Download Climate change and habitat heterogeneity drive a population

Ibis (2014), 156, 97–106
Climate change and habitat heterogeneity drive a
population increase in Common Buzzards Buteo buteo
through effects on survival
€
RUDY M. JONKER,* NAYDEN CHAKAROV & OLIVER KRUGER
Department of Animal Behaviour, University of Bielefeld, Bielefeld, Germany
The effect of changing climatic conditions on wild populations has been the subject of
much recent research. Most attention has been on the direct effects of climate changes
on species of lower trophic levels and on the negative consequences of climate change.
However, a deeper understanding of how climate change affects apex predators is vital,
as they are keystone species that have a disproportionate effect on ecosystems. Studying
survival in an apex predator requires individual-based data from long-term studies and is
complicated by the integration of climatic effects on lower trophic levels. Here we assess
how climate affects the survival of the Common Buzzard Buteo buteo. We analysed the
survival of 670 males and 669 females over the period 1989–2011, during which time
our study population quadrupled. We used mark–recapture survival analysis of individual
resightings of breeding adults to identify the environmental factors best explaining
survival. A decrease in the North Atlantic Oscillation (NAO) index increased survival to
an extent that largely explains the population increase. This might be caused by higher
Common Vole Microtus arvalis survival in drier conditions and under snow cover.
Buzzard survival appeared to increase more for males than for females, possibly due to
the males’ higher sensitivity to winter food availability resulting from their smaller body
mass. However, we also found that the effect of NAO strongly depended on the area
in which individuals lived, especially for females. This may have been caused by the
recolonization of Eagle Owls Bubo bubo in some parts of our study area. This study
suggests that climatic changes can have complex effects on species of higher trophic
levels via an interaction with their prey.
Keywords: apex predators, life-history, North Atlantic Oscillation, population dynamics, raptors.
During the past decade, ample evidence has been
presented on the effects of climatic changes on
wild populations (Walther et al. 2002, Root et al.
2003, Moyes et al. 2011). Rapid climatic changes
should favour adaptations in organisms to cope
with these changes. However, when environmental
change happens at such a rapid rate that organisms
cannot adapt or use phenotypic plasticity to adjust
their behaviour, consequences might include population declines or even extinction (Both et al.
2006, Møller et al. 2008, Jones & Cresswell 2010).
Nevertheless, not all effects of climatic changes are
negative, as some species may benefit from the
*Corresponding author.
Email: [email protected]
© 2013 British Ornithologists’ Union
changes in temperature (D’Alba et al. 2010) or
winds (Weimerskirch et al. 2012). The effects of
climatic change may differ between trophic levels
because of the sensitivity for different environmental parameters or seasons (Visser et al. 1998), or
because of interactions between trophic levels
(Both et al. 2009). Additionally, the net effect of
climatic change on species of a higher trophic level
may be absent because of the integration of contrasting effects on species at a lower trophic level.
Whereas most studies have found significant
effects of climate change on life-history parameters
in small, migratory bird species, effects at higher
trophic levels are less well understood (Nielsen &
Møller 2006, Laidre et al. 2008, Rolland et al.
2008). Insights from apex predators are particularly
98
R. M. Jonker, N. Chakarov & O. Krüger
few (Parmesan 2006, Wynn et al. 2007), with
iconic high Arctic predators such as the Polar Bear
Ursus maritimus being an exception (Stirling &
Parkinson 2006, Stirling & Derocher 2012). A
deeper understanding of how climate change affects
apex predators is necessary as they are keystone
species that have a disproportionate effect on
ecosystems (Sergio et al. 2005, Duffy et al. 2007).
In addition, they are often of regional or global
conservation concern (Borrvall & Ebenman 2006).
The Common Buzzard Buteo buteo breeds
across Eurasia (BirdLife International 2012), and
its global population is increasing rapidly, although
some local populations are declining (Lehikoinen
et al. 2009). The population of Buzzards in Eastern Westphalia, Germany, has been studied since
1989 and has quadrupled over the last two decades (Kr€
uger et al. 2012). During the same period,
winter temperatures have decreased in this area,
although there has been no change in annual temperature. Because the winter period is the most
critical period of survival for Buzzards (Tubbs
1974), we assessed whether climatic changes over
the past 20 years have affected their survival.
To estimate survival accurately, detailed individual-based resighting data are required. Moreover,
for long-lived species, data covering many years
are required to detect significant trends in survival.
The long-term, individual-based data that have
been gathered during the course of this study provide such a dataset that allows an accurate estimation of yearly survival, and an assessment of the
extent to which climatic changes have increased
Buzzard survival, or whether other potential
factors are involved. For example, we have previously shown that the three different colour morphs of Buzzards differ significantly in lifetime
reproductive success (Kr€
uger et al. 2001, Chakarov
et al. 2008), and this could potentially be caused
by a difference in yearly survival between morphs.
Subsequently, we test whether there is an effect of
recruitment cohort on survival, as the conditions
experienced in early life are often good predictors
of reproductive success and survival in the future
(Lindstr€
om 1999).
METHODS
Study site and data collection
We studied a population of Buzzards in a 300-km2
study area in Eastern Westphalia, Germany, which
© 2013 British Ornithologists’ Union
is described in more detail elsewhere (Kr€
uger &
Lindstr€
om 2001, Kr€
uger 2004). Data have been
collected since 1989 by identifying individuals
based on the distinct plumage colour and pigmentation patterns (Cramps & Simmons 1980),
combined with the location at which they were
seen and the distinct differences in behaviour
between individuals. In more recent years, photographing individual adults and genetic fingerprinting of Buzzard chicks were also used to
increase the resighting accuracy. Based on a
comparison of the photographs and the genetic
fingerprinting, this method is up to 95% reliable
(Kr€
uger and Lindstr€
om (2001) give more details
on general data collection procedures in the
field). Resighting started for each individual with
the first breeding attempt. When an individual
was recorded breeding again, we recorded this as
a resighting. When it was not recorded breeding,
and hence not seen in the territory, it was treated as not resighted. Whenever an individual was
not recorded breeding in two consecutive years,
it was assumed to have died, an assumption that
is commonly made (Newton 1989), especially
because permanent emigration of individuals that
have already established a territory is highly unlikely (Melde 1983). This method allowed us to
track the breeding history of 670 male and 669
female Buzzards.
In addition to these breeding attempts, the
amount of food was characterized per annum by a
vole index (low, intermediate and high), which
was determined by counting how many Common
Vole Microtus arvalis holes were re-opened 24 h
after closing them (Heise et al. 1991). While the
index is crude, it is relevant to other parameters of
our population. For example, measures of annual
breeding productivity collected over 25 years at
the site correlated significantly with the vole index,
e.g. average number of fledged chicks per breeding
pair (r = 0.632, n = 23, P < 0.002), average
number of fledged chicks per successful breeding
pair (r = 0.611, n = 23, P < 0.002) and percentage
of failed breeding pairs (r = –0.430, n = 23,
P < 0.05). Furthermore, the percentage of voles in
the total prey remains is a good indication of the
availability of the staple food source for Buzzards,
and was correlated strongly with the vole index
over an 11-year period (r = 0.746, n = 11,
P < 0.01). For the entire study period we obtained
weather data such as the North Atlantic Oscillation (NAO) index. For NAO, we used the DJFM
Climate change and Common Buzzard survival
NAO (Osborn 2012), which is often used as a
predictor of winter weather (Hurrell 1995). In
addition, we obtained the number of days with
snow cover in winter, mean winter (December–
February) temperature, mean spring (March–May)
temperature and mean spring rainfall from the
German meteorological office (DWD). Because all
of these weather variables strongly correlated with
NAO, except the cumulative rainfall in spring, we
used only NAO and cumulative rainfall in spring
in further analyses to prevent collinearity between
independent variables. Absence of collinearity
between NAO, rainfall and vole index was confirmed by assessing the variance inflation factors
and intercorrelations (see also Supporting Information Fig. S1). To test for alternative influences on
survival, we used a variable for the area in which
each individual lived, based on the four distinct
areas in our study population: (1) the Teutoburger
forest, which is hilly and forested, and harbours an
important predator of Buzzards, the Eagle Owl
Bubo bubo; (2) Kiefernheide, south of the forest,
has a sandy soil and is consequently much drier,
and has more coniferous forest; (3) Ravensberger
Land, in the state of North-Rhine-Westfalia; and
(4) Lower-Saxony, the area north of the forest
within the state of the same name.
Statistical analysis
We analysed males and females separately to prevent within breeding pair dependencies affecting
the results. We used the R (R Development Core
Team 2012) package RMARK (Laake 2012) to
construct models for program MARK (White &
Burnham 1999). We used Cormack–Jolly–Seber
models with time, colour morph and recruitment
cohort as covariates for survival (φ) and time and
territory identity for resighting (p) utmc pt =Tid
as a general model. We first established that our
data met the assumptions of these models (goodness of fit), that all individuals sighted at one time
have an equal probability of surviving until
time+1, and that all individuals sighted at one
time have the same probability of being resighted
at time+1. To test these assumptions, we used the
program RELEASE (TEST 2 & TEST 3; Burnham
et al. 1987) and the median-^c method (White &
Burnham 1999). Subsequently, we defined a list
of candidate variables for both survival and resighting probability, from which RMARK built candidate
models for the corrected Akaike information
99
criterion (AICc) comparison (Burnham & Anderson
2002). The variables included for estimating survival were: Time in calendar years, NAO, Rain
measured as cumulative rainfall in spring, Vole
index and Area, and the factors Morph (dark,
intermediate and light: factor), Cohort (year of
first breeding), in addition to a number of biologically plausible interactions between these variables. We did not include age (or breeding career
length) as a variable because the use of age
together with time and cohort variables would
seriously reduce the degrees of freedom for our
analysis.
We compared models based on DAICc (difference between the model with the lowest AICc
value and the AICc value of all other models) and
used the normalized AICc weights (xi) to evaluate
the level of support for each candidate model.
Because there was not one ‘best’ model, we averaged the predicted estimates over all models based
on the AICc weight per model. In addition to this,
using the top 20 models (which ensured a
summed model weight of at least 0.95), we calculated the cumulative AICc weight (Σxi) per independent variable by summing, for each
independent variable, the AICc weights of the
models in which they were included.
To evaluate how the estimated survival
accounted for the observed population change,
we calculated the dominant eigenvalue of the
population matrix using the estimated average
survival for the period 1989–1998, in which the
population size changed little, and the estimated
average survival for the period 1999–2011, in
which a steep population increase was observed.
We then compared for the period 1999–2011
the predicted population sizes under both scenarios with the observed population change. We
calculated the asymptotic population growth rate
(k) from a fully age-structured Leslie matrix projection model (Caswell 2001), combining all
males and females from all years with the mean
observed fertility (number of young fledged per
pair) of 1989–2012 (1.36) and our estimated
survival probabilities. As first-year survival probability, we used 0.536 from the literature (Cramps
& Simmons 1980). We used standard annotations
as provided in Caswell (2001) and a post-fledging
birth-pulse approach, so there was a separate
fledgling age class, and hence first-year survival
was not incorporated into fertility entries (Caswell
2001).
© 2013 British Ornithologists’ Union
100
R. M. Jonker, N. Chakarov & O. Krüger
RESULTS
NAO significantly declined over the course of the
study. Winter rainfall correlated positively with
NAO, as well as with winter temperature. The
number of days with snow cover correlated negatively with NAO (Supporting Information Figs
S2–S5).
For both females and males, the results from the
goodness of fit test indicated that there was no overdispersion in either females (Release test 2:
v236 ¼ 18:6, P = 0.993; Release test 3: v2102 ¼ 65:3,
P = 0.998; median-^c = 1) or males (Release test 2:
v250 ¼ 35:3, P = 0.942; Release test 3: v288 ¼ 53:1,
P = 0.998; median-^c = 1) and no adjustments of ^c
were necessary. The model that estimated survival
best for females was the model that included Time,
Morph and NAO*Area as predictors for survival and
a constant resighting probability, which had an
AICc weight of 0.431. For males, the best model
was the model including cohort, Time, Morph
and NAO*Area as predictors for survival and a
resighting probability dependent on Time, which
had an AICc weight of 0.135. However, a more reliable estimate of the importance of the independent
variables for estimated survival is shown by the top
10 models for both males and females (Tables 1 &
2). For females, the top three models had a DAICc
of approximately 2 or less, which is usually taken
as a threshold value for model support (Burnham
& Anderson 2002). The cumulative AICc weights
per independent variable show that for females,
the effects of Time (Σxi = 0.999), Morph
(Σxi = 0.998), Area (Σxi = 0.999) and NAO
(Σxi = 0.999) were strongly supported. However,
from these weights of 0.999, most was due to the
interaction between Area and NAO (Σxi = 0.859).
For males, the effect of Time (Σxi = 0.951), NAO
(Σxi = 0.857) and Area (Σxi = 0.800) received
most support. The strength of support for an interaction between NAO and Area was relatively lower
in males than females (Σxi = 0.405).
Visualizing the main effect of Morph showed
that differences between the morphs are detectable but not particularly large. Intermediate
females and males have slightly higher survival
Table 1. List of top 10 models for estimating survival and resighting probabilities for females according to AICc comparison, with the
number of parameters K and the normalized AICc weight xi of each model (see Supporting Information Table S1 for full model list).
Model
K
AICc
DAICc
xi
Deviance
φ(~Morph + Time + NAO*Area) p (~1)
φ(~Morph + Time + NAO*Area + Rain2) p (~1)
φ(~Morph + Time + NAO*Area) p (~Time)
φ(~Morph + Time + NAO*Area + Rain2) p (~Time)
φ(~Morph + NAO + Time + Area) p (~1)
φ(~Morph + Time + NAO + Rain2 + Area) p (~1)
φ(~Morph + NAO + Time + Area) p (~Time)
φ(~Morph + Time + NAO + Rain2 + Area) p (~Time)
φ(~Cohort + Time + Morph + NAO*Area) p (~1)
φ(~Cohort + Time + Morph + NAO*Area) p (~Time)
12
13
13
14
9
10
10
11
33
34
3027.54
3029.17
3029.57
3031.12
3031.21
3032.69
3033.23
3034.71
3036.31
3038.04
0
1.63
2.03
3.66
3.67
5.15
5.69
7.17
8.77
10.50
0.431
0.191
0.156
0.069
0.069
0.033
0.025
0.012
0.005
0.002
1759.3327
1758.9293
1759.3326
1758.9274
1769.0775
1768.5339
1769.0757
1768.5268
1724.9335
1724.5882
Table 2. List of top 10 models for estimating survival and resighting probabilities for males according to AICc comparison, with the
number of parameters K and the normalized AICc weight xi of each model (see Supporting Information Table S1 for full model list).
Model
K
AICc
DAICc
xi
Deviance
φ(~Cohort + Time + Morph + NAO*Area) p (~Time)
φ(~Cohort + Time + Morph + NAO + Area) p (~Time)
φ(~Cohort + Time + NAO*Area) p (~Time)
φ(~Cohort + NAO + Time + Area) p (~Time)
φ(~Cohort + Morph + Time + NAO*Area + Rain2) p (~Time)
φ(~Cohort + Morph + Time + NAO + Rain2 + Area) p (~Time)
φ(~Morph + Time + NAO*Area) p (~Time)
φ(~Cohort + Time + Morph + NAO) p (~Time)
φ(~Morph + Time + NAO*Area + Rain2) p (~Time)
φ(~Cohort + Time + Morph + Area) p (~Time)
34
31
32
29
35
32
13
28
14
30
3139.28
3139.41
3140.00
3140.03
3140.58
3140.69
3140.85
3140.97
3141.76
3142.15
0
0.13
0.72
0.75
1.30
1.41
1.57
1.70
2.48
2.87
0.135
0.126
0.094
0.093
0.070
0.066
0.061
0.058
0.039
0.032
1890.795
1897.167
1895.68
1901.933
1890.007
1896.369
1935.585
1904.952
1934.465
1901.986
© 2013 British Ornithologists’ Union
Climate change and Common Buzzard survival
Rho = 0.02, n = 19, P = 0.92) between the
survival of a particular cohort for males and the
amount of voles two seasons earlier, the most
likely year in which these individuals were born,
as Buzzards usually start breeding at the age of 2
(Newton 1979).
Because there is one value for NAO per year, we
show the effect of NAO and Time combined. Moreover, because of the strongly supported interaction
between NAO and Area, we show the survival per
area per year, and for clarity we show this for
females (Fig. 3a) and males (Fig. 3b) separately. For
females, this showed that the change of survival differs strongly between the different areas (Fig. 3a).
Survival in the Lower-Saxony area increased steadily, and opposite to NAO, i.e. survival increased
with lower NAO (colder, less rain and more snow).
In the other areas, we observed the opposite pattern. This difference became particularly prominent
from 2002 onwards, with survival decreasing again
in the Teutoburger Forest, Kiefernheide and
Ravensberger Land. For males, this difference in
response to NAO between the areas is not detectable (Fig. 3b), as survival increases steadily and
opposite to NAO, similar to the females in the
Lower-Saxony area. For males and females, the survival in Lower-Saxony was consistently higher than
in the other areas, and survival in the Teutoburger
Forest and Kiefernheide the lowest. Rainfall affected
survival in a quadratic way, i.e. the highest survival
was associated with medium rainfall, although this
Males + s.e.
Females + s.e.
0.75
0.60
0.65
0.70
Survival
0.80
0.85
0.90
(females = 0.67, 0.76, 0.71 and males = 0.72,
0.78, 0.77 for dark, intermediate and light morphs,
respectively, Fig. 1). The visualization of the effect
of Cohort on survival showed clearly that there
was more fluctuation in survival between cohorts
for males than females (Fig. 2), but a formal test
of this interaction was not possible due to interdependence of males and females within pairs in our
data. There was also no correlation (Spearman’s
Dark
Intermediate
101
Light
Morph
0.7
0.6
0.5
Survival
0.8
0.9
Figure 1. The effect of morph on survival for males and
females. The average survival (se) per morph is shown for
both sexes. The average is calculated across all estimated
survival probabilities after model averaging.
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1990
1991
1989
0.4
Males + s.e.
Females + s.e.
Recruitment cohort
Figure 2. The effect of cohort on the survival for males and females. The average survival (se) per cohort (year of start of breeding) is shown for both sexes. The average is calculated across all estimated survival probabilities after model averaging.
© 2013 British Ornithologists’ Union
102
R. M. Jonker, N. Chakarov & O. Krüger
1
0
–1
NAO
0.7
0.6
0.5
–2
2008
2009
2010
2011
2009
2010
2011
–3
2007
2008
2006
2007
2005
2004
2003
2002
2000
1999
1998
1996
1997
1995
1994
1993
1992
1991
1990
0.4
Ravensberger Land
Teutoburger Forest
Kiefernheide
Lower-Saxony
NAO
2001
Survival
0.8
2
0.9
3
1.0
(a) Females
Year
1
0
–1
NAO
0.7
0.6
0.5
2006
2005
2004
2003
2002
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
–2
–3
0.4
Ravensberger Land
Teutoburger Forest
Kiefernheide
Lower-Saxony
NAO
2001
Survival
0.8
2
0.9
3
1.0
(b) Males
Year
Figure 3. The effect of time on the survival of males and females. The average survival (se) per year is shown for both sexes. The
average is calculated across all estimated survival probabilities after model averaging.
effect was more pronounced in females than in
males.
The predicted trajectory of the population,
under the scenario of the estimated survival for
the period 1989–1998 for females, shows that if
survival had remained unchanged, a population
decline is predicted (Fig. 4b). Under the scenario
of increased survival, the predicted and observed
population changes are very similar, although
some differences are still evident.
DISCUSSION
Survival of both female and male Buzzards in our
study area has increased over the past 20 years.
© 2013 British Ornithologists’ Union
Previous estimates of survival derived directly from
life tables for the same population were 0.68 for
females and 0.61 for males over the period 1989–
1998 (Kr€
uger & Lindstr€
om 2001). Our estimates
for the same period are very similar, at 0.63 for
females and 0.61 for males. For the period after
1998, we estimated a mean survival of 0.74 for
females and 0.80 for males (Fig. 4a). The first
result provides confidence in the earlier estimation
of the survival, as relatively similar survival rates
were estimated using two different methods. The
second result suggests that, especially in the past
10 years, survival has increased rapidly, as is
evident from Figure 3. For females, this increase in
survival mostly happened in the Lower-Saxony
(a)
1.0
Climate change and Common Buzzard survival
1989–1998 from Krüger & Lindström 2001
0.8
0.2
0.4
0.6
Estimate 1999–2010
0.0
Estimated survival (+/– s.e.)
New estimate 1989–1998
200
150
100
50
0
Population size (breeding pairs)
(b)
Males
250
Females
1990
1995
2000
2005
2010
Year
Figure 4. (a) Comparison of estimated survival for females
€m (2001) and this study. The
€ger and Lindstro
and males in Kru
first bar shows the estimated survival for both sexes from 1989
€ m (2001). The second bar shows
€ger and Lindstro
to 1998 in Kru
€ ger
our estimated mean survival for the same period as in Kru
€m (2001). The third bar shows the estimated surand Lindstro
vival for the period 1999–2010. (b) The observed and predicted
trajectories of the population size in our study area. In the period 1989–98 the population size did not change drastically.
Thereafter, the greatest change was observed and we compare
this change with predicted population size based on the dominant eigenvalue (k) of the Leslie population matrix. We predict
the population size for unchanged survival estimates (dotted
line) and changed survival estimates (dashed line). The
observed change after 1998 is shown with black squares.
area, which contains approximately 31% of our
breeding pairs. When we compare the development of the population size before and after 1998,
the rapid increase in the population coincides with
103
the rapid increase of survival. This observation is
confirmed by the predicted population trajectory
(Fig. 4b), which closely resembles that observed.
This suggests that the increase in survival can
account for a large part of the observed increase
(but note that these predictions are relatively
coarse because some parameter values have been
derived from other studies).
Reproductive success in Buzzards is known to
be affected by climate. Across Finland, at the
northern limit of their distribution, Buzzards have
advanced egg-laying following warmer springs, but
hatchlings face a less rapidly changing summer
climate, increasing the risk of hatching in unfavourable conditions (Lehikoinen et al. 2009), especially
as precipitation has increased. Our study, however,
focused more on the changes in winter, as they are
known to be more important for survival in our
resident population, whereas the Finnish population
is migratory. In addition, the climates in central
Germany and Finland differ greatly. This makes
direct comparisons between these studies complicated, but as our population is increasing and the
Finnish population is decreasing, it seems plausible
to suggest that different factors are operating on
each population.
The strong support for the NAO*Area interaction for females in particular suggests that the effect
of NAO on female survival strongly depends on the
area in which a female lives. In Kiefernheide and
Teutoburger Forest, with their sandy soils and
mostly coniferous forest, survival increased in high
NAO years and decreased in low NAO years,
whereas the opposite pattern was observed for
Lower-Saxony. Ravensberger Land, lying between
Lower-Saxony and Teutoburger Forest, was intermediate between these estimates. This finding links
to the importance of habitat heterogeneity for
reproduction in Buzzards (Kr€
uger et al. 2012). For
males, all areas responded similarly to NAO, with
increasing survival with lower NAO. This difference
in habitat sensitivity between males and females is a
surprising result, and we can only hypothesize about
the cause of this difference. A likely candidate is
that since the early 2000s, the Eagle Owl has recolonized our study area, mostly in Teutoburger Forest
and Kiefernheide. Eagle Owls have been observed
to take over Buzzard nests in our study area, consuming the occupants in the process. Also, we have
shown that breeding close to an Eagle Owl nest
reduces reproductive success of Buzzards and
Goshawks Accipiter gentilis in our study population
© 2013 British Ornithologists’ Union
104
R. M. Jonker, N. Chakarov & O. Krüger
(Chakarov & Kr€
uger 2010). Because females
incubate the eggs, they would naturally be more
vulnerable than males to this type of predation.
Indeed, it is striking that in the areas where Eagle
Owls breed, there has been a marked reduction in
female survival since the Eagle Owls’ return. An
alternative explanation is that in times of food shortage males fail to provision the female with sufficient
food, and can only feed themselves. This may lead
to a poor condition of the female during incubation.
During the study period, the NAO index has
decreased, and decreasing NAO has coincided with
decreases in winter rainfall and increases in the
number of snow days. Although more snow days
should limit the access of Buzzards to voles, their
main food source, snow also insulates and protects
voles against harsh winter weather, whereas rain
and subsequent freezing severely limits their access
to food (Aars & Ims 2002, Tkadlec et al. 2006).
Our estimate of vole abundance, however, was too
coarse to pick up a direct effect between vole index
and survival statistically. Our results differed from
other studies of raptors that show that more snow
actually decreases the access to food (e.g. voles may
hide under the snow), which may affect breeding
success (Sel
as 2001, Lehikoinen et al. 2011) or habitat use because preferred hunting habitat is covered
with snow (Wikar et al. 2008). These studies, however, come from Scandinavia, in which the winters
are far more severe. In an earlier study on the effect
of winter weather on Buzzard density and breeding
success (Kostrzewa & Kostrzewa 1990), no effect of
winter weather on the density and number of laying
pairs was found, but the density of Buzzards in that
study was much lower than during most of our
study, which could explain the absence of an effect.
Our study also suggests that survival in males
increased more than in females. This difference in
increased survival between males and females
could be interpreted in the context of the interaction between the reversed sexual dimorphism
present in Buzzards (Kr€
uger 2005) and the selection on survival and selection on reproductive
performance (Ydenberg & Forbes 1991). Ydenberg
and Forbes (1991) proposed the small male
hypothesis, which suggests that males are smaller
than females in raptors because the size difference
provides a benefit during provisioning of the
offspring, although there is a cost of decreased
survival during winter in males due to their smaller size (Hansen 1986). Under this hypothesis, an
increase in food availability during winter, as a
© 2013 British Ornithologists’ Union
result of climatic changes, should have a larger
effect on males than females. As there is ample
support for the small male hypothesis, we tentatively argue that this difference in increase in
survival between males and females might be
explained by the hypothesis that climatic changes
in winter are driving this increase in survival,
which is also illustrated by the presence of NAO
in the best supported models. Theoretically, we
would subsequently expect reverse sexual dimorphism to increase, which has been shown in a similar way for Goshawks (Tornberg et al. 1999).
We also showed that survival differs per cohort,
but this effect was more pronounced in males than
in females. We could not link this directly to the
food conditions in the year of hatching of each
cohort, but our result is in line with many other
studies that have documented cohort effects on
survival and reproduction (Lindstr€
om 1999, Reid
et al. 2004). A possible cause of the cohort effects,
and them being stronger in males than females, is
that the cohorts before 1996 seem to have lower
survival than those after 1996. The winter of 1996/
1997 was very severe for Buzzards, as emphasized
by the much lower survival in 1997 (Fig. 3), which
would of course only affect birds from cohorts of
1996 and before. That the effect was stronger in
males than females could be due to the relationship
between reversed sexual size dimorphism and
winter survival, as previously discussed.
The difference in survival we found between the
three colour morphs is consistent with earlier findings on life expectancy and consequent lifetime
reproductive success (Kr€
uger & Lindstr€
om 2001,
Kr€
uger et al. 2001). Although the differences in
estimated survival are small when viewed as a main
effect, the variable Morph was well supported. The
mechanism that causes this difference in survival
may lie in the difference in parasite load between
the morphs (Chakarov et al. 2008). Alternatively,
the differences in aggressiveness (Boerner & Kr€
uger
2009) may affect survival, as being the most
aggressive (light morph) might incur a higher
mortality because of higher risk-taking, while being
the least aggressive (dark morph) might cause territory or loss or insufficient predator deterrent with
consequent higher mortality.
In addition to the explored climate and area variables, other factors could have potentially affected
survival of Buzzards, most notably agricultural
changes and persecution. In Germany, the hunting
of Buzzards was banned after 1970. Hence, we
Climate change and Common Buzzard survival
think it is safe to assume that this would not have
affected the increase in survival greatly during our
study period. Changes in agricultural practice in our
study area have mainly consisted of increased maize
and rapeseed farming for biofuels, neither of which
are likely to provide suitable foraging habitat for
Buzzards because of the density of the vegetation
and the opportunity for prey concealment. However, we had no access to detailed spatio-temporal
data on land use, so we could not analyse this.
In summary, survival of Common Buzzards has
increased dramatically over the past 20 years, coincident with changing winter weather, but this
effect differs between areas, especially for females.
This increase in survival seems to account for a
large part of the marked increase in population
size. Survival of males appears to have increased
more strongly than survival of females, which suggests that improved food conditions in winter may
play a role in the observed increase in survival.
We are indebted to M. Boerner, S. Kalinski, U. Osterm€
uller and U. Stefener who helped with data collection.
We are also grateful to F. Trillmich for comments on the
manuscript. We thank Fabrizio Sergio and the anonymous
reviewers for their helpful comments. This study was
funded by grants of the German Science Foundation
(DFG) to O.K. (KR 2089/2-1; KR 2089/5-1). N.C.
was supported by the Volkswagen Foundation within its
Evolutionary Biology initiative, grant I/84-196.
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Received 5 February 2013;
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Associate Editor: Fabrizio Sergio.
SUPPORTING INFORMATION
Additional Supporting Information may be found
in the online version of this article:
Figure S1. The relationship between NAO and
cumulative rainfall in spring in the study area.
Figure S2. The development of NAO during
the study period.
Figure S3. The relationship between NAO and
cumulative rainfall in winter in the study area.
Figure S4. The relationship between NAO and
mean temperature in winter in the study area.
Figure S5. The relationship between NAO and
number of days with snow cover during winter in
the study area.
Table S1. Results of model comparison for
males and females.