Supplementary Figures and Tables
Supplementary Figure 1: Niche overlap, niche equivalency and similarity tests according to the
PCA-env approach of Broennimann et al. (2012). Upper panels show the kernel density estimates of
the native and invasive populations in PCA space. Darker shading indicate higher density of
occurrence records, wherein the available climate space of native and invasive populations is indicated
by solid (100%) and dashed lines (50%). The histograms show the results of null-models of niche
equivalency and similarity according to Warren et al. (2008). The red diamonds indicate the observed
niche overlap. For abbreviations of the climate variables see Methods.
Supplementary Figure 2: N-dimensional hypervolumes of native and invasive populations of the
wasp spider. Solid points indicated actual species records wherein light dots indicate the density of
species records in environmental space. The correlations of PCs with climatic variables are provided in
Suppl. Table 1.
Supplementary Figure 3: Population genetic parameters, estimated from our modelling
approach for 100 datasets of each 400 ancestry informative SNPs in invasive Northern European
populations. a(f) represents the estimated founding allele frequency for European alleles. a(m) shows
the estimated frequency of European migrant alleles. M shows the migration rate (4Nm). t represents
the time scaled by 2N generations.
Supplementary Table 1 Bioclimatic variables used for SDM development and niche quantification.
Variable
bio1 = Annual Mean Temperature
bio2 = Mean Diurnal Range
bio3 = Isothermality
bio4 = Temperature Seasonality
bio5 = Max Temperature of Warmest Month
bio6 = Min Temperature of Coldest Month
bio7 = Temperature Annual Range
bio8 = Mean Temperature of Wettest Quarter
bio9 = Mean Temperature of Driest Quarter
bio10 = Mean Temperature of Warmest Quarter
bio11 = Mean Temperature of Coldest Quarter
bio12 = Annual Precipitation
bio13 = Precipitation of Wettest Month
bio14 = Precipitation of Driest Month
bio15 = Precipitation Seasonality
bio16 = Precipitation of Wettest Quarter
bio17 = Precipitation of Driest Quarter
bio18 = Precipitation of Warmest Quarter
bio19 = Precipitation of Coldest Quarter
PET_HE_bio1 = Annual Mean Potential Evapotranspiration
PET_HE_bio4 = Potential Evapotranspiration Seasonality
PET_HE_bio5 = Max Monthly Potential Evapotranspiration
PET_HE_bio6 = Min Monthly Potential Evapotranspiration
PET_HE_bio7 = Annual Range of Potential Evapotranspiration
PET_HE_bio10 = Mean Potential Evapotranspiration of Highest Quarter
PET_HE_bio11 = Mean Potential Evapotranspiration of Lowest Quarter
Supplementary Table 2: Sampling sites for all populations used for genome and transcriptome
sequencing.
ID
Japan
Baltic
Sweden
Portugal
Italy
Reference
Genome
Reference
transcriptome
Region
East Asia
Invasive
European
Invasive
European
Native
European
Native
European
Extra European
Invasive
European
Native
European
Thermal stress
RNA seq
Invasive
European
Country
Region
City
GPS N
GPS E
Japan
Ibaraki
36
140,1
Japan
Kagoshima
31,6
130,56
Japan
Nagano
Tsukuba
Shimofukmotochö
Azusagawa-Azusa
36,23
137,97
Japan
Kagoshima
Amami-Oshima
28,28
129,43
Japan
Shiga
Ozigaoka
35,12
136,07
Latvia
Dobele
Annenieki Parish
56,7
23,13
Latvia
Limbazi
Skulte
57,35
24,45
Estonia
Pärnu
Pärnu
58,3
24,62
Latvia
Limbazi
Ainazi
57,87
24,37
Sweden
Kalmar county
Soderakra
56,45
16,67
Sweden
Kalmar county
Kalmar
56,63
16,22
Sweden
Skane county
Kristianstad
55,95
14, 10
Portugal
Alentejo
Corte Pequena
37,7
-7,85
Portugal
Alentejo
Santo Jao
37,67
-7,83
Portugal
Alentejo
Mertola
37,68
-7,6
Portugal
Alentejo
Mertola
37,65
-7,6
Portugal
Alentejo
Beja
37,8
-7,85
Italy
Campania
Giugliano
40,93
14,05
Italy
Calabria
Crosia
39,57
16,72
Italy
Puglia
Taranto
40,47
17,3
Portugal
Madeira
Camacha
32,7
-16,8
Germany
Schleswig Holstein
Ascheberg
54,13
10,32
Portugal
Alentejo
Corte Pequena
37,7
-7,85
Portugal
Alentejo
Mertola
37,68
-7,6
Portugal
Alentejo
Mertola
37,65
-7,6
Lithuania
Klaipeda
Klaipeda
55,8
21,15
Latvia
Liepaja
Niedasciems
56,08
21, 12
Latvia
Kurzemes
Viduskrogs
56,68
22,13
Latvia
Dobele
Annenieki Parish
56,7
23,13
Estonia
Pärnu
Pärnu
58,3
24,62
Estonia
Pärnu
Pärnu
58,31
24,6
Latvia
Limbazi
Ainazi
57,87
24,37
Sweden
Kalmar county
Soderakra
56,45
16,67
Sweden
Skane county
Kristianstad
55,95
14,1
Supplementary Table 3: Factor loadings, eigenvalues and explained variance of the PCs used to
compute 4-dimensional hypervolumes of native and invasive populations of the Wasp spider.
Variable
BIO1
BIO2
BIO3
BIO4
BIO5
BIO6
BIO7
BIO8
BIO9
BIO10
BIO11
BIO12
BIO13
BIO14
BIO15
BIO16
BIO17
BIO18
BIO19
PET_HE_bio1
PET_HE_bio4
PET_HE_bio5
PET_HE_bio6
PET_HE_bio7
PET_HE_bio10
PET_HE_bio11
PC1
-0.91
-0.92
-0.75
0.14
-0.97
-0.72
-0.29
-0.16
-0.85
-0.95
-0.81
0.76
0.69
0.78
-0.74
0.70
0.78
0.89
0.41
-0.98
-0.81
-0.96
-0.94
-0.85
-0.97
-0.95
Eigenvalues
16.4
Explained Variance [%] 62.9
PC2
-0.34
0.06
-0.56
0.90
0.00
-0.64
0.85
0.50
-0.45
-0.05
-0.55
-0.53
-0.51
-0.37
-0.04
-0.53
-0.41
-0.01
-0.76
-0.14
0.26
0.01
-0.23
0.18
-0.01
-0.24
PC3
-0.07
-0.12
0.15
-0.34
-0.22
0.04
-0.33
-0.18
-0.01
-0.19
0.04
-0.35
-0.37
-0.25
0.23
-0.36
-0.27
-0.23
-0.31
-0.11
-0.48
-0.24
0.09
-0.45
-0.23
0.06
PC4
0.16
-0.13
0.04
-0.16
0.03
0.22
-0.25
0.66
-0.10
0.10
0.17
-0.05
-0.23
0.27
-0.37
-0.21
0.26
0.24
-0.30
0.02
0.03
-0.02
-0.03
0.00
0.00
-0.02
5.1
19.5
1.7
6.5
1.2
4.6