If). T he patterns show n in Fig. 1
are qualitatively similar to those reported in other studies
that have com pared soil microbial biomass values across
ecosystems using smaller datasets (e.g. Insam 1990; Wardle
1992; Z ak et al 1994; W right & Coleman 2000; 8antm ckova
et al. 2003). A t the simplest level, these patterns suggest that
globally, microbial biomass parallels the well-established
patterns o f plant biomass and productivity (Chapin et al.
2002). However, 8 0 C concentrations could explain m ore o f
the variance in microbial biomass than plant productivity or
plant biomass (Fig. 1). In particular, at our global scale o f
inquiry, m ean microbial biomass in the tundra was higher
than would be predicted based on plant productivity and
soil respiration rates, but very close to expectations based on
8 0 C concentrations (Fig. 1). There are a num ber o f
possible explanations for this observation. First, high annual
variability in microbial biomass levels in tundra soils (Wardle
1998), com bined w ith the fact that a disproportionate
num ber o f samples were obtained during the growing
season (when microbial biomass is probably highest;
G rogan & Jonasson 2005) may be leading to an overesti­
m ation o f total microbial biomass in tundra soils. I f so, we
would expect actual annual average soil microbial biomass in
tundra soils to be lower than those depicted in Fig. 1 and
m ore closely correlated with annual rates o f plant primary
production or respiration. A n alternative hypothesis is that
extreme tem perature and m oisture conditions in tundra soils
strongly limit decom position rates. As a result, relatively
undecom posed and unprotected organic m atter accumulates
(Weintraub & 8chimel 2003; D avidson & Janssens 2006)
and provides the dom inant C source for tundra soil
microbes. In other environm ents, where decom position is
less constrained by climatic conditions, 8 0 C stocks may be
m ore advanced in their decay and hence m ore chemically
a n d /o r physically protected (Davidson & Janssens 2006)
with microbes deriving a larger percentage o f their C from
recent plant inputs. I f so, this hypothesis would suggest a
tem poral decoupling o f plant inputs and standing stocks o f
microbial biomass in tundra ecosystems, highlighting the
im portance o f docum enting tem poral variability to better
understand C dynamics in certain biomes (Bardgett et al.
© 2009 BlackweU PubUshing L td/C N R S
6 N. Fierer e t al.
#
■
V
A
(a)
Review an d Synthesis
Boreal forest
Desert
Temperate coniferous forest
Temperate deciduous forest
^ Temperate grassland
O Tropical forest
□ Tundra
(b)
= 0.50
y = 0.01x+ 70
P = 0 .0 8
o 120
6000
9000
12000
15000
18000
0
200
Soil organic carbon (g C m"^)
400
600
800
1000
1200 1400
Soil respiration (g C m"^ y e a r i)
(d)
a^ =
1^ = 0.61
y = 0.1x + 70
P = 0.04
r
160-
.2
£1
120 -
«
100-
0
200
0.69
y = 0.26X + 64
P = 0.02
400
600
800
1000
0
1200
Total plant NPP (g C m"^ year^ )
100
200
300
400
500
(e)
(f)
220
= 0.41
y = 0.006x+ 84
P = 0 .1 1
r
160-
.2
120
200
180
r^= 0.40
y = 0.04X+ 66
P = 0.12
160
140
120
-
100
80
60
40
20
0
3000
6000
9000
12000
Total plant biomass (g C m"^)
15 000
Belowground plant biomass (g C m~^)
2005). These com peting mechanistic hypotheses dem and
the attention o f soil microbial ecologists.
Microbial biomass was strongly constrained across
biomes ranging from 0.6% to 1.1% o f total SOC concen­
trations (Fig. la). These percentages o f microbial biomass
vs. SOC levels are similar to those reported elsewhere:
including those reported by W ardle (1992) (1—3%), Insam
(1990) (1-5% ) and Zak et al. (1994) (0.3-3%). O ur
percentages are probably at the lower end o f these
previously reported ranges because we com pared microbial
biomass and SOC concentrations to 1 m, n o t just within
surface soil horizons. D eeper horizons often present
environm ental hmitations (e.g. low O 2 tensions) to m icro­
bial biomass accumulation and often contain relatively m ore
organic C than Mic^ owing to the lower quahty o f the
organic C stores at depth (Paul et al. 1997; T rum bore 2000;
Fierer et al 2003). Nonetheless, the relatively consistent
© 2009 Blackwell PubHshing Ltd/C N RS
600
Belowground plant NPP (g C m"^ y e a r i)
Figure 1 Relationships betw een m icrobial
biom ass and soil organic carbon (a), soil
respiration rates (b), total net primary
production (NPP) (c), below ground N P P
(d), total plant biom ass (both aboveground
and below ground) (e), and below ground
plant (root) biomass (f) across biomes. N ote
that we used biome-level averages to deter­
m ine these relationships because rarely are
the param eters show n on the x-axes o f
panels (b—f) reported together with the
m icrobial biom ass m easurem ents.
ratio o f Mic^ to SOC across biomes is remarkable given the
hkely variations in SOC quahty, plant C inputs and rates o f
C turnover. T he consistency suggests a num ber o f potential
explanations for the close correlation between microbial
biomass and SOC levels across biomes (Fig. la). First, these
results may lend support to the observation that SOC
quahty, defined as the availabihty o f SOC to microbial
minerahzation, is often fairly constant across biomes due to
similarities in SOC characteristics under different vegetation
types (Grandy & N eff 2008) or similarities in the abiotic
controls on SOC availabihty (Sohins et al 1996; Six et al
2002). Alternatively, different C inputs may n o t necessarily
lead to ‘high’- or ‘low’-quahty SOC pools because C quahty
may partly be a function o f the resource input history
experienced by the resident microbial community, i.e. the
‘htter quahty is in the eye o f the beholder’ hypothesis
(Strickland et al 2009a,b). Alternatively, SOC may simply be
Review an d Synthesis
Global patterns in below g ro un d com munities 7
a better predictor o f microbial biomass than other site
characteristics because it provides an integrated measure o f
the biotic and abiotic factors that regulate the size o f the
microbial biomass pool. Future research com paring the
relationships between SOC quantity, quality, and microbial
biomass across ecosystem or vegetation types will be
required to elucidate the specific mechanisms contributing
to the pattern evident in Fig. la.
Across all biomes, microbial biomass averaged 7% o f
total plant biomass (both aboveground and belowground),
but the percentages varied widely across biomes (ranging
from 1% to 20%; Fig. 2). Relative to plant biomass,
microbial biomass was higher in desert, tundra and
tem perate grassland biomes (9—20% o f plant biomass) than
in forested biomes (1-1.4% o f plant biomass). This
discrepancy may result from our exclusion o f the microbial
biomass contained in litter from the biom e estimates or the
larger am ounts o f w oody plant tissue and larger diameter
roots in forests relative to grasslands. We would expect
estimates o f non-w oody plant biomass to be m ore closely
correlated with microbial biomass as structural C decom ­
poses relatively slowly and is n o t likely to represent
(a)
104
I
W //A
103
£
O
2
Plants
I Soil microbes
All fauna
Soil fauna
102
V)
tA
101
£
o
10°
TO
ioQ
10-1
10-2
10-3
im portant short-term resource pools fuelling microbial
biomass accumulation. Future w ork should evaluate
w hether non-w oody plant biomass inputs to soils provide
a better predictor o f microbial biomass than total plant
biomass inputs.
G lobal p a tte rn s in b e lo w g ro u n d fau n al b io m ass
Across all biomes, soil faunal biomass averaged 2% o f
microbial biomass (Fig. 3; Table 1), a percentage that is
similar to that reported for individual soils where both
microbial and faunal biomass have been estimated simulta­
neously (Faustian et al. 1990; Zw art et al. 1994; H u n t & Wall
2002). Although we have likely underestim ated soil faunal
biomass due to the limited am ount o f data on soil
macro fauna, our estimate that faunal biomass averages 2%
o f m icrobial biomass is reasonable given that it is similar to
w hat we would expect based on detrital foodweb models
(Cebrian 2004). While the ability to correct for differences in
faunal extraction efficiencies or biomass depth distributions
may alter our absolute estimates o f soil faunal biomass, our
overall conclusion that soil faunal biomass is a small
percentage o f microbial biomass is still likely accurate given
the magnitude o f the difference between faunal and
microbial biomass. However, it is im portant to note the
faunal : microbial biomass ratio was far lower in desert soils
(faunal biomass C is < 0.02% o f Mic^) than in the other six
biomes where faunal biomass ranged from 1.5% to 3.6% o f
microbial biomass. This may suggest that the desert biome
represents a (relatively) m ore inhospitable environm ent for
soil fauna than for microbes given that a larger portion o f
the belowground biomass is microbial. However, a m ore
parsim onious explanation is that this disparity simply
reflects an underestim ation o f faunal biomass in desert soils
since we excluded larger fauna that are likely to be im portant
(b)
220 n
> ^
(/)J
^Q
)O
TO
200
CM
180
E
0 160
O
<u Iq
<
/) iS
CO Q _
2
140
rs _
TO
E 2
OO
b
^ = 0.70
y = 25.5X + 52
P = 0.02
(
ua
Desert
Temp.
grassland
Boreal
forest
Temp.
decid.
forest
Temp,
con if.
forest
Tropical
forest
Figure 2 T otal plant biomass, faunal biomass, soil faunal biom ass
and soil m icrobial biom ass per biom e (a) w ith (b) showing faunal
and m icrobial biom ass values as a percentage o f total plant biom ass
(percentages calculated by com paring biom ass in g C m ).
Sources for biom ass estimates are described in the M ethods. N ote
that the j-a x e s are on a log-scale.
1
12 0
1
100
n
2
o 80
6040
20
Boreal forest
Desert
Temperate coniferous forest
Temperate deciduous forest
Temperate grassland
Tropical forest
Tundra
Fauna! biomass (g C m” ^)
Figure 3 Relationship betw een soil m icrobial biom ass and faunal
biom ass across biomes.
© 2009 BlackweU PubUshing Ltd/C N RS
8 N. Fierer e t al.
Review an d Synthesis
contributors to desert soil biomass (Petersen & Luxton
1982) and did no t include fauna residing in deeper soil
depths where desert fauna may be relatively m ore abundant
due to m oisture/tem perature constraints or the limited
am ount o f organic m atter on the soil surface (Freckman &
Virginia 1989; LaveUe & Spain 2001).
Soil faunal biomass across the biomes was n o t strongly
associated with either total plant biomass (r = 0.15) or
ro o t biomass (r = 0.20). These weak relationships are
driven by the high faunal : plant biomass ratios in tundra
and tem perate grasslands (Fig. 2); these two biomes
support relatively high levels o f soil faunal biomass
(relative to plant biomass). While the exact mechanistic
explanation for this is unclear, unique vegetation charac­
teristics, cUmate an d /o r soil properties in tundra and
tem perate grasslands are speculated to contribute to the
pattern (LaveUe & Spain 2001). A pattern that was
consistent across biomes, except for the desert, was that
40—80% o f the total animal biomass within biomes can be
found in the soil (Fig. 2), confirming results from w ork
conducted at individual locations (Zlotin & K hodashova
1980; Faustian et al 1990). This finding emphasizes the
need to consider the belowground environm ent w hen
investigating factors (e.g. global change) that may influence
the biomass and distribution o f organisms within and
(a)
100
I
I Acari
r7~71 Collembola
V7~A Enchytraeld
Nematoda
I
I Earthworms
-Q O
CD [q
(D
O
TO^
(D
.>
40-
Desert
(b )
across ecosystems. Studies restricted to the aboveground
may om it the responses o f a significant com ponent o f the
biomass in terrestrial ecosystems.
We used the estimates o f soil faunal biomass to assess
changes in faunal com m unity com position across biomes
(Fig. 4a). A lthough the estimates are poorly constrained
(Table 1), we observed m ajor differences in the com position
o f soil fauna across biomes. For example, we find that
nem atodes, although num erous in m ost soils, represented a
smaU portion o f the total faunal biomass in aU biomes
except for the desert (see also Petersen & L uxton 1982).
Enchytraeids and earthworms have relatively large body
sizes and thus, although they are generaUy less num erous in
terms o f num bers o f individuals per unit area than smaller
fauna, they represent the majority o f faunal biomass in all
non-desert biomes we assessed. Enchytraeids were partic­
ularly abundant in tundra, tem perate forests and boreal
forests; earthworms dom inated in the tropical forest and
tem perate grassland biomes. A lthough these biome-level
patterns do confirm those reported elsewhere (Petersen &
Luxton 1982; LaveUe & Spain 2001; Coleman et al 2004),
the low confidence in our biome-level faunal biomass
estimates, even with our m uch larger dataset, highUghts an
im portant knowledge gap in our understanding o f the global
distribution o f soil fauna. This is particularly true for soil
100n
80-
Tundra
Temp.
grassland
Boreal
forest
Temp.
Temp.
Tropical
deciduous coniferous forest
forest
forest
yyyyNH e
Desert
Tundra
Temp.
grassland
Boreal
forest
© 2009 BlackweU PubUshing Ltd/C N RS
Temp.
Temp.
Tropical
deciduous coniferous forest
forest
forest
I
I
]
]
I
I
I
I
]
Acldobacterla
Actlnobacteria
Alphaproteobacteria
Gammaproteobacterla
Deltaproteobacterla
Bacteroldetes
ChloroflexI
FIrmlcutes
Other
Figure 4 Estim ates o f the relative abun­
dances o f the m ajor fauna w ithin soUs o f
different biom es (a) and relative abundances
o f the m ajor soU bacterial phyla across
biom es (b). Faunal com m unity com position
determ ined from the faunal biom ass esti­
mates provided in Table 1. Bacterial com ­
m unity com position determ ined using a
pyrosequencing-based analysis o f 88 individ­
ual soUs.
Review an d Synthesis
Global patterns in below g ro un d com munities 9
macro faunal biomass which can be difficult to estimate
given that many m acrofauna are n o t strictly soil dwellers,
spending only a portion o f their life-cycle belowground, and
m acrofauna often exhibit distributions that are spatially
a n d /o r temporally patchy due to localized colonies or
infrequent emergence events.
P = 0.65
y = O.OOSe
P = 0 .0 1
A
V
Temperate deciduous forest
Temperate coniferous forest
C hanges in m icrobial co m m u n ity co m p o sitio n a cro ss
biom es
In contrast to the soil faunal communities, which exhibit
m arked biome-level differences in com position (Fig. 4a), we
found few differences in the general structure o f soil
bacterial communities across biomes (Fig. 4b). AU biomes
were dom inated by the same soil bacterial phyla (Acidobacteria, Actlnobacteria, Proteobacteria and Bacteroldetes),
and in proportions that are roughly equivalent across
biomes (Fig. 4b). There are Ukely to be some im portant
differences between biomes at finer scales o f taxonomic
resolution, but phylogenetic-based analyses show that
bacterial com munity com position is not significantly differ­
ent across biomes. Instead, soil pH explains the m ost
variance in bacterial com m unity com position (Lauber et al
2009), suggesting that, although distinct biomes typicaUy
harbour distinct plant communities, the same is not true for
bacterial communities. To phrase this another way, the
variabiUty in bacterial com m unity com position within a
given biom e is higher than that between biomes and this
spatial pattern is a product o f variance in soil pH levels. The
net effect is that biogeography is distinctly different for
plant and bacterial communities at broad spatial scales
(Fierer & Jackson 2006).
Extending the microbial com m unity com position anal­
yses, we observed that across the five biomes for which
we had sufficient data, fungal : bacterial ratios were highly
variable ranging from 0.007 to 0.34. Soils from coniferous
forests had particularly high fungal : bacterial ratios, with
soils from deserts and grasslands having the lowest
m easured ratios (Fig. 5). These patterns are further
exempUfied by the differences in m ean fungal : bacterial
ratios across individual biomes with forest biomes having
the highest m ean fungal : bacterial ratios (Fig. 5, inset).
These data confirm the assumed pattern that in general
grasslands are m ore bacterial-dominated than forested
soils (Paul & Clark 1989; LaveUe & Spain 2001; Bardgett
2005; Joergensen & W ichern 2008). The low fungal : bac­
terial ratios in deserts suggests that fungi may n o t
necessarily be m ore resistant to desiccation than bacteria,
as is often assumed (Harris 1981; Zak et al 1995). This
observation highUghts the need to ensure that taxonomic
classifications for soil microbes are no t simply used as
surrogates for ecological classification (see Fierer et al
(2007).
decid.
forest
Tundra Temp,
grassland
0
10
20
30
40
Soil C : N ratio
Figure 5 Fungal ; bacterial ratios estim ated using quantitative
polymerase chain reaction vs. m easured soU C ; N ratios.
A fungal ; bacterial ratio o f 1 m eans that fungal and bacterial
rR N A gene copies are in equal abundance. T he bar chart in the
insert shows averages and standard errors o f the fungal ; bacterial
ratios determ ined for each o f the five biomes.
The range o f fungal : bacterial ratios we report (Fig. 5)
is approximately similar to the range o f fungal : bacterial
ratios reported for individual studies that have used a
similar molecular technique (Boyle et al 2008; Lauber et al
2008; N em ergut et al 2008). A lthough all o f the ratios are
< 1, this does n o t imply that bacterial biomass always
exceeds fungal biomass. Instead, the values represent
ratios o f fungal to bacterial smaU-subunit rRN A gene
copies and it is the relative differences in ratios that are
im portant.
Across the range o f soils examined only one factor, soil
C : N ratio, was significantly correlated {P < 0.05; Fig. 5)
w ith fungal : bacterial ratios. There are a num ber o f possible
mechanisms that may explain the positive correlation
between fungal : bacterial ratios and soil C : N ratios
(Fig. 5). Soil C : N ratio may directly influence the relative
abundance o f fungi due to stoichiometric constraints as
bacteria are generaUy considered to require m ore nitrogen
per unit biomass C accumulation than fungi (Bardgett &
McAlister 1999; K uijper et al 2005; D eD eyn et al 2008).
Conversely, higher fungal : bacterial ratios may lead directly
to higher soil C : N ratios given the wider C : N ratio o f
fungal biomass and the possibiUty that dead microbial
biomass represents a m ajor fraction o f the organic m atter in
mineral soils (Guggenberger et al 1999; Six et al 2006;
Simpson et al 2007). Alternatively, there may be only a weak
mechanistic fink between fungal : bacterial ratios and soil
C : N ratios; instead soil C : N ratios may reflect the
integrated effects o f a suite o f other soil characteristics,
including soil pH , SOC quality, quaUty o f plant C inputs and
plant com m unity com position, that may drive differences in
fungal : bacterial ratios across sites (Wardle et al 2004; Six
et al 2006; D eD eyn et al 2008; van der H eijden et al 2008).
© 2009 BlackweU PubUshing Ltd/C N RS
10 N. Fierer e t al.
Clearly, detailed cross-site research Is required to elucidate
the mechanism, or set o f mechanisms, responsible for the
apparent relationship between soil C : N and fungal :
bacterial ratios (Fig. 5).
Review arid Syrnthesis
given methodological advances and the globalization o f
research efforts. Such studies are worthwhile In that they
allow us to Identify ecological patterns that are not
Immediately obvious from individual studies focusing on
only a few soils o r o n select taxa.
D ata lim itatio n s
In addition to docum enting biome-level patterns In
belowground biomass levels and com m unity com position,
this data compilation highlights key gaps In our current
knowledge o f belowground biota and the associated data
needs. In particular, there are relatively few studies that
have simultaneously quantified total faunal and microbial
biomass In Individual soils. Such studies would enable
m ore robust com parisons o f belowground foodweb
structure, particularly If coupled w ith detailed Inform ation
on microbial and faunal com m unity com position, biomass
depth distributions and the changes In biomass across
seasons. likew ise, to make robust com parisons between
plant productivity, plant biomass and belowground faunal
and microbial biomass. It would be useful to have
m easurem ents on aU o f these parameters from the same
plot (or even better, num erous plots In different biomes).
W ith the emergence o f m ore com prehensive, cross-site
studies, such as those proposed by National Ecological
O bservatory N etw ork (Keller et al. 2008) and other
contlnental-scale research efforts, these data may become
m ore readily available. In the datasets compiled here. It Is
also apparent that some biomes are m ore thoroughly
sampled than others. F or example, there Is a pronounced
bias towards tem perate biomes and sites In close
proximity to established research centres In the United
States and E urope with poor representation from certain
biomes (e.g. deserts and tropical rainforests) that make up
a large portion o f the terrestrial land surface. Although
this geographic bias Is not unique to surveys o f
belowground biota. It constrains our ability to describe
global patterns.
CONCLUSI ONS
We find distinct patterns In belowground community
biomass and com position across biomes. In particular,
these results dem onstrate remarkable consistency In the
biomass patterns o f belowground communities, suggesting
that there are similar mechanistic constraints o n below­
ground biota that are shared across biomes, constraints
that w arrant m ore detailed study. O ur w ork highlights
some key knowledge gaps, particularly with regards to
the estimation o f faunal biomass In a consistent m anner
and the characterization o f global-scale patterns In soU
microbial and faunal com m unity com position. However,
cross-blome comparative studies are Increasingly tractable.
© 2009 Blackwell PubHshing L td/C N R S
A C K N O WL E D G E ME N T S
The authors thank KeUy Ramirez, Katie Eilers, Chris
Lauber, Chris Gray and R obert Bowers for their helpful
com m ents on previous versions o f this manuscript. The
authors also w ant to thank Chris Lauber, Rob Jackson, Josh
Schimel and Ben Colman for help with soil analyses and
sample collection. N .F. and M.A.B. are grateful to the
Andrew W. Mellon Foundation for funding to explore
belowground microbial communities.
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S U P P O R T I N G I N F O R MA T I O N
Additional Supporting Inform ation may be found in the
online version o f this article:
Appendix SI R eferences fo r th e soil m icrobial biom ass
estim ates.
Appendix S2 R eferences
fo r
the
soil
faunal
biom ass
estim ates.
As a service to our authors and readers, this journal provides
supporting inform ation supplied by the authors. Such
materials are peer-reviewed and may be re-organized for
online delivery, b u t are n ot copy-edited or typeset. Technical
support issues arising from supporting inform ation (other
than missing files) should be addressed to the authors.
E ditor, Richard Bardgett
M anuscript received 13 April 2009
First decision made 18 May 2009
M anuscript accepted 10 July 2009