Skip to main content

Ancient rivers shaped the current genetic diversity of the wood mouse (Apodemus speciosus) on the islands of the Seto Inland Sea, Japan


The current distributions of organisms have been shaped by both current and past geographical barriers. However, it remains unclear how past geographical factors—currently cryptic on the sea floor—affected the current distributions of terrestrial animals. Here, we examined the effects of currently cryptic ancient rivers on current genetic differentiation of the large Japanese wood mouse, Apodemus speciosus, which inhabits islands in the Seto Inland Sea, Japan. Genome-wide polymorphisms were identified by GRAS-Di (Genotyping by Random Amplicon Sequencing, Direct) analysis of 92 A. speciosus individuals. Maximum-likelihood analysis was performed with 94,142 single nucleotide polymorphisms (SNPs) identified by GRAS-Di analyses. Ancient rivers were visualized by Geographic Information System (GIS) channel analysis. Maximum-likelihood analysis showed strong support for the monophyly of each population in the islands in the Seto Inland Sea; it also showed close relationships between Innoshima-Ikuchijima, Ohmishima-Hakatajima-Oshima, Ohmishima-Hakatajima, Ohsakikamijima-Ohsakishimojima, Kamikamagarijima-Shimokamagarijima, and Kurahashijima-Etajima islands. The principal component analyses of the SNPs also supported these relationships. Furthermore, individuals from islands located on the east and west sides of the main stream of the ancient river were clustered on each side with strong support. These phylogenetic relationships were completely congruent with the paleogeographic relationships inferred from ancient rivers. In conclusion, the findings demonstrated that the current distribution of genetically distinct island lineages was shaped by ancient rivers that are currently submerged beneath the Seto Inland Sea, Japan.


Biogeography is the most fundamental research field in evolutionary biology to explore the geographical factors that shape the current distribution of organisms [1]. The distribution of terrestrial animals is often defined by the presence of contemporary factors on the surface of the land or water. However, the effects of cryptic geographical factors that existed in the past on the evolution of terrestrial animals have not been extensively explored. Past global climate change during the Pleistocene glacial/nonglacial cycles induced sea level fluctuations [2, 3]. Therefore, islands surrounded by shallow sea floor would have been considerably affected by changes in sea level [4, 5], whereby the current sea floor may have characteristics that contributed to the current biogeographic patterns of terrestrial animals. Although the effects of changes in the island-area and/or connectivity due to Late Quaternary sea-level oscillations on the current biodiversity were examined previously (e.g., plants [6]; insects [7]), those of currently submerged structures have not been assessed. An understanding of these cryptic geographical factors would provide novel insights into the lineage differentiation of organisms that cannot be understood in the framework of contemporary biogeography.

The Japanese archipelago is composed of 6852 islands with more than 0.1 km of coastline (Geospatial Information Authority of Japan); this provides the opportunity to explore the effects of cryptic geographical factors because of the arrangement of the islands, surrounded by shallow seas. Among the islands in the Japanese archipelago, the Seto Inland Sea (the largest inland sea in Japan) possesses more than 700 islands (Ministry of Environment, Japan). Because the mean depth of the inland sea is ca. 38 m (Ministry of Environment, Japan), the current Seto Inland Sea was not present during the last glacial period, when the sea level dropped below this depth; the global sea level was ca. 120–135 m lower than today at the last glacial maximum (LGM) [2, 3, 8]. Therefore, the origin of the sea is comparatively young, beginning in the earlier Holocene because of the marine transgression (i.e., the Jomon transgression; [9]). Previous geological and ostracod paleobiogeographical studies inferred that the islands in the Seto Inland Sea began formation because of sea water invasion at ca. 11 ka (kilo annum); the current form was established by ca. 8 ka [10,11,12]. However, the geological history of the islands of the Seto Inland Sea is poorly known because of limited geological information present at the sea floor. Two major paleo-drainages (hereafter referred to as ancient rivers; Additional File 1) were inferred from the seafloor topography, the Hoyo and Kitan Rivers; these may have flowed into the Pacific Ocean through Bungo (between Shikoku and Kyushu islands) and Kii (between Shikoku and Honshu islands) channels, respectively, affecting the formation process of the islands of the Seto Inland Sea [13, 14]. These ancient rivers are assumed to have already been present during LGM and played an initial role to form the islands in the transgression. Previous biogeographic studies of a plant (Rhododendron ripense; [15]), snails (Semisulcospira spp.; [16]), and fish (Japanese rosy bittering Rhodeus ocellatus kurumeus; [17]) in the main Japanese islands (Honshu, Shikoku, and Kyushu) suggested the influence of ancient rivers on the current population genetic structures of these species. To our knowledge, there have been no reports regarding the relationships between ancient rivers and the phylogeography of organisms inhabiting the islands in the Seto Inland Sea; the formation of this phylogeography was presumably directly influenced by ancient rivers. Clarifying the effects of the ancient river on the population genetic structure of organisms in the Seto Inland Sea requires additional lines of evidence.

Terrestrial organisms on islands are potential candidates to understand the geological relationships among islands. The large Japanese wood mouse, Apodemus speciosus, is an old terrestrial mammal in Japan, originating at around 6 Ma [18, 19] (see [20] for review of origins of mammals in Japan); it is distributed in major and adjacent small islands throughout the Japanese archipelago except the Ryukyu Islands [21]. It is also adapted to the deciduous forest ecosystems in the islands of the Seto Inland Sea [22, 23]. Therefore, analysis of the wood mice in these islands is expected to provide information concerning the geological history of the islands in the Seto Inland Sea. Sato et al. (2017) examined the genetic variations of the wood mouse populations in the islands of the Seto Inland Sea using a portion of the nucleotide sequence of the mitochondrial Dloop region (ca. 300 bp); they reported that the genetic diversity was reduced on smaller islands and that the Dloop haplotypes were not shared among islands [24]. These results suggested that the wood mouse populations are genetically homogeneous on each island because of genetic drift and differentiated from each other by island separation [24]. However, interrelationships among the Dloop haplotypes from these islands were unclear because of the low resolution related to the use of only a short fragment of the Dloop region (ca. 300 bp).

Because of recent progress in next-generation sequencing (NGS) technology, this methodology can be used to obtain data regarding large numbers of polymorphisms in the genome. Several genome-wide reduced-representation sequencing approaches have been developed, such as RAD-seq [25], MIG-seq [26], and GRAS-Di [27, 28]. The GRAS-Di method (i.e., Genotyping by Random Amplicon Sequencing, Direct) was first reported in conference proceedings by Toyota Motor Corporation [27, 28]; it is a PCR-based genotyping-by-sequencing technique that uses only 3-bp random multiplexed primers. Although it has attracted little attention, GRAS-Di is considered a useful alternative to other genotyping-by-sequencing methods, such as RAD-seq or MIG-seq [29]. It can detect thousands of SNPs in the genome, usually larger than the SNPs detected by MIG-seq, and does not require a large amount of high-quality DNA, in contrast to RAD-seq [29]. This method has only recently been used for various organisms in the ecological and evolutionary context, including vertebrates [29], invertebrates [30], and plants [31]. For example, Hosoya et al. (2019) examined the estuarine mangrove fishes around the Ryukyu Islands using the GRAS-Di method; they also resolved the population genetic structures among islands for some mangrove fish species, suggesting that this is a powerful method to assess intraspecific genetic variations [29]. The ability of this method to identify genome-wide polymorphisms could be informative for wood mice on the islands of the Seto Inland Sea.

In this study, we examined genetic variations among wood mice inhabiting the islands of the Seto Inland Sea using the GRAS-Di method; we tested the hypothesis that ancient rivers could explain the genetic relationships among the wood mice on these islands.


Samples examined in this study

We used DNA samples from 92 individuals of the large Japanese wood mouse, A. speciosus, consisting of 80 samples extracted in our previous studies [24, 32] and 12 newly obtained samples from individuals in the field survey described below (Table 1). Wood mouse individuals were captured in Kure (Kre) on Honshu Island, Kurahashijima Island (Kra), and Etajima Island (Eta) using Sherman live traps baited with oats (Avena sativa) in 2019 (Fig. 1B; Table 1). After the mice had been captured, we collected several tissue samples using an ear punch for genetic analyses; we then released the animals to each sampling point. We obtained permission from Hiroshima Prefecture to perform the field survey and laboratory experiments on the wood mice; the study protocol was approved by the Animal Care and Use Committee of Fukuyama University (H30-Animal-8). We also followed the guidelines of the Procedure of Obtaining Mammal Specimens established by the Mammal Society of Japan.

Table 1 Samples and sequence reads examined in this study
Fig. 1
figure 1

A Maximum likelihood tree estimated using the GTR + G model by Iqtree based on 94,142 single nucleotide polymorphisms detected in GRAS-Di analysis. Mid-point rooting was used to construct the phylogeny. The numerals above the branches are bootstrap values estimated by ultrafast bootstrap approximation (10,000 replications). These values are only shown for the clades of each island in the Seto Inland Sea and close relationships between these islands A–F. Full information regarding the terminal samples and bootstrap values is shown in Additional File 4. B Sampling localities of the examined individuals and genetic relationships (red enclosures) demarcated on the basis of the phylogenetic tree A. The inset in the lower right corner shows a wider map of western Japan with longitude and latitude information. Letters (AF and each locality code) are consistent with the letters in A. The gray lines in the Seto Inland Sea are ancient rivers that are currently submerged, according to estimations by QGIS. The gray lines in Honshu and Shikoku are extant rivers. C Comparisons between PC1 and PC2 obtained by a principal component analysis with the program PLINK1.9. Letters (AE and each locality code in the legend) are consistent with the letters in A and B. D The same as for C except for removal of individuals AE. Letter F and locality code in the legend are consistent with those in A, B, and C

DNA extraction, PCR, and sequencing

DNA was extracted from ear tissue samples using a DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany), in accordance with the manufacturer’s instructions. The extracted DNA concentration was calculated using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and used to determine the amount of DNA input for PCR analyses.

Two-step PCR was used to construct a library for NGS via the GRAS-Di method. Two consecutive rounds of PCR were performed using a PrimeSTAR® HS DNA Polymerase kit (Takara, Kusatsu, Japan) on a LifeECO thermal cycler (Bioer Technology, Hangzhou, China). The first PCR mixture (25 μL) contained 5 × PrimeSTAR Buffer (Mg2+), 0.2 mM of each dNTP, 40 pM of multiplexed primer mix (for each random primer, see Additional File 2), PrimeSTAR HS DNA Polymerase (0.625 U/sample), template DNA (15 ng), and PCR-grade water. The first round of PCR was performed with an initial denaturation step at 98 °C for 2 min, followed by 30 cycles of denaturation at 98 °C for 10 s, annealing at 50 °C for 15 s, and extension at 72 °C for 20 s.

The second round of PCR was performed directly on PCR products obtained from the first round of PCR in a mixture (50 μL) containing 5 × PrimeSTAR Buffer (Mg2+), 0.2 mM of each dNTP, 0.25 pM of forward (AATGATACGGCGACCACCGAGATCTACAC-Index2-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG) and reverse (CAAGCAGAAGACGGCATACGAGAT-Index1-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG) primers (where Index 1 and 2 are 8-bp strings of bases for demultiplexing samples after the NGS run), PrimeSTAR HS DNA Polymerase (1.25 U/sample), product from the first round of PCR (1.5 μL), and PCR-grade water. The 3′-end sequences of each primer were designed to prime the primer regions from the first round of PCR. The 5′-end sequences of each primer were adapters for binding to the Illumina NGS flow cell (Illumina, San Diego, CA, USA). The second round of PCR consisted of an initial denaturation step at 95 °C for 2 min, followed by 25 cycles of denaturation at 98 °C for 15 s, annealing at 55 °C for 15 s, and extension at 72 °C for 20 s, with a final extension at 72 °C for 1 min. Products from the second round of PCR were mixed in equal volumes and purified using a Mini Elute PCR Purification Kit (Qiagen).

The concentrations of the libraries were assessed using a Synergy H1 microplate reader (BioTek, Winooski, VT, USA) with QuantiFluor dsDNA System (Promega, Madison, WI, USA); the qualities of these libraries were examined using a Fragment Analyzer (Agilent Technologies, Santa Clara, CA, USA) with a dsDNA 915 Reagent Kit (Agilent Technologies). Finally, NGS was performed using an Illumina HiSeq X Ten with HiSeq X Ten Reagent Kit v2 (150 cycles, paired-end). Raw FASTQ data for each sample generated from the NGS run were deposited in the DDBJ SRA database (DRA013162). Experiments from PCR to sequencing were performed by Bioengineering Lab. Co., Ltd. (Sagamihara, Japan).

Data filtering and SNP calling

For FASTQ data generated from the NGS run and demultiplexed into each individual, Sickle version 1.33 [33] was used to trim sequence data into only data with quality > Q30 and length > 100 bp. FASTX-Toolkit version 0.0.14 (, Accessed 2 December 2021) was then used to unify the length of the examined sequence to 100 bp (fastx_trimmer).We then used Bowtie2 version [34] to map these trimmed sequences to the reference genome of Apodemus speciosus (Accession code in NCBI: Aspe_assembly01 [35]) and obtained “.sam” file, followed by conversion of “.sam” into sorted “.bam” file in Samtools version 1.7 [36]. With these sorted “.bam” files, we applied STACKS version 2.59 [37, 38] with the commands “” and “populations” to obtain the SNPs among samples and the input files for the subsequent phylogenetic and principal component analyses. We extracted a randomly selected SNP from each locus using the “write-random-snp” option in “populations”. We also omitted SNPs sites that showed more than 0.5 heterozygosity and less than 0.05 mean allele frequency. Other parameters in the STACKS were set by default.

Phylogenetic inferences

For phylogenetic analysis, Iqtree version 2.0.3 [39] was used to construct the maximum likelihood tree with the GTR + G substitution model; nodal support in the phylogenetic tree was evaluated using the Ultrafast bootstrap proportion method (10,000 replications) [40]. Because there was no outgroup information in our samples, the midpoint rooting method was used to construct the phylogeny.

Principal component analysis

Using the extracted SNPs data from STACKS, we performed a principal component analysis (PCA). We obtained “.map” and “.ped” files generated by STACKS for examining the PCA in the program PLINK1.9 (, Accessed 10 January 2022; [41]). PLINK produces eigen vector and eigen value files. We then used tidyverse library in the R environment [42] to depict the PCA graphs with ggplot function.

Assessment of correlation between genetic and geographic distances

To assess the correlation between genetic and geographic distances, we compared p-distances calculated in MEGA X [43] based on the SNP data and geographic distances calculated from coordinates (Additional File 3) captured in QGIS version 3.4.12 [44]. The calculation of the geographic distances from coordinates was performed with geosphere library in the R environment. We used vegan library in R to conduct a Mantel test to evaluate the statistical significance between genetic and geographic distances with the pearson method and 9999 permutations.

Visualization of ancient rivers

To visualize ancient rivers from the geological data, channel analysis was performed using QGIS and SAGA GIS version 2.3.2 [45]. The dataset used in this analysis was GEBCO_2019 Grid [46], a continuous terrain model for ocean and land with a spatial resolution of 15 arc seconds. This dataset is composed of digital elevation data obtained by satellites, bathymetry data obtained with multi-beam sonar from ships, or the sea floor topography estimated from satellite altimeters. The SAGA GIS hydrological modeling tool was used for channel analysis. The tool Fill sinks xxl [47] was used to fill surface depressions in the Digital Elevation Model, thereby generating a depression-less elevation layer. A Strahler stream order raster was created from the filled elevation data using the Strahler order tool. Finally, using the SAGA GIS tool, Terrain Analysis Channels, tributaries with Strahler’s stream order 4–8 were extracted.


Extracted data

From GRAS-Di sequencing with the Illumina NGS platform, we obtained a mean (standard deviation) of 531,306 (242,846) total reads among the 92 samples examined (Table 1). These data were filtered for quality > Q30 and length > 100 bp. Consequently, the filtered data yielded a mean (standard deviation) of 401,983 (186,770) reads (Table 1). Sequences examined in the STACKS analyses had a mean (standard deviation) length of 2,688,332 (932,656) bp (Table 1).

Phylogenetic relationships

Through SNP-calling in STACKS analyses, we obtained 94,142 SNPs among 92 individuals. The maximum likelihood tree inferred from these 94,142 SNPs showed that all individuals on each island in the Seto Inland Sea were clustered (Fig. 1A). These clades were supported with high bootstrap values (100%; Fig. 1A; Additional File 4). Individuals in the larger Shikoku Island (Ima, Koc, and Tsu) were also clustered in a clade with high bootstrap value (97%; Fig. 1A; Additional File 4).

With regard to the interrelationships among island individuals, we observed six highly supported relationships among adjacent islands (Fig. 1A and B; labeled A–F): A, Inn (Innoshima)-Iku (Ikuchijima); B, Ohm (Ohmishima)-Hak (Hakatajima)-Ohs (Ohshima); C, Ohm-Hak; D, Osk (Ohsakikamijima)-Oss (Ohsakishimojima); E, Kk (Kamikamagarijima)-Sk (Shimokamagarijima); F, Kra (Kurahashijima)-Eta (Etajima). These relationships were supported by high bootstrap values (100%; Fig. 1A; Additional File 4). Additionally, individuals from eastern islands (A + B; 99% for Inn, Iku, Ohm, Hak, and Ohs) and western islands (D + E; 95% for Osk, Oss, Kk, and Sk) located in each side of the main stream of the hypothetical ancient Hoyo river were clustered in each clade with strong support (Fig. 1A; Additional File 4). Individuals from Muk (Mukaishima) and Kra-Eta were not included in the eastern and western clades, respectively, and were closely related to those from Honshu (Fuk, Ono, and Kre). See Additional File 4 for all bootstrap values in the phylogeny.

Principal component analysis

The result of the PCA analysis was consistent with that observed in the phylogenetic analyses (Fig. 1C). Five of six clusters that were observed in the phylogeny (A, B, C, D, E in Fig. 1A) were also clustered in the PCA plot (Fig. 1C). The cluster F including Kra and Eta individuals was weakly supported in the comparison excluding the individuals within the clade A, B, C, D, and E (Fig. 1D).

Correlation between genetic and geographic distances

There was no correlation between genetic and geographic distances (Additional File 5). Mantel test provided a coefficient of r = -0.08681, which was statistically not significant (P = 0.9413). These results show that the close relationships among island lineages cannot be explained by geographical proximity between the islands in the Seto Inland Sea.

Ancient rivers detected

Through channel analyses, we detected ancient rivers that were presumed to be present in the Seto Inland Sea. We assessed the Strahler’s stream order through 4–8 (Additional File 6) and focused on major streams by combining streams concerned with the island relationships for visual reasons (Fig. 1B). See Additional File 6 for precise information of extracted rivers from each Strahler’s stream order. The major stream (hypothesized to be an ancient river known as Hoyo River) was found to have divided several islands; their division patterns were completely congruent with the phylogenetic relationships estimated above (Fig. 1A and B).


Compared with our previous study using a portion of the mitochondrial Dloop sequence (ca. 300 bp; [24]), we obtained much a larger data set (2,688,332 bp) for phylogenetic analysis. The number of SNPs (94,142) extracted from these data was much greater than that in a previous study of estuarine mangrove fishes, which detected 4000–9000 SNPs after quality filtering [29]. This may be because we used reference-genome based SNP calling. Our initial analysis with de novo SNP calling provided 7678 SNPs that were consistent with those in the previous study. The large number of SNPs generated by the GRAS-Di analysis provided novel findings concerning the implications of the ancient river in shaping the current genetic diversity of A. speciosus on the islands of the Seto Inland Sea.

We used a concatenated SNPs dataset for the phylogenetic analyses. Concatenating the data might ignore coalescent variance and assume that all loci share the same genealogy [48]. Especially, incomplete lineage sorting (ILS) is often a problem for inferring the phylogenetic relationships. However, the phylogenetic node where the ILS is concerned is an “anomaly zone”, which has a tight span between consecutive divergences (e.g., due to rapid radiation) and in a condition in which the ancestral population size was large [49]. Such a node is difficult to resolve using either concatenation or ILS-aware methods, often producing low support values. As inferred from the high bootstrap values obtained in this study, the supported relationships should not have been affected by the ILS. Furthermore, considering the small population sizes in the island populations as inferred from the low genetic diversity of each island population compared with the Honshu or Shikoku populations [24], it is not likely that ancestral polymorphisms have been considerably maintained in such a small population as observed in mtDNA analyses where haplotypes in islands were not observed in Honshu and Shikoku [24]. Moreover, it is difficult to detect the ILS in this study because a small fragment sequence of less than 150 bp from each locus (HiSeq sequencer produces 150 bp at maximum) would have poor phylogenetic information [50]. In other words, the topology obtained from the concatenated sequences could not be rejected by any gene-tree discordances from each locus. It should also be noted that there is much hidden phylogenetic information in each locus that would not be emergent in the single locus analysis [50]. Using the concatenation method that would reveal the hidden information in each locus, a correct phylogenetic relationship would be supported even under the presence of gene tree discordance [50]. Extraction of only one SNP randomly from each locus might provide open and/or hidden phylogenetic information, enabling fair analyses. For these reasons, we performed a concatenated phylogenetic analysis.

As shown in Fig. 1B, we found complete consistency between phylogenetic affinity among some island lineages and the geographic relationship predicted by ancient rivers that were detected by channel analyses. The PCA analyses also provided consistent results (Fig. 1C, D). Overall, the results strongly support the hypothesis of the presence of the ancient Hoyo River flowing on the west side of the Seto Inland Sea. Although it is not clear that the fluid water flowed in the cold environment during LGM at around 20 ka, the channel analysis using sea level 120 m lower than today suggested the presence of the structure of ancient rivers in the Seto Inland Sea at that time (Additional file 1). We therefore assumed that the gene flows should have been restricted by the ancient rivers during the LGM. To supplement the geological evidence, which has been difficult to explore without large-scale investigation on the sea floor surface, the presence of ancient rivers in the Seto Inland Sea has been suggested by biological (particularly biogeographical) data. Kondo et al. (2009) examined microsatellite variations in a plant species, Rhododendron ripense, which has a seed dispersal strategy involving water; they suggested that populations were genetically different among river systems on Honshu, Shikoku, and Kyushu Islands that could be interconnected via two major ancient rivers in the Seto Inland Sea [15]. The current genetic structure of East Asian freshwater snails in Japan was presumably affected by past mtDNA introgressions that were facilitated by ancient rivers in the Seto Inland Sea [16]. Furthermore, Takahashi et al. (2020) showed genetic differentiation of the Japanese rosy bitterling, Rhodeus ocellatus kurumeus, between Honshu-Shikoku and Kyushu lineages using the Double Digest RAD-seq technique; they suggested the effect of ancient rivers on the lineage differentiation [17]. These previous studies, considered with the present study, supported the presence of the ancient river, thereby suggesting that the ancient river hypothesis is correct. Although interrelationships among the island lineages were not resolved in our previous study through the analysis of short Dloop sequences [24], much more abundant sequence data from the GRAS-Di analyses in the present study greatly increased the resolution concerning phylogenetic relationships among island lineages in the Seto Inland Sea. Notably, the individuals from islands on the east and west sides of the Hoyo River were closely related on each side, further supporting the ancient river hypothesis. It is also remarkable that Muk and Kra-Eta individuals were not clustered with the eastern and western clades, respectively, and instead were closely related to the Honshu individuals, also highlighting the effect of the ancient river. In future studies, application of the same methodology used in the present study for the other island populations of wood mice would illuminate the overall formation process of islands in the Seto Inland Sea. Furthermore, understanding the effects of current rivers on the genetic diversity of the wood mice would also be expected.

The observation that the island lineages were clustered in each clade in the phylogeny suggested that the effects of genetic drift through the bottleneck were stronger on the smaller islands in the Seto Inland Sea, making island-specific lineages in combination with independent mutations that occurred on each island after separation of the islands or with differential fixation of ancestral genetic variations. Such an island trend is also congruent with the results reported by Sato et al. [24], whereby the Dloop haplotypes detected on each island were not shared among different islands. However, the trend that the island haplotypes were more closely related to Honshu haplotypes than to Shikoku haplotypes, as observed in the Dloop analyses [24], was not confirmed in the present study, except for Muk and Kra-Eta islands, which would not have been separated from Honshu by ancient rivers. This may have been related to the stochastic effect caused by the low resolution of the Dloop sequences or the difference between mitochondrial DNA genealogy and phylogeny based on genome-wide SNP data. Further studies with more samples from Honshu and Shikoku Islands and high-throughput genome sequencing are needed to test these trends. Such studies should also clarify the effect of ancient rivers on the Honshu and Shikoku Islands. It should be further noted that effective population size of the ancestral population has affected the degree of maintenance of ancestral genetic variation, thereby explaining the long terminal branches in the phylogeny. However, it is difficult to estimate the population size parameter in this study with only a few individuals from each island. Analyses of more samples from islands would clarify the reasons for the island specificity in more detail.


Using the GRAS-Di high-throughput sequencing method, we clarified the genetic diversity of the large Japanese wood mouse that would have been shaped by an ancient river, the Hoyo River. To our knowledge, this is the first study to clarify the implications of an ancient river—currently submerged on the sea floor—in the genetic differentiation of island organisms. Application of the same method to various terrestrial organisms would elucidate the geological history of the islands of the Seto Inland Sea and the mechanisms that have shaped ecosystems on each island.

Availability of data and materials

Obtained DNA sequences are deposited to DDBJ sequence read archive (SRA) in the FASTQ format under the accession number DRA013162.


  1. Lomolino MV, Riddle BR, Whittaker RJ, Brown JH. Biogeography. 4th ed. Sunderland: Sinauer Associates, Inc.; 2010.

    Google Scholar 

  2. Rohling EJ, Fenton M, Jorissen FJ, Bertrand P, Ganssen G, Caulet JP. Magnitudes of sea-level lowstands of the past 500,000 years. Nature. 1998;394(6689):162–5.

    Article  CAS  Google Scholar 

  3. Clark PU, Mix AC. Ice sheets and sea level of the Last Glacial Maximum. Quat Sci Rev. 2002;21(1–3):1–7.

    Article  Google Scholar 

  4. Fernández-Palacios JM, Rijsdijk KF, Norder SJ, Otto R, de Nascimento L, Fernández-Lugo S, Tjørve E, Whittaker RJ. Towards a glacial-sensitive model of island biogeography. Glob Ecol Biogeogr. 2016;25(7):817–30.

    Article  Google Scholar 

  5. Norder SJ, Baumgartner JB, Borges PAV, Hengl T, Kissling WD, van Loon EE, Rijsdijk KF. A global spatially explicit database of changes in island paleo-area and archipelago configuration during the late Quaternary. Glob Ecol Biogeogr. 2018;27(5):500–5.

    Article  Google Scholar 

  6. Weigelt P, Steinbauer MJ, Cabral JS, Kreft H. Late Quaternary climate change shapes island biodiversity. Nature. 2016;532:99–102.

    Article  CAS  PubMed  Google Scholar 

  7. Papadopoulou A, Knowles L. Linking micro- and macroevolutionary perspectives to evaluate the role of Quaternary sea-level oscillations in island diversification. Evolution. 2017;71(12):2901–17.

    Article  PubMed  Google Scholar 

  8. Oba T, Irino T. Sea level at the Last Glacial Maximum, constrained by oxygen isotopic curves of planktonic foraminifera in the Japan Sea. J Quaternary Sci. 2012;27(9):941–7.

    Article  Google Scholar 

  9. Masuda F, Irizuki T, Fujiwara O, Miyahara B, Yoshikawa S. A Holocene sea-level curve constructed from a single core at Osaka, Japan (A preliminary note). Mem Fac Sci, Kyoto Univ, Ser Geol Mineral. 2002;59(1):1–8.

    Google Scholar 

  10. Masuda F, Miyahara B, Hirotsu J, Irizuki T, Iwabuchi Y, Yoshikawa S. Temporal variation of Holocene Osaka Bay conditions estimated from a core in off-Kobe. J Geol Soc Japan. 2000;106(7):482–8 (in Japanese with English abstract).

    Article  Google Scholar 

  11. Shioya F, Mii T, Iwamoto N, Inouchi Y. Late Pleistocene to Holocene variations in sea conditions within the Seto Inland Sea, offshore Matsuyama City. Japan Earth Sci (Chikyu Kagaku). 2007;61(2):103–5.

    Google Scholar 

  12. Yasuhara M. Holocene ostracod palaeogeography of the Seto Inland Sea, Japan: impact of opening of the strait. J Micropalaeontol. 2008;27:111–6.

    Article  Google Scholar 

  13. Nakano T, Kobayashi K. Nature in Japan. 1959. Iwanami Shinsho (in Japanese).

    Google Scholar 

  14. Kuwashiro I. Topographical development history of the Seto Inland Sea. Isao Kuwashiro manuscript publishing committee (in Japanese); 1972.

  15. Kondo T, Nakagoshi N, Isagi Y. Shaping of genetic structure along Pleistocene and modern river systems in the hydrochorous riparian Azalea, Rhododendron ripense (Ericaceae). Am J Bot. 2009;96(8):1532–43.

    Article  PubMed  Google Scholar 

  16. Miura O, Urabe M, Mori H, Chiba S. Ancient drainage networks mediated a large-scale genetic introgression in the East Asian freshwater snails. Ecol Evol. 2020;10(15):8186–96.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Takahashi T, Nagano AJ, Kawaguchi L, Onikura N, Nakajima J, Miyake T, Suzuki N, Kanoh Y, Tsuruta T, Tanimoto T, Yasui Y, Oshima N, Kawamura K. A ddRAD-based population genetics and phylogenetics of an endangered freshwater fish from Japan. Conserv Genet. 2020;21:641–52.

    Article  Google Scholar 

  18. Suzuki H, Sato JJ, Tsuchiya K, Luo J, Zhang Y-P, Wang Y-X, Jiang X-L. Molecular phylogeny of wood mice (Apodemus, Murinae) in East Asia. Biol J Linn Soc. 2003;80(3):469–81.

    Article  Google Scholar 

  19. Suzuki H, Filippucci MG, Chelomina GN, Sato JJ, Serizawa K, Nevo E. A Biogeographic view of Apodemus in Asia and Europe inferred from nuclear and mitochondrial gene sequences. Biochem Genet. 2008;46:329–46.

    Article  CAS  PubMed  Google Scholar 

  20. Sato JJ. A review on the process of mammalian faunal assembly in Japan—insight from the molecular phylogenetics. In: Motokawa M, Kajihara H, editors. Species Diversity of Animals in Japan. Tokyo: Springer Japan; 2016. p. 49–116.

    Google Scholar 

  21. Ohdachi SD, Ishibashi Y, Iwasa MA, Fukui D, Saitoh T. The Wild Mammals of Japan. 2nd ed. Kyoto: Shoukadoh, Kyoto; 2015.

    Google Scholar 

  22. Sato JJ, Kyogoku D, Komura T, Maeda K, Inamori C, Yamaguchi Y, Isagi Y. Potentials and pitfalls of the DNA metabarcoding analyses for the dietary study of the large Japanese wood mouse Apodemus speciosus on Seto Inland Sea islands. Mamm Study. 2019;44(4):221–31.

    Article  Google Scholar 

  23. Sato JJ, Ohtsuki Y, Nishiura N, Mouri K. DNA metabarcoding dietary analyses of the wood mouse Apodemus speciosus on Innoshima Island, Japan, and implications for primer choice. Mammal Res. 2022;2021(67):109–22.

    Article  Google Scholar 

  24. Sato JJ, Tasaka Y, Tasaka R, Gunji K, Yamamoto Y, Takada Y, Uematsu Y, Sakai E, Tateishi T, Yamaguchi Y. Effects of isolation by continental islands in the Seto Inland Sea, Japan, on genetic diversity of the large Japanese field mouse, Apodemus speciosus (Rodentia: Muridae), inferred from the mitochondrial Dloop region. Zool Sci. 2017;34(2):112–21.

    Article  Google Scholar 

  25. Andrews KR, Good JM, Miller MR, Luikart G, Hohenlohe PA. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat Rev Genet. 2016;17(2):81–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Suyama Y, Matsuki Y. MIG-seq: An effective PCR-based method for genome-wide single-nucleotide polymorphism genotyping using the next-generation sequencing platform. Sci Rep. 2015;5:16963.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Enoki H. The construction of pseudomolecules of a commercial strawberry by DeNovoMAGIC and new genotyping technology, GRAS‐Di. Proceedings of the Plant and Animal Genome Conference XXVII. San Diego, CA. 2019. Accessed 2 Dec 2021.

  28. Enoki H, Takeuchi Y. New genotyping technology, GRAS‐Di, using next generation sequencer. Proceedings of the Plant and Animal Genome Conference XXVI. San Diego, CA. 2018. Accessed 2 Dec 2021.

  29. Hosoya S, Hirase S, Kikuchi K, Nanjo K, Nakamura Y, Kohno H, Sano M. Random PCR-based genotyping by sequencing technology GRAS-Di (genotyping by random amplicon sequencing, direct) reveals genetic structure of mangrove fishes. Mol Ecol Resour. 2019;19(5):1153–63.

    Article  CAS  PubMed  Google Scholar 

  30. Hirase S, Yamasaki YY, Sekino M, Nishisako M, Ikeda M, Hara M, Merilä J, Kikuchi K. Genomic Evidence for Speciation with Gene Flow in Broadcast Spawning Marine Invertebrates. Mol Biol Evol. 2021;38(11):4683–99.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Ikeda H, Yakubov V, Barkalov V, Sato K, Fujii N. East Asian origin of the widespread alpine snow-bed herb, Primula cuneifolia (Primulaceae), in the northern Pacific region. J Biogeogr. 2020;47(10):2181–93.

    Article  Google Scholar 

  32. Sato JJ, Kawakami T, Tasaka Y, Tamenishi M, Yamaguchi Y. A few decades of habitat fragmentation has reduced population genetic diversity: A case study of landscape genetics of the large Japanese field mouse, Apodemus speciosus. Mamm Study. 2014;39(1):1–10.

    Article  Google Scholar 

  33. Joshi NA, Fass JN. Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files (Version 1.33) [Software]. 2011. Accessed 2 Dec 2021.

  34. Langmead B, Salzberg S. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Matsunami M, Endo D, Saitoh N, Suzuki H, Onuma M. Draft genome sequence of Japanese wood mouse, Apodemus speciosus. Data Brief. 2018;16:43–6.

    Article  PubMed  Google Scholar 

  36. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup. The Sequence alignment/map (SAM) format and SAMtools. Bioinform. 2009;25(16):2078–9.

    Article  CAS  Google Scholar 

  37. Catchen J, Amores A, Hohenlohe P, Cresko W, Postlethwait J. Stacks: building and genotyping loci de novo from short-read sequences. G3-Genes Genom Genet. 2011;1(3):171–82.

    CAS  Google Scholar 

  38. Catchen J, Hohenlohe P, Bassham S, Amores A, Cresko W. Stacks: an analysis tool set for population genomics. Mol Ecol. 2013;22(11):3124–40.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Mol Biol Evol. 2015;32(1):268–74.

    Article  CAS  PubMed  Google Scholar 

  40. Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Vinh LS. UFBoot2: Improving the ultrafast bootstrap approximation. Mol Biol Evol. 2018;35(2):518–22.

    Article  CAS  PubMed  Google Scholar 

  41. Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience. 2015;4:7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2021;URL (Accessed 13 Jan 2022).

  43. Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: Molecular Evolutionary Genetics Analysis across computing platforms. Mol Biol Evol. 2018;35:1547–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. QGIS Development Team. QGIS Geographic Information System, Version 3.4.12-Madeira. Open Source Geospatial Foundation. 2018; (Accessed 2 Dec 2021).

  45. Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, Wehberg J, Wichmann V, Böhner J. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geosci Model Dev. 2015;8(7):1991–2007.

    Article  Google Scholar 

  46. GEBCO Compilation Group. GEBCO 2019 Grid. 2019. Accessed 2 Dec 2021.

  47. Wang L, Liu H. An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling. Int J Geogr Inf Sci. 2006;20:193–213.

    Article  CAS  Google Scholar 

  48. Foote AD, Morin PA. Genome-wide SNP data suggest complex ancestry of sympatric North Pacific killer whale ecotypes. Heredity. 2016;117:316–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Pease JB, Hahn MW. More accurate phylogenies inferred from low-recombination regions in the presence of incomplete lineage sorting. Evolution. 2013;67–8:2376–84.

    Article  Google Scholar 

  50. Gatesy J, Springer MS. Phylogenetic analysis at deep timescales: Unreliable gene trees, bypassed hidden support, and the coalescence/concatalescence conundrum. Mol Phylogenet Evol. 2014;80:231–66.

    Article  PubMed  Google Scholar 

Download references


We thank Hiromi Morita, Nana Morita, and Yuya Ohtsuki for their help in the field work for sampling, and Yasushi Takada, Yasushi Uematsu, Eiichi Sakai, and Takashi Tateishi for providing tissue samples. We are also grateful to Yasunori Yamaguchi for his valuable advice for this study.


This work was supported by JSPS KAKENHI (Grant No. 18K06395 to JJS).

Author information

Authors and Affiliations



JJS planned and financially supported the research project. JJS was involved in the sampling of Apodemus speciosus around the Seto Inland Sea, Japan. JJS undertook the laboratory work with the aid of services (PCR and sequencing) from Bioengineering Lab. Co., Ltd. JJS and KY conducted the bioinformatics and QGIS analyses. JJS drafted the manuscript. KY reviewed the manuscript and joined discussions with JJS. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Jun J. Sato.

Ethics declarations

Ethical approval and consent to participate

We obtained permission from Hiroshima Prefecture to perform the field survey and laboratory experiments on the wood mice and conducted protocols approved by the Animal Care and Use Committee of Fukuyama University (H30-Animal-8). We also followed the guidelines of the Procedure of Obtaining Mammal Specimens established by the Mammal Society of Japan.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The English in this document (Black colored parts) has been checked by at least two professional editors, both native speakers of English (Sentences changed in the process of two revisions have not been checked). For a certificate, please see:

Supplementary Information

Additional file 1.

Hypothetical ancient Hoyo and Kitan rivers. Dotted lines around the main Japanese islands show the past coastlines when the sea level dropped by 120 m.

Additional file 2.

Random primers used in the first round of PCR.

Additional file 3.

Geographic coordinates for samples examined in this study.

Additional file 4.

Maximum likelihood tree estimated using the GTR+G model by Iqtree based on 94,142 single nucleotide polymorphisms detected in GRAS-Di analysis. Mid-point rooting was used to construct the phylogeny. The nodal values are bootstrap values estimated by ultrafast bootstrap approximation (10,000 replications).

Additional file 5.

A graph showing comparison between genetic and geographic distances made by ggplot analysis in the R environment. See text for calculation of the genetic and geographic distances.

Additional file 6.

Ancient rivers estimated through channel analyses using QGIS (Strahler’s stream order 4–8). Orders 4–8 (Upper left) show major streams combining rivers detected in the figures for orders 4–8.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sato, J.J., Yasuda, K. Ancient rivers shaped the current genetic diversity of the wood mouse (Apodemus speciosus) on the islands of the Seto Inland Sea, Japan. Zoological Lett 8, 9 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: