Soil food web properties explain ecosystem services
across European land use systems
Franciska T. de Vriesa,b,1, Elisa Thébaultc,d, Mira Liirie, Klaus Birkhoferf, Maria A. Tsiafoulig, Lisa Bjørnlundh,
Helene Bracht Jørgensenf, Mark Vincent Bradyi, Søren Christensenh, Peter C. de Ruiterc, Tina d’Hertefeldtf, Jan Frouzj,
Katarina Hedlundf, Lia Hemerikc, W. H. Gera Holk, Stefan Hotesl,m, Simon R. Mortimern, Heikki Setäläe,
Stefanos P. Sgardelisg, Karoline Utesenyo, Wim H. van der Puttenk,p, Volkmar Woltersl, and Richard D. Bardgetta,b
a
Soil and Ecosystem Ecology, Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom; bFaculty of Life Sciences, University of
Manchester, Manchester M13 9PT, United Kingdom; cBiometris, Wageningen University and Research Centre, 6700 AC, Wageningen, The Netherlands;
d
Bioemco, Unité Mixte de Recherche 7618 (Centre National de la Recherche Scientifique, Université Pierre et Marie Curie, Ecole Normale Supérieure, Institut
de Recherche pour le Développement, AgroParisTech), Ecole Normale Supérieure, F-75230 Paris Cedex 05, France; eDepartment of Environmental Sciences,
University of Helsinki, FI-15140, Lahti, Finland; fDepartment of Biology, Lund University, S-223 62 Lund, Sweden; gDepartment of Ecology, School of Biology,
Aristotle University, 54124 Thessaloniki, Greece; hBiologisk Institut, Terrestrisk Økologi, 1353 København K, Denmark; iDepartment of Economics, Swedish
University of Agricultural Sciences (SLU), S-220 07 Lund, Sweden; jInstitute of Soil Biology, Biology Centre Academy of Sciences of the Czech Republic, 370 05
Ceske Budejovice, Czech Republic; kDepartment of Terrestrial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), 6700 AB, Wageningen, The
Netherlands; lDepartment of Animal Ecology, Justus Liebig University, D-35392 Giessen, Germany; mDepartment of Ecology, Philipps University, 35043
Marburg, Germany; nCentre for Agri-Environmental Research, University of Reading, Reading RG6 6AR, United Kingdom; oDepartment of Conservation
Biology, Vegetation and Landscape Ecology, University of Vienna, 1030 Vienna, Austria; and pLaboratory of Nematology, Wageningen University, 6700 ES,
Wageningen, The Netherlands
soil fauna
| modeling | soil microbes | nitrogen
S
oils are of central importance for delivering ecosystem services, such as food production and climate mitigation. These
services strongly depend on carbon (C) sequestration and nutrient cycling, processes that are governed by soil biota. Increasing demand for the production of food, fiber, and biofuel
has resulted in intensification of agricultural production, which
reduces soil organic matter content (1) and the biomass and diversity of most soil biota (2), with consequent impacts on processes of C and nutrient cycling. Specifically, land use-induced
shifts to more bacterial-dominated microbial communities have
been linked to increased nitrogen (N) losses (3–5) and reduced C
sequestration (6). Conversely, fungal-dominated microbial communities, which are common in less intensively managed land use
systems, are linked to more conservative nutrient cycling and
greater storage of C (5, 7, 8). Although soil microbes are the
primary actors in C and N cycling, their biomass and activity are
www.pnas.org/cgi/doi/10.1073/pnas.1305198110
greatly influenced by higher trophic levels of the soil food web.
For instance, animals that consume microorganisms can stimulate rates of nutrient mineralization (9) and plant productivity
(10), whereas bioturbators, such as earthworms, can further increase nutrient availability for plants (11), although they can also
increase N2O emissions from soil (12).
Although there is evidence from field studies that soil microbial communities are linked to ecosystem functioning (13, 14),
most studies on relationships between soil fauna and ecosystem
function have been done in controlled (microcosm) experiments
(15). As a result, our understanding of the functional importance
of different groups of soil biota and the connections between
them (the soil food web) in the field is limited, and it is not
known how changes in soil food web structure across contrasting
locations and land use systems impact on ecosystem functioning.
There is some evidence to suggest that the role of the soil food
webs relative to abiotic factors in regulating ecosystem functions
will vary across geographical locations and environmental gradients (16). Moreover, differences in land use have been shown
to affect the resistance and the resilience of soil food webs to
simulated drought, with consequences for processes of C and N
cycling (17). Therefore, quantifying general relationships between soil biota and processes of C and N cycling is of pivotal
importance for predicting how these processes will be affected by
global change.
Our aim was to quantify, across geographically contrasting
locations in Europe, how changes in soil food web composition
resulting from land use systems influence the ecosystem services
that they deliver. We hypothesized that, across European land
use systems, processes of C and N cycling are explained by soil
food web properties on top of variation explained by other factors, such as land use and soil physical and chemical properties.
Specifically, we hypothesized that (i) more intensive land use
consistently reduces the biomass of soil fungi and their consumers
Author contributions: F.T.d.V., H.B.J., S.C., P.C.d.R., T.d., J.F., K.H., S.R.M., H.S., W.H.v.d.P.,
V.W., and R.D.B. designed research; F.T.d.V., E.T., M.L., K.B., M.A.T., L.B., H.B.J., S.C., T.d.,
L.H., W.H.G.H., S.H., S.R.M., H.S., S.P.S., and K.U. performed research; F.T.d.V., E.T., M.L.,
and K.B. analyzed data; and F.T.d.V., P.C.d.R., K.H., W.H.v.d.P., and R.D.B. wrote
the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
1
To whom correspondence should be addressed. E-mail: [email protected].
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1305198110/-/DCSupplemental.
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Intensive land use reduces the diversity and abundance of many
soil biota, with consequences for the processes that they govern
and the ecosystem services that these processes underpin. Relationships between soil biota and ecosystem processes have mostly
been found in laboratory experiments and rarely are found in the
field. Here, we quantified, across four countries of contrasting
climatic and soil conditions in Europe, how differences in soil food
web composition resulting from land use systems (intensive wheat
rotation, extensive rotation, and permanent grassland) influence
the functioning of soils and the ecosystem services that they
deliver. Intensive wheat rotation consistently reduced the biomass
of all components of the soil food web across all countries. Soil
food web properties strongly and consistently predicted processes
of C and N cycling across land use systems and geographic locations, and they were a better predictor of these processes than
land use. Processes of carbon loss increased with soil food web
properties that correlated with soil C content, such as earthworm
biomass and fungal/bacterial energy channel ratio, and were
greatest in permanent grassland. In contrast, processes of N cycling
were explained by soil food web properties independent of land
use, such as arbuscular mycorrhizal fungi and bacterial channel
biomass. Our quantification of the contribution of soil organisms to
processes of C and N cycling across land use systems and geographic
locations shows that soil biota need to be included in C and N
cycling models and highlights the need to map and conserve soil
biodiversity across the world.
ENVIRONMENTAL
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Edited by William H. Schlesinger, Cary Institute of Ecosystem Studies, Millbrook, NY, and approved July 16, 2013 (received for review March 18, 2013)
and increases the dominance of bacteria and their consumers
[i.e., decrease the fungal/bacterial (F/B) channel ratio] and (ii)
a shift to greater dominance of bacteria and their consumers (i.e.,
decrease F/B channel ratio) increases rates of C and N cycling
and loss.
To test these hypotheses, we measured C and N fluxes at 60
sites in four European countries (Sweden, United Kingdom,
Czech Republic, and Greece) distributed across five locations in
each country representing intensive annual crop rotation [high
intensity (H)], extensive rotation, including legumes or ley [medium intensity (M)], or permanent grassland [low intensity (L)].
Measurements included potential N mineralization, which is
a measure of the release of N for plant uptake, and losses of N
and C from soil both as gases and in drainage waters. Gaseous
emissions from agricultural soils, as N2O, CO2, and CH4, contribute significantly to global warming and atmospheric pollution
(18), and leaching of C and N in drainage waters contributes to
eutrophication of ground and surface water (19). We also quantified the biomass of key functional groups in the soil food web,
including fungi and bacteria, protozoa, nematodes, earthworms,
Enchytraeids, mites, and collembolans. To relate the structure of
the soil food web to C and N fluxes, we calculated traditional soil
food web properties, such as the number of feeding groups in the
food web. In addition, to test our hypothesis that land use will
alter the relative importance of the fungal and bacterial energy
channels, we calculated measures based on the fungal, bacterial,
and root energy channels (SI Appendix, SI Methods).
Soil food webs and their corresponding C and N fluxes are
likely to be affected by factors, such as land use, soil properties,
and spatial structure of sampling sites. To determine whether soil
food web characteristics explained a unique proportion of variation in ecosystem services and deduce meaningful relationships
between soil food web properties and C and N fluxes, we also
accounted for variation caused by the spatial structure of the
sites (which can be caused by autocorrelation between values of
the response variable or underlying factors, such as climate and
geology), land use, and soil properties. We accounted for spatial
autocorrelation in the measured variables by calculating spatial
filters using principle coordinates of neighbor matrices (20), and
we used a hierarchical modeling approach that has previously
been used to explain landscape-scale variation in soil microbial
communities on the basis of climatic factors, soil properties, and
plant traits (21) (Materials and Methods and SI Appendix).
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Results and Discussion
Across all four countries, soil food web structure was strongly
influenced by land use (SI Appendix, Tables S1 and S2). The
number of feeding groups, total biomass of the soil food web, and
biomass of the fungal, bacterial, and root energy channel [which
consists of arbuscular mycorrhizal fungi (AMF), root-feeding
fauna, and their predators] were all lower under the M and H land
use categories relative to the L category (SI Appendix, Table S1).
The biomass of many individual feeding groups of soil biota was
lower under these more-intensive land uses (SI Appendix, Table
S2). These results indicate that, across contrasting sites in Europe,
land use intensification consistently reduces the biomass of all
components of the soil food web. However, in contrast to our
hypothesis, land use intensification did not influence the ratio of
fungal to bacterial biomass or the ratio of fungal energy channel to
bacterial energy channel biomass in any of the countries sampled.
Instead, land use intensification equally reduced the biomass of
most feeding groups in the soil food web. However, the biomass of
the groups that are part of the root energy channel was reduced
more than the biomass of the organisms of the fungal and bacterial
energy channel together (the detritus energy channel) (SI Appendix, Table S1). This difference can be explained by the effect of
tillage, which was included in the M and H land use forms, because
it disrupts root-associated organisms and their consumers (8, 22).
All our models explaining processes of C and N cycling included one or more soil food web measures, indicating that
relationships between soil biota and ecosystem functioning are
surprisingly consistent across contrasting sites in Europe (Tables
1 and 2). In four of six models, land use was not included as an
explanatory variable, which indicates that soil food web properties
are better predictors of processes of C and N cycling than the
three land use systems. Land use might not be included in our
models because within our three broad land use categories, differences in management might have impacted on soil food web
structure, which in turn, affected ecosystem processes (SI Appendix, Tables S4–S7 shows management details of all sites). The
inclusion of spatial filters in all final models illustrates the importance of accounting for the spatial structure of sampling sites
across this scale (Tables 1 and 2) and shows that C and N fluxes
varied both among and within European regions. Still, only the
model for potential N mineralization included an interaction term
between a soil food web property (namely bacterial energy
channel biomass) and a spatial filter (filter 3), indicating that the
relationship between bacterial energy channel biomass and N
mineralization was dependent on geographical location (Box 1
de Vries et al.
Table 1. Selected models for potential N mineralization, total N leached, and N2O production
Potential N mineralization
Parameter
value
Intercept
Spatial filters
Soil physical properties
Land use
N and C stocks
Soil food web structure
Biomass of individual
functional groups
Model R2
Total N leached
Parameter
value
P
Parameter
value
P
774
−1,932*Filter2
<0.0001
0.0004
0.606
−2.445*filter5
0.0009
0.0054
−60,114*AM fungi;
+16,357,441* bacnem
0.34
0.004; 0.024
−4,678*flagellates
0.0196
P
−17.33
+224.7*Filter3
+65.7*moist;
−752.2*Filter3*moist
0.0096
<0.0001
<0.0001; <0.0001
+3.64*pathbact;
−38.2*Filter3*pathbact
0.0074; 0.0027
0.45
N 2O
0.17
of two functional groups, which together accounted for more than
one-half of the variation explained by the full model (SI Appendix,
Table S3). N leaching increased with greater biomass of bacterialfeeding nematodes (Fig. 1 and Table 1), which is in line with our
hypothesis and the stimulating effect of bacterial grazers on N
mineralization. In addition, we found that N leaching decreased
with increasing biomass of AMF across all sites. Laboratory
studies have shown that AMF reduce leaching of N and phosphorus (P) (28), but we are not aware of such a relationship
being detected in the field, which we show here. Surprisingly, N
leaching was not affected by land use across sites (SI Appendix,
Fig. S1), which shows that its relationship with AMF is independent of the impact of land use on AMF.
Production of N2O—a product of the denitrification process in
soil—decreased across all locations with increasing biomass of
flagellates, a group of protozoa that are part of the bacterial
energy channel (Table 1). A mechanistic link between protozoa
and N2O production has never been reported before. Because
protozoa are aquatic organisms, this correlation probably reflects
that denitrification predominantly occurs in anoxic zones in the
soil (12). Although N2O emission is generally strongly affected
by agricultural management (29), we did not find a link with land
use here (SI Appendix, Fig. S1).
Across all sites, we found that the three land use types were all
methane sinks, and the intensive rotation and permanent grassland were stronger methane sinks than the extensive rotation.
Table 2. Selected models for CO2 production, CH4 production, and DOC leached
CO2
Parameter
value
Intercept
Spatial filters
Soil physical properties
Land use
N and C stocks
Soil food web structure
Biomass of individual
functional groups
Model R2
0.74
−5.17*Filter2
+1.0*pathFB
+400*worms
0.53
CH4
P
0.033
0.0003
0.0003
<0.0001
DOC leached
Parameter
value
P
−0.27
0.044
296
−658*Filter2; −230*Filter4
−0.08*L; +0.17*M
0.0078
+326*L; −1,317*Filter4*L
<0.0001; 0.0001
−0.08*F/B ratio
+6.65*bacteria
0.046
0.049
+8,106,164*fungcoll;
+5,798,305*bacnem
0.77
<0.0001; 0.017
0.24
Parameter
value
P
<0.0001
0.001; 0.28
For each C cycling process, the best explaining model is shown, with intercept, parameters, their parameter value (within each category of parameters), and
P value as obtained by an L-ratio deletion test (SI Appendix, SI Methods). Interpretation of the models is in Box 1. Bacnem, biomass of bacterial-feeding
nematodes; fungcoll, biomass of fungal-feeding Collembola; pathFB, fungal-to-bacterial energy channel biomass ratio; worms, earthworm biomass.
de Vries et al.
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and Table 1). For all other models, the relationship between soil
food web properties and the process of C or N cycling was independent of location.
In line with our second hypothesis, across all 60 European
farmland sites, the biomass of the bacterial energy channel was
positively related to rates of N mineralization (Fig. 1 and Table
1). Interestingly, although the bacterial energy channel was reduced by intensive land use, N mineralization was not affected by
land use (SI Appendix, Fig. S1), suggesting that the relationship
between the bacterial energy channel and N mineralization was
independent of land use. Field studies have shown that fungalbased soil food webs have lower N leaching losses from soil (5,
23) and lower rates of N mineralization (24). In laboratory
studies, greater bacterial abundance has been linked to increased
rates of N mineralization, and the presence of bacterial feeders in
soil has often been shown to increase rates of N mineralization
both indirectly through stimulating bacterial activity and directly
through excreting N compounds (9, 25, 26). However, our study
shows that N mineralization rates increase with greater biomass of
the entire bacterial decomposition channel. This observation suggests that the intensification-induced reduction in bacterial channel
biomass might increase the dependency on mineral fertilizer.
Mineralization of N can turn into a disservice when N supply is
too high for crop uptake and excess N is washed away in drainage
waters or lost to the atmosphere through denitrification (27).
Across all sites, leaching of N was strongly explained by the biomass
ENVIRONMENTAL
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For each N cycling process, the best explaining model is shown, with intercept, parameters, their parameter value (within each category of parameters),
and P value as obtained by an L-ratio deletion test (SI Appendix, SI Methods). Interpretation of the models is in Box 1. Bacnem, biomass of bacterial-feeding
nematodes; moist, moisture content; pathbact, standardized biomass of the bacterial energy channel.
Fig. 1. Fitted relationships between ecosystem services and soil food web properties. Variables that were included in the models but not shown in the graphs
(Tables 1 and 2) were kept constant at their mean value in the dataset. (A) Potential N mineralization explained by standardized biomass of the bacterial
energy channel. (B) Total N leached explained by AMF biomass and biomass of bacterivorous nematodes. (C) N2O production explained by biomass of
flagellates. (D) CO2 production explained by F/B channel ratio and earthworm biomass. (E) CH4 production explained by F/B ratio and bacterial biomass
(relationship shown is for intensive wheat rotation and permanent grassland) (extensive rotation CH4 production increases with 0.17 mg m−2 d−1 are shown in
Table 2). (F) DOC leached from soil explained by fungivorous collembolans and bacterivorous nematodes (relationship shown is for intensive wheat rotation
and extensive rotation) (permanent grassland DOC leaching increases with 1,317 mg m−2 as shown in Table 2).
Legumes were included in the extensive rotation in three of four
countries (SI Appendix, Fig. S1 and Tables S4–S7) and have been
shown to reduce the strength of the methane sink in grasslands
(30). Methane consumption also decreased with decreasing F/B
biomass ratio and increasing biomass of bacteria (Table 2), which
suggests that the decrease in bacterial biomass as a result of land use
intensification, such as was found here, might affect the abundance
of methanotrophs (for example, through an increase in nitrifiers
at the expense of methanotrophs) (31).
Production of CO2 measured in situ is a measure of soil heterotrophic activity and root respiration, and it forms a pathway
of C loss from soil. Production of CO2 was greatest in the permanent grassland (SI Appendix, Fig. S1), which is consistent with
these soils having the greatest C content (SI Appendix, Tables
S4–S7). Production of CO2 was also positively related to the
biomass of earthworms, which were most abundant in the permanent grassland (SI Appendix, Table S2). Several field-based
experiments have shown significant impacts of earthworms on C
and N cycling (12), but evidence for impacts of earthworms on
respiration in the field is scarce. In addition and in contrast to our
hypothesis, CO2 production increased with greater importance of
the fungal energy channel (greater F/B channel ratio) (Fig. 1 and
Table 2), a relationship that was independent of land use. Fungaldominated soil food webs are thought to be more efficient in their
C use, although evidence is limited (6). The positive relationship
found here between the fungal decomposition pathway and CO2
production might be a consequence of the fact that C-rich soils
are generally fungal-dominated (5); consistent with this explanation, we found a positive relationship between biomass of the
fungal energy channel and soil organic C (SI Appendix, Table
S1). However, a greater CO2 production does not necessarily
mean a greater loss of soil C given that soil C content is
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determined by the balance between C loss by respiration and C
gain by photosynthesis.
Similar to CO2 production, leaching of dissolved organic carbon
(DOC) was greatest from permanent grassland across all sites
(Table 2). In addition, DOC leaching increased with the biomass
of fungal-feeding collembolans and bacterial-feeding nematodes.
This increase might be a consequence of the greater biomass of
the fungal energy channel with greater soil C stocks (SI Appendix,
Table S1), although the biomass of fungal-feeding collembolans
itself was not related to soil organic C (SI Appendix, Table S2).
The link between DOC leaching and fungal-feeding collembolans
suggests that this functional group might be a sensitive indicator
for changes in labile C availability. In addition, labile C constitutes
an easily decomposable food source for microbes, which might
stimulate microbial growth and increase the biomass of bacterial
and fungal grazers through bottom-up effects (32).
In sum, we found strong and consistent impacts of land use on
the structure of soil food webs across land use systems in four
climatically different regions in Europe; land use intensification
reduced the abundance of most functional groups of soil organisms. In turn, soil food web properties strongly influenced processes of C and N cycling, and these relationships were consistent
across land use types and sampling locations. The predictive
power of soil food web structure or functional groups varied
between the processes measured but was of equal importance as
abiotic factors (SI Appendix, Table S3). Although relationships
between soil food web properties and processes of C cycling were
mostly related to land use intensity, relationships with N cycling
processes were not. In all cases, soil food web properties were
better predictors of processes of C and N cycling than the tree land
use systems. Although ultimately correlative, the relationships that
we found between bacterial-feeding animals, AMF, and earthworms and C and N cycling are in line with results from mechanistic
de Vries et al.
Materials and Methods
Field Sites and Sampling. We selected four countries across Europe: Sweden,
United Kingdom, Czech Republic, and Greece. In each country, sampling was
done at five locations, and each location had three managements: intensive
rotation (H), extensive rotation (M), and permanent grassland (L). This nested
design resulted in 60 sampling sites (4 countries × 5 farms × 3 managements).
Between May and July of 2009, in each site, two 1-m2 plots were randomly
selected, and for each soil nutrient, microbial and faunal analyses of separate replicate soil cores (5-cm diameter and 10-cm depth) were taken from
each plot and kept cool (4 °C) until analysis (see below). Gas samples were
taken in situ: in each plot, a 10-cm inner diameter collar consisting of a PVC
cylinder was pushed 5 cm into the soil. Then, a 5-cm-high PVC lid was fitted
into a butyl rubber-lined groove in each collar. An 8-mL gas sample was
taken immediately and 30 min after attaching the lids. SI Appendix, Tables
S4–S7 has climate data of sampling regions and details on soil properties
and management.
determined by placing saturated undisturbed soil cores on a suction pressure
plate, and after drying at 105 °C, bulk density was calculated. All soil, food
web, and nutrient flux measures were expressed per meter squared, except
potential N mineralization.
Food Web Analyses. Phospholipid fatty acids (PLFAs) were extracted from 3 g
soil according to the work by Frostegård and Bååth (40). The PLFAs 15:0,
i15:0, a15:0, i16:0, 16:0ω9, i17:0, a17:0, cy17:0, 18:1ω7, and cy19:0 were used
as markers of bacterial biomass (40). The amount of PLFA 18:2ω6 was used as
a marker of nonmycorrhizal fungal biomass, and the neutral lipid fatty acid
16:1ω5 was used as a marker for AMF (41). Fatty acids were converted into
biomass C using the following factors: bacterial biomass, 363.6 nmol PLFA =
1 mg carbon (40); fungal biomass, 11.8 nmol PLFA = 1 mg carbon (42); and
AMF biomass, 1.047 nmol neutral lipid fatty acid = 1 μg carbon (41). Protozoa numbers were estimated using a modified most probable number
method, and enchytraeid worms were extracted from intact soil core samples using wet funnels. Nematodes were extracted from a 150-mL sample
with the modified Cobb sieving and decanting method (43), and soil mesofauna were extracted from undisturbed samples using Tullgren funnels.
Nematodes were identified to the genus level and allocated to trophic
groups; Collembola, Acari, and Oribatida were determined to species level.
More information on food web analyses and biomass calculations is in
SI Appendix.
Statistical Analysis. We generated statistical models for each ecosystem service using spatial filters, soil properties, land use, and soil food web characteristics. We used linear mixed effects models with a farm-level random
effect term to account for the clustering of fields in sampling locations.
Analysis was conducted using the lme function of R version 2.11.1 (R Development Core Team 2009). Model selection followed the hierarchical procedure used in the work by de Vries et al. (21). In short, the order in which
variables were added to linear mixed effects models followed a hypothesized sequence of controls, being such that variables added later in the
modeling process are unlikely to affect those variables added earlier. The first
terms added to the models were spatial filters, after which we sequentially
added soil properties, land use, soil C and N contents, and finally, soil food
web properties. Models were selected based on Akaike Information Criterion,
and true significance of retained terms was assessed by a χ2 likelihood ratio
deletion test. Detailed information on the modeling procedure is in SI Appendix.
Soil Analyses. Total soil C and N were analyzed on air-dried soil with a Leco
CNS-2000 analyzer, and total organic C was measured in a PrimacsSLC TOC
Analyzer on dried (100 °C) soil. Soil pH and gravimetric moisture content
were determined using standard methods. Water-holding capacity was
ACKNOWLEDGMENTS. We thank all landowners for kindly letting us use
their fields, and George Boutsis, Maria Karmezi, Sofia Nikolaou, Evangelia
Boulaki, Charisis Argiropoulos, Annette Spangenberg, and Helen Quirk for
help in the field and laboratory. We also thank two anonymous referees for
their helpful comments on the manuscript. This project was part of the
European Union Seventh Framework funded SOILSERVICE Project.
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ENVIRONMENTAL
SCIENCES
C and N Fluxes. Gas samples were analyzed for CO2, N2O, and methane as
described in the work by Priemé and Christensen (39). Soil leachates were
obtained and analyzed for concentrations of inorganic N and DOC and total
N as described in the work by de Vries et al. (5). Potential N mineralization
was assessed by incubating a 5-g soil sample at 60% water holding capacity
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SUSTAINABILITY
SCIENCE
experiments (9, 12, 28). Therefore, our results strongly suggest that
including soil food web parameters will enhance the predictive
capacity of C and N cycling models.
Process-based C and N cycling models require detailed input
information that is often not available on regional scales (33),
and general relationships between soil food web properties and
processes of C and N cycling have the potential to simplify these
models. Although more validation is needed (for example, within
the countries and soil types sampled), the simple relationships between earthworms and CO2 production or between AMF abundance and N leaching might help parameterize C cycling (34) and
ecosystem service models (35). Moreover, explicitly incorporating
soil food web properties and their response to land use and climate
change (17) in dynamic global vegetation models might improve
predictions of climate change impacts on terrestrial ecosystem
functions and their feedbacks to climate change (36). Finally,
there is an urgent need to identify and evaluate indicators for
soil-based ecosystem services (37). The quantitative relationships
between relatively simple soil food web measures and ecosystem
services shown in our analysis could be used to assess soil-based
ecosystem services and disservices, such as N leaching from soil.
Although the relationships revealed by our analysis require additional validation, they are an important first step to quantifying
general relationships between soil food web properties and
ecosystem processes in the field. Soil biodiversity is under threat
by a range of pressures but remains severely understudied (38);
our results explicitly quantify the contribution of soil organisms
to processes of C and N cycling across a range of management
and environmental conditions and thus, warrant efforts to map
and conserve soil biodiversity across the world.
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its application at regional scale. Ecol Appl 23(2):408–420.
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management on the European cropland carbon balance. Glob Chang Biol 17(1):
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SI Appendix
Supplementary Tables
Table S1. Selected models explaining soil food web parameters.
Intercept
Spatial filters
Parameter
value
Parameter
Land use
P
Parameter
value
2.20
2.27
Soil properties
Parameter
P
IntensityL
IntensityM
0.04
S
16.00
DivS
2.06
TLm
3.30
TLM
3.92
-0.33
Filter3
0.032
Log10(PathRD)
Log10(PathFB)
-1.70
-0.14
-1.80
-1.23
0.20
Filter1
Filter1
Filter3
0.0024
<0.0001
0.60
Pathfungi
0.46
-1.92
Filter3
0.0002
0.05
Filter1
<0.0001
0.01
IntensityL
<0.0001
Pathbact
0.39
IntensityL
Parameter
P
moist
moist*IntL
moist*IntM
Ntot
moist
0.042
0.034
0.0005
-0.24
0.01
pH
pH2
0.034
0.044
0.009
-0.15
TOC
Filter3*TOC
0.42
0.009
0.08
TOC
0.0001
0.05
0.03
0.04
0.10
0.008
-0.063
moist
bulkdens
Ntot
bulkdens
TOC
Filter5*TOC
0.0011
0.0007
0.01
0.0001
0.009
0.014
-0.79
0.24
moist
bulkdens
0.0016
0.016
<0.0001
Total biomass
-0.08
0.08
Filter5
0.21
0.049
IntensityL
<0.0001
Log10(Pathroot)
Log10(FB)
-1.67
-0.79
Filter1
Filter1
0.0045
<0.0001
0.57
IntensityL
<0.0001
0.08
1
Parameter
value
9.32
-5.71
-8.43
-2.02
1.73
Abbreviations: S = number of functional groups, DivS = diversity of functional groups, TLm = mean trophic level, TLM = maximum trophic level, PathRD =
ratio of standardized biomass of root and detritus energy channel, PathFB = ratio of standardized biomass of fungal and bacterial energy channel, Pathfungi =
standardized biomass of fungal energy channel, Pathbact = standardized biomass of bacterial energy channel, Total biomass = total biomass of the soil food web,
Pathroot = standardized biomass of the root energy channel, FB = fungal/bacterial biomass ratio, moist = moisture content, bulkdens = bulkdensity, IntL =
permanent grassland, IntM = medium intensity rotation, Ntot = total soil N content, TOC = total soil organic C content, WHC = water holding capacity.
2
Table S2. Selected models for explaining the biomass of individual functional groups of the soil food web.
Intercept
Fungi
Bacteria
0.06
-0.02
Log10(F/B ratio)
Log10(AM fungi)
-2.93
Spatial filters
Land use
Parameter
value
-0.06
0.05
Parameter
P
Filter5
Filter1
0.008
<0.0001
-0.79
Filter1
<0.0001
-1.70
Filter1
0.0046
Soil properties
Parameter
value
0.03
0.01
Parameter
P
IntensityL
IntensityL
<0.0001
<0.0001
0.58
IntensityL
<0.0001
-6
Fungivorous
1.88*10
nematodes
Log10(Bacterivor
-5.25
ous nematodes)
Omnivorous and
2.8*10-6
predatory
nematodes
Plant parasitic
-5.8*10-6
nematodes
Log10(Plant-6.25
associated
nematodes)
Fungivorous mites 8.1*10-7
Fungivorous
Collembola
Log10(Predatory
mites
Earthworms
7.29*10-5
-5.9
-0.0012
Parameter
value
-0.07
0.08
0.017
-0.79
0.24
Parameter
P
WHC
moist
bulkdens
moist
bulkdens
0.0001
<0.0001
0.001
0.0016
0.016
-3.8*10-6
moist
0.039
1.06
Filter1
0.01
-1.9*10-6
Filter2
0.0055
-2.2*10-7
-6.2*10-8
pH
TOC
0.018
0.035
1.44*10-5
-9.9*10-6
-1.04
Filter1
Filter2
Filter4
0.0017
0.011
0.03
1.30*10-6
pH
0.044
0.25
IntensityL
0.047
1.49*10-5
Filter3
0.033
2.35*10-6
IntensityL
0.01
-3.05*10-6
5.17*10-7
-1.07*10-4
WHC
LOI
Filter3*WHC
0.57
0.029
<0.0001
-7.3*10-5
3.09*10-5
4.00*10-5
-2.80
Filter3
Filter4
Filter6
Filter3
<0.0001
0.0031
0.0002
0.0004
0.29
IntensityL
0.04
0.0012
0.0015
IntensityM
IntensityL
0.0024
-0.03
0.10
0.0029
moist
(moist)2
bulkdens
0.0082
<0.0001
0.01
3
Intercept
Spatial filters
Parameter
value
Enchytraeids
Log10(Flagellates
)
Log10(Amoebae)
Parameter
Land use
P
0.00008
-5.93
-6.27
1.67
-0.98
1.28
Filter1
Filter3
Filter2
Parameter
value
0.001
<0.0001
0.0064
0.0004
Abbreviations: LOI = loss-on-ignition, Ctot = total soil C, rest as in table S1.
4
Soil properties
Parameter
P
IntensityL
<0.0001
Parameter
value
-0.00001
-0.0001
0.0004
2.02
0.86
2.19
1.37
0.05
Parameter
P
pH
IntL*pH
moist
moist
bulkdens
moist
bulkdens
Ctot
<0.0001
0.025
0.0004
0.0007
0.0003
<0.0001
0.0039
Table S3. Effect of removal of variable classes from selected models (Tables 1) explaining ecosystem processes on model R-squared
values and AIC.
Full model
Filters
removed
Soil properties
removed
Land use
removed
N and C
stocks
removed
Soil food web
structure
removed
Ind. group
biomass
removed
Pot N min
Explained
variance
0.45
0.14
392.3
412.5
0.14
429.9
0.32
AIC
N leached
Explained
variance
0.34
0.17
AIC
893.9
904.4
N2O
Explained
variance
0.17
0.06
AIC
156.7
162.5
400.5
0.12
906.1
0.10
160.2
CO2
Explained
variance
0.53
0.41
AIC
211.8
223.1
CH4
Explained
variance
0.24
AIC
9.63
0.11
15.36
0.42
222.9
0.19
11.57
0.33
230.7
0.19
11.64
5
DOC leached
Explained AIC
variance
0.77
759.3
0.56
785.1
0.46
804.8
0.57
788.7
Table S4. Management and soil properties for the three land use forms in Sweden
(Scania, 7.8/6.6/9.6 °C mean/min/max annual temperature, 666 mm mean annual
precipitation).
[SE]
Description of
crop/vegetation during
sampling (2008/2009)
L
Pasture
M
Rotation
H
Intensive Rotation
permanent grassland
ley in rotation
winter wheat
lay for hay or grass seed
production or catch crop
before winter wheat: carrot or
permanent grassland
during winter/ winter
winter wheat or spring barley/
Management regime – history (not tilled for at least 10
wheat or spring barley or
sugar beets or spring barley or
years)
lay for hay/ potato or
winter wheat
spring barley or winter
wheat
cutting/harvesting, no
harvesting, tillage (annually),
Most important management
grazing by cows or
tillage during the year of
weed and pest management
practices
horses
lay
when necessary
once/year in spring;
once/year in spring;
once/year in spring;
Fertilizer input *
granules
granules
granules
N (kg ha-1 y-1)
10 (0)
169 (112)
166 (134)
P (kg ha-1 y-1)
1 (0)
18 (16)
16 (21)
K (kg ha-1 y-1)
1 (0)
35 (33)
41 (36)
FAO soil type **
Calcaric Cambisol
Calcaric Cambisol
Calcaric Cambisol
Total Organic Carbon (%)
5.21
2.61
2.54
Total Carbon (%)
6.34
2.70
2.86
Total N (%)
0.41
0.21
0.17
C/N
13.84
13.15
16.55
pH
7.60
7.53
7.65
Moisture (g g-1)
0.31
0.20
0.19
Bulk Density (g cm-3)
0.95
1.31
1.19
Ca (g kg-1)
10.93
5.80
7.10
P (mg kg-1)
56.20
17.60
38.60
K (mg kg-1)
229.20
89.50
123.50
Mg (mg kg-1)
314.70
101.40
101.60
S (mg kg-1)
27.50
9.60
20.90
*average values for the years of sampling and for the previous three years before sampling (in parenthesis)
**European soil database (http://eusoils.jrc.ec.europa.eu)
6
Table S5. Management and soil properties for the three land use forms in United
Kingdom (Chilterns, 9.5/5.5/13.5 °C mean/min/max annual temperature, 625 mm mean
annual precipitation).
L
Pasture
M
Rotation
H
Intensive Rotation
Description of
crop/vegetation during
sampling (2008/2009)
permanent grassland
field beans
winter wheat
Management regime –
history
permanent grassland
(not tilled for at
least 10 years)
continuous 6 or 7 year rotation with
wheat/barley and two different break
crops (oil seed rape and field beans)
grazing by sheep
harvesting
tillage (annually)
fungicides/herbicides/insecticides
(biannually)
continuous 3 or 4 year rotation
with wheat/barley and oil seed
rape as the only break crop
harvesting
tillage (annually),
fungicides/herbicides (3 times
per year), insecticides
(annually), growth regulator
(biannually)
sewage sludge or municipal
compost (at 20% of sampling
sites)
[UK]
Most important
management practices
Fertilizer input *
N (kg ha-1 y-1)
P (kg ha-1 y-1)
K (kg ha-1 y-1)
FAO soil type**
once/year; March;
only for one site;
granules
9 (9)
5 (5)
5 (5)
Chromic
Luvisol/Leptosol
once/year; after soil analyses; granules
2 times/year; late March and late
April; granules
0 (169)
99 (93)
60 (111)
173 (171)
35 (25)
25 (74)
Chromic Luvisol/Leptosol
Chromic Luvisol/Leptosol
Total Organic Carbon
(%)
3.71
2.12
3.00
Total Carbon (%)
6.46
4.11
6.49
Total N (%)
0.39
0.24
0.27
C/N
9.36
8.94
11.04
pH
7.22
7.41
7.64
Moisture (g g-1)
0.18
0.16
0.16
Bulk Density (g cm-3)
0.82
1.30
1.15
Ca (g kg-1)
15.90
11.20
12.80
P (mg kg-1)
38.06
21.88
22.16
K (mg kg-1)
316.79
179.76
182.52
Mg (mg kg-1)
190.03
129.15
134.35
S (mg kg-1)
28.15
16.78
17.90
*average values for the years of sampling and for the previous three years before sampling (in parenthesis)
**European soil database (http://eusoils.jrc.ec.europa.eu)
7
Table S6. Management and soil properties for the three land use forms in Czech Republic
(Ceske Budejovice, 7.9/3/13 °C mean/min/max annual temperature, 700 mm mean
annual precipitation)
L
Pasture
M
Rotation
H
Intensive Rotation
Description of
crop/vegetation during
sampling (2008/2009)
permanent
grassland
clover
wheat
Management regime –
history
permanent meadow
(not tilled for at
least 10 years)
continuous rotation with clover - barley
or oats - wheat or oil seed rape or potato maize or winter barley.
before wheat: oil seed
rape/wheat/barley/potato
[CZ]
harvesting, tillage
(annually), weed and pest
cutting for forage
management when
necessary
3 times/year (3:2:1) in
3 times/year (3:2:1) in early spring,
early spring, during
Fertilizer input *
once/year; granules
during intensive growth, and after spike
intensive growth, and
appearance; granules
after spike appearance;
granules
N (kg ha-1 y-1)
3 (3)
26 (138)
138 (138)
P (kg ha-1 y-1)
0 (5)
5 (5)
K (kg ha-1 y-1)
0 (5)
5 (5)
Stagnic
Stagnic Luvisol/Stagnic
FAO soil type**
Luvisol/Dystric
Stagnic Luvisol/Dystric Cambisol
Cambiso/Dystric
Cambisol
Cambisoil
Total Organic Carbon (%)
5.54
1.91
1.98
Total Carbon (%)
5.54
1.91
1.98
Total N (%)
0.37
0.15
0.15
C/N
13.63
13.11
13.69
pH
6.41
6.74
6.74
Moisture (g g-1)
0.27
0.23
0.23
Bulk Density (g cm-3)
0.69
1.26
1.29
Ca (g kg-1)
3.20
1.30
1.60
P (mg kg-1)
8.00
8.60
17.40
K (mg kg-1)
488.10
235.20
272.30
Mg (mg kg-1)
309.20
174.70
151.80
S (mg kg-1)
91.20
7.80
15.40
*average values for the years of sampling and for the previous three years before sampling (in parenthesis)
Most important
management practices
cutting/harvesting, no tillage in the year
of clover
**European soil database (http://eusoils.jrc.ec.europa.eu)
8
Table S7. Management and soil properties for the three land use forms in Greece (Kria
Brisi, 14/4/31 °C mean/min/max annual temperature, 485 mm mean annual precipitation)
L
Pasture
M
Rotation
H
Intensive Rotation
Description of
crop/vegetation during
sampling (2008/2009)
permanent grassland
clover
barley
Management regime –
history
permanent natural
grassland (not tilled
for at least 20 years)
perennial rotation with clover (Medicago
for at least 4 years) -tobacco or maize or
vetch or barley.
Most important
management practices
grazing by sheep or
horses
cutting (biannually), no tillage during the
years of clover
before barley:
maize/various
legumes/barley/set aside.
harvesting, tillage
(annually), weed and pest
management when
necessary
once/year; granules
80 (53)
- (3)
- (3)
Fluvisol
[GR]
Fertilizer input *
once/year; granules
N (kg ha-1 y-1)
- (65)
P (kg ha-1 y-1)
- (17)
K (kg ha-1 y-1)
- (17)
FAO soil type**
Fluvisol
Fluvisol
Total Organic Carbon
(%)
2.61
2.41
1.79
Total Carbon (%)
3.76
2.63
2.42
Total N (%)
0.21
0.21
0.15
C/N
12.27
12.01
12.33
pH
8.63
8.48
8.60
Moisture (g g-1)
0.19
0.26
0.20
Bulk Density (g cm-3)
1.26
1.37
1.37
Ca (g kg-1)
15.9
12.2
13.9
P (mg kg-1)
9.0
16.4
40.3
K (mg kg-1)
245.3
140.2
138.2
Mg (mg kg-1)
1260.6
607.6
559.8
S (mg kg-1)
29.4
21.1
115.9
*average values for the years of sampling and for the previous three years before sampling (in parenthesis)
**European soil database (http://eusoils.jrc.ec.europa.eu)
9
Table S8. ANOVA table of country and land use effects on soil properties (with farm as a random factor). Underlined values are
significant.
Moisture
pH
Total N
Total C
Organic C
C/N
Ca
P
K
Mg
S
F3,16
3.46
12.7
1.49
1.12
0.62
9.53
4.70
2.99
5.84
58.2
2.12
Country
P
0.05
0.00017
0.25
0.37
0.62
0.0008
0.015
0.06
0.007
<0.0001
0.14
Land use
F2,32
0.91
1.29
6.51
5.04
4.41
2.42
4.70
0.93
5.40
2.06
8.65
10
P
0.41
0.29
0.0043
0.013
0.02
0.10
0.16
0.41
0.009
0.14
0.001
F6,32
1.43
0.62
0.63
0.63
0.64
0.57
0.29
0.76
0.31
0.415
0.44
Country*Land use
P
0.24
0.72
0.70
0.71
0.70
0.75
0.94
0.61
0.93
0.87
0.85
Table S9: Values for the physiological parameters of the different trophic groups in the
food web. For each parameter for each trophic group, we averaged the estimations
reported in the literature after review of recent literature on the subject (see references
listed below).
Trophic groups
assimilation
efficiency
production
efficiency
death rate
(yr-1)
plant parasitic nematodes
0.42
0.31
2.3
phytophagous collembolan
0.34
0.37
1.96
plant associated nematodes
0.42
0.31
6
AM fungi
1
0.44
3.7
saprophytic fungi
1
0.44
3.7
bacteria
1
0.51
9
fungivorous mites
0.5
0.4
1.42
fungivorous nematodes
0.42
0.31
6
fungivorous collembolan
0.34
0.37
1.96
omnivorous collembolan
0.34
0.37
1.96
bacterivorous collembolan
0.34
0.37
1.96
amoeba
0.55
0.58
7.3
flagellates
0.52
0.6
7.3
enchytraeids
0.28
0.29
1.95
earthworms
0.22
0.32
0.14
bacterivorous nematodes
0.54
0.49
14.1
omnivorous and predaceous nematodes
0.55
0.28
5.8
predaceous collembolan
0.34
0.37
1.96
predaceous mites
0.75
0.3
3.44
References used for estimating trophic group physiological parameters:
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communities to temperature: comparison of fungi and bacteria in a laboratory experiment. Global Change
Biology 15:2950–2957.
Berg M et al. (2001) Community food web, decomposition and nitrogen mineralisation in a stratified Scots
pine forest soil. Oikos 94:130–142.
Bolton P, Phillipson J (1976) Burrowing, feeding, egestion and energy budgets of Allolobophora rosea
(Savigny)(Lumbricidae). Oecologia 245:225–245.
11
Brown D, Coiro M (1985) The reproductive capacity and longevity of Xiphinema index(Nematoda:
Dorylaimida) from three populations on selected host plants. REV NEMATOL 8:171–173.
Burn A (1984) Life cycle strategies in two Antarctic Collembola. Oecologia 64:223–229.
Cabrera AR, Cloyd R a., Zaborski ER (2005) Development and reproduction of Stratiolaelaps scimitus
(Acari: Laelapidae) with fungus gnat larvae (Diptera: Sciaridae), potworms (Oligochaeta: Enchytraeidae)
or Sancassania aff. sphaerogaster (Acari: Acaridae) as the sole food source. Experimental and Applied
Acarology 36:71–81.
Chen J, Carey JR, Ferris H (2001) Comparative demography of isogenic populations of Caenorhabditis
elegans. Experimental gerontology 36:431–40.
Chen J, Ferris H (1999) The effects of nematode grazing on nitrogen mineralization during fungal
decomposition of organic matter. Soil Biology and Biochemistry 31:1265–1279.
Coiro M, Sasanelli N (1995) Life cycle studies of individual Longidorus athesinus (Nematoda) on S. Lucie
Cherry. Nematol medit 23:329–333.
Coûteaux M, Ogden C (1988) The growth ofTracheleuglypha dentata (Rhizopoda: Testacea) in clonal
cultures under different trophic conditions. Microbial ecology 15:81–93.
Ferris H, Eyre M, Venette RC, Lau SS (1996) Population energetics of bacterial-feeding nematodes: Stagespecific development and fecundity rates. Soil Biology and Biochemistry 28:271–280.
Ferris H, Venette R, Lau S (1997) Population energetics of bacterial-feeding nematodes: carbon and
nitrogen budgets. Soil Biology and Biochemistry 29:1183–1194.
Gems D (2000) Longevity and ageing in parasitic and free-living nematodes. Biogerontology 1:289–307.
Gotoh T, Yamaguchi K, Mori K (2004) Effect of temperature on life history of the predatory mite
Amblyseius (Neoseiulus) californicus (Acari: Phytoseiidae). Experimental & applied acarology 32:15–30.
Gregoire-Wibo C, Snider R (1977) The intrinsic rate of natural increase: its interest to ecology and its
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Hunt H, Coleman D, Ingham E (1987) The detrital food web in a shortgrass prairie. Biology and Fertility of
Soils 3:57–68.
Kojima K (1985) The life history of Hypogastrura denisana in a culture situation (Collembola:
Hypogastrurudae). Edaphologia 32:1–10.
12
Marchant R, Nicholas WL (1974) An energy budget for the free-living nematode Pelodera (Rhabditidae).
Oecologia 16:237–252.
Mulder C, Baerselman R, Posthuma L (2007) Empirical maximum lifespan of earthworms is twice that of
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decomposition processes. Oikos 39:288–388.
Phillipson J, Abel R, Steel J, Woodell S (1979) Enchytraeid numbers, biomass and respiratory metabolism
in a beech woodland-Wytham Woods, Oxford. Oecologia 193:173–193.
Rodriguez P, Arrate JA, Martinez-Madrid M (2002) Life history of the oligochaete Enchytraeus coronatus
(Annelida, Enchytraeidae) in agar culture. Invertebrate Biology 121:350–356.
Rogerson A (1981) The ecological energetics of Amoeba proteus (Protozoa). Hydrobiologia 85:117–128.
Römbke J (1991) Estimates of the Enchytraeidae (Oligochaeta, Annelida) contribution to energy flow in
the soil system of an acid beech wood forest. Biology and fertility of soils 11:255–260.
Rousk J, Bååth E (2007) Fungal and bacterial growth in soil with plant materials of different C/N ratios.
FEMS microbiology ecology 62:258–67.
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12:217–220.
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13
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of Animal Ecology 42:219–245.
Testerink G (1983) Metabolic adaptations to seasonal changes in humidity and temperature in litterinhabiting Collembola. Oikos 40:234–240.
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2:195–201.
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comparison of two approaches. Oecologia 124:561–573.
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experimentalis et applicata 85:177–187.
14
Table S10: Values for the coefficients of feeding preferences used to calculate the diet
fraction each prey represents for each predator trophic group (these coefficients were
further weighted by corresponding prey biomasses measured in the field). These values
were based on estimations reported in the literature (see references listed below).
Predator trophic group
Prey trophic groups
Feeding preference
plant parasitic nematodes
phytophagous collembolan
plant associated nematodes
AM fungi
saprophytic fungi
bacteria
fungivorous mites
fungivorous mites
fungivorous nematodes
fungivorous nematodes
fungivorous collembolan
fungivorous collembolan
fungivorous collembolan
omnivorous collembolan
omnivorous collembolan
omnivorous collembolan
omnivorous collembolan
bacterivorous collembolan
amoeba
amoeba
amoeba
amoeba
flagellates
flagellates
flagellates
enchytraeids
enchytraeids
enchytraeids
earthworms
earthworms
earthworms
bacterivorous nematodes
bacterivorous nematodes
omnivorous and predaceous nematodes
omnivorous and predaceous nematodes
omnivorous and predaceous nematodes
omnivorous and predaceous nematodes
omnivorous and predaceous nematodes
omnivorous and predaceous nematodes
omnivorous and predaceous nematodes
omnivorous and predaceous nematodes
predaceous collembolan
predaceous collembolan
root
aboveground plant
root
root
detritus
detritus
AM fungi
saprophytic fungi
AM fungi
saprophytic fungi
AM fungi
saprophytic fungi
bacteria
detritus
AM fungi
saprophytic fungi
bacteria
bacteria
bacteria
flagellates
saprophytic fungi
AM fungi
AM fungi
saprophytic fungi
bacteria
detritus
saprophytic fungi
bacteria
detritus
saprophytic fungi
bacteria
bacteria
flagellates
bacteria
amoeba
flagellates
plant parasitic nematodes
plant associated nematodes
fungivorous nematodes
bacterivorous nematodes
enchytraeids
fungivorous nematodes
fungivorous collembolan
1
1
1
1
1
1
0.5
0.5
0.1
0.9
0.475
0.475
0.05
0.25
0.25
0.25
0.25
1
0.08
0.9
0.01
0.01
0.05
0.05
0.9
0.2
0.4
0.4
0.2
0.4
0.4
0.05
0.95
0.0001
0.001
0.001
0.2
0.2
0.2
0.2
0.2
0.05
0.05
15
predaceous collembolan
predaceous collembolan
predaceous collembolan
predaceous collembolan
predaceous mites
predaceous mites
predaceous mites
predaceous mites
predaceous mites
predaceous mites
predaceous mites
predaceous mites
omnivorous collembolan
bacterivorous collembolan
bacterivorous nematodes
omnivorous and predaceous nematodes
predaceous collembolan
omnivorous and predaceous nematodes
enchytraeids
fungivorous mites
fungivorous collembolan
omnivorous collembolan
bacterivorous collembolan
phytophagous collembolan
0.05
0.05
0.05
0.8
0.2
0.001
0.001
0.2
0.2
0.2
0.2
0.2
References used for estimating coefficients of predator feeding preferences:
Berg M et al. (2001) Community food web, decomposition and nitrogen mineralisation in a stratified Scots
pine forest soil. Oikos 94:130–142.
Bjørnlund L, Rønn R, Péchy-Tarr M, Maurhofer M, Keel C, Nybroe O (2009) Functional GacS in
Pseudomonas DSS73 prevents digestion by Caenorhabditis elegans and protects the nematode from killer
flagellates. The ISME Journal 3: 770-779.
Ekelund F (1998) Enumeration and abundance of mycophagous protozoa in soil, with special emphasis on
heterotrophic flagellates. Soil Biology and Biochemistry 30: 1343-1347.
Hunt H, Coleman D, Ingham E (1987) The detrital food web in a shortgrass prairie. Biology and Fertility of
Soils 3:57–68.
Schmidt O, Curry JP, Dyckmans J, Rota E, Scrimgeour CM (2004) Dual stable isotope analysis (δ13C and
δ15N) of soil invertebrates and their food sources. Pedobiologia 48: 171-180.
16
Table S11. Parameter combinations fitted in the statistical modelling procedure.
Variable class
1. Spatial
autocorrelation
Parameter combinations tested
a)
Filters 1-6
2.
a)
pH, moisture, WHC
b)
pH+pH2, moisture+moisture2, WHC+WHC2
c)
All combinations of terms from a and b that were found to
improve AIC in phases a and b. (retain quadratic term of any of
these two term combinations if found to improve AIC in phase
b).
d)
Interaction terms between parameters that were found to be
significant in a and b with parameters from class 1
a)
H, M, L
b)
H+M, L
c)
Interaction terms between parameters that were found to be
significant in a and b with parameters from class 1.
a)
Total C, total N, total organic C, C/N, LOI
b)
Total C+Total C2, Total N+Total N2, TOC+TOC2, LOI+LOI2
c)
All combinations of terms from a and b that were found to
improve AIC in phases a and b. (retain quadratic term of any of
these two term combinations if found to improve AIC in phase
b).
d)
Interaction terms between parameters that were found to be
significant in a and b with parameters from class 1 and 3
a)
Number of functional groups, diversity of functional groups,
mean trophic level, maximum trophic level, fungal channel
biomass, bacterial channel biomass, root channel biomass, total
biomass, F/B channel ratio, R/D channel ratio, F/B ratio
b)
All combinations of terms from a that were found to improve
AIC in phase a.
c)
Interaction terms between parameters that were found to be
significant in a and b with parameters from class 1 and 3
a)
fungi, bacteria, AM fungi, nematodes (fungivorous,
bacterivorous, omnivorous, predatory, plant-feeding, plantassociated), Collembola (fungal-feeding) , mites (fungalfeeding, predators), earthworms, enchytraeids, protozoa
(amoebae, flagellates)
b)
All combinations of terms from a that were found to improve
3.
4.
5.
6.
Soil physical and
chemical properties
Land use
C and nutrient stocks
Soil
food
structure
web
Biomass
of
individual functional
groups
17
AIC in phase a.
c)
Interaction terms between parameters that were found to be
significant in a and b with parameters from class 1 and 3
18
Supplementary Figures
Figure S1. Differences in, and ranges of, ecosystem services and disservices as affected by land use across the four European
countries. Boxes represent median and 25th and 75th percentiles, whiskers show maximum and minimum value unless extreme values
are present (circles). Intensity effects were significant for CO2 production (F2,32 = 6.94, P = 0.003), CH4 production (F2,32 = 3.35, P =
0.047), and DOC leaching (F2,30 = 19.6, P < 0.000).
19
Figure S2: Soil food web diagrams. A. General food web diagram used to estimate the
flow-based soil food webs at the different sites. B. Flow-based soil food web in a
Swedish farm in a high land use intensity field. Circles represent trophic groups of soil
organisms and arrows represent the feeding links between these groups. For panel B, the
size of the circles is proportional to the biomass of the trophic groups and the width of the
arrows is proportional to the carbon flows between the groups.
20
Supplementary Methods
Food web analyses and calculations
Biomass calculations
First, the biomass of all measured and counted groups of the soil food web was calculated
in terms of kg C m-2. Fatty acids were converted into biomass C using the following
factors: bacterial biomass 363.6 nmol PLFA = 1 mg carbon (1). Fungal biomass: 11.8
nmol PLFA = 1 mg carbon (2), AMF biomass: 1.047 nmol NLFA = 1 µg carbon (3).
After counting total numbers, nematodes were fixed in 4% formaldehyde and 150
randomly selected nematodes were identified to the genus level (4) and allocated to
trophic group (5), and nematode biomass was individually estimated by analysing digital
microscope images with a specially developed software tool (6). Collembola were
determined to species using keys of Gisin (7), Babenko et al. (8), and Zimbars and
Dunger (9). Acari were sorted to suborders using Krantz and Walter (10), and Oribatida
were determined to species using keys of Balogh and Mahunka (11) and Weigman (12).
Biomass of microarthropods was estimated from body dimensions following (13).
Biomasses for all groups were then expressed per square meter using bulk density values.
Estimation of the “flow-based” soil food webs
Carbon flows, expressed as kg C m-2 yr-1, between trophic groups in soil food webs were
estimated as in Hunt et al. (14) from the biomasses of the different trophic groups at a
given site (see above), , and from feeding preferences and physiological parameters of the
different trophic groups (Table S9 and S10). Feeding rate (kg C m-2 yr-1) of trophic group
21
j on trophic group i is expressed as
∑
=
where Bj is the biomass of
group j, dj, aj and pj are respectively group j death rate, assimilation and production
efficiencies, and gij corresponds to the fraction each prey i represents in diet of trophic
group j depending on predator relative feeding preference
biomasses (
=∑
weighted by prey
). The basic assumption underlying this way of calculating
feeding rates is that this feeding rate, on an annual basis, balances losses through natural
death (
) and losses through predation (∑
) (14). Parameters used were taken
from Hunt et al. (14) and further updated by a review of recent literature on the subject
(see Table S9 and S10).
Measures of soil food web structure
We use three types of soil food web measures: diversity indices (number of trophic
groups in the food web and Shannon diversity of trophic groups), measures based on
trophic position (mean trophic level and maximum trophic level in the food web), and
measures based on energy channels (Fungi/Bacteria biomass ratio, fungal channel
biomass, bacterial channel biomass, root channel biomass, Fungal/Bacterial channel ratio,
Root/Detritus channel ratio).
The trophic position of a species is defined here by the average of the trophic
position of the species it consumes weighted by the diet fraction these species represents:
= 1 + ∑!
"
, where TLj is the trophic level of species j and gij the fraction of
the consumer j’s diet derived from the prey i. These “flow-based” trophic levels are
computed following the method of Williams and Martinez (15). Average trophic level for
22
each consumer is the sum of all entries in each column of A = [I –G]-1 with I the identity
matrix and G = (gij).
Fungal, bacterial and root energy channels are measured by the biomass of all the
groups belonging to that channel weighted by their contribution to this channel. The
contribution of a group to a channel is defined by # = ∑!
"
# and thus the
contribution of each group is equal to the product of A by a vector V, with Vi=1 for the
source of the energy channel (either fungi, bacteria or root) and 0 otherwise. We
measured two different indices to quantify the fungal and bacterial energy channel. First
we summed the biomass of all groups belonging to a given channel weighted by their
contribution # to this channel. Second, because the order of magnitude of biomasses
differs strongly between the trophic groups, we also calculated the energy channels with
standardized biomasses of each group by dividing the biomass of one group by the
overall mean of that group over all considered food webs (16).
23
Spatial filters
Two different types of mechanisms can cause spatial structure in a measured variable, (i)
known or unknown explanatory variables or (ii) autocorrelation between values of the
measured variable. To explicitly incorporate spatial structure into our statistical models
we calculated spatial filters using principal coordinates of neighbor matrices (PCNM)
(17, 18). This method accounts for the fact that measured variables are structured at
different spatial scales; not just at a country scale (which could have been modeled as a
random factor), but also within countries and within farms. The following steps were
used to create the spatial filters:
1. A distance matrix was calculated from the geographic coordinates of all sites based on
Euclidean distances.
2. This distance matrix was truncated at distances above 29.5 km as the minimum
spanning tree in the region with the largest spread of field sites (Czech Republic) and all
distances larger than 29.5 km were replaced by four times that value prior to PCO as
recommended (19)
3. The principal coordinates were computed from this distance matrix.
4. All principal coordinates that corresponded to positive eigenvalues were retained as
spatial filters for further analyses as they represent a spectral decomposition of the spatial
relationships between sampling sites.
24
Statistical modeling
Model selection followed a modified version of the procedure described in De Vries et al
et al. (20). In this method, we added groups of terms according to a fixed sequential
order, compared their influence on model likelihood, selected the variables that gave the
greatest improvement to model likelihood, assessed by selecting the model with the
lowest Akaike’s Information Criterion (AIC), and then retained these terms in the model
if they were found to be significant in a chi-squared likelihood ratio deletion test (LRTs)
(21). After these tests, another set of variables representing different controls over
function were then added and the process was repeated.
The order of addition followed a hypothetical hierarchy of controls over function,
starting with spatial filters that either account for autocorrelation between values of the
response variable, or for underlying, measured or unmeasured factors such as climate and
geology, and ending with soil food web properties. While interrelationships and
correlations between predictor variables are unavoidable, we kept the order of the
hierarchy such that variables added later in the modelling process were unlikely to
influence those that had previously been added. Therefore, if soil food web properties
shared explained variance with parameters previously added, but were retained in the
model, they explained a unique proportion of variance. In contrast, if they accounted for
all the variation explained by a parameter that was added earlier in the modeling process,
this parameter then became non-significant. Addition of variables according to this
hierarchy of controls does not allow for the disentangling of causative relationships, but if
variation accounted for by the more proximate factors was entirely shared by the ultimate
causes then these variables would not improve model likelihood when added. For
25
variables for which an optimum of biological activity was expected, quadratic terms were
added alongside main terms (e.g. pH, moisture).
First, spatial filters and first order interactions between them were added. For a full
list of terms added see Table S11. In the second stage, terms representing hydrology and
soil physical properties were added: soil pH, moisture content, and water holding
capacity. These variables are largely driven by underlying geology and local hydrology.
Once the effects of spatial structure and soil abiotic properties were estimated, we added
first order interactions between the retained spatial filters and soil variables, and removed
these sequentially by using LRTs, starting with the least significant until only significant
interactions remained. The third set of terms consisted of the three land use forms of
intensive wheat rotation, extensive rotation, and permanent grassland. The fourth stage of
the process was including total N and C stocks, variables that will be affected by
management, soils, and climate, but which might explain more or additional variation.
Fifth, we estimated soil food web structure effects on processes of C and N cycling, and
finally, we tested for effects of individual functional groups of the soil food web on
processes of C and N cycling. At the end of each of these steps, interaction terms between
retained variables were added to the model and removed by LRTs, until only significant
interaction terms remained.
Once this final model was reached we assessed the significance of each term by
removing it from the model and performing a LRT. When it was found that terms that
were significant earlier in the modelling process were no longer significant in the
presence of new variables, the non-significant terms were removed from the model. A
measure of model fit of the final model was calculated as the R-squared when fitting a
26
linear regression to the actual data, with the predicted values of the model as the
explanatory variable. We also explored how much influence each category of variable
had upon overall fit by removing each class of variable from the fitted models and
observing the change in AIC and model fit, as calculated above.
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27
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