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


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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.
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Journal of Animal Ecology
, 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
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.
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.
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.
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.
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.
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
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
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:
†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
© 2008 The Authors. Journal compilation © 2008 British Ecological Society,
Journal of Animal Ecology
, 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.
The Isle of May (56
N, 2
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.
M. Frederiksen
et al.
© 2008 The Authors. Journal compilation © 2008 British Ecological Society,
Journal of Animal Ecology
, 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.
We analysed the ring-recovery data in
(White & Burnham
1999) using the Seber parameterization (Williams, Nichols &
Conroy 2002), in which the estimated parameters are
, the annual
survival probability; and
, 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):
2 or
3 indicates
a model with two or three age classes,
indicates a fully time-specific
structure (year as a factor),
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
, and the estimated variance inflation factor
was used to
adjust standard errors and calculate QAIC
, the bias- and small
sample-adjusted Akaike’s information criterion (Burnham &
Anderson 2002). Models were then ranked according to QAIC
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;
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
, with all parameters time-dependent.
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
, 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
), where
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
to estimate process
correlations between survival probabilities of the three age classes.
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
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:
0·09; AT/OC:
0·46; OC/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
© 2008 The Authors. Journal compilation © 2008 British Ecological Society,
Journal of Animal Ecology
, 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
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
(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
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 (
Skalski, Hoffmann & Smith 1993)
-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
. We calculated the proportion of the
total between-year variation (deviance) in survival or recovery
probabilities explained by a given covariate as (DEV
), where
indicate, respectively, models with
no temporal variation, with the covariate, and with full time-dependence.
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
2001) was constructed in
(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
were run for 500 years
. 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
We first attempted to identify a parsimonious model for the
recovery probability
. The most general model (
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
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
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
(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
(model 1).
The model selected at this stage for
was retained for survival
modelling, and all
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).
nT nT
exp ln( ( )) ln( ( )) ()==− =
500 0
500 2001
Table 1. Model selection for recovery probabilities of ringed
European shags on the Isle of May
Model QDeviance KΔQAICc
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
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
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.
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).
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).
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.
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
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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
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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... Certain species or populations may benefit from the new conditions (e.g., reduced predation or competition) whereas for others the consequences can be detrimental, with reduced survival and reproduction rates among other impacts (Doney et al., 2012). Environmental conditions, inferred using Sea Surface Temperature (SST), wind intensity and other integrative climate proxies (e.g., North Atlantic Oscillation [NAO] index and Southern Oscillation Index [SOI]), affect the survival and demography of many marine top predators, mostly due to indirect trophic effects (Frederiksen et al., 2008;Guéry et al., 2017;Jenouvrier et al., 2003;Trathan et al., 2007). For instance, in the Southern Ocean, climate change has been shown to be the driver of changes in distribution, breeding phenology, adult survival, and growth rate of several seabird populations due to increases in SST and changes in ice extent (Barbraud et al., 2012(Barbraud et al., , 2011Croxall et al., 2002;Jenouvrier et al., 2018). ...
... To do so, we removed areas <200 m deep, given that Bulwer's petrels tend to avoid the continental shelf (Cruz-Flores et al., 2019) and, in the case of SST, we used an equal-area projection before calculating the average for the whole 50 % or 75 % KDE area. To evaluate whether these variables could have a delayed effect from basal trophic organisms to top predators, we also considered a lagged effect of one year, except for wind speed, since we only expected a direct effect of wind speed on Bulwer's petrel survival through an increase in energy demands or a decrease in foraging efficiency due to turbidity, as observed in the European shag (Phalacrocorax aristotelis; Frederiksen et al., 2008). In the case of SST, we also considered a lagged effect between seasons; thus, we combined the breeding distribution of the individuals (50 % or 75 % KDE) with SST values from the non-breeding season, and the nonbreeding distributions with SST values from the breeding season ( Fig. D1 in Appendix D). ...
... Finally, other environmental variables apart from SST can affect Bulwer's petrel survival, such as wind speed. The effect of wind on seabirds demography was already reported, for instance, on the breeding performance of wandering albatrosses (Diomedea exulans; Weimerskirch et al., 2012), as well as on the survival of European shags during the nonbreeding season (Frederiksen et al., 2008), both through a reduction in their foraging efficiency. However, in our case, although wind speed (during the breeding season at the breeding area) explains 38 % of the temporal variability of Bulwer's petrel survival, its effect is not as relevant as that of SST. ...
Climate change has repeatedly been shown to impact the demography and survival of marine top predators. However, most evidence comes from single populations of widely distributed species, limited mainly to polar and subpolar environments. Here, we aimed to evaluate the influence of environmental conditions on the survival of a tropical and migratory seabird over the course of its annual cycle. We used capture-mark-recapture data from three populations of Bulwer's petrel (Bulweria bulwerii) spread across the NE Atlantic Ocean, from the Azores, Canary, and Cabo Verde Islands (including temperate to tropical zones). We also inferred how the survival of this seabird might be affected under different climatic scenarios, defined by the Intergovernmental Panel on Climate Change. Among the environmental variables whose effect we evaluated (North Atlantic Oscillation index, Southern Oscillation Index, Sea Surface Temperature [SST] and wind speed), SST estimated for the breeding area and season was the variable with the greatest influence on adult survival. Negative effects of SST increase emerged across the three populations, most likely through indirect trophic web interactions. Interestingly, our study also shows that the survival of Bulwer's petrel will be profoundly affected by the different scenarios of climate change, even with the most optimistic trajectory involving the lowest greenhouse gas emission. Furthermore, for the first time, our study predicts stronger impacts of climate change on tropical populations than on subtropical and temperate ones. This result highlights the devastating effect that climate change may also have on tropical areas, and the importance of considering multi-population approaches when evaluating its impacts which may differ across species distributions.
... Combining population-specific tracking data, weather conditions experienced throughout the annual cycle, and long-term data on survival could help to understand the mechanisms behind the population changes in migratory species 23 . Despite the need for such knowledge, the responses of populations to year-round weather conditions are unknown in many terrestrial birds 8,12,24 . ...
... This may benefit some long-distance migrants such as the little ringed plover via increased survival. However, marked temporal variation in weather conditions may also increase variation in survival potentially translating to population growth rates and consequently depress population size in the long-term (e.g., 8 ). ...
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Understanding how weather conditions affect animal populations is essential to foresee population changes in times of global climate shifts. However, assessing year-round weather impacts on demographic parameters is hampered in migratory animals due to often unknown occurrence in space and time. We addressed this by coupling tracking and weather data to explain extensive variation in apparent survival across 19 years in a northern European population of little ringed plovers (Charadrius dubius). Over 90% (n = 21) of tracked individuals followed migration routes along the Indo-European flyway to south India. Building on capture–recapture histories of nearly 1400 individuals, we found that between-year variation in precipitation during post-breeding staging in northern South Asia explained 47% of variation in apparent adult survival. Overall, the intensity of the monsoon in South Asia explained 31–33% of variability in apparent survival. In contrast, weather conditions in breeding, final non-breeding and pre-breeding quarters appeared less important in this species. The integration of multi-source data seems essential for identifying key regions and periods limiting population growth, for forecasting future changes and targeting conservation efforts.
... Combined immature survival and natal philopatry, estimated within the IPM framework as a latent variable, was similar mean adult survival at Isle of May, and similar to results from a previous study (Harris 1983). Since survival of younger age classes is expected to be lower (and more variable) in seabirds (e.g., Frederiksen et al. 2008;Fay et al. 2015), this high estimate could reflect a combination of moderate immature survival and net immigration, which cannot be distinguished here. Estimated combined immature survival and natal philopatry were lower for Hornoya, compared to a previous study, where immature survival rates were similar to that of adults (Sandvik et al. 2008). ...
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Demographic correlations are pervasive in wildlife populations and can represent important secondary drivers of population growth. Empirical evidence suggests that correlations are in general positive for long-lived species, however little is known about the degree of variation among populations in relation to local conditions. For three widely geographically separated Atlantic puffin populations ( Fratercula arctica ), we compared the relative importance of survival-reproduction correlations for two cross-season correlations, reflecting either effects of non-breeding season or breeding season conditions. Demographic rates and their correlations were estimated with an integrated population model, and their respective contributions to variation in population growth were calculated using a transient-LTRE. Demographic correlations were positive for all three populations, but their strength differed. By comparing three populations with geographically distinct foraging areas throughout the year, this study shows that demographic correlations are, in part, driven by environmental conditions, which impacts their population viability and vulnerability to environmental change.
... Coastal ice build-up and sustained northeasterly winds have contributed to wrecks of thick-billed murres in Newfoundland, where murres trapped in coastal bays by ice starved within 2-3 days [43]. Similarly, many seabirds wintering in the North Atlantic are vulnerable to extended periods of stormy weather, where high wind and rough seas are thought to limit birds ability to forage or access prey over extended periods of time [8,11,59]. Body temperature increases during flight [60], particularly for aquatic species with high wing loading [61]. In Cold water habitat, increased flying may have additional benefits for thermoregulation as well as locating prey. ...
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Background Homeothermic marine animals in Polar Regions face an energetic bottleneck in winter. The challenges of short days and cold temperatures are exacerbated for flying seabirds with small body size and limited fat stores. We use biologging approaches to examine how habitat, weather, and moon illumination influence behaviour and energetics of a marine bird species, thick-billed murres (Uria lomvia). Methods We used temperature-depth-light recorders to examine strategies murres use to survive winter in the Northwest Atlantic, where contrasting currents create two distinct marine habitats: cold (−0.1 ± 1.2 °C), shallower water along the Labrador Shelf and warmer (3.1 ± 0.3 °C), deep water in the Labrador Basin. Results In the cold shelf water, murres used a high-energy strategy, with more flying and less diving each day, resulting in high daily energy expenditure and also high apparent energy intake; this strategy was most evident in early winter when day lengths were shortest. By contrast, murres in warmer basin water employed a low-energy strategy, with less time flying and more time diving under low light conditions (nautical twilight and night). In warmer basin water, murres increased diving at night when the moon was more illuminated, likely taking advantage of diel vertically migrating prey. In warmer basin water, murres dove more at night and foraging efficiency increased under negative North Atlantic Oscillation (calmer ocean conditions). Conclusions The proximity of two distinct marine habitats in this region allows individuals from a single species to use dual (low-energy/high-energy) strategies to overcome winter energy bottlenecks.
... Effects of inter-annual changes in vital rates on population dynamics have been widely studied (e.g., Aberg 1992;Frederiksen et al. 2008;Keith et al. 2008;Frick et al. 2010;Hunter et al. 2010). However, there remains a knowledge gap regarding how populations respond to changes in the periodic, non-random patterns of vital-rate variation. ...
The fate of natural populations is mediated by complex interactions among vital rates, which can vary within and among years. While the effects of random, among‐year variation in vital rates have been studied extensively, relatively little is known about how periodic, non‐random variation in vital rates affects populations. This knowledge gap is potentially alarming as global environmental change is projected to alter common periodic variations, such as seasonality. We investigated the effects of changes in vital‐rate periodicity on populations of three species representing different forms of adaptation to periodic environments: the yellow‐bellied marmot (Marmota flaviventer), adapted to strong seasonality in snowfall; the meerkat (Suricata suricatta), adapted to inter‐annual stochasticity as well as seasonal patterns in rainfall; and the dewy pine (Drosophyllum lusitanicum), adapted to fire regimes and periodic post‐fire habitat succession. To assess how changes in periodicity affect population growth, we parameterized periodic matrix population models and projected population dynamics under different scenarios of perturbations in the strength of vital‐rate periodicity. We assessed the effects of such perturbations on various metrics describing population dynamics, including the stochastic growth rate, log λS. Overall, perturbing the strength of periodicity had strong effects on population dynamics in all three study species. For the marmots, log λS decreased with increased seasonal differences in adult survival. For the meerkats, density dependence buffered the effects of perturbations of periodicity on log λS. Finally, dewy pines were negatively affected by changes in natural post‐fire succession under stochastic or periodic fire regimes with fires occurring every 30 years, but were buffered by density dependence from such changes under presumed more frequent fires or large‐scale disturbances. We show that changes in the strength of vital‐rate periodicity can have diverse but strong effects on population dynamics across different life histories. Populations buffered from inter‐annual vital‐rate variation can be affected substantially by changes in environmentally‐driven vital‐rate periodic patterns; however, the effects of such changes can be masked in analyses focusing on inter‐annual variation. As most ecosystems are affected by periodic variations in the environment such as seasonality, assessing their contributions to population viability for future global‐change research is crucial.
... However, it could have been male biased because we have found males are more likely to be sighted in the colony than females (Boersma and Rebstock 2010). Punctuated adult mortality events, like this one, will likely contribute to population decline (Frederiksen et al. 2008) and could further skew sex ratios. ...
Extreme weather events are becoming more frequent and severe, leading to an increase in direct, adverse thermoregulatory impacts on wildlife. Here, we document an unprecedented, single-day, heat-related mortality event of Magellanic Penguins (Spheniscus magellanicus) at Punta Tombo, Chubut Province, Argentina, one of the largest breeding colonies for this species. We found 264 dead adults and 90 dead chicks in the breeding colony and along the beaches after recording the highest temperature in the shade (44°C on January 19, 2019) since the study started in December 1982. We found dead adults and chicks in postures used to release heat (i.e. lying prone with flippers and feet extended away from the body and/or bills open). We found no evidence for other causes of mortality other than heat (e.g., disease, toxic algae, starvation). Adults potentially died of dehydration, because dead adults were in significantly worse body condition than adults that survived. Dead adults had either empty stomachs or <50 g of food, and 27% of the dead adults died traveling between the nesting area and the water. More males died than females (83% male and 17% female; n = 94). In one section of the colony, ~5% of 1,153 adults died in the heat. Mortality rates of adults were unevenly distributed across the colony, suggesting that the presence of microclimates or easier beach access was an important factor to penguin survival. The body condition indices of dead and live chicks were similar and chicks that died from heat had food in their stomachs (mean = 405 ± 128 g; n = 14), suggesting that food likely inhibited their ability to thermoregulate. Documenting the effects of extreme weather events on populations is crucial to predicting how they will respond to climate change because these events, although rare, are expected to become more frequent and could have severe impacts on populations.
... Arguably, only field experiments (e.g., time-area fisheries closures) are well placed to demonstrate causal fisheries impacts on seabirds (Sydeman et al., 2017). Field experiments, so far limited to the North Sea and Benguela ecosystems, suggest small but measurable impacts on foraging behavior (Pichegru et al., 2010) and reproductive success (Frederiksen et al., 2008;Sherley et al., 2015), which should translate into population-level impacts over time (Sherley et al., 2015). However, these experiments may need decades to account for changing environmental conditions, and not all species, traits. ...
Dissecting joint micro-evolutionary and plastic responses to environmental perturbations fundamentally requires quantifying interacting components of genetic and environmental variation underlying expression of key traits. This ambition is particularly challenging for phenotypically discrete traits where multiscale decompositions are required to handle non-linear transformations of underlying genetic and environmental variation into phenotypic variation, especially when effects have to be estimated from incomplete field observations. We devised a novel joint multistate capture-recapture and quantitative genetic animal model, and fitted this model to full-annual-cycle resighting data from partially migratory European shags (Gulosus aristotelis) to estimate key components of genetic, environmental and phenotypic variance in the ecologically critical discrete trait of seasonal migration versus residence. We demonstrate non-trivial additive genetic variance in latent liability for migration, resulting in estimated micro-evolutionary responses following two episodes of strong survival selection. Yet, underlying additive genetic effects interacted with substantial permanent individual and temporary environmental effects to generate complex non-additive effects, causing large intrinsic gene-by-environment interaction variance in phenotypic expression. Our findings reveal how temporal dynamics of seasonal migration result from combinations of instantaneous micro-evolution and within-individual phenotypic inertia, and highlight how plastic phenotypic variation could expose cryptic genetic variation underlying discrete traits to complex forms of selection.
Understanding how ecological processes combine to shape population dynamics is crucial in a rapidly changing world. Evidence has been emerging for how fundamental drivers of density dependence in mobile species are related to two differing types of environmental variation – temporal variation in climate, and spatio‐temporal variation in food resources. However, to date, tests of these hypotheses have been largely restricted to mid‐trophic species in terrestrial environments and thus their general applicability remains largely unknown. We tested if these same processes can be identified in marine upper trophic level species. We assembled a multi‐decadal data set on population abundance of ten species of colonial seabirds comprising a large component of the UK breeding seabird biomass, and covering diverse phylogenies, life histories and foraging behaviours. We tested for evidence of density dependence in population growth rates using discrete time state‐space population models fit to long time‐series of observations of abundance at seabird breeding colonies. We then assessed if the strength of density dependence in population growth rates was exacerbated by temporal variation in climate (sea temperature and swell height), and attenuated by spatio‐temporal variation in prey resources (productivity and tidal fronts). The majority of species showed patterns consistent with temporal variation in climate acting to strengthen density dependent feedbacks to population growth. However, fewer species showed evidence for a weakening of density dependence with increasing spatio‐temporal variation in prey resources. Our findings extend this emerging theory for how different sources of environmental variation may shape the dynamics and regulation of animal populations, demonstrating its role in upper trophic marine species. We show that environmental variation leaves a signal in long‐term population dynamics of seabirds with potentially important consequences for their demography and trophic interactions.
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Reviews, in four steps, mathematical models of the dynamics of bird populations which are relevant to conservation and management: 1) demographic models with constant parameters, with an emphasis on sensitivity analysis and on the comparison of model results with population censuses; 2) environmental variability and its effect on population growth rate; 3) density-dependent models, with comments on the difficulties to assess density dependence using censuses; and 4) models incorporating density dependence, spatial aspects, and various kinds of stochasticity. -from Authors
Theory predicts that temporal variability plays an important role in the evolution of life histories, but empirical studies evaluating this prediction are rare. In constant environments, fitness can be measured by the population growth rate λ, and the sensitivity of λ to changes in fitness components estimates selection on these traits. In variable environments, fitness is measured by the stochastic growth rate λs, and stochastic sensitivities estimate selection pressure. Here we examine age‐specific schedules for reproduction and survival in a barn owl population (Tyto alba). We estimated how temporal variability affected fitness and selection, accounting for sampling variance. Despite large sample sizes of old individuals, we found no strong evidence for senescence. The most variable fitness components were associated with reproduction. Survival was less variable. Stochastic simulations showed that the observed variation decreased fitness by about 30%, but the sensitivities of λ and λs to changes in all fitness components were almost equal, suggesting that temporal variation had negligible effects on selection. We obtained these results despite high observed variability in the fitness components and relatively short generation time of the study organism, a situation in which temporal variability should be particularly important for natural selection and early senescence is expected.
Reviews the main current ideas on how seabird populations may be regulated, considering evidence for the role of density-independent (catastrophe) effects and density-dependent factors, eg predation, parasitism, breeding space, and food. The authors then consider the potential relative importance of adult and juvenile survival and breeding frequency for influencing population change in different types of seabird, focusing on the wandering albatross Diomedea exulans at South Georgia, comparing its theoretical sensitivity to those factors with current data on breeding success, recruitment, and adult survival which illustrate the actual present vulnerability of this species. Some of the practical constraints relevant to incorporating demographic parameters into routine monitoring studies using seabirds are considered. -from Authors
Conference Paper
Matrix models for population dynamics have recently been studied intensively and have many applications to theoretical and applied problems (conservation, management). The computer program ULM (Unified Life Models) collects a good part of the actual knowledge on the subject. It is a powerful tool to study the life cycle of species and meta-populations. In the general framework of discrete dynamical systems and symbolic computation, simple commands and convenient graphics are provided to assist the biologist. The main features of the program are shown through detailed examples: a simple model of a starling population life cycle is first presented leading to basic concepts (growth rates, stable age distribution, sensitivities); the same model is used to study competing strategies in a varying environment (extinction probabilities, stochastic sensitivities); a meta-population model with migrations is then presented; some results on migration strategies and evolutionary stable strategies are eventually proposed.
Demography is the study of the size and structure of populations and of the process of replacing individuals constituting the population. The study of demography was developed to forecast population growth. The rate at which a population increases or decreases depends basically on the fecundity (number of eggs laid) and survivorship of the individuals that belong to the population (Figure 5.1, bottom), but also to a lesser extent (especially for seabirds) on migration. Because many organisms, and especially seabirds, breed several times in their lives, a population consists of cohorts of individuals of different ages, born in different years. Moreover, mortality and fecundity rates are generally age-specific; life tables represent these birth and death probabilities. The relationship between the rate of increase or decrease and demographic parameters can be translated into more or less complex equations. The basic equation is the Euler-Lotka equation (Euler 1760, Lotka 1907) that specifies the relationships of age at maturity, age at last reproduction, probability of survival to age classes, and number of offspring produced for each age class, to the rate of growth of the population (r).
Over 13,000 chicks and 1,800 adult European Shags (Phalacrocorax aristotelis) were banded at a colony in southeast Scotland between 1963 and 1987. Survival estimates for adults (birds three or more years old) were calculated from subsequent retrapping of these birds back at the colony and recoveries of birds found dead in the colony and elsewhere. The mean annual survival for the period 1967-1992 was estimated at 0.878 (95% C.I. = 0.859, 0.897). European Shags exhibit considerable annual variation in several breeding parameters, but there was no evidence that survival was lower in years when breeding was late or reproductive output reduced. Survival in years when the number of nests in the colony showed a dramatic decline was not significantly lower than normal years. An age-related effect was found indicating that survival declined significantly in birds older than 13 years.
During a 12 year study of European Shags (Phalacrocorax aristotelis), we recorded 22 cases where pairs laid a second clutch after successfully fledging their first brood. Pairs that were double-brooded invariably bred early in the season and the 2 years in which double-brooding was recorded were the 2 earliest breeding seasons. Only 5 second clutches resulted in fledged chicks. The mean number of chicks produced in these cases was 4.2 young/pair, markedly higher than pairs that reared only a single brood (1.96 young/pair). However, in population terms, the contribution of second broods to annual production was negligible (<2%). We speculate that double-brooding may also occur in other cormorant species and members of the sub-order Pelecani.