Milcu et al. 2016
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Supplementary information for the article entitled “Systematic variability increases the
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reproducibility of an ecological study”, by Milcu and colleagues.
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This file contains:
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1) Supplementary discussion
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2) Extended data tables 1-3
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3) Extended data figures 1-7
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4) Supplementary acknowledgements
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1) Supplementary discussion
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The results of our multi-laboratory study indicated that genotypic CSV was more effective in
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increasing reproducibility than environmental CSV. However, we cannot discount the possibility
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that we observed this result because our treatments with environmental CSV were less successful
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in increasing within-treatment variability. A similar argument could be used for the observation
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that CSV introduced among the replicated microcosms was more effective at increasing
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reproducibility than CSV introduced within individual microcosms. Therefore, additional
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experiments are needed to test the importance of within- and among-microcosm CSV and to
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explore further whether other types of environmental CSV such as soil nutrients, texture, or
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water availability might be more effective for increasing reproducibility.
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Concerning the direction of the net legume effect, we expected that productivity of the grass
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B. distachyon would be higher in the presence of the nitrogen (N)-fixing M. truncatula.
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However, these species were not selected because of their routine pairings in agronomic or
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ecological experiments (they are rarely used that way), but rather because they are frequently
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used in controlled environment experiments for functional genomics. Contrary to our
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expectation, in this experiment, productivity of B. distachyon did not seem to increase in the
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presence of the N-fixing M. truncatula. Although the total plant biomass increased in the
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presence of legumes, this effect was the result of the higher biomass production of M. truncatula;
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the biomass of B. distachyon was actually lower in the microcosms with M. truncatula. Shoot
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%N data show (Extended Data Fig. 2) that the presence of M. truncatula in mixtures led to a
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reduction in B. distachyon shoot %N, suggesting that the two species were competing for N. We
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argue that the lack of any N fertilization effect of M. truncatula on B. distachyon can be
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explained by the asynchronous phenologies of the two species, with the life cycle of B.
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distachyon (8-10 weeks) being too short to benefit from the N fixation by M. truncatula.
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5) Supplementary Tables
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Extended Data Table 1 | Physicochemical properties of the soils used in growth chamber and glasshouse setups.
SETUP
Lab ID
C
N
(g/kg)
(g/kg)
Organic
P
Cation exchange
Clay
Silt
Sand
matter (g/kg)
(g/kg)
(cmol+/kg)
(g/100g)
(g/100g)
(g/100g)
7.26
0.57
12.67
12.57
0.09
3.46
10.53
19.23
70.23
5.88
Glasshouse L2
7.41
0.45
15.23
12.83
<0.005
3.06
8.43
23.87
67.70
8.68
Glasshouse L4
19.73
1.63
12.13
34.17
0.12
10.80
18.57
36.63
44.80
6.66
Glasshouse L11, L12
50.03
4.58
10.90
86.53
0.05
16.73
22.83
25.00
52.17
5.35
Glasshouse L13
16.83
1.94
8.67
29.10
0.19
8.02
18.00
10.00
72.00
5.78
Glasshouse L14
20.13
1.83
11.00
34.77
0.06
10.70
22.60
45.97
31.23
8.23
Growth
L1, L3, L5,
chamber
L6, L7, L8,
C/N
pH
L9, L10
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40
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Extended Data Table 2 | The net legume effect on measured response variables as affected by SETUP (glasshouse vs. growth
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chamber). Selected variables are typical for plant-soil microcosm experiments measuring plant productivity, biomass allocation,
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shoot tissue chemistry, evapotranspiration and litter decomposability. † symbol indicates a response variables measured for B.
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distachyon grass only, while the rest of the variables have been measured at the microcosm level, i.e. including the contribution of
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both the legume and the grass species.
Variable abbreviation
Description
Unit
Mean net legume effect (±SE)
Glasshouse
Growth chamber
Shoot BM
shoot biomass
g DW
5.05 ± 0.29
2.72 ± 0.11
Root BM
root biomass
g DW
0.80 ± 0.08
0.96 ± 0.08
Seed BM†
B. distachyon total seed biomass
g DW
-1.28 ± 0.07
0.88 ± 0.05
Total BM
total biomass (shoot + root + seeds)
g DW
4.57 ± 0.34
2.80 ± 0.15
Shoot.Root
shoot (+seed) to root biomass ratio
dimensionless
-2.51 ± 0.28
-0.88 ± 0.13
Grass height†
B. distachyon average size
cm
1.17 ± 0.72
-1.87 ± 0.28
Shoot N%†
B. distachyon shoot (+seed) nitrogen %
%
-0.26 ± 0.04
-0.16 ± 0.02
Shoot C%†
B. distachyon shoot (+seed) carbon %
%
0.32 ± 0.0
0.73 ± 0.07
Shoot δ15N†
B. distachyon shoot (+seed) δ15N signature
‰
-0.27 ± 0.09
-0.29 ± 0.1
Shoot δ13C†
B. distachyon shoot (+seed) δ13C signature
‰
0.26 ± 0.04
0.05 ±0.03
ET
evapotranspiration prior to experimental
ml -24h
67.27 ± 5.41
59.8 ± 3.29
g DW
0.01 ± 0.009
0.04 ± 0.01
harvest
Litter
litter substrate remaining at the end of
experiment
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Extended Data Table 3 | Within-laboratory standard deviation (SD) of net legume effect. Mixed effects output summarizing the
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impact of CSV and SETUP experimental treatments on within- and among-laboratory SD of net legume effect for the twelve response
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variables. We also present the impact of treatments on the first second principal components (PC1 and PC2) of the within-laboratory
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SD from all twelve variables. Response variables shown represent a typical ensemble of variables measured in plant-soil microcosm
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experiments (see Extended Data Table 1 for details). † symbol indicates a response variables measured for B. distachyon grass only,
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while the rest of the variables have been measured at the microcosm level, i.e. including the contribution of both the legume and the
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grass species.*** for P < 0.001; ** for P < 0.01; * for P < 0.05; + for P < 0.1; ns for P >0.1; numDF = numerator degrees of freedom,
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denDF = denominator.
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Within-laboratory SD
numDF denDF
Shoot BM
Root BM
Seed BM†
Total BM
Shoot/Root Grass height†
Shoot N%†
CSV
5
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3.38 (***)
> 100 (***)
>100 (***)
> 100 (***)
>100 (***)
26.98 (***)
> 100 (***)
SETUP
1
12
0.08 (ns.)
4.14 (+)
0.57(ns.)
0.45(ns.)
24.18(***)
0.01 (ns.)
2.00 (ns.)
CSV×SETUP
5
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1.8 (ns.)
2.33 (+)
9.03 (***)
>100 (***)
31.85 (***)
16.71 (***)
>100 (***)
Shoot C%†
Shoot δ15N†
Shoot δ13C†
ET
Litter
PC1
PC2
numDF denDF
CSV
5
60
0.58 (ns.)
> 100 (***)
0.81 (ns.)
> 100 (***)
12.73 (***)
2.61 (*)
>100 (***)
SETUP
1
12
4.04 (+)
0.11 (ns.)
1.30 (ns.)
1.32 (ns.)
3.71 (+)
0.28 (ns.)
7.38 (*)
CSV×SETUP
5
60
0.42 (ns.)
11.88 (***)
1.13 (ns.)
8.14 (***)
5.31 (***)
0.27 (ns.)
2.21 (ns.)
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Extended Data Fig. 1 | Response variables as affected by laboratory and SETUP
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(growth chamber vs. glasshouse treatment). Grey and blue bars represent laboratories that
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used growth chamber and glasshouse setups, respectively. Bars show means over all CSV
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treatments, with error bars representing ± 1 s.e.m. (n = 72 microcosms per laboratory).
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6)
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Extended Data Fig. 2 | Net legume effect in 14 laboratories as affected by laboratory and
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SETUP (growth chamber vs. glasshouse) treatment. The grey and blue bars represent
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laboratories that used growth chamber and glasshouse setups, respectively. Bars show means
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over all CSV treatments, with error bars indicating ± 1 s.e.m (n = 72 microcosms per laboratory).
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Extended Data Fig. 3 | The net legume effect as affected by CSV and SETUP treatments. Grey and blue bars indicate laboratories
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that used growth chamber and glasshouse setups, respectively. Bars with error bars represent means ± 1 s.e.m. (n = 6 microcosms).
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Extended Data Fig. 4 | Z-scored standard deviation (SD) of net legume effect as affected by
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controlled systematic variability (CSV). a, Within-laboratory SD. b, Among-laboratory SD. P-
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values represent the result of a priori planned contrasts (Welch’s t-test) between CTR and CSV-AB
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treatment levels in the growth chamber setup. Bars with error bars indicate means ± 1 s.e.m., n = 12
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(representing the z-scored SDs of the twelve measured response variables).
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Extended Data Fig. 5 | The net legume effect on the twelve response variables expressed as the
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difference from the grand mean as affected by controlled systematic variability (CSV). The grand
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mean of the legume effect for response variable represents the mean across all laboratories for each
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CSV treatment level. The grand mean is assumed to be the best available estimate of the real effect of
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the legume presence. Data are presented as means ± 1 s.e.m., n = 14 laboratories.
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Extended Data Fig. 6 | Relationship between within- and among-laboratory SD (zscores) as affected by the CSV and SETUP treatments. The twelve data points per CSV
treatment represent z-scored SD of the twelve measured response variables. None of the
regressions are significative at P < 0.05.
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Extended Data Fig. 7 | Frequency distribution of Spearman’s correlation coefficients
between each pair of the twelve response variables (n = 36 pairs).
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4) Supplementary Acknowledgements
We further thank to David Degueldre, Thierry Mathieu, Pierrick Aury, Nicolas Barthès,
Bruno Buatois, Raphaëlle Leclerc, Agnes Fastnacht, Rainer Fuchs, Susanne Remus, Martina
Peters, Grit von der Waydbrink, Maximillian Lörch and the students of the MSc. course
“Ecology and Evolutionary Biology” at the University of Freiburg. A.M. thanks Simon
Fellous for comments on the manuscript. Additional funding was received from the Deutsche
Forschungsgemeinschaft Project GL 262/14, 19 within the research unit “Exploring the
mechanisms underlying the relationship between biodiversity and ecosystem functioning”
(FOR 1451). N.M. was supported by the SNF project 315230_149955. The IJPB benefited
from the support of the LabEx Saclay Plant Sciences-SPS (ANR-10-LABX-0040-SPS).
A.M.E.’s participation in this project was supported by the Harvard Forest Long Term
Ecological Research Program (US NSF grant 12-37491).
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