Salvelinus from microsatellite DNA and morphological analysis namaycush

Salvelinus from microsatellite DNA and morphological analysis namaycush
Population structure of lake trout (Salvelinus
namaycush) in a large glacial-fed lake inferred
from microsatellite DNA and morphological
Sara Northrup, Mark Connor, and Eric B. Taylor
Abstract: Understanding the structure of intraspecific genetic and morphological diversity within and across habitats is a
fundamental aspect of biodiversity research with conservation value. Atlin Lake is the largest lake in British Columbia,
Canada, and contains relatively pristine populations of lake trout (Salvelinus namaycush) that are key components of the
lake’s fish community and local fisheries. Lake trout from Atlin Lake were examined for genetic and phenotypic variation
using eight microsatellite DNA loci, body form, and colouration. Genetic assays were also conducted on lake trout from
the adjoining Tagish Lake and from 17 other localities to provide spatial context for the variation within Atlin Lake. The
genetic data suggested that there were three genetic subpopulations within the Atlin–Tagish lake system. Morphological
analysis identified two morphological groups of lake trout within Atlin Lake. Genetic and morphological groupings in Atlin Lake were not associated with each other. A mixed-stock analysis of samples collected from Atlin Lake commercial
and recreational fisheries suggested that all genetic subpopulations contributed to the fishery and that there was some contribution from fish originating from within Tagish Lake.
Résumé : La compréhension de la structure de la diversité intraspécifique génétique et morphologique au sein des habitats
et entre les habitats est une composante essentielle de la recherche sur la biodiversité reliée à la conservation. Le lac Atlin
est le plus grand lac de la Colombie-Britannique, Canada, et il contient des populations de touladis (Salvelinus namaycush)
encore relativement dans leur état originel qui forment une composante essentielle de la communauté de poissons du lac et
de la pêche locale. Nous avons examiné la variation génétique et phénotypique des touladis du lac Atlin à l’étude de huit
locus microsatellites d’ADN, de la forme corporelle et de la coloration. Nous avons aussi procédé à des analyses génétiques des touladis du lac Tagish voisin et de 17 autres localités pour obtenir un contexte spatial pour étudier la variation au
sein du lac Atlin. Les données génétiques indiquent qu’il existe trois sous-populations génétiques dans le réseau des lacs
Atlin et Tagish. L’analyse morphologique a permis d’identifier deux groupes morphologiques de touladis dans le lac Atlin.
Les regroupements génétiques et morphologiques dans le lac Atlin ne sont pas associés l’un à l’autre. Une analyse de
stock mixte d’échantillons récoltés dans les pêches commerciales et sportives au lac Atlin montre que toutes les souspopulations génétiques contribuent à la pêche et qu’il y a une certaine contribution des poissons provenant du lac Tagish.
[Traduit par la Rédaction]
The origin, extent, and patterns of genetic diversity within
species are central issues for evolutionary biologists (Futuyma 1997) and are increasingly important for conservation
because genetic diversity can influence the persistence of
populations (Frankham et al. 2002). Consequently, there is
a growing need to study genetic diversity of contemporary
populations to help understand how population viability
might change in response to environmental fluctuations
(Schwartz et al. 2007). One aspect of genetic variation is
population subdivision, the partitioning of a species into
two or more independent or semi-independent genetic subpopulations that may exist in the same or different habitats
(e.g., Waples and Gaggiotti 2006).
In many fishes, including salmonids (salmon, trout, char,
whitefish, and grayling), assays of population distinctiveness
have been employed in a variety of conservation contexts
such as separating native from introduced fish (e.g., Taylor
et al. 2007) or to identify distinct populations to facilitate
population-specific management initiatives (Gunn et al.
2003). As dominant predators of north-temperate freshwater
lakes in the Nearctic, lake trout (Salvelinus namaycush) are
Received 12 April 2009. Accepted 22 March 2010. Published on the NRC Research Press Web site at on 25 June 2010.
Paper handled by Associate Editor Michael Hansen.
S. Northrup and E.B. Taylor.1 Department of Zoology and Native Fishes Research Group, University of British Columbia,
6270 University Boulevard, Vancouver, BC V6T 1Z4, Canada.
M. Connor. Taku River Tlingit First Nation Fisheries Department, P.O. Box 132, Atlin, BC V0W 1A0, Canada.
author (e-mail:
Can. J. Fish. Aquat. Sci. 67: 1171–1186 (2010)
Published by NRC Research Press
important members of aquatic communities across their
range. Lake trout are exploited, to varying degrees, in traditional, commercial, and recreational fisheries and declines in
abundance and loss of specific populations are occurring in
a number of areas (Guinand et al. 2003; Gunn et al. 2003).
In addition, lake trout populations are vulnerable to other
human activities, including the introduction of non-native
species (Smith and Tibbles 1980; Guinand et al. 2003; Dextrase and Mandrak 2006). In an effort to better manage
exploitation-based and environment-based stressors on lake
trout populations, the species has been the subject of a number of studies to resolve population distinctiveness. For instance, the study of sympatric and allopatric lake trout
populations has resolved genetic differences in survival,
morphology, early development rate, physiology, allozymes,
and microsatellite allele frequencies (Brown et al. 1981; Ihssen et al. 1988; Guinand et al. 2003). Genetic ‘‘inventories’’
may help to identify populations of conservation significance; populations with low levels of genetic variability
might be compromised in their ability to persist in current
or changing environments, and populations with high levels
of diversity might be of great value for opposite reasons
(Frankham et al. 2002; Piller et al. 2005).
Many north-temperate fish species consist of two or more
major genetic lineages thought to have arisen from divergence during isolation in distinct glacial refugia — divergence perhaps enhanced by postglacial adaptation to their
present environments (Bernatchez and Wilson 1998; Power
2002). The many episodes of isolation while in separate
refugia followed by secondary contact between divergent
lineages have probably contributed to the tremendous morphological, ecological, and genetic variability observed
within lake trout that makes them an excellent model for
studying evolutionary process of divergence (Magnan et al.
Atlin Lake is located in northwestern British Columbia
(BC), Canada, and is the province’s largest natural lake ecosystem, with a surface area of 792 km2. Atlin Lake forms
one of the headwater lakes of the Yukon River system and
interconnects with Tagish Lake. Lake trout are thought to
have colonized this area from both the Nahanni and Berinigian refugia using freshwater dispersal corridors (no anadromous populations of lake trout are known) beginning about
10 000 years ago (Wilson and Hebert 1998). In addition, because of its large size and diversity of habitats, Atlin Lake
has the potential to contain multiple genetically distinct populations of lake trout. There have, in fact, been anecdotal accounts of different lake trout phenotypes (based on
observations of shape, size, and colour) occurring in different parts of the lake (N. DeGraaf, Taku River Tlingit First
Nation, Atlin, BC, unpublished data). In addition to the importance of lake trout from Atlin Lake to the local community, the lake is of interest to a broader understanding of lake
trout biology and conservation. This is because relatively
few populations in the western part of the geographic range
of lake trout have been examined in detail genetically, especially for pristine areas such as Atlin Lake. Furthermore,
there are longstanding accounts of variation in lake trout in
body shape and colour, variability initially thought to be restricted to the Laurentian Great Lakes (Dehring et al. 1981;
Gunn et al. 2003; Bronte and Moore 2007). Subsequent in-
Can. J. Fish. Aquat. Sci. Vol. 67, 2010
vestigations have found morphological variants in other
northern lakes (Blackie et al. 2003; Alfonso 2004; Zimmerman et al. 2006). How geographically widespread such variants are and how they originate or are maintained in their
respective environments, however, remain unknown.
Despite the fact that lake trout from different areas of Atlin Lake appear to spawn at the same time of year, variation
in food source, water depth, and water turbidity may promote local differentiation. If more than one morphotype exists in Atlin Lake, it would be important to determine if
these morphotypes signal genetically distinct populations
such that a better understanding of the basis of within-lake
biodiversity can be obtained (Keeley et al. 2007). For instance, should distinct morphotypes be a purely phenotypic
response to variable environments within the lake, then
management might focus on maintaining a minimum population size and a diversity of habitats across the entire lake
so that this plasticity is sustained. Alternatively, if morphotypes are associated with genetically distinct populations of
lake trout, then these morphotypes provide a means to monitor genetic diversity and efforts should be directed towards
understanding the biological processes that sustain such genetic variation (spatial or ecological segregation) such that a
plan for the maintenance of within-lake biodiversity can be
designed. Maintenance of such intraspecific genetic biodiversity is considered important for the long-term persistence
of species (Bowen 1999; Allendorf and Luikart 2007) and
their productivity within specific habitats (Hilborn et al.
2003), as well as for ecosystems as a whole (Booth and
Grime 2003; Reusch et al. 2005). Further, a common problem in fisheries management is that sometimes the management of only fish occurs when it should include assessment
of fishers and their effort (Hilborn 2007). Lake trout in Atlin
Lake are exploited in commercial and recreational fisheries
but have not been subjected to fish stocking programmes.
Consequently, if Atlin Lake contains genetically distinct
subpopulations of lake trout, an understanding of the relative
levels of exploitation of each component through mixedstock fishery analysis would help guide a conservation plan
for each subpopulation integrated across the whole lake.
To contribute to a greater understanding of the structure
of diversity in Atlin Lake S. namaycush and provide guidelines for more effective management and conservation, we
conducted a survey of microsatellite DNA and morphological variation in collections of lake trout from Atlin Lake, as
well as from several other localities in BC and Yukon.
Although investigations on lake trout from Lake Mistassini,
Québec, and Great Bear Lake, Northwest Territories, are recent exceptions (Blackie et al. 2003; Zimmerman et al.
2007), the vast majority of data on variability in lake trout
have been collected for highly perturbed populations of lake
trout in the Laurentian Great Lakes. Consequently, our data
contribute to much needed comparative data from relatively
understudied and less perturbed northwestern ecosystems.
Materials and methods
Sample collections
We obtained lake trout tissue samples from adult fish that
were collected between 2000 and 2006 from 19 lakes in BC,
Yukon, and Northwest Territories using a variety of gillnet
Published by NRC Research Press
Northrup et al.
gear (1.5- to 3-inch mesh sizes) that captured fish ranging
from 192 to 887 mm fork length. The lakes sampled varied
in surface area (determined from topographic maps) from
1.1 to 792 km2 (Table 1; Fig. 1). Three lakes (Trapper, Arctic, and Muncho) had sample sizes <10 individuals, and we
used them only in general descriptions of variability. We
sampled two lakes from the upper Yukon River watershed
in northwestern BC and adjacent Yukon Territory (Atlin
and Tagish lakes) more extensively. These two lakes are interconnected by the Atlin River, are very large (>300 km2 in
surface area), and are characterized by very low levels of
development and, presumably, human disturbance. For about
one-half of its length, Atlin Lake is surrounded by Atlin
Provincial Park. In Atlin Lake, we collected fish during general lake surveys (N = 186) using small-mesh gillnets deployed at 1 km intervals for 1 h, as well as by sampling
catches landed at the Great Northern Fish Company
(GNFC, N = 101). We also sampled tissue from adults obtained in the recreational fishery (N = 33). Fish from Atlin
Lake were weighed and measured for fork length, and adipose fin tissue was removed and stored in 95% ethanol. We
calculated condition factor of each fish as (weight (in
grams) 100)/length3 (in cm). In 2005 and 2006, samples
from the Atlin Lake survey were photographed for morphological and colouration analyses (see below). In Tagish
Lake, we collected adult fish using gillnets from known
spawning reefs (N = 175), as well as from nonspawning
areas (N = 52), analogous to the whole-lake surveys conducted in Atlin Lake.
Genetic data collection and analyses
We extracted total genomic DNA using the QIAgen DNeasy
kit system, and DNA was precipitated and resuspended in
Tris–EDTA (pH 8.0) buffer and then stored at –20 8C. Polymerase chain reactions (PCR) were performed across eight
loci in 10 mL volumes in MJ PTC 100 thermal cyclers using
fluorescently labeled primers and assayed on a BeckmanCoulter CEQ 8000 automated capillary sequencer/genotyper
(Supplemental Table S12). The loci used were Sco2,
Sco102, Sco107, Sco19, Sco215, Sfo18, Ssa197, and
Smm22 (Supplemental Table S12). We routinely re-ran individual samples across all loci to check for consistency of
scoring; in no case did these re-runs produce results that differed from the original analyses.
We tested for departures from Hardy–Weinberg equilibrium (HWE) and linkage equilibrium using Fisher’s exact
test in which P values were estimated using the Markov
chain method as implemented in GENEPOP (version 3.1;
Raymond and Rousset 1995). We used the sequential Bonferroni correction method (Rice 1989) to adjust critical
type-I error rates when making multiple simultaneous hypothesis tests. For tests within populations, we adjusted alpha levels sequentially by the number of loci for tests of
HWE and by the number of pairwise comparisons between
loci for tests of linkage disequilibrium. Basic descriptive statistics of sample size (N), number of alleles (A), observed
heterozygosity (HO), and expected heterozygosity (HE) were
compiled using TFPGA (version 3.2; Miller 1997), whereas
allelic richness (AR) was determined using FSTAT (version
2 Supplementary
2.9; Goudet 2001). Weir and Cockerham’s (1984) FST (q)
values were calculated using ARLEQUIN (version 3.0; Excoffier et al. 2005) to test population differentiation via pairwise comparisons. There was no evidence from permutation
tests in SPAGeDi (Hardy and Vekemans 2002) that mutation
contributed to population differentiation, so we limited our
genetic distance inferences to those based on FST. Next, we
used PCAgen (Goudet 1999) to conduct a principal component analysis (PCA) on allele frequency data to visualize genetic differentiation among the sample localities.
Microsatellite variation was also partitioned into its components by performing an analysis of molecular variance
(AMOVA) following Excoffier et al. (1992) and as implemented in ARLEQUIN. Different hierarchical arrangements
of samples such as watershed groupings, PCA groupings,
and Atlin Lake and Tagish Lake subpopulations (see below)
were tested.
We tested for a correlation between geographic distance
and genetic distance (FST) and its significance to determine
if the observed genetic structure could be explained by the
isolation-by-distance model (IBD; Slatkin 1993). We used
TFPGA to perform the Mantel test (Mantel 1967), and significance of any correlation was determined using the default setting of 999 matrix permutations. We used Google
Earth ( to calculate the distances between localities.
We adopted several approaches to estimating the population structure of lake trout within Atlin and Tagish lakes.
First, the number of subpopulations, K, within Atlin Lake
and Tagish Lake was estimated by employing the program
STRUCTURE (version 2.2; Pritchard et al. 2000). Because
Atlin and Tagish lakes are interconnected by the Atlin River
and lake trout are present in this waterway, we did not conduct STRUCTURE analyses separately by lake. A Bayesian
clustering algorithm, Markov chain Monte Carlo based approach assumes a model of K subpopulations, characterized
by allele frequencies at each locus and assigns individuals
probabilistically to each of these subpopulations. This analysis then uses a likelihood approach to determine the most
probable number of K subpopulations using a general genetic inheritance model to minimize Hardy–Weinberg and
linkage disequilibrium within the subpopulations. We ran
multiple trials following Evanno et al. (2005), with a ‘‘burnin’’ period of 10 000 iterations followed by 10 000 iterations
to estimate K and subpopulation membership; longer runs
with more iterations did not alter the basic results. Initial
analyses examining values of K from 1 to 30 suggested that
the true value of K was ~5 within Atlin and Tagish lakes.
Consequently, we conducted more detailed simulations employing the admixture model with correlated allele frequencies at each K from 1 to 8 using a burn-in of 50 000
iterations followed by 450 000 iterations and replicated each
five times per K value. The model of K with the highest loglikelihood value was chosen as the most probable level of
population subdivision. We did not employ the ad hoc statistic, DK (Evanno et al. 2005), because of the generally low
levels of subdivision resolved where this statistic performs
poorly at resolving the true K (Waples and Gaggiotti 2006).
After the most likely number of subpopulations was deter-
data for this article are available on the journal Web site (
Published by NRC Research Press
Can. J. Fish. Aquat. Sci. Vol. 67, 2010
Table 1. Sample sites, sample size (N), locations, watersheds, elevations, and surface area of lakes sampled for lake trout (Salvelinus namaycush).
1. Atlin (AL)
2. Tatsemerie (T)
3. Tagish (TAG)
4. Trapper (TR)
5. Muncho (M)
6. Frances (F)
7. Travaillant (TV)
8. Bednesti (B)
9. Hautete (H)
10. Tezzeron (TE)
11. Arctic (AR)
12. Cluculz (CL)
13. Fraser (FRA)
14. Nakinlerak (K)
15. Natowite (N)
16. Cunningham (CU)
17. Airline (A)
18. Klawli (KL)
19. Pinchi (P)
Latitude (N)
Longitude (W)
Note: BC, British Columbia; NWT, Northwest Territories.
Fig. 1. Locations in western Canada where lake trout (Salvelinus namaycush) were collected in this study. Population codes are given in
Table 1. Inset at the top left shows Atlin and Tagish lakes in northwestern British Columbia and Yukon; inset on the right shows the upper
Fraser River, British Columbia; inset at the bottom left shows the whole study region in relation to Canada.
Published by NRC Research Press
Northrup et al.
mined, each fish was assigned to the subpopulation for
which its inferred ancestry coefficient, q, was at least 0.5
(i.e., the proportional contribution to its genome across the
eight loci from a specific subpopulation was estimated to
constitute at least 0.5; Pritchard et al. 2000).
We partitioned Atlin Lake into four large geographic regions that corresponded to areas that are partially isolated
from each other or that had distinct limnological conditions:
north arm (northernmost 50 km of lake), central arm (next
50 km of lake), west arm (distinct restrictions between this
area and central and south arms), and south arm (southernmost 25 km of lake headed by the Llewellyn Glacier field).
Contingency tests were performed to assess statistical significance of differences in the frequencies of the STRUCTUREgenerated genetic subpopulations among these geographic
regions within Atlin Lake using PAST, a general spreadsheet-based package of statistical analyses (Hammer et al.
2001). Similar tests were also performed to assess whether
the genetic subpopulations within Tagish Lake were associated with distinct spawning locations.
We next calculated individual pairwise identity indices
(Mathieu et al. 1990) in the Atlin Lake and Tagish Lake
subpopulations identified by STRUCTURE with IDENTIX
(Belkhir et al. 2002). The identity index varies from 0 to
1.0 and provides a measure of relatedness because closely
related individuals are more likely to produce homozygous
offspring and IDENTIX estimates the expected proportion
of loci that are homozygous in the offspring of any pair of
individuals. IDENTIX also implements a permutation test
of the null hypothesis of no relatedness by comparing the
distribution of the observed pairwise relatedness coefficients
in a population sample with its null expectation. The identity
values were calculated because the sampling was not based
on actual spawning locations, at least in Atlin Lake, and examining this estimate of individual relatedness may resolve
finer patterns of genetic differentiation especially because
kin groups have been resolved in other char at postjuvenile
life stages (Fraser et al. 2005).
The composition of baseline populations defined by genetic data for Atlin and Tagish lakes and for use in subsequent fisheries mixture analyses was determined using
STRUCTURE by identifying the K most likely subpopulations for both Atlin and Tagish lakes’ survey samples and
assigning each individual to its most likely population (i.e.,
q > 0.5) as described above. These baseline subpopulations
were used in conjunction with genetic mixture analysis to
determine if the commercial and recreational fisheries in Atlin Lake were sampling proportionately from each population using the Bayesian approach to mixed-stock analysis
implemented in ONCOR (Anderson et al. 2008). This analysis utilizes the conditional maximum likelihood algorithm to
estimate mixture proportions (Miller 1987) and the Rannala
and Mountain (1997) method for estimating the probability
of observing a genotype in series of baseline populations,
each of which was characterized across a number of genetic
loci. We estimated the robustness of our analyses in two
ways. First, 95% confidence intervals were generated by
bootstrap resampling with replacement in both the mixture
samples (across individuals) and the baseline samples (alleles across genotypes) during 5000 replicate analyses. Second, we produced simulated mixtures (N = 200) in which we
fixed the proportional contribution of each subpopulation in
turn to 1.0 and then estimated the mixture proportions for all
baseline subpopulations contributing to the simulated mixtures. In this case, ‘‘perfect’’ performance of the mixture
analysis would return an estimated proportional contribution
for each subpopulation of 1.0. Initially, this analysis compared the commercial and angling samples with all 19 lakes
sampled to determine the general performance of our data
and ONCOR (i.e., no contributions from lakes other than Atlin and Tagish should be observed). Next, commercial and
recreational fishing samples were analysed, both separately
and in a combined analysis, to assess the contributions of
the various genetic subpopulations from Atlin and Tagish
Morphological analyses
We photographed individual fish from Atlin Lake using a
digital camera with a measuring board as a consistent background for light standardization. Body shape was measured
with the aid of TPSDIG software (Rohlf 1997). The digital
images were imported into TPSDIG and x and y coordinates
were established using 16 (each with an x and y coordinate)
landmarks to measure the shape of each fish (Zimmerman et
al. 2006; Bronte and Moore 2007; Supplemental Fig. S12).
These data were then imported into a companion program
TPSRELW (Rohlf 1997) that centers, scales, and aligns the
coordinates using the Procrustes method and calculates an
average configuration to describe the consensus body shape
among a sample of fish. The program then compares each
set of coordinates with the consensus body shape using
thin-plate spline analysis (Bookstein 1991). The method produces a value for each fish that represents its deviation at
each x and y coordinate from the consensus shape, generating 32 such deviation scores for each fish (x, y coordinates
across 16 landmarks). The TPSDIG software was also used
to measure head depth, midbody depth, and caudal peduncle
depth and to confirm body length, which had been measured
in the field. To account for variation in body size related
(isometric) differences, head depth, midbody depth, and caudal peduncle depth were size-corrected to the overall sample
mean fork length (521 mm) by employing the residuals from
the linear relationship between fork length and individual
trait in subsequent statistical tests (Thorpe 1976). These latter three measurements, in combination with condition factor
and the 32 deviation scores from the landmark analysis, resulted in 36 body shape measurements for each fish.
We quantified body colouration, or more accurately
brightness, of each lake trout image with the aid of Image J
software (version 1.32j; following
the methodology of Zimmerman et al. (2006). All images
were converted to black and white and the measurements
were taken on a 0 (black) to 256 (white) scale and averaged
for a total overall ‘‘brightness factor’’. Brightness was measured at six positions along the lateral surface of each fish as
defined by Zimmerman et al. (2006): midoperculum, two
equidistant points along the body above the lateral line, two
below the lateral line, and one at the midpoint of the caudal
peduncle. We performed ANOVAs to assess the level of
brightness differentiation among (i) the genetic subpopulations resolved via STRUCTURE, (ii) body shape based morPublished by NRC Research Press
phological clusters (see below), and (iii) within-lake geographical units.
To summarize morphology, condition factor, and brightness into a measure of overall phenotypic variation, we conducted principal component analysis (PCA) on the
correlation matrix among all variables using PAST. To determine the number of phenotypic clusters that could be resolved among all lake trout, a model-based clustering
method was employed without a priori designation of populations using the program MCLUST as implemented within
the R Statistical Software Project (Fraley and Raftery 2003;
R Development Core Team 2008). The method fits the observed frequency distribution of PCA scores obtained from
the analysis above to alternative models of structure in terms
of the number, shape, and variability within phenotypic clusters. In the first model, a single morphological cluster is assumed to best represent the data. In subsequent models, two
or more (up to eight in our case) clusters are assumed to exist. The model with the highest Bayesian information criterion (BIC) is selected as the model best describing
morphological variation, with differences in BIC values between alternative models that are greater than 6 indicating
‘‘strong’’ evidence and those greater than 10 indicating
‘‘very strong’’ evidence in favour of the model with the
highest BIC value (Fraley and Raftery 2003). The MCLUST
analysis includes a function allowing classification of individual observations (fish) to the resolved morphological
We conducted a discriminant function analysis (DFA)
with jackknifing in PAST to measure the reliability of classification of individual fish to morphological groups resolved in the MCLUST analysis. A contingency test was
also performed using PAST to determine (i) if the morphological groups resolved above were associated with the four
geographic regions in Atlin Lake and (ii) if there was a relationship between the STRUCTURE-defined genetic subpopulations and the morphological clusters. Finally, we
estimated the number of genetic groups within the sample
of fish examined morphologically using STRUCTURE as
detailed above, testing among potential K values of 1–5.
Intrapopulation genetic variation
We assayed microsatellite variation across 818 individuals
at eight loci. The number of alleles ranged from four
(Sco102) to 30 (Smm22) across all populations, with an
average of 14.8 alleles per locus for the entire study region
and an average of 5.3 alleles per locus per lake (Supplemental Tables S1 and S22). Observed heterozygosity ranged
from 0.17 to 0.91, with an average of 0.51 across all loci
and populations (Supplemental Table S22). The two most
variable loci were Smm22 and Ssa197, with 30 and 24 alleles globally, respectively. Fourteen out of 152 (8 loci 19 populations) tests showed statistically significant deviations from HWE, and all were heterozygote deficiencies.
Seven such deficiencies were from Atlin Lake (four loci)
and Tagish Lake (three loci); the remaining samples were
largely in HWE. Tests for linkage disequilibrium resulted in
none of the 532 tests being significant. Consequently, we
Can. J. Fish. Aquat. Sci. Vol. 67, 2010
considered each locus to represent an independent measure
of genetic variation and divergence.
The level of variation within populations varied greatly.
Expected heterozygosity over all eight loci ranged from a
low of 0.30 (Bednesti Lake) to a high of 0.75 (Atlin Lake)
(Supplemental Table S22). Fourteen populations were found
to have at least one of the eight loci fixed, with Sco102
being the most commonly fixed locus. No more than two
loci, however, were fixed in any one population. Between
watersheds, there were no fixed allele differences among
the eight microsatellites studied; however, there were several alleles found to be unique to individual watersheds, especially within the Yukon and Fraser rivers’ watersheds.
There was a strong, positive correlation between lake surface area and allelic richness (r = 0.68, P = 0.001).
Population structure among lakes
There were 120 pairwise comparisons of FST calculated
across the eight loci between populations with sample sizes
greater than 10 (N = 16), and all but two of these were significant at a = 0.05 (Supplemental Table S32). The average
FST across all comparisons was 0.258 and ranged from 0.009
to 0.616. A principal component analysis (PCA) of allele
frequency for all lakes revealed two groupings, with PCA 1
being the major (and only significant, P < 0.01) axis of differentiation between groupings (Fig. 2). Lakes in the Fraser
River watershed and Travaillant Lake (Mackenzie River,
Northwest Territories) formed one group that was separate
from the remaining samples (Yukon, northwestern BC). We
next examined genetic differentiation by grouping the various populations by watershed; AMOVA indicated that variation among the watershed groups (19.6%, P < 0.001)
exceeded that among populations within watersheds (13.8%,
P < 0.001; Table 2). Further tests were conducted based on
the groups suggested by the principal component analysis
(Fraser River – Travaillant Lake vs. all others; Fig. 2).
Here, there was a considerable narrowing of the difference
between the two PCA groups (3.3%, P < 0.001), as well as
that among populations within PCA groups (6.0%, P <
When all populations were included, a test for IBD was
not significant (r = 0.27, P = 0.09). The populations were
then grouped according to the two PCA clusters and tested
for IBD within each group, which was also not significant
within either group (r = 0.34, P = 0.25, and r = 0.24, P =
0.22, respectively). When lakes from the Fraser River watershed were analyzed separately, however, we detected a significant pattern of IBD (r = 0.47, P = 0.025).
Population structure within lakes
Four hundred and thirteen individuals from Atlin and
Tagish lakes were genotyped at eight microsatellite loci:
186 from Atlin Lake and 227 from Tagish Lake. The most
variable loci were Smm22 and Ssa197, with 26 and 18 alleles, respectively. STRUCTURE analysis indicated the
presence of genetic substructure, with three clusters (ATLA, N = 94 fish; ATL-B, N = 123 fish; ATL-C, N = 196
fish) being the most likely number of subpopulations within
these interconnected lakes (Supplemental Table S42). When
we pooled individual fish by membership within a subpopulation, only three of the 24 (eight loci three subpopulaPublished by NRC Research Press
Northrup et al.
Fig. 2. Principal component analysis of allele frequency variation at
eight microsatellite loci among all sample lakes. The numbers correspond to the population codes listed in Table 1.
tions) tests showed statistically significant HWE deviations.
Tests for linkage disequilibrium resulted in none of the 84
pairwise tests being significant. Within any of the three subpopulations, the number of alleles ranged from three
(Sco102, subpopulations ATL-A and ATL-B) to 24
(Smm22, subpopulation ATL-A), with the three subpopulations averaging 10.6, 10.1, and 11.8 alleles, respectively
(Supplemental Table S52). Observed heterozygosity ranged
from 0.18 (subpopulations ATL-A and ATL-B, Sco102) to
~0.90 (subpopulations ATL-A and ATL-C, Smm22), with
an average of 0.65 across all loci and populations (Supplemental Table S52).
Estimates of FST between the three subpopulations averaged 0.022 and ranged from 0.005 between subpopulations
ATL-A and ATL-C to 0.027 between ATL-A and ATL-B
to 0.034 between ATL-B and ATL-C (all P < 0.001). Pairwise identity values averaged 0.25, 0.38, and 0.27 in subpopulations ATL-A, ATL-B, and ATL-C, respectively, and
none was significantly different from that expected under
random mating within subpopulations (all P > 0.20).
Contingency tests failed to find any geographic pattern in
the distribution of the three genetic groupings across the
four areas of Atlin Lake (P = 0.25; Fig. 3). In contrast to
Atlin Lake, however, there was a highly nonrandom association between membership of individual fish in one of the
three genetic subpopulations and area of capture (spawning
bed) within Tagish Lake (P < 0.0001; Fig. 3). Furthermore,
there was a strong difference in the frequency of the three
genetic subpopulations between Atlin and Tagish lakes (contingency test, P < 0.0001; Fig. 3). In particular, Tagish Lake
possessed a higher frequency of genetic subpopulation ATLB than did Atlin Lake. We performed an analysis of molecular variation by pooling all STRUCTURE-generated subpopulations from Atlin and Tagish lakes by geographic region
within lake. The AMOVA indicated that only a small, but
significant, percentage of the total variation found was attributable to differences between lakes (1.2%, P = 0.04),
but slightly more was attributable to differences among regions within lakes (2.3%, P < 0.001), and most variation
was attributable to differences among individual fish within
subpopulations (96.6%, P < 0.001).
Mixed-stock fisheries analysis
Initial comparisons of lake trout commercial and angling
samples (N = 134) from Atlin Lake with baseline samples
from all 19 lakes in this study estimated that 79.6% of these
samples were from Atlin Lake, while 20.4% originated from
Tagish Lake. As expected, no other lakes were identified as
contributing to the Atlin Lake fisheries. Lake trout from
commercial fisheries and those caught by local anglers were
compared with the three genetic subpopulations identified
both in Atlin and Tagish lakes. Commercial fisheries appeared to draw most heavily from subpopulation ATL-C
(82.7%), followed by subpopulation ATL-B (15.1%), and finally from subpopulation ATL-A (2.2%) (Table 3). The angling catch also appeared to sample most heavily from
subpopulation ATL-C, followed by ATL-B, but few fish
from ATL-A were apparently exploited (Table 3). When we
partitioned the baseline and commercial samples by lake,
mixture analysis suggested that about 75% of all fish exploited originated from Atlin Lake (Table 3), with the remainder from Tagish Lake. The angling catch was even
more dominated by fish from Atlin Lake (91% vs. 9%; Table 3). Finally, all samples were partitioned by lake region
(Atlin) or spawning ground (Tagish). In this analysis, 75%
of commercially exploited fish were estimated to have originated from Atlin region 4 and Tagish Lake region SW
(Table 3). The angling catch was also dominated by fish
from Atlin Lake region 4, but other Atlin Lake regions and
Tagish Lake region DB also appeared to contribute to the
recreational fishery (Table 3). Interestingly, both in commercial and angling analyses, Tagish Lake region W did not appear to contribute any fish (Table 3). Confidence intervals
for real and simulated mixture analyses by genetic subpopulation and lake were largely nonoverlapping and associated
with minimum baseline sample sizes of about 100 (Table 3).
This was in marked contrast to the poor performance of
mixture analyses conducted by region where simulated proportions were highly variable with broad confidence intervals that all included 0. A baseline sample size of at least
50 appears to be required for simulated mixtures to approach
1.0 with relatively narrow confidence limits (Table 3).
Morphological analysis
We examined 104 lake trout morphologically and in terms
of body brightness. Two components in the PCA accounted
for 56.2% of total variation and axes 1 and 2 (PC1 and PC2)
made up 40.7% and 15.5% of the total variation, respectively (Supplemental Table S62). Fish length was uncorrelated, with scores along PC1 or PC2 (both P > 0.1)
suggesting the lack of an allometric effect on body shape
differences. A model describing two distinct morphological
clusters with unequal variances within them had the highest
BIC of –1080 (Fig. 4). Alternative models had BIC values at
least 20 points lower than the model describing two populations: MCLUST reported a BIC of –1110 for models with
one morphological cluster and a minimum BIC of –1100
for models with three or more clusters. The separation of
the two clusters was evident, however, only along PC2,
which appeared to differentiate fish with long heads and
Published by NRC Research Press
Can. J. Fish. Aquat. Sci. Vol. 67, 2010
Table 2. Analysis of molecular variance within and among western lake trout (Salvelinus namaycush) populations based on allele frequency variation across eight microsatellite DNA loci.
Yukon vs. Taku vs. Mackenzie vs.
Fraser watersheds
Between regions or
Among populations within
regions or lakes
Within populations
among regions or
Between PCA groups
Atlin vs. Tagish
% variation
% variation
% variation
Note: Comparisons between Atlin and Tagish lakes incorporated variation among three genetic subpopulations resolved within each lake, respectively.
Fig. 3. Map of the distribution of lake trout (Salvelinus namaycush) genetic subpopulations within Atlin and Tagish lakes according to
STRUCTURE analysis. The bold dashed lines indicate the separation between geographical regions in Atlin Lake. Shading within each pie
chart depicts the frequency of each of the three genetic subpopulations at that locality, with the sample size shown as the numeral to the
right of each pie chart. Solid circles represent known or suspected spawning locations in Atlin and Tagish lakes (M. Connor, Taku River
Tlingit First Nations Fisheries Department, unpublished data). Locality codes are given as underlined numbers for each of the four regions
in Atlin Lake or abbreviations for the three spawning reefs in Tagish Lake (DB, Deep Bay; W, Windy Arm; SW, Squaw Point).
caudal regions from those with deeper heads and bodies
overall (Supplemental Table S62; Fig. 4). The five topranked positive coefficients along PC2 were for x1, x3, x4,
x10, and x11 associated with head, eye, and caudal region
landmarks (Supplemental Fig. S1 and Supplemental Table
S62). The most negative coefficients for PC2 were for x6,
x7, x14, x15, and y5 associated with fin position and head
landmarks (Supplemental Fig. S1 and Supplemental Table
S62). Condition factor and ‘‘brightness’’ score had low PC
coefficients along PC2, typically in the order of five or six
times lower than for the landmarks with the highest coefficients (Supplemental Table S62). In general, fish that scored
Published by NRC Research Press
Northrup et al.
Table 3. Genetic mixture analysis results for 101 commercial samples and 33 recreational angling samples of
lake trout (Salvelinus namaycush) from Atlin Lake as generated using ONCOR (Anderson et al. 2008) and variation at eight microsatellite DNA loci.
Sample mixture
Simulated mixture
0.022 (0.000–0.110)
0.151 (0.053–0.267)
0.827 (0.688–0.924)
0.258 (0.034–0.462)
0.002 (0.000–0.262)
0.741 (0.456–0.927)
0.990 (0.971–1.000)
0.979 (0.954–0.999)
0.995 (0.977–1.000)
Atlin Lake
Tagish Lake
0.773 (0.587–0.884)
0.227 (0.116–0.410)
0.908 (0.612–0.999)
0.092 (0.001–0.385)
0.953 (0.898–0.999)
0.964 (0.917–0.999)
Atlin Lake 1
Atlin Lake 2
Atlin Lake 3
Atlin Lake 4
Tagish Lake W
Tagish Lake SW
Tagish Lake DB
Note: Tagish Lake: W, Windy Arm; SW, Squaw Point; DB, Deep Bay. Mixture values represent the estimated contribution
of each genetic subpopulation within Atlin and Tagish lakes, for lakes, and for geographic regions (Atlin Lake) or spawning
localities (Tagish Lake) within lakes averaged (95% confidence intervals (CI) in parentheses) over 5000 bootstrap replicates.
Subpopulations or lakes or regions with the highest average proportional contributions are indicated in bold. Simulated mixtures indicate average (95% CI) proportional contribution of each subpopulation or lake or region in simulated mixtures of
200 fish, where the true contribution of each is 1.0 under conditions of original baseline sample sizes (N).
most positively on PC2 were ‘‘stockier’’ with shorter and
shallower heads, bodies, and caudal regions relative to the
more elongated shapes of fish that had high negative scores
along PC2 (Supplemental Fig. S22).
The jackknifed DFA resulted in 75.9% correct morphological assignment, with two-thirds of the miss-assignments
occurring owing to fish from morphological group 1 (MG1)
assigned to group 2 (MG2). There was slight, but significant, association between geography and morphological
group; MG1 predominated in the south arm of Atlin Lake
(P = 0.046; Fig. 4). By contrast, there was no significant association (P = 0.35) between morphotype composition and
the three STRUCTURE-generated genetic subpopulations
identified earlier.
The brightness scores ranged from a low of near 100 (representing dark shading with distinct white spotting) to over
120 (representing silver–white shading with indistinct light
spotting; Fig. 5). Average brightness scores were not statistically different among the three Atlin Lake genetic subpopulations (P = 0.13) or the two morphological clusters (P =
0.15). The observed geographical distribution of brightness
scores was, however, found to be significant (P = 0.016),
with the higher mean scores (lighter variety) being more
common in the south arm (Fig. 5). An FST of 0.004 (95%
CI of –0.001 to 0.009) was calculated between the two morphological clusters, six times lower than the value of the
global FST of 0.022 among the three genetic subpopulations.
Finally, analysis using STRUCTURE provided no evidence
of more than one genetic population within the group of
fish examined morphologically; log-likelihood was highest
at K(1) = –2731, with K(2) having the next highest likelihood at –2939.
The ability to detect genetic substructure within populations has greatly increased over the last decade, which can
provide information to promote effective management
(Schwartz et al. 2007). Our study examined the level of genetic diversity and subdivision of lake trout within Atlin
Lake and other western lake trout populations to help facilitate comparisons with other populations in more heavily
studied and perturbed systems of eastern North America.
These data can be used as baseline levels of genetic diversity for more effective conservation and management decisions for lake trout both in Atlin Lake and more generally.
Genetic variability across lakes and regions
Our study yielded significant FST values that ranged from
0.014 between two lakes within the same watershed and
connected by a short river (Atlin and Tagish lakes) to 0.616
between two lakes (Trapper Lake, Taku River watershed,
and Bednesti Lake, Fraser River watershed) that are separated by about 780 km, located in different watersheds, and
are thought to have been colonized by fish from distinct glacial refugia (Wilson and Hebert 1998). The overall mean
pairwise FST of 0.25 is well within the range reported
among populations of freshwater resident salmonid fishes
that, as a group, tend to have higher levels of genetic subdivision than anadromous salmonids (Hendry et al. 2004).
Substantial subdivision among freshwater populations of salmonids, despite the excellent dispersal potential of salmonid
fishes, is consistent with the generally greater constraints on
interlocality dispersal imposed by the underlying landscape
in freshwater environments.
Where opportunities for dispersal do exist such as bePublished by NRC Research Press
Fig. 4. (a) Morphological variation among Atlin Lake lake trout
(Salvelinus namaycush) along the first and second principal components (PC1 and PC2). Ellipses represent the covariances of the values along each component, solid triangles correspond to
morphological group 1 (MG1), and open squares correspond to
morphological group 2 (MG2). (b) Map of the distribution of the
two morphological groups among four geographic regions within
Atlin Lake. The value to the right of each pie chart is the sample
size for each region of the lake.
tween Atlin and Tagish lakes, genetic subdivision is lower.
More modest levels of subdivision (expressed as GST and
FST) of between 0.024 and 0.058 were reported among historical and contemporary wild and hatchery populations of
Can. J. Fish. Aquat. Sci. Vol. 67, 2010
lake trout within Laurentian Great Lakes Huron and Superior (Guinand et al. 2003; Page et al. 2004). Measures of
subdivision are expected to be lower when more continuously distributed populations (e.g., within lakes Huron and
Superior) are compared relative to widely separated or completely isolated populations (e.g., our study lakes, among
Lakes Superior and Huron, and upper Mississippi River
drainage lakes; DeWoody and Avise 2000; Page et al.
2004; Piller et al. 2005). Consequently, the large spatial
scale of our study (Northwest Territories to central BC)
probably accounts in large part for the strong differentiation
that we observed. These results reinforce the importance of
geographic proximity as an important factor influencing genetic structure among populations (Slatkin 1993; reviewed
by Bohonak 1999). It is, however, more surprising that a
signature of isolation-by-distance was detected among lakedwelling populations within the Fraser River basin because
these populations are strongly isolated currently. Consequently, there is almost surely very little contemporary
movement between these lakes (i.e., although lake trout
may occur in rivers and occasionally spawn there, they are
generally associated with deepwater, lacustrine habitats;
Scott and Crossman 1998). These results suggest that historical patterns of recolonization (timing, direction, sequence of
events) may play some role in influencing current patterns
of genetic structure even among relatively isolated populations (Crispo and Hendry 2005).
Analyses based on morphology and mtDNA have suggested that the Atlin–Tagish lakes area was colonized postglacially by fish from two refugia: the Nahanni Refuge and
the Bering Refuge. By contrast, the lower Mackenzie River
(e.g., Travaillant Lake) and the Fraser River watersheds are
thought to have been colonized by fish from the Bering Refuge only (Bodaly and Lindsey 1977; Wilson and Hebert
1998). Our results are consistent with these hypotheses
given that the Atlin–Tagish samples were very distinct from
the Travaillant Lake and Fraser River samples, suggesting
that they have had distinct origins postglacially. Further, our
microsatellite data suggested that the Yukon and Taku rivers’ watershed lakes are as similar to each other as are the
lakes within the Fraser River watershed. The similarity between the Yukon (draining to the Bering Sea) and Taku (Pacific drainage) rivers is consistent with a common origin for
these fish and (or) with suspected historical drainage connections between watersheds of these major river systems in
the southern Yukon Territory (Lindsey 1975). By contrast,
the sources of lake trout in the Mackenzie River watershed
appear to be more variable. Travaillant Lake (lower Mackenzie River, Arctic drainage) lake trout were more similar
to lake trout from the Fraser River watershed (Pacific drainage) than to fish from a lake tributary to the upper Mackenzie River (via the upper Liard River, Arctic drainage) —
Frances Lake. Fish from Frances Lake, in turn, were more
similar to lake trout from the Yukon and Taku rivers. The
latter result is consistent with the shorter distance between
Frances Lake and the upper Yukon River, even though Frances Lake is part of the upper Liard River, which ultimately
flows to the Mackenzie River via the Peace River. In addition, Frances Lake lies just west of the area thought to have
encompassed the Nahanni Refuge, which probably provided
some postglacial colonists to the southwestern Yukon
Published by NRC Research Press
Northrup et al.
Fig. 5. Results of comparisons of average (± standard deviation, SD) brightness values for lake trout (Salvelinus namaycush) sampled from
the four geographic regions within Atlin Lake. Sample sizes are given in Fig. 2, and images illustrate the extremes of colouration of lake
trout found within Atlin Lake.
(Wilson and Hebert 1998; Stamford and Taylor 2004).
These patterns highlight the importance of considering historical patterns of connectivity in the interpretation of genetic affinities among contemporary populations in lake
trout, a phenomenon shared with other fishes from geologically active areas (Currens et al. 1990; Waters and Wallis
2000; Zemlak et al. 2008).
Genetic variability within and between Atlin and Tagish
Atlin and Tagish lakes were the only two populations that
displayed deviations from HWE across multiple loci, which
suggested that some substructure might be present, an inference supported by the STRUCTURE analysis. Genetic subdivision within single geographic localities is common in
salmonids (summarized by Hendry et al. 2004), including
lake trout (Ihssen et al. 1988; Page et al. 2003), and might
have been expected in Atlin and Tagish lakes as they are by
far the largest lakes in our survey. The level of subdivision
found among the three genetic subpopulations in Atlin and
Tagish lakes (FST = 0.022) was greater than between Atlin
and Tagish lakes when the fish were simply grouped by
lake and not accounting for genetic substructure within lake
(FST = 0.014). Atlin Lake and Tagish Lake are connected by
the Atlin River where lake trout do occur (M. Connor, Taku
River Tlingit First Nation Fisheries Department, Atlin, BC,
unpublished data). Interlake dispersal via this river probably
explains the finding of the same genetic groups between the
lakes and, therefore, that fish found in either Atlin Lake or
Tagish Lake may be more similar to fish found in the other
lake. In addition, the generally higher levels of subdivision
observed in Tagish Lake relative to Atlin Lake may result
from our sampling of actual spawning reefs in Tagish Lake,
sampling that should yield a better estimate of genetic structure as a function of reproductive isolation than the more
general lake survey sampling of nonreproductive adults
completed in Atlin Lake.
Subpopulations within Atlin and Tagish lakes appeared to
exhibit comparable levels of subdivision (FST = 0.005–
0.034) with those reported among populations separated
over similar distances within individual Laurentian Great
Lakes (Ihssen et al. 1988; Page et al. 2004). By contrast,
these values are somewhat lower than the maximum values
reported from a study of spawning reefs within Lakes
Huron, Michigan, and Superior (FST = >0.1) (Page et al.
2003), but these latter samples represented spawning populations that had been repopulated after stocking of fish from
diverse hatchery sources.
Biochemical genetic and tagging data both strongly suggest that some lake trout exhibit very precise homing, returning to their spawning site year after year (Ihssen et al.
1988). These studies also indicated that the typical dispersal
range of most lake trout is about 30 km but can be upwards
of 50 km, but usually no more than 100 km, from their
spawning shoals (Ihssen et al. 1988). Lake trout populations
may also utilize environmental cues to select spawning beds,
suggesting that distance is not the sole isolating factor (Perkins et al. 1995). In Atlin Lake, the spawning shoals identified thus far are all within the central portion of the lake.
Consequently, the location of these spawning beds in close
proximity to each other may be one of the reasons for such
apparent high gene flow in Atlin Lake, but large areas of the
lake remain unsurveyed for spawning areas. In Tagish Lake,
three spawning bed locations had sufficiently high sample
Published by NRC Research Press
numbers to determine if they were primarily from one genetic subpopulation or not. Genetic subpopulation ATL-C
dominated the spawning bed furthest west, while the spawning beds in the eastern and southern portions of the lake
were characterized by higher proportions of subpopulation
TAG-B, suggesting some independence among spawning
Phenotypic vs. genetic diversity
Local residents have reported multiple colour and shape
morphs of lake trout in Atlin Lake for generations, but
whether the different morphotypes were discrete populations
or represented a continuous range of variation was not
known (M. Connor, Taku River Tlingit First Nation Fisheries Department, Atlin, BC, unpublished data). Our study
suggests the presence of two morphological kinds of lake
trout within Altin Lake that appear to differ primarily in
head shape, body depth, and fin position. The three morphotypes found within the Laurentian Great Lakes exhibit significant differences in head shape (Moore and Bronte 2001)
and the Great Bear Lake ‘‘redfin’’ and ‘‘normal’’ morphotypes differ in head size and dorsal fin position (Alfonso
2004). Independent from these morphotype differences was
variation in brightness in fish from across Atlin Lake. Zimmerman et al. (2006, 2007), in contrast, found brightness
differences to be associated with different body forms and
that they tended to be collected from different water depths.
Similarly, geographic segregation of colour morphs of lake
trout has also been reported in Great Bear Lake (i.e., light
in the south, dark in the north), but the basis of this pattern
is unknown (Blackie et al. 2003).
The striking difference in brightness of fish observed in
Atlin Lake is most likely promoted by variability in the
lake’s environment, especially in terms of turbidity and possibly water colour. The high level of turbidity caused by the
influx of sediment from glacial meltwater characterizes the
lake’s south arm where the distinctive bright ‘‘silver’’ shading of the lake trout predominates. In fact, locally, these fish
are known as the ‘‘glacial’’ variety. Colouration differences
found in Lake Mistassini (Zimmerman et al. 2007) showed
individuals in the deeper waters to be lighter-shaded than
those in shallow water. Although all fish captured in the Atlin Lake survey were found in depths of less than 50 m, the
increased turbidity caused by the glacial meltwater in the
south end of the lake may be simulating the conditions in
deeper waters. By contrast, lake trout in Great Slave Lake
(Zimmerman et al. 2006) exhibited the reverse pattern (i.e.,
light in shallow water, and dark in deep water). Perhaps if
fish in Atlin Lake were caught in a greater range of water
depths, an association between depth and shading–colour–
brightness might also be found. The extent to which such
brightness variants are the result of genetic differences or result from an environmentally induced phenotypic response
is, however, unknown (Miller et al. 2007; Stuart-Fox and
Moussalli 2009). Indeed, there is a long history of morphological investigation in salmonids, and such variation can be
primarily environmental, genetic, or both in origin (e.g.,
Pakkasmaa and Piironen 2001; Imre et al. 2002; Keeley et
al. 2007). The three main morphotypes of lake trout found
in some portions of the Laurentian Great Lakes (so-called
‘‘humper’’, ‘‘siscowet’’, and ‘‘lean’’ varieties) explained a
Can. J. Fish. Aquat. Sci. Vol. 67, 2010
greater percentage of total microsatellite variation than locality, suggesting that morphological groups may also represent distinct genetic groups in lake trout (Page et al. 2004;
Bronte and Moore 2007). Furthermore, lake trout raised in
hatcheries maintain morphological differences among parental groups, suggesting that a genetic component is involved
(Moore and Bronte 2001). As yet, however, no genetic information has been provided for the other morphologically
diverse lake trout in the Mackenzie Great Lakes. Whether
that would be the case of Atlin Lake lake trout and whether
or not such differences are associated with fitness are uncertain as the necessary common garden, performance, or selection experiments have not been performed (Grewe et al.
1994; Perkins et al. 1995; Bronte and Moore 2007).
Despite the identification of two morphotypes (independent of brightness differences), there was considerable
overlap between the types, which was confirmed by a level
of miss-classification of approximately 26% by discriminant
function analysis. Because of the overlap and subtle differences between the morphotypes, it would be difficult to assign an individual fish to one type or the other in the field
by visual identification. This presence of different morphotypes in Atlin Lake could be driven, in part, by sexual dimorphism, and we were unable to discount this possibility
by sexing all of our fish. Sexual dimorphism in lake trout,
however, is much less distinct than in other char, even
when spawning (McPhail 2007), and there was no obvious
way for us to sex our prespawning samples externally.
Also, Zimmerman et al. (2007) found no association between sex and either morphological or brightness variation
in Lake Mistassini lake trout. In addition, in the few mortalities that did occur during our sampling, sex appeared to be
randomly distributed between morphotypes (two females
and one male were in MG1 and one male and one female
were in MG2). The brightness differences are unlikely to be
explained solely by sexual dimorphism given the clear association of brightness scores with lake water colour (clear vs.
glacial flour colour in the south arm) and associated crypsis
via background matching (sensu Stevens and Merilaita
2009). Finally, because the morphotypes–brightness differences showed significant geographic associations in Atlin
Lake, if such variation was driven by sexual dimorphism,
one would need to invoke sexual differences in habitat use
to explain the association between phenotype and geography.
The morphological subpopulations resolved in Atlin Lake
did not correspond to the subpopulations defined through
microsatellite analysis. This could result from environmental
differences among localities that control the expression of
the phenotypes among members of different genetic subpopulations that use a range of environments during nonspawning periods. Alternatively, there could be considerable
gene flow at neutral microsatellite loci among lake trout
subgroups characterized by adaptive differences in morphology, and there are many theoretical and empirical grounds
for expecting adaptive divergence in phenotype in the presence of some gene flow at neutral loci (e.g., McKay and
Latta 2002; Saint-Laurent et al. 2003; Hendry and Taylor
Our data are too few for a definitive analysis of any association between the established morphotypes from the LauPublished by NRC Research Press
Northrup et al.
rentian Great Lakes and those found in Atlin Lake. Casual
visual identifications based on the morphological descriptions suggest, however, that Atlin Lake contains both a lean
(streamlined shape and generally inhabiting water < 70 m
deep) and a siscowet-like variety (deeper-bodied inhabitants
of waters 70–150 in depth; Page et al. 2004). Unfortunately,
none of the siscowet type could be included in our morphological analysis because of poor quality photographs. A
more comprehensive comparative analysis across lakes
could determine if the recognized morphotypes such as the
siscowet reported from the Laurentian and Mackenzie Great
Lakes are morphologically similar to those in Atlin Lake
and if such similarity results from parallel evolution. Regardless of their exact identity or affinities, Atlin Lake is
relatively small compared with other lakes where multiple
morphotypes and genetic populations have been reported.
This morphological diversity may be promoted by the physical connectivity between Atlin and Tagish lakes and the increasing habitat complexity that this represents.
Fisheries contributions and conservation
It is not surprising that some percentage (~22% on average) of the samples collected from Atlin Lake recreational
and commercial fisheries was estimated to constitute fish
from Tagish Lake owing to known incidences of dispersal
of lake trout through the Atlin River, which connects Atlin
and Tagish lakes. Lake trout caught by angling have a lower
proportional contribution from Tagish Lake than do those
from the commercial fisheries. This seems reasonable because the Atlin River is closer to the commercial fisheries
locations than areas that receive the greatest angling effort.
Genetic mixture analysis estimated that genetic subpopulation ATL-C was most frequently sampled by both commercial operators and anglers. This subpopulation also
possessed the largest estimated effective population size,
which might explain its high estimated greater contribution
to fisheries if this reflects a higher census size (Northrup
2008). In fact, baseline fish assigned to ATL-C were the
most or second most abundant of all fish sampled in both
Atlin (60%) and Tagish (42%) lakes. The contribution of
ATL-C to fisheries in Tagish Lake is unknown due to the
lack of samples from fisheries in Tagish Lake. In addition,
given its high relative frequency, ATL-B probably contributes substantially to the Tagish Lake fisheries. Proximity of
fishery areas to main feeding or reproductive habitats of
subpopulations may contribute to unequal fishery contribution. In fact, the known spawning locations in Atlin Lake
are found in its central portion and this area is the most
heavily used by anglers. Therefore, if the genetic subpopulations are driven largely by homing to specific spawning
beds, then those subpopulations closer to popular angling locations could show a higher contribution. In terms of regions within lakes, however, our mixture estimates had
broad confidence intervals and thus interpretations of differential exploitation of the fish sampled from particular regions of either lake must be interpreted cautiously. Our
simulated mixture results suggested that sufficient microsatellite variability was assayed across the eight loci to generate resolution consistent with previous studies of mixture
analyses of lake trout, at least at the level of lake or genetic
subpopulation (Page et al. 2003; DeKoning et al. 2006). Our
simulations, however, also indicated that increasing baseline
sample sizes would improve confidence for mixture analyses
at smaller spatial scales within lakes.
Our study has resolved microsatellite allele frequency differences between samples across a broad geographic scale in
British Columbia, Yukon, and the Northwest Territories and
within single lakes of the upper Yukon River watershed. We
also found evidence of morphological and colouration differences between localities within Atlin Lake, BC, indicating that such phenomena are general ones for lake trout
across their geographic range. It is well known that the extent of genetic substructure within a population assemblage
linked by gene flow and its influence on effective population size can have important effects on the time to fixation
or loss of alleles, as well as which alleles are more likely to
fix (e.g., Whitlock and Barton 1997; Whitlock 2003). Following these principles, the lake trout substructure of Atlin
and Tagish lakes is an important attribute that can impact
the rate of evolution of lake trout and hence influence their
persistence in the face of environmental change (Whitlock
2003). Consequently, to sustain lake trout and their fishery
potential within the large and complex ecosystem of Atlin–
Tagish lakes, managers should consider and maintain the
morphological and molecular biocomplexity that we have
documented and the interaction of subpopulations within
and between these two lakes (cf. Hilborn et al. 2003). The
evolutionary potential of lake trout, and ultimately their persistence, in this large lake ecosystem and others like it most
likely depends on the survival of many subpopulations
across a diversity of habitats within such large lake basins.
More broadly, previous conceptions of lake trout ecology
such as a narrow ecological niche and limited tolerance to
temperature and oxygen levels, based largely on the Laurentian Great Lakes populations, have been revised based on
new information (Wilson and Mandrak 2003). If the Laurentian Great Lakes are quantitatively different, as some have
suggested (Wilson and Mandrak 2003), then obtaining
knowledge of populations outside this area such as we have
provided offers a more complete understanding of the biology of lake trout.
Funding and field assistance for this project was provided
by the Taku River Tlingit Fisheries Department and the Yukon Territory Government and an Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery
grant awarded to E.B.T. We appreciate the assistance of
Sean Rogers, Dolph Schluter, Patrick Tamkee, Jen Gow, and
Luke Harmon with the various analyses. We also thank all of
the people who assisted in collecting and providing samples
and three reviewers for helpful comments on the study.
Alfonso, N.R. 2004. Evidence for two morphotypes of lake charr,
Salvelinus namaycush, from Great Bear Lake, Northwest Territories, Canada. Environ. Biol. Fishes, 71(1): 21–32. doi:10.
Allendorf, F.W., and Luikart, G. 2007. Conservation and the genetics of populations. Blackwell Publishing, Oxford, UK.
Anderson, E.C., Waples, R.S., and Kalinowski, S.T. 2008. An improved method for predicting the accuracy of genetic stock idenPublished by NRC Research Press
tification. Can. J. Fish. Aquat. Sci. 65(7): 1475–1486. doi:10.
Belkhir, K., Castric, V., and Bonhomme, F. 2002. IDENTIX, a
software to test for relatedness in a population using permutation
methods. Mol. Ecol. Notes, 2(4): 611–614. doi:10.1046/j.14718286.2002.00273.x.
Bernatchez, L., and Wilson, C.C. 1998. Comparative phylogeography of Nearctic and Palearctic fishes. Mol. Ecol. 7(4): 431–452.
Blackie, C.T., Weese, D.J., and Noakes, D.L.G. 2003. Evidence for
resource polymorphism in the lake charr (Salvenlinus namaycush) population of Great Bear Lake, Northwest Territories, Canada. Ecoscience, 10: 509–514.
Bodaly, R.A., and Lindsey, C.C. 1977. Pleistocene watershed exchanges and the fish fauna of the Peel River Basin, Yukon Territory. J. Fish. Res. Board Can. 34: 388–395.
Bohonak, A.J. 1999. Dispersal, gene flow, and population structure.
Q. Rev. Biol. 74(1): 21–45. doi:10.1086/392950. PMID:
Bookstein, F.L. 1991. Morphometric tools for landmark data: geometry and biology. Cambridge University Press, New York.
Booth, R.E., and Grime, J.P. 2003. Effects of genetic impoverishment on plant community diversity. J. Ecol. 91(5): 721–730.
Bowen, B.W. 1999. Preserving genes, species, or ecosystems?
Healing the fractured foundations of conservation policy. Mol.
Ecol. 8(12, Suppl. 1): S5–S10. doi:10.1046/j.1365-294X.1999.
00798.x. PMID:10703547.
Bronte, C.R., and Moore, S.A. 2007. Morphological variation of
siscowet lake trout in Lake Superior. Trans. Am. Fish. Soc.
136(2): 509–517. doi:10.1577/T06-098.1.
Brown, E.H., Jr., Eck, G.W., Foster, N.R., Horrall, R.M., and Coberly, C.E. 1981. Historical evidence for discrete stocks of lake
trout (Salvelinus namaycush) in Lake Michigan. Can. J. Fish.
Aquat. Sci. 38(12): 1747–1758. doi:10.1139/f81-223.
Crispo, E., and Hendry, A.P. 2005. Does time since colonization
influence isolation-by-distance? A meta-analysis. Conserv.
Genet. 6(5): 665–682. doi:10.1007/s10592-005-9026-4.
Currens, K.P., Schreck, C.B., and Li, H.W. 1990. Allozyme and
morphological divergence of rainbow trout (Oncorhynhcus mykiss) above and below waterfalls in the Deschutes River, Oregon. Copeia, 1990(3): 730–746. doi:10.2307/1446439.
Dehring, T.R., Brown, A.F., Daugherty, C.H., and Phelps, S.R.
1981. Survey of the genetic variation among eastern Lake
Superior lake trout (Salvelinus namaycush). Can. J. Fish. Aquat.
Sci. 38(12): 1738–1746. doi:10.1139/f81-222.
DeKoning, J., Keatley, K., Phillips, R., Rhydderch, J., Janssen, J.,
and Noakes, M. 2006. Genetic analysis of wild lake trout embryos recovered from Lake Michigan. Trans. Am. Fish. Soc.
135(2): 399–407. doi:10.1577/T05-044.1.
DeWoody, J.A., and Avise, J.C. 2000. Microsatellite variation in
marine, freshwater and anadromous fishes compared with other
animals. J. Fish Biol. 56(3): 461–473. doi:10.1111/j.1095-8649.
Dextrase, A.J., and Mandrak, N.E. 2006. Impacts of alien invasive
species on freshwater fauna at risk in Canada. Biol. Invasions,
8(1): 13–24. doi:10.1007/s10530-005-0232-2.
Evanno, G., Regnaut, S., and Goudet, J. 2005. Detecting the number of clusters of individuals using the software STRUCTURE:
a simulation study. Mol. Ecol. 14(8): 2611–2620. doi:10.1111/j.
1365-294X.2005.02553.x. PMID:15969739.
Excoffier, L., Smouse, P.E., and Quattro, J.M. 1992. Analysis of
molecular variance inferred from metric distances among DNA
Can. J. Fish. Aquat. Sci. Vol. 67, 2010
haplotypes: application to human mitochondrial DNA restriction
data. Genetics, 131(2): 479–491. PMID:1644282.
Excoffier, L., Laval, G., and Schneider, S. 2005. Arlequin (version
3.0): an integrated software package for population genetics data
analysis. Evol. Bioinform. Online, 1: 47–50. PMID:19325852.
Fraley, D., and Raftery, A.E. 2003. Enhanced model-based clustering, density estimation, and discriminant analysis software:
MCLUST. J. Classif. 20(2): 263–286 http://www.stat. doi:10.1007/s00357-003-0015-3.
Frankam, R., Ballou, J.D., and Briscoe, D.A. 2002. Introduction to
conservation genetics. United Kingdom University Press, Cambridge, UK.
Fraser, D.J., Duchesne, P., and Bernatchez, L. 2005. Migratory
charr schools exhibit population and kin associations beyond juvenile stages. Mol. Ecol. 14(10): 3133–3146. doi:10.1111/j.
1365-294X.2005.02657.x. PMID:16101779.
Futuyma, D.J. 1997. Evolutionary biology. Sinauer, Sunderland,
Goudet, J. 1999. PCA-GEN for Windows. Available from the Institute of Ecology, Biology Building, University of Lausanne, Lausanne, Switzerland.
Goudet, J. 2001. FSTAT version Updated from Goudet, J.
(1995). J. Hered. 86: 485–486.
Grewe, P.M., Krueger, C.C., Marsden, J.E., Aquadro, C.F., and
May, B. 1994. Hatchery origins of naturally produced lake trout
fry captured in Lake Ontario: temporal and spatial variability
based on allozyme and mitochondrial DNA data. Trans. Am.
Fish. Soc. 123(3): 309–320. doi:10.1577/1548-8659(1994)
Guinand, B., Scribner, K.T., Page, K.S., and Burnham-Curtis, M.K.
2003. Genetic variation over space and time: analyses of extinct
and remnant lake trout populations in the upper Great Lakes.
Proc. R. Soc. Lond. Ser. B Biol. Sci. 270(1513): 425–433.
Gunn, J.M., Steedman, R.J., and Ryder, R.A. 2003. Boreal shield
watersheds: lake trout ecosystems in a changing environment.
CRC Press, Boca Raton, Florida.
Hammer, Ø., Harper, D.A.T., and Ryan, P.D. 2001. PAST: palaeontological statistics software package for education and data
analysis. Palaeontol. Electronica, 4: 1–9.
Hardy, O.J., and Vekemans, X. 2002. SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual
or population levels. Mol. Ecol. Notes, 2(4): 618–620. doi:10.
Hendry, A.P., and Taylor, E.B. 2004. How much of the variation in
adaptive divergence can be explained by gene flow? An evaluation using lake-stream stickleback pairs. Evolution, 58(10):
2319–2331. PMID:15562693.
Hendry, A.P., Castric, V., Kinnison, M.T., and Quinn, T.P. 2004.
The evolution of dispersal: homing versus straying in salmonids.
In Evolution illuminated: salmon and their relatives. Edited by
A.P. Hendry and S.C. Stearns. Oxford University Press, Oxford,
UK. pp. 52–91.
Hilborn, R. 2007. Managing fisheries is managing people: what has
been learned? Fish Fish. 8: 282–296.
Hilborn, R., Quinn, T.P., Schindler, D.E., and Rogers, D.E. 2003.
Biocomplexity and fisheries sustainability. Proc. Natl. Acad.
Sci. U.S.A. 100(11): 6564–6568. doi:10.1073/pnas.1037274100.
Ihssen, P.E., Casselman, J.M., Martin, G.W., and Phillips, R.B.
1988. Biochemical genetic differentiation of lake trout (Salvelinus namaycush) stocks of the Great Lakes region. Can. J.
Fish. Aquat. Sci. 45(6): 1018–1029. doi:10.1139/f88-125.
Imre, I., McLaughlin, R.L., and Noakes, D.L.G. 2002. Phenotypic
Published by NRC Research Press
Northrup et al.
plasticity in brook charr: changes in caudal fin induced by water
flow. J. Fish Biol. 61(5): 1171–1181. doi:10.1111/j.1095-8649.
Keeley, E.R., Parkinson, E.A., and Taylor, E.B. 2007. The origins
of ecotypic variation of rainbow trout: a test of environmental
vs. genetically based differences in morphology. J. Evol. Biol.
20(2): 725–736. doi:10.1111/j.1420-9101.2006.01240.x. PMID:
Lindsey, C.C. 1975. Proglacial lakes and fish dispersal in the southwestern Yukon. Verh. Internat. Verein. Limnol. 19: 2364–2370.
Magnan, P., Audet, C., Glemet, H., Legault, M., Rodriguez, A., and
Taylor, E.B. 2002. Developments in the ecology, evolution, and
behaviour of the charrs, genus Salvelinus: relevance for their
management and conservation. Environ. Biol. Fishes, 64(1/3):
9–14. doi:10.1023/A:1016010903489.
Mantel, N. 1967. The detection of disease clustering and a generalized regression approach. Cancer Res. 27(2): 209–220. PMID:
Mathieu, E., Autern, M., Roux, M., and Bonhomme, F. 1990.
Épreves de validation dans l’analyze de structures génétiques
multivariées : comment tester l’équilibre panmictique? Rev.
Stat. Appl. 38: 47–66.
McKay, J.K., and Latta, R.G. 2002. Adaptive population divergence: markers, QTL and traits. Trends Ecol. Evol. 17(6): 285–
291. doi:10.1016/S0169-5347(02)02478-3.
McPhail, J.D. 2007. The freshwater fishes of British Columbia. The
University of Alberta Press, Edmonton, Alberta.
Millar, R.B. 1987. Maximum likelihood estimation of mixed stock
fishery composition. Can. J. Fish. Aquat. Sci. 44(3): 583–590.
Miller, M.P. 1997. Tools for population genetic analysis (TFPGA)
1.3: a Windows program for the analysis of allozyme and molecular population genetic data. Computer software distributed by
author (
Miller, C.T., Beleza, S., Pollen, A.A., Schluter, D., Kittles, R.A.,
Shriver, M.D., and Kingsley, D.M. 2007. cis-Regulatory changes
in Kit ligand expression and parallel evolution of pigmentation
in sticklebacks and humans. Cell, 131(6): 1179–1189. doi:10.
1016/j.cell.2007.10.055. PMID:18083106.
Moore, S.A., and Bronte, C.R. 2001. Delineation of sympatric morphotypes of lake trout in Lake Superior. Trans. Am. Fish. Soc. 130(6):
1233–1240. doi:10.1577/1548-8659(2001)130<1233:DOSMOL>2.0.
Northrup, S. 2008. Population structure of lake trout (Salvelinus
namaycush) in Atlin Lake, British Columbia, and contributions
to local fisheries: a microsatellite DNA-based assessment. M.Sc.
thesis, Department of Zoology, University of British Columbia,
Vancouver, British Columbia.
Page, K.S., Scribner, K.T., Bennett, K.R., Garzel, L.M., and BurnhamCurtis, M.K. 2003. Genetic assessment of strain-specific sources
of lake trout recruitment in the Great Lakes. Trans. Am. Fish.
Soc. 132(5): 877–894. doi:10.1577/T02-092.
Page, K.S., Scribner, K.T., and Burnham-Curtis, M.K. 2004. Genetic diversity of wild and hatchery lake trout populations: relevance for management and restoration in the Great Lakes. Trans.
Am. Fish. Soc. 133(3): 674–691. doi:10.1577/T03-007.1.
Pakkasmaa, S., and Piironen, J. 2001. Water velocity shapes juvenile salmonids. Evol. Ecol. 14(8): 721–730. doi:10.1023/
Perkins, D.L., Fitzsimons, J.D., Marsden, J.E., Krueger, C.C., and
May, B. 1995. Differences in reproduction among hatchery
strains of lake trout at eight spawning areas in Lake Ontario: genetic evidence from mixed-stock analysis. J. Great Lakes Res.
21: 364–374. doi:10.1016/S0380-1330(95)71110-8.
Piller, K., Wilson, C.C., Lee, C.E., and Lyons, J. 2005. Conservation genetics of inland lake trout in the upper Mississippi River
basin: stocked or native ancestry? Trans. Am. Fish. Soc. 134(4):
789–802. doi:10.1577/T04-040.1.
Power, G. 2002. Charrs, glaciations and seasonal ice. Environ.
Biol. Fishes, 64(1/3): 17–35. doi:10.1023/A:1016066519418.
Pritchard, J.K., Stephens, M., and Donnelly, P. 2000. Inference of
population structure using multilocus genotype data. Genetics,
155(2): 945–959. PMID:10835412.
R Development Core Team. 2008. R: a language and environment
for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available from
Rannala, B., and Mountain, J.L. 1997. Detecting immigration by
using multilocus genotypes. Proc. Natl. Acad. Sci. U.S.A.
94(17): 9197–9201. doi:10.1073/pnas.94.17.9197. PMID:
Raymond, M., and Rousset, F. 1995. Genepop (version 1.2), population genetics software for exact tests and ecumenicism. J.
Hered. 86: 248–249.
Reusch, T.B.H., Ehlers, A., Hämmerli, A., and Worm, B. 2005.
Ecosystem recovery after climatic extremes enhanced by genotypic diversity. Proc. Natl. Acad. Sci. U.S.A. 102(8): 2826–
2831. doi:10.1073/pnas.0500008102. PMID:15710890.
Rice, W.R. 1989. Analyzing tables of statistical tests. Evolution,
43(1): 223–225. doi:10.2307/2409177.
Rohlf, F.J. 1997. TPSDIG and TPSRELW programs. Available
Saint-Laurent, R., Legault, M., and Bernatchez, L. 2003. Divergent
selection maintains adaptive differentiation despite high gene
flow between sympatric rainbow smelt ecotypes (Osmerus mordax Mitchill). Mol. Ecol. 12(2): 315–330. doi:10.1046/j.1365294X.2003.01735.x. PMID:12535084.
Schwartz, M.K., Luikart, G., and Waples, R.S. 2007. Genetic monitoring as a promising tool for conservation and management.
Trends Ecol. Evol. 22(1): 25–33. doi:10.1016/j.tree.2006.08.009.
Scott, W.B., and Crossman, E.J. 1998. Freshwater fishes of Canada. Galt House Publications, Ltd., Oakville, Ontario, Canada.
Slatkin, M. 1993. Isolation by distance in equilibrium and nonequilibrium populations. Evolution, 47(1): 264–279. doi:10.
Smith, B.R., and Tibbles, J.J. 1980. Sea lamprey (Petromyzon marinus) in Lakes Huron, Michigan, and Superior: history of invasion and control, 1936–78. Can. J. Fish. Aquat. Sci. 37(11):
1780–1801. doi:10.1139/f80-222.
Stamford, M.D., and Taylor, E.B. 2004. Phylogeographical lineages
of Arctic grayling (Thymallus arcticus) in North America: divergence, origins, and affinities with Eurasian Thymallus. Mol.
Ecol. 13: 1533–1549. doi:10.1111/j.1365-294X.2004.02174.x.
Stevens, M., and Merilaita, S. 2009. Animal camouflage: current
issues and new perspectives. Philos. Trans. R. Soc. B Biol. Sci.,
364(1516): 423–427. doi:10.1098/rstb.2008.0217.
Stuart-Fox, D., and Moussalli, A. 2009. Camouflage, communication and thermal regulation: lessons from colour-changing organisms. Philos. Trans. R. Soc. B Biol. Sci., 364(1516): 463–
470. doi:10.1098/rstb.2008.0254.
Taylor, E.B., Tamkee, P., Sterling, G., and Hughson, W. 2007. Microsatellite DNA analysis of rainbow trout (Oncorhynchus mykiss) from western Alberta, Canada: native status and
evolutionary distinctiveness of ‘‘Athabasca’’ rainbow trout. Conserv. Genet. 8(1): 1–15. doi:10.1007/s10592-006-9142-9.
Thorpe, R.S. 1976. Biometric analysis of geographic variation and
Published by NRC Research Press
racial affinities. Biol. Rev. Camb. Philos. Soc. 51(4): 407–425.
doi:10.1111/j.1469-185X.1976.tb01063.x. PMID:1088084.
Waples, R.S., and Gaggiotti, O. 2006. What is a population? An
empirical evaluation of some genetic methods for identifying
the number of gene pools and their degree of connectivity. Mol.
Ecol. 15(6): 1419–1439. doi:10.1111/j.1365-294X.2006.02890.x.
Waters, J.M., and Wallis, G.P. 2000. Across the Southern Alps by
river capture? Freshwater fish phylogeography in South Island,
New Zealand. Mol. Ecol. 9(10): 1577–1582. doi:10.1046/j.
1365-294x.2000.01035.x. PMID:11050552.
Weir, B.S., and Cockerham, C.C. 1984. Estimating F-statistics for
the analysis of population structure. Evolution, 38(6): 1358–
1370. doi:10.2307/2408641.
Whitlock, M.C. 2003. Fixation probability and time in subdivided
populations. Genetics, 164(2): 767–779. PMID:12807795.
Whitlock, M.C., and Barton, N.H. 1997. The effective size of a
subdivided population. Genetics, 126: 427–441.
Wilson, C.C., and Hebert, P.D.N. 1998. Phylogeography and postglacial dispersal of lake trout (Salvelinus namaycush) in North
America. Can. J. Fish. Aquat. Sci. 55(4): 1010–1024. doi:10.
Can. J. Fish. Aquat. Sci. Vol. 67, 2010
Wilson, C.C., and Mandrak, N.E. 2003. History and evolution of
lake trout in shield lakes: past and future challenges In Boreal
shield watersheds: lake trout ecosystems in a changing environment. Edited by J.M. Gunn, R.J. Steedman, and R.A. Ryder.
CRC Press, Boca Raton, Florida. pp. 21–33.
Zemlak, T.S., Habit, E.M., Walde, S.J., Battini, M.A., Adams,
E.D., and Ruzzante, D.E. 2008. Across the southern Andes on
fin: glacial refugia, drainage reversals and a secondary contact
zone revealed by the phylogeographical signal of Galaxias platei in Patagonia. Mol. Ecol. 17(23): 5049–5061. doi:10.1111/j.
1365-294X.2008.03987.x. PMID:19017262.
Zimmerman, M.S., Krueger, C.C., and Eshenroder, R.L. 2006. Phenotypic diversity of lake trout in Great Slave Lake: differences
in morphology, buoyancy, and habitat depth. Trans. Am. Fish.
Soc. 135(4): 1056–1067. doi:10.1577/T05-237.1.
Zimmerman, M.S., Krueger, C.C., and Eshenroder, R.L. 2007. Morphological and ecological differences between shallow and deepwater lake trout in Lake Mistassini, Quebec. J. Great Lakes Res.
33(1): 156–169. doi:10.3394/0380-1330(2007)33[156:MAEDBS]2.
Published by NRC Research Press
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF