Cryptic species diversity and reproductive isolation among sympatric lineages of Strongylocentrotus sea urchins in the northwest Atlantic
Abstract
Distinguishing between intra- and inter-specific variation in genetic studies is critical to understanding evolution because the mechanisms driving change among populations are expected to be different than those that shape reproductive isolation among lineages. Genetic studies of north Atlantic green sea urchins Strongylocentrotus droebachiensis (Müller, 1776) have detected significant population substructure and asymmetric gene flow from Europe to Atlantic Canada and interspecific hybridization between S. droebachiensis and Strongylocentrotus pallidus (Sars, 1871). However, combined with patterns of divergence at mtDNA sequences, morphological divergence at gamete traits suggests that the European and North American lineages of S. droebachiensis may be cryptic species. Here, we use a combination of cytochrome c oxidase subunit I (COI) sequences and single nucleotide polymorphisms (SNPs) to test for cryptic species within Strongylocentrotus sea urchins and hybrids between S. droebachiensis and S. pallidus populations. We detect striking patterns of habitat and reproductive isolation between two S. droebachiensis lineages, with offshore deep-water collections consisting of S. pallidus in addition to a cryptic lineage sharing genetic similarity with previously published sequences from eastern Atlantic S. droebachiensis. We detected only limited hybridization among all three lineages of sea urchins, suggesting that shared genetic differences previously reported may be a result of historical introgression or incomplete lineage sorting.
Introduction
Broadcast-spawning marine invertebrates offer unique prospects for studying reproductive isolation, because the opportunities for allopatric separation are fewer in the ocean, and both divergent selection along ecological boundaries and the evolution of gamete incompatibilities are expected to be key mechanisms driving speciation in the sea (Bowen et al. 2013). Genetic studies aimed at assessing the ecological and evolutionary processes that initiate divergence among populations hinge on the capacity to recognize (and target) species of interest, because the mechanisms driving intraspecific diversification can be different from those driving interspecific divergence (e.g., Palumbi 1994). Recent studies on marine invertebrates (e.g., Vogler et al. 2008; Ladner and Palumbi 2012; Pérez-Portela et al. 2013; Pante et al. 2015; Warner et al. 2015) have identified cryptic species in what were once thought of as divergent populations. In such cases, unrecognized diversity within or among populations containing cryptic species influences the partitioning of genetic variation among allopatric samples and can bias the estimation of population divergence. Thus, patterns of genetic structure are open to being misinterpreted as a result of restricted gene flow and increased genetic drift while overlooking additional extrinsic and intrinsic factors driving genetic differentiation among species.
Differentiating between intra- and inter-specific diversity in sympatric lineages also provides the opportunity to identify introgressive hybridization when reproductive isolation is incomplete. Recent empirical studies have resurrected ecological factors as a primary driver of marine speciation (Choat 2006; Bowen et al. 2013), and the ecological differences between adjacent habitats represent a middle ground between sympatric and allopatric isolation (Doebeli and Dieckmann 2003). Identifying hybrid zones that form across such habitat disjunctions provides a “natural laboratory” in which we can observe the evolutionary process of speciation. Offspring of mixed ancestries contain different combinations of parental genotypes that are tested by natural selection (Barton and Hewitt 1985), and the introgression of loci among hybrids will vary with their effect on fitness. Thus, hybrid zones offer important settings for studies of selection, gene flow, and genetic drift, because the introgression of foreign alleles is a direct measure of the degree of reproductive isolation.
Sea urchins have long been a model organism for the studies of developmental biology, and species in the genus Strongylocentrotus are becoming an increasingly popular in the study of population genetics and evolution (Addison and Hart 2004; Addison and Hart 2005; Addison and Pogson 2009; Pujolar and Pogson 2011; Kober and Bernardi 2013; Norderhaug et al. 2016). Genetic studies of Strongylocentrotus droebachiensis (Müller, 1776) detected local panmixis but significant population substructure between the east and west coasts of the north Atlantic, where the eastern populations had low genetic diversity at both microsatellite loci and mtDNA sequences (Addison and Hart 2004, 2005; Harper et al. 2007). Individuals of eastern descent were detected at low frequency in more diverse western populations, and the patterns of genetic subdivision were interpreted as the result of limited intraspecific gene flow and genetic drift. However, subsequent research on patterns of morphological variation in sperm traits have identified significant differences among populations of S. droebachiensis, with sea urchins from the northwest Atlantic and Pacific having longer and narrower sperm nuclei than those from the northeast Atlantic (Marks et al. 2008). Similarly, Manier and Palumbi (2008) reported that overall sperm morphology (head length, head width, axoneme length, and mid-piece area) was most divergent between the Pacific and the northeast Atlantic, and comparisons of quantitative divergence (QST) for sperm characters relative to divergence at neutral microsatellite markers (FST) suggest that directional selection may be accelerating the evolution of sperm traits, especially between the Pacific and northwest Atlantic. Significant divergence in both genetic and morphological characters among allopatric populations across the north Atlantic suggests that lineages of S. droebachiensis may be distinct (cryptic) species, and the presence of mtDNA lineages from the eastern populations in the west may simply be a result of incomplete lineage sorting and not gene flow per se. Distinguishing between intraspecific gene flow and interspecific lineage sorting in north Atlantic populations of S. droebachiensis will help identify the relevant scales to which population subdivision is an evolutionarily driver in this system.
In addition to potential for gene flow between the east and west lineages of S. droebachiensis, patterns of genetic diversity within Atlantic populations of S. droebachiensis may also be influenced by introgressive hybridization with the congener Strongylocentrotus pallidus (Sars, 1871). Strongylocentrotus droebachiensis and S. pallidus are circumpolar and have overlapping depth preferences, with S. dreobachiensis typically found in shallow habitats (0–30 m) and S. pallidus in deeper habitats (>30 m). Mixed aggregations of both species have been observed throughout their range at depths >15 m (Vasseur 1952; Swan 1953; Gagnon and Gilkinson 1994; Bluhm et al. 1998), and as both species broadcast sperm and eggs, opportunities exist for their gametes to interact in the plankton. However, levels of hybridization and introgression detected in natural populations (Addison and Hart 2005; Harper et al. 2007; Addison and Pogson 2009; Pujolar and Pogson 2011) are much lower than predicted based on their propensity to hybridize under laboratory conditions (Strathmann 1981). Analyses of nuclear DNA sequences detected asymmetric introgression (from S. pallidus into S. droebachiensis) in approximately 5% of individuals sampled in the northeast Pacific (Addison and Pogson 2009). Genetic studies of S. droebachiensis in the northwest Atlantic are consistent with the low asymmetric gene flow of mitochondrial genes from S. pallidus (Addison and Hart 2005), but a lack of resolution at nuclear loci (four microsatellites; Addison and Hart 2002, 2004) makes it difficult to distinguish between incomplete lineage sorting and recent hybridization.
Our goal with this study is to examine northwest Atlantic populations of Strongylocentrotus sea urchins for evidence of cryptic species and hybridization. To do this, we evaluated mitochondrial and nuclear DNA polymorphisms in both coastal populations of S. droebachiensis (10–30 m) and offshore populations of S. pallidus (65–120 m). If contemporary gene exchange occurs between S. pallidus and S. droebachiensis (interspecific hybridization) or among genetically distinct populations of S. droebachiensis (intraspecific gene flow), then patterns of mtDNA haplotype sharing among lineages should correspond to admixture at nuclear loci. Alternatively, if mtDNA haplotype sharing results from incomplete lineage sorting or historical introgression, then we predict limited admixture across the nuclear genome. Finally, the absence of both mtDNA haplotype sharing and admixture across the nuclear genome would suggest complete reproductive isolation among the lineages. Overall, this study contributes to a better understanding of reproductive isolation among Strongylocentrotus sea urchin lineages in the north Atlantic and provides a framework for distinguishing between mechanisms driving intra- and inter-specific divergence.
Methods
Specimen collection
Green sea urchins (S. droebachiensis; n = 60) were collected from coastal habitat near Duncan Cove, Nova Scotia (44.2954°N, 63.3120°W), ranging in depths from 10 to 32.5 m using SCUBA (“shallow” populations). We sampled along a depth gradient at one location in an attempt to identify hybrid individuals across the range of shallow habitats dominated by S. droebachiensis and deeper coastal habitats where (at least in the Pacific) individuals of mixed ancestry have been detected (Addison and Pogson 2009). Pale sea urchins (S. pallidus; n = 70) were collected from offshore populations using a fixed gear dredge (“deep” populations) at 65 m (42.2348°N, 64.1282°W), 72 m (43.3588°N, 64.4279°W), 92 m (43.4862°N, 67.0702°W), and 120 m (43.4367°N, 62.5083°W) (Fig. 1). Shallow-water specimens were confirmed as being S. droebachiensis based on colour characteristics (i.e., test, tube feet, and aboral spines) that strongly correlate with detailed identification keys (e.g., Jensen 1974; Gagnon and Gilkinson 1994). Deep-water samples were generally pale in color, but as they had been frozen at sea, most were in poor condition, and confirming species identity through secondary characteristics such as the color of the tube feet or aboral spines was difficult. Therefore prior to DNA extraction, these samples were categorized as S. pallidus following the morphological studies of Gilkinson et al. (1988) and Gagnon and Gilkinson (1994) who showed that Strongylocentrotus sea urchins sampled at depths >60 m in Atlantic Canada were S. pallidus. Gonad tissue was preserved in 95% ethanol, and total genomic DNA was extracted using DNAeasy Blood and Tissue columns (QIAGEN) following the manufacturer’s recommended protocols.
Fig. 1.
COI genotyping
A fragment of the cytochrome c oxidase subunit I (COI) mitochondrial gene was amplified using the polymerase chain reaction (PCR) primers COIJ and COIC (Edmands et al. 1996). Amplification was performed in a 30 μL volume consisting of 4 ng DNA, 1× ThermoPol reaction buffer (New England Biolabs, NEB), 0.2 mmol dNTPs (NEB), 2.0 mmol MgSO4, 0.5 μmol forward and reverse primers, and 1.0 unit of Taq polymerase (NEB). Thermal cycling conditions were 95 °C for 3 min, followed by 39 cycles of 95 °C (30 s), 45 °C (30 s), 72 °C (60 s), and a final extension at 72 °C for 3 min. Amplicons were separated using agarose gel electrophoresis and visualized under UV light prior to sequencing using forward, reverse, or both PCR primers at the Genome Quebec Innovation Centre (McGill University, Montreal, Quebec, Canada).
Sequences were aligned, edited, and trimmed using CLC Genomics WorkBench (CLC GW; QIAGEN) (GenBank accession No. MG098337-MG098440). For samples collected at each depth, we computed measurements of nucleotide diversity (π), number of segregating sites (S), number of haplotypes (H), and haplotype diversity (h) using Arlequin v3.5 (Excoffier and Lischer 2010). To examine our data for introgressive hybridization, we conducted a phylogenetic analysis of mtDNA and designated each haplotype as either S. droebachiensis or S. pallidus. First, we constructed a gene tree and estimated the posterior probability of the nodes with MrBayes 3 (Ronquist and Huelsenbeck 2003). We used the HKY model of substitution selected by the Bayesian implementation criterion implemented in the program jModelTest 2.1.3 (Darriba et al. 2012) and ran independent analyses of 10 chains (temp = 0.1) for 3 million generations, sampling trees every 1000 steps. Convergence was achieved by running the analysis long enough to ensure that the standard deviation of the split frequencies of both independent runs was <0.01. A second phylogenetic tree was inferred by maximum likelihood using PhyML 3.1 (Guindon et al. 2010), with an HKY85 substitution model, gamma distributed rate heterogeneity at sites, and an SPR tree search. Nodal support was estimated using nonparametric bootstrap analysis with 1000 replicates. We rooted both trees using the conger Strongylocentrotus intermedius (Agassiz, 1864) (see Kober and Bernardi 2013). Finally, to simultaneously visualize both the phylogenetic relationships and the frequency of each haplotype, we constructed 95% parsimony networks using the computer program tcs 1.18 (Clement et al. 2000).
SNP genotyping
We collected ddRAD-seq data following the protocol described by Poland et al. (2012). We selected a combination of restriction enzymes MluCI (common cutter; 5′-AATT-3′; NEB) and Sau96I (rare cutter; 5′-GGNCC-3′; NEB) to reduce the complexity of the genome and increase the sequencing depth at similar sites among individuals (Poland and Rife 2012). In addition, double digests using this combination of enzymes generated fragments in the size range of 200–300 bp, thus eliminating the need for a size selection step in the construction of the library. We used custom adapters that included 48 multiplex-identifier (MID) barcodes (4–9 bp), with a Sau96I overhang on one end (A1) and a common Y adapter with a MluCI overhang at the other end (A2). We included four different MID tags on Adapter 2 (3–8 bp) to increase sequence complexity during the first few bases to possibly improve resolution of captured florescent image in base-calling.
We double-digested 100 ng of genomic DNA in CutSmart® Buffer (NEB) by incubating 10 units of MluCI and five units of Sau96I in a single 20 μL reaction for 2 h at 37 °C, followed by 20 min at 65 °C. Adapters were ligated to the digested DNA in the same tubes that included 0.1 μmol/L A1, 15 μmol/L A2, 0.5× CutSmart® Buffer, 1 mmol/L ATP, and 400 units of T4 ligase. The 40 μL reaction volume was incubated at 22 °C for 2 h, 65 °C for 30 min, and 4 °C overnight. We pooled 12 individuals (10 μL each) and purified the mix using QIAquick PCR Purification kit in one column following the manufacturer’s protocol and eluting using 50 μL EB buffer (10 mmol/L Tris-Cl at pH 8.5; QIAGEN). Pooled ligation products were amplified in 50 μL volumes containing 10 μL DNA template, 1× NEBNEXT High-Fidelity PCR Master Mix, and 0.8 μmol/L each of the forward and reverse sequencing primer. Thermal cycling protocol consisted of 98 °C for 30 s, 15 cycles of 98 °C (10 s), 65 °C (30 s), 72 °C (30 s), and 72 °C for 5 min. To construct the final libraries, we pooled four PCR reactions (48 individuals total) and purified with a QIAquick PCR Purification kits and eluting with 50 μL EB buffer. We constructed a total of three libraries (48-plex) and each was pair-end sequenced (100 bp) on the Illumina HiSeq2000 platform at the Genome Quebec Innovation Centre (McGill University, Montreal, Quebec, Canada).
Bioinformatics
Raw Illumina reads were imported into CLC GW 8.0.3 (QIAGEN) and demultiplexed based on Y-adapter and unique barcodes. Reads lacking a complete barcode were discarded, and we filtered for quality by retaining reads with a minimum PHRED score of 30, a limit of two ambiguous nucleotides, and a minimum length of 30 bp. Pairing reads that include a minimum of seven overlapping nucleotides at the beginning or end of sequences were merged to improve quality in read mapping by increasing the sequencing length. We then mapped all the reads to the Strongylocentrotus purpuratus (Stimpson, 1857) reference genome (Sea Urchin Genome Sequencing Consortium et al. 2006) using the CLC Assembly Cell 4.3 algorithm with default parameters, a length fraction of 0.5, and similarity fraction of 0.8. A consensus sequence was extracted from this mapping and used as the reference for read mapping for SNP variants with the same parameters except a length fraction of 0.9. We detected variants for each individual and for the reference as a whole, using CLC’s Basic Variant Detection tool. We removed all polymorphisms except single nucleotide variants (SNVs), and we retained only SNVs with at least eight reads of coverage for each individual. Finally, SNPs were filtered for a minor allele frequency of ≥ 0.05.
SNP analysis
Summary statistics, including indices of nucleotide diversity (π), observed (Ho) and expected (He) heterozygosity, average gene diversity over loci (πn), and FST values, were calculated in Arlequin v3.5. Locus-specific FST values were calculated in genepop (Rousset 2008). We tested for the presence of distinct genetic lineages and hybrid individuals using the clustering method in structure v2.4.3 (Pritchard et al. 2000; Falush et al. 2003) to assign individual multilocus genotypes to k groups. We performed the analysis in structure using 1 < k < 10 assuming an admixture model, uncorrelated allele frequencies, and no population priors. We ran Bayesian MCMC searches for 1 million steps with an initial burn-in of 100 000. For each analysis, we performed five replicates and used the method of Evanno et al. (2005) to find the best-fit value of k.
We further tested for the presence and nature of hybrids using the Gensback command in structure. We assumed that individuals in each cluster had pure ancestry, but that some individuals may have ancestry from one or more of the other clusters. This analysis uses an informative prior based on k (from the admixture model) to calculate the posterior probability that an individual has an immigrant ancestor in the last G generations, where G = 0 corresponds to the individual being an immigrant itself. In this analysis, we limited k to 3 (see below), set the prior probability of migrants to 0.01, and allowed the detection of migrants up to four generations before the present by setting G = 4. The calculations were performed on the final 100 000 runs with an initial burn-in of 10 000.
Results
mtDNA variation
We obtained 399 bp COI sequences for 104 of the 110 sea urchins collected from shallow (n = 49) and deep-water (n = 55) habitats for which we obtained high-quality genomic DNA (Table 1). There were 36 variable sites and a total of 22 unique haplotypes. Phylogenetic analysis identified three well-supported clades (Fig. 2A) consisting of the expected lineages of S. droebachiensis and S. pallidus from the shallow and deep habitats, respectively, as well as an unexpected cryptic lineage of S. droebachiensis detected only in the deep habitat. Mean genetic divergence (Kimura 2-parameter) among all the sequences was 3.1%, whereas the pairwise divergence between the shallow and deep-water populations of S. droebachiensis was 2.3%.
Table 1.
| nDNA | mtDNA | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Putative species | Depth | n | Ho | He | πn | n | S | π | H | h |
| S. droebachiensis | 10 | 14 | 0.268 (0.206) | 0.260 (0.155) | 0.157 (0.076) | 11 | 22 | 0.012 | 6 | 0.800 |
| 13 | 7 | 0.335 (0.225) | 0.316 (0.146) | 0.161 (0.082) | 6 | 23 | 0.020 | 4 | 0.800 | |
| 17 | 7 | 0.347 (0.226) | 0.321 (0.145) | 0.159 (0.080) | 7 | 4 | 0.003 | 3 | 0.524 | |
| 21 | 7 | 0.346 (0.226) | 0.318 (0.146) | 0.160 (0.081) | 7 | 3 | 0.004 | 2 | 0.476 | |
| 28 | 11 | 0.301 (0.223) | 0.280 (0.154) | 0.157 (0.078) | 11 | 2 | 0.002 | 2 | 0.436 | |
| 32 | 7 | 0.366 (0.232) | 0.311 (0.147) | 0.155 (0.078) | 7 | 0 | 0.000 | 1 | 0.000 | |
| S. pallidus | 65 | 9 | 0.283 (0.225) | 0.312 (0.147) | 0.238 (0.119) | 8 | 19 | 0.012 | 3 | 0.464 |
| 72 | 19 | 0.241 (0.180) | 0.314 (0.137) | 0.263 (0.127) | 19 | 21 | 0.019 | 7 | 0.667 | |
| 92 | 14 | 0.277 (0.213) | 0.306 (0.142) | 0.248 (0.121) | 13 | 19 | 0.013 | 5 | 0.583 | |
| 120 | 15 | 0.218 (0.175) | 0.316 (0.153) | 0.259 (0.126) | 15 | 20 | 0.023 | 6 | 0.790 | |
| Total | — | 110 | 0.199 (0.134) | 0.277 (0.132) | 0.277 (0.131) | 104 | 36 | 0.029 | 22 | 0.801 |
Note: Values in parentheses are the standard deviation. Diversity indices: Ho, observed heterozygosity; He, expected heterozygosity; πn, average gene diversity over loci for the SNPs; S, the number of segregating sites; π, nucleotide diversity; H, number of unique haplotypes; and h, haplotype diversity.
Fig. 2.
Population-level analyses of the COI data revealed two instances of potential hybridization in which shallow-water S. droebachiensis individuals produced mtDNA that grouped within the well-supported clade of deep-water S. pallidus haplotypes (Fig. 2B). We failed to detect shallow-water (northwest Atlantic) S. droebachiensis haplotypes in individuals sampled from the deep-water habitat, but 17 of the 55 (31%) deep-water samples produced mtDNA haplotypes similar to the northeast Atlantic S. droebachiensis lineage (see Addison and Hart 2005; Fig. S1).
ddRAD-seq library results
Sequencing of the ddRAD-seq libraries generated a total of 889.0 million raw read dataset from 110 individuals (Table S1). A total of 372.7 million reads were retained following quality filtering, removal of adapter sequences, and merging pair-end reads. The total length of the processed (trimmed and merged) reads was 34.8 billion bases, corresponding to a length coverage of 37.1× against the purple sea urchin genome of 936.4 million bases (Sea Urchin Genome Sequencing Consortium et al. 2006). We mapped over 90% (342.0 million reads) of the Atlantic sea urchin ddRAD-seq dataset to the S. purpuratus genome, with an average coverage of 104.0× (total length of mapped reads per mapped region) (Table S2). Consensus genome sequences between Atlantic (S. droebachiensis and S. pallidus) and purple (S. purpuratus) sea urchins were extracted and used as a reference for variant detection. More reads were aligned to intergenic regions (55.8%) compared with genic regions (44.2%) of consensus genome of Atlantic–purple sea urchin (Table S3). We detected a total of 104 817 SNPs that were scored in all 110 individuals, not including indel polymorphisms, or multi nucleotide variants. After filtering for a minor allele frequency of 0.05, 13 186 bi-allelic SNPs remained. In contrast to the results from read mapping, more were detected in genic regions (10 137; 76.9%) compared with those located intergenic regions (3049; 23.1%), respectively. We focused our analyses on polymorphisms in putatively neutral intergenic regions to limit the impact of loci potentially under strong ecological or sexual selection among the lineages.
nDNA diversity
Measures of genetic variability at each sample for 3049 intergenic SNPs are summarized in Table 1. Observed heterozygosity (0.218–0.366), expected heterozygosity (0.260–0.321), and the average gene diversity over loci (πn; 0.155–0.263) were similar when computed for each sample site or for samples grouped into the distinct genotype clusters identified using both the mtDNA and structure analyses (see below). Global FST among all samples was 0.267, and pairwise FST between the three genetic clusters was 0.467 (S. pallidus and deep S. droebachiensis), 0.474 (S. pallidus and shallow S. droebachiensis), and 0.497 between shallow and deep samples of S. droebachiensis (for all comparisons p < 0.001). The frequency distribution of locus-specific FST values calculated among samples of all three lineages peaked at FST = 0.10–0.15, but there was a relatively even frequency distribution of loci with FST values between 0.5 and 1.0 (Fig. 3). A total of 796 (26.1%) of the SNPs had an FST >0.50, and 304 (10%) had an FST value >0.80. There was no evidence of population structure among sample sites within lineages (FST not significantly different from zero), and frequency distributions of locus-specific FST values peaked at zero (Fig. 3) for all three lineages. Maximum locus-specific FST values ranged from 0.271 to 0.287 in shallow S. droebachiensis and S. pallidus, respectively. Frequent FST values >0.40 detected in the deep lineage of S. droebachiensis were largely driven by small sample sizes (≤ 2) in two of the four sample sites. Removing these two individuals revealed a similar intraspecific distribution of locus diversity with a maximum value of 0.373 (Fig. 3).
Fig. 3.
Using all 3049 SNPs, we tested for distinct genetic clusters (lineages) and the presence of hybrid individuals using the program structure. The inspection of the log likelihoods identified three clusters of genotypes (K = 3; Fig. 2C) that corresponded to the shallow- and deep-water populations of S. droebachiensis and the deep-water species S. pallidus. Although Evanno’s method of selecting the best-fit model of K identified K = 2, the low negative log-likelihood values from runs with K = 1 likely artificially inflated ΔK resulting in an underestimation of the number of distinct genotypes. A recent review by Janes et al. (2017) suggests bias towards K = 2 when using Evanno’s ΔK correction. As likelihood scores reached an asymptote at K = 3 (Fig. S2), reanalysis of the structure output after removing K = 1 identified the highest value of ΔK at K = 3. Based on a combination of the log-likelihood scores from the structure analysis, broad consistency of assignment of primary assignment to three clusters across simulations of K = 1–10 (Fig. S3), and the reciprocal monophyly observed in the COI data from deep and shallow lineages of S. droebachiensis, we chose K = 3 as the best-fit model.
The clustering pattern detected in the nuclear genome was broadly consistent with the results of the mtDNA analysis (Fig. 2), with the exception of the two mtDNA S. pallidus haplotypes recovered in the shallow-water S. droebachiensis population. We failed to detect hybrid individuals in the shallow-water S. droebachiensis, including the two individuals (at 10 and 13 m) that had an S. pallidus mtDNA haplotype but whose admixture proportions (S. pallidus into S. droebachiensis; 90% probability intervals) were only 0.035 (0.022–0.050) and 0.054 (0.039–0.091), respectively. We detected the presence of two putative deep-water S. droebachiensis × S. pallidus hybrids, one at 65 m with an admixture proportion (deep-water S. droebachiensis into S. pallidus) of 0.093 (0.094–0.108), and another at 92 m with an admixture proportion of 0.091 (0.075–0.108), respectively. Using the Gensback setting in structure, the two admixed individuals were classified as third-generation backcrosses (p = 1.000).
Discussion
Our main goal was to assess northwest Atlantic populations of S. droebachiensis and S. pallidus for the presence of cryptic species and evidence of gene flow among them. Our data reveal a striking pattern of spatial segregation, genetic divergence, and reproductive isolation between two lineages of S. droebachiensis. Pairwise FST between shallow- and deep-water populations of S. droebachiensis was high (0.497, p < 0.001) and we failed to detect individuals of mixed ancestry using nuclear genetic markers. Shallow- and deep-water populations did not share mtDNA haplotypes, and the average genetic distance between the two mito-groups was 2.3% (K2P). Phylogenetic analysis of mtDNA identified the deep-water lineage as being genetically similar to sea urchins sampled in the northeast Atlantic (11 of the 17 haplotypes are identical to those found in Iceland and Norway, Fig. S1; Addison and Hart 2005). These results are consistent with earlier genetic studies in which significant subdivision between eastern and western Atlantic populations of S. droebachiensis was found for both mitochondrial (Addison and Hart 2005; Harper et al. 2007) and nuclear loci (sperm Bindin: Marks et al. 2008; microsatellites: Addison and Hart 2004, 2005). However, in combination with the significant morphological divergence identified in sperm traits (Manier and Palumbi 2008; Marks et al. 2008), the patterns of genome wide divergence and lack of hybridization we detected in this study suggest the presence of a cryptic species in north Atlantic S. droebachiensis. These results indicate that the patterns of significant genetic subdivision and asymmetric gene flow from eastern to western populations of S. droebachiensis (Addison and Hart 2004, 2005) may represent some degree of interspecific variation and cannot be strictly interpreted as limited intraspecific gene flow and genetic drift. Because both the eastern and western lineages of S. droebachiensis have been reported to co-occur in shallow-water populations at higher latitudes in the northwest Atlantic (e.g., Addison and Hart 2005), future genetic studies of these populations will provide insight evolutionary mechanisms driving both ecological and reproductive isolation.
Although studies in the Pacific Ocean have detected asymmetric hybridization and introgression from the pale sea urchin (S. pallidus) into the green sea urchin (S. droebachiensis) (Addison and Pogson 2009; Pujolar and Pogson 2011), our results suggest that northwest Atlantic lineages of these species are reproductively isolated. Divergence at mitochondrial (5.5% K2P distance) and nuclear loci (FST = 0.474) was high, and the admixture analysis of nuclear loci failed to identify hybrid individuals. We detected S. pallidus mtDNA haplotypes in two shallow-water S. droebachiensis individuals (4% of the samples), and this is consistent with the findings of Addison and Hart (2005), which reports a similar pattern in 3% of samples (n = 132) from Nova Scotia and Newfoundland. However, limited admixture in the nuclear genome (<5%) in our samples suggests that the shared mtDNA lineages are result of historical gene flow or incomplete lineage sorting, and not recent hybridization.
We detected only weak evidence of hybridization between the deep-water lineages of S. droebachiensis and S. pallidus. Although the levels of mtDNA and nDNA divergence were high (4.2% K2P and FST = 0.467, respectively), our structure analysis identified two deep-water S. droebachiensis individuals of mixed ancestry whose nuclear genome consisted of ∼10% S. pallidus. This level of introgression corresponds to a third-generation backcross, indicating that gene flow is contemporary and reproductive isolation between these two species may not yet be complete. Little is known about the spawning ecology of deep-water Strongylocentrotus sea urchins, but the relatively low frequency of hybrids detected in our deep S. droebachiensis population (∼12%) and absence of F1 or F2 crosses indicate that gene flow between the species is rare. As population densities of sea urchins are generally low in the deep-water habitat of the northwest Atlantic (Gilkinson et al. 1988), the low frequency of hybrids could be explained by the low probability of heterospecific gametes interacting in the plankton. Alternatively, hybrids may form readily, but a moderate level of post-zygotic selection may act on early life stages (larvae, recruits, and juveniles) to maintain the barriers to gene exchange. More extensive surveys of deep-water populations are required to fully assess the extent to which the S. pallidus and S. droebachiensis hybridize throughout their range.
Given that hybrids readily form in laboratory crosses among Pacific (Strathmann 1981; Levitan 2002a, 2002b) and Atlantic (Biermann and Marks 2000) species of Strongylocentrotus sea urchins, the relatively low levels of hybridization in natural populations we detected in the northwest Atlantic may be due to a variety of pre-zygotic or post-zygotic mechanisms. Like most marine invertebrates, sea urchins broadcast spawn by simply releasing their sperm and eggs into the water column. Habitat or temporal isolation are effective pre-zygotic barriers among broadcast-spawning species (e.g., Pernet 1999; Coppard and Campbell 2005), but for sympatric species that spawn simultaneously, reproductive barriers may largely depend on the compatibilities of their gametes (Palumbi 1994; Coyne and Orr 2004). In the Pacific Ocean, S. droebachiensis co-occurs with S. purpuratus and Mesocentrotus franciscanus (nee. S. franciscanus; Kober and Bernardi 2013), all of which vary in their egg traits and susceptibility to fertilization under sperm limitation (Levitan 1993, 2002a, 2002b). Strongylocentrotus droebachiensis typically has a lower population density and produces eggs that are easily fertilized by both conspecific and heterospecific sperm (Levitan 2002a, 2002b). However, in the north Atlantic, S. droebachiensis is the only species of sea urchin in the shallow subtidal (<30 m) where population densities can reach 400 m−2 (Scheibling and Hennigar 1997). High sperm density in these populations can result in high variance in reproductive success caused by polyspermy (Levitan 2004, 2005), leading to sexual conflict as males are selected for fast fertilization and females are selected to prevent polyspermy by reducing fertilization rates (Levitan 2004; Levitan and Ferrell 2006; Levitan et al. 2007; Levitan and Stapper 2010). Under this scenario, the eggs of S. droebachiensis in northwest Atlantic populations may have co-evolved mechanisms that indirectly reduce the susceptibility to heterospecific sperm and thus prevent hybridization.
The rapid evolution of gamete recognition molecules has long been considered an important driver of speciation in Echinoderms (Vacquier 1998; Palumbi 2009; Lessios 2011; Vacquier and Swanson 2011). Both proteins and carbohydrates (sulfated polysaccharides) coating the sperm and eggs mediate gamete interactions in the plankton and are involved in sperm competition and mate choice (Biermann et al. 2004). Positive selection at the coding regions of some of these genes, including sperm Bindin (e.g., Biermann 1998), correlates with the strength of reproductive isolation between species (Zigler et al. 2005). If the divergence within S. droebachiensis corresponds with the invasion of the east and west coasts of the Atlantic followed by population expansion throughout the ocean basin, then is it possible that continued selection at gamete signaling and recognition molecules continues to drive reproductive isolation (i.e., reinforcement selection; Lessios 2011). Patterns of divergence at sperm Bindin within Atlantic populations of S. droebachiensis support the importance of reinforcement selection in this system, but future studies are needed to link the function of genotypes with reproductive success and the presence of hybrids throughout the ocean basin.
Our failure to detect hybrids between S. pallidus and S. droebachiensis in adult sea urchins sampled from the shallow-water coastal habitat may also be a result of strong post-zygotic selection on individuals of mixed ancestry. As our sample site in Nova Scotia is close to the southern range limit of coastal populations of S. droebachiensis, decadal and seasonal variation in salinity (Breeze et al. 2002; Russell 2013; Bundy et al. 2014) may intensify the ecological gradient along which the shallow and deep lineages are adapted. Russell (2013) reported that over an eight year period the coastal salinity in New Hampshire frequently drop below 26‰ in March and April, with the lowest recorded salinity being 15‰. Laboratory studies have demonstrated that S. droebachiensis is one of the most tolerant echinoderms to hyposalinity (Russell 2013). Roller and Stickle (1985) showed that in the Pacific, larvae of S. pallidus failed to develop, and cultures experienced 100% mortality at salinities below 27.5‰. In contrast, although cultures of S. droebachiensis developed slowly at salinities ≤22.5‰ and below, survival was 100% at salinities as low as 20‰. In addition, hybrids generated by crossing female S. droebachiensis with male S. pallidus experienced 100% survivorship at salinities as low as 22.5‰. Himmelman et al. (1984) showed that S. droebachiensis collected in the Gulf of St. Lawrence tolerated salinities as low as 14‰. However in the same study, individuals collected from Nova Scotia experienced complete mortality when exposed to the same conditions, indicating that the geographical origin and possibly the genetic composition of populations contributed to the ability to tolerate hyposmotic stress. As both pure and hybrid S. pallidus are more susceptible to salinity <22.5‰, it is possible that seasonal variation in salinity could either limit the dispersal of hybrid larvae produced in overlapping populations in deeper habitats or impose strong post-settlement selection on newly metamorphosed recruits or juveniles. Future studies should aim to evaluate these ecological factors as primary drivers of both geographic range limits of and genetic divergence among north Atlantic lineages of Strongylocentrotus sea urchins.
Our results are consistent with a recent barcode survey of 145 Canadian Ecninoderm taxa by Layton et al. (2016), in which the authors detected 23 lineages that could not be identified to the species level. Of these individuals were two samples of Strongylocentrotus from the Arctic, and analysis of this confirms that these individuals cluster with others from the deep-water/northeast Atlantic lineage (Fig. S1). This finding suggests that the cryptic species is widespread throughout the Atlantic basin, segregating in sympatry with shallow-water S. droebachinesis at northern latitudes but being restricted to deeper habitats at southern latitudes. It is possible that the range and habitat limits are driven by similar ecological conditions discussed above, and future surveys of Strongylocentrotus populations throughout the Atlantic are required to better detail the species’ distributions.
Conclusion
Strongylocentrus sea urchins are an ideal system for understanding how reproductive barriers evolve both from the initial accumulation of divergence between populations (e.g., reduced trans-Arctic gene flow in S. droebachiensis; Addison and Hart 2005), to the evolution of gamete recognition molecules driven by sexual conflict within species (e.g., S. purpuratus; Levitan and Ferrell 2006) and Darwinian selection driving changes between species (e.g., Pujolar and Pogson 2011). Our results identified a cryptic lineage of S. droebachiensis that shifts the interpretation of trans-Atlantic genetic diversity from limited gene flow and genetic drift to the interactions among reproductively isolated species. We discovered the potential for strong ecological conditions to shape the distributions of shallow- and deep-water lineages of sea urchins in the northwest Atlantic and renewed the importance to further assess the biogeography, population genetics, and barriers to gene exchange among all three lineages in the Atlantic Basin.
Acknowledgements
We thank Don Clark at the Department of Fisheries and Oceans St. Andrew’s Biological Station and Karen Filbee-Dexter and Bob Scheibling at Dalhousie University for sample collections. Funding for this research was provided by grants to JAA from Natural Sciences and Engineering Research council (Discovery Grant), Canadian Foundation for Innovation, New Brunswick Innovation Foundation (Leaders Opportunities Fund; Research Assistant Initiative), and the University of New Brunswick.
References
Addison JA, and Hart MW. 2002. Characterization of microsatellite loci in sea urchins (Strongylocentrotus spp.). Molecular Ecology Resources, 2: 493–494.
Addison JA, and Hart MW. 2004. Analysis of population genetic structure of the green sea urchin (Strongylocentrotus droebachiensis) using microsatellites. Marine Biology, 144: 243–251.
Addison JA, and Hart MW. 2005. Colonization, dispersal, and hybridization influence phylogeography of North Atlantic sea urchins (Strongylocentrotus droebachiensis). Evolution, 59: 532–543.
Addison JA, and Pogson GH. 2009. Multiple gene genealogies reveal asymmetrical hybridization and introgression among strongylocentrotid sea urchins. Molecular Ecology, 18: 1239–1251.
Barton NH, and Hewitt GM. 1985. Analysis of hybrid zones. Annual Review of Ecology and Systematics, 16: 113–148.
Biermann CH. 1998. The molecular evolution of sperm bindin in six species of sea urchins (Echinoida: Strongylocentrotidae). Molecular Biology and Evolution, 15(12): 1761–1771.
Biermann CH, and Marks JA. 2000. Geographic divergence of gamete recognition systems in two species of the sea urchin genus Strongylocentrotus. Zygote, 8: S86–S87.
Biermann CH, Marks JA, Vilela-Silva A-CES, Castro MO, and Mourão PAS. 2004. Carbohydrate-based species recognition in sea urchin fertilization: another avenue for speciation? Evolution & Development, 6(5): 353–361.
Bluhm BA, Piepenburg D, and von Juterzenka K. 1998. Distribution, standing stock, growth, mortality and production of Strongylocentrotus pallidus (Echinodermata: Echinoidea) in the northern Barents Sea. Polar Biology, 20: 325–334.
Bowen BW, Rocha LA, Toonen RJ, Karl SA, and the ToBo Laboratory. 2013. The origins of tropical marine biodiversity. Trends in Ecology & Evolution, 28: 359–366.
Breeze H, Fenton DG, Rutherford RJ, and Silva MA. 2002. The Scotian Shelf: an ecological overview for ocean planning. Canadian Technical Report of Fisheries and Aquatic Sciences 2392. Fisheries and Oceans Canada, Dartmouth, Nova Scotia. x + 259 p. Available from publications.gc.ca/collections/collection_2012/mpo-dfo/Fs97-6-2393-eng.pdf.
Bundy A, Themelis D, Sperl J, and den Heyer N. 2014. Inshore Scotian Shelf ecosystem overview report: status and trends. DFO Canadian Science Advisory Secretariat (CSAS) Research Document 2014/065. Fisheries and Oceans Canada, Dartmouth, Nova Scotia. xii + 213 p. Available from waves-vagues.dfo-mpo.gc.ca/Library/360197.pdf.
Choat JH. 2006. Phylogeography and reef fishes: bringing ecology back into the argument. Journal of Biogeography, 33: 967–968.
Clement M, Posada D, and Crandall KA. 2000. tcs: a computer program to estimate gene genealogies. Molecular Ecology, 9: 1657–1659.
Coppard SE, and Campbell AC. 2005. Lunar periodicities of diadematid echinoids breeding in Fiji. Coral Reefs, 24: 324–332.
Coyne JA, and Orr HA. 2004. Speciation. Sinauer Associates, Sunderland, Massachusetts.
Darriba D, Taboada GL, Doallo R, and Posada D. 2012. jModelTest 2: more models, new heuristics and parallel computing. Nature Methods, 9: 772.
Doebeli M, and Dieckmann U. 2003. Speciation along environmental gradients. Nature, 421: 259–264.
Edmands S, Moberg PE, and Burton RS. 1996. Allozyme and mitochondrial DNA evidence of population subdivision in the purple sea urchin Strongylocentrotus purpuratus. Marine Biology, 126: 443–450.
Evanno G, Regnaut S, and Goudet J. 2005. Detecting the number of clusters of individuals using the software structure: a simulation study. Molecular Ecology, 14(8): 2611–2620.
Excoffier L, and Lischer HEL. 2010. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources, 10: 564–567.
Falush D, Stephens M, and Pritchard JK. 2003. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics, 164: 1567–1587.
Gagnon JM, and Gilkinson KD. 1994. Discrimination and distribution of the sea urchins Strongylocentrotus droebachiensis (O.F. Müller) and S. pallidus (G.O. Sars) in the Northwest Atlantic. Sarsia, 79: 1–11.
Gilkinson KD, Gagnon J-M, and Schneider DC. 1988. The sea urchin Strongylocentrotus pallidus (GO Sars) on the Grand Bank of Newfoundland. In Echinoderm biology. Edited by RD Burke, PV Mladenov, and P Lampert. Balkema, Rotterdam, the Netherlands. pp. 467–473.
Guindon S, Dufayard JF, Lefort V, Anisimova M, Hordijk W, and Gascuel O. 2010. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Systematic Biology, 59: 307–321.
Harper FM, Addison JA, and Hart MW. 2007. Introgression versus immigration in hybridizing high-dispersal echinoderms. Evolution, 61: 2410–2418.
Himmelman JH, Guderley H, Vignault G, Drouin G, and Wells PG. 1984. Response of the sea urchin, Strongylocentrotus droebachiensis, to reduced salinities: importance of size, acclimation, and interpopulation differences. Canadian Journal of Zoology, 62: 1015–1021.
Janes JK, Miller JM, Dupuis JR, Malenfant RM, Gorrell JC, Cullingham CI, et al. 2017. The K = 2 conundrum. Molecular Ecology, 26: 3594–3602.
Jensen M. 1974. The Strongylocentrotidae (Echinoidea), a morphologic and systematic study. Sarsia, 57: 113–148.
Kober KM, and Bernardi G. 2013. Phylogenomics of strongylocentrotid sea urchins. BMC Evolutionary Biology, 13: 88.
Ladner JT, and Palumbi SR. 2012. Extensive sympatry, cryptic diversity and introgression throughout the geographic distribution of two coral species complexes. Molecular Ecology, 21: 2224–2238.
Layton KKS, Corstorphine EA, and Hebert PDN. 2016. Exploring Canadian echinoderm diversity through DNA barcodes. PLoS ONE, 11(11): e0166118.
Lessios HA. 2011. Speciation genes in free-spawning marine invertebrates. Integrative & Comparative Biology, 51(3): 456–465.
Levitan DR. 1993. The importance of sperm limitation to the evolution of egg size in marine invertebrates. The American Naturalist, 141: 517–536.
Levitan DR. 2002a. Density-dependent selection on gamete traits in three congeneric sea urchins. Ecology, 83: 464–479.
Levitan DR. 2002b. The relationship between conspecific fertilization success and reproductive isolation among three congeneric sea urchins. Evolution, 56: 1599–1689.
Levitan DR. 2004. Density-dependent sexual selection in external fertilizers: variances in male and female fertilization success along the continuum from sperm limitation to sexual conflict in the sea urchin Strongylocentrotus franciscanus. The American Naturalist, 164: 298–309.
Levitan DR. 2005. The distribution of male and female reproductive success in a broadcast spawning marine invertebrate. Integrative and Comparative Biology, 45: 848–855.
Levitan DR, and Ferrell DL. 2006. Selection on gamete recognition proteins depends on sex, density, and genotype frequency. Science, 312: 267–269.
Levitan DR, and Stapper AP. 2010. Simultaneous positive and negative frequency dependent selection on sperm bindin, a gamete recognition protein in the sea urchin Strongylocentrotus purpuratus. Evolution, 64: 785–797.
Levitan DR, TerHorst CP, and Fogarty ND. 2007. The risk of polyspermy in three congeneric sea urchins and its implications for gametic incompatibility and reproductive isolation. Evolution, 61: 2007–2014.
Manier MK, and Palumbi SR. 2008. Intraspecific divergence in sperm morphology of the green sea urchin, Strongylocentrotus droebachiensis: implications for selection in broadcast spawners. BMC Evolutionary Biology, 8: 283.
Marks JA, Biermann CH, Eanes WF, and Kryvi H. 2008. Sperm polymorphism within the sea urchin Strongylocentrotus droebachiensis: divergence between Pacific and Atlantic oceans. The Biological Bulletin, 215: 115–125.
Norderhaug KM, Anglès d’Auriac MB, Fagerli CW, Gundersen H, Christie H, Dahl K, et al. 2016. Genetic diversity of the NE Atlantic sea urchin Strongylocentrotus droebachiensis unveils chaotic genetic patchiness possibly linked to local selective pressure. Marine Biology, 163: 36.
Palumbi SR. 1994. Genetic divergence, reproductive isolation, and marine speciation. Annual Review of Ecology and Systematics, 25: 547–572.
Palumbi SR. 2009. Speciation and the evolution of gamete recognition genes: pattern and process. Heredity, 102: 66–76.
Pante E, Abdelkrim J, Viricel A, Gey D, France SC, Boisselier MC, et al. 2015. Use of RAD sequencing for delimiting species. Heredity, 114: 450–459.
Pérez-Portela R, Arranz V, Ruis M, and Turon X. 2013. Cryptic speciation or global spread? The case of a cosmopolitan marine invertebrate with limited dispersal capabilities. Scientific Reports, 3: 3197.
Pernet B. 1999. Gamete interactions and genetic differentiation among three sympatric polychaetes. Evolution, 53: 435–446.
Poland JA, and Rife TW. 2012. Genotyping-by-sequencing for plant breeding and genetics. The Plant Genome, 5: 92–102.
Poland JA, Brown PJ, Sorrells ME, and Jannink JL. 2012. Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE, 7(2): e32253.
Pritchard JK, Stephens M, and Donnelly P. 2000. Inference of population structure using multilocus genotype data. Genetics, 155: 945–959.
Pujolar JM, and Pogson GH. 2011. Positive Darwinian selection in gamete recognition proteins of Strongylocentrotus sea urchins. Molecular Ecology, 20: 4968–4982.
Roller RA, and Stickle WB. 1985. Effects of salinity on larval tolerance and early developmental rates of four species of echinoderms. Canadian Journal of Zoology, 63: 1531–1538.
Ronquist F, and Huelsenbeck JP. 2003. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics, 19: 1572–1574.
Rousset F. 2008. genepop’007: a complete re-implementation of the genepop software for Windows and Linux. Molecular Ecology Resources, 8: 103–106.
Russell MP. 2013. Echinoderm responses to variation in salinity. Advances in Marine Biology, 66: 171–212.
Scheibling RE, and Hennigar AW. 1997. Recurrent outbreaks of disease in sea urchins Strongylocentrotus droebachiensis in Nova Scotia: evidence for a link with large-scale meteorologic and oceanographic events. Marine Ecology Progress Series, 152: 155–165.
Sea Urchin Genome Sequencing Consortium, Sodergren E, Weinstock GM, Davidson EH, Cameron RA, Gibbs RA, et al. 2006. The genome of the sea urchin Strongylocentrotus purpuratus. Science, 314: 941–952.
Strathmann RR. 1981. On barriers to hybridization between Strongylocentrotus droebachiensis (O.F. Müller) and S. pallidus (G.O. Sars). Journal of Experimental Marine Biology and Ecology, 55: 39–47.
Swan EF. 1953. The Strongylocentrotidae (Echinoidea) of the northeast Pacific. Evolution, 7: 269–273.
Vacquier VD. 1998. Evolution of gamete recognition proteins. Science, 281: 1995–1998.
Vacquier VD, and Swanson WJ. 2011. Selection in the rapid evolution of gamete recognition proteins in marine invertebrates. Cold Spring Harbor Perspectives in Biology, 3: a002931.
Vasseur E. 1952. Geographic variation in the Norwegian sea-urchins, Strongylocentrotus droebachiensis and S. pallidus. Evolution, 6: 87–100.
Vogler C, Benzie J, Lessios H, Barber P, and Wörheide G. 2008. A threat to coral reefs multiplied? Four species of crown-of-thorns starfish. Biology Letters, 4: 696–699.
Warner PA, van Oppen MJH, and Willis BL. 2015. Unexpected cryptic species diversity in the widespread coral Seriatopora hystrix masks spatial-genetic patterns of connectivity. Molecular Ecology, 24: 2993–3008.
Zigler KS, McCartney MA, Levitan DR, and Lessios HA. 2005. Sea urchin bindin divergence predicts gamete compatibility. Evolution, 59: 2399–2404.
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FACETS
Volume 3 • Number 1 • October 2018
Pages: 61 - 78
Editor: Vance L. Trudeau
History
Received: 26 June 2017
Accepted: 26 September 2017
Version of record online: 29 January 2018
Copyright
© 2018 Addison and Kim. This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Data Availability Statement
All relevant data are within the paper, the Supplementary Material, Dryad (SNPs; doi:https://doi.org/10.5061/dryad.dn160) and GenBank (ncbi.nlm.nih.gov/genbank/; sequence data, accession No. MG098337-MG098440).
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All conceived and designed the study.
All performed the experiments/collected the data.
All analyzed and interpreted the data.
JAA contributed resources.
All drafted or revised the manuscript.
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The authors have declared that no competing interests exist.
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Jason A. Addison and Jin-Hong Kim. 2018. Cryptic species diversity and reproductive isolation among sympatric lineages of Strongylocentrotus sea urchins in the northwest Atlantic. FACETS.
3(1): 61-78. https://doi.org/10.1139/facets-2017-0081
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