Supplementary Material
Species information
Table A1: Information on the 60 plant species of the Jena Experiment. Given are the species
names, their assignment to specific functional groups (FG), the harvest date of the mesocosms
(Harvest), whether individuals in the mesocosms flowered at harvest (1) or not (0)(Flower), the
available number of replicate mesocosms per species (Reps), the measured number of traits (n
Traits) and the availability of population biomass in the field monocultures (MonoBM).
Species
FG
Harvest
Flower Reps n Traits
MonoBM
Achillea millefolium
tall herb
Aug 2011 1
5
35
1
Ajuga reptans
small herb July 2012 0
5
32
1
Alopecurus pratensis
grass
Aug 2011 1
5
33
1
Anthoxanthum odoratum grass
July 2012 1
5
35
1
Anthriscus sylvestris
tall herb
Aug 2011 0
3
32
0
Arrhenatherum elatius
grass
Aug 2011 1
5
34
1
Avenula pubescens
grass
Aug 2011 0
5
30
1
Bellis perennis
small herb Aug 2011 1
5
35
1
Bromus erectus
grass
Aug 2011 1
5
35
1
Bromus hordeaceus
grass
Aug 2011 0
5
35
1
Campanula patula
tall herb
July 2012 0
4
32
1
Cardamine pratensis
tall herb
Aug 2011 0
5
35
1
Carum carvi
tall herb
Aug 2011 0
5
35
1
Centaurea jacea
tall herb
Aug 2011 1
5
35
1
Cirsium oleraceum
tall herb
Aug 2011 0
5
35
1
Crepis biennis
tall herb
Aug 2011 0
5
35
1
Cynosurus cristatus
grass
Aug 2011 0
5
35
1
Dactylis glomerata
grass
Aug 2011 0
5
35
1
Daucus carota
tall herb
Aug 2011 0
5
35
1
Festuca pratensis
grass
Aug 2011 0
5
35
1
Festuca rubra
grass
Aug 2011 0
5
35
1
Galium mollugo
tall herb
Aug 2011 0
5
35
1
Geranium pratense
tall herb
Aug 2011 0
5
35
1
Glechoma hederacea
small herb Aug 2011 0
5
35
1
Heracleum sphondylium
tall herb
Aug 2011 0
5
30
0
Holcus lanatus
grass
Aug 2011 0
5
35
1
Knautia arvensis
tall herb
Aug 2011 1
5
35
1
Lathyrus pratensis
legume
Aug 2011 0
5
35
1
Leontodon autumnalis
small herb Aug 2011 1
4
35
1
Leontodon hispidus
small herb Aug 2011 1
5
35
1
Leucanthemum vulgare
tall herb
Aug 2011 0
5
35
1
Lotus corniculatus
legume
Aug 2011 1
5
35
1
Luzula campestris
Medicago lupulina
Medicago x varia
grass
legume
legume
Aug 2011
Aug 2011
Aug 2011
0
1
1
5
5
5
26
35
35
1
1
1
Table A1: continued.
Species
Onobrychis viciifolia
Pastinaca sativa
Phleum pratense
Pimpinella major
Plantago lanceolata
Plantago media
Poa pratensis
Poa trivialis
Primula veris
Prunella vulgaris
Ranunculus acris
Ranunculus repens
Rumex acetosa
Sanguisorba officinalis
Taraxacum officinale
Tragopogon pratensis
Trifolium campestre
Trifolium dubium
Trifolium fragiferum
Trifolium hybridum
Trifolium pratense
Trifolium repens
Trisetum flavescens
Veronica chamaedrys
Vicia cracca
FG
legume
tall herb
grass
tall herb
small herb
small herb
grass
grass
small herb
small herb
tall herb
small herb
tall herb
tall herb
small herb
tall herb
legume
legume
legume
legume
legume
legume
grass
small herb
legume
Harvest
Aug 2011
Aug 2011
Aug 2011
Aug 2011
Aug 2011
Aug 2011
July 2012
Aug 2011
NA
Aug 2011
Aug 2011
Aug 2011
Aug 2011
Aug 2011
Aug 2011
Aug 2011
Aug 2011
Aug 2011
Aug 2011
Aug 2011
Aug 2011
Aug 2011
Aug 2011
Aug 2011
Aug 2011
Flower
0
0
1
0
1
1
1
1
NA
0
1
1
0
0
0
0
0
1
1
1
1
1
1
0
0
Reps
5
5
5
5
5
5
4
5
0
5
5
5
5
5
5
5
4
5
5
5
5
5
5
5
5
n Traits
35
35
35
35
32
35
34
35
0
35
35
35
35
33
35
35
35
35
35
35
35
35
32
35
35
MonoBM
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Trait measurements
In June we took up to five healthy and full developed leaves per plant and brought them to the
laboratory for measurements. Fresh leaves where weighted and scanned with a flatbed
scanner. We analyzed the pictures using the software WinFolia (Regent Instruments Inc.,
Canada) to obtain, leaf length (LLen), maximum leaf width (LW), leaf width/leaf length
(LW/LLen) and leaf area/leaf perimeter (LA/LP). We measured leaf thickness LTh using a
caliper in the center of an intercostal field next to the mid rib. At the same point we measured
leaf penetration persistence using a penetrometer to gouge leaf toughness (LTo). The
penetrometer is an electric test stand (TVM 5000N 230N, Sauter GmbH, Germany) with a
platform that is movable in vertical direction. On the top of the stand there is a fixed force
gauge (FH50, Sauter GmbH, Germany) with a cylindrical metal needle (diameter: 1.4mm)
pointing down to a small hole on the stands platform in which it fits exactly. When the leaf is
fixed on the platform, with the point supposed to be penetrated above the hole, the stand can
be moved upwards until the needle sticks into the leaf and cuts out a round peace of it. The
Sauter FH50 measures the power needed to stick the needle through the leaf. Afterwards we
dried the leaves for 48 hours at 70°C and measured their dry weights. Leaf dry matter content
(LDMC) is the ratio of leaf dry to fresh weight.
We measured ventral and dorsal leaf conductivity in June 2011 on a covered but rainless day
using a Leaf Porometer (SC-1, Decagon Devices Inc., USA). The trait conductivity (LCo) is the
sum of dorsal and ventral leaf conductivity.
From planting in May to the end of June we counted new emerged leaves weekly. We
multiplied the maximum number of leaves produced per week by the mean single leaf dry
weight to obtain maximum leaf mass production rate (LMP). LMP is the leaf component of
relative growth rate (RGR) which could also be considered as performance measure, rather
than as functional trait. However, due to differences in leaf turnover fast growth not necessarily
leads to high biomass. Although LMP correlates with both, individual and monoculture biomass
(both: r=0.59), plants can grow fast but stay small (e.g. Poa trivialis, LMP=0.6 g*d-1, individual
biomass=7.8 g), or grow more slowly but taller (Medicago lupulina, LMP=0.2 g*d-1, individual
biomass=12.3g). We included leaf mass production as a functional trait because it describes a
growth strategy (slow vs. fast) important to our approach.
We started to harvest the plants on August 15th 2011 beginning in block 1. We measured
standing plant height (staH) (from soil surface to the highest leaf) as well as stretched plant
height (strH) directly before we cut the shoot at soil level. Erectness (E) is the ratio of standing
to stretched height ranging between 0 and 1. Values close to 1 relate to erect growth, whereas
values near 0 relate to creeping growth. Then we cut the aboveground parts directly at the soil
surface, put them into a plastic bag with a moistened tissue to prevent them from dehydration
and transported them in a cool box immediately to the nearby laboratory for further
measurements. We separated the shoots into compartments (leaves, stems, flowers and fruits
for appropriate measurements. At the end we dried all materials separately at 70°C for 48h and
weighed them.
If possible we took 10 fully developed, healthy leaves and measured projected area using a leaf
area meter Li-3100C (Li-Cor, Bioscience Inc. USA).
Specific leaf area (sLA) is leaf area per leaf dry mass. Total leaf area is total plants leaf dry
mass*sLA. Leaf area ratio (LA/SM) is the ratio of total leaf area to shoot dry mass. The trait leaf
area (LA) is the average single leaf area. Leaf mass ratio (LM/SM) is the ratio of total plants leaf
dry mass to shoot dry mass. Shoot-root ratio (SM/RM) is the ratio of shoot dry mass (incl.
stems, leaves, flowers and fruits) to root dry mass. This trait cannot clearly be assigned to an
above- or a belowground cluster as it implies both, shoot and root sampling. We deliberately
assigned it to the stature cluster, to describe whole-plant biomass allocation and to avoid
overvaluation of root traits. Specific leaf density (sLDen) is leaf dry mass per leaf fresh volume
(leaf diameter * leaf area).
To measure root traits we cut the soil cylinder (mesocosm: 10cm (drainage)+50cm high and 15
cm in diameter) into four layers (from top 0-10cm, 10-20cm, 20-30cm and 30-50cm) and
washed the roots of each layer separately using tap water and fine sieves (0.2mm). After the
coarse root washing we cleaned the roots from remaining dust and non-root material using
forceps. We scanned the roots using a flat-bed scanner and analyzed them using the software
WinRizo (Regent Instruments Inc., Canada). From the WinRizo output we obtained mean root
diameter (RDia), root length, root area and root volume. After scanning we dried the roots for
48h at 70°C and weighed them. We calculated the following traits from measured data:
specific root length (sRL) = root length/root mass
specific root area (sRA) = root area/root mass
To measure nitrogen uptake into roots (RNU) and deposition into leaves (LNU) we applied
double labelled stable isotope tracer of Ammonium-Nitrate (15NH415NO3; 98% purity) 48 h
before a plant was harvested. For label injections we prepared two holes of 8 cm depth and 0.6
cm in diameter in the soil of each pot, using a drilling machine. Into each hole we applied 2 ml
of the label solution (0.02 Mol/l). Root subsamples of layer 1 (0-10 cm) and leaf samples were
analyzed for concentrations of nitrogen (RN, LN) and carbon (RC, LC) as well as 15N excess
(R15N/14N and L15N/14N in ‰) over natural abundance using a EA-IRMS (Delta V, Thermofisher).
Calculation of Nitrogen uptake:
As we applied 15N in very high concentrations compared to the natural background signal
(R15N/14N ranged between 24 and 22148 ‰) we neglected the background 15N concentration.
We calculated 15N content (15Ncont) in roots and leaves:
X15Ncont = XDW * XN /100 * X15N/14N/( X15N/14N + 1)
Here X always stays for either root (R) or leaf (L) material. The term XDW * XN /100 is the total
N content in g and the term X15N/14N/(R15N/14N + 1) is the 15N to total N ratio in roots or leaves
respectively (DW=dry weight).
L15Ncont is the amount of 15N captured and allocated into leaves within 48 hours and
corresponds with our trait LNU. As all 15N that was deposited into leaves had to be captured by
roots earlier L15Ncont was added to R15Ncont to get RNU. These traits have some limitations:
(1) uptake of natural 14N is not considered and (2) 15N that is located in other tissue than root or
leaf (e.g. stem) was also captured by roots but not considered in the calculation.
Correlations among the response variables
Fig. A1: paired correlations between individual biomass, population density and
population biomass. Numbers are squared correlation coefficients (r²)
Table A2. SEM path coefficients
trait
eveRA
eveRL
eveRM
RC
RCN
RDia
RN
RNU
sRA
sRDen
sRL
LA
LA/LP
LC
LCN
LCo
LDMC
LLen
LMP
LN
LNU
LTh
LTo
LW
LW/LLen
sLA
sLDen
E
LA/SM
LM/SM
SM/RM
sStDen
staH
StDMC
strH
DF
57
57
57
56
56
57
56
56
57
57
57
57
51
56
56
52
56
57
51
57
57
57
57
57
57
57
57
55
57
57
57
56
55
56
55
1a
0.49
0.52
0.49
0.16
0.19
-0.05
-0.01
0.23
-0.04
0.06
-0.01
-0.04
0.14
0.28
0.27
-0.05
0.11
0.00
0.59
-0.23
0.17
-0.24
0.24
-0.13
-0.10
-0.17
0.32
-0.06
-0.58
-0.52
0.23
0.28
0.62
0.43
0.69
Paths of SEM according to Fig. 1
1b
1a*1b
2a
2b
2a*2b
0.58
0.29
0.13 0.12
0.02
0.58
0.30
0.15 0.12
0.02
0.64
0.32
0.18 0.10
0.02
0.60
0.10 -0.02 0.13
0.00
0.64
0.12
0.50 0.18
0.09
0.62
-0.03 -0.49 0.37
-0.18
0.62
-0.01 -0.41 0.18
-0.08
0.63
0.14
0.04 0.10
0.00
0.63
-0.03
0.24 0.23
0.05
0.66
0.04 -0.04 0.11
0.00
0.61
-0.01
0.38 0.33
0.12
0.66
-0.03 -0.40 0.19
-0.08
0.64
0.09 -0.26 0.05
-0.01
0.65
0.18
0.08 0.10
0.01
0.64
0.17
0.41 0.10
0.04
0.67
-0.03 -0.12 0.11
-0.01
0.65
0.07
0.27 0.15
0.04
0.68
0.00 -0.01 0.10
0.00
0.45
0.27
0.44 0.00
0.00
0.68
-0.15 -0.39 0.10
-0.04
0.70
0.12
0.10 0.10
0.01
0.72
-0.17 -0.28 0.16
-0.04
0.68
0.16
0.63 0.26
0.16
0.62
-0.08 -0.68 0.41
-0.28
0.65
-0.07 -0.61 0.25
-0.15
0.65
-0.11 -0.06 0.10
-0.01
0.71
0.23
0.31 0.12
0.04
0.69
-0.04
0.05 0.11
0.01
0.68
-0.40 -0.46 0.10
-0.05
0.74
-0.39 -0.51 0.15
-0.08
0.69
0.16
0.02 0.09
0.00
0.65
0.18
0.43 0.06
0.03
0.68
0.42
0.39 0.10
0.04
0.65
0.28
0.38 0.11
0.04
0.64
0.44
0.39 0.10
0.04
3
0.17
0.17
0.07
0.21
-0.15
0.44
0.16
0.05
-0.38
0.20
-0.45
0.19
-0.05
-0.03
0.00
-0.02
-0.18
0.05
0.32
0.01
-0.14
0.27
-0.24
0.38
0.21
-0.14
-0.12
-0.04
0.03
0.17
-0.05
0.12
0.00
0.04
0.06
Multiple regression model selection
To explore the importance of root traits relative to leaf and stature traits in these models, we
deliberately included all traits directly as predictors, instead of using synthetic axes (e.g. PCA
axes) or removing correlated traits. This approach is valid because trait correlations are weak
between the majority of root traits and aboveground traits (Table 2). High trait-correlations
within trait clusters might lead to the replacement of individual traits in the selected model, e.g.
specific root length by specific root area, but the overall representation of traits per cluster
remains constant and thus our conclusions about the importance of trait clusters stay the same.
In order to find the “best” model, with respect to the principle of parsimony, one would have to
test all possible variable combinations of different variable numbers. This would often exceed
the available computer power. Thus variable selection algorithms have been developed to find
the best model without having to test all possibilities (e.g. Furnival & Wilson 1974). After testing
several of these algorithms without consistently satisfying results we decided to develop our
own algorithm. It is based on a stepwise forward selection with intermediate variable exchange
(five). The detection of the final model is done in two steps.
I.
Variable selection: In this step the “five “-algorithm selects the least-BIC model
comprising 1, 2,…n variables
The algorithm, executes the following steps:
1. Find the predictor, with the highest R².
2. Add the next variable, resulting in the least BIC (Bayesian information criterion).
3. Test if the replacement of x1-n by a variable, which is not in the model so far, improves
the BIC. If yes, keep the new variable.
4. If any variable was replaced, repeat steps 3 and 4.
5. Repeat steps 2 to 4 up to the specified maximum number of variables supposed to be in
the model.
6. Save the “best” model of each length (number of variables) in a data frame (similar to
Tables A3 – A8).
II.
Final model selection: The decision which of the models identified in step I (see Tabs A3A8) is considered the final model is made in two steps:
1. Find the model with the lowest BIC (minBIC)
2. Check, if there are models using a lower number of variables, with BIC ≤ minBIC+2
3. From these models keep the model with the lowest number of variables and define the
BIC of this model as the new minBIC
4. Repeat steps 2 and 3 until no smaller model can be found that fits the criteria.
To test our algorithm (step I), we fitted all possible models with up to 6 parameters (more
variables caused the computer to crash, because of the high number of computing steps).
As the algorithm always succeeded, we are confident, that this algorithm always finds the
model with lowest BIC. We also compared the final models (step II) found by our algorithm
with those found by other functions (e.g. R; package MASS: stepAIC). Our Algorithm always
performed better. For example the final individual biomass - model found by stepAIC
comprised 25 variables (BIC= -133.32, R²=0.97), whereas our algorithm found a model using
13 variables (BIC= -134.39, R²=0.95).
Tables A3 to A8 show the most parsimonious models for each number of variables. The model
with the lowest BIC (bold line) was always considered the “best” model for the prediction with
the respective traits.
Table A3. Model selection for individual biomass (indBM) using stature, leaf and root traits.
no.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
formula
indBM ~ strH
indBM ~ SM/RM + LMP
indBM ~ strH + LMP + SM/RM
indBM ~ staH + LMP + SM/RM + RNU
indBM ~ E + LMP + SM/RM + RNU + staH
indBM ~ E + LMP + SM/RM + RNU + staH + LN
indBM ~ LA + LMP + SM/RM + RNU + staH + RCN + E
indBM ~ LA + LMP + SM/RM + RNU + staH + RCN + E + sStDen
indBM ~ LTh + LMP + SM/RM + RNU + staH + LCN + E + LDMC + eveRA
indBM ~ LA + LMP + SM/RM + RNU + staH + RCN + E + LA/SM + LDMC + LTh
indBM ~ LA/SM + LMP + SM/RM + RNU + staH + RCN + E + LDMC + RN + LTh
+ LA
indBM ~ sStDen + LMP + SM/RM + RNU + staH + RCN + E + sRDen + eveRL
+ LTh + LA + LA/SM
indBM ~ sRA + LMP + SM/RM + RNU + staH + RCN + E + sRL + eveRL + LTh
+ LA + LA/SM + sStDen
indBM ~ LN + LMP + SM/RM + RNU + staH + LCN + E + LDMC + eveRL + LTh
+ LA + LA/SM + sStDen + sRA
indBM ~ LM/SM + LMP + SM/RM + RNU + staH + LCN + E + LDMC + eveRL
+ LTh + LA + LA/SM + sStDen + sRA + LN
BIC
-50.84
-70.92
-83.88
-98.93
-113.89
-119.52
-123.88
-125.43
-121.80
-128.26
-129.26
R2
-129.27
0.94
-134.39
0.95
-130.55
0.95
-132.21
0.95
0.39
0.62
0.73
0.82
0.87
0.89
0.91
0.92
0.92
0.93
0.94
Table A4. Model selection for individual biomass (indBM) using only stature and leaf traits.
no.
1
2
3
4
5
6
7
8
formula
indBM ~ strH
indBM ~ SM/RM + LMP
indBM ~ strH + LMP + SM/RM
indBM ~ strH + LMP + SM/RM + LNU
indBM ~ E + LMP + SM/RM + LNU + staH
indBM ~ LDMC + LMP + SM/RM + LNU + staH + E
indBM ~ LC + LMP + SM/RM + LNU + staH + E + LDMC
indBM ~ LA + LMP + SM/RM + LNU + staH + E + sStDen + LC
BIC
-50.84
-70.92
-83.88
-88.64
-103.32
-104.96
-105.54
-107.17
R2
0.39
0.62
0.73
0.77
0.84
0.86
0.87
0.88
9
10
11
12
13
14
15
indBM ~ sStDen + LMP + SM/RM + LNU + staH + E + LDMC + LC + LA
indBM ~ LTh + LMP + SM/RM + LNU + staH + E + LDMC + LC + LA + sStDen
indBM ~ LA/SM + LMP + SM/RM + LNU + staH + E + LDMC + LC + LA + sStDen
+ LTh
indBM ~ LCN + LMP + SM/RM + LNU + staH + E + LDMC + LN + LA + sStDen
+ LTh + LA/SM
indBM ~ LN + LMP + SM/RM + LNU + staH + E + LDMC + LTo + LA + sStDen
+ LTh + LA/SM + LCN
indBM ~ LTo + LMP + SM/RM + LNU + staH + E + LDMC + LW + LA + sStDen
+ LTh + LA/SM + LCN + LN
indBM ~ LTo + LMP + SM/RM + LNU + staH + E + LDMC + LW + LA + sStDen
+ LTh + LA/SM + LCN + LN + sLDen
-109.21
-111.11
-113.97
0.89
0.90
0.91
-120.57
0.93
-118.14
0.93
-117.49
0.93
-115.41
0.93
Table A5. Model selection for individual biomass (indBM) using only leaf traits.
no.
form
BIC
R2
1
indBM~LMP
-54.65
0.35
2
indBM~LMP + LC
-59.69
0.43
3
indBM~LMP + LC + LA/LP
-59.00
0.46
4
indBM~LMP + LC + LTh + LDMC
-58.41
0.48
5
indBM~LMP + LC + LTh + LDMC + LA/LP
-57.60
0.50
6
indBM~LMP + LTh + LDMC + LA/LP + LTo + LNU
-56.61
0.52
7
indBM~LMP + LTh + LDMC + LA/LP + LTo + LNU + LC
-56.45
0.54
8
9
indBM~LMP + LTh + LDMC + LA/LP + LTo + LNU + LC + LLen
indBM~LMP + LTh + LDMC + LA/LP + LTo + LNU + LC + LCN + LN
-53.19
-63.72
0.54
0.64
10
indBM~LMP + LTh + LDMC + LA/LP + LTo + LNU + LC + LCN + LN +
LW/LLen
indBM~LMP + LTh + LDMC + LA/LP + LTo + LNU + LC + LCN + LN + LA
+ LW
indBM~LMP + LTh + LDMC + LA/LP + LTo + LNU + LC + LCN + LN + LA
+ LLen + LW/LLen
indBM~LMP + LTh + LDMC + LA/LP + LTo + LNU + LC + LCN + LN + LLen +
LW/LLen + sLA + sLDen
indBM~LMP + LTh + LDMC + LA/LP + LTo + LNU + LC + LCN + LN + LLen +
LW/LLen + sLA + sLDen + LW
indBM~LMP + LTh + LDMC + LA/LP + LTo + LNU + LC + LCN + LN + LLen +
LW/LLen + sLA + sLDen + LW + LA
indBM~LMP + LTh + LDMC + LA/LP + LTo + LNU + LC + LCN + LN + LLen +
LW/LLen + sLA + sLDen + LW + LA + LCo
-60.17
0.64
-56.57
0.63
-52.92
0.62
-50.82
0.63
-46.89
0.62
-42.96
0.60
-36.39
0.61
11
12
13
14
15
16
Table A6. Model selection for population biomass (popBM) using stature, leaf and root traits.
no.
1
2
3
formula
BIC
adj. R2
popBM ~ LMP
popBM ~ LMP + RN
popBM ~ LMP + RN + LDMC
-9.05
-18.92
-21.15
0.38
0.52
0.57
4
5
6
7
8
9
10
popBM ~ LMP + RN + LDMC + staH
popBM ~ LMP + SM/RM + eveRL + RDia + sStDen
popBM ~ LMP + SM/RM + eveRL + eveRM + sStDen + RDia
popBM ~ LMP + LA/LP + RDia + SM/RM + sStDen + eveRM + eveRL
popBM ~ LMP + LA/LP + LA/SM + SM/RM + sStDen + RDia + eveRL + eveRM
popBM ~ LMP + RNU + LC + SM/RM + sStDen + RDia + eveRL + eveRM +
LA/LP
popBM ~ LMP + RDia + RNU + sLA + sStDen + LC + eveRL + eveRM + LA/LP
+ SM/RM
-21.39
-31.43
-34.56
-39.76
-39.24
-37.83
0.60
0.69
0.73
0.77
0.78
0.79
-36.89
0.79
Table A7. Model selection for population biomass (popBM) using only stature and leaf traits.
no. formula
1 popBM ~ LMP
2 popBM ~ LMP + strH
3 popBM ~ LMP + strH + LDMC
4 popBM ~ LMP + strH + LDMC + LN
5 popBM ~ LMP + strH + LDMC + LN + sStDen
6 popBM ~ LMP + strH + LDMC + LN + sStDen + LA
7 popBM ~ LMP + strH + LDMC + LN + sStDen + LA + LCN
8 popBM ~ LMP + strH + LDMC + LN + sStDen + LA + LCN + SM/RM
9 popBM ~ LMP + E + LDMC + LN + sStDen + LA + staH + SM/RM + LA/LP
10 popBM ~ LMP + StDMC + LDMC + LN + sStDen + LA + LCN + SM/RM + LTo
+ LNU
BIC
adj. R2
-9.05
-11.29
-13.31
-12.78
-12.03
-11.10
-9.44
-7.60
-6.17
-5.27
0.38
0.44
0.49
0.52
0.54
0.55
0.56
0.57
0.58
0.60
Table A8. Model selection for population biomass (popBM) using only leaf traits.
Nr.
formula
BIC
R2
1
popBM~LMP
-7.62
0.35
2
popBM~LMP + LNU
-9.17
0.39
3
popBM~LMP + LNU + LDMC
-8.19
0.41
4
popBM~LMP + LNU + LDMC + LN
-8.19
0.45
5
popBM~LMP + LDMC + LN + LCo + LCN
-8.64
0.48
6
popBM~LMP + LDMC + LN + LCo + LCN + LNU
-7.87
0.50
7
popBM~LMP + LDMC + LN + LCo + LCN + LNU + LTo
-5.44
0.51
8
popBM~LMP + LDMC + LN + LCo + LCN + LNU + LTo + LW/LLen
-2.79
0.51
9
popBM~LMP + LDMC + LN + LCo + LCN + LNU + LTo + LW/LLen + LA/LP
popBM~LMP + LDMC + LN + LCo + LCN + LNU + LTo + LW/LLen + LA/LP +
sLA
popBM~LMP + LDMC + LN + LCo + LCN + LNU + LTo + LW/LLen + LA/LP +
sLA + LLen
popBM~LMP + LDMC + LN + LCo + LCN + LNU + LTo + LW/LLen + LA/LP +
sLA + LLen + LW
popBM~LMP + LDMC + LN + LCo + LCN + LNU + LTo + LW/LLen + LA/LP +
sLA + sLDen + LTh + LA
popBM~LMP + LDMC + LN + LCo + LCN + LNU + LTo + LW/LLen + LA/LP +
sLA + sLDen + LTh + LA + LW
0.51
0.50
4.28
0.49
8.05
0.47
11.88
0.46
15.30
0.45
19.04
0.44
10
11
12
13
14
15
16
popBM~LMP + LDMC + LN + LCo + LCN + LNU + LTo + LW/LLen + LA/LP +
sLA + sLDen + LTh + LA + LW + LC
popBM~LMP + LDMC + LN + LCo + LCN + LNU + LTo + LW/LLen + LA/LP +
sLA + sLDen + LTh + LA + LW + LC + LLen
22.85
0.42
26.71
0.40
Table A9. Absolute and relative variable frequency in the model selection for individual biomass
(indBM, Tab. A2) and population biomass (popBM, Tab. A5) using stature, leaf and root traits.
Traits
RNU
RCN
eveRL
sRA
eveRA
RN
sRDen
RDia
eveRM
RC
LMP
LA
LTh
LDMC
LN
LA/LP
SM/RM
staH
E
LA/SM
sStDen
strH
LM/SM
Total
12
6
4
2
1
1
1
0
0
0
14
8
7
5
3
0
14
12
11
6
5
1
1
indBM
Rel. frequency
11
5
4
2
1
1
1
0
0
0
12
7
6
4
3
0
12
11
10
5
4
1
1
Total
2
popBM
Rel. frequency
4
6
11
3
6
6
5
2
10
11
9
4
19
2
4
4
6
1
7
11
2
1
6
2
11
Fig. A2: Comparison of the individual biomass models (see Tables A3-A5)
containing one to 13 factors, selected only from leaf traits (green), leaf and
stature traits (blue) and leaf, stature and root traits (black). Data points with open
circles mark the most parsimonious models
Fig. A3: Comparison of the population biomass models (see Tables A6-A8)
containing one to seven factors, selected only from leaf traits (green), leaf and
stature traits (blue) and leaf, stature and root traits (black). Data points with open
circles mark the most parsimonious models