The decline of Norwegian kittiwake populations: Modelling the role of ocean warming

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 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.

Full text

CLIMATE RESEARCH
Clim Res
Vol. 60: 91–102, 2014
doi: 10.3354/cr01227 Published online June 17
1. INTRODUCTION
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 · www.int-res.com*Corresponding author: hanno@evol.no
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
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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
populations?
2. MATERIAL AND METHODS
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.
92

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
form:
(1)
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
e(environ-
mental variance); Ntis the population size in year t;⎯r
is the long-term intrinsic population growth rate; σ2
d
is the demographic variance; Xi,tis the environmen-
tal covariate iin year t. The parameters βi,⎯rand σ2
e
were estimated from the population time series using
maximum likelihood such that the log-likelihood
(2)
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
d兾Nt. 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 parameters⎯r, 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 common⎯rand
σ2
e, with a common⎯rand 5 separate σ2
e, with a com-
mon σ2
eand 5 separate⎯r, and with 5 separate⎯rand
σ2
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-
NNrN X
tt tiitt
∑
=+−σ +β+ε
+−
ln ln
11
2d
21 ,
LNEN
kk
k
n
∑
=−−σ+πσ
−
=
ln {[ln (ln )] ln(2 )}
1
222 2
2
n
ii
∑=1
5
93
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− 8° 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
94
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.
3. RESULTS
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 rate⎯rof −0.055 ± 0.026
95
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
Supplement).
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
96
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, common⎯r, 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, separate⎯r, separate σ2
e 10 12.21 0.002
No covariate, common⎯r, common σ2
e ⎯r= −0.070 −0.106 to −0.035 2 45.64 0.000
No covariate, separate⎯r, 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. DISCUSSION
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
colonies.
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.
97
Model: covariate Estimate CI K ΔAICC R2
(time lag, yr)
Runde
Grand Banks (3) −0.127 −0.259 to +0.005 3 0.00 0.125
Null 2 0.79
Sklinna
Null 2 0.00
Grand Banks (1) −0.284 −0.661 to +0.088 3 0.70 0.110
Vedøy
Null 2 0.00
Grand Banks (3) −0.059 −0.145 to +0.025 3 0.65 0.063
Hjelmsøya
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
Hornøya
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
migration.
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
98
Model: covariate (time lag, yr) Time to extinction (yr)
with a probability of
50% 20% 10% 2.5%
Runde
Null 79 57 49 40
Grand Banks (3) 45 35 31 26
Grand Banks (3) + warming 35* 28 25 22
Vedøy
Null 89 66 58 48
Grand Banks (3) 60 46 41 37
Grand Banks (3) + warming 48* 37 33 30
Hjelmsøya
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
Hornøya
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
entirely.
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
99
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
100
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 (www.seapop.no), 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
thanked for help with population models.
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