Levels of Persistent Organic Pollutants (POPs) and Metals in Breeding Kittiwakes (Rissa tridactyla) from Kongsfjorden, Svalbard

Jose M. Castaño Ortiz

Grau de Biologia Department of Biology (NTNU) and Departament de Biologia Animal (UB) Supervisors: Veerle Jaspers (NTNU), Carolina Sanpera and Lluís Jover (UB) Submission date: February 2016

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Abstract This study investigated the levels of metals (Hg, Se, Cd, Pb, As), polychlorinated

biphenyls (PCBs), organochlorinated pesticides (OCPs) and brominated diphenyl

ethers (PBDEs) in female black-legged kittiwakes (Rissa tridactyla). Blood samples

were collected from adult birds within the same week of the chick-rearing period at two

breeding colonies (Blomstrandhalvøya and Krykjefjellet) in Kongsfjorden, Svalbard.

PCBs was the major organic pollutant class in plasma samples from

Blomstrandhalvøya (n=14, median 18.9 ng·ml-1, range 10.6-33.6 ng·ml-1) and

Krykkjefjellet (n=11, median 27.2 ng·ml-1, range 11.2-70.2 ng·ml-1). Selenium was the

most concentrated metal in red blood cells from Blomstrandhalvøya (n=14, median

52.3 µg·g-1 dw, range 24.8-79.4 µg·g-1 dw) and Krykkjefjellet (n=11, median 43.5

µg·g-1 dw, range 34.5-56.4 µg·g.1 dw). Significant differences in pollutant levels were

not found between the study colonies. Insights into foraging ecology were provided by

stable isotope analysis (δ15N, δ13C) of kittiwake blood and regurgitates. The estimated

trophic level ranged from 3.4 to 4.0, and did not significantly differ between study

colonies. Neither did the origin of the carbon source (δ13C) or the body condition of the

two kittiwake groups. Altogether, similar trophic levels, feeding habitats and body

condition are consistent with the lack of observed differences in pollutant levels

between colonies.

Acknowledgements This bachelor’s thesis has been conducted at the Department of Biology at the

Norwegian University of Science and Technology (NTNU) in collaboration with the

Departament de Biologia Animal at Universitat de Barcelona (UB). It has been written

under the supervision of Dr. Veerle Jaspers (NTNU), Dr. Lluís Jover (UB) and Dr.

Carolina Sanpera (UB).

Firstly, I would like to thank Veerle for letting me participate in this project. Thank you

for your attention, guidance and positivity throughout the process. It was great to be

part of the Bird Ecotoxicology group for a few months. Thanks to Syverin Lierhagen for

sharing his knowledge during the analysis of metals. Thanks to Nathalie Briels and

Grethe Stavik for their collaboration during the extraction of POPs. I am also grateful

to Igor Eulaers for his helpful comments, and to Solveig Nilsen for her detailed

description of the field work. Thanks to all members of the group because they all

contribute to create a good work environment. Adrian Covaci and Malarvannan

Govindan, from the University of Antwerp, thank you very much for the identification

and quantification of POPs during Christmas holidays. I acknowledge Geir Gabrielsen

and Martin Kristiansen, from the Norwegian Polar Institute, for their support in

processing the diet samples. I am equally thankful to Per Ambus from the CENPERM

at Copenhagen University for his collaboration with the stable isotope analysis of blood

and diet samples.

To my supervisors at UB, Lluís and Carolina, thank you for providing valuable feedback

and advice. Special thanks to Lluís for the provided statistical support, I highly

appreciated your criticism and suggestions.

To my family, thanks for making my stay at NTNU possible and being supportive all

the way. And last but not least, thanks to my friends in Trondheim for the good times

that we had. Amine, Fabio, Jorge aka Silver, J. Gustavo and others, your friendship

was greatly appreciated!

Table of contents

1. Introduction ................................................................................................... 1

1.1 The Arctic: an extreme environment for wildlife .......................................... 1

1.2 The Arctic as a sink for transported pollutants ............................................. 1

1.3 Pollutants considered in this study .............................................................. 3

1.3.1 Persistent organic pollutants (POPs) .................................................. 3

1.3.2 Metals ................................................................................................ 6

1.4 Bioaccumulation and biomagnification in Arctic food chains ........................ 8

1.5 Pollutant exposure in kittiwakes during the Arctic breeding season ............. 9

1.6 The use of stable isotopes as dietary tracers ............................................... 9

1.7 Objectives of the study .............................................................................. 10

2. Materials and methods ............................................................................... 11

2.1 Study area ................................................................................................. 11

2.2 Study species ............................................................................................ 12

2.2.1 The pelagic food web of Kongsfjorden ............................................. 13

2.3 Sampling methods..................................................................................... 13

2.4 Sex determination ..................................................................................... 14

2.5 Body condition ........................................................................................... 15

2.6 Contaminant analysis ................................................................................ 16

2.6.1 Persistent organic pollutants (POPs) analysis .................................. 16

2.6.2 Metal analysis .................................................................................. 17

2.7 Stable isotope analysis (SIA) ..................................................................... 18

2.7.1 Trophic level calculations ................................................................. 19

2.8 Statistical analysis ..................................................................................... 19

3. Results ........................................................................................................ 20

3.1 Sex determination ..................................................................................... 20

3.2 Body condition ........................................................................................... 21

3.3 Levels of pollutants.................................................................................... 21

3.3.1 Metals .............................................................................................. 21

3.3.2 Persistent organic pollutants (POPs) ................................................ 22

3.4 Relationship between body condition and pollutant levels ......................... 24

3.5 Stable isotope analysis (SIA) ..................................................................... 25

4. Discussion .................................................................................................. 26

4.1 Discriminant analysis for sex determination ............................................... 26

4.2 Levels of pollutants in kittiwakes from Kongsfjorden, Svalbard .................. 26

4.2.1 Metals .............................................................................................. 26

4.2.2 Persistent organic pollutants (POPs) ................................................ 27

4.3 Comparison of the study colonies .............................................................. 28

4.4 Influence of foraging ecology on pollutant levels ....................................... 29

5. Conclusions ................................................................................................ 30

6. References .................................................................................................. 32

7. Appendices ................................................................................................. 43

7.1 Appendix I: Morphometric data .................................................................. 43

7.2 Appendix II: Sex determination .................................................................. 45

7.3 Appendix III: POPs data ............................................................................ 46

1

1. Introduction The Arctic has typically been considered a pristine area, little affected by the human

activity at lower latitudes. However, environmental concern regarding pollution in the

Arctic has risen lately. Marla Cone, in her publication "Silent Snow: The Slow Poisoning

of the Arctic" (Cone, 2005), describes the Arctic Paradox. It refers to people living in

the Arctic, who are the most contaminated, despite the fact that they live far away from

any source of pollution. Early references, such as Nares in 1875, already speak of the

occurrence of a slight haze over the northern horizon which made the distant land

indistinct. A hundred years later, this phenomenon was mostly attributed to long-range

transport of pollution (Shaw and Rahn, 1982). At present, research in ecotoxicology is

continuously providing evidence that the Arctic environment and its wildlife are also

affected by global scale pollution (PAME, 2013; Letcher et al., 2010)

1.1 The Arctic: an extreme environment for wildlife

The living organisms in the Arctic are subject to harsh climatic conditions. Variation in

light and temperature, short summers, limited primary production, permafrost,

extensive snow and ice cover in winter are among the attributes that define the tough

Arctic environment (AMAP, 1998). Arctic marine ecosystems typically host simple food

webs, with few species serving as prey for a particular predator (Muir et al., 1992).

Arctic animals are adapted to this extreme environment and many rely on seasonal

deposition of thick layers of subcutaneous fat to make it through the winter (Blix, 2005).

All together makes the Arctic a tough environment for survival, development and

reproduction (Strathdee et al., 1998). Arctic organisms are more likely to be sensitive

to man-induced changes such as pollution and climate change than are those in more

temperate or tropical biomes (Poland et al., 2003). Recent changes in temperature,

snow, ice-cover and nutrient availability may have a major repercussion on biological

dynamics in the Arctic (Post et al., 2009). To this harsh and uncertain scenario of

extreme living conditions and climatic changes, we have to add the hazardous effects

of pollutants in the Arctic. Marine pollution was internationally defined as the

introduction by man, directly or indirectly, of substances and energy into the marine

environment resulting in deleterious effects on living organisms (GESAMP, 1986).

1.2 The Arctic as a sink for transported pollutants

The major pollutants of concern to the Arctic are trace metals and organic pollutants,

where the first are of natural origin and the second are man-made. Sources of these

potentially toxic pollutants are mainly outside the Arctic, although they are often

2

detected in northern waters, snow, air and wildlife (Barrie et al., 1992). The Arctic

contains military bases, mining operations, research stations and small settlements as

examples of local active sources of contamination, but it is mostly affected by long-

range transport (LRT) of pollutants associated to industrial activity at lower latitudes

(Poland et al., 2003; Kallenborn et al., 2007). LRT of pollutants is a concerning

phenomenon that involves use and emission of chemicals in the most populated areas

of the northern hemisphere, and transport through different environmental

compartments to the Arctic (Franklin, 2006). The geographical location and cold

climate make the Arctic behave as a sink for pollutants that are spread around the

world through major pathways: atmosphere, ocean, rivers and ice (AMAP, 1998).

Atmospheric circulation is the fastest and most direct route from distant sources of

pollution, transporting pollutants from lower latitudes to the Arctic within days

(Macdonald et al., 2000). The volatilization of chemicals in warm mid-latitude locations

and condensation in the cooler Arctic environment may lead to above-expected levels

of pollutants in the Arctic (Blais et al., 1998). This process is typically repeated in hops

across different latitudes, and is known as global distillation or the “grasshopper effect”

(Fig. 1) (Wania and Mackay, 1996). According to this, volatile (mercury) and semi-

volatile (persistent organic pollutants - POPs) chemicals will undergo LRT, through

repeated cycles of volatilization and partitioning to condensed phases, condensing at

Figure 1. The fraction transported by atmospheric transport from source regions to the Arctic is higher

in relatively high volatile than in less volatile compounds (Semeena and Lammel, 2005). Figure

design: modification of a figure provided by Veerle Jaspers and further adapted.

3

different temperatures and latitudes according to their volatility (Franklin, 2006).

Although the atmosphere is crucial for fast pollutant transport, it contains a relatively

low amount of pollutants (Gregor et al., 1998).

The ocean currents can redistribute contaminated water both horizontally and vertically

(Wania et al., 1995) and pollutants can reach the Arctic within years (AMAP, 2004).

The Beaufort Gyre and the Transpolar Drift are major currents responsible for the main

surface circulation within the Arctic (UNEP, 2000), whereas locations of deep-water

formation account for vertical mixing of surface-inserted pollutants (Booij et al., 2014).

Non-soluble organic pollutants may bound onto plastic particles from sea water, and

plastic litter can thus transport associated pollutants (Zarfl and Matthies, 2010).

Although atmospheric LRT is thought to be the major pathway by which conventional

POPs enter the Arctic, some emerging chemicals (e.g. perfluoroalkyl and

polyfluoroalkyl substances - PFASs) may be more likely transported by ocean currents

(Lohmann et al., 2007). Riverine input is an important source of pollutants to the Arctic

Ocean too, through north-flowing large rivers that collect significant amounts of

pollutants as they trickle through vast farming and industry areas (Barrie et al., 1992).

Drifting sea ice, formed in shallow regions, also contributes to the redistribution of

pollutants. (Pfirman et al., 1995). Although the biologically mediated movement of

pollutants is less studied, it is feasible that gregarious animals that bioaccumulate

(dietary accumulation of organic pollutants in lipid-rich tissues) and biomagnify

(increasing levels of organic pollutants with trophic level) pollutants, and then migrate

and congregate, may sometimes represent an important pathway for pollutant

transport (Gregor et al., 1998). These animals cover long distances on their migratory

routes, crossing international boundaries and linking industrialized and remote regions

(Gregor et al., 1998). For instance, Evenset et al. (2002) reported contaminated fish

and sediments on Lake Ellasjøen (Bjørnøya, Svalbard) due to deposited seabird guano

into the freshwater ecosystem.

1.3 Pollutants considered in this study

1.3.1 Persistent organic pollutants (POPs)

As defined in the Criteria for identification of new persistent POPs under the Stockholm

Convention (UNEP, 2001), POPs are lipophilic compounds which have the potential

for persistence in the environment, LRT and bioaccumulation. They may consequently

cause adverse effects to human health or to the environment. POPs include

intentionally produced chemicals currently or once used (e.g. industry and agriculture),

and unintentionally produced chemicals (e.g waste from industrial activities) (EPA,

4

2002). Cold conditions, occurrence of top predators and storage of lipids promote

bioaccumulation and biomagnification of POPs in the Arctic (AMAP, 2004). Arctic

indigenous communities, which rely on traditional diets of fish and large marine

mammals, are exposed to high levels of pollutants (Dudarev, 2012). As a consequence

of restrictions, temporal trends of legacy POPs seem to be decreasing in Arctic biota

(Rigét, 2010), but persistence and inputs due to geographical redistribution may

sometimes maintain concentrations at a steady state and blur decreasing patterns

(Aguilar et al., 2002). The POPs studied in this thesis include:

Polychlorinated biphenyls (PCBs). Because they are chemically stable and heat

resistant compounds, PCBs were once extensively used as transformer and capacitor

oils, hydraulic and heat-exchange fluids, lubricating oils and as plasticizers in joint

sealants (de March, 1998). There are 209 PCBs congeners, with different substitutions

on the biphenyl rings that influence their physical and biological activity (AMAP, 2004).

Since their early discovery in the 1960s, many studies have reported bioaccumulation

of PCBs in the Arctic biota, including mammals (Letcher et al., 2010; Wolkers et al.,

1999) and seabirds (Barret et al., 1996; Borgå et al., 2005). Endocrine disruption,

uncoupling of mitochondrial oxidative phosphorylation, uncontrolled cellular

proliferation (Vallack et al., 1998), suppressed immunity (Grasman et al., 1996) and

reproductive failure in wild birds (Fisk et al., 2005) are among the effects of PCBs.

Dichlorodiphenyltrichloroethane (DDT) is a chlorinated organic pesticide that,

although it was already banned by circumpolar countries three decades ago, continues

to be used for pest control in some developing regions (AMAP, 2004). DDT is usually

Polychlorinated biphenyls (PCBs) Dichlorodiphenyltrichloroethane (p,p’-DDT)

Hexachlorobenzene (HCB) Polybrominated diphenyl ethers (PBDEs)

Figure 2. General structure of the main investigated POPs. Source: Agency for Toxic Substances

and Disease Registry (ATSDR)

5

converted into metabolites dichlorodiphenyldichloroethylene (DDE) and

dichlorodiphenyldichloroethane (DDD) in the environment, which have the potential to

accumulate in fatty tissues of fish, birds and mammals (de March, 1998). The decrease

in the total release of DDT has been linked with a progressive increase of the

metabolized forms DDD and DDE (Aguilar et al., 2002). Exposure to high levels of DDT

and metabolites may lead to suppressed immune function (Gabrielsen, 2007),

endocrine disruption (Macdonald and Bewers, 1996), eggshell thinning and

reproductive failure (Poland, 2003) in birds.

Hexachlorobenzene (HCB) was widely used as a fungicide on grain seeds until the

late 1970s and is currently released as a by-product of industrial processes (e.g.

production of solvents and currently used pesticides, and incineration of chlorinated

waste material) (US EPA, 2000). Due to its relatively high lipophilicity and long half-life

in biota (Mackay et al., 1992), as well as its chemical stability and resistance to

biodegradation, HCB is considered a very persistent environmental pollutant (EPA,

2000). The major health hazards are porphyria and effects on reproduction and

immune system (de March et al., 1998). In glaucous gull (Larus hyperboreus) with high

levels of HCB, for instance, immune response was significantly lower (Bustnes et al,

2004). HCB also became a cause of concern for human health when fatal cases of

infant and adult poisoning were reported in the past, due to ingestion of highly

contaminated human milk and bread made of HCB-treated wheat (Sonawane, 1995).

Polybrominated diphenyl ethers (PBDEs) are structurally similar to PCBs, but with

bromine substitution instead of chlorine and an extra oxygen between the phenyl rings

(de March et al., 1998). They are used as flame retardants (FRs), by being added to

plastic-containing products to make them difficult to burn and to slow down burning

rates (Harley et al., 2010). Although there are different types of FRs, such as

chlorinated or phosphorus-containing, brominated flame retardants (BFRs) are popular

in the market because of their low cost and high efficiency (Birnbaum and Staskal,

2004). As they are not covalently bound to the polymer matrix (Vuong and Webster,

2015), they are easily released to the environment during degradation (ATSDR, 2004).

Levels of PBDEs seem low in comparison to legacy POPs, but several studies have

reported significant accumulation in Arctic biota (Braune et al., 2005; de Wit et al.,

2010). Disruption of thyroid hormones, neurological and developmental effects, as well

as cancer in laboratory animals, are among the potential health risks associated to

PBDE exposure (Verreault et al., 2005). Current-use BFRs alternatives to PBDEs are

tetrabromobisphenol A (TBBPA) and hexabromocyclododecane (HBCD) (Covaci et

6

al., 2006). HBCD is an additive FR that readily leaks into the environment and its

occurrence has already been reported (Covaci et al., 2006; Sun et al., 2012). In

contrast, TBBPA is a reactive BFR that binds covalently to the matrix and is therefore

less leachable (Covaci et al., 2009).

1.3.2 Metals

Naturally occurring concentrations of metals are usually low and vary among

geological areas, but large loads have been emitted since the beginning of the

industrial era (Zaborska, 2014). This may lead to high levels that affect marine biota

and seafood consumers (Gong and Barrie, 2005). Industrial activities are responsible

for the release of metals into the environment (AMAP, 2004), while LRT of metals

attached to aerosols may account for the enrichment of concentrations far from source

regions (Pacyna and Winchester, 1990). Environmental effects in the Arctic are

strongly influenced by the mobility of each metal through environmental compartments

(Dietz et al., 1998). The intake and ecological transfer of metals depends, for instance,

on the bioavailability of the different metal species (Grotti et al., 2013). Johansen et al.

(2000) found that Greenlanders, through their traditional seafood diets, are exposed to

high intakes of cadmium and mercury. The metals studied in this thesis include:

Mercury (Hg). The elemental form of mercury Hg(0) is emitted to the atmosphere and

can undergo LRT because it has a long (∼1 yr) atmospheric residence time (Selin,

2009). Coal combustion, non-ferrous metal and cement production, and waste

incineration are the main anthropogenic activities that release Hg to the atmosphere

(Streets et al., 2005). Hg(0) can react with oxidants in the atmosphere and be

transformed into reactive gaseous mercury (RGM) (Steffen et al., 2008), rapidly

deposited in water bodies through seasonal Hg depletion events, enhanced by arctic

conditions (Schroeder et al., 1998). Anaerobic sediments in aquatic systems allow the

conversion of this oxidized form into the organic methylmercury (MeHg) (Selin, 2009).

Although Hg(0) has low toxicity and is unavailable to the food web (Jaeger, 2009),

MeHg is toxic, lipophilic and readily taken up and accumulated in aquatic organisms

(Dietz, 1998). Higher trophic level (TL) species are particularly exposed to the organic

MeHg through their diet (Kirk et al., 2012). In seabirds, excretion of Hg occurs mainly

by egg laying (egg white) and molting (Dietz et al., 1998). Growing feathers are

responsible for sequestration of up to 93% of the body burden of Hg (Braune and

Gaskin, 1987).

7

Selenium (Se) is an essential nutrient that needs to be physiologically regulated to

satisfy nutritional needs and guarantee the normal functioning of necessary

biomolecules like catalytic enzymes (Daniels, 1996). Regulation is important because

Se has a narrow range of dietary concentrations providing adequate but nontoxic

amounts (Øverjordet et al., 2015b). A protective effect of Se against toxicity of Hg was

found a few decades ago (Pařízek and Ošt’ádalová, 1967). Se-containing compounds

bind to the MeHg that is taken up through diet, and may give rise to MeHg detoxification

via demethylation and production of biochemically inert solids (Khan and Wang, 2010).

Significant correlations between Hg and Se in Arctic seabirds suggests a role of Se in

Hg detoxification (Koeman et al., 1975; Campbell et al., 2005; Øverjordet, 2015b).

Organic Se (selenide and selenomethionine), may also cause hazardous effects (e.g.

reproductive impairment, duckling growth and survival, mortality) when present above

toxic thresholds (Ohlendorf and Heinz, 2009; Spallholz and Hoffman, 2002).

Lead (Pb) is a naturally occurring heavy metal that has dramatically increased in

concentration since the beginning of the industrial era (Zaborska, 2014) due to its many

industrial applications (e.g shot for shotguns, combustion of leaded gasoline, metallic

items, oil spills) (Komárek et al., 2008). Leaded petrol was the main source of emission

and environmental exposure to Pb (Landrigan, 2002) until the early 2000s, when

increasing concern led to the regulation and phase-out of leaded gasoline in developed

regions (Zaborska, 2014). Although exposure to Pb still affects developing regions

(Tong et al., 2000), decline in its commercial usage has been a key contributor to the

decrease of Pb in different environmental compartments (Singh et al., 2006). Although

it has lately declined in the Arctic (CACAR, 2003), Pb concentrations above thresholds

for human consumption have been detected in many Arctic biota (Muir et al., 1999).

Local sources of Pb, such as oil spills or ingested shots, can lead to sublethal or lethal

effects in seabirds (Flint et al., 1997)

Cadmium (Cd) is a widespread non-essential and toxic metal whose emissions to the

atmosphere come from both natural (e.g. windblown dust and volcanoes) and

anthropogenic sources (e.g. coal combustion, by-product of Cu-Ni-Zn production,

refuse incineration, cement manufacture) (Barrie et al., 1992). Anthropogenic

emissions have lately declined (OSPAR, 2010) and available data also suggest

declines in Cd deposition in the Arctic (Li et al., 2003). Once transported on aerosol

particles to remote areas, Cd may accumulate in lower TLs and biodilute through the

marine food web. Biodilution is the decrease of concentrations with increasing TL

(Campbell et al., 2005). Particularly high levels of Cd have been found in common

8

eiders (Somateria mollissima), kittiwakes (Rissa tridactyla) and Arctic terns (Sterna

paradisaea) feeding on invertebrates (Savinov et al., 2003). However, biodilution of Cd

remains unclear because some studies have not found such trend (Macdonald and

Bewers, 1996; Øverjordet et al., 2015b). Cd tends to accumulate in soft tissues, such

as kidneys and liver (Barrie, 1992), and might be responsible for deleterious effects in

seabirds (Eisler, 1985). Metal-binding proteins (e.g. metallothionein) (Liu et al., 1991)

and interactions with essential metals (e.g. Ca, Zn) (Moulis, 2010) could be important

mechanisms against Cd toxicity.

1.4 Bioaccumulation and biomagnification in Arctic food chains

Pollutants end up entering pelagic and benthic food webs. While in the case of metals

this may typically lead to accumulation in proteinaceous tissues (e.g. feathers, hair,

muscle, egg white), POPs tend to accumulate in lipid-rich tissues (Muir et al., 1992).

The following mechanisms, as originally described by Macek et al. (1979), are key to

understand the interaction of chemicals with aquatic biota:

"Bioconcentration refers to that process whereby chemical substances enter aquatic

organisms through the gills or epithelial tissue directly from the water. Bioaccumulation

is a broader term referring to a process which includes bioconcentration but also any

uptake of chemical residues from dietary sources. Finally, biomagnification refers to a

process by which the tissue concentrations of bioaccumulated chemical residues

increase as these materials pass up the food chain through two or more trophic levels.”

Marine organisms uptake pollutants directly from the water or through the food chain.

Food intake is the major pathway for bioaccumulation of pollutants in aquatic mammals

and birds (Walker et al., 2012). Once a pollutant enters a bird, it becomes available for

possible biotransformation and elimination through molting or egg laying (Newman,

2009). Dietary accumulation is due to low concentrations of chemicals in water, high

bioconcentration, and low rates of elimination in predators (Braune et al., 2005).

Dietary accumulation leads to an increase in pollutant concentrations with increasing

TL (Jæger et al., 2009). Bioaccumulation into organisms and biomagnification along

the food chain have led to high levels of pollutants and vulnerability in top predators

(seabirds and marine mammals), whose lipid-rich tissues readily concentrate POPs

(Vallack et al., 1998). Negative effects of pollution on adult survival (Erikstad et al.,

2013), pollutant-associated immunosuppression (Grasman et al., 1996) or

reproduction impairment through endocrine disruption (Tartu et al., 2014) may turn out

to be a threat to populations of these long-lived Arctic predators.

9

1.5 Pollutant exposure in kittiwakes during the Arctic breeding season

The black-legged kittiwake (Rissa tridactyla) is the study species of the present thesis.

Biological changes during its breeding season may lead to an increased exposure to

pollutants. The long breeding season of seabirds is characterized by periods of fasting

during incubation and energetically demanding during chick-rearing (Jacobs et al.,

2011). Significant reductions in body condition have been reported in kittiwakes during

the chick rearing period (Moe et al., 2002). Kittiwakes may experience a decrease in

body mass of almost 20% from pre-breeding to late chick rearing, due to reproductive

stress (Henriksen et. al, 1996). A considerable proportion of the kittiwake's stored lipids

is mobilized to active organs during reproduction (Henrisken et al., 1996), which

invariably causes the decrease in body mass (Landys et al., 2006). This high metabolic

activity leads to increased blood concentrations of POPs, previously stored in lipid-rich

tissues. Nordstad et al. (2012) found that POPs increased whereas body mass

decreased progressively from the pre-laying to the incubation and chick rearing period

in kittiwakes from Kongsfjorden, Svalbard. It therefore seems that negative effects of

POPs are likely to occur during reproduction (Bustnes et al., 2010). Although

thresholds levels for effects in captive animals are often used to estimate effects in wild

birds (AMAP, 2004), they should be used with caution because free-ranging birds,

unlike captive animals, are exposed to a more complex cocktail of pollutants

(Gabrielsen, 2007).

1.6 The use of stable isotopes as dietary tracers

Stable isotopes of nitrogen are often used to estimate TL, because heavy 15N is

enriched relative to lighter 14N in predators (Hobson and Welch, 1992; Minagawa and

Wada, 1984). Relative abundances of 15N in seabirds thus provide a continuous

signature (δ15N) that is useful as a quantitative approach to TL (Braune et al., 2005).

Similar to nitrogen, the isotopic signature of carbon (δ13C) provides a useful time-

integrated tool in foraging ecology (Hobson and Welch, 1992). Unlike nitrogen, 13C is

very slightly enriched (<1‰) with TL relative to its lighter isotope (12C) and does

therefore not serve as a TL estimator (Post, 2002; Hobson and Welch, 1992). However,

it does indicate the origin of carbon sources, because it is assumed that different

carbon sources will have distinct signatures (Post, 2002). δ13C values have been

commonly used to differentiate between seabirds foraging in pelagic versus

inshore/benthic food chains, because seabirds that feed benthically/inshore are

enriched in 13C relative to those that feed in pelagic environments (Hobson, 1993).

10

Similarly, consumers of terrestrial-derived carbon are usually more enriched in 13C than

consumers in marine phytoplankton food webs (Romanuk and Levings, 2005). The

use of stable isotope values of nitrogen (δ15N) and carbon (δ13C) as dietary tracers

may therefore provide a useful approach into foraging ecology (Ricca et al., 2008).

The changes in the relative abundances of isotopes along the food webs are known

as isotopic fractionation (Faure and Mensing, 2005). When animals process their food,

they make a metabolic difference between isotopes, being the heavier ones more

readily retained in their tissues (Criss, 2008). This normally leads to higher ratios of

15N to 14N and 13C to 12C in animal tissues compared to those in their diet, which turns

out in a trophic enrichment along food webs (Tibbets et al., 2008). The difference in

isotopic composition between a consumer’s tissue (δXtissue) and its diet (δXdiet) is called

trophic enrichment factor. In the case of N, pronounced enrichment is due to

preferential use of lighter amine groups during deamination and transamination

processes (Macko et al., 1986). Stable isotope ratios in tissues reflect the isotopic

signature of a particular diet, and the extent of the integrated period will partially

depend on the turnover rate of the sampled tissue (Hobson and Clark, 1992). Turnover

rates refer to the amount of time that tissues take to change to a novel isotopic

signature after a diet-switch (Martinez del Rio and Wolf, 2005). According to this, high

turnover tissues (e.g. liver, plasma), which are replaced rapidly relative to lower

turnover tissues (e.g. red blood cells, muscle, fat), are short-term dietary indicators

because they integrate isotopes incorporated in the recent past (Hobson and Clark,

1993; Foglia et al., 1994). Before the use of stable isotopes in dietary analysis, the

conventional approach to what an animal eats was exclusively based on direct

observations and stomach content analysis. Due to observer fatigue and differential

digestibility of different foods, these methods are time consuming and subject to biases

(DeNiro and Epstein, 1978). Soft-bodied prey, for instance, are underestimated when

analyzing regurgitates due to their rapid digestion (González-Solís et al., 1997).

1.7 Objectives of the study

When assessing pollutant exposure in wildlife, predators are appropriate as study

species because they are long-lived, feed high up on the food web, and are therefore

vulnerable to TL-related exposure to some pollutants. The present study aims to

investigate the levels of metals and POPs in kittiwakes from Svalbard by non-

destructive sampling of blood. This thesis is meant to be the follow-up of a MSc thesis

(Svendsen, 2015) that focused on the same kittiwake breeding colonies (Jul-Aug

2014). In that study, significant differences in the concentrations of plasma POPs were

11

found between the colonies (Blomstrandhalvøya and Krykkjefjellet). It was suggested

that higher levels of POPs in Blomstrandhalvøya were due to lower prey availability

and body condition of kittiwakes. Svendsen (2015) recommended that future research

should evaluate whether this interesting difference between colonies holds in time.

Insights into foraging ecology, through stable isotope analysis, were also

recommended for further investigation. In our study, blood from only female breeding

kittiwakes at both colonies was sampled within the same week, to avoid variation in

pollutant concentrations due to sex, breeding status, tissue or sampling date.

The main objectives of this study are: (1) to identify and quantify the target pollutants

(metals and POPs) in blood samples, (2) to compare concentrations in two different

kittiwake breeding colonies and (3) to take into consideration potential explanatory

factors (body condition, foraging ecology). It is hypothesized that most studied metals

and POPs will be similarly detected in Krykkjefjellet and Blomstrandhalvøya, because

foraging ecology and pollutant exposure are expected to be similar in neighboring

kittiwakes sampled at the same period. Furthermore, it is suspected that body condition

will be negatively correlated with pollutant levels in kittiwakes.

2. Materials and methods

2.1 Study area

The field work was carried out in July 2015 and sampling was conducted in two

different kittiwake breeding colonies: Krykkjefjellet (78° 59’ N, 12° 07’ E) and

Blomstrandhalvøya (78° 54’ N, 12° 13' E), both located in Kongsfjorden and relatively

close to Ny-Ålesund research station, which provides facilities for researchers (Fig. 3).

Kongsfjorden is a glacial fjord in the northwest coast of Spitsbergen (Svalbard

archipelago, Norway) that is influenced by Atlantic and Arctic water masses (Hop et

al., 2002b). Eleven and fifteen adult birds were sampled for this study in Krykkjefjellet

and Blomstrandhalvøya respectively, all within the same week in mid-July (15/07/2015

- 18/07/2015). Although these colonies are mainly inhabited by kittiwakes, breeding

northern fulmars (Fulmarus glacialis) are present at Blomstrandhalvøya, whereas

black guillemots (Cepphus grylle) also breed at Krykkjefjellet (Svendsen, 2015).

12

2.2 Study species

The black-legged kittiwake (Rissa tridactyla) is the commonest gull in the world and

the most oceanic in its habits (Strøm, 2006), and it is widely distributed in the

circumpolar Arctic (Hatch et al., 1993). Around 500000 pairs breed along the coastline

in the Barents region, and Svalbard accounts for approximately half of the Barents

breeding population (Gabrielsen, 2009). Kittiwakes are medium-sized gulls which can

be easily recognized by completely black wing tips and legs, grey upper sides of wings,

yellow bill and white head, neck and belly (Strøm, 2006). They return from southern

wintering areas to breeding colonies in Svalbard in April (Gabrielsen, 2007). Unlike

auks (Alle alle) and guillemots, kittiwakes are obligate surface-feeding seabirds and

mostly obtain food from the top meter of sea surface (Coulson, 2011). In the high Arctic,

polar cod (Boreogadus saida) is their dominant food source, but small crustaceans –

e.g. amphipods (Themisto spp.) and krill (Thysanoessa spp.) – may occur in large

amounts during the breeding season and become a substantial part of their diet

(Coulson, 2011; Mehlum and Gabrielsen, 1993). They breed colonially on narrow cliffs

along the coast (Porter, 1990). In the study area, nests are made out of plant material

held together with feces and are placed in steep rock outcrops near the sea (Svendsen,

2015). Egg laying takes place in June and the female typically produces 2 (1-3) eggs.

The eggs are incubated for ~25 days and the chicks are fed regurgitated food (Strøm,

2006) by both parents for 5-6 weeks, until they are completely developed (Gabrielsen,

2009). Although some birds may undertake very long trips and forage up to halfway

between Svalbard and Greenland (Claus Bech, personal communication), it seems

Figure 3. The two study colonies (black circles) are located in Kongsfjorden, a glacial fjord on the west

side of the Svalbard archipelago. Map source: TopoSvalbard (Norwegian Polar Institute, 2015)

13

that during the chick rearing adults tend to forage closer to the colony than earlier in

the reproductive period (Robertson et al., 2014).

2.2.1 The pelagic food web of Kongsfjorden

The review of the marine ecosystem in Kongsfjorden by Hop et al. (2002b) gives a

good approach to the pelagic food web in the study area. The primary production in

Kongfjorden is markedly seasonal, and diatoms and dinoflagellates are predominant

primary producers. The zooplankton community is mainly represented by copepods

(Calanus spp., Pseudocalanus spp.), amphipods and krill. Amphipods, planktivorous

fish (e.g. polar cod and capelin -Mallotus villosus) and little auks are among second-

order consumers. The higher TLs are dominated by marine mammals (seals, whales

and polar bear -Ursus maritimus) and seabirds, such as kittiwakes, Brünnich’s

guillemots (Uria lomvia), black guillemots, northern fulmars and glaucous gulls. While

most of them are predators on both fish and zooplankton, top predators (glaucous gull

and polar bear) may also feed on seabirds (glaucous gull) or other marine mammals

(polar bear). In summer, the discharge of fresh water in front of the glaciers is known

to cause an osmotic shock in upwelled zooplankton, which becomes moribund and

more vulnerable to predation by fish and birds (Weslawski and Legezytńska, 1998).

Kittiwakes often feed in these upwelling areas in front of glaciers (Coulson, 2011) and

large flocks are often seen foraging in front of Kongsbreen and Blomstrandbreen

glaciers, near the study colonies (see Fig. 1) (Solveig Nilsen, personal

communication).

2.3 Sampling methods

Adult kittiwakes were trapped in the vicinity of their nests by using a long telescopic

rod with a nylon noose at its end. The noose was put over the bird's head and the bird

was lifted off the nest. The breeding birds were caught within the early chick rearing

period. The age of the chicks probably ranged from 2 to 6 days old. Biometric

measurements (weight, skull-, tarsus- and wing length), blood (2 ml) and food samples

(only from birds that spontaneously regurgitated it) were taken within 15 minutes of

capture. Blood sampling from the brachial veins, on the inside of the wings, was carried

out first. The syringe was previously rinsed with heparin to avoid coagulation. As we

intended to study only females, birds were preliminary sexed in the field. It is assumed

that skull lengths exceeding 92.0 mm correspond to males in 87% of the cases (Barret

et al., 1985). DNA-based sex identification was later performed at NTNU to ensure

correct bird sexing. Before release, birds were colored on the head with markers to

avoid recaptures. Whole blood samples were stored on ice in the field and separated

14

into plasma and cells at the lab facilities. As the latter mainly contains red blood cells,

they will be hereafter referred to as RBC samples. Separation consisted in spinning

the blood for 5 min at 10000 rpm and transferring the supernatant (plasma) to clean

tubes. Sexing vials had previously been filled with whole blood. All tubes (RBCs,

plasma, sexing vials) were stored in the freezer (-20ºC) until further use.

2.4 Sex determination

The sexing vials were used in the molecular sexing of birds. The Chelex extraction

method (Walsh et al., 1991) was performed to isolate DNA from blood samples. 2-4 μl

of each sample were transferred to Eppendorf vials containing 200 μl of 5% Chelex

solution. The mixtures were incubated at 56˚C (20 min) and 96˚C (8 min) and vortexed

between and after incubations. DNA was isolated in the supernatant after

centrifugation at 12000 rpm (3 min) and 20 μl of the supernatant were taken out for

further processing. A stock mix was prepared with 0.05 μL Taq, 1.95 μL H2O

(autoclaved), 0.40 μL Mix (dNTP), 0.60 μL MgCl, 1 μL 10X, 1 μL Primer 2718 (10 μM),

1 μL Primer 2550 (10 μM), and 2 μL Q. The mixture was gently vortexed and 8 μL were

added into each PCR tube. 2 μL of the extracted DNA were also added. PCR was then

performed at GeneAMP® PCR System 9700 thermal cycler (PE Applied Biosystems,

Life Technologies, USA). The following program was run: initial denaturation step at

94˚C followed by 35 cycles of 94˚C (30 s), 46˚C (45 s) and 70˚C (45 s). The program

ends with 70˚C (10 min) and then the temperature drops to 4˚C. This DNA test employs

two different PCR primers for the two types of chromobox-helicase-DNA-binding

genes: CHD-W will only be amplified in females (ZW), whereas CHD-Z will occur both

in males (ZZ) and females. This will allow for later sex determination through gel

electrophoresis (Griffiths et al., 1998). The procedure for gel electrophoresis started by

preparing a 1% agarose gel and stain it with 6 μL ethidium bromide when all agarose

was dissolved. The liquid was then poured into a gel cast. When the gel was completely

set, it was put in a running chamber and 700 ml running buffer (686 ml water and 14

ml 50x TAE buffer) were added. The samples were then loaded into the wells and PCR

products were finally separated by electrophoresis at 70 V (45 min). The bands were

checked with UV light and it was concluded that samples showing one line

corresponded to males (CHD-Z amplification), whereas females showed two lines

(amplification of CHD-Z and CHD-W). Note that 24/26 birds were molecularly sexed,

because 2 samples could not be determined possibly due to an error in the procedure.

A linear discriminant analysis (LDA), based on our own morphometric data, was then

performed. LDA has been widely used to sex seabirds whose sexes look alike in the

15

field, but may be distinguished by slightly dimorphic traits (Evans et al., 1993; Bertellotti

et al., 2002; Calabuig et al., 2011). In this study, LDA aimed to evaluate if the traditional

skull threshold (92.0 mm) (Barret et al., 1985) is the most reliable way to field sex

kittiwakes from Kongsjorden, or whether a new combination of biometric variables

leads to a more accurate classification percentage. Due to geographical variation in

body size, existing models should be used with caution, and new area-specific models

are often recommended (Carey et al., 2011; Ellrich et al., 2009). All measurable traits

(body mass, skull-, tarsus- and wing length) were included in the analysis because

they were significantly higher in males than in females (p=0.0002, p<0.0001, p=0.034,

p=0.0005). The stepwise variable selection procedure examined which of our variables

(or combination thereof) provided the maximum discrimination between males and

females. The obtained LDA function only included skull length (Wilks’ lambda=0.328,

p<0.0001), whereas all other traits did not enter the model because they did not

significantly contribute to the discriminatory power of the function. The classification

functions allow for the classification of individual birds as males or females:

Where Si is the resultant classification score, wij is the weight of the xi variable in the

classification, and ci is a constant for each group. The individuals will be classified as

females if Sfemale>Smale, and viceversa.

2.5 Body condition

Many animal ecologists rely on non-destructive measurements (e.g. body mass and

body size) to assess the energetic status of individuals (Stevenson and Woods, 2006).

Body condition indices (BCI) are expected to be an estimate of the energy content

accumulated in the body as a result of feeding (Peig and Green, 2009). There is

currently no consensus about the most appropriate method among the many available

BCIs. The most widely accepted BCI uses the residuals from a regression of body

mass (M) on a linear measure of body size (L) (Schulte-Hostedde et al., 2005).

However, to compare body condition among individuals of different sizes, BCI methods

should remove the effects of growth on the M-L relationship through standardization

(Peig and Green, 2010). In this study, the BCI was based on a novel procedure (scaled

mass index M̂i) that controls for growth effects on the size of body and components

(Peig and Green, 2009). The scaling exponent (bSMA) determines the scaling

relationship between body mass and body size. Among morphometric data, wing

length was chosen because it showed the strongest correlation with body mass. The

Si = ci + wI1·x1 + wi2·x2….+ wim·xm

Sfemale = – 2490.4 + 55.350·Skull (H+B) Smale = – 2740.137 + 58.060·Skull (H+B)

16

slope of a standardized major axis (SMA) regression on ln-transformed data (ln-mass,

ln-wing) corresponds to the bSMA, which was then applied to the following formula:

scaled mass index (M̂i) = Mi W0

Wi

bSMA

Where Mi is any given value of weight, W i is any given value of wing length and W0 is

the arithmetic mean of wing length. Besides BCI, body mass itself was used as an

alternative approach to body condition.

2.6 Contaminant analysis

2.6.1 Persistent organic pollutants (POPs) analysis

2.6.1.1 Extraction and clean-up

Preparation of plasma samples was carried out at the Department of Biology, NTNU.

The applied extraction method (Dirtu et al., 2013) consisted of four main steps: serum

preparation, SPE cartridge prewashing and conditioning, solid-phase extraction (SPE)

and clean-up. Each plasma sample (750-1000 μl) was spiked with 100 µl mixture of

internal standards (CB 143 at 200 pg·µl-1, BDE 77 at 50 pg·μl-1, and Ɛ-HCH at 40

pg·µl-1 in acetone). MilliQ water (1000 µl) and formic acid (200 µl) were added prior to

ultrasonication in a water bath for 20 min. Intermittent vortexing was performed

between each of the additions above. The extraction step was carried out on OASIS

HLB cartridges (3 ml, 60 mg) prewashed and conditioned consecutively with

dichloromethane -DCM, MeOH and MilliQ water. Plasma samples were applied to the

cartridges, which were further washed with water, and eluted with 10 ml DCM. The

eluates were further evaporated to dryness under a gentle N2 flow and reconstituted in

0.5 ml of hexane. The concentrated extracts were then transferred to SPE cartridges

(3 ml containing 1 g of hexane-washed 44 % acid silica (w/w). Target analytes were

eluted with 10 ml hexane:DCM (1:1). After evaporation to near dryness under a gentle

stream of nitrogen, the extract was reconstituted in 100 μl recovery standard (CB 207

at 100 pg·µl-1 in iso-octane).

2.6.1.2 Identification and quantification (GC-MS)

Identification and quantification of POPs was carried out at Antwerp University,

Belgium. Briefly, PBDEs, CHLs, HCHs, and higher PCBs were measured with a gas

chromatograph (Agilent 6890-5973) coupled with a mass spectrometer system (GC-

MS). The GC was equipped with a 30 m x 0.25 mm x 0.25 µm DB-5ms capillary column

(J&W Scientific, Folsom, CA, USA) and the MS was operated in electron capture

negative ionisation (ECNI) mode. For the measurement of lower PCBs, DDT, DDE,

and HCB, an Agilent 6890 GC – 5973 MS system operated in electron ionisation (EI)

17

mode equipped with a 25 m x 0.22 mm x 0.25 µm HT-8 capillary column (SGE, Zulte,

Belgium) was used. Procedural blanks were analyzed simultaneously with every batch

of seven samples to check for interferences or contamination from solvent and

glassware. Procedural blanks were consistent (RSD < 30%) and the mean value was

calculated for each compound and subtracted from the values in the samples. The limit

of quantification (LOQ) was calculated as 3 x SD of the mean of the blank

measurements. For analytes that were not detected in procedural blanks, LOQs were

calculated for a signal-to-noise (S/N) ratio equal to 10. Mean ± SD recoveries of the

internal standards CB 143, Ɛ-HCH and BDE 77 were 84±15%, 100±9%, and 114±20%,

respectively. The analytical procedures were validated through the analysis of control

human serum for which deviations from certified values are usually less than 10%.

2.6.2 Metal analysis

2.6.2.1 Preparation and digestion of the samples

Metal analysis was fully carried out at the Department of Chemistry, NTNU. It involved

three different steps: sample preparation, acid digestion (UltraCLAVE), and metal

identification and quantification by inductive coupled plasma mass spectrometry (ICP-

MS). Approximately 300 mg ww of RBCs were weighted and transferred to acid

washed Teflon tubes. 6 ml 50% HNO3 were added to the transferred amount. Samples

were then acid digested at high temperature and pressure through a microwave

digestion system (Milestone UltraCLAVE, Leutkirch, Germany). The process consisted

in a gradual increase of temperature and pressure within an hour up to a maximum of

245ºC and 140 bar. This was followed by a cooling and depressurization step. Digested

RBCs were diluted to 60 ml with ultrapure water (PURELAB flex, ELGA LabWater),

resulting in a concentration of 0.6 M HNO3.

2.6.2.2 Inductive coupled plasma mass spectrometry (ICP-MS)

The diluted samples were transferred to 12 ml tubes and introduced into the ICP-MS

by the sample introduction system (SC2 DX and PrepFAST) as aerosol droplets. SC2

DX is an auto-sampler with a dustcover and ULPA filter. Samples were uncapped

inside this cover with as little opening as possible, to avoid contamination. Dilution and

addition of internal standard (Rhenium) was performed by prepFAST autodilution

system. A nebulizer (PFA-ST) accounted for the formation of the fine droplets that are

introduced into the argon plasma, which dissociates the molecules and forms ions after

electron removal. Methane (10%) was used as an additional gas because it allows for

efficient ionization of Se and As. Atomization and ionization occur at very high

temperatures (7000 ºC) and are necessary for further separation and detection in the

18

mass spectrometer (MS). The extracted ions were directed to a mass filtering device

(quadrupole) in the Element 2 ICP-MS instrument (Thermo Scientific, Bremen,

Germany), where they were separated based on their mass-to-charge ratio. When ions

emerge from the mass filter, they are converted into an electrical signal with an ion

detector. This electrical signal was handled by the software and converted into

concentrations through calibration curves. Our instrument was calibrated using 0.6 M

HNO3 solutions of matrix-matched multi-element standards (Custom 70 Element Mix,

Elemental Scientific, Omaha, USA). The sample introduction system was automatically

washed out after each sample. Procedural blanks were analyzed simultaneously to

check for background contamination.

The limit of detection (LOD) was defined as the highest value between instrument

detection limits (IDL) and 3 x SD of the mean of the blank measurements. LODs were

5.9, 0.5, 0.2, 0.5 and 35.3 ng·g-1 dw for As, Cd, Hg, Pb and Se, respectively. Note that

LOD is here given instead of LOQ (used for POPs). This is because the lab that

quantified POPs only provided the LOQ. To facilitate comparison with other studies,

original RBCs concentrations of metals (ng·g-1 ww) were expressed on a dry weight

basis (ng·g-1 dw), according to the estimated water content (%) in RBCs samples:

Water content (%) = (Wb – Wa )

Wb

· 100

Where Wb and Wa are the sample weight before and after freeze-drying, respectively.

Dry weight (g dw) = wet weight −water content (%)

100· wet weight

Concentration (ng·g-1 dw) = concentration (ng·g−1 ww)· volume

dry weight (g dw)

2.7 Stable isotope analysis (SIA)

RBCs were freeze-dried to obtain 10 mg dw. Food samples were separated in different

prey at the Norwegian Polar Institute. Analysis of stable isotope ratios was carried out

at the Institut for Bioscience (University of Aarhus, Denmark). The total carbon and

nitrogen contents and isotopic ratios of 13C/12C and 15N/14N were measured in solid

samples by Dumas combustion (1020 ºC) on an elemental analyser (CE 1110, Thermo

Electron, Milan, Italy, Thermo Scientific, Bremen, Germany) coupled in continuous flow

mode to a Finnigan MAT Delta PLUS isotope ratio MS (Thermo Scientific, Bremen,

Germany). Briefly, 2.51 (1.30-6.89) and 2.16 (0.91-4.18) mg samples of homogenized

and dried material (RBCs and food samples, respectively) were weighed out into tin

19

combustion cups for elemental analysis. Acetanilide and atropine were used for

elemental analyser mass calibration. As working standard for isotope ratio analysis we

used pure gases of CO2 and N2 calibrated against certified reference materials of 13C-

sucrose and 15N-(NH4)2SO4, respectively (IAEA, Vienna, Austria). The isotope ratio of

a sample (RSa) is compared to the ratio in a primary standard (RStd), and expressed by

the delta notation:

𝛿X (‰) = R𝑠𝑎 − 𝑅𝑠𝑡𝑑

Rstd x 1000

Where R is the concentration ratio (13C/12C or 15N/14N) in samples and standards.

Primary standards are a marine limestone fossil, Vienna Pee Dee Belemnite (VPDB),

for carbon, and atmospheric air for nitrogen.

2.7.1 Trophic level calculations

TL of kittiwakes was assessed based on nitrogen isotope ratios (δ15N) in kittiwakes

and their prey. Krill was by far the most common prey species after regurgitate

analysis. A trophic enrichment factor (TEF) of 2.4‰ between kittiwakes and krill was

assumed, resulting in the following equation: δ15Ntissue = δ15Ndiet + 2.4 (Mizutani et al.,

1991). Although this widely used TEF was originally calculated from muscle samples,

it should not differ much from diet-tissue fractionation in RBCs (Caut et al., 2009;

Ogden et al., 2004; Kurle, 2009). However, tissue-specific TEF are always preferred

and our TL estimations may therefore be slightly biased (Ramos and González-Solis,

2012). For the rest of the food web, a general TEF of 3.4‰ was applied (Søreide et

al., 2006). Isotopic fractionation in birds is therefore considered lower (2.4<3.4),

possibly due to the excretion of N waste as uric acid in birds (Hobson and Clark, 1992).

Mean δ15N value in krill was reported to be representative of TL 2.7 in Kongsfjorden

(Wold et al., 2011), and was therefore used as a baseline to estimate the TL of

kittiwakes, through a modification of the relationship suggested by Hop et al. (2002a):

TPbird= 3.7 + 𝛿15𝑁 𝑅. 𝑡𝑟𝑖𝑑𝑎𝑐𝑡𝑦𝑙𝑎

− (𝛿15𝑁 𝑇. 𝑖𝑛𝑒𝑟𝑚𝑖𝑠 +2.4)

3.4

2.8 Statistical analysis

The statistical analysis was performed using IBM SPSS Statistics 23, rejecting the null-

hypothesis at α=0.05. All tests with p-values<0.05 were therefore considered as

statistically significant. Exact p-values were provided, except for values below 0.0001

(p<0.0001). For POPs, samples with a concentration<LOQ were assigned the value

20

QF x LOQ, with QF (quantification frequency) being the proportion of samples>LOQ

(Voorspoels et al., 2002). Only compounds with QF>0.5 were included in statistical

analysis. Original concentrations were normalized (log10-transformation) in order to

fulfil the criteria of parametric tests. Although it is not indicated throughout the results,

all provided statistics was based on log-transformed concentrations.

T-tests for independent means were used to compare morphometric data, pollutant

levels, body condition, isotopic ratios and TL between colonies. Association between

the study variables was analyzed by means of Person’s correlation coefficient. Linear

regression was applied to examine the dependence of POPs on SI ratios and location.

LDA was used to test the hypothesis that morphometric data can distinguish between

male and female kittiwakes. Body condition and pollutant data (POPs) were visually

presented in box and whiskers plots, where each box indicates the interquartile range

(IQR) that includes values between the 25th (Q1) and 75th (Q3) percentiles. The

median (Q2) corresponds to the horizontal line through the box. Whiskers indicate

highest/lowest values laying within 1.5 x IQR from the upper/lower edge of the box

(hinge). Extreme values between 1.5-3 times the IQR from the hinge are marked by

circles, whereas values farther than 3 times the IQR are marked by stars. In addition,

a ternary diagram was used to display the relative contribution of POP classes to the

overall POPs.

3. Results

3.1. Sex determination

The results of sex determination through gel electrophoresis revealed that there were

25 females and 1 male among the kittiwakes sampled for this study. The blood samples

(RBCs and plasma) corresponding to the only male were thus removed from further

analysis. The skull length measurements (Barret et al., 1985) correctly determined sex

in 22/24 (91.7%) of the kittiwakes compared to the performed genetic determination.

All molecularly sexed individuals, along with their skull length and sex based on the

latter technique, are summarized in Appendix (Table 3). According to the performed

LDA, also 22/24 (91.7%) of the investigated kittiwakes were correctly sexed based on

our model. The threshold value that allowed for discrimination between males and

females (92.15 mm) was obtained by equating the functions of classification and

isolating the skull length (see 2.4). The predictive capacity of LDA models is generally

too optimistic, because a newly created model is expected to fit the used samples

better than the entire population or any other sample, and validation is therefore

21

recommended (Miller, 1990). Validation was performed by applying the model to

another set of molecularly sexed kittiwakes from the study area. In that case, the model

correctly determined sex in 79% of the kittiwakes, whereas the use of the traditional

threshold value (Barrett et al., 1985) also resulted in 79% of correct classification.

3.2. Body condition

The body mass of the 25 studied female kittiwakes ranged from 305 to 410 g, and no

significant difference was found between the study colonies (p=0.3) (Fig. 4). Similarly,

BCI did not differ between colonies (p=0.4) (Fig. 5). These condition variables, as well

as all morphometric data, are summarized in Appendix (Table 1 and Table 2).

3.3. Levels of pollutants

3.3.1. Metals

The studied metals (As, Cd, Hg, Pb and Se) were detected above the LOD in all

samples. Concentrations of the investigated elements in RBCs from

Blomstrandhalvøya and Krykkjefjellet are summarized in Table 1. It readily follows that

the mean concentration of these elements did not significantly differ between the study

sites (p=0.98 for As, p=0.66 for Cd, p=0.99 for Hg, p=0.25 for Pb, p=0.14 for Se). The

highest levels in RBCs were detected for Se, whereas As was the major contributor to

the total load of investigated non-essential elements. The relationship between Se and

Hg in RBCs was not found to be significant (p=0.1), but the mean ratio of Hg to Se

(Hg:Se) was clearly below 1 in Blomstrandhalvøya and Krykkjefjellet.

Figure 4. Body mass (g) of female kittiwakes from

Blomstrandhalvøya (n=14) and Krykkjefjellet

(n=11). Note that the median was not represented

in Krykkjefjellet because it matches with the upper

hinge (Q2=Q3=380)

Figure 5. Body condition index (BCI) of female

kittiwakes from Blomstrandhalvøya (n=14) and

Krykkjefjellet (n=11).

22

Table 1. Summary statistics for RBC concentrations (ng·g-1 dw) of studied metals in female kittiwakes

from Blomstrandhalvøya (n=14) and Krykkjefjellet (n=11).

3.3.2. Persistent organic pollutants (POPs)

Among the investigated POPs, the following compounds were detected above LOQ in

more than 50% of the analyzed plasma samples (QF>0.5). In order to facilitate their

study, these pollutants were grouped into six different POP classes according to their

properties: ΣPCBs (CB -99, -105, -118, -138, -153, -156, -170, -171, -177, -180, -183,

-187, -194, -196/203, -199, -206), ΣDDTs (p,p’-DDE), HCB, ΣCHL (cis-nonachlor,

trans-nonachlor, oxy-chlordane), ß-HCH and ΣPBDEs (BDE -47, -99, -100, -153)

(Appendix, Table 4). ΣPCBs turned out to be by far the most prevalent class of POPs

in plasma, representing a mean contribution ± SD to the overall load of POPs of 76.0

± 7.7 % (Blomstrandhalvøya) and 80.1 ± 8.0 % (Krykkjefjellet). The concentrations of

different POPs in kittiwake plasma from Blomstrandhalvøya and Krykkjefjellet are

shown in Fig. 6.

Blomstrandhalvøya (n=14) Krykkjefjellet (n=11)

Mean SD Median Min Max Mean SD Median Min Max

As 1178.8 308.3 1095.7 671.8 1832.3 1220.3 459.3 1399.0 624.6 1860.9

Cd 11.4 3.9 11.0 4.8 16.5 13.1 7.9 10.7 5.4 33.8

Hg 342.8 139.4 334.9 165.1 756.9 333.3 88.4 344.8 160.3 478.5

Pb 4.5 3.4 3.0 1.6 13.8 4.7 1.3 4.3 3.2 7.0

Se 52381.5 12286.4 52270.9 24826.5 79369.3 44767.0 7812.7 43482.1 34526.2 56379.7

Figure 6. The concentration of POPs (pg·ml-1) in plasma of kittiwakes from Blomstrandhalvøya (n=14)

and Krykkjefjellet (n=11) are here presented as box and whiskers plots on a logarithmic scale.

23

Although mean levels of all POP classes (ΣPCBs, ΣDDTs, ΣCHLs, HCB, ß-HCH and

ΣPBDEs) were higher in Krykkjefjellet than in Blomstrandhalvøya, significant

differences between colonies, evaluated in terms of geometric mean ratio (GMR), were

not found (p=0.07, p=0.78, p=0.33, p=0.26, p=0.55, p=0.44, respectively). The

geometric mean concentration of ΣPCBs was, for instance, 0.96-2.44 times higher in

Krykkjefjellet relative to Blomstrandhalvøya (95% CI). Because 95% CI included

GMR=1 in all investigated POPs, no significant differences within the fjord could be

demonstrated (Fig. 7). This is consistent with overlapped boxplots and large

variabilities shown in Fig 6.

The mean contribution of POP classes to the total load in plasma did not significantly

vary between colonies (Fig. 8). The relative contribution of ΣPCBs, ΣOCPs (ΣCHL +

HCB + ß-HCH) and ΣPBDEs in all kittiwakes are summarized in a ternary diagram

(Appendix, Fig. 1). Because ΣPCBs, ΣOCPs and ΣPBDEs represented a similar

proportion of POPs in the study colonies, color-coded circles were not visually

separated in the diagram (Appendix, Fig. 1). The major compounds in plasma (CB-

153, CB-138, p.p’-DDE), when studied separately, also constituted a similar fraction of

the ΣPOPs (mean % ± SD) in Blomstrandhalvøya (CB-153: 24.1 ± 2.6, CB-138: 19.3

± 2.3, p.p’-DDE: 12.9 ± 8.5) and Krykkjefjellet (CB-153: 27.2 ± 5.0, CB-138: 20.0 ± 1.3,

p.p’-DDE: 10.3 ± 5.2).

Figure 7. Geometric mean ratio (GMR) and 95% CI for levels of POPs in plasma of kittiwakes from

Krykkjefjellet (n=11) in relation to Blomstrandhalvøya (n=14).

24

3.4. Relationship between body condition and pollutant levels

BCI of birds from Blomstrandhalvøya and Krykkjefjellet was negatively correlated with

ΣPCBs (r=-0.52, p=0.008), ΣCHL (r=-0.55, p=0.005), HCB (r=-0.54, p=0.005), and ß-

HCH (r=-0.48, p=0.014). The relationship between BCI and ΣDDTs was not found to

be significant (p=0.12). Fig. 9 shows the scatter plot of ΣPCBs against BCI. Similarly,

a negative and significant relationship existed between body mass and ΣPCBs (r=

-0.43, p=0.03), ΣDDTs (r=-0.51, p=0.009), ΣCHLs (r=-0.6, p=0.002), HCB (r=-0.52,

p=0.007), and ß-HCH (r=-0.51, p=0.009). Nevertheless, BCI did not show a significant

relationship with ΣPBDEs (p= 0.11), As (p=0.21), Cd (p=0.74), Hg (p=0.89), Pb

(p=0.11) and Se (p=0.081).

Figure 9. Scatter plot of BCI and log ΣPCBs.

Kittiwakes from the two study colonies were

pooled together (n=25) to investigate this

relationship (r=-0.519, p=0.008).

Figure 8. Comparison of the contribution of investigated POPs (expressed as mean percentage of total

POP load) in plasma of kittiwakes from Krykkjefjellet (n=11) and Blomstrandhalvøya (n=14).

25

3.5. Stable isotope analysis (SIA)

Stable isotope values (δ13C and δ15N), as well as estimated TL, did not show significant

intraspecific variation between the study colonies (p=0.9 for δ13C, p=0.7 for δ15N, p=0.7

for TL). However, kittiwakes and krill are clearly separated (Fig. 10) because values of

δ13C and δ15N were significantly higher in kittiwakes than in their main prey (p<0.0001,

p<0.0001). Before statistical analyses on SIA data were performed, original plasma

δ13C values were lipid-normalized according to Post et al. (2007) because a strong

negative correlation between C:N ratio and δ13C was found (r=-0.71, p<0.0001). Taking

into consideration that TL 2.7 was set for krill, the estimated TL ranged from 3.39 to 4

in the studied kittiwakes (Table 2).

TL did not show a significant relationship with ΣPCBs (p=0.7), ΣDDTs (p=0.2), ΣCHL

(p=0.4), HCB (p=0.06) and ΣPBDEs (p=0.9). ß-HCH was the only investigated

pollutant that showed a significant decrease with TL (r=-0.42, p=0.038). A linear

regression model was applied to examine the dependence of POP classes on δ13C,

δ15N and location. In this study, these factors did not significantly contribute to explain

Species / Location

a δ13C δ15N TL

Mean SD Min Max Mean SD Min Max Mean SD Min Max

R. tridactyla (Blomstrandhalvøya) -21.34 0.89 -22.76 -19.74 10.39 0.54 9.70 11.77 3.59 0.16 3.39 4.00

R. tridactyla (Krykkjefjellet) -21.31 0.49 -22.21 -20.48 10.32 0.27 9.78 10.72 3.57 0.08 3.41 3.69

T. inermis -23.31 0.32 -23.70 -22.85 8.36 0.30 8.07 8.86

Table 2. Stable isotope ratios of carbon (δ13C), nitrogen (δ15N) and estimated TL for kittiwakes from

the study colonies and krill. a Lipid-normalized values (transformation of original δ13C) are here shown.

Figure 10. Scatter plot of δ15N (‰) and

δ13C (‰) in RBCs of kittiwakes from

Blomstrandhalvoya (n=14) and

Krykkjefjellet (n=11), as well as in krill

(n=5) as a representative kittiwake prey.

Note that δ13C correspond to the lipid-

normalized values.

26

the variation between colonies in pollutant concentrations, and no significant models

could therefore be fitted for most pollutants. ß-HCH was the only exception, because

its levels were partly explained by δ15N (intercept ± SE= 4.025 ± 0.863, slope ± SE= -

0.183 ± 0.083, R2=0.17, p=0.038).

4. Discussion

4.1. Discriminant analysis for sex determination

The LDA model for field sex determination did not seem to represent an improvement

relative to the traditional rule, and therefore kittiwakes should preferably be DNA-sexed

in order to validate field-sexing. However, even if the suggested threshold value did

had resulted in a better correct classification percentage, there are important limitations

underlying the LDA model used in this study, due to our sampling design. Because we

intended to capture only females, the sampling of kittiwakes was strongly skewed

towards the selection of small individuals. It is therefore incorrect to conclude that

91.7% of the kittiwakes were correctly sexed according to the model, because it was

not properly estimated due to a lack of male data input. Consequently, the validity of

the suggested threshold value could not be demonstrated, and its application to further

studies in the area is therefore not recommended.

4.2. Levels of pollutants in kittiwakes from Kongsfjorden, Svalbard

4.2.1. Metals

Mean levels of Hg in RBCs were 3 to 6 times lower than previously reported in RBCs

of kittiwakes from the study area (Goutte et al., 2015) and other Arctic gulls (Bond and

Robertson, 2015), and did not seem to represent a hazard to the investigated

kittiwakes because concentrations were below those thought to cause adverse effects

in seabirds (3 ppm) (Evers et al., 2008). Low levels of Hg during the chick-rearing

period may relate to a seasonal change in kittiwake prey, from a diet dominated by fish

to a diet mainly constituted of invertebrates (Øverjordet et al., 2015a). Se is an

essential element that protects kittiwakes against toxicity, but concentrations

exceeding 30 ppm dw in liver may associate with sublethal toxic effects in birds

(Ohlendorf and Heinz, 1996). In this study, mean concentration of Se was above this

threshold and 3 to 8 times higher than found in kittiwake liver (Hegseth et al., 2011;

Wenzel and Gabrielsen, 1995) and muscle (Savinov et al., 2003; Øverjordet et al.,

2015b) at the study area. Nonetheless, it is not sure that kittiwakes are exposed to

27

toxic levels of Se in this study, because hepatic concentrations of dietary Se can be

lower than the observed RBCs levels (Suzuki, 2005). The lack of a significant

relationship between Se and Hg has previously been found in seabirds (Wenzel and

Gabrielsen, 1995) and may relate to too-low Hg levels (Leonzio et al., 1986).

Nevertheless, Se is expected to give an efficient protective response when present in

molar excess (Hg:Se<1) (Khan and Wang, 2009), as found in the current study. Mean

concentration of Cd (0.012 µg·g-1 dw) was substantially lower than reported in liver of

kittiwakes from Kongsfjorden (37-48 µg·g-1 dw) (Savinov et al., 2003; Øverjordet et al.,

2015a) and relative to toxicity thresholds in blood of experimental birds (0.26 µg·g-1)

(Wayland and Scheuhammer, 2011). Similarly, mean Pb concentration was very low

(0.005 µg·g-1 dw) compared to whole blood levels in breeding common eiders (56 µg·g-

1 dw) from Kongsfjorden (Lervik, 2012). The mean levels of As in kittiwake RBCs (1200

ng·g-1 dw) were higher than in kittiwake eggs from Kongsfjorden (379 ng·g-1 dw)

(Miljeteig and Gabrielsen, 2009) and in RBCs of most studied Procellariiformes from

Bird Island, S Georgia (184-896 ng·g-1 dw) (Anderson et al., 2010). All individuals in

this study had concentrations above the reference values for As in RBCs of birds from

uncontaminated areas (400 ng·g-1 dw) (Burger and Gochfeld, 1997). Because local

anthropogenic inputs of As are not known in the area, high levels could relate to

disruption in the phosphorus/nitrogen ratio leading to greater accumulation of As by

phytoplankton (Anderson et al., 2010).

4.2.2. Persistent organic pollutants (POPs)

The concentrations of POPs were expressed on a volumetric basis (pg·ml-1) in this

thesis. To allow for comparison with other studies, it was assumed that 1 pg·ml-1 ≈ 1

pg·g-1 ww (Savard et al., 2015). The plasma concentrations of ΣPCBs, ΣDDTs, ΣCHLs

and HCB were similar to mean levels reported in plasma of female kittiwakes from

Kongsfjorden (Goutte et al., 2015; Nordstad et al., 2012). Nonetheless, levels of

ΣPBDEs were low (0.17 ng·g-1 ww) relative to previously found in plasma of female

kittiwakes from Hornøya, SW Barents Sea (0.55 ng·g-1 ww) (Sagerup et al., 2014) and

very low in comparison with female glaucous gulls from Kongsfjorden (4.17 ng·g-1 ww)

(Løseth, 2014). The latter applies for all investigated POPs (Verboven et al., 2010;

Haugerud, 2011) and is consistent with their potential for biomagnification along the

food web, because glaucous gulls feed at higher TLs than kittiwakes (Hop et al.,

2002b). All studied individuals were far below a general threshold for effects of

halogenated pollutants (1 ppm ww) (Letcher et al., 2010).

28

4.3. Comparison of the study colonies

Significant differences between colonies were not found in this study. This is likely

related to the similar period in the breeding season they were sampled, leading to

similar energy efforts. Food availability near the colonies was probably similar because

kittiwakes had similar body condition. As expected for neighboring colonies, in the

absence of obvious local sources of pollution, levels of metals in kittiwakes did not

significantly vary within the fjord. In the case of POPs, although significant differences

were not demonstrated, results showed a slight trend towards higher levels of POPs

in Krykkjefjellet. This was particularly clear for ΣPCBs, whose within-fjord variation

notably approached significance. The investigated geometric mean of all POP classes

was higher in Krykkjefjellet, and this seems unlikely to happen just by chance.

Furthermore, 95% CIs (see Fig. 7) indicate that pollutants load may be up to 2.43-fold

greater in Krykkjefjellet relative to that in Blomstrandhalvøya, which is not negligible. It

therefore seems feasible that our current study failed to demonstrate statistically

significant differences partly because our sample size was not sufficiently large to

provide enough statistical power. This makes these results inconclusive because one

cannot reject with certainty the possibility that colonies are actually different. However,

excepting that slight trend, concentrations of POPs did not differ between colonies.

This is in contrast to previously reported differences between the study colonies

(Svendsen, 2015). In that study, levels of POPs in plasma of chick-rearing kittiwakes

were significantly higher (more than twice as high) in Blomstrandhalvøya. Based on

the available results, this study also aimed to discuss why such differences were found

in 2014 (Svendsen, 2015) but not in 2015 (this study).

Several factors (e.g. biotransformation, reproduction, seasonality of body mass, age,

migration, foraging ecology) may account for the within-fjord variability in the levels of

POPs (Borgå et al., 2004). Although the biotransformation capacity varies between

species (Helgason et al., 2010), to the author’s knowledge, no study has yet suggested

spatial variation in the ability to metabolize pollutants within kittiwakes. During

reproduction, the pollutant load may decrease in females due to the formation of lipid-

rich eggs (Bustnes et al., 2010), but the influence of egg-laying seems negligible in this

study because all individuals were females sampled after egg laying. Also, the

redistribution of stored lipids may increase the concentrations of POPs during the

energetically demanding breeding season (Henriksen et al., 1996). This is partly shown

in this study by negative relationships between body condition and most POPs.

Nonetheless, as BCI did not differ between the colonies, the release of POPs from

29

adipose tissue was most likely similar within the fjord. The fact PBDEs did not correlate

with body condition may be due to their too low levels in plasma. Metals did not

correlate with body condition possibly because they are not lipophilic and lipid

mobilization is thus not expected to affect their concentrations. Although the

investigated kittiwakes may differ in age, which remained unknown, it is thought that

POPs do not considerably accumulate with age in seabirds (Bustnes et al., 2003).

Similarly, migration does not seem to have an influence on the concentration of POPs

in this study because, even if the investigated kittiwake groups differed in their

wintering grounds, plasma would not reflect such long-term dietary exposure to

pollutants (Hobson and Clark, 1993). All together, the lack of remarkable pollutant

differences in this study is consistent with the so far mentioned biological factors.

In contrast, the strong differences that were reported in 2014 (Svendsen, 2015) may

be party explained by the seasonal redistribution of lipids. That study included breeding

kittiwakes sampled at two different stages of the chick rearing period (mid-July and

early August). All kittiwakes from Blomstrandhalvøya (11) were sampled in August,

whereas birds from Krykkjefjellet (8) were sampled in July (4) and August (4). It could

be that mean BCI was lower in kittiwakes from Blomstrandhalvøya partly because

kittiwakes in the late chick rearing (August) were in worse condition than in the early

chick rearing (July) (Kytaysky et al., 1999). This would have led to the observed

increased levels of POPs relative to Krykkjefjellet. Variation in BCI due to breeding

stage was however not expected in the current study, because all kittiwakes were

sampled within the same week in the chick rearing period.

4.4. Influence of foraging ecology on pollutant levels

Among the biological factors that may account for the variation in POPs levels within

kittiwakes, this study also focused on the role of foraging ecology. According to the

estimated TL, kittiwakes from Blomstrandhalvøya and Krykkjefjellet were feeding at

similar TL. Because TL and most pollutants were not correlated, biomagnification

within kittiwakes was not demonstrated. This can be explained by the narrow TL range

(3.39-4.00), unable to show an increase of pollutant levels with TL. Biomagnification

studies, unlike this, generally include several species that represent a wide range of

TL in their food web (Jæger et al., 2009). The mean ± SD TL reported in this study (3.6

± 0.1) was comparable to kittiwakes collected in the same period (3.3 ± 0.1), but lower

than in kittiwakes collected earlier in the reproductive season (4.3 ± 0.1) at the study

area (Øverjordet et al., 2015b). This is supported by the previously mentioned change

30

in diet composition along the breeding season (Øverjordet et al., 2015a). The food

chain origin of the diet, reflected by δ13C, did not seem to vary between colonies as

well. This is consistent with the expected overlap of foraging ranges in neighboring

colonies. Spatial variation in the foraging ecology of kittiwakes that breed in the same

fjord, evaluated through SIA, was therefore not found in this study. Similar foraging

ecology and thereby dietary exposure to pollutants also corresponds well to the fact

that no clear differences regarding pollutant levels were detected in 2015. This

contrasts with the differences in 2014. Although they were most likely due to variation

in BCI between sampling periods, differences could also relate to within-fjord variation

in the diet or in the availability of food near the colony. The latter may determine

different foraging strategies in Kongsfjorden, with kittiwakes limited by food abundance

near the colony taking longer foraging trips (Goutte et al., 2014) and being in worse

condition, which would lead to increased plasma levels of POPs (Svendsen, 2015).

5. Conclusions

All studied pollutants were detected and quantified in blood of kittiwakes from

Kongsfjorden. Reported concentrations were generally below toxicity thresholds.

Significant differences in blood levels of these pollutants were not found between two

kittiwake breeding colonies. This was consistent with the lack of variation in body

condition and stable isotope ratios between the colonies. Stable isotope analysis

provided an interesting insight into the foraging ecology (trophic level and dietary

carbon source) of the two colonies. Our results revealed that kittiwakes from

Blomstrandhalvøya and Krykkjefjellet had similar trophic levels and feeding habitats. It

cannot be ruled out that differences in feeding behaviors may have driven previously

observed differences between the study colonies, although different body condition in

different sampling periods during the breeding season seems the most likely

explanation for the previously observed differences between the colonies.

Future research should continue to study kittiwakes at the same breeding stage in

order to approach the comparison between colonies and whether the effect of location

observed in 2014 holds in time. A larger sample size of both males and female

kittiwakes is strongly recommended for further study. This should be complemented

by deep insights into foraging ecology through different dietary tracers (δ15N, δ13C, δ34S

and analysis of fatty acids), tracking devices, conventional dietary analyses and

evaluation of spatial variation in prey availability within Kongsfjorden.

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7. Appendices

Appendix I: Morphometric data

Table 1. Field data for 26 kittiwakes (Rissa trydactyla) from Kongsfjorden, Svalbard (1/2). a There is at

least 1 chick, but nest content was not clearly visible from the base of the cliff (there may be more chicks).

ID Date Time Breeding colony Nest Chicks Skull (mm) Wing (mm) Tarsus (mm)

KBK15-01 15/07/2015 15:40 Krykkjefjellet A14 1 91.5 322 34.0

KBK15-02 15/07/2015 16:05 Krykkjefjellet A10 1 90.1 316 35.0

KBK15-03 15/07/2015 16:20 Krykkjefjellet A5 1 88.6 319 34.2

KBK15-04 15/07/2015 17:30 Krykkjefjellet AA12 1 90.5 314 33.9

KBK15-05 15/07/2015 18:05 Krykkjefjellet AA19 1 90.1 308 35.0

KBK15-06 15/07/2015 19:35 Krykkjefjellet AA21 1 90.2 315 34.2

KBK15-07 15/07/2015 20:00 Krykkjefjellet AA18 1 92.0 319 32.1

KBK15-08 17/07/2015 12:00 Blomstrandhalvøya BG6 1 88.0 308 34.4

KBK15-13 17/07/2015 14:45 Blomstrandhalvøya BG23 1 88.9 312 32.3

KBK15-14 17/07/2015 15:15 Blomstrandhalvøya BG31 1 90.6 311 32.9

KBK15-16 17/07/2015 16:20 Blomstrandhalvøya BG15 1 91.1 323 34.1

KBK15-17 17/07/2015 16:40 Blomstrandhalvøya BG14 1 90.6 328 34.0

KBK15-18 17/07/2015 17:20 Blomstrandhalvøya BG29 2 88.2 328 32.5

KBK15-19 17/07/2015 17:45 Blomstrandhalvøya BG28 1 88.0 319 33.6

KBK15-20 17/07/2015 18:00 Blomstrandhalvøya BG25 1 91.1 322 35.1

KBK15-21 17/07/2015 18:25 Blomstrandhalvøya BG4a 1 90.5 309 34.5

KBK15-23 17/07/2015 19:10 Blomstrandhalvøya BG22 1 89.6 320 32.9

KBK15-24 17/07/2015 19:35 Blomstrandhalvøya BG30 1 90.0 325 33.9

KBK15-25 17/07/2015 20:00 Blomstrandhalvøya BG24 1 92.2 315 33.1

KBK15-26 18/07/2015 16:35 Krykkjefjellet AA13 1 90.0 327 35.0

KBK15-27 18/07/2015 18:20 Krykkjefjellet AA20 1 91.8 326 34.1

KBK15-28 18/07/2015 18:45 Krykkjefjellet AA23 1 88.1 322 33.0

KBK15-29 18/07/2015 19:05 Krykkjefjellet AA12 ≥1a 88.0 309 33.5

KBK15-32 18/07/2015 22:42 Blomstrandhalvøya UBR2 1 89.3 326 33.1

KBK15-33 18/07/2015 23:10 Blomstrandhalvøya UBR13 1 86.9 317 32.0

KBK15-34 18/07/2015 23:45 Blomstrandhalvøya UBR8 1 88.2 320 33.8

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Table 2. Field data for 26 kittiwakes (Rissa trydactyla) from Kongsfjorden, Svalbard (2/2). a Because the

two birds were captured at the same next, it was thought that the one with the shorter skull would be the

female (Barrett et al., 1985).

ID Breeding colony Body mass

(g)

Food sample

Blood (2 ml)

Blood (sexing

vial) Comments

KBK15-01 Krykkjefjellet 380 x

KBK15-02 Krykkjefjellet 380 x

KBK15-03 Krykkjefjellet 375 x

KBK15-04 Krykkjefjellet 355 x x New rings. Potential partner of KBK15-29: this could be the malea

KBK15-05 Krykkjefjellet 370 x x New rings. 1.8 mL aprox. of blood

KBK15-06 Krykkjefjellet 380 x x New rings

KBK15-07 Krykkjefjellet 390 x

KBK15-08 Blomstrandhalvøya 380 x x

KBK15-13 Blomstrandhalvøya 360 x x New rings

KBK15-14 Blomstrandhalvøya 380 x x New rings

KBK15-16 Blomstrandhalvøya 410 x x x New rings

KBK15-17 Blomstrandhalvøya 400 x x

KBK15-18 Blomstrandhalvøya 375 x x Aggressive behavior

KBK15-19 Blomstrandhalvøya 375 x

KBK15-20 Blomstrandhalvøya 410 x

KBK15-21 Blomstrandhalvøya 375 x

KBK15-23 Blomstrandhalvøya 360 x x x New rings

KBK15-24 Blomstrandhalvøya 400 x

KBK15-25 Blomstrandhalvøya 385 x

KBK15-26 Krykkjefjellet 365 x

KBK15-27 Krykkjefjellet 395 x

KBK15-28 Krykkjefjellet 380 x

KBK15-29 Krykkjefjellet 305 x x New rings. Potential partner of KBK15-04: this could be the femalea

KBK15-32 Blomstrandhalvøya 335 x

KBK15-33 Blomstrandhalvøya 405 x x x New rings

KBK15-34 Blomstrandhalvøya 375 x

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Appendix II: Sex determination

Table 3. Sex of the 26 kittiwakes that were sampled for this study in Blomstrandhalvøya (n=15) and

Krykkjefjellet (n=11). Skull length measurements and predicted sex (field sex estimate) are also included

in the table. a Kittiwake could not be molecularly sexed, possibly due to an error in the procedure. Note

that this individual was included in the study because its considerably short skull would rarely correspond

to a male.

ID Location Molecular sex Skull H+B (mm) Field sex estimate

KBK15-01 Krykkjefjellet Female 91.5 Female

KBK15-02 Krykkjefjellet Female 90.1 Female

KBK15-03 Krykkjefjellet Female 88.6 Female

KBK15-04 Krykkjefjellet Female 90.5 Female

KBK15-05 Krykkjefjellet Female 90.1 Female

KBK15-06 Krykkjefjellet Female 90.2 Female

KBK15-07 Krykkjefjellet Female 92.0 Female

KBK15-08 Blomstrandhalvøya Female 88.0 Female

KBK15-13 Blomstrandhalvøya Female 88.9 Female

KBK15-14 Blomstrandhalvøya Female 90.6 Female

KBK15-16 Blomstrandhalvøya Female 91.1 Female

KBK15-17 Blomstrandhalvøya Female 90.6 Female

KBK15-18 Blomstrandhalvøya Female 88.2 Female

KBK15-19 Blomstrandhalvøya Female 88.0 Female

KBK15-20 Blomstrandhalvøya Male 91.1 Female

KBK15-21 Blomstrandhalvøya Female 90.5 Female

KBK15-23 Blomstrandhalvøya Female 89.6 Female

KBK15-24 Blomstrandhalvøya Female 90.0 Female

KBK15-25 Blomstrandhalvøya Female 92.2 Male

KBK15-26 Krykkjefjellet Female 90.0 Female

KBK15-27 Krykkjefjellet Female 91.8 Female

KBK 15-28 Krykkjefjellet Femalea 88.1 Female

KBK15-29 Krykkjefjellet Female 88.0 Female

KBK15-32 Blomstrandhalvøya Female 89.3 Female

KBK 15-33 Blomstrandhalvøya Femalea 86.9 Female

KBK15-34 Blomstrandhalvøya Female 88.2 Female

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Appendix III: POPs data

Table 4. Plasma concentrations (pg·ml-1) of detected compounds (in more than 50% of the samples)

from Blomstrandhalvøya (n=14) and Krykkjefjellet (n=11). Quantification frequency (QF) is referred to the

proportion of samples>LOQ. a The minimum corresponds to a value below the LOQ (see 2.8).

Blomstrandhalvøya (n=14) Krykkjefjellet (n=11)

QF Mean SD Median Min Max QF Mean SD Median Min Max

CB 99 14/14 1343 434 1243 771 2402 11/11 2060 991 1979 768 3838

CB 105 14/14 518 200 483 256 993 11/11 692 307 646 299 1205

CB 118 14/14 1682 597 1547 1008 3145 11/11 2286 1084 2050 967 4161

CB 138 14/14 4976 1872 4701 2460 8547 11/11 8263 4710 7165 2841 15857

CB 153 14/14 6191 2243 5936 3274 10527 11/11 11714 7895 8849 3518 27364

CB 156 14/14 263 99 250 137 453 11/11 443 286 319 135 934

CB 170 14/14 776 339 724 364 1531 11/11 1476 1046 1051 430 3489

CB 171 14/14 126 61 115 58 287 11/11 221 151 182 61 532

CB 177 14/14 90 46 90 29 199 11/11 118 80 97 40 258

CB 180 14/14 1846 798 1728 913 3609 11/11 3441 2399 2512 1010 7708

CB 183 14/14 432 178 410 217 819 11/11 783 513 663 243 1729

CB 187 14/14 860 332 846 370 1371 11/11 1362 950 996 387 3067

CB 194 14/14 122 54 110 60 252 11/11 255 201 209 74 719

CB 196/203 14/14 171 75 170 77 341 11/11 331 237 295 91 804

CB 199 14/14 142 57 147 58 231 11/11 236 161 197 63 513

CB 206 14/14 23 9 24 11 42 11/11 47 33 46 15 125

ΣPCBs 14/14 19562 7180 18900 10553 33581 11/11 33727 20733 27205 11216 70164

p,p'-DDE 14/14 3357 2284 2547 1038 7890 11/11 3549 2217 2903 844 8014

HCB 14/14 1530 565 1464 842 2630 11/11 2087 1343 1658 784 5465

Cis-nonachlor 14/14 64 41 50 21 176 11/11 45 26 34 24 111

Trans-nonachlor 13/14 59 47 39 10a 175 11/11 56 28 54 25 115

Oxy-chlordane 14/14 766 343 776 295 1452 11/11 1027 612 914 424 2538

ΣCHLs 14/14 889 347 863 407 1607 11/11 1129 638 1013 484 2668

ß-HCH 14/14 138 47 140 51 222 11/11 163 94 142 75 393

BDE 47 14/14 99 44 91 42 195 11/11 109 53 96 55 237

BDE 99 14/14 28 19 24 9 85 11/11 28 16 26 14 73

BDE 100 14/14 25 16 22 10 72 11/11 31 18 24 10 61

BDE 153 10/14 8 5 8 2a 18 10/11 17 16 14 2a 61

ΣPBDEs 14/14 159 71 145 63 294 11/11 185 97 167 86 432

ΣPOPs 14/14 25635 8940 23955 13517 43608 11/11 40840 22333 34834 15546 77593

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Figure 1. PCBs-OCPs-PBDEs ternary diagram for individual plasma samples of kittiwakes from

Krykkjefjellet (red circles) and Blomstrandhalvøya (black circles). Because the data was clustered in a small

area (upper diagram), original axes were re-scaled for a better circle discrimination (lower diagram).

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