4. Population structure, genetic diversity, and genetic
differentiation
Haplotype frequencies based on relative read counts (eDNA) or the number
of individuals (tissue) within each site were plotted and compared.
Additionally, the spatial patterns of haplotype frequencies were
compared with the population structure inferred from genome-wide SNP
data.
To identify overall trends in intrapopulation genetic diversity,
haplotype diversity (h S;\(h_{S}=1-\sum_{i}{p_{i}}^{2}\), where \(p_{i}\) is the haplotype
frequency of the i -th haplotype) and haplotype richness
(hr ; number of haplotypes corrected for differences in the number
of individuals or total read counts among populations; haplotype version
of allelic richness (El Mousadik and Petit 1996)) of each population
were calculated. For interpopulation genetic differentiation, we first
calculated Nei’s F ST (G ST;
Nei 1973), as haplotype differentiation in Hudson et al. (1992) (i.e.,
it does not matter whether two haplotypes differ by one nucleotide or by
tens). Other than G ST, we also used the allele
frequency distance (AFD; Berner 2019) with the modification of using
haplotypes instead of alleles. AFD is considered less susceptible to
differences in sample size between populations and more sensitive in the
range of weak differentiation that is of interest in local scale studies
(Berner 2019). In addition, this metric is identical toD PS (1 − proportion of shared alleles; Bowcock et
al. 1994), which is known to reflect recent gene flow
(~10 generations) particularly relevant to management
and the purpose of landscape genetics (Leroy et al. 2018; Savary et al.
2021). Hereafter, we refer to this metric as D PS,
although this notation has not yet been used for haplotype-based
analysis. The calculation process of D PS for two
arbitrary populations Pop A and Pop B is\(D_{\text{PS}}=(\ \sum_{i}{\left|p_{\text{iPop}A}-p_{\text{iPop}B}\right|\ )/2}\),
where \(p_{\text{iPop}A}\) and \(p_{\text{iPop}B}\) are the haplotype
frequencies of the i -th haplotype in Pop A and Pop B,
respectively. As this study focuses on local genetic differentiation
reflecting recent gene flow, subsequent analyses and discussions are
primarily based on D PS, while analyses using more
common and well-known G ST statistics were also
conducted.
To clarify the spatial scale at which gene flow is more dominant than
genetic drift for genetic differentiation, Mantel correlograms
displaying the spatial correlation of D PS (as
simple genetic distance available in eDNA) and geographic distance for
each 7.5 km distance class until 60 km and then from 60 km to 128 km
(maximum value) were generated. Within the distance classes where the
correlation coefficient is significantly positive, gene flow among
populations is considered to be particularly active (Diniz‐Filho and De
Campos Telles 2002). The correlogram in each distance class was assessed
with 9999 permutations using the package ecodist 2.0.9 (Goslee and Urban
2007) in R 4.2.1 (R Core Team 2022).
Genetic differentiation statistics were also calculated by SNP data
(Text S1). Generally, genetic diversity and genetic drift pressure
differ between nuclear DNA and mtDNA (Toews and Brelsford 2012; Morin et
al. 2018; Saitoh 2021). However, at the spatial scale at which gene flow
is dominant, because the theoretical pattern of being inversely
proportional to migration is common (Allendorf et al. 2022), genetic
differentiation should be synchronized regardless of markers in the
absence of sex differences. On the spatial scale explicitly stated, the
more informative SNP data were used as the known truth.