Molecular Ecology 1997, 6, 751–759
Lack of concordance between mtDNA gene flow and
population density fluctuations in the bank vole
J . E . S T A C Y , * P . E . J O R D E , † H . S T E E N , † ‡ R . A . I M S , † A . P U R V I S * § and K . S . J A K O B S E N *
* Division of General Genetics, Department of Biology, University of Oslo, PO Box 1031 Blindern, N-0315 Oslo, Norway, †Division
of Zoology, Department of Biology, University of Oslo, PO Box 1050 Blindern, N-0316 Oslo, Norway
Abstract
The genetic structure of bank voles Clethrionomys glareolus was determined from analyses of mitochondrial DNA (mtDNA) sequences, and compared with previous data on
geographical synchrony in population density fluctuations. From 31 sample sites evenly
spaced out along a 256-km transect in SE Norway a total of 39 distinct mtDNA haplotypes
were found. The geographical distribution of the haplotypes was significantly nonrandom, and a cladistic analysis of the evolutionary relationship among haplotypes
shows that descendant types were typically limited to a single site, whereas the ancestral
types were more widely distributed geographically. This geographical distribution
pattern of mtDNA haplotypes strongly indicates that the range and amount of female
dispersal is severely restricted and insufficient to account for the previously observed
synchrony in population density fluctuations. We conclude that geographical synchrony
in this species must be caused by factors that are external to the local population, such as
e.g. mobile predators.
Keywords: Clethrionomys, gene flow, mtDNA, polymorphism, population cycles, voles
Received 7 January 1997; revision received 21 March 1997; accepted 8 April 1997
Introduction
The pronounced and regular fluctuations in population
density that are observed for many small mammal species,
such as microtine rodents (e.g. Microtus, Lemmus and
Clethrionomys), have presented an enigma to biologists that
are trying to elucidate the causal mechanisms underlying
population dynamics (Stenseth & Ims 1993). Following
Elton (1942), an extensive number of hypotheses have
been put forward to explain both the fairly regular occurrence of density peaks, at intervals of 3–5 years, as well as
the large magnitude of peaks with abundances typically
representing an 25–200-fold increase (e.g. Krebs 1993). The
hypotheses for explaining these density fluctuations may
be classified as either extrinsic factor hypotheses, which
include both abiotic and biotic agents such as
Correspondence: J. E. Stacy. Fax: +47-22-85-46-05; e-mail: j.e.stacy
@bio.uio.no, K.S. Jacobsen, e-mail: kjetill.jakobsen@bio.uio.no
‡Present address: University of British Columbia, Department of
Zoology, 6270 University Boulevard, Vancouver, British
Columbia, Canada V6T 1Z4.
§Present address: School of Biological Sciences, University of
Wales, Bangor, Gwynedd LL57 DG, UK.
© 1997 Blackwell Science Ltd
predator–prey interactions (Hanski et al. 1993), and intrinsic factor hypotheses that assume density dependence in
reproduction, mortality, and/or dispersal, possibly
caused by physiological and behavioural responses to
crowding (for a summary of various hypotheses, see
Stenseth & Ims 1993, pp. 69–82).
Despite large research efforts made to discriminate
between the various proposed mechanisms for population
cycles, there is still no conclusive evidence that may settle
the controversy among different schools of small mammal
ecologists (Gaines et al. 1991; Stenseth & Ims 1993; Lambin
et al. 1995). One deficiency impeding the progress of most
studies is that the spatial scale of the phenomenon is not
treated explicitly. Almost all experimental and observational studies are conducted on study areas that rarely
exceed a few hundred metres, and comparative studies
have focused on differences in the population dynamics
between small areas separated at considerable distances
from each other (e.g., Hansson & Henttonen 1988; Bjørnstad
et al. 1995). Clearly, new insight into the dynamical processes may be gained by identifying the spatio-temporal
scale of the various population processes (Wiens 1989).
Here we report results from an ongoing project that
is specifically tailored to unravel the spatial scale of
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J. E. STACY ET AL.
population phenomena in the bank vole, Clethrionomys
glareolus, within the cyclic part of the species’ geographical
range (cf. Hansson & Henttonen 1988). The project
includes long-term measurements of population densities
in the field along a nearly linear ‘transect’ of 256 km
length. In a previous paper from this project (Steen et al.
1996), we analysed the spatial pattern of density fluctuations over the transect and found significant geographical
synchrony over a range of up to at least 30–40 km. In the
present paper we apply mitochondrial DNA (mtDNA)
analyses to bank voles collected from the same area as previously studied (Steen et al. 1996) and address the question
of how large the range of local populations of bank voles
are, and if dispersal (migration) within or among populations may account for the observed synchrony in density
changes. As dispersal is one of the principal mechanisms
proposed to underlie regular population fluctuations (the
intrinsic factor hypotheses: Lidicker 1975; Stenseth 1983;
Cockburn 1988; Bondrup-Nielsen & Ims 1988; Stenseth &
Lidicker 1992), the results have direct bearing upon the
long-standing problem of population cycles in small
mammal species.
Material and methods
Sampling
Bank voles were collected from a continuous boreal forest in
south-eastern Norway, between about 60–62 °N, uninterrupted by any obvious barriers to dispersal for this species.
A total of 31 sampling sites were spaced out with an average intersite distance of 8.5 km in a linear transect spanning
256 km (Fig. 1). Further details regarding the extent and the
location of the transect line and the spatial resolution of the
sampling effort are given by Steen et al. (1996).
To obtain an adequate sample for genetical analysis in
terms of quantity and quality of the material our withinsite sampling design differed from that of Steen et al.
(1996), which employed snap-trapping on fixed small
quadrats (Myllymäki et al. 1971) over a 5-year period
(1990–1994). The present sample was obtained during a
5-day period (in October 1993), simultaneous with the
small quadrat sampling. At each site parallel trap lines
were established on either side of the ‘transect road’ (the
sampling was aided by the use of a car). Five live traps
were spaced out at 40–50-m intervals in each of the two
traplines. The relatively large distance between the traps
and the separation of the two trap lines by a road (roads
are known to impede movements in small mammals;
Mader 1984) should diminish the influence of first-order
relatedness on the estimates of genetic variability components. Each site was trapped for 2 days and each half of
the transect was trapped simultaneously. Samples from
each site varied from two to 12 individuals (average five
individuals) and the total number of individuals included
in the analysis was 156. The two sexes were equally represented in the sample.
DNA analyses
From all sampled individuals we sequenced a 243
nucleotide portion of the mtDNA control region (D-loop),
according to standard procedures (Sequenase™, United
States Biochemical, Cleveland, OH, USA). (The consensus
sequence has been deposited in the EMBL database, with
accession number Y07543.) We applied PCR-mediated
sequencing Hultman et al. (1989), using control region
primer sequences 5’TCCCCACCATCAGCACCCAAAGC
and 5’TGGGCGGGTTGTTGGTTTCACGG, which are
similar to human primer positions L15997 and H16401,
respectively (Hopgood et al. 1992).
Statistical analyses
The amount and distribution of genetic variation in the
bank vole was characterized by the probability of geneidentity, Fd, within and between sample sites at various
distances, d, apart (in multiples of about 8.5 km; the average distance between adjacent sample sites); d ranging
from zero for individuals sampled at the same site to 30 for
individuals sampled from opposite ends of the transect.
The probabilities Fd were calculated by constructing a
gene-identity matrix of all pairwise comparisons among
individuals, consisting of 156 × 155/2 = 12 090 measures, f,
that equal 0 (zero) for pairs of individuals differing in at
least one nucleotide and 1 (one) for pairs with identical
mtDNA sequences. A distance matrix consisting of the
geographical distances (in units of 8.5 km) among all pairs
of individuals was also constructed and the Fd-values were
calculated as averages of f over all pairs separated by the
same distance d. This procedure is analogous to a spatial
autocorrelation analysis; the Fd values being directly
related to the usual Moran’s I statistics by Id = Fd/FST
(Barbujani 1987). We also calculated an average F over all
pairs of individuals from different sample sites (i.e. with
d > 0), regardless of their distances (Crow & Aoki 1984).
The averages for individuals at the same sites (F0) and at
–
different sites (F) were used to estimate Wright’s fixation
^
index (Wright 1951; Cockerham & Weir 1993), FS T = (F0 –
–
–
F)/(1– F). This quantity represents an estimate of the
degree of population subdivision relative to the limiting
amount under complete fixation, and provides an overall
measure of the degree of population subdivision over the
sampled area (Wright 1951, 1978). The statistical significance of the F-values were assessed from their 95%
confidence intervals, constructed from the 2.5% and 97.5%
percentiles of 5000 bootstrap samples (Efron & Tibshirani
1993, pp. 168–176).
© 1997 Blackwell Science Ltd, Molecular Ecology, 6, 751–759
GENE FLOW IN BANK VOLES
To check whether the observed population subdivision,
as measured by FST, is caused by a limited dispersal range
or arises from other causes, we used the information contained in the genealogical relationships among mtDNA
haplotypes and their geographical location, applying the
method described by Templeton and his co-workers
(Templeton et al. 1987, 1992, 1995).
Briefly, Templeton’s method consists of the following
steps. First, the evolutionary relationship among the
mtDNA haplotypes is estimated and used to construct a
cladogram (Templeton et al. 1992). As a preliminary step,
an estimate is made of the probability that reverse or parallel mutations will obscure the correspondence between
Fig. 1 Schematic representations of the
sampled area (upper left), with sample
sites enumerated (middle), a cladogram
over mtDNA haplotypes (bottom left),
and their geographical distribution
among sample sites (right). Major clades
are circled on the cladogram, and the
distributions of the clades’ member
haplotypes on the transect are ordered
into corresponding columns. Haplotype
occurrences along the transect are
represented by numerals, except in the
case of ancestral haplotypes, which are
represented by shaded rectangles, using
the same shades as in the cladogram (see
Table 1 for haplotype designations).
© 1997 Blackwell Science Ltd, Molecular Ecology, 6, 751–759
753
evolutionary relationship and sequence similarity. The
estimate is based on sample size and the number of polymorphic nucleotides relative to the total number of nucleotides examined (Hudson 1989). In our case, the estimate was
0.0482, and the null hypothesis of nonparsimony was therefore rejected at the 5% level of significance. Having
assured that evolutionary relationship will be reasonably
reflected by a parsimonious arrangement between haplotypes, a ‘single-step’ or ‘minimum-spanning’ network
was constructed (see Fig. 1). This was carried out by linking those haplotypes that differ by a single base, where
possible. Thirty-three of the haplotypes had a nearest
neighbour differing by only one base. The remaining six
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J. E. STACY ET AL.
Table 1 Observed mtDNA haplotypes in the
bank vole. Ancestral haplotypes are referred
to by letters and descendant types by Arabic
numerals. The nucleotide differences among
haplotypes are indicated relative to the most
common type (i.e. type a) by the position and
identity of the differing nucleotides. Thus,
‘31c’ indicates a mtDNA sequence that differs
from the common one at nucleotide position
31 in having a cytosine at this position, and so
on. n is the total number of individuals
observed for each haplotype and s is the
number of sample sites the type was observed
Haplotype
Nucleotide
differences
n
s
a
c
14
b
d
9
3
5
7
29
15
13
2
16
1
23
22
11
20
27
–
31c
105a172c
105a
230t
150c
132c
154t
15t
132c230t
104c105a
105a124g
118g
44g105a
88t
31c157g170a
31c157g
85c
31c128t
31c126t132c170a
34
13
17
10
7
4
4
4
4
3
6
5
3
2
6
4
3
2
2
2
19
8
6
6
4
4
3
3
3
3
2
2
2
2
1
1
1
1
1
1
differed from the next most similar haplotype by two
bases (haplotypes 12, 18, 24, 25, 26, 27) and were joined to
the network by the insertion of hypothetical intermediates (indicated by dotted boxes in Fig. 1). By this
procedure 12 equally parsimonious networks were
obtained, all indicating the occurrence of four major
groupings or clades of haplotypes (denoted A, B, C and
D). Also constant in each of the 12 alternative networks is
the root haplotype for each of the groups (‘ancestor’ haplotypes a, b, c, d). The differences between the alternative
networks are primarily to which of the major groups the
haplotypes 3, 18 and 29 are assigned, and for the most
part can be considered trivial in the present context. An
exception regards one of the possible arrangements
involving haplotypes 3, 29 and d, that places types 3 and
29 interior (and thereby ancestral) to type d, rather than
the other way around. In deciding among the 12 equally
parsimonious networks, we chose the one (Fig. 1) with
the least number of transversions relative to transitions
and that simultaneously resulted in the least number of
hypothetical intermediates.
In the second step, clades of haplotypes that differ by a
given number of base pairs (mutation steps) are identified
and grouped into one-step clades, two-step clades, and so
on, until the entire phylogenetic tree is included in a nested
set of clades (Templeton et al. 1987). In the present case most
haplotypes belong to either one of the main ancestors (a, b,
c and d), or differed from one of these by just one base pair
(cf. Fig. 1; Table 1). We chose to nest all the descendants
types within the same clade level, even if they differed by
Haplo- Nucleotide
type
differences
n
s
28
35
4
6
8
10
12
17
18
19
21
24
25
26
30
31
32
33
34
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
122t230t
132a193g230t
135t
132a
131c
150c242c
85c109c147c
44g105a170a
105a118g154t
31c149g
31c242c
31c126t127c
31c126t127c170a
126t127c170a228c
127c230t
193g230t
31c193g230t
44g193g230t
126t193g230t
more than one mutation step, and based the analyses on
four clades, A, B, C and D, that includes the respective
ancestral haplotype as well as their descendant types
(enclosed in circles in Fig. 1). This uniform treatment of haplotypes that differ by various numbers of mutation steps
obviously results in some loss of information, but leads to a
simplified analysis with more easily interpreted results and
avoids clade levels with few replicate observations.
Third, the geographical location of members of the various clades are tested for randomness (Templeton et al.
1995). This was carried out by first calculating the distances, Dc(anc) for ancestral types and Dc(desc) for descendant types, of all individuals of a particular haplotype
from the geographical centre of that type. Similarly, we
calculated the average distance of individuals to their
respective clade’s centre, and denoted these quantities by
Dc(clade). A second set of distances, Dn, was calculated as
the average distance of individuals of a particular haplotype from the centre of the clade to which they belong. The
tests for geographical association of haplotypes and clades
are based on comparisons of the observed Dc values
against those expected under the null-hypothesis of random geographical distributions. These expected values
were determined by random rearrangement (repeated
1000 times) of haplotypes against sample sites and calculating Dc for these randomized data (Templeton & Sing
1993). Finally, conclusions about general migration levels
were obtained by comparing the observed Dc and Dn values for each clade, and were based on the protocol of
Templeton et al. (1995, Appendix).
© 1997 Blackwell Science Ltd, Molecular Ecology, 6, 751–759
GENE FLOW IN BANK VOLES
Results
We found 39 distinct haplotypes among the 156 mtDNA
sequences analysed (Table 1). A total of 28 (11.5%) of the
243 nucleotide sites are variable, and individuals differ on
average in 2.3 nucleotide sites (range 0–7). The proportion
of individuals with identical mtDNA haplotypes (disregarding their geographical locations) is 0.0768. Thus, on
average for the region, two randomly chosen individuals
carry different mtDNA haplotypes with a probability of
about 92%. There was marked heterogeneity in the geographical distribution of the 39 haplotypes. One type (a)
was fairly widespread throughout the sampled area, being
observed at 19 of the 31 sample sites, whereas 25 haplotypes were found at one site only and 17 of these occurred
only once (Table 1). At three sites (site number 1, 10 and
13; Fig. 1) all sampled individuals had private haplotypes
that were not observed elsewhere. Although the sample
sizes are small, this observation indicates that the degree
of mixing of genes among neighbouring sites is low.
The geographical heterogeneity in haplotype occurrence is reflected in the considerably lower probability of
gene-identity for animals caught at different sample sites,
–
F = 0.0697, as compared with those from the same sites, F0
= 0.2879; there being essentially no overlap in the confidence interval for the within-site estimate and those for
greater distances (d > 0, Fig. 2). This observation clearly
indicates that the species is not panmictic throughout the
range, but, rather, substructured into local genetically differentiated populations. A quantitative measure of the
tendency for population substructuring is provided by
Wright’s fixation index, which was estimated as
^
FST = 0.234. This value is within the range of what is usually considered as moderate to strong differentiation
(Wright 1978, p. 85), and is comparable with those reported
Fig. 2 Probabilities of mtDNA haplotype
identity (Fd) among individuals separated
at various geographical distances (in units
of intersite distances of 8.5 km). Vertical
bars indicate 95% confidence intervals for
the point estimates, and were based on
5000 bootstrap replications.
© 1997 Blackwell Science Ltd, Molecular Ecology, 6, 751–759
755
for meadow voles Microtus pennsylvanicus at large geographical distances (Plante et al. 1989a).
The probabilities of gene-identity, Fd, varied markedly
with geographical distance, ranging from 0.2879 within
sites (d = 0) to 0.0000 for the most distantly separated sites
(d = 30, Fig. 2); a value of zero implying no common haplotypes among individuals at this distance. As is seen from
Fig. 2, the decline in Fd with increasing distance is very
rapid initially and drops to about half the within-site value
for nearest neighbours (F1 = 0.1582). At larger distances Fd
approaches a mean value of about 0.06 and from d = 3
onwards there is no indication of a further decline in Fd
with distance. Even for distances as small as d = 2 and 3,
corresponding to about 17–25 km, the observed Fds are
only marginally larger than those found at much greater
distances. At d = 4 (about 34 km) F4 = 0.0607, which is nearly identical to the average over larger distances. Thus,
while the previously mentioned demographic analyses
detected significant synchrony in population density
changes for distances up to at least 30–40 km (Steen et al.
1996), there is no indication for statistically detectable
amounts of mtDNA gene exchange at such distances.
The cladistic analysis revealed a strong association
between the geographical location of haplotypes and their
evolutionary relationship, as illustrated in Fig. 1. In this
figure, which depicts a minimum-spanning network providing an estimate of the evolutionary relationship among
haplotypes, four major branch-points are evident; rooted
by haplotypes a, b, c and d. These four ancestral types and
their respective descendant types (denoted with Arabic
numerals; see Table 1) evidently comprise four evolutionary lineages or clades, denoted A, B, C and D, respectively.
There are significant geographical associations of these
clades, with clades B, C and D clearly being confined to
limited portions of the transect (cf. Fig. 1, right-hand side).
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J. E. STACY ET AL.
Table 2 Measures of the geographical distribution of clades and haplotypes. Each line represent one clade, its ancestral haplotype (anc),
and descendant (desc) haplotypes (see Table 1 for haplotype designations). Dc(clade) is the average distance (in kilometres) of all members
of this clade from the clade’s geographic centre (i.e., its average position on the transect). For each haplotype, Dc represents the average
distance of individuals carrying a particular haplotype from that type’s geographic centre, and Dn that from the centre of the entire clade
to which the haplotype belongs. For descendant types, only the averages over all such types within each clade are given (values in parenthesis are the standard errors). Significantly small (s) or large (L) values, as determined from randomization tests, are indicated with asterisks (*P < 0.05; **P < 0.01)
Clade
Dc(clade)
Haplotype
Dc(anc)
Dn(anc)
Haplotype
–
Dc(desc)
–
Dn(desc)
Dc(anc) –
–
Dc(desc)
A
B
C
D
60.5s*
32.1s**
45.0s**
12.2s**
a
b
c
d
66.2
53.6
34.9s*
7.3s**
68.3
49.9
84.3
8.9s**
1–12
13–18
19–27
28–35
5.9 (10.4)
4.1 (4.5)
0.0 (0.0)
0.7 (2.0)
59.5 (37.7)
32.1 (22.5)
76.7 (56.0)
17.2 (15.9)
60.3 L**
49.5 L*
34.9
6.6s*
A quantitative representation of clade distributions,
Dc(clade), shows that the geographical association is statistically significant at the 5% level for each of the clades
(Table 2), and indicating restricted gene flow at the clade
level of resolution.
An association between geography and evolutionary
relationship is also evident at the level of the individual
haplotypes. The ancestral types a, b, c and d each occupy
a relatively large range, roughly equivalent to that of the
entire clade to which it belongs. This pattern is reflected in
similar values for Dc(anc) and Dn(anc), and in the similarity of these values to the Dc(clade) for the corresponding
clade (cf. Table 2). In contrast, descendant haplotypes have
very restricted distributions, and generally occur within
the range of their ancestor types (Fig. 1). The Dc(desc)
values for these haplotypes are therefore small, and significantly so for 16 out of 18 haplotypes (ignoring those that
were found in single copies only). This contrasting pattern
in the geographical distribution of ancestral and descendant haplotypes is quantified by the difference
––
Dc(anc) – Dc(desc) (Table 2, right column), which is significantly large for the two clades A and B, meaning that
the geographical range of the ancestral haplotype is significantly larger than the average for descendant types
within these two clades. The difference is not significant
for the two other clades, C and D; in fact, for clade D it is
significantly less than expected. One possible explanation
for this deviation is that clade D is located near one end of
the transect and may extend further outside the sampled
area (cf. Fig. 1). Another explanation could be that haplotype d is not really the ancestor of clade D, but, instead, a
descendant type (see above). However, because all
observed haplotypes in this clade have very limited geographical ranges, an erroneous assignment of ancestral
type for this clade should not affect the result to any great
extent.
A biological interpretation of the results from the
cladistic analyses is that the descendant mtDNA haplotypes represent fairly newly arisen mutants that have not
had time to spread far from their point of origin because of
restricted gene flow (dispersal) among populations. This
inference regarding limited gene flow is strengthened by
the fact that there are many (35) such descendant types,
and most of these (25) were observed at single sites only
(cf. Table 1). In contrast, the ancestral haplotypes have
wider, although fairly limited, geographical distributions,
and this observation is consistent with the fact that
ancestral types must be older than their descendants, and
therefore have had more time to diffuse throughout the
area. Note, however, that despite their greater age, two of
the ancestral haplotypes (c and d) still have significantly
restricted geographical distributions. This shows that diffusion of genes throughout the observed range must be
extremely slow and this observation is consistent with the
general finding of restricted gene flow and dispersal in
this species.
Discussion
We have shown that the bank vole displays a level of
mtDNA sequence variation appropriate for the elucidation of population structure at the sampling scale
employed. From the few comparable data sets published
(Prager et al. 1993; Jaarola & Tegelström 1995, 1996), it
appears that mitochondrial DNA variation in other small
rodents is at a similar level as observed for the bank vole,
suggesting that the approach used here might be widely
applied to studies of dispersal in other species too.
Because of the strict maternal inheritance of mtDNA
(Gyllensten et al. 1985), the observations reported herein
refer to females only and do not exclude the possibility
that males behave differently with respect to dispersal.
Indeed, direct observations of animal movements in the
field do indicate that males may have somewhat larger
dispersal ranges than females (Bondrup-Nielsen &
Karlson 1985; Steen 1994). Nevertheless, we conclude that
local populations of bank voles typically extend over areas
that are similar to, or less than, the average distance
© 1997 Blackwell Science Ltd, Molecular Ecology, 6, 751–759
GENE FLOW IN BANK VOLES
among sample sites in the present study (i.e. 8.5 km), and
that dispersal among populations is unlikely to generate
the observed geographical synchrony in density fluctuations (Steen et al. 1996). Below, we discuss the validity and
implications of this conclusion.
A key observation of this study is the finding that the
probability of gene identity, Fd, declines rapidly with distance for individuals farther apart than one sample site
(about 8.5 km). Even individuals from adjacent sites have
a considerably reduced probability (F1) of carrying identical mtDNA sequences as compared with those collected
from the same site (F0); the ratio of these probabilities
being F1/F0 = 0.55. Such a decline in genetic correlation
with distance resembles the situation expected under the
‘stepping-stone’ model of population structure (Kimura &
Weiss 1964), rather than, e.g. the more commonly assumed
‘island’ model (for a summary of population genetic
models see, e.g. Slatkin 1985). Tentatively assuming that
the two-dimensional stepping-stone model represents a
reasonable approximation for the bank vole populations,
the equilibrium expectation for the correlation (F1/F0 )
may be given as
1–
π
2
[ ln (
4
2µ
CD
m
)]
–1
(Kimura & Weiss 1964, equation 3.9), where m is the
migration rate and µ is the mutation rate. Substituting the
observed correlation (0.55) for this expectation results in
an estimate for µ/m of about 0.007. While the mutation
rate of the mtDNA D-loop region in voles is not known, it
seems reasonable to assume that it is similar to that for
hominids, which has been estimated to be about 7.5 × 10–8
per site per year (Tamura & Nei 1993). Extrapolating this
value to the full length of 243 nucleotides gives ^
µ = 6 × 10–6
per generation (assuming about three generations per
year), and leading to an estimated migration rate of
^ = 0.0008. This implies that less than one female
roughly m
out of 1000 is exchanged among neighbouring populations
per generation. While this estimate obviously is conditional on a number of assumptions, some of which
appears unrealistic for fluctuating vole populations (e.g.
Plante et al. 1989b), it does provide a strong indication that
the number of dispersing females is far too low to have
any noticeable effect on population density.
Regardless of what the exact value of the migration rate
might be, the observed rapid decline in genetic similarity
with distance is in marked contrast to the pattern of
geographical synchrony in population fluctuations. As
mentioned above, statistically significant synchrony in
population fluctuations was found for distances of at least
30–40 km (Steen et al. 1996), whereas we can find no indications of gene flow at such distances. Rather, the
© 1997 Blackwell Science Ltd, Molecular Ecology, 6, 751–759
757
observed Fd value at that approximate distance is well
within the range of those found for much greater distances. That Fd does not quite approach zero even for large
distances (except for d = 30) is due to the presence of haplotype a over almost the entire transect. This distribution is
likely a consequence of a fairly recent ancestry among
voles of the area and does not indicate long-distance
migration. Even quite low levels of long-distance migration seem unlikely in view of the marked geographical
clustering of the other haplotypes, including the three
ancestor types b, c and d.
The results presented here are based on the maternally
transmitted mtDNA genome. The comparison of demographic patterns with maternal genetics seems appropriate, because females are generally demographically ‘dominant’ to males (Charlesworth 1980), and this is
particularly so for Clethrionomys for which the local demographic process is regulated by female territory, dispersal,
and maturation (Stenseth 1985; Bondrup-Nielsen & Ims
1988). Only females that migrate and successfully reproduce, and thus deposit their mtDNA, would contribute
much to growth in the recipient population (Birky et al.
1983). Thus, the marked spatial pattern of mtDNA haplotypes implies a considerable demographic autonomy
among sites (Avise 1995). We conclude that dispersal is
unlikely to be the cause for the observed geographical
synchrony of population density fluctuations in the bank
vole, and possibly also in other small rodent species (Stacy
et al. 1994). Rather, external factors that are in common to
neighbouring vole populations, e.g. mobile predators or
other elements in the local environment, seem to be
required for these populations to grow and decline in a
synchronized fashion (Ims & Steen 1990). Such factors
have also been invoked as the possible cause of population
cycles in this and other species, and indicate that external
factors may be the common cause of both local and regional dynamics of boreal small rodent populations.
Acknowledgements
This study was supported by the Research Council of Norway
(NFR) through a research grant (no. 107622/420) to K.S.J. and a
NFR postdoc grant (no. 109332/410) to P.E.J., and by
Nansenfondet to R.A.I. and K.S.J. We thank Nils Chr. Stenseth for
valuable comments and support.
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This paper reports results of an ongoing collaborative work aimed
at understanding the population biology, phylogeny and evolution of small mammal species, and represents a part of John Erik
Stacy’s PhD dissertation. The laboratory analyses were carried
out at the DNA Laboratory for Systematics, Evolution and
Ecology at the University of Oslo.