2.3 Phylogenetic Analysis
A total of 35 species (3 newly determined in this study, 32 available
from GenBank) representing seven subfamilies of Hesperiidae were used to
construct the phylogenetic relationships. The ingroup contains 5 species
of Coeliadinae, 1 species of Euschemoninae, 2 species of Pyrginae, 4
species of Tagiadinae, 2 species of Eudaminae, 3 species of
Heteropterinae, 2 species of Barcinae and 16 species of Hesperiinae. The
4 Papilionidae species (P. machaon , P. helenus , G.
timur and P. apollo ) were selected as outgroups (Table 1).
The complete mitogenome genes were extracted using PhyloSuite v1.2.2 and
the sequences of 13 PCGs of the 39 species were aligned in batches with
MAFFT integrated into PhyloSuite. Nucleotide sequences were aligned
using the G-INS-i (accurate) strategy and codon alignment mode. All
rRNAs were aligned in the MAFFT with the Q-INS-i strategy (Katoh &
Standley, 2013). Poorly matched sites in the alignments were removed
using Gblocks v0.91b (Castresana, 2000). Individual genes were also
concatenated using PhyloSuite v1.2.2.
We used 3 datasets to reconstruct the phylogenetic relationship: (1) PCG
matrix, containing
all
codon positions of the 13 protein-coding genes; (2) PRT matrix,
concatenating all codon positions of the 13 protein coding genes, 22
tRNAs and 2 rRNAs; and (3) 12PRT matrix, including the first and second
codon positions of 13 protein-coding genes plus 22 tRNAs and 2 rRNAs.
Based on 3 datasets, the maximum likelihood (ML) and Bayesian inference
(BI) methods were used to reconstruct the phylogeny. The optimal
partitioning scheme and nucleotide substitution model for ML and BI
phylogenetic analyses were selected using PartitionFinder 2.1.1
(Lanfear, Frandsen, Wright, Senfeld, & Calcott, 2017) with the greedy
algorithm and BIC (Bayesian information criterion) criteria (Tables S3
and S4). Maximum likelihood analysis was inferred using IQ-TREE (Nguyen,
Schmidt, Von Haeseler, & Minh, 2015) with the ultrafast bootstrap (UFB)
approximation approach (Minh, Nguyen, & von Haeseler, 2013), as well as
the Shimodaira-Hasegawa-like approximate likelihood-ratio test (Guindon
et al., 2010), and the bootstrap value (BS) of each node of the ML tree
was evaluated via the bootstrap test with 10,000 replicates. Bayesian
inference was carried out using MrBayes 3.2.6 (Ronquist et al., 2012)
with the following requirements: 2 independent runs of
1×107 generations were conducted with four independent
Markov Chain Monte Carlo (MCMC) runs, including 3 heated chains and a
cold chain, by sampling every 1,000 generations. A consensus tree was
obtained from all the trees after the initial 25% of trees from each
MCMC run was discarded as burn-in, with the chain convergence assumed
after the average standard deviation of split frequencies fell below
0.01. The confidence value of each node of the BI tree was presented as
the Bayesian posterior probability (BP).
3. Results and Discussion