1
Table T1. Relative importance of predictor variables for plant  diversity in a circular
2
subregion of northern Europe determined by summing the coefficients of the I-splines from
3
GDM. The most important predictor for each dispersal mode and the entire flora are shown
4
in bold. Predictors found to be not significant are indicated by dashes. Italicized text
5
indicates gradients for which fitted functions are plotted in Figure S1b-e.
6
Ant
Passive
Vertebrate
Wind
All spp.
7
Geographic distance
0.270
0.490
0.522
0.337
0.409
8
Maximum temperature
0.202
0.611
0.326
0.541
0.439
9
Minimum temperature
0.323
0.970
0.344
0.337
0.343
Soil pH
0.158
0.495
0.165
0.104
0.132
10
11
1
12
Table T2. Relative importance of predictor variables for plant  diversity in a southern
13
subsection of northern Europe determined by summing the coefficients of the I-splines
14
from GDM. The most important predictor for each dispersal mode and the entire flora are
15
shown in bold. Predictors found to be not significant are indicated by dashes. Italicized text
16
indicates gradients for which fitted functions are plotted in Figure S2b-e.
17
Ant
Passive
Vertebrate
Wind
All spp.
18
Geographic distance
0.093
0.489
0.346
0.209
0.266
19
Maximum temperature
0.198
--
0.140
0.214
0.184
20
Minimum temperature
0.244
0.395
0.123
0.135
0.136
21
Summer precipitation
0.032
0.182
0.123
0.203
0.161
22
Soil pH
0.078
0.293
0.135
0.046
0.103
23
Sand
0.020
0.099
0.033
--
--
24
CaCO3
0.045
--
--
0.028
--
25
2
26
FIGURE S1: (a) Variation in species richness in 50 km  50 km cells for a circular subregion
27
of northern Europe (only shaded cells were used in model fitting). (b)-(e) Generalized
28
dissimilarity model-fitted I-splines (partial regression fits) for variables significantly
29
associated with plant  diversity for all four dispersal modes. The maximum height reached
30
by each curve indicates the total amount of compositional turnover associated with that
31
variable (and by extension, the relative importance of that variable in explaining 
32
diversity), holding all other variables constant. The shape of each function provides an
33
indication of how the rate of compositional turnover varies along the gradient. (f) The
34
proportion of total explained deviance attributable purely to (black) space, (grey) purely to
35
environment, and (white) jointly to both variables (shared).
36
37
FIGURE S2: Same as S1, but for a southern subregion of northern Europe.
38
39
FIGURE S3: Per cent contributions of environmental predictor variables from linear models
40
for (a) the entire floras of (a) southwest Australia and (b) all of northern Europe.
41
42
FIGURE S4: Differences in the deviance explained by linear models (LM) and GDM for all
43
four dispersal modes and the entire floras of southwest Australia and all of northern
44
Europe. Negative values indicate greater explanatory power by GDM.
3
45
FIGURE S1.
46
4
47
FIGURE S2.
48
5
49
FIGURE S3.
50
6
51
FIGURE S4.
52
7