The demographic impact of extreme events: stochastic weather drives survival and population dynamics in a long-lived seabird. Journal of Animal Ecology

Abstract

1. Most scenarios for future climate change predict increased variability and thus increased frequency of extreme weather events. To predict impacts of climate change on wild populations, we need to understand whether this translates into increased variability in demographic parameters, which would lead to reduced population growth rates even without a change in mean parameter values. This requires robust estimates of temporal process variance, for example in survival, and identification of weather covariates linked to interannual variability. 2. The European shag Phalacrocorax aristotelis (L.) shows unusually large variability in population size, and large-scale mortality events have been linked to winter gales. We estimated first-year, second-year and adult survival based on 43 years of ringing and dead recovery data from the Isle of May, Scotland, using recent methods to quantify temporal process variance and identify aspects of winter weather linked to survival. 3. Survival was highly variable for all age groups, and for second-year and adult birds process variance declined strongly when the most extreme year was excluded. Survival in these age groups was low in winters with strong onshore winds and high rainfall. Variation in first-year survival was not related to winter weather, and process variance, although high, was less affected by extreme years. A stochastic population model showed that increasing process variance in survival would lead to reduced population growth rate and increasing probability of extinction. 4. As in other cormorants, shag plumage is only partially waterproof, presumably an adaptation to highly efficient underwater foraging. We speculate that this adaptation may make individuals vulnerable to rough winter weather, leading to boom-and-bust dynamics, where rapid population growth under favourable conditions allows recovery from periodic large-scale weather-related mortality. 5. Given that extreme weather events are predicted to become more frequent, species such as shags that are vulnerable to such events are likely to exhibit stronger reductions in population growth than would be expected from changes in mean climate. Vulnerability to extreme events thus needs to be accounted for when predicting the ecological impacts of climate change.

Full text

Journal of Animal Ecology
2008,
77
, 1020–1029 doi: 10.1111/j.1365-2656.2008.01422.x
© 2008 The Authors. Journal compilation © 2008 British Ecological Society
Blackwell Publishing Ltd
The demographic impact of extreme events: stochastic
weather drives survival and population dynamics in
a long-lived seabird
M. Frederiksen*†, F. Daunt‡, M. P. Harris‡ and S. Wanless‡
Centre for Ecology and Hydrology, Hill of Brathens, Banchory AB31 4BW, UK
Summary
1.
Most scenarios for future climate change predict increased variability and thus increased frequency
of extreme weather events. To predict impacts of climate change on wild populations, we need to
understand whether this translates into increased variability in demographic parameters, which
would lead to reduced population growth rates even without a change in mean parameter values.
This requires robust estimates of temporal process variance, for example in survival, and identification
of weather covariates linked to interannual variability.
2.
The European shag
Phalacrocorax aristotelis
(L.) shows unusually large variability in popula-
tion size, and large-scale mortality events have been linked to winter gales. We estimated first-year,
second-year and adult survival based on 43 years of ringing and dead recovery data from the Isle of
May, Scotland, using recent methods to quantify temporal process variance and identify aspects of
winter weather linked to survival.
3.
Survival was highly variable for all age groups, and for second-year and adult birds process
variance declined strongly when the most extreme year was excluded. Survival in these age groups
was low in winters with strong onshore winds and high rainfall. Variation in first-year survival was
not related to winter weather, and process variance, although high, was less affected by extreme
years. A stochastic population model showed that increasing process variance in survival would
lead to reduced population growth rate and increasing probability of extinction.
4.
As in other cormorants, shag plumage is only partially waterproof, presumably an adaptation to
highly efficient underwater foraging. We speculate that this adaptation may make individuals
vulnerable to rough winter weather, leading to boom-and-bust dynamics, where rapid population
growth under favourable conditions allows recovery from periodic large-scale weather-related mortality.
5.
Given that extreme weather events are predicted to become more frequent, species such as shags
that are vulnerable to such events are likely to exhibit stronger reductions in population growth than
would be expected from changes in mean climate. Vulnerability to extreme events thus needs to be
accounted for when predicting the ecological impacts of climate change.
Key-words:
capture–mark–recapture, modelling climate impacts, random-effect models, stochastic
population dynamics
Journal of Animal Ecology
(2007) >doi: 10.1111/j.1365-2656.2007.0@@@@.x
Introduction
The Earth’s climate is changing rapidly, and there is an urgent
need to predict the ecological consequences of ongoing and
future climate change, including impacts on growth rates and
extinction probabilities of wild populations (Clark
et al
.
2001; Sutherland
et al
. 2006). To date, most such predictions
have focused on the effects of changes in mean values of
various climate parameters (Frederiksen
et al
. 2004; Thomas
et al
. 2004). However, under most scenarios for future climate
change, environmental variability is expected to increase and
extreme events are expected to become more common
(Solomon
et al
. 2007). Thus, we need to know how this will
affect populations, and whether and how species can adapt.
Because population growth is a multiplicative process,
increasing between-year variability in demographic parameters
*Correspondence author. E-mail: mfr@dmu.dk
†Present address: National Environmental Research Institute,
Department of Arctic Environment, University of Aarhus, Freder-
iksborgvej 399, DK-4000 Roskilde, Denmark.
‡Present address: Centre for Ecology and Hydrology, Bush Estate,
Penicuik EH26 0QB, UK

Weather and seabird population dynamics
1021
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Journal of Animal Ecology
,
77
, 1020–1029
and hence annual growth rate will inevitably lead to a
reduction in the long-term growth rate, even with no change
in mean parameter values (Lewontin & Cohen 1969). Natural
selection therefore should favour reduced variability in those
fitness components (demographic parameters) that are most
tightly linked to asymptotic population growth rate (that have
the highest sensitivity/elasticity), a process termed environ-
mental canalization (Gaillard & Yoccoz 2003; Morris &
Doak 2004).
To understand patterns, causes and consequences of
temporal variability in fitness components, we need to be able
to measure it accurately. Reliable estimation of temporal
variability requires separation of sampling variance, which in
this context is a nuisance parameter, from the underlying
‘process variance’, the true variance at the population level.
The best framework for this separation is a mixed (hierarchical)
modelling approach, where the variance term for a random
annual effect estimates temporal process variance (Gould
& Nichols 1998; Loison
et al
. 2002; Altwegg
et al
. 2006).
Similarly, identification of temporal environmental covariates
of demographic parameters is best done in a hierarchical
framework (Loison
et al
. 2002), because other methods suffer
from inflated power when temporal variability is pronounced
(Link 1999). Robust methods for estimating process variance
and identifying temporal covariates, for example using
capture–mark–recapture statistics, have been developed only
recently and are rarely used, and more detailed analyses of
existing long-term demographic data are needed to build up a
general understanding of the extent, causes and consequences
of temporal variation in demographic parameters.
In long-lived organisms, population growth rate is more
sensitive to variation in adult survival than in fecundity-
related fitness components (Lebreton & Clobert 1991). Most
such organisms are characterized by relatively stable popu-
lation size, and in accordance with the environmental
canalization hypothesis, adult survival varies relatively little
between years (Gaillard, Festa-Bianchet & Yoccoz 1998;
Sæther & Bakke 2000). Most seabirds fit this pattern, and
population change tends to be slow (Croxall & Rothery 1991;
Weimerskirch 2002). However, breeding populations of many
cormorant species (family Phalacrocoracidae) are prone to
periodic crashes, caused by large-scale mortality or non-breeding
events (European shag,
Phalacrocorax aristotelis
(L.): Potts,
Coulson & Deans 1980; Aebischer 1986; Harris & Wanless
1996; Brandt’s cormorant,
Phalacrocorax penicillatus
: Boekel-
heide & Ainley 1989; Nur & Sydeman 1999; Guanay cor morant,
Phalacrocorax bougainvillei
: Duffy 1983). Cormorants
typically also have higher potential fecundity than most other
long-lived birds (Weimerskirch 2002), and under favourable
environmental conditions, populations can grow by up to
20% per year (Frederiksen, Lebreton & Bregnballe 2001). It is
unclear whether this suite of demographic traits, often
thought to be adaptations to a highly variable environment
(Nur & Sydeman 1999; Weimerskirch 2002), will buffer these
species against further increases in environmental variability,
or whether a higher frequency of environment-related large-
scale mortality events will increase extinction risk. Resolving
this uncertainty requires detailed analyses of long-term demo-
graphic data covering a wide range of environmental conditions.
Here, we use 43 years of ring-recovery data to examine
temporal variability in juvenile, immature and adult survival
of European shags (hereafter shags) on the Isle of May in eastern
Scotland. The main aims of this paper are to (i) quantify and
compare temporal process variance in survival for different
age classes; (ii) identify environmental factors driving temporal
variation in survival; and (iii) evaluate the impact of extreme
mortality events on population dynamics.
Methods
STUDY
SITE
AND
FIELD
METHODS
The Isle of May (56
°
11
′
N, 2
°
33
′
W) is situated in the outer Firth of
Forth, eastern Scotland,
≈
8 km from the mainland. The population
dynamics and demography of shags at this colony have been studied
in detail over many years (Aebischer 1986; Aebischer & Wanless
1992; Harris
et al
. 1994b, 1994a; Harris & Wanless 1996; Harris,
Wanless & Elston 1998). Since 1961, the number of occupied shag
nests has fluctuated between 259 and 1916, with pronounced crashes
in 1975 –76, 1993 –94 and 2004 –05 (Fig. 1). Some of these fluctuations
were due to non-breeding events, for example 1975–76 (Aebischer
1986), 1993 (Harris & Wanless 1996) and 1999 (unpublished data),
while others were linked to major mortality events, for example
1994 (Harris & Wanless 1996). Shags are inshore foragers and
always spend the night on land (Daunt
et al
. 2006). During the
breeding season, Isle of May shags forage both around the island
and along the adjacent mainland coast (Wanless, Harris & Morris
1991). At other times of the year their distribution is still centred on
the colony with similar numbers dispersing both north and south
along the UK east coast, juveniles and immatures dispersing greater
distances, on average, than older birds (Harris & Swann 2002).
Adult shags (
≥
2 years old), as well as unfledged chicks, have been
ringed on the Isle of May with hard-metal British Trust for Orni-
thology (BTO) rings since 1963. Unique colour rings were first
introduced in 1981 for adults, and in 1997 for chicks. Many birds
colour-ringed as adults were originally metal-ringed as chicks, but
here these birds are treated as released in the year they were colour-
ringed. The extensive ringing effort has resulted in large numbers of
live recaptures and resightings at the colony, as well as dead recoveries.
Fig. 1. Counts of European shag nests on the Isle of May, 1961–
2006. No counts were made in 1967, 1968 and 1970–72.

1022
M. Frederiksen
et al.
© 2008 The Authors. Journal compilation © 2008 British Ecological Society,
Journal of Animal Ecology
,
77
, 1020–1029
In this study, we focused on quantifying temporal variability and
identifying environmental covariates of survival; we therefore used
only dead recoveries in order to obtain the longest time series
possible without unduly complicating the analysis. Ringed chicks
recovered as dead before fledging were not included in the data set.
A total of 28 221 individuals were released: 19 168 metal-ringed
chicks, 2590 metal-ringed adults, 5564 colour-ringed chicks and 899
colour-ringed adults (436 of these were originally ringed as chicks).
We used 2938 dead recoveries from the period 1963–2005 reported
by members of the public, excluding cases where only the ring was
found. The recovery year was defined as 1 July to 30 June, except for
the first year after ringing, which extended from the actual ringing
date of each bird to 30 June in the following year. The overall mean
ringing date was 5 July for chicks and 27 June for adults.
STATISTICAL
METHODS
We analysed the ring-recovery data in
mark
(White & Burnham
1999) using the Seber parameterization (Williams, Nichols &
Conroy 2002), in which the estimated parameters are
S
, the annual
survival probability; and
r
, the probability that a dead ringed bird is
found and the ring number reported. All model parameters are
probabilities, and a logit-link function ensures that estimates and
confidence limits remain within the interval [0;1]. A number of
statistical models, each representing a biological hypothesis, were
fitted to the data. Subscripts indicate the structure of the model,
following the principles of Lebreton
et al
. (1992):
a
2 or
a
3 indicates
a model with two or three age classes,
t
indicates a fully time-specific
structure (year as a factor),
T
indicates a logit-linear trend over time
(year as a covariate), CR indicates the presence/absence of a colour
ring, and environmental covariates are indicated as listed below. An
asterisk between two terms indicates that the model includes an interac-
tion term, and a plus that the model is additive (without interaction).
Goodness of fit was evaluated with the median c-hat procedure in
mark
, and the estimated variance inflation factor
:
was used to
adjust standard errors and calculate QAIC
c
, the bias- and small
sample-adjusted Akaike’s information criterion (Burnham &
Anderson 2002). Models were then ranked according to QAIC
c
,
with lower values indicating better approximating models with a
proper balance between under- and overfitting.
Previous studies of shags have shown that survival is lower during
the first 2 years of life than among adults (e.g. Aebischer 1986;
Catchpole
et al
. 1998), and there is also evidence for a senescent
decline in survival among older birds (Harris
et al
. 1994b; Harris
et al
. 1998). We restricted age-dependence in survival to three age
classes (first-year, second-year and adult), as we were primarily
interested in the temporal variability of survival for each of these age
classes, and the addition of further age classes would lead to a loss
of power to detect meaningful temporal patterns. For recovery
probabilities, we used a two-age-class structure, as first-year juve-
niles often have separate wintering areas and a different pattern of
vulnerability to various sources of mortality, both factors that could
lead to different probability of dead ringed birds being found and
reported (e.g. Frederiksen & Bregnballe 2000). We also allowed, in
the most general model, for dead birds with colour rings potentially
being more likely to be reported. Our general model thus had the
structure
S
a
3*
t
r
a
2*CR*
t
, with all parameters time-dependent.
ESTIMATING
AND
COMPARING
PROCESS
VARIANCES
When evaluating the impact of demographic variability on population
dynamics based on empirical data, it is important to separate
variance associated with the sampling process, which is irrelevant in
this context, from the underlying process variance (Gould & Nichols
1998). For capture–mark–recapture data, this can be done using the
random effects module in
mark
, which uses the method of moments
to provide an estimate of the process variance of a given set of estimated
parameters, typically annual estimates of survival (Franklin
et al
. 2000;
Burnham & White 2002). However, in order to compare process
variances among different sets of parameters, here age classes, math-
ematical restrictions on the variance of probabilities must also be taken
into account. Briefly, the maximum possible variance associated
with a probability is a function of the mean [
p
(1
– p
), where
p
is the
mean]; it is highest at a mean of 0·5 and declines to zero at means of
0 or 1 (Morris & Doak 2004). We followed previous authors (Gaillard
& Yoccoz 2003; Morris & Doak 2004; Altwegg
et al
. 2006) in scaling
process variance by the maximum possible variance for the given
mean, and used the term ‘relative process variance’. We used the Markov
chain Monte Carlo (MCMC) module in
mark
to estimate process
correlations between survival probabilities of the three age classes.
IDENTIFYING
COVARIATES
OF
SURVIVAL
We adopted a confirmatory rather than an exploratory approach
when identifying covariates of survival (Anderson
et al
. 2001). A
small number of candidate covariates were thus chosen, based on
theoretical considerations and the results of previous studies. Like
other cormorants, shags have a partially wettable plumage (Grémillet,
Tuschy & Kierspel 1998; Grémillet
et al
. 2005) and therefore
potentially suffer extensive heat loss in cold and wet conditions.
Low temperatures, strong winds and high rainfall could therefore
lead to increased mortality, particularly in winter when feeding con-
ditions deteriorate and the time available for foraging is limited
(Daunt
et al
. 2006, 2007). In addition, shags probably forage less
efficiently when water turbidity is high (cf. Strod
et al
. 2005), such
as during strong onshore winds or following heavy rainfall. Onshore
winds may thus have several potentially interacting negative effects
on shags: drenching by waves and spray (shags roost on rocks close
to the tide line), increased evaporative cooling and reduced foraging
efficiency. Large-scale mortality of Isle of May shags in late winter
1994 was related to an extended period of strong onshore (easterly)
winds (Harris & Wanless 1996), and similar patterns had been
shown previously in another colony (Potts 1969). In general, shag
mortality peaks in late winter. Among 851 Isle of May shags recovered
as freshly dead, 31% of first-year birds and 41% of older birds were
recovered in February and March. Late winter conditions have also
been shown to affect the extent of non-breeding and timing of
breeding in Isle of May shags (Aebischer & Wanless 1992; Daunt
et al
. 2006). Taking into account a likely 2–3-week mean lag
between death and recovery (cf. Daunt
et al
. 2007), we concentrated
on February weather in our search for relevant covariates of survival.
Daily weather data from Leuchars (28 km north-west of the study
site) were extracted and the following synthetic weather variables
were calculated: mean daily minimum air temperature (AT), total
precipitation (R), and summed onshore wind component (OC). The
onshore (easterly) wind component was calculated for each day as
mean daily wind speed (in knots)
×
sin(mean daily wind direction),
and set to 0 if wind direction was between 180 and 360
°
(i.e. west-
erly). The resulting variable was then summed over all days in
February The three environmental covariates were not highly corre-
lated (AT/R:
r
=
−
0·09; AT/OC:
r
=
−
0·46; OC/R:
r
= 0·34). Onshore
winds are also likely to increase the chance that dead birds are
recovered, so we first tested for an effect of OC on recovery prob-
abilities before modelling survival.

Weather and seabird population dynamics
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Journal of Animal Ecology
,
77
, 1020–1029
Traditionally, important temporal covariates of survival have
been identified by fitting ultrastructural models, where annual
survival is constrained to be a function (usually on the logit scale) of
one or more covariates, and comparing these models with constant
and fully time-dependent models using information-theoretic criteria
such as AIC
c
(Lebreton
et al
. 1992). However, in large data sets with
strong temporal variation in survival, this approach has inflated
power (Link 1999): fully time-dependent models are almost invariably
preferred over covariate models, and ultrastructural covariate
models are generally preferred over constant models, even when the
covariate is a series of random numbers. In the present data set, temporal
variation in survival was very strong and fully time-dependent
models always had the lowest QAIC
c
(see Results). We fitted models
including 10 different series of random numbers (uniformly distributed
between 0 and 1) as covariates of adult survival; eight of these were
preferred over the constant model by QAIC
c
by a margin of up to
63, indicating very strong support for some of these non-informative
models. The best framework for covariate selection is probably
hierarchical mixed models with random year effects, but guidelines
for this have not yet been established. We therefore selected covariates
using an alternative method. We used analysis of deviance (
anodev
,
Skalski, Hoffmann & Smith 1993)
F
-tests in a combined step-
up–step-down approach to identify the best combination of covariates
for each age class (Grosbois
et al
. 2006), starting from the model
with all main effects and two-way interactions. This approach does
not distinguish between process and sampling variance.
The amount of between-year variation explained by covariates
was assessed using
anodev
. We calculated the proportion of the
total between-year variation (deviance) in survival or recovery
probabilities explained by a given covariate as (DEV
c
– DEV
x
)/
(DEV
c
– DEV
t
), where
c
,
x
and
t
indicate, respectively, models with
no temporal variation, with the covariate, and with full time-dependence.
STOCHASTIC
POPULATION
MODEL
We used a stochastic matrix population model to explore how the
observed level of temporal variation in survival affected long-term
population growth rate. A three age-class model of the population
at the start of the breeding season (prebreeding census
sensu
Caswell
2001) was constructed in
ulm
(Legendre & Clobert 1995). Observed
means and variances of survival were taken from random-effect
models on the logit scale; annual values were drawn from these
distributions and back-transformed to the real scale, and thus
included only process variance. We estimated mean annual fecundity
(0·9 chicks per pair, SD 0·38) from our long-term records and drew
annual values from this distribution. Some birds start breeding at
age 2, but the majority commence at age 3, and some later (Potts
et al
. 1980; Aebischer 1986); in the model we assumed that all birds
start at age 3. The model did not include correlations between fitness
components; age of first breeding was assumed to be constant rather
than stochastic; and non-breeding of established breeders was not
accounted for. The starting population was 1000 females distributed
according to the stable age distribution of the equivalent deterministic
model. 1000 realizations
i
were run for 500 years
T
. We recorded
mean stochastic population growth rate:
as well as the proportion of extinct trajectories at the end of the
simulation (with an extinction threshold of 1). To explore the
implications of changes in environmental variability, we re-ran this
model with values of process variance for fecundity as well as
survival of all three age classes between 50 and 150% of the observed
values.
Results
MODELLING
RECOVERY
PROBABILITIES
We first attempted to identify a parsimonious model for the
recovery probability
r
. The most general model (
S
a
3*
t
r
a
2*CR*
t
,
with year-specific recovery probabilities separately for first-
year and older birds, and for birds with and without colour
rings) showed some lack of fit (
:
= 1·21), and we therefore
used QAIC
c
in model selection. This model (model 12 in
Table 1) could be simplified by eliminating the age and colour-
ring effects (models 5– 7,9,10), but year-to-year variation in
r
was strong (model 5 vs. 11). A substantial part of this
variation could be explained by a linear trend over time
(model 4) or by onshore winds in February (model 8), and the
model with both effects explained 56% of the interannual
variation according to
anodev
(model 2). At this stage, we
again tested whether additive age or colour-ring effects were
important. The two age-class effect was not needed (model 3),
whereas colour rings seemed to have an effect on
r
(model 1).
The model selected at this stage for
r
was retained for survival
modelling, and all
Δ
QAIC
c
values given are relative to this
model. Recovery probabilities declined strongly over the
study period (
β = −0·020 ± 0·0032 SE), from ≈17 to ≈7% for
non-colour-ringed birds. Onshore winds in February had a
positive effect on r (β = 0·0041 ± 0·0015 SE), corresponding
to an increase in recovery probability of up to 4 –5% in the
windiest winters. Colour-ringed birds were more likely to be
recovered (β = 0·22 ± 0·10 SE), although the effect was small
(≈2% higher recovery probability).
λ
s
i
ii
nT nT
exp ln( ( )) ln( ( )) ()==− =
⎡
⎣
⎢⎤
⎦
⎥
=
∑
1
1000
500 0
500 2001
1
1000
Caswell
Table 1. Model selection for recovery probabilities of ringed
European shags on the Isle of May
Model QDeviance KΔQAICc
Variation
explained (%)
1Sa3*t rCR+OC+T 1029·34 126 0
2Sa3*t rOC+T 1034·23 125 2·87 55·9
3Sa3*t ra2+OC+T 1033·87 126 4·54
4Sa3*t rT1040·96 124 7·59 49·6
5Sa3*t rt987·06 162 30·46
6Sa3*t rCR+t 985·87 163 31·30
7Sa3*t ra2+t 985·89 163 31·32
8Sa3*t rOC 1071·89 124 38·51 20·7
9Sa3*t rCR*t951·55 185 41·53
10 Sa3*t ra2*t957·16 189 55·25
11 Sa3*t r.1094·02 123 58·63
12 Sa3*t ra2*CR*t917·42 223 84·52
QDeviance is the deviance of the model adjusted for lack of fit; K is
the number of estimable parameters; ΔQAICc is the difference in
QAICc between the model in question and the best model; the
amount of total between-year variation explained by one or more
covariate(s) is calculated with anodev.

1024 M. Frederiksen et al.
© 2008 The Authors. Journal compilation © 2008 British Ecological Society, Journal of Animal Ecology, 77, 1020–1029
MODELLING SURVIVAL
Between-year variation in survival was very strong for all
three age classes (Fig. 2); ΔQAICc for models with constant
survival was 301 for first-year birds, 24·4 for second-year
birds and 649 for adults. As shown previously (Harris &
Wanless 1996), adult survival was extremely low (0·27) in
1993/94, and very low adult survival (<0·6) was also observed
in 1965/66 and 2004/05. First-year survival was particularly
low during 1976/77–1978/79 (cf. Aebischer 1986). Mean
survival, as estimated using a random-effects model on the
real scale, was 0·513 (± 0·038 SE) for first-year birds, 0·737
(± 0·028 SE) for second-year birds, and 0·858 (± 0·030 SE) for
adults. Estimated relative process variance was highest for
first-year birds (20·4% of maximum possible), and lower for
second-year birds (9·6%) and adults (14·1%). Relative process
variance declined more rapidly for adults and second-year
birds than for first-year birds when extreme years were
dropped (Fig. 3), indicating that extreme events were more
important for these older age classes. The MCMC analysis
indicated substantial process correlations in survival between
the three age classes (Table 2); in particular, second-year and
adult survival probabilities were highly correlated. We fitted a
set of additive models, where survival was constrained to vary
in parallel over time between two or three age groups. The
model with parallel variation between second-year and adult
survival was slightly better than the fully interactive model
(ΔQAICc = −5·32), whereas all other additive models performed
poorly (ΔQAICc > 33). We therefore explored relationships
between survival and environmental covariates separately for
each age class, and also using additive models for second-year
and adult survival.
Fig. 2. Estimated survival of first-year, second-year and adult
European shags from the Isle of May, 1963–2005. Estimates are
derived from a fully time-specific random-effects model on the logit
scale (see text for details). Error bars indicate 95% confidence limits.
Fig. 3. Process variance as a proportion of the maximum possible
(see text for details) in first-year, second-year and adult survival of
European shags on the Isle of May, as a function of the number of
extreme years dropped from the estimation.
Table 2. Process correlations (corrected for sampling covariance)
between annual time series of estimated survival probabilities of
first-year, second-year and adult European shags
Correlation Median SE 95% CI
First vs. second year 0·401 0·187 –0·004–0·731
First year vs. adult 0·466 0·156 0·145–0·736
Second year vs. adult 0·824 0·133 0·469–0·972
Medians, standard errors and 95% credible intervals are shown.

Weather and seabird population dynamics 1025
© 2008 The Authors. Journal compilation © 2008 British Ecological Society, Journal of Animal Ecology, 77, 1020–1029
ENVIRONMENTAL COVARIATES OF SURVIVAL
As expected with the large sample size and very pronounced
year-to-year variation in survival, covariate models were
never preferred over fully time-dependent models by QAICc.
Furthermore, for first-year and adult survival where year-
to-year variation was particularly pronounced, covariate
models invariably had a lower QAICc than the constant
model, and the most complex model (with all main effects and
two-way interactions) had the lowest QAICc of all covariate
models (Table S1 in Supplementary Material). QAICc was
thus not a useful tool for covariate selection. Stepwise anodev
resulted in the following covariates being selected: OC
(marginal) for first-year survival, and OC*R for second-year
and adult survival (Table S2). The selected covariate models
for first-year, second-year and adult survival explained,
respectively, 7·3, 15·1 and 42·9% of the annual variation.
Because the same covariates were selected for both second-year
and adult survival, we used a model including an additive
constraint on these two age classes to derive coefficients for
the relationship between survival, onshore winds and
precipitation (Table 3). Predicted adult survival was high
(0·85–0·95) under most conditions, but fell dramatically when
both OC and R were high, to 0·15 at the highest observed
values (Fig. 4). Similarly, predicted second-year survival was
0·7–0·9 under most conditions, but fell to 0·07 at the highest
observed values of OC and R. While the relationship with OC
and R accurately predicted the low survival in 1965/66 and
1976/77, it underestimated the magnitude of the mass
mortality in 1993/94, and the low observed survival in 2004/
05 was unexpected based on these weather variables (Fig. 5).
We tested whether variation in population size might explain
some of the lack of fit by including the number of breeding
pairs and interactions with the selected weather variables as
additional covariates of adult survival. According to anodev,
this model was far from being preferred (F4,32 = 0·23, P =
0·92), and population size thus could not explain the lack
of fit of the best weather-related model.
STOCHASTIC POPULATION MODEL
With the observed means and process variances for survival
of each age class, mean λs was 0·9838, and 745 of the 1000
trajectories were extinct after 500 years. We recalculated
process variances (but not means) of second-year and adult
survival after removing the most extreme year, 1993/94. Mean
λs was 1·0003, and none of the 1000 trajectories was extinct
after 500 years. To simulate the effect of potential changes in
environmental stochasticity, we re-ran the model with process
variances for survival of all three age classes ranging from 50
to 150% of the observed values. Increasing process variance
by 20% led to near-certain extinction after 500 years, whereas
reducing it by 30% led to a positive growth rate and certain
persistence of the population (Fig. 6).
Discussion
SHAG SURVIVAL AND WEATHER
Survival of second-year and adult shags was substantially
reduced in years when high precipitation (mostly rainfall)
and strong onshore (easterly) winds coincided in February
Table 3. Coefficients of the preferred logit-scale random-effect
model for second-year and adult survival
Coefficient Estimate SE
Intercept 1·728 0·121
Additive age effect –0·773 0·081
OC 0·008268 0·002219
R0·011919 0·001936
OC.R –0·000350 0·000027
Coefficients are given on the logit scale and thus are not immediately
interpretable; Fig. 4 plots the relationship for adult survival.
Fig. 4. Predicted adult survival as a function of summed onshore
component and total precipitation, both in February. Coefficients are
from a model with an additive constraint on second-year and adult
survival (Table 3).
Fig. 5. Predicted and observed adult survival, 1965–2005. Predicted
values are from a constrained model (Fig. 4); observed values from
an unconstrained random-effect model (Fig. 2).

1026 M. Frederiksen et al.
© 2008 The Authors. Journal compilation © 2008 British Ecological Society, Journal of Animal Ecology, 77, 1020–1029
(Fig. 4); the interactive model including these covariates
explained 43% of the temporal variation in adult survival. To
avoid data mining and to obtain a parsimonious model, we
decided a priori to focus on February weather rather than
search for the time window where the weather–survival cor-
relation was highest. February was chosen mainly because
mortality (measured as the number of birds recovered) peaks,
on average, at this time, and because major shag ‘wrecks’ in
the North Sea (mass occurrences of beached dead birds)
usually occur from February onwards (Potts 1969; Harris
& Wanless 1996). In addition, detailed studies of overwinter
time budgets showed that foraging effort in February was
linked to timing of breeding in the following season, with
females spending more time foraging in February laying later
(Daunt et al. 2006). This suggests that late winter is a stressful
period for shags, and it is likely that individuals in poor body
condition may struggle to survive if weather is poor. Never-
theless, the timing of peak mortality varied substantially
between years (data not shown), and it is likely that a more
detailed search would allow us to explain a larger proportion
of the between-year variation in survival. First-year survival
was highly variable between years (Fig. 2), but not strongly
correlated with February weather (Table S1; Table S2).
Consistent with this, Potts (1969) showed that the timing of
peak mortality of first-year shags varied between both years
and colonies. The less clear relationship between late winter
weather and survival for first-year birds relative to adults may
reflect higher vulnerability of juveniles to environmental con-
ditions in autumn or early winter and/or greater importance
of food abundance relative to weather in this age class, due to
less developed foraging skills. First-year survival thus may
not be highly correlated with any individual weather covariate,
because the period of greatest vulnerability varies between
years. In addition, shags disperse further from the colony
during their first winter than later, on average, which would
tend to make the weather covariates we have used here less
appropriate for this age class.
The combination of strong easterly winds and heavy
rainfall is likely to have had both direct and indirect impacts
on shags. Gales and associated heavy rainfall during the
breeding season can cause mass mortality among unfledged
shag chicks, presumably through hypothermia, and this
mortality is most pronounced in nests exposed to the prevailing
wind (Aebischer 1993; unpublished data). Because shag
plumage is not completely waterproof (Grémillet et al. 1998;
Grémillet et al. 2005), adults may succumb to the same
factors, particularly in winter when ambient temperatures are
relatively low. For the double-crested cormorant Phalacrocorax
auritus, Hennemann (1983) showed that birds with wet
plumage suffered increased heat loss at low temperatures.
However, it is also likely that foraging is inhibited during
onshore gales, perhaps because of increased turbidity. Daunt
et al. (2006) showed that while the foraging effort of Isle of
May shags during winter generally increased when onshore
winds dominated, birds stopped foraging completely during
the strongest wind episodes. This could reflect increasing
energy demands during onshore winds, combined with
decreased foraging efficiency when turbidity was very high.
Prolonged episodes of onshore winds may thus lead to both
increased energy demand and decreased intake rates, a
potentially lethal combination for shags, which carry very
small fat reserves (D.N. Carss, pers. comm.). Interestingly,
while major mortality events (wrecks) of young shags are a
regular occurrence on the relatively linear east coast of
Britain, which has very few islands and thus little shelter from
onshore winds (Potts 1969; Harris & Wanless 1996), they
seem to be absent on the west coast of Scotland, which, with
its convoluted coastline and many small islands, offers shelter
from any wind direction (Swann & Ramsay 1979). Indeed,
over the period 1985–2005, variability in shag breeding
population size was higher on the Isle of May (CV = 0·55)
than on Canna in western Scotland (CV = 0·39; R.L. Swann,
pers. comm.) or two colonies in Shetland, where shelter is also
available from all wind directions (CV = 0·42 and 0·15;
M. Heubeck, pers. comm.).
Survival of adult shags showed an unusually high degree of
temporal variation, particularly for a generally long-lived
organism. In our study, adult survival varied from 0·27 to
0·98, similar to the range of mean values observed across
birds and other annually reproducing organisms (Sæther &
Bakke 2000). Like other cormorants (Duffy 1983; Nur &
Sydeman 1999), shags also show large between-year variation
in fecundity (Aebischer & Wanless 1992; unpublished data)
and are capable of rapid population growth. Unusually for
seabirds (Weimerskirch 2002), shags occasionally can raise a
brood of four chicks successfully (Harris et al. 1994a), and
exceptionally can rear two broods in a season (Wanless &
Harris 1997). Taken together, these life-history traits con-
stitute what could be termed a demographic ‘boom-or-bust’
syndrome among cormorants, where individuals and popu-
lations are able to take advantage of favourable conditions
through high fecundity and survival, while suffering high
mortality and breeding failures under unfavourable con-
ditions. This life-history syndrome is linked to a set of
presumed morphological adaptations to efficient underwater
foraging (Grémillet et al. 1999; Grémillet et al. 2001): large,
Fig. 6. Stochastic population growth rate (closed symbols) and
probability of extinction (open symbols) as functions of the process
variance in survival of all three age classes, relative to observed values.

Weather and seabird population dynamics 1027
© 2008 The Authors. Journal compilation © 2008 British Ecological Society, Journal of Animal Ecology, 77, 1020–1029
fully webbed (totipalmate) feet, partially wettable plumage,
and small fat stores to reduce buoyancy. These morphological
traits, particularly plumage and fat stores, are probably linked
to the high vulnerability to mortality due to inclement
weather demonstrated in this study. In contrast to the
standard image of seabirds as highly conservative, ‘prudent’
breeders, shags and other cormorants thus have a ‘risky’ life
style and can thrive only at times and locations where prey
availability is high (Grémillet, Wanless & Linton 2003).
THE IMPACT OF TEMPORAL VARIATION IN SURVIVAL
ON POPULATION DYNAMICS
We found strong temporal variability in survival for all three
age classes (Figs 2 and 3). Consistent with the environmental
canalization hypothesis (Gaillard & Yoccoz 2003), relative
process variance was highest for first-year survival, which in
long-lived organisms has a lower sensitivity/elasticity in terms
of mean population growth rate than adult survival (Leb reton &
Clobert 1991). The relatively low estimated process variance
for second-year survival (Fig. 3), which is also a prebreeding
parameter and therefore has the same elasticity as first-year
survival, may be related to the higher sampling variance for
this age class (mean annual sampling variance: first-year
0·069, second-year 0·079, adult 0·032, cf. confidence limits in
Fig. 2). Few studies have quantified relative process variance.
Our estimates of relative process variance in shag survival are
higher than that found in barn owls, Tyto alba (<0·1 for all age
classes) by Altwegg et al. (2006, 2007), but comparable with
that of European dippers, Cinclus cinclus (0·176 for adults,
Loison et al. 2002). A wider range of species need to be
studied before general conclusions can be drawn.
Process variance in second-year and adult survival
dropped by ≈50% when the most extreme year, 1993/94, was
excluded from the estimation (Fig. 3), whereas process
variance in first-year survival, while very high, was much less
driven by extreme events. A similar pattern was found for
barn owls (Altwegg et al. 2006). This has important implications
for the impact of extreme weather events on population
growth rate, as illustrated by the results of the stochastic
population model. Removing the most extreme year from the
estimation of process variance in second-year and adult
survival increased predicted growth rate to 1 and essentially
eliminated the risk of extinction. On the other hand, a 20 –
50% increase in process variance for all age classes had a
strong negative effect on predicted growth rate and caused
extinction of all model trajectories. For adults, one additional
year with survival as low as in 1994 would lead to ≈50%
increase in process variance. In other words, the frequency
and severity of extreme weather-driven mortality events has
strong implications for population growth in this species. In
fact, our model probably underestimates the impact of
increased variance in survival, because it does not account for
the high and positive process correlation between second-year
and adult survival (Table 2), nor for potential correlations
between fecundity and survival. Positive correlations between
demographic parameters imply that poor years for survival
and reproduction, for example, tend to coincide and thus
exacerbate the negative effect of temporal variability on
population growth rate (Fieberg & Ellner 2001).
With the exception of sudden cold periods, extreme
weather events are predicted to become more frequent under
most scenarios for future climate change (Solomon et al.
2007), although to our knowledge no specific predictions for
the frequency and duration of easterly gales in winter in the
North Sea area are currently available. Increased variability
and higher frequency of extreme events are likely to affect
most ecosystems in the coming decades. It is likely that species
for which one or more demographic parameters are directly
affected by high temperatures, wind or precipitation ex-
tremes, such as European shags, Manx shearwaters,
Puffinus puffinus (Thompson & Furness 1991), bearded tits,
Panurus biarmicus (Wilson & Peach 2006), and mouflon, Ovis
gmelini musimon × Ovis sp. (Garel et al. 2004), will be dispro-
portionately negatively affected (for review see Parmesan,
Root & Willig 2000). Predictions of the ecological effects of
climate change thus need to account not only for changes in
mean climate, but also for the expected increase in the
frequency of extreme events and the associated effects on
vulnerable species.
Acknowledgements
Thanks to the Natural Environment Research Council and the Joint Nature
Conservation Committee for supporting the long-term studies on the Isle of
May, to Scottish Natural Heritage for access to the island and for recent nest
counts, and to the Isle of May Bird Observatory for some nest counts, for
ringing most of the shags during the first few decades of the study, and for
supplying BTO rings throughout the study. Weather data were supplied by the
UK Meteorological Office through the British Atmospheric Data Centre.
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Received 16 August 2007; accepted 2 April 2008
Handling Editor: Henri Weimerskirsch

Weather and seabird population dynamics 1029
© 2008 The Authors. Journal compilation © 2008 British Ecological Society, Journal of Animal Ecology, 77, 1020–1029
Supplementary material
The following supplementary material is available for this
article.
Table S1. Results of model selection for covariates of survival
for the three age classes using ultrastructural models and QAICc.
Table S2. Results of stepwise anodev selection of covariate
models for survival of the three age classes.
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