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Journal of Animal
Ecology 2001
70, 173 – 181
Habitat heterogeneity affects population growth in
goshawk Accipiter gentilis
Blackwell Science, Ltd
OLIVER KRÜGER*† and JAN LINDSTRÖM†‡
*Department of Animal Behaviour, University of Bielefeld, Postfach 10 01 31, 33501 Bielefeld, Germany;
and †Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK
Summary
1. The concept of site-dependent population regulation combines the ideas of Ideal
Free Distribution-type of habitat settlement and density dependence in a vital rate
mediated by habitat heterogeneity. The latter is also known as habitat heterogeneity
hypothesis. Site-dependent population regulation hypothesis predicts that increasing
population density should lead to inhabitation of increasingly poor territories and
decreasing per capita population growth rate. An alternative mechanism for population
regulation in a territorial breeding system is interference competition. However, this
would be expected to cause a more even decrease in individual success with increasing
density than site-dependent regulation.
2. We tested these ideas using long-term (1975–99) population data from a goshawk
Accipiter gentilis population in Eastern Westphalia, Germany.
3. Goshawk territory occupancy patterns and reproduction parameters support predictions of site-dependent population regulation: territories that were occupied more often
and earlier had a higher mean brood size. Fecundity did not decrease with increasing
density in best territories.
4. Using time-series modelling, we also showed that the most parsimonious model
explaining per capita population growth rate included annual mean habitat quality, weather
during the chick rearing and autumn period and density as variables. This model explained
63% of the variation in per capita growth rate. The need for including habitat quality in
the time-series model provides further support for the idea of site-dependent population
regulation in goshawk.
Key-words: goshawk Accipiter gentilis, Ideal Free Distribution, population growth
rate, site-dependent population regulation, time-series analysis.
Journal of Animal Ecology (2001) 70, 173–181
Introduction
One of the fundamental objectives of ecology is to
understand the factors and mechanisms causing fluctuations in the population density of animals (Sinclair
1989; Royama 1992; Begon, Harper & Townsend 1996).
Since David Lack’s pioneering studies (1954, 1966),
birds have been studied intensively in regard to factors
influencing population fluctuations. In these studies,
population density, food availability and weather have
been recurrently reported to be significant predictors
© 2001 British
Ecological Society
Correspondence: Oliver Krüger, Department of Animal Behaviour,
University of Bielefeld, Postfach 10 01 31, 33501 Bielefeld,
Germany. E-mail: [email protected]
‡Present address: Institute of Biomedical and Life Sciences,
Division of Environmental & Evolutionary Biology, University
of Glasgow, Graham Kerr Building, Glasgow G12 8QQ, UK
of population growth (Korpimäki 1984, 1992; Nilsson
1987; Kostrzewa & Kostrzewa 1990, 1991; Newton 1991a;
Steenhof, Kochert & McDonald 1997; Both 1998;
Newton, Rothery & Dale 1998; Steenhof et al. 1999).
However, merely showing a feedback from population density to population growth – sometimes called
statistical density dependence (e.g. Royama 1977;
Wolda & Dennis 1993) – does not comment on the
mechanisms causing this feedback.
One possible regulating mechanism in animal populations is territoriality (Lack 1966; Sutherland 1996; Newton
1998). For instance, in the Oystercatcher (Haematopus
ostralegus) increasing usage of poorer quality territories
in high population densities decreases the per capita
breeding success (Ens et al. 1992). In territorial species,
the settlement pattern behind observations such as this
is called Ideal Pre-emptive Distribution (IPD) (Pulliam
& Danielson 1991). This is a form of the Ideal Free
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O. Krüger &
J. Lindström
Distribution (Brown 1969; Fretwell & Lucas 1970;
Sutherland 1996), where best territories are inhabited
first and occupied territories are no more available to
others. Examples of this phenomenon have been found
in several bird and mammal species (Lindén & Wikman
1983; Nilsson 1987; Morris 1989; Andrén 1990; Wauters
& Dhondt 1990; Bensch & Hasselquist 1991; Møller
1991; Both 1998). The habitat heterogeneity hypothesis
(Dhondt, Kempenaers & Adriansen 1992) links the
idea of IPD to density dependence: increased usage of
poor territories in high densities weakens per capita
performance (Both 1998) and is thus very closely related
to, if not inseparable from, the IPD. In the context of
population regulation, habitat heterogeneity coupled
with IPD settlement pattern to breeding territories
has been called site-dependent population regulation
(Rodenhouse, Sherry & Holmes 1997).
In a territorial species, interference competition
could also give rise to an inverse relationship between
density and population growth rate (Lack 1966; Dhondt
& Schillemans 1983). However, if this is the case one
would expect a smaller variance between individual
breeding performances than in IPD. A few earlier studies
that have tested these hypotheses in birds of prey have
lent support to both the IPD (Ferrer & Donazar 1996)
and interference competition (Fernandez, Azkona &
Donazar 1998). The reason for contrasting results in
raptors may be that the existence and strength of interference competition depends on the social system of the
species. Interference competition is likely to be stronger
in socially breeding species, whereas solitarily nesting
species are more likely to follow IPD (Fernandez et al.
1998).
In this study we confronted these hypotheses, IPD
and interference competition, using a 25-year data set of a
German goshawk (Accipiter gentilis, L.) population.
We analysed the territory settlement pattern and breeding
performance and modelled per capita population growth
rate using standard time-series analysis techniques.
Materials and methods
© 2001 British
Ecological Society,
Journal of Animal
Ecology, 70,
173–181
five–10) times during the breeding season to gather basic
breeding data: breeding success (failure or non-failure
of a breeding attempt), reproductive output (number
of fledged juveniles per breeding attempt) and brood
size (number of fledged juveniles per successful breeding attempt). Displaying behaviour and nest building
activities were also taken to indicate a breeding attempt,
thus underestimation of goshawk density is unlikely,
especially since most goshawks present attempt to breed
(Zang, Heckenroth & Knolle 1989). For territories that
always failed to produce any juveniles, reproductive
output = brood size = 0. All data recording was performed through careful observations from the ground,
thus nests were not climbed. However, this method
allowed us to collect all desired data reliably (Mebs
1981) because juveniles can be counted accurately
just before leaving the nest, and most nests also had
good visibility because slopes allowed observers to
look into the nest.
To assess habitat quality, we used three different statistics
based on the 1975–99 territory-specific occupancy
frequencies. (i) For an annual mean habitat quality, we
calculated the mean occupancy frequency across those
territories used in a given year. (ii) We also classified
territories into three quality groups according to their
occupancy frequency 1975–99 (low; occupancy below
12%, intermediate; occupancy 12–36%, high; occupancy
> 36%) and (iii) noted their first year of occupancy.
Occupancy category boundaries were chosen so that
roughly equal numbers of territories belonged to each
territory-quality category. To avoid circularity in our
arguments – good territories are good because they
are often occupied – we checked all the statistical tests
by repeating them after excluding all territories where
occupancy was over 70%. This ensured that the low
and intermediate quality territories have, a priori,
roughly the same chance to be occupied in a given year
and equally early during the study period. No breeding
forests were felled or underwent major management
changes during the study so that the underlying assumption about habitat constancy seems to us to be justified.
  
    
Goshawk population dynamics was monitored between
1975 and 1999 in a 250-km2 investigation area in Germany (52°10′N and 8°25′E). It consists of two 125 km2
grid squares, 3715 (Bissendorf) and 3816 (Spenge).
Meller Berge, the main habitat, is a low mountain region
reaching a maximum height of ≈ 300 m above sea-level.
Ridges are covered by Norway Spruce Picea abies, Beech
Fagus sylvatica and Oak Quercus robur and Q. petrea
forests at lower altitudes. The second main habitat type
is cultivated landscape to the north and south of the
main ridge, mainly composed of Beech and Oak forests.
Each year, all forest patches within the study area
were visited in late winter to look for signs of goshawk.
All nest-sites were marked on large-scale maps and
each active nest-site was visited at least three (mostly
We defined population growth rate as Rt = ln(Xt +1) –
ln(Xt), where Xt is the population density at time t.
First, to analyse the factors affecting the growth rate, Rt
in goshawk, we used variables giving current and previous population densities, Xt and Xt–1. Using standard
time-series analysis methodology, we started by analysing the order of the process (Box, Jenkins & Reinsel
1994). In more biological terms, this means exploring
whether the process involves delayed feedback in addition to direct density dependence. As we do not know the
number of floaters in the population, strictly speaking
our population growth rate refers to changes in the breeding population size rather than whole population density.
In addition, we included in the analysis abundance
indices of wood pigeon (Columba palumbus) and rabbit
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population growth
© 2001 British
Ecological Society,
Journal of Animal
Ecology, 70,
173– 181
Table 1. Principal components analysis of the seasonal (see text) weather variables. Only the first principal component of each
season was included in further analysis. For each principal component, component-specific loadings are given, together with
percentages of variance explained. P refers to precipitation, T to temperature and S to the number of days with snow cover
Variable
Winter PCA
P November (t –1)
P December (t –1)
P January
P February
P March
P April
P May
P June
P July
P August
P September
P October
T November (t –1)
T December (t –1)
T January
T February
T March
T April
T May
T June
T July
T August
T September
T October
S November (t –1)
S December (t –1)
S January
S February
S March
S April
Variance explained (%)
0·14
− 0·19
− 0·06
0·29
Egg PCA
− 0·1
− 0·18
Chick PCA
Autumn PCA
− 0·20
0·47
0·51
0·54
0·19
− 0·05
0·14
0·01
0·31
0·47
− 0·68
0·34
0·02
− 0·47
− 0·51
− 0·64
− 0·49
0·14
− 0·40
− 0·18
− 0·40
− 0·41
26·1
(Oryctolagus cuniculus), two main prey species of the
goshawk in the study area (Krüger & Stefener 1996).
These prey data were annual (April–March) shooting
records from Kreis Melle (248 km2, extensively overlapping with the study area). Since the number of hunters
has remained constant between years (300–320, Ernst
Rutsch, personal communication), these variables reflect
the overall abundance of the two species. Hunting effort
also did not vary systematically between years since
both species are not ‘sought after’ and are hunted only
in order to ‘control’ populations. A bias in these estimates,
however, cannot strictly be ruled out.
We obtained monthly weather variables giving the
number of days with snow cover, temperature and
precipitation. Weather data were obtained from the
Deutscher Wetterdienst, from the station Osnabrück
(12 km to the west of the study area) from 1974 to 1986
and from the station Melle (inside the study area) from
1987 to 1999.
To reduce the number of weather variables used
and to aid interpretation of the results, we divided the
year into biologically meaningful periods, winter
(November (t–1) to February (t)), egg-laying period
(March–April), chick growth (May–July) and autumn
(August–October). Within these four seasons, we formed
0·61
− 0·05
31·4
33·3
27·6
principal components, PCA, of the monthly observations on precipitation, temperature and snow conditions (snow occurred only from November to April and
is thus included only in the winter PCA; Table 1), thus
capturing year-to-year variation in weather conditions.
Also annual average breeding habitat quality was used
as a variable in time-series modelling.
Model performances were compared using the
Akaike Information Criterion (AIC; Akaike 1973) corrected for small sample sizes (Hurvich & Tsai 1989). We
refer to this corrected version of AIC as AICc. As AICc
‘penalizes’ the model for every free parameter used, it
renders it possible to compare models with different
complexity and helps in avoiding overfitting. Autocorrelation structures of residuals of all the fitted models
were checked using Box–Pierce test (e.g. Cromwell,
Labys & Terraza 1994). Only models with residuals
which did not deviate significantly from the assumption
of a white noise process (Box–Pierce P > 0·05) were
considered valid (Pindyck & Rubinfeld 1991).
Results
The number of breeding pairs (BPs) fluctuated between
6 and 18 during the 25 years of study (Fig. 1). Highest
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Fig. 1. Number of goshawk breeding pairs in the study area
from 1975 to 1999. Density was similar to other studies, 2·4 –
7·2 pairs per 100 km2 (Lindén & Wikman 1983; Kostrzewa
1991; Kennedy 1997; Penteriani & Faivre 1997).
© 2001 British
Ecological Society,
Journal of Animal
Ecology, 70,
173–181
densities in the study area were found at the end of the
1970s, after which the population decreased sharply
during the early 1980s. During the last decade, the
population returned, undergoing fluctuations, to the level
at the onset of the study. Reproductive output varied
between 0·67 and 2·18 juv/BP and brood size between
1·5 and 3·0 juv/successful BP. Between 0 and 72·7% of
breeding pairs failed to produce any chicks annually.
There were no correlations between reproduction parameters and time.
To test whether goshawk territory settlement patterns correspond to predictions of the IPD, we divided
population densities into three groups according to the
number of breeding pairs present in the study area
(low: 6–8 breeding pairs, intermediate: 9–12 pairs and
high: > 12 pairs). In low density years, most occupied
territories were of high quality (Fig. 2a) but the number
of occupied intermediate and low quality territories
increased rapidly in years of intermediate or high
density ( Fig. 2b,c). This distribution of territory qualities
between levels of population density differs significantly
from random (χ2 = 27·105, d.f. = 4, P = 0·00002). To
eliminate the effect of the fact that territories with high
occupancy frequency are bound to be present at low
density years, we also compared low and intermediate
quality territories only. The distribution was still significantly different from random (χ2 = 6·447, d.f. = 2,
P = 0·04). According to the IPD hypothesis, high quality
territories should also be occupied earlier than lower
quality territories. To test this, we compared the first
occupancy year of the different territory quality groups
(again, excluding all territories with over 70% occupancy
since these have to have been occupied early). High
quality territories were occupied earlier than intermediate
or low quality ones (Fig. 3, F2,29 = 4·197, P = 0·025).
Taken together, these results thus support the IPD in
territory settlement pattern in goshawk.
To test the two competing hypotheses, site-dependent
regulation and interference competition, explaining
density dependent effects of population growth, we first
Fig. 2. Occupation of low (black squares), intermediate (grey
triangles) and high (diamonds) quality goshawk territories in
years with a low (a), intermediate (b) and high (c) population
density.
plotted mean reproductive output against its coefficient
of variation. The interference–competition hypothesis
predicts no relationship between these variables,
whereas site-dependent regulation hypothesis predicts
a strong negative relationship here. As can be seen from
Fig. 4, the relationship is negative and highly significant (r = −0·881, n = 25, P < 0·001). Controlling
for different sample sizes due to different numbers of
breeding pairs did not change the strength of this relationship (r = −0·881, d.f. = 22, P < 0·001). Thus, in years
with an overall high reproductive output, differences
between territories were small whereas in years with an
overall low reproductive output, differences between
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Fig. 3. Mean year of first occupancy (+ SE) of the three territory
qualities.
© 2001 British
Ecological Society,
Journal of Animal
Ecology, 70,
173– 181
Fig. 4. Relationship between mean reproductive output and
the coefficient of variation.
Fig. 5. Mean (+ SE) reproductive output (a) and brood size
(b) of the three territory quality categories and periods of first
occupation.
territories became substantial. We elaborated this
further by comparing reproductive output and brood size
across territory quality groups and time-spans of first
occupation. High quality territories and those occupied early should have higher reproduction success than
late occupied territories of lower quality. For reproductive
output, differences between territory quality groups
and time-spans of first occupancy were not significant
(Fig. 5; one-way analyses of variance, territory quality
and reproduction: F2,32 = 0·524, P = 0·597, time-span of
first occupation and reproduction: F2,32 = 0·732, P = 0·493).
With regard to brood size, however, differences were
significant (Fig. 5; , territory quality and brood
size: F2,32 = 3·331, P = 0·048, time-span of first occupation and brood size: F2,32 = 3·991, P = 0·028). Thus
territories differed significantly in occupancy frequency
and time of first occupation, and also in brood size. This
matched the predictions of IPD and the habitat heterogeneity hypothesis.
Partial autocorrelation function (PACF) did not
suggest delayed density dependence in the process: the
value PACF with lag two, −0·24, was far from significance. We judged the significance by constructing 95%
confidence limits for the PACF using the so called
‘Bartlett’s bands’ (e.g. Chatfield 1996). Therefore, we
included only direct density dependence (Xt ) in the multivariate time-series model and could use 24 years of the
25-year period (‘losing’ the last year for calculating the
population growth rate). In the multivariate time-series
analysis, fitting all the model combinations using the
eight explanatory variables described in the methods
section yielded 255 candidate models. To minimize the
number of candidate two-way interaction terms, we used
all the a priori information we had on goshawk biology
(for including interaction terms see, e.g. Burnham &
Anderson 1998). We excluded interactions between wood
pigeon and rabbit abundance indices, and the interactions between either of these and population size because
the published literature on goshawk dynamics show
almost no evidence of a numerical response by goshawk
to its food abundance even though a strong functional
response has been reported many times (Lindén &
Wikman 1983; Sulkava, Huhtala & Tornberg 1994; Selås
& Steel 1998). To our knowledge, the only reported
correlation between grouse abundance and goshawk
dynamics is from Sweden (Höglund 1964). However,
goshawk diet in Fennoscandia is more specialized than
in Central Europe, where they are known to be more
opportunistic (Cramp & Simmons 1980). This left us with
15 possible two variable interaction terms. We included
these one by one in the full model and compared the
outcome with the full model fitted alone. These results
are given in Table 2b. The need for an interaction term
became evident here. Including Xt (APCA or Habitat
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© 2001 British
Ecological Society,
Journal of Animal
Ecology, 70,
173–181
Table 2. (a) The full model for population growth rate, R,
including all eight explanatory variables. R2 = 0·62, R 2 = 0·06,
F8,15 = 1·18, P = 0·37, AICc = − 38·44 (AICc becomes often
negative if calculated using sums of squared errors from least
squares fitting; Burnham & Anderson (1998). (b) The 15 two–
way interactions studied. (AICc gives the difference in AICc
compared to the full model without a given interaction
(AICcfull − AICcinteraction )
Variable
Coefficient
SE
t
P
(a)
Constant
Xt
Wood pigeon
Rabbit
Habitat
WPCA
EPCA
CPCA
APCA
0·0459
− 0·217
− 0·007
− 0·005
0·006
0·009
0·001
− 0·071
− 0·032
1·35
0·36
0·05
0·07
0·02
0·04
0·04
0·05
0·05
0·03
− 0·60
− 0·15
− 0·08
0·40
0·22
0·01
− 1·58
− 0·58
0·97
0·56
0·88
0·94
0·71
0·83
1·00
0·14
0·57
(b)
Interaction
Xt × Habitat
Xt × WPCA
Xt × EPCA
Xt × CPCA
Xt × APCA
Habitat × WPCA
Habitat × EPCA
Habitat × CPCA
Habitat × APCA
WPCA × EPCA
WPCA × CPCA
WPCA × APCA
EPCA × CPCA
EPCA × APCA
CPCA × APCA
∆AICc
− 7·03
− 5·29
− 3·77
− 5·12
8·28
− 5·09
− 2·26
− 3·80
8·25
− 5·87
− 7·08
− 6·45
− 6·66
− 6·82
− 7·03
(APCA interaction reduced the AICc significantly
(Table 2b). Following convention, we considered values
of (AICc larger than unity statistically significant
(Sakamoto, Ishiguro & Kitagawa 1986). Also, looking
at the regression statistics, shows that the full model
without any interaction term is not statistically significant, whereas including either of these interactions made
the whole model significant (full model + Xt × APCA
interaction: R2 = 0·83, R2 = 0·48, F9,14 = 3·39, P = 0·02;
full model + Habitat × APCA interaction: R2 = 0·82,
R2 = 0·47, F9,14 = 3·27, P = 0·02). Based on this, we included
these two interactions among the variables used in the
candidate models. Adding these interactions one by one to
each of the 255 models without interactions increased
the number of fitted candidate models to 765. After
checking for serial correlation structure in the model
residuals, 68 models remained valid. In these models,
the adjusted coefficient of determination, R2 ranged
from 0·01 to 0·62 and the AICc ranged from −71·21
to −54·18 (Fig. 6a). According to the AICc, the most
parsimonious of these 68 valid models was:
Rt = − 0·44 + 0·01(Habitatt) − 0·08(CPCAt ) + 0·48
× APCAt − 0·39Xt (APCAt).
Fig. 6. The ∆AICc values of all the 68 valid models against the
corresponding model ranks. The ∆AICc of the ‘best’ model = 0
(a). 1975 – 98 time-series of population growth rate, R (solid
line, filled markers), fit of the most parsimonious model (solid
line, empty markers) and fit of the model including direct
density dependence alone (grey line). The model for density
dependence alone is Rt = 0·55 – 0·39 (Xt) with an R 2 = 0·18;
the equation for the most parsimonious model is given in the
results section (b).
This model explained 63% of the variation in population growth rate, R, and its AICc value was −71·21. If
compared to a model explaining R only with population density X (Fig. 6b), it is obvious that using weather
factors as covariates aided in explaining the amplitude
of fluctuations in Rt. The model including density
alone (with constant) explained 18% of variation in R
and its AICc value was −61·56 being thus clearly higher
than in the ‘best’ model. In fact, none of the 10 best
performing models had fewer than three explanatory
variables, including a constant (Table 3). The four best
models include population density only in an Xt ×
APCA interaction term, whereas habitat entered into
two of the four best models. One should note, however,
that already the second ranking model is clearly weaker
than the best one. In addition, in all of the 10 best models
where Xt appears, it is alone, without Xt × APCA interaction only once, in the model ranking ninth (Table 3).
Thus, the important factors for population growth rate
in goshawk were habitat quality, weather conditions
during late breeding period and density. Note that as the
signs of the principal component loadings are arbitrary
(Wilkinson 1998), one cannot determine the direction
of a given effect from the signs of the coefficients in the
model. However, looking at the correlation matrix after
having chosen the ‘best’ model revealed that increased
precipitation during chick rearing and autumn periods
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Table 3. Summary of the 10 best models explaining population growth rate. Variables included in the models are indicated with filled
dots. All models include a constant term. ∆AICc shows the change in the Akaike Information Criterion compared to the best model
Model rank
Variable
Xt
Wood pigeon
Rabbit
Habitat
WPCA
EPCA
CPCA
APCA
Xt (APCA)
Habitat × APCA
Parameters
R2
∆AICc
1
2
3
d
4
6
d
d
7
8
d
9
d
d
d
d
d
d
d
d
d
5
0·63
0·62
0·0
5
0·64
0·61
− 3·44
3
0·43
0·45
− 3·67
d
d
d
d
d
d
d
d
d
d
d
d
d
4
0·50
0·50
− 4·00
6
0·63
0·59
− 4·66
4
0·47
0·47
− 5·20
4
0·44
0·44
− 6·71
4
0·44
0·44
− 6·74
Discussion
Moffat (1903), Elton (1927) and Andrewartha & Birch
(1954) were among the first to realize that abundance
and distribution are closely related issues in ecology.
However, studies directly addressing relationships between
habitat heterogeneity, density-dependent habitat selection and territory settlement patterns only started to surface after the ideas ‘buffer effect’ (Kluijver & Tinbergen
1953; Brown 1969) and Ideal Free Distribution (Fretwell
& Lucas 1970) were presented. Here, we followed suit
by exploring territory occupancy patterns in goshawk.
We also tested more specifically if the goshawk territory
system matches the predictions of Ideal Preemptive
Distribution (Pulliam & Danielson 1991), or whether
interference competition is a more likely candidate to
explain a negative feedback from population density to
per capita population growth rate. Our results match the
predictions of the site-dependent population regulation idea (Rodenhouse et al. 1997). Breeding territories
differ qualitatively, breeding pairs inhabit progressively
lower quality habitats as population density increases
and this leads to reduced per capita breeding output.
Interference competition was not supported by these
findings. This has been documented for some passerine
bird species (Andrén 1990; Bensch & Hasselquist 1991),
but so far examples from long-lived territorial birds are
very scarce (see Newton 1991b; Ferrer & Donazar 1996).
Rodenhouse et al. (1997) raise an interesting issue
claiming that in site-dependent population regulation,
there is no direct link between population density and
10
d
d
had a negative effect on population growth rate, R,
whereas increasing temperatures had a positive effect.
However, of these factors temperature in July and August
appeared to be the most important ones (correlation
between temperature in July and R, r = 0·66, n = 23,
P = 0·001; correlation between August temperature
and R, r = 0·45, n = 23, P = 0·03).
© 2001 British
Ecological Society,
Journal of Animal
Ecology, 70,
173– 181
5
d
d
3
0·34
0·34
− 7·18
d
d
d
5
0·50
0·48
− 7·45
demographic parameters. Rather, although population
regulation is not exactly density-free (see Hawkins &
Berryman 2000; Hunt & Law 2000), density effects in
regulation are mediated through the distribution of site
qualities (Rodenhouse et al. 1997; Rodenhouse, Sherry
& Holmes 2000). Thus, while site-dependent population regulation is a form of density dependence, it
specifies a mechanism for the operation of density
dependence in a territorial breeding system in a heterogeneous habitat. Our results from the time-series model
explaining population growth rate are in line with the
idea of site-dependent population regulation: no direct
link between population growth rate and density
was found. Density only entered into the model in
the form of an interaction between density and densityindependent weather conditions during autumn.
Presumably, this interaction could have a biological
interpretation in the survival prospects of young birds in
their first autumn if these were affected by both density
and environmental conditions. However, there is no direct
evidence for this. This result thus shows that densitydependent breeding success in goshawk is mediated by
habitat heterogeneity and territory settlement system.
Some earlier studies have also shown that individuals
breeding in suboptimal territories not only experience
lower breeding success but have also higher costs associated with breeding (Møller 1991; Martin 1995). This
could be manifested, for instance, in increased adult
mortality after a breeding attempt in a poor territory.
Again, we do not have direct evidence for this in goshawk.
In addition, the individual quality of birds rather than
habitat quality might also affect the results presented
here. However, we believe that the length of the study
compared to the mean lifespan of goshawks (1·9 years,
Zang et al. 1989) might mitigate against bird quality.
Interestingly, food abundance was not a major determinant of population growth rate in goshawk. Other raptor
studies have repeatedly demonstrated the importance
of food in determining reproduction (Korpimäki 1984,
JAE481.fm Page 180 Thursday, March 29, 2001 5:06 PM
180
O. Krüger &
J. Lindström
© 2001 British
Ecological Society,
Journal of Animal
Ecology, 70,
173–181
1992; Potapov 1997; Steenhof et al. 1997, 1999). However,
a numerical response has rarely been demonstrated. In
addition, this functional response mainly concerns
species relying on one or only a few principal prey species
(e.g. Nielsen 1999). Goshawk feeds on a large variety of
prey species (in our study area, 63 species have been
found, Krüger & Stefener 1996) and no relation between
predator and prey density has been found elsewhere
(Lindén & Wikman 1983; but see Sulkava et al. 1994).
In our study area, where the main prey species do not
fluctuate very strongly, goshawks might constantly predate several prey species and not be strongly influenced
by the abundance of principal prey. Although the 10
most important prey species contribute to more than
80% of goshawk prey biomass (Krüger & Stefener 1996).
Goshawks select between them according to whatever
species is currently most abundant (Cramp & Simmons
1980). However, we are aware that a potential bias in
the food abundance estimates might also be responsible for the lack of an association between growth rate
and food in this goshawk population.
The influence of weather variables on raptor reproduction has been shown in several species (Kostrzewa
& Kostrzewa 1990, 1991; Steenhof et al. 1997, 1999). In
our study, weather conditions, most importantly
temperature in July and August, during the breeding
season and autumn affected the per capita growth rate.
The negative effect of adverse conditions on population
growth rate, in our case rainy and cold weather, becomes
amplified by high population density. To support this,
the percentage of first-year breeders was very high during
the 1970s (up to 70%), but nowadays hardly any goshawk breeds at 1 year. Over the same time-span, autumn
weather became worse with more rainfall and lower
temperatures. We would like to emphasize, however, that
our conclusions based on the time-series analysis are
somewhat corroborative to the first part of the analysis
here than the main evidence. As so often in ecology,
even the longest time-series available tend to be short
and without replicates (Powell & Steele 1995). The danger
of ‘overfitting’, that is, including spurious parameters
into the model cannot easily be avoided (Burnham &
Anderson 1998). We tried to do this here as rigorously
as possible, using an Information Theoretical approach
and ‘penalizing’ the models for every free parameter
estimated. We are the first to admit, however, that 25 years
does not allow too-strong conclusions to be drawn
from time-series analysis. However, taken all these results
together, we conclude that the dynamics we observe in
goshawk support the idea of a site-dependent population regulation (Rodenhouse et al. 1997), where weather
acts as a stochastic modifier. These results also have
implications for conservation. In times of habitat loss
due to human demands, the desertion of a few optimal
territories can have a dramatic consequence for the whole
population (Ferrer & Donazar 1996; Sutherland 1996;
Kokko & Sutherland 1998). Clearly, in areas of sparse
goshawk populations, a high priority should be given
for protecting the most suitable habitats first.
Acknowledgements
We are indebted to Uwe Ostermüller and Ulrich Stefener
who collected most of the field data. This study was
supported by the German National Scholarship Foundation (O.K.), the TMR programme of the EC and Clare
Hall College, Cambridge (J.L.). We also thank Gavin
Alexander, Nick Davies, Dominic Johnson, Bill Sutherland and Fritz Trillmich for comments on the manuscript
and Dave Collett for statistical advice.
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