Oecologia (2002) 133:474–482
DOI 10.1007/s00442-002-1053-y
P O P U L AT I O N E C O L O G Y
Oliver Krüger
Dissecting common buzzard lifespan
and lifetime reproductive success: the relative importance
of food, competition, weather, habitat and individual attributes
Received: 4 April 2002 / Accepted: 9 August 2002 / Published online: 19 September 2002
© Springer-Verlag 2002
Abstract The relative importance of factors such as
food, competition, weather, habitat and individual attributes as determinants of fitness in natural populations is
difficult to assess. While each component alone can be
experimentally manipulated to study its influence on fitness, the relative importance of different factors is very
hard to establish experimentally. Here, I describe an attempt to include most major factors simultaneously in an
analysis of common buzzard (Buteo buteo) lifespan and
lifetime reproductive success (LRS), as two important
components of fitness. For both sexes, the most important factor complex determining lifespan and LRS was
weather, with high rainfall and cold temperatures reducing fitness. There was no evidence for density-dependent
factors influencing buzzard fitness. Habitat variables related to human disturbance were important predictor
variables for the dark and light morph but not to the
same extend for the intermediate morph. Thus selection
pressures resulting from different factors did not vary
much between sexes but varied between the three phenotypes in the population.
Keywords Buteo buteo · Density dependence · Lifespan ·
Lifetime reproductive success · Weather conditions
Introduction
Life history theory commonly uses lifetime reproductive
success (LRS) as an approximation of fitness (CluttonBrock 1988; Newton 1989; Korpimäki 1992; McGraw
and Caswell 1996; Benton and Grant 2000). Studying
LRS variation between individuals within and between
sexes provides a good estimate for fitness variance in a
relatively stable population (Brommer et al. 1998; Kruuk
et al. 1999). A common feature of studies of LRS is the
O. Krüger (✉)
Department of Zoology, University of Cambridge,
Downing Street, Cambridge CB2 3EJ, UK
e-mail: [email protected]
Tel.: +44-1223-336610, Fax: +44-1223-336676
enormous amount of variation among individuals
(Clutton-Brock 1988; Newton 1989). Analysing the factors
causing this variation sheds light on the selective pressures affecting the reproductive careers of individuals.
One of the strongest correlates of LRS is lifespan, longer
living individuals commonly achieving a higher LRS
(Clutton-Brock 1988; Newton 1989). Despite lifespan,
many other factors have been shown to influence variation in LRS, such as phenotype (Partridge 1988; CluttonBrock et al. 1997), habitat quality (Packer et al. 1988;
but see Conradt et al. 1999), territory quality (Newton
1989), food abundance (Korpimäki 1992; Brommer et al.
1998), weather (Rose et al. 1998; Kruuk et al. 1999) and
density dependence (Clutton-Brock et al. 1997). While
all these studies provide unequivocal evidence for the
one factor concerned being a significant determinant of
LRS, it is impossible to assess the relative importance of
all these factors in shaping animal life histories because
no study has included all these factors in a single analysis. This might, however, add greatly to our understanding of life history strategies because such an approach
could identify which factors are most important indicating their relative selection pressures (Travis et al. 1985).
Such an integrative attempt has not been done, owing to
great difficulties in collecting information on LRS, but
also on factors such as habitat quality, food abundance
and population dynamics.
Over the last 12 years, such a data set has been collected for a common buzzard (Buteo buteo) population in
Eastern Westphalia, Germany (Krüger and Lindström
2001b; Krüger et al. 2001). While some potentially important factors such as parasite load were not measured,
the data set covers most major factors and hence allows a
multivariate analysis of fitness to be done. In earlier papers (Krüger and Lindström 2001b; Krüger et al. 2001),
the paramount importance of phenotype as a predictor of
variation in buzzard fitness has been shown. Here I expand the analysis and not only include many additional
variables, but test whether selection pressures acting on
lifespan differ from those acting on LRS. By integrating
traits which are heritable, such as phenotype (morph)
475
with those which are not (weather, population density),
one can establish what type of selection pressure affects
this buzzard population. This allows, for example, to disentangle whether fitness differences between morphs are
more likely to result from intraspecific competition leading to competitively inferior phenotypes being ousted into poor quality territories or whether phenotypes are affected differently by weather.
The common buzzard is the most common accipitrid
bird of prey in Central Europe and feeds predominantly
on voles (Mebs 1964; Melde 1983). It shows great variability in plumage pigmentation and three main morphs
(dark, intermediate and light) can be recognised (Glutz
von Blotzheim et al. 1971; Melde 1983). Using data on
food supply, level of competition, weather, habitat and
individual phenotype, I established the relative importance of these factors in explaining variation in lifespan
and LRS. By analysing lifespan and LRS separately, it is
possible to examine whether the same variables affect
both or whether differences exist which might even be
sex-specific. This allows for evaluation of the strength of
selection pressures separately on lifespan and/or LRS in
this buzzard population.
Materials and methods
1982; Glaubrecht 1981; Janes 1984, for similar approaches, and
Bateson 1977; Scott 1979, for reliability tests on other species).
Thus it was not necessary to colour-mark birds, but if uncertainties
existed in recognising individuals they were excluded from any
analysis (n=15, or 6.3% of the birds of the 1990–1995 cohorts).
Recently, about 15% of the adult population have been colour
ringed which makes individual recognition much easier. Rarely,
birds moved to a nearby territory which could be detected using
the drawings (n=5, or 2.1% of the birds in the LRS data set). Birds
already breeding in 1989 were excluded since their previous
breeding career was unknown. While 1.1–4.3% (1–3 birds) of the
1990–1995 cohorts were still alive in 2000, this proportion increased to 12.8% (14 birds) for the 1996 cohort. Thus, data on
LRS and lifespan was limited to the 1990–1995 cohorts. A potential error source which could not be eliminated was birds breeding
at the edge of the study area. A few could have moved outside the
study area, hence I would have underestimated their LRS. I also
categorised individuals into three morphs (dark, intermediate and
light; Glutz von Blotzheim et al. 1971). Morphs are genetically inherited and stable over time (Glutz von Blotzheim et al. 1971;
Cramp and Simmons 1980). Here, I will only briefly describe fitness differences between morphs (see Krüger and Lindström
2001b; Krüger et al. 2001). As for other resident bird species, it
was assumed that once an individual was not found in the population for 2 years or more, it had died (Newton 1989). Sometimes,
birds skipped a breeding attempt (9.7% of all breeding years), that
is why 2 years was taken as the threshold to assume death in order
to determine which birds could be included in the analyses. Longlived birds still alive in 2000 were included in the analyses, although their reproductive career might not have ended, because
their exclusion would have introduced a much larger bias by leaving the most successful birds out of the analysis.
Study area and population
Explanatory variables
The study was conducted over 12 years from 1989 to 2000 in a
300 km2 area in Eastern Westphalia, Germany (8°25′E, 52° 6′N).
The habitat, a mosaic of forest and cultivated landscape, has been
described in detail elsewhere (Krüger and Stefener 1996, 2000;
Krüger and Lindström 2001a, b). Each year, all forest patches
were visited to look for buzzard pairs. This includes breeding pairs
(occupying a nest and showing signs of egg-laying activity) as
well as non-breeding pairs which just occupy a territory. Between
38 and 81 buzzard pairs were found annually over the 12 years.
Annual breeding success was recorded through careful observation from ground level, as nest trees were not climbed. Because
the landscape is hilly, slopes allow an observer to see most nests
well. At least three (normally 5–10) visits were made to each active nest to determine breeding success (failure or non-failure to
fledge any chicks), reproductive output (number of fledged chicks
per breeding pair) and brood size (number of fledged chicks per
successful breeding pair). Buzzards commonly start to breed in
their third summer (Glutz von Blotzheim et al. 1971) and most
birds (85%, Melde 1983) recruit from within 20 km of their birthplace. They establish a territory through aerial displays and sometimes fights (Melde 1983).
The set of 29 explanatory variables is given in Table 1. For information on abiotic and biotic variables for each year, I obtained
weather data (monthly precipitation, mean temperature and days
with snow cover) from the nearest meteorological station of the
Deutscher Wetterdienst at Melle, located at the northern edge of
the study area. The period “winter” refers to the period from December to February in the following year and “breeding” covers
the period from March to June. An index of vole abundance (low,
medium or high) of the main prey species, field vole (Microtus
arvalis) was obtained by counting the number of vole skulls in
buzzard and tawny owl (Strix aluco) pellets (Kostrzewa and
Kostrzewa 1990). Since 1994, the index was estimated using the
re-opened holes method where the number of active vole holes per
unit area is counted (Görner and Kneis 1981; Heise and Wieland
1991). Both methods provide site-specific, semi-quantitative data
(Heise and Wieland 1991) and so I used only three broad abundance categories: low, intermediate and high.
During the winter of 1998-1999 and the summer of 1999, each
nest in the study area (n=392) was visited and habitat measurements were taken (Krüger 2002b). No major habitat changes (felling of entire forest patches, major road construction, etc.) occurred
over the 12-year period so that measures of habitat in 1998–1999
can be taken as representative for the entire period. Variables were
selected to describe the macro habitat using a circular plot with a
radius of 500 m around the nest tree, covering 78.5 ha. This macro
habitat plot covers around 50% of a typical buzzard territory
(Mebs 1964; Newton 1979; Krüger 2000, 2002a, b). Buzzards (especially the female) spend certainly more than 50% of their foraging time within this core area of their territory (Melde 1983). Most
variables were measured in the field with 100-m measuring tape,
but some (breeding forest size, etc.) could only be measured on
small-scale maps. Disturbance from the nearest forest track and
street was measured by random visits (30 min or 1 h duration) and
counts of walkers, joggers, and cars per hour, which were subsequently lumped together into five disturbance categories (Krüger
2002b). A permanent disturbance was defined as a constant anthropogenic habitat feature (occupied house, highly frequented
Determining LRS
I define lifetime reproductive success as the total number of
chicks fledged during an adult’s lifetime, thus following Newton
(1989). This definition seems to be justified for birds of prey, at
least, since it has been shown that lifetime chick production correlates highly with lifetime number of recruits (Newton 1989;
Korpimäki 1992; Brommer et al. 1998) and is thus a good approximation of fitness.
Individual buzzards were drawn or photographed, because the
high variation in plumage colour and especially pigmentation pattern (Glutz von Blotzheim et al. 1971; Ulfstrand 1977; Cramp and
Simmons 1980) allows for individuals to be recognised from year
to year without artificially marking them (see Clutton-Brock et al.
476
Table 1 Explanatory variables used in the analysis, with their detailed description
Variable
Description
Complex
Vole index start year
Vole index mean
Population density start year
Population density mean
Territories within 2,000 m
Snow start year
Snow mean
Precipitation (winter) start year
Precipitation (winter) mean
Precipitation (breeding) start year
Prec ipitation (breeding) mean
Temperature (winter) start year
Temperature (winter) mean
Temperature (breeding) start year
Temperature (breeding) mean
Breeding forest size
Plot forest share
Plot clearing share
Plot water share
Plot building share
Plot street share
Forest edge length outside
Forest edge length inside
Usage frequency path
Usage frequency street
Permanent disturbances
Plot altitude difference
Habitat variety
Plumage morph
Vole index at first breeding year
Vole index mean across all breeding years
Buzzard population density at first breeding year
Population density mean across all breeding years
Maximum no. of buzzard territories within 2,000 m radius
No. of days with snow cover preceding first breeding year
Mean no. of days with snow cover across all breeding years
Monthly winter precipitation preceding first breeding year
Mean monthly winter precipitation across all breeding years
Monthly summer rain at first breeding year
Mean monthly summer rain across all breeding years
Monthly winter temperature preceding first breeding year
Mean monthly winter temperature across all breeding years
Monthly summer temperature at first breeding year
Mean monthly summer temp. across all breeding years
Absolute size of the breeding forest
Forested area in the plot
Area, covered by clearings in the plot
Area, covered by water in the plot
Area, covered by buildings in the plot
Area, covered by streets and roads in the plot
Total length of outside forest edges in the plot
Total length of forest clearing edges in the plot
Human usage frequency of the nearest forest track
Human usage frequency of the nearest street
No. of permanent human disturbances within 200 m
Maximum range of altitude difference in the plot
No. of main habitats in the plot (woods, fields, meadows, etc.)
Plumage morph scored as dark, intermediate or light
Food
street). For those few individuals which moved territories over
their lifespan, habitat measurements were averaged between the
two territories. For the population density variables estimating the
level of intraspecific competition, I included both breeding as well
as non-breeding pairs.
Statistical analysis
Prior to parametric statistical analysis, the skewed lifespan and
LRS variables were log-transformed to give them a normal distribution. I included a non-linear term for the morph variable since
earlier work has shown that this variable has a non-linear relationship with LRS (Krüger and Lindström 2001b). I then used stepwise forward multiple regression models to determine which variables explained variation in LRS. To avoid overfitting, the maximum number of variables was set to one for every ten cases (Hair
et al. 1995). In addition, multicollinearity was assessed by checking the tolerance values for each independent variable in the model. Following Hair et al. (1995), 0.19 was used as border criterion
to remove an independent variable. To check whether the models
were robust, I resampled 50% of the birds at random and performed a multiple regression analysis on this random subsample.
This was done 100 times and hence each variable has a percentage repeatability which is the percentage of subsamples significantly influenced by that variable (see Mac Nally 2000). I also
used stepwise multiple discriminant analysis to check for model
robustness and to see which variables discriminated between birds
with LRS and those failing to achieve any LRS. Following Hair et
al. (1995), I used the Mahalanobis distance criterion, which is considered to be especially suitable for large multivariate data sets.
The limitations of multiple regression analysis were pointed out
by James and McCulloch (1990) and, following their recommendations, variables were not ranked in importance according to
their coefficient and residuals of the models were checked for normality.
Competition
Weather
Habitat
Individual
Results
Basic pattern in LRS
LRS ranged between 0 and 22 fledged chicks for females
and between 0 and 20 fledged chicks for males (Fig. 1).
There was no significant difference in LRS between the
two sexes (t236=1.521, P=0.130). The distribution was
significantly skewed for both females (Lillieforstest:0.268, df=106, P<0.0001) and males (Lillieforstest:0.214, df=132, P<0.0001). For both sexes, there
were highly significant relationships between lifespan
and LRS (Fig. 2; females F1,104= 103.493, P<0.0001 and
males F1,130=72.963, P<0.0001).
Predictors of lifespan
The multiple regression model for female lifespan explained 57.9% of the variance in lifespan with six explanatory variables and was highly significant (Table 2 a;
F6,99=22.674, P<0.0001). Among the six explanatory
variables were five of the weather complex with more
precipitation affecting lifespan negatively and warmer
temperatures affecting lifespan positively. In addition,
population density mean entered with a positive sign
suggesting that overall good conditions affect lifespan
and result in higher population density as well. Repeatability was higher for these variables than for any other
477
tory variables. Four variables describing precipitation
levels all entered the model with a negative sign. While
population density mean entered the model with a positive sign, the population density of the first breeding
year entered with a negative sign, maybe indicating that
early in the breeding career, density dependent effects
could play a role in male lifespan. Finally, the absolute
size of the breeding forest entered with a positive sign.
Repeatabilities were high only for the population density
mean, and three other independent variables had higher
repeatabilities than variables in the model: the vole index
in the start year (34%), winter temperature of the start
year (32%) and morph (13%).
Fig. 1 Distribution of lifetime reproductive success for 106 females and 132 males of common buzzard (Buteo buteo). The lifetime reproductive success (LRS) data covers the period
1990–2000
Fig. 2 Relationship between reproductive lifespan and LRS for
a females and b males. Sample size and time span as in Fig. 1
variable with one exception’ the vole index in the start
year entered 26% of subsamples, always with a positive
sign.
The best model for male lifespan explained 41.7% of
the variance in lifespan, was highly significant (Table 2
b; F7,124=12.660, P<0.0001) and included seven explana-
Predictors of LRS
Because LRS is lifespan × average reproductive success
across a whole population, I first analysed variation in
annual reproduction rate over the 12 years, using weather, population density, vole index, goshawk density and
also the share of intermediate birds in the population.
The multiple regression model included one predictor
variable, namely precipitation in may decreased annual
reproduction rate. The model was significant
(F1,10=5.355, P=0.043) and explained 34.9% of the variation in annual reproduction rate.
The multiple regression model for female LRS explained 59.6% of the variance in LRS with eight explanatory variables and was highly significant (Table 3 a;
F9.96=15.769, P<0.0001). Residuals did not deviate significantly from a normal distribution but were very close
(Lilliefors-test:0.086, df=106, P=0.051). Among the nine
explanatory variables that entered the model were five of
the weather complex and one each of the competition,
habitat and individual complex. Precipitation variables
showed a negative association with female LRS and temperature and snow cover a positive one. Although at first
counterintuitive, the positive association between snow
cover and LRS makes sense biologically because voles
survive better under snow cover, resulting in greater numbers in spring. Population density mean was also positively associated with LRS indicating that overall breeding conditions were good, resulting in high LRS and
higher breeding density. The positive association between
forest edge length and LRS indicates that habitats with
high structural complexity are correlated with a high
LRS. Finally, plumage morph entered as a highly significant predictor in a non-linear fashion (see methods section). There were not only significant differences in the
mean LRS (F2,103=24.709, P<0.0001), but more specifically between both extreme morphs and the intermediate
one which had the highest mean LRS (Tukey-tests, dark
vs intermediate: P<0.0001, dark vs light: P=0.485 and intermediate vs light: P<0.0001). Repeatabilities were
again quite high, indicating model robustness and there
was only one variable with a higher repeatability compared to the variables in the model: winter precipitation
in the start year (18%) influenced female LRS negatively.
478
Table 2 Multiple regression
models for female and male
common buzzard (Buteo buteo)
lifespan. The standard error of
the estimate is 0.199 for the female and 0.214 for the male
model. Variables in italics are
also significant predictors of
LRS of the sex concerned. Repeatabilities give the percentage of 100 subsamples of 50%
of the birds where the variable
was a significant predictor
Table 3 Multiple regression
models for female and male
lifetime reproductive success.
The standard error of the estimate is 0.226 for the female
and 0.271 for the male model.
Variables in italics are also significant predictors of lifespan
of the sex concerned. Repeatabilities give the percentage of
100 subsamples of 50% of the
birds where the variable was a
significant predictor
Variable
β
SE
t
P
R2
Repeatability
Female
Constant
Population density mean
Precipitation (winter) mean
Precipitation (breeding) start year
Temperature (breeding) start year
Snow mean
Precipitation (winter) start year
–5.956
0.227
–0.008
–0.004
0.170
0.021
–0.002
0.656
0.026
0.001
0.001
0.036
0.004
0.001
9.072
8.867
8.477
6.441
4.699
5.016
3.838
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.186
0.273
0.396
0.451
0.516
0.579
85
48
32
24
21
15
Male
Constant
Population density mean
Precipitation (breeding) mean
Precipitation (winter) mean
Precipitation (winter) start year
Population density start year
Precipitation (breeding) start year
Breeding forest size
–0.917
0.254
–0.005
–0.008
–0.007
–0.182
–0.004
0.023
0.821
0.034
0.001
0.001
0.001
0.043
0.001
0.011
1.117
7.536
6.246
5.803
5.206
4.235
3.503
2.091
0.266
0.001
0.001
0.001
0.001
0.001
0.001
0.045
0.130
0.166
0.277
0.323
0.348
0.402
0.417
73
17
15
7
16
16
11
Variable
β
SE
t
P
R2
Repeatability
Female
Constant
Population density mean
Plumage morph2
Plumage morph
Precipitation (breeding) start year
Precipitation (winter) mean
Temperature (breeding) start year
Snow start year
Snow mean
Forest edge length outside
–10.399
0.166
–0.349
2.160
–0.004
–0.006
0.400
0.024
0.014
0.0001
1.193
0.030
0.054
0.346
0.001
0.001
0.073
0.006
0.004
0.0001
8.719
5.506
6.460
6.239
6.596
5.548
5.446
3.774
3.149
2.145
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.002
0.035
0.099
0.144
0.350
0.386
0.448
0.491
0.540
0.577
0.596
59
48
48
25
15
16
15
19
21
Male
Constant
Population density mean
Breeding forest size
Temperature (winter) start year
Snow mean
Temperature (breeding) start year
–2.113
0.039
0.035
0.023
0.020
0.154
0.488
0.019
0.016
0.012
0.005
0.046
4.330
2.101
2.050
1.982
4.157
3.327
0.001
0.038
0.044
0.049
0.001
0.001
0.077
0.112
0.138
0.178
0.244
50
41
19
36
37
Comparing the models for female lifespan and female
LRS, they show great similarities, sharing five explanatory variables. The only difference was that morph
seems to greatly influence female LRS, but not female
lifespan to the same extent.
For male LRS, the best model explained only 24.4%
of the variance in LRS with five explanatory variables
and was also highly significant (Table 3 b; F5,126=8.146,
P<0.0001). Again, residuals did not differ significantly
from a normal distribution (Lilliefors-test:0.065, df=132,
P=0.200). Among the five explanatory variables were
three of the weather complex and one of the competition
and habitat complex. Three variables (population density
mean, snow cover mean and temperature during the first
breeding year) were also significant predictor variables
in the female model, so there were no clear differences
in the importance of factor complexes determining LRS
between the sexes. In addition, there were positive asso-
ciations between winter temperature before the first
breeding year and the size of the breeding forest, indicating that more remote territories promoted a higher LRS.
Repeatbilities were very high for the variables in the
model, indicating high model robustness.
Although male morphs differed significantly in their
fitness with intermediate birds achieving a higher LRS
than both extreme morphs (F2,129=7.421, P=0.001,
Tukey-tests, dark vs intermediate: P=0.015, dark vs light:
P=0.785 and intermediate vs light: P=0.002), plumage
morph did not enter the model for male LRS, because
morph was interrelated with other explanatory variables.
Comparing the models for male lifespan and male LRS,
they show less similarities compared to females; sharing
only two explanatory variables (population density mean
and absolute breeding forest size). Whereas male lifespan is mainly influenced by precipitation levels, male
LRS seems to be influenced more by temperature.
479
Table 4 Multivariate discriminant function (DF) analysis
models for female and male.
LRS was categorised as either
0 = no LRS or 1 = at least one
chick raised. Models classified
64.2% of female LRS correctly
and 69.7% of male
Variable
Mahalanobis statistic
F
P
DF coefficient
Female
Constant
Precipitation (winter) start year
Plumage morph2
Plumage morph
0.356
0.627
1.265
5.558
4.842
6.447
0.020
0.010
0.001
–14.663
–0.006
–1.869
11.286
Male
Constant
Temperature (breeding) start year
Snow mean
Plot forest share
0.113
0.397
0.536
2.291
3.996
3.567
0.133
0.021
0.016
–17.676
1.560
0.151
–0.030
In order to find which variables discriminated between buzzards with at least one fledged chick produced
in their lifetime from those failing completely, discriminant models were developed. For females, 64.2% of buzzards were correctly classified as having either any LRS
or none by a model with two explanatory variables (Table 4), a highly significant improvement over the 50%
expected by chance (P<0.01). As in the multiple regression model, plumage morph was a significant discriminator as was the winter precipitation level before the first
breeding year. For males, the discriminant model classified 69.7% of buzzards correctly into the LRS or no LRS
group with three explanatory variables, again a significant improvement over 50% (P<0.01). Thus the discriminant model was much more successful in its predictive
power than the multiple regression model. Two variables, temperature during the first breeding year and
snow cover mean were also significant predictors in the
regression model whereas the forest share in the plot was
new. Its negative discriminant function coefficient indicates that open areas (fields, meadows) are important;
closed forests are not ideal habitat.
Morph specific analysis of LRS
Because of the significant LRS differences between the
morphs for both sexes, I analysed the relative importance
of the factors separately for each morph and sex. The regression models for the female morphs are given in Table 5. For dark females, the significant model
(F1,7=5.504, P=0.046) explained 43.6% of the variance
in LRS with one predictor variable, precipitation during
winter before the first breeding year. For intermediate females, 67.1% of variation in LRS was explained with
five predictor variables in a highly significant model
(F5,47=19.154, P<0.0001). Three of them (mean population density, mean precipitation during winter and precipitation during first breeding year) were also significant predictors in the total female model. In addition, the
area covered by buildings in the plot was negatively related to LRS and the vole index during the first breeding
year was positively related to LRS. For light females, the
regression model explained 6.5% of the variation and
Table 5 Morph-specific multiple regression models for female
and male. For females, the percentage of variance explained was
43.6% for dark, 67.1% for intermediate and 6.5% for light birds,
and, for males, 33.2% for dark, 36.4% for intermediate and 21.8%
for light
Variable
β
Female
Dark (n=9)
Constant
Precipitation (winter) start year
SE
t
P
0.607 0.173
–0.002 0.001
3.512
2.397
0.010
0.047
Intermediate (n=53)
Constant
Plot building share
Population density mean
Precipitation (winter) mean
Precipitation (breeding) start year
Vole index start year
–4.476
–0.138
0.248
–0.009
–0.005
0.138
0.714
0.059
0.034
0.001
0.001
0.044
6.272
2.339
7.386
7.533
7.344
3.158
0.001
0.031
0.001
0.001
0.001
0.003
Light (n=44)
Constant
Permanent disturbances
0.416 0.060
–0.071 0.041
6.946
1.714
0.001
0.094
Male
Dark (n=14)
Constant
Plot building share
0.522 0.088
–0.549 0.225
5.933
2.441
0.001
0.031
Intermediate (n=57)
Constant
Population density mean
Vole index mean
Forest edge length outside
–2.102
0.207
0.465
0.0001
3.892
5.093
4.125
2.612
0.001
0.001
0.001
0.012
2.553
2.765
3.100
2.191
0.013
0.008
0.003
0.033
0.540
0.041
0.113
0.0001
Light (n=61)
Constant
–1.484 0.582
Breeding forest size
0.060 0.022
Temperature (breeding) start year 0.154 0.050
Snow mean
0.015 0.007
only approached statistical significance (F1,42=2.938,
P=0.094) with one significant negative correlation between LRS and the number of permanent human disturbances within 200 m of the nest.
The regression models for males are given in Table 5.
The model for dark males (F1,12=5.960, P=0.031) explained 33.2% of the variance in LRS with one predictor
480
quality territories between the morphs, I calculated the
occupancy share of dark and light morphs combined in
relation to territory quality. As can be seen in Fig. 3,
there is a significant negative relationship for both sexes
(females: F1,106=6.055, P=0.015, males: F1,106=4.165,
P=0.044), but there is also substantial scatter (female
R2=0.054 and male R2=0.038). Nevertheless it indicates
that dark and light morphs are over-represented in poor
quality territories.
Discussion
Fig. 3 Relationship between territory quality and the occupancy
share of dark and light morphs combined for a females and
b males. A total of 108 territories were identified and occupancy
data covers the period 1990– 2000
variable, the area covered by buildings in the plot which
was negatively related to LRS. For intermediate males,
36.4% of the variation in LRS was explained with a
highly significant model (F3,53=10.131, P<0.0001) with
three predictor variables. One (mean population density)
was also a significant predictor in the overall male model. The vole index across all breeding years and the forest edge length were both positively related to LRS in intermediate males. For light males, the regression model
explained 21.8% of variation in LRS with a highly significant model (F3,57=5.291, P=0.003) with three predictor variables. Two (temperature during the first breeding
year and mean snow cover) were also significant predictors in the overall male model, but absolute forest size
entered as a new variable with a positive relationship, so
that remote, larger forests were important breeding habitats predicting a higher LRS.
Similarities between both sexes are that extreme
morphs tend to be influenced more by habitat features
describing the disturbance level of the territory than the
intermediates. There is a trend for extreme morphs to be
influenced more by these habitat variables than the intermediate morph (Table 5 : χ21=2.718, P=0.096). To test
whether this might be a result of competition for high
This study has tried to assess the relative importance of
food, competition, weather, habitat and individual quality as predictors of lifespan and LRS in an animal population. As predicted by life history theory for a monogamous species with no strong sexual dimorphism
(Lindström 1999), there was no clear difference between
the sexes with regard to the importance of different factors
in determining LRS. Overall, the results indicate that
weather is the most important factor influencing both
lifespan and LRS in this buzzard population. Although
other studies have demonstrated the effect of adverse
weather conditions on both annual (Steenhof et al. 1997,
1999) and LRS (Rose et al. 1998; Kruuk et al. 1999), or
survival (Smith et al. 1996), it remained unclear whether
these conditions were the main driving force since other
important variables were not measured simultaneously.
My results argue forcefully for density-independent
mechanisms to be more important determinants of lifespan and LRS since only one of the variables of the competition complex entered only one of the models (density
in the first breeding year affected male lifespan negatively). Life history theory also predicts that, under less predictable environmental conditions, reproductive investment should be selected to maximise parental survival
(Hirshfield and Tinkle 1975; Benton et al. 1995). Under
such a scenario, abiotic conditions are predicted to be
more important determinants of both lifespan and LRS
compared to a more predictable environment, a result
that is confirmed by this study (see Brommer et al. 1998,
for a more predictable environment scenario). Cold, wet
breeding seasons are likely to result in fewer surviving
offspring through direct effects, but they also indicate
the overall food abundance, since densities of small rodents (staple prey) are influenced by weather conditions
(Mebs 1964). Probably the vole index used here was too
crude a measure to reflect true vole abundance and hence
was not a significant predictor variable in many models.
Other published studies have shown the great importance
of food supply as a determinant of LRS (Korpimäki
1992; Brommer et al. 1998).
Predictor variables also emphasised the importance of
conditions experienced before and during the first breeding year. These links between environmental conditions
experienced early in life and fitness have only recently
been studied in detail (Partridge 1988; Borgerhoff Mulder
1988; Gustafsson et al. 1995; Lindström 1999). Even for
481
long-lived, iteroparous animals, the effect of adverse
conditions during early stages of life on LRS can be
marked (Brommer et al. 1998; Rose et al. 1998). Inferior
quality birds have been shown to suffer fitness costs related to territory settlement patterns (Hakkarainen and
Korpimäki 1996; Krüger and Lindström 2001a), reproduction (Kruuk et al. 1999; Lindström 1999) and survival (Thessing and Ekman 1994; Rose et al. 1998).
A potential problem with my approach is that the
number of fledglings produced is not necessarily a good
measure of fitness (Benton and Grant 2000). Van
Noordwijk and van Balen (1988) showed a clear qualityquantity trade-off in great tits (Parus major) resulting in
reduced number of recruits into the population from larger clutches. However, other studies, notably on birds of
prey (Newton 1989; Korpimäki 1992; Brommer et al.
1998), have shown a significant positive correlation between the number of fledglings and recruits. This gives
me some confidence that number of fledglings is a good
proxy for fitness. One additional point is that any measure failing to take into account individuals dying before
reproducing is prone to erroneous interpretations (Grafen
1988). For this fraction I do not have a direct individualbased estimate.
The morph-specific regression models showed that
determinants of LRS differ according to morph type. A
general feature was that models for the extreme morphs
that had a lower fitness than the intermediate morph
tended to include habitat variables (especially anthropogenic disturbance), whereas the intermediate morph
models predominantly included weather and vole variables. This implies that habitat features seem to be more
important for dark and light individuals compared to intermediate ones, i.e. habitat quality matters more for the
less fit morphs. This provides an explanation for the
clear fitness differences observed between the morphs,
because individuals of the extreme morphs more often
tend to occupy territories which rarely are used by intermediate individuals (Fig. 3). Individuals of the extreme
morphs seem to be ousted into poor quality territories
with a higher human disturbance, and this might explain
their lower fitness, because repeated human disturbance
is known to cause breeding failures in birds of prey and
other bird species (Glutz von Blotzheim et al. 1971;
Cramp and Simmons 1980). Hence at least part of the
fitness differences in this buzzard population are caused
by intraspecific competition for high quality territories
and the associated lower breeding success in the low
quality territories (Krüger et al. 2001).
These results highlight the need to integrate as much
information as possible to assess the relative importance
of factors influencing fitness. With more and longer-term
studies emerging, we might then be able to understand in
more detail the factors by which natural selection has
created the variety of life history strategies.
Acknowledgements I am indebted to S. Kalinski and U. Stefener
for help with the fieldwork and the German National Scholarship
Foundation and Marie Curie Fellowship programme of EU for
funding. I thank J. Brommer, S. Butchart, T. Coulson, N. Davies,
M. Fowlie, J. Lindström, I. Newton and A. Roulin for comments
which improved the manuscript. This study complies with current
laws in Germany.
References
Bateson PPG (1977) Testing an observer’s ability to identify individual animals. Anim Behav 25:247–248
Benton TG, Grant A (2000) Evolutionary fitness in ecology:
Comparing measures of fitness in stochastic, density-dependent environments. Evol Ecol Res 2:769–789
Benton TG, Grant A, Clutton-Brock TH (1995) Does environmental
stochasticity matter? Analysis of red deer life-histories on Rum.
Evol Ecol 9:559–574
Borgerhoff Mulder M (1988) Reproductive success in three Kipsigis
cohorts. In: Clutton-Brock TH (ed) Reproductive success. University of Chicago Press, Chicago, pp 419–439
Brommer JE, Pietiäinen H, Kolunen H (1998) The effect of age at
first breeding on Ural owl lifetime reproductive success and
fitness under cyclic food conditions. J Anim Ecol 67:359–369
Clutton-Brock TH (1988) Reproductive success. Chicago University Press, Chicago
Clutton-Brock TH, Guinness FE, Albon SD (1982) Red deer.
Behaviour and ecology of two sexes. Chicago University
Press, Chicago
Clutton-Brock TH, Wilson K, Stevenson IR (1997) Density-dependent selection on horn phenotype in Soay sheep. Philos
Trans R Soc Lond B 352:839–850
Conradt L, Clutton-Brock TH, Guinness FE (1999) The relationship
between habitat choice and lifetime reproductive success in
female red deer. Oecologia 120:218–224
Cramp S, Simmons KEL (eds) (1980) Birds of the western palearctic, vol 2. Oxford University Press, Oxford
Glaubrecht M (1981) Dreieinhalbjährige Untersuchung zur Lebensweise einer Mäusebussardpopulation (Buteo buteo) im Westen
Ahrensburgs unter Anwendung einer individuellen Erfassungsmethode. Hamb Avifauna Beitr 18:149–170
Glutz von Blotzheim U, Bauer K, Bezzel E (1971) Handbuch der
Vögel Mitteleuropas, vol IV. Aula, Wiesbaden
Görner M, Kneis P (1981) Angaben zur Häufigkeit der Feldmaus
in der DDR von 1950 bis 1979. Saugetierkundl Inf 1:88–93
Grafen A (1988) On the uses of data on lifetime reproductive
success. In: Clutton-Brock TH (ed) Reproductive success.
Chicago University Press, Chicago, pp 454–471
Gustafsson L, Qvarnström A, Sheldon BC (1995) Trade-offs between life-history traits and a secondary sexual character in
male collared flycatchers. Nature 375:311–313
Hair JF, Anderson RE, Tatham RL, Black WC (1995) Multivariate
data analysis with readings, 4th edn. Prentice Hall, Englewood
Cliffs, N.J.
Hakkarainen H, Korpimäki E (1996) Competitive and predatory
interactions among raptors: an observational and experimental
study. Ecology 77:1134–1142
Heise S, Wieland H (1991) Zu den Methoden der Abundanzbestimmung bei Feldmauspopulationen als Grundlage eines
umweltgerechten Pflanzenschutzes. Nachrichtenbl Dtsch
Pflanzenschutzdienst 43:30–33
Hirshfield MF, Tinkle DW (1975) Natural selection and the evolution of reproductive effort. Proc Natl Acad Sci USA 72:
2227–2231
James FC, McCulloch CE (1990) Multivariate analysis in ecology
and systematics: Panacea or Pandora´s box? Annu Rev Ecol
Syst 21:129–166
Janes SW (1984) Influences of territory composition and intraspecific competition on red-tailed hawk reproductive success. Ecology
65:862–870
Korpimäki E (1992) Fluctuating food abundance determines the
lifetime reproductive success of male Tengmalm’s owls.
J Anim Ecol 61:103–111
Kostrzewa A, Kostrzewa R (1990) The relationship of spring and
summer weather with density and breeding performance of the
482
Buzzard Buteo buteo, Goshawk Accipiter gentilis and Kestrel
Falco tinnunculus. Ibis 132:550–559
Krüger O (2000) Life history strategies and population dynamics
of Common Buzzard Buteo buteo and Goshawk Accipiter
gentilis. PhD thesis, University of Bielefeld, Bielefeld
Krüger O (2002a) Interactions between Common Buzzard Buteo
buteo and Goshawk Accipiter gentilis: trade-offs revealed by a
field experiment. Oikos 96:441–453
Krüger O (2002b) Analysis of nest occupancy and nest reproduction in two sympatric raptors: common buzzard Buteo buteo
and goshawk Accipiter gentilis. Ecography 25 (in press)
Krüger O, Lindström J (2001a) Habitat heterogeneity affects population growth in goshawk Accipiter gentilis. J Anim Ecol 70:
173–181
Krüger O, Lindström J (2001b) Lifetime reproductive success in
Common Buzzard Buteo buteo: from individual variation to
population demography. Oikos 93:260–273
Krüger O, Stefener U (1996) Nahrungsökologie und Populationsdynamik des Habichts (Accipiter gentilis) im östlichen Westfalen. Vogelwelt 117:1–8
Krüger O, Stefener U (2000) Populationsfluktuation und die Rolle
der Reproduktion in einer Population des Habichts Accipiter
gentilis. Populationsokol Greifvogel Eulen 4:263–271
Krüger O, Lindström J, Amos W (2001) Maladaptive mate choice
maintained by heterozygote advantage. Evolution 55:1207–1214
Kruuk LEB, Clutton-Brock TH, Rose KE, Guinness FE (1999)
Early determinants of lifetime reproductive success differ
between the sexes in red deer. Proc R Soc Lond B 266:
1655–1661
Lindström J (1999) Early development and fitness in birds and
mammals. Trends Ecol Evol 14:243–248
Mac Nally R (2000) Regression and model-building in conservation
biology, biogeography and ecology: the distinction between and
reconciliation of ‘predictive’ and ‘explanatory’ models. Biodivers Conserv 9:655–671
McGraw JB, Caswell H (1996) Estimation of individual fitness
from life-history data. Am Nat 147:47–64
Mebs T (1964) Zur Biologie und Populationsdynamik des Mäusebussards (Buteo buteo) unter besonderer Berücksichtigung der
Abhängigkeit vom Massenwechsel der Feldmaus (Microtus
arvalis). J Ornithol 105:247–306
Melde M (1983) Der Mäusebussard. Neue Brehm-Bücherei, Wittenberg Lutherstadt
Newton I (1979) Population ecology of raptors. Poyser, Berkhamsted
Newton I (1989) Lifetime reproduction in birds. Academic Press,
London
Packer C, Herbst L, Pusey A, Bygott J, Hanby J, Cairns S, Mulder
M (1988) Reproductive success of lions. In: Clutton-Brock TH
(ed) Reproductive success. Chicago University Press, Chicago,
pp 363–383
Partridge L (1988) Lifetime reproductive success in Drosophila.
In. Clutton-Brock TH (ed) Reproductive success. Chicago
University Press, Chicago, pp 11–23
Rose KE, Clutton-Brock TH, Guinness FE (1998) Cohort variation
in male survival and lifetime breeding success in red deer.
J Anim Ecol 67:979–986
Scott DK (1979) Identification of individual Bewick’s swans by bill
patterns. In: Stonehouse B (ed) Animal markings. Macmillan,
London, pp 160–174
Smith JNM, Taitt MJ, Rogers CM, Arcese P, Keller LF, Cassidy
ALEV, Hochachka WM (1996) A metapopulation approach to
the population biology of the Song Sparrow Melospiza melodia.
Ibis 138:120–128
Steenhof K, Kochert MN, McDonald TL (1997) Interactive effects
of prey and weather on golden eagle reproduction. J Anim
Ecol 66:350–362
Steenhof K, Kochert MN, Carpenter LB, Lehman RN (1999)
Long-term prairie falcon population changes in relation to
prey abundance, weather, land uses, and habitat conditions.
Condor 101:28–41
Thessing A, Ekman J (1994) Selection of the genetical and environmental components of tarsal growth in juvenile willow tits
(Parus montanus). J Evol Biol 7:713–726
Travis J, Keen WH, Juilianna J (1985) The effects of multiple factors
on viability selection in Hyla gratiosa tadpoles. Evolution
39:1087–1099
Ulfstrand S (1977) Plumage and size variations in Swedish Common
Buzzards Buteo buteo L. (Aves, Accipitriformes). Zool Scr
6:69–75
Van Noordwijk AJ, Van Balen JH (1988) The great tit, Parus
major. In: Clutton-Brock TH (ed) Reproductive success. Chicago University Press, Chicago, pp 119–135