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Fig. S1 Graphic representation of three scenarios on the colonization history of three R. affinis subspecies (R. a. himalayanus, R. a. macrurus and R. a. hainanus) based on the mtDNA control region dataset in the DIY ABC analysis.

Fig. S2 Graphic representation of three scenarios on the colonization history of three R. affinis subspecies (R. a. himalayanus, R. a. macrurus and R. a. hainanus) based on microsatellite dataset in the DIY ABC analysis.

Fig. S3 (a) Bayesian tree for Chd1; (b) Bayesian tree for SWS1; (c) Bayesian tree for USP9x. Trees were rooted with R. sinicus (not shown).

Fig. S4 (a) Mean likelihood values with SE based on ten runs for each K (from 2 to 5); Clustering of R. a. himalayanus and R. a. macrurus individuals based on no-admixture model (b) and admixture model (c) in STRUCTURE forced for K = 2 to 4 inclusive; (d) Clustering of all individuals of R. a. himalayanus, R. a. macrurus and R. a. hainanus based on the admixture model in STRUCTURE for K = 2. Each bar corresponds to one individual and the color of each bar represents an individual's probability to clusters.

Table S1 Summary of the number of individuals used for genetic analyses in each locality

Table S2 Geographical descriptions of mitochondrial and nuclear haplotypes in this study. Introgressed mtDNA haplotypes are shown in bold

Table S3 Demographic analysis parameters for populations showing evidence of population expansion

Table S4 Geographical descriptions of gene sequences used in this study with GenBank accession numbers

Table S5 Posterior probabilities of each scenario in DIY ABC analysis based on mtDNA dataset, obtained through a logistic regression computed every 10% of the number of selected datasets (a); mean, median and mode values and four quantiles of the posterior distribution of model parameters under scenario 2 (b)

Table S6 Posterior probabilities of each scenario in DIY ABC analysis based on microsatellite dataset, obtained through a logistic regression computed every 10% of the number of selected datasets (a); mean, median and mode values and four quantiles of the posterior distribution of model parameters under scenario 1 (b).

Fig. S1 Graphic representation of three scenarios on the colonization history of three R. affinis subspecies (R. a. himalayanus, R. a. macrurus and R. a. hainanus) based on the mtDNA control region dataset in the DIY ABC analysis.

Fig. S2 Graphic representation of three scenarios on the colonization history of three R. affinis subspecies (R. a. himalayanus, R. a. macrurus and R. a. hainanus) based on microsatellite dataset in the DIY ABC analysis.

Fig. S3 (a) Bayesian tree for Chd1; (b) Bayesian tree for SWS1; (c) Bayesian tree for USP9x. Trees were rooted with R. sinicus (not shown).

Fig. S4 (a) Mean likelihood values with SE based on ten runs for each K (from 2 to 5); Clustering of R. a. himalayanus and R. a. macrurus individuals based on no-admixture model (b) and admixture model (c) in STRUCTURE forced for K = 2 to 4 inclusive; (d) Clustering of all individuals of R. a. himalayanus, R. a. macrurus and R. a. hainanus based on the admixture model in STRUCTURE for K = 2. Each bar corresponds to one individual and the color of each bar represents an individual's probability to clusters.

Table S1 Summary of the number of individuals used for genetic analyses in each locality

Table S2 Geographical descriptions of mitochondrial and nuclear haplotypes in this study. Introgressed mtDNA haplotypes are shown in bold

Table S3 Demographic analysis parameters for populations showing evidence of population expansion

Table S4 Geographical descriptions of gene sequences used in this study with GenBank accession numbers

Table S5 Posterior probabilities of each scenario in DIY ABC analysis based on mtDNA dataset, obtained through a logistic regression computed every 10% of the number of selected datasets (a); mean, median and mode values and four quantiles of the posterior distribution of model parameters under scenario 2 (b)

Table S6 Posterior probabilities of each scenario in DIY ABC analysis based on microsatellite dataset, obtained through a logistic regression computed every 10% of the number of selected datasets (a); mean, median and mode values and four quantiles of the posterior distribution of model parameters under scenario 1 (b).

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