Estimating ploidy
Since the study was based on herbarium vouchers, chromosome counting or
genome size estimates by flow cytometry were not possible. Instead, we
bioinformatically estimated the ploidy of 32 specimens for which we
obtained an estimated nuclear genome coverage of at least three
following mapping. Data for each specimen were merged and used to
estimate ploidy in HMMploidy, a program that combines information of
sequencing depth and genotype likelihoods to estimate ploidy levels
(Soraggi et al., 2021). A multi-sample mpileup file was generated in
SAMtools v.1.10 for all genome scaffolds longer than 10kb using only
reads with a minimum mapping quality of 30 and only calling sites with a
minimum quality of 30, counting anomalous reads, and setting the maximum
per-file depth to 50. Genotype likelihoods were then calculated in
HMMPloidy using default settings and ploidy levels were inferred in 10kb
windows (total of 5,224 windows). The percentage of 10kb windows
supporting each ploidy level (1n-4n; no windows supported a ploidy level
larger than 4n) were calculated. Specimens for which at least 60% of
the windows supported a single ploidy were assigned to one of four
categories: 1n=“likely diploid”, 2n=“diploid”, 3n=“likely
polyploid”, or 4n=“polyploid”. The estimates for specimens not
assigned to any of these categories were considered uncertain.