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Map of Lake Erie, showing the sites where jaw-tagged Walleyes were released as part of the jaw tagging study. Also shown are the different regions (separated by dashed lines) for which demographic and fishery parameters were estimated: Lake Erie (regions 1–4) and the Huron–Erie Corridor (i.e., Detroit River, Lake St. Clair, and St. Clair River) and Lake Huron (region 5). Depth contours are only applicable to Lake Erie.
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Although the Lake Erie population of Walleyes Sander vitreus exhibits complex spatial structuring, the extent to which population demographics also vary spatially is unknown. Using a spatial tag recovery model, we estimated region- and age-specific mortalities and regional movement probabilities by using recoveries from a jaw tagging study initiate...
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... experiment study design .—Lake Erie is the shallowest and most productive of the Laurentian Great Lakes. The lake consists of three distinct basins (Figure 1; Ryan et al. 2003). The western basin is the shallowest, with a mean depth of 7.4 m, fol- lowed by the central basin (mean depth = 18.5 m) and eastern basin (mean depth = 24.5 m). Most of the lake is classified seasonally as coolwater (20–28 ◦ C), with coldwater ( < 20 ◦ C) habitat limited to the eastern basin and portions of the central basin (Hokanson 1977). For the purpose of this study, Lake Erie was partitioned into four regions (Figure 1). The western basin was partitioned into two regions (regions 1 and 2) corresponding to waters that are under Canadian (i.e., Ontario; region 1) and U.S. (i.e., Michigan and Ohio; region 2) management authority. Similarly, the combination of central and eastern basins (here- after, “central/eastern basin”) was partitioned into two regions (regions 3 and 4) corresponding to waters that are under Canadian (i.e., Ontario; region 3) and U.S. (i.e., Ohio, Pennsylvania, and New York; region 4) management authority. Total mortality rates between Ontario and U.S. waters were believed a priori to differ because in Ontario waters, Walleyes are exploited ...
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... experiment study design .—Lake Erie is the shallowest and most productive of the Laurentian Great Lakes. The lake consists of three distinct basins (Figure 1; Ryan et al. 2003). The western basin is the shallowest, with a mean depth of 7.4 m, fol- lowed by the central basin (mean depth = 18.5 m) and eastern basin (mean depth = 24.5 m). Most of the lake is classified seasonally as coolwater (20–28 ◦ C), with coldwater ( < 20 ◦ C) habitat limited to the eastern basin and portions of the central basin (Hokanson 1977). For the purpose of this study, Lake Erie was partitioned into four regions (Figure 1). The western basin was partitioned into two regions (regions 1 and 2) corresponding to waters that are under Canadian (i.e., Ontario; region 1) and U.S. (i.e., Michigan and Ohio; region 2) management authority. Similarly, the combination of central and eastern basins (here- after, “central/eastern basin”) was partitioned into two regions (regions 3 and 4) corresponding to waters that are under Canadian (i.e., Ontario; region 3) and U.S. (i.e., Ohio, Pennsylvania, and New York; region 4) management authority. Total mortality rates between Ontario and U.S. waters were believed a priori to differ because in Ontario waters, Walleyes are exploited ...
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... and recreational fisheries, whereas in U.S. waters only recreational fishing occurs. Finer-scale partitioning (e.g., by quota management units) was not possible because tagging was generally limited to the lake’s western and eastern basins. The central basin was combined with the eastern basin for partitioning because the central basin’s habitat and fishery characteristics are more similar to those of the eastern basin than to those of the western basin. Due to the possibility of tagged fish moving to the Huron–Erie Corridor (i.e., Detroit River, Lake St. Clair, and St. Clair River) or to Lake Huron, these areas were combined into an additional region (region 5; Figure 1). Between 1990 and 2007, mature Walleyes were collected from tributary and open-water reef spawning locations during the spring; a jaw tag (butt-end style, constructed of the alloy Monel) was affixed to the mandible or maxillary, and the fish was released. Tagging in region 1 was conducted by the Ontario Ministry of Natural Resources (OMNR). Tagging in region 2 was conducted by both the Michigan Department of Natural Resources (MDNR) and the Ohio Department of Natural Resources (ODNR). Tagging in region 4 was conducted by the New York State Department of Environmental ...
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... Walleye are tolerant of a broad range of environmental conditions, but are generally more successful in low light conditions, moderate turbidity, cool (< 24ºC) water temperatures and sufficiently high (> 5 mg/L O 2 ) dissolved oxygen (McMahon et al., 1984;references therein). Walleye are known to be a wide-ranging species with large home ranges in the Great Lakes (Hayden et al., 2014;Vandergoot & Brenden, 2014). They commonly occupy similar thermal habitats to Toronto Harbour throughout the year (Madenjian et al., 2018); yet, avoidance of > 20ºC has been hypothesized as a driver of walleye movement in Lake Erie (Raby et al., 2018). ...
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Fish habitat associations are important measures for effective aquatic habitat management, but often vary over broad spatial and temporal scales, and are therefore challenging to measure comprehensively. We used a 9-year acoustic telemetry dataset to generate spatial-temporal habitat suitability models for seven fish species in an urban freshwater harbour, Toronto Harbour, Lake Ontario. Fishes generally occupied the more natural regions of Toronto Harbour most frequently. However, each species exhibited unique habitat associations and spatial-temporal interactions in their habitat use. For example, largemouth bass exhibited the most consistent seasonal habitat use, mainly associating with shallow, sheltered embayments with high aquatic vegetation (SAV) cover. Conversely, walleye seldom occupied Toronto Harbour in summer, with the highest occupancy of shallow, low-SAV habitats in the spring, which corresponds to their spawning period. Others, such as common carp, shifted between shallow summer and deeper winter habitats. Community level spatial-temporal habitat importance estimates were also generated, which can serve as an aggregate measure for habitat management. Acoustic telemetry provides novel opportunities to generate robust spatial-temporal fish habitat models based on wild fish behaviour, which are useful for the management of fish habitat from a fish species and community perspective.
Supplementary information:
The online version contains supplementary material available at 10.1007/s10750-023-05180-z.
... We assumed M 0 was 0.1 for all analyses, because higher values of M 0 such as those obtained using data-poor methods (e.g., Kenchington 2014) resulted in a substantial mismatch between preliminary model fits and the observed survey data, whereas lower values seemed unlikely given the observed longevity of Walleye (e.g., fewer than 0.0001 of all Walleye sampled in Alberta survived > 25 years). We used a value of 0.85 for ϑ because Walleye in well-studied systems demonstrate modest increases in M at young ages (Hansen et al. 2011;Vandergoot and Brenden 2014). Relative length of fish at age a in lake k was calculated using lake-specific von Bertalanffy growth parameters from the life history meta-analyses as ...
... genetic stock identification, fisheries management, Great Lakes, Portfolio theory, RADcapture, RAD-seq, stock discrimination (Kershner et al., 1999;Raby et al., 2018), where they intermix with walleye from smaller spawning stocks in the central and eastern basins (Matley et al., 2020;Vandergoot & Brenden, 2014;Zhao et al., 2011). Migration of individuals from western basin stocks into Lake Erie's eastern basin is predicted to have a disproportionate influence on local commercial and recreational fisheries because of presumed differences in population productivity and abundance (Zhao et al., 2011). ...
Abstract Mixed‐stock analyses using genetic markers have informed fisheries management in cases where strong genetic differentiation occurs among local spawning populations, yet many fisheries are supported by multiple, weakly differentiated stocks. Freshwater fisheries exemplify this problem, with many populations supported by multiple stocks of young evolutionary age and isolated across small spatial scales. Consequently, attempts to conduct genetic mixed‐stock analyses of inland fisheries have often been unsuccessful. Advances in genomic sequencing offer the ability to discriminate among populations with weak population structure, providing the necessary resolution to conduct mixed‐stock assignment among previously indistinguishable stocks. We used genomic data to conduct a mixed‐stock analysis of eastern Lake Erie's commercial and recreational walleye (Sander vitreus) fisheries and estimate the relative harvest of weakly differentiated stocks (pairwise FST
... Also, sampling efforts to recapture tagged individuals can differ spatially. Both tag-55 recapture and tag-recovery (i.e., terminal recapture) frameworks for estimating mortality 56 components have been expanded to account for the inherent spatial structure to tagging data 57 (Royle et al. 2014;Vandergoot and Brenden 2014). In the case of tag-recapture studies, a spatial 4 58 CJS framework has been proposed to include the locations of where recapture events occur and 59 to address spatial aspects of recapture data (e.g., movement patterns of tagged individuals; 60 spatially-and/or temporally-varying mortalities), while also allowing for sample-site level 61 covariates. ...
Mortality rates are major determinants of long-term sustainability of exploited fish populations yet accurately estimating these rates can be challenging. We used simulations to evaluate a non-spatial and spatial modeling approach for estimating mortality rates from acoustic telemetry detection data. Data were generated assuming different receiver configurations (grids, lines), number of receivers, and mortality levels. Relative error rates for total mortality ranged from 0% to 83% for the non-spatial model and 1% to 141% for the spatial model. Absolute error rates ranged from 0.00 to 0.11 for the non-spatial model and 0.01 to 0.15 for the spatial model. Accuracy and precision in mortality estimates were sensitive to assumed mortality rates and receiver configurations; the high-density receiver grid resulted in the lowest error rates. Estimates were consistently positively biased. We recommend using grid receiver configurations for mortality rate estimation from acoustic telemetry studies. The potential for biased mortality estimation from acoustic telemetry detection data should be considered during study design, particularly for those species whose behavior and ecology may result in long periods of non-detection.
... Analysis of tagging data to estimate individual movement has received considerable attention in fisheries research [e.g. 16,20,21]. A spatial extension of the traditional Brownie model [22] have been widely used to derive movement probability from tagging data [20,21]. ...
... 16,20,21]. A spatial extension of the traditional Brownie model [22] have been widely used to derive movement probability from tagging data [20,21]. The spatial Brownie models separate parameters for survival and movement rates, and parameterize survival and recovery rates in terms of instantaneous natural morality and fishing mortality rates [20,21,23]. ...
... A spatial extension of the traditional Brownie model [22] have been widely used to derive movement probability from tagging data [20,21]. The spatial Brownie models separate parameters for survival and movement rates, and parameterize survival and recovery rates in terms of instantaneous natural morality and fishing mortality rates [20,21,23]. Interactions between individual movement and age class, time, and region make the movement process complex and difficult to understand [24]. ...
Tagging studies have been widely conducted to investigate the movement pattern of wild fish populations. In this study, we present a set of length-based, age-structured Bayesian hierarchical models to explore variabilities and uncertainties in modeling tag-recovery data. These models fully incorporate uncertainties in age classifications of tagged fish based on length and uncertainties in estimated population structure. Results of a tagging experiment conducted by the Ontario Ministry of Natural Resources and Forestry (OMNRF) on yellow perch in Lake Erie was analyzed as a case study. A total of 13,694 yellow perch were tagged with PIT tags from 2009 to 2015; 322 of these were recaptured in the Ontario commercial gillnet fishery and recorded by OMNRF personnel. Different movement configurations modeling the tag-recovery data were compared, and all configurations revealed that yellow perch individuals in the western basin (MU1) exhibited relatively strong site fidelity, and individuals from the central basin (MU2 and MU3) moved within this basin, but their movements to the western basin (MU1) appeared small. Model with random effects of year and age on movement had the best performance, indicating variations in movement of yellow perch across the lake among years and age classes. This kind of model is applicable to other tagging studies to explore temporal and age-class variations while incorporating uncertainties in age classification.
... Although such approaches have been used extensively in marine ecosystems (e.g., Li et al., 2018), they have not been employed as often in freshwater systems, even those that support valuable fisheries, the dynamics of which are similar to marine fisheries (e.g., Laurentian Great Lakes; Ludsin et al., 2014;Pritt et al., 2014;Vandergoot and Brenden, 2014). As with marine ecosystems, the Laurentian Great Lakes support numerous economically and culturally important fish populations. ...
... The west basin, which is the shallowest, warmest, and most biologically productive (Sly, 1976;Bolsenga and Herdendorf, 1993), is where the majority of walleye spawning occurs (DuFour et al., 2015), typically on offshore reefs and in major tributaries during early spring (typically March-April; Vandergoot et al., 2010;DuFour et al., 2015;Bade et al., 2019). After spawning, the majority of adult walleye migrate into the deeper and cooler central and east basins of Lake Erie (Wang et al., 2007;Vandergoot and Brenden, 2014). This large-scale seasonal migration out of the west basin in the spring and early summer into the central and east basins of the lake is presumably caused by walleye seeking more optimal thermal conditions and more preferred prey resources (Kershner et al., 1999;Raby et al., 2018). ...
... As a result, during years of high abundance, the age-structure of the stock is typically skewed towards younger individuals that recently recruited to the adult population (typically at age-2; [WTG] Walleye Task Group, 2018). Because walleye movement in Lake Erie is known to be age (size) and sex-specific (Wang et al., 2007;Vandergoot and Brenden, 2014;Bade et al., 2019), associations between spatial harvest patterns and population abundance could be caused by underlying demographic shifts that alter the movement and spatial dynamics of the population (Hsieh et al., 2010). Similar dynamics have been observed in marine ecosystems, where agespecific movement patterns have been shown to alter the availability of certain age-classes to the fishery (Maloney and Sigler, 2008). ...
Demographic and environmental factors can influence the spatial distribution of fish populations, potentially affecting the timing, location, and magnitude of harvest. Quantifying these relationships can be complicated, if their effects vary spatially over a population’s range or are non-additive (i.e., interactive), where one factor mediates the effect of another. Toward understanding the relative influence of demographic and environmental factors on fishery harvest in large freshwater lakes, we used varying-coefficient generalized additive models to explore the existence of non-additive, spatially-dependent effects of adult population size and thermal conditions on recreational harvest patterns of Lake Erie walleye (Sander vitreus) during 2006-2015. We identified nonlinear, additive, and generally positive effects of thermal conditions and adult population size on harvest rates. Their effects were, however, spatially-dependent, the accounting of which can help explain inter-annual and intra-annual variation in lake-wide harvest rates. Specifically, harvest rates increased more with increasing cumulative degree days in the eastern portion of the central basin, especially offshore, relative to the rest of the study area. Harvest rates also increased more with increasing walleye population size in the southwest portion of the west basin and the middle of the central basin compared to other study areas. As in marine ecosystems, our findings demonstrate the benefit of using modeling approaches that consider the spatial dependency of harvest rate on demographic and environmental factors to understanding broader harvest dynamics in large lakes. Their use could help managers and policy-makers ensure the sustained use of valued freshwater fish populations amidst demographic and environmental change.
... The MU1, MU2 and MU3 for Lake Erie Walleye are corresponding to the west, west central and east central basins, while the south portion of MU4 is the Pennsylvania ridge, and the remaining portion of MU4 and MU5 in Ontario waters is the east basin of Lake Erie geographically. A recent tagging study in Lake Erie documented different movement rates and natural mortality rates among Walleye populations from different basins (Vandergoot & Brenden, 2014), suggesting that Walleyes from different basins may develop a unique set of life history traits, including growth and maturation (Muth & Wolfert, 1986;Vandergoot & Brenden, 2014;Wolfert, 1969). As the foundation of the stock assessment, if the growth and maturation of Lake Erie Walley vary among different basins, different sex or other aspects, such variation needs to be considered in the population dynamics modelling and management decision making (Lorenzen, 2016). ...
... The MU1, MU2 and MU3 for Lake Erie Walleye are corresponding to the west, west central and east central basins, while the south portion of MU4 is the Pennsylvania ridge, and the remaining portion of MU4 and MU5 in Ontario waters is the east basin of Lake Erie geographically. A recent tagging study in Lake Erie documented different movement rates and natural mortality rates among Walleye populations from different basins (Vandergoot & Brenden, 2014), suggesting that Walleyes from different basins may develop a unique set of life history traits, including growth and maturation (Muth & Wolfert, 1986;Vandergoot & Brenden, 2014;Wolfert, 1969). As the foundation of the stock assessment, if the growth and maturation of Lake Erie Walley vary among different basins, different sex or other aspects, such variation needs to be considered in the population dynamics modelling and management decision making (Lorenzen, 2016). ...
Sexual and spatio-temporal variations have been observed in the life history parameters of many aquatic species and their causes have been related to harvesting pressure and environmental changes. This study aims to explore sexual, spatial and temporal variation in the growth and maturity through weight-at-length, length-at-age, and maturity-at-length relationships for Lake Erie Walleye (Sander vitreus) as a case to test some hypotheses. Hypotheses on whether harvest pressure and environmental changes (both local and global scale) caused the temporal changes of these life history traits were further diagnosed. Sexual and spatio-temporal variations in these life history traits were formulated using mixed-effects models. Our study found that geographic basin, sex, year and cohort all have substantial effects on the growth and maturity of Walleye based on survey data from 1989 to 2015. Multiple factors including water supply of Lake Erie, temperature, fishing pressure of Walleye, and global climate factors were found to correlate with the temporal variations of growth and maturity of Walleye significantly. Our findings should contribute to the future interpretation of Walleye life history variations and population dynamics. The methodology constructed in this study could be applied to explore the heterogeneity and impacting factors for other species in aquatic ecosystems.
... Walleye spawn in tributaries and on open-water reefs throughout Lake Erie, with the largest spawning populations in the western basin (WB) (DuFour et al., 2015;Kayle et al., 2015) and smaller populations occurring in the central (CB) and eastern (EB) basins (Zhao et al., 2011;Stepien et al., 2018). Post-spawn, WB walleye travel extensively within Lake Erie (Wang et al., 2007;Vandergoot and Brenden, 2014;Raby et al., 2018) and sometimes move between Great Lakes (Brenden et al., 2015;Hayden et al., 2019), while EB walleye tend to remain within the EB (Zhao et al., 2011). Despite extensive mixing between populations in the EB, particularly during the summer (Zhao et al., 2011), '13 m walleye habitat' quotas are allocated for the WB and CB of Lake Erie (management units 1-3; Fig. 1), whereas the EB (management units 4-5; Fig. 1) is managed as a separate entity by local managers. ...
... Only receivers present during the detection period of each individual were included in the calculation of p i . All receivers in Lake Erie and affiliated rivers were included in the analysis of WB walleye (except when conditions above were not met) based on the lake-wide distribution of WB walleye, while receivers used in the EB walleye analysis were filtered to only include receivers in the EB (Fig. 1) due to their limited movement outside this area (Zhao et al., 2011;Vandergoot and Brenden, 2014). Values of a range from 0 to 1, with values >1/number of depth intervals indicating positive selection for that depth and values <1/number of depth intervals indicating avoidance. ...
... Walleye from Lake Erie's western and eastern basins showed stark differences in their large-scale movements; specifically, WB walleye often made movements to the east up to~400 km, while EB walleye remained more local (although far within-basin movements were often made). These findings are consistent with previous work examining their spatial ecology (Wang et al., 2007;Zhao et al., 2011;Vandergoot and Brenden, 2014;Raby et al., 2018). Furthermore, our results suggested that these populations also differed in their habitat selection patterns. ...
Understanding the spatial ecology and habitat-use of Lake Erie’s commercially important walleye (Sander vitreus) population is imperative due to their large-scale seasonal migrations (>400 km) exposing them to five different jurisdictions in the USA and Canada. The objective of this study was to determine the habitat selected by walleye throughout the year and across Lake Erie. Here, we used acoustic telemetry to estimate walleye occurrence at three lake depth categories that were pertinent to biology (e.g., spawning) and management (e.g., quota allocation). Detection data from 851 adults during five continuous years identified consistent seasonal fluctuations in habitat selection across western (WB) and eastern (EB) basin walleye stocks. Sex-specific differences were also found during spawning periods (March-May) when males showed a stronger affinity to shallow waters <6 m than females. Also, EB stocks selected these shallow waters longer than WB stocks, likely due to differences in thermal patterns between basins. Deep water (>13 m) was readily selected between spring and winter (>6 months/year) for most WB and EB walleye despite stock-specific migration patterns. This study provides novel information about the space use patterns of one of the most economically important fish in North America at spatial and temporal scales relevant to management.
... In some instances, others have resorted to entirely different modeling theoretical approaches to compensate for the mixing of stocks (Michielsens et al., 2006;Molton et al., 2012). The need and challenge of accounting for movement in stock assessment efforts has been no less significant for the Laurentian Great Lakes (Berger et al., 2012;Nalepa et al., 2005;Thomas et al., 2011;Vandergoot and Brenden, 2014;Wilberg and Bence, 2008). ...
... Compounding the complexity of stock assessment for the Saginaw Bay stock of walleyes is potential immigration of walleyes from Lake Erie. Previous tag-return studies (Ferguson and Derksen, 1971;Thomas and Haas, 2005;Vandergoot and Brenden, 2014;Wang et al., 2007;Wolfert, 1963) and genetic analyses McParland et al., 1996) have provided an indication of the magnitude of Lake Erie contribution to the population and fisheries in Lake Huron. Simultaneous to Hayden et al. (2014) was a similar telemetry study of Lake Erie walleye movement (Raby et al., 2018) that provided up-to-date information and indicated that immigration by Lake Erie walleyes has probably declined or stopped in recent years possibly due to food-web changes in Lake Huron (Hayden et al., 2019). ...
... The model described by Fielder and Bence (2014) accounted for this immigration by estimating a seasonal contribution to the fisheries as a fraction of the same fishing mortalities estimated for the Saginaw Bay stock and further fractionalized by some proportion of the year. Contributions of Lake Erie fish were most evident when jaw tagging was employed by agencies and sample sizes were in the thousands resulting in immigration rates of <1-2% (Vandergoot and Brenden, 2014;Wang et al., 2007). Regardless, movement by walleyes in and among the Great Lakes is almost certainly a phenomenon that changes with density, prey availability, water temperatures, population age structure, and sex ratios. ...
Since achieving population recovery targets, management of the Saginaw Bay stock of walleye in Lake Huron is informed by a statistical-catch-at-age (SCAA) model providing estimates of abundance, spawning stock biomass, mortality rates, and exploitation rates. Movement was examined by an acoustic telemetry study and indicated that 37% of the adult population spends much of spring, summer, and fall in the main basin of Lake Huron and unavailable to the fishery in the bay. The current SCAA model used by managers accounts for this movement by including harvest estimates from main basin fisheries in addition to those within the bay. To quantify the effect of the inclusion of the migration information into the model, we constructed a reduced model version that was limited only to the bay harvest and data sources, and then compared these estimates to the full model estimates. All estimates from the reduced model deviated significantly from the full Lake Huron model’s estimates. Significantly different by year were 61% of population size estimates, 25% of total annual mortality estimates, 18% of recreational exploitation rates, and 52% of spawning stock biomass estimates. Differences between the two models were greatest in years of high walleye abundance (i.e., after recovery) and retrospective analysis indicated that this departure was not an estimation artifact. Generally, the reduced model underestimated predicted abundance. We concluded that incorporation of knowledge of movement of Saginaw Bay walleye from the acoustic telemetry study resulted in better informed stock assessment estimates.
... Toward achieving these ends, numerous artificial and natural tagging approaches have been employed to match individual fish to specific stocks. Artificial tags (e.g., jaw, PIT, acoustic) have been successfully used in Lake Erie to track movement and differentiate among individuals at the population level (Wang et al. 2007;Zhao et al. 2011;Vandergoot and Brenden 2014). Natural or "biomarker" tagging approaches (e.g., morphometrics, otolith microchemistry, microsatellites), which rely on environmentally induced or inherited genetic markers, have been attempted during recent decades, with limited success to identify natal origins of fish harvested in the open lake (Elsdon and Gillanders 2006;Elsdon et al. 2008). ...
Delineating population structure helps fishery managers maintain a diverse “portfolio” of local spawning populations (stocks), as well as facilitate stock‐specific management. In Lake Erie, commercial and recreational fisheries for walleye (Sander vitreus) exploit numerous local spawning populations, which cannot be easily differentiated using traditional genetic data (e.g., microsatellites). Here, we used genomic information (12,264 polymorphic loci) generated using RAD‐seq to investigate stock structure in Lake Erie walleye. We found low genetic divergence (FST = 0.0006–0.0019) among the four western basin stocks examined, which resulted in low classification accuracies for individual samples (40–60%). However, more structure existed between west and eastern basin stocks (FST = 0.0042–0.0064), resulting in> 95% classification accuracy of samples to a lake basin. Thus, our success in using genomics to identify stock structure varied with spatial scale. Based on our results, we offer recommendations to improve the efficacy of this new genetic tool for refining stock structure and eventually determining relative stock contributions in Lake Erie walleye and other Great Lakes populations.
