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. 2022 Mar 8;15(3):459–470. doi: 10.1111/eva.13359

Population genomics of the basket cockle Clinocardium nuttallii in the southern Salish Sea: Assessing genetic risks of stock enhancement for a culturally important marine bivalve

James L Dimond 1,2,✉, Ryan N Crim 1, Elizabeth Unsell 3, Viviane Barry 3, Jodie E Toft 1
PMCID: PMC8965374  PMID: 35386400

Abstract

Coastal Indigenous communities that rely on subsistence harvests are uniquely vulnerable to declines in nearshore species. The basket cockle Clinocardium nuttallii is among the favored foods of Indigenous people along the northwest Pacific coast of North America, yet localized declines in their abundance have led to interest in stock enhancement efforts. We used a population genomics approach to examine potential risks associated with stock enhancement of C. nuttallii in the southern Salish Sea, a large inland estuary that includes Puget Sound. More than 8000 single nucleotide polymorphisms across 349 individuals at 12 locations were assembled de novo using restriction site‐associated DNA sequencing. Results indicated that C. nuttallii within the southern Salish Sea were distinct from those along the outer Pacific coast (F ST = 0.021–0.025). Within the southern Salish Sea, C. nuttallii populations appear to be well‐connected despite numerous potential impediments to gene flow; Hood Canal, which experiences the lowest flushing rates of all Puget Sound sub‐basins, was a minor exception to this strong connectivity. We found evidence of isolation by distance within the southern Salish Sea, but the slope of this relationship was shallow, and F ST values were low (F ST = 0.001–0.004). Meanwhile, outlier analyses did not support the hypothesis that southern Salish Sea sub‐populations are locally adapted. Estimates of effective population size had no upper bound, suggesting potentially very high adaptive capacity in C. nuttallii, but also making it difficult to assess potential reductions in effective population size resulting from stock enhancement. We present several strategies to augment cockle populations for subsistence harvest that would limit risk to the genetic diversity of wild cockle populations.

Keywords: Clinocardium, population connectivity, RADseq, Salish Sea, stock enhancement, subsistence

1. INTRODUCTION

Shellfish have been an important food for people throughout the world for eons, as evidenced by the unearthing of shell middens on nearly every continent (Álvarez et al., 2011). In the Salish Sea, a large inland estuary along the northern Pacific coast of North America, Coast Salish people historically built and tended intertidal clam gardens, which are regarded as an ancient form of aquaculture and provide further testament to the nutritional and cultural importance of shellfish for Indigenous people (Groesbeck et al., 2014; Poe et al., 2016). Today, Coast Salish people continue to forage for shellfish as a primary means of subsistence (Poe et al., 2016). Unfortunately, however, several native shellfish species in the region have been severely depleted, such as Olympia oyster (White et al., 2009), pinto abalone (Carson & Ulrich, 2019), and native littleneck clam (Barber et al., 2019), threatening the persistence of the species at risk as well as the livelihoods of Indigenous people.

Among the principal shellfish species harvested by Coast Salish people is the basket or heart cockle, Clinocardium nuttallii (Conrad, 1837). This species ranges widely along the Pacific coast of North America, from southern California to western Alaska, and has also been found on the island of Hokkaido (Kilmer, 1973). It is a broadcast spawning simultaneous hermaphrodite, producing planktotrophic larvae with a duration up to approximately 10 days (Gallucci & Gallucci, 1982; Liu et al., 2010). Cockles populate low intertidal and shallow subtidal areas and have been reported at densities up to 232 g (~2–3 adult cockles) m−3 intertidally in the Salish Sea (Barber et al., 2019). In recent years, certain beaches have become closed to subsistence harvest due to population declines. Multi‐decadal surveys of intertidal clams in the southern Salish Sea have confirmed that cockles have declined on some beaches, while on other beaches their abundance has either increased or remained steady (Barber et al., 2019). Anecdotal evidence suggests that cockles may be susceptible to periodic localized die‐offs due to factors such as high temperatures and hypoxia, which are becoming increasingly common due to marine heatwaves and nearshore pollution (Khangaonkar et al., 2018; Oliver et al., 2018). Recently, widespread mortality of cockles was documented on beaches in the Salish Sea following an unprecedented summer heatwave that coincided with some of the lowest tides of the year (J. Barber, unpublished data). This has raised concerns among tribal fishers and fishery co‐managers about the ability of cockles on particular beaches to repopulate naturally, which could be related to factors such as low population connectivity or local adaptation.

Populations of marine species were once thought to be genetically homogenous, being highly interconnected by seemingly infinite populations of adults and larvae that could disperse over large expanses of water (Hauser & Carvalho, 2008). Over the last few decades, this notion has been overturned by modern genetic data indicating that a surprising number of marine species exhibit significant population structure, sometimes at relatively small spatial scales (Hauser & Carvalho, 2008). Causes of population structure in marine species include processes such as local retention of larvae, adaptation to local environments, spatial and temporal reproductive isolation, and high variance in reproductive success (Hauser & Carvalho, 2008). In the southern Salish Sea, which includes Puget Sound and its various sub‐basins, many potential opportunities for restricted gene flow are created by its estuarine circulation, fjord‐like bathymetry, and tortuous geography.

Localized cockle population declines have spurred interest in using aquaculture to supplement beaches with hatchery‐spawned and raised juveniles. In fact, C. nuttallii has long been viewed as an attractive species for aquaculture development in the region, and initial research on captive spawning and larval rearing practices in the past decade have proven that C. nuttallii is highly amenable to aquaculture production (Dunham et al., 2013a, 2013b; Liu, Alabi, & Pearce, 2008a, 2008b; Liu et al., 2009, 2010, 2011). However, fishery co‐managers in Washington State have agreed that genetic risks associated with aquaculture must be addressed before it can be used as a restoration practice.

To evaluate potential genetic risks of aquaculture enhancement for C. nuttallii, we evaluated the population genomics of cockles from throughout the southern Salish Sea, as well as the outer Washington coast. We used restriction site‐associated DNA sequencing to provide a comprehensive picture of genomic variability, population connectivity, and potential local adaptation in this bivalve.

2. METHODS

Cockles were collected intertidally (−0.15 to −0.55 m below mean lower low water) during low tides between October 2019 and January 2020. Locations of sampling sites were chosen to span a wide range of water bodies and sub‐basins in the southern Salish Sea and coastal Washington, including Puget Sound, the Strait of Juan de Fuca, and the Strait of Georgia (Figure 1). In addition to natural intertidal habitats, we collected cockles from inside geoduck (Panopea generosa) culture tubes at two sites. Cockles growing inside geoduck tubes are wild animals that have naturally recruited to these artificial habitats. One of the sites (Sequim Bay geoduck tubes) was within 50 m of a natural cockle collection habitat (Sequim Bay), while another (Eld Inlet geoduck tubes) was within 7 km of a natural collection habitat (Squaxin Island). At all sites, adult cockle specimens >~50 mm in length were targeted for collection, and specimens were kept in coolers with ice packs prior to dissection and tissue sampling within 12–48 h of collection.

FIGURE 1.

FIGURE 1

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Upper panel: Map of the study area showing Clinocardium nuttallii collection sites in the southern Salish Sea and coastal Washington, with inset globe map in the upper right showing location of the study area highlighted in red. Individual pie charts show ancestry proportions at each site based on LEA analysis with K = 2 ancestral populations. Lower panel: Detailed view of ancestry components for each sample at each site. Sites are ordered by water distance from Willapa Bay

2.1. Tissue collection, DNA extraction, library preparation, and sequencing

Tissue samples from cockles were taken by clipping small pieces of foot and mantle tissue and preserving them in 95% ethanol. Samples were kept at 4°C prior to DNA extraction, which took place within 1–3 months of collection. DNA was extracted using the Qiagen DNeasy Blood and Tissue 96‐well kit, following overnight lysis in Proteinase K. Mantle tissue was chosen over foot tissue for all samples based on slightly higher DNA quality in initial extractions as assessed by agarose gel electrophoresis. Samples were shipped to Eurofins Diagnostics (River Falls, WI, USA) for DNA quantitation and normalization, and then to Floragenex Inc. (Portland, OR, USA) for RADseq library preparation (Baird et al., 2008) with the restriction enzyme SbfI. A total of 380 samples, including 20 technical replicates sequenced in duplicate, were allocated randomly across four libraries, which were each sequenced on three lanes of an Illumina HiSeq producing paired‐end, 100 bp reads.

2.2. Bioinformatic processing and filtering

Sequencing reads were demultiplexed with the Stacks v2.55 process_radtags program (Rochette et al., 2019). Demultiplexed reads were then assembled de novo with the Stacks v2.55 pipeline using the denovo_map.pl program. Prior to running the pipeline on the entire set of samples, a range of assembly parameters were tested on a subset of samples using the r80 method (Paris et al., 2017; Rochette & Catchen, 2017). Based on these results, we chose M = 4 and n = 4 for the full analysis. The full analysis program settings included the PCR duplicate removal option, a minimum minor allele count of 3 to process a locus, and the requirement that a locus occurred in a minimum of 70% of individuals in a population in order to process it. Subsequent SNP filtering of the resulting Stacks VCF file was done with the R package vcfR v1.10.0 (Knaus & Grünwald, 2017). Variants with sequencing depth outside of 95% confidence intervals were excluded, as were those with quality scores below 20 and with greater than 20% missing data. To exclude variants according to linkage disequilibrium (LD), LD‐based SNP pruning was performed with the R package SNPRelate v1.20.1 (Zheng et al., 2012) using an LD threshold of r 2 = 0.2. Lastly, we tested for the presence of paralogous loci in the assembly using the proportion of heterozygotes and deviations in read ratios procedure described by McKinney et al. (2017); we reasoned that this would assist in identifying potential over‐clustering of loci in the assembly. We did not apply Hardy–Weinberg equilibrium (HWE) filtering for two reasons. First, recent work by Pearman et al. (2021) examining HWE filtering found limited support for its efficacy. Second, HWE is most commonly employed to reduce genotyping errors. Given that genotyping error as assessed by technical replicates was extremely low, we did not feel that it was necessary to implement additional filters.

Genotyping error was assessed in the final SNP dataset based on SNP mismatches between 18 samples sequenced in duplicate serving as technical replicates; 2 of the initial 20 replicates were dropped during filtering due to low coverage. Technical replicates were removed from the dataset for subsequent analyses.

2.3. SNP outlier tests

To identify outlier SNP markers, we used two different programs. The R package pcadapt v4.3.3 (Luu et al., 2017) was used to detect outliers based on principal components analysis. The R package OutFLANK v0.2 (Whitlock & Lotterhos, 2015) was then used to detect outliers based on the F ST distribution. Loci in consensus across these two methods were separated as outlier loci for subsequent analyses.

2.4. SNP analysis

Population statistics, including gene diversity, observed heterozygosity, inbreeding coefficients, and genetic differentiation, were computed with the R package hierfstat v0.04–22 (Goudet, 2005). Pairwise F ST was computed according to the method of Weir and Cockerham (1984) as implemented in hierfstat and StAMPP v1.6.1 (Pembleton et al., 2013), with 95% confidence intervals and p values computed with StAMPP. Population differentiation was first examined without a priori population groups using the R package LEA v3.6.0 (Frichot & François, 2015); admixture was tested with a range of K = 1–10, and cross‐entropy values were used to evaluate optimal K. Collection sites were then used as a priori groups for discriminant analysis of principal components (DAPC) with the R package adegenet v2.1.3 (Jombart, 2008). To determine the optimal number of principal components to include in the DAPC, cross‐validation was implemented with the xvalDapc function in adegenet using default parameters of 0.9 for the training set and 30 replicates.

Isolation by distance was tested by creating a pairwise matrix of lineal water distances between sites. The distance matrix and the F ST matrix were compared with a Mantel test using the R package ape v5.3 (Paradis & Schliep, 2019); to compare Willapa Bay and Salish Sea sites, for which full distance matrices were not relevant, Pearson correlation was used.

Relatedness between individuals was assessed using the genomic relatedness estimator of Yang et al. (2010) implemented in StAMPP (Pembleton et al., 2013). The effective population size was computed using the linkage disequilibrium method with the program NeEstimator v2.1 (Do et al., 2014).

3. RESULTS

3.1. Cockle size and approximate age classes

Cockles collected at each site generally spanned a range of shell lengths, corresponding to multiple age classes based on age‐size data from San Juan Island, Washington, reported by Gallucci and Gallucci (1982). These size ranges suggest that the majority of cockles collected were in the 2‐ to 4‐year age range (Figure S1). One notable exception was cockles collected from Eld Inlet geoduck tubes, which were composed almost entirely of a putative 2‐year‐old age class.

3.2. SNP data

After all filtering procedures, the final SNP dataset consisted of 8674 variants and 367 samples, with 13% missing data. No paralogous loci were identified based on the read ratio deviation versus proportion of heterozygotes procedure (Figure S2). Based on technical replicates sequenced in duplicate, mean genotyping error of the final assembly was 0.72% (Figure S3); in other words, 99.28% of SNPs were called identically between two identical samples. Final sample sizes included in the analysis ranged from 27 to 30 individuals per site (Table 1). All sites exhibited similar levels of observed (H o; 0.0722–0.0804) and expected heterozygosity (H e; 0.0695–0.0763), and no sites showed heterozygote deficiencies. The highest and lowest observed and expected heterozygosities were in Willapa Bay and Port Gamble Bay, respectively. Allelic richness was similar among populations; however, the number of private alleles was much higher among Willapa Bay cockles, followed by Hood Canal (Table 1).

TABLE 1.

Site names and codes, sample sizes, observed heterozygosity (H o), expected heterozygosity (H e), inbreeding coefficients (F IS), allelic richness, and private alleles for the Clinocardium nuttallii samples included in the final assembly

Site N H o H e F IS Allelic richness Private alleles
Hood Canal (HCL) 30 0.0748 0.0704 −0.0238 1.400 36
Agate Pass (AGP) 27 0.0752 0.0724 −0.0108 1.414 17
Neah Bay (NEB) 28 0.0725 0.0703 −0.0081 1.401 17
Penn Cove (PEN) 30 0.0745 0.0710 −0.0185 1.407 14
Port Gamble Bay (PGB) 30 0.0722 0.0695 −0.0118 1.402 22
Sequim Bay (SBY) 28 0.0749 0.0709 −0.0221 1.405 18
Sequim Bay geoduck (SBG) 29 0.0733 0.0697 −0.0173 1.401 10
Padilla Bay (PAD) 30 0.0726 0.0698 −0.0115 1.401 14
Semiahmoo Spit (SMS) 30 0.0740 0.0716 −0.0105 1.412 25
Eld Inlet geoduck (EIG) 29 0.0735 0.0702 −0.0156 1.404 20
Squaxin Island (SQI) 28 0.0726 0.0699 −0.0130 1.403 9
Willapa Bay (WPB) 30 0.0804 0.0763 −0.0193 1.421 172
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3.3. Population structure

Admixture analysis showed that K = 2 had only slightly higher cross‐entropy than K = 1, with subsequent values of K showing sharp increases in cross‐entropy (Figure S4). We therefore plotted admixture components at K = 2, which indicated a strong population break between Salish Sea sites and Willapa Bay, on the outer Pacific coast (Figure 1). In corroboration, pairwise F ST values indicated that divergence of Willapa Bay cockles from all other sites was an order of magnitude higher (F ST = 0.022–0.025) than divergence of remaining sites from each other (F ST = 0.001–0.004; Figure 2). Of these remaining sites, hierarchical clustering showed that Neah Bay and Sequim Bay were the least divergent from Willapa Bay (top of Figure 2). After Willapa Bay, Hood Canal had the second highest F ST values, suggesting that Hood Canal exhibited a small degree of differentiation from other Salish Sea sites. All pairwise F ST values were significantly different from zero at α = 0.05.

FIGURE 2.

FIGURE 2

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Pairwise F ST matrix with populations ordered by hierarchical clustering of F ST values. All F ST values were significantly different from zero

These results were mirrored by DAPC analysis. The xvalDapc function in adegenet determined that 150 principal components were associated with the lowest cross‐validation error (Figure S5). Sixty percent of the variance was explained by a DAPC with 150 principal components. The majority of the DAPC variance (55%) was explained by the first discriminant function (Figure 3). Along this first discriminant axis, Willapa Bay exhibited clear divergence from Salish Sea sites (Figure 3). Along the second discriminant axis (7% of variance), Hood Canal showed a less pronounced degree of differentiation from the remaining sites.

FIGURE 3.

FIGURE 3

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Discriminant analysis of principal components (DAPC) scatterplot. The percentage of DAPC variance captured by each axis is shown in axis labels

3.4. Neutral vs. outlier loci

Pcadapt and OutFLANK identified 38 and 14 SNP outliers, respectively; 13 of these were in consensus across both methods. Pairwise F ST based only on outliers indicated much more pronounced differentiation between Willapa Bay and all Salish Sea sites compared to the 8661 neutral markers (Figure 4). However, F ST values between Willapa Bay and all Salish Sea sites based on neutral markers were still an order of magnitude higher than pairwise differentiation within Salish Sea sites.

FIGURE 4.

FIGURE 4

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Pairwise F ST based on 8661 neutral (upper triangle) and 13 outlier (lower triangle) loci. Significant F ST values are shown in bold italics

3.5. Isolation by distance

Analysis of isolation by distance using the neutral SNP dataset revealed an overall positive relationship between pairwise water distance and F ST (Figure 5). However, the relationship was highly disjunct due to the order of magnitude higher F ST between Willapa Bay and Salish Sea sites compared to sites within the Salish Sea. For this reason, the analysis was separated between Willapa Bay vs. Salish Sea comparisons and within Salish Sea comparisons. There was a significant increase in pairwise F ST with increasing water distance both within the Salish Sea (Mantel test; r 2 = 0.46, p = 0.003) and between the Salish Sea and Willapa Bay (Pearson correlation; r 2 = 0.67, p = 0.024).

FIGURE 5.

FIGURE 5

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Tests of isolation by distance in Clinocardium nuttallii. Pairwise genetic differentiation and water distances within the Salish Sea were tested separately from those between Willapa Bay and the Salish Sea. Results of a Mantel test are shown for the within Salish Sea analysis, while results of a Pearson correlation are shown for Willapa Bay vs. Salish Sea

3.6. Relatedness

The genomic relatedness matrix indicated that no closely related individuals were found within a location or between distinct locations (Figure S6). Individuals at Willapa Bay exhibited the highest relatedness values, but these values were still too low to suggest inbreeding, which is also reflected by the low F IS value for Willapa Bay (Table 1).

3.7. Effective population size

Estimates of Ne based on the heterozygote excess method were in the range of 22.7–142.9, while for the linkage disequilibrium method estimates were infinite (Table 2).

TABLE 2.

Effective population size (Ne ) based on the heterozygote excess method (NeHt) and linkage disequilibrium (NeLD) method, for minimum allele frequencies of 0.01

Population NeHt (95% CI) NeLD (95% CI)
Hood Canal 67.6 (44.4–142.9) ∞ (∞–∞)
Agate Pass 22.7 (19.5–27.4) ∞ (∞–∞)
Neah Bay 28.9 (23.9–36.6) ∞ (∞–∞)
Penn Cove 24.3 (20.9–29.2) ∞ (∞–∞)
Port Gamble Bay 31.3 (25.5–40.7) ∞ (∞–∞)
Sequim Bay 51.4 (37–84.4) ∞ (∞–∞)
Sequim Bay geoduck 34.1 (27.2–45.8) ∞ (∞–∞)
Padilla Bay 45.5 (33.8–69.9) ∞ (∞–∞)
Semiahmoo Spit 47 (34.3–75) ∞ (∞–∞)
Eld Inlet geoduck 49.9 (35.9–82) ∞ (∞–∞)
Squaxin Island 41.6 (31.7–60.9) ∞ (∞–∞)
Willapa Bay 28.4 (23.4–36) ∞ (∞–∞)
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4. DISCUSSION

Our results indicate that C. nuttallii within the southern Salish Sea are distinct from those in Willapa Bay. Although we lack another outer coast population with which to corroborate this result, it is in agreement with the strong differentiation between coastal populations and those within the Salish Sea observed among other marine invertebrates and fishes (Buonaccorsi et al., 2005; Drinan et al., 2018; Iwamoto et al., 2015; Jackson & O’Malley, 2017; Silliman, 2019). Indeed, we found that cockles from Neah Bay, which is relatively close to Willapa Bay at the western limit of the Salish Sea (217 km), are much more similar to cockles from Squaxin Island, which is further away at the southern extent of Puget Sound (285 km). For Olympia oysters, Silliman (2019) concluded that the entrance to the Strait of Juan de Fuca is among the major barriers to gene flow along the Pacific coast of North America. This is likely due to oceanographic processes, principally the net outflow of Salish Sea surface waters through the Strait of Juan de Fuca resulting from estuarine circulation (Khangaonkar et al., 2017). Additionally, conditions within Willapa Bay may also restrict gene flow; modeling studies of green crab larval retention suggested that 5%–40% of larvae spawned during summer were retained within Willapa Bay (Banas et al., 2009). Although neutral processes appear to contribute most significantly to differentiation of Willapa Bay cockles, outlier loci were strongly associated with Willapa Bay, suggesting that adaptive processes may also play a role.

Within the southern Salish Sea, C. nuttallii appear to be a well‐connected metapopulation despite the numerous inlets, embayments, and other potential impediments to gene flow. Although we detected evidence of isolation by distance within the southern Salish Sea, the slope of this relationship was shallow, and F ST values were low. Moreover, based on outlier analyses, our results do not support the hypothesis that sub‐populations are locally adapted. However, cockles within Hood Canal showed slight evidence of divergence from other sub‐populations. Similarly, differentiation of Hood Canal populations from those in other Puget Sound basins has been observed among Dungeness crab (Jackson & O’Malley, 2017) and yelloweye rockfish (Andrews et al., 2018). Among all Puget Sound sub‐basins, water in Hood Canal is retained for the longest duration (Ebbesmeyer, 1984), making larval retention perhaps the most likely explanation for divergence of Hood Canal populations. At the mouth of Hood Canal, deep water flow is limited by a relatively shallow sill at 50 m depth, while surface flow is partially inhibited by a floating bridge with a draft of 3.7 m spanning approximately 90% of the width of the inlet (Khangaonkar & Wang, 2013). This bridge has been shown to impede movement of steelhead smolt out of Hood Canal (Moore et al., 2013).

High connectivity and gene flow among Salish Sea cockle populations are further supported by relatively high observed heterozygosity, negative inbreeding coefficients, and low levels of relatedness among cockles in a given location. These attributes are particularly notable given that C. nuttallii is a simultaneous hermaphrodite capable of self‐fertilization. Liu et al. (2008b) found that some level of self‐fertilization appears to be unavoidable in C. nuttallii, and reported a rate of up to 95% self‐fertilization among newly released eggs in the laboratory; however, this rate dropped to 25.7% among 2–4 cell embryos, perhaps as a result of deleterious alleles. The reduced fitness of inbred individuals through the expression of deleterious alleles among homozygotes (inbreeding depression) is thought to strongly limit the influence of inbreeding (Charlesworth & Charlesworth, 1987), and it is possible that reductions in the survival and fitness of selfed C. nuttallii significantly diminish their proportion among surviving adults. For example, selfed larvae of the scallop Argopecten circularis exhibited 41.6% lower survival than larvae from pair‐mated crosses (Ibarra et al., 1995). Nonetheless, selfed individuals that survive and reproduce have the potential to significantly reduce the genetic diversity of the population. Perhaps this is reflected in the expected heterozygosity (gene diversity), which is notably low yet remarkably consistent across populations (0.0695–0.0763). However, relatively low gene diversity has also been observed in two other Atlantic cockle species, Cerastoderma edule and C. glaucum (Coscia et al., 2020; Sromek et al., 2019), both nonselfing, dioecious species. Although an excess of heterozygosity can be caused by over‐clustering of loci during de novo catalog construction (O’Leary et al., 2018), no paralogs were detected by the proportion of heterozygotes and deviations in read ratios procedure (McKinney et al., 2017), and the extremely low genotyping error provides additional evidence against over‐clustering.

By contrast with the heterozygote excess and negative F IS we found in C. nuttallii, heterozygote deficiencies are common among bivalves and were observed in recent SNP‐based analyses of both of the Atlantic cockle species mentioned above, as well as in Olympia and eastern oysters (Bernatchez et al., 2019; Coscia et al., 2020; Silliman, 2019; Sromek et al., 2019). The heterozygote excess observed here among C. nuttallii is particularly unusual in light of the potential for selfing. It suggests that C. nuttallii has evolved mechanisms to overcome the effects of selfing, such as through strong selection against recessive alleles expressed in homozygotes (heterozygote advantage) and resulting associative overdominance (Ohta, 1971; Ohta & Cockerham, 1974). Additionally, the ~10‐day larval period is likely to allow dispersal of any potential selfed offspring far from parents, and indeed, the high connectivity of C. nuttallii observed here is consistent with strong dispersal.

The high population connectivity, and by inference, effective dispersal among southern Salish Sea C. nuttallii populations suggests that natural repopulation of beaches following localized disturbances should occur quickly. Moreover, since localized die‐offs are thought to primarily affect intertidal populations, subtidal C. nuttallii populations represent refuges that may facilitate reseeding of adjacent intertidal areas even in the case of limited larval supply from other locales (Ratti, 1977). In an Oregon estuary, for example, Ratti (1977) found that although subtidal populations were lower than those in the intertidal zone, subtidal individuals were more fecund and grew faster. However, repopulation of intertidal areas does not always occur quickly based on both anecdotal and quantitative evidence (Barber et al., 2019). Notably, while two other intertidal clam species in the region, Leukoma staminea and Saxidomus gigantea, showed multi‐year population synchrony throughout the southern Salish Sea, C. nuttallii population trends were beach specific (Barber et al., 2019). Together with our evidence that connectivity of C. nuttallii is strong, this suggests that factors other than larval supply, such as postsettlement survival or changes in habitat quality or suitability, may be responsible for population declines in certain areas. For example, large‐scale die‐offs or heavy harvests of cockles could lead to alternative stable states (Beisner et al., 2003) that make it difficult for cockles to become reestablished. Alternatively, climate change may be slowly bringing conditions on some beaches above survival thresholds for cockles, which are not well buffered from air temperature extremes during low tides due to their relatively shallow sediment burial depths.

Supplementation of C. nuttallii populations using captive breeding may offer a solution to fluctuations or localized declines in abundance. The high connectivity and lack of evidence for locally adapted populations in the southern Salish Sea suggest potentially low genetic risks to wild stocks. On the other hand, effective population size (Ne ) estimates based on the LD method were infinite, which makes it difficult to estimate the genetic consequences of supplementation. Assessments of Ne in large marine populations are notoriously problematic and may require extremely large sample sizes to obtain any degree of accuracy (Marandel et al., 2019). Although Ne values based on the heterozygote excess method were finite and could be used to estimate the Ryman–Laikre effect, which reflects the potential increase in inbreeding and reduced effective population size as a result of supplementation (Ryman & Laikre, 1991; Waples et al., 2016), the potentially much larger Ne as assessed by LD is more important, because populations with large Ne are at greatest risk for increased inbreeding due to supplementation (Hare et al., 2011; Waples et al., 2016). Supportive breeding effectively increases variance in reproductive success, and for populations with large Ne , maintaining large Ne in the face of supplementation would require unreasonably large captive broodstocks (Hare et al., 2011). Furthermore, the lack of an upper bound on Ne makes it impossible to calculate the potential change in Ne resulting from a supplemental breeding program.

The most risk‐averse solution may be to ensure that captive‐bred individuals have limited reproductive contribution to wild stocks (Waples et al., 2016). This can be done by either supplementing wild populations with a very small number of captive‐bred animals (e.g., <10% of the wild population; Waples et al., 2016), by developing techniques to produce reproductively sterile animals, or by harvesting captive‐bred individuals before they become reproductively mature (Waples et al., 2016). For example, in support of the latter strategy, a lack of introgression between hatchery‐bred and wild Sydney rock oysters (Saccostrea glomerata) may have been attributed to harvest of hatchery‐bred individuals prior to reproductive maturity (Thompson et al., 2017). Supplemental hatchery‐bred C. nuttallii could be collected at an acceptable harvest size before they reproduce in spring or summer of their second year, when they reach a size of approximately 50 mm (Gallucci & Gallucci, 1982; Ratti, 1977; Table 3). This strategy employs standard shellfish aquaculture techniques and can be considered a form of sea farming; it is distinguished from stock enhancement due the removal of individuals before they can interbreed with wild stocks (Grant et al., 2017).

TABLE 3.

Summary of the costs and benefits of strategies to augment populations of Clinocardium nuttallii for subsistence harvest

Augmentation strategy Costs Benefits Notes
Translocation of cockles from geoduck tubes to beaches Labor and transportation costs to collect and translocate cockles No impact on wild Ne . No hatchery production costs. Cockles in geoduck tubes would be removed anyway Least risky approach from genomic standpoint
Containerized sea farming of hatchery‐produced cockles Hatchery production costs. Cockles harvested at small size to avoid reproduction Low projected impact on wild Ne Cockles collected at age 2, ~50 mm
Stock enhancement through beach seeding Hatchery production costs. Potential for reduction in wild Ne Cockles harvested at larger size than with sea farming Genetic risk increases with size of breeding program. Greater attention to broodstock sizes recommended
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A potentially attractive sea farming‐based alternative to a hatchery breeding program would be to translocate wild cockles from geoduck culture tubes to beaches where they have declined. Cockles growing in geoduck culture tubes can be regarded as a nuisance to shellfish growers, and redistributing them to beaches where they are highly sought by tribal fishers would be a mutually beneficial solution for both parties. From a genomic standpoint, our analysis suggests that this would be a less risky solution than seeding beaches with captive‐bred individuals. Whereas captive‐bred cockles would have the potential to reduce the effective population size of the wild population, geoduck tube cockles exhibit comparable genomic profiles to those on natural beaches, including similar levels of H o, H e, F IS, Ne , and low F ST.

The ultimate success of a C. nuttallii stock enhancement program is a key consideration. Fortunately, hatchery production has been extensively tested in C. nuttallii, and it has long been considered a prime candidate for aquaculture in the region due to ease of culture (Dunham et al., 2013a, 2013b; Liu, Alabi, & Pearce, 2008a, 2008b; Liu et al., 2009, 2010, 2011). Pilot breeding projects by our group have produced large quantities of C. nuttallii seed (>1 M) at relatively low cost and effort (Puget Sound Restoration Fund, unpublished). Typical seeding densities of Manila clams in Puget Sound are 300–600 m−2 (Toba, 1992), and similar densities have been used successfully in cockle beach culture pilot studies (600 m−2; Brooks, 2001). Meanwhile, the wild seed source available in geoduck tubes would be more cost‐effective and pose fewer genetic risks, yet it is unclear how much of this resource exists. However, geoduck farms cover approximately 80 ha in Puget Sound and the potential cockle biomass in these farms is significant; surveys to estimate this biomass are planned for the near future. We suspect that, owing to both their wild origin and larger size, geoduck tube cockles would be more successful than hatchery‐raised seed. Some studies of shellfish stock enhancement programs have found limited efficacy using hatchery‐bred seed, with potential explanations including predation and artificial selection (e.g., Carlsson et al., 2008). Cockles from geoduck tubes may be less prone to these effects due to their larger size and wild origin, and could be planted at densities approximating the highest observed natural densities in Puget Sound (232 g m−3, equivalent to ~6 cockles @ 50 mm; Barber et al., 2019).

5. CONCLUSIONS

Our analysis suggests that C. nuttallii populations in the southern Salish Sea are well connected and have strong potential to adapt and recover from disturbances. We cannot therefore recommend a large‐scale stock enhancement strategy that would risk undermining the natural genetic diversity of the species, and instead suggest either a small enhancement program or a sea farming approach that limits reproductive contributions of captive‐bred individuals. Meanwhile, questions remain regarding the causes of localized declines and slow recovery in certain areas. Several characteristics of C. nuttallii, such as shallow sediment burial depth, relatively short life span, and high fecundity, may make it particularly susceptible to boom‐and‐bust cycles, which could be amplified by climate change. This highlights the disproportionate vulnerability to climate change of Indigenous populations that rely heavily on nearshore subsistence harvests (Donatuto et al., 2014; Weatherdon et al., 2016). We advocate for continued monitoring of C. nuttallii populations in the region, including research on causes of declines in certain areas, and potential reevaluation of the need for stock enhancement in coming decades.

Supporting information

Fig S1‐S6

Click here for additional data file. (1.7MB, docx)

ACKNOWLEDGEMENTS

We are grateful to many people who collected cockles or helped facilitate collections, often under cold, wet, and dark conditions, including Adrianne Akmajian, Julie Barber, Kevin Cagey, Tamara Gage, Jim Gibbons, Donovan Henry, Neil Harrington, Paul Harris, Megan Hintz, Nik Matsumoto, James McArdle, Matt Nelson, Blair Paul, Andy Pavone, Ralph Riccio, Marilyn Sheldon, Eric Sparkman, and Liz Tobin. We are thankful for the laboratory assistance in preparing and processing tissue samples by Paul Blodgett, Josh Bouma, Lisa Crosson, Claire Everett, Mackenzie Gavery, Rachael Giersch, Michael Metzger, Crystal Simchick, and Marisa Yonemitsu. Many specimen collections were made on tribal reservations or usual and accustomed fishing grounds, and we are grateful to the Jamestown S'Klallam Tribe, the Lummi Nation, the Makah Nation, the Port Gamble S'Klallam Tribe, the Skokomish Tribe, the Suquamish Tribe, the Squaxin Island Tribe, and the Swinomish Indian Tribal Community for granting permission to collect. We also thank the Seattle Shellfish Company for Eld Inlet geoduck tube collections. Additional collections were made under Washington Department of Fish and Wildlife Scientific Collection Permit 19‐319. Finally, we thank Dennis Hedgecock and Mackenzie Gavery for helpful discussions on technical details, and two anonymous reviewers for comments that improved the manuscript. This study was supported by U.S. Bureau of Indian Affairs Tribal Resilience Grant #A19AP00215.

Dimond, J. L. , Crim, R. N. , Unsell, E. , Barry, V. , & Toft, J. E. (2022). Population genomics of the basket cockle Clinocardium nuttallii in the southern Salish Sea: Assessing genetic risks of stock enhancement for a culturally important marine bivalve. Evolutionary Applications, 15, 459–470. 10.1111/eva.13359

DATA AVAILABILITY STATEMENT

Demultiplexed sequence reads are available under NCBI BioProject PRJNA771709 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA771709/) (Dimond, 2021).

REFERENCES

  1. Álvarez, M. , Briz Godino, I. , Balbo, A. , & Madella, M. (2011). Shell middens as archives of past environments, human dispersal and specialized resource management. Quaternary International, 239(1–2), 1–7. 10.1016/j.quaint.2010.10.025 [DOI] [Google Scholar]
  2. Andrews, K. S. , Nichols, K. M. , Elz, A. , Tolimieri, N. , Harvey, C. J. , Pacunski, R. , Lowry, D. , Yamanaka, K. L. , & Tonnes, D. M. (2018). Cooperative research sheds light on population structure and listing status of threatened and endangered rockfish species. Conservation Genetics, 19(4), 1–14. 10.1007/s10592-018-1060-0 [DOI] [Google Scholar]
  3. Baird, N. A. , Etter, P. D. , Atwood, T. S. , Currey, M. C. , Shiver, A. L. , Lewis, Z. A. , Selker, E. U. , Cresko, W. A. , & Johnson, E. A. (2008). Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS One, 3(10), e3376. 10.1371/journal.pone.0003376 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Banas, N. S. , McDonald, P. S. , & Armstrong, D. A. (2009). Green crab larval retention in willapa bay, washington: An intensive lagrangian modeling approach. Estuaries and Coasts, 32(5), 893–905. 10.1007/s12237-009-9175-7 [DOI] [Google Scholar]
  5. Barber, J. S. , Ruff, C. P. , McArdle, J. T. , Hunter, L. L. , Speck, C. A. , Rogers, D. W. , & Greiner, C. M. (2019). Intertidal clams exhibit population synchrony across spatial and temporal scales. Limnology and Oceanography, 64(Suppl 1), S284–S300. 10.1002/lno.11085 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Beisner, B. E. , Haydon, D. T. , & Cuddington, K. (2003). Alternative stable states in ecology. Frontiers in Ecology and the Environment, 1(7), 376–382. [Google Scholar]
  7. Bernatchez, S. , Xuereb, A. , Laporte, M. , Benestan, L. , Steeves, R. , Laflamme, M. , & Mallet, M. A. (2019). Seascape genomics of eastern oyster (Crassostrea virginica) along the Atlantic coast of Canada. Evolutionary Applications, 12(3), 587–609. 10.1111/eva.12741 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Brooks, K. (2001). Chugach Regional Resources Commission Bivalve Enhancement Program: Bivalve inventories and native littleneck clam (Protothaca staminea) culture studies. Exxon Valdez Oil Spill Trustee Council Project Number 95131, Final Report. [Google Scholar]
  9. Buonaccorsi, V. P. , Kimbrell, C. A. , Lynn, E. A. , & Vetter, R. D. (2005). Limited realized dispersal and introgressive hybridization influence genetic structure and conservation strategies for brown rockfish, Sebastes Auriculatus . Conservation Genetics, 6(5), 697–713. 10.1007/s10592-005-9029-1 [DOI] [Google Scholar]
  10. Carlsson, J. , Carnegie, R. B. , Cordes, J. F. , Hare, M. P. , Leggett, A. T. , & Reece, K. S. (2008). Evaluating recruitment contribution of a selectively bred aquaculture line of the oyster, Crassostrea virginica used in restoration efforts. Journal of Shellfish Research, 27(5), 1117–1124. 10.2983/0730-8000-27.5.1117 [DOI] [Google Scholar]
  11. Carson, H. S. , & Ulrich, M. (2019). Status report for the pinto abalone in Washington (p. 25). Washington Department of Fish and Wildlife. [Google Scholar]
  12. Charlesworth, D. , & Charlesworth, B. (1987). Inbreeding depression and its evolutionary consequences. Annual Review of Ecology and Systematics, 18(1), 237–268. 10.1146/annurev.es.18.110187.001321 [DOI] [Google Scholar]
  13. Coscia, I. , Wilmes, S. B. , Ironside, J. E. , Goward‐Brown, A. , O’Dea, E. , Malham, S. K. , & Robins, P. E. (2020). Fine‐scale seascape genomics of an exploited marine species, the common cockle Cerastoderma edule, using a multimodelling approach. Evolutionary Applications, 13(8), 1854–1867. 10.1111/eva.12932 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Dimond, J. L. (2021). Population genomics of the basket cockle Clinocardium nuttallii in the southern Salish Sea. NCBI BioProject PRJNA771709. https://www.ncbi.nlm.nih.gov/bioproject/PRJNA771709/ [DOI] [PMC free article] [PubMed]
  15. Do, C. , Waples, R. S. , Peel, D. , Macbeth, G. M. , Tillett, B. J. , & Ovenden, J. R. (2014). NeEstimator v2: Re‐implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Molecular Ecology Resources, 14(1), 209–214. 10.1111/1755-0998.12157 [DOI] [PubMed] [Google Scholar]
  16. Donatuto, J. , Grossman, E. E. , Konovsky, J. , Grossman, S. , & Campbell, L. W. (2014). Indigenous community health and climate change: Integrating biophysical and social science indicators. Coastal Management, 42(4), 355–373. 10.1080/08920753.2014.923140 [DOI] [Google Scholar]
  17. Drinan, D. P. , Gruenthal, K. M. , Canino, M. F. , Lowry, D. , Fisher, M. C. , & Hauser, L. (2018). Population assignment and local adaptation along an isolation‐by‐distance gradient in Pacific cod (Gadus macrocephalus). Evolutionary Applications, 11(8), 1448–1464. 10.1111/eva.12639 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Dunham, A. , Gurney‐Smith, H. , Plamondon, N. , Yuan, S. , & Pearce, C. M. (2013a). Aquaculture potential of the basket cockle (Clinocardium nuttallii). Part 2: effects of stocking density and depth on second year grow‐out performance of three size cohorts in intertidal and off‐bottom suspended culture. Aquaculture Research, 44(8), 1277–1299. 10.1111/j.1365-2109.2012.03133.x [DOI] [Google Scholar]
  19. Dunham, A. , Gurney‐Smith, H. , Plamondon, N. , Yuan, S. , & Pearce, C. M. (2013b). Aquaculture potential of the basket cockle (Clinocardium nuttallii). Part 1: effects of stocking density on first year grow‐out performance in intertidal and off‐bottom suspended culture. Aquaculture Research, 44(8), 1236–1253. 10.1111/j.1365-2109.2012.03125.x [DOI] [Google Scholar]
  20. Ebbesmeyer, C. C. (1984). Synthesis of current measurements in Puget Sound, Washington. Volume 3, Circulation in Puget Sound: An interpretation based on historical records of currents. [Google Scholar]
  21. Frichot, E. , & François, O. (2015). LEA: An R package for landscape and ecological association studies. Methods in Ecology and Evolution, 6(8), 925–929. 10.1111/2041-210X.12382 [DOI] [Google Scholar]
  22. Gallucci, V. F. , & Gallucci, B. B. (1982). Reproduction and ecology of the hermaphroditic cockle Clinocardium nuttallii (Bivalvia:Cardiidae) in Garrison Bay. Marine Ecology Progress Series, 7, 137–145. 10.3354/meps007137 [DOI] [Google Scholar]
  23. Goudet, J. (2005). Hierfstat, a package for R to compute and test hierarchical F‐statistics. Molecular Ecology Notes, 5(1), 184–186. 10.1111/j.1471-8286.2004.00828.x [DOI] [Google Scholar]
  24. Grant, W. S. , Jasper, J. , Bekkevold, D. , & Adkison, M. (2017). Responsible genetic approach to stock restoration, sea ranching and stock enhancement of marine fishes and invertebrates. Reviews in Fish Biology and Fisheries, 27(3), 615–649. 10.1007/s11160-017-9489-7 [DOI] [Google Scholar]
  25. Groesbeck, A. S. , Rowell, K. , Lepofsky, D. , & Salomon, A. K. (2014). Ancient clam gardens increased shellfish production: adaptive strategies from the past can inform food security today. PLoS One, 9(3), e91235. 10.1371/journal.pone.0091235 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Hare, M. P. , Nunney, L. , Schwartz, M. K. , Ruzzante, D. E. , Burford, M. , Waples, R. S. , Ruegg, K. , & Palstra, F. (2011). Understanding and estimating effective population size for practical application in marine species management. Conservation Biology, 25(3), 438–449. 10.1111/j.1523-1739.2010.01637.x [DOI] [PubMed] [Google Scholar]
  27. Hauser, L. , & Carvalho, G. R. (2008). Paradigm shifts in marine fisheries genetics: Ugly hypotheses slain by beautiful facts. Fish and Fisheries, 9(4), 333–362. 10.1111/j.1467-2979.2008.00299.x [DOI] [Google Scholar]
  28. Ibarra, A. M. , Cruz, P. , & Romero, B. A. (1995). Effects of inbreeding on growth and survival of self‐fertilized catarina scallop larvae, Argopecten Circularis . Aquaculture, 134(1–2), 37–47. 10.1016/0044-8486(95)00022-T [DOI] [Google Scholar]
  29. Iwamoto, E. M. , Elz, A. E. , García‐De León, F. J. , Silva‐Segundo, C. A. , Ford, M. J. , Palsson, W. A. , & Gustafson, R. G. (2015). Microsatellite DNA analysis of Pacific hake Merluccius productus population structure in the Salish Sea. ICES Journal of Marine Science, 72(9), 2720–2731. 10.1093/icesjms/fsv146 [DOI] [Google Scholar]
  30. Jackson, T. M. , & O’Malley, K. G. (2017). Comparing genetic connectivity among Dungeness crab (Cancer magister) inhabiting Puget Sound and coastal Washington. Marine Biology, 164(6), 123. 10.1007/s00227-017-3152-7 [DOI] [Google Scholar]
  31. Jombart, T. (2008). adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics, 24(11), 1403–1405. 10.1093/bioinformatics/btn129 [DOI] [PubMed] [Google Scholar]
  32. Khangaonkar, T. , Long, W. , & Xu, W. (2017). Assessment of circulation and inter‐basin transport in the Salish Sea including Johnstone Strait and Discovery Islands pathways. Ocean Modelling, 109, 11–32. 10.1016/j.ocemod.2016.11.004 [DOI] [Google Scholar]
  33. Khangaonkar, T. , Nugraha, A. , Xu, W. , Long, W. , Bianucci, L. , Ahmed, A. , Mohamedali, T. , & Pelletier, G. (2018). Analysis of hypoxia and sensitivity to nutrient pollution in salish sea. Journal of Geophysical Research: Oceans, 123(7), 4735–4761. 10.1029/2017JC013650 [DOI] [Google Scholar]
  34. Khangaonkar, T. , & Wang, T. (2013). Potential alteration of fjordal circulation due to a large floating structure—Numerical investigation with application to Hood Canal basin in Puget Sound. Applied Ocean Research, 39, 146–157. 10.1016/j.apor.2012.11.003 [DOI] [Google Scholar]
  35. Kilmer, F. H. (1973). The Cockle, Clinocardium nuttallii (Conrad) in Japan. Science Reports of the Tohoku University. Second Series (Geology), 6, 29–35. [Google Scholar]
  36. Knaus, B. J. , & Grünwald, N. J. (2017). vcfr: A package to manipulate and visualize variant call format data in R. Molecular Ecology Resources, 17(1), 44–53. 10.1111/1755-0998.12549 [DOI] [PubMed] [Google Scholar]
  37. Liu, W. , Alabi, A. O. , & Pearce, C. M. (2008a). Broodstock Conditioning in the Basket Cockle, Clinocardium nuttallii . Journal of Shellfish Research, 27(2), 399–404. [Google Scholar]
  38. Liu, W. , Alabi, A. O. , & Pearce, C. M. (2008b). Fertilization and embryonic development in the Basket Cockle, Clinocardium nuttallii . Journal of Shellfish Research, 27(2), 393–397. [Google Scholar]
  39. Liu, W. , Gurney‐Smith, H. , Beerens, A. , & Pearce, C. M. (2010). Effects of stocking density, algal density, and temperature on growth and survival of larvae of the basket cockle, Clinocardium Nuttallii . Aquaculture, 299(1–4), 99–105. 10.1016/j.aquaculture.2009.11.023 [DOI] [Google Scholar]
  40. Liu, W. , Pearce, C. M. , Alabi, A. O. , Beerens, A. , & Gurney‐Smith, H. (2011). Effects of stocking density, ration, and temperature on growth of early post‐settled juveniles of the basket cockle, Clinocardium Nuttallii . Aquaculture, 320(1–2), 129–136. 10.1016/j.aquaculture.2011.07.030 [DOI] [Google Scholar]
  41. Liu, W. , Pearce, C. M. , Alabi, A. O. , & Gurney‐Smith, H. (2009). Effects of microalgal diets on the growth and survival of larvae and post‐larvae of the basket cockle, Clinocardium Nuttallii . Aquaculture, 293(3–4), 248–254. 10.1016/j.aquaculture.2009.04.032 [DOI] [Google Scholar]
  42. Luu, K. , Bazin, E. , & Blum, M. G. B. (2017). pcadapt: An R package to perform genome scans for selection based on principal component analysis. Molecular Ecology Resources, 17(1), 67–77. 10.1111/1755-0998.12592 [DOI] [PubMed] [Google Scholar]
  43. Marandel, F. , Lorance, P. , Berthelé, O. , Trenkel, V. M. , Waples, R. S. , & Lamy, J.‐B. (2019). Estimating effective population size of large marine populations, is it feasible? Fish and Fisheries, 20(1), 189–198. 10.1111/faf.12338 [DOI] [Google Scholar]
  44. McKinney, G. J. , Waples, R. K. , Seeb, L. W. , & Seeb, J. E. (2017). Paralogs are revealed by proportion of heterozygotes and deviations in read ratios in genotyping‐by‐sequencing data from natural populations. Molecular Ecology Resources, 17(4), 656–669. 10.1111/1755-0998.12613 [DOI] [PubMed] [Google Scholar]
  45. Moore, M. , Berejikian, B. A. , & Tezak, E. P. (2013). A floating bridge disrupts seaward migration and increases mortality of steelhead smolts in Hood Canal, Washington State. PLoS One, 8(9), e73427. 10.1371/journal.pone.0073427 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. O’Leary, S. J. , Puritz, J. B. , Willis, S. C. , Hollenbeck, C. M. , & Portnoy, D. S. (2018). These aren’t the loci you’re looking for: Principles of effective SNP filtering for molecular ecologists. Molecular Ecology, 27(16), 3193–3206. [DOI] [PubMed] [Google Scholar]
  47. Ohta, T. (1971). Associative overdominance caused by linked detrimental mutations. Genetical Research, 18(3), 277–286. 10.1017/S0016672300012684 [DOI] [PubMed] [Google Scholar]
  48. Ohta, T. , & Cockerham, C. C. (1974). Detrimental genes with partial selfing and effects on a neutral locus. Genetical Research, 23(2), 191–200. 10.1017/S0016672300014816 [DOI] [PubMed] [Google Scholar]
  49. Oliver, E. C. J. , Donat, M. G. , Burrows, M. T. , Moore, P. J. , Smale, D. A. , Alexander, L. V. , Benthuysen, J. A. , Feng, M. , Sen Gupta, A. , Hobday, A. J. , Holbrook, N. J. , Perkins‐Kirkpatrick, S. E. , Scannell, H. A. , Straub, S. C. , & Wernberg, T. (2018). Longer and more frequent marine heatwaves over the past century. Nature Communications, 9(1), 1324. 10.1038/s41467-018-03732-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Paradis, E. , & Schliep, K. (2019). ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics, 35(3), 526–528. 10.1093/bioinformatics/bty633 [DOI] [PubMed] [Google Scholar]
  51. Paris, J. R. , Stevens, J. R. , & Catchen, J. M. (2017). Lost in parameter space: A road map for stacks. Methods in Ecology and Evolution, 8(10), 1360–1373. 10.1111/2041-210X.12775 [DOI] [Google Scholar]
  52. Pearman, W. S. , Urban, L. , & Alexander, A. (2021). Commonly used Hardy‐Weinberg equilibrium filtering schemes impact population structure inferences using RADseq data. bioRxiv. 10.1101/2021.06.15.448615 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Pembleton, L. W. , Cogan, N. O. I. , & Forster, J. W. (2013). StAMPP: An R package for calculation of genetic differentiation and structure of mixed‐ploidy level populations. Molecular Ecology Resources, 13(5), 946–952. 10.1111/1755-0998.12129 [DOI] [PubMed] [Google Scholar]
  54. Poe, M. R. , Donatuto, J. , & Satterfield, T. (2016). “Sense of place”: Human wellbeing considerations for ecological restoration in Puget Sound. Coastal Management, 44(5), 409–426. 10.1080/08920753.2016.1208037 [DOI] [Google Scholar]
  55. Ratti, F. D. (1977). Reproduction and growth of the Pacific basket‐cockle, Clinocardium nuttallii Conrad, from intertidal and subtidal environments of Netarts Bay. M.S. Thesis. Oregon State University. [Google Scholar]
  56. Rochette, N. C. , & Catchen, J. M. (2017). Deriving genotypes from RAD‐seq short‐read data using Stacks. Nature Protocols, 12(12), 2640–2659. 10.1038/nprot.2017.123 [DOI] [PubMed] [Google Scholar]
  57. Rochette, N. C. , Rivera‐Colón, A. G. , & Catchen, J. M. (2019). Stacks 2: Analytical methods for paired‐end sequencing improve RADseq‐based population genomics. Molecular Ecology, 28(21), 4737–4754. 10.1111/mec.15253 [DOI] [PubMed] [Google Scholar]
  58. Ryman, N. , & Laikre, L. (1991). Effects of supportive breeding on the genetically effective population size. Conservation Biology, 5(3), 325–329. 10.1111/j.1523-1739.1991.tb00144.x [DOI] [Google Scholar]
  59. Silliman, K. (2019). Population structure, genetic connectivity, and adaptation in the Olympia oyster (Ostrea lurida) along the west coast of North America. Evolutionary Applications, 12(5), 923–939. 10.1111/eva.12766 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Sromek, L. , Forcioli, D. , Lasota, R. , Furla, P. , & Wolowicz, M. (2019). Next‐generation phylogeography of the cockle Cerastoderma glaucum: Highly heterogeneous genetic differentiation in a lagoon species. Ecology and Evolution, 9(8), 4667–4682. 10.1002/ece3.5070 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Thompson, J. A. , Stow, A. J. , & Raftos, D. A. (2017). Lack of genetic introgression between wild and selectively bred Sydney rock oysters Saccostrea glomerata . Marine Ecology Progress Series, 570, 127–139. 10.3354/meps12109 [DOI] [Google Scholar]
  62. Toba, D. R. (1992). Guide to Manila clam culture in Washington. Washington Sea Grant Program, University of Washington. [Google Scholar]
  63. Waples, R. S. , Hindar, K. , Karlsson, S. , & Hard, J. J. (2016). Evaluating the Ryman‐Laikre effect for marine stock enhancement and aquaculture. Current Zoology, 62(6), 617–627. 10.1093/cz/zow060 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Weatherdon, L. V. , Ota, Y. , Jones, M. C. , Close, D. A. , & Cheung, W. W. L. (2016). Projected scenarios for coastal First Nations’ fisheries catch potential under climate change: Management challenges and opportunities. PLoS One, 11(1), e0145285. 10.1371/journal.pone.0145285 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Weir, B. S. , & Cockerham, C. C. (1984). Estimating F‐statistics for the analysis of population structure. Evolution, 38(6), 1358. 10.2307/2408641 [DOI] [PubMed] [Google Scholar]
  66. White, J. , Ruesink, J. L. , & Trimble, A. C. (2009). The nearly forgotten Oyster: Ostrea lurida Carpenter 1864 (Olympia Oyster) history and management in Washington State. Journal of Shellfish Research, 28(1), 43–49. 10.2983/035.028.0109 [DOI] [Google Scholar]
  67. Whitlock, M. C. , & Lotterhos, K. E. (2015). Reliable detection of loci responsible for local adaptation: inference of a null model through trimming the distribution of F(ST). The American Naturalist, 186(Suppl 1), S24–S36. 10.1086/682949 [DOI] [PubMed] [Google Scholar]
  68. Yang, J. , Benyamin, B. , McEvoy, B. P. , Gordon, S. , Henders, A. K. , Nyholt, D. R. , Madden, P. A. , Heath, A. C. , Martin, N. G. , Montgomery, G. W. , Goddard, M. E. , & Visscher, P. M. (2010). Common SNPs explain a large proportion of the heritability for human height. Nature Genetics, 42(7), 565–569. 10.1038/ng.608 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Zheng, X. , Levine, D. , Shen, J. , Gogarten, S. M. , Laurie, C. , & Weir, B. S. (2012). A high‐performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics, 28(24), 3326–3328. 10.1093/bioinformatics/bts606 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Dimond, J. L. (2021). Population genomics of the basket cockle Clinocardium nuttallii in the southern Salish Sea. NCBI BioProject PRJNA771709. https://www.ncbi.nlm.nih.gov/bioproject/PRJNA771709/ [DOI] [PMC free article] [PubMed]

Supplementary Materials

Fig S1‐S6

Click here for additional data file. (1.7MB, docx)

Data Availability Statement

Demultiplexed sequence reads are available under NCBI BioProject PRJNA771709 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA771709/) (Dimond, 2021).

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