P OPULATION STRUCTURE OF S TYRIA AND T YROL COMBINED

For the following analyses, Styrian and Tyrolean datasets were pooled and classified as regions, one hierarchy level above subpopulation level.

Analysis of molecular variance and F-statistics

According to the hierarchical analysis of molecular variance (AMOVA) with Tyrol and Styria classified as regions, 88.4 % (p < 0.0000) of total genetic variance was explained by differences within individuals and 6.1 % (p < 0.0000) among individuals within the same subpopulation. 3 % (p < 0.0000) of variance was based on differences among subpopulations in the same region and only 2.5 % (p = 0.0941) of total variance was explained by variation between regions, respectively Styria and Tyrol.

A hierarchical extension to the F statistics was computed via FSC and FCT. FSC, describing the variance among subpopulations within regions, was 0.0308 while FCT = 0.0255 described the variance among regions relative to the total variance. Together with the inbreeding coefficient FIS = 0.0646 and the overall fixation index FIT = 0.1165, FSC showed similar results to the FST values calculated before for Styria and Tyrol separately. The FCT value as an index of distance between the regions hinted to a possible distinction while still being relatively small.

25 Pairwise FST matrix

Pairwise FST values (Table 5) did not show a clear pattern of distinction between Styria and Tyrol based on absolute values. Regarding significant values, a differentiation within Styria was visible with Oststeiermark/Wechsel (OSW) as well as the southern subpopulations (ZIW, ZIO, GLS, KOR) being separated. Also Tyrol was differentiated, as nine of eleven comparisons showed significant FST values.

Table 5 Pairwise FST between subpopulations. Rounded to three decimals. Bold values are significant after correction for multiple testing (α = 0.00076)

AUS LIN HSS HSW TAU OSW TUR ZIW ZIO GLS KOR AUS

LIN 0.015

HSS 0.013 0.021

HSW 0.023 0.037 0.014

TAU 0.002 0.007 0.011 0.021 OSW 0.042 0.03 0.041 0.042 0.03

TUR 0.013 0.052 0.057 0.041 0.025 0.107

ZIW 0.076 0.062 0.097 0.071 0.047 0.062 0.141

ZIO 0.017 0.05 0.049 0.024 0.02 0.04 0.065 0.016

GLS 0.011 0.039 0.012 0.012 0.012 0.028 0.06 0.071 0.003

KOR 0.034 0.084 0.056 0.059 0.033 0.076 0.088 0.111 0.023 0.013 TIR 0.05 0.053 0.046 0.058 0.024 0.083 0.044 0.098 0.07 0.061 0.076

STRUCTURE analysis

The STRUCTURE analysis without LOCPRIOR information showed the most probable number of clusters to be K = 2, same as for the analysis including LOCPRIOR information. Without LOCPRIOR information, it was not possible to distinguish between Styria and Tyrol properly (Fig. 7). There were individuals in every region showing a closer membership to the other region. Especially Tauern (TAU) was not classifiable to either belong to Styria or Tyrol as there were individuals assigned to Styrian (yellow) as well as Tyrolean (blue) cluster.

Including LOCPRIOR information for K = 2, a clear distinction between Styria and Tyrol in general was possible, especially Oststeiermark/Wechsel (OSW) as most eastern subpopulation was differentiated the most. The Tauern (TAU) subpopulation, situated in the west of Styria, partly shared the Tyrolean cluster. This was even more pronounced when a third cluster (Fig. 7, K = 3) was introduced. Tauern (TAU) and in

26 succession the north-western part of Styria (LIN, HSW, HSS) displayed a clear decline in shared cluster with Tyrol while the south-eastern subpopulations (ZIW, ZIO, GLS, KOR) were separated. Compared to the separate analysis of Styria, the differentiation within the region stayed the same with the minor difference of the northern subpopulations (TAU, HSW, HSS, LIN, AUS) also sharing a cluster (red) with the southwestern subpopulations. This pattern however was due to the limited number of clusters, when introducing a fourth cluster, differentiation within Styria stayed the same as in the separate analysis (data not shown).

Fig. 7 Structure barplots for Styria and Tyrol combined. Without and with LOCPRIOR information for K = 2 and K = 3

27 Principal component analysis

The principal component analysis (PCA) resulted in 75 principal components, with little proportion of variance covered by the first 3 principal components, respectively 3.8 %, 3.2 % and 3.1 %. The first 22 principal components accounted for 50 %. The first three principal components were kept in the analysis as there was a significant decrease in eigenvalue visible. Covererd proportion of variance was low and therefore the power of interpretation of the PCA was reduced. Differentiation was almost not visible, as only principal component 1 was able to seperate subpopulations at all (Fig. 8). Tyrol was clearly differentiated from the most eastern subpopulation Oststeiermark/Wechsel (OSW), while overlapping with the other Styrian subpopulations. Therefore, no clear seperation between Styria and Tyrol was detectable. A gradient was visible from Tyrol into Styria up to Oststeiermark (OSW), corresonding to previously shown STRUCTURE results.

Fig. 8 Scatterplots for principal component analysis displaying the subpopulations, for PC1 against PC2 on the left and PC1 against PC3 on the right. PC1 is both times assigned to the x-axis. The inset displays the eigenvalues of the principal components

28 Discriminant analysis of principal components

Two discriminant analyses of principal components (DAPC) with different prior group membership assignments were calculated in order to complete the analysis of population structure.

The first DAPC was calculated based on group membership inferred with a K-means clustering approach implemented in adegenet (Jombart 2008; Jombart and Ahmed 2011). The most probable number of clusters, inferred from the lowest Bayesian Information Criterion (BIC), was K = 7. While a cluster (cluster 5) was clearly separated, all other clusters overlapped and no differentiation was possible. However, especially Tyrolean individuals were present in all clusters, with two clusters containing 29 (cluster 1) respectively 27 (cluster 5) individuals. Therefore, K-means clustering did not correspond to predefined subpopulations.

For the second DAPC calculated, the predefined subpopulations were used as groups.

A differentiation based on the first principal component was visible between Styria and Tyrol, although both regions overlapped (Fig. 9). Results corresponded to the PCA results, with Oststeiermark/Wechsel (OSW) differentiated the most and Tauern (TAU) situated between all subpopulations. Looking at the assignment of individuals into groups by the DAPC, 93 Tyrolean samples were assigned correctly to the Tyrolean cluster. Additionally, four Styrian individuals from different subpopulations were assigned to the Tyrolean cluster as well, while within Styria, individuals were interchanged between subpopulations resulting in high assignment of individuals to Tauern (TAU).

29 Fig. 9 Scatterplot for discriminant analysis of principal components based on the predefined subpopulations. Insets display the eigenvalues of the discriminant functions (DA) as well as of the principal component analysis (PCA). Discriminant function 1 is displayed on the x-axis

OSW ZIW

OSW

TAU

OSW

TUR

30