Alpine Botany

, Volume 128, Issue 1, pp 35–45 | Cite as

The phylogeographic structure of Arabis alpina in the Alps shows consistent patterns across different types of molecular markers and geographic scales

  • Aude Rogivue
  • René Graf
  • Christian Parisod
  • Rolf Holderegger
  • Felix Gugerli
Original Article


Glaciation during the Pleistocene confined alpine species to refugial areas. These range contractions had major impacts on the spatial genetic structure of alpine species. Consequently, one should take into account the often complex phylogeographic structure of species when performing genomic research, e.g. on signatures of local adaptation. Understanding the phylogeography of the widespread arctic and alpine Arabis alpina is particularly important, as this species is developing into a model species for ecological genetics. The first objective of this study was to assess the genetic variation of A. alpina across the Alps and to compare the spatial genetic patterns resulting from two different types of molecular markers, namely nuclear microsatellites and amplified fragment length polymorphisms (AFLPs). A second objective was to infer the distribution of genetic variation at the regional scale to understand the genetic structure of populations in the area of a previously suggested contact zone between genetic clusters that presumably recolonised their current range from different glacial refugia. We characterized the phylogeographic structure of 372 individuals from 127 populations across the entire Alpine range, complemented by 364 individuals from 22 populations in the western Swiss Alps. Nuclear microsatellite and AFLP markers described consistent population clustering, coherent with previous phylogeographic analyses. Furthermore, regional population structure in the western Alps of Switzerland highlighted a contact zone of genetic clusters associated with different presumed refugia. Again, this finding was in accordance with recolonisation routes formerly inferred for other plant taxa of the western Swiss Alps. Our results highlight the coincidence of large-scale patterns of genetic structure among alternative types of molecular markers and set a valuable basis for further studies on ecological genomics in A. alpina.


AFLPs Alps Brassicaceae Microsatellites Spatial genetic pattern 



We thank Maurice Moor, Alexandra Foetisch and Sabine Brodbeck for the sampling in the western Alps of Switzerland and the IntraBioDiv Consortium for the DNA samples of the entire Alpine range. Christian Rellstab contributed to discussions, and two anonymous reviewers provided valuable comments. This study was financially supported by the Swiss National Science Foundation (GeneScale; CR32I3_149741/1).

Author contributions

Aude Rogivue and Felix Gugerli designed the study, René Graf did the lab work and the genotyping, Aude Rogivue performed all the analyses and wrote the manuscript, with contributions from Christian Parisod, Rolf Holderegger and Felix Gugerli.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflicts of interests.

Supplementary material

35_2017_196_MOESM1_ESM.pdf (221 kb)
Online Resource 1 Three main putative recolonisation pathways in the western Alps of Switzerland based on Parisod (2008). The names of main mountain ranges are given in bold, country names and regions are in normal font. Dark grey arrows indicate the Rhodanian pathway, grey arrows the transalpine eastern pathway and white arrows the transalpine southern pathway, including the Simplon pass pathway. (PDF 221 KB)
35_2017_196_MOESM2_ESM.pdf (76 kb)
Online Resource 2 Sampling locations of the 22 populations of Arabis alpina from the regional scale study in the western Swiss Alps. (PDF 75 KB)
35_2017_196_MOESM3_ESM.pdf (265 kb)
Online Resource 3 Plot of mean likelihood LnP(D) and variance among repetitions per K value as well as the ∆K plots (Evanno et al. 2005) from the STRUCTURE analyses (Pritchard et al. 2000; Hubisz et al. 2009) of Arabis alpina for (a and b) the alpine data set using microsatellite markers, (c and d) the alpine dataset using AFLP markers, (e and f) the alpine including the regional data with microsatellite markers, (g and h) only the regional dataset with microsatellite markers and, (i and j) only the regional dataset with microsatellite markers but analysed with INSTRUCT (Gao et al. 2007). (PDF 266 KB)
35_2017_196_MOESM4_ESM.pdf (797 kb)
Online Resource 4 Genetic clustering of Arabis alpina determined by STRUCTURE analyses (Pritchard et al. 2000; Hubisz et al. 2009) of 19 microsatellite markers for K = 2–11 across the Alps. (PDF 797 KB)
35_2017_196_MOESM5_ESM.pdf (1018 kb)
Online Resource 5 Genetic clustering of Arabis alpina determined by STRUCTURE analyses (Pritchard et al. 2000; Hubisz et al. 2009) of 150 AFLP markers for K = 2–10 across the Alps. (PDF 1019 KB)
35_2017_196_MOESM6_ESM.pdf (66 kb)
Online Resource 6 Plot of the coefficients of similarity, calculated with CLUMPAK (Kopelman et al. 2015) for each K value, among the population membership coefficients of Arabis alpina using microsatellite and AFLP markers. (PDF 66 KB)
35_2017_196_MOESM7_ESM.pdf (61 kb)
Online Resource 7 Population genetic parameters inferred from 19 microsatellite markers in 18 populations of Arabis alpina. Number of sampled individuals per population, observed (H o) and expected heterozygosity (H e) and the inbreeding coefficient F IS. (PDF 61 KB)
35_2017_196_MOESM8_ESM.pdf (70 kb)
Online Resource 8 Pairwise F ST values among 18 populations of Arabis alpina in the western Swiss Alps, based on 19 nuclear microsatellite markers. (PDF 69 KB)
35_2017_196_MOESM9_ESM.pdf (1 mb)
Online Resource 9 Genetic clustering determined by STRUCTURE analyses (Pritchard et al. 2000; Hubisz et al. 2009) of the 364 individuals from 22 populations of Arabis alpina from the western Swiss Alps using 19 microsatellite markers for K = 2–15. (PDF 1033 KB)
35_2017_196_MOESM10_ESM.pdf (3.8 mb)
Online Resource 10 Genetic clustering of the 18 populations of Arabis alpina in the western Swiss Alps, using 19 microsatellite markers, determined (a) by STRUCTURE analyses (Pritchard et al. 2000; Hubisz et al. 2009) and by (b) INSTRUCT (Gao et al. 2007), which uses the estimated selfing rate to infer individual memberships. (PDF 3933 KB)


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Copyright information

© Swiss Botanical Society 2017

Authors and Affiliations

  1. 1.WSL Swiss Federal Research InstituteBirmensdorfSwitzerland
  2. 2.Institute of Plant SciencesUniversity of BernBernSwitzerland
  3. 3.Institute of Integrative BiologyETH ZürichZurichSwitzerland

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