Results
An average of 2848 reads per sample were assigned to the reference ofC. nozawae (Table 1), all with sequence identity of
>99.4%. A total of 66 haplotypes were detected from eDNA,
whereas 58 haplotypes were detected from tissue samples. Among the
haplotypes detected from eDNA, 35 haplotypes matched with 100% identity
to haplotypes detected from tissue samples, and the other haplotypes
matched with 99.4%–99.7% identity (representing differences of 1–2
bases). The number of haplotypes detected in each population was 2–17
(mean: 8.62) in eDNA and 1–11 (mean: 6.43) in tissue samples, and the
haplotype diversity h S ranged from 0.01–0.83
(mean: 0.57) in eDNA and 0.00–0.89 (mean: 0.64) in tissue samples.
Global G ST was 0.33 in eDNA and 0.29 in tissue
samples, and the average D PS was 0.86 in both
eDNA and tissue samples. Pairwise G ST ranged from
0.01–0.90 (eDNA) or 0.01–0.89 (tissue), and D PSranged from 0.17–1.00 (eDNA) or 0.25–1.00 (tissue) (Table S3).
The spatial distribution of haplotypes was generally consistent between
eDNA and tissue samples (Figure 2). Some major patterns of the spatial
structure could be commonly found, such as the dominance of a unique
haplotype in Pop 3, the presence of haplotypes commonly found at many
sites in Pop 1–11, and Pop 12 being composed almost entirely of one
haplotype also found in the downstream sites (Pop 13–15). Compared with
the results of the STRUCTURE analysis using SNP data, some patterns such
as the presence of spatial structure or the uniqueness of Pop 3 were
common, but there were some differences from D-loop region results, such
as the absence of a clear differentiation between Pop 1–11 and Pop
12–13 in SNP data.
The statistics of genetic diversity and differentiation calculated from
eDNA were positively correlated with those calculated from tissue
samples (Figures 3 and S2). For genetic differentiation, the correlation
coefficient was r = 0.73 (p < 0.001) forG ST and r = 0.83 (p < 0.001) forD PS. From the spatial autocorrelation analysis by
the Mantel correlogram, r values were consistently positive up to
approximately 30 km and significant up to at least 15 km in all genetic
markers, indicating that gene flow can be considered particularly strong
in this range. Correlations of genetic differentiation calculated
between eDNA and SNP data were low when calculated for all 21
populations, but there was some correlation when calculated only for
population pairs less than 15 km apart (Figure 3, Table 2).
Additional data treatment of the eDNA dataset did not increase the
correlation of its statistics with tissue-based statistics (Tables 2 and
S4). Treatment 2, which involved bold data removal, significantly
worsened the correlation in most datasets (95% CI not overlapping). In
terms of the correlation coefficient alone, treatment 3
(semi-quantitative approach) sometimes outperformed the no treatment
dataset, although the differences were not significant.
From the basic analysis including spatial and water temperature data,
the detection patterns from our eDNA data were congruent with those of
previous studies, with a significant correlation between genetic
differentiation and waterway distance (r = 0.50, p <
0.001 for 19 populations; r = 0.52, p < 0.01 for
upstream populations; Figures 4 and S3) and no significant correlation
between genetic differentiation and water temperature differences (r =
0.20, p = 0.16 for 19 populations; r = 0.32, p = 0.06 for
upstream populations).