Journal of Fish Biology (2007) 70 (Supplement C), 381–398
doi:10.1111/j.1095-8649.2007.01519.x, available online at http://www.blackwell-synergy.com
The effect of migratory behaviour on genetic
diversity and population divergence: a comparison of
anadromous and freshwater Atlantic salmon
Salmo salar
A. T ONTERI *, A. J E . V ESELOV †, S. T ITOV ‡, J. L UMME §
C. R. P RIMMER *k
AND
*Department of Biology, Division of Genetics and Physiology, FIN-20014
University of Turku, Finland, †Institute of Biology, Karelian Research Centre,
Pushkinskaya 11, 185610 Petrozavodsk, Russia, ‡State Research Institute on
Lake and River Fisheries, Makarova 26, 199053 St. Petersburg,
Russia and §Department of Biology, P. O. Box 3000,
FIN-90014 University of Oulu, Finland
(Received 11 September 2006, Accepted 15 March 2007)
The genetic diversity of anadromous and freshwater Atlantic salmon (Salmo salar) populations
from north-west Russia and other north European locations was compared using microsatellite variation to evaluate the importance of anadromous migration, population size and
population glacial history in determining population genetic diversity and divergence. In
anadromous Atlantic salmon populations, the level of genetic diversity was significantly higher
and the level of population divergence was significantly lower than among the freshwater
Atlantic salmon populations, even after correcting for differences in stock size. The phylogeographic origin of the populations also had a significant effect on the genetic diversity
characteristics of populations: anadromous populations from the basins of the Atlantic
Ocean, White Sea and Barents Sea possessed higher levels of genetic diversity than
anadromous populations from the Baltic Sea basin. Among the freshwater populations, the
result was the opposite: the Baltic freshwater populations were more variable. The results of
this study imply that differences in the level of long-term gene flow between freshwater
populations and anadromous populations have led to different levels of genetic diversity,
which was also evidenced by the hierarchical analysis of molecular variance. Furthermore, the
results emphasize the importance of taking the life history of a population into consideration
when developing conservation strategies: due to the limited possibilities for new genetic
diversity to be generated via gene flow, it is expected that freshwater Atlantic salmon
populations would be more vulnerable to extinction following a population crash. Hence,
high conservation status is warranted in order to ensure the long-term survival of the limited
# 2007 The Authors
number of European populations with this life-history strategy.
Journal compilation # 2007 The Fisheries Society of the British Isles
Key words: anadromy; dispersal; gene flow; microsatellite; phylogeography; salmonid.
kAuthor to whom correspondence should be addressed. Tel.: þ358 2 3335571; fax: þ358 2 3336680;
email: craig.primmer@utu.fi
381
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Journal compilation # 2007 The Fisheries Society of the British Isles
#
382
A. TONTERI ET AL.
INTRODUCTION
The genetic composition of populations in previously glaciated areas is determined by pre-glacial, glacial and post-glacial history (e.g., Poissant et al., 2005;
Johansson et al., 2006). For freshwater spawning fish species, the population unit
is usually defined at the level of a river or a lake, and the primary determinant of
genetic diversity is historical: from where and when and by how many individuals the river or lake was colonized (e.g., Sato et al., 2003; Barluenga & Meyer,
2005). After this initial founding period, the standard evolutionary forces—
mutation, gene flow (immigration), natural selection and genetic drift—also contribute to population diversity. The relative importance of these evolutionary factors in shaping the post-glacial genetic structure of a population depends, among
other things, on the life history of the species. For example, it is known that in
marine fishes, the level of genetic diversity is higher and the level of population
divergence is lower than in freshwater species, most likely due to the better
dispersal capabilities and larger effective population sizes of marine species
(Gyllensten, 1985; Ward et al., 1994; DeWoody & Avise, 2000).
Anadromy is a life-history trait of fishes that refers to migration from a freshwater breeding habitat to a marine feeding habitat and back to freshwater for
spawning (McDowall, 2001). Several summaries of genetic diversity in fishes
have been published (Gyllensten, 1985; Ward et al., 1994; DeWoody & Avise,
2000). These have shown that there is some evidence that the level of genetic
diversity and population divergence of anadromous species is intermediate to
that of marine and freshwater species. This seems logical given that, for example, the level of between population migration may be expected to be higher in
rivers connected to the ocean compared to completely isolated water bodies.
There, however, has been little statistical support for some of these findings,
most probably due to the small number of anadromous populations assessed
and to the fact that earlier studies have compared genetic diversity indices
based on completely different sets of loci. A small number of studies have also
addressed the issue of within species differences in genetic diversity (Bouza
et al., 1999; Castric et al., 2001; Castric & Bernatchez, 2003) and it has been
observed that populations with a lower level of connectivity to other populations tend to have lower levels of genetic diversity and a higher level of genetic
divergence. None of these earlier studies, however, have considered the relative
roles of the various evolutionary forces and processes in shaping the genetic
diversity of fish populations. Such a study is not only important from a population genetic perspective but also has implications for determining appropriate
conservation strategies for anadromous populations.
Populations of Atlantic salmon (Salmo salar L.) offer an opportunity to
more thoroughly investigate the effects of anadromy and glacial history on
genetic diversity as both anadromous and freshwater (non-anadromous) populations occur in the species (Mills, 1989). Anadromous Atlantic salmon populations spawn in rivers and the offspring spend up to 5 years in freshwater
after which they migrate to the sea to feed and mature (Hutchings & Jones,
1998). After spending one to four winters at sea, mature individuals usually return to their natal river for spawning (Mills, 1989; Hutchings & Jones, 1998).
Non-anadromous freshwater populations were formed following the last ice
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EFFECT OF ANADROMOUS MIGRATIONS ON GENETIC VARIATION
383
age as rapid land upheaval isolated these populations from the sea and created
lakes large enough for the freshwater-adapted refugial populations to thrive
(Berg, 1985). In such populations, smolts migrate from a river to a lake and
back again as the migration to and from the sea is inhibited by geographical
barriers. In Europe, freshwater Atlantic salmon populations are clearly the
minority and can be found in 13 locations in Norway, Sweden, Finland and
north-west Russia (MacCrimmon & Gots, 1979; Berg, 1985). In North America,
freshwater populations are found in several small lakes in Maine, New Brunswick,
Nova Scotia, Quebec, Labrador and Newfoundland (MacCrimmon & Gots,
1979).
North European Atlantic salmon colonized its current habitats following the
last glaciation but despite abundant research, no consensus has been reached
on the origin of the current populations. In recent times, it has been suggested
that the Baltic Sea has been colonized exclusively from a single eastern ice lake
refugium (Nilsson et al., 2001; Tonteri et al., 2005), or from up to three distinct
refugia: the Gulf of Bothnia from an Atlantic refugium, the Gulf of Finland
from an eastern ice lake refugium and the southern Main Basin from a refugium that was presumably located in the basin of rivers Neman, Vistula, Odra
and Elbe (Säisä et al., 2005). The freshwater populations from lakes Ladoga
and Onega have been proposed to originate from the eastern ice lake refugium
(Nilsson et al., 2001; Säisä et al., 2005; Tonteri et al., 2005).
North-west Russia has often been proposed to be colonized from two directions. Evidence of colonization of the northern Kola Peninsula from the eastern Atlantic Ocean (Iberian peninsula, the British Isles and the North Sea) has
been presented in several studies (Verspoor et al., 1999; Consuegra et al., 2002;
Asplund et al., 2004; Makhrov et al., 2005; Säisä et al., 2005) as well as immigration from the western Atlantic Ocean (Asplund et al., 2004; Makhrov et al.,
2005; Tonteri et al., 2005). The Atlantic salmon found in the area of the White
Sea and the eastern Barents Sea probably originate from an north-eastern glacial refugium (Kazakov & Titov, 1991; Asplund et al., 2004; Tonteri et al.,
2005), although some support for western Atlantic immigration has been found
on the west coast of the White Sea as well (Makhrov et al., 2005; Tonteri et al.,
2005). In addition, Makhrov et al. (2005) suggested colonization from the Baltic
basin into the White Sea.
In this study, 14 anadromous and 7 freshwater Atlantic salmon populations
from north-west Russia and other north European locations were genotyped at
14 microsatellite loci and the effects of life-history type (anadromous v. freshwater), stock size, phylogeographic origin (Baltic Sea v. Atlantic Ocean, and
White and Barents Seas) and population bottlenecks were assessed with a view
to establishing the importance of anadromous migration, population size and
population glacial history in determining population genetic diversity and
divergence.
MATERIALS AND METHODS
Microsatellite data of 846 Atlantic salmon individuals from 14 anadromous and 7
freshwater north European populations from Tonteri et al. (2005) were used in the
study (Fig. 1; Table I). Microsatellite methods, estimation of genetic diversity (number
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Journal compilation # 2007 The Fisheries Society of the British Isles, Journal of Fish Biology 2007, 70 (Supplement C), 381–398
384
A. TONTERI ET AL.
FIG. 1. A map of northern Europe indicating the sampling locations of the Atlantic salmon populations
included in the study. Black circles indicate anadromous and grey circles freshwater populations. See
Table I for population names.
of alleles, allelic richness and observed and expected heterozygosities) and testing of
Hardy–Weinberg and genotypic linkage equilibria are described in Tonteri et al.
(2005). Global population differentiation was estimated using y (Weir & Cockerham,
1984), an estimator of Wright’s FST, using FSTAT 2.9.3.2 (Goudet, 1995). FSTAT
was also employed to compare genetic diversity and the level of population divergence
between anadromous and freshwater populations with statistical significance being assessed by 2000 permutations. To examine whether population divergence was correlated
with genetic diversity either in the anadromous or in the freshwater populations, expected heterozygosity and FST were calculated for each locus separately employing
FSTAT. The statistical significance of the correlation was assessed with Spearman’s r
as implemented in SPSS 11.0 for Windows. In addition, estimates of G’ST, a standardized measure of genetic differentiation independent of the degree of within-population
genetic variation (Hedrick, 2005), were calculated for the anadromous and freshwater
populations separately. A hierarchical analysis of molecular variance (AMOVA) was
performed for anadromous and freshwater populations separately using Arlequin version 2.0 (Schneider et al., 2000). The anadromous populations were divided into two
groups according to the known or predicted basin of origin (Tonteri et al., 2005) as follows: 1) Baltic Sea basin and 2) Atlantic Ocean, White and Barents Sea basins. Among
the freshwater populations, the division was made between 1) Baltic Sea basin and 2)
White Sea basin.
To examine the effect of stock size and phylogeographic origin (Baltic Sea basin v.
Atlantic Ocean, White and Barents Sea basins) on genetic diversity, the populations
were categorized according to the estimated number of adults ascending to the river
each year (see Table I for references). A general linear model (GLM) was used to study
the relationship between allelic richness, expected heterozygosity or observed heterozygosity (Ar, He, and Ho, respectively) and stock size and phylogeographic origin in
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2007 The Fisheries Society of the British Isles, Journal of Fish Biology 2007, 70 (Supplement C), 381–398
Sampled populations
Microsatellite diversity
Stock size
Nr.
Population
Anadromous
1 Dee
Basin
Nr. of ascending
adults/year
Dee
Atlantic Ocean
50 000–100 000
Abbr.
2
Teno
Ten
Atlantic Ocean
50 000–100 000
3
Tuloma
Tul
Atlantic Ocean
1000–5000
4
Pizhma
Piz
Barents Sea
1000–5000
5
Megra
Meg
White Sea
500–1000
6
7
8
Kitsa
Varzuga
Nilma
Kit
Var
Nil
White Sea
White Sea
White Sea
1000–5000
10 000–50 000
<100
9
Pulonga
Pul
White Sea
100–500
10
Pongoma
Pon
White Sea
100–500
11
Suma
Sum
White Sea
500–1000
Reference
J. Taggart, University of Stirling, U.K.,
pers. comm.
J. Erkinaro, Finnish Game and Fisheries
Research Institute, pers. comm.
Sharov et al. (1990); Zubchenko (1994);
Tretjak et al. (1997)
Zubchenko et al. (1995); Kazakov & Veselov
(1998); (A. Je. Veselov, unpublished data)
Zubchenko et al. (1995); Kazakov & Veselov
(1998); (A. Je. Veselov, unpublished data)
Zubchenko & Sharov (1993)
Zubchenko & Sharov (1993)
Zubchenko et al. (1995); Kazakov & Veselov
(1998); (A. Je. Veselov, unpublished data)
Zubchenko et al. (1995); Kazakov & Veselov
(1998); (A. Je. Veselov, unpublished data)
Zubchenko et al. (1995); Kazakov & Veselov
(1998); (A. Je. Veselov, unpublished data)
Zubchenko et al. (1995); Kazakov & Veselov
(1998); (A. Je. Veselov, unpublished data)
N
A (range)
Ar
Ho
He
48 106 (3–23) 69 072 073
46
97 (2–20) 63 066 071
42
91 (2–16) 61 067 072
21
55 (2–10) 47 062 062
48
78 (2–15) 55 068 066
45
47
35
84 (2–17) 54 07 067
84 (2–16) 53 061 065
45 (2–7) 36 061 055
40
54 (1–9)
44
66 (2–12) 49 067 066
38
50 (2–11) 41 061 058
41 057 057
EFFECT OF ANADROMOUS MIGRATIONS ON GENETIC VARIATION
385
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Journal compilation # 2007 The Fisheries Society of the British Isles, Journal of Fish Biology 2007, 70 (Supplement C), 381–398
TABLE I. Details and genetic diversity indices of the Atlantic salmon populations included in the study (from Tonteri et al., 2005). For the
freshwater populations, the lake where the salmon migrates to is given in parentheses. Nr., population number; Abbr., the abbreviation of the
population name; N, number of individuals; A, average number of alleles in the population; Ar, allelic richness; Ho, observed heterozygosity;
He, expected heterozygosity; pers. comm., personal communication
386
Journal compilation
TABLE I. Continued
Sampled populations
Microsatellite diversity
Stock size
#
Population
Abbr.
Basin
Nr. of ascending
adults/year
Reference
N
A (range)
ICES (2006); A. Romakkaniemi,
Finnish Game and Fisheries
Research Institute, pers. comm.
Swedish University of Agricultural
Sciences. Available at: http://
www-umea.slu.se/fisk/sve/trappa/
norrfors/trappan/stat/statistik.cfm
Zubchenko et al. (1995); Kazakov & Veselov
(1998); (A. Je. Veselov, unpublished data)
42
74 (3–14) 49 059 059
47
48 (2–12) 36 047 048
43
64 (2–12) 46 063 06
Ar
Ho
He
12
Tornio
Tor
Baltic Sea
10 000–50 000
13
Vindelälven
Vin
Baltic Sea
500–1000
14
Neva
Nev
Baltic Sea
500–1000
Tai
Baltic Sea
500–1000
Valetov (1999)
38
63 (2–14) 45 059 057
Sys
Baltic Sea
100–500
Valetov (1999)
42
43 (1–12) 34 049 047
Shu
Baltic Sea
1000–5000
Shchurov et al. (2000)
20
44 (2–10) 38 057 055
Liz
Baltic Sea
100–500
26
39 (2–8)
32 054 051
Luz
White Sea
100–500
40
39 (1–8)
32 047 045
Kam
White Sea
100–500
41
27 (1–5)
24 038 035
Pis
White Sea
500–1000
Zubchenko et al. (1995); Kazakov & Veselov
(1998); (A. Je. Veselov, unpublished data)
Zubchenko et al. (1995); Kazakov & Veselov
(1998); (A. Je. Veselov, unpublished data)
Zubchenko et al. (1995); Kazakov & Veselov
(1998); (A. Je. Veselov, unpublished data)
Zubchenko et al. (1995); Kazakov & Veselov
(1998); (A. Je. Veselov, unpublished data)
53
36 (1–7)
29 043 041
Freshwater
15 Taipale
(Ladoga)
16 Sysky
(Ladoga)
17 Shuja
(Onega)
18 Lizhma
(Onega)
19 Luzhma
(Segozero)
20 Kamennaya
(Kimasozero)
21 Pisto
(Kuitozero)
A. TONTERI ET AL.
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Nr.
EFFECT OF ANADROMOUS MIGRATIONS ON GENETIC VARIATION
387
anadromous and freshwater populations. The upper limit of each size class was used to
represent the stock size in the model, and the linearity of the model was gained by
applying a log10 transformation on the stock size. To study whether the effect of stock
size or phylogeographic origin on genetic diversity was similar in both anadromous and
freshwater populations, the interaction of the explanatory variables (life-history type
and phylogeographic origin or stock size) was included in the model. The interaction
between phylogeographic origin and stock size was also included in the model to determine if the effect of stock size on genetic diversity was similar in salmon from different
phylogeographic origins. As a significant interaction was found only between phylogeographic origin and life-history type, the final model included stock size, phylogeographic
origin, life history type and the interaction between the latter two as explanatory variables. For interpretation of the interaction least square means (LSMEAN) of Ar, He
and Ho were calculated for anadromous and freshwater populations from the Baltic
Sea basin and the basins of Atlantic Ocean, White Sea and Barents Sea. Analyses were
performed separately for each of the dependent variables (Ar, He and Ho) by applying
the GLM procedure of the SAS 8.2 statistical analysis software.
To test for recent severe reductions in effective population size (Ne), the Wilcoxon’s
test, which is especially useful when <20 polymorphic loci are used (Piry et al., 1999),
and the graphical mode shift indicator as implemented in Bottleneck 1.2.02 (Piry et al.,
1999), were employed. As rare alleles are lost rapidly after a population bottleneck,
allele number reduces faster than the expected heterozygosity, He (Maruyama & Fuerst,
1985; Cornuet & Luikart, 1996). Thus, the bottlenecked population will exhibit a transient excess of heterozygosity as He, which is calculated from allele frequencies, becomes
larger than the expected mutation-drift equilibrium heterozygosity, Heq, which is calculated from allele number (Cornuet & Luikart, 1996). Also, the allele frequency distribution becomes distorted as alleles with low frequency (<01) become less common than
alleles at higher frequencies (>01) (Luikart et al., 1998). For these analyses, loci were
assumed to evolve under the two-phase mutation model (Di Rienzo et al., 1994) with
5% of the mutations involving multiple steps with a variance of 12 as recommended
by Piry et al. (1999). A sequential Bonferroni type method [a9 ¼ 1 (1 a)1/k; Rice,
1989; Gotelli & Ellison, 2004] was employed to correct for multiple significance tests.
RESULTS
A summary of population-wise genetic diversity indices is given in Table I.
All populations and loci were in Hardy–Weinberg and linkage equilibria (see
Tonteri et al., 2005). The anadromous river Dee population had the most variation among the studied populations as measured with allelic richness (Ar ¼
69), and observed (Ho ¼ 072) or expected heterozygosity (He ¼ 073) while
the freshwater Kamennaya population was the least variable in all three measures (24, 038 and 035, respectively; Table I). The level of genetic diversity of
the anadromous populations was significantly higher (Ar ¼ 57, Ho ¼ 063 and
He ¼ 063) than that of the freshwater populations (Ar ¼ 37, Ho ¼ 048 and
He ¼ 046; 2000 permutations, all P values 0001; Fig. 2). In addition, the
level of genetic divergence among the freshwater populations (FST ¼ 031) was
significantly greater than among the anadromous populations (FST ¼ 012;
2000 permutations, P 0001; Fig. 2). Considering each locus separately, there
was a significant negative association between He and FST both in anadromous
(Spearman’s r ¼ 081, d.f. ¼ 12, P < 0001) and freshwater (Spearman’s
r ¼ 081, d.f. ¼ 12, P < 0001) populations, although the reduction in FST
was not as great in the anadromous populations as in the freshwater populations
(Fig. 3). Apart from one exception, the FST estimates of the anadromous
populations, however, were lower than of the freshwater populations regardless
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388
A. TONTERI ET AL.
FIG. 2. A comparison of genetic diversity indices and the level of genetic divergence between anadromous
and freshwater Atlantic salmon populations. Error bars indicate standard deviation. Ar, allelic
richness; Ho, observed heterozygosity; He, expected heterozygosity; Anadr., anadromous populations; Freshw., freshwater populations.
of the expected heterozygosity (Fig. 3). The G’ST estimate, which corrects for
differences in variability between loci, was 034 for anadromous and 062 for
freshwater populations.
When the populations were divided into two groups according to their basin
of origin, AMOVA analyses indicated that 49% of the variation among the
anadromous populations was explained by differences between the two basins
(Atlantic, including the Barents and White Seas v. Baltic; d.f. ¼ 1, P 0001;
Table II). Among the freshwater populations, the differentiation between
the White Sea v. Baltic basins was nearly four times higher, that is, 186%
(d.f. ¼ 1, P < 005; Table II). In both cases, the proportion of within-population
variance was the highest (856% in anadromous, d.f. ¼ 1158, P < 0001 and
641% in freshwater populations, d.f. ¼ 513, P < 0001; Table II).
FIG. 3. The relationship between expected heterozygosity and genetic divergence for each locus. The black
squares represent the values for each locus averaged across anadromous populations and the grey
circles represent the values for each locus averaged across freshwater populations. Lines connect the
values of anadromous and freshwater populations for each homologous locus.
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Partitioning of the populations
Baltic Sea basin
Anadromous
Nev, Tor, Vin
Freshwater
Liz, Shu, Sys,
Tai
Atlantic Ocean and White and
Barents Seas basins
Dee, Kit, Meg, Nil, Piz, Pon, Pul,
Sum, Ten, Tul, Var
Kam, Luz, Pis
*001 < P 005; ***P 0001.
Among basin
Within basin
Within populations
%
P
FCT
%
P
FSC
%
49
***
005
95
***
010
*
019
173
***
021
186
P
FST
856
***
014
641
***
036
EFFECT OF ANADROMOUS MIGRATIONS ON GENETIC VARIATION
389
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TABLE II. Hierarchical analysis of molecular variance for the anadromous and the freshwater populations. See Table I for population
abbreviations
390
A. TONTERI ET AL.
The GLM did not reveal any interaction between the stock size and the lifehistory type hence indicating a similar effect of stock size on allelic richness
[interaction F(1,20) ¼ 002, P ¼ 089], expected heterozygosity [interaction
F(1,20) ¼ 048, P ¼ 050] and observed heterozygosity [interaction F(1,20) ¼
063, P ¼ 044] both in anadromous and freshwater populations. Likewise,
the interaction between stock size and phylogeographic origin was found to
be non-significant suggesting a similar effect of stock size on allelic richness
[interaction F(1,20) ¼ 105, P ¼ 032], expected heterozygosity [interaction
F(1,20) ¼ 090, P ¼ 036] and observed heterozygosity [interaction F(1,20) ¼
043, P ¼ 052] both in the Baltic Sea basin and in the basins of Atlantic
Ocean, and White and Barents Seas. These interactions were thus excluded
from further analysis. The model, which included just stock size, phylogeographic origin, life-history type and the interaction between the latter two as
explanatory variables, accounted for 79, 83 and 76% of the variability in allelic
richness, expected heterozygosity and observed heterozygosity, respectively
(Table III). Both stock size, life-history type and the interaction between lifehistory type and phylogeographic origin explained significant components of
the variability in allelic richness and expected heterozygosity, whereas only
life-history type and the interaction between life history type and phylogeographic origin explained significant portions of the variation in observed heterozygosity (Table III).
A closer examination of the interaction revealed that anadromous salmon
populations from the basins of the Atlantic Ocean, White Sea and Barents
Sea were significantly more variable (LSMEAN Ar ¼ 5041, Ho ¼ 0644 and
He ¼ 0640) than the freshwater populations with the same origin (LSMEAN
Ar ¼ 3268, Ho ¼ 0437 and He ¼ 0427, all P values <0001), while in the Baltic
Sea basin, no difference was observed between the anadromous (LSMEAN
Ar ¼ 4059, Ho ¼ 0556 and He ¼ 0540) and the freshwater populations
(LSMEAN Ar ¼ 3993, Ho ¼ 0554 and He ¼ 0540, all P values 09; Table
IV). Among the anadromous salmon, populations from the basins of Atlantic
Ocean, White Sea and Barents Sea were more variable than populations from
the Baltic Sea basin in terms of both Ar (P ¼ 0025), Ho (P ¼ 0018) and He
(P ¼ 0006). Among freshwater salmon, the difference in Ar between the basins
of origin was not significant (P ¼ 0138), while Ho and He were significantly
higher in the Baltic freshwater populations than in the freshwater populations
from the White Sea basin (both P values ¼ 0008; Table IV).
The Wilcoxon’s test revealed a significant excess of heterozygosity (He > Heq)
in four populations (Dee, Lizhma, Pizhma and Pongoma). After the sequential
Bonferroni type correction for multiple tests, however, none of them remained
significant. Likewise, the graphical method gave no indication of a mode shift
in allele frequencies as alleles at low frequency (<01) were the most common
in each population and the frequency distributions were L-shaped.
DISCUSSION
Overall, Atlantic salmon populations with an anadromous life-history strategy displayed significantly more genetic variation (Ar, Ho and He) and lower
interpopulation divergence (FST and G’ST) than the freshwater populations
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Ar (R2 ¼ 79%)
Source of variation
Log10 (stock size)
Life-history type
Phylogeographic
origin
Life-history type phylogeographic
origin
He (R2 ¼ 83%)
Ho (R2 ¼ 76%)
Sum of
squares
d.f.
F
P
Sum of
squares
d.f.
F
P
Sum of
squares
d.f.
F
P
6738
2815
0064
1
1
1
1838
768
018
***
*
NS
0021
0038
0000
1
1
1
903
1634
006
**
***
NS
0004
0037
0001
1
1
1
142
1425
032
NS
**
NS
2889
1
788
*
0045
1
1957
***
0042
1
1620
***
NS, not significant.
*001 < P 005; **0001 < P 001; ***P 0001.
EFFECT OF ANADROMOUS MIGRATIONS ON GENETIC VARIATION
391
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TABLE III. The analysis of variance table for stock size, life-history type (anadromous v. freshwater) and phylogeographic origin (Baltic Sea
basin v. Atlantic Ocean, White and Barents Sea basins) in explaining the variability of allelic richness (Ar), expected (He) and observed
heterozygosity (Ho)
392
A. TONTERI ET AL.
TABLE IV. A comparison of the least squares means (LSMEAN) of allelic richness (Ar),
expected (He) and observed heterozygosity (Ho) between anadromous and freshwater
populations (in rows) and between populations form the Baltic Sea basin and the basins
of Atlantic Ocean, White Sea and Barents Sea (in columns). Anadr., anadromous
populations; Freshw., freshwater populations
Ar LSMEAN
Phylogeographic
Anadr. Freshw.
origin
Atlantic Ocean,
White and
Barents Seas
Baltic
P
He LSMEAN
P
Anadr. Freshw.
Ho LSMEAN
P
Anadr. Freshw.
P
5041
3268
***
0640
0427
***
0644
0437
***
4059
*
3993
NS
NS
0540
**
0540
**
NS
0556
*
0554
**
NS
NS, not significant.
*001 < P 005; **0001 < P 001; ***P 0001.
(Fig. 2). The results are congruent with the findings of DeWoody & Avise
(2000) who found a significant difference in the number of microsatellite alleles
and He in an interspecific comparison of anadromous (A ¼ 113 and He ¼ 068)
and freshwater fishes (A ¼ 75 and He ¼ 046). Using allozymes as genetic
markers, Gyllensten (1985) and Ward et al. (1994) reported heterozygosities
many magnitudes lower than the ones reported here for both anadromous
(He ¼ 0041 & 0057, Gyllensten, 1985; Ward et al., 1994, respectively) and
freshwater populations (He ¼ 0043 & 0062, Gyllensten, 1985; Ward et al.,
1994, respectively) and found no significant difference between the two groups.
Their GST estimates were similar to those described here but again, no significant difference in the GST estimates of anadromous (GST ¼ 0085 & 011,
Gyllensten, 1985; Ward et al., 1994, respectively) and freshwater species (033
& 022, Gyllensten, 1985; Ward et al., 1994, respectively) was found.
The result obtained here holds even when accounting for differences in stock
sizes: within a given population census size class, anadromous populations
almost always had a higher level of genetic diversity than freshwater populations of the same size class (Fig. 4; Table I). The observed difference between
anadromous and freshwater populations cannot be due to population bottlenecks in the latter as our analysis revealed no sign of recent severe reductions
of Ne in any of the studied populations. The observed positive correlation
between estimated census stock size and genetic diversity (Fig. 4; Table III)
can be explained by basic population genetic theory, whereby the effect of
genetic drift is expected to be higher in population with small Ne than in populations with high Ne (e.g., Hartl & Clark, 1997).
The census size of the populations was not, however, the only factor found
to affect the genetic diversity characteristics of the populations: the interaction
between life-history strategy and phylogeographic origin also contributed significantly to the level of genetic diversity observed in a population (Table
III). Hence, the glacial history of the studied populations’ distribution area
and phylogeography of the populations must be considered as well since the
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EFFECT OF ANADROMOUS MIGRATIONS ON GENETIC VARIATION
393
FIG. 4. Linear regression depicting the association between genetic diversity (allelic richness) and
stock size for populations with alternative migration behaviours [anadromous (black squares)
v. freshwater (grey circles)]. The stock size is represented by the log10 transformed upper limit of each
size class.
number of refugia where the present populations are derived from and, in the
case of the freshwater populations, also the time when the populations became
isolated may have affected the current-day genetic diversity and population
divergence.
The anadromous populations from the basins of Atlantic Ocean, White Sea
and Barents Sea were found to be more variable than the freshwater populations from the White Sea basin (Table IV). This seems logical as it is known
that after the deglaciation of Lakes Kimasozero and Kuitozero <11 590 years
ago (Saarnisto & Saarinen, 2001), the waters of the post-glacial White Sea
never reached their elevation and that the lakes were separated from the White
Sea fairly soon after the deglaciation as waterfalls preventing salmon immigration were formed c. 11 000 years ago (J.-P. Lunkka, University of Oulu, pers.
comm.). The observed difference could thus be due to a small number of
founding individuals and genetic drift in the freshwater populations that have
been isolated for a long period of time.
In the Baltic Sea basin, the result was different as no difference in genetic
diversity characteristics was found between the anadromous and the freshwater
populations (Table IV) possibly due to the very different post-glacial history
lakes Ladoga and Onega as compared to Kimasozero and Kuitozero. The
basins of Ladoga and Onega were deglaciated by 12 750 years ago after which
Ladoga remained as a part of the Baltic Ice Lake until the end of the ice lake
stage 11 590 years ago (Björck, 1995; Saarnisto & Saarinen, 2001). Later, c.
10 700–10 100 years ago (Andren et al., 2000), Ladoga was a bay of the
Ancylus Lake (Björck, 1995) allowing more immigration to the area and even
today Ladoga is connected to the Baltic Sea via river Neva and Onega to
Ladoga via river Svir. Thus, the number of individuals that took part in
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Journal compilation # 2007 The Fisheries Society of the British Isles, Journal of Fish Biology 2007, 70 (Supplement C), 381–398
394
A. TONTERI ET AL.
establishing the freshwater populations of Ladoga and Onega may have been
sufficiently large to retain their level of variability similar to that of the anadromous populations of the Baltic Sea. The differing post-glacial histories and differences in the extent of isolation of Ladoga, Onega, Kimasozero and Kuitozero
may also explain the higher heterozygosity levels of the Baltic freshwater populations as compared to the White Sea basin freshwater populations (Table IV).
Among the anadromous Atlantic salmon, more diversity was observed in
populations from the Atlantic Ocean, and White and Barents Seas than in populations from the Baltic Sea. Previous studies have shown that the northwest
Russian populations originate both from the east (Kazakov & Titov, 1991;
Asplund et al., 2004; Tonteri et al., 2005) and from the west (Verspoor et al.,
1999; Consuegra et al., 2002; Asplund et al., 2004; Makhrov et al., 2005; Säisä
et al., 2005) while evidence of immigration from the western Atlantic Ocean exists as well (Makhrov et al., 2005; Tonteri et al., 2005). Colonization by that
many refugial lineages may have lead to high diversity among these populations. Theories regarding the origin of the Baltic salmon are controversial (colonization from only the east v. colonization from east, west and south)
although it is agreed that freshwater refugia have contributed to the colonization of a substantial part of the Baltic Sea (Säisä et al., 2005) if not to the colonization of the whole Baltic Sea (Nilsson et al., 2001; Tonteri et al., 2005).
Possibly the high proportion of colonizers from freshwater refugia have resulted in a lower level of variability in Baltic salmon as during their isolation
in a freshwater refugium, genetic drift may have reduced the variability of the
Atlantic salmon populations living in them.
When interpreting the results, it is worthwhile keeping in mind that the
anadromous populations almost always had a higher level of genetic diversity
than the freshwater populations of the same size class (Fig. 4; Table I). This
implies that the long-term gene flow between the anadromous populations is
efficient, which was also evidenced by the hierarchical AMOVA (Table II). Individuals from geographically distant populations come together during the sea
phase and, instead of returning to their natal rivers to spawn, some may stray
to other rivers and consequently introduce novel alleles into the populations,
that is, gene flow, therefore increasing their genetic diversity and lowering their
population divergence with the donor population(s). In freshwater populations,
gene flow is only possible between rivers belonging to the same water system,
which might be a large lake with numerous Atlantic salmon spawning rivers,
such as Lakes Ladoga and Onega, or a small lake with merely a few Atlantic
salmon spawning rivers, as Kuitozero (just three salmon rivers) or Kimasozero
and Segozero (only one salmon river) (Table I, Fig. 1). Consequently, the proportion of molecular variation between basins and between populations within
a basin was greater in the freshwater populations than in the anadromous populations (Table II).
Another factor, which should be considered, is that loci with high withinpopulation heterozygosity (He), such as microsatellites, may give underestimates of population divergence when measured with F-statistics (Hedrick,
1999, 2005). In our study, the expected heterozygosity estimated over all loci
was higher among the anadromous than among the freshwater populations,
which might explain the lower genetic divergence of the anadromous populations
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EFFECT OF ANADROMOUS MIGRATIONS ON GENETIC VARIATION
395
as compared to the freshwater (Fig. 2). Indeed, the difference in FST between
anadromous and freshwater populations was lower for loci with high expected
heterozygosity (Fig. 3), supporting the proposed effect of high heterozygosity
on FST estimation (Hedrick, 1999, 2005). As the standardized G’ST, however,
also indicated greater population divergence for the freshwater populations
than for the anadromous populations, the observed low population divergence
of the anadromous populations cannot be merely due to high polymorphism of
the loci used. It should also be noted that the populations were divided into
two groups of anadromous and non-anadromous populations although they
do not represent monophyletic groups. Thus, the global FST estimates for each
group are based only on pair-wise distances within the group in question and
a number of pair-wise distances between the groups were excluded in the calculation. Despite this fact, the trend in the difference in genetic divergence was
strikingly clear.
The results also emphasize the importance of taking the life history of a population into consideration when developing conservation strategies. Although
the census sizes of freshwater Atlantic salmon populations in this study are
generally smaller than those of anadromous populations (Table I) and have
lower levels of genetic diversity (Fig. 2), the majority of populations appear
to be relatively stable, indicating that the natural level of genetic variation expected in freshwater populations is lower than in anadromous populations.
Nevertheless, due to the limited possibilities for new genetic diversity to be generated via migration, it is expected that freshwater populations would be more
vulnerable to extinction following a population crash, and hence, high conservation status is warranted in order to ensure the long-term survival of populations with this relatively rare life-history strategy.
We thank Irma Saloniemi for statistical advice, Timo Saarinen for enlightening discussions on dating the events of the Weichselian glaciation and Anti Vasemägi, two
anonymous reviewers and the subject editor for helpful comments on the manuscript.
We also acknowledge Alexander Zubchenko, Svjatoslav Kaluzhin and Igor Bakhmet
for their contribution to sampling the populations. This work was funded by the Russia
in Flux research program of the Finnish Academy, the Finnish Graduate School of
Population Genetics, and the Finnish Ministry of Agriculture and Forestry.
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