Demographic History Inference
Hybrid individuals were removed from downstream analyses due to the levels of heterozygosity, and the remaining individuals were used for the inference of demographic history. The gene flow between each lineage was inferred based on the allele frequency by applying the ABBA-BABA or D statistic using the qpDstat module in AdmixTools 7.0 (Patterson et al., 2012). Based on the phylogeny (((P1, P2), P3), O), the four topologies were tested, with Paraquilegia microphylla andSemiaquilegia adoxoides as outgroups. In addition to the D statistic, we performed gene flow analysis between different populations using allele frequency data with 1-10 migration events by TreeMix v1.1 (Pickrell & Pritchard, 2012). The Python script easySFS was used to calculate the joint site frequency spectrum (SFS) for demographic analysis (https://github.com/isaacovercast/easySFS). We also calculated the likelihood of different demographic scenarios in fastsimcoal2 software (Excoffier et al., 2021) using the joint SFS to infer demographic parameters. Twenty scenarios were set up involving genetic structure and gene flow, including three monophyletic models and sixteen paraphyletic models (Figure S8). For each scenario, fastsimcoal2 performed 10000 coalescent simulations to approximate the expected SFS in each cycle and will run 40 optimization cycles to estimate the parameters. To ensure the accuracy of evaluating the best scenario, each scenario was run 100 times, and the run with the highest likelihood was compared by calculating the Akaike information criterion (AIC) to determine the best scenario. The parameter estimation was run under the best scenario 100 times with each of bootstrapped SFS. A neutral mutation rate of 10-8 and a generation time of 1 year were used to estimate the effective population size, divergence times and migration rates (M. Li et al., 2019).