1 INTRODUCTION

The conservation of freshwater mussels is essential to the health of freshwater aquatic ecosystem given their important ecosystem services, including biofiltration, nutrient cycling, and sediment formation (Elderkin et al., 2007; Vaughn et al., 2008). Freshwater mussels (Bivalvia: Unionidae) are one of the most imperiled groups of freshwater organisms (Ricciardi & Rasmussen, 1999), and like many species, they face tremendous declines due to global climate change and anthropogenic threats. Pollution, legacy contaminants, and invasive species have led to a dramatic loss in aquatic biodiversity (Haag & Williams, 2014). For freshwater mussels, the pearl and button industry heavily exploited populations in the early 1900s. Subsequent construction of dams led to the destruction of irreplaceable habitat and impeded dispersal of host fish for freshwater mussels, while increased agricultural land use and associated run-off into aquatic systems has also been detrimental (Haag, 2012). Lastly, in the early 1990s, Dreissena polymorpha and Dreissena rostriformis bugensis (Zebra and Quagga mussels) became established in the Great Lakes and can out-compete native freshwater mussels and even form large aggregates that suffocate native mussel species (Lucy et al., 2013). Current management guidelines for imperiled or endangered species strive to preserve unique or rare genetic variation (Fraser, 2008; Jones et al., 2006) and examining trends in the diversity and structure of imperiled freshwater species is crucial to management and recovery planning (FMCS, 2016; Petit et al., 1998).

Obovaria olivaria (common name: Hickorynut, Rafinesque, 1820) is a member of the freshwater bivalve family Unionidae and is widely distributed in central North America (COSEWIC, 2011). Obovaria olivaria is a species typically found in large rivers from the Mississippi River drainage system and the Great Lakes-St. Lawrence basin, extending south to Missouri, Arkansas, and Louisiana, east to Quebec, New York, and Pennsylvania, and west to Kansas (Parmalee & Bogan, 1998). While considered least concern by the IUCN, O. olivaria is considered imperiled (e.g., endangered, threatened, or special concern) across much of its distribution, especially in the Great Lakes region (COSEWIC, 2011; Natureserve, 2021), and is endangered in Canada due to declines in habitat, host fish declines, and the introduction of dreissenid mussels (COSEWIC, 2011).

Like most North American freshwater mussels, O. olivaria is dioecious (COSEWIC, 2011; Hoeh et al., 1995). A female mussel typically broods glochidia (a parasitic larvae) until a suitable host fish is near before discharging mature glochidia to successfully parasitize the host (Barnhart et al., 2008). Glochidia eventually metamorphose into juveniles and dislodge from the gills of the host fish to drop to the bottom of the riverbed (Oesch, 1995). Once mussels develop into free-living filter-feeding adults, their natural movement is severely limited and thus are virtually dependent on the brief period of attachment to the host fish or the supplemental stocking of adult mussels for long-distance dispersal (Haag & Warren, 2011). Transformation of O. olivaria glochidia has been documented on Acipenser fulvescens (common name: Lake Sturgeon, Rafinesque 1817), and O. olivaria in the Great Lakes and the St. Lawrence drainage are only known from areas where A. fulvescens are present (Brady et al., 2004; COSEWIC, 2011). Obovaria olivaria may also utilize Scaphirhynchus platorynchus (Shovelnose Sturgeon) in parts of its range (Coker et al., 1921), particularly in the Upper Mississippi River basin where A. fulvescens is rare and considered vulnerable (Knights et al., 2002; Natureserve, 2021) and S. platorynchus is more abundant and secure (Knights et al., 2002; Natureserve, 2021).

Understanding how genetic diversity is structured among O. olivaria populations across this large distribution will be an important component of future conservation and management plans (Fraser, 2008; Jones et al., 2006). As discussed above, the distribution of freshwater mussels is tied to the distribution and movement of their host fish species (Barnhart et al., 2008; Leibold et al., 2004; Newton et al., 2008; Schwalb et al., 2013; Zanatta & Murphy, 2006; Zanatta & Wilson, 2011). Sturgeons are capable of dispersing over 200 km during spawning periods (Auer, 1996; Wildhaber et al., 2011). Thus, the large distribution of O. olivaria may be driven by long-distance dispersal of its glochidia by sturgeon hosts, combined with stream capture events of waterways during the Pleistocene glaciation (Brady et al., 2004; Coker et al., 1921; Underhill, 1986). These factors may also contribute to a history of extensive gene flow within O. olivaria. However, given limited contemporary connectivity among drainages and the potential for increasingly limited dispersal of fish hosts discussed above, distinguishing whether populations exhibit substantial connectivity (e.g., isolation-by-distance) or more discrete genetic structure among rivers will be important. Beyond impacting gene flow and population genetic structure, recent and historical changes in connectivity among river drainages can leave signatures on levels of genetic diversity, and quantifying variation in diversity among less threatened populations (e.g., Mississippi River) and endangered populations (e.g., St. Lawrence River) could provide important information on how genetic diversity relates to conservation status.

The objectives of this study are to (1) examine the genetic diversity of O. olivaria across its range and identify populations or regions harboring relatively low genetic diversity and (2) examine the genetic structure of O. olivaria across its range to identify patterns of connectivity or presence of discrete population structure and dispersal barriers that can assist in focal management planning. We employ two complementary molecular approaches, mitochondrial DNA (mtDNA) sequencing and restriction site-associated DNA sequencing (RAD-seq), to examine the phylogeography, genetic structure, and genetic diversity of O. olivaria populations throughout its range. Mitochondrial DNA has been widely used in range-wide phylogeographic studies for decades (Avise et al., 1987; Beebee & Roe, 2008), including numerous analyses of freshwater mussels to determine population structure at broad geographic scales (Inoue & Berg, 2017; Inoue et al., 2014; Zanatta & Harris, 2013), but represents only a single genetic marker. RAD-seq is a powerful high-throughput sequencing approach that is increasingly used in population genetics analyses (Andrews et al., 2016). By providing large numbers of genome-wide single-nucleotide polymorphisms (SNPs), RAD-seq is of great value in population and conservation genetics because of the improved power to detect differentiation even in small or geographically restricted populations without requiring prior genomic resources (Andrews et al., 2016; Kang et al., 2017). Considering the imperiled status and rarity of O. olivaria in many parts of its distribution, RAD-seq offers a robust and innovative means to detect genomic differences among populations. Assessing both mitochondrial and genomic diversity in O. olivaria should provide insights into the population structure and diversity that will be valuable for management of this species.

2 MATERIALS AND METHODS

2.1 Sampling locations

Obovaria olivaria samples were collected from 19 sites across its distribution in the upper Mississippi River system, the Ozark highlands, tributaries of the Great Lakes, and the St. Lawrence River system. Sampling locations included the Upper Mississippi River drainage [Wisconsin River (WI), Mississippi River, St. Croix River (MN/WI), and Chippewa River (WI)], the Ohioan River drainage [Wabash River and White River (IN)], the Great Lakes drainage [Mississagi River (ON), Wolf River (WI) and Menominee River (WI/MI)] the St. Lawrence River drainage [St. Lawrence River (QC), St. François River (QC), Batiscan River (QC), L’Assomption River (QC), and Ottawa River (ON/QC)] (Figure 1; Tables 1 and 2). Existing mtDNA sequences from Inoue et al. (2013) were downloaded from GenBank and included in the analyses. Additionally, tissue samples from Inoue et al. (2013) from the White River in Arkansas (Mississippi Embayment) were provided by colleagues at Arkansas State University (J. Harris). For newly collected specimens, mantle tissue was nonlethally biopsied from each specimen (Berg et al., 1995), placed in 95% ethanol, and stored at −80°C.

TABLE 1. Collection sites, including abbreviations (code), major drainage basin, state/province, and the number of Obovaria olivaria samples sequenced for the mitochondrial gene COI, the number of haplotypes found at each collection location, number of unique haplotypes, the mean number of pairwise differences among haplotypes, and the mean nucleotide diversity (π)

Major River Drainage (Region)	Collection Site	Code	N sequenced @ COI	No. haplo.	No. unique haplo.	Mean No. of pairwise differences	π
St. Lawrence R.			48	4	1	0.7438	0.00144
	St. Lawrence R. (Grondines)	GRON-STL	9	2	0	1.0000	0.00194
	St. Lawrence R. (Domaine Joly)	DOJO-STL	9	2	0	0.4444	0.00086
	Rivière Saint Franςois	STFR-STL	8	1	0	0.000	0.000
	Batiscan River	BAT-STL	7	2	0	0.4762	0.00092
	Rivière L’Assomption	AS-STL	7	4	1	1.0476	0.00203
	Ottawa R. (Lac Coulonges)	LCOU-OTT	8	2	0	0.5000	0.00097
Great Lakes			18	4	1	1.0458	0.00203
	Mississagi R.	MISS-ON	5	2	1	0.6000	0.00116
	Menominee R.	MEN	4	1	0	0.000	0.000
	Wolf River	WOLF	9	2	0	0.2222	0.00043
Upper Mississippi R.			31	9	4	2.3226	0.00455
	Wisconsin R.	WIPS/WIOR	11	7	2	3.2364	0.00635
	St. Croix R.	STCR	6	1	0	0.000	0.000
	Chippewa R.	CHIP	5	3	0	3.0000	0.00588
	Mississippi R. Pool 15, 24, 25	MISS-P15/ MISS-P24/ MISS-P25	8	4	2	1.6071	0.00312
	Ohio Rivera	OHIOR	1	1	0	-	-
Mississippi Embayment			15	5	4	1.7524	0.00344
	White Rivera	WHIT-AR	12	4	2	1.4394	0.00282
	Black Rivera	BLACK-AR	3	2	1	3.3333	0.00646

Notes

• Values presented for each drainage (shaded rows) were calculated from pools of all samples for the drainage.
• ^a From Inoue et al. (2013), sites with sample, Ohio River excluded from per-population averages presented in Results text for small sample size.

TABLE 2. Obovaria olivaria collection sites, including abbreviations (code), major drainage basin, state/province, number genotyped, observed heterozygosity (H_o), expected heterozygosity (H_e), inbreeding coefficients (F_IS), and private alleles for the SNP dataset

Major River Drainage (Region)	Collection Site	Code	n	H o	H e	Private alleles
Northern populations
St. Lawrence R.	Rivière L’Assomption	AS-STL	5	0.189	0.171	0
	Batiscan R.	BAT-STL	3	0.192	0.164	0
	St. Lawrence R. (Domaine Joly)	DOJO-STL	6	0.199	0.181	0
	St. Lawrence R. (Grondines)	GRON-STL	10	0.195	0.193	0
	Rivière Saint Franςois	STFR-STL	5	0.188	0.167	0
	Ottawa R. (Lac Coulonges)	LCOU-OTT	5	0.195	0.169	0
	Ottawa R. (Lac Deschênes)	LDES-OTT	1	0.201	0.100	0
Great Lakes populations
Great Lakes	Wolf R.	WOLF	5	0.268	0.230	0
	Menominee R.	MEN	3	0.215	0.176	0
	Mississagi R.	MISS-ON	4	0.216	0.192	1
Southern populations
Upper Mississippi R.	Chippewa R.	CHIP	5	0.240	0.215	0
	St. Croix R.	STCR	5	0.235	0.209	0
	Mississippi R. Pool 15	MISS-P15	6	0.227	0.213	0
	Mississippi R. Pool 25	MISS-P25	1	0.243	0.122	0
	Wisconsin R. at Praire Du Sac	WPS	5	0.244	0.225	0
	Wisconsin R. at Orion	WIOR	5	0.246	0.226	0
Mississippi Embayment	White R.a	WHIT-AR	4	0.216	0.178	1
Ohioan	White R.	WHIT-IN	8	0.223	0.221	2
	Wabash R.	WABASH	7	0.219	0.211	0

Notes

• Although H_o was generally greater than H_E, no significant deviations from Hardy–Weinberg Equilibrium were detected (Genepop combined results across loci using Fisher's method, tests could not be performed for LDES-OTT and MISS-P25 for sample size). Northern, Great Lakes, and Southern population codes refer to the regions assigned for the DIYABC analyses.
• ^a Museum specimens from Inoue et al. (2013).

2.3 Bioinformatics and statistical analyses

2.3.1 Mitochondrial DNA barcoding dataset

The mtDNA sequences were proofread and aligned using BIOEDIT (Hall, 1999). Additional COI sequences from the White River drainage in the southern Ozark Highlands of Arkansas and the Ohio River were included in the analyses (Inoue et al., 2013; GenBank Accession Numbers: KF035244-KF035229). Metrics for genetic diversity (e.g., number of haplotypes, number of polymorphic sites, and nucleotide diversity – π) were calculated for each population sampled using ARLEQUIN v. 2.0 (Excoffier et al., 2005). Haplotype networks were created based on the number of nucleotide mutations between different haplotypes using POPART software and the TCS algorithm (Clement et al., 2000; Leigh & Bryant, 2015). To determine whether mussels were significantly differentiated within drainages and among sites, hierarchical analysis of molecular variance (AMOVA) was used to estimate haplotype partitioning within and among sampling locations (Excoffier et al., 1992) in Arlequin (Excoffier et al., 2005) with significance of variance components and F-statistics assessed using 1,000 permutations. Pairwise Φ_ST values were also calculated to determine pooled sampling location differentiation at the drainage level (Figure 2).

2.3.2 RAD-seq generated SNP dataset

The quality of Illumina reads was assessed using FASTQC (Andrews, 2010), and the data were cleaned, processed, and called using Stacks v2.53 (Catchen et al., 2013). To demultiplex and clean reads, we used process_radtags (parameters -c, -q, -r, --best-rad, others default). Because there is no reference genome for O. olivaria, de novo locus assembly and SNP calling were performed using denovo_map.pl, with removal of PCR duplicates (parameters: --rm-duplicates, --paired, --time-components, others default). We used vcftools (Danecek et al., 2011) to create a final dataset with SNPs present in ≥75% of individuals and present in all sampling locations. We required a minimum minor allele frequency of 0.02 to reduce the impact of low-frequency alleles and possible genotyping error (Rochette et al., 2019, but see Supplementary materials Appendix S1 and S2 for results with all SNPs) and thinned the dataset to retain a single SNP per RAD-tag locus to remove tightly linked sites. Individuals with >50% missing data were removed. Unless otherwise stated, all remaining analyses were conducted in R version 4.0.3 (R Core Team, 2018). The R package radiator v1.1.9 was used for import and data format conversion (Gosselin, 2017). To confirm that the removal of rare alleles did not impact the genetic structure observed in the dataset, a principal component analysis (PCA) was performed. First, the vcf file was converted to a genind object in adegenet v2.1.3, and then, the function tab was used to replace missing data with mean allele frequency (Jombart & Ahmed, 2011). The PCA was conducted in R package using ade4 v1.7.16 (Dray & Dufour, 2007) with the function dudi.pca (see Appendix S3).

To assess genetic diversity, we calculated observed and expected heterozygosity and the inbreeding index (H_o, H_e, F_IS, and private alleles) of SNPs for each sampling location using the radiator package's summary_rad and private_alleles functions (Gosselin, 2017). The radiator package's write_genepop function (Gosselin, 2017) was used to convert the vcf to genepop format (Raymond & Rousset, 1995; Rousset et al., 2020), and Hardy–Weinberg calculations were conducted using the R package genepop v1.1.7 with the function test_HW (Rousset et al., 2020). We tested for significant Hardy–Weinberg deviations for each locus within each population and across populations (combining results across populations using Fisher's Method) and overall for each population (combining results across SNPs using Fisher's Method). Genetic structure was assessed using several complementary methods. Individual-level genotype clustering was first determined using the snmf function in the R package LEA v3.2.0 (Frichot & François, 2015) with K (number of genetic populations) determined by the value producing the lowest cross-entropy (Frichot & François, 2015). Plots of snmf qmatrix results were made with the R package ggplot2 v3.3.0 (Frichot & François, 2015; Villanueva & Chen, 2019). We also performed a discriminant analysis of principal components (DAPC) using adegenet v2.1.3 (Jombart & Ahmed, 2011), with the optimal number of clusters (K) determined by the value producing the lowest Bayesian information criterion from k-means clustering (find.clusters function). The DAPC scatterplot was created with ggplot2 and ggforce v0.3.1 (Pederson, 2019; Villanueva & Chen, 2019). Pairwise measures of genetic differentiation (F_ST) were computed in the R package StAMPP v1.6.1 (Pembleton et al., 2013), with significance determined using 1000 bootstrap replicates. We used AMOVA in Arlequin v3.5 as above (Excoffier et al., 2005) to estimate genotype partitioning between and among sampling locations and genetic clusters determined by the DAPC in adegenet (Jombart & Ahmed, 2011).

To assess the location of potential geographic barriers to gene flow exist with the SNP dataset, Monmonier's algorithm was used to find the boundaries of maximum differences between contiguous polygons in a tessellation and to detect geographic locations of barriers to gene flow among genotypes (Monmonier, 1973). The Monmonier's algorithm was executed in R package adegenet using the monmonier function with pairwise F_ST and site coordinates (Jombart & Ahmed, 2011; Manni et al., 2004; Monmonier, 1973).

To test for isolation-by-distance, we determined geographic river distances between sites in R package riverdist v0.15.3. Prior to riverdist calculations, shapefiles from DivaGIS v7.5 (Villordon, 2019) for North American and Canadian rivers were cropped and merged in ArcMap v10.8 to only include relevant segments in the shapefile. The outline of the Great Lakes was used to create a line segment around the Great Lakes for distance calculations (ESRI, 2018; Villordon, 2019). The function cleanup in riverdist (Tyers, 2017) was used to measure straight-line distances (snapping to closest point in riverdist) to account for lakes and short overland distances between drainages (i.e., Wisconsin River and Fox/Wolf River into the Great Lakes and the Lake Nipissing drainage in the Great Lakes and Ottawa River/St. Lawrence River drainages). After assigning the Mississippi (above STCR site) as the river mouth in riverdist, the detectroute function was used to measure the distances between points on the river network segment (Tyers, 2017). Mantel tests for isolation-by-distance were performed in R package adegenet with the function mantel.randtest with 999 permutations using pairwise F_ST and pairwise river distances (Jombart & Ahmed, 2011). We excluded sites with only one sampled individual (MISS-P25, LDES-OTT) and one site (DOJO-STL) because of channel braiding, which can produce incorrect distance calculations (Tyers, 2017).

Finally, we were interested in using the SNP data to test various demographic scenarios that might explain genetic diversity and structure among the major regional populations. We were interested in varying patterns of divergence that reflect postglacial expansion from likely glacial refugia from southern into northern populations. Frequently, freshwater mussels exhibit a stepping-stone model of postglacial colonization, entering the Great Lakes from the Mississippi or Wabash refugia and then expanding northward (Beaver et al., 2019; Elderkin et al., 2007, 2008; Mathias et al., 2018). For model testing, we conducted approximate Bayesian computation with random forests using DIYABC RF v1.0 and the R package diyabcGUI v 1.0.14 (Collin et al., 2021). We grouped mussels into three regional population clusters identified by DAPC: (1) Southern populations including the Upper Mississippi River basin and Mississippi Embayment, (2) Great Lakes basin populations, (3) Northern populations in St. Lawrence River basin (see Table 2 for population assignments to regions). We first used DIYABC with the mitochondrial dataset to determine reasonable upper bounds on prior distributions for modeling with the SNP dataset. Default mutation model parameters were used to model COI sequence data, and maximum effective population sizes and generation times were varied to determine the best fit for the SNP dataset. We ran the COI testing models with 140,000 iterations and validated models with 500 random forests. Based on the effective population size estimates from mtDNA sequence data, in the SNP data, we set uniform prior distributions for effective population size (henceforth, N_e) and time to be a maximum of 2.5 million individuals for all regional populations and set a maximum divergence time of 50,000 generations. We also examined models constraining N_e to 1/10^th this value (250k) to examine sensitivity to this prior. We tested seven potential scenarios of postglacial colonization (Appendix S4), including a null model and variations of a bottleneck pre- and postdivergence for our populations. (1) First, we chose a null model with a single divergence event for the Southern, Great Lakes, and Northern populations from a single common ancestor at the same time point. (2) Next, we simulated a model with divergence between Southern and Northern populations with a single admixture event forming the Great Lakes (prior on admixture proportion: admixture rate of 0.05–0.95 with a uniform distribution, default setting). Three related models were intended to model stepping-stone colonization of postglacial colonization using multiple divergence times: (3) Southern populations splitting to form the Great Lakes and then a subsequent Great Lakes split leading to the formation of the Northern populations, (3a) one of our stepping-stone models had with constant population sizes throughout, (3b) one with a variation in population sizes at the Great Lakes split only to simulate a potential bottleneck, (3c) one with variation in population sizes at both the Great Lakes split and Northern split to simulate two potential bottlenecks. (4) A fourth model simulated a divergence between the Southern populations and the Great Lakes with admixture (prior of 0.05–0.95 with a uniform distribution) following with the formation of the Northern populations from the Great Lakes ancestor. (5) Lastly, we simulated a model where the Northern populations and Southern populations split from a common ancestor and then Northern populations split to form the Great Lakes. ABC modeling was conducted from command line on an HPC cluster with 140,000 (20,000 simulations per model for model choice) simulations. A random forest estimation was then used to determine the best model fit, and parameter estimates from the best fit model were determined from 120,000 simulations using random forest with 500 trees for each parameter, prediction of models and parameter estimates was with 500 trees for each parameter estimated.

3 RESULTS

3.2 SNP dataset

Illumina sequencing of the Obovaria olivaria BestRAD libraries yielded an average of 2,041,264 (1,596,292 SD) paired-end reads per sample. The final filtered dataset included 1,237 polymorphic SNPs present in the 93 individuals from 19 collection locations (Table 2), with an average SNP depth of 19.15 (Appendix S5), <30% missing data and a minor allele frequency >0.02.

There were clear spatial patterns in diversity in the population genomics dataset that were consistent with COI sequences, with northern and eastern populations having reduced heterozygosity compared to populations from south of the Great Lakes, with diversity especially low in the St. Lawrence River and highest in the Wolf and Mississippi River sites (Table 2; Appendix S6). Observed heterozygosity was higher than expected heterozygosity across all collection locations (Table 2), but no significant deviations from Hardy–Weinberg equilibrium were detected in any population (Table 2). Four private alleles were found in the SNP dataset, and all were found south of the St. Lawrence (Table 2).

The DAPC and snmf analyses of the SNP dataset both revealed two major clusters, but there was clear admixture at spatially intermediate sites (Figure 3; Appendix S7). For the DAPC, K = 2 was identified as the optimal number of ancestral populations, with St. Lawrence and Ontario River populations loading negatively on axis 1 and the Upper Mississippi, Mississippi Embayment, and Ohioan River populations loading positively, and with the Great Lakes populations intermediate between the two groups; axis 2 discriminated among sites within the Upper Mississippi, White, and Ohioan Rivers. For snmf, K = 2 had the lowest cross-entropy, indicating two ancestral population groupings: (1) all the individuals from the St. Lawrence River drainage, (2) all individuals from the Mississippi watershed, Great Lakes watershed, the White River, Wabash, and White River in Illinois, but as above the Great Lakes mussels were intermediate (Figure 4). We repeated the snmf analysis for K = 3, which partly separated sites from the Great Lakes region; however, admixture among ancestral populations was still apparent. AMOVA showed significant genetic differentiation among the two regional clusters (F_CT = 0.11, p < .001) (Table 4), as well as among sites within clusters (F_SC = 0.05, p < .001) and among sites overall (F_ST = 0.16, p < .001).

TABLE 4. Analysis of molecular variance (AMOVA) for Obovaria olivaria using SNP data completed in Arlequin with 1000 permutations

Source of variation	Sum of squares deviation	Percentage of variation	F-statistics	p
Among clusters	1589.99	10.9%	F CT	.11***
Among populations within clusters	3459.01	4.8%	F SC	.05***
Within populations	19703.10	84.3%	F ST	.16***
Total	24752.09

Notes

• The two clusters are (1) St. Lawrence and Ottawa River sites; (2) Great Lakes, Upper Mississippi R., White R. (Mississippi Embayment), and Ohioan drainage collection locations.
• *** p < .001.

When differentiation among pairs of populations was examined, there was significant low to moderate genetic differentiation (F_ST) among most collection sites (Appendix S8), with values ranging from F_ST = 0.00–0.20. Monmonier's algorithm detected a barrier to gene flow between the St. Lawrence/Ottawa collection locations and the remaining collection locations at Lake Erie (42.0894, −81.70649), consistent with the clusters identified by DAPC. However, also consistent with the DAPC and snmf clustering that suggested admixture among ancestral populations via intermediate sites, Mantel tests showed a strong isolation-by-distance relationship between pairwise F_ST and river distances at the range-wide scale (Mantel r = 0.85; p < 0.001; Figure 5).

The DIYABC scenario choice determined that the null model (simultaneous divergence of three regional populations) was the best fit model for the SNP and mitochondrial datasets, suggesting limited power in this system to resolve any directional colonization history. However, DIYABC models did consistently show that relative N_e was sharply reduced in the Northern populations of Obovaria olivaria (Table 5; Appendix S9). DIYABC models were sensitive to prior parameter settings and although Northern N_e was always well-estimated, N_e from Southern and Great Lakes populations had broad posterior distributions (Appendix S9). We thus urge caution when interpreting results as real-world values but suggest the variation in N_e from population to population is more indicative of general trends in relative effective size (Table 5; Appendix S9). The median generation time since population split was 26,498 generations in the upper bound model (although as for N_e the posterior was broad), but similar to geologic estimates of the Great Lakes formation of ~14,000 YBP (Larsen, 1985) in the lower bound model (13,166 generations). Overall, we were unable to precisely estimate parameters in this dataset with DIYABC, but results are consistent with summary statistics presented above that suggest substantially reduced genetic diversity in O. olivaria from the northern St. Lawrence River populations.

TABLE 5. Prior and posterior distributions for effective population sizes (Ne) and generation time (t) for Obovaria olivaria genetic clusters inferred from the DAPC analysis

Parameter	Population	Prior distributions				Posterior parameter estimates
		Minimum	Maximum	Distribution	Median	95% credible interval
Ne	Southern	10	250,000	Uniform	175,101	65,200	247,255
Ne	Great Lakes	10	250,000	Uniform	112,264	21,129	236,280
Ne	Northern	10	250,000	Uniform	9,863	1,507	45,150
t1		10	50,000	Uniform	13,166	3,163	36,452
Ne	Southern	10	2,500,000	Uniform	1.80 × 106	643,676	2.45 × 106
Ne	Great Lakes	10	2,500,000	Uniform	1.10 × 106	228,206	2.32 × 106
Ne	Northern	10	2,500,000	Uniform	63,420	19,221	220,130
t1		10	50,000	Uniform	26,498	7,717	46,939

Notes

• Two sets of upper prior bounds on N_e are presented, indicating the sensitivity of absolute posterior distributions to selection of priors, while relative patterns are similar. The Southern populations comprise the Upper Mississippi River, the Ohioan and the Mississippi Embayment sites, the Great Lakes populations comprise the Great Lakes drainage sites, and the Northern populations comprise the St. Lawrence River drainage sites.

4 DISCUSSION

Our combined analysis of mitochondrial and RAD-tag sequences in O. olivaria provides evidence for ongoing or recent connectivity driven by spatial separation within and between regions, which also exhibit differences in genetic diversity. The patterns of range-wide isolation-by-distance presented here imply that O. olivaria dispersal is distance-limited, but such limitations occur at large scales and suggest the potential for long-distance movement on host fish. We also find that O. olivaria exhibits notably reduced genetic diversity in northern sites (the St. Lawrence and Ottawa Rivers), which is especially concerning given the endangered status of these populations.

In the mitochondrial DNA dataset, diversity declined from south to north, with reduced haplotype and nucleotide diversity in the Great Lakes and St. Lawrence drainages and the greatest number of haplotypes and unique haplotypes is found in the Mississippi River drainage. The White (AR) and Black River individuals had 4 unique haplotypes out of 5 haplotypes, which is consistent with other phylogeographic studies on freshwater taxa from this region that display similar endemism (Crandall, 1998; Mayden, 1985; Vaughn et al., 1996; Zanatta & Murphy, 2008). Despite the presence of some unique haplotypes in the southern populations and significant regional Φ_ST values, the COI data indicate no extensive phylogeographic barriers across the O. olivaria range.

Results from the SNP data largely parallel those from the mitochondrial sequences. Heterozygosity was greatest in the Mississippi and Great Lakes drainage sites and declined toward the St. Lawrence collecting sites (Table 2). As anticipated, the O. olivaria SNP dataset revealed more fine-scale genetic structure than analyses of the mtDNA dataset, indicating the presence of two major population clusters, one predominantly in the St. Lawrence River drainage and one in the Upper Mississippi, Mississippi Embayment, and Ohioan, and some weaker separation of mussels in the Great Lakes drainages. Weak structure was seen between the Mississippi and Great Lakes populations, which historically were not connected for decades due to changes in the configuration of the Great Lakes after the Pleistocene and were then subsequently artificially connected by canals. Although the Great Lakes sampling locations appear to have some unique genetic variation, with one private allele found in the Mississagi River location and high genetic diversity at the Wolf River site (Table 2; Figure 4), these populations are for the most part genetically intermediate between the more southern and northern localities and provide evidence of historical connectivity across the species range.

Patterns of genetic structure in freshwater mussel species often reflect changes in the patterns of hydrologic connections in the Great Lakes at the end of the last glacial period (Elderkin et al., 2007, 2008; Mathias et al., 2018). For example, at the end of the Pleistocene glaciation (~6000 to ~4500 YBP), the Great Lakes and the St. Lawrence River drainages remained connected near North Bay, Ontario (Larsen, 1985; Teller, 1985), but these connections were later severed (~4500 YBP) (Larsen, 1985). This change in the configuration of the Great Lakes drainage left only one remaining outlet to the St. Lawrence River drainage, via Lake Erie and Lake Ontario. A disruption of gene flow in the Great Lakes would be consistent with the location of the genetic barrier at Lake Erie inferred by the Monmonier's algorithm in O. olivaria. Further, the declining genetic diversity from south to north supports the hypothesis that the Mississippi River drainage may have served as a glacial refuge for O. olivaria populations. We were thus interested in testing whether patterns of structure and declining south-to-north diversity are related to a stepping-stone model of postglacial colonization (Hewitt et al., 2018; Kimura & Weiss, 1964; Mathias et al., 2018; Zanatta & Harris, 2013). While a more complex demographic history for O. olivaria than one of ongoing distance limited dispersal among contemporary populations is likely, DIYABC was unable to distinguish scenarios of postglacial colonization. This is perhaps due to limitations in software's ability to simulate ongoing migration or complex population structure within our broad regional populations, especially in the south (Collin et al., 2021). Our data are thus most consistent with a model of population structure involving contemporary regional populations undergoing regular distance-limited genetic exchange, which has likely complicated historical phylogeographic signatures. Given this inability to clearly distinguish among models and the observed sensitivity of parameters to N_e priors, we urge caution in interpreting DIYABC results as “real-world” estimates. However, models consistently support the conclusion that the St. Lawrence and Ottawa Rivers harbor substantially smaller population sizes than other remaining regions sampled for this study. These Canadian O. olivaria populations are endangered (COSEWIC, 2011), and density in the Ottawa River is relatively low compared to other unionid species in the area (Martel et al., 2011). In contrast, N_e estimates for the Southern (Upper Mississippi, Mississippi Embayment, and Ohio Rivers) and Great Lakes regions were high (>1 million individuals in our upper bound model), suggesting that these populations have retained high levels of ancestral genetic diversity and may not have suffered the genetic impacts of any population declines.

Genetic differentiation and diversity of O. olivaria largely mirrors that seen in confirmed and potential host fish with analogous distributions. Mitochondrial haplotypes of O. olivaria host fish A. fulvescens reveal patterns of limited genetic structure (DeHaan et al., 2006; Ferguson & Duckworth, 1997). Mitochondrial (DeHaan et al., 2006; Ferguson & Duckworth, 1997) and microsatellite data from A. fulvescens (Welsh et al., 2008) show a pattern of genetic structure consistent with our O. olivaria SNP dataset. Welsh et al. (2008) also found F_ST for A. fulvescens among the Great Lakes drainage sampling locations (including samples from the Wolf, Menominee, and Mississagi rivers) was low, but significant (0.02–0.05, p < .002), and F_ST among St. Lawrence River sampling locations was also low, but significant (0.03–0.04, p < .002). The lack of discrete genetic structure in O. olivaria is likely linked to A. fulvescens and other host fish and therefore prioritizing management of habitats where both species coexist will likely ensure greater conservation success.

CONFLICT OF INTEREST

None declared.

AUTHOR CONTRIBUTION

Jamie R. Bucholz: Conceptualization (supporting); Data curation (lead); Formal analysis (lead); Investigation (lead); Methodology (lead); Validation (lead); Visualization (lead); Writing – original draft (lead); Writing – review & editing (lead). Nicholas M. Sard: Data curation (supporting); Methodology (equal); Resources (equal); Supervision (supporting); Writing – original draft (supporting); Writing – review & editing (supporting). Nichelle M. VanTassel: Data curation (supporting); Writing – review & editing (supporting). Jeffrey D. Lozier: Resources (equal); Software (equal); Supervision (supporting); Validation (supporting); Visualization (supporting); Writing – original draft (supporting); Writing – review & editing (supporting). Todd J. Morris: Conceptualization (supporting); Data curation (supporting); Funding acquisition (supporting); Project administration (supporting); Writing – review & editing (supporting). Annie Paquet: Conceptualization (supporting); Data curation (supporting); Funding acquisition (supporting); Investigation (supporting); Project administration (supporting). David T. Zanatta: Conceptualization (lead); Data curation (equal); Formal analysis (equal); Funding acquisition (lead); Investigation (equal); Methodology (equal); Resources (lead); Software (equal); Supervision (lead); Validation (lead); Visualization (lead); Writing – original draft (equal); Writing – review & editing (equal).