Abstract The genetic diversity of one of the most abundant species in the Arctic and subarctic oceans, the pelagic snail Limacina helicina, has not yet been characterized in the north Pacific. This species has different ‘forma’ (L. helicina forma helicina, acuta, pacifica and ochotensis), but whether or not the morphological differences between these forma are caused by phenotypic plasticity or genetic differentiation remains unclear. Here, we analysed partial nucleotide sequences of the mitochondrial cytochrome c oxidase subunit I gene in L. helicina from the subarctic western North Pacific Ocean (SWNP; L. helicina f. acuta) and compared them with those from Svalbard (L. helicina f. helicina) and other localities (Beaufort Sea, eastern Pacific, northern Sea of Japan and western Atlantic). The results show clear genetic differentiation between populations in the SWNP and Svalbard (ΦCT = 0.59282, P < 0.001). These genetic differences are consistent with the previous description of the two forma L. h. f. acuta (SWNP) and L. h. f. helicina (Svalbard) based on shell morphology. INTRODUCTION Planktonic organisms (including swimming larvae of benthic organisms) can disperse across relatively wide areas. Their population size, population structure and genetic diversity are influenced by oceanographic events and structural changes in the water mass and currents. For example, many high-latitude marine organisms underwent evolutionary bottleneck events when expansion of polar ice sheets reduced their habitats and relegated them to refugia during the Quaternary ice ages (Hewitt, 2000; Wares & Cunningham, 2001; Marko, 2004; Marko et al., 2010). Subsequent secondary contact between the isolated populations may have increased genetic polymorphisms during interglacial periods (Wilson et al., 2007), allowing some polar organisms to recover genetic diversity. However, it remains unclear whether the low genetic diversity in high-latitude marine species is caused by the small size of former refugia at lower latitudes or by the rapid recolonization of high latitudes and consequent founder effects. Studies of intraspecific genetic variation offer a basic understanding of the biogeographic and/or demographic history of the organism (Avise, 2000) and may be useful for predicting population dynamics in future environments. Global climate change will likely impact the biodiversity of species or populations to varying degrees (Pauls et al., 2013). Responses to changing environmental conditions have been observed as phenotypic plasticity or evolutionary adaptations (Riddle et al., 2008; Scoble & Lowe, 2010; Hoffmann and Sgrò, 2011) and as changes in habitat (Parmesan et al., 1999; Chen et al., 2011). Organisms with low genetic diversity are generally more susceptible to rapid environmental changes than those with high genetic diversity, which also have higher stress resistance (Nowak et al., 2007; Markert, Schelly & Stiassny, 2010) and adaptive potential (Hughes & Stachowicz, 2004). Accordingly, previous studies have demonstrated the importance of genetic diversity for understanding ecological dynamics (Bolnick et al., 2011) and investigations of genetic diversity are considered to be central to understanding the ecosystem impacts of current rapid environmental changes. Ocean acidification caused by increasing atmospheric CO2 dating to the industrial revolution is one of the main environmental stressors in marine ecosystems (Rockström et al., 2009). Because these changes lead to decreased calcium carbonate saturation states (Ω), ongoing ocean chemical changes are expected to have severe effects on marine organisms that have calcifying mechanisms and calcareous structures, such as coccolithophores, foraminiferans, corals, molluscs and sea urchins (Doney et al., 2009). Although the effects of ocean acidification on marine invertebrates have been documented, response patterns differ not only by species but also among populations within species (Fabry et al., 2008). These observations suggest that consideration of genetic background and population structure is needed in order to understand the impact of ocean acidification. The pelagic snail Limacina helicina is widely distributed in the Arctic and subarctic oceans where it can comprise more than 50% of the biomass of polar zooplankton and is one of the key food resources for large zooplankton, fish and seabirds (Lalli & Gilmer, 1989; Hunt et al., 2008, 2010; Comeau et al., 2009). Limacina helicina is a notable species in ocean acidification research (Comeau et al., 2009; Lischka et al., 2011; Lischka & Riebesell, 2012; Bednaršk et al. 2012a, b, 2014; Koh et al., 2015; Johnson & Hofmann, 2016; Peck et al., 2016). It is classified as four ‘forma’ in the northern hemisphere (North Atlantic: L. h. f. helicina Phipps, 1774; western North Pacific: L. h. f. acutaSpoel, 1967; eastern North Pacific: L. h. f. pacifica Dall, 1871; Okhotsk Sea: L. h. f. ochotensis Shkoldina, 1999; Spoel, 1967: 257, 349), defined by shell morphology and habitat (Spoel, 1967: 36–43, 257, 349; Shkoldina, 1999), but their genetic diversity and genetic population structure have not been clarified. Hunt et al. (2010) found that large genetic differences exist between Arctic and Antarctic populations of L. ‘helicina’ and the two morphotypes helicina and antarctica are now considered to be distinct species, L. helicina (Phipps, 1774) and L. antarctica Woodward, 1854 (for which the valid name is L. rangii (d’Orbigny, 1834); World Register of Marine Species, http://www.marinespecies.org accessed 6 October 2017). Sromek, Lasota & Wolowicz (2015) performed population genetic analyses of the mitochondrial cytochrome c oxidase subunit I (COI) gene of L. helicina s. l. and showed lower genetic diversity in the Arctic species (haplotype diversity, H = 0.778; nucleotide diversity, π = 0.0031) than in the Antarctic species (H = 0.967, π = 0.0061). However, L. helicina individuals from only five localities around Svalbard were used in the comparison, so that the resulting data may not be sufficient to understand the genetic structure of this species. Here, we examined more samples from the subarctic western North Pacific (SWNP) in order to clarify further the population structure and genetic diversity of L. helicina. MATERIAL AND METHODS Samples A total of 101 individuals of Limacina helicina f. acuta were collected at five stations (Tsugaru, KNOT, K2, ESM, NWP) in the subarctic western North Pacific Ocean (SWNP; Fig. 1, Table 1) during cruises of the R/V Hakuho Maru (KH15-J01, K2), T/S Ushio Maru (US348, Tsugaru), T/S Osyoro Maru (OS026-Leg2, KNOT and ESM) and R/V Yokosuka (YK16-16, NWP). Samples were obtained from vertical hauls from 150 m to the surface with a ring net (45 cm in diameter, 100-μm mesh). Specimens were fixed in 100% ethanol and stored at −20 °C. Table 1. Station and sampling information for the subarctic western North Pacific population of Limacina helicina. Name Latitude Longitude Date N GenBank Accession Nos Tsugaru 41°83′N 141°22′E 17 Nov. 2015 16 LC185064-LC185073 LC229751-LC229757 KNOT 43°11′N 153°19′E 4–8 July 2016 30 LC185015-LC185032 LC229727-LC229734 LC229766-LC229769 K2 47°00′N 160°00′E 4 July 2015 19 LC185045-LC185063 ESM 44°11′N 170°24′E 7 July 2016 20 LC185033-LC185044 LC229758-LC229765 NWP 41°83′N 141°22′E 13 Nov. 2016 16 LC229735-LC229750 Name Latitude Longitude Date N GenBank Accession Nos Tsugaru 41°83′N 141°22′E 17 Nov. 2015 16 LC185064-LC185073 LC229751-LC229757 KNOT 43°11′N 153°19′E 4–8 July 2016 30 LC185015-LC185032 LC229727-LC229734 LC229766-LC229769 K2 47°00′N 160°00′E 4 July 2015 19 LC185045-LC185063 ESM 44°11′N 170°24′E 7 July 2016 20 LC185033-LC185044 LC229758-LC229765 NWP 41°83′N 141°22′E 13 Nov. 2016 16 LC229735-LC229750 Figure 1. View largeDownload slide Sampling locations for Limacina helicina in the subarctic western North Pacific Ocean. Figure 1. View largeDownload slide Sampling locations for Limacina helicina in the subarctic western North Pacific Ocean. Micro X-ray CT The shell morphology of individuals L. h. f. acuta collected from K2 was visualized using a Microfocus X-ray CT scanner (ScanXmateD160TSS105, Comscantechno Co., Shin-Yokohama, Japan) at Japan Agency for Marine-Earth Science and Technology (JAMSTEC). High-resolution settings (X-ray focus diameter 0.8 μm, voltage 80 KeV, detector array size 1024 × 1024 pixels, 1500 projections in 360° rotations) were applied for 3D quantitative densitometry of individual shells. Geometric resolution of isotropic voxel size was varied from 2 to 4 μm depending on each shell size. We used ConeCTexpress (White Rabbit Corp., Tokyo, Japan) for corrections and reconstruction of tomography data, and the general principle of Feldkamp cone beam reconstruction was followed to reconstruct the image cross-sections based on filtered back-projections. We obtained 2D images from 3D data and measured shell height (H) and diameter (D) using ImageJ v. 1.50i. Morphometric analysis We attempted to evaluate two morphological aspects of coiling patterns and aperture shapes of shells, based on theoretical morphological and geometric-morphometric approaches. The coiling patterns were approximated using the growing tube model (Okamoto, 1988a, b, c) and patterns were characterized by three parameters of the model (see Okamoto, 1988a for details): expansion rate (EG), standardized curvature (CG) and standardized torsion (TG). We estimated the parameters of the growing tube model using Raup’s model, following a method proposed by Noshita (2014). Raup’s model is a theoretical morphological model for describing coiling patterns based on three parameters (Raup 1962, 1966, 1967; Raup & Michelson, 1965): whorl expansion rate (WR), whorl translation rate (TR) and relative width of the umbilicus (DR). These parameters were estimated from 2D images. We analysed the shape of the helicocone bisected by a plane passing through the coiling axis instead of using the changes in aperture shapes through growth stages. These ‘aperture shapes’ were quantified by elliptic Fourier descriptors (EFDs) (Kuhl & Giardina, 1982). To estimate the parameters of the growing tube model and EFDs, we developed the following simplified procedure. First, we resampled a slice image passing through the coiling axis from a 3D CT model with Amira® (FEI). Second, centroids, areas and outlines of ‘apertures’ were digitized from the slice image using the image-processing software Fiji in ImageJ (Schneider, Rasband & Eliceiri, 2012). Third, Raup’s W, T and D (WR, TR, DR) were estimated from areas and centroid coordinate values in a specimen by fitting Raup’s model describing the growth trajectory. Fourth, Raup’s parameters were converted into the parameters of the growing tube model based on an equation by Noshita (2014). Finally, the outlines of ‘apertures’ were quantified with EFDs using Momocs, which is an R package for outline-based geometric morphometrics (Bonhomme et al., 2014). In this way, our simple procedure could be used to evaluate the coiling patterns and aperture shapes of L. h. f. acuta shells and to provide quantitative information characterizing the changes in growth. Coiling patterns and aperture shape analysis For coiling patterns, a pairwise multivariate analysis of variance (MANOVA) of the parameters of the growing tube model was performed to evaluate the effect of growth stage (first half and second half). For analysis of aperture shapes, principal component analysis (PCA) was used to contrast morphological variation in terms of the EFDs of aperture shapes. A pairwise MANOVA of the scores of PC1 to PC18, for which the cumulative contribution ratio reached 99%, was performed to evaluate the effect of growth stage, first half (D = 687–814 μm) vs. second half. In both analyses, Pillai’s trace test was used for this purpose (Hand & Taylor, 1987). To avoid multiple testing problems, P-values were adjusted by the Holm method (Holm, 1979). DNA extraction, amplification and sequencing DNA extraction was performed using GeneReleaser (BioVentures, Murfreesboro, TN) in accordance with the manufacturer’s protocol. Gene sequences for the COI gene were amplified by PCR using the universal primers (LCO1490 and HCO2198) as described by Folmer et al. (1994). PCR products were purified using a commercial kit (ExoSAP-IT for PCR product clean-up; Thermo Fisher Scientific, Waltham, MA). Sequencing reactions were performed using ABI BigDye Terminators v. 3.1 (Applied Biosystems, Foster City, CA) and purified by ethanol precipitation, and sequencing products were sequenced using an ABI 3130 genetic analyser with the same primers as for the PCR. Sequences were deposited in GenBank (for accession numbers see Table 1). Molecular phylogenetic analysis We obtained 101 COI sequences of L. helicina from SWNP and retrieved an additional 44 sequences of L. helicina f. helicina, L. antarctica, L. helicoidea, L. inflata, L. retroversa and Hyalocylis striata obtained from GenBank (Supplementary Material Table S1) for phylogenetic analysis. Sequence alignments were conducted using the online version of MAFFT (v. 7.310; http://mafft.cbrc.jp/alignment/server/index.html; Katoh et al., 2002) based on 503 bp of COI. The best-fit nucleotide substitution model with the lowest Bayesian information criterion (BIC) score was selected using MEGA v. 5.1 (Tamura et al., 2011). Maximum-likelihood (ML) trees were inferred with MEGA v. 5.1 using Tamura’s 3-parameter model + G + I (Tamura, 1992) with 1,000 bootstrap replications. Sequences of H. striata were used as the outgroup. Population genetic analysis The 101 sequences gathered by this study were combined with 77 from Svalbard and other regions derived from previous studies (Supplementary Material Fig. S1, Table S2; Hunt et al., 2010; Jennings et al., 2010; Corse et al., 2013; Layton, Martel, Hebert, 2014; Sromek et al., 2015; Chichvarkhin, 2016). Sequence alignment was conducted using the online version of MAFFT (Katoh et al., 2002) and sequences were trimmed to 503 bp using MEGA v. 5.1. Population genetic analyses (diversity indices and neutrality test) were performed using Arlequin v. 184.108.40.206 (Excoffier & Lischer, 2010). Pairwise ΦST (among localities) and ΦCT (between SWNP and Svalbard) with AMOVA were performed by Arlequin and significance tested by 10,000 permutations. Parsimony networks were constructed using TCS v. 1.21 software with the connection probability set at 95% (Clement, Posada & Crandall, 2000). RESULTS Shell morphology in SWNP D and H of Limacina helicina f. acuta shells from K2 were 3.168 and 3.153 mm, respectively (Supplementary Material Table S3). The average shell height to diameter ratio (H/D) was 0.841 ± 0.080 (SD) (Fig. 2A). The shape of small individuals with D < 0.8 mm differed from that of the larger ones (small: H/D = 0.806 ± 0.036; large: H/D = 0.987 ± 0.196). We then analysed the aperture shapes and growth patterns using 3D data and compared small and large individuals (i.e. early and late stages measured on the same individuals). We found that aperture shapes and growth patterns differed significantly between early and late stages of large individuals (Fig. 2B, C and Supplementary Material Table S4), but not between small individuals and the early stage of large individuals (Fig. 2B, C and Supplementary Material Table S4). Figure 2. View largeDownload slide Shell morphology of Limacina helicina f. acuta at locality K2. A. Shell height to shell diameter ratio (H/D). B. Cube plot of the three estimated parameters of the growing tube model (EG, expansion rate; CG, standardized curvature, TG, standardized torsion). ‘Small’ indicates D < 1 mm. C. Principal component analysis of aperture shape by size and stage. Figure 2. View largeDownload slide Shell morphology of Limacina helicina f. acuta at locality K2. A. Shell height to shell diameter ratio (H/D). B. Cube plot of the three estimated parameters of the growing tube model (EG, expansion rate; CG, standardized curvature, TG, standardized torsion). ‘Small’ indicates D < 1 mm. C. Principal component analysis of aperture shape by size and stage. Molecular phylogeny, phylogeography and population genetic analysis Phylogenetic analysis using the ML method placed all COI sequences of L. helicina in a single clade (Fig. 3). There were 24 variable sites (4.77%) among the 503 bp of the COI sequences. H and π were markedly lower for the SWNP population than for the Svalbard population (Table 2). Based on analyses of pooled samples from SWNP and other regions, we constructed a haplotype network that showed two major haplotypes (H1 and H2) and multiple singletons (Fig. 4). Haplotype H1 was shared by samples from the northern Sea of Japan, eastern North Pacific, Hudson Bay, the Canadian Arctic Ocean and Svalbard, while Haplotype H2 (two mutational steps from H1) was restricted to the Svalbard population (Fig. 4). Most samples from SWNP were haplotype H1 (75/101, 74.2%), while most individuals from Svalbard were haplotype H2 (32/67, 47.76%). Significant population differences were found between SWNP (Tsugaru, KNOT, K2, ESM and NWP) and Svalbard based on pairwise ΦST and ΦCT comparisons (ΦCT = 0.59282, P < 0.001); (Table 3). The haplotype network shows a typical star-like structure with multiple singletons and Tajima’s D (Tajima, 1989) and Fu’s Fs neutrality tests (Fu, 1997) were significantly negative (Table 2). Table 2. Population genetic analyses (diversity indices and neutrality tests) of populations of Limacina helicina from subarctic western North Pacific and Svalbard, based on partial COI sequence data. Subarctic western North Pacific Svalbard Number of individuals 101 67 Number of sites 503 503 Number of haplotypes (k) 25 24 Polymorphic sites (S) 24 25 Haplotype diversity (H) 0.450 (±0.064) 0.764 (±0.054) Nucleotide diversity (π) 0.00121 (±0.00021) 0.00324 (±0.00049) Average number of nucleotide differences (Π) 0.610 1.629 Tajima’s D −2.60025*** −2.268** Fu’s Fs −40.848*** −23.916*** Subarctic western North Pacific Svalbard Number of individuals 101 67 Number of sites 503 503 Number of haplotypes (k) 25 24 Polymorphic sites (S) 24 25 Haplotype diversity (H) 0.450 (±0.064) 0.764 (±0.054) Nucleotide diversity (π) 0.00121 (±0.00021) 0.00324 (±0.00049) Average number of nucleotide differences (Π) 0.610 1.629 Tajima’s D −2.60025*** −2.268** Fu’s Fs −40.848*** −23.916*** *P < 0.05; **P < 0.01; ***P < 0.001. Table 3. Population differentiation in Limacina helicina based on pairwise ΦST values calculated from COI data. Tsugaru KNOT K2 ESM NWP Svalbard Tsugaru − KNOT 0.01186 − K2 0.00120 −0.00479 − ESM −0.00483 −0.00526 −0.01430 − NWP 0.00000 −0.02961 −0.00117 −0.02796 − Svalbard 0.47470*** 0.52570*** 0.51465*** 0.49440*** 0.50745*** − Tsugaru KNOT K2 ESM NWP Svalbard Tsugaru − KNOT 0.01186 − K2 0.00120 −0.00479 − ESM −0.00483 −0.00526 −0.01430 − NWP 0.00000 −0.02961 −0.00117 −0.02796 − Svalbard 0.47470*** 0.52570*** 0.51465*** 0.49440*** 0.50745*** − *P < 0.05; **P < 0.01; ***P < 0.001. For sampling localities see Figure 1 and Supplementary Material Figure S1. Figure 3. View largeDownload slide Molecular phylogenetic analysis of Limacina species based on 503 bp of COI gene. A. Maximum-likelihood trees inferred using Tamura’s 3-parameter model + G + I (Tamura, 1992) with 1,000 bootstrap replications. B. Details of L. helicina clade. Open circles indicate Svalbard samples (from Sromek et al., 2015) and filled circles the subarctic western North Pacific (SWNP) samples (this study). H1 and H2 indicate haplotypes H1 and H2 (Fig. 4). Figure 3. View largeDownload slide Molecular phylogenetic analysis of Limacina species based on 503 bp of COI gene. A. Maximum-likelihood trees inferred using Tamura’s 3-parameter model + G + I (Tamura, 1992) with 1,000 bootstrap replications. B. Details of L. helicina clade. Open circles indicate Svalbard samples (from Sromek et al., 2015) and filled circles the subarctic western North Pacific (SWNP) samples (this study). H1 and H2 indicate haplotypes H1 and H2 (Fig. 4). Figure 4. View large Download slide Parsimony haplotype network of COI sequences from Limacina helicina. Haplotype circle sizes indicate frequency and colours indicate sampling location (Supplementary Material Fig. S1, Table S2). SWNP, subarctic western North Pacific; NSJ, northern Sea of Japan; ENP, eastern North Pacific; HB, Hudson Bay; CAO, Canadian Arctic Ocean. Figure 4. View large Download slide Parsimony haplotype network of COI sequences from Limacina helicina. Haplotype circle sizes indicate frequency and colours indicate sampling location (Supplementary Material Fig. S1, Table S2). SWNP, subarctic western North Pacific; NSJ, northern Sea of Japan; ENP, eastern North Pacific; HB, Hudson Bay; CAO, Canadian Arctic Ocean. DISCUSSION Based on morphological descriptions, four different forma of Limacina helicina have been identified in subarctic and arctic regions (North Atlantic: L. h. f. helicina; western North Pacific: L. h. f. acuta; eastern North Pacific: L. h. f. pacifica; Okhotsk Sea: L. h. f. ochotensis; Spoel, 1967). Whether the morphological differences between these forma are the result of phenotypic plasticity or genetic differentiation is unknown. In this study we characterized the shell ratio H/D of L. helicina collected from SWNP (K2). Two of the forma of L. helicina are recorded from the North Pacific, L. h. f. acuta and L. h. f. pacifica, and are described as having a high-spired and low-spired shell, respectively. The former is known to occur in the SWNP (McGowan, 1963). Our samples could be categorized by shell shape into two groups: large individuals with high-spired shells and small individuals with low-spired shells (Fig. 2A). The large individuals are consistent with previous descriptions of L. h. f. acuta (H/D = 0.85–1.2; D = 0.7–1.8 mm; McGowan, 1963). Our analysis of aperture shapes and growth patterns revealed a change in aperture shape and growth pattern between early and late stages of the same shells, such that the early stage of large individuals showed aperture shapes and growth patterns similar to those of small individuals (Fig. 2B, C). These results suggest that the two different forms found at K2 were simply early and late stages of a common ontogenetic pattern. Most samples from SWNP were haplotype H1 and most from Svalbard were haplotype H2, indicating significant genetic differentiation between the two populations (Table 3). In addition, our SWNP samples had a shell shape different from that of L. h. f. helicina in the North Atlantic Ocean (H/D = 0.75; H up to 6 mm, D up to 8 mm; Spoel, 1967). The genetic results are thus consistent with the previous establishment of two distinct forma, L. h. f. acuta and L. h. f. helicina. Although few COI sequences of L. helicina have been reported from the northern Sea of Japan, eastern North Pacific, Hudson Bay and the Canadian Arctic Ocean, most are of haplotype H1 (Fig. 4). This suggests the possibility of dispersal from the SWNP to the Arctic Ocean via the Bering Strait (McLaughlin et al., 1996). Similarly, the copepod Pseudocalanus newmani shows no regional differentiation between the Gulf of Alaska and the Chukchi Sea, and northward directional gene flow from the Gulf of Alaska to the Beaufort Sea (Questel et al. 2016). Despite the significant differentiation of the SWNP and Svalbard populations of L. helicina, a few of the Svalbard individuals have haplotype H1 (6/67). This could be explained by dispersal among the Chukchi Sea, Beaufort Sea and around Svalbard by the Arctic intermediate layer circulation (Jones, 2001). Genetic data for individuals from the Chukchi and Beaufort Seas is required to test this hypothesis. Our haplotype network analysis showed a typical star-like structure (Fig. 4) and Tajima’s D (Tajima, 1989) and Fu’s Fs neutrality tests (Fu, 1997) were both significantly negative for samples from both the SWNP and Svalbard (Table 2). This suggests a recent dramatic population expansion in both regions, as has been reported for L. h. f. helicina (Sromek et al., 2015) and other high-latitude organisms (Bernatchez & Wilson, 1998; Marko, 2004; Maggs et al., 2008; Grant et al., 2011). These patterns are also consistent with population bottlenecks caused by restriction of Arctic marine organisms to small southern refugia during the ice ages and their subsequent population expansions (Hewitt, 2004; Hardy et al., 2010). MtDNA diversity in L. h. f. acuta is very low, lower than that of L. h. f. helicina (Table 2; Sromek et al., 2015), which may reflect differences in life history and physical dispersal abilities. Sromek et al. (2015) estimated that L. h. f. helicina in Svalbard showed increased population size and genetic diversity at 131 kyr BP (confidence interval 44–225 kyr BP) and this was related to the Eemian interglacial period, based on mismatch distribution analyses. This agrees with simulations of historical changes in the continent ice sheets of Greenland and North American by Abe-Ouchi et al. (2013), which showed shrinkage of ice sheets, ending the isolation between the SWNP, the Arctic Ocean and Svalbard, at 131 ky BP. This and similar geological events likely triggered secondary contact between the SWNP and Svalbard populations (via the Chukchi and Beaufort Seas), leading to an increase in genetic polymorphism during interglacial periods. Importantly, the current through the Bering Strait is in a northward direction, from the SWNP to the Arctic Ocean (McLaughlin et al., 1996), and this directionality permitted genetic divergence between the SWNP population and the Svalbard population. This pattern is not universal. For example, the high-latitude planktonic foraminiferan Neogloboquadrina pachyderma shows evidence of a geographical barrier between the Bering Sea and the Chukchi Sea (Darling, Kucera, Wade, 2007), while some rocky-shore species show significant genetic differentiation even within the northeastern Pacific itself (Marko et al., 2010). These contrasting patterns reflect the dispersal characteristics and habitats of the organisms inhabiting the same sea region. Invertebrates have been reported to show different responses to ocean acidification, not only between species but also between populations within a species (Fabry et al., 2008). Thus, these forma L. h. helicina and L. h. acuta might respond differently to ocean acidification and we must take genetic differences into careful consideration in future studies of responses to environmental change. In the SWNP (K2 and KNOT), wintertime surface waters have a lower calcium carbonate saturation state (Ω about 1.4) and a shallower CaCO3 saturation horizon (about 120 m; Wakita et al., 2013) than at other monitoring stations (Bates et al., 2014). In addition, acidification in winter in this region occurs at a slower rate than the annual mean rate because of a reduced rate of increase of dissolved inorganic carbon and an increase of total alkalinity (Wakita et al., 2017). This increase of total alkalinity is possibly caused by the weakening of calcification by organisms in winter. Thus, the SWNP is expected to be among the regions in which organisms will be most seriously impacted (Orr et al., 2005). It is recommended that future studies of L. helicina and other organisms at high risk in the SWNP should investigate genetic structure in more detail using variable markers such as microsatellites or SNPs regions (i.e. RADseq, Miller et al., 2007 and MIGseq, Suyama & Matsuki, 2015). SUPPLEMENTARY MATERIAL Supplementary material is available at Journal of Molluscan Studies online. ACKNOWLEDGEMENTS We thank Yoshiyuki Abe, Souta Komeda and Marie Maekakuchi (Hokkaido University) for the collection of Limacina helicina. We wish to thank Yuriko Nakamura (JAMSTEC) for help in micro X-ray CT analysis. We thank Minoru Kitamura, Naomi Harada and Masashi Tsuchiya (JAMSTEC) for invaluable discussion and suggestion. We would like to thank the captains, crews and cruise members (R/V Hakuho Maru, KH-15-J01; R/V Yokosuka, YK16-16; T/S Ushio Maru, US348; T/S Oshoro Maru, OS026-Leg2). This study was supported by JSPS Grants-in-Aid for Scientific Research 15H06908 and 16H04961. REFERENCES Abe-Ouchi, A., Saito, F., Kawamura, K., Raymo, M., Okuno, J., Takahashi, K. & Blatter, H. 2013. Insolation driven 100,000-year glacial cycles and hysteresis of ice sheet volume. Nature , 500: 190– 193. Google Scholar CrossRef Search ADS PubMed Avise, J.C. 2000. 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Journal of Molluscan Studies – Oxford University Press
Published: Feb 1, 2018
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