Fisheries Research 206 (2018) 247–258

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Fisheries Research

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Comprehensive evaluation of genetic population structure for anadromous T river with single nucleotide polymorphism data

Kerry Reida,b, Eric P. Palkovacsa, Daniel J. Hasselmana,1, Diana Baetscherb,c, Jared Kibeled, ⁎ Ben Gahagane, Paul Bentzenf, Meghan C. McBridef, John Carlos Garzab,c, a Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA 95060, USA b Fisheries Ecology Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Santa Cruz, CA 95060, USA c Department of Ocean Sciences, University of California, Santa Cruz, CA 95064, USA d National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara, CA 93101, USA e Division of Marine Fisheries, Gloucester, MA 01930, USA f Marine Gene Probe Laboratory, Biology Department, Dalhousie University, Halifax, NS B3H 4R2, Canada

ARTICLE INFO ABSTRACT

Handled by J Viñas Anthropogenic activities are placing increasing pressure on many species, particularly those that rely on more Keywords: than one ecosystem. River herring (, pseudoharengus and blueback herring, A. aestivalis collectively) Alosa are anadromous fishes that reproduce in rivers and streams of eastern North America and migrate to the western Alewife Atlantic Ocean. Here, we use data from single nucleotide polymorphisms (SNPs) to provide a comprehensive Blueback herring analysis of population structure for both species of river herring throughout their native ranges. We sampled Mixed stock analysis river herring spawning runs in rivers from Newfoundland to , examining a total of 108 locations, and Population genetic structure genotyping over 8000 fish. We identified geographic population groupings (regional genetic groups) in each Single nucleotide polymorphisms species, as well as significant genetic differentiation between most populations and rivers. Strong correlations between geographic and genetic distances (i.e., isolation by distance) were found range-wide for both species, although the patterns were less consistent at smaller spatial scales. River herring are caught as bycatch in fisheries and estimating stock proportions in mixed fishery samples is important for management. We assessed the utility of the SNP datasets as reference baselines for genetic stock identification. Results indicated high accuracy of individual assignment (76–95%) to designated regional genetic groups, and some individual po- pulations, as well as highly accurate estimates of mixing proportions for both species. This study is the first to evaluate genetic structure across the entire geographic range of these species and provides an important foun- dation for conservation and management planning. The SNP reference datasets will facilitate continued multi- lateral monitoring of bycatch, as well as ecological investigation to provide information about ocean dispersal patterns of these species.

1. Introduction aggregations, can be identified to their demographic and genetic unit of origin (Milner et al., 1981; Rannala and Mountain, 1997; Anderson Natural populations that are affected by anthropogenic activities et al., 2008). This is particularly relevant for anadromous fishes, as they require monitoring and management to avoid demographic and other spawn in freshwater, migrate long distances from their natal rivers and risks. Genetic data allow accurate evaluation of population structure streams to the ocean and then return, and are often encountered in and patterns of migration, which is critical for the identification of mixed stock aggregations while at sea. Genetic data from reference demographic independence and appropriate management units “baseline” databases of established population units can allow for the (Palsbøll et al., 2007). When extensive population structure exists, it determination of which stocks are present in a mixed sample and in can be used with genetic stock identification (GSI) techniques so that what proportions (Milner et al., 1981; Seeb et al., 2007; Clemento et al., individuals sampled away from their natal areas, or in mixed 2014).

⁎ Corresponding author at: Fisheries Ecology Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 110 McAllister Way, Santa Cruz, CA 95060, USA. E-mail address: [email protected] (J.C. Garza). 1 Present address: Columbia River Inter-Tribal Fish Commission, Hagerman, ID 83332, USA. https://doi.org/10.1016/j.fishres.2018.04.014 Received 8 December 2017; Received in revised form 12 April 2018; Accepted 19 April 2018 0165-7836/ Published by Elsevier B.V. K. Reid et al. Fisheries Research 206 (2018) 247–258

Fig. 1. Maps of sampling locations from coastal rivers for river herring (A) sampling sites for alewife (B) blueback herring. Sampling location codes correspond to Table 1.

Population structure for anadromous fishes is typically understood populations spawning in the same rivers. River herring are of sig- by sampling populations in freshwater spawning habitat. Populations of nificant ecological and conservation concern due to declining popula- anadromous fishes often show signals of hierarchical structure and tions and the effects of habitat loss, pollution and harvest (Limburg and patterns of isolation by distance, due to high rates of homing to natal Waldman, 2009; Atlantic States Marine Fisheries Commission [ASMFC] rivers, with migration usually to proximate river basins (Garza et al., 2012; Palkovacs et al., 2014; McBride et al., 2015; Hasselman et al., 2014, Ozerov et al., 2017). This restricted gene flow among river ba- 2016). sins, and among tributaries within larger river systems, leads to popu- Previous population genetic studies of river herring have provided lation structure even when individuals move thousands of kilometers important insights into the species biology, conservation, and man- over their lifetimes. The resulting population structure allows in- agement (McBride et al., 2014; Palkovacs et al., 2014; Turner et al., dividuals sampled in the ocean to be assigned back to rivers and re- 2015, Hasselman et al., 2014; Hasselman et al., 2016; Ogburn et al., gional stocks of origin using GSI techniques (Anderson et al., 2008; 2017). Palkovacs et al. (2014), examining populations from rivers south Seeb et al., 2007; Clemento et al., 2014). Such information can provide of the US-Canada border, identi fied three regional genetic units of insight about differential exploitation of populations or regional de- alewife and four genetic units of blueback herring. McBride et al. mographic units and patterns of marine migration and distribution in (2014), examining populations from Canadian rivers, detected weak space and time (e.g., Larson et al., 2012; Bradbury et al., 2016; differentiation among populations of alewife. These studies provided Anderson et al., 2017). important information to facilitate the conservation and management River herring is the collective name given to alewife (Alosa pseu- of river herring, but derived their genetic data from microsatellite doharengus) and blueback herring (A. aestivalis). These anadromous markers. Despite their high variability and extensive use in the study of sister species, native to eastern North America and the northwestern fish and wildlife over the last several decades, microsatellites have Atlantic Ocean, have similar life-history characteristics, including important limitations when applied to fisheries management. Primary spawning in freshwater during spring and spending two to five years in among them are a lack of portability across laboratories and instru- the marine environment, where they undertake migrations along the ments, which prevents the integration of datasets without extensive continental shelf, following food resources and schooling with other standardization efforts (Seeb et al., 2007, Clemento et al., 2011, Seeb species such as and Atlantic mackerel (Turner et al., et al., 2011). To overcome this limitation, single nucleotide poly- 2016, 2017), and then return to their natal rivers to spawn (Scott and morphism (SNP) genetic markers have been developed to assess po- Crossman, 1973). Hasselman et al. (2014) and McBride et al. (2014) pulation structure and employ GSI techniques for the study of ana- documented hybridization between alewife and blueback herring dromous fishes and other migratory species. SNP markers can be

248 K. Reid et al. Fisheries Research 206 (2018) 247–258 consistently genotyped across laboratories and instruments, making 2.3.2. Genetic diversity and differentiation them robust tools for GSI analyses. Observed and expected heterozygosity and percent polymorphic Here, we use recently developed panels of SNP markers for alewife loci were calculated for each collection using Microsatellite Toolkit v. and blueback herring (Baetscher et al., 2017) to provide a compre- 3.1 (Park, 2001). Allelic heterogeneity among rivers was assessed with hensive evaluation of population structure for these species. We build genic tests in GENEPOP using default parameters. Tests were combined on previous work and increase the geographic range and number of across loci using Fisher’s method. Due to small sample sizes, the Con- rivers sampled for each species to include nearly all of the freshwater necticut EMR 2011 collection was combined with River spawning ranges for both species. We then used simulations to assess collections from the same year and James River collections (Herring, power for stock identification at multiple hierarchal levels and to de- Walker and Chickahominy) were combined by year (2011 and 2015) monstrate the utility of these SNP markers to identify demographically for summary statistic estimates in blueback herring. Overall and pair- connected groups of populations for the purposes of management and wise FST values (θ; Weir and Cockerham, 1984) were estimated using conservation. Fstat v. 2.3.9.2 (Goudet, 2001). To account for variation in genetic diversity among populations, standardized estimates of differentiation 2. Materials and methods (F'ST) were calculated as F'ST = FST/FST(max) (Hedrick, 2005) using RECODEDATA v. 0.1 (Meirmans, 2006). FSTAT was used to estimate 2.1. Sampling FST(max) for each pairwise comparison.

Tissue samples from anadromous alewife and blueback herring were 2.3.3. Population genetic structure obtained from across the species’ geographic ranges (i.e., Genetic relationships among populations were examined with un- Newfoundland to Florida; Fig. 1). Specimens were identified to species rooted neighbor-joining (NJ) dendrograms using the DA distance (Nei based on a combination of external morphological characteristics and et al., 1983). Bootstrapping over loci (10,000 replicates) was performed peritoneal color (Jordan and Evermann, 1896; Scott and Crossman, using POPTREEW (Takezaki et al., 2014) to examine stability of re- 1973). Muscle or fin tissue samples were obtained from adult and ju- lationships. The Bayesian model-based clustering method implemented venile fish captured in spawning rivers above the influx of salt water. in STRUCTURE v. 2.3.4 (Pritchard et al., 2000; Falush et al., 2003) was Temporal replicates for a subset of locations were collected to evaluate used to infer the number of genetic clusters represented by the collec- temporal genetic variation. The alewife samples included a total of 137 tions. A burn-in of 50,000 replicates, followed by 150,000 replicates of collections (n = 6783) and the blueback herring samples were from 54 the MCMC simulation, was performed employing the admixture model collections (n = 2502). Tissue samples were either preserved in 95% and correlated allele frequencies among populations, with no geo- ® ethanol or stored dry on Whatman blotting paper in coin envelopes graphic prior. Ten iterations were performed for each value of K – until DNA extraction. (number of clusters) = 2 10, allowing an estimation of the most likely number of clusters by consistency of patterns indicating “geographic barriers” (Rosenberg et al., 2005). The results were visualized using 2.2. Laboratory protocols CLUMPP v. 1.1.2 (Jakobsson and Rosenberg, 2007) and DISTRUCT v. 1.1 (Rosenberg, 2004). Genomic DNA was extracted with DNeasy 96 Tissue kits using a To further characterize population structure, Discriminate Analysis BioRobot 3000 (Qiagen, Inc.). Samples were genotyped using species- of Principal Components (DAPC; Jombart et al., 2010) was performed fi speci c SNP assays described by Baetscher et al. (2017), and included with the package ‘adegenet’ v. 2.0.1 (Jombart, 2008) in R 3.3.1 (R Core 93 SNPs for alewife (Suppl. Table 1A) and 96 SNPs for blueback herring Team, 2016). The variance of each dataset was broken into principal (Suppl. Table 1B). Genotyping was done with 96.96 Dynamic SNP components and the most likely number of biologically meaningful Genotyping Arrays on an EP1 system using SNP Type assays (Fluidigm clusters described by the data was determined using the change in ’ fi Corporation) according to manufacturer s speci cations. SNP genotypes Bayesian Information Criterion (BIC), as recommended by Jombart were called using the Fluidigm Genotyping Analysis Software v. 2.1.1. et al. (2010). These clusters were used to inform the DAPC analysis. Previous studies of river herring revealed low rates of species mis- Finally, pairwise genetic differentiation (F ) between identified fi fi ST identi cation in the eld, and low frequencies of hybridization between regional genetic groups for both species was calculated using Weir and anadromous alewife and blueback herring (McBride et al., 2015; Cockerham’s (1984) estimator implemented in GENETIX (Belkhir et al., fi Hasselman et al., 2016). We screened for misidenti ed individuals and 1996–2004), with 10,000 permutations to quantify the amount of di- “ ” “ ” hybrid specimens by genotyping known pure and hybrid specimens vergence between groups. with both of the species-specific SNP panels. Misidentified specimens fi and potential hybrid individuals were identi ed by both stock assign- 2.3.4. Isolation by distance ment and by extremely low heterozygosity at the SNP loci, as very few Tests for potential correlations between geographic distance and loci are reciprocally polymorphic in both species, with polymorphism genetic differentiation, so-called isolation by distance (IBD), were generally restricted to a single species (Clemento et al., 2014). In- conducted across the entire range of each species using a Mantel test dividuals missing data from more than 10% of the genotyped loci, with 10,000 permutations (Pearson’s correlation) in the R-package fi misidenti ed individuals, and potential hybrids were excluded from ‘vegan’ (Oksanen et al., 2007, 2013). Differentiation was evaluated with further analyses. both pairwise F'ST values and their linearized counterparts (F'ST/1-F'ST) following Rousset (1997). Geographic distances between sampling lo- 2.3. Data analyses cations were estimated using a swimmable distance method (i.e., the shortest distance through rivers and ocean without crossing land). 2.3.1. Data conformance to model assumptions and temporal replicates These distances were estimated for each pair of sampling locations Tests for linkage disequilibrium (LD) and departures from Hardy- using a novel network theory approach and implemented using the Weinberg equilibrium (HWE) expectations were performed with NetworkX open source Python library (Hagberg et al., 2008) which is GENEPOP v. 4.2 (Rousset, 2008) using default parameters for all tests. available from an open source license code repository (https://github. Sequential Bonferroni adjustments were used to determine significance com/jkibele/pyriv). levels (Holm, 1979; Rice, 1989). Genic differentiation was assessed First, a network of potential ocean paths was created from a sim- among temporal collections to assess the stability of populations plified coastline polygon of the study area. All coastline polygon ver- through time. tices were represented as network nodes and every possible line

249 K. Reid et al. Fisheries Research 206 (2018) 247–258 between two nodes was considered as a potential edge. Only those lines from 0.214 (Otter Pond, Newfoundland) to 0.281 (Saint John River, that did not cross the land polygon were incorporated as network edges. ). The range was wider for blueback herring collections Next, hydrography network shapefiles were obtained (United States (Table 1B), and varied from 0.246 (St. John’s River, Florida) to 0.362 Forest Service; Nagel et al., 2017) and amended where necessary to (James River, ). The proportion of polymorphic loci for alewife intersect the coastline network. In cases where river features were not ranged from 0.64 (Otter Pond, Newfoundland) to 1.0 (, found within four km of site locations, a straight line to the coast was ) (Table 1A), and for blueback herring from 0.86 (St. John’s added to the network if that location was within four km of the coast. River, Florida) to 1.0 (Connecticut River) (Table 1B). The alewife col- Where site locations were more than four km from a river feature and lection from Otter Pond also had the lowest number of alleles per locus more than four km from the coast, rivers were drawn manually with (1.64) of any anadromous alewife or blueback herring collection reference to OpenStreetMap raster basemaps (http://www. (Table 1A). openstreetmap.org/copyright). The completed river network was then For alewife, 87.8% (8299/9453) of pairwise tests of allelic hetero- combined with the ocean path network. Geographic paths were calcu- geneity were significant (Suppl. Table 2A) and standardized pairwise lated between each pair of sites using the NetworkX implementation of estimates of genetic differentiation (F'ST) for alewife ranged from Dijkstra’s shortest path algorithm (Dijkstra, 1959) weighted by distance −0.026 to 0.311 (FST = −0.005–0.188) (Suppl. Table 3A). Non-sig- and saved to shapefile format. Distances were then derived from these nificant differentiation was primarily among temporal replicates and line features. between neighboring drainages, but there were also a few instances between more distant locations. For example, lack of differentiation 2.3.5. GSI baseline simulations was observed between several collections within the Gulf of St. The utility of the SNP panels to assign individuals back to collection Lawrence, among numerous collections in northern New England, and and regional genetic unit of origin was evaluated using a self-assign- between collections from Chesapeake Bay to the Albemarle Sound ment analysis (Rannala and Mountain, 1997) implemented in the mixed (Suppl. Tables 2A, 3A). stock analysis program GSI_SIM (Anderson et al., 2008). A simulation For blueback herring, 90.1% (1241/1378) of pairwise tests were approach implemented in GSI_SIM was then used to evaluate mixing significant (Suppl. Table 2B) and F'ST for blueback herring ranged from proportion estimates for regional genetic units following the approach −0.005 to 0.397 (FST = −0.004–0.212) (Suppl. Table 3B). Non-sig- previously described by Hasselman et al. (2016), with regional genetic nificant differentiation was found primarily among temporal replicates unit designations based on the observed patterns of genetic population and between neighboring drainages, including most comparisons from structure for each species. This involved simulations of 50 mixtures within Chesapeake Bay, and among collections from Albemarle Sound with different proportions of the constituent stocks, using Dirichlet (Suppl. Tables 2B, 3B). distributions = 1.5. From each of these 50 mixtures, four samples of 1000 fish each were taken and mixing proportions then estimated using 3.3. Population genetic structure maximum likelihood. A correlation was then assessed between esti- mated mixing proportions and “True” mixing proportions from the si- The neighbor joining trees (Suppl. Fig. 1A, B) indicated that most mulations, which allowed for the detection of potential biases. Because river herring collections cluster by geography, but there was generally of a clear signal of non-native ancestry (see below), due to documented low bootstrap support for branching relationships, due to the relatively translocation events (McBride et al., 2015), we removed the Dresden low genetic divergence among populations. The STRUCTURE analysis Mills collections from the reference datasets for both species and also supported four distinct groups for alewife with most clustering patterns removed the Sewall Pond collection from the alewife dataset for all coinciding with geography. Populations at the boundaries of the ge- simulations, mixing proportion estimation and individual assignments. netically distinct groups showed some degree of admixture, which re- flects gene flow between adjacent regional groups. Several collections 3. Results not at genetic group boundaries also appeared admixed − Argyle Brook in as a mixture of groups from Canada and Northern New 3.1. Data conformance to model assumptions and temporal replicates England, while Dresden Mills and Sewell Pond within the basin of Maine had a mixture of Northern New England and One locus from each species was removed (alewife: Aps_4413, Southern New England ancestry. Argyle Brook is at the southern tip of blueback herring: Aae_1454) as they showed deviations from HWE and Nova Scotia, making natural gene flow with populations in Maine missing data for several collections. The final dataset for alewife con- likely, whereas admixture at Dresden Mills and Sewell Pond is likely the sisted of genotypes for 92 SNPs from 137 collections at 99 locations result of anthropogenic stocking (McBride et al., 2015). (n = 5678), and included one or multiple temporal replicates for 28 For blueback herring, the clustering analyses indicated five regional locations (Table 1A). The final blueback herring dataset consisted of 95 genetic units (Fig. 2B). As with alewife, blueback herring also had SNPs genotyped for 54 collections from 42 locations (n = 2247), and patterns of gene flow at the boundaries of genetic groups and this was including one or multiple temporal replicates for 10 locations particularly prevalent in the Mid-Atlantic region. Additionally, at (Table 1B). Most collections of alewife and blueback herring sampled K = 2, there was a clear gradient of ancestry in the Mid-Atlantic region over multiple years (ranging from two to five years) showed temporal between the Northern and Southern genetic units. There were also stability. These results were broadly supported by the finding of allelic several collections that appeared admixed, including Dresden Mills, and homogeneity between temporal replicates with genic tests. However, the James, Connecticut and Metedeconk Rivers (Fig. 2B). there was allelic heterogeneity between alewife collections from the Significant isolation by distance (IBD) was evident across the ranges in 2010 and 2015, and between blueback herring collections of both alewife (R2 = 0.408, p < 0.001) and blueback herring from the James River and Cape Fear River in 2010 and 2011 (Suppl. (R2 = 0.465, p < 0.001) (Fig. 3), demonstrating that gene flow be- Tables 2A, 2B), although these temporal collections still grouped to- tween basins is greater the closer they are to each other. However, gether in cluster analyses (Fig. 2). much of the range-wide pattern in both species was due to comparisons between regional genetic groups, as significant correlations between 3.2. Genetic diversity and differentiation geographic and genetic distance were not present within all of the re- gional groups and were generally not as strong as at the range wide The genotype data were used to evaluate relative levels of genetic level (Suppl. Fig. 2). The exception was in the southernmost genetic diversity in river herring populations. Unbiased heterozygosity fell in a group, where distances between populations are greater and IBD was relatively narrow range for alewife collections (Table 1A) with values strong and significant in both species. DAPC found similar clustering

250 K. Reid et al. Fisheries Research 206 (2018) 247–258

Table 1A Sampling locations for alewife and summary statistics from 92 SNP loci.

Sampling location Code Cluster Latitude Longitude Year Sample Size HE HO % poly. loci

Garnish River GAR CAN 47.2267 −55.3452 2015 35 0.28 0.27 96.7 Otter Pond OTT CAN 51.0872 −56.8668 2014 29 0.21 0.26 64.1 Miramichi River MIR CAN 46.9665 −65.5783 2011 41 0.26 0.25 95.7 Richibucto River RIC CAN 46.6579 −64.8628 2011 46 0.26 0.24 91.3 Tidnish River TID CAN 45.9768 −64.0447 2011 30 0.26 0.26 87.0 River Phillip RPH CAN 45.8389 −63.7576 2011 40 0.25 0.27 93.4a Wallace River WAL CAN 45.8116 −63.5158 2011 31 0.24 0.24 93.5 Waughs River WAU CAN 45.6969 −63.2685 2011 28 0.26 0.26 92.3a Hillsborough River HIL CAN 46.3515 −62.8713 2011 37 0.25 0.25 95.7 Tracadie Bay TRA CAN 46.3896 −62.9909 2011 42 0.25 0.24 93.5 MAR CAN 46.4167 −61.0815 2011 42 0.24 0.23 93.5 Bras d'Or Lakes BRA CAN 45.8464 −60.8211 2011 39 0.27 0.27 92.4 WES CAN 44.9265 −62.5445 2011 38 0.27 0.27 94.6 Sullivan Pond Outlet SUL CAN 44.6715 −63.5633 2011 47 0.26 0.26 97.8 SAK CAN 44.7308 −63.6620 2011 45 0.27 0.27 93.5 LaHave River LHV CAN 44.3967 −64.5369 2011 46 0.27 0.27 94.6 Argyle Brook ARG CAN 43.7930 −65.8669 2011 45 0.26 0.26 91.3 Kiack Brook KIA CAN 43.8209 −65.9468 2011 46 0.26 0.26 93.5 TUS CAN 43.8633 −65.9816 2011 48 0.26 0.26 94.6 GAS CAN 45.0773 −64.3160 2011 43 0.28 0.27 92.4 SHU CAN 44.9318 −63.5343 2011 45 0.26 0.26 94.6 Peticodiac River PET CAN 46.0537 −64.8417 2011 40 0.25 0.25 92.4 Saint John River SJR CAN 45.9535 −66.8651 2011 41 0.28 0.28 96.7 St. Croix River (Dennis Stream) SCDEN NNE 45.1938 −67.2593 2004 42 0.26 0.26 92.4 St. Croix River (Milltown Dam) SCMIL NNE 45.1776 −67.2938 2004 40 0.25 0.24 90.2 LIT NNE 44.9722 −67.0894 2010 27 0.25 0.24 84.6a DEN NNE 45.0385 −67.3577 2010 29 0.25 0.26 92.4 2015 46 0.25 0.24 88.0 East EMA NNE 44.7566 −67.3624 2010 40 0.25 0.25 90.2 2015 48 0.24 0.24 94.4a NAR NNE 44.4906 −68.0050 2010 22 0.27 0.26 87.0 2015 45 0.25 0.25 92.4 West Bay Pond WBP NNE 44.4882 −68.0460 2010 42 0.25 0.24 92.3a Mt. Desert Island MDI NNE 44.3591 −68.3466 2010 40 0.25 0.25 91.3 UNI NNE 44.5437 −68.4288 2009 46 0.25 0.24 90.2 2011 48 0.26 0.25 93.5 (Wight Pond) WIG NNE 44.4509 −68.6731 2009 41 0.26 0.25 93.5 2011 45 0.25 0.24 90.2 2015 43 0.26 0.25 94.6 Bagaduce River (Walker Pond) BWAL NNE 44.3395 −68.6872 2015 45 0.25 0.24 89.1 Bagaduce River (Pierce Pond) PIE NNE 44.4808 −68.7259 2015 45 0.25 0.24 90.2 ORL NNE 44.5697 −68.7430 2010 72 0.27 0.27 97.8 2011 43 0.25 0.25 92.4 (Milford) PMIL NNE 44.9404 −68.6443 2015 45 0.25 0.24 94.6 Penobscot River (Souadabscook Stream) PSOU NNE 44.7491 −68.8326 2009 46 0.26 0.26 93.5 2010 68 0.26 0.26 95.7 2011 45 0.27 0.26 95.7 Penobscot River (Veazie Dam) PVEA NNE 44.8327 −68.7006 2009 37 0.26 0.25 92.4 2010 44 0.26 0.26 93.5 2011 44 0.27 0.25 93.5 Penobscot River (Blackman Stream) PBLA NNE 44.8860 −68.6476 2015 45 0.27 0.27 96.7 St. George River STG NNE 44.2383 −69.2766 2008 46 0.25 0.25 93.5 2010 40 0.24 0.25 93.5 2011 47 0.24 0.25 90.2 Falls MDK NNE 44.0959 −69.3780 2010 23 0.24 0.25 89.0a 2015 30 0.26 0.27 88.0 DAM NNE 44.0605 −69.5260 2009 33 0.24 0.26 84.8 2010 70 0.25 0.25 93.5 2011 39 0.25 0.25 85.9 SHE NNE 44.0763 −69.6014 2010 40 0.25 0.26 90.2 Kennebec River (Sewell Pond) SEW NNE 43.8691 −69.7823 2009 48 0.25 0.25 95.7 2011 39 0.25 0.24 91.3 Eastern River (Dresden Mills Dam) DRE NNE 44.1065 −69.7292 2008 20 0.26 0.26 91.3 2009 20 0.26 0.26 91.3 2010 76 0.27 0.28 100.0 2011 40 0.27 0.26 92.4 Seven Mile Stream (Webber Pond Dam) WEB NNE 44.4028 −69.6717 2009 31 0.25 0.26 89.1 2010 84 0.25 0.25 93.5 2011 42 0.24 0.24 93.5 SEB NNE 44.5802 −69.5542 2009 45 0.25 0.25 93.5 2010 36 0.25 0.26 92.4 2011 44 0.25 0.24 91.3 Kennebec River (Lockwood Dam) LOC NNE 44.5458 −69.6272 2009 45 0.25 0.25 93.5 2011 28 0.26 0.25 89.1 (continued on next page)

251 K. Reid et al. Fisheries Research 206 (2018) 247–258

Table 1A (continued)

Sampling location Code Cluster Latitude Longitude Year Sample Size HE HO % poly. loci

Androscoggin River AND NNE 43.9204 −69.9670 2009 48 0.25 0.24 92.4 2010 80 0.25 0.25 94.6 2011 47 0.24 0.23 91.3 PRE NNE 43.7202 −70.2723 2010 27 0.26 0.25 88.0 Saco River SAC NNE 43.4953 −70.4461 2010 31 0.27 0.26 91.3 2015 23 0.26 0.25 92.4 Cocheco River COC NNE 43.1964 −70.8738 2011 23 0.25 0.24 89.1 2012 44 0.25 0.25 91.3 Oyster River OYS NNE 43.1309 −70.9187 2011 26 0.26 0.25 89.1 Lamprey River LAM NNE 43.0809 −70.9339 2011 32 0.26 0.27 91.3 2012 19 0.25 0.25 89.1 Exeter River EXE NNE 42.9811 −70.9444 2015 42 0.26 0.25 93.5 Merrimack River MER NNE 42.7016 −71.1645 46 0.26 0.25 95.7 Parker River PAR SNE 42.7501 −70.9292 2015 46 0.27 0.27 94.6 Mystic River MYS SNE 42.4153 −71.1381 38 0.25 0.25 91.3 Back River BAC SNE 42.2154 −70.9231 2015 46 0.27 0.27 93.5 Town Brook TOW SNE 41.9543 −70.6642 2011 45 0.25 0.25 93.5 Stony Brook STN SNE 41.7445 −70.1125 2015 52 0.25 0.24 91.3 Herring River HER SNE 41.6819 −70.1224 2015 42 0.26 0.25 92.4 Mashpee River MAS SNE 41.6158 −70.4792 2015 42 0.26 0.27 94.6 Coonamessett River COON SNE 41.5853 −70.5726 2015 41 0.25 0.24 89.1 Monument River MON SNE 41.7368 −70.6245 2011 45 0.26 0.26 95.7 Nemasket River NEM SNE 41.8907 −71.0846 2010 38 0.26 0.27 92.4 2012 38 0.26 0.27 94.6 Nonquit Pond NON SNE 41.5607 −71.1968 2012 39 0.26 0.27 94.6 Gilbert-Stuart Brook GIL SNE 41.5184 −71.4463 2011 43 0.25 0.24 94.6 Saugatucket River SAU SNE 41.4521 −71.4954 2012 44 0.27 0.25 93.5 Thames River THA SNE 41.4662 −72.0702 2012 33 0.27 0.26 97.8 Bride Lake BRI SNE 41.3278 −72.2373 2012 43 0.26 0.26 95.7 2014 47 0.25 0.25 91.3 2015 17 0.26 0.26 85.9 Dodge Pond DOD SNE 41.3275 −72.1986 2014 51 0.27 0.26 93.5 2015 47 0.26 0.26 94.6 Latimer Brook LAT SNE 41.3689 −72.2020 2013 36 0.26 0.26 90.2 Connecticut River CON SNE 41.4791 −72.4986 2011 31 0.25 0.26 92.4 Connecticut River (Wethersfield Cove) WTH SNE 41.7252 −72.6558 2015 23 0.27 0.27 92.4 Mattabessett River MAT SNE 41.5907 −72.6668 2015 48 0.26 0.26 92.4 Mill Creek − Mary Steube MCR SNE 41.3506 −72.3023 2015 124 0.26 0.25 98.9 Mill Brook MBR SNE 41.3426 −72.3113 2014 45 0.26 0.25 91.3 Quinnipiac River QUI SNE 41.3635 −72.8770 2009 20 0.26 0.26 93.5 2012 20 0.26 0.26 89.1 Housatonic River HOU SNE 41.2412 −73.0979 2012 11 0.25 0.23 83.5a Pequonnock River PEQ SNE 41.1969 −73.1864 2012 33 0.27 0.26 90.2 Mianus River MIA SNE 41.0490 −73.5853 2012 36 0.25 0.25 90.2 Peconic River PEC SNE 40.9172 −72.6534 2009 45 0.26 0.25 92.4 Carlls River CAR SNE 40.7038 −73.3276 2009 38 0.25 0.24 89.1 Hudson River HUD MAT 41.2121 −73.9427 2012 47 0.26 0.25 93.5 Hudson River (Black Creek) BLA MAT 41.8245 −73.9590 2015 47 0.24 0.24 96.7 River DEL MAT 39.7274 −75.4912 2011 38 0.24 0.23 91.3 Nanticoke River NAN MAT 38.4516 −75.8357 2011 36 0.23 0.23 84.8 Choptank River CHOP MAT 38.7124 −75.9975 2015 41 0.23 0.23 85.9 Northeast River NRT MAT 39.5985 −75.9456 2012 48 0.22 0.22 91.3 Susquehanna River SUS MAT 39.6345 −76.1555 2012 29 0.23 0.22 82.6 2015 45 0.22 0.22 88.0 Patuxent River PAT MAT 38.5875 −76.6779 2012 46 0.22 0.21 80.4 2015 45 0.23 0.23 92.4 Potomac River POT MAT 38.9314 −77.1168 2015 37 0.24 0.23 87.0 Rappahannock River RAP MAT 37.9176 −76.8189 2011 42 0.23 0.24 88.0 James River JAM MAT 37.3727 −76.8985 2015 44 0.23 0.22 85.9 Chowan River CHO MAT 36.1860 −76.7309 2011 44 0.22 0.22 85.6a 2015 44 0.22 0.22 88.0 Roanoke River ROA MAT 35.8265 −76.8586 2011 47 0.22 0.22 89.1 Alligator River ALL MAT 35.6977 −76.1368 2011 50 0.22 0.22 84.8

HE = Unbiased heterozygosity; HO = observed heterozygosity. a Includes data from 90 (EMA2015 & CHO) or 91 SNP loci. patterns for both species (Suppl. Fig. 3) and indicated that, in both alewife (Fig. 2A) and blueback herring (Fig. 2B). These regional genetic species, the geographically southernmost group is the most distinct units for alewife are as follows: Canada (CAN) from Garnish River and from the other populations. Otter Pond in Newfoundland to the Saint John River in New Brunswick; Northern New England (NNE) from the St. Croix River to the Merrimack River; Southern New England (SNE) from the Parker River to the Carll’s 3.4. Identification of regional genetic units River; and Mid-Atlantic (MAT) from the Hudson River to the Alligator River. The designations for these genetic units of blueback herring are The genetic clustering analyses identified regional genetic units for

252 K. Reid et al. Fisheries Research 206 (2018) 247–258

Table 1B Sampling locations for blueback herring and summary statistics from 95 SNP loci.

Sampling location Code Cluster Latitude Longitude Year Sample Size HE HO % poly. loci

Margaree River MAR CAN-NNE 46.4167 −61.0815 2011 41 0.30 0.28 96.8 Peticodiac River PET CAN-NNE 46.0537 −64.8417 2011 32 0.27 0.30 88.2a Saint John River SJR CAN-NNE 45.9535 −66.8651 2011 43 0.28 0.28 96.8 EMA CAN-NNE 44.7509 −67.3815 2010 46 0.29 0.29 95.8 Orland River ORL CAN-NNE 44.5697 −68.7430 2009 34 0.27 0.28 91.6 CAN-NNE 2015 25 0.27 0.26 93.7 St. George River STG CAN-NNE 44.2019 −69.2764 2010 48 0.29 0.30 95.7a Eastern River – Dresden Mills Dam DRE CAN-NNE 44.1065 −69.7292 2015 46 0.30 0.29 97.9 Kennebec River – Lockwood Dam LOC CAN-NNE 44.5458 −69.6272 2015 46 0.30 0.30 98.9 Sebasticook River SEB CAN-NNE 44.5802 −69.5542 2015 45 0.29 0.28 96.8 Oyster River OYS MNE 43.1309 −70.9187 2015 34 0.29 0.27 95.8 Exeter River EXE MNE 42.9811 −70.9444 2011 47 0.29 0.29 94.7 2015 20 0.30 0.28 92.6 Parker River PAR MNE 42.7501 −70.9292 2015 46 0.29 0.28 95.8 Mystic River MYS SNE 42.4153 −71.1381 39 0.28 0.29 95.8 Herring River HER SNE 41.6819 −70.1224 2015 47 0.27 0.28 88.4 Coonamessett River COON SNE 41.5853 −70.5726 2015 23 0.28 0.28 86.3 Monument River MON SNE 41.7368 −70.6245 2011 47 0.28 0.29 90.5 Gilbert-Stuart Brook GIL SNE 41.5184 −71.4463 2011 46 0.28 0.28 92.5a Connecticut River CON MAT 41.4791 −72.4986 2011 43 0.29 0.30 100.0 Connecticut River (Wethersfield Cove) WCV MAT 41.7252 −72.6558 2015 29 0.29 0.29 96.8 Connecticut River (Farmington River) FAR MAT 41.8735 −72.6501 2015 21 0.29 0.31 93.7 Quinnipiac River QUI MAT 41.3635 −72.8770 2012 28 0.29 0.29 96.8 Mianus River MIA MAT 41.0490 −73.5853 2012 28 0.29 0.29 96.8 Hudson River HUD MAT 41.2121 −73.9427 39 0.29 0.29 97.9 Hudson River ESO MAT 42.0741 −73.9472 2015 46 0.29 0.30 96.8 Metedeconk River MET MAT 40.0613 −74.1250 2015 34 0.29 0.28 94.7 Delaware River DEL MAT 39.7274 −75.4912 2011 47 0.29 0.29 96.8a Nanticoke River NAN MAT 38.4516 −75.8357 2011 21 0.29 0.29 94.7 2012 27 0.30 0.30 94.7 Choptank River CHOP MAT 38.7124 −75.9975 2015 46 0.29 0.29 98.9 Susquehanna River SUS MAT 39.6345 −76.1555 2012 43 0.29 0.29 94.7 2015 47 0.30 0.30 95.8 Patuxent River PAT MAT 38.5875 −76.6779 2012 44 0.30 0.30 96.8 2015 46 0.30 0.30 95.8 Potomac River POT MAT 38.9314 −77.1168 2011 51 0.29 0.28 98.9 Rappahannock River RAP MAT 37.9176 −76.8189 2011 43 0.30 0.31 97.9 YOR MAT 37.5161 −76.7903 2011 48 0.29 0.29 97.9 James River JAM MAT 37.3727 −76.8985 2011 47 0.29 0.29 94.7 2015 24 0.33 0.37 90.5 Chowan River CHO MAT 36.1860 −76.7309 2011 45 0.29 0.29 96.8a Roanoke River ROA MAT 35.8265 −76.8586 2011 48 0.30 0.29 98.9 Neuse River NEU MAT 35.3240 −77.3428 2011 47 0.30 0.29 96.8a 2015 45 0.30 0.29 96.8 Cape Fear River – Rice/Town CF SAT 34.1285 −77.9515 2011 91 0.29 0.29 97.9 2012 46 0.30 0.29 94.7 2015 45 0.29 0.28 96.8 Santee River SAN SAT 33.2401 −79.5001 2011 44 0.28 0.27 92.4a 2015 42 0.27 0.27 91.5a Savannah River SAV SAT 32.2314 −81.1460 2011 48 0.27 0.26 94.7 2015 47 0.27 0.26 92.6 Altamaha River ALT SAT 31.4728 −81.6145 2011 47 0.27 0.28 94.7 St. John's River STR SAT 29.6033 −81.6022 2011 47 0.25 0.25 86.3 2015 48 0.26 0.25 87.4

HE = Unbiased heterozygosity; HO = observed heterozygosity. a Includes data from 92 (SAN2011), 93 or 94 (DEL, CHO & SAN2015) SNP loci. as follows: Canada-Northern New England (CAN-NNE) from the Mar- indicated that the genetic baselines provide accurate mixing proportion garee River to the Kennebec River; Mid-New England (MNE) from the estimates for alewife and blueback herring encountered in mixture Oyster River to the Parker River; Southern New England (SNE) from the samples back to their larger genetic units. This pattern was also sup- Mystic River to Gilbert-Stuart Pond; Mid-Atlantic (MAT) from the ported by the self-assignment analysis of populations back to larger Connecticut River to the Neuse River; and South Atlantic (SAT) from geographical units (Figs. Figure 4B, Figure 5B). For alewife, the accu- the Cape Fear River to the St. John’s River. All pairwise comparisons of racy of self-assignment of individuals to their regional genetic group of genetic differentiation between regional genetic groups in both species origin was 90% to the CAN regional group, 91% to the NNE, 86% to the were highly significant (Suppl. Tables 4A, 4B). SNE and 92% to the MAT regional group (Suppl. Table 5A). For blue- back herring, individual assignments to regional genetic group of origin 3.5. GSI simulations were similarly high, with 89% accuracy to the CAN-NNE regional group, 76% to the MNE, 90% to the SNE, 89% to the MAT and 95% to There was broad concordance between the simulated and estimated the SAT regional group (Suppl. Table 5B). Simulation results and self- mixing proportions for each regional genetic unit for both alewife and assignment tests revealed that assignment to population/river of origin blueback herring (Figs. Figure 4A, Figure 5A ). These simulations was not as accurate as assignment to regional genetic units, with some

253 K. Reid et al. Fisheries Research 206 (2018) 247–258

Fig. 2. Bayesian clustering analysis for (A) alewife and (B) blueback herring. Codes correspond to Table 1 and each K produced consistent patterns across ten iterations. populations exhibiting considerable bias in assignments (Suppl. Fig. 4A, monitoring exploited species, as well as methods to identify age-specific B). and stock-specific migration patterns within the marine and freshwater environments. The utility of genetic methods to facilitate such efforts has been highlighted by recent studies indicating that certain fisheries 4. Discussion (e.g., Atlantic herring, harengus, and Atlantic mackerel, Scomber scombrus) have major bycatch of alewife, blueback herring and We describe here comprehensive evaluations of genetic population American shad (A. sapidissima)(Bethoney et al., 2017). A dispropor- structure for alewife and blueback herring using single nucleotide tionate amount of the river herring bycatch is from the SNE alewife fi polymorphism (SNP) data from almost 8000 sh that encompass nearly group and the MAT blueback herring group, which includes popula- their entire geographic ranges on the Atlantic coast of North America. tions around Long Island Sound (Hasselman et al., 2016). These re- We also describe how these SNP genotype datasets function as reference gional genetic groups have also experienced among the most severe fi baseline databases for genetic stock identi cation that allow accurate recent population declines (Palkovacs et al., 2014; Hasselman et al., assignment of individuals back to their regional genetic group of origin 2016). and the estimation of stock proportions in mixed fisheries with high accuracy. We extended our understanding of genetic structure for these sister species by including population samples from essentially all of the 4.1. Inferring regional genetic units of river herring from population major areas with river herring populations in their native range, and structure improved the ability to use genetic stock identification techniques to facilitate ecological investigation and management monitoring of river Our primary goal here was to evaluate genetic differentiation of herring. river herring populations throughout their native ranges, which extend Defining patterns of population structure and migration for river from Newfoundland to for alewife and from Nova Scotia herring is imperative for continued management and conservation ef- to Florida for blueback herring, using recently described sets of SNP forts. Identification of genetic population structure provides an under- markers (Baetscher et al., 2017). Our results largely coincide with the standing of ecological and demographic interactions between popula- large-scale genetic patterns observed in previous studies of alewife and tions and regional groups, and is critical for predicting responses to blueback herring; however, prior studies only covered portions of the management actions. Genetic tools that allow accurate estimation of ranges of the two species (McBride et al., 2014; Palkovacs et al., 2014). stock contributions to fisheries provide important capabilities for Extensive sampling of natal rivers, including most basins with

2 Fig. 3. Regression between pairwise genetic distance (F’ST) and geographic distance (km) for river herring. (A) alewife (R = 0.408; P < 0.001) and (B) blueback herring (R2 = 0.465; P < 0.001).

254 K. Reid et al. Fisheries Research 206 (2018) 247–258

Fig. 4. Assessment of reporting group assignment using simulations and self-assignment tests for alewife. (A) Correlation of simulated and estimated mixing pro- portions by designated reporting unit. (B) Proportion of self-assignment of individuals within rivers to designated reporting groups.

Fig. 5. Assessment of reporting group assignment using simulations and self-assignment tests for river herring. (A) Correlation of simulated and estimated mixing proportions by designated reporting unit. (B) Proportion of self-assignment of individuals within rivers to designated reporting groups.

255 K. Reid et al. Fisheries Research 206 (2018) 247–258 demographically important river herring populations, provided in- likely a consequence of stocking events. A previous study focusing on creased resolution that enabled us to define transitions between re- river herring stocking practices in the northern part of the range found gional genetic groups more clearly than in previous studies. In alewife, that the Sewell Pond and Dresden Mills populations in the Kennebec multiple analyses supported the identification of four primary geneti- River were distinct from the rest of the populations in that region cally distinct units range-wide (Fig. 2A; Suppl. Fig. 2A). At the northern (McBride et al., 2015), a finding confirmed in this study for both alewife extent of the sampling, populations from Otter Pond and Garnish River and blueback herring (Figs. 2, 4 and 5). These populations have an in Newfoundland to the Saint John River in New Brunswick (Canada) earlier spawn time than other populations in surrounding basins were identified as a regional genetic group, consistent with the results (McBride et al., 2015), suggesting that they maintain characteristics of McBride et al. (2014). There was not strong support for additional from their likely genetic group of origin (SNE). River herring have a regional structuring among rivers in the Canadian portion of the range, gradient of spawning time across the range, with spawning starting as found by McBride et al. (2014) with a distinct set of molecular earlier (March) in the south and later (June) in Canada (Scott and markers, although there was significant differentiation between many Crossman, 1973). Spawning time has been shown to be highly heritable populations. The Northern New England (NNE) regional genetic group in other anadromous fish (Hendry and Day, 2005; Abadía-Cardoso extended from the St. Croix River at the Canada/US international et al., 2013). While there are some additional alewife stocking events boundary to the Merrimack River, which was again consistent with that have been documented, none involved donor and recipient popu- previous work (Palkovacs et al., 2014), but substantially refined the lations that are in different regional genetic groups (Personal Commu- transition due to much denser population sampling. The Southern New nication, J. Sheppard, MDMF) and are therefore unlikely to have af- England (SNE) regional genetic group extended from the Parker River fected boundaries of regional genetic groups. to the Carll’s River on Long Island. The Mid-Atlantic (MAT) regional Another process that likely contributes to the admixture observed in genetic group encompassed all populations from the Hudson River to transition zones is metapopulation dynamics. Local extirpations or ex- the Alligator River. Although the Hudson River populations occur in the treme reductions in size of smaller populations in such systems can lead transition zone between the SNE and MAT genetic groups, we desig- to recolonization from adjacent populations, some of which will be in nated them as part of the MAT group, as the majority of individuals and other genetic groups, on the boundaries of regional population sub- their ancestry assigned to the MAT group (Figs. Figure 2A, Figure 4B), divisions. The patterns of correlation between geographic and genetic whereas Palkovacs et al. (2014) and Hasselman et al. (2016) previously distances observed in this study are also consistent with this. McBride assigned the Hudson to SNE. Pairwise FST values between the alewife et al. (2015) found significant isolation by distance in regions with regional genetic groups ranged from 0.007 to 0.022 and were all highly limited stocking, but not in regions with extensive stocking. Strong significant (p<0.001, Suppl. Table 4A). patterns of range-wide isolation by distance were found here for both Blueback herring populations were divided into five regional ge- species (Fig. 3), consistent with the findings of Palkovacs et al. (2014) netic groups (Fig. 2B). The clustering with Structure at K = 2 found a and McBride et al. (2014), and highlighting the importance of straying geographic cline in ancestry between two major genetic groups, with and gene flow in shaping observed population structure, by maintaining one corresponding to the southern-most populations, the (South connectivity and avoiding or mitigating local extirpations. However, Atlantic) SAT regional group, and the other including all other popu- within regional genetic groups, the patterns of correlations were less lations. At values of K = 3, 4, 5 the three additional boundaries became consistent, indicating that gene flow is less restricted to adjacent basins apparent, separating the Mid-Atlantic (MAT), Southern New England at smaller spatial scales. Such patterns of isolation by distance have (SNE), Mid-New England (MNE) and Canada-Northern New England been observed in other networks of populations of anadromous fish, (CAN-NNE) genetic groups from one another, respectively. These ge- particularly salmonids (e.g. Garza et al., 2014). netic patterns were also generally supported by the DAPC analysis (Suppl. Fig. 2B), in which clusters mostly corresponded to geographic 4.3. Mixed stock and assignment analyses groups. Only one of the boundaries between regional genetic groups, the MNE and SNE at the Parker River, was coincident between alewife The analysis of population structure that we provide was used to and blueback herring. The composition of blueback herring genetic identify regional genetic groups for river herring throughout the native groups was generally consistent with those described by Palkovacs et al. geographic ranges of both species and establish the utility of these SNP (2014). However, with the addition of multiple, additional populations data as comprehensive reference datasets (baselines) for genetic stock in the northern part of the range, the newly designated CAN-NNE ge- identification. These alewife and blueback herring baselines include netic group included some (East Machias and St. George River) but not data from much more densely sampled populations (alewife n = 99 and all of the populations that previously were included in the NNE genetic blueback herring n = 42) than in previous studies and unify genetic group from Palkovacs et al. (2014). Pairwise FST values between these data collection from the northern-most parts of the species ranges with regional genetic groups were all highly significant (p<0.001) and those from the central and southern-most parts of the ranges. This ap- ranged from 0.026 to 0.114 (Suppl. Table 4B). proach allowed us to more clearly identify transitions between genetic groups than was previously possible and substantially increase con- 4.2. Connectivity across genetic group boundaries fidence in range-wide diversity estimates, which will likely prove useful for future research. Using SNPs for genetic stock identification baselines The extent of gene flow between regional genetic groups inferred in facilitates collaborative research for species that are migratory or found this study is substantially greater than previously estimated (Palkovacs in large geographic areas, as they are more economical to genotype and et al., 2014), with clearly transitional populations present in many amenable to high-throughput genotyping for large-scale fisheries or areas. This is likely a result of increased sampling from populations in genome-scale projects (Seeb et al., 2011; Clemento et al., 2014). between those that constituted boundaries of genetic groups in previous We found high individual assignment and mixing proportion accu- studies. However, there are some discrepancies that could also be due racy to regional genetic units (Figs. 4 and 5) with these SNP baselines partially to differences in the genetic datasets. For example, the Con- that avoid the slight biases observed in previous work with micro- necticut River blueback herring populations appear transitional/ad- satellites (Hasselman et al., 2016). However, accuracy of assignment of mixed between the SNE and MAT groups with the SNP data, but were individuals to population and river of origin was low in most cases more clearly part of the MAT stock in previous work with microsatellite (Suppl. Fig. 4A, B), indicating that proximate rivers are usually not data (Palkovacs et al., 2014), which could be the influence of rare al- demographically independent or genetically differentiated, which is leles that are characteristic of such data. Additionally, we found several likely a consequence of straying. Gene flow and straying among prox- instances of populations with clearly admixed ancestry, which was imate rivers promotes admixture and is a mechanism to recolonize

256 K. Reid et al. Fisheries Research 206 (2018) 247–258 available habitat after local extirpation. In addition, gene flow supports Bethoney, N.D., Schondelmeier, B.P., Kneebone, J., Hoffman, W.S., 2017. Bridges to best the maintenance of effective population size and population viability. management: effects of a voluntary bycatch avoidance program in a mid-water trawl fishery. Mar. Policy 83, 172–178. At larger spatial scales, signals of genetic subdivision indicate regions of Belkhir, K., Borsa, P., Chikhi, L., Raufaste, N., Bonhomme, F., (1996-2004). GENETIX 4. restricted gene flow, which are potentially driven by environmental and 05, logiciel sous WindowsTM pour la genetique des populations. Laboratoire habitat differences, including temperature gradients, throughout the Genome, Populations, Interactions, CNRS UMR 5000, Universite de Montpellier II, ’ Montpellier, France. species geographic ranges. 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