Population dynamics in a cyclic environment: consequences of cyclic food abundance on tawny owl...

Population dynamics in a cyclic environment:

consequences of cyclic food abundance on tawny

owl reproduction and survival

Patrik Karell1*, Kari Ahola2, TeuvoKarstinen3, Aniko Zolei4 and Jon E. Brommer1

1Bird Ecology Unit, Department of Biological and Environmental Sciences, P.O. Box 65 (Viikinkaari 1), FI-00014 University

of Helsinki, Finland; 2Tornihaukantie 8D 72, FI-02620 Espoo, Finland; 3Juusinkuja 1, FI-02700 Kauniainen, Finland; and4Duna-Ipoly National Park Directorate, 1021 Budapest, Hu¢vosvolgyi ut 52

Summary

1. Understanding which factors regulate population dynamics may help us to understand how a

population would respond to environmental change, and why some populations are declining.

2. In southern Finland, vole abundance shows a three-phased cycle of low, increase and decrease

phases, but these have been fading out in recent years. During five such cycles (1981–1995), all

tawny owls Strix alucowere censused in a 250-km2 study area, and their reproduction and survival

were monitored.

3. Males and females showed similar dynamics, but experienced breeders recruited more offspring

and had higher survival than first breeders. Offspring recruitment, but not survival of breeding

individuals varied in accordance with vole abundance.

4. The population’s numerical response to prey abundance was primarily due to first-breeding

individuals entering the population in the increase phase when immigration was the highest. First-

breeding birds were younger, but experienced breeders were older in more favourable vole years.

5. A stage-specific matrix population model integrating survival and fecundity showed that,

despite obvious variation in fecundity between vole cycle phases, this variation had limited impor-

tance for overall tawny owl population dynamics, but that the survival of experienced breeders

during the low phase is most important for population growth.

6. Model and data agreed that the vole cycle drives the dynamics of this avian predator by limiting

the recruitment of new breeders during the low phase. Population dynamics hence differ not only

from the classic example of the species in a more temperate region in the UK where the number of

territories is stable across years, but also from the dynamics of other avian vole predators in Fen-

noscandia where the recurring crash in vole abundance drastically lowers adult survival thereby

creating vacancies.

Key-words: Clethrionomys glareolus, life stage, Microtus agrestis, population cycle, predator-

prey interaction

Introduction

Since the seminal work of Charles Elton (1927), population

cycles of predators and their prey have been a focal topic of

animal ecology and population dynamics (Southern 1970;

Hanski, Hansson & Henttonen 1991; Hanski & Korpimaki

1995; Krebs et al. 1995; Lambin, Petty & MacKinnon 2000;

Lindstrom et al. 2001; Gilg, Hanski & Sittler 2003; Sundell

et al. 2004; Korpimaki et al. 2005a, b). Cyclic fluctuations in

the abundance of herbivores are commonly found in popula-

tions on high latitudes and ⁄or high altitudes (Lindstrom

et al. 2001). Because these herbivores typically are basal to

the ecosystem, the effect of the cycles in their abundance

reverberates across the foodweb (Ims,Henden&Killengreen

2008). The consequences of herbivore cycles are thus appar-

ent also on higher trophic levels, even when these predators

do not directly drive the cycle. This perspective is in contrast

to the classic view of predator–prey dynamics as a Lotka–

Volterra type of dynamics, where the predator drives the fluc-

tuations in the abundance of the prey and shows similar

cycles as its prey but lagging in time. A classic example of

predator-prey population dynamics where the predator’s*Correspondence author. E-mail: [email protected]

Journal of Animal Ecology 2009, 78, 1050–1062 doi: 10.1111/j.1365-2656.2009.01563.x

� 2009TheAuthors. Journal compilation� 2009 British Ecological Society

population size does not track the fluctuations in the abun-

dance of its main prey, voles, is provided by Southern’s

(1970) study of tawny owls Strix aluco Lin. in southern Eng-

land.

In Northern boreal environments in Fennoscandia, tawny

owls and other birds of prey occur in such low densities that

they do not have the potential to impose sufficient predation

pressure to make a serious impact on the vole dynamics and

hence are, themselves, not driving the cyclic fluctuations in

their main prey (Korpimaki et al. 2002; Norrdahl et al. 2004).

Resident owl species (Ural owlStrix uralensisPall. and tawny

owl) respond to fluctuations in food abundance by adjusting

their reproduction, but – once they have occupied a territory

– do not disperse to breed where there are plenty of voles as

other (semi-) nomadic species do (Andersson 1980).

By refraining from breeding when food is scarce, the pro-

portion of breeding site-tenacious owls can increase rapidly

with increasing numbers of voles, without any delay (South-

ern 1970; Brommer, Pietiainen & Kolunen 2002; see also

Korpimaki & Norrdahl 1989, 1991; Rohner 1996). On the

other hand, the mortality of territorials (and their offspring)

is drastically increased when the voles crash in abundance

every third year (Brommer et al. 2002). This recurring ‘bottle-

neck’ creates opportunities for prebreeding individuals (float-

ers; Rohner 1996) to start breeding when food abundance

increases again. As a consequence, fluctuations in food abun-

dance generate changes in the population’s age distribution,

as the proportion of young, first-breeding individuals in the

population increases when food abundance becomes more

favourable (Brommer, Pietiainen &Kolunen 1998).

For a variety of reasons, young and ⁄or inexperienced indi-

viduals may respond differently to environmental fluctua-

tions than older and experienced ones (Metcalf & Pavard

2007). The change in age structure over a cycle therefore

potentially creates marked variation in the population’s

reproductive output and survival. One powerful way to

incorporate such individual differences in performance is to

group the individuals in relevant stages. In general, such

grouping has important consequences for the understanding

of population growth and dynamics (Caswell 2001). In case

of a population living in a cyclic environment, changes in

population structure across the cycle need to be incorporated

and the consequences of a variable population structure for

reproduction and survival need to be understood when mod-

elling cyclic population dynamics.

In many places, and particularly in Fennoscandia, herbi-

vore cycles are fading out (Ims et al. 2008), which is expected

to present a major change in the environment for many other

species that are (partly) dependent on these herbivores. Avian

predators of voles are prime candidates for species likely to

be negatively affected by changes in the vole dynamics

(Hornfeldt, Hipkiss & Eklund 2005). Although the tawny

owl is a generalist predator in Northerly populations, it

almost non-exclusively uses voles as a prey when vole abun-

dance is high (Petty 1999) and it is highly dependent on voles

for reproduction (Kekkonen et al. 2008). In this study, we

determine how reproduction and survival, which together

define population growth, of different stages of tawny owls

respond to variation in food supply during 15 years of persis-

tent cycles in vole abundance. Our aim was to provide a

benchmark for understanding the cyclic tawny owl – vole sys-

tem to evaluate changes in this system when vole cycles fade

out. In particular, we aim to assess the relative importance of

variation in reproduction and survival for the dynamics of a

predator population subsisting on prey that shows periodic

fluctuations in its abundance. Previous studies have shown

that reproduction and survival vary in such an environment

(e.g. Brommer et al. 1998), but no study has – to our knowl-

edge – quantitatively compared the importance of such varia-

tion for the population dynamics. It is not obvious how

variation in reproduction can be compared with variation in

survival without formal consideration in a population

dynamical model. We therefore construct a matrix popula-

tion model based on our study of fecundity and survival to

perform an elasticity analysis, in which we quantitatively

resolve how a change in survival and fecundity rate of differ-

ent stages across the vole cycle would alter population

growth rate.

Materials andmethods

STUDY AREA

Tawny owls were studied in a study area of c. 250 km2 in southern

Finland (60�15¢ N, 24�15¢ E). Between 1975 and 1980, the study area

was established by setting up boxes. Pairs nested almost exclusively

in nest boxes, which were provided in high abundance (c. 125 were

available from 1980 onwards). Tawny owls were also ringed and con-

trolled by ornithologists in regions surrounding the study area. We

here consider data collected from the study area during 1981–1995,

when vole dynamics were cyclic.

FIELD PROTOCOL

The census and handling of all tawny owls were carried out by KA

and TK. Territorial tawny owls are rather vocal, especially prior to

the breeding season. In early spring (March–April), recordings of

hooting tawny owls were played at regular locations along roads

transecting the study area. The location where tawny owls responded

was marked on a map. Starting at the end of April, all boxes and

other possible breeding sites were checked. Considerable effort was

put into finding the nests of all tawny owls by searching for natural

nest sites and using new boxes set up by private individuals in the

approximate area where a hooting owl was recorded and where a

breeding thus was expected.We here distinguish between the number

of territories and the number of pairs that are breeding, where the

former refers to the number of sites where tawny owls responded to

the playback, and the latter to the number of pairs observed breed-

ing.

Practically all females and males were trapped when the offspring

were 1–2 weeks old. Brooding females were taken from their nest

boxes in the evening by netting them at the opening of the nest box.

After handling, the female was put back into the nest box and a

swing-door trap for the male was mounted in front of it and left over

night. In the following, morning traps were checked and the males

were handled. When the oldest chick was c. 25 days old, all offspring

in the brood were ringed with a unique aluminium ring to allow life-

Tawny owl population dynamics and the vole cycle 1051

� 2009 TheAuthors. Journal compilation� 2009 British Ecological Society, Journal of Animal Ecology, 78, 1050–1062

long individual identification. Adult birds were categorized into three

age classes at ringing )1, 2 and +2 years old – using characteristic

patterns on juvenile and adult primary and secondary feathers (Ah-

ola & Niiranen 1986). Individuals were considered as first breeders if

they were unringed when first caught, or if they had been ringed as

pulli and did not have a previous breeding record. Because consider-

able effort has been put into finding all nests and ringing all nestlings,

we considered all individuals that were unringed when first caught as

immigrants.

PREY ABUNDANCE

Spring and autumn snap trapping of small mammals was carried out

by KA and TK within the study area from 1981 onwards to estimate

the abundance of prey. Snap trapping was conducted in two localities

on each trapping event: one in the eastern part of the study area and

one in the western part of the study area. Each trapping locality con-

sists of open (field ⁄ clear-cut) habitat and forest habitat. Traps were

set as a transect of 16 trapping spots (15 m between) with three traps

each, giving a total of 48 traps per habitat (96 traps per replicate). All

traps were triggered for two consecutive nights (192 traps for two

nights = 384 trap nights in total per trapping). Not only field voles

Microtus agrestis and bank voles Clethrionomys glareolus were

caught in the traps, but also, to some extent, wood mice Apodemus

flavicollis and shrews Sorex araneus. We include all species in the

analysis of prey abundance (number of individuals caught per 100

trap nights). Based on these trap indices, we categorized years into

low, increase and decrease phases of the vole cycle (see, e.g. Brommer

et al. 2002).

L IFE-H ISTORY STAGES

We here distinguish owls according to their sex and whether they are

first breeders or have bred before (termed experienced breeders). Dif-

ferences in reproductive performance between the stages of first and

experienced breeders are expected (Metcalf & Pavard 2007). Our

main motivation for classifying individuals into first vs. experienced

breeders is that such a classification is possible in a non-arbitrary way

for any individual in a population, whereas ageing all individuals is

not always possible. For example, age classes in tawny owls older

than 2 years cannot be distinguished (Ahola & Niiranen 1986), such

that the exact ages of only a subset of individuals is known, and simi-

lar constraints apply to most avian populations. We here consider

the sex of an individual as a second-stage variable. An individual’s

sex may be particularly important in birds of prey, where males are

smaller and mainly provide food (almost exclusively so prior and

during incubation), whereas the larger females incubate and defend

the brood. Such sex differences may conceivably lead to differences

in their sensitivity to changes in the environmental conditions that

occur when food supply fluctuates. Sex differences in reproduction

and survival in birds of prey have rarely been examined (but see Altw-

egg, Schaub &Roulin 2007), mainly due to the difficulty of capturing

males.

STATIST ICAL ANALYSES

Statistical analyses were carried out using r 2.5.0 (R Development

Core Team 2007). To investigate associations between vole and owl

numbers, we assumed a saturating nonlinear response and used linear

regression with square root of the prey trapping indices in the preced-

ing autumn to explain the number of breeding individual owls. A sat-

urating response is expected and biologically motivated as follows: (i)

voles are the only available prey of tawny owls in Finland prior to

breeding (most birds migrate); and (ii) a previous study of Finnish

tawny owls has shown that breeding parameters correlate with voles

but not with other prey items (Kekkonen et al. 2008). In the other

analyses (on reproduction and survival), vole phase was entered as a

three-level categorical variable (low, increase and decrease phases).

We grouped years according to vole cycle phases rather than describ-

ing variation across years due to vole abundance (i.e. as a continuous

function of prey abundance), because the phase-based approach cap-

tures not only the present, but also the future dynamics of the vole

cycle. For example, in March–April when the breeding season for

tawny owls starts in Finland, the vole abundance is reasonably high

both in vole increase and in vole decrease phases, but the autumn

density of voles is high in the former whereas voles are almost absent

in the latter (Karell et al. 2009). Because we study five vole cycles, the

phase-based approach constitutes a replicated design (each phase

occurs five times). Hence, to explicitly explore whether such categori-

zation indeed captures an intrinsic part of the dynamics, we specifi-

cally modelled the effect of years (nested in phase) in all analyses

[denoted ‘year(phase)’ in the results]. In case phases are irrelevant to

the dynamics and variation across years is in fact the main driver,

analyses should show that phase is insignificant.

The effects of breeding experience on breeding activity and recruit

production were analysed separately for males and females, and we

thus onlymade a qualitative comparison of the sex-related differences

inbreedingperformance.Dataonboth sexeswerenot combined in the

sameanalysesbecause thiswouldrequireanalysing thedataperpair.

Survival of first and experienced breeders in relation to the vole

cycle was estimated using capture–mark–recapture (CMR) method-

ology on live encounters data (Cormack–Jolly–Seber model, CJS)

using the program MARK (White & Burnham 1999). With the CJS

model, one can separate survival probability (F) from recapture

probability (P) using a maximum likelihood approach. We used data

on individual encounters from 1981 to 1996 in order to also get an

estimate of survival for the last year of the study (1995). Individuals

were categorized into males and females to test for sex-specific differ-

ences in survival. We built a full model coding for sex and including

two experience categories (first breeder denoted by ‘first’ and experi-

enced breeder denoted by ‘exp’) and full-time dependence in survival

and recapture [F(sex · timefirst ⁄ timeexp)p(sex · timefirst ⁄ timeexp)].

To correct for overdispersion, we calculated the parameter c as the

ratio of the observed deviance of the full model over the mean devi-

ance achieved from 500 bootstrap simulations of the same model.

Time dependence was replaced by vole cycle phase to test if F and p

of the different age and sex categories were affected by the vole cycle.

Models were built by entering the vole cycle phases directly into the

Parameter IndexMatrix in programMARK as a three-class variable

coding for low, increase and decrease phase. Therefore, F and p

could be either time dependent (time), constant (c) or vole phase

dependent (phase) for each experience class (first or experienced bree-

der) and sex. Models were ranked on the basis of their quasi-likeli-

hood Akaike information criterion (QAIC) calculated as

)2 ln(L) ⁄ c + 2K + (2K(K + 1) ⁄ (n – K – 1), where L is the likeli-

hood of the model, K the number of parameters and n the effective

sample size. We used the following approach in model selection: for

‘F’ we first tested all candidate models nested under the full model

(n = 22) while keeping ‘p’ constant at ‘sex · time ⁄ time’. Models

including ‘sex’ as a factor did not receive high support (QAIC), and

therefore two-way interactions of ‘sex · experience’ and ‘sex · -

phase’ were not tested. The models with the ighest QAIC among

these 22 models (QAIC weight more than 5%, n = 5) were then

tested with all the different ‘p’ possibilities (n = 22) giving a total of

1052 P. Karell et al.


127 models [n = 22 + (5 · 21)]. The five models with the highest

QAIC were ranked in an equal order for all the different recapture

possibilities. Because we did not find strong support for a single

model (see Results), we obtained estimates of survival and recapture

probabilities through model averaging. Model averaging calculates

an average value over all models in the candidatemodel set with com-

mon elements in the parameter structure, weighted by normalized

QAIC model weights [exp()DQAIC ⁄ 2) ⁄P

(exp()DQAIC ⁄ 2))]. Formore information on the modelling approach, see Burnham &

Anderson (1998) andCooch&White (2001).

Proportional data on population composition were analysed using

generalized linear models (GLM) with binomial errors and logit

links.We estimated reproductive success in the population as the pro-

portion of recruits per number of eggs produced in a given year. To

allow qualitative comparison between sexes recruitment probability

was analysed separately for males and females in (nested) GLMswith

binomial errors emphasizing vole cycle and experience-dependent

effects. We constructed the minimal adequate models by stepwise

backwards modelling in which we compared the models using likeli-

hood ratio tests (LRT, v2; Crawley 2002). We also compared the

models using AIC. Both the AIC and LRT produced the same mini-

mal adequate models for the data. Although our data on reproduc-

tion is individual-based, we chose to analyse it on an annual basis.

Our aim with the analysis of recruitment was to evaluate the popula-

tion-level productivity of first and experienced breeders, and hence,

to deliver a general picture of reproduction in relation to the vole

cycle as a baseline for the population dynamical model. Although

generalized linear mixed models (LMMs) can account for individual

characteristics, we chose not to use such a model because the focus is

on the population-level contribution of first and experienced breed-

ers.We thus accepted some level of pseudo-replication (120 ⁄ 258 indi-viduals are included both as a first breeder and as an experienced

breeder) as the preference of a straightforward and robust method of

analysis. We report in the results the minimal adequate models

achieved through the stepwise backwards procedure.

Age composition was based on individuals that were ringed as nes-

tlings and whose age was therefore known exactly. The data were

analysed with LMMs with ‘individual identity’ as a random effect,

because certain individuals occurred repeatedly in the analysis. We

constructed the minimal adequate model in the same manner as for

GLMs by stepwise backwards modelling in which we compared

LMMs solved under maximum likelihood using LRT between mod-

els. In an LRT, )2 times the difference in the likelihood of a model

with and without an effect was tested as a chi-squared value with the

effect’s degrees of freedom (Pinheiro & Bates 2000). The significance

of the random effect was also based on an LRT. Significance of fixed

effects in LMMswas based on F-tests.

Results

Atotal of 351 clutchesweremonitored in the studypopulation

between1981and1995.Oftheseclutches,300producedhatch-

lings and 274 succeeded to fledge offspring. In 294 breeding

attempts, the femaleparentswere caughtand identifiedand, in

278cases,also themaleparentswerecaughtandidentified.

PAIR FORMATION

Pairs were formed assortatively with respect to breeding

experience as most (68%, 125 of 184) pair formations were

between either two inexperienced breeders or two experi-

enced breeders (Table 1). After first pair formation mate

change was rare: in total 32 of 122 (26%)males changedmate

during the study, of which 13 changed more than once [in

total 51 mate changes of which 43 changes (84%) was due to

death of the partner]. Even fewer females (29 ⁄ 129, 16%)

changed mate, with only six females changing more than

once (in total 37 mate changes of which 28 changes (76%)

was due to death of the partner).

TERRITORY OCCUPANCY AND BREEDING ACTIV ITY IN

RELATION TO THE VOLE CYCLE

Territories were considered occupied when an owl responded

to play-back calls prior to the breeding season. Once estab-

lished on a territory, most owls remained faithful to their ter-

ritory. Only 23 ⁄ 122 (19%) males and 29 ⁄ 136 (21%) females

changed territory during the study. Territory change

occurred after the first breeding event in 15 ⁄ 23 cases (65%)

for males and in 19 ⁄ 29 (66%) of the cases for females.

There was variation in the number of occupied territories

between vole cycle phases (Fig. 1a): in low phases 28 ± 4 ter-

ritories were occupied and the number increased to 34 ± 3

territories in increase phases and further to 39 ± 2 territories

in decrease phases [anova: phase, F2,12 = 3.76, P = 0.05;

decrease phase vs. low phase, t12 = 2.74, P = 0.02; year

(phase) dropped]. This difference in territory occupancy

between years was less clear when prey abundance in the pre-

ceding autumn was entered as an explanatory variable

(Fig. 1a; linear regression: R2 = 0.14, B = 2.70 ± 1.96,

F1,12 = 1.89,P = 0.19).

The proportion of occupied territories where the residing

owl pair attempted to breed increased as vole abundance

increased (Fig. 1b; logistic regression, B = 0.27 ± 0.10,

v2 = 7.39, d.f. = 1, P = 0.007). Also when categorizing

vole abundance into vole cycle phases, a substantially higher

proportion of territorial pairs bred in the increase and

decrease phase years than in the low phase years [Fig. 1b;

GLM: phase, v2 = 9.64, d.f. = 2, P < 0.0001; year(phase)

dropped].

Some nests failed at an early stage before parents could be

caught and identified (16.2%, 57 ⁄ 351). There was no differ-

ence in the proportion of failed nests between vole cycle

phases (v2 = 1.09, d.f. = 2, P = 0.58). Among the failed

nests, 48% (31 ⁄ 65) were in newly established territories

and this proportion of failed newly established territories

did not vary between vole cycle phases (v2 = 1.80, d.f. = 2,

Table 1. Pair formation in the tawny owl population

##

$$

First

breeder (%)

Experienced

breeder (%)

First breeder 96 (52) 26 (14)

Experienced breeder 33 (18) 29 (16)

There were in total 184 new pair formations of whichmost were

between two first breeders.



P = 0.41). Therefore, we found no bias in the data with

respect to capture of individuals from newly established terri-

tories (indicating that they are first breeders) and formerly

established territories (indicating that they are experienced

breeders) in different vole cycle phases.

The numerical response of breeding male and female owls

to the yearly prey density was affected by the owls’ breeding

experience (Fig. 2): the yearly vole density tended to explain

the number of first breeding males (linear regression, R2 =

0.27, B = 2.20 ± 1.03 SE, F1,13 = 4.54, P = 0.055) and

females (R2 = 0.26, B = 2.06 ± 0.99 SE, F1,13 = 4.31, P =

0.060). In contrast, there was no evidence for a relationship

between the number of experienced owls and prey density

(males,R2 = 0.05, B = 0.89 ± 1.08 SE, F1,13 = 0.67, P =

0.43; females, R2 = 0.13, B = 1.35 ± 1.00 SE, F1,13 =

1.81,P = 0.20).

AGE STRUCTURE IN THE POPULATION

The vole cycle had differential effects on the age structure of

first and experienced breeders (Fig. 3). Despite the age

variation between years [Table 2, year(phase)], the vole cycle

caused variation in the age structure of the population

(Fig. 3), where especially the first breeders were younger in

the increase and decrease phase than in the low phase (Fig. 3;

Table 2, phase). First breeders and experienced breeders

showed opposite patterns in age composition in different

vole phases (Fig. 3). Both male and female individuals that

started to breed in a decrease phase were younger than those

that started in a low phase, whereas experienced breeders

were the oldest in the decrease phase (Fig. 3; Table 2,

phase · experience).

REPRODUCTION, PATTERNS OF RECRUITMENT AND

IMMIGRATION

Recruits were produced in all three phases of the vole cycle

(Fig. 4). Recruitment probability depended on the vole

phase, as eggs produced in the increase phase had the highest

recruitment probability (phase: females, v2 = 19.07, d.f. = 2,

P < 0.0001; males, v2 = 16.36, d.f. = 2, P = 0.0003).

Furthermore, experienced individuals produced proportion-

ally more recruits than first breeding individuals (experience:

females, v2 = 7.61, d.f. = 1, P = 0.006; males, v2 = 4.36,

d.f. = 1, P = 0.04). Nearly all (39 ⁄ 40) of the locally

produced recruits were recruited to the breeding population

as 1- or 2-year olds. More specifically, a larger proportion of

these local recruits were able to start breeding as 1-year olds

if they were born in an increase phase compared with those

that were born in a low or decrease phase (Table 3).

The proportion of locally recruited and immigrant first

breeders in the population varied between vole cycle phases.

These proportions were similar for both sexes and we there-

fore pooled the sexes. The proportion of immigrant first

breeders was phase-dependent (v2 = 6.17, d.f. = 2, P =

0.046). In low vole phases, 82% (32 ⁄ 39) of first breeders wereimmigrants, whereas in increase vole phases the proportion

of immigrants was higher (102 ⁄ 117, 87%) and in decrease

vole phases lower (91 ⁄ 122, 77%).

SURVIVAL

A CMR analysis of the data revealed clear differences in sur-

vival probabilities between first and experienced breeders

(Table 4). Survival was fairly stable across different phases of

the vole cycle as the most parsimonious model had constant

survival over years in both first and experienced breeders.

Prey abundance

0

10

20

30

40

50

N te

rrito

ries

5 10 15 20 25 30

5 10 15 20 25 30

Prey abundance

0

20

40

60

80

100

Pro

port

ion

bree

ding

(a)

(b)

Fig. 1. Number of occupied territories in which tawny owls

responded to play-back calls (a) and proportion of occupied territo-

ries where a clutchwas produced (b) as a response of prey abundance.

Prey abundance stands for vole numbers caught per 100 trap nights.

Vole cycle phases are denoted by different symbols: filled diamonds

represent low phase, open circles represent increase phase and filled

triangles represent decrease phase.



However, some indications of a vole cycle effect on survival

could be detected as models including phase-dependent sur-

vival in either first (phasefirst ⁄ cexp) or experienced breeders

(cfirst ⁄phaseexp) or both (phasefirst ⁄phaseexp) had almost as

low QAIC values as the most parsimonious model (Table 4).

Because none of the models could be considered a single best

model, we employed model averaging to calculate the sur-

vival and recapture values for first and experienced breeders.

Estimates of annual variation in survival did vary to some

extent according to phases with higher survival of experi-

enced breeders (Table 4). There were no differences in sur-

vival between sexes as all sex-specific models received lower

support. Also recapture probabilities were equal for both

sexes, but they varied differently for first and experienced

breeders in the models that received the highest support

(Table 4). Model averaging revealed that recapture probabil-

ity of first breeders was dependent on phase, and was inter-

mediate after low phases (average 0.60 ± 0.13), highest after

increase phases (average 0.78 ± 0.08) and lowest after

decrease phases (average 0.25 ± 0.06). Among experienced

breeders, recapture probability varied annually but indepen-

dently of the vole cycle (average 0.67 ± 0.09).

Apopulation dynamical model

MODEL STRUCTURE AND SIMULATION

We found heterogeneity in the contribution different stages

(first and experienced breeders) make to the population in

terms of their reproduction and survival. Nevertheless, in a

cyclic environment where survival and fecundity vary

between cycle phases, it is not immediately obvious how a

change in these values would affect the overall population

dynamics. We therefore constructed a stage-specific matrix

model, where we considered first and experienced breeders as

separate stages. Because both sexes showed very similar

dynamics, we considered females only.

A standard matrix population model is based on annual

values of reproduction and survival, forecasting year-to-year

dynamics. In a cyclic environment, annual values for repro-

duction and survival are not the same from year to year.

However, cycles will repeat themselves with a fixed period.

Hence, matrices describing the yearly population contribu-

tions (reproduction and survival) over one cycle can be com-

bined in a block matrix to describe the cycle-to-cycle

population dynamics (Brommer, Kokko& Pietiainen 2000).

We denoted recruitment (average number of offspring per

breeding pair that joined the breeding population in a future

year) for phase ‘ph’ and a bird of experience ‘exp’ as Fph,exp.

By definition, all first breeders breed, but not all experienced

females with a territory breed in a given phase (see above),

and we therefore included the fraction breeding bph. Because

this population is not closed, we included a phase-specific

immigration rate mph (see above), which was assumed to act

as a scaling parameter for all reproductive output. Some

recruits join the population in the next year, whereas others

join the breeding population in later years (in practice this

was never later than 3 years, Table 3). We therefore denote

Prey abundance

0

4

8

12

16

20

N e

xper

ienc

ed m

ales

Experienced breeders

Prey abundance

0

4

8

12

16

20

N fi

rst b

reed

ing

mal

es

First breeders

5 10 15 20 25 30

5 10 15 20 25 30 5 10 15 20 25 30

5 10 15 20 25 300

4

8

12

16

20

N e

xper

ienc

ed fe

mal

esPrey abundance Prey abundance

0

4

8

12

16

20

N fi

rst b

reed

ing

fem

ales

(a)

(b)

Fig. 2. Relationship of owl and vole numbers

separately for first-breeding and experienced

(a) male and (b) female tawny owls. Prey

abundance stands for vole numbers caught

per 100 trap nights. Owl numbers are actual

numbers. Symbols are as in Fig. 1. Statistics

are given in the text.



the fraction of recruits born in phase ‘ph’ and recruiting ‘y’

(1, 2 or 3) years later as rph,y.

Survival for phase ‘ph’ to the next phase for a bird of expe-

rience ‘exp’ was denoted by Pph,exp. The cycle-to-cycle popu-

lation dynamics of a three-phase cycle are then given by the

blockmatrix

We parameterized the model with the observed data. The

fractions (r) of offspring that were born in a low, increase or

decrease phase and recruited 1, 2 or 3 years later are given in

Table 3. The values for the reproductive parametersm, b and

F and their rationale are outlined in Table 5. Stage- and

phase-specific survival values are reported in Table 4.

The dominant eigenvalue k1 of B [k1(B)] denotes the

change in population size (i.e. the number of females with a

territory) across cycles. Elasticity analysis is a prospective

analysis in which one can explore how much k would change

in response to changes in reproduction and survival (Caswell

2000). Elasticity can be calculated for each stage ⁄phase com-

bination by using standard methods (Caswell 2001) to calcu-

late the elasticities of B. Each elasticity value gives a

proportional contribution to k1(B) as the sum of elasticities

always equals 1. In a stable cyclic population k1(B) = 1. The

right eigenvector of B that corresponds with the dominant

eigenvalue [w1(B)] gives the stable stage distribution, i.e. the

proportion of first and experienced breeders and non-breed-

ers in each phase during a complete cycle. Again, this vector

can be calculated using standardmethods (Caswell 2001).

The elasticities of matrix B indicate the proportional con-

tribution of each matrix element to population dynamics.

The sum of all elasticities equals 1 by definition, and the elas-

ticity values therefore provide a ranking of importance. This

procedure models both demographic stochasticity and uncer-

tainty in the estimates of fecundity and survival. To gain

insight into the variability of our findings with respect to sto-

chasticity in fecundity and survival, we calculated k1(B) andthe elasticities for matrixB based on 10 000 simulations vary-

ing Fph,exp and Pph,exp. We treated all other parameters as

constants as we have no prior expectation of their uncer-

tainty. Hence, the variability addressed by our simulations is

conservative.

Simulated reproductive output was the average of a ran-

domly drawn Poisson distribution over the number of phase-

and stage-specific territorieswithmeanFph,exp (Table5 forval-

ues).We used the estimated error of the CMR survival values

(Table 4) to vary the survival values. The logit of the simulated

survival value was logit �Pph;exp

� �þN 0; r2 �Pph;exp

� �� , where

�Pph;exp is the estimated mean survival (Table 5), and

N 0; r2 �Pph;exp

� �� is a random draw of a normal distribution

with zero mean and variance r2ð �Pph;expÞ (i.e. the square of thestandard error inTable 5).For eachof the simulations,we cal-

culated thedominant eigenvalueand the elasticities.

We used the simulated dominant eigenvalues to calculate

the 95% confidence interval (CI) around k1(B), and tested

whether the elasticities showed a significant difference using

the distribution of the simulated elasticities. Pairwise com-

parisons were made between all simulated fecundity elastici-

ties and between all simulated survival elasticities. We first

calculated the proportion p of the differences between the

10 000 simulated values that was either smaller or larger than

zero (whichever was smallest). This gives a pairwise test value

(e.g. Manly 1997) for whether two elasticities are equal after

taking into account demographic uncertainty in the estimates

of fecundity and survival. Overall significance of the multiple

comparison of all pairwise P-values was based on the Holm–

Bonferroni method (Holm 1979) retaining the overall signifi-

cance level a = 0.05. The elasticity values can then be

grouped into groups that differ from each other significantly.

POPULATION DYNAMICS: MODEL RESULTS

The dominant eigenvalue of the population dynamical model

was0.981 (95%CI:0.867–1.100).Weconsidered thisa reason-

ablereflectionoftheobserveddynamics,becausetheCIclearly

overlaps with 1 (stable cycle-to-cycle population dynamics).

The expected stage ⁄phase distribution (given by the right

eigenvector) produced a satisfactory fit (v2 = 9.60, d.f. = 5,

P = 0.09) to the observed distribution (Fig. 5b), although the

model predicted a relatively large proportion of first breeders

in the increase phase. In the field data, the number of experi-

encedbreeders stayedfairlyconstantacrossphases (Figs5b,c,f

and 2). The matrix model showed that the survival of experi-

enced breeders was more important for population growth

than that of inexperienced breeders and that the main differ-

ence stemmed from the survival elasticity of an experienced

breeder in the low phase (Fig. 5a). The elasticity values of

reproduction showed only small differences, with none of the

non-zeroelasticitiesdifferingsignificantly (Fig.5a).

Discussion

NUMERICAL RESPONSE OF TAWNY OWLS

We have analysed 15 years (five 3-year vole cycles) of data on

both sexes of tawny owls and their small mammal prey in a

B ¼

rlow;3mlowFlow;f rlow;3mlowblowFlow;e rinc;2mlowFinc;f rinc;2mlowbincFinc;e rdec;1mlowFdec;f rdec;1mlowbdecFdec;e

0 0 0 0 Pdec;f Pdec;e

rlow;1mincFlow;f rlow;1mincblowFlow;e rinc;3mincFinc;f rinc;3mincbincFinc;e rdec;2mincFdec;f rdec;2mincbdecFdec;e

Plow;f Plow;e 0 0 0 0rlow;2mdecFlow;f rlow;2mdecblowFlow;e rinc;1mdecFinc;f rinc;1mdecbincFinc;f rdec;3mdecFdec;f rdec;3mdecbdecFdec;e

0 0 Pinc;f Pinc;e 0 0

26666664

37777775:



population in southern Finland. Based on responses to play-

back, the number of active territories has been censused in

this population, allowing us to investigate the response of the

total population size to fluctuations in vole abundance. We

find that territorial activity (potential breeders) in the tawny

owl population in southern Finland varies between vole cycle

phases in response to fluctuations in the abundance of their

main prey. Thus, our result differs from studies of tawny owls

in temperate regions without multi-annual fluctuations in the

abundance of their main prey. In particular, territory num-

bers in temperate regions are stable between years (Southern

1970; Hirons 1985; Jedrzejewski et al. 1996; Sunde & Bølstad

2004; Desfor, Boomsma& Sunde 2007).We find that the var-

iation in territory number between vole cycle phases is not

driven by extensive mortality after the decrease phase as in

the closely related Ural owl living in the same environment

(Brommer et al. 1998, 2002), because tawny owl survival is

fairly stable between vole phases. Instead, recruitment of new

breeders to the population (including immigration) drives the

variation in territory numbers between vole phases and con-

stitutes the main part of the observed numerical response of

the species. Because first breeders survive less well than expe-

rienced breeders, they are less important for population

growth, and a population dynamical model emphasizes that

the survival of experienced breeders is most important for

population growth above other components.

STAGE-DEPENDENT REPRODUCTION AND SURVIVAL

A numerical response of the number of breeding pairs to the

abundance of their main prey has been documented in several

owl studies (Tengmalm’s owl Aegolius funereus and voles:

Korpimaki & Norrdahl 1991; barn owl Tyto alba and voles:

Taylor 1994; tawny owl and voles: Jedrzejewski et al. 1996;

Ural owl and voles: Brommer et al. 2002; Northern spotted

owl Strix occidentalis caurina and deer mice: Rosenberg,

Swindle & Anthony 2003). These studies have, however, not

accounted for any sex- or stage-related differences. We find

that fluctuating prey abundance has distinctive consequences

for the age composition of first breeding and experienced

individuals. In particular, the majority of tawny owl off-

spring hatched in an increase phase starts to breed already as

1-year olds in the following decrease phase when conditions

(at least at the onset of reproduction) are favourable (see also

Brommer et al. 1998). This flexible and potentially young age

at first breeding allows for a rapid tracking of the vole cycle.

As a further consequence, one stage in the population (first

breeders) becomes younger as voles increase in abundance,

whereas already established territorial individuals get older

when voles become more abundant. Hence, over the course

of a cycle, the structure of the population changes, in terms

of both the proportion of first vs. experienced breeders and

their age distributions.

Bird populations consist of two sexes, but most studies on

birds of prey consider one sex only. In birds of prey, males

and females are sexually dimorphic in size and havemarkedly

different parental roles. Males and females may therefore

0

2

4

6

8

14

17

17

28

28

26


Low Incr Decr

Low Incr Decr

0

2

4

6

8A

ge in

yea

rsA

ge in

yea

rs

9

6

22 17

27

27

First breeders

Fig. 3.Mean age (±SE) of first-breeding and experienced females

(open circles) and males (filled diamonds) in different phases of the

vole cycle. Included are individuals ringed as nestlings, which were

born either within or outside the study area, but which bred within

the study area are included. See Table 2 for statistics.



respond differently to variations in prey availability. The

smaller males are the main hunters during courtship, prior

and during incubation, and they do most of the hunting dur-

ing the nestling stage. The larger female incubates and con-

centrates on defending the brood (e.g. Lundberg 1986;

Wallin 1987; Newton 1989; Meijer, Daan & Hall 1990; Sun-

de, Bølstad &Moller 2003). However, in spite of these differ-

ent sex roles, both males and females largely respond

similarly to variations in prey availability in terms of their

numerical response, reproduction and survival. Our results

thus correspond with those of Kruger (2002), who found that

the lifetime reproductive success of male and female common

buzzardButeo buteowere affected by similar factors.

We find strong contrasts between first and experienced

breeders also in terms of their reproduction. First-breeding

tawny owls have a much lower production of recruits in all

phases of the vole cycle. The future prospects of tawny owl

offspring thus depend to a large extent on whether their par-

ents have reproduced before or not. Again, this effect may be

due to the parents gaining experience (individually or

together, as most pairs breeding the first time consist of two

inexperienced parents and parents rarely divorce). Alterna-

tively, poor parents are selected out of the population after

their first breeding attempt. In addition to parental experi-

ence, the vole cycle itself creates different future prospects for

the offspring. Over 80% of the local recruits produced in the

increase phase are able to start breeding as 1-year olds,

whereas the majority of recruits born in low or decrease

phases start breeding as 2-year olds. This aspect of tawny owl

reproduction is similar to Tengmalm’s owls (Korpimaki

1988, 1992) and Ural owls (Brommer et al. 1998). Intrigu-

ingly, experienced tawny owls are rather successful in pro-

ducing recruits even in a low phase as recruitment probability

of offspring hatched in a low phase is almost equal to that of

offspring hatched in the decrease phase. This latter finding is

in contrast to Tengmalm’s owls, which rarely breed during

low phases (Korpimaki & Norrdahl 1989; Laaksonen, Kor-

pimaki & Hakkarainen 2002), and is also in contrast to Ural

owls, which do breed in low numbers, but rarely produce

recruits in low phases (Brommer et al. 1998). This difference

is likely caused by the fact that tawny owl reproduction is less

dependent on small voles than the reproduction of the other

two owl species. In particular, water voles Arvicola terrestris,

which do not follow the 3-year vole cycle, are an important

prey item affecting tawny owl reproduction in Finland (Ke-

kkonen et al. 2008).

We find only marginal effects of the vole cycle on tawny

owl survival. This is in contrast to findings in Finnish Ural

owls, where CMR analysis revealed a reduced survival prob-

ability after a decrease phase (Brommer et al. 2002). Simi-

larly, in a recapture and recovery analysis of long-term

Table 2. Minimal adequate model of factors that explain the

variation in age in the tawny owl population

Variable F d.f. P

Fixed effects

Phase 28.24 2, 122 <0.0001

Experience 3059.32 1, 122 <0.0001

Year (phase) 856.90 3, 122 <0.0001

Phase · experience 21.73 2, 122 <0.0001

Variance (95%CI) % v2 P

Random effect

Individual 0.98 (0.84–1.13) 99.4 268.29 <0.0001

The results are from a linear mixed effects model with normal errors

and individual ID as the random effect. The significance of the ran-

dom effect is tested with a likelihood ratio test (v2).

Low Incr Decr

Low Incr Decr

0

0·05

0·1

0·15

0·2

Pro

port

ion

recr

uite

d

54

178

231

35

240

233

First breeders

0

0·05

0·1

0·15

0·2

Pro

port

ion

recr

uite

d

108

252203

107

250291


Fig. 4. Proportion of offspring that recruited to the breeding popula-

tion in low, increase and decrease vole phases produced by first

breeders and experienced breeders. Error bars (mean ± SE,

n = 5 years for each phase) are clustered by the sex of parent (open

circles: females; filled diamonds: males). Total number of eggs pro-

duced is given above bars.



nationwide data on Finnish tawny owls, Francis & Saurola

(2004) found that most (more than 50%) of the variation in

adult survival is explained by winter temperature, with only

9% of variation explained by the vole cycle. In addition to

winter temperature, which increases energy expenditure in

owls (Mosher & Henny 1976), also the depth of the snow

cover may affect mortality in tawny owls. A deep snow cover

makes vole prey less accessible during winter. For example,

in Swiss barn owls Tyto alba, the number of days with snow

cover during winter explained substantial variation in annual

survival (Altwegg et al. 2003, 2006). We find that first-breed-

ing tawny owls of both sexes have lower survival than experi-

enced individuals. It is therefore possible that, in Northern

tawny owl populations such as the one studied here, winter

severity has prominent effects on survival, especially in first

breeders. We do not have sufficient data to distinguish

whether the higher survival of experienced breeders is due to

their older age or because they have gained more experience

than first breeders. In addition, experienced birds represent,

by definition, a subset of owls that have already undergone

survival selection as a first breeder and may therefore,

because of intrinsic or extrinsic factors, enjoy a higher sur-

vival than the unselected group of first breeders. Whatever

the reason for the observed differences in survival, our pri-

mary interest here is to understand its consequences for pop-

ulation dynamics.

Our CMR analysis also revealed that recapture probability

varied between vole cycle phases for first breeders whereas it

varied annually for experienced breeders. Previously recap-

ture rate has been found to vary in tawny owls due to individ-

ual characteristics (Roulin et al. 2003). We find here that

recapture rate of first breeders is the lowest in a low vole

phase. This is unlikely to be due to movement out of the pop-

ulation as we find that both first and experienced breeders are

highly site tenacious. Instead, we find it most likely that these

individuals refrain from breeding under such poor food con-

ditions (see also Southern 1970; Brommer et al. 2002) and

not that theymove out of the population, because we find sig-

nificantly lower breeding activity in the population in low

phases compared with other vole phases. Therefore, we sug-

gest that refraining from breeding is more common after the

first breeding event than at later stages and explains the vole

cycle effect on recapture rate of first breeders.

THE VOLE CYCLE AND TAWNY OWL POPULATION

DYNAMICS

Individuals of different stages of a population are often

affected differently in an environment where food availability

fluctuates annually or in a cyclic manner (Pietiainen 1988;

Ratcliffe, Furness & Hamer 1998; Cam & Monnat 2000;

Kruger & Lindstrom 2001; Laaksonen et al. 2002). To draw

conclusions on the persistence of a population, it is therefore

crucial to evaluate how these environmental fluctuations

affect reproduction and survival of different stages, and how

Table 3. Age at recruitment of offspring born in different phases of

the vole cycle

Phase at birth (%)

Low Increase Decrease

Age at recruitment

1 1 ⁄ 3 (33) 24 ⁄ 28 (85.7) 3 ⁄ 9 (33)2 2 ⁄ 3 (67) 3 ⁄ 28 (10.7) 6 ⁄ 9 (67)3 – 1 ⁄ 28 (3.6) –

Included are local recruits that were born in the study area between

1981 and 1995 (n = 40, 21males and 19 females). Data from both

sexes are pooled as there was no difference in age at recruitment

between sexes (Fisher’s exact test,P = 0.59). The age distribution of

recruits differ between phases of the vole cycle (Fisher’s exact test,

P = 0.002).

Table 4. Survival and recapture probabilities of tawny owl males and females between 1981 and 1995

Model QAIC QAICweight N par Likelihood

F(cfirst ⁄ cexp)p(phasefirst ⁄ timeexp) 993.3 0.344 19 356.60

F(cfirst ⁄ phaseexp)p(phasefirst ⁄ timeexp) 994.0 0.246 21 352.97

F(phasefirst ⁄ cexp)p(phasefirst ⁄ timeexp) 994.6 0.181 21 353.58

F(phasefirst ⁄ phaseexp)p(phasefirst ⁄ timeexp) 995.4 0.123 23 350.02

First breeder F Experienced breeder F

Model averaging

Low 0.56 ± 0.07 (0.427–0.688) 0.73 ± 0.05 (0.620–0.819)

Increase 0.55 ± 0.06 (0.437–0.635) 0.78 ± 0.07 (0.598–0.896)

Decrease 0.60 ± 0.08 (0.440–0.739) 0.71 ± 0.06 (0.578–0.823)

Themodels separate between survival (F) and recapture (p) and have two age categories (reported as ‘first breeder ⁄ experienced breeder’) thatcan be either constant (c), phase dependent (phase) or time dependent (time).We tested all possible model permutations for both experience

categories, and for both sexes including their interactions. Shown here are the four best models as judged by their QAIC values, which together

provide 89.4% support for the data (models including sex-specific effects received little support). Statistics given are quasi-likelihoodAkaike

information criterion (QAIC; the AIC corrected for overdispersion, c = 1.15), proportional support for themodel (QAICweight), number of

parameters (N par) andmodel likelihood. The lower part of the table shows the estimates of survival (F)±SE (95%CI) achieved by averaging

the estimates over all candidate models.



these differences translate into population dynamics (Caswell

2001). We have here taken a pragmatic view, and rather than

considering age-dependent performance (on which we have

incomplete information), we distinguish stages according to

their breeding experience (first time vs. experienced breeders).

Our cycle-to-cycle population dynamical matrix model,

which was parameterized with stage- and phase-specific esti-

mates of reproduction, survival and immigration, produced a

reasonable estimate of population growth and the numerical

distribution of the two stages across the vole cycle’s phases.

First breeders have a strong numerical response as prey

abundance increases, whereas the number of territories of

experienced breeders remain fairly stable between vole cycle

phases. Hence, our population dynamical model captures the

essentials of the tawny owl population dynamics.

The matrix model shows that experienced breeders are

most important for population growth mainly due to their

higher survival. Similarly, barn owl population growth rate is

highly sensitive to changes in the survival of adults but mark-

edly less to changes in the survival of yearlings (Altwegg et al.

2007). To some extent, these differences are inflated by struc-

turing the population into two stages (cf. Reid et al. 2004).

The first-breeder stage necessarily equals 1year, whereas the

experienced-breeder stage lumps all experienced breeders

into a single category independently of how long they have

been experienced breeders [breeding life span in the popula-

tion is c. 2.5 years (Brommer, Ahola & Karstinen 2005)].

However, clear differences between phase-specific elasticity

in tawny owl survival between the two stages remain. Intrigu-

ingly, the population-dynamical impact of survival of the

experienced breeders after a low vole phase is higher com-

pared with increase and decrease phases, whereas this pattern

is absent in the first breeders. This difference is because expe-

rienced breeders that survive a low phase contribute strongly

to the population during increase phases when reproduction

is favourable. The elasticity of survival of first breeders

mainly tends to increase from low to increase and decrease

phases as they becomemore abundant in the population.

A second striking finding of our modelling exercise was

that there was no strong population-dynamical impact of the

almost threefold difference in offspring recruitment proba-

bility between increase and decrease phases that occurred in

both first and experienced breeders. In fact, the proportion-

ally much higher recruitment of offspring hatched in the

increase phase than in any other phase is a general character-

istic of an avian predator preying on cyclically fluctuating

vole populations (Korpimaki 1992; Brommer et al. 1998).

Table 5. Data summary per vole cycle phase of parameters used in the population dynamical model

Phase Nfirst Nexperienced bph Ffirst Fexperienced mph

Low 26 41 41 ⁄ 110 (0.37) 0 ⁄ 26 (0) 5 ⁄ 41 (0.12) 4.57

Increase 70 57 57 ⁄ 97 (0.59) 20 ⁄ 70 (0.29) 25 ⁄ 57 (0.43) 6.80

Decrease 77 80 80 ⁄ 117 (0.68) 4 ⁄ 77 (0.05) 17 ⁄ 80 (0.21) 2.94

The data includes observations of all clutches produced in the population during five vole cycles. Parents of those clutches that failed (andwhere

parents could therefore not be caught) are categorized according to whether a clutch was produced in the territory in the previous year (experi-

enced breeder) or not (first breeder).Nfirst andNexperienced are the total number of observed first and experienced (female) breeders in the popula-

tion in a given phase. Parameter bph is the fraction of experienced breeders that produced a clutch over the total number of experienced breeders

(reproducing and territorials) in the population. Ffirst and Fexperienced are fecundity estimates of first and experienced breeders (number of recruit-

s ⁄ total number of pairs), andmph is the immigration coefficient (mph = Nimmigrants ⁄Nrecruits) when both sexes are pooled (data reported in text).

Low Incr Decr Low Incr DecrFirst breeders Experienced breeders

0

10

20

30

N T

errit

orie

sE

xpec

ted

/ Obs

erve

d

0

0·05

0·1

0·15

0·2

0·25

Ela

stic

ity

a

ab a

d

cd

bc

a

bab ab

b

ab

(a)

(b)

Fig. 5. The graph at the top shows elasticity of survival (filled dots)

and fecundity (open dots) for both stages in the different phases of

the vole cycle (x-axis is the same for both panels). Grouping of elas-

ticities in significantly different groups is indicated with letter coding

and is based on Bonferroni–Holm-corrected multiple comparison of

10 000 simulated elasticity values that take into account demo-

graphic stochasticity in fecundity and survival values. Elasticities that

share a common letter do not differ significantly. Dotted lines are for

visual presentation only. The graph at the bottom shows the expected

number of reproductively active territories (black bars) derived from

the population matrix model compared with the observed number of

territories (white bars) where a clutch was produced. In those territo-

ries of the actual data where parents were not identified, the parents

were assumed to be first breeders if a clutch was not produced in the

territory in the previous year, and experienced breeders if a clutch

was produced in the territory in the previous year. There is no signifi-

cant difference in the number of territories expected by the model

and observed in nature.



Increase vole phases have therefore been considered to be of

major importance for population persistence and viability.

For example, offspring hatched in the increase phase starts

to breed at an earlier age (this study; Brommer et al. 1998;

Laaksonen et al. 2002). However, no study has – to our

knowledge – investigated the actual population dynamical

impact of this difference in recruitment probability by inte-

grating fecundity and survival differences in a proper model.

We find that the elasticity of reproduction in the increase

phase was not any higher than the value for the decrease

phase in both first and experienced breeders. Hence, this

finding shows that explicit modelling of the dynamics can

provide non-intuitive insights.

Conclusions

Experience-dependent reproductive performance and sur-

vival are key aspects to be taken into account whenmodelling

population dynamics in variable environments. This is

because changes in food supply will have a clear impact on

the proportion of first breeders in the population. Whenever

first breeders have a markedly lower fecundity and survival

than experienced breeders, such changes in the composition

of the population will have population dynamical conse-

quences, especially when the population dynamics are pri-

marily driven by the recruitment of new breeders in years of

abundant food supply. Our findings further caution for inter-

preting possibly large differences in fecundity across years of

varying quality as indicative of having population dynamical

impact, and we emphasize the need to integrate fecundity

and survival in a model for making robust conclusions on the

population dynamics.

Acknowledgements

This is report number 6 of Kimpari Bird Projects. We thank the other

members of KBP – Juhani Ahola, Pentti Ahola, Bo Ekstam, Arto

Laesvuori and Martti Virolainen – for the many hours spent con-

ducting field work.We thankHannu Pietiainen, Peter Sunde and two

anonymous referees for insightful comments. Jari Valkama and Sep-

po Niiranen from the Finnish Ringing Bureau kindly provided data

on tawny owl recruits. Author contributions: all data were collected

byKA andTK; initial analyses were carried out byAZ; final analyses

and writing by PK and JEB. AZwas supported by CIMO, PK by the

Academy of Finland (project 1118484 to JEB) and JEB was

employed as anAcademyResearcher.

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Received 9 June 2008; accepted 17 April 2009

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