Characterization of the Gray Whale Eschrichtius robustus Genome and a Genotyping Array Based on Single-Nucleotide Polymorphisms in Candidate Genes.
The gray whale Eschrichtius robustus (Lilljeborg, 1861) was once common in both the North Pacific and the North Atlantic Ocean but disappeared from the Atlantic by the early 1700s because of overhunting (Mead and Mitchell, 1984). Contemporary gray whales are found only along the eastern and western coasts of the North Pacific Ocean (Andrews, 1914; Rice and Wolman, 1971; Fig. 1). Here, we follow the convention established by the International Union for Conservation of Nature (IUCN, 2008; Reilly et al., 2008) and refer to an eastern gray whale (EGW) population and a western gray whale (WGW) population. We use WGW to refer to the gray whales that feed in the western North Pacific off the coast of Sakhalin Island, Russia (Fig. 1). The IUCN considers the extant WGW population to be critically endangered (IUCN, 2008; Reilly et al., 2008), as there were only about 140 WGW adults in 2012 (Cooke et al., 2013), and the population was previously believed to be extinct (Bowen, 1974; Weller et al., 2002). The EGW population was reduced to a low of about 2000 individuals and has since made a strong recovery, the contemporary population numbers about 19,000 individuals (Laake et al., 2009; Durban et al., 2015).
Like so many of the great whales, modern gray whale populations declined steeply during the commercial whaling era (Mead and Mitchell, 1984; Alter et al., 2012). On the basis of whaling records, Henderson (1984) estimated the size of the EGW population at 15,000-20,000 individuals prior to commercial hunting. The prewhaling WGW population is thought to have been much smaller (see Berzin and Vladimirov, 1981), but they were apparently not as restricted in geographic range as they are today (Reeves et al., 2008).
Gray whales are occasionally sighted along a historical western migration corridor that includes waters near Russia's Sakhalin Island in the Sea of Okhotsk (Weller et al., 2008; Weller and Brownell, 2012; Fig. 1). Studies based on the maternally inherited mitochrondrial DNA (mtDNA) genome have documented genetic differentiation between the WGWs sampled near Sakhalin and the EGW population (LeDuc et al., 2002; Alter et al., 2012; Meschersky et al., 2015). Geospatial and genetic data (e.g., Alter et al., 2015; Mate et al., 2015) suggest that the extant WGW and EGW populations have the potential to mix, and thus contemporary signals of population differentiation may represent historical patterns. Gray whales have great capacity to travel long distances (>22,000 km; Scheinin et al., 2011; Shpak et al., 2013), and in other great whales (e.g., sperm whales; Alexander et al., 2016) nuclear gene pools are nearly homogenous across vast geographic scales. The development of additional, more powerful genetic assays would be a boon to global gray whale conservation, as the ability to identify individuals and family groups, delineate populations, and track patterns of genetic diversity over space and time would result in more informed management decisions.
Suites of single-nucleotide polymorphisms (SNPs) can be used to delineate breaks in genetic structure, but they can also be used for definitive individual identifications (e.g., from biopsies) and for categorical assignment of parentage or relatedness (e.g., Ruegg et al., 2014; Doyle et al., 2016). Here, we developed a gray whale SNP panel to facilitate genetic studies of population differentiation, parentage, and individual identity. Instead of using anonymous SNP markers, we used SNPs from protein-coding genes thought to be targets of selection in other marine mammals because, in addition to providing a rich source of targets for future evolutionary genetic studies, they also have the potential to reveal subtle genetic differentiation sooner than neutral markers because of selection (Hoban et al., 2016; Schweizer et al., 2016).
To develop our SNP panel, we sequenced the genomes of two Sakhalin Island gray whales (a male and a female WGW) and a female EGW from Barrow, Alaska. Subsequently, multilocus SNP genotypes were generated for the single EGW plus a set of WGW biopsy samples (n = 35) collected near Sakhalin Island to provide a preliminary estimate of genomic diversity in gray whales. Our sample sizes are small despite three years of effort because these whales are rare and, like other baleen whales, difficult to sample because of both the expense involved and their extensive movements. However, the genome we describe here will allow researchers to explore evolutionary aspects of gray whales (e.g., rates of nucleotide substitution) and perhaps identify genes and pathways unique to the species.
Materials and Methods
Sample collection and DNA extraction
Skin biopsies were obtained from WGWs on their summer feeding grounds near Sakhalin Island using methods approved by the Scientific Committee of the International Whaling Commission. These include samples from six calves as well as adults. Biopsy samples were frozen and shipped to Purdue University for processing. Skin samples from putative EGWs (n = 2) were cut from two dead gray whales that beached near Barrow, Alaska, and then frozen until processing at Purdue University. Genomic DNA was extracted using a standard potassium acetate protocol (Sambrook and Russell, 2001).
Genome sequencing, assembly, and annotation
The quality and quantity of DNA were greatest from the two WGWs (male ER-14-168 and female ER-14-0147), but we also sequenced one EGW (female GFD-02). For the male WGW, we constructed one paired-end (PE) library without polymerase chain reaction (PCR) amplification and one mate-paired (MP) no-gel library. For the two female gray whales (WGW and EGW), we constructed both MP and PE libraries. PE libraries were constructed per the instructions from the TruSeq DNA PCR-Free Library Preparation Kit (Illumina, San Diego, CA) using the 550-bp insert size method, whereas MP libraries were constructed per the instructions from the Nextera Mate Pair Library Preparation Kit (Illumina) using the "no gel size selection" method. In total, we sequenced 7 lanes of the PE libraries and 1 lane of the MP libraries using an Illumina HiSeq 2500 sequencing system (2 x 100).
We used FastQC software (ver. 0.11.2; Babraham Bioinformatics, 2016) to generate summary statistics for the sequencing data. Trimmomatic software (ver. 0.32; Bolger et al., 2014) was used to remove adaptor sequences and low-quality bases (Phred scores <20). Multiple genome assemblies were generated using ABySS software (ver. 1.9.0; Simpson et al., 2009) with a variety of k-mer values to produce the most comprehensive assemblies. We used the PE data during the contig-building steps and MP data during the scaffold-building steps.
Genome annotation was based on the male WGW to include genes found on the Y chromosome (Zfy) and the X chromosome (Zfx). We used the MAKER pipeline (ver. 2.28; Cantarel et al., 2008) for annotation, following Doyle et al. (2014). Briefly, RepeatMasker software was used to identify and mask stretches of repetitive DNA. We subsequently used SNAP software (ver. 2013-02-16; Korf, 2004) to generate ab initio gene predictions. SNAP was trained (i.e., gray whale gene models were generated) using cetacean protein sequences from the UniProtKB/Swiss-Prot database. SNAP gene predictions supported by expressed sequence tag (EST), protein, or InterProScan evidence were elevated to gene annotations. Cetacean protein data were downloaded from the UniProt Knowledgebase (Uniprot Consortium, 2017), and the manually annotated sequences were used with EST evidence derived from an assembled transcriptome of an Alaskan bow-head whale (Keane et al., 2015). We used CEGMA software to assess the completeness of our genome assemblies (Parra et al., 2007).
SNP identification and development
From published cetacean data, we generated a manually curated list of candidate genes potentially subject to strong natural selection. These include genes associated with osmoregulation, oxygen binding and delivery, and many other aspects of marine life (hereafter referred to as "nonneutral" or "candidate" genes; see Table S1 [available online], Table S2 [available online], and Kosiol et al., 2008). We targeted SNPs from these genes because they can provide insights into adaptive divergence (Rico et al., 2016), because they can help determine the biological significance of population genetic structure (Miller et al., 2010), and because they can provide more power for population assignments (Freamo et al., 2011; Helyar et al., 2011). Using the MAKER transcripts from our genome annotation, we used BLAST software (ver. 2.2.31 +) to annotate these candidate genes in the gray whale genome. For SNP identification, we used BWA software (ver. 0.7.12; Li and Durbin, 2009) to map all PE reads to the male assembly. We used GATK software (ver. 3.4; DePristo et al., 2011; Van der Auwera et al., 2013) to (1) identify and realign reads around insertions/deletions (indels), (2) identify SNPs with a minimum Phred quality score of 30 and a minimum depth of 10 reads, and (3) disregard SNPs within 20 bp of each other. BEDOPS software (ver. 2.0; Neph et al., 2012) was used to identify variable sites (i.e., SNPs) within nonneutral genes. We used IGV software (ver. 2.3; Thorvaldsdottir et al., 2013) to identify target SNPs with at least 60 nucleotides (nt) of high-quality flanking sequence upstream and downstream and guanine and cytosine (GC) content less than 65%. We deliberately minimized linkage disequilibrium (LD) by choosing only single SNPs from a given scaffold.
Ultimately, we attempted to develop 92 autosomal nuclear markers from protein-coding genes specifically targeted because of evidence for selection in other marine mammals. Following preliminary population surveys via dideoxy sequencing (Alter and Palumbi 2009), we designed two mtDNA markers for haplotyping. For sexing, we designed two redundant SNP assays to assess differences in nucleotide sequence between the Zfx and Zfy genes, as reported in GenBank entries AF260789.1, AF260790.1, and AF260791.1.
SNP genotyping, error rates, and variability
We genotyped 36 gray whale biopsy samples at the 96 candidate SNP markers. Samples were genotyped using a Fluidigm Juno Genotyping System (Fluidigm, South San Francisco, CA), and a specific target amplification (STA) step was incorporated to facilitate genotyping of the low-quantity DNA samples that are often associated with endangered species. The STA step refers to Fluidigm's multiplex PCR, which uses a low molar concentration of each primer and limited thermal cycles to increase template material for downstream amplification. Individual SNP calls were visualized and edited using Fluidigm's genotyping analysis software; data from nuclear loci that did not produce obvious clusters of homozygotes and heterozygotes (see Fig. 2) were excluded from further analyses.
In theory, our 35 WGW biopsies could include whales that were inadvertently sampled more than once (e.g., in subsequent years). We used allelematch in R software (Galpern et al., 2012; R Development Core Team, 2008) to group replicate genotypes into unique records that represent individual whales, allowing for a maximum of two mismatches between replicates. Preliminary analyses conducted with the diagnostic function amUniqueProfile were used to determine the number of mismatches most appropriate for clustering replicate genotypes into groups (Galpern et al., 2012). All genotypes identified as replicates were subsequently confirmed by manually evaluating the SNP calls.
Error rates were calculated using replicate DNA samples (n = 69 in total) from 27 individual gray whales. The plurality principle was used to determine a consensus genotype for each individual whale by using SNP calls across replicate samples (see Doyle et al., 2016). The SNP typing error rate (e) was calculated according to the equation e = m/[d(s)], where m represents the total number of mismatches between each replicate sample and the consensus sequence across multiple samples from the same individual, d represents the total number of loci per replicate sample, and s represents the total number of replicate samples. Both incorrect SNP calls (errors of commission) and instances where no amplification occurred (errors of omission) contributed to m.
We used GENALEX software (ver. 6.5; Peakall and Smouse, 2012) to quantify observed ([H.sub.O]) and expected ([H.sub.E]) heterozygosity, the mean probability of identity ([P.sub.I]), and the mean probability of exclusion with neither parent known ([P.sub.E]). We tested for deviations from Hardy-Weinberg equilibrium (HWE) and for LD after applying a sequential Bonferroni correction to account for multiple tests (Holm, 1979). The inbreeding coefficient/(Weir and Cockerham, 1984) was evaluated in GENODIVE software (ver. 2.0b27; Meirmans and Van Tienderen, 2004) using [10.sup.3] permutations.
Molecular sexing and haplotyping
For validation purposes, all samples were sexed using a traditional PCR/gel method (Berube and Palsboll, 1996) as well as by using our novel sexing SNPs. Similarly, mtDNA haplotypes were generated using both traditional dideoxy methods (Alter and Palumbi, 2009) and novel SNP markers. We then compared the concordance among assays to validate our novel markers.
Relatedness and effective size
Relatedness among individuals was first estimated using RELATED software (ver. 1.0; Wang, 2011; Pew et al., 2015), which implements five widely used moment-based relatedness estimators. To identify the estimator that performed best with our data, we used our empirical allele frequencies to simulate 100 data sets for 4 types of dyads: parent-offspring pairs (expected r = 0.5), full siblings (r = 0.5), half siblings (r = 0.25), and unrelated individuals (r = 0.0). Point estimates and 95% confidence intervals (CIs) were estimated on the basis of 100 bootstrap replicates, and the best estimator was identified using Pearson's correlation coefficient. This estimator was used to compare the observed mean pairwise relatedness of each individual whale ([r.sub.pw]) to the mean for the population ([r.sub.pop]). Thus, [r.sub.pw] represents the mean relatedness of a single individual whale to every other whale in the population, whereas [r.sub.pop] represents the mean relatedness of the population as a whole. By comparing [r.sub.pw] to [r.sub.pop], individual whales were identified that were more or less related to the population than expected by chance alone.
Because several commonly used relatedness estimators can be problematic when used with biallelic loci (Oliehoek et al., 2006), we also calculated a pairwise genotypic similarity index following Blouin et al. (1996). The [M.sub.xy] statistic quantifies the mean number of shared alleles ([M.sub.xy]) among pairs of samples (in our case, 630 pairs). For each locus, the number of matching allelic positions (0, 1, or 2) between pairs of individuals was determined using only loci with no missing data among a given pair of whales. For each individual sample (n = 36), we calculated mean observed [M.sub.xy] and 95% CI, which was compared with the population mean and 95% CI.
Conventional relatedness and allele-sharing approaches like those described above do not differentiate between relationship categories. For example, both parent-offspring pairs and full-sibling pairs are expected to have r = 0.5. CERVUS software (ver. 3.0.7; Marshall et al., 1998; Kalinowski et al., 2007) was used to assign parentage to calves. All adults identified in the field were considered candidate parents. Simulations included 100,000 replicate cycles with the number of candidate males and females set to 200 and the proportion of candidate males and females sampled set to 0.1. The proportion of loci mistyped was set to 0.0002, and the minimum allowable confidence level at which a parent assignment was accepted was 95%.
To provide preliminary estimates of contemporary effective population size ([n.sub.e]) and effective number of breeders ([n.sub.eb]) of the critically endangered WGW population, we used NeESTI-MATOR software (ver. 2.01; Do et al., 2014). The software implements two different methods for estimating [n.sub.e] from a single sample, based on (1) LD (Waples and Do, 2008) and (2) heterozygosity excess (Zhdanova and Pudovkin, 2008). A molecular coancestry approach (Nomura, 2008)--also implemented in NeESTIMATOR--was used for estimating [n.sub.eb]. Note that these methods assume selectively neutral markers and closed populations, so these [n.sub.e] and [n.sub.eb] estimates may be biased. Moreover, the influence of selection and migration on such estimates is unknown, as it has not been investigated in a thorough, systematic manner (Waples, 2006).
Genome sequencing, assembly, and annotation
Our WGW sequencing results are compiled in Tables 1 and 2; the EGW genome yielded a poor-quality sequence and was not assembled. We generated >2.5 billion reads (~2 billion high-quality reads after quality control) that collectively span ~200 billion bases and contain nearly all of the core genes common to eukaryotes (Tables 1, 2). The scaffold N50, which represents the value where more than half of the assembly is contained in larger contiguous regions, was ~180,000 bp for the best assembly. We annotated roughly 22,700 genes, a number similar to other cetacean genome studies (Tables S1, S6, available online).
SNP identification and development
We identified 2,057,254 candidate SNPs from the WGWs, of which 1,474,749 passed quality-filtering criteria. Of this high-quality subset, 8413 SNPs were located in exons that were identified in the genome annotation. From these exonic SNPs, we designed and tested 96 SNPs for our genotyping assay. Four were ultimately excluded because they were monomorphic, clustered poorly, or were otherwise of insufficient quality. These novel SNP loci and flanking sequences are described in Table S5, available online. The 92 informative loci include 88 gene-associated nuclear markers, 2 mitochondrial markers, and 2 nuclear sexing markers (Table S2, available online).
SNP genotyping, variability, and error rates
The vast majority of markers amplified in each individual DNA sample. By genotyping 27 of the 36 biopsy samples multiple times, we calculated an overall genotyping error rate (e) of 0.021%. The mean probability of identity ([P.sub.I]; Waits et al., 2001) was 1.6 x [10.sup.-25], and the mean probability of exclusion ([P.sub.E], Jamieson and Taylor, 1997) with neither parent known was >0.999. According to allelematch, the 36 biopsies represented a total of 29 unique multilocus genotypes that correspond to 29 individual gray whales (i.e., we inadvertently sampled 7 whales twice). Observed and expected heterozygosities at autosomal SNPs were 0.32 [+ or -] 0.19 (mean [+ or -] SD) and 0.31 [+ or -] 0.17, respectively, for all WGWs sampled (n = 28). All autosomal SNP loci were in HWE following sequential Bonferroni correction (Table S3, available online). The [H.sub.O] and [H.sub.E] of male whales (n = 11) averaged 0.31 [+ or -] 0.02 and 0.29 [+ or -] 0.02, respectively, across all autosomal markers, whereas the [H.sub.O] and [H.sub.E] of female whales (n = 17) averaged 0.33 [+ or -] 0.02 and 0.31 [+ or -] 0.02, respectively. The mean inbreeding coefficient (f) was -0.05.
Molecular sexing and haplotyping
Samples from the 29 individual gray whales were sexed using both our novel SNP assays and the traditional method with PCR and gel electrophoresis (Table 3). The results of the 2 methods were in complete concordance with one another (see Fig. 3), indicating that our samples were derived from 11 males and 18 females. These same samples were haplotyped using both our mtDNA SNP assays as well as traditional Sanger sequencing. Although our marker set queries only two mtDNA sites, this marker panel could easily be extended to other SNP sites known to be variable in gray whales; we provide here the proof-of-concept data.
Relatedness and effective size
Mean pairwise relatedness ([r.sub.pop]) observed among all 29 individual gray whales (including one EGW individual) was -0.032, and the 95% CI ranged from -0.055 to -0.009. In general, relatedness estimates based on r and on [M.sub.xy]. were qualitatively similar, so we focused on [M.sub.xy] for reasons discussed in Oliehoek et al. (2006). Our analysis of pairwise relatedness confirmed the allelematch results in that 7 of our biopsies were duplicates. For example, the mean number of shared alleles ([M.sub.xy]) ranged from 0.667 to 1.0 for all 630 pair-wise comparisons (Fig. 4), including the 7 duplicate biopsy pairs identified by allelematch, all of which had [M.sub.xy] = 1.0. Many dyads were likely first-degree relatives (e.g., full siblings or parent-offspring pairs). The mean number of shared alleles for each of the 29 individual whales ranged from 0.768 to 0.851 (Fig. 5). The population mean [M.sub.xy(pop)] was 0.834 (95% CI: 0.831-0.838; Fig. 5).
The CERVUS analyses were used to investigate suspected relationships among two ostensible cow-calf pairs. CERVUS confirmed one such relationship (ER14-0159/ER14-0I73) on the basis of field observations but not the other (ER14-0152/ER14-0172). No sires were identified in our sample, but our relatedness analyses identified two potential full sisters (ER14-0162 and ER14-0173; pairwise r = 0.483). These two adult females apparently share the same mother (ER14-0174/ER14-0159).
The [n.sub.e] estimates of contemporary WGW were similar based on LD (mean [n.sub.e] = 14.1; 95% CI: 12.1-16.7) and heterozygosity excess (mean [n.sub.e] = 14.4; 95% CI: 7.6-254.1). The effective number of breeders, based on individual co-ancestry, was 1.3 (95% CI: 1.0-1.7).
Genome sequencing, assembly, and annotation
The gray whale genome assemblies we describe (Tables 1, 2) consist of ~22,700 genes and contain ~95% of the genes known to be highly conserved among eukaryotes (Parra et al., 2007). These assemblies are relatively complete and reveal that the gray whale genome appears to be fairly typical of cetaceans in terms of genome size and gene complement (Table S1, available online). Foote et al. (2015) have argued that the genomes of marine mammals have evolved in a convergent fashion, and future research will determine what proportion of the gray whale genes we annotated (Table S1) are orthologues of the 16,878 genes shared by the killer whale, manatee, walrus, and bottlenose dolphin (Foote et al., 2015) and what proportion are paralogues resulting from gene duplication. We expect that many of these species have faced similar selection pressures with regard to genes involved in processes such as osmoregulation (e.g., adaptation to a saline environment), thermoregulation (e.g., adipose deposition), and oxygen binding.
SNP identification and development
From the genomic sequence data, we identified hundreds of thousands of SNPs that could potentially be used in population genetic surveys. Of these, we evaluated 96 and found that 92 were polymorphic, had low error rates, and could be amplified in a single assay. This panel of markers should be sufficient for investigations of genetic parentage, relatedness, individuality (i.e., DNA fingerprinting), demographic turnover, and population structure. However, the much larger set of SNPs we identified could inform future genome scans based on restriction-site-associated DNA sequencing (Miller et al., 2007) or other queries of anonymous SNPs that might also be informative for studies of natural selection and/or demographic history. We note that our markers are useful in the critically endangered WGWs, so we expect them to be variable in the more plentiful EGWs. However, robust allele frequency estimates will be needed to assess their utility in EGWs (and to search for potential ascertainment biases).
SNP genotyping, variability, and error rates
The oligonucleotides we developed and the SNPs they query were assessed with the Fluidigm platform because it requires very little DNA. We note, however, that our markers could instead be assayed with alternative genotyping technologies. Because these markers are polymorphic and have low error rates (~0.02%), we expect laboratory-to-laboratory variability to be low, as 99.98% of our replicate data were identical across independent genotyping runs. For example, a whale from the western Pacific that is genotyped with these SNPs could be identified as one also sampled in the eastern Pacific provided the genotypes are stored in a common database that could easily be queried by independent research groups (e.g., in a public database on the cloud).
The power available for individual identification and parentage is captured by [P.sub.I] and [P.sub.E], respectively. In theory (based on allele frequencies in our small sample of WGWs), mean [P.sub.I] = 1.6 x [10.sup.-25]. In practice, our limited sampling revealed multiple biopsies from the same donor whales (see below). Similarly, a calf's unknown parentage could be determined among hundreds of candidate parents with virtual certainty if all were genotyped with this SNP panel ([P.sub.E] > 0.999). For applications that require more power, our genome data include a large pool of additional SNPs that could serve as supplemental markers.
Relatedness and effective size
The WGW is critically endangered, and although our sample consisted of only 28 WGW individuals, this is ~20% of the estimated population (Cooke et al., 2013). Our relatedness analysis indicated that a number of our biopsies came from the same whales; seven pairs of samples were found to have identical SNP genotypes across all loci. Given the paucity of WGWs, it is not entirely surprising that over multiple field seasons we (inadvertently) sampled seven individuals twice. Furthermore, a number of individual pairs were apparently derived from close relatives (Figs. 4, 5) that may represent parent-offspring pairs, full siblings, half siblings, and other close relatives. We identified two distinct cow/calf pairs and one ostensible full-sibling pair. Finally, the mean inbreeding coefficient is generally consistent with random mating within the WGW population. Overall, these results illustrate the power of genetic analyses for corroborating or overturning relationships suspected on the basis of fieldwork.
All of our population inferences are necessarily preliminary due to small sample sizes, but we include them here because of the keen interest in the conservation of the WGW population. Whether the population near Sakhalin Island is composed of WGWs, EGWs, or a mixed aggregation of the two stocks is under study by the International Whaling Commission (IWC, 2015). The putative EGW we sampled was no more or less related to the WGW population than expected by chance alone (Fig. 5). If the EGW was derived from an independent gene pool, it should have been more distantly related to the WGW population.
Populations with small effective sizes diverge rapidly due to drift and inbreeding (Wang and Caballero, 1999). Our small estimates of gray whale [n.sub.e] ([n.sub.e] [approximately equal to] 14 by both LD and heterozygosity excess) are consistent with each other, with the idea that the extant WGW population is very small, and with published data indicating that the WGW population is genetically differentiated from the EGW population (e.g., LeDuc et al., 2002; Alter et al., 2012; Meschersky et al., 2015). Such genetic differentiation may be due to population structure associated with divergent selection, genetic drift, and/or a lack of migration (gene flow) among gray whale populations. Additional sampling across the range of the gray whale will be required to differentiate among these possibilities, but the genome sequence and the genotyping platform we describe here should enable those efforts.
Here, we describe the gray whale genome and the development of a gray whale genotyping assay that queries 92 autosomal nuclear SNPs (88 gene-associated autosomal markers, 2 mitochondrial markers, and 2 sex-chromosome markers). We validated these markers by repeated genotyping of 36 gray whale samples and determined that the error rates were low and the markers were polymorphic despite small effective population sizes. The single whale we sampled from the eastern population could not be genetically distinguished from the 28 gray whales we sampled near Sakhalin Island, but our markers provide a powerful platform for distinguishing among individuals and kin (e.g., identifying close relatives). Ultimately, these markers should prove a useful resource for biologists and for the broader conservation community given the difficulty and expense associated with sampling and identifying baleen whales. Furthermore, the genome sequence will serve as a resource for basic studies across a diversity of disciplines.
We thank the International Whaling Commission (IWC) and Aimee Lang for curating the 2011 western gray whale samples provided via the IWC. This work was conducted under the National Marine Fisheries Service (NMFS) Office of Protected Resources' Marine Mammal Health and Stranding Response Program permits 932-1905-MA-009526 and 18786 and Convention on the International Trade in Endangered Species of Wild Fauna and Flora permit 13US082589/9. This work was supported by Exxon Neftegas Limited and the Sakhalin Energy Investment Company. Both collaborative agencies and funding parties received annual progress reports for the past several years. The content presented here is solely the responsibility of the authors and does not necessarily represent the official views of the funding parties. We also thank Teri Rowles (NMFS) for assistance with the project. A. Aziz, J. Dupont, M. Scott, and M. Swindoll provided support in obtaining the biopsies and associated metadata. The Institute of Ecology and Evolution of the Russian Academy of Sciences, the A. V. Zhirmunsky Institute of Marine Biology Far Eastern Branch, and Oregon State University provided invaluable support for the collection of the western gray whale samples. We thank E. Srour and A. Cardoso (Indiana University School of Medicine) for laboratory assistance and support. C. George and R. Suydam (Department of Wildlife Management, North Slope Borough of Alaska) provided the putative eastern gray whale samples. Finally, we thank S. Fears, Y. Ji, M. Sundaram, and J. Willoughby for constructive comments.
We have deposited the primary data underlying these analyses into the National Center for Biotechnology Information's Short Read Archive (study accession SRP105779; BioProject PRJNA384396), in the Dryad Digital Repository (DeWoody et al., 2017), and in the associated online supplementary material (Tables S1-S6).
Alexander, A., D. Steel, K. Hoekzema, S. Mesnick, D. Engelhaupt, I. Kerr, R. Payne, and C. S. Baker. 2016. What influences the worldwide genetic structure of sperm whales (Physeter macrocephalus)? Mol. Ecol. 25: 2754-2772.
Alter, S. E., and S. R. Palumbi. 2009. Comparing evolutionary patterns and variability in the mitochondrial control region and cytochrome b in three species of baleen whales. J. Mol. Evol. 68: 97-111.
Alter, S. E., S. D. Newsome, and S. R. Palumbi. 2012. Pre-whaling genetic diversity and population ecology in eastern Pacific gray whales: insights from ancient DNA and stable isotopes. PLoS One 7: e35039.
Alter, S. E., M. Meyer, K. Post, P. Czechowski, P. Gravlund, C. Gaines, H. C. Rosenbaum, K. Kaschner, S. T. Turvey, J. van der Plicht, el al. 2015. Climate impacts on transocean dispersal and habitat in gray whales from the Pleistocene to 2100. Mol. Ecol. 24: 1510-1522.
Andrews, R. C. 1914. Monograph of the Pacific Cetacea. I. The California Gray Whale (Rhachianectectes glaucus Cope). Mem. Am. Mus. Nat. Hist. 1: 227-287.
Babraham Bioinformatics. 2016. FastQC: a quality control tool for high throughput sequence data. [Online]. Available: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/[2017, June 12].
Berube, M., and P. Palsboll. 1996. Identification of sex in cetaceans by multiplexing with three ZFX and ZFY specific primers. Mol. Ecol. 5: 283-287.
Berzin, A. A., and V. L. Vladimirov. 1981. Changes in the abundance of whalebone whales in the Pacific and the Antarctic since the cessation of their exploitation. Rep. Int. Whal. Comm. 31: 495-499.
Blouin, M. S., M. Parsons, V. Lacaille, and S. Lotz. 1996. Use of micro-satellite loci to classify individuals by relatedness. Mol. Ecol. 5: 393-401.
Bolger, A. M., M. Lohse, and B. Usadel. 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30: 2114-2120.
Bowen, S. L. 1974. Probable extinction of the Korean stock of the gray whale (Eschrichtius robustus). J. Mammal. 55: 208-209.
Cantarel, B. L., I. Korf, S. M. Robb, G. Parra, E. Ross, B. Moore, C. Holt, A. Sanchez Alvarado, and M. Yandell. 2008. MAKER: an easy-to-use annotation pipeline designed for emerging model organism genomes. Genome Res. 18: 188-196.
Cooke, J. G., D. W. Weller, A. L. Bradford, O. Sychenko, A. M. Burdin, and R. L. Brownell, Jr. 2013. Population assessment of Sakhalin gray whale aggregation. Paper SC/65a/BRG27 presented to the 16th Meeting of the International Whaling Commission Scientific Committee, November 2015. [Online]. Available: https://swfsc.noaa.gov/publications/CR/2013/2013Cooke.pdf [2017. June 12].
DePristo, M. A., E. Banks, R. Poplin, K. V. Garimella, J. R. Maguire, C. Hartl, A. A. Philippakis, G. del Angel, M. A. Rivas, M. Hanna, et al. 2011. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43: 491-498.
DeWoody, J. A., N. B. Fernandez, A. Briiniche-Olsen, J. D. Antonides, J. M. Doyle, P. San Miguel, R. Westerman, V. V. Vertankin, C. A. J. Godard-Codding, and J. W. Bickham. 2017. Data from: Characterization of the gray whale Eschrichtius robustus genome and a genotyping array based on single-nucleotide polymorphisms in candidate genes. [Online]. Dryad Digital Repository. Available: http://dx.doi.org/10.5061/dryad.dc04s [2017, June 20].
Do, C., R. S. Waples, D. Peel, G. M. Macbeth, B. J. Tillett, and J. R. Ovenden. 2014. NeEstimator V2: re-implementation of software for the estimation of contemporary effective population size ([N.sub.e]) from genetic data. Mol. Ecol. Resour. 14: 209-214.
Doyle, J. M., T. E. Katzner, P. H. Bloom, Y. Ji, B. K. Wijayawardena, and J. A. DeWoody. 2014. The genome sequence of a widespread apex predator, the golden eagle (Aquila chrysaetos). PLoS One 9: e95599.
Doyle, J. M., T. E. Katzner, G. W. Roemer, J. W. Cain III, B. Milsap, C. McIntire, S. Sonsthagen, N. Fernandez, M. Wheeler, Z. Bulut, et al. 2016. Novel single nucleotide polymorphisms reveal genetic structure and viability selection in the golden eagle (Aquila chrysaetos), a vagile raptor with a holarctic distribution. Conserv. Genet. 17: 1307-1322.
Durban, J. W., D. W. Weller, A. R. Lang, and W. L. Perryman. 2015. Estimating gray whale abundance from shore-based counts using a multilevel Bayesian model. J. Cetacean Res. Manag. 15: 61-68.
Foote, A. D., Y. Liu, G. W. Thomas, T. Vinar, J. Alfoldi, J. Deng, S. Ougan, C. E. van Elk, M. E. Hunter, V. Joshi, et al 2015. Convergent evolution of the genomes of marine mammals. Nat. Genet. 47: 272-275.
Freamo, H., P. O'Reilly, P. R. Berg, S. Lien, and E.G. Boulding. 2011. Outlier SNPs show more genetic structure between two Bay of Fundy metapopulations of Atlantic salmon than do neutral SNPs. Mol. Ecol. Resour. 11: 254-267.
Galpern, P., M. Manseau, P. Hettinga, K. Smith, and P. Wilson. 2012. Allelematch: an R package for identifying unique multilocus genotypes where genotyping error and missing data may be present. Mol. Ecol. Resour. 12: 771-778.
Helyar, S. J., J. Hemmer-Hansen, D. Bekkevold, M. I. Taylor, R. Ogden, M. T. Limborg, A. Cariani, G. E. Maes, E. Diopere, G. R. Carvalho, et al. 2011. Application of SNPs for population genetics of nonmodel organisms: new opportunities and challenges. Mol. Ecol. Resour. 11: 123-136.
Henderson, D. A. 1984. Nineteenth century gray whaling: grounds, catches and kills, practices and depletion of the whale population. Pp. 159-186 in The Gray Whale Eschrichtius robustus, M. L. Jones, S. L. Swartz, and S. Leatherwood, eds. Academic Press, Orlando, FL.
Hoban, S., J. L. Kelley, K. E. Lotterhos, M. F. Antolin, G. Bradburd, D. B. Lowry, M. L. Poss, L. K. Reed, A. Storfer, and M. C. Whitlock. 2016. Finding the genomic basis of local adaptation: pitfalls, practical solutions, and future directions. Am. Nat. 188: 379-397.
Holm, S. 1979. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6: 65-70.
IUCN (International Union for Conservation of Nature). 2008. IUCN Red List of Threatened Species: A Global Species Assessment. IUCN, Gland, Switzerland.
IWC (International Whaling Commission). 2015. Report of the Second Workshop on the Rangewide Review of the Population Structure and Status of North Pacific Gray Whales, San Diego, 1-3 April 2015. IWC, SC/66a/Rep08.
Jamieson, A., and S. C. S. Taylor. 1997. Comparisons of three probability formulae for parentage exclusion. Anim. Genet. 28: 397-400.
Kalinowski, S. T., M. L. Taper, and T. C. Marshall. 2007. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16: 1099-1106.
Keane, M., J. Semeiks, A. E. Webb, Y. Li, V. Quesada, T. Craig, L. B. Madsen, S. van Dam, D. Braw, P. I. Marques, et al 2015. Insights into the evolution of longevity from the bowhead whale genome. Cell Rep. 10: 112-122.
Korf, I. 2004. Gene finding in novel genomes. BMC Bioinformatics 5: 59.
Kosiol, C., T. Vinar, R. R. da Fonseca, M. J. Hubisz, C. D. Bustamante, R. Nielsen, and A. Siepel. 2008. Patterns of positive selection in six mammalian genomes. PLoS Genet. 4: el000144.
Laake, J., A. Punt, R. Hobbs, M. Ferguson, D. Rugh, and J. Breiwick. 2009. Re-analysis of gray whale southbound migration surveys 1967-2006. U.S. Department of Commerce, NOAA Technical Memorandum. NMFS-AFSC-203.
LeDuc, R. G., D. Weller, J. Hyde, A. M. Burdin, P. E. Rosel, R. L. Brownell, Jr., B. Wursig, and A. E. Dizon. 2002. Genetic differences between western and eastern gray whales (Eschrichtius robustus). J. Cetacean Res. Manag. 4: 1-5.
Li, H., and R. Durbin. 2009. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25: 1754-1760.
Marshall, T. C., J. Slate, L. E. B. Kruuk, and J. M. Pemberton. 1998. Statistical confidence for likelihood-based paternity inference in natural populations. Mol. Ecol. 7: 639-655.
Mate, B. R., V. Y. Ilyashenko, A. L. Bradford, V. V. Vertyankin, G. A. Tsidulko, V. V. Rozhnov, and L. M. Irvine. 2015. Critically endangered western gray whales migrate to the eastern North Pacific. Biol. Lett. 11: 20150071.
Mead, J. G., and E. D. Mitchell. 1984. Atlantic gray whales. Pp. 33-53 in The Gray Whale Eschrichtius robustus, M. L. Jones, S. L. Swartz, and S. Leatherwood, eds. Academic Press, Orlando, FL.
Meirmans, P. G., and P. H. Van Tienderen. 2004. GENOTYPE and GENODIVE: two programs for the analysis of genetic diversity of asexual organisms. Mol. Ecol. Notes 4: 792-794.
Meschersky, I. G., M. A. Kuleshova, D. I. Litovka, V. N. Burkanov, R. D. Andrews, G. A. Tsidulko, V. V. Rozhnov, and V. Y. Ilyashenko. 2015. Occurrence and distribution of mitochondrial lineages of gray whales (Eschrichtius robustus) in Russian Far Eastern seas. Biol. Bull. Russ. Acad. Sci. 42: 34-42.
Miller, H. C., F. Allendorf, and C. H. Daugherty. 2010. Genetic diversity and differentiation at MHC genes in island populations of tuatara (Sphenodon spp.). Mol. Ecol. 19: 3894-3908. doi: 10.1111/j.1365-294X.2010.04771.x.
Miller, M. R., J. P. Dunham, A. Amores, W. A. Cresko, and E. A. Johnson. 2007. Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers. Genom. Res. 17: 240-248.
Neph, S., M. S. Kuehn, A. P. Reynolds, E. Haugen, R. E. Thurman, A. K. Johnson, E. Rynes, M. T. Maurano, J. Vierstra, S. Thomas, et al. 2012. BEDOPS: high-performance genomic feature operations. Bioinformatics 28: 1919-1920.
Nomura, T. 2008. Estimation of effective number of breeders from molecular coancestry of single cohort sample. Evol. App. 1: 462-474.
Oliehoek, P. A., J. J. Windig, J. A. M. van Arendonk, and P. Bijma. 2006. Estimating relatedness between individuals in general populations with a focus on their use in conservation programs. Genetics 173: 483-496.
Parra, G., K. Bradnam, and I. Korf. 2007. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics 23: 1061-1067.
Peakall, R., and P. E. Smouse. 2012. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research--an update. Bioinformatics 28: 2537-2539.
Pew, J., P. H. Muir, J. Wang, and T. R. Frasier. 2015. related: an R package for analysing pairwise relatedness from codominant molecular markers. Mol. Ecol. Resour. 15: 557-561.
Queller, D. C., and K. F. Goodnight. 1989. Estimating relatedness using genetic markers. Evolution 43: 258-275.
R Development Core Team. 2008. R: a language and environment for statistical computing. [Online]. R Foundation for Statistical Computing, Vienna. Available: http://www.R-project.org [2017, June 12].
Reeves, R. R., T. D. Smith, and E. A. Josephson. 2008. Observations of western gray whales by ship-based whalers in the 19th century. J. Cetacean Res. Manag. 10: 247-256.
Reilly, S. B., J. L. Bannister, P. B. Best, M. Brown, R. L. Brownell, Jr., D. S. Butterworth, P. J. Clapham, J. Cooke, G. P. Donovan, J. Urban, et al. 2008. Eschrichtius robustus. [Online]. IUCN Red List Threatened Species, ver. 2013.2. Available: http://www.iucnredlist.org/details/8099/0 [2017, June 12].
Rice, D. W., and A. A. Wolman. 1971. The Life History and Ecology of the Gray Whale (Eschrichtius robustus). American Society of Mammalogists, Special Publication No. 3, Stillwater, OK.
Rico, Y., D. M. Ethier, C. M. Davy, J. Sayers, R. D. Weir, B. J. Swanson, J. J. Nocera, and C. J. Kyle. 2016. Spatial patterns of immunogenetic and neutral variation underscore the conservation value of small, isolated American badger populations. Evol. Appl. 9: 1271-1284. doi: 10.1111/eva.12410.
Ruegg, K. C., E. C. Anderson, K. L. Paxton, V. Apkenas, S. Lao, R. B. Siegel, D. F. DeSante, F. Moore, and T. B. Smith. 2014. Mapping migration in a songbird using high-resolution genetic markers. Mol. Ecol. 23: 5726-5739.
Sambrook, J., and D. Russell. 2001. Molecular Cloning: A Laboratory Manual, 3rd ed. Cold Spring Harbor Laboratory Press, Cold Spring Harbor. NY.
Scheinin, A. P., D. Kerem, C. D. MacLeod, M. Gazo, C. A. Chicote, and M. Castellote. 2011. Gray whale (Eschrichtius robustus) in the Mediterranean Sea: anomalous event or early sign of climate-driven distribution change? Mar. Biodivers. Rec. 4: e28.
Schweizer, R. M., B. M. vonHoldt, R. Harrigan, J. C. Knowles, M. Musiani, D. Coltman, J. Novembre, and R. K. Wayne. 2016. Genetic subdivision and candidate genes under selection in North American grey wolves. Mol. Ecol. 25: 380-402.
Shpak, O. V., D. M. Kuznetsova, and V. V. Rozhnov. 2013. Observation of the gray whale (Eschrichtius robustus) in the Laptev Sea. Biol. Bull. Russ. Acad. Sci. 40: 797-800.
Simpson, J. T., K. Wong, S. D. Jackman, J. E. Schein, S. J. M. Jones, and I. Biron. 2009. ABySS: a parallel assembler for short read sequence data. Genome Res. 19: 1117-1123.
Thorvaldsdottir, H., J. T. Robinson, and J. P. Mesirov. 2013. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. 14: 178-192.
Uniprot Consortium. 2017. UniProt: the universal protein knowledge-base. Nucl. Acids Res. 45: D158-D169.
Van der Auwera, G. A., M. O. Carneiro, C. Hartl, R. Poplin, G. Del Angel, A. Levy-Moonshine, T. Jordan, K. Shakir, D. Roazen, J. Thibault, et al. 2013. From FastQ data to high-confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43: 11.10.1-11.10.33.
Waits, L. P., G. Luikart, and P. Taberlet. 2001. Estimating the probability of identity among genotypes in natural populations: cautions and guidelines. Mol. Ecol. 10: 249-256.
Wang, J. 2011. Coancestry: a program for simulating, estimating and analysing relatedness and inbreeding coefficients. Mol. Ecol. Resour. 11: 141-145.
Wang, J., and A. Caballero. 1999. Developments in predicting the effective size of subdivided populations. Heredity 82: 212-226.
Waples, R. S. 2006. A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Conserv. Genet. 7: 167-184.
Waples, R. S., and C. Do. 2008. LDNE: a program for estimating effective population size from data on linkage disequilibrium. Mol. Ecol. Resour. 8: 753-756.
Weir, B., and C. Cockerham. 1984. Estimating F-statistics for the analysis of population structure. Evolution 38: 1358-1370.
Weller, D. W., and R. L. Brownell, Jr. 2012. A re-evaluation of gray whale records in the western North Pacific. [Online]. Report SC/64/BRG10. Available: https://swfsc.noaa.gov [2017, July 7].
Weller, D. W., A. M. Burdin, B. Wursig, B. L. Taylor, and R. L. Brownell, Jr. 2002. The western gray whale: a review of past exploitation, current status and potential threats. J. Cetacean Res. Manag. 4: 7-12.
Weller, D. W., A. L. Bradford, H. Kato, T. Bando, S. Ohtani, A. M. Burdin, and R, L. Brownell, Jr. 2008. Photographic match of a western gray whale between Sakhalin Island, Russia, and Honshu, Japan: first link between feeding ground and migratory corridor. J. Cetacean Res. Manag. 10: 89-91.
Zhdanova, O. L., and A. I. Pudovkin. 2008. Nb_HetEx: a program to estimate the effective number of breeders. J. Hered. 99: 694-695.
J. ANDREW DEWOODY (1,2,*), NADIA B. FERNANDEZ (1), ANNA BRUNICHE-OLSEN (1), JENNIFER D. ANTONIDES (1), JACQUELINE M. DOYLE (1,3), PHILLIP SAN MIGUEL (4), RICK WESTERMAN (4), VLADIMIR V. VERTYANKIN (5), CELINE A. J. GODARD-CODDING (6), AND JOHN W. BICKHAM (7)
(1) Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana 47907;
(2) Department of Biological Sciences, Purdue University, West Lafayette, Indiana 47907; (3) Department of Biological Sciences, Towson University, Towson, Maryland 21252; (4) Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, Indiana 47907; (5) Kronotsky State Nature Biosphere Reserve, Elizovo, Kamchatka 684100, Russia; (6) Institute of Environmental and Human Health, Texas Tech University (TTU) and TTU Health Sciences Center, Box 41163, Lubbock, Texas 79409-1163; and (7) Department of Wildlife and Fisheries Sciences, Texas A &M University, College Station, Texas 77845
Received 27 September 2016; Accepted 30 May 2017; Published online 1 September 2017.
* To whom correspondence should be addressed. E-mail: firstname.lastname@example.org.
Abbreviations: EGW, eastern gray whale; EST, expressed sequence tag; HWE, Hardy-Weinberg equilibrium; IUCN, International Union for Conservation of Nature; IWC, International Whaling Commission; LD. linkage disequilibrium; MP, mate paired; PCR, polymerase chain reaction; PE, paired end; SNP, single-nucleotide polymorphism; STA, specific target amplification; WGW, western gray whale.
Table 1 Sequencing statistics associated with the western gray whale genome Mean insert Mode insert Sample name size (bp) size (bp) Total reads Paired-end libraries ER-14-0147 471 481 1,331,820,280 ER-14-0168 497 506 1,254,676,990 Mate-paired libraries ER-14-0147 2223 1463 184,404,728 ER-14-0168 2363 1426 213,408,466 Sample name Total bases Quality reads Quality bases Paired-end libraries ER-14-0147 134,513,848,280 988,439,854 97,099,230,094 ER-14-0168 126,722,375,990 974,126,824 95,896,562,771 Mate-paired libraries ER-14-0147 18,624,877,528 146,095,780 13,350,014,231 ER-14-0168 21,554,255,066 168,844,292 15,387,314,171 Table 2 Summary statistics generated by ABySS software for the western gray whale genome assemblies Sample Contig N50 Scaffolds Assembly size Min. length ER-14-0147 8336 60,534 3,082,450,012 500 ER-14-0168 8690 57,219 2,849,466,389 500 Sample Max. length Scaffold N50 n : N50 Comp% ER-14-0147 1,943,192 180,882 4539 95 ER-14-0168 1,944,941 187,455 4089 96 Table 3 Haplotype and sex data mtDNA Sex chromosomes Sample Population CR_82 CR_104 ZFY_288 ZFY_342 Sex ER-14-0147 Western G : G G : G C : C G : G Female ER-14-0148 Western A : A G : G C : C G : G Female ER-14-0149 Western A : A G : G C : C G : G Female ER-14-0150 Western G : G A : A C : T A : G Male ER-14-0153 Western G : G G : G C : C G : G Female ER-14-0155 Western A : A G : G C : C G : G Female ER-14-0156 Western A : A G : G C : T A : G Male ER-14-0160 Western G : G A : A C : T A : G Male ER-14-0161 Western G : G A : A C : C G : G Female ER-14-0162 Western G : G G : G C : C G : G Female ER-14-0163 Western G : G G : G C : C G : G Female ER-14-0164 Western A : A G : G C : T A : G Male ER-14-0165 Western A : A G : G C : T A : G Male ER-14-0167 Western G : G G : G C : T A : G Male ER-14-0168 Western G : G G : G C : T A : G Male ER-14-0169 Western A : A G : G C : T A : G Male ER-14-0170 Western G : G A : A C : T A : G Male ER-14-0171 Western G : G A : A C : C G : G Female ER-14-0172 Western G : G G : G C : C G : G Female ER-14-0173 Western G : G G : G C : C G : G Female ER-14-0174 Western G : G G : G C : C G : G Female ER-14-0175 Western A : A G : G C : T ... Male Zl12743 Western G : G G : G C : T A : G Male Zl12744 Western G : G G : G C : C G : G Female Zl12745 Western G : G G : G C : C G : G Female Zl12746 Western A : A G : G C : C G : G Female Zl12747 Western A : A G : G C : C G : G Female Zl12748 Western A : A G : G C : C G : G Female GFD02 Eastern G : G A : A C : C G : G Female Shown are genotypes for two mitochondrial (CR_82 and CR_104) and two sexing (ZFY_288 and ZFY_342) markers. A missing genotype is represented by an ellipsis.
|Printer friendly Cite/link Email Feedback|
|Author:||Dewoody, J. Andrew; Fernandez, Nadia B.; Bruniche-Olsen, Anna; Antonides, Jennifer D.; Doyle, Jacque|
|Publication:||The Biological Bulletin|
|Date:||Jun 1, 2017|
|Previous Article:||Parallel Patterns of Host-Specific Morphology and Genetic Admixture in Sister Lineages of a Commensal Barnacle.|
|Next Article:||Crossing the Divide: Admixture Across the Antarctic Polar Front Revealed by the Brittle Star Astrotoma agassizii.|