OIKOS 95: 185–197. Copenhagen 2001
Mechanisms for delayed density-dependent reproductive traits in
field voles, Microtus agrestis: the importance of inherited
environmental effects
Torbjørn Ergon, James L. MacKinnon, Nils Chr. Stenseth, Rudy Boonstra and Xavier Lambin
Ergon, T., MacKinnon, J. L., Stenseth, N. C., Boonstra, R. and Lambin, X. 2001.
Mechanisms for delayed density-dependent reproductive traits in field voles, Microtus
agrestis: the importance of inherited environmental effects. – Oikos 95: 185 – 197.
Reproductive traits of voles vary with the phases of the population density fluctuations. We sought to determine whether the source of this variation resides in the
individuals or in their environment. Overwintering field voles (Microtus agrestis) from
two cyclic out-of-phase populations (increase and peak phases) were sampled in early
spring and bred in the laboratory for two generations under standardised conditions
with ambient light and temperature. Monitoring of the source populations by
capture-mark-recapture showed large differences in reproductive performance. In the
increase area, reproduction started six weeks earlier, the probability of maturation of
young-of-the-year was more than ten times higher during mid-summer, and reproduction continued nearly two months later in the autumn than in the peak area. These
differences were not found to be associated with a difference in age structure of
overwintered animals between the two areas (assessed by the distribution of eye lens
masses from autopsy samples). Although the population differences in reproductive
traits were to some degree also present among the overwintered animals in the
laboratory, we found no difference in reproductive traits in the laboratory-born
generations. There was a strongly declining seasonal trend in probability of sexual
maturation both in the field and in the laboratory under ambient light conditions.
However, in the field there were large population differences in the steepness of the
seasonal decline that were not seen under the standardised laboratory conditions. We
conclude that seasonal decline in maturation rates is governed by change in photoperiod, but that the population level variation in the shape of the decline is caused by
a direct response to the environment and not due to variation in any intrinsic state
of the individuals reflecting the environment experienced by the previous generation(s).
T. Ergon and N. C. Stenseth (correspondence), Di6. of Zoology, Dept of Biology, Uni6.
of Oslo, P.O. Box 1050, Blindern, N-0316 Oslo, Norway (n.c.stenseth@bio.uio.no). –
J. L. MacKinnon and X. Lambin, Dept of Zoology, Uni6. of Aberdeen, Tillydrone
A6enue, Aberdeen, UK AB24 2TZ. – R. Boonstra, Di6. of Life Sciences, Uni6. of
Toronto, 1265 Military Trail, Scarborough, ON, Canada M1C 1A4.
Since the first scientific description of small rodent
population cycles by Elton (1924), much variation has
been documented in the density fluctuations of different
populations. Through time-series analysis, this variation has been described in terms of differences in the
strength of direct and delayed density dependence on
population growth rate (Bjørnstad et al. 1995, Turchin
1995, Stenseth et al. 1996, Stenseth 1999). However, less
is known about the demographic mechanisms of the
regulation. Indeed, the ecological mechanisms of the
large variation in life histories of individuals within
many animal populations are poorly understood (McNamara and Houston 1996).
In fluctuating small rodent populations, there is profound between-year variation in body size, timing of
maturation and reproductive performance of individu-
Accepted 16 May 2001
Copyright © OIKOS 2001
ISSN 0030-1299
Printed in Ireland – all rights reserved
OIKOS 95:2 (2001)
185
als. In years with increasing population densities, overwintering animals generally start to breed earlier in the
spring and more animals mature in their year of birth
than in other years (Krebs and Myers 1974, Hansson
and Henttonen 1985, Bernshtein et al. 1989, Gilbert
and Krebs 1991, Boonstra 1994). This general life-history pattern seems to be a universal characteristic of
microtine population fluctuations and is also seen in
some non-cyclic but multi-annually fluctuating populations (Agrell et al. 1992).
If the properties of individuals vary in relation to
previous densities, there must be a ‘memory’ within the
system. This memory may reside in the environment
(interactions with predators, parasites or food resources) and/or within the population itself. The population may ‘remember’ past conditions in two ways.
First, environmental conditions changing over time
may affect demographic processes that alter the age
structure of the population, and this may in turn affect
future demographic rates. Boonstra (1994) and Tkadlec
and Zejda (1998) found that the demographic processes
commonly observed in fluctuating small rodent populations cause a shift in age structure towards older animals after peak population densities, and they argued
that declines become inevitable because of senescence
(i.e., the ‘senescence hypothesis’). Second, environmental conditions may cause a variation in the internal
states of the individuals, through genetic selection and/
or through persistent changes in the individuals’ physiological state. Assumptions of hypotheses for
population regulation involving fluctuating genetic selection that generates delayed density dependence in
reproduction, like Chitty’s (1960, 1967) polymorphic
genetic-behavioural hypothesis and its variants (Krebs
1978), have not been supported empirically, and intraspecific variation in life-history traits of small rodents does not seem to have an important genetic basis
(Boonstra and Boag 1987, Boonstra and Hochachka
1997). However, hypotheses involving time lags maintained by phenotypic changes and maternal effects have
been little explored until recently. The environment in
early life, including maternal effects operating during
gestation and lactation, is often an important determinant of life histories in mammals (Bernardo 1996,
Rossiter 1996, Inchausti and Ginzburg 1998), including
microtines (Boonstra and Boag 1987, Boonstra and
Hochachka 1997, Hansen and Boonstra 2000).
In this paper we examine the proximate causes of
variation in reproductive performance of overwintering
animals and their offspring in cyclic populations of field
voles (Microtus agrestis). We tested whether variation
in reproductive traits seen in the field can be explained
by mechanisms whereby the memory of past conditions
resides in the individual voles. Overwintering voles
from two areas that differed in previous densities were
bred under standardised conditions in the laboratory
alongside monitoring the source populations in the
186
field. A similar experiment was undertaken by Mihok
and Boonstra (1992) on a fluctuating population of M.
pennsyl6anicus. However, whereas Mihok and Boonstra
(1992) sampled voles from two different years (decline
and increase) in the same area, we sampled voles simultaneously from two cyclic out-of-phase populations.
First, we tested if variation in breeding performance
seen in the field was maintained under standardised
laboratory conditions. Secondly, we bred the voles for
two generations to test whether the population differences would be reinforced by genetic and/or maternal
effects. By comparing the age distributions through the
distribution of eye lens masses in autopsy samples (cf.
Hagen et al. 1980), we sought to assess the potential for
senescence in causing the population differences.
Material and methods
Study system
Field voles (Microtus agrestis) were sampled from the
Kielder and Kershope forests on the border between
England and Scotland (55°13% N, 2°33% W). These manmade conifer forests have been planted over the last 70
yr and now have a mosaic of clear-cuts that are separated by dense spruce forest. Voles only inhabit the
grassland clear-cuts, which are connected by road
verges and fire breaks throughout the forest. The
mesotrophic vegetation in these areas is dominated by
Deschampsia caespitosa, Holcus lanatus, Agrostis spp.
and Juncus effusus.
The two forests are in adjacent water sheds approximately 20 km apart within the larger (620 km2) forested
area. Long-term monitoring of field voles in Kielder has
shown cyclic population fluctuations with a 3–4-yr
period (Petty 1992, Lambin et al. 2000). Although
populations on nearby clear-cuts fluctuate in synchrony, out-of-phase populations are found within the
larger forest region. Kershope forest has been one year
ahead of Kielder since 1993 (Petty and Fawkes 1997,
Lambin et al. 1998). The voles at Kershope reached
high population densities in the autumn of 1996 and
maintained high densities in 1997, while the population
densities in Kielder were lower in 1996 but increased in
1997 (Fig. 1).
Sampling of parental animals for the laboratory
Voles for breeding in the laboratory and for autopsy
were sampled from nine clear-cuts within a 50-km2 area
in Kielder forest (‘increase area’) and three clear-cuts
1 –2 km apart in Kershope forest (‘peak area’) in two
different periods, 12 –24 March and 13–15 April 1997.
Voles were caught in Ugglan Special multiple capture
traps placed in active runways. Trapping continued on
OIKOS 95:2 (2001)
the sites for 2 –3 d to avoid sampling only the most
trappable animals. On capture, animals were individually marked with ear-tags, weighed and their reproductive status was noted. Pregnancy of captured females
was determined by swollen abdomen, parturition in the
laboratory, or by autopsy.
A random subset of the March sample was autopsied. For determination of relative age (see Hagen et al.
1980), eyes from autopsied voles (freshly sacrificed)
were fixed in 4% formaldehyde for one week and then
stored in 70% ethanol until being dissected and dried
for one week at 70°C. Eye-lenses were allowed to cool
to room temperature in a desiccator where they were
kept until being weighed as pairs on a Mettler™ M3
balance (precision 0.1 mg).
Laboratory procedures
While field sampling was taking place, animals were
kept in single-sex, multi-animal cages (up to 10 individuals per cage) after capture until being transported to
rearing facilities in Aberdeen, Scotland. In the laboratory, animals were caged in pairs in cages with sawdust
and placed in sheds with windows and no heating.
Hence, photoperiod and temperature varied with the
ambient conditions. Placement of the cages within the
laboratory was randomised.
Animals were fed on fortified cereal pellets (‘Rat and
Mouse No. 1’ from Special Diets Services; 14.7%
protein) and provided with hay for bedding and extra
food. Parental animals were also given apples and
carrots for the first two weeks until they learned to
drink from the water bottles. Each cage was supplied
with a wooden nest box and two water bottles. The
voles regularly chewed on the nest boxes, hence reduced
teeth wear was not a problem. After one of the authors
(TE) became infected with leptospirosis (Leptospira
saxkoebing), all animals in the colony were treated with
antibiotics (Tetramycine) in the first week of July.
Adults were weighed and checked for reproductive
status every two weeks. Cages with pregnant females
were checked every two to three days in order to
determine the exact date of parturition. Females were
classified as breeders if they gave birth in the laboratory
or were shown to be pregnant by autopsy. The males
were usually left in the cages with their mate during
pregnancy and after parturition, but when there was
shortage of sires, males were sometimes taken away to
be paired with another female.
Juveniles were separated from their mother at 18–20
d of age, and one female offspring from each litter was
selected at random for breeding in the next generation.
These females were paired with non-paternal, known
breeder, adult males from the parental generations. In
some cases, when excess males were available, two
females from the same litter were paired but only one
of them (drawn at random) entered the analysis if both
survived to parturition.
Population monitoring in the field
Fig. 1. Density trajectories at the sampling sites before (1996)
and after (1997) sampling voles to the laboratory. Densities
are plotted on a logarithmic scale. Filled symbols are average
density estimates of eight sites in Kielder forest (increase area),
and the open symbols are average estimates of two sites in
Kershope forest (peak area). The 1996 estimates from the peak
area are estimated from ‘Vole-sign indexes’ (Lambin et al.
2000) and the other estimates are estimated with closed capture-mark-recapture models by MacKinnon (1998). Error bars
indicate plus/minus standard error of the averages. Inset shows
long-term fluctuations in Kielder forest (from Lambin et al.
2000).
OIKOS 95:2 (2001)
Eight sites in the increase-phase area and two sites in
the peak-phase area were monitored before and after
sampling to provide estimates of density trajectories
and data on breeding and maturation in the field (the
eight increase sites were part of another study, MacKinnon 1998). At each site, a 0.3-ha live-trapping grid
was established, consisting of 100 Ugglan Special
Mousetraps in a 10×10 configuration with 5-m spacing. The trapping regime followed the ‘robust design’
(Pollock 1982) with primary sessions at 27–31-d intervals consisting of six secondary sessions over 2 d. The
first trapping session was at the end of March in the
increase area and in mid-April in the peak area. Traps
were pre-baited for 3 d, set between 06:00 and 08:00
and checked three times per day at 4-h intervals. The
traps were not set overnight. Individuals were marked
with a pair of uniquely numbered Hauptner™ ear-tags.
The first time an animal was caught in each primary
session it was weighed and checked for reproductive
state.
187
Estimating timing of reproduction and maturation
rates in the field data
In order to compare the pattern of maturation in the
laboratory with maturation rates in the field, we employed multi-state capture-mark-recapture models
(Hestbeck et al. 1991, Brownie et al. 1993) in the
program MARK (White and Burnham 1999, White
2001). Males were classified as reproductive if they had
scrotal testes. Females were classified as reproductive if
they were lactating, if their pubic symphysis or vaginal
opening indicated that they had recently given birth, or
if their nipples indicated that they had recently weaned
young. We primarily wanted to estimate maturation
rate, and not the rate at which reproducing animals
cease reproducing for the season. Hence, animals were
classified as reproductive if they had been captured in
reproductive condition in an earlier session, and the
parameter in the likelihood function representing the
transition probability from reproducing to non-reproducing state was fixed to zero.
Data from each trapping site were fitted with a
separate model, and the most parsimonious models
were selected based on Akaike’s Information Criterion
adjusted for sample size, AICc (see Burnham and Anderson 1998). The seasonal variation in maturation
probability of young-of-the-year females was modelled
as a logit-function of capture date, assuming a
monotonic decline in maturation probability over the
season. This assumption was assessed graphically, and
a complete time specific structure never increased the
parsimony (i.e., never lowered the AICc) of the selected
models. To simplify the model selection procedure, we
started with a model structure for apparent survival (f)
and recapture probability (p) that was found to be most
parsimonious in standard Cormack-Jolly-Seber models
without multiple states. A general model for maturation
probability (different slope and intercept for the two
sexes) was then used to determine whether additive or
interacting effects of reproductive state on f and p
would increase the parsimony of the models. With the
new model for f and p, we then searched for the most
parsimonious model for the maturation probability.
Alternative models for f and p were then again tested
to ensure that the most parsimonious model had been
found.
Since almost all overwintered animals matured during the first one-month interval between trapping sessions (April –May), we could not estimate the timing of
onset of spring reproduction from the capture-mark-recapture data. To compare the onset of spring reproduction between the areas we relied on data on the
frequency distributions of reproductive states of captured females. We also compared the seasonal patterns
of reproduction by studying the recruitment of juveniles
to the trapping sites.
188
Statistical analysis
As animals taken to the laboratory were sampled from
a small number of sites within only one year and
geographical region, we stress that the sampled individuals do not represent a random sample from particular
‘phases’ of the cycles as such, but rather from two areas
having experienced contrasting densities in the previous
year. Phase dependence is a likely cause of the population (area) differences, but we cannot make statistical
inferences about the area differences. Our intention is
to test whether population differences in the field were
maintained under standardised conditions in the laboratory. Hence, in the analysis of the laboratory data we
considered all sampled animals within each of the populations and sample periods as independent, and compare the population differences in the field with the
population differences in the laboratory at the level of
the individuals (not sampling sites).
Each response variable was investigated by two different models. First, to assess differences in population
means, models with only population and sampling time
were used. Second, models including intrinsic state variables (body mass and reproductive state, i.e., pubic
symphysis, perforate/non-perforate vagina and pregnancy status) were used to search for underlying mechanisms behind the differences.
There was high mortality among the parental generation in the laboratory. A total of 69.5% (n = 141)
‘increase-area females’ and 41.6% (n = 113) ‘peak-area
females’ died before they were paired or within 12 d
after pairing. As the animals were transported and kept
in multi-animal cages prior to pairing, and a possible
disease may have been transmitted between individuals,
these differences in mortality in the laboratory may
have little relevance to the wild populations because
individuals were not treated independently. However,
females of the two populations were very different in
their initial states, so care must be taken to avoid
erroneous inferences due to experiment-induced bias
caused by selective mortality. In order to reduce possible biases when testing for population differences in the
laboratory, each observation was weighted with the
inverse of the estimated probability of survival in the
estimation (see Littell et al. 1996). Hence, the sum of
weightings of animals of a particular characteristic divided by the total sum of weightings for all animals will
be the same among the survivors as in the original
sample (where all have equal weightings). Since the
groups differed greatly in intrinsic state variables, survival probabilities used to calculate weightings were
estimated with separate logistic models for each population and sample period, and the best models were
selected by the lowest AIC. These models are given in
Table 1.
As we did not have any prior knowledge of the
importance of maternal effects, we considered models
OIKOS 95:2 (2001)
Table 1. Survival models (logit[probability of survival], binomial error distribution) used to calculate the weightings used to test
for differences between samples of females. 95% confidence intervals of the parameters are given in brackets. Models were
selected according to the lowest AIC. Weightings for each observation were calculated as the inverse of the predicted survival
probability (see Material and methods), and the ratios between maximum and minimum weightings in each group are given in
the righthand column.
Sample
Peak area – March
Increase area – March
Peak area – April
Increase area – April
Intercept
0.41 [−0.02, 0.85]
2.36 [−1.63, 6.74]
Pregnant= ‘no’
Capture body mass
(g)
% survived of
total
Ratio max/min
weights
−1.76
[−3.42, −0.30]
−0.08 [−0.22, 0.03]
60% of 85
24% of 91
1.0
3.7
+0.21 [0.08, 0.39]
53% of 28
42% of 50
1.0
6.4
0.14 [−0.60,0.90]
−6.30 [−11.32, −2.62]
both with and without weighted observations in the
laboratory-born generations and report the most conservative result. For logistic and log-linear models, we
applied quasi-likelihood techniques implemented in
the GLIMMIX-macro (version 30 April 1998) in SAS
(Littell et al. 1996). When using weighted observations in GLIMMIX, the result is independent of the
scale of the weights.
Terms were included in the statistical models if
they reduced the AIC value (Akaike 1985). Tests of
effects when all other selected terms are included in
the model (type-III tests) are presented. All date variables were centralised by subtracting the mean value.
Litter size had generally larger variance with increasing expected value. Hence, litter size was modelled
with log-linear models (Poisson distributed error and
log-link), which proved to give reasonable fits (Pearson residuals).
Fig. 2. Cumulative average of new litters per trapping site in
the increase-area sites (solid line) and the peak-area sites
(stippled line). Date of birth of captured juveniles was estimated from their body mass using estimated growth curves of
juveniles in the laboratory. Captured juveniles were grouped
into presumptive litters based on their body mass and location
at capture.
Results
Reproductive traits and demography in the field
The voles at Kershope reached high population densities in the autumn of 1996 and maintained high densities in 1997, while the population densities in
Kielder were lower in 1996 but increased in 1997
(Fig. 1). To increase readability we will in the following refer to Kershope as the ‘peak area’ and Kielder
as the ‘increase area’. The first spring born juveniles
appeared more than six weeks earlier in the increase
area than in the peak area (Fig. 2). The large difference in the onset of reproduction is also evident from
the distribution of body mass and reproductive state
of the overwintered animals taken to the laboratory
in both March and April (see below). Young-of-theyear in the increase area continued to mature later in
the season (Fig. 3), and reproduction continued for
nearly two months later in the autumn than in the
peak area (Fig. 2).
OIKOS 95:2 (2001)
Fig. 3. Estimated probability that an immature female at
various dates will reach maturity during the next 30 d. The
figure shows the arithmetic mean9SE of estimates from the
eight increase-area trapping sites (filled squares) and the two
peak-area sites (open circles). The estimates were obtained by
multi-state capture-mark-recapture models (see Material and
methods).
189
Only the largest peak-area females had an estimated
breeding probability equal to the increase-area females
(Fig. 7).
Increase-area females were still heavier after parturition than peak-area females (weighted linear model,
population effect: F=9.05; 1, 82 df; p=0.004; 95% c.i.,
increase area: 38.391.2 g, peak area: 35.59 1.4 g).
Thirty of the breeding peak-area females and 27 of the
increase-area females had a mate at parturition of the
first litter, and hence the opportunity to re-conceive. All
of these females became pregnant except for four increase-area females that only had a mate for 4 –10 d
after giving birth to their first litter. Time between first
and second parturition ranged from 19–42 d (mean=
21.2 d) but there was no significant pattern in this
variation.
Fig. 4. Absolute frequency distributions of eye lens masses
(mg per lens) of autopsy sample from March. Hatched bars
are females, open bars stacked on top are males. There are no
significant population or sex differences.
Initial characteristics of the laboratory sample
Distributions of eye lens mass (an index of age) from
the autopsy (Fig. 4) were not significantly different
between the two populations (two-sample KolmogorovSmirnov tests, p\0.4). However, increase-area animals
of both sexes had generally larger body mass (Fig. 5)
and many females had already started to reproduce in
the increase-area sample (Table 2).
Litter size
Increase-area females had on average larger litters (least
square mean= 4.5; 95% c.i. [4.2, 4.9]) than did peakarea females (least square mean=4.0; 95% c.i. [3.6,
4.4]; weighted log-linear model, population effect: x2 =
4.04, 1 df, p = 0.044; sampling month: x2 = 2.83, 1 df,
p = 0.093). Animals with closed pubic symphyses when
captured had smaller litters (mean= 3.9; 95% c.i. [3.6,
4.2]) than those with open pubic symphyses when captured (mean=5.2; 95% c.i. [4.6, 6.0]; log-linear model:
x2 = 14.4, 1 df, p=0.0002). When state of pubic symphysis was included in the model, the population effect
was no longer significant (x2 = 0.99, 1 df, p= 0.32;
interaction effect: x2 = 2.75, 1 df, p=0.10). The size of
litters conceived in the laboratory was also related to
Breeding performance of the overwintered
generation
Frequency and timing of breeding in the lab
In the laboratory as in the field, the overwintered
animals from the two populations showed different
patterns of reproduction. A higher proportion of the
increase-area females conceived and the increase-area
females had shorter average time between pairing and
parturition (26 vs 32 d) than the peak-area females
(Fig. 6a; weighted Cox proportional hazard regression,
population effect: Z=2.44, p=0.015). Excluding nine
females that died within 12 d after pairing, 95% of the
increase-area females vs only 77% of the peak-phase
females either gave birth or showed signs of pregnancy
when autopsied (weighted logistic model, population
effect: x2 =6.40, 1 df, p=0.013). Non-breeding females
lived up to 205 d (1st to 3rd quartile: 32 to 111)
together with their mate. Breeding probability of peakarea females increased significantly with body mass at
capture (logistic regression: x2 =5.15, 1 df, p= 0.023).
190
Fig. 5. Body mass distributions of sampled animals grouped
by sex, month sampled and population. Both data from autopsy sample and animals used in the laboratory experiment
are used in the March samples. Box-plots show median and
inter-quartile distance. Whiskers show 1.5 times inter-quartile
distance (approx. 95% of data) and outliers are plotted as
horizontal lines. There are highly significant differences in
distributions between the populations in all four groups (Kolmogorov-Smirnov two-sample tests, p B0.0001), also when
excluding all pregnant females (March: p B 0.0001, April: p =
0.011).
OIKOS 95:2 (2001)
Table 2. Reproductive state of sampled females. The March sample includes animals sampled for autopsy as well as those
brought into the laboratory. The April sample is only animals sampled for the laboratory. p-values were calculated using
Fisher’s exact test.
March sample
Pregnant
Nipples
Small
Lactating
Post-lact.1
Perforate vagina
Pubic
Closed
symphysis
B2 mm
\2 mm
Mature uterus2
(B0.5 mm diameter)
1
2
April sample
Peak area
n= 123
Increase area
n= 118
p-value
Peak area
n =29
Increase area
n =55
p-value
2.4%
100.0%
28.0%
88.5%
B0.0001
6.9%
92.6%
32.7%
58.3%
0.008
0.0%
0.0%
7.8%
89.5%
10.5%
0.0%
60.5%
(n= 38)
0.03%
8.2%
52.7%
53.8%
38.7%
7.5%
81.5%
(n=27)
0.001
0.0%
7.4%
37.0%
88.9%
11.1%
0.0%
29.2%
12.5%
60.4%
68.1%
21.3%
10.6%
0.005
B0.0001
B0.0001
0.059
0.078
0.10
Reproduced earlier in life.
Data on uterus are only from the autopsy sample.
pregnancy status at capture in the increase-area sample
(there were very few pregnant females in the peak-area
sample, see Table 2); mean of 5.2 (95% c.i. [4.4, 6.1])
among those pregnant at capture versus 4.1 (95% c.i.
[3.6, 4.7]) for those not pregnant at capture. Litter size
of those females that re-conceived in the laboratory
increased significantly from first to second parturition
(average increase: 1.1 pups; Wilcoxon matched pairs
test: Z=4.30, pB 0.0001). Together, these results suggest that the population difference in litter size may be
due to a parity effect, as a larger proportion of the
increase-area females had previously bred in the field.
Breeding performance of the laboratory-born
generations
As there was large variation in date of birth of the
laboratory-born females, and since date of birth differed between the two populations, date of birth was
used as a covariate in all further analysis.
Body mass at weaning
Body mass at weaning (18–22 d old, age included as a
covariate in the model) of the first laboratory-born
generation (F1s) generally decreased with increasing
number of littermates (95% c.i. − 0.7390.69 g per
additional littermate) and increased with date of birth
(95% c.i. 0.09 90.05 g d − 1). However, there was no
difference between the two populations (model including littermates and date of birth, population effect:
F =1.16; 1, 82 df; p= 0.28, alone: F=1.68; 1, 84 df;
p=0.20).
In the second lab-born generation (F2s), body mass
at weaning decreased with increasing date of birth (95%
c.i. −0.1690.08 g d − 1), and estimated weaning mass
was 2.47 g ( 92.12 g; 95% c.i.) higher for the peak-area
OIKOS 95:2 (2001)
females than for the increase-area females born on the
same date.
Frequency and timing of breeding – F1s
Four of the 49 breeding peak-area parental females did
not wean any daughters, and a further two of the
peak-area F1-females died shortly after pairing. Of the
remaining F1 females, 49% (n= 41) of the increase-area
females and 55% (n=43) of the peak-area females
conceived (revealed by parturition or autopsy) in the
laboratory (Fisher’s exact: p =0.7). There was no significant population effect on breeding pattern (Fig. 6b,
Cox proportional hazard regression, population effect:
Z =0.12, p= 0.9). However, date of birth under the
ambient photoperiod conditions had a highly significant effect on breeding probability (logistic regression
model: x2 = 16.05, 1 df, pB0.0001). Females born early
in the season had a much higher estimated probability
of becoming a breeder than those born late in the
season (Fig. 8a).
Frequency and timing of breeding – F2s
In the F2 generation, four of the 21 (19%) increase-area
females versus seven of the 24 (29%) peak-area females
conceived (Fisher’s exact test: p= 0.5). As in the F1
generation, there was a decreasing probability of breeding for F2 females born later in the season (Fig. 8b;
logistic regression model: x2 = 4.77, 1 df, p=0.029),
but there was no significant population effect on the
breeding pattern (Fig. 6c; Cox proportional hazard
regression, population effect: Z = 1.03, p= 0.3). However, a different pattern in timing of breeding was
generally seen in the F2 generation than in the F1
generation. Only one F2 female raised pups before
mid-August, even though they were paired 50–95 d
earlier. This female had only one pup in mid-July,
whereas all other females had three pups. The five
191
earliest born F2 females had their first litter killed by
one of the parents, but all except one of these pairs
raised young later in the season. Infanticide of own
litters occurred only twice among the 90 breeding
parental females and never among the 44 breeding F1
females.
Non-breeding females remained small throughout the
summer, and by 1 August they were significantly
smaller (mean =22.8 g, SD=3.1) than breeding fe-
Fig. 7. Estimated probability that females bred in the laboratory given that they survived. The broken line and open
symbols are peak-area females, while the solid line and symbols are increase-area females. Observations are symbolised
with circles above (breeders) and below (non-breeders).
males (mean= 28.0, SD= 2.4, one pregnant female
omitted; two-tailed t-test: p B0.0001).
Litter size in the laboratory generations
There was no significant difference in average litter size
of F1s between the two populations (increase area 3.9
vs 4.1 for peak area; x2 = 0.15, 1 df, p= 0.7). All
females in the F2 generation had three offspring except
one increase-area female having only one pup.
Cross-generational correlations
Despite large variation in reproductive traits within
both the parental generation and the first laboratoryborn generation, there was no indication of any strong
effects carrying over between the generations (Table 3).
The weak correlation between time before parturition
in the parental generation and the pre-weaning growth
rate of their daughters (Table 3) may be a date (photoperiod) effect. Heavier animals at capture also tended
to be heavier after giving birth, and laboratory-born
animals with higher pre-weaning growth rate also
tended to be heavier after parturition.
Fig. 6. Maturation in a) the parental generation, b) the F1generation, and c) the F2-generation. Figures show the cumulative daily probabilities (Kaplan-Meier estimates) of giving
birth for peak-area (stippled lines) and increase-area (solid
lines) females. Crosses indicate censored animals; i.e, animals
that either died, lost their mate before maternity, or had still
not reproduced by the end of the study. There is a significant
difference between the curves only in the overwintered
parental generation (see text).
192
Discussion
We have demonstrated large variation in the seasonal
trends in reproductive traits between female field voles
from two nearby cyclic populations that had experienced contrasting densities in the previous year. However, except for the parental laboratory generation,
reproductive traits varied only seasonally under stanOIKOS 95:2 (2001)
dardised laboratory conditions. Hence, the large population differences in maturation rates that we found in
the field are unlikely to have been caused by maternal
or genetic effects, as assumed by the maternal effects
hypothesis for population cycles (Boonstra and Boag
1987, Boonstra and Hochachka 1997) and Chitty’s
fluctuating selection hypothesis (Chitty 1960, 1967).
Although we cannot make statistical inferences about
the phase of the cycles from this study, the reproductive
traits that we have been investigating (onset of spring
reproduction and maturation of young-of-the-year) are
known from many studies to vary consistently with the
phase of small rodent cycles (see review in Krebs and
Myers 1974, Hansson and Henttonen 1985, Bernshtein
et al. 1989, Gilbert and Krebs 1991, Boonstra 1994).
Fig. 8. Estimated breeding probability (solid line) as a function of date of birth of laboratory-born females; a) F1s, and b)
F2s. Broken lines show the 95% confidence limits of the
estimate. Observations are symbolised with circles above
(breeders) and below (non-breeders); open circles represent
peak-area females, while solid circles are increase-area females.
All breeding F1 females bred before the summer, while all but
one of the breeding F2 females postponed reproduction until
after the summer (see text for details).
OIKOS 95:2 (2001)
Our field observations are in agreement with these
general findings; later onset of spring reproduction and
‘poorer’ reproduction when the populations have gone
through high densities.
Overwintered animals – does the memory for
delayed density dependence reside in the individual
voles?
Onset of spring reproduction in the field was more than
a month later in the peak area than in the increase area.
The large differences between the sites are shown by
body mass distributions, frequency of reproductive
states among overwintered animals when sampled and
the time that the first juveniles in the spring appeared.
Such differences in onset of reproduction were also
found under the standardised laboratory conditions.
Increase-area animals had also a larger proportion of
breeders as well as larger litters in the laboratory. Our
evidence indicates that the latter may be due to the fact
that more animals in the increase area had previously
reproduced in their lives.
Although the population differences in onset of reproduction were much smaller in the laboratory than in
the field, the clear differences between the populations
seen in the laboratory must represent different states of
the animals when they were sampled. These initial state
differences may have resulted in larger differences in
reproductive performance in a natural but shared environment (or with a different experimental protocol).
However, it is not clear from our experiment at what
stage the individuals’ internal state diverged between
the populations. On the one hand, the state differences
may have been shaped early in the life of the individuals or resulted from genetic differences, in which case
there would be a time-lag residing in the individuals.
Hansson (1989) reported that laboratory-born Microtus
agrestis started to breed two months earlier in the
spring and had a higher frequency of winter breeding
under laboratory conditions with ambient light than
wild-born females living under the same conditions.
This suggests that the environment in early life may be
an important determinant of onset of spring reproduction. On the other hand, since some of the voles had
already started to reproduce in the increase area when
they were sampled in the field, it may also be that the
increase-area animals had reacted to some cue in the
environment just before sampling, and that the peakarea animals had not yet received this stimulus. The
fact that the smallest peak-area animals failed to breed
in the laboratory supports the latter suggestion – that
the smallest animals may not yet have received an
environmental stimulus to initiate spring growth and
reproduction.
Boonstra (1994) and Tkadlec and Zejda (1998) found
that low juvenile recruitment during the peak phase of
193
Table 3. Spearman rank correlations between reproductive traits of females within and across the parental (mothers) and the
first lab-born (daughters) generation. The top number is the correlation coefficient for both populations pooled, the middle is
for the peak-area population, and the lower number is for the increase-area population. Asterisks indicate significance levels;
*: pB0.05, **: pB0.01, ***: pB0.001.
Pooled peak
increase
Parental generation
Time before Litter
parturition
size
Parental
generation
Mother body Mother body Pre-weaning Age at first Litter
mass at
mass after
growth rate parturition size
capture
parturition
Litter size
−0.42***
−0.38**
−0.36*
Mother body
−0.23*
mass at capture −0.19
0.05
Mother body
−0.01
mass after
0.02
parturition
0.11
F1 generation Pre-weaning
0.25*
growth rate
0.13
0.34
Age at first
0.20
parturition
0.22
0.30
Litter size
−0.25
−0.29
−0.24
Mother body
0.19
mass after
−0.13
parturition
0.36
0.30*
0.34*
0.02
0.26*
0.31*
0.15
−0.35**
−0.30
−0.33
−0.17
−0.08
−0.26
0.01
0.15
−0.16
−0.01
0.53*
−0.42
0.37***
0.19
0.39*
0.04
0.07
0.11
0.18
0.21
0.10
−0.08
−0.21
0.02
0.21
0.38
0.40
the cycles causes a shift in age structure toward older
animals, and they argued that poor reproduction of
overwintered animals in the decline phase is due to
senescence. We assessed differences in age structure
between the two populations in our study by comparing
the distributions of eye lens masses (Hagen et al. 1980),
but found no evidence for a difference in age structure.
Hence, there was no support for the idea that senescence was responsible for the large differences in onset
of reproduction and reproductive performance in this
study. However, since the relationship between eye lens
mass and age flattens out with increasing age, detecting
differences in the prevalence of older age classes may be
difficult.
Laboratory-born animals – there is no support
for maternal or genetic effects
Although the parental generation showed clear differences in reproductive performance in the laboratory,
these differences did not carry over to the laboratoryborn generations. In fact, despite large variation in
reproductive traits in all generations, there were no
convincingly strong cross-generational correlations for
any trait. Thus, maternal and genetic effects are not
major sources of variation in reproductive traits of
these cohorts under the laboratory conditions. Hence,
the notion that maternal effects are important determinants of demographic traits in small rodent populations
194
F1 generation
0.07
0.08
0.13
0.11
0.24
0.00
0.06
−0.05
0.07
0.38*
0.34
0.43
−0.12
−0.09
−0.14
0.07
−0.04
0.44
0.51*
0.25
0.84***
−0.12
−0.24
0.05
−0.09
0.05
−0.19
0.43*
0.51*
0.38
in general (Boonstra and Boag 1987, Boonstra and
Hochachka 1997) is not supported by our study. Boonstra and Hochachka (1997) found strong maternal influence on growth rate and age at maturity of collared
lemmings (Dicrostonyx groenlandicus) under laboratory
conditions. However, such effects remain to be demonstrated under natural conditions.
The most prominent patterns seen in the laboratoryborn generations were the differences between the generations and the changes over the season. In both of the
laboratory-born generations, the estimated probability
of breeding declined with date of birth of the female.
All F2 females that raised any pups (except for one
female having a single pup) did not do so until end of
August to end of September. The only environmental
change over time in the laboratory was that of seasonal
change in ambient light conditions and temperature.
Change in photoperiod is known to be an important
cue for reproductive regression and inhibition of maturation before the winter in several species of voles
(reviewed in Bronson and Heideman 1994) including
field voles (Spears and Clarke 1988). However, regulation of reproduction by natural change in photoperiod
during mid-summer has, to our knowledge, not been
previously described.
The seasonal decline in maturation seen in the laboratory closely resembles the pattern seen in our livetrapping sites, especially in the peak area where
maturation ended early in the season. Spears and
Clarke (1988) suggested, based on their findings of
OIKOS 95:2 (2001)
strong heritability of photo-responsiveness on maturation of juvenile field voles, that phase-dependent variation in the seasonal reproductive pattern in cyclic vole
populations may be due to genetic differences (see also
Nelson 1987, Bronson and Heideman 1994). However,
in our study, the large season-specific difference in
maturation between the areas was not maintained under the standardised laboratory conditions. This suggests that phase-dependent variation in juvenile
maturation is not genetically based but rather caused
by differences in the environment.
Mihok and Boonstra (1992) reported that female
meadow voles (Microtus pennsyl6anicus) from a low
year after a severe winter decline had a much poorer
breeding performance under laboratory conditions than
females sampled during a strong increase the following
year. In their study, however, low-year animals were
sampled in early June and July (50/50 overwintered/
young-of-the-year), whereas the increase-year animals
were sampled at the start of the breeding season in May
and had all overwintered. In the light of our own
results, it is possible that the difference in reproductive
performance observed by Mihok and Boonstra (1992) is
due to seasonal effects rather than to phase-dependent
differences.
Possible environmental effects on reproductive
traits in voles
Although deterministic seasonal change in photoperiod
can alone cause the seasonal trends in maturation probability, environmental mechanisms must be invoked to
explain the large between-year/site variability in life
histories of cohorts born in mid-summer cohorts which
we found in this study.
Social constraints may affect juvenile maturation in
the field (Krebs and Myers 1974, Morris 1989, Pusenius
and Viitala 1993, Boonstra 1994). At the beginning of
the year, there were differences in density between our
two study areas, so the differences in maturation rate
may partly have been due to direct density-dependent
social suppression of maturation (e.g. Boonstra and
Rodd 1983, Rodd and Boonstra 1988, Boonstra 1989,
Gilbert and Krebs 1991). However, the difference in
maturation frequencies was largest late in the year
when the densities had more or less converged. Nor can
social suppression of breeding explain why all overwintered animals in the peak area delayed maturation until
late in the spring. The above points to the importance
of extrinsic effects.
Suppressed reproduction and delayed maturation in
microtines have been suggested to be an adaptive response to high risk of predation, and several laboratory
studies have found strong effects of mustelid odours on
reproduction (Ylönen 1989, Korpimäki et al. 1994,
Koskela and Ylönen 1995, Mappes and Ylönen 1997).
OIKOS 95:2 (2001)
However, such an effect may be a laboratory artefact
with little relevance for natural populations (Hansson
1995, Lambin et al. 1995, Korpimäki and Krebs 1996).
A recent field experiment failed to find any effects of
mustelid odours despite simulating very high predator
density, which led the authors to conclude that their
previous findings in the laboratory had little relevance
(Mappes et al. 1998).
Feore et al. (1997) found that non-lethal cowpox
infections in bank voles (Clethrionomys glareolus) and
wood mice (Apodemus syl6aticus) increased the time
before first litter under laboratory conditions. Such
effects may be profound under natural conditions and
may have interacting effects with, e.g., food availability.
Cowpox is highly prevalent in our study system (Cavanagh et al. unpubl.).
Food quality and/or availability directly affect the
physiological state of the individuals and may cause a
delay in maturation (Berger et al. 1977, 1981, Gallaher
and Schneeman 1986, Andreassen and Ims 1990, Ryan
1990). Negus et al. (1977) provide evidence that considerable variation in onset of reproduction in M. montanus between years may be related to the availability of
green plants in the spring, which in turn is related to
the time of snow melt. The phenolic plant compound,
6-methoxy-2-benzoxazolinone (6-MBOA), which is
present in all growing grasses and sedges but has no
nutritional value (Moffatt et al. 1991, Nelson 1991), is
known to enhance gonadal development and trigger
reproduction in several north-American Microtus species (Sanders et al. 1981, Korn and Taitt 1987, Gower
and Berger 1990, Nelson and Blom 1993, Meek et al.
1995). Korn and Taitt (1987) found that Microtus
townsendii in natural populations that were fed with
oats coated with 6-MBOA started to breed four weeks
earlier than voles in a control site 200 m away where
oats coated with the solvent only were provided. Several mechanisms may cause a delayed response in food
quality and/or availability to earlier grazing; e.g. quantitative reduction and slow recovery (Kalela and Koponen 1971, Oksanen and Oksanen 1981, Moen et al.
1993), depletion of the plants’ stored resources in the
roots or altered allocation of resources to growth and
defence (Herms and Mattson 1992), and induced plant
resistance (Karban and Baldwin 1997). It is also well
known from agricultural science that harvesting or
grazing in a critical period in the autumn can interrupt
cold acclimation, and thereby strongly reduce tiller
survival and grass production in the following spring
(Sheaffer and Marten 1990, Sheaffer et al. 1992, Shimada 1994). Bergeron and Jodin (1993) found a 52%
reduction of green biomass in the spring after manipulating high densities of Microtus pennsyl6anicus in enclosures the previous year.
To understand the mechanisms of population fluctuations of microtines, there is clearly a need for both
better descriptions of the direct and delayed density-de195
pendent structure of variation in life-history traits, as
well as studies on the individual level responses to the
environment. Diseases and food quality/availability
may impose severe constraints on the individuals’ performance, and they therefore represent plausible agents
to cause variation in reproductive traits. However, the
role of diseases and food quality/availability in the
population dynamics of microtines is as yet largely
unexplored.
Acknowledgements – The laboratory part of this work was
funded by a special grant from the Univ. of Oslo to NCS and
by support from the Natural Sciences and Engineering Council
to RB. The field monitoring was supported by a studentship
(to JLM) and grants from Natural Environmental Research
Council, UK to XL. The Centre for Advanced Study of the
Norwegian Academy of Science and Letters together with the
Univ. of Oslo supported RB to visit Oslo and the field sites in
Scotland. TE has been supported by the Norwegian Science
Foundation. Rearing facilities were generously provided at
Culterty Field Station and we thank Robert Donaldson and
Sue Way for assistance with maintaining the animals. We
thank George Batzli, Karen E. Hodges, Robert Moss and
Nigel Yoccoz for constructive comments on the manuscript.
References
Agrell, J., Erlinge, S., Nelson, J. and Sandell, M. 1992. Body
weight and population dynamics: cyclic demography in a
noncyclic population of the field vole (Microtus agrestis). –
Can. J. Zool. 70: 494–501.
Akaike, H. 1985. Prediction and entropy. – In: Atkinson, A.
C. and Fienberg, S. E. (eds), A celebration of statistics.
Springer-Verlag, pp. 1–24.
Andreassen, H. P. and Ims, R. A. 1990. Responses of female
gray-sided voles Clethrionomys rufocanus to malnutrition: a
combined laboratory and field experiment. – Oikos 59:
107 – 114.
Berger, P. J., Sanders, E. H., Gardner, P. D. and Negus, N. C.
1977. Phenolic plant compounds functioning as reproductive inhibitors in Microtus montanus. – Science 195: 575–
577.
Berger, P. J., Negus, N. C., Sanders, E. H. and Gardner, P. D.
1981. Chemical triggering of reproduction in Microtus
montanus. – Science 214: 69–70.
Bergeron, J.-M. and Jodin, L. 1993. Intense grazing by voles
(Microtus pennsyl6anicus) and its effect on habitat quality.
– Can. J. Zool. 71: 1823–1830.
Bernardo, J. 1996. Maternal effects in animal ecology. – Am.
Zool. 36: 83–105.
Bernshtein, A. D., Zhigalsky, O. A. and Panina, T. V. 1989.
Multi-annual fluctuations in the size of a population of the
bank vole in European part of the USSR. – Acta Theriol.
34: 409 – 438.
Bjørnstad, O. N., Falck, W. and Stenseth, N. C. 1995. A
geographic gradient in small rodent density fluctuations: a
statistical modelling approach. – Proc. R. Soc. Lond. B.
262: 127– 133.
Boonstra, R. 1989. Life history variation in maturation in
fluctuating meadow vole populations (Microtus pennsyl6anicus). – Oikos 54: 265–274.
Boonstra, R. 1994. Population cycles in microtines: the senescence hypothesis. – Evol. Ecol. 8: 196–219.
Boonstra, R. and Rodd, F. H. 1983. Regulation of breeding
density in Microtus pennsyl6anicus. – J. Anim. Ecol. 52:
757 – 780.
Boonstra, R. and Boag, P. T. 1987. A test of the Chitty
hypothesis: inheritance of life-history traits in meadow
voles Microtus pennsyl6anicus. – Evolution 41: 929– 947.
196
Boonstra, R. and Hochachka, W. M. 1997. Maternal effects of
additive genetic inheritance in the collared lemming Dicrostonyx groenlandicus. – Evol. Ecol. 11: 169– 182.
Bronson, F. H. and Heideman, P. D. 1994. Seasonal regulation of reproduction in mammals. – In: Knobil, E. and
Neill, J. D. (eds), The physiology of reproduction. Raven
Press, pp. 541– 583.
Brownie, C., Hines, J. E., Nichols, J. D. et al. 1993. Capturerecapture studies for multiple strata including non-Markovian transitions. – Biometrics 49: 1173 – 1187.
Burnham, K. P. and Anderson, D. R. 1998. Model selection
and inference: a practical information-theoretic approach.
– Springer-Verlag.
Chitty, D. 1960. Population processes in the vole and their
relevance to general theory. – Can. J. Zool. 38: 99– 113.
Chitty, D. 1967. The natural selection of self-regulatory behaviour in animal populations. – Proc. Ecol. Soc. Aust. 2:
51 – 78.
Elton, C. 1924. Periodic fluctuations in the numbers of animals: their causes and effects. – Br. J. Exp. Biol. 2:
119 – 163.
Feore, S. M., Bennett, M., Chantrey, J. et al. 1997. The effect
of cowpox virus infection on fecundity in bank voles and
wood mice. – Proc. R. Soc. Lond B 264: 1457– 1461.
Gallaher, D. and Schneeman, B. O. 1986. Nutritional and
metabolic response to plant inhibitors of digestive enzymes.
– Adv. Exp. Med. Biol. 199: 167– 184.
Gilbert, B. S. and Krebs, C. J. 1991. Population dynamics of
Clethrionomys and Peromyscus in southwestern Yukon
1973 – 1989. – Holarct. Ecol. 14: 250– 259.
Gower, B. A. and Berger, P. J. 1990. Reproductive responses
of male Microtus montanus to photoperiod, melatonin,
and 6-MBOA. – J. Pineal Res. 8: 297– 312.
Hagen, A., Stenseth, N. C., Østbye, E. and Skar, H. J. 1980.
The eye lens as an age indicator in the root vole. – Acta
Theoriol. 25: 39– 50.
Hansen, T. F. and Boonstra, R. 2000. The best in all possible
worlds? A quantitative genetic study of geographic variation in the meadow vole, Microtus pennsyl6anicys. – Oikos
89: 81 – 94.
Hansson, L. 1989. Tracking or delay effects in microtine
reproduction? – Acta Theriol. 34: 125– 132.
Hansson, L. 1995. Is the indirect predator effect a special case
of generalized reactions to density-related disturbances in
cyclic rodent populations? – Ann. Zool. Fenn. 32: 159–
162.
Hansson, L. and Henttonen, H. 1985. Regional differences in
cyclicity and reproduction in Clethrionomys species: are
they related? – Ann. Zool. Fenn. 22: 277– 288.
Herms, D. A. and Mattson, W. J. 1992. The dilemma of
plants: to grow or to defend. – Q. Rev. Biol. 63: 283 – 335.
Hestbeck, J. B., Nichols, J. D. and Malecki, R. A. 1991.
Estimates of movement and site fidelity using mark-resight
data of wintering Canada geese. – Ecology 72: 523– 533.
Inchausti, P. and Ginzburg, L. R. 1998. Small mammal cycles
in northern Europe: patterns and evidence for maternal
effect hypothesis. – J. Anim. Ecol. 67: 180– 194.
Kalela, O. and Koponen, T. 1971. Food consumption and
movements of the Norwegian lemming in areas characterized by isolated fells. – Ann. Zool. Fenn. 8: 80– 84.
Karban, R. and Baldwin, I. T. 1997. Induced responses to
herbivory. – Univ. of Chicago Press.
Korn, H. and Taitt, M. J. 1987. Initiation of early breeding in
a population of Microtus townsendii (Rodentia) with the
secondary plan compound 6-MBOA. – Oecologia 71: 593–
596.
Korpimäki, E. and Krebs, C. J. 1996. Predation and population cycles of small mammals. – BioScience 46: 754– 764.
Korpimäki, E., Norrdahl, K. and Valkama, J. 1994. Reproductive investment under fluctuating predation risk: microtine rodents and small mustelids. – Evol. Ecol. 8:
357 – 368.
OIKOS 95:2 (2001)
Koskela, E. and Ylönen, H. 1995. Suppressed breeding in the
field vole (Microtus agrestis): an adaptation to cyclically
fluctuating predation risk. – Behav. Ecol. 6: 311– 315.
Krebs, C. J. 1978. A review of the Chitty hypothesis of
population regulation. – Can. J. Zool. 56: 2463– 2480.
Krebs, C. J. and Myers, J. H. 1974. Population cycles in small
mammals. – Adv. Ecol. Res. 8: 267–399.
Lambin, X., Ims, R. A., Steen, H. and Yoccoz, N. G. 1995. Vole
cycles. – Trends Ecol. Evol. 10: 179–220.
Lambin, X., Elston, D., Petty, S. and MacKinnon, J. 1998.
Spatial patterns and periodic travelling waves in cyclic field
vole, Microtus agrestis, populations. – Proc. R. Soc. Lond.
265: 1491– 1496.
Lambin, X., Petty, S. J. and MacKinnon, J. L. 2000. Cyclic
dynamics in field vole populations and generalist predation.
– J. Anim. Ecol. 69: 106–118.
Littell, R. C., Milliken, G. A., Stroup, W. W. and Wolfinger,
R. D. 1996. SAS Systems for Mixed Models. – SAS Inst.,
Cary, NC.
MacKinnon, J. 1998. Spatial dynamics of cyclic field vole
Microtus agrestis populations. – Univ. of Aberdeen.
Mappes, T. and Ylönen, H. 1997. Reproductive effort of bank
vole females in a risky environment. – Evol. Ecol. 11:
591 – 598.
Mappes, T., Koskela, E. and Ylönen, H. 1998. Breeding
suppression in voles under predation risk of small mustelids:
laboratory or methodological artifact? – Oikos 82: 365– 369.
McNamara, J. M. and Houston, A. I. 1996. State-dependent life
histories. – Nature 380: 215–221.
Meek, L. R., Lee, T. M. and Gallon, J. F. 1995. Interaction of
maternal photoperiod history and food type on growth and
reproductive development of laboratory meadow voles (Microtus pennsyl6anicus). – Physiol. Behav. 57: 905– 911.
Mihok, S. and Boonstra, R. 1992. Breeding performance in
captive meadow-voles (Microtus pennsyl6anicus) from decline- and increase-phase populations. – Can. J. Zool. 70:
1561 – 1566.
Moen, J., Lundberg, P. A. and Oksanen, L. 1993. Lemming
grazing on snowbed vegetation during a population peak,
northern Norway. – Arct. Alp. Res. 25: 130–135.
Moffatt, C. A., Bennett, S. A. and Nelson, R. J. 1991. Effects
of photoperiod and 6-methoxy-2-benzoxazolinone on maleinduced estrus in prairie voles. – Physiol. Behav. 49: 27– 32.
Morris, D. W. 1989. Density-dependent habitat selection: testing the theory with fitness data. – Evol. Ecol. 3: 80– 94.
Negus, N. C., Berger, P. J. and Forslund, L. G. 1977. Reproductive strategy of Microtus montanus. – J. Mammal. 58:
347 – 353.
Nelson, R. J. 1987. Photoperiod-nonresponsive morphs: a
possible variable in microtine population-density fluctuations. – Am. Nat. 130: 350–369.
Nelson, R. L. 1991. Maternal diet influences reproductive
development in male prairie vole offspring. – Physiol.
Behav. 50: 1063–1066.
Nelson, R. J. and Blom, J. M. C. 1993. 6-Methoxy-2-benzoxazolinone and photoperiod: prenatal and postnatal influences
on reproductive development in prairie voles (Microtus
ochrogaster). – Can. J. Zool. 71: 776–789.
Oksanen, L. and Oksanen, T. 1981. Lemmings (Lemmus lemmus) and grey-sided voles (Clethrionomys rufocanus) in
interactions with their resources and predators on
Finnmarksvidda, northern Norway. – Rep. Kevo Subarct.
Res. Stn. 17: 7–31.
Petty, S. J. 1992. The ecology of the tawny owl, Strix aluco, in
the spruce forests of Northumberland and Agryll. – The
Open Univ., Milton Keynes, p. 295.
OIKOS 95:2 (2001)
Petty, S. J. and Fawkes, B. L. 1997. Clutch size variation in
tawny owls (Strix aluco) from adjacent valley systems: can
this be used as surrogate to investigate temporal and spatial
variation in vole density. – In: Duncan, J. R., Johnson, D.
H. and Nicholls, J. H. (eds), Biology and conservation of
owls of the northern hemisphere. USDA Forest Service
North-Central Forest Research Station, General Technical
Report 140– 190, pp. 314 – 324.
Pollock, K. H. 1982. A capture-recapture design robust to
unequal probability of capture. – J. Wildl. Manage. 46:
752 – 757.
Pusenius, J. and Viitala, J. 1993. Varying spacing behaviour of
breeding field voles, Microtus agrestis. – Ann. Zool. Fenn.
30: 143 – 152.
Rodd, F. H. and Boonstra, R. 1988. Effects of adult meadow
voles, Microtus pennsyl6anicus, on young conspecifics in field
populations. – J. Anim. Ecol. 57: 755– 770.
Rossiter, M. C. 1996. Incidence and consequences of inherited environmental effects. – Annu. Rev. Ecol. Syst. 27:
451 – 476.
Ryan, C. A. 1990. Proteinase inhibitors in plants. Genes for
improving defenses against insects and pathogens. – Annu.
Rev. Phytopathol. 28: 425– 449.
Sanders, E. H., Gardner, P. D., Berger, P. J. and Negus, N. C.
1981. 6-Methoxybenzoxazolinone: a plant derivative that
stimulates reproduction in Microtus montanus. – Science
214: 67– 69.
Sheaffer, C. C. and Marten, G. C. 1990. Alfalfa cutting
frequency and date of fall cutting. – J. Prod. Agric. 3:
486 – 491.
Sheaffer, C. C., Barnes, D. K., Warnes, D. D. et al. 1992.
Seeding-year cutting affects winter survival and its association with fall growth score in alfalfa. – Crop Sci. 32:
225 – 231.
Shimada, T. 1994. A new concept on the critical harvest period
of forage crops in autumn. Japan-Russia workshop: Low
temperature physiology and breeding of northern crops.
Hokkaido National Agricultural Experiment Station, Sapporo, pp. 35 – 41.
Spears, N. and Clarke, J. R. 1988. Selection in field voles
(Microtus agrestis) for gonadal growth under short photoperiod. – J. Anim. Ecol. 57: 61– 70.
Stenseth, N. C. 1999. Population cycles in voles and lemmings:
density dependence and phase dependence in a stochastic
world. – Oikos 87: 427– 461.
Stenseth, N. C., Bjørnstad, O. N. and Falck, W. 1996. Is spacing
behaviour coupled with predation causing the microtine
density cycle? A synthesis of current process-oriented and
pattern-oriented studies. – Proc. R. Soc. Lond. B 263:
1423 – 1435.
Tkadlec, E. and Zejda, J. 1998. Small rodent population
fluctuations: the effects of age structure and seasonality. –
Evol. Ecol. 12: 191– 210.
Turchin, P. 1995. Population regulation: old arguments and a
new synthesis. – In: Cappuccino, N. and Price, P. W. (eds),
Population dynamics; new approaches and synthesis. Academic Press, pp. 19– 40.
White, G. 2001. Program MARK. – Available at Žhttp://
www.cnr.colostate.edu/ gwhite/.
White, G. C. and Burnham, K. P. 1999. Program MARK:
survival estimation from populations of marked animals. –
Bird Study 46 Suppl.: 120– 138.
Ylönen, H. 1989. Weasels Mustela ni6alis suppress reproduction
in cyclic bank voles Clethrionomys glareolus. – Oikos 55:
138 – 140.
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