Table 1.
Details of the investigated sites, number of samples and haplogroups.
Archaeological cultures according to Leesch [28]. Mitochondrial haplogroup nomenclature follows Cieslak et al. [11]. Haplogroups identified from directly dated specimens are indicated in italics. For individual dates, skeletal elements and references see S1 Table.
Fig 1.
Map of investigated sites in Switzerland and added sites in Germany.
Site numbers according to Table 1.
Table 2.
Datasets used for analyses.
Fig 2.
Median Joining Network (MJN) of horse populations (97 samples, max. 241 bp).
Nodes are proportional to frequencies and branch length according to number of substitutions. Haplogroup nomenclature follows Cieslak et al. [11].
Table 3.
Nucleotide and haplotype diversities in horse populations from Switzerland and the Swabian Jura (dataset 2).
Fig 3.
Temporal Median Joining Networks (MJN) of Swiss and Swabian horse populations (97 samples, max. 241 bp) in the context of environmental conditions and temperature.
Light grey boxes and arrows show the age of the sequences the particular MJN is based on. Empty nodes represent haplotypes absent from particular time bin. Vertical arrows indicate continuity of haplotypes from subsequent time bins. δ18O record of the North Greenland Ice Core Project (NGRIP) after [49–53], vegetation data from [21, 54].
Table 4.
Tajima’s D, Fu’s FS, sum of squared deviations (SSD) and Harpending’s raggedness index results for horse populations from Switzerland and the Swabian Jura (dataset 2).
Significant results are in bold.
Fig 4.
Nucleotide (left panel) and haplotype (right panel) diversity in Asia (black squares), Ural region (red diamonds) and the Swiss and Swabian Jura (blue circles) before (BLGM), during (LGM) and after (PLGM) the Last Glacial Maximum.
Fig 5.
PCA graph based on relative haplogroup frequencies.
Asia (black), Ural region (red) and Swiss/Swabian (blue) samples divided in time bins: square = before the LGM, diamond = LGM, circle = after the LGM. Sample numbers are given in parenthesis. The first two components explain 84% of the variation (see S4 Fig for loadings).