The geographical and environmental determinants of genetic diversity for four alpine conifers of the European Alps
Article first published online: 12 OCT 2012
© 2012 Blackwell Publishing Ltd
Volume 21, Issue 22, pages 5530–5545, November 2012
How to Cite
Mosca, E., Eckert, A. J., Di Pierro, E. A., Rocchini, D., La Porta, N., Belletti, P. and Neale, D. B. (2012), The geographical and environmental determinants of genetic diversity for four alpine conifers of the European Alps. Molecular Ecology, 21: 5530–5545. doi: 10.1111/mec.12043
- Issue published online: 29 OCT 2012
- Article first published online: 12 OCT 2012
- Manuscript Accepted: 21 AUG 2012
- Manuscript Revised: 10 AUG 2012
- Manuscript Received: 18 MAY 2012
Table S1 Sampling locations for Abies alba (A), Larix decidua (B), Pinus cembra (C) and Pinus mugo (D).
Table S2 Environmental variables that were used in the multivariate analysis.
Table S3 (A) Hierarchical estimation of FST at three different levels: across sample sites across geographic areas and across sample sites nested in geographic areas (Hierarchical FST). (B) Estimation of fixation index (FST) of population within geographic area. Each geographic area was analysed separately.
Table S4 (A) Results of the canonical correlation analysis (CCorA) between the arcsine of the population assigned frequency and geographic region, latitude and longitude. Multivariate statistic based on F value and 999 permutations tests. (B) Asymptotic tests of statistical significance on the CCorA coefficients, using F-approximations of Wilks' Lambda.
Table S5 Extract of the correlation matrix for latitude (y) and longitude (x) and the climatic variables (codes are in Table S2) calculated with Pearson's r.
Table S6 (A) Results of the canonical correlation analysis (CCorA) between the climatic variables and geographic region, latitude and longitude. Multivariate statistic based on F value and 999 permutations tests. (B) Asymptotic tests of statistical significance on the CCorA coefficients, using F-approximations of Wilks' Lambda.
Table S7 In each species, several SNPs had a moderate to strong support for association with the first 10 climatic PCs.
Fig. S1 Genotyping data generation.
Fig. S2 The relationship between the log probability of data and the number of clusters (K) in each species.
Fig. S3 Visualization of the correlation matrix for 35 climate and geographical variables calculated with Pearson's r and visualized using HEATMAP function in the base distribution of r.
Fig. S4 Biplot on the reduced PCA on climatic variables.
Fig. S5 Loadings of the first 6 principal components (PCs) for 33 climatic variables in A. alba (A), L. decidua (B), P. cembra (C) and P. mugo (D).
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