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Supporting Information
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The importance of litter traits and decomposers for litter decomposition: A
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comparison of aquatic and terrestrial ecosystems within and across biomes
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Pablo García-Palacios1,*, Brendan G. McKie2, I. Tanya Handa3, André Frainer2,4, Stephan
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Hättenschwiler1
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Centre d’Ecologie Fonctionnelle et Evolutive (CEFE) UMR 5175, CNRS - Université
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de Montpellier - Université Paul-Valéry Montpellier - EPHE, 1919 Route de Mende,
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34293 Montpellier, France
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Sciences, P.O. Box 7050, 75007 Uppsala, Sweden
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succ. Centre-Ville, Montréal, Qc, H3C 3P8 Canada
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Norway.
Department of Aquatic Sciences and Assessment, Swedish University of Agricultural
Département des sciences biologiques, Université du Québec à Montréal. C.P. 8888,
Department of Arctic and Marine Biology, University of Tromsø, 9037 Tromsø,
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*
Corresponding author: +33 (0)467 613 236; E-mail pablogpom@yahoo.es
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Running headline: Aquatic and terrestrial litter decomposition
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Appendix S1. Field site locations, experimental duration and characteristics (measured according to Handa et al. [2014]) of the stream (S) and
2
forest floor (FF) sites surveyed in each of the five biomes.
Subarctic
Site
Experimental
duration†
Coordinates
Elevation*
MAT (°C)
MAP (mm)
Soil/water pH
Water N-NO3-‡
Water N-NH4+‡
Soil C††
Soil N††
3
4
5
Boreal
S
FF
Kopperåsen Abisko
Sweden
76
714
68º26' N
68º21' N
18º28' N
18º49' E
445
0.9
0.9
352
352
4.63
7.3
0.02
n.d.
27.36
0.95
S
FF
Krycklan Krycklan
Sweden
63
570
64º16' N 64º14' N
19º50' E 19º50' E
200
1.8
1.8
643
643
4.7
7.1
0.27
<20
3.63
0.19
Temperate
Mediterranean
S
FF
S
FF
Mosbeek Leuvenemse B. Maureillas Barroubio
Netherlands
France
56
414
52º26' N
5º32' E
52º18' N
5º41' E
37
10.2
814
6.8
7.45
154
154
10.7
911
3.29
25.21
1.14
48
338
42º28' N 43º23' N
2º48' E
2º51' E
180
15.4
14.6
766
670
6.56
7.9
0.18
20
4.57
0.39
Tropical
S
FF
PetitSaut Paracou
French Guiana
97
278
5º04' N
53º00' W
5º18' N
52º55' W
35
25.4
2519
7.7
<0.02
<5
25.4
2519
4.8
2.84
0.19
† days *m a.s.l., ‡ mg/L, ††% of dry mass, MAT mean annual temperature, MAP mean annual precipitation, n.d. no data
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2
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Appendix S2. Tree or shrub species belonging to the four plant functional types evaluated in each biome and their corresponding plant litter traits.
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Med = Mediterranean, RDD = rapidly decomposing deciduous, SDD = slowly decomposing deciduous, E = Evergreen, WSC = water soluble
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compounds, S. phenols = soluble phenols, T. phenols = total phenols, C. tannins = condensed tannins, SLA = specific leaf area, LWS = leaf water
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saturation. All traits are shown as percentage dry mass except for ratios, leaf toughness (g H2O), 3D (cm2cm-3), SLA (cm2g-1) and LWS (% H2O
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in dry mass).
Biome
Plant
functional
type
Subarctic Subarctic Subarctic
Subarctic
Boreal
Boreal
Boreal
Boreal
Temperate Temperate Temperate Temperate
N-fixing
RDD
SDD
E
N-fixing
RDD
SDD
E
N-fixing
RDD
SDD
E
Alnus
incana
Sorbus
aucuparia
Populus
tremula
Vaccinium
vitis-idaea
Alnus
Incana
Prunus
padus
Betula
pubescens
Rhododendron
tomentosum
Alnus
glutinosa
Salix
cinerea
Fagus
sylvatica
Ilex
aquifolium
N
2.55
0.68
0.54
0.73
2.75
1.20
0.69
0.83
2.49
1.23
1.03
1.69
P
0.12
0.23
0.09
0.07
0.14
0.17
0.09
0.07
0.08
0.11
0.06
0.16
Litter
K
nutrient traits Ca
0.91
1.34
0.96
0.31
1.07
0.15
0.45
0.29
0.28
0.69
0.55
0.77
4.02
1.66
1.89
0.61
1.59
3.45
1.17
0.67
1.59
1.31
0.73
1.69
Mg
0.33
0.46
0.43
0.10
0.15
0.39
0.33
0.16
0.24
0.23
0.09
0.52
Na
0.01
0.02
0.01
0.01
0.01
0.02
0.01
0.01
0.14
0.19
0.09
0.03
C
44.64
44.63
45.98
48.85
48.16
41.28
48.25
52.12
47.83
47.75
48.04
46.78
Lignin
9.27
7.86
8.81
34.77
18.72
8.99
18.75
22.35
19.40
24.19
22.53
10.59
Hemicellulose
9.52
13.41
16.72
15.08
13.10
14.19
13.67
15.42
15.03
14.43
21.67
15.84
Cellulose
22.30
26.28
21.66
13.02
25.42
24.08
24.35
13.47
23.80
17.17
20.47
22.59
WSC
58.91
52.45
52.81
37.12
42.76
52.74
43.23
48.76
41.77
44.20
35.34
50.97
S. phenols
3.56
7.54
6.70
2.34
2.15
2.84
5.44
5.02
1.63
4.64
3.39
3.79
Species
Litter C
quality traits
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T. phenols
7.93
11.40
7.12
7.19
5.58
3.92
9.00
9.24
3.51
8.65
8.78
4.63
C. tannins
0.75
3.00
1.35
1.90
0.58
1.62
3.52
1.92
0.29
2.73
2.77
0.13
C:N
17.50
65.23
85.45
66.63
17.52
34.48
70.41
62.56
19.23
38.73
46.72
27.63
C:P
378.52
193.73
492.62
663.48
340.65
246.86
527.91
777.98
566.60
433.04
791.46
298.36
N:P
21.63
2.97
5.77
9.96
19.45
7.16
7.50
12.44
29.47
11.18
16.94
10.80
Lignin:N
Litter
stoichiometry Lignin:P
traits
T. phenols:N
3.63
11.49
16.37
47.43
6.81
7.51
27.36
26.83
7.80
19.62
21.91
6.26
78.63
34.13
94.40
472.30
132.41
53.74
205.13
333.61
229.81
219.41
371.11
67.56
3.11
16.66
13.22
9.81
2.03
3.28
13.13
11.09
1.41
7.02
8.54
2.74
T. phenols:P
67.28
49.49
76.24
97.65
39.50
23.45
98.43
137.94
41.54
78.49
144.61
29.56
C. tannins:N
0.29
4.38
2.51
2.60
0.21
1.35
5.14
2.31
0.12
2.22
2.70
0.08
C. tannins:P
6.35
13.02
14.47
25.84
4.09
9.68
38.56
28.73
3.47
24.77
45.66
0.86
Leaf
toughness
79.87
86.73
301.23
301.23
73.07
132.23
118.53
270.17
98.90
128.20
129.73
572.80
3D
0.03
0.75
0.02
5.20
0.04
0.04
0.15
2.80
0.03
0.18
0.07
0.03
SLA
179.84
323.01
53.87
53.87
211.95
377.21
210.58
77.81
154.51
149.32
275.61
79.75
LWS
310.92
444.53
87.97
87.97
243.37
441.53
333.71
108.08
227.33
185.49
229.83
191.27
Litter
physical
traits
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3
4
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6
4
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Appendix S2 continued
Biome
Plant
functional
type
Med.
Med.
Med.
Med.
Tropical
Tropical
Tropical
Tropical
N-fixing
RDD
SDD
E
N-fixing
RDD
SDD
E
Alnus
Fraxinus
Pistacia Quercus Diplotropis Qualea Vochysia Eperua
glutinosa angustifolia terebinthus
ilex
purpurea
rosea densiflora falcata
Species
N
1.74
1.00
0.47
0.69
1.21
0.75
0.92
1.28
P
0.04
0.04
0.14
0.04
0.02
0.01
0.02
0.04
Litter nutrient K
traits
Ca
0.45
0.40
0.50
0.46
0.06
0.20
0.22
0.62
2.31
1.89
2.21
1.21
0.43
0.93
0.86
0.62
Mg
0.29
0.33
0.23
0.12
0.16
0.18
0.07
0.19
Na
0.09
0.54
0.10
0.04
0.20
0.28
0.07
0.08
C
46.12
47.03
49.08
48.60
50.97
43.44
44.56
49.30
Lignin
8.94
11.09
11.77
15.13
29.60
7.80
20.56
29.22
Hemicellulose
12.00
13.10
12.04
19.47
17.00
18.60
18.92
21.56
Cellulose
21.61
19.32
15.23
18.40
15.39
28.11
15.10
12.05
WSC
57.45
56.49
60.96
47.01
38.01
45.50
45.42
37.17
S. phenols
5.12
3.85
13.48
7.61
5.36
2.21
0.61
2.54
T. phenols
8.01
4.25
21.24
11.71
13.80
3.61
4.08
9.28
C. tannins
0.68
0.13
3.73
1.88
5.10
1.28
2.03
2.08
Litter C
quality traits
C:N
26.58
47.25
103.61
70.20
42.04
57.86
48.46
38.42
C:P
1028.68
1175.11
345.28
1350.49
3008.34
4487.63
1979.05
1379.07
N:P
38.70
24.87
3.33
19.24
71.55
77.57
40.84
35.90
Lignin:N
Litter
stoichiometry Lignin:P
traits
T. phenols:N
5.15
11.14
24.85
21.85
24.42
10.38
22.36
22.77
199.44
277.00
82.82
420.32
1747.34
805.42
913.26
817.29
4.62
4.27
44.83
16.92
11.38
4.80
4.43
7.23
T. phenols:P
178.69
106.23
149.40
325.50
814.51
372.65
181.07
259.56
C. tannins:N
0.39
0.13
7.88
2.71
4.21
1.71
2.21
1.62
C. tannins:P
15.25
3.27
26.27
52.16
300.90
132.49
90.35
58.25
Leaf
toughness
107.20
92.70
183.67
441.00
221.27
300.20
208.97
221.27
Litter
3D
physical traits
SLA
0.03
0.45
0.29
0.75
0.04
0.03
0.08
0.02
161.45
271.31
108.27
60.84
141.93
70.89
106.25
141.93
LWS
255.82
452.80
135.19
110.45
117.22
134.57
107.19
117.22
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Appendix S3: Materials and Methods
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Experimental design and field litter incubations
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This study design encompassed a range of contrasting climatic conditions and local
4
environmental parameters (Table S1), allowing us to evaluate the relative importance of
5
different drivers of decomposition in streams vs. forest floors across a broad range of
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environmental variation. Site-specific, native leaf litter was collected in each biome in
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2007, and included one woody species belonging to each of four distinct functional types:
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(1) N-fixing, (2) rapidly decomposing deciduous, (3) slowly decomposing deciduous, and
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(4) broadleaved evergreen plant species. A total of 18 species (Alnus incana L. and Alnus
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glutinosa L. were used in two biomes) were investigated (Table S2). Leaf litter was
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collected from multiple individuals of each species. After leaf collection, leaf litter was
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oven-dried at 40 ºC to constant weight and kept in dry conditions until field incubations.
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The approach to achieve different complexities of the decomposer guilds (e.g.
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microcosms of varying mesh size) has been widely used to assess the contribution of
15
decomposers on litter decomposition (Kampichler & Bruckner 2009; García-Palacios et
16
al. 2013). Stream microcosms were attached to a metal chain anchored to the stream
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bottom using 30 cm long iron bars. One metal chain was placed in each of five stretches
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of faster-flowing, rocky “riffle” habitat along the same stream, with each riffle
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representing a block in our statistical models. One replicate of every litter and mesh
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combination was attached to each chain using plastic cable ties. Forest floor microcosms
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were covered with 50 µm mesh on top and at the bottom to maintain the same water
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infiltration and leaching into and out of microcosms across all decomposer and litter
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treatments. Five homogeneous areas within the same site were used as blocks, containing
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one replicate of each combination of decomposer and litter treatment. Microcosms
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contained a total of five (streams) or eight (forest floors) grams of air-dried leaf litter,
6
1
with the exception of the subarctic forest floor site where only 4 grams of litter were used
2
due to limited litter availability. Leaf species in mixtures had an even biomass
3
distribution.
4
We retrieved microcosms when the fastest decomposing litter had reached 40–
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50% remaining mass at each site. To this end, extra microcosms containing the most
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rapidly decomposing litter type were retrieved at regular intervals in each ecosystem type
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in each biome. Forest floor field incubations ranged between 278 and 714 days (tropical
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and subarctic biomes, respectively), and the stream field incubations ranged between 48
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and 97 days (Mediterranean and tropical sites, respectively) (Table S1).
10
11
Trait measurements of the initial leaf litter
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Traits were measured on all 20 individual litter types from three random samples from
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each species pool with the exception of water saturation and three-dimensionality, which
14
were measured on five samples, and leaf toughness and specific leaf area which were
15
measured on ten individual leaves per litter type. Based on these measurements, we also
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calculated several commonly used relationships between some litter traits related to litter
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stoichiometry such as lignin, C, total phenols and condensed tannins to N and P ratios.
18
19
Litter decomposition measurements
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We focused on C and N loss instead of the commonly used bulk litter mass loss metrics
21
for two main reasons: 1) to correct for potential incorporation of mineral material into
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litter microcosms, and 2) to directly analyze the dynamics of two major elements allowing
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to put our results within a broader biogeochemical context. Remaining C and N were
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measured for each individual species within each mixture after field incubation.
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7
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Analytical procedures
2
Data reduction before litter traits evaluation in structural equation models
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We used Principal Components Analysis (PCA) across the 15 litter combinations from
4
each biome to extract a reduced number of variables (multivariate axes) capturing most
5
of the variance in these trait categories, and to avoid redundancy among correlated suites
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of traits. The original data were used for all concentration-based measurements, because
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they were all expressed in the same unit (% dry mass). However, for physical traits that
8
varied in their specific measurement units, we applied standardization using Z scores.
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Equamax rotation was employed to minimize overdispersion of variable loadings over
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several axes and reduce the number of extracted factors. Before PCA, we checked for
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collinearity between litter traits using the Variance Inflation Factor (VIF). When VIF > 5
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(Kutner, Nachtsheim & Neter 2004), we excluded the less informative collinear trait. This
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was usually the trait which explained less variation in both response variables (C and N
14
loss), but in cases where this was not clear, the choice was made based on the literature.
15
For example, we selected SLA, lignin, total C, lignin:N, C:N and C:P (rather than leaf
16
water saturation, WSC, hemicellulose, N:P, lignin:P, phenols:N, phenols:P, tannins:N and
17
tannins:P) because they are more commonly used in large-scale models linking litter
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quality and decomposition (Parton et al. 2007; Cornwell et al. 2008). No nutrient element
19
was removed from the nutrient matrix.
20
21
Comparison of streams and forest floors across biomes
22
All SEMs are contingent on the structure imposed by the modelers. The model structure
23
proposed in the a priori model (Fig. 1) was supported by current ecological knowledge,
24
allowing a causal interpretation of the model outputs (Shipley 2002). When modeling
25
categorical dummy variables, it is necessary to omit one level (Grace 2006), and we
8
1
omitted the biome (subarctic) and decomposer community (mesofauna) that allowed us
2
to interpret the categorical variable in a more straightforward way. The interpretation of
3
‘biome’ is the influence of being at different biomes for litter C and N loss, and that of
4
‘decomp’ is the effects of increasingly complex decomposer communities (from
5
microorganisms to macrofauna). We could not evaluate whether biome effects were also
6
indirectly mediated by decomposer community complexity, due to the categorical nature
7
of the latter (Grace 2006). Because the initial litter quality was determined at the plant
8
species level and not at the individual field microcosm level, we averaged C and N loss
9
data for each litter treatment combination across the five sampling blocks in order to
10
account for the nested structure of the dataset. This procedure reduced the sample size (to
11
n = 225 per ecosystem, including all five biomes) but helped with issues related to sample
12
non-independence. Since some of the variables introduced in the model were not normally
13
distributed, the probability that a path coefficient differs from zero was estimated with
14
bootstrapping (Schermelleh-Engel, Moosbrugger & Müller 2003) in stream and forest
15
floor models. To increase the degrees of freedom, any path with a coefficient <0.10 was
16
removed from the model when not significant (Delgado-Baquerizo et al. 2013). Overall
17
goodness-of-fit of the models were tested against the dataset and checked according to
18
Schermelleh-Engel, Moosbrugger & Müller (2003). Correlations between litter traits, and
19
between C and N loss, were also introduced to acknowledge for relationships where no
20
direction is specified, possibly due to shared causal influences.
21
22
Comparison of streams and forest floors at the biome level
23
We first constrained the model in which all free parameters (seven path coefficients and
24
two error terms; Fig. S1) were forced to be equal across ecosystems, and tested it against
25
the experimental data. Then, equality constraints were removed one at a time to detect
9
1
which one would significantly improve the model fit (Shipley 2002). In this case, path
2
coefficients were obtained using the maximum likelihood estimation technique, as all the
3
endogenous variables were normal at the biome level. The difference in the maximum
4
likelihood χ2 statistic between the fully constrained model and the specific 1-free
5
parameter model was used to test the value of a parameter between the stream and the
6
forest floor model. We built nine models this way and applied Bonferroni corrections to
7
adjust the overall significance. χ2, RMSEA and GFI were used as goodness-of-fit tests.
8
9
References
10
Cornwell, W.K., Cornelissen, J.H.C., Amatangelo, K., Dorrepaal, E., Eviner, V.T., Godoy,
11
O. et al. (2008) Plant species traits are the predominant control on litter
12
decomposition rates within biomes worldwide. Ecology Letters, 11, 1065-1071.
13
Delgado-Baquerizo, M., Maestre, F.T., Gallardo, A., Bowker, M.A., Wallenstein, M.D.,
14
Quero, J.L. et al. (2013) Decoupling of soil nutrient cycles as a function of aridity
15
in global drylands. Nature, 502, 672-676.
16
García-Palacios, P., Maestre, F.T., Kattge, J. & Wall, D.H. (2013) Climate and litter
17
quality differently modulate the effects of soil fauna on litter decomposition across
18
biomes. Ecology Letters, 16, 1045-1053.
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20
Grace, J.B. (2006) Structural Equation Modeling and Natural Systems. Cambridge
University Press, Cambridge, USA.
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Handa, I.T., Aerts, R., Berendse, F., Berg, M.P., Bruder, A, Butenschoen, O. et al. (2014)
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Consequences of biodiversity loss for litter decomposition across biomes. Nature,
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509, 218-221.
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1
Kampichler, C. & Bruckner, A. (2009) The role of microarthropods in terrestrial
2
decomposition: a meta-analysis of 40 years of litterbag studies. Biological
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Reviews, 84, 375-389.
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Kutner, M.H., Nachtsheim, C.J. & Neter, J. (2004) Applied Linear Regression Models,
4th edn. McGraw Hill, New York, USA.
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Parton, W., Silver, W.L., Burke, I.C., Grassens, L., Harmon, M.E., Currie, W.S. et al.
7
(2007) Global-scale similarities in nitrogen release patterns during longterm
8
decomposition. Science, 315, 361-364.
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Schermelleh-Engel, K., Moosbrugger, H. & Müller, H. (2003) Evaluating the fit of
10
structural equation models: Test of significance and descriptive goodness-of-fit
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measures. Methods of Physiological Research Online, 8, 23-74.
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Shipley, B. (2002) Cause and Correlation in Biology: A User's Guide to Path Analysis,
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Structural Equations and Causal Inference. Cambridge University Press,
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Cambridge, USA.
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22
23
24
25
26
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1
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Appendix S4. A priori conceptual structural equation model depicting pathways by
3
which decomposers (decom), litter nutrients and stoichiometry may influence litter C and
4
N loss at the biome level. This a priori model was used for multigroup comparisons for
5
streams and forest floors in each biome (results in Table 1 and 2). Single-headed black
6
arrows signify a hypothesized causal influence of one variable upon another. Double-
7
headed grey arrows signify a correlation in which no direction is specified, possibly
8
owing to shared causal influences. The circle ‘Decom’ (decomposer community
9
complexity) represents the effects of the levels of this categorical variable (modelled
10
using dummy variables) on measured endogenous variables (represented with boxes). The
11
litter traits are the component 1 from the PCAs conducted in Fig. 2.
12
13
14
12
1
Appendix S5. Individual path coefficients from each biome relative to the level omitted
2
(subarctic) and selected as reference in the across-biome stream and forest floor structural
3
equation models. The different biomes were represented in the structural equation models
4
of Fig. 3 by dummy variables. A significant positive or negative, individual path
5
coefficient from a certain biome means a larger or smaller, value of the endogenous
6
variable (e.g. litter traits, C or N loss) with respect to the subarctic biome. ***P < 0.001,
7
**P < 0.01, *P < 0.05. Average C and N loss data can be found in Fig. S2.
8
Stream
Boreal Temperate Mediterranean Tropical
Litter nutrients
‒0.37*** ‒0.33***
‒0.15**
‒0.81***
Litter C quality
0.15*
0.31***
0.26***
0.34***
Litter stoichiometry ‒0.26** ‒0.55**
‒0.09
‒0.20**
C loss
‒0.28*
‒0.01
‒0.26*
‒0.21*
N loss
‒0.12
‒0.06
‒0.59
0.07
9
Forest floor
Boreal Temperate Mediterranean Tropical
Litter nutrients
‒0.37*** ‒0.33***
‒0.15**
‒0.81***
Litter C quality
0.15*
0.31***
0.26***
0.34***
Litter stoichiometry ‒0.26** ‒0.55**
‒0.09
‒0.20**
Litter physical
‒0.31*
0.12
0.09
0.3*
0.14**
0.41***
‒0.22***
0.67***
C loss
0.15***
0.26***
‒0.17**
0.55***
N loss
10
11
13
1
2
3
Appendix S6. Mean (a) C loss and (b) N loss (± SE, n = 15) are shown for the two
4
ecosystem types, five biomes and three increasingly complex decomposer communities
5
(excluded by microcosm mesh size) evaluated. Data from the 15 litter combinations used
6
per biome were pooled to simplify the figure and highlight the ecosystem and biome
7
differences. Statistical analyses of treatment differences can be found in Handa et al.
8
(2014).
9
10
11
14
1
Appendix S7. Pearson correlations (ρ) between the litter traits and the component 1 and
2
2 of the litter nutrients PCA. Significant P values are highlighted in bold.
3
Mg
Ca
P
K
N
Na
PC1 litter nutrients
ρ
P
0.90
<0.001
0.81
<0.001
0.67
<0.001
0.57
<0.001
0.35
0.002
‒0.01
0.942
PC2 litter nutrients
ρ
P
0.22
0.063
0.06
0.625
0.63
<0.001
0.60
<0.001
0.09
0.451
‒0.94
<0.001
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
15
1
2
3
4
FFFf
5
6
16
1
Appendix S8. Across-biome structural equation models describing the influences of
2
biome, decomposers (decom) and litter trait on litter C and N loss in streams (a) and forest
3
floors (b). The model represents the same relationships evaluated in Fig. 3 but with the
4
additional assessment of the component 2 of litter C quality and litter stoichiometry
5
PCAs. Continuous and dashed arrows represent positive and negative relationships,
6
respectively. The widths of the arrows are proportional to the strengths of the path
7
coefficients. The litter traits are the component 1 (litter nutrients-1, C quality-1 and
8
stoichio-1 and physical) and 2 (litter C quality-2 and stoichiometry-2) from the PCAs
9
conducted in Fig. 2, which are positively related with the following variables. Nutrients-
10
1: Mg and C. C quality-1: cellulose (negative), C and lignin. C quality-2: condensed
11
tannins. Stoichiometry-1: C:N and lignin:N ratios. Stoichiometry-2: C:P ratio. Physical-
12
1: SLA (negatively) and leaf toughness. ‘Litter stoichiometry-2’ was omitted from the
13
stream model, and ‘Litter physical-1’ was omitted from both the stream and forest floor
14
models, because of their non-significant and marginal effects (<0.1) on C and N loss.
15
Correlations among litter traits and among litter C and N loss, as well as the proportion
16
of variance explained (r2) of each litter trait are not shown for simplicity. Goodness-of-
17
fit tests are: streams (χ2 = 26.79, P = 0.265, RMSEA = 0.027, P = 0.820, Bootstrap P =
18
0.312) and forest floors (χ2 = 31.58, P = 0.248, RMSEA = 0.028, P = 0.839, Bootstrap P
19
= 0.287). ***P < 0.001, **P < 0.01, *P < 0.05.
20
17