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Appendix S1 - Supplementary Methods Study group Androsace is a plant genus of c. 110 species, most of them found in alpine habitats. The map below (Fig. S1) shows that a high proportion of Androsace species are found in mountainous areas.

Fig. S1 World map of Androsace species richness with a resolution of 1 arc-degree. The grey layer shows areas with an altitude higher than 1000 m. Occurrence data was extracted from the Global Biodiversity Information Facility database (GBIF, http://www.gbif.org).

 

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Biogeographic inference Biogeographic analyses were conducted with Lagrange (Ree & Smith, 2008) at two scales: continental and regional. At the continental scale, we aimed to search for broader patterns in the biogeographic reconstruction and to test which continental delimitation (Fig. S1) suits best

Androsace

species

based

on

their

likelihood.

Fig. S2 Four biogeographic models with different continental delimitation that where tested with the dispersal-extinction-cladogenesis method. The differences (remarked by a circle) are on whether the Caucasus and Asia Minor regions are grouped with Europe or with the rest of Asia; and whether the Beringian Asian region is grouped with North America or with the rest of Asia. The best scoring model was A.

At the regional scale, two types of biogeographic models were compared: a baseline model without dispersal constraints; and a stepping-stone model, with different dispersal probabilities for neighbouring and non-neighbouring areas (Fig. S2), testing for a range of probabilities: we fist tested 10 values equally ranged from 0.1 to 1, and because the best value was obtained with 0.1, we tested then 10 values equally ranged from 0.01 to 0.1. Neighbour areas were defined as areas that are adjacent or at least that do not have another area or sea between them that could act as a barrier, except for Arctic Asia and Arctic North America, which were considered as neighbouring areas due to the lability of the Bering strait, where a land bridge has emerged at various geological periods (Hopkins, 1967; Wen, 1999). The species geographic ranges were defined integrating data from the Global Biodiversity Information Facility database (GBIF, http://www.gbif.orf) and information of several floras (Flora of China (Hu & Kelso, 1996); Flora Europaea (Ferguson, 1972); Flora Ibérica (Kress, 1997); Flora of North America (Cholewa & Kelso, 2009); Flora of Pakistan (Nasir, 1984); Flora of the U.R.S.S. (Shishkin & Bobrov, 1952)).

 

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Fig. S3 Diagram showing the stepping-stone biogeographical model used with Lagrange. Arrows show dispersals for which probability was set as equal to 1, while dispersals between areas without arrows were set to a lower value. All dispersals are bidirectional.

  Climatic vicariance analysis To detect whether diversification in Androsace has been influenced by climatic vicariance, we used the statistical analyses implemented in SEEVA (Struwe et al. 2011). The rationale behind SEEVA is that phylogenetic nodes (i.e. cladistic splits) can reflect ecological or geographical splits (i.e. ecological or spatial vicariance). Statistically, it can be evaluated whether nodes are associated to specific ecological splits. SEEVA tests this expectation, with a null hypothesis that ecological and phylogenetic splits are independent. For instance, if species’ divergence were systematically associated with events of climatic vicariance, one would conclude that climate caused parapatric speciation in the study-group. To address this issue, we tested whether there was a significant divergence for the climatic niche between all sister lineages within Androsace with SEEVA. This software works using data from individuals or populations and a phylogeny. Quantitative data is grouped into quantiles; a contingency table for each node and variable is created; and a Fisher exact test is performed to test for skewed patterns between sister clades. Because an independent test is computed for each node (here, 41 in total), a Bonferroni correction (Rice, 1989) has to be applied to define the significance level of P. In addition, SEEVA computes for each node and variable an index of divergence (D, with values from 0 to 1) that allows to compare the strength of phylogenetic-ecological associations of different variables for a given node.

 

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Occurrence data for the SEEVA analyses was extracted from Boucher et al. (2011), for which 19 « bioclim » variables were obtained for 51 species from the wordclim database (Hijmans et al. 2005, http://worldclim.org). We selected 5 variables based on a principal component

analysis

(PCA):

annual

mean

temperature,

temperature

seasonality,

isothermality, annual precipitation and precipitation seasonality. The phylogeny used corresponded to the 50% majority-rule consensus phylogeny from Boucher et al. (2011)

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