2.5.2 Genetic diversity, inbreeding and mean kinship
We used the genetic_diversity function within the “gstudio”
package v1.5.3 (Dyer 2021) to calculate mean (± standard error and
standard deviation) genetic diversity metrics across the 10
subpopulations. These metrics included the number of alleles (A),
effective number of alleles (AE), observed
heterozygosity (HO), expected heterozygosity
(HE). We used the standard correction for small sample
size to account for potential biases in the estimates of
HE due to variation in the number of samples obtained
from subpopulations. We used “diveRsity” v1.9.90 (Keenan et al. 2013)
to calculate subpopulation-level Wright’s inbreeding coefficients
(FIS) values with confidence intervals via 1000
bootstraps. We then used “PopGenReport” v3.0.7 (Gruber & Adamack
2022) to calculate mean allelic richness (AR) in the
subpopulations and “poppr” to calculate (i) mean private alleles
within all subpopulations bootstrapped to sample size of 5; and (ii) the
number of private alleles only within subpopulations of the King Island
scrubtit. Finally, we calculated individual inbreeding coefficients
(IIC) using the ‘mom.weir’ method with thesnpgdsIndInbCoef function in “SNPRelate” (Zheng et al. 2012).
This method uses a modified Visscher’s estimator (Yang et al. 2010).
We calculated relatedness using COANCESTRY (Wang, 2011), firstly only
within the King Island subpopulations, and then within all 10
subpopulations together (King Island and mainland Tasmania). We first
ran simulations as per Hogg et al (2019) to determine the best moment
estimator to use. This was the triadic likelihood method (TrioML), as it
weights loci relative to the number of alleles and accounts for
genotyping error and inbreeding (Wang, 2007). Results are presented as
mean kinship values (MK), calculated by dividing the TrioML value by 2.