2.6 Inference of demographic history
For ancestral area reconstruction, we used seven groups of C.
chuniana for S-DIVA (statistical dispersal-vicariance analysis)
analysis implemented in RASP v3.2 (Ronquist 1997; Yu, Harris, Blair, &
He, 2015). The analysis was based on the BEAST MCMC trees and the
maximum clade credibility tree derived from the Bayesian analysis with
BEAST and TreeAnnotator (Matuszak, Muellner-Riehl, Sun, & Favre, 2016).
With this method, the frequencies of an ancestral area at a node in the
ancestral reconstructions are averaged over all trees. Dispersal or
vicariance events were also detected with S-DIVA.
We applied coalescent simulations with the program fastsimocoal2 (FSC2;
Excoffier, Dupanloup, Huerta-Sanchez, Sousa, & Foll, 2013) to provide
model evidence of divergence, secondary contact, bottleneck effects, and
demographic expansion. The populations in the Nanling Mts., which formed
a monophyletic group and showed a distinct geographic location, were
considered as one group (NL), and the remaining populations as another
(ES). We used easySFS
(https://github.com/isaacovercast/easySFS)
to transform SNPs into a folded site frequency spectrum (SFS), based on
the construction of 10 demographic models with the two groups (Figure
S2). The models are: without isolation (NIS), isolation only (IS),
isolation followed by migration (MIG), bottleneck effect (BOT), or
secondary contact (SEC). Models including ancient (AMIG) or recent
migration (RMIG), bidirectional or one-way migration, and demographic
expansion (EXP) were also applied. In each model, NL or ES were
alternatively used as the split source that was subjected to each
scenario. We estimated effective population size (Ne ), time
(T ) and migration rates in individual migrants per generation
(MNL-ES and MES-NL ) for the two
groups in each model from posterior distributions. To scale parameter
estimates into real values, we used the substitution/site/generation
mutation rate of 1.16×10-7 based on our research (Liu
et al., unpublished data, 2020), because no genomic mutation rate has
been calculated for Cercis . We estimated a generation time of
five years for C. chuniana based on estimates for its congeners
(Aldworth, 1998; Chen & Mao, 1999). We ran 100 replicate FSC2 analyses
under each model with 10,000 simulations for optimal parameters and
composite likelihood estimation. All 10 demographic models were compared
(Figure S2, Tables S2‒S4). The composite likelihood of arbitrarily
complex demographic models under the given SFS was calculated by using
best-fit models based on the Akaike information criterion (AIC). The
models with the lowest AIC were chosen as the best fit of the data.