MARINE ECOLOGY PROGRESS SERIESMar Ecol Prog Ser

Vol. 509: 289302, 2014doi: 10.3354/meps10825

Published August 27

Inter-Research 2014 www.int-res.com*Corresponding author: tone.reiertsen@nina.no

Prey density in non-breeding areas affects adultsurvival of black-legged kittiwakes Rissa tridactyla

Tone K. Reiertsen1,2,*, Kjell E. Erikstad2,3, Tycho Anker-Nilssen4, Robert T. Barrett1, Thierry Boulinier5, Morten Frederiksen6, Jacob Gonzlez-Sols7, David Gremillet5,

David Johns8, Brge Moe4, Aurore Ponchon5, Mette Skern-Mauritzen9, Hanno Sandvik3, Nigel G. Yoccoz10

1Troms University Museum, Department of Natural Sciences, 9037 Troms, Norway2Norwegian Institute for Nature Research,

FRAM High North Research Centre for Climate and the Environment, Hjalmar Johansens gt 14, 9296 Troms, Norway3Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, 7491 Trondheim, Norway

4Norwegian Institute for Nature Research, PO Box 5685 Sluppen, 7485 Trondheim, Norway5Centre dEcologie Fonctionnelle et Evolutive, CNRS UMR 5175, 1919 Route de Mende, 34293 Montpellier cedex 5, France

6Department of Biosciences, Aarhus University, Fredriksborgvej 399, 4000 Roskilde, Denmark7Institut de Recerca de la Biodiversitat (IRBio) and Departament de Biologia Animal (Vertebrats), Facultat de Biologia,

Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Spain8Sir Alister Hardy Foundation for Ocean Science (SAHFOS), Citadel Hill, PL1 2PB Plymouth, UK

9Institute of Marine Research, PO Box 1870, 5817 Bergen, Norway10Department of Arctic and Marine Biology, Faculty of Biosciences, Fisheries and Economics, University of Troms,

9037 Troms, Norway

ABSTRACT: In migratory birds, environmental conditions in both breeding and non-breedingareas 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, hasseverely limited such studies. This study explored how annual adult survival rates of black-leggedkittiwakes Rissa tridactyla on Hornya in the southern Barents Sea were related to temporal vari-ation in prey densities and climatic parameters in their breeding and non-breeding areas. Weused information on the kittiwakes spatiotemporal distribution in the non-breeding seasongained from year-round light-based tracking devices (geolocators) and satellite transmitters, andkittiwake annual adult survival rates gained from a multistate capture-mark-recapture analysis ofa 22 yr time series of colour-ringed kittiwakes. In the post-breeding period, kittiwakes concen-trated in an area east of Svalbard, in the winter they stayed in the Grand Banks/Labrador Seaarea, and in the pre-breeding period they returned to the Barents Sea. We identified 2 possibleprey categories of importance for the survival of kittiwakes in these areas (sea butterflies Theco-somata in the Grand Banks/Labrador Sea area in winter and capelin Mallotus villosus in the Bar-ents Sea in the pre-breeding season) that together explained 52% of the variation in adult survivalrates. Our results may have important implications for the conservation of kittiwakes, which aredeclining globally, because other populations use the same areas. Since they are under the influ-ence of major anthropogenic activities including fisheries, international shipping and the offshoreoil and gas industry, both areas should be targeted for future management plans.

KEY WORDS: Black-legged kittiwake Pteropods Capelin Capture-mark-recapture analyses Non-breeding distribution

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Mar Ecol Prog Ser 509: 289302, 2014

INTRODUCTION

The relationship between adult survival in long-lived migratory species and environmental condi-tions in both breeding and non-breeding areas isimportant in understanding demographic processesand population dynamics. For many migratory birds,however, including most marine birds, the very lim-ited knowledge of their distribution outside of thebreeding season has constrained studies of the rela-tionship between demography and environmentalconditions (Runge & Marra 2005, Ratikainen et al.2008, Schaub et al. 2011, Smith & Gaston 2012). Forpelagic seabirds, of which many species are severelythreatened by global extinction, such studies are crucial (Croxall et al. 2012, Frederiksen et al. 2012,Lewison et al. 2012).

The recent advance of tracking technologies, suchas miniaturized year-round light-based tracking de-vices (GLS loggers or hereafter geolocators), has revo-lutionized our knowledge of the non-breeding rangeof migrant species (e.g. Phillips et al. 2004, Gonzles-Sols et al. 2007, Guilford et al. 2009, Egevang et al.2010, Seavy et al. 2012, Smith & Gaston 2012). Com-bining such data with statistical modeling of long-term capture-mark-resighting (CMR) data now allowsus to explore in detail the relationships between envi-ronmental conditions in both breeding and non-breeding areas and adult survival rates. As yet, veryfew studies have investigated such relationships (butsee Schaub et al. 2011), and we know of only 2 seabirdstudies that link adult survival to environmental con-ditions in both the breeding and non-breeding areas(Ramos et al. 2012, Smith & Gaston 2012) and nonethat address temporal changes in prey availability innon-breeding areas and adult survival rates.

Even small changes in adult survival can signifi-cantly impact the population dynamics in long-livedseabirds, since adult survival has a strong effect onlifetime reproduction (Lebreton & Clobert 1991,Stearns 1992). Worrying in this respect is the growingnumber of studies documenting a negative im pact ofclimate on the adult survival rates of marine birds(e.g. Harris et al. 2005, Jenouvrier et al. 2005, Sandviket al. 2005, LeBohec et al. 2008). Climatic effects are,however, most often indirectly mediated through tem-poral changes in prey availability, via changes in preyabundance (e.g. Sandvik et al. 2005), but they mayalso affect prey availability directly through shifts inthe preys spatial distribution (e.g. Peron et al. 2010).

Most marine ecosystems show pronounced tempo-ral and spatial variation in oceanographic processesand trophic interactions that are often driven by cli-

matic processes, and these may lead to fluctuationsin important fish stocks and disruptions in preda-torprey interactions (God 2003, Durant et al. 2005,Grebmeier et al. 2006, Gjster et al. 2009, Stige etal. 2010, Certain et al. 2011). Especially for top pred-ators such as seabirds, such fluctuations in prey spe-cies may have negative effects on both survival andreproduction (Lack 1968, Cairns 1992, Furness 2003,Oro et al. 2004, Cury et al. 2011) and in some casesmay even drive populations towards extinction (Erik-stad et al. 2013).

Since day length and food availability tend to be ata minimum in winter, conditions in the winteringrange may strongly influence survival (Gaston 2003,Frederiksen et al. 2008a). The effects of environmen-tal variation on adult survival may, therefore, bemore profound outside the breeding season (Schaubet al. 2011, Smith & Gaston 2012). However, duringthe bree ding season, the costs of reproduction arealso known to impact survival (Golet et al. 1998, Oro& Furness 2002), and may also have direct effectson subsequent winter ecology (so called carry-overeffects) (Golet et al. 1998, Ylnen et al. 1998, Harri-son et al. 2010). To be able to determine what factorsand time periods have the greatest impact on adultsurvival, it is important to consider factors in both thebreeding and non-breeding seasons.

The Atlantic population of the black-legged kitti-wake Rissa tridactyla (hereafter kittiwake) has expe-rienced widespread population declines in recentdecades (Frederiksen 2010). The Norwegian popula-tion has decreased by 68% per year since the mid-1990s (Barrett et al. 2006) and is currently catego-rized as endangered on the Norwegian Red List(Kls et al. 2010). The reasons for the declines oversuch a large scale are not fully understood, but thereare indications that food shortages, possibly linked toan increase in fishery activity, are im portant (e.g.Frederiksen et al. 2004, 2008b, 2012).

Kittiwakes are good candidate species as sentinelsof environmental change in the marine system. Theyare surface feeders with limited capacity to switchprey, and they seem to operate at their energetic ceil-ing (Welcker et al. 2010), which makes them par -ticularly vulnerable to changes in the marine system(Fur ness & Tasker 2000). As such, both spatial andtemporal environmental changes can affect theirdemographic traits. A recent study by Frederiksen etal. (2012) documented the non-breeding distributionof kittiwakes from a widespread selection of coloniesacross the North Atlantic (including the presentstudy population), providing the unique opportunityto extract environmental covariates (such as sea sur-

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Reiertsen et al.: Prey density and kittiwake adult survival

face temperature [SST] and temporal data of preydensity) from the identified non-breeding areas andcombine them with important demographic traits,such as adult survival rates.

In order to collate representative data on prey avail-ability for kittiwakes in their non-breeding areas,knowledge of their diet throughout the year is im -portant. Such data stem, however, mainly from thebreeding season, when the kittiwakes predominantlyfeed on invertebrates and small energy-rich schoolingfish (up to 1520 cm), the composition of which differsbetween different ecosystems. In the North Sea, theymainly feed on lesser sandeel Ammodytes marinus(Lewis et al. 2001), in the Newfoundland and Labra -dor Sea area on capelin Mallotus villosus (Carscaddenet al. 2002), and in the Barents Sea they switch be-tween capelin and herring Clupea harengus (Barrett2007). In the pre-breeding season, polar cod Bore-ogadus saida have been found in stomach samples ofkittiwakes (Erikstad 1990). Knowledge of their diet inthe non-breeding season is poor (e.g. Frederiksen etal. 2012), but a study by Gonzlez-Sols et al. (2011) ofkittiwakes breeding on Hornya suggests that theyfeed on a lower trophic level (zooplankton) outside ofthe breeding season. Some studies have shown thatthey may feed on a variety of large zooplankton species, e.g. Calanus spp., amphi pods (Hyperiidea),euphausiids and pteropods (Thecosomata) (Lydersenet al. 1989, Mehlum & Ga brielsen 1993, Lewis et al.2001, Karnovsky et al. 2008).

The aim of the present study was to explore therelationship between potential prey availability andclimatic factors from known non-breeding areas andadult survival rates of kittiwakes breeding in acolony in the southern Barents Sea. Our main goalwas to assess which environmental conditions andwhich time period had the greatest impact on thiskey demographic trait. By combining tracking data ofkittiwakes from the colony and knowledge of kitti-wake diet in their main non-breeding areas, we wereable to extract data on potential important environ-mental factors from large-scale databases and enterthem as covariates in a multistate CMR analysis and,hence, assess their impact on annual adult survivalrate of kittiwakes from the study colony.

MATERIALS AND METHODS

We used a 22 yr (19902011) time series of re -sighted breeding kittiwakes that were individuallymarked with colour- or letter-coded rings at thecolony of Hornya (70 23 N, 31 09 E) in the south-

ern Barents Sea, where searches for the birds weremade every year during the breeding season andwhere the population has declined by 70% since1990 (Barrett et al. 2006).

Kittiwakes are long-lived, cliff-nesting seabirdsthat lay 13 eggs and have a circumpolar, subarcticand Arctic distribution (Coulson 2011). Their meanbreeding life-span on Hornya in 19892003 wasestimated to be 8 yr (Sandvik et al. 2005); however,one individual was sighted at an age of >26 yr (Bar-rett 2010).

Non-breeding distribution

The non-breeding distribution of adult kittiwakes inthe North Atlantic has recently been extensivelymapped using light level geolocators (Bogdanova etal. 2011, Gonzlez-Sols et al. 2011, Frederiksen et al.2012). The data used in this study concern birds nest-ing on Hornya, which were used in both the 20082009 study by Gonzlez-Sols et al. (2011) and in20082009 and 20092010 by Frederiksen et al.(2012). These data give important information on thenon-breeding distribution of the study population andare used as background information for the analysisin the present study. The initial data processing is de-tailed in Frederiksen et al. (2012). Birds were trackedfrom one breeding season to the next (n = 6 in20082009, n = 14 in 20092010) and the monthlydata included in the present study are 110 Septem-ber, 2131 October, 130 November, 131 December,131 January, 120 February and 430 April. Datafrom July and August were excluded because of theconstant daylight in the Barents Sea. Data around theequinoxes were also excluded because latitude esti-mates are then unreliable (Phillips et al. 2004).

We also included data from 5 adult kittiwakes nest-ing in Hornya deployed with platform terminaltransmitters (PTTs or hereafter satellite transmitters;A. Ponchon et al. unpubl.) from 2010 (n = 5). Fixedkernel densities of kittiwake distribution were esti-mated with Hawths Analysis Tools for ArcGis usingthe quartic approximation and a raster cell size of20 km (Beyer 2004). The smoothing factor (alsoknown as band width or the h statistic) was 50 and200 km for the satellite transmitter and geolocatordata, respectively. Kernel contours (%) are presentedin maps with a North Pole stereographic projection.

In 2008, 2009 and 2010, all kittiwakes nesting inHornya equipped with geolocators and satellitetransmitters went straight north after the breedingseason to an area east of the Svalbard archipelago

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(EOS hereafter) (between 75 N and 80 N, and 15 Eand 35 E,) and stayed there until October (Figs. 1A,B& 2C). In October they started to migrate westtowards the Grand Banks/Labrador Sea area (GBLShereafter) in the NW Atlantic (Fig. 1B),where most birds stayed from Novem-ber to February (Fig. 1C). In February,the first birds migrated back to the Bar-ents Sea (BS), and by March all birdshad returned. In April, all birds wereclose to the breeding colony (Fig. 1D).

CMR modeling of adult kittiwake survival

The CMR analysis started with theCormack JollySeber (CJS) model (Le -breton et al. 1992). All birds were cap-tured only once, and attempts made toresight them in the following years. We,therefore, denote the recapture rate asresighting rate.

We assessed the goodness-of-fit(GOF) of the CJS model using U-CAREsoftware (Choquet et al. 2009a). Bydoing so, we examined whether themodel fitted the data and whether therewas any heterogeneity in the resightingprobabilities. This was done by evaluat-ing the overdispersion coefficient, c-hat.Test 3.SR, a test component in the GOFtest that tests the assumption that allmarked individuals alive at time i havethe same probability of surviving to i + 1(transient effect), showed that there wasa transient effect in the data (N[0,1] =0.105, df = 13, 2 = 87.98, p 0, c-hat =6.77), and test 2.CT, which tests theassumption of independence in the re-sighting rate (i.e. trap-happiness ortrap-shyness) showed that there wastrap-happiness in the data (N[0,1] =23.40, df = 19, 2 = 677.09, p 0, c-hat =35.64). We, therefore, used the methodproposed by Gimenez et al. (2003), whoused a multi-state model, with 3 states,adding an unobservable state for non-resighted birds in the previous year (theprobability of having not been seenbefore), to correct for heterogeneity inresighting probabilities (details in Rei -ertsen et al. 2012).

To estimate and compare the annual survivalrates of adult kittiwakes, we used the program E-SURGE (Choquet et al. 2009b). A time-dependentmodel was the starting point for the model selec-

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Fig. 1. Distribution of adult kittiwakes from Hornya at different times ofthe year, as revealed by geolocators over 2 non-breeding seasons. The pan-els in the first row show an overview of the Barents Sea, and those in the sec-ond row show an overview of the North Atlantic including the Barents Sea.The 25, 50, 75 and 90% kernel contours (red, orange, yellow and green, re-spectively) are shown for the periods (A) 110 September, (B) 2131 October,(C) 131 Decem ber and (D) 430 April. n = 6 (20082009), n = 14 (20092010)

Reiertsen et al.: Prey density and kittiwake adult survival

tion, denoted as phi(t)p(f+t), where phi was theannual adult survival and p was the re-sightingprobability, t was time dependence and f was thetransition between states when we took intoaccount trap- happiness in the model. By comparingthe time-dependent model with the constant sur-vival, denoted as phi(i)p(f+t), the constant re-sight-ing model phi(t)p(f ), or both, phi(t)p(f ), where idenotes a constant model, we assessed the appro-priate model for annual survival and re-sightingprobability.

Inclusion of covariates

We included covariates in the models to examinewhether they could explain the variation observed inthe kittiwake adult survival rates. We started byincluding only one covariate at a time to assess theimpact of each covariate on the survival rate, andthen we extended the models using 2 covariates.

In wild populations, adult survival is most likely tobe influenced by multiple factors (e.g. Burnham &Anderson 2002), and models with full time variationusually provide the best description for rich data sets.

Finding a model with covariates that explain all thevariation in survival is, therefore, unlikely, and resid-ual unexplained variation after accounting for theeffect of covariates can be expected (Grosbois et al.2008). We assessed the ability of each covariate todescribe significant variation in survival, usinganalysis of deviance tests (ANODEV: F-test statisticwith ncov and nncov1 degrees of freedom, wherencov represents the number of covariates includedand n is the number of parameters of the time-depen-dent model) (Skalski et al. 1993, Grosbois et al. 2008,Lebreton et al. 2012), which is an established way ofevaluating the significance of covariates. To assessthe effect of the covariates on the kittiwake adult sur-vival rate, we used an approximated R2 statistic,which compared the constant and the time-depen-dent survival models, which is done using the for-mula [Deviance (covariate model) Deviance (con-stant model)]/[Deviance (time-dependent model) Deviance (constant model)] (Gaillard et al. 1997, Bar-braud et al. 2000, Jenouvrier et al. 2006). This givesus the proportion of explained variation (Grosbois etal. 2008, Lebreton et al. 2012), which is denoted as R2

in Tables 1 & S3 (in the Supplement at www. int-res.com/ articles/ suppl/ m509 p289 _ supp .pdf).

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Fig. 2. Spatial distribution of capelin (coloured areas) and kittiwakes (black-contoured areas) in (A) 2008, (B) 2009 and (C)2010. The data from 2008 (n = 6) and 2009 (n = 14) are based on light level geolocators, and the data from 2010 (n = 5) are basedon satellite transmitters. The lower 3 panels show the survey areas of capelin between 20 and 30 August in (D) 2008, (E) 2009and (F) 2010. Kernel contours for capelin are 25, 50, 75 and 90% (red, orange, yellow and green, respectively) and for

kittiwakes 50 and 75% (black; hatched and unhatched, respectively). Red stars: Hornya

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The model selection was performed using QAICc(quasi-likelihood Akaikes information criterion cor-rected for small sample size and overdispersion)(Burnham & Anderson 2002), where the model withthe lowest QAICc value was considered the best.QAICc is the difference between the QAICc of agiven model and the QAICc of the best model.According to Burnham & Andersons (2002) scale ofQAICc model interpretation, models with scores ofQAICc 2 are strongly plausible, 47 less plausibleand 10 improbable. Survival and resighting proba-bility estimates are given with 95% confidence inter-vals. We also used model averaging to assess theeffect of each covariate on adult survival rate. Modelaveraging provides an average weighted estimate ofthe covariates used in a given number of models(Burnham & Anderson 2002).

A detailed description of how the covariates fromthe different areas and time periods were selected isgiven in the Supplement (see www. int-res. com/articles/ suppl/ m509 p289 _ supp .pdf), and a list of allthe covariates used in the CMR analysis is given inTable 2. For EOS in the autumn, we used acousticdata of capelin (CapelinEOS) and polar cod(PcodEOS) from the yearly joint NorwegianRussianecosystem surveys as covariates (for a detailed de -scription see Skern-Mauritzen et al. 2011). Anoverview of the survey area covered in 2008, 2009and 2010 and the resulting capelin distribution isgiven in Fig. 2. The acoustic data of capelin used inthe figures were log-transformed and are from20 August to 30 September. Kernel contours (%)were calculated and presented for capelin distribu-

tion using the same method as described for kitti-wakes (smoothing factor was 50 km). We also usedSST data (SSTautEOS), which were extracted fromthe Extended Reconstruction SST data set (availableon a 2 2 grid; Smith et al. 2008, NOAA 2012), forEOS (75 N, 80 N, 15 E and 35 E) during the post-breeding period (mean of AugustSeptember). ForGBLS, we used data from the continuous planktonrecorder survey (CPR data) from the winter period(NovemberFebruary) from 1990 to 2010. The datawere provided by the Sir Alister Hardy Foundation(SAHFOS) and consisted of monthly mean number ofindividuals of amphipods (Hyperiidea), euphausiids(Euphausiacea total), Calanus finmar chicus andpteropods (Thecosomata total). The area from whichwe extracted CPR data was within 40 N, 62 N,38 W and 60 W. CPR methods are described inLindley (1982). We also used SST from the same areain winter (mean of OctoberJanuary) and the princi-pal-component-based indices of the North AtlanticOscillation (NAO) (NCARS 2012) as climatic covari-ates for this area and time period.

For the pre-breeding in BS, we used the data fromICES (2011) for the total biomass of capelin, (1-group)herring, and the eastern and the western distributionof polar cod in BS, which is defined as stock numbersof a given age on 1 January multiplied by weight atage and reflect the estimated densities of the fish spe-cies on 1 January. Additionally, for the area aroundHornya in the pre-breeding (monthly means of FebruaryApril) and breeding periods (means ofMayJuly) (between 70 N, 72 N, 28 E and 34 E)SST was used as climatic covariate. Average breeding

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Model k Deviance QAICc QAICc QAICc wt F p R2

phi(t )p(f+t) 42 10349.93 10434.58phi(CapelinTot+ThecoDec)p(f+t) 25 10405.13 10455.36 0 0.97 9.17 0.002 0.52phi(CapelinTot+PcodEOS)p(f+t) 25 10412.01 10462.24 6.87 0.03 7.21 0.005 0.46phi(CapelinTot+EuphDec)p(f+t) 25 10417.54 10467.77 12.41 0.00 5.93 0.01 0.41phi(ThecoDec+PcodEOS)p(f+t) 25 10429.32 10479.55 24.19 0.00 3.78 0.041 0.31phi(PcodEOS)p(f+t) 24 10432.47 10480.68 25.32 0.00 7.02 0.016 0.28phi(CapelinTot)p(f+t) 24 10433.87 10482.08 26.71 0.00 6.61 0.019 0.27phi(EuphDec)p(f+t) 24 10451.51 10499.72 44.36 0.00 2.33 0.143 0.11phi(ThecoDec)p(f+t) 24 10458.36 10506.58 51.21 0.00 1.05 0.318 0.05phi(i )p(f+t) 23 10464.66 10510.86 55.49 0.00 0.00 0 0

Table 1. Overview of the most important models of kittiwake adult survival and their neighbouring models, with different en-vironmental covariates. Phi is the survival rate and p is the re-sighting rate. The notation t indicates time-dependent, f indi-cates the transition between 2 states and is a model where the re-sighting rate has been corrected for trap-happiness and tran-sience, and i indicates a constant model. The notations and explanations for the covariates are given in Table 2. Models aresorted by ascending QAICc (quasi-likelihood Akaikes information criterion corrected for small sample size and overdisper-sion) and QAICc (the difference between the QAICc of a given model and the QAICc of the best model) is given for covariatemodels only. QAICc wt: QAICc weight. A total overview of the model selection is given in Table S3 in the Supplement

(see www.int-res. com/ articles/ suppl/m509p289_supp.pdf)

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Reiertsen et al.: Prey density and kittiwake adult survival

success in the previous year (BSL1) was also used as apopulation-level covariate for the breeding season.

Some of the covariates had missing values for someyears. These included Calanus finnmarchicus, Hy -peri idea, Euphausiacea and Thecosomata in Decem-ber 1991. To obtain a complete time series, we usedthe method developed by Colebrook (1975), whichmultiplies the long-term monthly mean correspon-ding to a missing month in a given year by the ratioof the sum of the non-missing values in that year tothe sum of the corresponding long-term monthlymeans. Fish and plankton covariates were log-trans-formed prior to analysis to achieve a linear relation-ship on a log scale.

Covariates with linear temporal trends were de -trended, using the residuals from the regressionbetween the parameter and the year (Table S2 in theSupplement). All covariates were checked for auto-correlation after detrending (see Table S1 and Fig. S1in the Supplement for an overview of colinearitiesbetween and temporal variation in the covariates),and we did not enter covariates with high correlationin the same model (Table S1 in the Supplement). Sig-nificant autocorrelation (p < 0.05) was when R 0.44.

RESULTS

An overview of the best models in the model se -lection and their neighbouring models is given inTable 1, and a total overview of the model selection isgiven in Table S3 in the Supplement.

Overall, the model with the lowest QAICc (Table 1)was the model with time dependency in both the sur-vival rate and the resighting rate, where we had cor-rected for transience and trap-happiness (Fig. 3). Sur-vival varied extensively with time (mean phi = 0.85,range = 0.660.98). There were 2 severe drops in thesurvival rate; one in 1994 (phi = 0.66, SE = 0.05) andone in 2003 (phi = 0.74, SE = 0.04; Fig. 3). Models cor-rected for trap-happiness were clearly better (lowerAICc) than models without such a correction(Table S3 in the Supplement).

None of the models with covariates improved themodel with full time-dependency of both the survivalprobability and the re-sighting rate, because of thepresence of residual unexplained variation as ex -plained in the methods. We, therefore, only com-pared the difference between the QAICc values(QAICc) for models that in cluded covariates.

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Time period Area Covariate Covariate explanation Sourcenotation

Autumn East of Sval- CapelinEOS Acoustic assessment of the capelin stock IMR(Sep) bard (EOS) PcodEOS Acoustic assessment of the polar cod stock IMR

SSTautEOS Sea surface temperature NOAAWinter Grand Banks/ ThecoDec Continously plankton recorded (CPR) SAHFOS(NovDec) Labrador Sea data of the suborder Thecosomata

(GBLS) ThecoNov CPR data of Thecosomata SAHFOSHypDec CPR data of Hyperiidea SAHFOSHypNov CPR data of Hyperiidea SAHFOSEuphDec CPR data of Euphausiacea SAHFOSEuphNov CPR data of Euphausiacea SAHFOSCalDec CPR data of Calanus finmarchicus SAHFOSCalNov CPR data of Calanus finmarchicus SAHFOSSSTwinterGBLS Sea surface temperature NOAA

Winter North Atlantic NAOPC Principal component based indices of NCARS(DecMar) the North Atlantic Oscillation (2012)

Spring Barents Sea CapelinTot Total biomass of capelin ICES(MarApr) (BS) Herring1Y 1-group herring ICES

PcodEast Total biomass of polar cod in the eastern ICESpart of the Barents Sea

PcodWest Total biomass of polar cod in the western ICESparts of the Barents Sea

SSTspringHorn Sea surface temperature NOAASummer Around SSTsummerHorn Sea surface temperature NOAA(breeding season) Hornya BSL1 Breeding success, lagged 1 yr

Table 2. Overview of all covariates used in the capture-mark-resighting analysis with time periods and areas of interest. Thenotation used in the model selection is explained, and data sources are indicated. IMR: Norwegian Institute of Marine Re-search; NOAA: National Oceanic and Atmospheric Administration (USA); SAHFOS: Sir Alistair Hardy Foundation (UK); ICES:

International Council for the Exploration of the Sea (Denmark)

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Among the covariate models, the model that in -cluded temporal variation of the total biomass ofcapelin in BS (CapelinTot) and the temporal variationof Thecosomata density in December in the GBLS(ThecoDec) was clearly the best (QAICc weight =0.97; Table 1). This model was 6.87 QAICc units bet-ter than the second best covariate model that in -cluded the total biomass of capelin in BS and theacoustic data of polar cod in EOS (PcodEOS) (QAICcweight = 0.03; Table 1). Model averaging of the 3covariates included in these models gives estimatesof 0.40, 0.50 and 0.008 for CapelinTot, ThecoDecand PcodEOS, respectively. The effect of both Theco-somata in GBLS in December and the total biomassof capelin in BS on adult kittiwake survival are thusvery strong compared with polar cod in EOS in thepost-breeding period. Estimates of the covariatesfrom the top model explaining adult survival wereboth positive (0.41 [range = 0.300.52] and 0.52[range = 0.330.70] for CapelinTot and ThecoDecwith 95% confidence intervals). The temporal trendsin both the time-dependent model and the bestcovariate model are shown in Fig. 4.

When considering models with single covariates,they all had a very high QAICc, and QAICc weightswere very close to zero. Thus models with 2 covari-ates were clearly better than single-covariate mod-els (Table 1). The best single-covariate model wasthe one with acoustic data of polar cod in EOS,which explained 28% of the variation in adult sur-vival but was 26.32 QAICc units higher than theoverall best covariate model (Table 1). Capelin inBS and the density of Thecosomata in GBLS were

26.71 and 51.21 QAICc units higher, respectively,than the overall best covariate model (Table 1).None of the models that included climatic covari-ates was ever better than models including preydensities. The best climatic model included The-coDec and SST in EOS in the post-breeding season(SSTautEOS) (QAICc = 22.48, R2 = 0.32; Table S3in the Supplement). The models including covari-ates from the breeding season had a much higherQAICc than the best covariate models (QAICc =55.19 and 56.71, R2 = 0.02 and 0.01, for SSTsum-merHorn and BSL1, respectively).

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Fig. 3. Temporal variation of kit-tiwake adult survival rates(solid line; error bars are 95%confidence limits) and the bestcovariates, total biomass ofcapelin in the Barents Sea (greyarea) and detrended Thecoso-mata density (monthly mean no.of ind. for specified area; seeMaterials and methods) in De-cember in the Grand Banks/Labrador Sea area (dotted line)

between 1990 and 2010

Year

1989 1992 1995 1998 2001 2004 2007 2010

Ad

ult

surv

ival

0.5

0.6

0.7

0.8

0.9

1.0No covariatesBest model

Fig. 4. Temporal variation in kittiwake adult survival fromthe time-dependent model with no covariates (d) and themodel with total biomass of capelin in the Barents Sea andThecosomata in the Grand Banks/Labrador Sea in Decem-

ber (s) between 1991 and 2010

Reiertsen et al.: Prey density and kittiwake adult survival

DISCUSSION

The best covariate model explaining the temporalvariation of kittiwake adult survival included thetotal biomass of capelin in the BS and the monthlymean density of Thecosomata in GBLS in December.This suggests that prey densities in the BS and theGBLS area, which both seem to be important non-breeding areas for kittiwakes from Hornya (andmany other kittiwake populations, e.g. Bogdanova etal. 2011, Frederiksen et al. 2012), affect adult survivalof kittiwakes.

Capelin in the Barents Sea

Capelin is one of the most abundant fish species inthe BS (Orlova et al. 2010), and the stocks fluctuategreatly (Ushakov & Ozhigin 1987). During the periodof the present study, the capelin stock collapsedtwice (around 1994 and 2003) (Gjster et al. 2009),and both collapses coincided with a correspondingdrop in the adult survival rate of kittiwakes (Fig. 2).The collapse in 2003 was accompanied by an excep-tional die-off of adult birds in the region in earlyspring (Barrett et al. 2004). Capelin plays a key rolein the pelagic ecosystem of the Barents Sea (Orlovaet al. 2010) and follows a characteristic seasonalmigration pattern (Fauchald et al. 2006, Gjster etal. 2011). It is an important grazer of zooplankton(Hassel et al. 1991, Gjster et al. 2002, Stige et al.2009) and an important food source for Atlantic codGadus morhua, seabirds and marine mammals (Fol -kow et al. 2000, Nilssen et al. 2000, Bogstad &Gjster 2001, Fauchald et al. 2011).

In general, kittiwakes are known to feed on school-ing fish (Lewis et al. 2001, Carscadden et al. 2002),and in the southwest BS (Hornya), capelin is themain prey during the breeding season (Furness &Barrett 1985, Barrett 2007). It has also been shownthat kittiwake breeding success on Hornya de -creases when the abundance of capelin is low (Bar-rett 2007, A. Ponchon et al. unpubl.). Although we donot know in detail the diet of kittiwakes during thenon-breeding season, the results of this study andthose of Barrett et al. (2004) show that the temporalvariation in the BS capelin stock may have a strongimpact on the yearly variation in kittiwake survivalrates. Kittiwakes from Hornya stay in the BS fromMarch to October, i.e. the pre-breeding season, thebreeding season and post-breeding season, whenthey move to EOS. It is likely that the capelin densi-ties in the BS when the kittiwakes return in March

April are critical for their survival. This return coin-cides with the capelin spawning migration to wardsthe coast of Norway and Russia in March April(Gjster et al. 2011), a time when the fish are full ofenergy-rich gonads (Montevecchi & Piatt 1984). Inthe pre-breeding season, the northern BS is still cov-ered with ice and primary production is low, suchthat the seabirds are constrained to feed on mainlypelagic fish such as 1-group herring and capelin(Fauchald et al. 2011). The capelin spawning migra-tion is highly concentrated in space and time(Fauchald et al. 2011), and it is very likely that kitti-wakes are especially sensitive to the abundance anddistribution of this energy-packed fish at this time ofthe year, since they need to build energy reservesprior to reproduction.

After the breeding season, the kittiwakes movequickly to EOS (Figs. 1A & 4C). Capelin distributionsin the post-breeding period show a limited overlapwith the kittiwake distribution (Fig. 4). Acoustic dataof capelin and polar cod from this area in this timeperiod did not, however, correlate with kittiwake sur-vival, which suggests that kittiwakes find other prey,e.g. zooplankton in EOS in the post-breeding period,as also suggested by Gonzlez-Sols et al. (2011).Unfortunately, there are no temporal plankton datafrom EOS that could have been used to test directlywhether conditions from this area also were impor-tant for kittiwake survival. Another implication isthat capelin seems to have the highest impact on kittiwake survival during the pre-breeding and/orbreeding season.

Thecosomata in the GBLS

The other prey covariate which entered the top-rank model was the density of Thecosomata in GBLSin December, a time when most of the kittiwakes fromHornya were present in that area. There is some ear-lier evidence that kittiwakes feed at lower trophic lev-els in winter and that they probably feed more on avariety of large zooplankton species, such as am-phipods, pteropods and euphausiids (Mehlum &Gabrielsen 1993, Karnovsky et al. 2008, Gonzlez-Sols et al. 2011, Frederiksen et al. 2012), than in thebreeding season when their main prey is fish (e.g.Lewis et al. 2001, Carscadden et al. 2002, Barrett2007, A. Ponchon et al. unpubl.). Furthermore, Kar -novsky et al.s (2008) study from the North WaterPolynya showed that kittiwakes there fed extensivelyon the pteropod Limacina helicina and that they hadfatty acid signatures resembling the signatures of the

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little auks Alle alle that feed exclusively on zooplank-ton (Karnovsky et al. 2008). This supports our hypo -thesis that Thecosomata is a likely food for kittiwakes.

Thecosomata is a taxon of pelagic swimming seasnails also called sea butterflies or pteropods. Theyare rich in lipids, live their whole life in a planktonicform, and can be found in large concentrations in theupper layer of the water. They play an important rolein the marine food web, in particular at high latitudes(Comeau et al. 2012), and are important food sourcesfor herring and capelin and for higher predators, suchas whales and seabirds (Hunt et al. 2008, Orlova et al.2011, Comeau et al. 2012). The density of Thecoso-mata in December declined steeply be tween 1990and 2011 (Fig. 2), possibly as a result of ocean acidifi-cation (Lischka et al. 2011). Thecosomata, such as Li-macina helicina, have an aragonitic shell, whichmakes them particularly sensitive to acidification, aprocess that is expected to be most serious in Arcticoceans (Steinacher et al. 2009, Comeau et al. 2012)and which will be more severe when ocean tempera-tures increase (Lischka et al. 2011). Kittiwakes frommost of the breeding colonies in the north Atlanticspend their winter in this area (Frederiksen et al.2012); hence, the steep de cline in Thecosomata abun-dances could be an important driver of the overall de-cline in the North Atlantic kittiwake populations.

SST

Since both the temporal and spatial variance inSST may influence the distribution of prey independ-ent of total density, we used un-lagged SST from thedifferent areas and time periods together with preycategory data in the models but found no apparentdirect effects of SST. Although kittiwakes are verysensitive to changes in the marine system, they arealso highly mobile, especially in the non-breedingperiod, when they are not constrained by the need toreturn regularly to the colony and can easily movearound searching for available food (Fauchald 1999,Pinaud & Weimerskirch 2005, Certain et al. 2011).This could indicate that kittiwakes are more sensitiveto overall prey density as such and not to small-scalechanges in the spatial distribution of prey linked tooceanographic changes. Increasing SST, however,could also result in a vertical shift of preys in thewater masses (i.e. prey move to deeper and colderwater), thus making them inaccessible to kittiwakesthat cannot dive, even if prey densities are high.

None of the models that included any climaticcovariates (SST and NAO) could compete with the

best models in the model selection. This may indicatethat kittiwakes are not directly affected by the cli-mate factors in these areas and that their abilities tomove around may buffer this effect. Additionally, thismay give some support to the explanation that kitti-wakes are more sensitive to overall prey densities inthese areas than to the spatial changes of the prey.

None of the breeding-season covariates (i.e. breed-ing success or SST in the summer near the colony)could explain any temporal variability in adult sur-vival, which is in contrast to other studies (Oro & Fur-ness 2002, Frederiksen et al. 2004). However, asstressed by Frederiksen et al. (2004), identifying fac-tors outside the breeding season was very difficult atthat time because of the limited knowledge of kitti-wakes non-breeding distribution, and the impor-tance of the conditions in the non-breeding areascould not be ruled out as an explanation of the adultsurvival variability.

CONCLUSIONS

This study demonstrates the importance of linkingthe knowledge of the winter distribution of seabirdsand density of the prey they feed on in their winter-ing areas to temporal variation of adult survival rates.Although it has been suggested that adult survivalmay be primarily influenced by prey densities andoceanographic conditions in the wintering area (Gas-ton 2003, Daunt et al. 2006, Wanless et al. 2007, Fred-eriksen et al. 2008a, Harris et al. 2010), lack of dataon migration patterns and the whereabouts of impor-tant wintering areas has precluded the search forcausal explanations. This study is among the first toexamine the influence of prey densities in non-breeding areas on the survival of seabirds. The studyis highly exploratory and based on scarce loggerheaddata, but it suggests that Thecosomata in the GBLSand capelin in the BS are important prey species forkittiwakes in the non-breeding season. This needs tobe tested further. Both the BS and the GBLS areimpacted by intense an thropo genic activities, such asfisheries, international shipping and the offshore oiland gas industry, with several reports of operationaland accidental discharges of hydrocarbons (Hedd etal. 2011). All these activities pose risks to seabirds,and the knowledge of how important these areas areto a species that is globally declining has strongimplications for conservation and management ofboth these areas.

The migration part of our study is based on only3 yr of data and a rather low number of tracked

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individuals. There is therefore a strong need for alarger sample of logger data over more years toexamine the stability and evidence of wintermigration patterns. However, although the samplesize from Hornya is small, there is evidence thatbirds from several populations return to the sameareas in different years (Frederiksen et al. 2012, B.Moe et al. unpubl., A. Ponchon et al. unpubl.).Also, the time series of CMR data is 22 yr long,and since we found such strong correlationsbetween the survival rate and the covariates fromthe main areas revealed by migration data, it isplausible that there is some stability in the migra-tion pattern. A challenge for future studies ofdemography and causal mechanisms will be tomap both the stability of wintering areas over timeand not only the density of prey, but also the spa-tial distribution of the prey in different areas. Suchinformation is essential for the conservation andmanagement of any seabird, including the kitti-wake, a species that is declining globally.

Acknowledgements. We thank all the fieldworkers whohave read colour rings over the years. The NorwegianCoastal Administration is thanked for the use of the light-house on Hornya as a base for the fieldwork. This studywas funded by the University of Troms and the Norwe-gian Directorate for Nature Management. Since 2005, thestudy has also been financed through the Norwegian Sea-bird Program (SEAPOP). The work was also partly sup-ported by programme no. 333 of the French Polar Institute(IPEV) and the Norwegian Research Council (project no.216547/E40 to K.E.E.). We also thank Marit Reigstad forvaluable input on the ecological importance of the hotspot area east of Svalbard, and Karen McCoy and VictorGarcia Matarrantz for help in the field. We acknowledgecomments from 2 anonymous referees that helped improvethis article.

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Editorial responsibility: Rory Wilson, Swansea, UK

Submitted: April 18, 2013; Accepted: April 11, 2014Proofs received from author(s): August 13, 2014

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