as a function of time
We inferred the demographic history of the B. schroederi by using
the WGS data generated by DNBSEQ-T1 from one individual. We
simultaneously perform the same analysis on a giant panda by using
resequencing short reads of an individual download from SRA database
(accession SRA053353). For this analysis, we used BWA (v0.7.13-r1126)
(H. Li & Durbin, 2009) to map the clean reads to each genome with the
default parameters. Next, the PSMC method (H. Li & Durbin, 2011) was
used to evaluate the dynamic changes of the effective population size
(Ne ) of B. schroederi and the giant panda. Following Li’s
procedure (H. Li & Durbin, 2011), we applied a bootstrapping approach,
repeated sampling 100 times to estimate the variance of simulated
results for both B. schroederi and giant panda. We used 0.17 and
12 years per generation (g) and a mutation rate (μ ) of
9×10-9 and 1.29×10-8 for B.
schroederi and giant panda, respectively (Cutter, 2008). Since
fluctuations in the effective population size of giant pandas have been
reported to closely reflect changes in climate and atmospheric dust (S.
Zhao et al., 2013), we added the
mass accumulation rate (MAR) of
Chinese loess over the past 250,000 years for comparison. In addition,
we implemented the MSMC2 (Schiffels & Durbin, 2014) which can infer the
recent effective population size history. We phased all SNPs of each
individual by using beagle (v5.0) (Browning & Browning, 2007) using the
following parameters: -i 20 -t 6 -p ’10*1+15*2’. The mutation rate
(μ ) of B. schroederi for MSMC2 was the same as used for
PSMC.