Supplementary Material
Appendix 1 – Detailed experimental protocol for the quantification of the abundance of
nitrifiers, ammonia-oxidizing bacteria and archaea
The composition of each reaction mix is given in Table S1. For all genes dilution series of the
DNA extracts were previously tested to avoid inhibition of PCR, resulting in an optimal
dilution of 1:64 for all samples with 2 µl as template. Forty and ten ng of soil DNA extract
were used as templates (5µl) for nxrA and 16S rRNA Nitrospira (Ns) respectively, and
samples were run at least twice. For these genes, the presence of PCR inhibitors in DNA
extracts was checked by co-amplification of standards with randomly chosen soil DNA
samples; no inhibition was observed. Serial plasmid dilutions of the respective functional
genes ranging from 101 to 107 gene copies/µL were used as standards. PCR runs with an
initial step for enzyme activation at 95 °C for either 10 min (nirK, nirS, amoA (AOA), amoB
(AOB)) or 15 min (Ns, nxrA). To confirm the amplicons specificity after each PCR run, a
melting curve and a 2 % agarose gel stained with ethidium bromide were done to validate the
amplified fragment length. The efficiencies (Eff) of the amplification were calculated from the
standard curve with the formula Eff = [10 (−1/slope) − 1] * 100 % and resulted in the following
values: amoA -AOB (AOB) 93-95 %, amoA-AOA 91–98 %, nirS 99–100 %, nirK 94–99 %,
nxrA 91-94%, Ns 83-94%.
Table S1 – Quantitative PCR conditions used to estimate abundances of N-related microbial functional groups (Ammonium oxidizers, Nitrite
oxidizers and Nitrite reducers)
Target gene Master Mix
Thermal profile
Power SybrGreen
amoA AOA (Applied Biosystems)
Power SybrGreen
amoA AOB (Applied Biosystems)
Power SybrGreen
nirS
(Applied Biosystems)
Power SybrGreen
nirK
(Applied Biosystems)
94°C–45
s/55°C–45s/72°C–45 s
94°C–60
s/58°C–60s/72°C–60 s
94°C–45
s/57°C–45s/72°C–45 s
95°C–15
s/63°C–30s/72°C–30 s
95°C–15
s/58°C–30s/72°C–30 s
16S rRNA
Nitrospira
95°C-30 s/64°C30s72°C- 60s
95°C-30s/55°C45s/72°C-45s
nxrA
QuantiTect SybrGreen
PCR Master (Qiagen)
QuantiTect SybrGreen
PCR Master (Qiagen)
No. of Reactional
Primer*
Primer
DMSO* BSA* Source of standard
cycles
volume*
(10 μM)
amo19F,
0.5
0.5
40
25
CrenamoA16r48x
Fosmid clone 54d9
amoA1F,
0.5
0.5
40
25
amoA2R
Nitrosomonas sp.
40
25
cd3aF, R3cd
5a
25
nirK876, nirK5R
40
25
Ns675f, Ns746r
45
20
F1norA, R2norA
Devices
7300 (Applied
Biosystems, Germany)
7300 (Applied
Biosystems, Germany)
7300 (Applied
0.5
Pseudomonas stutzeri Biosystems, Germany)
7300 (Applied
0.5
Azospirillum irakense Biosystems, Germany)
0.5
0.625
0.5
0.625
1
-
-
1
-
-
References
Treusch et al. 2005
Leininger et al. 2006
Rotthauwe et al.
1997
Throback et al. 2004
Braker et al. 1998
40
Clone accession
number FJ529918
Nitrobacter
hamburgensis X14
LC 480 (Roche
Diagnostics, France)
LC 480 (Roche
Diagnostics, France)
Graham et al. 2007
Attard et al. 2010
a=touchdown PCR
*volumes in µl
References
Treusch, A.H., Leininger, S., Kletzin, A., Schuster, S.C., Klenk, H-P., Schleper, C. (2005) Novel genes for nitrite reductase and Amorelated proteins indicate a role of
uncultivated mesophilic crenarchaeota in nitrogen cycling. Environ Microbiol, 7, 1985–1995
Leininger, S., Urich, T., Schloter, M., Schwark, L., Qi, J., Nicol, G.W., Prosser, J.I., Schuster, S.C., Schleper, C. (2006) Archaea predominate among ammonia-oxidizing
prokaryotes in soils. Nature, 442, 806–809
Rotthauwe, J.H., Witzel, K.P., Liesack, W. (1997) The ammonia monooxygenase structural gene amoA as a functional marker: molecular fine-scale analysis of natural
ammonia-oxidizing populations. Appl Environ Microbiol, 63, 4704–4712
Throback, I.N., Enwall, K., Jarvis, A., Hallin, S. (2004) Reassessing PCR primers targeting nirS, nirK and nosZ genes for community surveys of denitrifying bacteria with DGGE.
FEMS Microbiol Ecol, 49, 401–417
Braker, G., Fesefeldt, A., Witzel, K.P. (1998) Development of PCR primer systems for amplification of nitrite reductase genes (nirK and nirS) to detect denitrifying bacteria in
environmental samples. Appl Environ Microbiol, 64, 3769–3775
Attard, E., Poly, F., Commeaux, C., Laurent, F., Terada, A., Smets, B.F., Recous, S. & Roux, X.L. (2010) Shifts between Nitrospira- and Nitrobacter-like nitrite oxidizers underlie
the response of soil potential nitrite oxidation to changes in tillage practices. Environmental Microbiology, 12, 315-326.
Graham, D.W., Knapp, C.W., Van Vleck, E.S., Bloor, K., Lane, T.B. & Graham, C.E. (2007) Experimental demonstration of chaotic instability in biological nitrification. ISME
Journal, 1, 385-393.
Table S2 –Results of REML tests of individual plant and microbial parameters as explanatory factors for each ecosystem parameter. Presented
are the P values, the amount of variation explained by each plant or microbial parameter and its standardised effect
p
Plant Traits
CWM SLA (mm² mg-1dry)
CWM Height (cm)
Fdvar SLA (mm² g-1dry)
Fdvar Height (cm)
CWM LNC (mg g-1)
CWM LCC (mg g-1)
CWM C/N
Fdvar LNC
Fdvar LCC
Fdvar C/N
CWM LDMC
Fdvar LDMC
Microbial Traits
nir S
nir K
amo A (AOA)
amo B (AOB)
AOA:AOB ratio
nir K:nir S ratio
Vmax (µg N-NH4+.g-1.h-1)
Km (µg N-NH4+.l-1)
DEA (µg N-N2O.g-1.h-1)
Ns
nxr A
Ns : nxr A
Fungi : Bacteria ratio
Peak green biomass t/ha
% variance
std. effect
0,032
0,004
ns
0,005
<0,001
ns
<0,001
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
0,024
12,96
33,12
32,00
44,32
54,24
22,88
0,01
0,01
-0,22
0,01
-0,01
-2,03
p
Total Microbial Biomass N
% variance
std. effect
<0,001
0,001
ns
0,003
ns
ns
ns
0,010
ns
ns
ns
ns
ns
0,053
ns
ns
ns
0,037
ns
ns
ns
ns
ns
ns
<0,001
40,76
39,64
40,31
29,18
17,59
21,60
67,30
-0,04
0,03
0,63
-0,28
0,45
0,21
-7,16
p
Standing Litter t/ha
% variance
std. effect
0,001
0,001
ns
0,007
ns
0,002
ns
0,002
ns
ns
ns
ns
38,77
39,54
-0,04
0,03
<0,001
0,019
0,017
ns
0,003
<0,001
ns
0,028
ns
0,023
ns
0,018
ns
47,79
21,88
23,99
-0,52
0,50
-0,20
34,36
52,40
-0,30
0,36
15,16
-0,33
18,43
0,65
35,12
12,47
35,70
-0,34
17,08
-0,23
20,15
-0,32
p
Soil organic matter (%)
% variance std. effect
0,011
ns
ns
ns
ns
0,006
ns
ns
0,032
ns
0,006
ns
ns
0,003
ns
ns
ns
ns
ns
ns
0,014
ns
ns
ns
0,016
28,08
-0,02
15,76
0,27
14,78
6,81
17,24
-0,19
30,54
13,30
1,40
-0,13
32,57
0,28
26,11
0,41
26,11
-3,74
Potential leached NO3-N ( µg g-1 soil)
p
% variance
std. effect
ns
ns
0,007
ns
ns
ns
ns
ns
ns
0,029
ns
ns
ns
0,02
0,013
0,003
ns
ns
<0,001
ns
<0,001
<0,001
0,01
ns
ns
29,88
15,38
-2,31
-0,37
-16,27
27,81
31,95
-0,90
0,47
0,82
45,56
1,12
32,25
54,14
23,67
2,00
0,88
0,63
Mineralisation (NH4-N µg g-1 d-1)
p
% variance
std. effect
ns
ns
ns
0,04
ns
ns
ns
ns
ns
ns
0,04
ns
0,03
ns
0,07
0,06
ns
ns
0,03
ns
<0,001
ns
ns
ns
ns
0,03
0,56
0,11
2,04
20,39
0,36
6,80
9,67
0,17
0,26
16,92
0,30
46,22
1,01
Potential leached NH4-N ( µg g-1 soil)
p
% variance std. effect
0,043
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
0,008
ns
ns
ns
ns
6,33
-0,03
24,18
0,73
Table S3 - Alternative multivariable REML models for each of the ecosystem properties resulting from the replacement of plant and microbial
traits retained in the original final models (Table 2) by alternatively significant collinear traits with an adjusted R² of greater than 0.3. Co-linearity
of explanatory variables was tested using pair wise REML’s between all of the individually significant variables (Table S2) using site as a
random factor. The stepwise model fitting and optimisation procedure for the multiple variable models was the same as described in the
methods for the original models, apart from the replacement of each the originally chosen variables, in turn by each of the significantly co-linear
variables.
1) Peak Green Biomass (Best original final model % variation explained by fixed effects = 87.33 (94.54% plant traits / 5.46% microbial traits))
Parameters
retained in best
final model
CWM_Height
Significant co-linearities with other
individually significant variables
dropped from final model.
CWM_C :N
CWM_SLA
None
FDvar_Height
None
Fungi : Bacteria
None
p value
Adjusted R²
<0.001
0.37
Most parsimonious alternative multiple variable REML models fitting co-linear variables
in place of original best final model parameters
CWM_C :N
FDvar_Height
Fungi : Bacteria
p value
Wald
% variation
explained by
fixed effects
proportion of %
variation
explained due to
plant traits
proportion of %
variation
explained due
to microbial
traits
<0.001
0.003
0.012
44.22
11.67
7.72
74.72
87
13
2) Microbial biomass N (Best original final model % variation explained by fixed effects = 83.27 (85.32% plant traits / 14.68% microbial traits))
Parameters
retained in
best final
model
Significant co-linearities with other
individually significant variables
dropped from final model.
p value
Adjusted R²
Most parsimonious alternative multiple variable models fitting co-linear variables in
place of original final model parameters
p value
Wald
% variation
explained
by fixed
effects
proportion of %
variation
explained due to
plant traits
proportion of
% variation
explained due
to microbial
traits
CWM_SLA
nirK :nirSa
0.006
0.31
CWM_Height
FDvar_Height
<0.001
<0.001
28.07
18.78
71.49
100
0
CWM_Height
nirK : nirSb
<0.001
0.38
CWM_SLA
<0.001
24.09
40.75
100
0
FDvar_LNCc
<0.001
0.41
CWM_SLA
<0.001
24.09
40.75
100
0
nirK : nirS
<0.001
0.56
CWM_SLA
CWM_Height
FDvar_Height
nirK : nirS
<0.001
<0.001
0.04
0.005
52.51
18.22
5.05
12.47
79.31
90
10
FDvar_LNCd
<0.001
0.48
CWM_SLA
CWM_Height
FDvar_Height
<0.001
0.003
0.022
29.84
12.41
6.38
71.05
100
0
FDvar_Height
nirK
a
None
nirK : nirS not significant in multivariate model including CWM_Height and FDvar-Height
nirK : nirS not significant in multivariate model including CWM_SLA
c FDvarLNC not significant in multivariate model including CWM_SLA
d FDvarLNC not significant in multivariate model including CWM_SLA, CWM_Height and FDvar_Height
b
3) Litter mass (Best original final model % variation explained by fixed effects = 77.74 (82.96% plant traits / 17.04% microbial traits))
Parameters
retained in best
final model
CWM_Height
CWM_SLA
Km
a
b
Significant co-linearities with other
individually significant variables
dropped from final model.
p value
Adjusted R²
nirK : nirS
0.002
0.37
nirS
0.008
FDvar_LNCa
Most parsimonious alternative multiple variable models fitting co-linear variables in place
of original final model parameters
p value
Wald
% variation
explained by
fixed effects
proportion of %
variation
explained due to
plant traits
proportion of %
variation
explained due
to microbial
traits
nirK : nirS
<0.001
24.5
52.40
0
100
0.29
nirS
CWM_SLA
<0.001
0.012
20
20.3
62.18
62
38
<0.001
0.42
CWM_SLA
Km
<0.001
0.008
18.23
8.69
54.70
71
29
nirK : nirS
0.006
0.31
CWM_Height
nirK : nirS
<0.001
0.005
18.92
9.77
56.04
71
29
nirKb
0.004
0.33
CWM_Height
Km
<0.001
0.022
17.07
6.2
50.86
78
22
CWM_LCC
0.007
0.30
CWM_Height
CWM_LCC
Km
<0.001
0.014
0.046
20.39
7.42
4.59
58.73
88
12
FDvar_Height
0.002
0.36
CWM_Height
FDvar_Height
<0.001
0.008
17.92
8.8
53.93
100
0
Ns
<0.001
0.42
CWM_Height
CWM_SLA
Ns
<0.001
<0.001
0.005
35.67
21.25
9.95
75.62
85
15
FDvar_LNC not significant in multivariable model including CWM_SLA and Km
nirK not significant in multivariate model including CWM_Height and Km
4) Soil organic matter % (Best original final model % variation explained by fixed effects = 82.61 (50.09% plant traits / 49.91% microbial traits))
Parameters
retained in best
final model
CWM_LDMC
CWM_SLA
DEA
a
b
Significant co-linearities with other
individually significant variables
dropped from final model.
p value
Adjusted R²
CWM_LCC
<0.001
0.77
FDvar_LCC
<0.001
nirK
Most parsimonious alternative multiple variable models fitting co-linear variables in place
of original final model parameters
p value
Wald
% variation
explained by
fixed effects
proportion of %
variation
explained due to
plant traits
proportion of %
variation
explained due
to microbial
traits
CWM_LCC
CWM_SLA
DEA
<0.001
<0.001
<0.001
21.03
16.67
59.59
82.02
35
65
0.46
FDvar_LCC
CWM_SLA
DEA
<0.001
<0.001
<0.001
25.34
32.61
41.25
82.36
53
47
0.002
0.37
nirK
CWM_SLA
DEA
0.002
0.004
<0.001
13.24
10.37
31.25
71.77
39
61
nirKa
0.004
0.33
CWM_LDMC
DEA
<0.001
<0.001
17.81
15.72
61.08
50
50
CWM_LCCb
0.007
0.30
CWM_LDMC
DEA
<0.001
<0.001
17.81
15.72
61.08
50
50
None
nirK not significant in multiple variable model with CWM_LDMC and DEA
CWM_LCC not significant in multiple variable model with CWM_LDMC and DEA
5) Potential N Mineralisation (Best original final model % variation explained by fixed effects = 61.18 (17.53% plant traits / 82.47% microbial traits))
Parameters
retained in best
final model
Significant co-linearities with other
individually significant variables
dropped from final model.
p value
Adjusted R²
Most parsimonious alternative multiple variable models fitting co-linear variables in place
of original final model parameters
p value
Wald
% variation
explained by
fixed effects
proportion of %
variation
explained due to
plant traits
proportion of %
variation
explained due
to microbial
traits
CWM_LDMC
None
DEA
Vmax
<0.001
0.70
CWM_LDMC
Vmax
0.02
0.005
6.32
9.71
37.46
29
71
nirS
0.004
0.31
CWM_LDMC
nirS
0.03
0.01
5.51
8.06
36.40
29
71
6) Potential leached NO3-N (Best original final model % variation explained by fixed effects = 60.95 (25.24% plant traits / 74.76% microbial traits))
Parameters
retained in best
final model
Significant co-linearities with other
individually significant variables
dropped from final model.
p value
Adjusted R²
Most parsimonious alternative multiple variable models fitting co-linear variables in place
of original final model parameters
p value
Wald
% variation
explained by
fixed effects
proportion of %
variation
explained due to
plant traits
proportion of %
variation
explained due
to microbial
traits
FDvar_C : N
None
Vmax
amoA (AOA)
<0.001
0.71
amoA (AOA)
0.013
9.27
27.81
0
100
DEA
<0.001
0.70
DEA
<0.001
17.34
33.72
0
100
Ns
<0.001
0.93
FDvar_C : N
Ns
0.005
<0.001
10.00
20.63
55.32
28
72
nxrAa
<0.001
0.47
FDvar_C : N
0.029
11.03
15.38
100
0
a
nxrA not significant in multiple variable model with FDvar_C : N
7) Potential leached NH4-N (Best original final model % variation explained by fixed effects = 39.73 (15.94% plant traits / 84.06% microbial traits))
Parameters
retained in best
final model
Significant co-linearities with other
individually significant variables
dropped from final model.
p value
CWM_SLA
None
DEA
None
Adjusted R²
Most parsimonious alternative multiple variable models fitting co-linear variables in place
of original final model parameters
p value
Wald
% variation
explained by
fixed effects
proportion of %
variation
explained due to
plant traits
proportion of %
variation
explained due
to microbial
traits