Figure 1.

B. bruxellensis population clusters identification by combining different tools and parameters. (A) Dendrogram using Bruvo’s distance and NJ clustering. The figure was produced using the poppr package in R. (B) Dendrogram using Bruvo’s distance and UPGMA clustering. The figure was produced using poppr. Isolates are shown in the same colours as in A. (C) Multidimensional scaling performed with Bruvo’s distance matrix on the same dataset and using the cmdscale function on R. For isolates with incomplete genotyping, the missing data was inferred from the closest neighbour using Bruvo’s distance. Isolates are shown with the same colours as in A. (D) Node reliability using the partition method⁵⁰. Only the nodes with reliability >90% are shown on the NJ tree. (E) Cluster identification using successive K-means. The find.cluster function from the adegenet package in R was applied, using within-groups sum of squares (WSS) statistics and the default criterion diffNgroup. This tool identifies an optimal number of 3 clusters, represented on the NJ tree using different arbitrary colours. (F) Inferred ploidy. The maximum number of alleles per locus was computed. Isolates with up to 2 alleles/locus were considered as diploid (2n). Isolates with up to 3 alleles/locus were considered as triploid (3n), and the number of loci showing up to 3 alleles was recorded (1–2 loci, or more than 2 loci showing up to three alleles). Finally, isolates with up to 4 or 5 alleles/locus were noted as 4n/5n. The inferred ploidy is represented on the NJ tree.