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The black-legged kittiwake Rissa tridactyla is a pelagic seabird whose population has recently declined in most parts of the North Atlantic and which is red-listed in most bordering countries. To investigate a possible cause for this decline, we analysed the population dynamics of 5 kittiwake colonies along the Norwegian coast, ranging from 62° to 71° N, over the last 20 to 35 yr. By quantifying the importance of sea surface temperatures (SST) in relevant areas of the North Atlantic, we tested the importance of climatic conditions throughout the populations’ annual cycles. We found no synchrony among colonies; however, SST affected population dynamics, explaining between 6% and 37% (average 18%) of the variation in annual population growth rate. While dynamics of the southerly colonies were mainly affected by winter conditions in the Grand Banks area, dynamics of the northernmost colonies were dominated by autumn conditions off Svalbard. Negative slopes indicated stronger population decline under warmer ocean conditions. Population dynamics were affected both via adult survival and offspring recruitment, as evidenced by the presence of unlagged effects as well as effects lagged by the age at recruitment. Finally, we performed population viability analyses taking into account the projected warming trends for the future. The median time to extinction of the Norwegian colonies was 52 to 181 yr without considering covariates; 45 to 94 yr when considering the effects of SST but ignoring future warming; and 10 to 48 yr when ocean warming, based on a ‘business as usual’ scenario, was taken into account.
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Clim Res
Vol. 60: 91–102, 2014
doi: 10.3354/cr01227 Published online June 17
Predicting the impact of human activities, includ-
ing global climate change, on the biosphere has
become one of the most important efforts in ecology.
Ecosystems worldwide are changing rapidly as a
con se quence of anthropogenic impacts such as glo -
bal warming (IPCC 2007), yet our understanding of
the consequences of these changes on populations is
limited. To be able to predict population trajectories,
it is crucial to understand the mechanisms underly-
ing variation of, and co-variation among, popula-
tions. Population fluctuations are determined by
parameters such as intrinsic population growth rate
and carrying capacity, as well as by stochastic fluctu-
ations in the environment (Lande et al. 2003). Fur-
thermore, temporal variation in climate and other
environmental variables may synchronise population
© Inter-Research 2014 ·*Corresponding author:
The decline of Norwegian kittiwake populations:
modelling the role of ocean warming
Hanno Sandvik1,*, Tone K. Reiertsen2,3, Kjell Einar Erikstad1, 3,
Tycho Anker-Nilssen4, Robert T. Barrett2, Svein-Håkon Lorentsen4, Geir Helge
Systad3, Mari S. Myksvoll5
1Centre for Biodiversity Dynamics (CBD), Department of Biology, Norwegian University of Science and Technology (NTNU),
7491 Trondheim, Norway
2Tromsø Museum, University of Tromsø, PO Box 6050 Langnes, 9037 Tromsø, Norway
3Norwegian Institute for Nature Research (NINA), FRAM - High North Research Centre for Climate and the Environment,
9296 Tromsø, Norway
4Norwegian Institute for Nature Research (NINA), PO Box 5685 Sluppen, 7485 Trondheim, Norway
5Institute for Marine Research, PO Box 1870 Nordnes, 5817 Bergen, Norway
ABSTRACT: The black-legged kittiwake Rissa tridactyla is a pelagic seabird whose population has
recently declined in most parts of the North Atlantic and which is red-listed in most bordering
countries. To investigate a possible cause for this decline, we analysed the population dynamics of
5 kittiwake colonies along the Norwegian coast, ranging from 62° to 71° N, over the last 20 to 35 yr.
By quantifying the importance of sea surface temperatures (SST) in relevant areas of the North
Atlantic, we tested the importance of climatic conditions throughout the populations’ annual cycles.
We found no synchrony among colonies; however, SST affected population dynamics, explaining
between 6% and 37% (average 18 %) of the variation in annual population growth rate. While
dynamics of the southerly colonies were mainly affected by winter conditions in the Grand Banks
area, dynamics of the northernmost colonies were dominated by autumn conditions off Svalbard.
Negative slopes indicated stronger population decline under warmer ocean conditions. Population
dynamics were affected both via adult survival and offspring recruitment, as evidenced by the
presence of unlagged effects as well as effects lagged by the age at recruitment. Finally, we per-
formed population viability analyses taking into account the projected warming trends for the
future. The median time to extinction of the Norwegian colonies was 52 to 181 yr without consider-
ing covariates; 45 to 94 yr when considering the effects of SST but ignoring future warming; and 10
to 48 yr when ocean warming, based on a ‘business as usual’ scenario, was taken into account.
KEY WORDS: Global warming · Non-breeding distribution · Population dynamics · Population
viability analysis · Rissa tridactyla · Sea surface temperature
Resale or republication not permitted without written consent of the publisher
Clim Res 60: 91–102, 2014
fluctuations over large distances (Moran 1953, Bjørn-
stad et al. 1999, Lande et al. 1999, Post & Forchham-
mer 2002). Population synchrony, defined as the
inter-annual correlation of population growth rates
across colonies, can therefore indicate the presence
of environmental factors affecting population dy -
nam ics on large spatial scales. Moreover, a high
degree of inter-annual synchrony in population fluc-
tuations increases the risk of local and global extinc-
tion (Esler 2000, Engen et al. 2002). Herein lies the
main importance of population synchrony for popula-
tion management.
In seabirds, knowledge about their distribution out-
side the breeding season has for a long time been a
limiting factor in analysing and understanding the im-
portance of environmental conditions for population
processes (e.g. Smith & Gaston 2012). Seabirds are
normally philopatric and return to the same breeding
colony each year, but may disperse over vast ocean
ranges during the rest of the year (e.g. Egevang et al.
2010, Frederiksen et al. 2012). Quantitative analyses
of environmental conditions during non-breeding
have only recently become feasible through advances
in tracking technologies, such as miniaturized year-
round light-based tracking devices (GLS loggers or
geolocators; Phillips et al. 2004, González-Solís et al.
2007, Egevang et al. 2010, Seavy et al. 2012).
We here use the novel knowledge of non-breeding
distribution (Frederiksen et al. 2012) to search for
environmental covariates explaining population
dynamics of the black-legged kittiwake Rissa tridac -
tyla (hereafter called kittiwake) in Norway. Kittiwake
numbers have declined over most of the North
Atlantic over the last 2 decades, particularly in the
North Sea and adjacent areas (Frederiksen 2010).
Ac cording to the IUCN Red List, the species is of
least concern globally; in national Red Lists, how-
ever, it is currently listed as near threatened in Den-
mark, France and Svalbard, as vulnerable (or
‘amber’) in the Faroes, Greenland, Great Britain and
Ireland and as endangered in Norway and Sweden
(Wind & Pihl 2004, Fosaa et al. 2005, Boertman 2007,
Lynas et al. 2007, Eaton et al. 2009, Gärdenfors 2010,
Kålås et al. 2010, UICN France et al. 2011).
At a study colony in the North Sea, the decline was
caused by low reproductive success as well as low
adult survival (Frederiksen et al. 2004), both appar-
ently linked to increasing sea temperatures affecting
their main prey (Frederiksen et al. 2006). To under-
stand whether the ocean-wide decline of kittiwakes
is governed by a common factor, it is important to
establish whether these findings can be generalised
to kittiwake populations in other areas.
We here study the dynamics of 5 kittiwake popula-
tions along the coast of Norway in order to address 4
questions: (1) Is there any population synchrony
between the colonies? (2) How are the population
dynamics related to the local climatic conditions (sea
surface temperature [SST]) in the areas where the
birds stay during different parts of their annual
cycle? (3) Through which demographic trait (adult
survival or offspring recruitment) is climate affecting
the population growth rate? (4) How will the pre-
dicted future warming trend affect the viability of the
2.1. Population monitoring
Kittiwakes breed in many places along the Norwe-
gian coast. We analysed population counts from the 5
kittiwake Rissa tridactyla colonies that are part of the
long-term Norwegian Monitoring Programme for
Sea birds, covering the geographic range from the
southern Norwegian Sea to the Barents Sea (Fig. 1).
From southwest (boreal climate) to northeast (Arctic
climate), the colonies included were Runde (62° 24’ N,
5° 38’ E), Sklinna(65° 12’ N, 10° 59’ E), Vedøy(67° 29’ N,
12° 1’ E), Hjelmsøya (71° 4’ N, 24° 43’ E) and Hornøya
(70°23’ N, 31° 9’E).
The populations were monitored according to stan-
dardised methods (e.g. Walsh et al. 1995) using ap -
parently occupied nests or nest sites (AON) as the
counting unit. At Sklinna, the whole colony was
counted, while averaged counts in randomly selected
study plots were used in the other colonies. Annual
estimates of AONs were based on a total count made
in 2010/2011 and the annual rates of change docu-
mented in the monitoring plots. The AONs in the
study plots represented 1.1% (10 study plots), 100%
(whole colony count), 3.7% (6 study plots), 5.2%
(5 study plots) and 12.3% (9 study plots) of the total
population in Runde, Sklinna, Vedøy, Hjelmsøya and
Hornøya, respectively. All counts were made late in
the incubation period or early in the chick period.
Colony sizes varied by 3 orders of magnitude
among colonies, ranging from 170 pairs in Sklinna
to 158 000 in Runde in 1980. All 5 populations
declined during the monitoring period that spanned
20 to 35 yr (Fig. 2). The breeding population at
Sklinna went ex tinct in 2011; in our models, we dis-
regarded all counts at this colony after 2001, when
population size for the first time dropped below 20
breeding pairs.
Sandvik et al.: Declining populations and ocean warming
2.2. Population models
Population dynamics of the 5 colonies were
density-independent, as evidenced by the absence
of any negative correlation between annual growth
rates rtand population sizes Nt(all correlation co -
efficients R > −0.25, all p > 0.3; see the Supplement
at www. int-res. com/ articles/ suppl/ c061 p091_ supp.
pdf for density-dependent models). We therefore
used Brownian population models of the following
with βias the slope of the ith environmental covariate
Xi; εis the environmental noise, i.e. an independent
variable with zero mean and variance σ2
mental variance); Ntis the population size in year t;r
is the long-term intrinsic population growth rate; σ2
is the demographic variance; Xi,tis the environmen-
tal covariate iin year t. The parameters βi,rand σ2
were estimated from the population time series using
maximum likelihood such that the log-likelihood
was maximised over the nelements of the time series
(Sæther et al. 2009), where E(lnNk) is the predicted
log-population size based on the observed popula-
tion size Nk−1 and Eq. (1), and σ2= σ2
e+ σ2
dNt. In the
absence of estimates of life-time reproductive suc-
cess of the 5 colonies, demographic variance was
assumed to be 0.1 in all colonies, which is a realistic
value for long-lived birds (Lande et al. 2003).
Population models were either fitted to one colony
at a time or to all populations simultaneously. In the
former case, the optimal set of parameters for each
colony could be identified. The latter approach en -
abled us to test for the presence of synchrony and
whether population parameters differed among
colonies. It was carried out by modifying Eq. (1) in
such a way that any of the parametersr, σ2
eor βi
could be replaced by a vector of length 5, containing
the growth rates, environmental variances or slopes
for each of the 5 colonies; log-likelihood was max-
imised over the elements of all 5 time series
using Eq. (2). These modifications could be combined
such that, for example, models with a commonrand
e, with a commonrand 5 separate σ2
e, with a com-
mon σ2
eand 5 separater, and with 5 separaterand
ecould be compared with each other (likewise for
each of the environmental variables βi). If a model
with a common parameter was preferred over a
model with separate parameters, this indicated that
this specific parameter did not differ significantly
among colo nies. Specifically, a common estimate for
the slope βwould indicate that the corresponding
environmental covariate is common to all 5 colonies
and synchronises their dynamics.
Models with different parameterisations or covari-
ates were compared using Akaike’s information cri-
terion corrected for small sample sizes (AICC), prefer-
ring models with the lowest ΔAICC(or the highest
AICCweight or model likelihood; see Burnham &
Anderson 2002). Non-nested models within 2 AICCof
each other were considered equally well supported.
Confidence intervals were obtained by nonparamet-
ric bootstrapping of the model parameters using
10 000 replicates.
2.3. Population viability analyses
Population viability analyses (PVA) were carried
out separately for each colony. In each case, 10000
future population trajectories were modelled using
Eq. (1). The quasi-extinction threshold was set at 20
pairs. Confidence limits around the median popula-
tion trajectory were estimated as population predic-
tt tiitt
=+−σ +β+ε
ln ln
21 ,
ln {[ln (ln )] ln(2 )}
222 2
Fig. 1. Map of Norway showing all registered breeding
colonies of black-legged kittwakes. The 5 study colonies are
highlighted (orange). The size of the circles indicates the
number of apparently occupied nests in 2005
Clim Res 60: 91–102, 2014
tion intervals. A population prediction interval is ‘the
stochastic interval that includes the unknown popu-
lation size at a specified future time with a given
probability or confidence level’ (Lande et al. 2003,
p. 108) and incorporates stochasticity as well as
parameter uncertainty. The effects of demographic
and environmental stochasticity are included via
Eq. (1). Uncertainty about parameter estimates was
taken into account by simulating the population time
series using the estimated parameters 10 000 times
and re-estimating the parameters from each simula-
tion. This method produces sampling distributions for
all parameters (
r, σ2
eand β), from which a random set
of population parameters is drawn, and accounts for
the presence of sampling correlation (Lande et al.
2003). No PVA was performed for
Sklinna because the population is
already extinct and crossed the quasi-
extinction threshold of 20 pairs for the
first time in 2001.
For each colony, a set of at least 3
different PVAs was carried out. The
first PVA was based on the null popu-
lation model, i.e. without covariates.
The re maining PVAs were based on
the estimates de rived from the best
population model(s) incorporating
SST as a covariate; half of the latter
PVAs as sumed average SST to stay
constant at the actual level of the years
2000 to 2011, the other half assumed
average SST to increase in line with
predictions of ocean warming (see
Fig. 3). Using this ap proach, it is possi-
ble to directly compare the viability of
each colony under different assump-
tions (effect of SST present vs. absent,
and warming present vs. absent).
2.4. Climatic variables
The climatic covariate considered as
an explanatory variable was SST,
based on the Extended Reconstruction
SST data set available on a 2° × 2° grid
(ERSST v 3b, NOAA 2012; cf. Smith et
al. 2008). We considered spring and
summer SSTs around each of the
breeding colonies calculated as sea-
sonal means (March to May and June
to August, respectively) of the 2 grid
cells adjacent to the colonies (Runde,
62− 64° N, 2− E; Sklinna, 64−66° N, 8−14° E; Vedøy,
66− 68° N, 8− 14° E; Hjelmsøya, 70−72° N, 22−28° E;
Horn øya, 70− 72°N, 28−34° E). SSTs for the non-
breeding season were taken from the following areas
and periods: autumn SST off Svalbard was defined as
the spatial mean September SST within the area 74−
80° N, 14− 36° E; winter SST in the Grand Banks area
as the spatial and seasonal mean SST during Novem-
ber to January within the area 40−62° N, 38−60° W.
These choices of areas and periods were based on
the actual spatiotemporal distribution of kittiwakes
from the relevant colonies outside the breeding sea-
son (Frederiksen et al. 2012).
SSTs were considered as covariates at different time
lags, allowing for different biological explanations of
Fig. 2. Population trajectories (thick lines,
left-hand y-axes) and annual intrinsic
population growth rates (thin lines, right-
hand y-axes) of 5 black-legged kittiwake
breeding colonies along the Norwegian
coast. Entities counted were apparently
occupied nests. Note that the left-hand y-
axes differ in scale (but not in intercept,
which is zero in all cases). While the
mean growth rates are rather similar
(horizontal dotted lines), the variability
differs both geographically and tempo-
rally. In the analyses, population counts
at Sklinna have been disregarded after
2001 (grey lines; the dot in 1980 is a
single population count)
Sandvik et al.: Declining populations and ocean warming
potential effects: if SST affects breeding propensity
(i.e. absence/presence of adult birds during the popu-
lation count), this would be visible in population mod-
els as an unlagged effect of SST. In contrast, if SST af-
fects adult survival after breeding, the corresponding
change in population size would not become evident
before the population count of the following year,
showing up in the population model as an SST effect
lagged by 1 yr. Effects of SST on recruitment would
entail even longer time lags: most kittiwakes that re-
turn to their breeding colony to breed do so at an age
of 3 to 4 yr (Coulson 2011). If breeding success (or sur-
vival of juveniles during their first winter) is affected
by SST, the corresponding change in population size
would therefore not be counted before the cohort af-
fected recruits to the breeding population, i.e. 3 to 4 yr
later. In a population model, an SST effect on repro-
duction would thus become evident as an effect of
SST lagged by the number of years that corresponds
to the mean age at recruitment. Based on these as-
sumptions, we considered SSTs around colonies at
time lags of 0, 1, 3 and 4 yr; SSTs off Svalbard and in
the Grand Banks area were considered at time lags of
1, 3 and 4 yr (where the time period from autumn or
winter to the following breeding season is considered
to be a time lag of 1).
Estimates of SST in a future climate scenario were
extracted from the Norwegian Earth System Model
(NorESM; Iversen et al. 2013), a global coupled cli-
mate model. The scenario chosen was RCP8.5 (Rep-
resentative Concentration Pathway) with radiative
forcing target level at 8.5 W m−2 in 2100, which is a
very high baseline emission scenario leading to CO2
concentrations at 1370 ppm in 2100 (van Vuuren et
al. 2011). The RCP8.5 scenario does not include any
specific climate mitigation target, corresponding to a
doubling in greenhouse gas emissions by 2050 and 3-
fold increase by 2100 (Riahi et al. 2011). SSTs were
extracted for the exact positions of each colony and
for points within the wintering areas (off Svalbard,
78° N, 26° E; Grand Banks, 51°N, 46° W).
For each of these points, SSTs for the period 2006 to
2100 were used to estimate a linear trend. The period
of overlap between the ERSST and NorESM data
(2006 to 2011) was used to adjust the historical with
the future time series to ensure that the projected
SST trend started at the same value as the empirical
SST data end point. For each of the 10 000 PVA runs,
an independent SST time series was generated. This
time series consisted of white noise (zero mean and
assuming the same variance as in the past, inferred
from the relevant ERSST time series), added either to
the mean SST of the years 2000 to 2011 or to the pro-
jected SST trend (Fig. 3).
All models were run in the R environment (R
Development Core Team 2011). Estimates are pro-
vided with 95% confidence intervals.
The 5 Norwegian kittiwake Rissa tridactyla colo -
nies studied (Runde, Sklinna, Vedøy, Hjelmsøya and
Hornøya) declined steeply during the study period
(Fig. 2). Beyond the decline, there was no strong tem-
poral covariation among the colonies. If the negative
trend was not removed, population counts were
highly correlated (except Sklinna, all pairwise R >
0.7, all p < 0.01; Sklinna was only correlated to
Vedøy). Upon removal of the trend, however, counts
were un correlated (all |R| < 0.3, all p > 0.18). Nor were
annual population growth rates correlated across
colonies (whether de-trended or not, all |R| < 0.4, all
p > 0.14). Population synchrony across colonies was
thus virtually absent.
The long-term mean rate of decline was similar in
all colonies; however, the temporal variability was
much higher in some colonies (Fig. 2). This is evident
from the best population model without covariates
(Table 1), which assumed a common long-term
intrinsic population growth raterof −0.055 ± 0.026
Fig. 3. Actual and predicted sea surface temperatures (SST)
at Hornøya. The population viability analyses conducted in
this study follow 3 scenarios: (i) no effect of SST (not shown);
(ii) constant average SST at the level of the past 11 yr; (iii) in-
creasing average SST assuming the warming trend pre-
dicted by NorESM (see text, this page). Black lines represent
observed SSTs (thick) and predicted trends (thin); grey lines
represent the observed trend (thick) and one realisation of
modelled future SSTs (thin)
Clim Res 60: 91–102, 2014
(corresponding to an annual reduction of 5.7 ± 2.7 %),
while the environmental variance σ2
ediffered tenfold
between 0.011 ± 0.006 at Vedøy and 0.118 ± 0.078 at
Sklinna. Variability was not merely a function of
colony size; for instance, Hjelmsøya was a larger and
more variable colony than Hornøya (correlation be -
tween mean log population size and environmental
variance, R = −0.83, p = 0.080, n = 5).
The best model without covariates could be im -
proved using SST as covariate (Table 1). In all cases,
SST was negatively related to population growth
rate. The 2 best-supported models indicated that SST
during the non-breeding season had the strongest
effect. According to the first model, population
growth rate declined 3 yr after a warm winter in the
Grand Banks area. This time lag suggests an effect
on the first-year survival of future recruits. The vari-
ance in population growth rate explained by this
effect was 14% in Runde, 11% in Hornøya, 6% in
Vedøy and <1% in Sklinna and Hjelmsøya. Accord-
ing to the second model, population growth rate
declined in years following a warm autumn southeast
of Svalbard. This unlagged effect suggests an effect
on adult return rate and explained a fifth of the vari-
ance in population growth rate in the 2 northernmost
colonies (21% in Hornøya, 19% in Hjelmsøya, <5%
elsewhere). When de-trending these 2 covariates, the
corresponding models were somewhat poorer but
still at least 2 AICCunits better than models without
covariates (Table 1). This indicates that the model
support is not merely due to unrelated trends in pop-
ulation growth rate and temperature.
Of the remaining covariate models, 2 were better
supported than the model without covariates, but
somewhat poorer (ΔAICC> 2) than the 2 top-ranked
models. These indicated an effect of SST at the
Grand Banks in the previous winter and of SST
around the colonies in the previous summer
(Table 1). No combinations of 2 or more covariates
achieved more support than single-covariate models.
When fitting separate models to each colony,
results were somewhat different (Table 2). The best
models for Hornøya and Hjelmsøya contained SST
southeast of Svalbard in the previous autumn. In the
case of Hjelmsøya, this model could be improved by
adding the previous year’s SST around the colony.
The latter effect was estimated to be positive after
SST southeast of Svalbard was accounted for. The
effect of winter SST in the Grand Banks area 3 yr ear-
lier was only supported as a covariate to the popula-
tion dynamics at Runde. The population dynamics at
Sklinna and Vedøy could not be explained using the
covariates considered (Table 2; cf. Table S2 in the
Based on models without covariates, all extant
colo nies except Hornøya had a median time to ex -
tinction of <90 yr and a lower 95% confidence limit of
<50 yr (Table 3). Estimated extinction probabilities
increased when using covariate models and espe-
cially when adding a warming trend to the predicted
future SST values. This pattern is evident from Fig. 4,
which shows population trajectories for 1 colo ny
under the 3 different assumptions. For all colonies,
median time to extinction was significantly shorter
Model: covariate (time lag, yr) Estimate CI K ΔAICC ML
Grand Banks (3) b= −0.079 −0.132 to −0.027 7 0.00 1.000
Svalbard (1) b= −0.244 −0.404 to −0.081 7 0.12 0.942
Svalbard (1), de-trended b= −0.240 −0.450 to −0.035 7 3.60 0.165
Grand Banks (1) b= −0.059 −0.113 to −0.004 7 4.07 0.131
Grand Banks (3), de-trended b= −0.088 −0.174 to −0.004 7 4.40 0.111
Colonies (1) b= −0.065 −0.133 to +0.003 7 5.12 0.077
No covariate, commonr, separate σ2
e r= −0.055 −0.081 to −0.030 6 6.42 0.040
Grand Banks (1), de-trended b= −0.031 −0.116 to +0.052 7 8.09 0.018
Colonies (1), de-trended b= −0.027 −0.142 to +0.085 7 8.44 0.015
No covariate, separater, separate σ2
e 10 12.21 0.002
No covariate, commonr, common σ2
e r= −0.070 −0.106 to −0.035 2 45.64 0.000
No covariate, separater, common σ2
e σ2
e= 0.038 +0.027 to +0.046 6 51.89 0.000
Table 1. Population models for 5 Norwegian black-legged kittiwake populations, fitted to all populations simultaneously and
assuming Brownian population dynamics. Covariates used were sea surface temperatures from different ocean areas. Mod-
els are sorted by decreasing support and presented using estimates and 95% confidence intervals (CI), number of parame-
ters (K), ΔAICCand model likelihood (ML). Bold: best-supported models. Models with covariates assumed a common growth
rate (
r), and separate environmental variances (σ2
e), in the 5 colonies (which was the best supported parameterisation of mod-
els without covariates). The 2 top models had AICCweights of 0.40 and 0.38, respectively. See Table S1 for density-
dependent models
Sandvik et al.: Declining populations and ocean warming
under the assumption of a causal link to SST and a
warming trend (reduced by as much as 56 %, 46%,
81% and 90%, respectively) than without these as -
sumptions (Table 3, Fig. 4).
One of the models (Hjelmsøya) had covariates with
opposite signs (Table 2). While this lead to increased
estimates of time to extinction compared to the 1-
parameter model under the assumption of constant
SST, the 2- and 1-parameter models did not differ
under the assumption of warming (Table 3).
4.1. Retrospective models of population dynamics
Our analyses of population dynamics of 5 Nor -
wegian kittiwake Rissa tridactyla colonies have
shown that their overall rates of population decline
were similar (~5.7% yr–1), although we found no evi-
dence of synchrony among their annual changes in
breeding numbers. SSTs in different areas of the
North Atlantic explained between 6 and 37% of the
inter-annual variation in population
growth rates, but regions and time
lags differed between the colonies.
Most slopes were estimated to be ne -
gative, i.e. warmer conditions were
related to stronger population de -
crease. We ascertained that the ef -
fects of SST were not just artefacts
created by un correlated trends by
verifying the findings with de-
trended time series.
The oceanic regions considered
had been chosen based on recent
evidence of the non-breeding distri-
bution of kittiwakes from these
colonies, and the regions identified
as most relevant by the population
models are fully compatible with this
evidence. Geolocator data singled
out 2 areas as especially important
for adult kittiwakes outside the
breeding season (Frederiksen et al.
2012, B. Moe et al. unpubl. data): an
area southeast of Svalbard, visited by
kittiwakes from the northernmost
colonies after the breeding season
(September), and the Grand Banks,
visited by birds from all colonies in
winter (November to January). This
explains why, in our analyses, SST
east of Svalbard in September accounts for roughly
20% of population dynamics at Hornøya and Hjelm-
søya, which are the colonies utilising this area the
most. Models incorporating SST at the Grand Banks
were better than the null model and/or the best sup-
ported covariate model for all colonies ex cept Hjelm-
søya, although this variable entered the optimal
model for Runde only, where it explained some 12%
of the inter-annual variation in population dynamics.
Unfortunately, no geolocator data are available from
Runde and Sklinna. However, based on our findings,
it is un likely that these colonies deviate from the
multi-colony pattern revealed by geolocators in other
The absence of population synchrony between the
colonies is somewhat surprising given the evidence
that birds from several breeding colonies use the
same oceanic regions during winter. This is an impor-
tant finding in itself, as the degree of population syn-
chrony may affect the extinction probability (Engen
et al. 2002). The most likely explanation for the
absence of synchrony is the presence of environmen-
tal noise and measurement error.
Model: covariate Estimate CI K ΔAICC R2
(time lag, yr)
Grand Banks (3) −0.127 −0.259 to +0.005 3 0.00 0.125
Null 2 0.79
Null 2 0.00
Grand Banks (1) −0.284 −0.661 to +0.088 3 0.70 0.110
Null 2 0.00
Grand Banks (3) −0.059 −0.145 to +0.025 3 0.65 0.063
Svalbard (1) −1.186 −1.865 to −0.490 4 0.00 0.372
+ colony (1) +0.589 + 0.102 to +1.068
Svalbard (1) −0.733 −1.391 to −0.090 3 1.95 0.192
Null 2 3.47
Svalbard (1) −0.310 −0.526 to −0.084 3 0.00 0.210
Grand Banks (3) −0.077 −0.151 to −0.002 3 2.78 0.127
colony (4) −0.140 −0.285 to + 0.005 3 3.33 0.110
Null 2 4.11
Table 2. Separate population models for 5 Norwegian black-legged kittiwake
populations, based on Brownian population dynamics. Covariates used were sea
surface temperatures from specific ocean areas. For each colony, the best covari-
ate model and/or other models with better support than the null model (without
covariates) are shown, along with estimate, 95% confidence intervals (CI), num-
ber of parameters (K), ΔAICCand the variance explained (R2). Models are sorted
by decreasing support (increasing ΔAICC) within each colony (ΔAICCvalues are
not comparable across colonies). See Table S2 in the Supplement for density-
dependent models and de-trended covariates
Clim Res 60: 91–102, 2014
Environmental noise may not only explain the ab-
sence of population synchrony but also be invoked as
an alternative explanation for the population decline
as such. Factors that have been documented to affect
population dynamics in other areas and/or other spe-
cies of seabirds include predation pressure (e.g. by
white-tailed eagles Haliaeetus albicilla, Hipfner et al.
2012), competition with larger gulls (e.g. Oro et al.
2009) or interactions with commercial fisheries (e.g.
Frederiksen et al. 2004). While such factors may have
also contributed to the decline in some Norwegian
colonies, they cannot explain the overall pattern. The
population decline has a very similar slope in colonies
experiencing predation and harrassment by eagles
and those that did not (e.g. Vedøy vs. Hornøya). Pop-
ulations of greater black-backed gulls Larus marinus
and European herring gulls L. argentatus, which may
act as competitors as well as predators on kittiwake
eggs and chicks, have been declining at Hornøya and
Sklinna (Norwegian Monitoring Programme for Sea-
birds unpubl. data). Although reliable data are miss-
ing from the other colonies, this likewise excludes
competition as an explanation of the large-scale
trend. Kittiwakes may be hypothe-
sised to compete with commercial
fisheries, e.g. for herring Clupea
haren gus in the Norwegian Sea or for
capelin Mallotus villosus in the Bar-
ents Sea. As far as data are available
for relevant fish species, however,
there have not been any increases in
landings from commercial fisheries or
decreases in stock sizes over the time
period in question (ICES 2012).
Regarding the time lags consid-
ered, population effects of SST east of
Svalbard were significant at a 1 yr
time-lag, indicating that the survival
of breeding birds was affected nega-
tively by warm conditions in that
area. While the effects of mortality,
intermittent breeding and permanent
emigration would be indistinguish-
able in our models, the latter 2 pro-
cesses are rather unlikely to cause the
pattern. First, non-breeding should
create a positive effect in the follow-
ing year (lag 2), which was not
observed. Second, the strong and
parallel declines in all colonies rule
out that there is a large degree of
SST at the Grand Banks was mostly
relevant if lagged by 3 yr (Runde, Vedøy and
Hornøya). A time lag of 3 yr is compatible with an
effect of SST on recruitment, i.e. on the survival of
immature birds (cf. Sandvik et al. 2012). Fledglings
that later recruit to their natal colony will enter the
population count when they first return to the breed-
ing colony to build a nest (not necessarily to lay
eggs), which in kittiwakes happens at 3 to 4 yr of age
(Coulson 2011). The findings thus suggest that
recruitment is poor in cohorts that experience warm
conditions during their first winter after fledging.
This interpretation does not involve the assumption
that immatures were un affected by environmental
conditions in other years; however, consistently find-
ing a 3 yr lag across cohorts is convincing evidence
that the effect was strong enough not to be masked
by the environmental conditions of the intervening
seasons. On the other hand, the interpretation pre-
supposes that im matures use the same areas as
adults. Geolocator data from immatures that would
allow us to test this assumption are, however, not
available at present. Time lags in climate ecology can
also originate from effects that are mediated through
Model: covariate (time lag, yr) Time to extinction (yr)
with a probability of
50% 20% 10% 2.5%
Null 79 57 49 40
Grand Banks (3) 45 35 31 26
Grand Banks (3) + warming 35* 28 25 22
Null 89 66 58 48
Grand Banks (3) 60 46 41 37
Grand Banks (3) + warming 48* 37 33 30
Null 52 35 29 23
Svalbard (1) 35 26 23 19
Svalbard (1) + warming 10* 8 7 6
Svalbard (1) + colony (1) 49 33 28 23
Svalbard (1) + colony (1) + warming 9* 8 7 6
Null 181 119 101 79
Svalbard (1) 94 72 64 55
Svalbard (1) + warming 18* 15 14 13
Table 3. Modelled times to extinction of 4 Norwegian black-legged kittiwake
populations, based on population viability analysis and different sets of assump-
tions (i.e. presence/absence of covariates and a warming trend in SST). Covari-
ates used were sea surface temperatures from specific ocean areas. The column
‘50%’ provides the median time to extinction, and the column ‘2.5%’ provides
its lower 95% confidence limit. *Times to extinction that differ significantly from
a model without covariates and warming (i.e. a median time to extinction of less
than the 2.5% quartile of the corresponding null model). Bold: threshold values
that correspond to Red List criteria (50% within 10 yr: critically endangered;
20% within 20 yr: endangered; 10% within 100 yr: vulnerable)
Sandvik et al.: Declining populations and ocean warming
the food chain (e.g. Hjermann et al. 2004). Although
this alternative explanation is less likely in this case,
because kittiwakes seem to feed at a low trophic
level during winter, it cannot currently be ruled out
The findings thus corroborate results from the
North Sea, where adult survival was negatively
related to SST (Frederiksen et al. 2004, 2006); in con-
trast, the present study did not find effects of SST on
chick production (which would have resulted in a 3
yr lag of local SST), but rather on first-year survival.
A study of adult survival rates of kittiwakes from
the Hornøya colony can shed some light on the likely
mechanisms underlying population responses to
SST. Adult survival was strongly affected by prey
abundance, notably of capelin in the Barents Sea and
of sea butterflies (Thecosomata) at Grand Banks
(Reiertsen et al. in press). Capelin and sea butterflies
are known to be important prey of kittiwakes during
summer and autumn, respectively (Barrett 2007, Kar -
novsky et al. 2008), and may represent the causal
link between SST and survival. No biotic link ex -
plaining the importance of the region off Svalbard
has been identified so far (Reiertsen et al. in press).
Local conditions, i.e. summer SST around the colo -
nies, entered the population model of Hjelmsøya but
only after the effect of the non-breeding season had
been taken into account (Table 2). This is the only
covariate estimated to have a positive slope, indica-
ting that adult survival was higher after warmer
breeding seasons.
4.2. Population viability analyses
The kittiwake is currently classified as endangered
in the Norwegian Red List (Kålås et al. 2010). This
decision was based on Criterion A2b, because the
Norwegian mainland population has decreased by
almost 80% within 3 generations. According to the
PVAs of the present study, most Norwegian colonies
would be categorised as vulnerable (10% extinction
risk within 100 yr; see Table 3) when applying Red
Fig. 4. Simulated extinction trajectories of black-legged kitti-
wakes at the colony of Runde. Three different models are de-
picted (cf. Table 2 and Fig. 3): (a) population dynamics are un-
affected by sea surface temperature (SST); (b) population
dynamics are affected by winter SST at Grand Banks, but
ocean warming is not taken into account; (c) population dy-
namics are affected by winter SST at Grand Banks, and ocean
warming is taken into account. The figure shows actual
counts for the past 20 yr (thick line) and modelled population
sizes for the next 100 yr. Thin grey lines show 60 of the 10 000
trajectories simulated; black lines are the 5, 10, 20, 50 (thicker
line), 80, 90 and 95% population prediction quantiles. The
quasi-extinction threshold was fixed at 20 individuals.
Note the logarithmic scale of the y-axis
Clim Res 60: 91–102, 2014
List criterion E (IUCN 2001) to each of them. In the
absence of any covariate effect, Hornøya is the most
viable colony, classified as near threatened (5% ex -
tinction risk within 100 yr), which would change to
endangered (20% extinction risk within 20 yr) ac -
cording to the PVA that includes a warming trend.
The Hjelmsøya colony even crosses the threshold to
critically endangered (50% extinction risk within
10 yr) according to PVAs that include warming
trends (Table 3). The Sklinna population, which was
the smallest of the colonies studied, went extinct dur-
ing the study period.
These results are only indicative because Red List
criteria are not applicable to single populations
(Hartley & Kunin 2003). For example, local popula-
tions may have a high turnover rate, without the spe-
cies as such being at threat (viz., if new populations
are established at the same rate as other populations
go extinct). There is no indication that this is the case
for kittiwakes. Moreover, the negative trend was
very similar in all study colonies, which covered a
large part of the species’ breeding range in Norway.
No comparable time series are available from other
Norwegian colonies, but there is no indication that
other colonies are better off than the 5 colonies stud-
ied here (Erikstad & Systad 2009).
The warming scenario may have overestimated the
extinction risk (or underestimated time to extinction)
for several reasons. First, the warming model chosen
corresponds to a ‘business as usual’ scenario, which
may be too pessimistic an assumption. Our main pur-
pose was to compare 2 extreme models, one assum-
ing constant SST and the other a drastic but realistic
warming trend. As such, these 2 models represent
reasonable limits that embrace the actual future
trend. Most emission scenarios do, however, assume
trends that are closer to the ‘business as usual’ sce-
nario than to a ‘no change’ scenario (van Vuuren et
al. 2011).
Second, the PVAs assume that the mechanisms of
the past remain unchanged in the future, which, of
course, is uncertain. For example, we do not yet
know a great deal about the temporal stability of for-
aging patterns and wintering areas of kittiwakes. If
they remain stable, the PVAs offer realistic viability
estimates. However, at least 2 factors may decouple
kittiwake population dynamics from autumn/winter
SST: (1) kittiwakes may follow their main prey spe-
cies as they migrate to other (presumably colder)
areas, and/or (2) kittiwakes may shift to other prey.
The latter may occur either because the current prey
species are replaced by more warm-tolerant prey
species originating from more southerly waters or be -
cause kittiwakes move to areas where more warm-
tolerant species are abundant during winter.
A third factor that might have resulted in overesti-
mated extinction risks is that past (actual) and future
(modelled) SSTs may not be directly comparable.
SST is not sampled to the same extent in all areas. A
coarser sampling in Arctic waters would, for exam-
ple, cause underestimation of inter-annual SST vari-
ation in the ERSST dataset. This would, in turn, result
in inflated slopes in our population models, which
would then overestimate the effect of warming. Fur-
ther studies are needed to rule out this potential
source of systematic errors.
Even when excluding a warming trend, however,
the PVAs with SST covariates suggested 33 to 48%
shorter median times to extinction than PVAs without
covariates (Table 3, Fig. 4). Because of the high un -
certainty and correspondingly wide population pre-
diction intervals, these reductions are not statistically
significant, although they certainly would be biolog-
ically so. The reasons for these drastically increased
extinction risks are the negative effect of SST and the
fact that even constant SST at current levels repre-
sents conditions that are considerably warmer than
the long-term average (cf. Fig. 3).
Frederiksen et al. (2012) have hypothesised that
the decline of kittiwakes in the North Atlantic may be
due to environmental conditions at the Grand Banks,
which is an overwintering area that seems to be com-
mon to the whole Atlantic population. Our findings
from Runde, and to some degree from Sklinna and
Vedøy, are compatible with this hypothesis. How-
ever, the decline in the 2 northernmost colonies
(Hjelmsøya and Hornøya) was more closely related to
autumn conditions off Svalbard, while the population
declines were similar. The support for the importance
of the Grand Banks area is thus somewhat equivocal.
In conclusion, although ocean warming is not the
sole explanation for the decline of Norwegian kitti-
wake populations, it aggravates the situation consid-
erably. Unless kittiwakes are able to switch to other
foraging areas or prey, especially outside the breed-
ing season, the populations surveyed will reach
quasi-extinction within a couple of decades.
Acknowledgements. We thank all the field workers involved
in monitoring kittiwake numbers over the years, none men-
tioned, none forgotten. The Norwegian Coastal Administra-
tion kindly allowed us to use the lighthouses on Hornøya and
Sklinna as bases for the field work there. Access to the
colonies was granted by the County Governors of Møre &
Romsdal, Nord-Trøndelag, Nordland and Finnmark counties.
The population monitoring was mainly funded by the Norwe-
gian Environment Agency (formerly the Norwegian Direc-
Sandvik et al.: Declining populations and ocean warming
torate for Nature Management), with initial support from the
Zoological Museum at Oslo University and the Tromsø Uni-
versity Museum, and as part of the Norwegian seabird moni-
toring programme from its start in 1988. Since 2005, the study
has also been an integrated part of the Norwegian seabird
programme SEAPOP (, which is funded as a
consortium between Norwegian environmental management
authorities and the Norwegian Oil and Gas Association.
SEAPOP provided funds for the analysis, with additional sup-
port from the institutions of the authors. Vidar Grøtan is
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Submitted: August 22, 2013; Accepted: March 25, 2014
Proofs received from author(s): June 4, 2014
... In particular, Northern Atlantic Oscillation and East Atlantic patterns changed and coupled with each other, leading to a rise in sea surface temperature (SST) and to a decline in the intensity of coastal upwellings. Similar cases have been reported for declining Norwegian colonies, where changes in SST explained an average of 18% of the variation in annual population growth rate (Sandvik et al. 2014). ...
... The lack of synchrony in the decline of nesting pairs between Sisargas and Cape Vilán may be explained by the location of the latter further south along the west coast of Galicia where kittiwakes can access resources other than sardines (such as sandeels) in sandy-bottom fjords (locally 'rías'). Lack of synchrony in the population dynamics of kittiwake colonies has been previously found both in the North Atlantic (see Sandvik et al. 2014) and Alaska, USA (Kildaw et al. 2008). The scant data for the Cape Vilán colony precludes any rigorous quantitative ana lysis of its dynamic and of the role that the Cape Vilán colony could have played as a satellite of Sisargas for the functioning of the whole population. ...
ABSTRACT: We analysed the long-term (1975−2017) population response of a colony of a marine top predator, the black-legged kittiwake Rissa tridactyla, to the population dynamics of sardine Sardina pilchardus, its main local prey. The study site (Sisargas Islands, Spain) is located at the southernmost edge of the geographical distribution of the predator. Kittiwake counts of breeding pairs started with the discovery of the colony (1975), likely close to the actual year of first colonization. Sardine landings by age class (1978−2016) were taken from the International Council for the Exploration of the Sea (ICES) database. Sequential t-test analysis revealed that a regime shift of the oldest sardine age class (age 6+) took place in 1991 and that kittiwakes experienced a regime shift in the number of breeding pairs in 1993, 2 yr after the prey shift. Multiple autocorrelation functions for the detrended time series of sardines and kittiwakes indicated an autocorrelation with a time lag of 2 yr. Despite much reduced fishing effort, sardine densities have not recovered since the collapse, likely due to changes in large-scale atmospheric circulation in the Northern Hemisphere in the late 1990s. Kittiwakes at Sisargas have not recovered demographically, re - maining nearly extinct during the last ca. 20 yr. Although we lack detailed demographic data for the studied kittiwake population, we suggest that massive breeding failure and subsequent dispersal to higher-quality patches might explain the rapid non-linear collapse in breeding population density. We discuss some behavioural social responses that may have occurred during and after the collapse to explain the dynamics of the study colony.
... The fact that some regional kittiwake populations in the North-East Atlantic are in decline indicate that some of them are not coping well with ongoing rapid changes in environmental conditions, in addition to other natural and anthropogenic pressures (e.g. Sandvik et al. 2014). Although there is good evidence of connectivity between clusters of colonies in the North-East Atlantic (e.g. ...
... Furthermore, changes in oceanographic conditions can affect seabirds at both their breeding and nonbreeding foraging areas, with potential lag effects of a decline in immature survival during the autumn and winter months on population growth rates (e.g. Sandvik et al. 2014). ...
Technical Report
Full-text available
Offshore windfarms are seen as a key part of efforts to combat climate change. However, there are a number of significant concerns about the potential of these windfarms to have a negative impact on wildlife and biodiversity, particularly in relation to birds. This is of particular concern as the scale of offshore windfarm development expands so that the risk of reaching unacceptable levels of cumulative impacts also increases. This work has been undertaken on behalf of the Offshore Wind Strategic Monitoring and Research Forum (OWSMRF). The report presents a summary of existing evidence, and potential research opportunities, to better understand the population dynamics of black-legged kittiwakes and how their populations might respond to potential additional mortality from offshore windfarm development and conservation management measures. The intention is that this report provides a signpost towards research that can facilitate meaningful and precise cumulative impact assessments, and thus contribute to reducing uncertainty in decision making around offshore windfarm consenting in the next few years. Full report can be found here:
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Technical Report
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Status report (in Norwegian with English summary) for the Norwegian Sea ecosystem. This is delivered to the Norwegian Ministries every third year. The listed "authors" are the editors, while the repoert has contribution from a list of authors, presented in the report
... Changes in climatic conditions can have profound effects on the demography, phenology and population trajectories of marine top predators such as seabirds (Dias et al., 2019;Jenouvrier, 2013;Oro, 2014;Sydeman et al., 2015). In light of projected climatic change due to global warming (IPCC, 2013), this raises concerns for the viability of populations that respond negatively to increasing temperatures, either directly or mediated by effects at lower trophic levels (Jones et al., 2018;Sandvik et al., 2014;Trathan et al., 2020). ...
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The current warming of the oceans has been shown to have detrimental effects for a number of species. An understanding of the underlying mechanisms may be hampered by the non-linearity and non-stationarity of the relationships between temperature and demography, and by the insufficient length of available time series. Most demographic time series are too short to study the effects of climate on wildlife in the classical sense of meteorological patterns over at least 30 years. Here we present a harvest time series of Atlantic puffins (Fratercula arctica) that goes back as far as 1880. It originates in the world's largest puffin colony, in southwest Iceland, which has recently experienced a strong decline. By estimating an annual chick production index for 128 years, we found prolonged periods of strong correlations between local sea surface temperature (SST) and chick production. The sign of decennial correlations switches three times during this period, where the phases of strong negative correlations between puffin productivity and SST correspond to the early 20th century Arctic warming period and to the most recent decades. Most of the variation (72%) in chick production is explained by a model in which productivity peaks at an SST of 7.1°C, clearly rejecting the assumption of a linear relationship. There is also evidence supporting non-stationarity: The SST at which puffins production peaked has increased by 0.24°C during the 20th century, although the increase in average SST during the same period has been more than three times faster. The best supported models indicate that the population's decline is at least partially caused by the increasing SST around Iceland.
... In Norway, this appears to be primarily a result of reduced productivity (e.g. Reiertsen et al. 2013), likely enforced by increased predation from White-tailed Eagles Haliaeetus albicilla (Anker-Nilssen & Aarvak 2009;Hipfner et al. 2012) corvids and large gulls, but reduction in over-winter survival of adults is likely also at play Sandvik et al. 2014). ...
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In recent decades, the population of Black-legged Kittiwake Rissa tridactyla has declined substantially in most parts of the North Atlantic. Concurrently, there has been an increased urbanisation of the species, with Kittiwakes colonising nearshore buildings and other man-made structures. Here we document the prevalence and performance of Kittiwakes breeding on offshore oil rigs on the Norwegian shelf and compare their reproductive output with parallel data from the nearest Kittiwake colonies monitored on the Norwegian coast. At least six (10%) of the 63 rigs addressed in the study were reported to have breeding Kittiwakes, four of which had a total of 1,164 breeding pairs in 2019. One of these offshore colonies was situated in the Barents Sea, the other five in the Norwegian Sea. Overall the Kittiwakes breeding on oil rigs had a moderate to high productivity, ranging on average between 0.61–1.07 large chicks per nest. This was higher than the productivity in most (but not all) colonies on man-made structures on the coast in the same period, and much higher than that in natural breeding habitats. The differences in Kittiwake productivity between offshore and coastal habitats are likely related to parallel differences in food availability and exposure to predators, but this warrants further study. Besides helping us explore key drivers of Kittiwake productivity, the increasing numbers of Kittiwakes breeding on man-made structures both offshore and on the coast clearly provide a significant contribution of juveniles to the impoverished Kittiwake population in Norwegian waters.
... Kittiwakes feed mainly on fish and crustaceans, organisms that tend to have a narrower temperature tolerance than seabirds. Hence, increasing temperatures can affect the abundance and distribution of kittiwake main prey with cascading consequences on their breeding success, productivity and phenology (Hunt Jr et al. 2002;Frederiksen et al. 2006;Moe et al. 2009;Sandvik et al. 2014). A study on longterm changes in kittiwake diet from Svalbard showed a clear increase in the abundance of sub-Arctic and Atlantic species in the chick's diet, but these changes did not appear to have any negative effect on breeding success (Vihtakari et al. 2018). ...
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The black-legged kittiwake (Rissa tridactyla, hereafter kittiwake) is a small pelagic seabird and is the most numerous gull species in the world. It has a circumpolar distribution, and breeds in the arctic and boreal zones of the Northern Hemisphere. It’s breeding distribution is widespread and ranges across the North Atlantic from the west coast to the Barents Sea, including Arctic Canada, Newfoundland and the Gulf of St. Lawrence, Greenland, Iceland, Faroe Islands, United Kingdom, Republic of Ireland, mainland Norway, Svalbard, Murman Coast, Novaya Zemlya and Franz Josef Land. In the Pacific, the kittiwake breeds in the Russian Far East and Alaska, USA. The kittiwake spends most of the non-breeding period offshore. Most of those breeding in the North Atlantic spend the winter in the North-West Atlantic, over the shelf, slope and deep waters off Newfoundland and Labrador and south of Greenland, whereas the Pacific birds stay in cool, productive waters north of the North Pacific Subtropical Convergence Zone.
... Over the scale of its full distribution across the Northern Hemisphere, synchrony in the fluctuations of colony sizes has generally been nonexistent or very low, apart from a synchronised decline during a period of rapid ocean warming (Descamps et al. 2017). Similarly, a study looking at smaller scale synchrony, using colonies along the coast of Norway, found no evidence that colony sizes fluctuated in unison (Sandvik et al. 2014). However, synchrony has been identified in kittiwake breeding success in the UK and Ireland, where colonies formed geographically distinct clusters in which breeding success fluctuated in unison (Furness et al. 1996, Frederiksen et al. 2005. ...
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Synchrony in demographic rates between spatially disjunct populations is a widespread phenomenon, although the underlying mechanisms are often not known. This synchrony and its spatial patterns can have important consequences for the long-term persistence of metapopulations and can also be used to infer drivers of population dynamics. Here, we examined spatial patterns of synchrony in the breeding success of black-legged kittiwakes Rissa tridactyla in the UK, using an extensive dataset on kittiwake breeding success and 2 different ways of measuring synchrony: one reflecting synchrony in inter-annual fluctuations only ( rdiff ) and one reflecting synchrony in both inter-annual fluctuations and long-term trends ( r ). We found that between-colony synchrony in breeding success decreased with distance up to just over 200 km but that some colony pairs showed stronger or weaker synchrony than expected based on distance. This was also reflected in the configuration of spatially coherent clusters of kittiwake colonies with synchronous breeding success. Further, we compared the support for different drivers of these spatial patterns, including trophic interactions and weather conditions. We found that the spatial dynamics of the kittiwakes’ main prey in this region, the lesser sandeel Ammodytes marinus , appeared to play some role in generating synchrony in long-term patterns, but their role in generating synchrony in inter-annual fluctuations was less clear. The study shows that examining spatial patterns in synchrony can provide useful information for inferring potential drivers and the spatial scale over which they are acting.
... southern (Norwegian Sea) colonies were mainly affected by winter SST in the western North Atlantic wintering grounds, while dynamics of the northernmost colonies (Barents Sea) were mainly affected by autumn SST off Svalbard. Warmer conditions see faster population declines. Modelling suggested that colonies would become extinct in 10 to 100 years.Sandvik et al. (2014) Brandt's cormorant Survival and probability of breeding were both related by a quadratic model to SST and ENSO index. Lower survival occurred at extremes of SST or ENSO index. Survival was particularly low when SST was high.Schmidt et al. (2015) ...
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Seabirds throughout the world are vulnerable to increasing sea temperatures, and associated climate change such as rainfall and storminess, partly through direct effects on bird physiology, but mostly through indirect bottom-up effects, with adverse effects far more frequent than new opportunities. • Some populations have already declined considerably due to climate change (emperor penguins declined by 50% due to reduction in adult survival related to reduced sea ice extent, rockhopper penguins at Campbell Island declined by 96% as sea temperature increased, a 70% reduction in ivory gull numbers has been attributed to increasing temperatures reducing Arctic sea ice, Arctic skua breeding numbers in Scotland declined by 74% during 1986-2011, black-legged kittiwake breeding numbers by 66% in the same period, both declines being thought due to increasing temperatures (predominantly affecting sandeel productivity). • Modelling predicts extinction of black-legged kittiwakes in Norway within the next 10-100 years because their breeding success and survival are both reduced by increasing sea temperature. • Some populations may redistribute polewards, but scope for redistribution is limited. • Climate envelope models predict the near-future loss as breeding species in the British Isles of Arctic tern, Arctic skua, great skua and Leach's storm-petrel, all of which are at the southern edge of breeding range in the British Isles.
... Although we cannot investigate changes in migratory paths, environmental conditions, and breeding productivity over time with our current dataset, our findings suggest that large puffin colonies may not be sustainable anymore, perhaps because of long-term changes in environmental conditions near the breeding [55] or wintering [56] grounds, affecting the birds' ability to both refuel in winter and feed their offspring in summer. This is also likely to be the case for other species that have undergone similar declines in large northern colonies [57]. ...
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Wildlife watching tourism is a growing industry and can become eco-friendly if sufficient conservation measures are actively included in the operative tourism strategies. Research on visitor behaviour is necessary to evaluate and understand peoples’ behaviour towards wildlife. The focus of this case-study is birdwatching tourism in a protected nature reserve in Northern Norway. Qualitative methods have been used with The Theory of Planned Behaviour as a framework. The aim of the study is to understand visitor behaviour that may disturb the seabird on the island of Hornøya. The research is based on 48 interviews with 61 participants, in addition to participatory and systematic observations of tourists at the island. Interpretation of bird behaviour and the affective responses to the wildlife experience are identified as factors that influence unwanted visitor behaviour. Informants’ understandings of disturbance towards the seabirds are reflected in negative perceptions of inappropriate behaviour. However, most participants believe that the birds are not disturbed by tourists, or at least not easily disturbed. However, some variation exists, and some informants think that the birds are negatively affected to some extent. The social norms support that visitors express a responsibility for respecting the rules of the nature reserve. The findings also suggest that visitors performing intentional non-conforming behaviour have not internalised the social norm, it has not become a personal norm. The willingness among visitors for social sanctioning towards depreciative behaviour was relatively low, suggesting that stricter formal regulations may be more effective measures for reduced depreciative behaviour. The study also identifies persuasive communication through interpretive information as a management approach with potential of reducing inappropriate behaviour. Further, alternative tourism experiences of seabirds can lessen the pressure on birdlife on Hornøya, as well as creating new business opportunities.
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NorESM is a generic name of the Norwegian earth system model. The first version is named NorESM1, and has been applied with medium spatial resolution to provide results for CMIP5 ( without (NorESM1-M) and with (NorESM1-ME) interactive carbon-cycling. Together with the accompanying paper by Bentsen et al. (2012), this paper documents that the core version NorESM1-M is a valuable global climate model for research and for providing complementary results to the evaluation of possible anthropogenic climate change. NorESM1-M is based on the model CCSM4 operated at NCAR, but the ocean model is replaced by a modified version of MICOM and the atmospheric model is extended with online calculations of aerosols, their direct effect and their indirect effect on warm clouds. Model validation is presented in the companion paper (Bentsen et al., 2012). NorESM1-M is estimated to have equilibrium climate sensitivity of ca. 2.9 K and a transient climate response of ca. 1.4 K. This sensitivity is in the lower range amongst the models contributing to CMIP5. Cloud feedbacks dampen the response, and a strong AMOC reduces the heat fraction available for increasing near-surface temperatures, for evaporation and for melting ice. The future projections based on RCP scenarios yield a global surface air temperature increase of almost one standard deviation lower than a 15-model average. Summer sea-ice is projected to decrease considerably by 2100 and disappear completely for RCP8.5. The AMOC is projected to decrease by 12%, 15–17%, and 32% for the RCP2.6, 4.5, 6.0, and 8.5, respectively. Precipitation is projected to increase in the tropics, decrease in the subtropics and in southern parts of the northern extra-tropics during summer, and otherwise increase in most of the extra-tropics. Changes in the atmospheric water cycle indicate that precipitation events over continents will become more intense and dry spells more frequent. Extra-tropical storminess in the Northern Hemisphere is projected to shift northwards. There are indications of more frequent occurrence of spring and summer blocking in the Euro-Atlantic sector, while the amplitude of ENSO events weakens although they tend to appear more frequently. These indications are uncertain because of biases in the model's representation of present-day conditions. Positive phase PNA and negative phase NAO both appear less frequently under the RCP8.5 scenario, but also this result is considered uncertain. Single-forcing experiments indicate that aerosols and greenhouse gases produce similar geographical patterns of response for near-surface temperature and precipitation. These patterns tend to have opposite signs, although with important exceptions for precipitation at low latitudes. The asymmetric aerosol effects between the two hemispheres lead to a southward displacement of ITCZ. Both forcing agents, thus, tend to reduce Northern Hemispheric subtropical precipitation.
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Climate variability can affect population dynamics via adult survival or via offspring production and recruitment. The relative importance of both processes is still an unresolved matter, especially in long-lived species, where the time lags between the climate signal and the population response differ greatly depending on the process involved. We address the issue using 378 time series from 29 seabird species from 187 breeding colonies throughout the North Atlantic. The effect of climate on population growth rate is estimated as the slope of the North Atlantic Oscillation (NAO) index at different time lags when used as a covariate in population models. Using nonlinear mixed effects models, we can demonstrate that climate affects the population dynamics of seabirds, both through adult survival and through the recruitment of offspring produced. The latter effect is stronger, and the long time lags involved make it likely that its magnitude is still underestimated. Because different processes are involved, the sign of the relationship with the NAO differs between time lags. The relationship between the NAO and the population growth rate is also highly variable, both within and across species. In a second analytical step, we address the factors that may cause this interspecific and inter-colony variation, considering the ecological, demographic and geographical characteristics of the populations. Among comparatively 'fast-lived' seabirds, i.e. species with large clutches, the relationship with the NAO reverses its sign depending on latitude, while no such trend is apparent among 'slow' species.
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In migratory birds, environmental conditions in both breeding and non-breeding areas may affect adult survival rates and hence be significant drivers of demographic processes. In seabirds, poor knowledge of their true distribution outside the breeding season, however, has severely limited such studies. This study explored how annual adult survival rates of black-legged kittiwakes Rissa tridactyla on Hornoya in the southern Barents Sea were related to temporal variation in prey densities and climatic parameters in their breeding and non-breeding areas. We used information on the kittiwakes' spatiotemporal distribution in the non-breeding season gained from year-round light-based tracking devices (geolocators) and satellite transmitters, and kittiwake annual adult survival rates gained from a multistate capture-mark-recapture analysis of a 22 yr time series of colour-ringed kittiwakes. In the post-breeding period, kittiwakes concentrated in an area east of Svalbard, in the winter they stayed in the Grand Banks/Labrador Sea area, and in the pre-breeding period they returned to the Barents Sea. We identified 2 possible prey categories of importance for the survival of kittiwakes in these areas (sea butterflies Thecosomata in the Grand Banks/Labrador Sea area in winter and capelin Mallotus villosus in the Barents Sea in the pre-breeding season) that together explained 52% of the variation in adult survival rates. Our results may have important implications for the conservation of kittiwakes, which are declining globally, because other populations use the same areas. Since they are under the influence of major anthropogenic activities including fisheries, international shipping and the offshore oil and gas industry, both areas should be targeted for future management plans.
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Conditions in arctic marine environments are changing rapidly, and understanding the link between environmental and demographic parameters could help to predict the consequences of future change for arctic seabirds. Over 20 yr (1988 to 2007), we studied colony attendance, adult survival and reproductive success of thick-billed murres, as well as the departure masses and diets of their chicks at Coats Island, Nunavut, Canada (62.95 degrees N, 82.00 degrees W). We evaluated how each parameter responded to climatic conditions near the colony during the breeding season, and in the winter range during the non-breeding period (delineated using geolocation). We used the Arctic Oscillation (AO) and North Atlantic Oscillation indices, as well as local variables to describe ice, oceanographic and weather conditions. We demonstrate that adult survival varied little among years but was higher after winters with lower AO indices, more ice in the south-western part of the winter range in spring, and cooler sea surface temperatures (SST). By comparison, interannual variation in breeding parameters (breeding success, chick mass and diet) was pronounced and responded to SST and ice conditions near the colony. Counts of birds attending the colony, influenced heavily by pre-breeders, were most strongly related to the conditions that influenced adult survival; counts were positively related to ice concentration in the southwest of the winter range. Relationships between climatic conditions and demographic parameters were often lagged, suggesting effects mediated through the food web. The trend towards higher SST and less ice in the vicinity of the colony has not yet reduced reproductive success. However, a significant, ongoing decline in the rate of energy delivery to nestlings suggests that a critical threshold may eventually be crossed.
The hypothesis that animal population dynamics may be synchronized by climate is highly relevant in the context of climate change because it suggests that several populations might respond simultaneously to climatic trends if their dynamics are entrained by environmental correlation. The dynamics of many species throughout the Northern Hemisphere are influenced by a single large-scale climate system, the North Atlantic Oscillation (NAO), which exerts highly correlated regional effects on local weather. But efforts to attribute synchronous fluctuations of contiguous populations to large-scale climate are confounded by the synchronizing influences of dispersal or trophic interactions. Here we report that the dynamics of caribou and musk oxen on opposite coasts of Greenland show spatial synchrony among populations of both species that correlates with the NAO index. Our analysis shows that the NAO has an influence in the high degree of cross-species synchrony between pairs of caribou and musk oxen populations separated by a minimum of 1,000 km of inland ice. The vast distances, and complete physical and ecological separation of these species, rule out spatial coupling by dispersal or interaction. These results indicate that animal populations of different species may respond synchronously to global climate change over large regions.