- Email: [email protected]
Morphological evidence of discrete stocks of yellow perch in Lake Erie
Journal of Great Lakes Research 38 (2012) 534–539
Contents lists available at SciVerse ScienceDirect
Journal of Great Lakes Research journal homepage: www.elsevier.com/locate/jglr
Morphological evidence of discrete stocks of yellow perch in Lake Erie Patrick M. Kocovsky a,⁎, Carey T. Knight b, 1 a b
U.S. Geological Survey, Great Lakes Science Center, Lake Erie Biological Station, 6100 Columbus Avenue, Sandusky, OH 44857, USA Ohio Department of Natural Resources, Division of Wildlife, 1190 High Street, Fairport Harbor, OH 44077, USA
a r t i c l e
i n f o
Article history: Received 18 November 2011 Accepted 20 April 2012 Available online 29 May 2012 Communicated by Wendylee Stott Index words: Exploitation Harvest Management Whole-body morphometrics
a b s t r a c t Identification and management of unique stocks of exploited fish species are high-priority management goals in the Laurentian Great Lakes. We analyzed whole-body morphometrics of 1430 yellow perch Perca flavescens captured during 2007–2009 from seven known spawning areas in Lake Erie to determine if morphometrics vary among sites and management units to assist in identification of spawning stocks of this heavily exploited species. Truss-based morphometrics (n = 21 measurements) were analyzed using principal component analysis followed by ANOVA of the first three principal components to determine whether yellow perch from the several sampling sites varied morphometrically. Duncan's multiple range test was used to determine which sites differed from one another to test whether morphometrics varied at scales finer than management unit. Morphometrics varied significantly among sites and annually, but differences among sites were much greater. Sites within the same management unit typically differed significantly from one another, indicating morphometric variation at a scale finer than management unit. These results are largely congruent with recently-published studies on genetic variation of yellow perch from many of the same sampling sites. Thus, our results provide additional evidence that there are discrete stocks of yellow perch in Lake Erie and that management units likely comprise multiple stocks. Published by Elsevier B.V. on behalf of International Association for Great Lakes Research.
Introduction Yellow perch Perca flavescens in Lake Erie support one of the largest commercial and recreational fisheries in the Great Lakes. In 2010 gillnet, trapnet, and recreational fisheries harvested a combined 4389 metric tons (YPTG, 2011) valued at several hundred million US$. Management agencies presently use management units (MU, Fig. 1) for allocating harvest quotas, which correspond coarsely to the three major basins of Lake Erie, with the larger central basin divided into two MUs. The MU boundaries intersect shores at easily identifiable landmarks, such as lighthouses, and were drawn with full consideration to socioeconomic concerns (e.g., at least one major port exists within each MU) and political boundaries (e.g., counties in Ontario). Hence, MUs capture coarse differences in trophic conditions and are convenient for landing and reporting of harvest, but they may lack ecological relevance if they do not adequately protect distinct stocks. Yellow perch have been managed sustainably under the current MU structure since the late 1970s, but recent poor recruitment (e.g., YPTG, 2011) coupled with a general interest in identifying and managing unique stocks whenever possible (Ryan et al., 2003) has increased interest in examining stock structure of yellow perch.
⁎ Corresponding author. Tel.: + 1 419 625 1976x17. E-mail addresses: [email protected] (P.M. Kocovsky), [email protected] (C.T. Knight). 1 Tel.: + 1 440 352 4199.
Evidence for discrete stocks of yellow perch in Lake Erie has been accumulating for the past two decades. Henderson and Nepszy (1989) reported that central basin yellow perch had greater length at age and reached longer lengths than western basin yellow perch. They also noted differences in mortality and growth rates, although differences were correlated with fishing effort. This study demonstrated that there were likely different stocks within the major basins of Lake Erie. Genetic research has provided additional evidence of variation at scale finer than basin. Ford and Stepien (2004) reported 10 distinct mitochondrial DNA (mtDNA) haplotypes, some of which were unique to a basin, and some of which were found in only two of the three basins. They attributed these observations to differences in glacial refugia of yellow perch that colonized Lake Erie following retreat of the most recent Wisconsinan glaciers. This effort demonstrated the potential for differentiation at a scale finer than MU. Ryan et al. (2003) explicitly acknowledged the need for stock-based management by calling for identification of distinct spawning stocks for several exploited Lake Erie species. The usefulness and value of morphometrics for stock discrimination have been acknowledged for some time (e.g., Begg and Waldman, 1999), although Swain and Foote (1999) cautioned about the effects of environmental influences on morphology. In the Great Lakes, Bronte and Moore (2007) used whole-body morphometrics to successfully distinguish siscowet lake trout Salvelinus namaycush based on whole-body morphometrics. Similarly, Elliott et al. (1995) distinguished orange roughy Hoplostethus atlanticus stocks in Australian waters that vary little genetically. Tiffan et al. (2000) distinguished spring and fall runs of Chinook salmon Oncorhynchus tschawytscha using
0380-1330/$ – see front matter. Published by Elsevier B.V. on behalf of International Association for Great Lakes Research. doi:10.1016/j.jglr.2012.04.006
P.M. Kocovsky, C.T. Knight / Journal of Great Lakes Research 38 (2012) 534–539
535
was included in images to provide a scale for converting image units to millimeters. Color images were taken with a Sony Super Steady Shot, 5.1 megapixel digital camera mounted on a tripod. A level was used to ensure that the lens of the camera was parallel to the table surface on which fish were placed. Following image capture, total length (TL in mm) was measured, sex was confirmed, otoliths were removed for age analysis, and a pectoral fin was removed from a sub-sample for genetic analysis. We used the box-truss method (Bookstein et al., 1985) to characterize whole-body shape. Morphometrics were collected using SigmaScan software, version 5.0. Ten landmarks corresponding to skeletal features were identified and distances among them were calculated using the distance formula. A total of 21 morphometrics were used to characterize whole body shape (Fig. 2). Fig. 1. Sampling locations (solid circles) and management units (MU) for yellow perch in Lake Erie. Solid lines are boundaries between MUs. Dashed lines are state and international boundaries.
morphometrics. The combined use of morphometric and genetic differences can provide strong evidence of stock-level differences within larger populations (Begg and Waldman, 1999). We examined whole-body morphometrics of male yellow perch captured during spawning from several spawning locations throughout Lake Erie to test three hypotheses for morphometric variation: 1) spatial variation was greater than temporal variation for sites sampled in multiple years; 2) morphometrics varied at a scale finer than MU; and 3) morphometric distance (i.e., the magnitude of morphometric differences) was unrelated to physical distance, as has been observed for genetic distance (Sepulveda-Villet and Stepien, 2011). Our objective was to determine the scale of morphometric differentiation of yellow perch in Lake Erie to support identification and improved management of unique yellow perch stocks. Methods Fish collection Yellow perch were collected as part of annual assessment sampling by fisheries management agencies and from commercial trap nets at seven sites within the four MUs (Fig. 1) during 2007–2009. Samples from Monroe were captured with a fishery-independent fyke net. Samples from Cedar Point were obtained from a commercial trap net. At the time of the Cedar Point sample the commercial fishery for yellow perch in MU 1 was closed. The area was still being fished for white perch Morone americana. Yellow perch were taken from the net under a US Geological Survey collection permit. All other fish were captured in bottom trawls during spawning season near known spawning locations. Only mature males>150 mm total length (TL) and expressing milt were retained for analyses. Females were excluded to eliminate effects of enlarged ovaries on morphometrics. Yellow perch are not sexually dimorphic in the literal sense (i.e., female and male body shapes do not differ; Craig, 2000), hence eliminating females did not bias results. We restricted collection of males for analysis to fish at least 150 mm TL because our age estimation efforts for this work revealed that most were age 2 and older by that length, which greatly reduces the potential for allometric growth confounding results. The target minimum sample size (N) was 77 fish per site following recommendations by Kocovsky et al. (2009). All fish were placed on ice immediately after capture and were transported to laboratories for image capture for morphometric analysis.
Statistical analyses Multivariate outlier analysis was conducted to identify discordant fish and to determine if any particular morphometric was causal using Scout software (Stapanian et al., 2008). Fish identified as discordant were re-measured. Fish still discordant after re-measurement were removed from further analyses (see results for exceptions). Two separate analyses were conducted on subsets of the data to address our hypotheses regarding morphological differences. For each we followed the same general analytical framework. Morphometric data were reduced using principal component analysis (PCA) using the covariance matrix. Prior to PCA morphometrics were standardized to fish standard length using the procedure described by Elliott et al. (1995). This step was taken to eliminate the influence of fish length on the first principal component, which can be substantial and which can confound interpretation of shape differences independent of fish size [Bookstein et al., 1985; see also Humphries et al. (1981), Bookstein (1989), and Sundberg (1989) for discussion of “size” and “shape” in morphometric analyses]. Next, either an ANOVA or nested ANOVA was conducted on the first, second and third principal components followed by Duncan's multiple range test to test for differences among sites and MUs. The magnitude of spatial versus temporal variation was examined using nested ANOVA of principal components from all sites (locations on Fig. 1) sampled in at least two years: Cleveland 2007 and 2008; Perry, Chagrin, Erie, and Dunkirk 2007–2009; and Monroe 2008–2009. Year was nested within each site for ANOVA to avoid constraining annual differences in morphology to be the same for all sites. This analysis permitted examination of spatial versus temporal variation for the broadest range of sites. Spatial variation was assessed with ANOVA of principal components using data from Monroe, Cedar Point, Cleveland, Chagrin, Perry, Erie, and Dunkirk in 2008, when we had greatest spatial coverage and at least two sites within all MUs except MU 4. Fixing year allowed for assessment of spatial differences within a year. The relationship between morphometric distance and geographic distance was analyzed using linear regression to test whether morphometric differences were related to geographic distance. Morphometric distance was calculated as Mahalanobi's distance between all possible pairs of sampling sites using PROC DISCRIM in SAS version 9.2. Geographic distance between all possible pairs of sites was calculated
Morphometric data collection All fish were processed within 24 h of capture. Fish were placed in a dissecting pan on their right side and medial fins were pinned in place to permit easy identification of fin origins and insertions. When pinning fins we ensured that body shape was not distorted. A centimeter scale
Fig. 2. Drawing of a yellow perch with morphometric landmarks (filled circles) and truss measurements (lines between landmarks) identified.
536
P.M. Kocovsky, C.T. Knight / Journal of Great Lakes Research 38 (2012) 534–539
using latitude–longitude coordinates and a distance calculator provided by the National Oceanic and Atmospheric Administration (http:// www.nhc.noaa.gov/gccalc.shtml). This analysis was restricted to 2008 data because it had the broadest spatial coverage within a year and to avoid confounding of geographic and annual differences. Results Fish total lengths varied among sites and years (Table 1). Fish from Monroe were longer than fish from all other sites in both years, but among the other sites there was broad spatial and temporal overlap in total lengths. Ages of fish examined ranged between 1 and 19 years, with 85% of fish 3 to 6 years old. Samples in 2007 (57%) and 2008 (49%) had large percentages of individuals from the 2003 year class, one of the largest on record for yellow perch in Lake Erie (YPTG, 2006). Discordancy analysis revealed that most fish less than 180 mm TL had several morphometrics that were discordant suggesting that they were from a different statistical population. Thus, all fish less than 180 mm TL (n = 103) were removed from analyses. Several unusually large fish were also identified as potential outliers. Morphometrics identified as discordant for those fish were re-measured, and most were within 1 mm of original measurements, indicating that discordancies did not result from misidentifying landmarks. All of the unusually large fish that had discordant morphometrics that were not due to misidentification of landmarks were retained for analysis in order to capture the full range of morphometric diversity. Doing so meant higher variation, hence greater difficulty identifying differences among the various groups analyzed. The first, second, and third PCs accounted for 32%, 18%, and 14% of the variation in morphometrics, respectively, for sites sampled from 2007 through 2009. The remaining PCs each accounted for less than 9% of variation and were not interpreted. Variation in site (F5,1424 =76.1, Pb 0.0001) and year within site (F10, 1419 = 17.1, Pb 0.0001) were both significant on the first PC. Both site (F5, 1424 =90.6, Pb 0.0001) and year within site (F10, 1419 =28.8, Pb 0.0001) were also significant on the second PC. For both of the first two PCs, which combined accounted for 50% of the variation in fish shape, the magnitude of F-values indicates that both are primarily a gradient in spatial variation. Variation in year within site (F10, 1419 =67.5, Pb 0.0001) exceeded site variation (F5, 1424 = 46.6, Pb 0.0001) on the third PC, indicating that it was primarily a gradient in annual variation. Greater variation accounted for by site on the two PCs with the greatest variation explained and a comparatively low differential between variation explained by site and year on the third PC strongly indicates that spatial variation exceeds annual variation in yellow perch morphometrics at the sites examined. Duncan's multiple range test revealed spatial differences at the site scale (Table 2). Sites within the same MU were typically different than one another as well as from sites in different MUs on all three PCs (Table 2; Fig. 3). This outcome supports the research hypothesis that spatial variation was greater than annual variation in yellow perch morphometrics at those sites where we had multiple years of data. The first, second, and third PCs accounted for 32%, 18%, and 11% of the variation in morphometrics, respectively, for seven sites sampled
Table 2 Mean site scores on the first three principal components of morphometrics of yellow perch from six different sites in Lake Erie sampled from 2007 through 2009. Superscript letters indicate significant differences following Duncan's multiple range test. Sites with different letters are significantly different. Site Monroe Chagrin Cleveland Erie Perry Dunkirk
MU 1 2 2 3 3 4
PC1
PC2 d
− 0.0140 − 0.0283e 0.0062c 0.0200b − 0.0179d 0.0523a
PC3 d
− 0.0427 0.0111b 0.0109b − 0.0027c 0.0214a − 0.0077c
− 0.0106d 0.0013c 0.0218a − 0.0219e 0.0046bc 0.0088b
in 2008. The remaining PCs each accounted for less than 10% of variation and were not interpreted. The first (F6, 745 = 42.8, P b 0.0001), second (F6, 745 = 59.1, P b 0.0001), and third (F6, 745 = 4.67, P b 0.0001) PCs differed significantly by site. Duncan's multiple range tests revealed that sites in the same MU differed from one another on all three PCs except for the two MU1 sites on PC3 (Table 3). Sites from the same MU were clearly distinguishable from one another visually (Fig. 3). This result supports the research hypothesis that morphometrics vary at scales finer than MU. Geographic distance was positively related to morphometric distance when all possible pairs of sites were analyzed (linear regression; F1, 19 = 4.74, P = 0.04, r 2 = 0.20; Table 4). When inter-site differences were analyzed for each site individually, only geographic distance of the six other sites from Cedar Point was positively and significantly related to morphometric distance (Table 4). Thus, the lake-wide relationship of increasing morphometric distance with increasing geographic distance was strongly influenced by distances to Cedar Point.
Discussion Our analyses revealed significant spatial differences in whole-body morphometrics of yellow perch. Significant annual variation was also observed, but spatial variation was greater. For several pairs of sites, those geographically distant from one another were more similar morphometrically than those geographically proximal. All fish used in analyses were mature males, eliminating potential distortion from females owing to enlarged ovaries, and were sampled during the spawning period in known spawning areas. Although it is possible that some fish sampled were not successful spawners, were intercepted on their way to another spawning location, or spawned at more than one location, such phenomena would decrease the probability of detecting morphometric differences at the site scale. The overall outcome of our analyses supports the research hypothesis that whole-body morphology of yellow perch varies at scales finer than MU in Lake Erie. Begg and Waldman (1999) called for a ‘holistic’ approach to stock identification that included complimentary techniques. Here we follow the stock concept provided by Begg and Waldman (1999), where stocks are “semi-discrete groups of fish with some definable attributes of interest to managers.” We also agree with Ryan et al. (2003) that
Table 1 Mean total length (± SE) of yellow perch captured for morphometric analysis from sites in Lake Erie, 2007–2009. Means with the same superscript letter are not significantly different. Site
Cedar Point Monroe Chagrin Cleveland Erie Perry Dunkirk
Management unit 1 1 2 2 3 3 4
2007 Mean TL
207.4 ± 2.5fgh 195.1 ± 2.1i 206.3 ± 2.7gh 207.2 ± 2.1fgh 209.7 ± 3.5efgh
2008
2009
N
Mean TL
N
69 68 53 85 61
216.8 ± 1.3cde 241.7 ± 1.9a 219.6 ± 2.1cd 212.0 ± 1.7efgh 205.4 ± 2.3h 228.8 ± 1.5b 213.7 ± 2.6cdefg
133 133 111 109 66 116 84
Mean TL
N
234.5 ± 3.3b 214.9 ± 1.7cdef
85 115
211.0 ± 1.9efgh 209.0 ± 1.9fgh 213.0 ± 3.4cdefgh
115 96 64
P.M. Kocovsky, C.T. Knight / Journal of Great Lakes Research 38 (2012) 534–539
537
Fig. 3. Mean scores on the first three principal components of morphometrics of yellow perch sampled from six sites 2007-2009 (A) and from seven sites in 2008 (B) in Lake Erie. Sites represented by the same shape are in the same management unit.
identification and conservation of unique spawning stocks provides the best pathway for sustainable management of exploited species. Although we did not employ multiple techniques in this effort, results of efforts to examine genetic differences among yellow perch from the same sites we sampled that were concurrent to our efforts (Sepulveda-Villet and Stepien, 2011; Sepulveda-Villet et al., 2009) mostly agree with our morphometric results. Sepulveda-Villet et al. (2009) reported that variation in mtDNA control region sequences among sites within MUs in Lake Erie was greater than variation among MUs. They also reported that inter-site variation was not congruent with MU (i.e., sites more similar genetically were not necessarily from the same or a neighboring MU), which is also consistent with our morphometric analyses. Sepulveda-Villet and Stepien (2011) also examined variation in 15 nuclear microsatellite loci extracted from pectoral fin clips from yellow perch from 13 spawning locations in Lake Erie. Many of those fin clips were provided by us, and for several sites and years fin clips were taken from the same fish used in morphometric analyses after images were captured. Sepulveda-Villet and Stepien (2011) reported significant differences in nuclear microsatellites for nearly all pairwise comparisons among sampling sites. Differences were not related to basin, MU, or spatial distance. The mostly congruent genetic and morphometric results provides strong evidence that yellow perch exist as discrete stocks in Lake Erie and that stocks exist at scales finer than MU. Gear differences, dominance of the 2003 YC, and smaller-thandesired sample sizes for a few sites were sampling liabilities, but these liabilities probably did not affect the overall outcome of morphometric analyses. Although some fish were sampled in trap nets or fyke nets and others in bottom trawls, which may have different size selectivities, restricting our analyses to mature males >180 mm TL and standardizing
morphometrics to standard length eliminated any effects of potential gear bias in size selectivity. The longer mean total length of fish from Monroe was most likely attributable to no commercial harvest near Monroe (YPTG, 2009) rather than gear differences. Any effect a large percentage of individuals from the 2003 year class, one of the strongest on record (YPTG, 2006), had on 2007 and 2008 morphometric samples is unclear. Incubation and rearing temperatures can have strong effects on morphometrics (Beacham, 1990; Swain et al., 1991; Todd et al., 1981) and meristics (Lindsey, 1988), but both are also partly controlled by genetics (Hagen and Blouw, 1983). The three basins of Lake Erie differ greatly in thermal regimens, with the western basin warmer in summer and warming more quickly in spring than the eastern and central basins. These differences persist from year to year, although there may be interannual differences among sites in warming rates, maximum temperatures, etc. Thermal differences during early ontogeny are probably partly responsible for morphological differences among sites, but whether dominance of the 2003 YC may have increased or decreased those differences cannot be known without detailed site-by-site thermal records, which are not available. Gene flow in 2003 was particularly high compared to other years examined by Sepulveda-Villet and Stepien (2011). Higher gene flow, which resulted in lesser ability to distinguish potential genetic spawning stocks owing to higher diversity of genotypes, would likely result in reduced ability to distinguish morphometric differences because morphometrics are partially controlled by genetics. Similar rearing environment experienced by the 2003 year class might also result in less distinct morphometric differences for those traits influenced by rearing environment. Hence, the most likely effect of the dominance of the 2003 YC in samples would have been to reduce ability to discriminate stock differences, but we were still able to observe differences.
Table 3 Mean site scores on the first three principal components of morphometrics of yellow perch from seven different sites in Lake Erie sampled in 2008. Superscript letters indicate significant differences following Duncan's multiple range test. Sites with different letters are significantly different.
Table 4 Slope, r-squared, and P-values for slopes for linear regressions of Mahalanobi's distance of whole-body morphometrics of yellow perch versus geographic distance between sites for seven sites sampled in Lake Erie in 2008.
Site
MU
PC1
PC2
PC3
Cedar Point Monroe Chagrin Cleveland Erie Perry Dunkirk
1 1 2 2 3 3 4
− 0.0374d − 0.0139c − 0.0126c 0.0244b 0.0307b − 0.0141c 0.0617a
− 0.0011d − 0.0372e 0.0060cd 0.0186b − 0.0421e 0.0346a 0.0137bc
0.0050ab − 0.0021abc 0.0080a − 0.0035bc 0.0074a − 0.0120c 0.0002ab
Site
N
Slope
r-squared
P
Cedar Point Chagrin Cleveland Dunkirk Erie Monroe Perry All possible pairs
6 6 6 6 6 6 6 21
0.0716 0.031 0.0225 0.0237 − 0.0257 0.02 0.0753 0.0344
0.86 0.49 0.55 0.12 0.08 0.13 0.31 0.2
0.007 0.12 0.09 0.50 0.58 0.48 0.25 0.04
538
P.M. Kocovsky, C.T. Knight / Journal of Great Lakes Research 38 (2012) 534–539
Sample sizes smaller than those recommended for whole-body morphometrics of yellow perch probably did not affect outcomes of PCA analyses. Sample sizes, N, for some sites sampled in 2007 were below the 3.5:1 ratio of fish to number of morphometrics, P, recommended by Kocovsky et al. (2009). The primary risk in using an N:P ratio less than 3.5:1 is unstable loadings on PC1, which means failing to adequately account for the overall size component (Kocovsky et al., 2009) when size is not otherwise accounted for. Because we used standardized morphometrics to account for differences in length among groups the risk of instability of PC1 when N is small is reduced. Thresholds reported by Kocovsky et al. (2009) for other outcomes of PCA were lower than 3.5:1, and we met those recommendations in all cases. Hence, we are satisfied that sample sizes were adequate. The origin and maintenance of stock structure of yellow perch in Lake Erie is probably the joint effects of post-glacial origins, water currents on dispersal of juveniles, and adult dispersal. Most yellow perch in Lake Erie likely derived from the Mississippian refugium, but eastern Lake Erie yellow perch probably derived from both the Mississippian and Atlantic refugia (Sepulveda-Villet et al., 2009; Sepulveda-Villet and Stepien, 2011). Yellow perch become limnetic shortly after hatching and generally move passively with currents (Craig, 2000), hence currents and gyres likely act both as avenues for and barriers to dispersal. Beletsky et al. (1999) described Lake Erie as having a as two-gyre anticyclonic circulation pattern in summer 1979–1980. The two gyres were in central Lake Erie, a smaller gyre from east of Point Pelee to Erieau, Ontario, and the larger gyre from Erieau to the Pennsylvania Ridge. León et al. (2005) presented simulated circulation patterns for 1994 and 2001 that were similar to one another and to Beletsky et al. (1999) in that there were two dominant gyres in central Lake Erie. The simulations also identified single gyres in eastern Lake Erie and the Sandusky-Huron sub-basin near the border between MU1 and MU2 and unidirectional flow from west to east along the southern shore (León et al., 2005). Schwab et al. (2009) reported a similar two-gyre pattern for May–October 1994 as well as a single gyre in eastern Lake Erie and unidirectional flow from west to east along the southern shore. These currents and gyres, which seem to persist across years, probably promote stock structure by passive transport of larvae as was proposed by Gerlach et al. (2001) for European perch Perca fluviatilis in Lake Constance. Knutsen et al. (2003) also invoked this mechanism for Atlantic cod Gadus morhua. Currents may also act as a retention or blocking mechanism following hatch and initial drift of yellow perch as suggested for Atlantic cod by Ruzzante et al. (1998). Cryptic barriers to dispersal (sensu Bergek and Björklund, 2007; Bergek et al., 2010), such as areas of seasonal hypoxia or thermal stratification, which are common in central Lake Erie and parts of western Lake Erie (Burns et al., 2005), may also restrict movements of juveniles and adults. Yellow perch in Lake Erie tend to associate with nearshore areas and use protected areas for spawning (Wei et al., 2004), although they do migrate into areas up to 20 m deep during summer prior to thermal stratification and onset of hypoxia. Deep (>20 m in central Lake Erie, >60 m in eastern Lake Erie) or open water areas, which exist in central and eastern Lake Erie, can also act as barriers to dispersal as has been demonstrated for stone loach Barbatula barbatula (Barluenga and Meyer, 2005) and blue mbuna Labeotropheus fuelleborni (Arnegard et al., 1999). In addition to mechanisms that promote or restrict dispersal, spawning site fidelity and kin recognition can maintain population structure. Marking experiments by Kipling and LeCren (1984) in Windermere demonstrated homing of tagged European perch. Kipling and LeCren (1984) recaptured only 2.29% of all fish tagged, but the percentage of recaptured fish that homed was as high as 100% for those released 100–200 m from their tagging location to 44% for those released 1600–3200 m away. This experiment demonstrated not only homing, but also that homing decreases with distance released from capture site. Egg removal experiments by Aalto and Newsome (1990) in Lochaber Lake demonstrated spawning site fidelity of yellow perch. The authors demonstrated that removal of egg masses at one of 11 spawning locations
for a period of four years resulted in declines in number of egg masses at the site from which egg masses were removed in the following two years, whereas increases were observed at nine of the other ten sites. The magnitude of the decrease at the one site from which egg masses were not removed was much less than at the site where removals occurred. Lochaber Lake is only 8.4 km long and 0.7 km at its widest, and spawning sites were separated by only 0.4–4.5 km (Aalto and Newsome, 1989). This work demonstrated that yellow perch can have spawning site fidelity at a very fine scale. Bergek and Björklund (2009) also reported genetic and morphometric differences of European perch at the scale of a few kilometers. The shortest distance between neighboring sites that we sampled was 18 km (mean 62 km). Gerlach et al. (2001) observed two distinct groups of European perch in the Bodensee and a higher degree of relatedness among individuals within sampling sites than between sampling sites. Behrmann-Godel et al., 2006 also reported kin recognition using olfactory cues, which would maintain spawning site fidelity and, thus, stock structure. Three tagging studies in Lake Erie provide insights into fish movements and potential spawning site fidelity of yellow perch in Lake Erie. Rawson (1980) reported that most recaptures of fish tagged during spawning season at five sites in Ohio waters were within a few kilometers of their tagging site. A small proportion (less than 20%) of fish were recaptured more than 100 km from their tagging site a few months after release. MacGregor and Witzel (1987) reported that half of yellow perch captured during spawning season in Long Point Bay (LPB) and released at several locations within LPB and south of Long Point were recaptured near their original point of capture within two weeks of being released. Similarly, half of yellow perch captured during spawning season in LPB and released along the northern shore east of LPB were recaptured near their original point of capture within two weeks of being released. Many of those fish traveled 11 km to reach their point of capture; one fish traveled 71 km from Evans Point. An Ontario Ministry of Natural Resources study (OMNR, 2011) revealed movement patterns similar to those observed by Rawson (1980), with 90% of recaptures proximal to tagging sites within the western basin and the remaining 10% captured many kilometers from tagging locations in the central basin a few months after tagging. Both OMNR (2011) and Rawson (1980) suggest that a majority of fish remain close to their spawning location while a small number migrate to other areas, sometimes quite distant from tagging locations. MacGregor and Witzel (1987) demonstrated that yellow perch can return to areas of original capture during a spawning season, which suggests homing. The OMNR (2011) study further suggests the potential for a ‘return migration’ of yellow perch in October and November, when recaptures proximal to tagging sites increased disproportionately to increases in harvest per unit effort compared to August and September recaptures. Collectively, these studies suggest that yellow perch in Lake Erie may have spawning site fidelity. Evidence from our study, corroborated by genetic evidence, demonstrates that there are likely unique spawning stocks of yellow perch in Lake Erie and that stocks exist at scales finer than MU. That fish migrate, sometimes great distances, from spawning locations (OMNR, 2011; Rawson, 1980) and may have spawning site fidelity further demonstrates that yellow perch in Lake Erie may be a mixed stock fishery. Under current management practices, measures of recruitment or exploitation, such as length at age, average age of harvest, and average length of harvest are estimated at the scale of MU. Data are not presently gathered on discrete spawning stocks as defined here. Thus, there is risk of overexploiting one or more stocks, which could lead not only to local reductions in stock size or biomass but potentially MU-wide population decreases in the event a particular stock is supporting exploitation in a MU (analogous to year class domination). It may also negatively affect long-term sustainability of yellow perch as a species if unique stocks that are better adapted to future conditions in Lake Erie are greatly reduced or extirpated. Despite the evidence of discrete stocks, yellow perch populations have been managed sustainably to date at the MU scale. Evidently, a composite of stocks or single stocks within MUs have sufficed in
P.M. Kocovsky, C.T. Knight / Journal of Great Lakes Research 38 (2012) 534–539
generating recruits to the fishery to date. Conserving stocks has the same benefit on a species as conserving species has on an ecosystem: stock diversity ensures long-term overall population stability, with concomitant ecosystem and economic benefits (Schindler et al., 2010). That we lacked samples from the northern shore and that we had only one or two replicates in each MU limited our ability to understand lake-wide stock diversity. How many stocks there are and the scale of stock-level differentiation is not known. Future efforts that expand sampling to the northern shore and that increase sampling in each of the four management units will improve understanding of stock diversity. As our knowledge of the spatial extent and number of stocks grows, it may become possible to determine, with some degree of error, the likely stock origin of an individual from morphometric characters, but at present this is not possible. Addition of genetic characters in combination with morphometric characters may enhance this ability. A broad, lake-wide effort over multiple years, combining genetics and morphometrics, and multiple sampling sites on each shore within each MU, or more extensive tagging and telemetry studies would be required to characterize stock structure of yellow perch in Lake Erie. Acknowledgments We thank M. Thomas, D. Einhouse, C. Murray, and T. Reynolds for providing fish for analysis and the vessel operators and crews of the research vessels Argo, Channel Cat, Grandon, and Perca used for collections. Advice on statistical analyses was provided by J. Adams. Constructive reviews of previous drafts were provided by A. Cook, R. Kraus, and three anonymous reviewers. Use of trade, product, or firm names does not imply endorsement by the U.S. Government. This article is Contribution 1689 of the U.S. Geological Survey Great Lakes Science Center. References Aalto, S.K., Newsome, G.E., 1989. Evidence of demic structure for a population of yellow perch (Perca flavescens). Can. J. Fish. Aquat. Sci. 46, 184–190. Aalto, S.K., Newsome, G.E., 1990. Additional evidence supporting demic behavior of a yellow perch (Perca flavescens) population. Can. J. Fish. Aquat. Sci. 47, 1959–1962. Arnegard, M.E., Markert, J.A., Danley, P.D., Stauffer Jr., J.R., Ambali, A.J., Kocher, T.D., 1999. Population structure and colour variation of the cichlid fish Labeotropheus fuelleborni Ahl along a recently formed archipelago of rocky habitat patches in southern Lake Malawi. Philos. Trans. R. Soc. Lond. B Biol. Sci. 266 (1415), 119–130. Barluenga, M., Meyer, A., 2005. Old fish in a young lake: stone loach (Pisces: Barbatula barbatula) populations in Lake Constance are genetically isolated by distance. Mol. Ecol. 14, 1229–1239. Beacham, T.D., 1990. A genetic analysis of meristic and morphometric variation in chum salmon (Oncorhynchus keta) at three different temperatures. Can. J. Zool. 68, 225–229. Begg, G.A., Waldman, J.R., 1999. An holistic approach to fish stock identification. Fish. Res. 43, 35–44. Behrmann-Godel, J., Gerlach, G., Eckmann, R., 2006. Kin and population recognition in sympatric Lake Constance perch (Perca fluviatilis L.): can assortative shoaling drive population divergence? Behav. Ecol. Sociobiol. 59, 461–468. Beletsky, D., Saylor, J.H., Schwab, D.J., 1999. Mean circulation in the Great Lakes. J. Great Lakes Res. 25, 78–93. Bergek, S., Björklund, M., 2007. Cryptic barriers to dispersal within a lake allow genetic differentiation of Eurasian perch. Evolution 61, 2035–2041. Bergek, S., Björklund, M., 2009. Genetic and morphological divergence reveals local subdivision of perch (Perca fluviatilis L.). Biol. J. Linnean Soc 96, 746–758. Bergek, S., Sundblad, G.S., Björklund, M., 2010. Population differentiation in perch Perca fluviatilis: environmental effects on gene flow? J. Fish Biol. 76, 1159–1172. Bookstein, F.L., 1989. “Size and shape”: a comment on semantics. Syst. Zool. 38, 173–180. Bookstein, F.L., Chernoff, B., Elder, R.L., Humphries Jr., J.M., Smith, G.R., Strauss, R.E., 1985. Morphometrics in Evolutionary Biology. Special Publication, 15. The Academy of Natural Sciences, Philadelphia, PA. Bronte, C.R., Moore, S.A., 2007. Morphological variation of siscowet lake trout in Lake Superior. Trans. Am. Fish. Soc. 136, 509–517. Burns, N.M., Rockwell, D.C., Bertram, P.E., Dolan, D.M., Ciborowski, J.J.H., 2005. Trends in temperature, Secchi depth, and dissolved oxygen depletion rates in the central basin of Lake Erie, 1983–2002. J. Great Lakes Res. 31 (Supplement 2), 35–49. Craig, J.F., 2000. Percid Fishes: Systematics, Ecology, and Exploitation. Blackwell Science, Ltd., Oxford, England. Elliott, N.G., Haskard, K., Koslow, J.A., 1995. Morphometric analysis of orange roughy (Hoplostethus atlanticus) off the continental slope of southern Australia. J. Fish Biol. 46, 202–220.
539
Ford, A.M., Stepien, C.A., 2004. Genetic variation and spawning population structure in Lake Erie yellow perch, Perca flavescens: a comparison with a Maine population. In: Barry, T.P., Malison, J.A. (Eds.), Proceedings of Percis III, the 3rd International Symposium on Percid Fishes. University of Wisconsin Sea Grant Institute, Madison, Wisconsin, pp. 131–132. Gerlach, G., Schardt, U., Eckmann, R., Meyer, A., 2001. Kin-structured subpopulations in Eurasian perch (Perca fluviatilis L.). Heredity 86, 213–221. Hagen, D.W., Blouw, D.M., 1983. Heritability of dorsal spines in the fourspine stickleback (Apeltes quadracus). Heredity 50, 275–281. Henderson, B.A., Nepszy, S.J., 1989. Yellow perch (Perca flavescens) growth and mortality rates in Lake St. Clair and the three basins of Lake Erie, 1963–86. J. Great Lakes Res. 15, 317–326. Humphries Jr., J.M., Bookstein, F.L., Chernoff, B., Smith, G.R., Elder, R.L., Poss, S.G., 1981. Multivariate discrimination by shape in relation to size. Syst. Zool. 30, 291–308. Kipling, C., LeCren, E.D., 1984. Mark-recapture experiments on fish in Windermere, 1943–1982. J. Fish Biol. 24, 395–414. Knutsen, H., Jorde, P.E., André, C., Stenseth, N.C.H.R., 2003. Fine-scaled geographical population structuring in a highly mobile marine species: the Atlantic cod. Mol. Ecol. 12, 385–394. Kocovsky, P.M., Adams, J.V., Bronte, C.R., 2009. The effect of sample size on the stability of principal components analysis of truss-based fish morphometrics. Trans. Am. Fish. Soc. 138, 487–496. León, L.F., Imberger, J., Smith, R.E.H., Hecky, R.E., Lam, D.C.L., Schertzer, W.M., 2005. Modeling as a tool for nutrient management in Lake Erie: a hydrodynamics study. J. Great Lakes Res. 31 (Suppl. 2), 309–318. Lindsey, C.C., 1988. Factors controlling meristic variation. In: Hoar, W.S., Randall, D.J. (Eds.), Fish Physiology Volume XI. The Physiology of Developing Fish. Academic Press, Inc., San Diego, California, pp. 197–274. MacGregor, R.B., Witzel, L.D., 1987. A twelve year study of the fish community in the Nanticoke Region of Long Point Bay, Lake Erie. Lake Erie Fisheries Assessment Unit Report 1987‐3. Ontario Ministry of Natural Resources, Port Dover, Ontario. 393 pp. Ontario Ministry of Natural Resources (OMNR), 2011. 2006–2009 Annual Report. Lake Erie MU. ISSN 1715‐8087 (Print), ISBN 978-1-4435-6376-5 (Print, 2006–2009 ed.), ISSN 1925-539X (Online), ISBN 978-1-4435-6377-2 (PDF, 2006–2009 ed.) 117 pp. + iv. Rawson, M.R., 1980. Lake Erie Fisheries Investigations: Yellow Perch Movements. Ohio Department of Natural Resources, Division of Wildlife, Federal Aid in Fish Restoration Project F-35-R-18, Columbus, Ohio. 25 pp. Ruzzante, D.E., Taggart, C.T., Cook, D., 1998. A nuclear DNA basis for shelf- and bankscale population structure in northwest Atlantic cod (Gadus morhua): Labrador to Georges Bank. Mol. Ecol. 7, 1663–1680. Ryan, P.A., Knight, R., MacGregor, R., Towns, G., Hoopes, R., Culligan, W., 2003. Fish Community Goals and Objectives for Lake Erie. Great Lakes Fishery Commission, Windsor. Ont. Spec. Publ. 03‐02. Schindler, D.E., Hilborn, R., Chasco, B., Boatright, C.P., Quinn, T.P., Rogers, L.A., Webster, M.S., 2010. Population diversity and the portfolio effect in an exploited species. Nature 465, 609–613. Schwab, D.J., Beletsky, D., DePinto, J., Dolan, D.M., 2009. A hydrodynamic approach to modeling phosphorus distribution in Lake Erie. J. Great Lakes Res. 35, 50–60. Sepulveda-Villet, O.J., Stepien, C.A., 2011. Fine-scale population genetic structure of the yellow perch Perca flavescens in Lake Erie. Can. J. Fish. Aquat. Sci. 68, 1435–1453. Sepulveda-Villet, O.J., Ford, A.M., Williams, J.D., Stepien, C.A., 2009. Population genetic diversity and phylogeographic divergence patterns of the yellow perch (Perca flavescens). J. Great Lakes Res. 35, 107–119. Stapanian, M.A., Kocovsky, P.M., Garner, F.C., 2008. Application of two tests of multivariate discordancy to fisheries data sets. Environ. Biol. Fishes 82, 325–339. Sundberg, P., 1989. Shape and size-constrained principal components analysis. Syst. Zool. 38, 166–168. Swain, D.P., Foote, C.J., 1999. Stocks and chameleons: the use of phenotypic variation in stock identification. Fish. Res. 43, 113–128. Swain, D.P., Riddell, B.E., Murray, C.B., 1991. Morphological differences between hatchery and wild populations of Coho salmon (Oncorhynchus kisutch): environmental versus genetic origin. Can. J. Fish. Aquat. Sci. 48, 1783–1791. Tiffan, K.F., Rondorf, D.W., Garland, R.D., Verhey, P.A., 2000. Identification of juvenile fall versus spring Chinook salmon migrating through the lower Snake River based on body morphology. Trans. Am. Fish. Soc. 129, 1389–1395. Todd, T.N., Smith, G.R., Cable, L.E., 1981. Environmental and genetic contributions to morphological differentiation in ciscoes (Coregoninae) of the Great Lakes. Can. J. Fish. Aquat. Sci. 38, 59–67. Wei, A., Chow-Fraser, P., Albert, D., 2004. Influence of shoreline features on fish distribution in the Laurentian Great Lakes. Can. J. Fish. Aquat. Sci. 61, 1113–1123. YPTG, 2006. Annual Report of the Lake Erie Yellow Perch Task Group. Lake Erie Committee, Great Lakes Fishery Commission, Yellow Perch Task Group, Ann Arbor, Michigan. 48 pp. YPTG, 2009. Annual Report of the Lake Erie Yellow Perch Task Group. Yellow Perch Task Group, Lake Erie Committee, Great Lakes Fishery Commission, Ann Arbor, Michigan. 46 pp. YPTG, 2011. Annual Report of the Lake Erie Yellow Perch Task Group. Yellow Perch Task Group, Lake Erie Committee, Great Lakes Fishery Commission, Ann Arbor, Michigan. 40 pp.
Contents lists available at SciVerse ScienceDirect
Journal of Great Lakes Research journal homepage: www.elsevier.com/locate/jglr
Morphological evidence of discrete stocks of yellow perch in Lake Erie Patrick M. Kocovsky a,⁎, Carey T. Knight b, 1 a b
U.S. Geological Survey, Great Lakes Science Center, Lake Erie Biological Station, 6100 Columbus Avenue, Sandusky, OH 44857, USA Ohio Department of Natural Resources, Division of Wildlife, 1190 High Street, Fairport Harbor, OH 44077, USA
a r t i c l e
i n f o
Article history: Received 18 November 2011 Accepted 20 April 2012 Available online 29 May 2012 Communicated by Wendylee Stott Index words: Exploitation Harvest Management Whole-body morphometrics
a b s t r a c t Identification and management of unique stocks of exploited fish species are high-priority management goals in the Laurentian Great Lakes. We analyzed whole-body morphometrics of 1430 yellow perch Perca flavescens captured during 2007–2009 from seven known spawning areas in Lake Erie to determine if morphometrics vary among sites and management units to assist in identification of spawning stocks of this heavily exploited species. Truss-based morphometrics (n = 21 measurements) were analyzed using principal component analysis followed by ANOVA of the first three principal components to determine whether yellow perch from the several sampling sites varied morphometrically. Duncan's multiple range test was used to determine which sites differed from one another to test whether morphometrics varied at scales finer than management unit. Morphometrics varied significantly among sites and annually, but differences among sites were much greater. Sites within the same management unit typically differed significantly from one another, indicating morphometric variation at a scale finer than management unit. These results are largely congruent with recently-published studies on genetic variation of yellow perch from many of the same sampling sites. Thus, our results provide additional evidence that there are discrete stocks of yellow perch in Lake Erie and that management units likely comprise multiple stocks. Published by Elsevier B.V. on behalf of International Association for Great Lakes Research.
Introduction Yellow perch Perca flavescens in Lake Erie support one of the largest commercial and recreational fisheries in the Great Lakes. In 2010 gillnet, trapnet, and recreational fisheries harvested a combined 4389 metric tons (YPTG, 2011) valued at several hundred million US$. Management agencies presently use management units (MU, Fig. 1) for allocating harvest quotas, which correspond coarsely to the three major basins of Lake Erie, with the larger central basin divided into two MUs. The MU boundaries intersect shores at easily identifiable landmarks, such as lighthouses, and were drawn with full consideration to socioeconomic concerns (e.g., at least one major port exists within each MU) and political boundaries (e.g., counties in Ontario). Hence, MUs capture coarse differences in trophic conditions and are convenient for landing and reporting of harvest, but they may lack ecological relevance if they do not adequately protect distinct stocks. Yellow perch have been managed sustainably under the current MU structure since the late 1970s, but recent poor recruitment (e.g., YPTG, 2011) coupled with a general interest in identifying and managing unique stocks whenever possible (Ryan et al., 2003) has increased interest in examining stock structure of yellow perch.
⁎ Corresponding author. Tel.: + 1 419 625 1976x17. E-mail addresses: [email protected] (P.M. Kocovsky), [email protected] (C.T. Knight). 1 Tel.: + 1 440 352 4199.
Evidence for discrete stocks of yellow perch in Lake Erie has been accumulating for the past two decades. Henderson and Nepszy (1989) reported that central basin yellow perch had greater length at age and reached longer lengths than western basin yellow perch. They also noted differences in mortality and growth rates, although differences were correlated with fishing effort. This study demonstrated that there were likely different stocks within the major basins of Lake Erie. Genetic research has provided additional evidence of variation at scale finer than basin. Ford and Stepien (2004) reported 10 distinct mitochondrial DNA (mtDNA) haplotypes, some of which were unique to a basin, and some of which were found in only two of the three basins. They attributed these observations to differences in glacial refugia of yellow perch that colonized Lake Erie following retreat of the most recent Wisconsinan glaciers. This effort demonstrated the potential for differentiation at a scale finer than MU. Ryan et al. (2003) explicitly acknowledged the need for stock-based management by calling for identification of distinct spawning stocks for several exploited Lake Erie species. The usefulness and value of morphometrics for stock discrimination have been acknowledged for some time (e.g., Begg and Waldman, 1999), although Swain and Foote (1999) cautioned about the effects of environmental influences on morphology. In the Great Lakes, Bronte and Moore (2007) used whole-body morphometrics to successfully distinguish siscowet lake trout Salvelinus namaycush based on whole-body morphometrics. Similarly, Elliott et al. (1995) distinguished orange roughy Hoplostethus atlanticus stocks in Australian waters that vary little genetically. Tiffan et al. (2000) distinguished spring and fall runs of Chinook salmon Oncorhynchus tschawytscha using
0380-1330/$ – see front matter. Published by Elsevier B.V. on behalf of International Association for Great Lakes Research. doi:10.1016/j.jglr.2012.04.006
P.M. Kocovsky, C.T. Knight / Journal of Great Lakes Research 38 (2012) 534–539
535
was included in images to provide a scale for converting image units to millimeters. Color images were taken with a Sony Super Steady Shot, 5.1 megapixel digital camera mounted on a tripod. A level was used to ensure that the lens of the camera was parallel to the table surface on which fish were placed. Following image capture, total length (TL in mm) was measured, sex was confirmed, otoliths were removed for age analysis, and a pectoral fin was removed from a sub-sample for genetic analysis. We used the box-truss method (Bookstein et al., 1985) to characterize whole-body shape. Morphometrics were collected using SigmaScan software, version 5.0. Ten landmarks corresponding to skeletal features were identified and distances among them were calculated using the distance formula. A total of 21 morphometrics were used to characterize whole body shape (Fig. 2). Fig. 1. Sampling locations (solid circles) and management units (MU) for yellow perch in Lake Erie. Solid lines are boundaries between MUs. Dashed lines are state and international boundaries.
morphometrics. The combined use of morphometric and genetic differences can provide strong evidence of stock-level differences within larger populations (Begg and Waldman, 1999). We examined whole-body morphometrics of male yellow perch captured during spawning from several spawning locations throughout Lake Erie to test three hypotheses for morphometric variation: 1) spatial variation was greater than temporal variation for sites sampled in multiple years; 2) morphometrics varied at a scale finer than MU; and 3) morphometric distance (i.e., the magnitude of morphometric differences) was unrelated to physical distance, as has been observed for genetic distance (Sepulveda-Villet and Stepien, 2011). Our objective was to determine the scale of morphometric differentiation of yellow perch in Lake Erie to support identification and improved management of unique yellow perch stocks. Methods Fish collection Yellow perch were collected as part of annual assessment sampling by fisheries management agencies and from commercial trap nets at seven sites within the four MUs (Fig. 1) during 2007–2009. Samples from Monroe were captured with a fishery-independent fyke net. Samples from Cedar Point were obtained from a commercial trap net. At the time of the Cedar Point sample the commercial fishery for yellow perch in MU 1 was closed. The area was still being fished for white perch Morone americana. Yellow perch were taken from the net under a US Geological Survey collection permit. All other fish were captured in bottom trawls during spawning season near known spawning locations. Only mature males>150 mm total length (TL) and expressing milt were retained for analyses. Females were excluded to eliminate effects of enlarged ovaries on morphometrics. Yellow perch are not sexually dimorphic in the literal sense (i.e., female and male body shapes do not differ; Craig, 2000), hence eliminating females did not bias results. We restricted collection of males for analysis to fish at least 150 mm TL because our age estimation efforts for this work revealed that most were age 2 and older by that length, which greatly reduces the potential for allometric growth confounding results. The target minimum sample size (N) was 77 fish per site following recommendations by Kocovsky et al. (2009). All fish were placed on ice immediately after capture and were transported to laboratories for image capture for morphometric analysis.
Statistical analyses Multivariate outlier analysis was conducted to identify discordant fish and to determine if any particular morphometric was causal using Scout software (Stapanian et al., 2008). Fish identified as discordant were re-measured. Fish still discordant after re-measurement were removed from further analyses (see results for exceptions). Two separate analyses were conducted on subsets of the data to address our hypotheses regarding morphological differences. For each we followed the same general analytical framework. Morphometric data were reduced using principal component analysis (PCA) using the covariance matrix. Prior to PCA morphometrics were standardized to fish standard length using the procedure described by Elliott et al. (1995). This step was taken to eliminate the influence of fish length on the first principal component, which can be substantial and which can confound interpretation of shape differences independent of fish size [Bookstein et al., 1985; see also Humphries et al. (1981), Bookstein (1989), and Sundberg (1989) for discussion of “size” and “shape” in morphometric analyses]. Next, either an ANOVA or nested ANOVA was conducted on the first, second and third principal components followed by Duncan's multiple range test to test for differences among sites and MUs. The magnitude of spatial versus temporal variation was examined using nested ANOVA of principal components from all sites (locations on Fig. 1) sampled in at least two years: Cleveland 2007 and 2008; Perry, Chagrin, Erie, and Dunkirk 2007–2009; and Monroe 2008–2009. Year was nested within each site for ANOVA to avoid constraining annual differences in morphology to be the same for all sites. This analysis permitted examination of spatial versus temporal variation for the broadest range of sites. Spatial variation was assessed with ANOVA of principal components using data from Monroe, Cedar Point, Cleveland, Chagrin, Perry, Erie, and Dunkirk in 2008, when we had greatest spatial coverage and at least two sites within all MUs except MU 4. Fixing year allowed for assessment of spatial differences within a year. The relationship between morphometric distance and geographic distance was analyzed using linear regression to test whether morphometric differences were related to geographic distance. Morphometric distance was calculated as Mahalanobi's distance between all possible pairs of sampling sites using PROC DISCRIM in SAS version 9.2. Geographic distance between all possible pairs of sites was calculated
Morphometric data collection All fish were processed within 24 h of capture. Fish were placed in a dissecting pan on their right side and medial fins were pinned in place to permit easy identification of fin origins and insertions. When pinning fins we ensured that body shape was not distorted. A centimeter scale
Fig. 2. Drawing of a yellow perch with morphometric landmarks (filled circles) and truss measurements (lines between landmarks) identified.
536
P.M. Kocovsky, C.T. Knight / Journal of Great Lakes Research 38 (2012) 534–539
using latitude–longitude coordinates and a distance calculator provided by the National Oceanic and Atmospheric Administration (http:// www.nhc.noaa.gov/gccalc.shtml). This analysis was restricted to 2008 data because it had the broadest spatial coverage within a year and to avoid confounding of geographic and annual differences. Results Fish total lengths varied among sites and years (Table 1). Fish from Monroe were longer than fish from all other sites in both years, but among the other sites there was broad spatial and temporal overlap in total lengths. Ages of fish examined ranged between 1 and 19 years, with 85% of fish 3 to 6 years old. Samples in 2007 (57%) and 2008 (49%) had large percentages of individuals from the 2003 year class, one of the largest on record for yellow perch in Lake Erie (YPTG, 2006). Discordancy analysis revealed that most fish less than 180 mm TL had several morphometrics that were discordant suggesting that they were from a different statistical population. Thus, all fish less than 180 mm TL (n = 103) were removed from analyses. Several unusually large fish were also identified as potential outliers. Morphometrics identified as discordant for those fish were re-measured, and most were within 1 mm of original measurements, indicating that discordancies did not result from misidentifying landmarks. All of the unusually large fish that had discordant morphometrics that were not due to misidentification of landmarks were retained for analysis in order to capture the full range of morphometric diversity. Doing so meant higher variation, hence greater difficulty identifying differences among the various groups analyzed. The first, second, and third PCs accounted for 32%, 18%, and 14% of the variation in morphometrics, respectively, for sites sampled from 2007 through 2009. The remaining PCs each accounted for less than 9% of variation and were not interpreted. Variation in site (F5,1424 =76.1, Pb 0.0001) and year within site (F10, 1419 = 17.1, Pb 0.0001) were both significant on the first PC. Both site (F5, 1424 =90.6, Pb 0.0001) and year within site (F10, 1419 =28.8, Pb 0.0001) were also significant on the second PC. For both of the first two PCs, which combined accounted for 50% of the variation in fish shape, the magnitude of F-values indicates that both are primarily a gradient in spatial variation. Variation in year within site (F10, 1419 =67.5, Pb 0.0001) exceeded site variation (F5, 1424 = 46.6, Pb 0.0001) on the third PC, indicating that it was primarily a gradient in annual variation. Greater variation accounted for by site on the two PCs with the greatest variation explained and a comparatively low differential between variation explained by site and year on the third PC strongly indicates that spatial variation exceeds annual variation in yellow perch morphometrics at the sites examined. Duncan's multiple range test revealed spatial differences at the site scale (Table 2). Sites within the same MU were typically different than one another as well as from sites in different MUs on all three PCs (Table 2; Fig. 3). This outcome supports the research hypothesis that spatial variation was greater than annual variation in yellow perch morphometrics at those sites where we had multiple years of data. The first, second, and third PCs accounted for 32%, 18%, and 11% of the variation in morphometrics, respectively, for seven sites sampled
Table 2 Mean site scores on the first three principal components of morphometrics of yellow perch from six different sites in Lake Erie sampled from 2007 through 2009. Superscript letters indicate significant differences following Duncan's multiple range test. Sites with different letters are significantly different. Site Monroe Chagrin Cleveland Erie Perry Dunkirk
MU 1 2 2 3 3 4
PC1
PC2 d
− 0.0140 − 0.0283e 0.0062c 0.0200b − 0.0179d 0.0523a
PC3 d
− 0.0427 0.0111b 0.0109b − 0.0027c 0.0214a − 0.0077c
− 0.0106d 0.0013c 0.0218a − 0.0219e 0.0046bc 0.0088b
in 2008. The remaining PCs each accounted for less than 10% of variation and were not interpreted. The first (F6, 745 = 42.8, P b 0.0001), second (F6, 745 = 59.1, P b 0.0001), and third (F6, 745 = 4.67, P b 0.0001) PCs differed significantly by site. Duncan's multiple range tests revealed that sites in the same MU differed from one another on all three PCs except for the two MU1 sites on PC3 (Table 3). Sites from the same MU were clearly distinguishable from one another visually (Fig. 3). This result supports the research hypothesis that morphometrics vary at scales finer than MU. Geographic distance was positively related to morphometric distance when all possible pairs of sites were analyzed (linear regression; F1, 19 = 4.74, P = 0.04, r 2 = 0.20; Table 4). When inter-site differences were analyzed for each site individually, only geographic distance of the six other sites from Cedar Point was positively and significantly related to morphometric distance (Table 4). Thus, the lake-wide relationship of increasing morphometric distance with increasing geographic distance was strongly influenced by distances to Cedar Point.
Discussion Our analyses revealed significant spatial differences in whole-body morphometrics of yellow perch. Significant annual variation was also observed, but spatial variation was greater. For several pairs of sites, those geographically distant from one another were more similar morphometrically than those geographically proximal. All fish used in analyses were mature males, eliminating potential distortion from females owing to enlarged ovaries, and were sampled during the spawning period in known spawning areas. Although it is possible that some fish sampled were not successful spawners, were intercepted on their way to another spawning location, or spawned at more than one location, such phenomena would decrease the probability of detecting morphometric differences at the site scale. The overall outcome of our analyses supports the research hypothesis that whole-body morphology of yellow perch varies at scales finer than MU in Lake Erie. Begg and Waldman (1999) called for a ‘holistic’ approach to stock identification that included complimentary techniques. Here we follow the stock concept provided by Begg and Waldman (1999), where stocks are “semi-discrete groups of fish with some definable attributes of interest to managers.” We also agree with Ryan et al. (2003) that
Table 1 Mean total length (± SE) of yellow perch captured for morphometric analysis from sites in Lake Erie, 2007–2009. Means with the same superscript letter are not significantly different. Site
Cedar Point Monroe Chagrin Cleveland Erie Perry Dunkirk
Management unit 1 1 2 2 3 3 4
2007 Mean TL
207.4 ± 2.5fgh 195.1 ± 2.1i 206.3 ± 2.7gh 207.2 ± 2.1fgh 209.7 ± 3.5efgh
2008
2009
N
Mean TL
N
69 68 53 85 61
216.8 ± 1.3cde 241.7 ± 1.9a 219.6 ± 2.1cd 212.0 ± 1.7efgh 205.4 ± 2.3h 228.8 ± 1.5b 213.7 ± 2.6cdefg
133 133 111 109 66 116 84
Mean TL
N
234.5 ± 3.3b 214.9 ± 1.7cdef
85 115
211.0 ± 1.9efgh 209.0 ± 1.9fgh 213.0 ± 3.4cdefgh
115 96 64
P.M. Kocovsky, C.T. Knight / Journal of Great Lakes Research 38 (2012) 534–539
537
Fig. 3. Mean scores on the first three principal components of morphometrics of yellow perch sampled from six sites 2007-2009 (A) and from seven sites in 2008 (B) in Lake Erie. Sites represented by the same shape are in the same management unit.
identification and conservation of unique spawning stocks provides the best pathway for sustainable management of exploited species. Although we did not employ multiple techniques in this effort, results of efforts to examine genetic differences among yellow perch from the same sites we sampled that were concurrent to our efforts (Sepulveda-Villet and Stepien, 2011; Sepulveda-Villet et al., 2009) mostly agree with our morphometric results. Sepulveda-Villet et al. (2009) reported that variation in mtDNA control region sequences among sites within MUs in Lake Erie was greater than variation among MUs. They also reported that inter-site variation was not congruent with MU (i.e., sites more similar genetically were not necessarily from the same or a neighboring MU), which is also consistent with our morphometric analyses. Sepulveda-Villet and Stepien (2011) also examined variation in 15 nuclear microsatellite loci extracted from pectoral fin clips from yellow perch from 13 spawning locations in Lake Erie. Many of those fin clips were provided by us, and for several sites and years fin clips were taken from the same fish used in morphometric analyses after images were captured. Sepulveda-Villet and Stepien (2011) reported significant differences in nuclear microsatellites for nearly all pairwise comparisons among sampling sites. Differences were not related to basin, MU, or spatial distance. The mostly congruent genetic and morphometric results provides strong evidence that yellow perch exist as discrete stocks in Lake Erie and that stocks exist at scales finer than MU. Gear differences, dominance of the 2003 YC, and smaller-thandesired sample sizes for a few sites were sampling liabilities, but these liabilities probably did not affect the overall outcome of morphometric analyses. Although some fish were sampled in trap nets or fyke nets and others in bottom trawls, which may have different size selectivities, restricting our analyses to mature males >180 mm TL and standardizing
morphometrics to standard length eliminated any effects of potential gear bias in size selectivity. The longer mean total length of fish from Monroe was most likely attributable to no commercial harvest near Monroe (YPTG, 2009) rather than gear differences. Any effect a large percentage of individuals from the 2003 year class, one of the strongest on record (YPTG, 2006), had on 2007 and 2008 morphometric samples is unclear. Incubation and rearing temperatures can have strong effects on morphometrics (Beacham, 1990; Swain et al., 1991; Todd et al., 1981) and meristics (Lindsey, 1988), but both are also partly controlled by genetics (Hagen and Blouw, 1983). The three basins of Lake Erie differ greatly in thermal regimens, with the western basin warmer in summer and warming more quickly in spring than the eastern and central basins. These differences persist from year to year, although there may be interannual differences among sites in warming rates, maximum temperatures, etc. Thermal differences during early ontogeny are probably partly responsible for morphological differences among sites, but whether dominance of the 2003 YC may have increased or decreased those differences cannot be known without detailed site-by-site thermal records, which are not available. Gene flow in 2003 was particularly high compared to other years examined by Sepulveda-Villet and Stepien (2011). Higher gene flow, which resulted in lesser ability to distinguish potential genetic spawning stocks owing to higher diversity of genotypes, would likely result in reduced ability to distinguish morphometric differences because morphometrics are partially controlled by genetics. Similar rearing environment experienced by the 2003 year class might also result in less distinct morphometric differences for those traits influenced by rearing environment. Hence, the most likely effect of the dominance of the 2003 YC in samples would have been to reduce ability to discriminate stock differences, but we were still able to observe differences.
Table 3 Mean site scores on the first three principal components of morphometrics of yellow perch from seven different sites in Lake Erie sampled in 2008. Superscript letters indicate significant differences following Duncan's multiple range test. Sites with different letters are significantly different.
Table 4 Slope, r-squared, and P-values for slopes for linear regressions of Mahalanobi's distance of whole-body morphometrics of yellow perch versus geographic distance between sites for seven sites sampled in Lake Erie in 2008.
Site
MU
PC1
PC2
PC3
Cedar Point Monroe Chagrin Cleveland Erie Perry Dunkirk
1 1 2 2 3 3 4
− 0.0374d − 0.0139c − 0.0126c 0.0244b 0.0307b − 0.0141c 0.0617a
− 0.0011d − 0.0372e 0.0060cd 0.0186b − 0.0421e 0.0346a 0.0137bc
0.0050ab − 0.0021abc 0.0080a − 0.0035bc 0.0074a − 0.0120c 0.0002ab
Site
N
Slope
r-squared
P
Cedar Point Chagrin Cleveland Dunkirk Erie Monroe Perry All possible pairs
6 6 6 6 6 6 6 21
0.0716 0.031 0.0225 0.0237 − 0.0257 0.02 0.0753 0.0344
0.86 0.49 0.55 0.12 0.08 0.13 0.31 0.2
0.007 0.12 0.09 0.50 0.58 0.48 0.25 0.04
538
P.M. Kocovsky, C.T. Knight / Journal of Great Lakes Research 38 (2012) 534–539
Sample sizes smaller than those recommended for whole-body morphometrics of yellow perch probably did not affect outcomes of PCA analyses. Sample sizes, N, for some sites sampled in 2007 were below the 3.5:1 ratio of fish to number of morphometrics, P, recommended by Kocovsky et al. (2009). The primary risk in using an N:P ratio less than 3.5:1 is unstable loadings on PC1, which means failing to adequately account for the overall size component (Kocovsky et al., 2009) when size is not otherwise accounted for. Because we used standardized morphometrics to account for differences in length among groups the risk of instability of PC1 when N is small is reduced. Thresholds reported by Kocovsky et al. (2009) for other outcomes of PCA were lower than 3.5:1, and we met those recommendations in all cases. Hence, we are satisfied that sample sizes were adequate. The origin and maintenance of stock structure of yellow perch in Lake Erie is probably the joint effects of post-glacial origins, water currents on dispersal of juveniles, and adult dispersal. Most yellow perch in Lake Erie likely derived from the Mississippian refugium, but eastern Lake Erie yellow perch probably derived from both the Mississippian and Atlantic refugia (Sepulveda-Villet et al., 2009; Sepulveda-Villet and Stepien, 2011). Yellow perch become limnetic shortly after hatching and generally move passively with currents (Craig, 2000), hence currents and gyres likely act both as avenues for and barriers to dispersal. Beletsky et al. (1999) described Lake Erie as having a as two-gyre anticyclonic circulation pattern in summer 1979–1980. The two gyres were in central Lake Erie, a smaller gyre from east of Point Pelee to Erieau, Ontario, and the larger gyre from Erieau to the Pennsylvania Ridge. León et al. (2005) presented simulated circulation patterns for 1994 and 2001 that were similar to one another and to Beletsky et al. (1999) in that there were two dominant gyres in central Lake Erie. The simulations also identified single gyres in eastern Lake Erie and the Sandusky-Huron sub-basin near the border between MU1 and MU2 and unidirectional flow from west to east along the southern shore (León et al., 2005). Schwab et al. (2009) reported a similar two-gyre pattern for May–October 1994 as well as a single gyre in eastern Lake Erie and unidirectional flow from west to east along the southern shore. These currents and gyres, which seem to persist across years, probably promote stock structure by passive transport of larvae as was proposed by Gerlach et al. (2001) for European perch Perca fluviatilis in Lake Constance. Knutsen et al. (2003) also invoked this mechanism for Atlantic cod Gadus morhua. Currents may also act as a retention or blocking mechanism following hatch and initial drift of yellow perch as suggested for Atlantic cod by Ruzzante et al. (1998). Cryptic barriers to dispersal (sensu Bergek and Björklund, 2007; Bergek et al., 2010), such as areas of seasonal hypoxia or thermal stratification, which are common in central Lake Erie and parts of western Lake Erie (Burns et al., 2005), may also restrict movements of juveniles and adults. Yellow perch in Lake Erie tend to associate with nearshore areas and use protected areas for spawning (Wei et al., 2004), although they do migrate into areas up to 20 m deep during summer prior to thermal stratification and onset of hypoxia. Deep (>20 m in central Lake Erie, >60 m in eastern Lake Erie) or open water areas, which exist in central and eastern Lake Erie, can also act as barriers to dispersal as has been demonstrated for stone loach Barbatula barbatula (Barluenga and Meyer, 2005) and blue mbuna Labeotropheus fuelleborni (Arnegard et al., 1999). In addition to mechanisms that promote or restrict dispersal, spawning site fidelity and kin recognition can maintain population structure. Marking experiments by Kipling and LeCren (1984) in Windermere demonstrated homing of tagged European perch. Kipling and LeCren (1984) recaptured only 2.29% of all fish tagged, but the percentage of recaptured fish that homed was as high as 100% for those released 100–200 m from their tagging location to 44% for those released 1600–3200 m away. This experiment demonstrated not only homing, but also that homing decreases with distance released from capture site. Egg removal experiments by Aalto and Newsome (1990) in Lochaber Lake demonstrated spawning site fidelity of yellow perch. The authors demonstrated that removal of egg masses at one of 11 spawning locations
for a period of four years resulted in declines in number of egg masses at the site from which egg masses were removed in the following two years, whereas increases were observed at nine of the other ten sites. The magnitude of the decrease at the one site from which egg masses were not removed was much less than at the site where removals occurred. Lochaber Lake is only 8.4 km long and 0.7 km at its widest, and spawning sites were separated by only 0.4–4.5 km (Aalto and Newsome, 1989). This work demonstrated that yellow perch can have spawning site fidelity at a very fine scale. Bergek and Björklund (2009) also reported genetic and morphometric differences of European perch at the scale of a few kilometers. The shortest distance between neighboring sites that we sampled was 18 km (mean 62 km). Gerlach et al. (2001) observed two distinct groups of European perch in the Bodensee and a higher degree of relatedness among individuals within sampling sites than between sampling sites. Behrmann-Godel et al., 2006 also reported kin recognition using olfactory cues, which would maintain spawning site fidelity and, thus, stock structure. Three tagging studies in Lake Erie provide insights into fish movements and potential spawning site fidelity of yellow perch in Lake Erie. Rawson (1980) reported that most recaptures of fish tagged during spawning season at five sites in Ohio waters were within a few kilometers of their tagging site. A small proportion (less than 20%) of fish were recaptured more than 100 km from their tagging site a few months after release. MacGregor and Witzel (1987) reported that half of yellow perch captured during spawning season in Long Point Bay (LPB) and released at several locations within LPB and south of Long Point were recaptured near their original point of capture within two weeks of being released. Similarly, half of yellow perch captured during spawning season in LPB and released along the northern shore east of LPB were recaptured near their original point of capture within two weeks of being released. Many of those fish traveled 11 km to reach their point of capture; one fish traveled 71 km from Evans Point. An Ontario Ministry of Natural Resources study (OMNR, 2011) revealed movement patterns similar to those observed by Rawson (1980), with 90% of recaptures proximal to tagging sites within the western basin and the remaining 10% captured many kilometers from tagging locations in the central basin a few months after tagging. Both OMNR (2011) and Rawson (1980) suggest that a majority of fish remain close to their spawning location while a small number migrate to other areas, sometimes quite distant from tagging locations. MacGregor and Witzel (1987) demonstrated that yellow perch can return to areas of original capture during a spawning season, which suggests homing. The OMNR (2011) study further suggests the potential for a ‘return migration’ of yellow perch in October and November, when recaptures proximal to tagging sites increased disproportionately to increases in harvest per unit effort compared to August and September recaptures. Collectively, these studies suggest that yellow perch in Lake Erie may have spawning site fidelity. Evidence from our study, corroborated by genetic evidence, demonstrates that there are likely unique spawning stocks of yellow perch in Lake Erie and that stocks exist at scales finer than MU. That fish migrate, sometimes great distances, from spawning locations (OMNR, 2011; Rawson, 1980) and may have spawning site fidelity further demonstrates that yellow perch in Lake Erie may be a mixed stock fishery. Under current management practices, measures of recruitment or exploitation, such as length at age, average age of harvest, and average length of harvest are estimated at the scale of MU. Data are not presently gathered on discrete spawning stocks as defined here. Thus, there is risk of overexploiting one or more stocks, which could lead not only to local reductions in stock size or biomass but potentially MU-wide population decreases in the event a particular stock is supporting exploitation in a MU (analogous to year class domination). It may also negatively affect long-term sustainability of yellow perch as a species if unique stocks that are better adapted to future conditions in Lake Erie are greatly reduced or extirpated. Despite the evidence of discrete stocks, yellow perch populations have been managed sustainably to date at the MU scale. Evidently, a composite of stocks or single stocks within MUs have sufficed in
P.M. Kocovsky, C.T. Knight / Journal of Great Lakes Research 38 (2012) 534–539
generating recruits to the fishery to date. Conserving stocks has the same benefit on a species as conserving species has on an ecosystem: stock diversity ensures long-term overall population stability, with concomitant ecosystem and economic benefits (Schindler et al., 2010). That we lacked samples from the northern shore and that we had only one or two replicates in each MU limited our ability to understand lake-wide stock diversity. How many stocks there are and the scale of stock-level differentiation is not known. Future efforts that expand sampling to the northern shore and that increase sampling in each of the four management units will improve understanding of stock diversity. As our knowledge of the spatial extent and number of stocks grows, it may become possible to determine, with some degree of error, the likely stock origin of an individual from morphometric characters, but at present this is not possible. Addition of genetic characters in combination with morphometric characters may enhance this ability. A broad, lake-wide effort over multiple years, combining genetics and morphometrics, and multiple sampling sites on each shore within each MU, or more extensive tagging and telemetry studies would be required to characterize stock structure of yellow perch in Lake Erie. Acknowledgments We thank M. Thomas, D. Einhouse, C. Murray, and T. Reynolds for providing fish for analysis and the vessel operators and crews of the research vessels Argo, Channel Cat, Grandon, and Perca used for collections. Advice on statistical analyses was provided by J. Adams. Constructive reviews of previous drafts were provided by A. Cook, R. Kraus, and three anonymous reviewers. Use of trade, product, or firm names does not imply endorsement by the U.S. Government. This article is Contribution 1689 of the U.S. Geological Survey Great Lakes Science Center. References Aalto, S.K., Newsome, G.E., 1989. Evidence of demic structure for a population of yellow perch (Perca flavescens). Can. J. Fish. Aquat. Sci. 46, 184–190. Aalto, S.K., Newsome, G.E., 1990. Additional evidence supporting demic behavior of a yellow perch (Perca flavescens) population. Can. J. Fish. Aquat. Sci. 47, 1959–1962. Arnegard, M.E., Markert, J.A., Danley, P.D., Stauffer Jr., J.R., Ambali, A.J., Kocher, T.D., 1999. Population structure and colour variation of the cichlid fish Labeotropheus fuelleborni Ahl along a recently formed archipelago of rocky habitat patches in southern Lake Malawi. Philos. Trans. R. Soc. Lond. B Biol. Sci. 266 (1415), 119–130. Barluenga, M., Meyer, A., 2005. Old fish in a young lake: stone loach (Pisces: Barbatula barbatula) populations in Lake Constance are genetically isolated by distance. Mol. Ecol. 14, 1229–1239. Beacham, T.D., 1990. A genetic analysis of meristic and morphometric variation in chum salmon (Oncorhynchus keta) at three different temperatures. Can. J. Zool. 68, 225–229. Begg, G.A., Waldman, J.R., 1999. An holistic approach to fish stock identification. Fish. Res. 43, 35–44. Behrmann-Godel, J., Gerlach, G., Eckmann, R., 2006. Kin and population recognition in sympatric Lake Constance perch (Perca fluviatilis L.): can assortative shoaling drive population divergence? Behav. Ecol. Sociobiol. 59, 461–468. Beletsky, D., Saylor, J.H., Schwab, D.J., 1999. Mean circulation in the Great Lakes. J. Great Lakes Res. 25, 78–93. Bergek, S., Björklund, M., 2007. Cryptic barriers to dispersal within a lake allow genetic differentiation of Eurasian perch. Evolution 61, 2035–2041. Bergek, S., Björklund, M., 2009. Genetic and morphological divergence reveals local subdivision of perch (Perca fluviatilis L.). Biol. J. Linnean Soc 96, 746–758. Bergek, S., Sundblad, G.S., Björklund, M., 2010. Population differentiation in perch Perca fluviatilis: environmental effects on gene flow? J. Fish Biol. 76, 1159–1172. Bookstein, F.L., 1989. “Size and shape”: a comment on semantics. Syst. Zool. 38, 173–180. Bookstein, F.L., Chernoff, B., Elder, R.L., Humphries Jr., J.M., Smith, G.R., Strauss, R.E., 1985. Morphometrics in Evolutionary Biology. Special Publication, 15. The Academy of Natural Sciences, Philadelphia, PA. Bronte, C.R., Moore, S.A., 2007. Morphological variation of siscowet lake trout in Lake Superior. Trans. Am. Fish. Soc. 136, 509–517. Burns, N.M., Rockwell, D.C., Bertram, P.E., Dolan, D.M., Ciborowski, J.J.H., 2005. Trends in temperature, Secchi depth, and dissolved oxygen depletion rates in the central basin of Lake Erie, 1983–2002. J. Great Lakes Res. 31 (Supplement 2), 35–49. Craig, J.F., 2000. Percid Fishes: Systematics, Ecology, and Exploitation. Blackwell Science, Ltd., Oxford, England. Elliott, N.G., Haskard, K., Koslow, J.A., 1995. Morphometric analysis of orange roughy (Hoplostethus atlanticus) off the continental slope of southern Australia. J. Fish Biol. 46, 202–220.
539
Ford, A.M., Stepien, C.A., 2004. Genetic variation and spawning population structure in Lake Erie yellow perch, Perca flavescens: a comparison with a Maine population. In: Barry, T.P., Malison, J.A. (Eds.), Proceedings of Percis III, the 3rd International Symposium on Percid Fishes. University of Wisconsin Sea Grant Institute, Madison, Wisconsin, pp. 131–132. Gerlach, G., Schardt, U., Eckmann, R., Meyer, A., 2001. Kin-structured subpopulations in Eurasian perch (Perca fluviatilis L.). Heredity 86, 213–221. Hagen, D.W., Blouw, D.M., 1983. Heritability of dorsal spines in the fourspine stickleback (Apeltes quadracus). Heredity 50, 275–281. Henderson, B.A., Nepszy, S.J., 1989. Yellow perch (Perca flavescens) growth and mortality rates in Lake St. Clair and the three basins of Lake Erie, 1963–86. J. Great Lakes Res. 15, 317–326. Humphries Jr., J.M., Bookstein, F.L., Chernoff, B., Smith, G.R., Elder, R.L., Poss, S.G., 1981. Multivariate discrimination by shape in relation to size. Syst. Zool. 30, 291–308. Kipling, C., LeCren, E.D., 1984. Mark-recapture experiments on fish in Windermere, 1943–1982. J. Fish Biol. 24, 395–414. Knutsen, H., Jorde, P.E., André, C., Stenseth, N.C.H.R., 2003. Fine-scaled geographical population structuring in a highly mobile marine species: the Atlantic cod. Mol. Ecol. 12, 385–394. Kocovsky, P.M., Adams, J.V., Bronte, C.R., 2009. The effect of sample size on the stability of principal components analysis of truss-based fish morphometrics. Trans. Am. Fish. Soc. 138, 487–496. León, L.F., Imberger, J., Smith, R.E.H., Hecky, R.E., Lam, D.C.L., Schertzer, W.M., 2005. Modeling as a tool for nutrient management in Lake Erie: a hydrodynamics study. J. Great Lakes Res. 31 (Suppl. 2), 309–318. Lindsey, C.C., 1988. Factors controlling meristic variation. In: Hoar, W.S., Randall, D.J. (Eds.), Fish Physiology Volume XI. The Physiology of Developing Fish. Academic Press, Inc., San Diego, California, pp. 197–274. MacGregor, R.B., Witzel, L.D., 1987. A twelve year study of the fish community in the Nanticoke Region of Long Point Bay, Lake Erie. Lake Erie Fisheries Assessment Unit Report 1987‐3. Ontario Ministry of Natural Resources, Port Dover, Ontario. 393 pp. Ontario Ministry of Natural Resources (OMNR), 2011. 2006–2009 Annual Report. Lake Erie MU. ISSN 1715‐8087 (Print), ISBN 978-1-4435-6376-5 (Print, 2006–2009 ed.), ISSN 1925-539X (Online), ISBN 978-1-4435-6377-2 (PDF, 2006–2009 ed.) 117 pp. + iv. Rawson, M.R., 1980. Lake Erie Fisheries Investigations: Yellow Perch Movements. Ohio Department of Natural Resources, Division of Wildlife, Federal Aid in Fish Restoration Project F-35-R-18, Columbus, Ohio. 25 pp. Ruzzante, D.E., Taggart, C.T., Cook, D., 1998. A nuclear DNA basis for shelf- and bankscale population structure in northwest Atlantic cod (Gadus morhua): Labrador to Georges Bank. Mol. Ecol. 7, 1663–1680. Ryan, P.A., Knight, R., MacGregor, R., Towns, G., Hoopes, R., Culligan, W., 2003. Fish Community Goals and Objectives for Lake Erie. Great Lakes Fishery Commission, Windsor. Ont. Spec. Publ. 03‐02. Schindler, D.E., Hilborn, R., Chasco, B., Boatright, C.P., Quinn, T.P., Rogers, L.A., Webster, M.S., 2010. Population diversity and the portfolio effect in an exploited species. Nature 465, 609–613. Schwab, D.J., Beletsky, D., DePinto, J., Dolan, D.M., 2009. A hydrodynamic approach to modeling phosphorus distribution in Lake Erie. J. Great Lakes Res. 35, 50–60. Sepulveda-Villet, O.J., Stepien, C.A., 2011. Fine-scale population genetic structure of the yellow perch Perca flavescens in Lake Erie. Can. J. Fish. Aquat. Sci. 68, 1435–1453. Sepulveda-Villet, O.J., Ford, A.M., Williams, J.D., Stepien, C.A., 2009. Population genetic diversity and phylogeographic divergence patterns of the yellow perch (Perca flavescens). J. Great Lakes Res. 35, 107–119. Stapanian, M.A., Kocovsky, P.M., Garner, F.C., 2008. Application of two tests of multivariate discordancy to fisheries data sets. Environ. Biol. Fishes 82, 325–339. Sundberg, P., 1989. Shape and size-constrained principal components analysis. Syst. Zool. 38, 166–168. Swain, D.P., Foote, C.J., 1999. Stocks and chameleons: the use of phenotypic variation in stock identification. Fish. Res. 43, 113–128. Swain, D.P., Riddell, B.E., Murray, C.B., 1991. Morphological differences between hatchery and wild populations of Coho salmon (Oncorhynchus kisutch): environmental versus genetic origin. Can. J. Fish. Aquat. Sci. 48, 1783–1791. Tiffan, K.F., Rondorf, D.W., Garland, R.D., Verhey, P.A., 2000. Identification of juvenile fall versus spring Chinook salmon migrating through the lower Snake River based on body morphology. Trans. Am. Fish. Soc. 129, 1389–1395. Todd, T.N., Smith, G.R., Cable, L.E., 1981. Environmental and genetic contributions to morphological differentiation in ciscoes (Coregoninae) of the Great Lakes. Can. J. Fish. Aquat. Sci. 38, 59–67. Wei, A., Chow-Fraser, P., Albert, D., 2004. Influence of shoreline features on fish distribution in the Laurentian Great Lakes. Can. J. Fish. Aquat. Sci. 61, 1113–1123. YPTG, 2006. Annual Report of the Lake Erie Yellow Perch Task Group. Lake Erie Committee, Great Lakes Fishery Commission, Yellow Perch Task Group, Ann Arbor, Michigan. 48 pp. YPTG, 2009. Annual Report of the Lake Erie Yellow Perch Task Group. Yellow Perch Task Group, Lake Erie Committee, Great Lakes Fishery Commission, Ann Arbor, Michigan. 46 pp. YPTG, 2011. Annual Report of the Lake Erie Yellow Perch Task Group. Yellow Perch Task Group, Lake Erie Committee, Great Lakes Fishery Commission, Ann Arbor, Michigan. 40 pp.