1
Supporting Information
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3
Appendix S1: Quantification of liana infestation in tree crowns
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5
The crown infestation index (COI) was higher in the liana-infested forest than in the transition
6
area (Wilcoxon test, p<0.001) (Figure S1). Consistently with our results with large liana, COI
7
was negatively correlated with tree basal area growth rate even if the strength of the
8
correlation was weak (Spearman rho=-0.04, p=0.008).
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Appendix S2: Impact of liana infestation on individual tree growth
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12
Across the 10-ha plot, the number of lianas and tree stems >= 10 cm DBH were stable
13
between 2008 and 2012 (lianas: 303 to 307; trees: 4660 to 4676 see Table 1). The number of
14
trees infested by large lianas (≥ 10 cm DBH) decreased slightly from 2008 to 2012 (from 261
15
to 240). Trees infested by liana were significantly larger than non-infested ones
16
(Kolmogorov-Smirnov test, p<0.001). Although large trees displayed higher growth rates on
17
average (Spearman’s rho= 0.51 between growth rate or relative growth rate and diameter;
18
p<0.001), we found a negative effect of liana infestation on tree growth; e.g. mean diameter
19
growth was significantly higher for trees not infested by large lianas (0.91 % year-1) than for
20
infested ones (0.40 % year-1; Wilcoxon test, p<0.001). This result was consistent with
21
previous findings(Clark & Clark 1990; Phillips et al. 2005; Schnitzer, Kuzee & Bongers
22
2005). We found no difference in mortality rate between infested and non-infested trees (2.1
23
% year-1 in both cases). There was no relationship between liana infestation and mortality at
1
24
the tree level. This result contrasts with the findings of Phillips et al. (2005) probably because
25
of the shorter duration of our survey (4 years) compared to their (24-years). It also contrasts
26
with the findings of Ingwell et al. (2010), who considered smaller lianas than in this study.
27
To evaluate the importance of below-ground competition we checked for the relation between
28
tree growth and distance of the closest liana rooting point, between tree growth and the
29
average distance of the five closest lianas rooting points and finally between tree growth and
30
the number of large liana rooting points in a 20-m circle around the bases of the tree trunks.
31
None of these variables improved tree growth models with or without COI as co-variable.
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Appendix S3: Soil analyses
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35
The bedrock of this area is metamorphic, derived from ancient volcanism of the Paramaca
36
series (green rocks). The rock substratum of our study area should be similar, however,
37
geological heterogeneity could occur at a very short distance.
38
To quantify the chemical composition in trace elements in the soil, samples were dried during
39
3 days at 50°C. We separated the fine soil fraction (< 2 mm mesh sieve), the large soil
40
fraction (> 2 mm), and surface litter. These three fractions were ground finely into a powder
41
(<80 µm), and they were analyzed independently at each sampling site.
42
To measure elemental concentrations, all samples were digested in Teflon Savillex® vial
43
within individual polycarbonate compartments (A100) containing Teflon hot plates in a clean
44
room (ISO3). For each sample, ~100 mg of powder was dissolved using HNO3 and HCl,
45
H2O2 and HF. We also performed a second method of sample dissolution, alkali fusion. In this
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second method, ca. 100 mg of sample was mixed with 600mg Spectroflux 100B fusing (Alfa
2
47
Aesar®), which is composed of 80% lithium methaborate (LiBO2) and 20% lithium
48
tetraborate (Li2B4O7). The mix was heated at 1100°C, and the resulting pellet was diluted into
49
10% HNO3.
Appendix S4: Airborne LiDAR data acquisition processing, and analyses
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51
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The first LiDAR acquisitions overflight, based on a portable Riegl laser rangefinder
53
(LMS6Q140i-60) flown on a helicopter, took place in November 2007. It measured the time-
54
of-flight of 30 kHz infra-red laser pulses (0.9 µm). The average laser pulse density was ca. 4
55
points per square meter (pt m-2) with a scanning angle of 60° and a footprint of 0.45 m leading
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to a mean density of points of ca. 3.5 pt m-2 in the zone of interest. Acquisition was conducted
57
in last return mode to maximize vegetation penetration. The second acquisition was
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conducted in March 2012, based on a portable Riegl laser rangefinder (LMSQ 560, 200 kHz
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infra-red laser pulses at 1.5 µm) flown on a Britten-Norman BN-2 Islander aircraft at ca. 400
60
m above the ground. The average laser pulse density was ca. 5 pulse m-2 with a scanning
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angle of 45° and a footprint of 0.25m. The system had multiple returns registering capacity
62
leading to a mean density in last return mode of ca. 12.5 pt m-2 in the zone of interest. Two
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dual-frequency GPS receivers coupled to an inertial navigation system allowed a sub-
64
decimeter differential positioning.
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Our LiDAR datasets consisted of a cloud of laser echoes originating from ground and
66
vegetation. Ground points were identified using the TerraScan (TerraSolid, Helsinki)
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‘Ground’ routine’. To maximize the ground point density and the elevation model accuracy,
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we combined the 2007 and the 2012 cloud point datasets in this process. This led to a mean
69
number of ground points of 0.35 pt m-2 over the entire area. Based on this dataset, we
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constructed a 1-m resolution elevation model using the “GridSurfaceCreate” procedure
3
71
implemented in FUSION (McGaughey 2009). This procedure computes the elevation of each
72
grid cell using the average elevation of all points within the cell (cells containing no ground
73
points are filled by interpolation using neighbouring cells).
74
For both 2007 and 2012 cloud point datasets, 1-m and 5-m canopy models were built
75
after outlier extraction, using the “CanopyModel” procedure implemented in FUSION. This
76
procedure subtracts the elevation model from the height of each return and then uses the
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highest return value to compute the canopy surface model. A 3x3 cell median filter was
78
applied to smooth the surface and avoid local unrealistic maxima. For the 2012 LiDAR
79
dataset, we only considered the last return points in order to limit systematic biases in the
80
comparison of the two LiDAR datasets (Vincent et al. 2012)
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82
Supplementary figures and table
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Figure S1: Crown occupation index (COI). COI ranges between 0 (absence of liana in the
85
tree crown) and 4 (over 75% of the tree crown covered by liana leaves).. Difference
86
significance was tested using a Chi² test. (Chi²:218.85, degree of freedom : 4, p<10-10)
5
87
)
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Figure S2: Size class distribution of trees in 2012. Proportions of trees in the different
90
classes are reported. Classes are intervals of 10cm dbh (Class 1: dbh between 10 and 20 cm,
91
class 2: dbh between 20 and 30 cm, and so forth).
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92
93
Figure S3: Principal Component Analysis (PCA) performed on the tasseled-cap indices
94
of the 2006 Landsat image. Pixels located within the liana infested-area delineated with the
95
2007 LiDAR dataset are illustrated in red, and the other pixels in blue. The first axis opposing
96
brightness/greenness to wetness represents 75% of the variance and discriminates the liana-
97
infested forest from the high-canopy forest.
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100
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Figure S4: PCA of the chemical tracers in rocks of the Guiana Shield and in the
103
available soil sample. Black: Plutonic rocks grey: volcanic rocks. Values for the soil samples
104
in the Nouragues were plotted in the PCA coordinate system. Red dots: liana-infested forest
105
soil, blue dots: high-canopy forest soil.
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107
Figure S5: Total soil composition below the different forest types Principal Component
108
Analysis (PCA) of total chemical macro-element concentration (dataset 1). Al: Aluminum,
109
Fe: Iron, C: Carbon, Ca: Calcium, K: Potassium, Mg: Magnesium, N: Natrium P: phosphorus,
110
pH: (log ([H+]).
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111
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Figure S6: Available elements composition of the soil below the different forest types.
113
Two principal axes of the PCA of bio-available chemical macro-element concentration
114
(dataset 2). In grey: high-canopy soil samples. In black: liana-infested forest soil samples. Al=
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Aluminum, Fe: Iron, Na: Sodium, Mn: Manganese, Mg: Magnesium, Ca: Calcium, K:
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Potassium, P: phosphorus, pH: (log ([H+]), TEB: total exchangeable bases, ECEC: effective
117
cation exchange capacity.
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Figure S7: Changes in aboveground biomass stocks in the study area between 1992 and
121
2012. Solid lines represent trees aboveground biomass. Dotted lines represent the total
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aboveground biomass (trees and lianas). Vertical bars represent the standard errors on the
123
mean computed from 25x25m² subplots as in Table 1 (n: 87 for the high-canopy forest; n: 17
124
for the liana-infested forest).
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126
Table S1: Chemical composition in trace elements of the fraction of soil sample < 2 mm (in ppm)
127
128
Sample n°
1
2
3
4
5
6
Sample n°
1
2
3
4
5
6
Sample n°
1
2
3
4
5
6
Sample n°
1
2
3
4
5
6
12
Forest type
Ba
Bi
Ca_(3_isotopes)
Cd
High canopy forest
24.8
0.45
175
0.023
High canopy forest
26.7
0.333
218
NA
High canopy forest
14.2
0.55
104
NA
High canopy forest
16.2
0.63
93.3
NA
Liana-infested forest
29.9
0.45
232
NA
Liana-infested forest
41.2
0.57
149
NA
Forest type
Eu
Ga
Gd
Ge
High canopy forest
0.57
33.2
1.71
0.16
High canopy forest
0.37
35.3
1.16
0.15
High canopy forest
0.50
34.7
1.28
0.36
High canopy forest
0.47
36.9
1.41
0.39
Liana-infested forest
1.00
35.9
2.54
1.15
Liana-infested forest
1.75
33.4
4.98
0.27
Forest type Pb_(4_isotopes)
Pb_(sans_204)
Pr
Rb
High canopy forest
14.6
14.5
3.14
3.49
High canopy forest
10.3
10.3
2.92
4.72
High canopy forest
11.4
11.3
2.35
1.83
High canopy forest
10.2
10.0
2.70
1.72
Liana-infested forest
12.4
12.31
5.11
3.52
Liana-infested forest
14.9
15.06
7.50
1.56
Forest type
Th
Ti
Tl
Tm
High canopy forest
9.45
8198
0.13
0.20
High canopy forest
8.88
9794
0.09
0.14
High canopy forest
9.45
8265
0.04
NA
High canopy forest
11.5
8388
0.030
0.16
Liana-infested forest
10.7
7354
0.10
0.20
Liana-infested forest
13.0
4261
0.33
0.30
Ce
32.1
27.3
17.9
20.9
40.5
59.1
Hf
3.98
4.42
3.42
3.75
4.10
3.07
Sb
1.22
0.71
1.42
1.31
1.02
1.20
U
1.87
2.01
1.65
1.83
2.13
2.39
Co
24.7
10.3
15.0
18.3
17.1
46.7
Ho
0.393
0.26
0.32
0.32
0.46
0.77
Sc
78.1
53.0
61.9
69.5
69.5
87.3
V
621
381
838
680
573
781
Cr
5422
2430
5673
7627
6631
8361
La
13.6
14.45
10.2
12.5
20.3
27.5
Sm
2.38
1.87
1.88
1.94
3.87
6.86
Y
7.80
5.88
6.08
6.52
8.41
12.9
Cs
1.43
1.63
0.61
0.51
1.15
1.78
Lu
0.23
0.18
0.19
0.19
0.22
0.30
Sn
0.99
NA
0.68
1.08
2.54
0.88
Yb
1.44
1.12
1.28
1.21
1.46
2.17
Cu
132
70.6
146
127
144
282
Nb
14.8
18.4
14.6
16.9
16.0
8.31
Sr
20.8
23.2
16.0
18.9
39.12
26.0
Zn
98.4
78.5
93.0
104
111
106
Dy
1.92
1.27
1.54
1.53
2.28
4.16
Nd
11.7
10.3
8.82
9.99
19.8
30.3
Ta
1.11
1.30
1.00
1.11
1.06
0.52
Zr
135
161
134
147
141
115
Er
1.30
0.880
1.04
NA
1.33
2.17
Ni
330
196
227
300
310
373
Tb
0.30
0.20
0.24
0.26
0.38
0.75
129
130
131
Reference cited in the Supporting Information:
132
133
134
Clark, D.B. & Clark, D.A. (1990) Distribution and effects on tree growth of lianas and woody
hemiepiphytes in a Costa Rican tropical wet forest. Journal of Tropical Ecology, 6,
321–331.
135
136
137
Ingwell, L.L., Wright J., S., Becklund, K.K., Hubbell, S.P. & Schnitzer, S.A. (2010) The
impact of lianas on 10 years of tree growth and mortality on Barro Colorado Island,
Panama. Journal of Ecology, 98, 879–887.
138
139
140
McGaughey, R.J. (2009) FUSION/LDV: Software for LIDAR data analysis and visualization.
US Department of Agriculture, Forest Service, Pacific Northwest Research Station:
Seattle, WA, USA, 123.
141
142
143
Phillips, O.L., Vásquez Martínez, R., Monteagudo Mendoza, A., Baker, T.R. & Núñez
Vargas, P. (2005) Large lianas as hyperdynamic elements of the tropical forest
canopy. Ecology, 86, 1250–1258.
144
145
146
Schnitzer, S.A., Kuzee, M.E. & Bongers, F. (2005) Disentangling above- and below-ground
competition between lianas and trees in a tropical forest. Journal of Ecology, 93,
1115–1125.
147
148
149
150
Vincent, G., Sabatier, D., Blanc, L., Chave, J., Weissenbacher, E., Pélissier, R., Fonty, E.,
Molino, J.F. & Couteron, P. (2012) Accuracy of small footprint airborne LiDAR in its
predictions of tropical moist forest stand structure. Remote Sensing of Environment,
125, 23–33.
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