GREAT LAKES FISHERY COMMISSION

2015 Project Completion Report1

JOINT ANALYSIS OF GENETIC AND AGE DATA TO ESTIMATE TRENDS IN STRAIN-SPECIFIC RECRUITMENT

OF EMERGING WILD LAKE TROUT POPULATIONS IN LAKE HURON

by:

Kim Scribner1,2

, IyobTsehaye1,3

*, Travis Brenden1,3

, Wendylee Stott1,4

, Jeannette Kanefsky1, James Bence

1,3

1Dept. Fisheries & Wildlife, Michigan State Univ., 480 Wilson Rd., 13 Natural Resources Bldg, East Lansing, MI 48824

2Dept. Integrative Biology, Michigan State Univ., 201 Natural Sciences Bldg, East Lansing, MI 48824

3Quantitative Fisheries Center, 293 Farm Ln., 153 Giltner Hal, Michigan State Univ., East Lansing, MI 48824

4Great Lakes Science Center, 1451 Green Rd., Ann Arbor, MI 48105-2807 (wstott@usgs.gov)

*present address – Wisconsin Dept. Natural Resources, Madison, WI

[July 2015]

1 Project completion reports of Commission-sponsored research are made available to the Commission’s Cooperators in

the interest of rapid dissemination of information that may be useful in Great Lakes fishery management, research, or

administration. The reader should be aware that project completion reports have not been through a peer-review process

and that sponsorship of the project by the Commission does not necessarily imply that the findings or conclusions are

endorsed by the Commission. Do not cite findings without permission of the author.

CONTACT INFORMATION:

Kim Scribner – email: scribne3@msu.edu; tel. (517)-353-3288

ABSTRACT:

During the past decade, assessments have indicated the emergence of wild lake trout (Salvelinus namaycush)

recruitment in many areas of Lake Huron based on collections of young-of–the-year (YOY) and unmarked adult and

sub-adult fish. Because wild fish derived from different strains are not phenotypically distinct and are unmarked,

managers lack the ability to distinguish what lake trout hatchery strains are contributing to this wild recruitment or

whether there are any temporal and/or spatial differences in hatchery strain contributions. We used 15 microsatellite

loci to characterize the originating strains of wild lake trout (N=1567) collected in assessment fisheries conducted by

agency cooperators during an early (2002-2004) and late (2009-2012) sampling period. Individuals from 13 US and

Canadian hatchery strains stocked into Lake Huron (N=1143) were genotyped to develop standardized baseline

information on the potential source strains. The basis for our analysis was a model originally proposed to quantify the

contributions of different source populations to newly founded colonies. Models were estimated within a Bayesian

context and deviance information criteria (DIC) was used to compare competing models evaluating strain

contributions at different spatial and temporal scales. Known annual stocking numbers of hatchery strains and

stocking locations combined with survival estimates based on statistical catch-at-age (SCAA) assessment models and

movement patterns of fish among different regions of the lake based on analyses of coded-wire tag returns were used

to derive expectations of strain contributions for the samples. The best performing models based on DIC were the

most complex models, suggesting that hatchery strain contributions to wild lake trout differed both spatially and

temporally. Contributions of Seneca strain lake trout were consistently high across Lake Huron statistical districts,

with contributions increasing from early to late time periods. Strain contributions deviated from expectations based

on historical stocking levels, suggesting that hatchery strains differed with respect to survival, reproductive success,

and/or dispersal. Strain type was extremely important to emergence of wild stocks, which underlies the importance of

genetic bases of adaptation in the development of more efficient and effective rehabilitation strategies across the

Great Lakes. Knowledge of recruitment levels of hatchery strains stocked in different management units and how

strain-specific recruitment varies across years, locations, and as a function of local or regional stocking efforts is

important to target strains to prioritize future stocking and management of the transition process from primarily

hatchery stocks to wild stocks in Lake Huron and potentially other Great Lakes as well.

PRESS RELEASE:

Title – Emergence of wild lake trout in Lake Huron genetically tied to contributions from few hatchery strains

Lake Huron is in the early stages of a lake trout restoration. Stocking began in Lake Huron in 1973, and since

1990 large numbers have been stocked annually. During the past decade, agency assessments have indicated the

emergence of natural lake trout recruitment in most management units and year-class strength or annual relative

abundance of wild lake trout has increased dramatically over time. A critical information need to support lake

trout restoration efforts is improved understanding of recruitment variation over space and time. Because wild

fish are untagged, managers lack the ability to characterize strain-specific rates of recruitment and whether

recruitment at specific locales can be attributed to either reproductive success of strains stocked at or near each

sampling station, or to dispersal and immigration of individuals from strains stocked elsewhere. We used

microsatellite genetic markers and genetic stock identification statistical methods to determine the relative

contributions of stocked lake trout strains and inter-annual, age-specific, and geographic variation in strain

contributions. We found that Seneca strain lake trout contributed substantially to wild lake trout in the two time

periods evaluated (2002-2004 and 2010-2012) in US and Canadian waters of the basin. Estimated strain

contributions were not consistent with expected proportions based on the numbers of each strain stocked annually

or based on estimates of age-specific survival and movements. Discovering a relationship between strain type

and successful reproduction will enhance managers’ understanding of adaptive mechanisms and contribute to the

development of more efficient and effective rehabilitation strategies across the Great Lakes.

SUMMARY STATEMENT:

The overall objectives were successfully accomplished. Our analytical approach deviated from the models

we originally proposed. As stated in our original proposal, we did utilize a general model developed by

Guo et al. (2008) (originally published by Gaggiotti et al. (2002 and 2004) utilizing a Bayesian-based

approach for estimating the proportional contribution of source populations or strains to newly founded

colonies, as a form of Genetic Stock Identification (GSI). Further, as proposed, we expanded on the

Gaggiotte et al. model to evaluate whether there was evidence for temporal and spatial variation in strain

contributions (DIC analyses reported in Table 3). We also accounted for inter-strain mixing in our models.

We successfully estimate proportional contributions of 13 US and Canadian hatchery strains of lake trout to

management units in two time periods (2002-2004 and 2010-2012) in US and Canadian waters of Lake

Huron (Tables 4 and 5). It was our desire to use methodologies that PIs Brenden, Bence, and Scribner have

previously developed (Brenden et al. in review, Tsehaye et al. in review) to characterize age (or cohort)

specific differences in recruitment by strain and to more rigorously model effects of strain-specific stocking

numbers, survival, and movements to estimated strain contributions. However, due to constrains posed by

limited sample sizes of agency-based collections of wild fish (Table 1), particularly during the early

sampling period, we were unable to develop a more detailed model that would serve to reconcile differences

between estimated strain contributions and expectations based on stocking numbers in the difference

management units and rather simplistic SCAA models that did not incorporate strain-specific differences in

survival. Further, details concerning movements outside of the main basin (units MH1, MH23, MH345,

OH1) were lacking (Supplemental Table S3).

DELIVERABLES:

Talks at the Lake Huron Technical Committee meetings in January 2013, January 2014, July 2014,

January 2015, July 2015.

Invited talk at the Lake Huron session of the GLFC annual meeting (March 2015).

Final report to the GLFC

Data set of microsatellite genotypes for all combined US and Canadian hatchery strains used in analyses

as well as all genotypes for wild lake trout genotyped from Lake Huron. The data represent the first

compilation and standardization of lake trout data in the Great Lakes for lake trout that include both

Canadian and US hatchery stocks. The data set is in the final stages of construction. The data will be

deposited in Science Base at (https://www.sciencebase.gov/catalog/item/5540f811e4b0a658d793a535).

Manuscript to be submitted to the Canadian Journal of Fisheries and Aquatic Sciences

Kim Scribner, K.T., I. Tsehaye, T. Brenden, W. Stott, J. Kanefsky, and J. Bence. Joint analysis of genetic and age

data to estimate trends in strain-specific recruitment of emerging wild lake trout populations in Lake Huron.

Canadian Journal of Fisheries and Aquatic Sciences. In prep.

APPENDIX:

See attached manuscript

1

Hatchery Strain Contributions to Emerging Wild Lake Trout Populations in Lake Huron

Final Report to the Great Lakes Fishery Commission

Kim Scribner1,2

, IyobTsehaye1,3

*, Travis Brenden1,3

, Wendylee Stott1,4

, Jeannette Kanefsky1, James Bence

1,3

1Dept. Fisheries & Wildlife, Michigan State Univ., 13 Natural Resources Bldg, East Lansing, MI 48824

2Dept. Integrative Biology, Michigan State Univ., 201 Natural Sciences Bldg, East Lansing, MI 48824

3Quantitative Fisheries Center, Michigan State Univ., 153 Giltner Hall, East Lansing, MI 48824

4Great Lakes Science Center, 1451 Green Rd., Ann Arbor, MI 48105-2807 (wstott@usgs.gov)

*present address – Wisconsin Dept. Natural Resources, Madison, WI

2

Abstract

During the past decade, assessments have indicated the emergence of wild lake trout (Salvelinus namaycush)

recruitment in many areas of Lake Huron based on collections of young-of–the-year (YOY) and unmarked adult and

sub-adult fish. Because wild fish derived from different strains are not phenotypically distinct and are unmarked,

managers lack the ability to distinguish what lake trout hatchery strains are contributing to this wild recruitment or

whether there are any temporal and/or spatial differences in hatchery strain contributions. We used 15 microsatellite

loci to characterize the originating strains of wild lake trout (N=1567) collected in assessment fisheries conducted by

agency cooperators during an early (2002-2004) and late (2009-2012) sampling period. Individuals from 13 US and

Canadian hatchery strains stocked into Lake Huron (N=1143) were genotyped to develop standardized baseline

information on the potential source strains. The basis for our analysis was a model originally proposed to quantify

the contributions of different source populations to newly founded colonies. Models were estimated within a

Bayesian context and deviance information criteria (DIC) was used to compare competing models evaluating strain

contributions at different spatial and temporal scales. Known annual stocking numbers of hatchery strains and

stocking locations combined with survival estimates based on statistical catch-at-age (SCAA) assessment models and

movement patterns of fish among different regions of the lake based on analyses of coded-wire tag returns were used

to derive expectations of strain contributions for the samples. The best performing models based on DIC were the

most complex models, suggesting that hatchery strain contributions to wild lake trout differed both spatially and

temporally. Contributions of Seneca strain lake trout were consistently high across Lake Huron statistical districts,

with contributions increasing from early to late time periods. Strain contributions deviated from expectations based

on historical stocking levels, suggesting that hatchery strains differed with respect to survival, reproductive success,

and/or dispersal. Strain type was extremely important to emergence of wild stocks, which underlies the importance of

genetic bases of adaptation in the development of more efficient and effective rehabilitation strategies across the

Great Lakes. Knowledge of recruitment levels of hatchery strains stocked in different management units and how

strain-specific recruitment varies across years, locations, and as a function of local or regional stocking efforts is

important to target strains to prioritize future stocking and management of the transition process from primarily

hatchery stocks to wild stocks in Lake Huron and potentially other Great Lakes as well.

Introduction

In the Laurentian Great Lakes of North America, lake trout (Salvelinus namaycush) experienced considerable

reductions in population abundance and distribution over the last couple of centuries (Hansen 1999). Historically, lake

trout were a dominant predator in the lakes and were important to human settlement around each of the Great Lakes

(Muir et al. 2013). During the 19th and 20

th centuries, native lake trout stocks declined in each lake owing to over-

exploitation, parasitism by sea lamprey (Petromyzon marinus), predation and competition stemming from the

3

introductions and spread of alewife (Alosa pseudoharengus) and rainbow smelt (Osmerus mordax), and

anthropogenic effects on water quality (Hansen 1999, Muir et al. 2013). Management actions were undertaken in the

1950s to restore lake trout populations in the Great Lakes, including stocking of juvenile fish, closure of commercial

fisheries, and reduction of sea lamprey populations through lamprey control efforts (Muir et al. 2013). However, after

decades of considerable effort, lake trout restoration in the Great Lakes has still not been fully realized except for

Lake Superior (Muir et al. 2013). In Lake Superior, detailed information on hatchery strain-specific recruitment and

survival were not available at the time stocks were recovering to unambiguously quantify the relative contributions of

environmental, ecological and management actions to the restoration of self-sustaining lake trout populations. For

example, debate exists surrounding the relative importance of recruitment from remnant wild stocks or hatchery

strains to lake trout restoration (Schram et al. 1995, Guinand et al. 2004).

Lake Huron is in the early stages of a lake trout restoration success like that seen on Lake Superior (Johnson et al.

2015). Stocking began in Lake Huron in 1973, and since 1990 annual stocking levels have averaged between 1.39

and 1.95 million yearlings (He et al. 2012). During the past decade, agency assessments have indicated the emergence

of wild lake trout recruitment in most management units based on collections of age-0 and untagged sub-adult and

adult fish (Riley et al. 2007, He et al. 2012). The year-class strength or annual relative abundance of wild lake trout

has increased dramatically over time in three locations where long-term monitoring has occurred: Drummond Island,

US off-shore reefs, and Ontario jurisdictions of the central main basin (He et al. 2012, Johnson et al. 2015). Wild

year-class abundances in different areas of Lake Huron are positively correlated, suggesting that factors that are

contributing to improvements in wild recruitment are occurring at lake-wide scales. Basin-wide adult catch per effort

(CPE) was correlated with wild year class strength suggesting a connection between the abundance of spawning

stocks and levels of natural reproduction. (Fitzsimons et al. 2010, He et al. 2012). Wild recruitment further increased

as thiamine concentration in lake trout eggs increased after the alewife population collapse in 2003 (Riley et al.

2012). Presently, wild lake trout have been estimated to comprise approximately 50% of the total lake trout spawning

biomass in Lake Huron.

A critical information need to support lake trout restoration efforts is improved understanding of recruitment

variation over space and time (Zimmerman and Krueger 2009). Because wild fish derived from different hatchery

parents are not phenotypically distinct (at least easily), and obviously are untagged, managers lack the ability to

characterize strain-specific rates of recruitment and whether recruitment at specific locales can be attributed to either

reproductive success of strains stocked at or near each sampling station, or to dispersal and immigration of

individuals from strains stocked elsewhere (e.g., source-sink effects; Pullium 1988). Previous research (Page et al.

2003, Madenjian et al. 2004) has shown that strain abundance at spawning locales are poor predictors of strain-

specific reproductive success, but this work was not spatially extensive enough to address the general questions of

4

the effects of dispersal and local stocking on the abundance and spatial distribution of wild lake trout origination

from different strains. Knowledge of recruitment levels of hatchery strains stocked in different management units

and how recruitment varies across time periods, locations, and as a function of local or regional (i.e., statistical

reporting districts or combinations of districts in U.S. and Canadian waters) stocking efforts will provide information

needed to improve future stocking efforts in Lake Huron and potentially other Great Lakes.

Measuring strain contributions and inter-annual variability in strain contributions to mixed-stock fisheries is not easy.

Tagging experiments, where fish from different spawning stocks are distinctly tagged and stock contribution is

determined based on proportions of harvested fish with different tag types can be logistically difficult to implement

on the Great Lakes because of the large number of spawning locations and hatchery strains involved. The use of

meristics or elemental analysis of bony structures requires killing individuals to determine stock contribution. Using

genetic data to determine stock contribution to mixed-stock fisheries has become increasingly common due to the

development of new, highly polymorphic molecular markers. Genetic stock identification methods have been

extensively used outside the Great Lakes (e.g., Scribner et al. 1998, Begg et al. 1999, Ensing et al. 2012) and to a

lesser extent in Great Lakes waters (e.g., Page et al. 2003, Bott et al., 2009, Stott et al. 2012, Brenden et al. 2015).

The main goal of our project was to determine the relative contributions of stocked hatchery strains to the emerging

wild lake trout populations in Lake Huron. Specific objectives were to: (1) determine whether strain contributions

differed spatially among Lake Huron statistical districts or combinations of districts (e.g., US versus Ontario

jurisdictional waters); (2) determine the degree of temporal consistency in hatchery strain contributions within

statistical districts (3) determine whether strain contributions deviate from what is expected given previous stocking

efforts incorporated current knowledge about dispersal and lake trout survival, stocking proportions or due to

dispersal; (4) quantify levels of assortative mating and inferentially the proportion of inter-strain hybrids in wild

mixtures in different regions of Lake Huron during different years. Our specific hypotheses to be tested included the

following:

(a) The strain composition of wild lake trout will differ among Lake Huron statistical districts.

(b) The strain composition of wild lake trout will differ among time periods within a statistical district.

(c) The strain composition of wild lake trout within a statistical district will be consistent with stocking levels of

hatchery fish in that region which are sexually mature at the time wild-caught lake trout were produced.

(d) Dispersal by lake trout strains from locations of stocking contributed to recruitment of wild lake trout in

different statistical districts.

We note that these are hypotheses to test and not statements of belief. For example if (d) were true this would mean

that (c) would not be.

5

Methods

We utilized genotypes from microsatellite DNA loci from naturally produced wild (N=1567) lake trout and from 13

hatchery strains (N=1143) stocked into Lake Huron (Table 1). We used samples of wild subadults and adults (age

range 4-10 yrs) collected in assessment fisheries conducted by agency and tribal cooperators during March-

December during two time periods that we designated as ‘early’ (2002-2004) and ‘late’ (2009-2012) sampling

periods that generally correspond to periods of wild lake trout emergence and establishment, respectively (see

Supplemental Figure S1 for details concerning timing and ages of samples from US and Canadian locations).

Sample collections - Samples of wild lake trout were collected during early (2002-2004) and late (2009-2012)

periods by cooperating agencies including the Michigan Department of Natural Resources (MDNR), Chippewa

Ottawa Resource Authority (CORA), the U.S. fish and Wildlife Service (USFWS), Ontario Ministry of Natural

Resources (OMNR) and the U.S. Geological Survey (USGS) from 10 US (MH1, MH2, MH3) and Canadian (OH1,

OH2&3, OH4&5, NC1&2, NC3, GB1-3, GB4) around the Lake Huron basin. Samples were collected using

multifilament nylon gillnets or trap nets that are bottom set overnight across depth contours (see He et al. 2012 for

details). Sample sizes by period and location are detailed in Table 1. Individuals were identified as “wild” based on

absence of coded wire tags (CWT) and absence of clipped fins. Tissue samples were preserved in 95% ethanol (fins,

muscle) or were placed in scale envelopes and allowed to dry (fins, scales). The date of capture, total length, capture

site, and age were recorded for each lake trout. Age determinations were made for all individuals using either pectoral

fin rays, otoliths, or (for smaller fish) scale annuli.

Genetic data - DNA was extracted from fin or scale tissue for both mixture and baseline individuals using QIAGEN

DNeasy kits (QIAGEN Inc., Germantown, MD) using the manufacturer’s protocols. A spectrophotometer was used

to quantify DNA concentrations for all samples for use in PCR reactions.

Individuals were genotyped at 15 microsatellite loci: Ogo1a (Olsen et al. 1998); One9 (Scribner et al. 1996); Sco19

(E.B. Taylor, unpublished data); Sfo1, Sfo12 and Sfo18 (Angers et al. 1995); Sco202 (DeHaan and Ardren 2005);

SfoC38 and SfoC88 (King et al. 2012); and SnaMSU01, SnaMSU03, SnaMSU05, SnaMSU08, SnaMSU10 and

SnaMSU11 (Rollins et al. 2009). All loci were amplified by PCR in single locus reactions. Samples of US lake trout

hatchery strains and samples from US (Michigan) statistical districts in Lake Huron were genotyped at Michigan

State University. Samples of Canadian lake trout hatchery strains and samples from Canadian (Ontario) statistical

districts in Lake Huron were genotyped at the U.S. Geological Survey Great Lakes Science Center.

PCR and genotyping conditions Michigan State University - PCR reactions for Ogo1a, One9, Sco19, Sfo1, Sfo12 and

Sfo18 were carried out in 25-ul volume reactions using 100 ng of DNA and included PCR buffer (10-mM Tris-HCl at

6

pH 8.3, 50-mM KCl, 0.01% gelatin, 0.01% NP-40, and 0.01% Triton-X 100), 0.2-mM dNTPs (except for Sfo1 which

contained 0.08-mM dNTPs), varying concentrations of MgCl2, 1 unit of Taq DNA polymerase, and forward and

reverse primer pairs in which one primer was fluorescently labeled. PCR cycling conditions consisted of: 1 cycle of

an initial denaturation step of 2 minutes at 94° C and a varying number of cycles of 1 minute at 94°C, 1 minute at the

annealing temperature, and 1 minute at 72° C. Locus-specific details of PCR amplification conditions including

MgCl2 concentration (mM), annealing temperature (oC), and number of cycles, respectively, for Ogo1a, One9, Sco19,

Sfo1, Sfo12 and Sfo18 were as follows: 1.5, 52, 30; 2.5, 54, 32; 2.5, 46, 35; 2.5, 60, 35; 2.25, 57, 35; and 3.0, 50, 40.

PCR products for these 6 loci were separated by size on 6% denaturing polyacrylamide gels and visualized using a

Hitachi FMBIO-II laser scanner (Hitachi Solutions America, Ltd., San Bruno, CA).

PCR for SfoC38, SfoC88, SnaMSU01, SnaMSU03, SnaMSU05, SnaMSU08, SnaMSU10, SnaMSU11 and Sco202

was conducted in 10-μl volume reactions using 40 ng of DNA and included PCR buffer (10-mM Tris-HCl at pH 8.3,

50-mM KCl, 0.01% gelatin, 0.01% NP-40, and 0.01% Triton-X 100), 0.2-mM dNTPs, varying concentrations of

MgCl2 and amounts of Taq DNA polymerase, and infrared fluorescently-labeled forward primers and unlabeled

reverse primers. PCR cycling conditions for all loci excepting Sco202 consisted of: 1 cycle of an initial denaturation

step of 2 minutes at 94° C; a varying number of cycles of 1 minute at 94°C, 1 minute at the annealing temperature,

and 1 minute at 72° C; and 1 cycle of 5 minutes at 72° C. PCR conditions for Sco202 were as follows: 1 cycle of an

initial denaturation step of 2 minutes at 94° C; a varying number of cycles of 45 seconds at 94°C, 45 seconds at the

annealing temperature, and 1 minute 30 seconds at 72° C; and 1 cycle of 5 minutes at 72° C. Locus-specific details

of PCR amplification conditions including MgCl2 concentration (mM), Taq amount (U), annealing temperature (oC),

and number of cycles, respectively, for SfoC38, SfoC88, SnaMSU01, SnaMSU03, SnaMSU05, SnaMSU08,

SnaMSU10, SnaMSU11 and Sco202 were as follows: 1.5, 0.75, 58, 33; 2.0, 0.75, 58, 35; 1.5, 0.75, 57, 30; 3.0, 0.75,

60, 30; 1.5, 0.9, 65, 36; 1.5, 1.0, 66, 35; 1.5, 0.75, 62, 30; 1.5, 0.75, 62, 33; and 1.5, 0.9, 60 36. PCR products for

these 9 loci were separated by size on denaturing 6.5% polyacrylamide gels and visualized using the LI-COR 4300

DNA Analyzer (LI-COR Biosciences, Lincoln, NE).

All genotypes were independently scored by two experienced lab personnel and 10% of the samples were randomly

selected and re-genotyped at all 15 loci. Genotype scores were compared with the original scores to derive an

empirical estimate of scoring error, which was estimated to be 0.4% as averaged among all 15 loci.

PCR and genotyping conditions USGS Great Lakes Science Center- One of each primer pair was labeled with a

fluorescent dye (6-FAM, HEX, or NED). Some of the primers were combined into multiplex reactions. Working

solutions for genotyping were made by diluting 1μl of each PCR product in 9μl of water and further diluting 1μl of

this solution in 9μl of formamide and a 400 base pair size standard labeled with a fluorescent dye (400HD ROX by

7

Life Technologies, Foster City CA, U.S.A.). This mixture was heated to 95°C for 4 minutes and then chilled on ice

for 3 minutes. Electrophoresis was performed and fragment size data were collected using the ABI3130 or ABI3130xl

Genetic Analyzer (Life Technologies, Foster City CA, USA.). The Genetic Analyzer (Life Technologies, Foster City

CA, USA) software was used to collect data and the GeneMapper (Life Technologies, Foster City CA, USA)

software package was used to calculate fragment sizes and assign genotypes.

Development of a standardized genetics data base - Microsatellite DNA markers are commonly used for such

purposes in freshwater and marine fish species. Many loci are commonly available for many species, including lake

trout, and researchers typically use a large set of loci for specific projects. However, different labs seldom employ

the same suite of loci. Even if the same loci are used by different researchers, the nomenclature used to characterize

alleles (typically based on size of the PCR product in bp), is rarely standardized. Accordingly, synthesis of data

beyond the focal area of a single study is not possible without efforts expended by all labs to coordinate loci used and

scoring nomenclature. Standardization of genetic markers and data across multiple labs increases the utility of the

data to address questions of management interest. Such efforts have been made for commercially valuable inter-

jurisdictional fisheries (e.g., Moran et al. 2008; Stephenson et al. 2008; Seeb et al. 2007) and ESA-listed species.

Similar efforts have occurred in the Great Lakes (e.g., lake sturgeon (Acipenser fulvescens) - Welsh et al. 2010;

brook trout (Salvelinus fontinalis) -Wilson et al. 2005) but are not routine.

Since data sets from two laboratories were used in the analysis, the scoring systems (allele size ranges and binning

rules) had to be standardized. A set of 96 samples was chosen from among the hatchery samples for this purpose.

Samples were chosen from three hatchery strains; Slate Island (Dorion FCS, N=30), Lake Manitou (N=33), and

Seneca Lake (Harwood FCS, N=32). These strains were selected because the represented strains separated by the

largest genetic divergence and had the largest numbers of observed alleles and therefore should represent most of the

alleles observed in the data set. The samples were genotyped and scored independently at the USGS-GLSC and in

the Scribner laboratory at Michigan State University. The scores were then compared to look for consistency in

whether an individual was scored as a homozygote or a heterozygote and if a consistent correction factor could be

applied to convert the allele sizes.

Development and merging of standardized microsatellite genetic databases for all US (Michigan State University

lab) and Canadian (Great Lakes Science Center lab) lake trout strains stocked into the Great Lakes will allow

comparative analysis and modeling that would not be possible using smaller regional datasets collected to date.

Once this has been achieved, the standardized datasets will be combined.

Standardized data from this project will be established at

8

(https://www.sciencebase.gov/catalog/item/5540f811e4b0a658d793a535).

Data Analysis

Estimation of allele frequency and summary measures of genetic diversity-

Estimates of allele frequency and summary measures of genetic diversity for hatchery strains and wild individuals

collected during the two time periods and from different management units including heterozygosity, number of

alleles per locus, and Wright’s inbreeding coefficient (Fis) were estimated using program FSTAT (Guodet 2001). F-

statistics (Weir and Cockerham 1984) quantifying the variance in allele frequency among lake trout hatchery strains

was also conducted using FSTAT.

As a demonstration that our lake trout hatchery baseline data would permit accurate assignment of individuals to their

strain of origin, we simulated single-population samples (i.e., 100% mixture simulations). Simulations were

conducted in program ONCOR (Kalinowski et al. 2008). The program implements simulations described by

Anderson et al. (2007) involving the generation of multiple (1000 iterations) mixtures comprised solely of one

hatchery strain. Bootstrapped mixture sample sizes were simulated for 200 fish, and the hatchery baseline sample

sizes were set equal to the actual sample sizes for empirical US and Canadian hatchery strains. Our target accuracy

level for the 100% mixture simulations was 90% for each spawning populations which has been established in the

empirical fisheries literature (Seeb and Crane 1999, Beacham et al. 2012).

Estimation of hatchery strain contributions to first-generation lake trout mixtures in Lake Huron -

The analysis to estimate hatchery strain contributions to emerging wild lake trout populations in Lake Huron was

based on a model described by Gaggiotti et al (2002, 2004) for quantifying source population contributions to newly

founded colonies. A basic assumption of the model is that all wild produced lake trout in Lake Huron are first-

generational (F1) descendants of lake trout hatchery fish previously stocked by Great Lakes fishery management

agencies (Gaggiotti et al. 2002, 2004). Potential violations of this assumption are addressed in the Discussion. A

feature of the estimation model is that allows for assortative mating of individuals from the same hatchery strain

(Gagiotti et al. 2002, 2004), which could arise from a number of factors such as subtle differences in spawning

habitat or even limited dispersal from stocking locations, which could rise to population stratification. In the original

formulation of the model by Gaggiotti et al. (2002, 2004), source population contributions are modeled as functions

of biotic or abiotic data, such as distance of source populations from the newly formed colony or some measure of

reproductive productivity of the populations. In our implementation, we chose to not use this approach for the

hatchery strain contributions and instead estimated the contributions as freely-varying parameters (albeit recognizing

the unit-sum constraint of the contributions).

9

Hatchery strain contributions to the wild lake trout populations were estimated in a Bayesian context, which we

believed would better allow for characterization of uncertainty in parameter estimates.

Under a Bayesian context, the posterior probability distribution for the unknown parameters [i.e., hatchery strain

contributions (p), assortative mating coefficient , and allele relative frequencies of the hatchery strains (Q)], can be

specified as

)()()X|(),,|Y()XY,|,,( ppp QQQ (1)

where Y is the multi-locus genotypes observed in a sample of wild lake trout, X is the genotypes observed in samples

taken from the hatchery strains, )( p and )( are the prior probability distributions assumed for the hatchery strain

contributions and assortative mating coefficient, respectively, )X|(Q is the prior probability distribution for allele

relative frequencies of the hatchery strains given the collection and genotyping of individuals from the strains, and

),,|( pY Q is the likelihood of observing the multi-locus genotypes observed in a sample of wild lake trout for

given values of Q, p, and .

A Dirichlet probability density function was assumed for )( p with concentration parameters set equal to the inverse

of the number of hatchery strains, meaning that prior to data collection each hatchery strain was assumed to

contribute equally to the mixture. A uniform probability density function with lower and upper bounds of 0.0 and 1.0

was assumed for )( . Our specification of )X|(Q followed that of Rannala and Mountain (1997). Specifically,

the Rannala and Mountain (1997) approach assumes that before individuals are sampled from the source populations,

the alleles at a locus in each source population are considered equally likely. This prior belief is then updated based

on the number of observed copies of an allele for a particular locus and hatchery strain once individuals from the

strains have been collected and genotyped (Rannala and Mountain 1997). Thus, even though )X|(Q in eqn 1 is a

prior probability distribution, it is obtained as a posterior probability distribution from a separate analysis and reflects

how naïve beliefs regarding allele relative frequencies of the source populations are updated based on samples from

the populations. As in Rannala and Mountain (1997), a Dirichlet probability density function was assumed for

)X|(Q . When fitting the models, the parameters of )X|(Q were fixed so that the distribution of Q was not

updated as part of the model fitting process

The likelihood of observing the multi-locus genotypes observed in a sample of wild lake trout for given values of Q,

p, and followed directly from Gaggiotti et al. (2002, 2004) as well as from Guo et al. (2008)

M

m

I

i

I

i ij

mjimi

I

i

mi ijyfppiiyfpiiyfppY1 1 1

2

1

)|()|()1()|(),,|( Q , (2)

10

where M is the total number of sampled wild lake trout, pi and pj are the proportional contributions of the i-th and j-th

hatchery strains to the wild lake trout population (elements of p), and f(ym|ij) is a genetic model describing the

probability a lake trout having the genotype of the m-th individual given that one parent is of the i-th strain and

another parent is from the j-th strain (potentially i = j when both parents are from the same strain). For individuals

with both parents from the same strain (i.e., i = j), the probability of a lake trout having the genotype of the m-th

individual is

L

l

aalmm lilmlilmqqiiyf

1;2;1

)|( (3)

where lilmaq

;1is the allele frequency of the l-th locus in the i-th hatchery strain corresponding to the first allele

observed in the m-th individual at the l-th locus, lilmaq

;2is the allele frequency of the l-th locus in the i-th hatchery

strain corresponding to the second allele observed in the m-th individual at the l-th locus, and δlm is an indicator

variable defined as

lmlm

lmlm

lmaa

aa

21

21

if 2

if 1 . (4)

For individuals with both parents from the same strain (i.e., i = j), the probability of a lake trout having the genotype

of the m-th individual is

L

l

aalmaam ljlmlilmljlmlilmqqqqijyf

1

)()|(;1;2;2;1

(5)

with where

lmlm

lmlm

lmaa

aa

21

21

if 1

if 0 . (6)

, Given that corresponds to the probability of a wild lake trout arising from assortative mating among hatchery

strains, 1- corresponds to the probability of a wild lake trout arising from random mating among the strains

(Gaggiotti et al. 2002, 2004).

To assess the degree of spatial and temporal consistency in the hatchery strain contributions, we fit a series of models

to different groupings of wild lake trout data. The groupings consisted of two categories: 1) spatial/temporal, and 2)

spatial. The spatial/temporal grouping involved wild lake trout collected from MH-1, MH-2, MH-345, and OH-1

where samples were available from both early and late time periods. For most other Lake Huron statistical districts,

sample sizes of wild lake trout during early periods were very low (in many cases 0; Table 1), which is why we

limited analyses to just these four statistical districts. The spatial grouping involved wild lake trout collected from

GB-1234, MH-1, MH-2, MH-345, OH-1, OH-23, OH-45, NC-12, and NC-3 during the late period only. For each

11

grouping, four models were fit. In the case of the spatial/temporal grouping, the four models that were fit were

pooled (common strain contributions and assortative mating coefficients for all statistical districts and time periods),

separate (unique strain contributions and assortative mating coefficients for each statistical district and time period),

spatial (unique strain contributions and assortative mating coefficients for each statistical district but pooled over time

periods), and temporal (unique strain contributions and assortative mating coefficients for each time period but

pooled over statistical districts). In the case of the spatial grouping, the four models that were fit were pooled

(common strain contributions and assortative mating coefficients for all statistical districts), separate (unique strain

contributions and assortative mating coefficients for each statistical district), Michigan vs. Ontario (unique strain

contributions and assortative mating coefficients by jurisdictional authority), and basin [unique strain contributions

and assortative mating coefficients by Lake Huron basin (i.e., main basin, Georgian Bay, North Channel)].

The estimation procedure was programmed in AD Model Builder, which includes a Metropolis-Hasting algorithm for

conducting Markov chain Monte Carlo (MCMC) approximation to posterior probability distributions (Fournier et al.

2012). The objective function used to estimate the models equaled the sum of the negative loge likelihood and

negative loge priors specified above. For most models, MCMC chains were run for 1 million steps, sampling every

100th step, and discarding the initial 3,000 saved steps as a burn-in period. For the separate model under the

spatial/temporal grouping MCMC chains were run for 1.5 million steps, sampling every 100th step and discarding the

initial 5,000 saved steps as a burn-in period. For the separate model under the spatial grouping, MCMC chains were

run for 3.0 million steps, sampling every 100th step and discarding the initial 20,000 saved steps as a burn-in period

Convergence of the MCMC chain for each model was evaluated by constructing trace plots for the model negative

loge likelihood as a visual check to ensure the chain was well-mixed and using Z-score tests to evaluate differences

between the means of the first 10% and last 50% of the saved chain (Geweke 1992). Additionally, we compared

effective sample size of the saved MCMC chain with the actual chain sample size as a method for evaluating

autocorrelation among the saved MCMC samples. Convergence diagnostics were conducted on the model negative

loge likelihood as an omnibus test of convergence given that some estimated models had in excess of 100 parameters

being estimated. Means of the posterior probability distributions for the model parameters were used as parameter

point estimates. Ninety-five percent highest posterior density intervals were used to characterize the uncertainty

associated with each parameter. All MCMC diagnostic measures and highest posterior density interval calculations

were conducted in R (R Core Team, 2012) using the “coda” package (Plummer et al. 2006).

Performance of the four models fit to each grouping of the lake trout data was assessed using deviance information

criteria (DIC) (Spiegelhalter et al. 2002). DIC was calculated as

DpD DIC (7)

12

where D is the average deviance for a model measuring fit and pD and is the effective number of parameters. The

average deviance for a model was calculated as

C

c

e pYC

D1

),,|(log21

Q (8)

with C equal to the number of MCMC steps saved minus the burn-in, whereas pD was calculated as

DDpD (9)

where D is the deviance evaluated at the posterior mean parameter estimates.

Expected hatchery strain contributions

Expected contributions of hatchery strains to wild lake trout populations were calculated by combined numbers of

hatchery strains stocked in various management units in Lake Huron (Supplemental Table S1) with mortality

estimates for Lake Huron lake trout from statistical catch-at-age assessment models fit to different regions of the lake

(Supplemental Table S2) and an assumed movement matrix (Supplemental Table S3) that was used to allocate

stocked lake trout to different regions of the lake and based on analyses of coded-wire tagging data from lake trout

(Adlerstein et al. 2007). We used numbers of hatchery strains stocked in various management units in Lake Huron

from US agencies (data available at http://www.glfc.org/fishstocking/) and the OMNR (Adam Cottrill, personal

communication). When calculating expected contributions of hatchery strains, we assumed that all early lake trout

data were obtained in 2003 and all late lake trout data were obtained in 2011. Assuming lake trout of ages 7–14 were

mature, the fish that contributed to the spawning event in 1997 that resulted in the fish collected in 2003 (which for

simplicity we assumed to be all 6 year olds) were stocked from 1983-1990. Similarly, the fish that contributed to the

spawning event in 2005 that resulted in the fish collected in 2011 were stocked from 1991-1998. The contributions to

these spawning events of stocked fish stocked in a given year were assumed to vary depending on their age (or

stocking year), calculated using the mortality estimates derived from catch-at-age models

Results

Characteristics of US and Canadian hatchery strains used in mixture analyses - In total, 1567 wild caught and 1143

hatchery lake trout were genotyped (Table 1). Loci were scored consistently between the two laboratories and a base

pair correction factor could be identified to standardize allele designations for all 15 loci. Thus, the combined inter-

jurisdictional data set represents the first unified and standardized data set for US and Canadian (Ontario) strains of

lake trout used in the Great Lakes. Estimates of allele frequency and summary measures of genetic diversity for

hatchery lake trout are provided in Supplemental Tables S4. US hatchery strains generally were characterized by

higher levels of genetic diversity than Canadian hatchery strains (Supplemental Table S4) including allelic richness

(AR range 6.67-9.04 across US strains vs 5.68-8.21 across Canadian strains), multi-locus expected heterozygosity (HE

13

ranged from 0.502-0.678 for US strains vs 0.410-0.592 for Canadian strains) and Wright’s inbreeding coefficient (Fis

range -0.037-0.037 for US strains vs -0.017-0.093 for Canadian strains). With the exception of Canadian Big/Parry

Sound and Michopicotan strains, genotype frequencies were in approximate Hardy Weinberg equilibrium

(Supplemental Table S4) and there was no evidence of significant gametic disequilibrium between loci for any strain

(P>0.05 after Bonferonni correction for multiple testing).

We documented high levels of inter-strain variance in allele frequency (mean Fst=0.061, P<0.001; Supplemental

Tables S5). Supplemental Table S6 shows pair-wise estimates of variance (Fst). Estimates of variance in allele

frequency (inter-strain Fst) ranged from 0.0091 to 0.091 (P<0.001 for all inter-strain comparisons). Pairwise inter-

strain estimates of Fst were highest for the US and Canadian Seneca strain lake trout, which were the only strains that

originated outside the Great Lakes. Seneca lake trout were most genetically diverged from the other strains

(Supplemental Table S6). Large inter-strain variance in allele frequency was further reflected in high strain

allocation accuracy estimated based on 100% simulations (Table 2). Slightly lower levels of hatchery strain

allocation accuracy for the US Marquette and Traverse Island strains resulted from the 100% simulations (Table 2),

with misallocations primarily occurring between these two strains. We attribute this to the US Marquette and

Traverse Island strains having both originated from Marquette Bay in Lake Superior. Collectively, simulation results

revealed that empirical strain allocation estimates from open water mixtures could be made with high accuracy.

Characteristics of open-water mixtures during the early and late sampling periods- Estimates of allele frequency and

measures of genetic diversity for the 10 location/period sampling units are presented in Supplemental Table 7.

Generally, expected heterozygosity across the US statistical districts in both early and late time periods were

comparable (HE ranges 0.591-0.620). Greater variability was observed among Canadian statistical districts (HE range

0.561-0.633). Allelic diversity was generally higher in US statistical districts (range 7.53-11.33) than in Canadian

statistical districts (7.60-10.60) generally reflecting the greater genetic diversities of US hatchery strains

(Supplemental Table 4) stocked into US than Canadian waters of Lake Huron. Estimates of Fis, which is a measure of

deviation from Hardy-Weinberg expected genotype frequencies, revealed higher positive Fis values from mixtures in

US waters during the early relative to late period (Supplemental Table 7) indicating less inter-strain mixing or

interbreeding in early relative to late periods. Statistical districts MH1 and MH34 were characterized by significant

positive Fis indicating heterozygote deficiency (0.056 and 0.054, P<0.05, respectively; Supplemental Table S7).

Estimates of Fis were generally higher in Canadian relative to US sampling units. The magnitude of heterozygote

deficiencies are likely attributed to sample mixture composition and lack of mixing among hatchery strains. High

positive Fis values were particularly notable in the Canadian north channel (NC3) and units in Georgian Bay (GB123

and GB4; Supplemental Table S7).

14

We observed significant differences in allele frequency among the 10 tatistical districts (mean Fst+SE= 0.019+0.005;

Supplemental Table S8). Pair-wise estimates of variance in allele frequency (Fst) among statistical districts

(Supplemental Table S9) revealed that 35 of 45 pair-wise comparisons (78%) were statistically different from one

another in allele frequency.

Differentiation between samples from the early and late periods from the same management unit - Samples were

available in both early and lake periods for the same units in main-basin regions of Lake Huron (MH1, MH2, MH3-5

for US and OH1 for Canada; Table 4). We observed significant differences in strain contributions between periods

for three of the four districts (MH1, Fst=0.025, P<0.001; MH3-5, Fst=0.005, P<0.001; OH1, Fst=0.058, P<0.001;

Supplemental Table S9). Differences were most likely attributed to differences in the hatchery strains used to stock

in each unit and to estimates of hatchery strain contributions across regions (Tables 4 and 5).

Differentiation among samples from management units – We observed greater differences in allele frequency among

Canadian statistical districts than among districts in the US (mean Fst among US statistical districts were 0.0296 and

0.0065, respectively; Tables 4 and 5). Levels of genetic differentiation between US and Canadian statistical districts

were also consistently high (mean Fst=0.024, P<0.001; Supplemental Table S9). Significant differences in allele

frequency were documented even for adjacent statistical districts (e.g. NC12 vs NC3, Fst=0.012, P<0.001; OH23 vs

GB123, Fst=0.019, P<0.001)

Hatchery strain contributions to the wild Lake Huron lake trout population – MCMC chains for the negative loge

likelihoods for each model fit to the spatial/temporal and spatial grouping of the wild lake trout data were found to

have converged on stationary distributions. Examination of trace plots (not shown) indicated that chains were well

mixed with no apparent stickiness. Geweke (1992) Z-scores for testing convergence of the models ranged from -

1.216 to 0.770 (Table 3). Effective sample sizes of the models ranged from 5011.4 to 7000 (Table 3). Based on DIC,

the separate models had the best performance for both the spatial/temporal (MH-1, MH-2, MH-345, OH-1 statistical

districts early and late period) and spatial (GB-1234, MH-1, MH-2, MH-345, OH-1, OH-23, OH-45, NC-12, and NC-

3 late period only) groupings (Table 3). The DIC weights indicated that the strength of evidence for the separate

models for both grouping was overwhelming and there essentially was no empirical support for the other models

(Table 3).

For US statistical districts of Lake Huron, Lewis Lake, US Seneca, and Marquette hatchery strains were in general

the greatest contributors to wild lake trout (Table 4). For the MH-1 statistical district, the Apostle Island and Green

Lake strains were estimated to have fairly large (13 to 29%) contributions during the early time period, but the

contributions of these strains declined to zero during the late time period. The Green Lake strain was also estimated

15

to have had around a 7% contribution during the early time period for the MH-2 statistical district, but similar to MH-

1 the contribution declined to near zero during the late time period (Table 4). The relative contributions of the US

Seneca strain increased dramatically from the early to the late period in all US management units (Table 4), most

notably in MH-1 and MH-345 statistical districts. The Canadian Seneca strain was the only strain from Canada to

have a notable contribution to wild lake trout production in US statistical districts. For MH-345 during the early time

period, the Canadian Seneca strain contributed around 17% to wild lake trout. During the late time period, the

Canadian Seneca strain contributed around 13, 11, and 14% to the MH-1, MH-2, and MH-345 statistical districts,

respectively. Contributions from the US Seneca, Lewis Lake and Apostle Island strains exceeded expectations based

on historical stocking and historical stocking considering survival and dispersal (Table 4). Representation of the

Marquette strain was consistently below expectations based on the same criteria (Table 4). Allele frequency

differences between US management units were not significant in the early sampling period (Fst for comparisons

between MH1, MH2, MH345 not statistically significant, P>0.05; Supplemental Table S9) likely reflecting

similarities in relative proportions of strains stocked into all units (Table 4) and high movements expected between

units (Supplemental Table S3).

Unlike US statistical districts, there was not a single hatchery strain that composed a majority of contributions to wild

lake trout production in Canadian statistical districts. The Lake Manitou strain had the largest contribution in the

OH-1 statistical district during the early period and the GB-1234 and NC-3 during the late period (Table 5).

Canadian Seneca strain had the largest contribution in the OH-23, OH-45, and NC-12 statistical districts (Table 5).

Lake Manitou lake trout were present in high frequency in NC3 during the late period and in all GB units and in

OH1. The US Seneca strain was a large contributor to wild lake trout in several of the Canadian statistical districts.

It was the largest contributor to the OH-1 statistical district during the late sampling period, and contributed anywhere

from 22 to 43% for the GB-1234, NC-12, OH-23, and OH-45 statistical districts during the late sampling period. The

Michipicotan strain was estimated to have contributed 6% and 17% for the GB1234, OH-23, and OH-45 statistical

districts during the late sampling period. The Big/Parry Sound strain was estimated to have contributed around 6% to

the GB-1234 and NC-12 statistical districts during the late sampling period. The Iroquois Bay strain was estimated to

have contributed around 17% in the NC-3 statistical district, which was in close alignment to the stocking rate in this

district (Table 5). The Slate Island strain was not estimated to have made any contribution to any of the statistical

districts (Table 5).

Estimates of the assortative mating coefficient ( in equation 1) were relatively high in the early sampling period in

US waters (0.519, 0.558, and 0.270 in statistical districts MH-1, MH-2, and MH-345, respectively; Table 4).

However, for the late sampling period, estimates of the assortative mating coefficient for the same units ranged from

0.016 to 0.121 (Table 4). For Canadian waters, the only statistical district for which samples were available during

16

the early period was OH-1; the estimated assortative mating coefficient for this district and period was 0.01, which

was considerably lower than the corresponding values for US waters. During the late sampling period, estimates of

the assortative mating coefficient for the OH-1, NC-12, NC-3, OH-45 statistical districts were comparable to those

observed in US waters, ranging from 0.010 to 0.114 (Table 5). However, for OH-23 and GB-1234, the estimates of

the assortative mating coefficient ranged from 0.230 to 0.452 (Table 5).

Discussion

Sustainability of economically, ecologically, and culturally important natural populations of Great Lakes fishes

requires greater understanding of relationships between recruitment from natural and supplemental sources and

dispersal and habitat occupancy by wild recruits. This project applied methodology for quantifying temporal and

strain-specific contributions to mixed stocks of wild offspring from hatchery strains occupying open-waters in the

Great Lakes. We identified which lake trout hatchery strains contributed disproportionately to the open-water

assessments in US and Canadian management units and how individual strain contribution varied spatially and

temporally. Identification of regions used by individuals of different ages and strains (populations in general)

originating from different management jurisdictions or stocking locations is a fundamental requisite for lake trout

management and recovery in Lake Huron and elsewhere.

Populations of many fish species are spatially genetically structured as a function and rates of straying and due to

genetic drift associated with small effective population size (Allendorf et al. 2013). In the case of lake trout in Lake

Huron, native populations were extirpated in all locations with the exception of Parry Sound and Iroquois Bay (Berst

and Spangler 1972; Reid et al. 2001). Therefore, spatial genetic structure observed in the form of significant

differences in allele frequency among management units (Supplemental Table S9) are due to compositional

differences in hatchery strain relative abundance and their respective reproductive contributions (Tables 4 and 5), as

these strains themselves are all genetically differentiated (Supplemental Table S6).

The numbers of fish of each hatchery strain stocked into waters of each management unit were not predictive of

strain contributions to mixtures sampled (% stocked columns in Tables 4 and 5). Likewise, stocking combined with

age-specific mortality (from statistical catch at age models) and movements based on observations of aged stocked

fish (Alderstein et al. 2007) (Tables 4 and 5) were not generally reflective of mixture composition either (Tables 4

and 5). The high proportional representation of Seneca Lake lake trout suggests that members of this strain have

higher survival, and/or are more fecund than lake trout of other strains. In contrast, several US and Canadian strains

contributed little to wild fish sampled (Tables 4 and 5). Given limitations of hatchery space and pending

recommendations to alter stocking prescriptions in Lake Huron, the success of Seneca strain lake trout should be

considered if stocking continues.

17

The relative abundance of US strains of lake trout in multiple Canadian management units that were not stocked with

US strains suggests that fish from US strains are straying from waters in which they are stocked and are reproducing

in Canadian waters. For example, allele frequencies and estimates of strain contributions to the Canadian N12

management unit and US management unit MH1 were similar. An alternative explanation would be that wild lake

trout stray to a greater degree than hatchery lake trout, which is not likely. Straying of hatchery fish can be

widespread (Quinn 1993).

Large non-zero estimates of assortative mating coefficients () in the early sampling period in US waters and in

some of the Canadian management units in the later period suggest that mating among different strains is not always

random. Values of in later periods were generally low and in many cases essentially zero suggesting high levels of

strain mixing. Formally a zero value indicates that the genotypes reflect the combinations you would expect given

the proportional contributions of the different strains, if they were combined at random. The model cannot produce

more strain mixing than is expected based on random combinations, but a zero estimated value may occur when this

is the case. Although assortative mating coefficients generally were characterized by large confidence intervals

(Table 4 and 5), the results show general spatial and temporal trends that may have implications for management.

One potential explanation for the decrease in the assortative mating coefficient between the early and later period is

that a substantial proportion of wild individuals sampled in the late period may not be F1 (first generation wild fish)

but rather represent offspring from matings among wild parents. These fish would tend to reflect more mixing of

strains. An additional contributing factor would be if the more mixed genotypes of F2 fish survived better. Given

that hatchery strains have been under domestication for a number of generations (e.g., Page et al. 2003), some level

of inbreeding depression may exist. Matings among members of the same strain (or even between two pure strains)

may have not been as successful (in terms of relative reproductive success) than individuals produced from outbreed

matings among members of different strains. Heterosis or hybrid vigor has been commonly observed in situations

where populations (or domestic stocks) have existed in low numbers and have experienced some levels of inbreeding

depression (Lynch 1991). Indeed, wild lake trout across most management units were more genetically variable than

were the progenitor hatchery strains (comparisons for HE, AR, and Fis in Supplemental Tables S4 and S6). Either

scenario has significant implications for management.

Non-zero and changing levels of assortative mating that indicate the interbreeding of individuals of different strains

was not surprising and was previously incorporated in Genetic Stock Identification analyses for lake trout in the

Great Lakes (Marsden et al. 1989). Marsden et al. constructed artificial baseline hatchery strains that were hybrids of

existing strains to use as unique ‘baselines’. Here we take a model-based approach (equation 3) and estimate the

18

proportion of individuals mating assortatively () and randomly (1-) as parameters in the overall Bayesian mixture

model. The levels of mixing among strains we observed based on the estimated values of indicate that caution

should be used when estimating proportional contributions of strains to mixtures using traditional likelihood

approaches (e.g., Pella and Milner 1987, Pella and Masuda 2001)). Likewise, considerable attention has been

focused on use of individual assignment tests for purposes of mixture analysis (e.g., Manel et al. 2005). For the same

reason, caution is advised when assigning individuals to strain of origin given evidence for inter-breeding between

strains.

While the overall objectives were successfully accomplished. Our analytical approach deviated from the models we

originally proposed. As stated in our original proposal, we did utilize a general model developed by Guo et al. (2008)

utilizing a Bayesian-based approach for estimating the proportional contribution of source populations or strains to

newly founded colonies, as a form of Genetic Stock Identification (GSI). Further, as proposed, we expanded on the

Guo et al. (2008) evaluate model fit to evaluate whether there was evidence for temporal and spatial variation in

strain contributions (DIC analyses reported in Table 3). We also accounted for inter-strain mixing in our models. It

was our desire to use methodologies that PIs Brenden, Bence, and Scribner have previously developed (Brenden et al.

in review, Tsehaye et al. in review) to characterize age (or cohort) specific differences in recruitment by strain and to

more rigorously model effects of strain-specific stocking numbers, survival, and movements to estimated strain

contributions. However, due to constrains posed by limited sample sizes of agency-based collections of wild fish

(Table 1), particularly during the early sampling period, we were unable to develop a more detailed model that would

serve to reconcile differences between estimated strain contributions and expectations based on stocking numbers in

the difference management units and rather simplistic SCAA models that did not incorporate strain-specific

differences in survival. Further, details concerning movements outside of the main basin (units MH1, MH23,

MH345, OH1) were lacking (Supplemental Table S3).

While the emergence of wild lake trout in Lake Huron is promising, restoration is in an early stage. In comparison

with Lake Superior where the lake experienced a successful transition from a hatchery stocked population to a wild

fish dominated population, the current spawning biomass in Lake Huron is still low (70 versus 12-15 adult per km

gillnet per night; He et al. 2012). In order to maintain sufficient top-down influence on a dynamically changing food

web, and to ensure the success of natural reproduction and recruitment, adult density of the top predator like lake

trout should be higher than a minimum level (Walters and Kitchell 2001).

Discovering a relationship between strain type and successful reproduction will enhance managers’ understanding of

adaptive mechanisms and contribute to the development of more efficient and effective rehabilitation strategies

19

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Table 1. Sample sizes of hatchery lake trout genotyped at 15 microsatellite

loci (N=1143) for use in mixed stock analyses of wild Lake Huron lake trout and

sample sizes for wild lake trout mixture samples (N=1567) collected in Lake Huron

during the early (2002-2004) and late (2009-2012) sampling periods.

Hatchery strains used Strain Sample

in mixed stock analyses abbreviation size

US hatchery strains

Lewis Lake US_LLW 85

US Seneca Lake US_SLW 90

Apostle Island US_SAW 95

Marquette US_SMD 93

Green Lake US_GLW 95

Isle Royale US_SIW 97

Traverse Island US_STW 68

Canadian hatchery strains

Big/Par Sound CAN_BigPar 128

Lake Manitou CAN_Lman 89

Michopicotan CAN_Mich_Isle 77

Iroquois Bay CAN_Iroq_Isle 95

Can Seneca Lake CAN_Sen 67

Slate Island CAN_Slate_Isle 64

Open lake mixtures1

Early period Late period

US sampling locations (2002-2004) (2009-2012)

MH1 35 276

MH2 107 212

MH3/4/5 87 207

Canadian sampling locations

OH1 80 96

OH2/3 58

OH4/5 63

GB 1/2/3/4 157

NC3 74

NC1/2 1151Management Units in Lake Huron.

Table 2. Results of 100% simulations estimating the accuracy of

genetic stock identification methodology for determining lake trout

hatchery strain composition in mixed stock analyses.

Hatchery Strain Mean Std. Dev 95% CI

US strains

Lewis Lake 0.990 0.009 (0.970, 1.000)

US Seneca Lake 0.999 0.002 (0.994, 1.000)

Apostle Island 0.932 0.025 (0.879, 0.976)

Marquette 0.905 0.027 (0.848, 0.955)

Green Lake 0.987 0.010 (0.964, 1.000)

Isle Royale 0.949 0.021 (0.904, 0.986)

Traverse Island 0.907 0.026 (0.854, 0.954)

Canadian strains

Big/Parry Sound 0.999 0.002 (0.994, 1.000)

Lake Manitou 0.990 0.008 (0.970, 1.000)

Michopicotan 0.999 0.003 (0.992, 1.000)

Iroquois Bay 0.999 0.002 (0.994, 1.000)

Can. Seneca Lake 0.999 0.002 (0.994, 1.000)

Slate Island 0.994 0.007 (0.978, 1.000)

Table 3. Effective sample size and Geweke (1992) Z-score from the MCMC convergence diagnostics

conducted on the negative loge likelihood for each model used to evaluate spatial and temporal

consistency in hatchery strain contributions to the emerging wild lake trout population in Lake Huron.

Deviance information criteria (DIC), effective number of parameters (pD), and DIC weights for each

model are also shown. See text for a description of each model.

Group Model E.S.S. Geweke Z-

score

DIC pD DIC

weights

Spatial/temporal Pooled 7000.0 0.164 80939.1 7.2 0.0

Separate 6503.1 -0.936 80220.3 25.5 1.0

Spatial 5011.4 -0.625 80805.0 20.0 0.0

Temporal 6753.0 -0.938 80612.4 13.3 0.0

Spatial Pooled 6375.6 0.770 92663.5 9.3 0.0

Separate 6430.4 -1.216 91841.5 28.0 1.0

Michigan vs. Ontario 6664.4 -0.397 92204.1 12.4 0.0

Basin 6075.9 -0.358 92290.5 16.5 0.0

Table 4. Estimates (posterior means) and measures of uncertainty [lower and upper 95% highest posterior density limits (HPDL)] of contributions and assortative mating coefficients of US and Canadian hatchery strains

to the emerging wild lake trout population in Lake Huroon during the early and late sampling periods. Also shown are the expected contributions based only on stocking levels (% stocked) and stocking levels combined

with SCAA assessment model survival estimates and lake trout disperal (expected % (SCAA)).

US sampling locations - Early time period (2002-2004) US sampling locations (late period - 2009-2012)

Statistical Hatchery Mean Lower Upper Expected % Mean Lower Upper Expected %

District Strain Posterior 95% HPDL 95% HPDL % Stocked (SCAA) Posterior 95% HPDL 95% HPDL % Stocked (SCAA)

US strains

MH1 Lewis Lake 0.290 0.255 0.324 0.007 0.014 0.040 0.021 0.061 0.353 0.172

US Seneca Lake 0.291 0.255 0.324 0.202 0.154 0.781 0.720 0.842 0.252 0.481

Apostle Island 0.293 0.257 0.326 0.000 0.000 0.000 0.000 0.000 0.014 0.039

Marquette 0.000 0.000 0.000 0.705 0.752 0.046 0.024 0.068 0.272 0.161

Green Lake 0.127 0.025 0.232 0.000 0.000 0.000 0.000 0.000 0.006 0.016

Isle Royale 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.002 0.004

Traverse Island 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Canadian strains

Big/Parry Sound 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Lake Manitou 0.000 0.000 0.000 0.074 0.079 0.000 0.000 0.000 0.017 0.006

Michopicotan 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.080 0.121

Iroquois Bay 0.000 0.000 0.000 0.012 0.000 0.000 0.000 0.000 0.000 0.000

Can. Seneca Lake 0.000 0.000 0.000 0.000 0.000 0.133 0.078 0.192 0.000 0.000

Slate Island 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.005 0.001

Assort. Mating Coef. 0.519 0.242 0.790 0.028 0.000 0.049

US strains

MH2 Lewis Lake 0.176 0.106 0.253 0.024 0.049 0.217 0.168 0.265 0.356 0.187

US Seneca Lake 0.515 0.426 0.608 0.173 0.102 0.530 0.460 0.603 0.222 0.422

Apostle Island 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.011 0.023

Marquette 0.240 0.159 0.326 0.681 0.735 0.085 0.047 0.126 0.223 0.118

Green Lake 0.068 0.022 0.121 0.000 0.000 0.000 0.000 0.000 0.007 0.015

Isle Royale 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.006 0.012

Traverse Island 0.000 0.000 0.000 0.000 0.000 0.061 0.025 0.097 0.000 0.000

Canadian strains

Big/Parry Sound 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Lake Manitou 0.000 0.000 0.000 0.106 0.114 0.000 0.000 0.000 0.021 0.008

Michopicotan 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.103 0.163

Iroquois Bay 0.000 0.000 0.000 0.017 0.000 0.000 0.000 0.000 0.000 0.000

Can. Seneca Lake 0.000 0.000 0.000 0.000 0.000 0.106 0.046 0.165 0.000 0.000

Slate Island 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.050 0.052

Assort. Mating Coef. 0.558 0.393 0.722 0.121 0.000 0.237

US strains

MH345 Lewis Lake 0.240 0.179 0.293 0.028 0.032 0.143 0.102 0.185 0.266 0.167

US Seneca Lake 0.296 0.256 0.341 0.153 0.080 0.624 0.542 0.701 0.209 0.335

Apostle Island 0.000 0.000 0.000 0.000 0.000 0.055 0.024 0.090 0.006 0.009

Marquette 0.300 0.259 0.345 0.802 0.876 0.041 0.014 0.069 0.244 0.155

Green Lake 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.007 0.010

Isle Royale 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.017 0.024

Traverse Island 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Canadian strains

Big/Parry Sound 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Lake Manitou 0.000 0.000 0.000 0.015 0.011 0.000 0.000 0.000 0.003 0.001

Michopicotan 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.035 0.040

Iroquois Bay 0.000 0.000 0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.000

Can. Seneca Lake 0.165 0.081 0.253 0.000 0.000 0.138 0.068 0.210 0.000 0.000

Slate Island 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.213 0.258

Assort .Mating Coef. 0.270 0.068 0.482 0.016 0.000 0.039

Table 5. Results of genetic stock identification analysis describing estimated hatchery strain contributions of US and Canadian hatchery strains of lake trout to wild lake trout sampled in management areas in Canadian waters of Lake Huron

during early (2002-2004) and late (2010-2012) sampling periods.

Management Hatchery Mean Lower Upper Management Hatchery Mean Lower Upper

Unit Strain Posterior 95% HPDL 95% HPDL % Stocked Unit Strain Posterior 95% HPDL 95% HPDL % Stocked

US strains US strains

OH1-Early Lewis Lake 0.000 0.000 0.000 OH1-Late Lewis Lake 0.000 0.000 0.000

US Seneca Lake 0.000 0.000 0.000 US Seneca Lake 0.433 0.396 0.467

Apostle Island 0.000 0.000 0.000 Apostle Island 0.000 0.000 0.000

Marquette 0.104 0.045 0.169 Marquette 0.061 0.015 0.115

Green Lake 0.000 0.000 0.000 Green Lake 0.000 0.000 0.000

Isle Royale 0.000 0.000 0.000 Isle Royale 0.000 0.000 0.000

Traverse Island 0.000 0.000 0.000 Traverse Island 0.000 0.000 0.000

Canadian strains Canadian strains

Big/Parry Sound 0.000 0.000 0.000 Big/Parry Sound 0.000 0.000 0.000

Lake Manitou 0.896 0.831 0.955 Lake Manitou 0.100 0.041 0.161

Michopicotan 0.000 0.000 0.000 Michopicotan 0.000 0.000 0.000

Iroquois Bay 0.000 0.000 0.000 Iroquois Bay 0.000 0.000 0.000

Can. Seneca Lake 0.000 0.000 0.000 Can. Seneca Lake 0.406 0.372 0.440

Slate Island 0.000 0.000 0.000 Slate Island 0.000 0.000 0.000

Assort. Mating Coef. 0.010 0.000 0.021 Assort. Mating Coef. 0.058 0.000 0.115

US strains US strains

GB-Late Lewis Lake 0.000 0.000 0.000 0.000 OH23-Late Lewis Lake 0.000 0.000 0.000

US Seneca Lake 0.216 0.152 0.289 0.000 US Seneca Lake 0.349 0.251 0.438

Apostle Island 0.000 0.000 0.000 0.000 Apostle Island 0.000 0.000 0.000

Marquette 0.000 0.000 0.000 0.000 Marquette 0.000 0.000 0.000

Green Lake 0.000 0.000 0.000 0.000 Green Lake 0.000 0.000 0.000

Isle Royale 0.000 0.000 0.000 0.000 Isle Royale 0.000 0.000 0.000

Traverse Island 0.000 0.000 0.000 0.000 Traverse Island 0.000 0.000 0.000

Canadian strains Canadian strains

Big/Parry Sound 0.055 0.015 0.096 0.113 Big/Parry Sound 0.000 0.000 0.000

Lake Manitou 0.373 0.317 0.435 0.071 Lake Manitou 0.000 0.000 0.000

Michopicotan 0.055 0.023 0.090 0.465 Michopicotan 0.165 0.081 0.252

Iroquois Bay 0.000 0.000 0.000 0.000 Iroquois Bay 0.000 0.000 0.000

Can. Seneca Lake 0.300 0.239 0.360 0.000 Can. Seneca Lake 0.485 0.388 0.593

Slate Island 0.000 0.000 0.000 0.351 Slate Island 0.000 0.000 0.000

Assort. Mating Coef. 0.452 0.302 0.614 Assort. Mating Coef. 0.230 0.001 0.462

US strains US strains

NC3-Late Lewis Lake 0.000 0.000 0.000 0.000 OH45-Late Lewis Lake 0.000 0.000 0.000 0.000

US Seneca Lake 0.000 0.000 0.000 0.000 US Seneca Lake 0.372 0.329 0.417 0.000

Apostle Island 0.000 0.000 0.000 0.000 Apostle Island 0.000 0.000 0.000 0.000

Marquette 0.000 0.000 0.000 0.000 Marquette 0.094 0.037 0.156 0.000

Green Lake 0.000 0.000 0.000 0.000 Green Lake 0.000 0.000 0.000 0.000

Isle Royale 0.000 0.000 0.000 0.000 Isle Royale 0.000 0.000 0.000 0.000

Traverse Island 0.000 0.000 0.000 0.000 Traverse Island 0.000 0.000 0.000 0.000

Canadian strains Canadian strains

Big/Parry Sound 0.000 0.000 0.000 0.000 Big/Parry Sound 0.000 0.000 0.000 0.000

Lake Manitou 0.826 0.748 0.896 0.794 Lake Manitou 0.000 0.000 0.000 0.000

Michopicotan 0.000 0.000 0.000 0.000 Michopicotan 0.151 0.078 0.229 0.000

Iroquois Bay 0.174 0.104 0.252 0.206 Iroquois Bay 0.000 0.000 0.000 0.000

Can. Seneca Lake 0.000 0.000 0.000 0.000 Can. Seneca Lake 0.383 0.336 0.426 0.000

Slate Island 0.000 0.000 0.000 0.000 Slate Island 0.000 0.000 0.000 1.000

Assort. Mating Coef. 0.020 0.009 0.027 Assort. Mating Coef. 0.033 0.016 0.057

US strains

NC12-Late Lewis Lake 0.000 0.000 0.000 0.000

US Seneca Lake 0.427 0.364 0.484 0.000

Apostle Island 0.000 0.000 0.000 0.000

Marquette 0.000 0.000 0.000 0.000

Green Lake 0.000 0.000 0.000 0.000

Isle Royale 0.000 0.000 0.000 0.000

Traverse Island 0.000 0.000 0.000 0.000

Canadian strains

Big/Parry Sound 0.057 0.021 0.096 0.000

Lake Manitou 0.000 0.000 0.000 0.405

Michopicotan 0.000 0.000 0.000 0.493

Iroquois Bay 0.000 0.000 0.000 0.000

Can. Seneca Lake 0.516 0.459 0.583 0.102

Slate Island 0.000 0.000 0.000 0.000

Assort. Mating Coef. 0.114 0.000 0.260

Supplemental Table S1. Total number of juvenile lake trout of each US and Canadian hatchery strain stocked into Lake Huron during years consistent with adults age classes that could have produced wild

individuals sampled during the early (2002-2004) and late (2010-2012) collection periods.

Cohorts from early sampling period (2002-2004) Cohorts from late sampling period (2010-2012)

Hatchery Strain 1990 1989 1988 1987 1986 1985 1984 1983 1998 1997 1996 1995 1994 1993 1992 1991

US

Lewis Lake 47000 0 0 0 0 0 0 0 284800 91460 294150 256450 1004180 436300 1259784 61200

US Seneca Lake 10350 141050 74900 106685 0 57132 0 0 910690 674300 561640 308879 37900 0 0 117000

Apostle Island 0 0 0 0 0 0 0 0 40400 73240 0 0 0 0 0 0

Marquette 137100 1E+06 78900 116441 0 108454 126200 400200 164350 307951 471600 784800 453800 133700

Green Lake 0 0 0 0 0 0 0 0 18000 60040 0 0 0 0 0 0

Isle Royale 0 0 0 0 0 0 0 0 8200 105300 0 0 0 0 0 0

Traverse Island 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Canadian

Big/Parry Sound 0 0 31764 30148 33585 19025 33840 0 406459 338507 47731 27888 0 44110 12874 0

Lake Manitou 155805 285930 416515 125135 20858 6729 2000 7500 171717 79564 95028 252842 56640 110151 547399 156309

Michopicotan 0 0 0 0 0 0 0 0 638178 764247 878286 871922 686372 771662 260658 0

Iroquois Bay 5250 3123 11366 15570 7500 30185 35457 23600 14396 0 0 0 0

Can. Seneca Lake 0 0 0 0 0 0 0 0 94767 0 0 0 0 0 0 0

Slate Island 0 0 0 0 0 0 0 0 748626 876882 559832 602171 981929 559181 392624 0

Supplemental Table 2. Survival of hatchery lake trout lake trout strains by year class and spawning year, derived from SCAA models.

Management

unit

Spawning

year

Ages of mature fish (year class)

7

(1983/1991)

8

(1984/1992)

9

(1985/1993)

10

(1986/1994)

11

(1987/1995)

12

(1988/1996)

13

(1989/1997)

14

(1990/1998)

MH1 1997 1.16×10-02

6.74×10-03

2.13×10-03

1.69×10-03

4.29×10-04

7.52×10-05

1.07×10-05

5.91×10-06

2005 1.59×10-01

6.24×10-02

3.25×10-02

1.19×10-02

8.91×10-03

5.3310-03

2.52×10-03

8.12×10-04

MH2 1997 4.42×10-03

2.73×10-03

7.20×10-04

3.46×10-04

2.38×10-04

9.47×10-04

2.42×10-05

2.42×10-05

2005 3.50×10-02

1.50×10-02

7.89×10-03

3.00×10-03

2.03×10-03

1.16×10-03

6.54×10-04

1.27×10-04

MH345 1997 1.13×10-02

1.21×10-02

3.22×10-03

1.68×10-03

1.69×10-03

1.31×10-03

1.31×10-03

1.31×10-03

2005 5.50×10-02

2.88×10-02

2.75×10-02

1.15×10-02

1.22×10-02

5.44×10-03

4.82×10-03

1.37×10-03

Average 1997 9.11×10-03

7.20×10-03

2.02×10-03

1.24×10-03

7.85×10-04

4.94×10-04

4.48×10-04

5.91×10-06

2005 8.29×10-02

3.54×10-02

2.26×10-03

8.82×10-03

7.72×10-03

3.98×10-03

2.66×10-03

7.70×10-04

Supplemental Table S3. Movement matrix characterizing expected rates of movement from stocked to settled

management units. Data are available for the regions of the Lake Huron main basin only (in box). Movements

associated with other regions are unknown and were not used in analyses to develop expected strain proportions.

Management units settled1

MH1=N MH2=C MH345=S GB-123 NC3 GB-4 NC-12 OH-45 total

MH1 0.720 0.229 0.051 0.000 0.000 0.000 0.000 0.000 1.000

DI 0.973 0.013 0.014 0.000 0.000 0.000 0.000 0.000 1.000

MH2 0.349 0.548 0.103 0.000 0.000 0.000 0.000 0.000 1.000

MH3 0.097 0.355 0.548 0.000 0.000 0.000 0.000 0.000 1.000

SFB 0.048 0.091 0.861 0.000 0.000 0.000 0.000 0.000 1.000

MH4 0.000 0.132 0.868 0.000 0.000 0.000 0.000 0.000 1.000

Management units stocked MH5 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 1.000

MH6 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 1.000

QMA4-3 0.349 0.548 0.103 0.000 0.000 0.000 0.000 0.000 1.000

QMA4-4 0.000 0.132 0.868 0.000 0.000 0.000 0.000 0.000 1.000

GB-123 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 1.000

NC3 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 1.000

GB-4 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 1.000

NC-12 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 1.000

Suppl OH-45 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 1.000

1Note OH1 = QMA 4-3; GB123=QMA 5-3 and QMA 5-1; NC12 = QMA 6-1; OH45=QMA 4-5; OH2 3=QMA 4-4;OH45=QMA 4-5

Supplemental Table S4. Estimated allele frequency and summary measures of genetic diversity for 13 lake trout hatchery strains

US Strains Canadian Strains

Locus Alleles (bp)

Lewis

Lake

US

Seneca

Lake

Apostle

Island Marquette

Green

Lake

Isle

Royale

Traverse

Island

Big/Parry

Sound

Lake

Manitou Michopicotan

Iroquois

Bay

Can

Seneca

Lake

Slate

Island

Sample size 85 88 95 93 95 96 68 124 89 77 95 67 64

One9 222 0 0 0 0.011 0 0 0 0 0 0 0 0 0

224 0 0 0 0.011 0 0 0 0 0 0 0 0 0

226 0.941 0.915 0.937 0.898 0.932 0.932 0.956 0.923 0.994 1 1 0.91 0.953

228 0.041 0.08 0.037 0.048 0 0.036 0.022 0.073 0.006 0 0 0.09 0.008

230 0.018 0 0.026 0.022 0.068 0.031 0 0.004 0 0 0 0 0.039

232 0 0.006 0 0.011 0 0 0.022 0 0 0 0 0 0

Ogo1a 138 0 0 0 0 0.005 0 0 0 0 0 0 0 0

140 0.006 0 0 0 0 0 0 0 0 0 0 0 0

142 0.247 0.149 0.053 0.118 0.195 0.063 0.103 0.327 0.247 0.182 0.347 0.227 0.048

144 0.018 0 0 0 0 0 0 0 0.006 0 0 0 0

146 0.035 0.006 0 0 0 0 0 0 0 0 0 0 0

148 0.518 0.322 0.663 0.715 0.637 0.807 0.779 0.398 0.416 0.487 0.532 0.386 0.754

150 0.176 0.523 0.279 0.167 0.163 0.13 0.103 0.276 0.331 0.331 0.121 0.386 0.198

152 0 0 0.005 0 0 0 0.015 0 0 0 0 0 0

Scou19 155 0 0 0 0.005 0 0 0 0 0.006 0 0 0 0

157 0 0 0 0.005 0 0.005 0 0.004 0 0.099 0 0.119 0.016

159 0.065 0.242 0.174 0.113 0.106 0.057 0.194 0 0.006 0.086 0 0.261 0.102

161 0 0 0 0 0 0 0 0 0.006 0 0.037 0 0.023

163 0.029 0 0.016 0.011 0 0.015 0 0.004 0.006 0 0.037 0 0.031

165 0.024 0.006 0 0 0.005 0 0 0 0.006 0 0 0 0.031

167 0 0.006 0 0.005 0.027 0 0.007 0.069 0.011 0.039 0.058 0 0.039

169 0.241 0.416 0.253 0.269 0.277 0.356 0.231 0.214 0.242 0.23 0.137 0.239 0.266

171 0 0.051 0.016 0.011 0.005 0.062 0.03 0.101 0.039 0.007 0.121 0.03 0.07

173 0.471 0.23 0.468 0.452 0.372 0.474 0.425 0.601 0.624 0.467 0.605 0.313 0.398

175 0.071 0.051 0.037 0.081 0.101 0.015 0.09 0.008 0.022 0 0.005 0.037 0

177 0.1 0 0.037 0.048 0.106 0.005 0.022 0 0.034 0.072 0 0 0.023

179 0 0 0 0 0 0.01 0 0 0 0 0 0 0

Sfo12 252 0.024 0.045 0.047 0.065 0.084 0.13 0.191 0.035 0.084 0.095 0.037 0.03 0.23

254 0.042 0.188 0.084 0.038 0.068 0.052 0.022 0.059 0.034 0 0 0.201 0.071

256 0.928 0.767 0.847 0.866 0.847 0.813 0.757 0.906 0.86 0.905 0.963 0.769 0.698

258 0 0 0.021 0.016 0 0.005 0.029 0 0.022 0 0 0 0

260 0.006 0 0 0.016 0 0 0 0 0 0 0 0 0

Sfo18 167 0 0 0 0 0 0.005 0 0.059 0 0.02 0.195 0.007 0.016

169 0.363 0.744 0.564 0.597 0.473 0.532 0.61 0.461 0.5 0.347 0.537 0.731 0.516

171 0 0.023 0 0 0.027 0.027 0 0 0.006 0 0.016 0.007 0.008

173 0.006 0.205 0.048 0.043 0.005 0.059 0.015 0.098 0 0 0 0.157 0

175 0 0 0 0 0 0 0 0.18 0 0 0.005 0 0

177 0.006 0 0 0 0 0 0 0.027 0 0.013 0.011 0.015 0

179 0.476 0.028 0.234 0.28 0.441 0.282 0.272 0.086 0.453 0.52 0.116 0.082 0.313

181 0.101 0 0.074 0.005 0 0.011 0.007 0.063 0.012 0.027 0 0 0.016

183 0.036 0 0.005 0.005 0 0.037 0 0 0 0.02 0.063 0 0.008

185 0.012 0 0.069 0.059 0.043 0.043 0.059 0.027 0.012 0.053 0.058 0 0.125

187 0 0 0 0.011 0.011 0.005 0.037 0 0 0 0 0 0

189 0 0 0.005 0 0 0 0 0 0.017 0 0 0 0

Sfo1 103 0 0.006 0.021 0.033 0.005 0.026 0.059 0 0.079 0.065 0.058 0 0.032

105 0.982 0.71 0.895 0.917 0.953 0.911 0.824 0.969 0.871 0.909 0.916 0.761 0.929

109 0 0 0 0 0 0 0 0 0 0.006 0 0 0

111 0.018 0.284 0.084 0.05 0.042 0.063 0.118 0.031 0.045 0 0.026 0.239 0.04

115 0 0 0 0 0 0 0 0 0.006 0 0 0 0

121 0 0 0 0 0 0 0 0 0 0.019 0 0 0

SfoC38 143 0.876 0.789 0.889 0.903 0.742 0.866 0.831 0.9 0.915 0.844 0.979 0.724 0.787

149 0.124 0.206 0.1 0.07 0.253 0.124 0.154 0.1 0.085 0.156 0.021 0.254 0.189

152 0 0.006 0 0 0 0 0 0 0 0 0 0.022 0

155 0 0 0.005 0 0 0 0 0 0 0 0 0 0

158 0 0 0.005 0.027 0.005 0.01 0.015 0 0 0 0 0 0.025

SfoC88 169 0 0 0 0 0 0 0 0 0.012 0 0 0 0

172 0.653 0.6 0.821 0.688 0.789 0.778 0.721 0.43 0.471 0.597 0.463 0.672 0.648

175 0.347 0.4 0.179 0.312 0.163 0.222 0.279 0.57 0.517 0.403 0.537 0.328 0.352

178 0 0 0 0 0.047 0 0 0 0 0 0 0 0

SnaMSU03 163 0.03 0 0 0 0 0 0 0 0 0 0 0 0

183 0 0 0.005 0 0 0.005 0 0 0 0 0 0 0.018

187 0 0.011 0 0.011 0 0 0 0.059 0.013 0 0.225 0 0

191 0.054 0.182 0.074 0.14 0 0.083 0.045 0.118 0.136 0.049 0.1 0.164 0.073

195 0.286 0.341 0.411 0.344 0.29 0.302 0.276 0.248 0.312 0.417 0.238 0.448 0.264

199 0.22 0.102 0.268 0.177 0.403 0.219 0.313 0.193 0.279 0.042 0.025 0.134 0.164

201 0 0.006 0.011 0 0.005 0.026 0 0 0 0 0 0 0

203 0.119 0.102 0.068 0.065 0.081 0.12 0.09 0.134 0.117 0.125 0.075 0.052 0.155

205 0 0.091 0 0.005 0 0.047 0.007 0 0.013 0 0 0.097 0.045

207 0.042 0.034 0.047 0.048 0.005 0.031 0.007 0.076 0.026 0.069 0 0.007 0.018

209 0 0.068 0.005 0.022 0.022 0.036 0.015 0.046 0 0.063 0.188 0.007 0.018

211 0 0.017 0.005 0.011 0.011 0.016 0.09 0 0.006 0.042 0 0.007 0.045

213 0.03 0.011 0 0.059 0.048 0 0 0.038 0.013 0 0 0.007 0.027

215 0 0 0.005 0.011 0 0.01 0 0.013 0 0 0 0 0.018

217 0 0 0 0 0 0 0 0 0 0 0 0.015 0.045

219 0 0 0 0 0 0 0.007 0 0 0 0 0 0

225 0 0 0 0.011 0.005 0.01 0.007 0 0 0 0 0 0.009

227 0 0 0 0 0 0 0 0 0 0 0.15 0 0

229 0 0 0 0.005 0 0.036 0.03 0 0 0 0 0 0

231 0 0 0 0 0 0.01 0 0.025 0 0 0 0 0

233 0 0 0.053 0 0.005 0 0.007 0 0 0.049 0 0 0

235 0.012 0.017 0 0.005 0 0 0 0.008 0.006 0 0 0 0

239 0 0 0.005 0.005 0.005 0 0.022 0.025 0.006 0.014 0 0 0

241 0 0 0 0 0 0.005 0 0 0 0 0 0 0

243 0 0.006 0.005 0 0 0.021 0 0 0.013 0.028 0 0.03 0

245 0 0 0 0 0 0.005 0 0 0 0 0 0 0

247 0 0.011 0.005 0 0.022 0.005 0 0 0 0.104 0 0 0.055

249 0 0 0 0 0.005 0 0 0 0 0 0 0 0

251 0.03 0 0.016 0.011 0 0 0.037 0 0.019 0 0 0 0.009

255 0.095 0 0 0.011 0.043 0 0 0 0.006 0 0 0.03 0.027

259 0.03 0 0 0.022 0 0 0.007 0 0 0 0 0 0.009

263 0.054 0 0.005 0 0 0 0.007 0 0.013 0 0 0 0

267 0 0 0 0.011 0 0 0 0 0.006 0 0 0 0

271 0 0 0 0 0 0 0 0 0.013 0 0 0 0

275 0 0 0.011 0.005 0 0 0.015 0 0 0 0 0 0

279 0 0 0 0 0 0.005 0.007 0 0 0 0 0 0

291 0 0 0 0 0 0 0 0.017 0 0 0 0 0

297 0 0 0 0.005 0 0.005 0 0 0 0 0 0 0

301 0 0 0 0.005 0.048 0 0 0 0 0 0 0 0

309 0 0 0 0 0 0 0.007 0 0 0 0 0 0

313 0 0 0 0.005 0 0 0 0 0 0 0 0 0

317 0 0 0 0.005 0 0 0 0 0 0 0 0 0

SnaMSU11 207 0 0 0.005 0 0 0 0 0 0 0 0 0 0

209 0 0 0 0.005 0.011 0 0 0 0 0 0 0 0

213 0 0 0 0 0 0.016 0 0 0 0 0 0 0

215 0 0 0.026 0 0.032 0.021 0.007 0 0 0.112 0.088 0 0.042

217 0.006 0 0 0 0 0.011 0.022 0 0 0 0.005 0 0

219 0.029 0 0.011 0.049 0.086 0.011 0.06 0 0.023 0.007 0.005 0 0.01

221 0.018 0 0 0 0 0.005 0 0 0 0 0 0 0

223 0.476 0.376 0.358 0.397 0.102 0.279 0.299 0.274 0.614 0.25 0.297 0.295 0.365

225 0 0 0.042 0.065 0.016 0.016 0 0 0 0.007 0 0 0.042

227 0.047 0.09 0.053 0.049 0.07 0.079 0.03 0.016 0.076 0.059 0.044 0.053 0.125

229 0 0 0.037 0.027 0.065 0.047 0.03 0 0 0.013 0 0 0

231 0.029 0 0.047 0.033 0 0.042 0.045 0.048 0 0.02 0 0.008 0

233 0 0 0.032 0 0 0.005 0.03 0.083 0 0.237 0 0 0

235 0.012 0.006 0.053 0.103 0 0.037 0.015 0.056 0.023 0.039 0.005 0.008 0

237 0 0 0.026 0 0.005 0.005 0.015 0.012 0 0.007 0 0 0.021

239 0.276 0.022 0.147 0.092 0.048 0.089 0.104 0.099 0.182 0.184 0.181 0.038 0.135

241 0.041 0.045 0 0.005 0.07 0 0.007 0 0.008 0 0 0.045 0.01

243 0.053 0.101 0.058 0.065 0.108 0.074 0.06 0.123 0.03 0.013 0.286 0.136 0.104

245 0.006 0.253 0.032 0.033 0.301 0.111 0.097 0.198 0.008 0 0.033 0.341 0.021

247 0 0.006 0.016 0.022 0.043 0.011 0.045 0 0.015 0.033 0.005 0 0.021

249 0 0.101 0.026 0.011 0.022 0.032 0 0.008 0 0.007 0 0.076 0.01

251 0.006 0 0 0.005 0 0 0.007 0 0 0 0.038 0 0

253 0 0 0 0 0 0.005 0 0 0 0 0 0 0

257 0 0 0 0 0 0 0.007 0 0 0 0 0 0

265 0 0 0.005 0 0.011 0 0.007 0 0.008 0 0 0 0

267 0 0 0 0 0 0 0 0 0 0.007 0 0 0

269 0 0 0.011 0 0 0 0.03 0 0 0 0 0 0

271 0 0 0.016 0.011 0 0 0 0 0 0 0 0 0.031

273 0 0 0 0 0 0 0.045 0 0.008 0 0.011 0 0.021

277 0 0 0 0.005 0 0.005 0 0 0.008 0 0 0 0.021

279 0 0 0 0 0 0.011 0 0 0 0 0 0 0

281 0 0 0 0.011 0 0.021 0 0.083 0 0 0 0 0

283 0 0 0 0 0 0.032 0 0 0 0 0 0 0

285 0 0 0 0 0 0 0 0 0 0.007 0 0 0

289 0 0 0 0 0.011 0 0 0 0 0 0 0 0

291 0 0 0 0 0 0.037 0 0 0 0 0 0 0

293 0 0 0 0 0 0 0.022 0 0 0 0 0 0.021

299 0 0 0 0.011 0 0 0.015 0 0 0 0 0 0

SnaMSU01 205 0 0 0 0 0 0.005 0 0 0 0 0 0 0

209 0.006 0 0 0 0 0 0.023 0 0 0 0 0 0.052

213 0.025 0 0 0.011 0 0 0 0.004 0.008 0 0 0 0

217 0.15 0 0.063 0.054 0.038 0.046 0.098 0.028 0.008 0.007 0.006 0 0.083

221 0.138 0 0.132 0.156 0.077 0.227 0.167 0.169 0.015 0.06 0.228 0 0.156

225 0.106 0 0.089 0.07 0.027 0.062 0.121 0.016 0 0.007 0 0 0.115

229 0.156 0 0.095 0.118 0 0.021 0.076 0.008 0 0.013 0 0 0.063

233 0.056 0.028 0.058 0.027 0.044 0.021 0.023 0 0 0 0 0.007 0.01

237 0.006 0.006 0.005 0.016 0.005 0.015 0.023 0 0 0 0 0 0

241 0.056 0.05 0.042 0.022 0.049 0.098 0.045 0.039 0.023 0.093 0 0.09 0

245 0.025 0.106 0.074 0.048 0 0.041 0.015 0 0.023 0.033 0 0.082 0.021

249 0.038 0.067 0.053 0.059 0.044 0.036 0.038 0.012 0.015 0.187 0 0.045 0.063

253 0.019 0.1 0.111 0.134 0.44 0.129 0.121 0.075 0.146 0.227 0.044 0.052 0.156

257 0.063 0.1 0.063 0.032 0.093 0.149 0.008 0.091 0.108 0.087 0.306 0.157 0.115

261 0.056 0.106 0.089 0.027 0.082 0.041 0.189 0.079 0.185 0.167 0.278 0.209 0.073

265 0.1 0.061 0.047 0.075 0.027 0.077 0.045 0.323 0.038 0.047 0.017 0.09 0.021

269 0 0.039 0.026 0.07 0.005 0.031 0.008 0.024 0.062 0 0.028 0.03 0.052

273 0 0.05 0 0.011 0 0 0 0.071 0.038 0.007 0.017 0.075 0

277 0 0.078 0 0.011 0.044 0 0 0.012 0.046 0.02 0 0.067 0

281 0 0.111 0 0 0 0 0 0.039 0.192 0.033 0.039 0.067 0.021

285 0 0.072 0 0 0 0 0 0 0.031 0 0 0.015 0

289 0 0.017 0 0.027 0 0 0 0 0.031 0.013 0 0.007 0

293 0 0.006 0 0.032 0 0 0 0.004 0 0 0.011 0.007 0

297 0 0.006 0 0 0 0 0 0 0.008 0 0.011 0 0

301 0 0 0.053 0 0.022 0 0 0.008 0 0 0 0 0

305 0 0 0 0 0 0 0 0 0.008 0 0 0 0

309 0 0 0 0 0 0 0 0 0.015 0 0.017 0 0

SnaMSU10 166 0 0 0 0 0.005 0 0 0 0 0 0 0 0

178 0 0.017 0.005 0.027 0 0.016 0.03 0.035 0 0.02 0 0 0

182 0 0 0.005 0.005 0.022 0 0.053 0 0 0.046 0 0 0

186 0 0.006 0.016 0.016 0 0.005 0.03 0 0.037 0.02 0.011 0 0.11

190 0.006 0.05 0.047 0.054 0.198 0.111 0.015 0.015 0.031 0 0.046 0.022 0.025

192 0 0 0 0 0 0 0 0.03 0 0 0 0 0

194 0.018 0.322 0.121 0.016 0.027 0.074 0.015 0.1 0.062 0.171 0.04 0.224 0

196 0 0 0 0 0 0 0 0 0 0.013 0 0 0

198 0.078 0.133 0.063 0.152 0.115 0.016 0.083 0.165 0.037 0.039 0.236 0.157 0.076

200 0 0 0 0 0 0 0 0 0.012 0 0 0 0

202 0.151 0.039 0.032 0.109 0.165 0.037 0.03 0.015 0.093 0.02 0.218 0.037 0.076

204 0 0 0.005 0 0 0 0 0.01 0.019 0 0 0 0

206 0.096 0 0.026 0.049 0.159 0.011 0.038 0.1 0.099 0.013 0.126 0 0.034

208 0 0 0.026 0.005 0 0 0.061 0.015 0.228 0 0.057 0 0

210 0.024 0 0.074 0.033 0.055 0.011 0.068 0 0.08 0.072 0 0 0.017

212 0.006 0 0.037 0.016 0.005 0.011 0.023 0.065 0.123 0.02 0.052 0 0.093

214 0 0 0 0.011 0 0.011 0 0 0.012 0 0 0.007 0.025

216 0.024 0 0.047 0.038 0.038 0.032 0.045 0.045 0.006 0.033 0.011 0 0.068

218 0 0 0.047 0.016 0.016 0 0.038 0.005 0.012 0 0 0 0.008

220 0.114 0.011 0.042 0.076 0.088 0.342 0.144 0.02 0.006 0 0.034 0.015 0.085

222 0.018 0 0.042 0.005 0.005 0 0 0.005 0 0 0 0 0

224 0.108 0.067 0.137 0.06 0 0.137 0.167 0.035 0.012 0.026 0 0.119 0.085

228 0 0 0 0.005 0 0.011 0.008 0.005 0 0.007 0 0 0.008

230 0.084 0.05 0.1 0.054 0.005 0.095 0.121 0.085 0.025 0.02 0.017 0.022 0.102

232 0 0 0 0.005 0.011 0 0 0 0 0 0 0 0

234 0.078 0.089 0.032 0.027 0.022 0.011 0 0.14 0.012 0.059 0 0.149 0.11

236 0 0 0 0 0 0 0.008 0 0 0 0 0 0

238 0.12 0.094 0.079 0.076 0.011 0.068 0 0.105 0.037 0.164 0 0.112 0.034

242 0.066 0.056 0 0.054 0.016 0.005 0 0.005 0.019 0.138 0.138 0.022 0.008

246 0 0.039 0.016 0 0 0 0 0 0.019 0.118 0.011 0.052 0.034

250 0.006 0.006 0 0.076 0.033 0 0 0 0 0 0 0.007 0

254 0 0.022 0 0.011 0 0 0.023 0 0.006 0 0 0.052 0

258 0 0 0 0 0 0 0 0 0.012 0 0 0 0

SnaMSU05 157 0 0 0.048 0.027 0.05 0.005 0.023 0.135 0.038 0 0.007 0 0.05

165 0 0.034 0 0 0 0 0.008 0 0 0 0 0.076 0

169 0.006 0 0 0.005 0 0 0 0 0.013 0 0 0.008 0

173 0.006 0.011 0.053 0.022 0.117 0.026 0.008 0 0.05 0.038 0 0 0

177 0.165 0.034 0.213 0.17 0.133 0.121 0.091 0.009 0.05 0.192 0.391 0.023 0.17

181 0.195 0.213 0.335 0.203 0.272 0.263 0.47 0.279 0.463 0.438 0.203 0.265 0.39

183 0 0 0 0 0.028 0 0 0 0 0 0 0 0

185 0.159 0.236 0.053 0.17 0.183 0.205 0.121 0.009 0.088 0.054 0.007 0.341 0.13

187 0 0 0 0.005 0 0 0 0 0 0 0 0 0

189 0.177 0.017 0.043 0.038 0.039 0.084 0.083 0.005 0.05 0.054 0 0.015 0.03

193 0 0 0 0.038 0.028 0.005 0.053 0.018 0.05 0 0.014 0 0

197 0.024 0 0.069 0.027 0.061 0.005 0.03 0.005 0.019 0.023 0 0 0.05

201 0.067 0.022 0.037 0.005 0 0 0 0 0.019 0 0.022 0 0

205 0.006 0.163 0.037 0.011 0.006 0.016 0 0.099 0 0.023 0 0.083 0.01

209 0 0 0 0.077 0.039 0.016 0 0 0.019 0.054 0 0 0.03

211 0 0 0 0 0 0.016 0 0 0 0 0 0 0

213 0 0 0.021 0.077 0.006 0.116 0.076 0.027 0.05 0 0.043 0 0.05

217 0.024 0.022 0.069 0.044 0.006 0.084 0.03 0.396 0.031 0.023 0.174 0.008 0.08

221 0.152 0.028 0.016 0.005 0 0.037 0.008 0.005 0.031 0 0.109 0 0

225 0 0.202 0.005 0.06 0.033 0 0 0.014 0.025 0.1 0.029 0.152 0

229 0.018 0.017 0 0.011 0 0 0 0 0 0 0 0.015 0

237 0 0 0 0 0 0 0 0 0 0 0 0.015 0

269 0 0 0 0 0 0 0 0 0.006 0 0 0 0.01

SnaMSU08 124 0 0 0 0 0 0 0 0.012 0 0 0 0 0

128 0 0 0 0 0 0 0 0.04 0 0 0 0 0

142 0 0 0.021 0.033 0 0.084 0.03 0 0.031 0.009 0 0 0.026

146 0 0 0.005 0.011 0.085 0.005 0 0 0 0 0 0 0

150 0.256 0.063 0.133 0.071 0.069 0.068 0.112 0.028 0.055 0 0 0.144 0.039

154 0.024 0.017 0.016 0.005 0.032 0.058 0.022 0.008 0.047 0 0.017 0.045 0.026

158 0.067 0.011 0 0.005 0.021 0.005 0.007 0 0.031 0.009 0 0 0

162 0.14 0.04 0.128 0.141 0.186 0.132 0.112 0.27 0.109 0.064 0.172 0.03 0.026

166 0.03 0.115 0.059 0.049 0.122 0.021 0.06 0.008 0.023 0 0 0.053 0.013

170 0.439 0.534 0.484 0.473 0.415 0.505 0.522 0.54 0.648 0.345 0.672 0.652 0.684

174 0 0.011 0.09 0.098 0.011 0.079 0.052 0.016 0.047 0 0.128 0.008 0.092

178 0.043 0.052 0.059 0.109 0.032 0.016 0.06 0.079 0 0.009 0.011 0.023 0.092

182 0 0.034 0 0 0.027 0.026 0 0 0 0 0 0.015 0

186 0 0.121 0 0.005 0 0 0.022 0 0.008 0.018 0 0.03 0

190 0 0 0 0 0 0 0 0 0 0.036 0 0 0

194 0 0 0 0 0 0 0 0 0 0.264 0 0 0

198 0 0 0 0 0 0 0 0 0 0.027 0 0 0

202 0 0 0 0 0 0 0 0 0 0.064 0 0 0

206 0 0 0 0 0 0 0 0 0 0.073 0 0 0

210 0 0 0 0 0 0 0 0 0 0.055 0 0 0

230 0 0 0.005 0 0 0 0 0 0 0 0 0 0

234 0 0 0 0 0 0 0 0 0 0.027 0 0 0

Sco202 137 0 0 0 0 0 0 0 0 0 0 0 0 0.017

138 0 0 0.016 0 0 0 0 0 0 0 0 0 0

141 0 0 0 0 0 0 0 0 0 0.045 0 0 0

142 0 0 0 0.011 0.006 0.043 0 0 0 0 0 0 0

145 0 0 0 0 0 0 0 0 0.006 0 0 0 0.127

146 0.006 0 0.011 0.006 0 0 0.022 0 0 0 0 0 0

149 0 0 0 0 0 0 0 0.004 0.124 0.039 0.037 0 0.008

150 0 0 0.021 0.05 0.034 0.096 0 0 0 0 0 0 0

153 0 0 0 0 0 0 0 0.018 0.1 0.071 0.268 0 0.034

154 0.042 0 0.053 0.044 0.006 0.005 0.015 0 0 0 0 0 0

157 0 0 0 0 0 0 0 0.075 0.124 0.143 0.121 0.104 0.068

158 0.083 0.144 0.106 0.106 0.115 0.037 0.112 0 0 0 0 0 0

161 0 0 0 0 0 0 0 0.268 0.212 0.357 0.174 0.463 0.263

162 0.321 0.391 0.186 0.167 0.178 0.176 0.358 0 0 0 0 0 0

165 0 0 0 0 0 0 0 0.228 0.1 0.162 0.258 0.194 0.144

166 0.196 0.115 0.207 0.172 0.218 0.298 0.201 0 0 0 0 0 0

169 0 0 0 0 0 0 0 0.268 0.071 0.078 0.068 0.067 0.11

170 0.113 0.075 0.261 0.194 0.167 0.112 0.067 0 0 0 0 0 0

173 0 0 0 0 0 0 0 0.101 0.1 0.013 0.047 0.007 0.051

174 0.077 0.011 0.074 0.133 0.172 0.101 0.127 0 0 0 0 0 0

177 0 0 0 0 0 0 0 0.039 0.082 0.078 0.026 0.037 0.051

178 0.095 0.046 0.005 0.067 0.052 0.016 0.037 0 0 0 0 0 0

181 0 0 0 0 0 0 0 0 0.059 0.013 0 0.127 0.093

182 0.024 0.213 0.053 0.05 0.029 0.106 0.052 0 0 0 0 0 0

185 0 0 0 0 0 0 0 0 0.018 0 0 0 0.034

186 0 0.006 0 0 0.023 0 0 0 0 0 0 0 0

189 0 0 0 0 0 0 0 0 0.006 0 0 0 0

190 0.042 0 0.005 0 0 0.005 0.007 0 0 0 0 0 0

194 0 0 0 0 0 0.005 0 0 0 0 0 0 0

Allelic richness 6.98 6.67 8.07 9.04 7.44 8.22 8.28 6.77 8.06 7.31 5.68 6.30 8.21

Mean Fis 0.037 0.004 -0.002 0.009 -0.037 -0.028 0.015 0.059 -0.006 0.093 -0.017 0.018 0.023

HE 0.527 0.502 0.604 0.678 0.560 0.610 0.613 0.498 0.594 0.536 0.410 0.463 0.604

Supplemental Table S5. F-statistics (including SE) and Rst characterizing differences in allele

frequency among the 13 US and Canadian lake trout hatchery strains stocked into Lake Huron.

F-statistics

Locus Capf Theta Smallf # alleles Rst

One9 -0.008 0.02 -0.028 6 0.009

(0.021) (0.007) (0.022)

Ogo1a 0.062 0.083 -0.023 8 0.055

(0.028) (0.020) (0.024)

Scou19 0.072 0.041 0.033 13 0.083

(0.024) (0.013) (0.020)

Sfo12 0.046 0.04 0.006 5 0.036

(0.033) (0.012) (0.027)

Sfo18 0.101 0.079 0.024 12 0.088

(0.053) (0.020) (0.050)

Sfo1 0.118 0.066 0.056 6 0.076

(0.040) (0.031) (0.040)

SfoC38 0.196 0.035 0.167 5 0.03

(0.052) (0.014) (0.052)

SfoC88 0.006 0.076 -0.075 4 0.063

(0.049) (0.025) (0.040)

SnaMSU03 0.017 0.03 -0.013 42 0.026

(0.018) (0.009) (0.014)

SnaMSU11 0.05 0.055 -0.005 38 0.053

(0.021) (0.013) (0.019)

SnaMSU01 0.049 0.055 -0.006 27 0.211

(0.017) (0.012) (0.013)

SnaMSU10 0.091 0.053 0.04 33 0.047

(0.024) (0.010) (0.025)

SnaMSU05 0.06 0.065 -0.006 23 0.055

(0.023) (0.019) (0.012)

SnaMSU08 0.124 0.04 0.087 22 0.21

(0.036) (0.011) (0.032)

Sco202 0.11 0.119 -0.01 29 0.061

(0.024) (0.012) (0.015)

All loci 0.072 0.061* 0.012 0.093

(0.010) (0.008) (0.011)

* P<0.001 based on jackknife resampling across loci (Guodet 1995).

Supplemental Table 6. Pair-wise Fst characterizing variance in allele frequency among lake trout hatchery strains stocking into Lake Huron

US Hatchery Strains Canadian Hatchery Strains

Lewis Lake US Seneca

Lake

Apostle

Island Marquette

Green

Lake

Isle

Royale

Traverse

Island

Big/Parry

Sound

Lake

Manitou Michopicotan

Iroquois

Bay

Can Seneca

Lake Slate Island

Lewis Lake 0.0807 0.0283 0.0224 0.049 0.0367 0.0347 0.0749 0.053 0.0599 0.0835 0.091 0.0487

US Seneca Lake 0.0587 0.059 0.0823 0.0728 0.0659 0.0894 0.0862 0.0931 0.1171 0.0353 0.0785

Apostle Island 0.0091 0.0358 0.0182 0.0153 0.0746 0.0579 0.0569 0.0858 0.0683 0.0326

Marquette 0.0327 0.0151 0.017 0.0649 0.0517 0.0567 0.0697 0.0687 0.0279

Green Lake 0.0348 0.0347 0.094 0.0806 0.0706 0.1079 0.0846 0.0536

Isle Royale 0.0167 0.08 0.071 0.0703 0.0883 0.0795 0.0321

Traverse Island 0.0823 0.0608 0.0679 0.0921 0.0748 0.0283

Big/Parry Sound 0.0535 0.0661 0.0556 0.0697 0.0579

Lake Manitou 0.0445 0.0633 0.0717 0.0387

Michopicotan 0.0788 0.0709 0.042

Iroquois Bay 0.0989 0.0637

Can Seneca Lake 0.0549

Slate Island

Pair-wise Fst for all inter-strain comparisons significant at P<0.001.

Supplemental Table S7. Allele frequencies and summary measures of genetic diversity for wild lake trout collected during 2002-2004 (Early period) and 2010-2012 (Late period) from management units

in US and Canadian waters of Lake Huron.

Management Unit and Time period (Early vs Late)

US management units Canadian management units

MH1-Early MH2-Early MH345-Early MH1-Late MH2-Late MH345-Late OH1-Early OH1-Late NC3-Late GB123-Late NC12-Late OH45-Late GB4-Late OH23-Late

Locus Allele

One9 Sample size 35 148 87 334 212 207 80 97 74 127 114 63 33 59

222 0 0 0 0 0 0 0.006 0 0 0 0 0 0 0

224 0 0 0 0 0 0.002 0 0 0 0 0 0 0 0

226 0.929 0.962 0.954 0.944 0.955 0.959 0.956 0.927 0.959 0.947 0.947 0.952 0.953 0.922

228 0.071 0.033 0.029 0.054 0.04 0.039 0.025 0.073 0.027 0.037 0.053 0.048 0.047 0.06

230 0 0 0.017 0.002 0.005 0 0.013 0 0.014 0.012 0 0 0 0

232 0 0.005 0 0 0 0 0 0 0 0.004 0 0 0 0.017

Ogo1a

140 0 0 0 0 0 0 0 0 0 0.004 0 0 0 0

142 0.229 0.22 0.201 0.154 0.177 0.214 0.194 0.198 0.257 0.205 0.2 0.23 0.317 0.267

144 0 0 0 0 0 0 0.006 0 0 0 0 0 0 0

148 0.429 0.402 0.443 0.361 0.368 0.381 0.456 0.344 0.392 0.35 0.35 0.341 0.35 0.31

150 0.343 0.379 0.356 0.486 0.455 0.405 0.344 0.458 0.351 0.44 0.45 0.429 0.333 0.422

Scou19

149 0 0 0 0.002 0 0 0 0 0 0 0 0 0 0

155 0 0 0 0 0 0 0.025 0 0.007 0 0 0 0 0

157 0 0 0 0 0 0 0.013 0.269 0.007 0.134 0.278 0.336 0.242 0.319

159 0.129 0.21 0.23 0.225 0.208 0.229 0 0.011 0 0 0 0.043 0 0.009

161 0 0 0 0 0 0 0.025 0 0.041 0.009 0 0 0 0

163 0.014 0.037 0.029 0.004 0.009 0.012 0.006 0 0 0.004 0 0 0 0.009

165 0.014 0.019 0.006 0.002 0.002 0.005 0.013 0 0 0 0 0 0 0

167 0 0.009 0 0 0 0 0 0.005 0.027 0.004 0.013 0 0 0.009

169 0.2 0.285 0.282 0.341 0.314 0.329 0.206 0.263 0.176 0.313 0.352 0.216 0.303 0.328

171 0.029 0.075 0.034 0.092 0.057 0.056 0.025 0.091 0.034 0.04 0.07 0.078 0.076 0.043

173 0.529 0.304 0.345 0.295 0.351 0.316 0.631 0.349 0.655 0.455 0.274 0.276 0.364 0.25

175 0.014 0.042 0.052 0.036 0.045 0.041 0.044 0.011 0.02 0.018 0.009 0.043 0.015 0.034

177 0.071 0.014 0.023 0.004 0.012 0.012 0.013 0 0.034 0.022 0.004 0.009 0 0

179 0 0 0 0 0.002 0 0 0 0 0 0 0 0 0

181 0 0.005 0 0 0 0 0 0 0 0 0 0 0 0

Sfo12

252 0.057 0.084 0.069 0.038 0.059 0.048 0.075 0.02 0.054 0.063 0.036 0.035 0.031 0.017

254 0.071 0.079 0.115 0.16 0.097 0.176 0.044 0.217 0.034 0.109 0.152 0.184 0.125 0.181

256 0.871 0.827 0.816 0.795 0.833 0.771 0.881 0.763 0.899 0.811 0.813 0.781 0.813 0.802

258 0 0.009 0 0.004 0.002 0.002 0 0 0.014 0.017 0 0 0 0

260 0 0 0 0.004 0.009 0.002 0 0 0 0 0 0 0 0

262 0 0 0 0 0 0 0 0 0 0 0 0 0.016 0

276 0 0 0 0 0 0 0 0 0 0 0 0 0.016 0

Sfo18

163 0 0 0 0 0 0 0.025 0 0.008 0 0 0 0.048 0

165 0 0 0 0 0.002 0 0 0 0 0 0 0 0 0

167 0 0 0 0 0 0 0 0.012 0 0 0 0 0 0.011

169 0.457 0.579 0.621 0.725 0.623 0.694 0.45 0.741 0.517 0.605 0.775 0.845 0.714 0.691

171 0 0.014 0.006 0.022 0.024 0.022 0.038 0.047 0.017 0.019 0.056 0 0 0.043

173 0.129 0.14 0.069 0.152 0.123 0.153 0 0.112 0 0.086 0.141 0.083 0.048 0.128

175 0 0 0 0 0 0 0 0 0.008 0.019 0 0 0 0

177 0.014 0.005 0 0 0.002 0.002 0.05 0.018 0.008 0 0 0 0 0

179 0.343 0.22 0.23 0.091 0.177 0.102 0.388 0.065 0.408 0.272 0.028 0.048 0.024 0.128

181 0.043 0.019 0.023 0.007 0.033 0.019 0.05 0 0.008 0 0 0 0.024 0

183 0 0.005 0.017 0 0.005 0 0 0 0 0 0 0.012 0 0

185 0.014 0.019 0.034 0.004 0.012 0.005 0 0 0.008 0 0 0.012 0.095 0

187 0 0 0 0 0 0 0 0 0 0 0 0 0.024 0

189 0 0 0 0 0 0 0 0.006 0.017 0 0 0 0 0

191 0 0 0 0 0 0 0 0 0 0 0 0 0.024 0

Sfo1

103 0 0.009 0.017 0.004 0.002 0.005 0.006 0.161 0.507 0.008 0 0.008 0 0

105 0.929 0.818 0.828 0.788 0.809 0.783 0.937 0.594 0.446 0.853 0.786 0.746 0.766 0.759

109 0 0 0 0 0 0 0 0.057 0.034 0.004 0.009 0 0 0

111 0.071 0.173 0.155 0.208 0.189 0.213 0.057 0.188 0.014 0.134 0.205 0.23 0.203 0.241

SfoC38

143 0.886 0.855 0.828 0.822 0.791 0.794 0.931 0.729 0.946 0.861 0.773 0.798 0.766 0.655

149 0.114 0.14 0.161 0.172 0.197 0.201 0.069 0.255 0.054 0.126 0.214 0.194 0.219 0.336

152 0 0 0 0.005 0.005 0.005 0 0.016 0 0.008 0.009 0.008 0.016 0.009

158 0 0.005 0.011 0 0.007 0 0 0 0 0.004 0.005 0 0 0

SfoC88

169 0 0 0 0 0 0 0 0 0.007 0 0 0 0 0

172 0.714 0.687 0.713 0.625 0.682 0.65 0.456 0.615 0.432 0.508 0.659 0.611 0.597 0.621

175 0.286 0.308 0.287 0.375 0.316 0.35 0.544 0.385 0.561 0.492 0.341 0.389 0.403 0.379

178 0 0.005 0 0 0.002 0 0 0 0 0 0 0 0 0

SnaMSU03

163 0 0 0.006 0 0.002 0 0 0 0 0 0 0 0 0

179 0 0 0.006 0 0 0 0 0 0 0 0 0 0 0

183 0 0 0 0 0 0.002 0 0.013 0 0 0 0 0 0

187 0 0.023 0 0.005 0.014 0.01 0.044 0.071 0.047 0.017 0.009 0.034 0.032 0

191 0.057 0.112 0.144 0.187 0.19 0.152 0.138 0.218 0.088 0.113 0.196 0.181 0.21 0.211

195 0.371 0.336 0.345 0.377 0.322 0.4 0.363 0.288 0.345 0.404 0.362 0.259 0.323 0.263

199 0.186 0.21 0.138 0.12 0.145 0.13 0.231 0.135 0.203 0.135 0.143 0.172 0.161 0.211

201 0 0 0 0.007 0.002 0.005 0 0.006 0.007 0 0 0.009 0 0

203 0.114 0.089 0.103 0.114 0.118 0.113 0.1 0.071 0.108 0.135 0.076 0.138 0.097 0.14

205 0.057 0.051 0.069 0.071 0.059 0.056 0.013 0.071 0.007 0.026 0.063 0.103 0.016 0.061

207 0.043 0.019 0.029 0.02 0.017 0.022 0.006 0.026 0.014 0.022 0.027 0.052 0 0.018

209 0 0.023 0.023 0.022 0.017 0.017 0 0.032 0.02 0.009 0.04 0.009 0.016 0.018

211 0 0.037 0.006 0.016 0.017 0.015 0 0.013 0 0.009 0.022 0.009 0.016 0.026

213 0.043 0.023 0.011 0.007 0.021 0.007 0 0.013 0 0.013 0 0.009 0.016 0

215 0.014 0 0.011 0.002 0 0 0 0 0 0 0.009 0.009 0 0.009

219 0 0 0.006 0 0 0 0 0 0 0 0 0 0 0

221 0 0.005 0 0 0 0 0 0 0 0 0 0 0 0

223 0 0.005 0 0 0.005 0 0 0 0 0 0 0 0 0

225 0 0.005 0.011 0 0 0 0 0 0 0.004 0 0 0 0

227 0 0 0 0 0 0 0.006 0 0.014 0.004 0 0 0 0

229 0 0.005 0 0 0 0 0 0 0 0 0 0 0 0

231 0 0 0 0 0 0 0 0 0 0.009 0 0 0.032 0

233 0 0.005 0 0 0 0 0 0 0 0.004 0 0 0 0

235 0.029 0.014 0.017 0.013 0.012 0.01 0.025 0.006 0.041 0.039 0.004 0.009 0 0

239 0 0 0.006 0 0.002 0 0 0 0 0 0 0 0 0

243 0 0 0.006 0.004 0.005 0.007 0.019 0.006 0.007 0.026 0.022 0 0.016 0.035

247 0.014 0 0 0.009 0.007 0.012 0.006 0.013 0.02 0.004 0.004 0 0.016 0

251 0.014 0.009 0.017 0.004 0.009 0.007 0.025 0.006 0.034 0.009 0 0 0 0

255 0.014 0.009 0.017 0.016 0.014 0.012 0 0.013 0 0.004 0.022 0.009 0.032 0

259 0.014 0.005 0.006 0.005 0.007 0.007 0 0 0 0 0 0 0 0

263 0.014 0.005 0.023 0 0.009 0.007 0.006 0 0.02 0.004 0 0 0 0

267 0 0.009 0 0.002 0.005 0.005 0 0 0.014 0.009 0 0 0 0

271 0 0 0 0 0 0 0.013 0 0.014 0 0 0 0 0

275 0.014 0 0 0 0 0 0 0 0 0 0 0 0.016 0.009

283 0 0 0 0 0 0 0.006 0 0 0 0 0 0 0

301 0 0 0 0 0 0.002 0 0 0 0 0 0 0 0

SnaMSU11

211 0 0 0 0 0 0 0.013 0 0.007 0 0 0 0 0

215 0.014 0 0 0 0 0.002 0.006 0 0.027 0.008 0 0 0 0

217 0 0.005 0.006 0.002 0.01 0.007 0 0.005 0 0 0 0 0 0

219 0.029 0.028 0.018 0.004 0.012 0.007 0.032 0.011 0.007 0.016 0 0.016 0 0.026

221 0 0 0.006 0 0.002 0 0 0 0.007 0.004 0 0 0 0

223 0.414 0.397 0.387 0.339 0.4 0.4 0.589 0.389 0.432 0.394 0.314 0.325 0.313 0.388

225 0 0.009 0.018 0.002 0.002 0 0 0.005 0 0 0 0.016 0 0.017

227 0.1 0.042 0.042 0.08 0.043 0.051 0.07 0.042 0.088 0.089 0.067 0.032 0.125 0.034

229 0 0.009 0.006 0 0.002 0 0 0.005 0 0 0 0 0 0

231 0.029 0.023 0.018 0 0.014 0.007 0.006 0 0.007 0.004 0.01 0.008 0.016 0

233 0 0 0 0 0 0 0 0 0.007 0.02 0 0.008 0 0.009

235 0 0.023 0.054 0.013 0.019 0.017 0.025 0.005 0.034 0.037 0.005 0 0 0.009

237 0 0 0.006 0 0.002 0.007 0 0.005 0 0 0 0.008 0 0

239 0.1 0.042 0.143 0.02 0.076 0.083 0.158 0.016 0.243 0.089 0.033 0.032 0.078 0.026

241 0.029 0.079 0.042 0.082 0.083 0.071 0.006 0.068 0.007 0.041 0.095 0.048 0.031 0.078

243 0.057 0.07 0.077 0.112 0.093 0.095 0.025 0.089 0.081 0.093 0.152 0.135 0.078 0.052

245 0.129 0.187 0.131 0.257 0.188 0.212 0.006 0.163 0 0.146 0.267 0.151 0.219 0.216

247 0.014 0.014 0.006 0 0 0 0.025 0.137 0.014 0.004 0.005 0.183 0.047 0.034

249 0.057 0.051 0.024 0.085 0.048 0.037 0.006 0.037 0.007 0.045 0.048 0.024 0.094 0.086

251 0 0.005 0 0.002 0 0 0 0.021 0.007 0.004 0 0.016 0 0.026

253 0 0 0 0.002 0 0 0.006 0 0 0 0 0 0 0

255 0 0 0.006 0 0 0 0.013 0 0.007 0 0 0 0 0

265 0 0 0 0.002 0.002 0 0 0 0 0 0 0 0 0

273 0 0 0 0 0 0 0 0 0 0.004 0 0 0 0

277 0 0.005 0 0 0.002 0 0 0 0 0 0 0 0 0

279 0 0 0 0 0 0.002 0 0 0 0 0 0 0 0

281 0 0.005 0 0 0 0 0 0 0.014 0 0.005 0 0 0

285 0.029 0 0.012 0 0 0 0 0 0 0 0 0 0 0

291 0 0 0 0 0 0 0.006 0 0.007 0 0 0 0 0

293 0 0 0 0 0 0 0.006 0 0 0 0 0 0 0

299 0 0.005 0 0 0 0 0 0 0 0 0 0 0 0

SnaMSU01

209 0.014 0 0 0.004 0.005 0 0 0.005 0 0 0 0 0 0

213 0 0.005 0 0 0.002 0 0 0 0 0 0 0 0 0

217 0.071 0.028 0.04 0.005 0.043 0.017 0.006 0 0.014 0.008 0.009 0.016 0 0.017

221 0.071 0.042 0.063 0.007 0.064 0.032 0 0.005 0.041 0.028 0.009 0.008 0 0

225 0.057 0.047 0.069 0.013 0.033 0.024 0.013 0 0.02 0.008 0 0.008 0 0.009

229 0.014 0.065 0.023 0.011 0.024 0.015 0.006 0.011 0 0.004 0 0.008 0 0

233 0.014 0.014 0.034 0.022 0.031 0.022 0.013 0.021 0 0.02 0.009 0.016 0.015 0.009

237 0 0.014 0 0.009 0.024 0.02 0 0.011 0 0.004 0.014 0.016 0.015 0

241 0.057 0.042 0.063 0.058 0.045 0.044 0.013 0.032 0 0.048 0.075 0.008 0.015 0.06

245 0.1 0.065 0.04 0.074 0.045 0.083 0.019 0.058 0.027 0.024 0.033 0.063 0.106 0.043

249 0.071 0.107 0.144 0.103 0.104 0.093 0.057 0.089 0.007 0.105 0.123 0.119 0.045 0.103

253 0.1 0.098 0.092 0.082 0.088 0.068 0.025 0.142 0.081 0.089 0.123 0.111 0.106 0.112

257 0.057 0.084 0.086 0.067 0.057 0.078 0.196 0.068 0.189 0.133 0.085 0.143 0.121 0.069

261 0.114 0.107 0.109 0.149 0.126 0.149 0.209 0.153 0.155 0.121 0.118 0.175 0.136 0.155

265 0.157 0.061 0.057 0.072 0.059 0.088 0.076 0.047 0.081 0.056 0.052 0.048 0.106 0.078

269 0 0.042 0.017 0.034 0.031 0.032 0.095 0.047 0.074 0.089 0.028 0.024 0.076 0.026

273 0.029 0.056 0.034 0.051 0.052 0.059 0.051 0.074 0.054 0.032 0.08 0.04 0.045 0.06

277 0.014 0.042 0.029 0.103 0.057 0.056 0.038 0.084 0.034 0.089 0.113 0.04 0.061 0.069

281 0.029 0.042 0.029 0.087 0.057 0.049 0.101 0.089 0.088 0.069 0.052 0.119 0.061 0.103

285 0.029 0.028 0.046 0.033 0.021 0.046 0.038 0.042 0.074 0.048 0.038 0.04 0.061 0.026

289 0 0 0.006 0 0 0.007 0.044 0.005 0.041 0.004 0.005 0 0 0.009

293 0 0.005 0.011 0.009 0.017 0.007 0 0.011 0.007 0.004 0 0 0 0.017

297 0 0.005 0 0.007 0.014 0.005 0 0 0.007 0.012 0.033 0 0.015 0.026

301 0 0 0 0 0 0.007 0 0.005 0 0 0 0 0 0.009

305 0 0 0 0 0 0 0 0 0 0.004 0 0 0 0

309 0 0 0 0 0 0 0 0 0 0 0 0 0.015 0

317 0 0 0 0 0 0 0 0 0.007 0 0 0 0 0

321 0 0 0.006 0 0.002 0 0 0 0 0 0 0 0 0

SnaMSU10

166 0 0 0 0 0 0 0 0 0.007 0 0 0 0 0

170 0 0.005 0 0 0 0 0 0 0 0 0 0 0 0

178 0 0.01 0.006 0.022 0.019 0.012 0 0.005 0 0.004 0.026 0.024 0.033 0

180 0 0 0 0 0 0 0.006 0 0 0 0 0 0 0

182 0 0 0.012 0 0.002 0.002 0 0 0 0.004 0 0 0 0

186 0.014 0.014 0.006 0.007 0.007 0.002 0.025 0.01 0.034 0.017 0 0.008 0 0.009

190 0.057 0.052 0.088 0.043 0.043 0.044 0.025 0.083 0.041 0.033 0.083 0.063 0.1 0.043

194 0.1 0.224 0.171 0.268 0.182 0.254 0.063 0.292 0.047 0.2 0.298 0.278 0.267 0.224

196 0 0 0 0 0.005 0 0.013 0 0.014 0 0 0 0 0

198 0.1 0.129 0.106 0.145 0.156 0.154 0.044 0.109 0.074 0.083 0.118 0.103 0.083 0.198

200 0 0 0 0 0 0 0.006 0 0 0 0 0 0 0

202 0.071 0.062 0.071 0.043 0.073 0.061 0.088 0.036 0.074 0.075 0.057 0.056 0.05 0.052

204 0 0 0 0 0 0 0.05 0 0.027 0.013 0 0 0 0

206 0.014 0.048 0.024 0.011 0.038 0.027 0.094 0.01 0.095 0.054 0.013 0.016 0 0.009

208 0 0 0 0 0.005 0.002 0.1 0.016 0.162 0.071 0 0 0.083 0

210 0.029 0.019 0.012 0 0.005 0.002 0.075 0 0.074 0.029 0.013 0 0 0

212 0 0.005 0.006 0.002 0.005 0.002 0.125 0.005 0.095 0.05 0.004 0 0.05 0

214 0 0 0.012 0.002 0.002 0 0.038 0.005 0 0.008 0 0 0 0

216 0.029 0.019 0.071 0.004 0.014 0.015 0 0 0 0.008 0 0 0.033 0

218 0 0.005 0 0 0.005 0 0.019 0 0.02 0.013 0 0 0 0

220 0.057 0.038 0.047 0.009 0.043 0.015 0.05 0.016 0.027 0.021 0.018 0.008 0 0

222 0.029 0.005 0 0.002 0.005 0 0 0 0 0 0.004 0 0 0

224 0.129 0.095 0.118 0.067 0.076 0.066 0.038 0.073 0.02 0.05 0.07 0.056 0.033 0.026

228 0 0 0 0.002 0 0 0 0 0 0 0 0 0 0

230 0.057 0.071 0.047 0.043 0.073 0.054 0.006 0.036 0.014 0.042 0.039 0.071 0.017 0.052

232 0 0 0.006 0 0 0 0 0 0 0 0 0 0 0

234 0.071 0.076 0.065 0.089 0.057 0.08 0.031 0.089 0.047 0.054 0.07 0.119 0.1 0.103

236 0.029 0.005 0.006 0 0 0 0 0 0 0 0 0 0 0

238 0.129 0.057 0.047 0.134 0.085 0.093 0.038 0.099 0.041 0.092 0.083 0.087 0.083 0.147

242 0.057 0.029 0.047 0.053 0.055 0.049 0.013 0.094 0.041 0.038 0.048 0.04 0 0.052

246 0.014 0.005 0.006 0.031 0.019 0.039 0.025 0.01 0.02 0.017 0.035 0.048 0.033 0.043

250 0.014 0.029 0.018 0.011 0.007 0.007 0.019 0 0.02 0.004 0.004 0 0.033 0.017

254 0 0 0.012 0.013 0.017 0.017 0.006 0.01 0 0.021 0.009 0.024 0 0.026

258 0 0 0 0 0.002 0.002 0.006 0 0.007 0 0.004 0 0 0

SnaMSU05

157 0.015 0.005 0.018 0.004 0.019 0.005 0.013 0.007 0.034 0.008 0.009 0 0.052 0

165 0.015 0.039 0.024 0.042 0.017 0.044 0 0.04 0 0.013 0.063 0.009 0.086 0.018

169 0 0 0 0 0 0 0.013 0 0 0.008 0 0 0 0.009

173 0.045 0.029 0.012 0.013 0.014 0.01 0.05 0.007 0.041 0.033 0.004 0.026 0 0.035

177 0.136 0.093 0.065 0.046 0.076 0.067 0.094 0.047 0.088 0.083 0.049 0.009 0.086 0.026

181 0.212 0.235 0.208 0.187 0.2 0.197 0.356 0.233 0.372 0.3 0.183 0.207 0.19 0.228

185 0.182 0.162 0.167 0.234 0.205 0.236 0.088 0.287 0.041 0.183 0.286 0.259 0.224 0.219

189 0.091 0.074 0.161 0.07 0.081 0.059 0.025 0.067 0.034 0.054 0.054 0.103 0.034 0.096

193 0 0.015 0.012 0.007 0.002 0.007 0.038 0.007 0.02 0.013 0.004 0.009 0.017 0.009

197 0.03 0.015 0.012 0.002 0.002 0.012 0.069 0 0.054 0.008 0 0 0.017 0

201 0.091 0.054 0.03 0.009 0.04 0.032 0.05 0.007 0.041 0.025 0.009 0.026 0 0.018

205 0.015 0.049 0.048 0.132 0.09 0.099 0.013 0.1 0 0.096 0.098 0.103 0.121 0.132

209 0.03 0.01 0.024 0.002 0.005 0 0.038 0 0.027 0.008 0 0.009 0.017 0

213 0 0.015 0.024 0.002 0.005 0.007 0.05 0.007 0.054 0.017 0.004 0 0 0

217 0.015 0.029 0.03 0.015 0.033 0.027 0.019 0.013 0.047 0.017 0.027 0.017 0.034 0.009

221 0.061 0.01 0.024 0.027 0.036 0.042 0.056 0.04 0.115 0.025 0.013 0.026 0 0.035

225 0.061 0.123 0.113 0.19 0.133 0.128 0.031 0.113 0.034 0.092 0.152 0.164 0.086 0.158

229 0 0.01 0.024 0.018 0.031 0.025 0 0.027 0 0.017 0.045 0.026 0 0.009

233 0 0.005 0 0 0 0 0 0 0 0 0 0 0 0

237 0 0 0 0 0.005 0.002 0 0 0 0 0 0.009 0.034 0

261 0 0.005 0 0 0.002 0 0 0 0 0 0 0 0 0

265 0 0.005 0 0 0.002 0 0 0 0 0 0 0 0 0

269 0 0.015 0.006 0 0 0 0 0 0 0 0 0 0 0

273 0 0.005 0 0 0 0 0 0 0 0 0 0 0 0

SnaMSU08 124 0 0 0 0 0 0 0 0 0 0 0 0.008 0 0

128 0 0 0 0 0 0 0 0 0 0 0 0.008 0 0

134 0 0.005 0.012 0 0 0 0.007 0 0 0 0 0 0 0

142 0.015 0.005 0.012 0 0 0 0 0 0 0 0 0 0 0

146 0 0.014 0.006 0 0 0.002 0 0 0 0 0 0 0 0

150 0.176 0.16 0.163 0.103 0.175 0.156 0.066 0.16 0.086 0.074 0.112 0.111 0.177 0.135

154 0 0.009 0.03 0.004 0.014 0.012 0.013 0.005 0.029 0.025 0.009 0.008 0.032 0

158 0 0.009 0.018 0.004 0.031 0.01 0.02 0.005 0.029 0.012 0 0 0 0

162 0.118 0.075 0.114 0.066 0.064 0.097 0.158 0.032 0.157 0.099 0.063 0.056 0.097 0.106

166 0.029 0.099 0.06 0.075 0.088 0.089 0.026 0.08 0.007 0.058 0.107 0.071 0.048 0.087

170 0.632 0.448 0.458 0.56 0.521 0.485 0.645 0.564 0.586 0.607 0.54 0.405 0.532 0.394

174 0.029 0.038 0.012 0.011 0.007 0.007 0.039 0.016 0.043 0.033 0.004 0.024 0 0

178 0 0.052 0.048 0.066 0.04 0.072 0.013 0.043 0.021 0.037 0.094 0.024 0.081 0.048

182 0 0.038 0.03 0.037 0.017 0.035 0 0.011 0.007 0.012 0.022 0.016 0 0.029

186 0 0.047 0.036 0.075 0.043 0.035 0.013 0.085 0.007 0.041 0.049 0.063 0.032 0.01

190 0 0 0 0 0 0 0 0 0.007 0 0 0.024 0 0.038

194 0 0 0 0 0 0 0 0 0.014 0 0 0.087 0 0.115

198 0 0 0 0 0 0 0 0 0.007 0 0 0.048 0 0

202 0 0 0 0 0 0 0 0 0 0 0 0.024 0 0

206 0 0 0 0 0 0 0 0 0 0 0 0.008 0 0.029

210 0 0 0 0 0 0 0 0 0 0 0 0.016 0 0

214 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01

Sco202

137 0 0 0 0 0.002 0 0 0 0 0 0 0 0 0

141 0 0 0 0 0 0 0.006 0 0 0.008 0 0 0 0

145 0 0.014 0.006 0.004 0.014 0.015 0.025 0.005 0.014 0.004 0 0 0 0

149 0.014 0.005 0.006 0 0.007 0 0.05 0.005 0.034 0.045 0.004 0.01 0.033 0.009

153 0 0.023 0.035 0.006 0.029 0.017 0.119 0.022 0.185 0.107 0.022 0.01 0.05 0.018

157 0.086 0.131 0.116 0.142 0.112 0.125 0.169 0.147 0.11 0.145 0.155 0.087 0.1 0.17

161 0.257 0.341 0.262 0.384 0.36 0.358 0.206 0.391 0.205 0.285 0.394 0.385 0.333 0.42

165 0.243 0.182 0.221 0.149 0.162 0.15 0.094 0.12 0.137 0.112 0.137 0.192 0.183 0.107

169 0.143 0.098 0.087 0.055 0.083 0.093 0.1 0.071 0.082 0.054 0.044 0.048 0.117 0.071

173 0.129 0.07 0.116 0.022 0.071 0.042 0.113 0.033 0.089 0.066 0.027 0.038 0.033 0.036

177 0.057 0.028 0.041 0.07 0.036 0.029 0.063 0.071 0.11 0.074 0.062 0.067 0.067 0.045

181 0.057 0.084 0.099 0.149 0.112 0.157 0.038 0.12 0.014 0.091 0.15 0.154 0.083 0.125

185 0 0.014 0.006 0.015 0.01 0.012 0.019 0.011 0.021 0.008 0.004 0.01 0 0

189 0.014 0.009 0.006 0.004 0 0.002 0 0.005 0 0 0 0 0 0

201 0 0 0 0.002 0.002 0 0 0 0 0 0 0 0 0

HE 0.591 0.619 0.620 0.599 0.614 0.613 0.561 0.631 0.594 0.603 0.599 0.615 0.628 0.633

mean # alleles 7.93 11.07 10.47 9.87 11.33 10.20 9.73 9.20 10.00 10.60 8.27 8.87 7.60 8.00

Fis 0.056* 0.028 0.054* 0.017 0.04 0.018 0.032 0.044 0.098* 0.064* 0.017 0.009 0.122* 0.047

*P<0.05.

Supplemental Table S8. F-statistics (including SE) and Rst characterizing differences in allele

frequency among the 14 wild lake trout mixtures sampled in Lake Huron.

F-Statistics

Locus Fit Fst Fis # alleles Rst

One9 -0.007 -0.001 -0.006 6 -0.001

(0.022) (0.001) (0.022)

Ogo1a -0.014 0.003 -0.018 5 0.005

(0.018) (0.002) (0.019)

Scou19 0.086 0.056 0.032 15 0.05

(0.021) (0.013) 0.014)

Sfo12 0.004 0.009 -0.005 7 0.003

(0.018) (0.004) (0.018)

Sf018 0.127 0.038 0.093 15 0.045

(0.038) (0.013) (0.036)

Sfo01 0.181 0.074 0.116 4 0.007

(0.113) (0.053) (0.072)

SfoC38 0.15 0.02 0.132 4 0.02

(0.025) (0.011) (0.026)

SfoC88 0.023 0.022 0.001 4 0.021

(0.025) (0.011) (0.021)

SnaMSU03 0.025 0.004 0.022 36 0.007

(0.01) (0.001) (0.01)

SnaMSU11 0.035 0.019 0.016 31 0.023

(0.014) (0.008) (0.013)

SnaMSU01 0.021 0.006 0.015 28 0.051

(0.006) (0.002) (0.007)

SnaMSU10 0.046 0.013 0.033 34 0.001

(0.012) (0.006) (0.011)

SnaMSU05 0.021 0.013 0.008 24 0.006

(0.013) (0.006) (0.01)

SnaMSU08 0.146 0.01 0.137 22 0.033

(0.017) (0.003) (0.017)

Sco202 0.034 0.01 0.024 15 0.011

(0.017) (0.004) (0.017)

All loci 0.054 0.019* 0.036 0.015

(0.013) (0.005) (0.011)

* P<0.05.

Supplemental Table S9. Pair-wise estimates of Fst among wild samples from Lake Huron

US lake locations Canadian lake locations

MHI-Early MH2-Early MH345-E MH1-Late MH2-Late MH345-Late OH1-Early OH1-Late NC3-Late GB123-Late NC12-Late OH45-Late GB4-Late OH23-Late

MHI-Early 0.006 0.0037 0.0253 0.0095 0.0175 0.0197 0.0349 0.0456 0.0118 0.0347 0.0376 0.0194 0.0344

MH2-Early -0.0006 0.0079 0.0002 0.0028 0.036 0.019 0.0564 0.0116 0.0151 0.0177 0.0091 0.0157

MH345-Early 0.0118 0.0014 0.0047 0.0349 0.022 0.0538 0.013 0.019 0.0196 0.0109 0.0198

MH1-Late 0.0042 0.0015 0.0556 0.0129 0.0747 0.0155 0.0069 0.0138 0.0085 0.0135

MH2-Late 0.0011 0.0395 0.0148 0.0601 0.0113 0.0114 0.0161 0.0081 0.0126

MH345-Late 0.0474 0.0128 0.0662 0.0138 0.0084 0.013 0.0054 0.0112

OH1-Early 0.0581 0.0273 0.0153 0.0645 0.0633 0.0425 0.0608

OH1-Late 0.0594 0.018 0.0046 0.0034 0.002 0.0053

NC3-Late 0.0373 0.0818 0.0754 0.0562 0.0767

GB123-Late 0.0165 0.0204 0.0068 0.0188

NC12-Late 0.0047 0.0002 0.0041

OH45-Late 0.0017 0.0025

GB4-Late 0.0016

OH23-Late

Pair-wise estimates of Fst that are italicized are not statistically significant after Bonferroni correction.