genetic and morphometric differences demonstrate finescale

Journal of Fish Biology (2013) 82, 2015–2030

doi:10.1111/jfb.12129, available online at wileyonlinelibrary.com

Genetic and morphometric differences demonstratefine-scale population substructure of the yellow perch

Perca flavescens: need for redefined management units

P. M. Kocovsky*†, T. J. Sullivan‡, C. T. Knight§ and C. A. Stepien‡

*US Geological Survey Lake Erie Biological Station, 6100 Columbus Avenue, Sandusky, OH44870, U.S.A., ‡Great Lakes Genetics/Genomics Laboratory, Lake Erie Center and

Department of Environmental Sciences, The University of Toledo, 6200 Bayshore Drive,Toledo, OH 43616, U.S.A. and §Ohio Department of Natural Resources, Division of Wildlife,

1190 High Street, Fairport Harbor, OH 44077, U.S.A.

(Received 13 August 2012, Accepted 14 March 2013)

Whole-body morphometrics and 15 nuclear DNA microsatellite loci were analysed for 158Perca flavescens collected during the spawning season from four spawning locations in centralLake Erie, two along the northern shore and two along the southern shore, to evaluate fine-scale vari-ation (spanning 17–94 km). Results showed significant morphological and genetic differences amongP. flavescens from the four locations. The magnitudes of differences were unrelated togeographic distance, demonstrating spatially heterogeneous levels of genetic divergence. Theseresults linked morphometric and genetic variation, showing a discontinuity of scale betweencurrently defined management units and population structure of P. flavescens in Lake Erie,and support that P. flavescens might exist as one or more metapopulations. Findingsdemonstrate the value of using complementary techniques for evaluating populationstructure. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

Key words: exploitation; fish ecology; fishery management.

INTRODUCTION

There is often a discontinuity of scale between the units used for species conservationand management and those matching population substructure. Such discontinuitiescan lead to overexploitation and loss of genetic variation over time (Martínez et al .,2002; Consuegra et al ., 2005). Thus, it is important to examine the linkage betweenthe two, and to incorporate multiple sources of variation, including morphologicaldifferences and genetic divergence, into studies of population substructure.

The Laurentian Great Lakes support the world’s largest freshwater fishery. Theyellow perch Perca flavescens (Mitchill 1814) fishery in Lake Erie is one of the GreatLakes’ primary resources, yielding 4389 t in 2010 (YPTG, 2011). The P. flavescensfishery is managed by the Lake Erie Committee (LEC) using recommendations ofthe Yellow Perch Task Group (YPTG), which is composed of provincial and state

†Author to whom correspondence should be addressed. Tel.: +1 419 625 1976; email: [email protected]

2015Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

2016 P. M . KO C OV S K Y E T A L .

biologists, and is charged with establishing a recommended allowable harvest (RAH)annually. The RAH is determined using a scientific approach and is used to determinethe total allowable catch within each management unit (MU). The LEC and YPTGare standing, binational committees of the Great Lakes Fishery Commission, whichwas formed by a 1955 treaty between Canada and the U.S.A.

The current, cooperative, bi-national framework for managing P. flavescens har-vest was developed following a tagging study by Rawson (1980), who demonstratedthat some P. flavescens travelled many kilometres and crossed the Canada–UnitedStates boundary after being tagged during the spawning season in Ohio waters ofwestern Lake Erie. Management agencies presently allocate harvest quotas to MUs(Fig. 1), which correspond coarsely to the three basins of Lake Erie, with the cen-tral basin divided into two MUs (Fig. 1). Boundaries of the MUs intersect shoresat political boundaries (e.g . Ontario counties) and other easily identified landmarks(e.g . lighthouses) and were drawn to include at least one major port within each MU.Although they are drawn primarily with these socioeconomic concerns in mind toaid landing and reporting, MUs capture coarse differences in trophic conditions andP. flavescens seem to have been managed sustainably under the current MU struc-ture. With the exception of the data on post-spawning movements (Rawson, 1980),biological characteristics of the P. flavescens population (or populations) were notexplicitly considered when MU boundaries were drawn.

Poor recruitment since 2003 (YPTG, 2011), a stated policy of identifying andmanaging unique stocks whenever possible (Ryan et al ., 2003), and an emergingconcern that MUs may lack ecological relevance in light of recent lake-wide morpho-metric and genetic evidence for discrete population subunits of P. flavescens haveincreased interest in examining stock structure at finer scales. Sepulveda-Villet &Stepien (2011) examined P. flavescens stock structure at a broader scale across LakeErie using the genetic stock definition of Hallermann et al . (2003), defining stocks aspopulation subunits that share a common gene pool, freely interbreed and are genet-ically distinguishable from other such groups. Using this definition, Sepulveda-Villet& Stepien (2011) reported significant differences at 15 microsatellite loci among P.flavescens from spawning sites throughout Lake Erie, finding that genetic distancewas not related to geographic distance between sampling locations. Kocovsky &Knight (2012) reported similar trends using morphometric data from P. flavescenssampled from many of the same locations used by Sepulveda-Villet & Stepien (2011)in the central and western basins. Both Sepulveda-Villet & Stepien (2011) andKocovsky & Knight (2012) relied almost entirely on southern-shore sites, and eachemployed a single method for evaluating stock differences. In this investigation, mor-phometric and genetic data from new samples from four sites in the central basin ofLake Erie were used to test the research hypothesis that spawning aggregations on thenorthern and southern shores of the central basin of Lake Erie differ morphometricallyand genetically. These sites were selected because they are in a region of the lake thatis experiencing increasing exploitation pressure. This study is unique as morphomet-ric and genetic data from the same samples at a much finer geographic scale (shortestover-water distances among sampling sites 17–94 km) than used previously (over-water distances > 200 km) are analysed concurrently. Such an approach is commonwhen diagnosing new species (Stauffer et al ., 2003), but rare when identifying stockswithin a species. This approach permitted examination of potential mechanisms thatcreate and sustain population structure in order to improve P. flavescens conservation.

Published 2013. This article is a U.S. Government work and is in the public domain in the USA.Journal of Fish Biology 2013, 82, 2015–2030

P E R C A F L AV E S C E N S P O P U L AT I O N S T RU C T U R E 2017

20 0 20 40 60 km

Lake Erie

42° N

82° W

MU 2

AB

C

MU 3

D

FST=0·0434

FST=0·0014

FST=0·0160

L a k e E r i eMU 3

MU 4

MU 2MU 1

N

Fig. 1. Map of Perca flavescens sample sites on the north and south shores of Lake Erie (letteredA–D). , primary barriers to gene flow (ranked I–III, in order of decreasing magni-tude) from barrier analysis (Manni et al ., 2004b). Support for each barrier is given as percent bootstrap support and number of loci supporting: barrier I (between north and south shoresites): 77%, 15/15 loci, 100%; barrier II (between Erieau West and Erieau East): 100%, 15/15loci, 100%; barrier III: 70%, 14/15 loci, 93%. , northern shore; , southern shore; ,international boundary between U.S. and Canadian waters; , management units (MU; GLFC,www.glfc.org/lakecom/lec/YPTG_docs/annual_reports/YPTGexesum2012.pdf).

MATERIALS AND METHODS

F I S H C O L L E C T I O N

Perca flavescens were sampled from four sites in central Lake Erie, two along the northernshore and two along the southern shore (Fig. 1) during the spawning season in May 2009.Sampling sites were areas where P. flavescens were known or anticipated to aggregate forspawning during the documented spawning period at temperatures 5–9 ◦ C based on researchto date in Ohio waters (C. Knight, unpubl. data). Sampling was conducted with bottom trawls.At each site, surface and bottom dissolved oxygen and temperature were measured with aYSI meter (www.ysi.com) just before trawling began at each trawling station. Trawls weretowed on the bottom at 7–24 m for 10–20 min at 3 knots (c. 5·6 kmh−1). All fishes capturedwere enumerated by species and sex, and all male and female P. flavescens were placed onice for subsequent data collection in the laboratory. Only mature fish were used in analyses.Maturity was determined by obviously enlarged abdomens (females) or flowing milt (males).

M O R P H O M E T R I C DATA C O L L E C T I O N A N D A NA LY S I S

Male P. flavescens were placed in a dissecting pan on their right side and medial fins werepinned in place, ensuring that body shape was not distorted. Females were excluded from



morphometric analysis owing to distortion of body shape caused by enlarged ovaries. Percaflavescens are not sexually dimorphic in the strict sense that body dimensions do not varybetween males and females (Craig, 2000); hence, eliminating females did not bias results. Acentimetre scale was included in the images to enable calibration of image units to the nearestmillimetre. Colour images were taken with a Sony Super Steady Shot, 5·1 megapixel digitalcamera (www.sony.com) mounted on a tripod. A level was used to ensure that the lens ofthe camera was parallel to the table surface on which the fish were placed. Following imagecapture, total length (LT; mm) was measured, sex was confirmed and otoliths were removedfor age analysis. Pectoral fin clips were taken from a sub-sample of the males used formorphometric analyses and from a sub-sample of the females excluded from morphometricanalyses (total n = 158), and preserved in 95% ethanol for genetic analyses. The targetminimum sample size for morphometrics was 32 fish per site, following recommendationsby Kocovsky et al . (2009) for achieving stability of proportion of variation explained whenconducting principal components analysis (PCA) of morphometric data. The box-truss method(Bookstein et al ., 1985) was used to characterize whole-body shape. Morphometric data werecollected using SigmaScan software 5.0 (www.sigmaplot.com). Ten landmarks correspondingto skeletal features were identified and distances among them were calculated using thedistance formula. A total of 21 morphometrics were used to characterize whole-body shape(Kocovsky & Knight, 2012). All fish were processed within 24 h of capture.

Multivariate outlier analysis was conducted to identify discordant fish and to determine ifany particular morphometric was causal in the discordancy using Scout software (Stapanianet al ., 2008). Fish identified as discordant were re-measured. Most fish remaining discordantafter re-measurement were removed from further analyses. Morphometric data were reducedusing PCA using the covariance matrix of log10-transformed morphometrics. Prior to PCAand log10 transformation, morphometrics were standardized to fish standard length (LS; mm)using the procedure described by Elliott et al . (1995). This step was taken to eliminate theinfluence of fish length on the first principal component (PC), which can be substantial andmay confound interpretation of shape differences independent of fish size (Humphries et al .,1981; Bookstein et al ., 1985; Bookstein, 1989; Sundberg, 1989). Although more recentlydeveloped geometric morphometric (GM) techniques for morphometric analysis are available,at least four comparisons of GM v . truss-based morphometrics have concluded that GM andtruss-based methods perform equally well when the objective is to determine if groups differmorphometrically (Douglas et al ., 2001; Parsons et al ., 2003; Trapani, 2003; Busack et al .,2007). The first three PCs of morphometric data were analysed by MANOVA using PROCGLM in SAS 9.2 (www.sas.com). Following a significant MANOVA, separate ANOVAs ofthe individual PCs were conducted followed by Duncan’s multiple range test (PROC GLM)to determine which groups differed from one another morphometrically.

The relationship between morphometric distance and two measures of geographic distancewas analysed using Mantel’s test (Mantel, 1967) with 10 000 permutations in the ecodistpackage in the R statistical analysis software suite 2.13.1 (R Development Core Team, 2011;www.r-project.org). Morphometric distance was calculated as Mahalanobis’ distance (Maha-lanobis, 1936) of morphometric data between all possible pairs (n = 6) of sampling sites usingPROC DISCRIM in SAS 9.2. The shortest waterway distance between all possible pairs ofsites was calculated using latitude–longitude co-ordinates and a distance calculator providedby the National Oceanic and Atmospheric Administration (www.nhc.noaa.gov/gccalc.shtml).The distance between all possible pairs of sites along paths of prevailing water currents asreported by Beletsky et al . (2012) was estimated using latitude–longitude points and thedistance measuring tool in Google Earth (www.daftlogic.com/projects-google-maps-distance-calculator.htm).

G E N E T I C DATA C O L L E C T I O N A N D A NA LY S I S

Genomic DNA was extracted and purified with a DNeasy Qiagen kit (QIAGEN, Inc.;www.qiagen.com). Aliquots of DNA were frozen and archived, then used to analyse allelicvariation at 15 nuclear DNA microsatellite loci that had been employed for broad-scale analy-ses of P. flavescens variation across Lake Erie (Sepulveda-Villet & Stepien, 2011) and acrosstheir native North American range (Sepulveda-Villet & Stepien, 2012). These loci included:



Svi2, Svi3 and Svi7 from Eldridge et al. (2002), Svi4, Svi17 and Svi33 from Borer et al .(1999), YP13 and YP17 from Li et al . (2007) and Mpf1-7 from Grzybowski et al . (2010).

The PCRs contained 50 mM KCl, 1·5 mM MgCl2, 10 mM Tris–HCl buffer, 50 μM of eachdeoxynucleotide triphosphate (dNTP), 0·5 μM each of the forward and reverse primers, 2%dimethyl sulphoxide, 5–30 ng DNA template and 0·6–1·2 μM of Taq polymerase per 10 μlof reaction volume, and were run with negative and positive controls. The forward PCRprimers were synthesized with one of four 5′ fluorescent labels, allowing pool-plexing on theDNA analyser (grouped as follows: Svi2+7, Svi3+33, Svi4+17, YP13+17, Mpf1+2+5+6and Mpf3+4+7). An initial cycle of 2 min at 94◦ C for strand denaturation was followedby 40 cycles of denaturation (94◦ C, 30 s), primer annealing (1 min) at a primer-specifictemperature given by Sepulveda-Villet & Stepien (2011) and polymerase extension (72◦ C,30 s). A final extension at 72◦ C for 5 min was run to minimize partial strands.

Amplification products were diluted (1:50) with dH2O, of which a 1 μl aliquot was addedto 13 μl of a formamide and ABI GeneScan-500 size standard solution, loaded onto a 96well plate and denatured for 2 min at 95◦ C. The denatured products were analysed on anABI 3130XL Genetic Analyzer with GeneMapper 3.7 software (Applied Biosystems Inc.;www.appliedbiosystems.com) in the Great Lakes Genetics/Genomics Laboratory at the LakeErie Center of the University of Toledo. All electropherograms were reviewed manually toconfirm allelic size variants.

Samples were tested for conformance to Hardy–Weinberg equilibrium (HWE) at eachlocus, with significance estimated using the Markov Chain Monte-Carlo (MCMC) methodwith 1000 randomization procedures (Guo & Thompson, 1992) in Genepop 4.0 (Rousset,2008). Population samples were evaluated for heterozygote deficiency or excess, and the lociwere tested for linkage disequilibrium (LD) and for null (non-amplified) alleles with Micro-Checker 2.2.3 (van Oosterhout et al ., 2004, 2006). Significance of HWE and LD tests wasadjusted using sequential Bonferroni correction (Rice, 1989). To evaluate genetic diversity,observed and expected heterozygosity values for each sample were computed in Genepop, andnumbers of alleles and allelic richness were determined from FSTAT v2.9.3.2 (Goudet, 2002).Friedman rank sum tests in the R statistical analysis software suite v2.13.1 (R DevelopmentCore Team, 2011) were used to evaluate differences in heterozygosity and allelic richnessamong samples.

Genetic similarity or differences among samples were determined using two methods: (1)pair-wise θ estimates of F -statistics (Weir & Cockerham, 1984) in FSTAT and (2) exact(G) non-parametric tests whose probabilities were estimated from an MCMC procedure inGenepop (Raymond & Rousset, 1995). Although the exact G method had less statistical power(Goudet et al ., 1996) than the F ST analogue θST, it did not rely on a normal distribution andits results were less sensitive to sample size effects (Goudet et al ., 1996). For both pair-wisetests, sequential Bonferroni corrections were used to minimize type I statistical error (Rice,1989).

Genetic relationships among the samples were depicted by a three-dimensional facto-rial correspondence analysis (3D-FCA; Benzecri, 1973) in GENETIX 4.05 (Belkhir et al .,1996–2004), which made no a priori assumptions. Mantel’s (1967) procedure with 10 000permutations in Genepop (Rousset, 1997) was used to test whether genetic divergence ofthe samples [θST(1 − θST)−1] reflected isolation by geographic distance, according to twopossible scenarios (1) measured as the shortest waterway distance between spawning sites or(2) by distance along prevailing water currents as reported by Beletsky et al . (2012).

Partitioning of genetic variation (1) between spawning P. flavescens from the northand south shores and (2) between their respective spawning sites was evaluated usingAMOVA (Excoffier et al ., 1992) in Arlequin 3.5.12 (Excoffier & Lischer, 2010). Geneticdiscontinuity among the spawning groups was assessed further using Barrier 2.2 (Manniet al ., 2004a, b), in which θST values were mapped onto a matrix of sample geo-graphical co-ordinates (latitude and longitude), and barriers were placed where geneticdivergences were significantly greater than predicted from spatial proximity. Rankingsupport for the genetic barriers was determined from the relative number of support-ing loci and from a bootstrap process in Geneland 3.1.4 that involved resampling ofthe multilocus dataset to generate 2000 iterated θST matrices (Guillot et al ., 2005a ,b, 2008).



RESULTS

M O R P H O M E T R I C DATA

Aggregations of mature adults in spawning condition occurred at temperaturesbetween 7 and 9◦ C on both the north and south shores and in slightly deeperwaters and over a broader range of depths in Ohio (15·5–18 m) than in Ontario(15·2–15·8 m) waters. Ages of P. flavescens in spawning aggregations that were atleast 180 mm LT varied among sites (ANOVA, F 3,712 = 31·4, P < 0·001). Notably,Fairport fish were the youngest on average (2·9 years) followed by Perry (3·4 years),Erieau West (4·2 years) and Erieau East (4·7 years). All pair-wise differences weresignificant. The fish retained for morphometric and genetic analyses did not differ inmean LT among the sites (ANOVA, F 3,335 = 2·13, P > 0·05).

Five unusually large fish (250–302 mm LT) were identified as potential outliers.Morphometrics identified as discordant for those fish were re-measured, and mostwere within 1 mm of original measurements, indicating that the discordancies didnot result from misidentifying landmarks. All of these large fish with discordantmorphometrics that were not due to misidentification of landmarks were retained foranalysis in order to capture the full range of morphometric diversity. Doing so meanthigher variation, hence greater difficulty identifying differences among the variousgroups analysed.

The first, second and third PCs accounted for 29·5, 18·6 and 12·4% of the vari-ation in fish shape, respectively. Subsequent PCs each accounted for <10% of thevariation and were not interpreted. Multivariate ANOVA of the first three PCs wassignificant (Wilk’s λ = 0·601, P < 0·001), indicating that whole-body shape signif-icantly differed among the four spawning group samples. Univariate ANOVAs ofthe individual PCs were significant for the first (F 3,335 = 43·9, P < 0·001) and third(F 3,335 = 16·7, P < 0·001) PCs, but not the second (F 3,335 = 0·83, P > 0·05). Onthe first PC, P. flavescens samples from Perry (south shore) and Erieau East (northshore) were morphometrically distinguishable from each other and from both of theother sites (Table I). On the third PC, fish from Perry and Erieau West (north shore)were not morphometrically different from one another and samples from Erieau Eastand Fairport (south shore) did not differ, but Perry and Erieau West fish divergedfrom Erieau East and Fairport samples (Table I). Morphometric differences were notrelated to either shortest geographic distance (Mantel’s test, R = 0·83, P > 0·05) orto geographic distance by way of prevailing water currents (Mantel’s test, R = 0·95,P > 0·05).

G E N E T I C DATA

All four samples conformed to HWE expectations, all loci were unlinked and thusall 15 loci were retained for analysis. Similar levels of genetic variation characterizedall sampling sites (mean ± s.e. H O = 0·57 ± 0·09, range = 0·54–0·59), includingbetween the population groups spawning along the northern v . the southernLake Erie shores (Table II). Allelic richness values were also similar among theindividual sampling sites (mean ± s.e. RA = 8·8 ± 0·2, range = 8·5–9·2), as well asbetween the northern and southern shores. AMOVA (Table III) revealed significantpartitioning of genetic variation between the northern and southern populationgroups (3·75% of variation explained, P > 0·05), as well as among the population



groups along each shore (0·59%, P < 0·001). Mean ± s.e. F ST of the northern v .southern shore populations was 0·044 ± 0·010 (range = 0·001–0·056). The twospawning samples from the northern shore (sites A and B) significantly differedfrom each other based on θST (θST = 0·016) and non-parametric exact G tests, aswell as from all other samples (Table IV). The southern shore spawning samples(sites C and D) did not differ using θST (θST = 0·001), but significantly divergedunder the G test (χ2 = 51·2). It is likely that those two spawning groups diverge,and that the difference among the test results was due to deviations from a normaldistribution.

Barrier analysis revealed an analogous pattern to the results of AMOVA and F STtests, placing the highest-ranking genetic barrier [barrier I: supported by 100% of theloci (15/15), 77% bootstrap replications] between the northern and southern shorespawning population groups of P. flavescens (mean θST = 0·044; Fig. 1). The secondbarrier separated the Erieau West population (A) from that in Erieau East (B) onthe northern shore (mean θST = 0·016; barrier II: 100% of the loci, 100% bootstrapsupport) and barrier III distinguished between the two southern shore samples[mean θST = 0·001; Fairport (C) and Perry (D) 93% of the loci, 70% bootstrapsupport].

The first three axes of the 3D-FCA (Fig. 2) explained 100% of the variation amongthe four samples, and were congruent with results from the pair-wise comparison andAMOVA tests. All results supported the largest genetic difference between populationunits (spawning stocks) from the northern and southern shores, with additional signif-icant difference between the west and east spawning stock groups along each shore.The results did not correspond to a pattern of genetic isolation by (1) geographicdistance (P > 0·05) or (2) prevailing water current distance (P > 0·05).

DISCUSSION

These findings demonstrate clear differences between northern and southern popu-lation units (spawning stocks) of P. flavescens and between reproductive populationunits of P. flavescens sharing a common shoreline in central Lake Erie. Genetic andmorphometric differentiation patterns were nearly identical. Neither genetic nor mor-phometric differences were related to geographic distance, but one of the measuresof geographic v . morphometric distance was marginal, and small sample size for

Table I. Mean site scores (different superscript lower-case letters indicate significant dif-ferences by Duncan’s multiple range test) on the first three principal components of mor-phometrics of Perca flavescens from four different sites in Lake Erie sampled in 2009

(see Fig. 1)

Site n PC1 PC2 PC3

Erieau West 46 −0·00503b −0·00779a 0·00766a

Erieau East 82 −0·04241c −0·00109a −0·01023b

Fairport 115 0·00548b 0·00316a −0·00968b

Perry 96 0·03207a 0·00088a 0·01666a

n , sample size.



Table II. Genetic variables of Perca flavescens spawning groups analysed includinglocations, sample size (n) and mean genetic variability from 15 microsatellite loci:mean ± s.e. observed (H O) and expected (H E) heterozygosity, mean ± s.e. deviation fromHardy–Weinberg expectations within subpopulations (F IS), number of alleles (N A) and allelicrichness adjusted by rarefaction (RA). Mean values are the average among all four sites (see

Fig. 1)

LocationLatitude

(◦N)Longitude

(◦W) n H O ± s.e. H E ± s.e. F IS ± s.e. N A RA

NorthShore

– – 62 0·53 ± 0·08 0·53 ± 0·08 −0·01 ± 0·05 182 12·0 ± 2·3

ErieauWest

42·1974 82·1396 26 0·59 ± 0·09 0·54 ± 0·08 −0·06 ± 0·06 129 8·5 ± 1·6

ErieauEast

42·2215 81·8800 36 0·54 ± 0·09 0·52 ± 0·07 0·03 ± 0·06 150 8·6 ± 1·6

SouthShore

– – 96 0·56 ± 0·08 0·56 ± 0·08 −0·01 ± 0·02 223 12·8 ± 2·4

Fairport 41·8167 81·3139 48 0·57 ± 0·08 0·53 ± 0·08 −0·02 ± 0·03 173 9·2 ± 1·7Perry 41·8497 81·1263 48 0·56 ± 0·08 0·47 ± 0·09 −0·01 ± 0·03 171 9·0 ± 1·6

inter-site distances may have contributed to the lack of statistical significance. Theseresults demonstrate that mechanisms other than geographic distance are operatingto maintain genetic diversity and that morphometric diversity is at least partly dueto genetic composition. This is strong evidence (Begg & Waldman, 1999) of theexistence of fine-scale population units of P. flavescens within central Lake Erie.

The concurrent analysis of more than one measure of differentiation has provensuccessful for distinguishing stocks of other species in the Great Lakes andelsewhere. The results presented here are similar to those of Bergek & Bjorklund(2009), who reported genetic and morphometric differences of perch Perca fluviatilisL. 1758 at the scale of a few km. Similarly, Vonlanthen et al . (2009) discernedgenetic and morphometric clines in sympatric Coregonus sp. that inhabit differentdepth layers in Lake Neuchatel, and Gíslason et al . (1999) reported that genetics andmorphometrics distinguished sympatric morphs of Arctic charr Salvelinus alpinus(L. 1758) in an Icelandic lake. In the Laurentian Great Lakes, Turgeon et al . (1999)identified morphotypes of ciscoes (Coregonus sp.) using morphometrics, nuclearmicrosatellites and ecological characteristics. The combination of genetic and one ormore morphometric or ecological methods is a rigorous approach for distinguishinggroups and for understanding evolutionary patterns.

Table III. Relative distribution of Perca flavescens genetic variation between shores andsampling sites within shores using analysis of molecular variance

Source of variation Variation (%) Fixation index P -value

Between shores 3·75 0·16 <0·05Among sites within shores 0·59 0·03 <0·001Within sampling sites 95·66 4·15 <0·001



Table IV. Summary statistics (bold values are significant following sequential Bonferronicorrectiona) for Perca flavescens divergence using 15 loci, including the F ST analogue θST

b

(below diagonal) and exact (G) tests of differentiationc (above diagonal) (see Fig. 1)

Location A B C D

Erieau West – 54·671 ∞ ∞Erieau East 0·016 – ∞ ∞Fairport 0·038 0·056 – 51·207Perry 0·033 0·047 0·001 –

aRice (1989).bWeir & Cockerham (1984).cGoudet et al . (1996).

This study revealed appreciable levels of genetic diversity (observed heterozygos-ity and number of alleles) in the central basin of Lake Erie that were consistent withvalues reported for other sampling years of central Lake Erie spawning groups alongthe southern shore by Sepulveda-Villet & Stepien (2011). A difference among studieswas that Sepulveda-Villet & Stepien (2011) found that the P. flavescens spawninggroup at Fairport (sampled in 2003) was very distinct from others (including Perry),whereas the present results (for the 2009 spawning group) showed its greater similar-ity to P. flavescens from Perry. This difference may be related to the very large yearclass produced in 2003, which has dominated catches since 2006 (YPTG, 2011).

Mechanisms that might contribute to the maintenance of differences include limno-logical characteristics or barriers and spawning group affinity. Deep water is probablya barrier to fish movement between shores. Perca flavescens in Lake Erie tend toassociate with nearshore areas (Wei et al ., 2004), although they are captured inwaters of 20 m or deeper during summer prior to thermal stratification and the onsetof hypoxia (P. Kocovsky & C. Knight, unpubl. data). Deeper (>20 m) or open

BD

A

C

500

−8000 −4000 4000

4000

0

−4000

0

Component 1 (43.75%)

Compo

nent

3 (2

7.26

%)

−50−100C

ompo

nent

2 (

28.9

9%)

Fig. 2. Three-dimensional factorial correspondence analysis (Benzecri 1973; GENETIX 4.05, Belkhir et al .,1996–2004) showing relationships among Perca flavescens sampling sites (A–D) and individuals alongthe north ( ) and south ( ) shores of Lake Erie, based on 15 microsatellite loci. The eastern-mostsites along each shoreline are indicated ( , ). Average values of each collection are indicated by largelettered shapes, with individuals represented by smaller shapes.



water areas can also act as barriers to dispersal, which was demonstrated for stoneloach Barbatula barbatula (L. 1758) (Barluenga & Meyer, 2005) and blue mbunaLabeotropheus fuelleborni Ahl 1926 (Arnegard et al ., 1999). The deepest water incentral Lake Erie is c. 25 m. Rawson (1980) reported that a few P. flavescens cap-tured and tagged in Ohio waters of western Lake Erie were harvested on the northshore near Erieau, ON, demonstrating that they sometimes move between the shores.Those recaptures, however, were not during spawning season. Perca flavescens thatswim from the southern shore of the western basin to the northern shore of thecentral basin would not have to cross water deeper than 18 m, as they could followwater shallower than 10 m along the many islands in the western basin and thentravel along Point Pelee. Fish from the sites sampled could move in similar ways,following shallower water to the west and then north, a path that would follow circu-lation pathways described by Beletsky et al . (2012). Following contours from southto north with prevailing currents would be a greater distance than the maximumdistances P. flavescens in Lake Erie have been reported to move (Rawson, 1980;MacGregor & Witzel, 1987). Results of this study and those of Sepulveda-Villet &Stepien (2011) and Kocovsky & Knight (2012) suggest such movements, if they wereto occur in the central basin, do not appreciably contribute to gene flow among P.flavescens spawning at these sites. In contrast, the present results indicate long-timegenetic isolation among all four spawning groups, which is especially pronouncedbetween the northern and southern shores. The data presented here suggest that P.flavescens may either return to natal sites to spawn or remain in genetically andmorphologically differentiated groups throughout their lives, or both.

Other limnological characteristics and barriers that may contribute to origin andmaintenance of spawning stocks include gyres, water currents and seasonal hypoxia.Beletsky et al . (1999) described a two-gyre, anticyclonic circulation pattern in centralLake Erie during summer 1979–1980. One gyre extended from east of Point Pelee toErieau, ON, and a larger gyre extended from Erieau to the Pennsylvania Ridge, whichextends northwesterly from just north-west of Erie, Pennsylvania. Similar circulationpatterns were reported for 1994 (Leon et al ., 2005; Schwab et al ., 2009) and for2001 (Leon et al ., 2005). More recently, Beletsky et al . (2012) reported a singleanticyclonic gyre encompassing the entire central basin using data from 2005 and2007. Leon et al . (2005) also reported unidirectional flow from west to east alongthe southern shore of the central basin. These currents and gyres may promote andmaintain population subunit structure by passive transport of larvae, which has beeninvoked for maintaining population structure of P. fluviatilis (Gerlach et al ., 2001)and Atlantic cod Gadus morhua L. 1758 (Knutsen et al ., 2003). In Lake Michigan,Dettmers et al . (2005) reported P. flavescens larvae were delivered from 2 to 120 kmby water currents and that current-caused drift of larvae might be responsible forgenetic homogeneity (Miller, 2003) of P. flavescens in southern Lake Michigan.Currents also might act as retention or blocking mechanisms (Ruzzante et al ., 1998).

Spawning group affinity can take two forms: spawning site fidelity and kin recog-nition. These mechanisms may operate together or independently. Aalto & Newsome(1990) demonstrated spawning site fidelity of P. flavescens with egg removal exper-iments in Lochaber Lake. After a few years of removing egg masses from spawningsites, the number of egg masses deposited at those same sites in subsequent yearswas greatly reduced. Kipling & LeCren (1984) demonstrated that 100% of P. fluvi-atilis tagged during spawning and released 100–200 m away returned to their tagging



location. Only 44% of those released 1600–3200 m away returned, demonstratingthat homing varied with distance between capture and release sites. The LochaberLake and Windermere work is consistent with spawning site fidelity (e.g . entrain-ment hypothesis; Secor et al ., 2009). MacGregor & Witzel (1987) reported thatP. flavescens captured and tagged during spawning season and released many kmdistant along the northern shore of the eastern basin of Lake Erie returned to tag-ging locations, implying homing. Kin recognition was reported by Gerlach et al .(2001) and Behrmann-Godel et al . (2006) in Lake Constance for P. fluviatilis . Ger-lach et al . (2001) observed that genetically similar individuals tended to aggregatewith one another and in close proximity to other aggregations of genetically similarindividuals. Behrmann-Godel et al . (2006) demonstrated that olfactory recognitionof related individual P. fluviatilis played a role in kin aggregations and that the abil-ity to detect began at the fry life stage. Kin recognition has not been studied in P.flavescens , but might yield insights on the observed population structure in Lake Erie.

Better understanding of population structure is necessary for effective managementto conserve populations and ensure long-term sustainability of this commercial andrecreational fishery. The stock concept as framed by Begg & Waldman (1999), wherestocks are ‘semi-discrete groups of fish with some definable attributes of interest tomanagers’, clearly is relevant for management purposes but lacks ecological context.Aalto & Newsome (1990) considered the P. flavescens population of Lochaber Laketo be composed of demes, which seem analogous to the Hallermann et al . (2003)definition of genetic stocks. While more satisfying ecologically, deme is also a mostlydescriptive term that lacks spatial and temporal context required for an in-depthunderstanding of population structure for an exploited population with the ecologicaland economic significance of Lake Erie P. flavescens .

The results presented here demonstrating population structuring at fine spatialscales similar to that observed for other Great Lakes species [e.g . lake whitefishCoregonus clupeaformis (Mitchill 1818); VanDeHey et al ., 2009] suggests that themetapopulation concept as summarized by Kritzer & Sale (2004) may be a usefulconstruct for guiding future research on P. flavescens population structure in LakeErie and eventual modification of MUs that better reflect population structure. Kritzer& Sale (2004), summarizing applications of metapopulation theory in marine envi-ronments, argue that allowing coupling of spatial scales for defining metapopulations,as opposed to strict presence–absence (i.e. the potential for local extinction) as inthe original conceptualization (Levins, 1969), provides the flexibility necessary toinclude changes in population size, age and genetic structure, which are critical forassessing populations, tracking effects of exploitation and estimating total allowablecatches. Kritzer & Sale’s (2004) view of a metapopulation as ‘a system of discretelocal populations, each of which determines its own internal dynamics to a largeextent, but with a degree of identifiable and nontrivial demographic influence fromother local populations through dispersal of individuals’ potentially describes whatSepulveda-Villet & Stepien (2011), Kocovsky & Knight (2012) and others (Rawson,1980; MacGregor & Witzel, 1987) have observed in Lake Erie. The Kritzer & Sale(2004) formulation of the metapopulation concept has been successfully applied toguide management of Atlantic herring Clupea harengus L. 1758 (McQuinn, 1997).

At present, insufficient data are available to estimate size, age structure, exploita-tion rate or other critical features of P. flavescens population units because demo-graphic data on exploited P. flavescens are collected at the scale of MU. Continued



efforts to identify population units and a better understanding of pre and post-spawning movements of P. flavescens will provide a more comprehensive view ofthe structure of the P. flavescens population or populations in Lake Erie. Understand-ing the ecological basis for population structure will inform effective managementnecessary to conserve spawning stocks, which will further ensure lake-wide popula-tion persistence with concomitant economic and ecosystem benefits (Schindler et al .,2010).

Field data collection was base funded by the Ohio DNR Fairport Fisheries Research Stationand the U.S. Geological Survey Lake Erie Biological Station. Genetic analyses were supportedby grants to C.A.S.: NOAA Ohio Sea Grant R/LR-13‘Temporal and spatial analyses of walleyeand P. flavescens genetic stock structure: A high-resolution database for fisheries manage-ment’ and USEPA CR-83281401-0‘High-resolution delineation of Lake Erie fish populations:DNA databases for fishery management’. Support for T.J.S. was provided by an NSF GK-12DGE#0742395 fellowship ‘Graduate fellows in high school STEM education: An environ-mental science learning community at the land-lake ecosystem interface’ (for which C.A.S.is PI) and a summer research assistantship from NOAA project #NA09OAR4170182 ‘Effectsof Bayshore power plant on ecosystem function in Maumee Bay, western Lake Erie’ (C.A.S.and P.M.K. are coPIs). Logistic support was provided by LEC staff members: P. Uzmann, M.Grey and R. Lohner. Vessel support was provided by B. Bennett (R.V. Grandon), T. Cherry,D. Hall and M. Porta (R.V. Musky II ). Constructive reviews of previous drafts were providedby R. Kraus and two anonymous reviewers. Use of trade, product or firm names does notimply endorsement by the U.S. Government. This article is Contribution 1741 of the U.S.Geological Survey Great Lakes Science Center and publication #2013-13 from the Lake ErieResearch Center.

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