Fluctuating life-history traits
in overwintering field voles (Microtus agrestis)
Torbjørn Ergon
Dissertation presented for the degree of Doctor Scientiarum
Department of Biology
Faculty of Mathematics and Natural Sciences
University of Oslo
2003
ii
Contents
1 INTRODUCTION
1.1 Vole life-cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Hypotheses for variation in life-history traits . . . . . . . . . . . .
1.3 Study system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
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4
5
2 SUMMARY OF THE PAPERS
2.1 Main results . . . . . . . . . . . . . . . . . . . . . . . .
2.1.1 Paper I — A “common garden experiment” . .
2.1.2 Paper II — A transplant experiment . . . . . .
2.1.3 Paper III — Body size and energy expenditure
2.1.4 Paper IV — Optimal onset of reproduction in
don’t know everything . . . . . . . . . . . . . .
2.2 Conclusions from the empirical results . . . . . . . . .
2.3 Relevance for management . . . . . . . . . . . . . . . .
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voles that
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3 GENERAL DISCUSSION — Experimental and observational approaches in population studies
12
4 SUMMARY
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5 REFERENCES
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6 LIST OF PAPERS
31
iv
Introduction
1
Imagine going to a forest clear-cut to check vole traps an early morning in the
spring. You weigh the voles, record their data and release them. All the voles you
see are less than 30 grams and no female show signs of reproducing. You then
go to another site a few kilometers away. Here the males are all above 40 grams
and all the females are either highly pregnant or nursing young - the spring has
come! Any curious person would wonder why? So did I, and indeed, so have many
before me. Dennis Chitty (1952) was the first to note that overwintered field voles
during a population decline at Lake Vyrnwy, Wales, had lower body weights in
the spring and initiated spring reproduction later compared to the previous spring
when the population reached peak densities1 . This has later been recognised as a
widespread phenomenon in fluctuating populations of voles, lemmings and even
snowshoe hares: Overwintering animals initiate spring reproduction earlier and
reach higher adult body size at increasing and peak densities than in years when
population densities are declining2 . This dissertation is about the causes of the
variation in these two key life-history traits of overwintering small rodents: body
size maintained over winter and the time of reproductive onset in the spring.
1.1
Vole life-cycles
Life history patterns of voles and lemmings3 are characterised by short life-spans
(usually less than one year), potentially young age at maturation, high fecundity, short gestation period and repeated breeding within the same reproductive
season4 . There is also typically very high flexibility in these life-history traits.
This is most evident in seasonal environments where breeding is restricted to the
growth season of the food plants. Individuals born early in the breeding season
will usually breed at young age whereas those born later in the season may delay
maturation until the next year (sometimes more than 8 months). One peculiarity
of voles’ life-histories is that they maintain a lower body size during winter than
in the summer: young animals that delay reproduction stop growing at small
body size and do not resume growth until the next breeding season, and reproductive animals typically reduce body mass by 20-40% as a response to declining
day-length prior to the winter5 . A sketch of this general life-history pattern of
1
The large differences in body mass reported in this pioneering work were probably mainly
caused by differences in the time that reproduction was initiated in the spring. However, it
has later been demonstrated that individuals grow faster and reach higher adult size during the
increase and peak phases of the population fluctuations than when the populations are declining
(see Chitty 1996 pp. 81-87 and Paper II).
2
Reviews in Chitty (1996), Boonstra and Krebs (1979), Krebs and Myers (1974), and introduction of Paper III.
3
Subfamily Arvicolinae (= Microtinae).
4
In field voles, females may conceive before 3 weeks of age, gestation period is 18-20 days
and litters usually have 3-8 pups.
5
See introduction of Paper III.
2
A - Reproduction
B - Growth
Body mass
OW
YB
OW
YB
NB
Winter Spring
Summer
Autumn
Winter Spring
NB
Summer
Autumn
Figure 1: Seasonal life-history patterns of voles. A, Overwintered animals (OW) will
breed repeatedly during the reproductive season (spring and summer in northern and
alpine environments). Young of the year may breed in the year of birth (young breeders;
YB), or they may delay reproduction until the next spring (non-breeders; NB). B, Overwintered animals maintain a low body mass throughout the winter but grow rapidly at
the onset of reproduction in the spring (females start growing when they conceive their
first litter). Young breeders grow large, whereas non-breeders suspend growth at as small
size and do not resume growing until they become reproductively active in the following
spring. Breeding animals loose body mass before the winter. It is mainly the animals
that delay reproduction that survive the winter and breed in the following spring. (The
sketch is based on descriptions by Agrell et al. (1992), Gliwicz (1996), Gyug and Millar
(1980), Lambin and Yoccoz (2001), Negus and Berger (1988), and own observations (see
Paper I and Paper II). Note that there is large variation in these patterns between
species, environments, and between years within the same population.)
voles is presented in Figure 1.
The patterns of growth, maturation and reproduction are not just related to
the time of the year the individuals are born, but there is typically also large
variation in the seasonal patterns between years. Onset of spring reproduction,
for example, may in some populations occur more than six weeks earlier in some
years than in other years6 . The tradition in population studies of small rodent
cycles has been to describe such year-to-year variation in life-history traits in
relation to “cyclic phase” (defined by current densities and rate of change7 ) rather
than in relation to present and previous densities. There are in particular three
traits that have been described to vary in relation to the phases of the population
cycles8 : 1. reproducing animals become heavier during the ‘increase’ and ‘peak’
6
See introduction of Paper IV.
See Krebs and Myers (1974) for definitions of the different phases.
8
See reviews by Batzli (1992), Krebs and Myers (1974), and Stenseth and Ims (1993).
7
3
years than in other years, 2. fewer young animals mature in their year of birth
when densities are high, and 3. reproduction commences early in the spring
when the population is increasing and late during population declines. Body
size will in itself not directly affect the population dynamics or the fitness of
the individuals. Nevertheless, growth and maintenance of a given body size are
generally important life-history traits because they are intimately linked to tradeoffs and constraints involving fecundity, scheduling of reproduction and survival9 .
Furthermore, demographic processes of birth and survival may also alter the
body-size distribution at the population level.
Traditional studies of life-history variation between species see traits such as
growth, sexual maturation, fecundity and survival only as functions of the organisms’ age10 . This approach is obviously of little use when studying variation
within populations where there are large differences in the schedules of growth
and reproduction between individuals born at different times of the year. Even
among animals born at the same time of the year there may be large variation in
traits such as onset of spring reproduction and adult body mass. In such cases one
must apply a more general approach where life-history traits are considered to be
functions of the total internal and external ‘state’ of the individuals and their surrounding environment11 . ‘Internal state variables’ describe the condition of the
body and include variables such as body size, fat reserves, age and genotype12 ,
whereas ‘external state’ refers to environmental variables such as day-length (season), availability of food and density of predators and competitors. Internal state
variables may be flexible, such as fat reserves or the state of the immune system,
or they may be fixed in the sense that they are not affected by the environment
(e.g. age and genotype). Flexible internal state variables typically depend on earlier environmental conditions or previous life-history decisions (e.g., poorer body
condition after periods of food shortage or after reproduction), and they may
vary in degree of flexibility13 . The ability of a single genotype to produce different phenotypes (e.g. life-history trait values) in different environments is called
‘phenotypic plasticity’, and the functional relations between the phenotypic values and the environmental state variables are called ‘norms of reaction’14 .
9
See Sauer and Slade (1988) and Sibly (1986).
Roff (1992), Stearns (1992).
11
McNamara and Houston (1996).
12
In the context of the hypotheses presented below it is convenient to define ‘age’ and ‘genotype’ as internal state variables, although in other contexts they may not be defined as such.
13
Growth in mammals may for example be severely retarded by adverse conditions in early
life due to poor conditions of the mother during gestation or lactation, which may affect the
condition throughout the lives of the individuals (if compensatory growth (Sibly 1986) is not
effective), i.e., “maternal effects” (Bernardo 1996; Rossiter 1996).
14
Roff (2002).
10
4
Population
mechanisms
Intrinsic
regulation
1. Memory in the environment due to trophic interactions with:
a. Food resources (Batzli 1992, see also Discussion in
Paper IV).
b. Predators (Korpimäki and Krebs 1996; Ylönen 1994).
c. Pathogens (Anderson and May 1978; Feore et al.
1997).
2. Memory in the individuals:
a. Persistent phenotypic changes in the quality of the
individuals due to the environment experienced in
early life (including maternal effects) (Boonstra and
Boag 1987; Inchausti and Ginzburg 1998).
3. Memory in population composition:
a. Genetic differences due to density dependent
selection (Charnov and Finerty 1980; Chitty 1967; Nelson
1987).
b. Alteration in the age structure of the population due
to density dependent demographic processes (Boonstra
1994; Tkadlec and Zejda 1998).
Extrinsic
regulation
Individual
mechanisms
Box 1. Hypotheses for general mechanisms of delayed density-dependent patterns in lifehistory traits. Where does the memory of past densities reside?
Hypotheses 1 and 2 represent individual level mechanisms in the sense that individuals of
the same age and genotype are assumed to behave differently depending on earlier
population densities (phenotypic plasticity), whereas Hypothesis 3 represents population
level changes in the structure of the population with respect to fixed phenotypes. As for
mechanisms for population regulation, Hypothesis 1 represents ‘extrinsic’ mechanisms,
whereas Hypotheses 2 and 3 represent ‘intrinsic’ mechanisms for delayed density
dependence (although changes in population structure or the internal state of the
individuals may be due to extrinsic trophic interactions in the earlier environment).
1.2
Hypotheses for variation in life-history traits
One way to structure hypothesised mechanisms for delayed density dependence
in onset of spring reproduction and other life-history traits is to ask the question
“Where does the ‘memory’ of past densities reside?” (Box 1). If the performance
of individuals of the same age and genotype varies in relation to previous densities,
then there must either be some “memory” in the surrounding environment due to
trophic interactions (Hypothesis 1 in Box 1) or the individuals must “remember”
past densities through their physiology (Hypothesis 2). Delayed density dependence at the population level may also occur due to changes in the composition
of the population with respect to genotypes and age (Hypothesis 3).
One of the most stimulating debates in population ecology is about to what
degree populations are regulated through trophic interactions with other species
5
(Hypothesis 1) or through intra-population (self-regulating) mechanisms (Hypotheses 2 and 3)15 . Population regulation is a result of density dependent
(direct or delayed) variation in life-history traits of individuals (including time
of death)16 . The studies in this dissertation are hence relevant for the debate
about population regulation, although I address mechanisms for delayed density dependence only in some life history traits: body mass and onset of spring
reproduction17 .
1.3
Study system
The field studies took place in Kielder forests on the border between England
and Scotland, one of the areas where the pioneering work on population fluctuations by Charles Elton and co-workers were carried out18 . In this region, which is
largely covered by spruce plantations, field voles (Microtus agrestis) are confined
to distinct grassland clear-cuts surrounded by dense tree stands that lack ground
vegetation and are hence uninhabitable for voles. The fact that sub-populations
of voles inhabiting these clear-cuts fluctuate somewhat asynchronously, but nevertheless with a regular period of 3—5 years, enables replicated short-term studies
of density dependence in life-history traits. Studies of wintering voles and onset
of spring reproduction are also made easy by the fact that there is no permanent
snow cover during winter19 .
Summary of the Papers
2
2.1
Main results
In the two first papers of this dissertation we address the question of whether
life-history variation in overwintering field voles is due to variation in the internal
state of the individuals (Hypotheses 2 and 3 in Box 1) or due to variation in the
external state of the immediate environment (Hypothesis 1 in Box 1). Paper I
presents a “common garden experiment” where voles from different areas were
bred under standardized conditions in the lab, and Paper II presents a “field
transplant experiment” where voles were swapped between study sites during
mid-winter and later monitored by capture-mark-recapture live-trapping. Both
these studies suggest that the main cause of variation in life-history traits of
field voles are individual responses to the immediate environment. Paper III
15
See Berryman (2002c), Chitty (1996), Krebs (1978) and Stenseth (1999).
Density regulation on a local scale may, of course, also be due to immigration and emigration
17
Note, however, that some of my colleagues have studied the impact of weasel predation
on survival (Graham and Lambin 2002), and studies on disease dynamics are currently being
undertaken (see Cavanagh et al. 2002).
18
See Chitty (1996 pp. 27-55); Newcastleton is located within this study area.
19
For detailed descriptions of the study system see Lambin et al. (1998; 2000), MacKinnon
et al. (2001) and Paper I and III of this dissertation.
16
6
and Paper IV investigate the mechanisms of these responses in more detail.
Paper III focus on the relations between body mass and energy expenditure
(measured by the use of doubly labelled water) of wintering voles, and Paper IV
focus on the optimal time to initiate spring reproduction when animals perceive
the state of their environment with varying degrees of precision.
2.1.1
Paper I — A “common garden experiment”
In the study of Paper I we sampled voles from two neighbouring out-of phase
valleys and bred them under standardized lab conditions for two generations.
We also monitored the source populations by capture-mark-recapture in the field
and compared the patterns of reproduction observed in the lab with patterns
of maturation and recruitment in the field. In one of the valleys (Kershopearea) densities were high in the previous year, but, unexpectedly, densities did
not decline during the year of study but remained at peak densities throughout
the breeding season. However, animals in this area initiated spring reproduction
late and did not reach very high body mass, and few young animals matured
during the following summer. These characteristics are more typical for the
‘decline phase’ of population cycles than for the ‘peak phase’20 . In the other
area (Kielder-valley) where densities in the previous year were low, reproduction
commenced about six weeks earlier, reproducing animal reached high body mass
and juveniles continued to mature until late in the summer (typical ‘increase
phase’). Among the overwintered animals brought to the lab, we found that some
of these differences were retained under the standardized conditions. However,
this may be pertaining to the fact that we sampled voles when some reproduction
was already initiated in the increase-area. Among the lab-born generations, on
the other hand, we found no differences in the patterns of reproduction, suggesting
that the large differences seen in the field were not due to genetic differences21
(cf. Hypothesis 3).
One interesting observation from the lab was that there was an early seasonal
decline in the probability that lab-born females, from both areas alike, would
initiate reproduction (conceive). The pattern of decline was similar to the seasonal decline in maturation probability seen in the peak-area (Kershope). As
the animals were reared in open sheds with no lighting, the probable reason for
this strong seasonal effect was a response to the change in ambient photoperiod
(day-length). This difference between animals in the lab versus animals at the
increase-phase field-sites may suggest that the voles need some stimuli from their
20
At the time of writing of Paper I, the delayed density dependence in onset of spring
reproduction was not well established in this study system. However, data from a larger number
of sampling sites and years presented in Paper IV clearly shows that early commencement of
the breeding season is associated with low densities in the previous spring (1 year lag) and a
population increase during the previous summer.
21
Note however that, due to genotype-environment interactions, two genotypes may display
different phenotypes in one environment (the field) but not in others (the lab).
7
food plants in order to mature22 . This may also explain why as many as 23% of
the overwintered peak-area females never reproduced in the lab.
2.1.2
Paper II — A transplant experiment
The transplant experiment, Paper II, was more rigorous than the lab experiment because it took place under natural field condition. Voles were here swapped
between four field sites after the breeding season and then monitored by capturemark-recapture in the following spring in order to separate the effects of previous
and current environment on life-history traits of the overwintering voles (Hypothesis 1 vs. 2 and 3). Average body mass differed by about 18% between sites at
the time of transplant (November/December), but body mass of the transplanted
voles had already converged to the values prevailing at the target sites during the
first trapping session in January/February. Over the following spring, voles at
the different sites showed large variation in individual weight gain, onset of spring
reproduction and survival (estimates of the latter are presented in Paper III and
Paper IV). At the sites with the lowest overwintering body mass, individuals
grew slower in the spring, reproduced later and had lower survival rates, but also
for these traits there were no significant effects of source population. Thus, this
experiment showed an overriding effect of the current environment on life-history
traits of overwintering individuals (Hypothesis 1).
2.1.3
Paper III — Body size and energy expenditure
The study presented in Paper III was carried out in conjunction with the transplant experiment, measuring daily energy expenditure (DEE) by the use of doubly
labelled water on voles at the four study sites during mid-winter. Also here were
there no significant effect of the source population, but the DEE measurements
differed largely between sites. Voles had higher DEE at the sites where average
body mass was lowest despite a positive relation between body mass and DEE
among individuals within sites. An optimality model, focusing on the trade-off
between energy acquisitioning and avoiding mortality risks of foraging, shows that
such patterns between body mass and DEE should be expected if voles became
smaller at the sites with the lowest body mass because energy was less available in the food plants at these sites. In contrast, if voles had become smaller
because they restricted energy intake to avoid predation, then one should expect to see the same relationship between average body mass and DEE between
the site-means as the relationship between individuals within sites (assuming a
homogenous environment within sites).
The model presented in this paper illustrates potential mechanisms for variation in body mass and energy expenditure of non-breeding animals more generally.
22
Animals were fed on unlimited high-quality protein-rich food in the lab, but they did not
receive any fresh plant material. See discussion relating to 6-MBOA in Paper IV.
8
By explicitly taking into account how energy expenditure, energy assimilation and
survival in cold climates are related to body size and foraging time, the model
predicts the optimal body size in environments that differ in the energetic costs
of maintaining a given size and/or in the survival costs of foraging. Specifically,
it may facilitate understanding of how food quality, temperature and risk of predation influence variation in body mass and energy expenditure between seasons,
populations (e.g., trends with climate) and species. Although exact mechanisms
cannot of course be deduced from observed patterns, the model enables more
meaningful interpretations of the variation in body mass and energy expenditure
if these two traits are measured together (as a bi-variate response) and when the
patterns of variation and co-variations are compared within and between locations/seasons/species23 . As we were mainly interested in studying causes of the
variation in body mass of non-breeding wintering animals, the model does not
explicitly account for effects of body mass on fecundity and interspecific competition, but it may be modified to do so.
2.1.4
Paper IV — Optimal onset of reproduction in voles that don’t know everything
Paper IV focuses on the optimal time to commence seasonal reproduction for
multivoltine organisms (organisms having several generations per year). In this
paper I present a theoretical model focusing on the trade-off between early reproduction and high success of the first breeding attempt, but I also investigate how
dependencies between pre-breeding survival and onset of reproduction (due to
trade-offs, senescence or seasonal variation in survival) will influence the optimal
strategies.
In the case where there is only a dependency between time of reproduction
and breeding success, and when animals (hypothetically) have perfect information
about their environment, the model predicts that: 1) reproduction should start
earlier when the difference between population growth in the breeding season
and pre-breeding winter survival is high, and 2) breeding success at the optimum
depends on how, but not when, breeding conditions improve. The latter implies
that a one-week delay in the time that breeding conditions improve will lead
to a one-week delay in the optimal time to initiate reproduction (if breeding
conditions improve in the same manner). Thus, if the variation on onset of spring
reproduction is only caused by variation in the time that breeding conditions
improve, then expected success of the first breeding attempt should be constant,
whereas if the variation is caused by a response to variation in population growth
rate and/or winter survival, breeding success should be lower when reproduction
is initiated early.
23
In the paper we discuss potential causes of geographical trends (cf. ‘Bergmann’s rule’)
and reasons for the fact that collared lemmings, unlike most microtines at northern latitudes,
maintain a larger body mass during winter than during summer.
9
I further investigate how these predictions about breeding success and optimal
time to initiate reproduction change when the animals, more realistically, cannot
measure the state of their environment without error. I do this by using a simulation approach to search for optimal weightings of uncertain (imprecise) cues
about the environmental state variables24 , and I show how the optimal norms
of reaction, the expected phenotypic correlations and selection pressures depend
on the precision of the cues used by the animals. Specifically, because it is optimal to be conservative in responding to imprecise environmental cues, there
should be less variation in the time of reproductive initiation when cues are more
unreliable, and breeding success should be highest in years when breeding conditions improve early (and when reproduction is initiated early; i.e., a negative
correlation between breeding date and breeding success).
The predicted patterns in the variation of optimal breeding date and breeding success will also be greatly modified by dependencies between pre-breeding
survival and breeding date. Such dependencies may arise due to trade-offs (i.e.,
if early reproduction requires the animals to maintain a morphological or physiological state that renders lower winter survival; e.g. body mass, see Paper III),
senescence (pre-breeding survival declines with time due to ageing of the individuals) or to seasonal variation in survival (pre-breeding survival declines with time
due to increased extrinsic mortality later in the spring; e.g., due to higher predation). If such dependencies are important, expected (optimal) breeding success
may be drastically reduced in years when breeding conditions improve late.
In this paper I also present data showing that onset of spring reproduction
is delayed density dependent: breeding is generally initiated early in the spring
when densities in the previous spring were low. However, there is no correlation
between onset of spring reproduction and population growth during the previous
non-reproductive season (winter survival) or during the following reproductive
season in the direction predicted by the model. This is probably because the
animals cannot “measure” these environmental state variables precisely.
Also presented in Paper IV is an analysis on survival costs of reproducing
at the four study sites included in Paper II and Paper III. This analysis shows
that survival was particularly low for reproducing females at the site where reproduction was initiated the latest (a typical ‘decline site’ with poor survival,
low body masses and high rates of energy expenditure). This is consistent with
the model if females at this site were forced to reproduce while the environment
was still unfavourable due to dependencies between pre-breeding survival and the
time that reproduction could be initiated (see above), or that breeding conditions
improved very slowly. Such an association between late onset of reproduction
(which is delayed density dependent) and low breeding success may greatly reinforce the delayed negative density dependence on population growth.
24
To simplify, I only studied the optimal response to cues about one state variable at a time,
but it should be possible to expand the simulation approach to study the optimal responses to
a set of dependent cues reflecting several state variables.
10
2.2
Conclusions from the empirical results
In summary, I have derived at the following conclusions about variation in lifehistory traits of overwintering field voles in the Kielder forest area:
1. The time that spring reproduction is initiated is delayed density dependent
with a one-year lag (Paper IV).
2. The “memory” of past conditions (see Box 1) resides in the environment
rather than in the internal states of the individuals (Paper II).
3. Variation in overwintering body mass is due to variation in the energetic
constraints rather than foraging responses to variation in risk of predation
(Paper III).
4. Variation in onset of spring reproduction is related to variation in the time
that breeding conditions improve rather than responses to cues reflecting
variation in prospects of survival and future population growth (Paper IV).
Although I have not studied any specific mechanisms explicitly with respect
to environmental state variables and trophic interaction, I suggest that variation
in the food quality/availability during winter and early spring is a likely cause of
these results (Fig. 2).
2.3
Relevance for management
Although the basic scientific issues relating to variation in life-history traits and
demography are interesting in its own right, these issues are also relevant for
conservation and management of endangered or harvested populations.
Time-series analyses focusing on describing the direct and delayed densitydependence, as well as influence of density-independent factors, help us to understand how for example climatic change influence the dynamics of populations
and the interactions between them25 . However, predictions from such models
may fail when environmental state variables (or the covariance between them)
are perturbed outside their normal (or previous) range. In building models intended to predict population responses to environmental change, it is essential
to understand how individuals respond to their environment. In particular, it
is important to know what environmental cues animals use in their life-history
decisions and how they respond to these cues. In Paper IV, I discuss how the
population’s ability to sustain environmental change depends on the precision
and accuracy of the environmental cues used by animals in their reproductive
decisions. One situation where rapid environmental change may be particularly
harmful is when reaction norms evolved under one set of environmental conditions become maladaptive when the covariation in environmental state variables
25
See the review by Stenseth et al. (2002a) for specific examples.
11
A – Increase phase
Spring
Summer
Winter
Spring
Summer
Winter
Spring
B – Decline phase
Spring
Figure 2: Hypothesised mechanism for delayed density dependence in winter/spring
body mass and onset of spring reproduction. A. When rodent densities are low and
grazing is moderate in the spring, perennial plants will accumulate resources in the
roots. This provides an abundant energy source for the rodents during the following
winter, enabling the maintenance of a larger wintering body mass (see Paper III). The
plants use the stored energy reserves in the roots for an early and fast re-growth of
assimilating shoots in the following spring, which enables the rodents to initiate spring
reproduction early in the season (see Paper IV). B. When rodent densities are high,
repeated grazing and re-growth of the emerging plant shoots in the spring depletes the
stored energy reserves in the roots. This reduces the energy availability for the voles in
the following winter, impede early re-growth of the vegetation in the following spring and
hence prohibit an early start of the reproductive season of the rodents. See Discussion
of Paper IV for details on the rationale of this hypothesis.
are distorted. For example, if the time that food availability becomes sufficient
to rear offspring changes, but the animals initiate reproduction at a certain daylength (enabling them to initiate reproduction before food becomes abundant),
then there will be a ‘mismatch’ between the reproductive strategies and food
12
availability, possibly causing severe population declines26 .
Eco-physiology may help us to understand how individuals will respond to
environmental change27 . Knowledge of simple physiological mechanisms will,
however, not allow direct predictions of life-history responses when the environment changes. This is because the environmental conditions may affect a set of
inter-dependent individual traits in intricate ways. For example, improved food
quality may entertain both a higher body mass and a reduced foraging time,
but the trade-off between maintaining a high body mass and reducing foraging time may depend on the prevailing risk of predation. The model presented
in Paper III is an example of how physiological considerations and life-history
trade-offs may be included in the same model to understand and predict individual responses to environmental change.
General Discussion —
3
Experimental and observational ap-
proaches in population studies28
There were two bears yesterday and there are three
bears today. Does this mean that one bear has been
born, or that 101 have been born and 100 have died?
Wood (1994)29
Much, if not most, population studies on small rodents have been motivated
by a fascination for the pronounced population cycles observed in many populations of lemmings and voles. At least in Norway, one of the most commonly
asked questions addressed to population ecologists by the public is: “What causes
lemming-years, and why do they occur regularly?”. We usually give them answers
of the type formulated by the Finnish naturalist Ehrström in 1852, who wisely
introduced his report on mass movements of lemmings by saying: “We are not
aware of the causes of pronounced periodicity, while smaller fluctuations in nature, we understand only incomplete.”30
Although specific mechanisms are controversial, ecologists now seem to agree
that small rodent population cycles are due to delayed negative density-dependent
feedback processes, most likely caused by trophic interactions31 . My studies are
hence relevant to the question of what causes cycles because I address mechanisms for delayed density-dependent life-history traits in the overwintering co26
See specific examples in Stenseth and Mysterud (2002).
Le Maho (2002).
28
I have elaborated on some of the ideas presented here in an essay written as part of a PhDcourse in philosophy of science that I followed in 1999 (available at http://folk.uio.no/torbjore/):
“Questions, approaches and paradigms in studies of small rodent population cycles; a search for
the Holy Grail”.
29
Cited from Caswell (2000), p. 142.
30
Cited from Klemola (1999) who referred to Ehrstöm (1852).
31
Stenseth (1999) and Berryman (2002c).
27
13
horts. However, as this is just one aspect of the complex demographic processes
that shape the multi-annual population fluctuations, I will not speculate on how
relevant the mechanisms that I have observed (see section 2.2) are for the occurrence of regular population cycles32 . Instead, I will discuss some approaches
that may be taken to understand the mechanisms of such cycles; in particular
the roles of experimental and observational studies.
Levels of understanding; mechanisms, processes and patterns
Population dynamic patterns, such as cycles, may be understood at many levels.
At the ‘population level’ the dynamics may be understood in terms of direct and
delayed density dependent structures of population growth (as well as exogenous
forcing). This is often the aim of time-series analysis of animal numbers. The
dynamic results of density dependent structures involving non-linearity, seasonality and stochasticity are far from trivial, and mathematical theory is essential
in understanding the dynamics at this level33 . Such mathematical modelling may
be used to describe and predict the change in animal numbers without understanding the causes of variation in population growth at the ‘demographic’ or
‘mechanistic’ levels. With knowledge at the ‘demographic’ level one understands
the causes of variation in population growth in terms of variation in demographic
rates such as maturation rates, fecundity and survival without necessarily understanding the causes, or ‘mechanisms’, of this variation beyond descriptions
relating to present and previous densities, seasons or exogenous variables. With
such knowledge one are able to construct population dynamic models as some
sort of book-keeping of individuals34 . By knowledge at the ‘mechanistic level’ I
mean that one has some sort of understanding of what causes the variation in
the demographic rates (or life-history traits), for example that delayed density
dependent variation in survival is due to predation. Such ‘mechanisms’ may be
determined at a general level as in Box 1 or at a much more detailed level with respect to physiological and behavioural responses, and with respect to the nature
of the delays in trophic interactions (e.g. Fig. 2).
It is a goal of science in general to understand patterns and processes at a much
32
Preliminary simulations of matrix projection models suggest that the delayed density dependence in onset of spring reproduction observed in Paper IV may not be sufficiently strong
to cause population cycles on its own. However, if late onset of spring reproduction is associated
with particularly low survival during the first breeding attempt (as was observed at one of my
study sites, see Paper IV), then regular cycles may occur. I believe, however, that more information about the demographic processes is needed before such specific modelling becomes very
relevant (e.g., variation in onset of spring reproduction has less relevance for the multi-annual
cycles if population growth is generally high in the summer and direct density dependence is
strong).
33
E.g. Turchin (2003).
34
See ‘The individual based reductionistic school’ in Box 2. Note that population level models
may be required to obtain a theoretical understanding of the population level processes of the
dynamics, see Turchin (2003).
14
as possible detailed mechanistic level. Indeed, this is the motivation for most
population studies of small rodents. However, trying to understand population
dynamic patterns such as regular cycles in terms of the underlying mechanisms
is not an easy task. When undertaking this task I think it is important to reflect
upon two points, which may be posed as postulates:
Postulate 1 Different mechanisms may lead to the same population dynamics.
Postulate 2 The same mechanism may lead to different population dynamics.
Postulate 1 is obvious: a reduction in fecundity due to impaired food quality
may result in the same change in numbers as decreased survival due to predation.
Postulate 2 requires explanation. By ‘the same mechanism’ I mean “structurally”
or “qualitatively” the same mechanism. Structurally equal mechanisms may be
modelled by the same model, but parameter values may differ35 . Anyone who has
played with non-linear dynamic models knows that even minute changes in the
(system specific) parameter values may completely change the dynamics of the
model36 . The difference in parameter values required to move from a ‘non-cyclic’
to a ‘cyclic’ dynamic is often, in parts of the parameter space, far more subtle
than one would specify in a specific hypothesis. Thus, in some sense two ‘cyclic’
populations may have less in common than a ‘non-cyclic’ and a ‘cyclic’ population
in terms of the underlying demographic mechanisms of the dynamics (although
in the latter case the two populations, or systems, would occupy different regions
of the variable-space). Further, an apparently drastic change in the population
dynamic pattern (e.g. a drastic shift in the amplitude of the cycles) does not imply
that there necessarily has been any major change in the underlying mechanisms of
the dynamics (especially if non-linear density dependence is involved; see footnote
36).
General approaches
There are many approaches taken to study the causes of small rodent population
cycles. I have described three traditions, or schools, in Box 2. These approaches
look at the problem of what causes cycles in different, and largely complementary,
ways. There have, however been much discussion about “how science should be
35
See McCauley and Murdoch (1987) for a discussion on this topic.
For example, consider the discrete logistic equation, Nt+1 = rNt (K − Nt ), where Nt is
the population size at time t, and where r and K is the system specific parameters (‘intrinsic
growth rate’ and ‘carrying capacity’). Even this very simple model may produce a wide range
of dynamics including constant population size, cycles of any period and chaotic dynamics, and
the dynamics may switch from one type to another with small changes in r (May and Oster
1976; Schaffer 1988). The chaotic dynamics of such models often have a rather regular period
but erratic amplitude, and the dynamics may go through phases with highly variable amplitude
and periodicity (Schaffer 1986, 1988; see McCauley et al. (1999) for examples of coexisting
dynamic attractors in Daphnia—algae systems.).
36
15
done” between followers of the different traditions37 . Much of this discussion
highlights the strengths and weaknesses of the different approaches, and much of
the discussion just reflects that people have different scientific interests. However,
it is also clear that people genuinely disagree on fundamental science-philosophic
issues such as “what justifies a conclusion”. I will contribute to this debate
with some views on how observational and experimental studies may be used to
uncover the mechanisms of population dynamic patterns such as cycles.
The role of observations and experiments
When trying to uncover mechanisms of population dynamics (specifically how
life-history traits respond to variation in environmental state variables) one face
the problem that a large number of state variables may be relevant, and that
these variables do not vary independently. Observational studies aim to describe
the variation and covariation in the response variables (e.g. life-history traits)
and a set of relevant state variables. Specific causations, or mechanisms, cannot
be inferred from such observational data because correlations do not imply that
there are any casual links, although one may often be able to “narrow down” to
a set of plausible mechanisms when the patterns of variation and covariation in
the data are viewed in the light of general theory or specific models (Paper III
and Paper IV are examples of this).
In experiments one ensures that the focal state variables vary independently
(i.e. zero covariance) through manipulations and by assigning the experimental
units at random to different treatments38 . Non-focal state variables may be kept
constant (to increase precision at the expense of realism and inference space;
typical lab-experiments, e.g. Paper I) or be allowed to vary and co-vary in a
“natural” way (typical field-experiments, e.g. Paper II). Experiments are design
to reveal casual mechanisms. However, there will often be a nagging uncertainty
about the relevance of experimental results because the background environment
may not have been realistic (i.e., interaction effects that were not incorporated
in the experimental design may be important in the real system) or because
treatments were too extreme.
There are additional problems that are often overlooked when using experimental approaches to understand population level phenomena such as cycles or
population declines. For example, consider an experiment where predators were
removed from a random set of locations and where other random locations were
37
See discussions in Berryman (2002b), Chitty (1996), Krebs (1996; 2002), Lambin et al.
(2002), L
à omnicki (1992), and Stenseth (1999).
38
I am here using a rather strict definition of ‘experiments’. Some texts refer to any study
that involves manipulation as an ‘experiment’. However, the effects of any factors that are
not ensured to vary independently of other factors through the experimental design must be
interpreted in an ‘observational’ way (although the observations apply to the manipulated, or
perturbed, system). Indeed, both ‘observational’ and ‘experimental’ inferences may often be
drawn from the same study.
16
Box 2. Traditional approaches in studies of small rodent cycles
1. The phenomenological school: This is the oldest and most influential tradition
initiated by Dennis Chitty and co-workers after Charles Elton’s first descriptions of
population cycles in Norwegian lemmings. The approach follows to a large extent the
older naturalistic traditions of qualitative descriptions and categorisations (see Łomnicki
(1992) and Mayr (1997) for historical reviews). Central classifications in this tradition are
the distinction between ‘cyclic’ and ‘non-cyclic’ populations and a classification of
different ‘phases’ of the population cycles. The descriptions of such defined classes often
involve variation in population structure with respect to life-history traits, but this
variation is rarely linked to quantitative dynamic models. The motivation for much of this
work has been to find a simple and universal cause of cycles, and workers often assume a
priori that all cycles are caused by the same mechanisms (e.g. Krebs 1996; Krebs and
Myers 1974). The advocates of this school adhere to the hypothetico-deductive method,
and strongly believe that scientific progress is primarily made through proposing and
rejecting hypotheses (e.g. Chitty 1996; Lambin et al. 2002).
2. The population level analytical school: This school uses time-series data and
mathematical modelling to describe and understand the dynamic properties of single
populations as well as trophic interactions between species and population level effects of
inter-specific and intra-specific competition (see Stenseth 1999; Turchin 2003). The timeseries data applied often consist of only numbers of individuals (often snap-trap data), and
models usually ignore any structure of the populations and incorporate only average rates
of birth and death (or even just population growth). Analytical models have often been
used to determine the plausibility of proposed hypotheses for population cycles, and timeseries analyses have been used to suggest what types of mechanisms that are likely to
operate (especially if the time-series include auxiliary data (e.g., other species or climate)
or if data are obtained at two or more times each year so that season-specific parameters
can be estimated (e.g. Hansen et al. 1999a; Hansen et al. 1999b; Stenseth et al. 2002b)).
3. The individual based reductionistic school: This school builds models that may be
parameterised by estimates of demographic parameters or life-history responses of
individuals. Such models may be formulated as a “book-keeping” of individuals in
simulation models (DeAnglies and Gross 1992), but more commonly the structure of the
population is modelled through matrix projection models (Caswell 2000; Tuljapurkar and
Caswell 1997). Such models are often applied where one has data on individual animals
(capture-mark-recapture or radio-telemetry data), and where one is mainly interested in
predicting the development of the population rather than explaining any particular class of
dynamics. This is a much more bottom-up approach to understand population dynamics
than the two approaches mentioned above (Łomnicki 1992), and has only recently been
applied in small rodent studies (e.g. Lima et al. 1999; 2001).
left unmanipulated as controls (predator removal was done on a large scale and
the populations were independent). If, in one season, all control populations
consistently declined but the predator-removal populations all remained at high
densities, then one might be tempted to conclude that high predation was the
cause of the decline in the control populations. This would indeed be an un-
17
justified conclusion. The control populations could have declined for almost any
reason; the only thing one can conclude is that one was able to compensate for
whatever caused the decline in the control populations by removing predation.
Similarly, if the predator-removal populations as well as the control populations
all declined, then this does not refute the hypothesis that the control populations
declined due to predation. It may well be that the manipulated populations
declined for other reasons than in the controls. I will chose a somewhat complicated, but not unrealistic, example to illustrate the complexity of the problem:
Increased adult survival when predators were removed may have caused a saturation of breeding territories, which together with reduced food availability in
the denser populations prevented recruitment, and the population subsequently
declined due to a combination of low recruitment and lower survival due to senescence and poor food quality. This could have been a transitory behaviour of the
manipulated system. Alternatively, another feedback mechanism (for example
interactions with a pathogenic parasite) may have taken effect in the absence of
the predator-prey interactions39 .
The obvious remedy to the problems described above is to describe the response to the experimental treatment as a detailed account of the demographic
processes. This would take the form of detailed observational studies within
each of the populations, and direct comparisons of single variables in isolation
would be little meaningful because experimental treatments at the population
level will affect a set of dynamically interacting demographic variables as well as
environmental state variables. Interpretations of the demographic processes in
the experimental populations also have to deal with the fact that the structure of
eco-system may change after some time with experimental manipulations, making
it difficult to distinguish between direct and indirect effects of the experimental
treatments. Hence, the demographic processes in the manipulated system may
be little helpful in trying to understand the mechanisms in the unmanipulated
(control) system, as is usually the purpose of the experiment40 . Another problem with population level manipulations is that it is often difficult to manipulate
one state variable without simultaneously affecting others (e.g. it may be difficult to exclude predators without simultaneously excluding competitors, and it
is difficult to supplement food in unenclosed areas without attracting immigrants
39
See Berryman (2002a) for a discussion of feedback hierarchies and McCauley et al. (1999)
for a specific example in Daphnia laboratory systems.
40
A different situation is if the purpose of the population level experiment is to find out how
the system responds to the experimental treatment (e.g. to investigate how the population (or
community) dynamics is affected by pollution or the introduction of an alien predator). See Moe
et al. (2001; 2002a; 2002b) and Lindgjærde et al. (2001) for good examples of how time-series
analysis and mathematical modelling can aid the interpretation of population level experiments
investigating the influence of a toxicant on blowfly populations. See also Caswell (2000 chap. 10)
for how “Life table response experiments” can be used to decompose differences in population
growth between treatments to differences in the demographic rates.
18
and competitors41 ). Perhaps it would be better to start with detailed observational studies just in the control populations? With a detailed understanding of
the demographic processes in the unmanipulated system one would be in a better
position to design such population level experiments in order to learn more about
the mechanisms in the system42 .
An alternative approach is to use experiments to distinguish between different
mechanisms for variation in individual level traits (such as time or probability of
maturation or death) rather than population level patterns. Such experiments
manipulate the environment of the individuals (or their internal state, such as parasite infection) rather than applying treatments to whole populations (Paper I
and Paper II are two examples). However, to be sure that such experiments
are relevant for the dynamics of a particular system, one would also here need
detailed observational data of the system.
Research strategies
An approach embraced by many followers of the phenomenological tradition (see
Box 2) is to, through experimental testing, search for factors that are necessary
or sufficient causes of given population phenomena (e.g., cyclic declines)43 . “A
necessary condition must always be present for an effect to occur, but may not
be sufficient to cause the effect. A cause may be sufficient to result in an effect,
but if the same effect occurs in its absence, the cause is not necessary”44 . While
these distinctions may be useful in structuring our research agendas, they may
also be misleading when used uncritically to interpret experimental results. For
example, proponents of this approach will argue that ‘predation’ is a ‘necessary’
cause of cyclic population declines if experimental removal of predators prevents
the decline from occurring. Conversely, if the populations decline despite removing predators, then ‘predation’ is not a ‘necessary’ cause of population declines.
Hence, considering my example above, predation may be deemed ‘necessary’ to
cause declines even if predation rates are constant, and ‘predation’ may be concluded to be ‘unnecessary’ to cause the declines even when predation is in fact
representing the dominant delayed feedback mechanism causing cyclic declines.
Thus, when used to interpret experimental results in this way, the terms ‘neces41
See Boutin et al. (2002) for a specific study on snow-shoe hares where these problems makes
the interpretations of the results difficult.
42
My critique is not that population level experiments are not useful. One may learn much
about the dynamics of the systems by studying the response to perturbation (i.e., exciting some
state variable outside its normal range or trying to de-couple some feedback mechanisms by
e.g. predator removal). However, to make sound interpretations, detailed information about the
response to the whole system, as well as a theoretical understanding of the dynamics, is required.
Only when a detailed understanding of the system is obtained and detailed predictions are made
will population level experiments be useful in testing dynamic models.
43
See Chitty (1996) and Lambin et al. (2002) for advocacies of this approach
44
Definitions in Lambin et al. (2002).
19
sary’ and ‘sufficient’ just becomes semantic classification labels that tell us very
little about the mechanisms responsible for the population phenomena.
Biologists have repeatedly been criticised for an overemphasis on statistical
null-hypothesis testing (p—values) at the expense of quantitative interpretations of
biologically meaningful parameter estimates45 . This critique is particularly relevant for analysis of observational data where the null-hypotheses being tested are
almost always false on a priory grounds: Two individuals, groups of animals or
populations in nature are never equal. Whether there is a statistically significant
difference between them is only a matter of how much effort we put into estimating their values, relative to how different the values really are. Hence, if used
uncritically, the ‘hypothesis testing’ approach may be used to classify predictor
variables as “necessary” or “unnecessary” with little concern about whether the
information is available in the data to justify such conclusions and whether the
size of the effects (contrasts) are biologically relevant. The outcomes of statistical
hypothesis tests of specific effects are also highly dependent on what other effects
are included in the models applied. Hence, since the null-hypotheses are given
by the selected model, hypothesis tests are meaningless when models are not
selected a priori but instead are selected based on some sort of data-based procedures (e.g. step-wise regression). Indeed, ad hoc methods for model selection,
such as step-wise regression, may give rather arbitrary results if the predictor
variables co-vary46 .
An alternative and more recently developed approach is the ‘information theoretic approach’, which is based on theoretically well-founded methods for model
selection and focus on drawing inferences from a set of pre-selected ‘candidate
models’47 . This approach recognizes that nature is infinitely complex and one
aims to find good ‘approximating models’; statistical models with a level of complexity (number of parameters) that reflects the amount of information in the
available data. Model selection is based on objective information criteria such as
the AIC (‘Akaike’s Information Criterion’), which are based on finding the optimal trade-off between models yielding precise but biased predictions (too simple
models) and models yielding predictions with low bias but also low precision
(too complex models). Statistical inference is then based on the AIC-ranking
of (preferably) competing and biologically meaningful models, as well as on the
confidence intervals of the parameter estimates conditional on specific models48 .
One of the benefits of this approach is a more objective presentation of obser45
See Anderson et al. (2000), Boyce (2002), Johnson (1999) and Yoccoz (1991). See also
http://www.cnr.colostate.edu/∼anderson/null.html for an extensive compilation of more than
300 papers and books that are critical to the use of hypothesis testing in biological and medical
sciences.
46
See Burnham and Anderson (2002 pp. 43—45)
47
Burnham and Anderson (2002) and Anderson et al. (2000).
48
By the use of ‘Akaike weights’ one may also compute parameter estimates and confidence
intervals that are conditional on a set of models (i.e. “unconditional” of any specific model).
See Burnham and Anderson (2002 pp. 149-203).
20
vational data with many co-varying (confounded) predictor variables49 . I have
used this approach in a rather exploratory data-analysis in Paper III50 .
In my opinion
I believe that, in order to understand “What causes small rodent population cycles?”, it is useful to structure our research strategies around the ‘levels of understanding’ presented above. Knowledge at the ‘demographic level’ is essential
because this describes the fundamental processes of the dynamics: births and
deaths (as well as immigration and emigration if only a local population is considered)51 . To have any knowledge at the ‘mechanistic’ level of the dynamics one
must also have an understanding of the dynamics at the ‘demographic level’52 .
Despite the fundamental importance of knowledge at the demographic level,
there are very few populations of small rodents where a detailed understanding
of the dynamics at this level has been obtained53 . The existing demographic
descriptions of cyclic dynamics of small rodent populations are very general
and are related to “cyclic phase” rather than quantitative estimates of densitydependence54 . These descriptions build on rather fragmented data and lack in
particular quantitative descriptions of season specific rates of maturation, fecundity, juvenile survival and recruitment. Perhaps in an eager to test specific hypotheses about the causes of population cycles, one has skipped trying to obtain
a detailed quantitative understanding of the cyclic dynamics at the demographic
level55 .
49
Whether one use the ‘hypothesis testing’ approach or the ‘information theoretic’ approach, it
is of course important to recognise both the variation and co-variation of the predictor variables
in the data.
50
I realize that the presentations in Paper I (and partly Paper II) may be criticised for an
“overemphasis on statistical hypothesis testing”, although I have also focused on magnitude of
the effects and confidence intervals.
51
If demographic rates are constant, then the population will either increase exponentially
towards infinity or decline exponentially towards extinction. Variation in population growth
rate can only occur due to variation in the demographic rates. In the long run, population size
stays within limits due to density dependent demographic rates (Turchin 2003; Caswell 2000).
52
To simply know that, for example, delayed density-dependent quality of the vegetation
cause large variation in onset of spring reproduction does not give any information about how
population growth is affected unless one also know the other demographic rates (e.g. fecundity
and survival). Indeed there may be large variation in a given (season specific) demographic
rate that has very little impact on population growth (see Caswell 2000, chap. 9). Further, to
understand the impact on the long-term (multi-annual) dynamic patterns, one must also know
the density dependent relations of other (season specific) demographic rates.
53
See Lima et al. (2001) for a good example of a detailed demographic description of the
population dynamics of a neotropical small rodent (see also Crespin et al. (2002), Graham and
Lambin (2002), and Yoccoz et al. (1998) for studies focusing on survival estimates).
54
e.g. Krebs and Myers (1974).
55
Probably the main reason for the lack of detailed long-term demographic studies in small
rodent populations is that sampling of such data with sufficiently long duration, high intensity
and large spatial scale is expensive and requires long-term commitment by researchers and
21
The best way to obtain detailed knowledge at the ‘demographic level’ is
to carry out long term observational capture-mark-recapture programs56 . Such
studies should focus on describing the variation in demography and life-history
traits in relation to present and previous densities in a seasonal context (see Fig.
1). When an understanding of the dynamics at the demographic level is obtained,
one may use experiments (together with observational studies) to find the mechanisms of this variation at a general or more specific level (see Box 1 and Fig. 2).
Population dynamic theory57 may then help us to understand the dynamics at
the population and eco-system levels. In addition, population level experiments
may be used to learn more about the properties of the system. However, considering the problems I have addressed above, I believe such experiments should
be viewed as “observations of perturbed systems” rather than as tests of specific
mechanisms.
I believe that, when trying to find the mechanisms of demographic variations,
a reductionistic approach can be very useful. The studies by Negus, Berger and
co-workers on reproductive strategies of mountain voles (Microtus montanus) is
a good example: Noticing that initiation and cessation of seasonal reproduction
in this species was closely correlated to the availability of vegetatively growing
plants, they hypothesised that that initiation of reproduction was triggered by
some component in the growing plants, and demonstrated that this was indeed the
case in carefully controlled laboratory experiments (Negus et al. 1977; Sanders
et al. 1981). By bioassay methods they further succeeded in isolating the active
compound to ‘6-MBOA’ (Sanders et al. 1981). This secondary plant compound
that is thought to be abundant in all growing grasses (Moffatt et al. 1991; Nelson
1991), and hence serve as a general cue indicating that the plant growth season has
begun. Field experiments have later demonstrated that supplying this compound
to Microtus montanus (Berger et al. 1981) and Microtus townsendii (Korn and
Taitt 1987) may precipitate onset of spring reproduction by several weeks. A
next step would be to investigate this mechanism explicitly in cyclic populations
where delayed density-dependent onset of spring reproduction is observed (see
Fig. 2 and the Discussion of Paper IV). Another example of a reductionistic
approach is to use observational radio-telemetry studies58 to find the causes of
the patterns of variation in survival discovered by demographic analyses. Despite
the success of reductionistic approaches in other disciplines of science (such as
funding agencies (see discussion in Yoccoz et al. 1998). However, there has historically also
been an assumption that the demographic mechanisms of small rodent population cycles is very
general (within and across populations and species), and, since one has been searching for simple
and universal causes of the cycles, the existing descriptions may have been regarded as sufficient
(see Chitty (1996) and Krebs (1996)).
56
Monitoring individually marked animals with sufficient intensity (monthly or shorter intervals) to obtain meaningful estimates of maturation rates, recruitment, survival, etc. See
Williams et al. (2002) for statistical methodology.
57
E.g. Caswell (2000) and Turchin (2003).
58
See Steen (1995).
22
physics, biochemistry and genetics) such approaches has had a rather late entry
in ecological science59 .
There has been a tendency to disregard the usefulness of observational work
to understand the mechanisms of small rodent population dynamics because “observed correlations do not imply causations” (see above)60 . I think this has lead
to a preoccupation of experimental studies with little discussion about the limitations and difficulties of experimental approaches (see above). Thus one has
often tried to test specific hypotheses that do not have a sufficient observational
support. For example, much effort has been devoted to test the hypothesis that
interactions with specialist predators “drive” the population cycles61 , without
thoroughly establishing that delayed density dependence in survival is an important part of the population dynamics at the demographic level. Another example
relates to Krebs’ (1978) genetic-based version of Chitty’s polymorphism hypothesis: for more than two decades much effort was made to test the predictions
of this hypothesis until it was discovered that one of the key assumption of this
hypothesis, strong heritability of variation in life-history and behavioural traits,
did not hold (Boonstra and Boag 1987).
An essential benefit of using open-minded exploratory observational studies
to discover potential mechanisms (at some level of detail), is that one may discover mechanisms that one have not thought about in the first place62 . I think
that observational studies where the patterns of variation and co-variation are
compared to predictions from theoretical models and general theory can be very
useful in “narrowing down” the set of possible specific mechanisms. Examples of
this approach are presented in Paper III and Paper IV.
59
See L
à omnicki (1992) and Caplan (1988) for historical reviews and discussion on this topic.
Chitty (1996; p. 17), for example, takes the view that even observational studies must always
have replicates and controls, arguing that the best (if not only) way of discovering what factors
are relevant for a particular phenomenon is to search for factors that are consistently associated
with the phenomenon (e.g. cyclic declines) but not its control (other cyclic phases). Chitty
seems to disregard the usefulness of more reductionistic approaches (i.e., study the properties
and interrelations of the elements of the system, and from this try to predict how the entire
system works — just like one may understand how a watch works by taking it apart and study
its components and construction; see footnote 59). He is quite harsh in his critique: “That
correlations are worthless without controls is one of the features that makes the critiques of
population ecology doubt whether it can be regarded as a science.” (Chitty 1996; p. 17)
61
See review in Graham and Lambin (2002).
62
When seeking to discover patterns and mechanisms by exploratory data-analysis, one must
recognize the dangers of data-dredging in such analysis (Burnham and Anderson 2002). Nevertheless, I think it is important that such data-analysis is not entirely centred around a priori
models that are based on our current ideas about the phenomena: to discover new mechanisms
one must have an open mind.
60
23
4
Summary
Overwintering field voles (Microtus agrestis) in Kielder forest show large variation
in life-history traits such as wintering body size and the time that reproduction
is initiated in the spring. Initiation of spring reproduction appears to be delayed
density dependent with a one-year lag (Paper IV). Whether such delayed density
dependence of life-history traits is caused by intrinsic mechanisms or are due to
delayed feedback from trophic interactions has been a long-standing source of
controversy (see Box 1).
A field transplant experiment where voles were swapped between locations
during mid-winter showed that the characteristics of the source population are
soon lost as the animals adapt to their new environment (Paper II). Hence, variation in life-history traits of the overwintering voles seems to be primarily caused
by flexible responses to the immediate environment. In a study of energy expenditure of wintering voles, we further conclude that variation in body size between
locations is primarily caused by variation in the energetic constraints rather than
foraging responses to variation in risk of predation (Paper III). Finally, variation in the initiation of spring reproduction seems to be related to the time that
breeding conditions improve rather than responses to cues reflecting variation
in prospects of survival and future population growth (Paper IV). I have also
presented more general theoretical models for optimal body size during winter
(Paper III) and optimal onset of spring reproduction (Paper IV). Although I
have not studied specific mechanisms explicitly, I suggest that the empirical results may be due to delayed density dependent quality of the vegetation (see Fig.
2). More information is needed about the demographic processes in the system to
assess how this affects the multi-annual population dynamic patterns. I discuss
how observational and experimental approaches may be used to investigate the
link between such mechanisms and the population dynamic patterns (Section 3).
24
5
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6
List of Papers
Paper I:
Ergon, T., J. L. MacKinnon, N. C. Stenseth, R. Boonstra, and X.
Lambin. 2001. Mechanisms for delayed density-dependent reproductive traits in field voles, Microtus agrestis: the importance of
inherited environmental effects. Oikos 95:185-197.
Paper II:
Ergon, T., X. Lambin, and N. C. Stenseth. 2001. Life-history traits
of voles in a fluctuating population respond to the immediate environment. Nature 411:1043-1045.
Paper III:
Ergon, T., J. R. Speakman, M. Scantlebury, R. Cavanagh, and X.
Lambin. 2003. Optimal body size and energy expenditure during
winter: Why are voles smaller in declining populations? The
American Naturalist (in press).
Paper IV:
Ergon, T. 2003. Optimal onset of seasonal reproduction in multivoltine organisms: When should overwintering small rodents start
breeding? Manuscript.