Examining Niche Partitioning and Dietary Change in Pygoscelis Penguins Using Stable Isotopes

EXAMINING NICHE PARTITIONING AND DIETARY CHANGE IN PYGOSCELIS PENGUINS USING STABLE ISOTOPES

Michael J. Polito

A Dissertation Submitted to the University of North Carolina Wilmington in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

Department of Biology and Marine Biology

University of North Carolina Wilmington

2012

Approved by

Advisory Committee

Stuart R. Borrett Heather N. Koopman

Bongkeun Song William P. Patterson

Steven D. Emslie Chair

Accepted by

Robert D. Roer Dean, Graduate School

TABLE OF CONTENTS

ABSTRACT……………………………………………………………………………………………….v ACKNOWLEDGMENTS………………………………………………………………………………..vii DEDICATION…………………………………………………………………………………………...viii LIST OF TABLES……………………………………………………………………………………..….ix LIST OF FIGURES………………………………………………………………………………………..x

CHAPTER ONE: Tissue-Specific Isotopic Discrimination Factors In Gentoo Penguin ( Pygoscelis Papua ) Egg Components: Implications For Dietary Reconstruction Using Stable Isotopes……………………... 11 Introduction...... 11 Materials and Methods...... 13 Captive Penguin Diet and Tissue Collection ...... 13 Wild Penguin Diet and Tissue Collection...... 13 Sample Preparation and Isotopic Analysis ...... 14 Model and Statistical Analysis...... 19 Results...... 21 Captive Penguin Diet ...... 21 Isotopic Values of Captive Penguin Tissues...... 21 Diet-Tissue Discrimination Factors ...... 22 Stable Isotope Values and Diet Composition of Wild Penguins ...... 22 Discussion...... 23 Discrimination Factors in Avian Egg Components ...... 25 Mixing Models, Discrimination Factors, and Dietary Reconstruction...... 27 References...... 30

CHAPTER TWO: Dietary Isotopic Discrimination In Gentoo Penguin ( Pygoscelis Papua ) Feathers…..35 Introduction...... 35 Materials and Methods...... 37 Results...... 39 Discussion...... 41 References...... 47

CHAPTER THREE: Ontogenetic And Oceanographic Factors Influence The Stable Isotope Values Of A Keystone Species, The Antarctic Krill ( Euphausia Superba ): Implications For Dietary Studies Of Krill Predators…………………………………………………………………………………………………..51 Introduction...... 51 Materials and Methods...... 54 Krill and oceanographic data collection...... 54 Sample preparation and isotopic analyses...... 56 Statistical analysis of krill samples...... 57

ii Krill predator stable isotope values and dietary models...... 58 Results...... 61 Variation in Antarctic krill and oceanographic factors...... 61 Factors affecting the stable isotope values of Antarctic krill...... 63 Krill predator stable isotope values and dietary modeling...... 63 Discussion...... 67 Ontogenetic variation in the stable isotope values of krill...... 69 Oceanographic factors and krill stable isotope values...... 70 Krill predator stable isotope values and dietary mixing models...... 71 Conclusion ...... 74 References...... 74

CHAPTER FOUR: Integrating Stomach Content and Stable Isotope Analyses to Quantify the Diets of Pygoscelid Penguins ……………………………………………………………………………………... 81 Introduction...... 81 Materials and Methods...... 83 Ethics statement ...... 83 Stomach contents, feather and prey samples ...... 84 Stable isotope analysis ...... 85 Isotopic mixing models...... 86 Statistical analysis...... 87 Results...... 89 Stomach content analysis...... 89 Isotopic signatures of chick feathers and prey...... 91 Two-source SIAR models...... 94 Multi-source SIAR models ...... 94 Discussion...... 98 Stomach content analysis...... 98 Two-source, SIAR models...... 100 Multiple-source, SIAR models ...... 102 Integrating SCA and SIA when estimating seabird diets...... 103 References...... 104

CHAPTER FIVE: Stable Isotopes Reveal Regional Heterogeneity In The Pre-Breeding Distribution And Diets Of Sympatrically Breeding Pygoscelis Penguins ……………………………………………... …108 Introduction...... 108 Materials and Methods...... 112 Study area and sample collection...... 112 Sample preparation and isotopic analysis...... 113 Statistical and dietary analysis...... 114 Results...... 116 Inter-specific and spatial variation in eggshell isotopic values ...... 116 Prey items and pre-breeding diet composition...... 123 Discussion...... 126

iii Habitat use prior to breeding...... 126 Distribution prior to breeding and the Adélie ‛gap’...... 128 Penguin diets prior to breeding...... 130 Isotopic mixing model and discrimination factors...... 132 Conclusions...... 134 References...... 135

CHAPTER SIX: Sympatrically Breeding Pygoscelis Penguins Balance Niche Plasticity And Segregation Under Variable Environmental Conditions……………………………………………………………... 141 Introduction...... 141 Materials and methods ...... 145 Study site and data collection ...... 145 Stable isotope analysis ...... 147 Data analysis ...... 148 Results...... 152 Environmental conditions ...... 152 Intraspecific comparisons ...... 152 Site comparisons ...... 158 Interspecific comparisons ...... 159 Discussion...... 160 Niche position plasticity in Pygoscelis penguins...... 160 Niche width plasticity in Pygoscelis penguins...... 162 Site-specific trends in the isotopic niche of Gentoo penguins...... 163 Interspecific niche partitioning and overlap ...... 164 Pygoscelis penguin ecological niches and recent declines in Antarctic krill...... 166 References...... 169

APPENDICES…………………………………………………………………………………………...176 Appendix 1...... 177 Appendix 2...... 178 Appendix 3...... 179 Appendix 4...... 180

iv ABSTRACT

The three Pygoscelis penguin species: the chinstrap ( P. antarctica ), gentoo ( P. papua )

and Adélie penguin ( P. adeliae ) breed sympatrically in the Antarctic Peninsula region. These species utilize similar nesting habitat, have similar breeding schedules and all consume primarily

Antarctic krill ( Euphausia superba ). Previous studies suggest a general pattern of niche partitioning of these species during the breeding season. However, tradition methods of studying penguin foraging ecology are time intensive, invasive, limited in spatial and temporal scale, and biased towards dietary items that are not readily digestible. The use of naturally occurring stable isotopes ( δ15 N and δ13 C) represent an alternative approach to studying penguin foraging ecology as isotope ratios in animal tissues are largely determined by isotopic abundances in the animal’s food. However, the success of this approach is dependent on having an understanding of the isotopic values of prey and habitat sources, and an understanding how physiology affects the isotopic composition of animal tissues. To address these issues, this research quantified the stable isotope values of common penguin prey species and calculated isotopic discrimination factors between penguin diet and tissues using controlled dietary studies. These studies allowed for the quantitative estimation of Pygoscelis penguin diet composition using stable isotope mixing models. Furthermore, the accuracy of these dietary models can be increased through the use of temporally, spatially and ontogenetically appropriate prey sources and with the integration of independent data sources. When applied to egg tissues, these techniques suggest that fish and/or other high trophic-level prey species comprise a significant portion Pygoscelis penguin

diets prior to breeding, and identifies geographically distinct migratory habitats within Adélie

penguin populations along the Antarctic Peninsula. The stable isotope approach was then applied

to examine niche plasticity and segregation between Pygoscelis penguin during five breeding

v seasons that differed in the abundance and demography of Antarctic krill. All three species generally increased their consumption of higher trophic prey such as fish in years when large

Antarctic krill was less abundant. However, the higher degrees of individual niche variation, proclivity for foraging on fish, and use of nearshore, benthic habitats all likely buffer gentoo penguins from recent declines in the abundance of Antarctic krill which have been linked to declines of Adélie and chinstrap penguin populations in the Antarctic Peninsula.

vi ACKNOWLEDGMENTS

Thank you to my advisor, S. Emslie and dissertation committee (S. Borrett, H. Koopman,

W. Patterson, BK Song) for their guidance and support. Many thanks to the numerous researchers from the US Antarctic Marine Living Resources program, Oceanites Inc, Friedrich-

Schiller-University, Omaha’s Henry Doorly Zoo, the Polar Oceans Research Group, and the

British Antarctic Survey for their assistance with the collection penguin and prey tissues. Thank you to J. Evans, A. Raya Rey, M. Rider, Raytheon Polar Services, One Ocean, Linblad

Expeditions, and the National Geographic Society for invaluable logistical support. Without the help of S. Able, D. Besic, J. Blum, A. Briggs, T. Cossio, K. Dietrich, K. Durenberger, M.

Goebel, J. Hinke, C. Jones, N. Karnovsky, C. Lane, T. Lankford, H. Lynch, R. Naveen, E. Ng,

A. Miller, T. Prokopiuk, C. Reiss, J. Seminoff, C. Tobais, W. Trivelpiece, S. Trivelpiece, A.

VanCise and J. Walsh this research would not have been possible. Thank you to the many Emslie

Lab colleagues who supported me and this research: R. Brasso, B. Drummond, E. Guber, A.

Hydrusko, C. McDougall, B. McLean, A. Michaelis, E. Strickland, E. Unger, C. Vasil, G.

Winger and C. Zavalaga. This research was funded by U.S. National Science Foundation (NSF)

Office of Polar Programs (OPP) grants to S. Emslie (ANT-0125098 and ANT-0739575) and W. and S. Trivelpiece (ANT-0344275). Animal use in this study was conducted under approved animal use protocols from the Institutional Animal Care and Use Committees at the University of

California San Diego (S05480), University of North Carolina Wilmington (A0910-020), and

Omaha’s Henry Doorly Zoo (HDZ#07-800) and in accordance to Antarctic Conservation Act permits provided by NSF OPP to S. Emslie (2006-001), R. Holt (2008-008), R. Naveen (2005-

005) and G. Watters (2011-05).

vii DEDICATION

I would like to dedicate this dissertation to Steve Emslie, and Wayne and Susan

Trivelpiece. My career in science is a product of their endless guidance and encouragement. I would also like to dedicate this research to Alison Satake whose love and support made this work possible.

viii LIST OF TABLES

CHAPTER TWO: Dietary Isotopic Discrimination In Gentoo Penguin ( Pygoscelis Papua ) Feathers...... 35 Table 1...... 40 Table 2...... 43

CHAPTER FOUR: Integrating Stomach Content and Stable Isotope Analyses to Quantify the Diets of Pygoscelid Penguins………………………………………………………………………………………81 Table 1 ...... 90 Table 2...... 92 Table 3...... 95 Table 4...... 97

CHAPTER FIVE: Stable Isotopes Reveal Regional Heterogeneity In The Pre-Breeding Distribution And Diets Of Sympatrically Breeding Pygoscelis Penguins………………………………………………….108 Table 1...... 117 Table 2...... 119 Table 3...... 122 Table 4...... 124 Table 5...... 125

ix LIST OF FIGURES

CHAPTER TWO: Dietary Isotopic Discrimination In Gentoo Penguin ( Pygoscelis Papua ) Feathers…..35 Figure 1...... 42

CHAPTER THREE: Ontogenetic And Oceanographic Factors Influence The Stable Isotope Values Of A Keystone Species, The Antarctic Krill ( Euphausia Superba ): Implications For Dietary Studies Of Krill Predators…………………………………………………………………………………………………...51 Figure 1...... 55 Figure 2 ...... 64 Figure 3 ...... 66 Figure 4...... 67

CHAPTER FOUR: Integrating Stomach Content and Stable Isotope Analyses to Quantify the Diets of Pygoscelid Penguins ……………………………………………………………………………………...81 Figure 1...... 93 Figure 2...... 96 Figure 3...... 99

CHAPTER FIVE: Stable Isotopes Reveal Regional Heterogeneity In The Pre-Breeding Distribution And Diets Of Sympatrically Breeding Pygoscelis Penguins …………………………………………………108 Figure 1...... 109 Figure 2...... 120 Figure 3...... 121 Figure 4...... 127

CHAPTER SIX: Sympatrically Breeding Pygoscelis Penguins Balance Niche Plasticity And Segregation Under Variable Environmental Conditions………………………………………………………………141 Figure 1...... 146 Figure 2...... 153 Figure 3 ...... 154 Figure 4...... 156 Figure 5...... 157

x CHAPTER ONE: TISSUE -SPECIFIC ISOTOPIC DISCRIMINATION FACTORS IN GENTOO

PENGUIN (PYGOSCELIS PAPUA ) EGG COMPONENTS : IMPLICATIONS FOR DIETARY

1 RECONSTRUCTION USING STABLE ISOTOPES

Introduction

Stable isotope abundances of carbon and nitrogen in animal biomass are largely determined by isotopic abundances in the animal’s food, with specific tissues reflecting the diet at the time of synthesis (Mizutani et al., 1990). Therefore by sampling tissues produced at different locations, or times of the year, it is possible to examine variations in an animal’s diet over both time and space (Schell et al., 1989; Hobson, 1999; Cherel et al., 2000; Rubenstein and

Hobson, 2004; Quillfeldt et al., 2005). For example, the isotopic analyses of specific avian tissues have the potential to provide information on the diets and foraging habitats of penguins throughout much of their annual cycles. Egg tissues can provide information on female diets during a brief period prior to breeding (Astheimer and Grau, 1985; Emslie and Patterson, 2007;

Strickland et al., 2008). Chick feathers reflect parental diets during the chick-rearing period, while adult feathers provide information on diets and foraging habitats after the breeding season when adults undergo molt (Penny, 1967; Cherel et al., 2000; Ainley et al., 2003; Cherel et al.,

2005; Quillfeldt et al., 2005).

The isotopic values of animal tissues are useful for examining diets due to a general enrichment in the heavier isotopes from prey tissues to the tissues of the predators consuming them (DeNiro and Epstein, 1978; DeNiro and Epstein, 1981; Minagawa and Wada, 1984).

1 This chapter has been published as: Polito M.J., Fisher S., Tobias C.R., and Emslie S.D. 2009. Tissue- specific isotopic discrimination factors in gentoo penguin (Pygoscelis papua) egg components: Implications for dietary reconstructions using stable isotopes. Journal of Experimental Marine Biology and Ecology 372:106-112 However, the amount of enrichment depends on the tissues and the physiological pathways that produce them (Hobson and Clark, 1992). This can result in predator tissues of differing composition having unique isotope values even though they are synthesized under the same diet.

These differing discrimination factors (the differences in isotopic ratios between prey items and consumer tissues) in animal tissues make it difficult to both initially identify prey items and directly compare the isotopic values of different tissue types.

Researchers have been able to determine the discrimination factors of specific animal tissues using controlled laboratory or zoo experiments (Mitzutani et al., 1992; Hobson and Clark,

1992; Hobson, 1995; Cherel et al., 2005; Seminoff et al., 2007; Reich et al., 2008). These experiments generally involve keeping animals on an isotopically consistent diet for a period of time. Samples of the animal tissues are then compared to the isotopic values of their food. Most avian studies, including those on penguins, have focused on discrimination factors in blood and feathers and little is known about the magnitude of enrichment in other tissues such as egg components (Vanderklift and Ponsard, 2003).

Using a captive collection of gentoo penguins ( Pygoscelis papua ) at the Henry Doorly

Zoo, Omaha, Nebraska, we determined the tissue-specific isotopic discrimination factors

between penguin diet and specific egg tissues. When examined in light of their specific

discrimination factors it is likely that, similar to blood and feathers, egg tissues can be used to

provide information on the diets and foraging habitats of penguins. We tested this possibility by

using a single isotope, two-source linear mixing model to reconstruct the diet composition of a

wild population of gentoo penguins at Cape Sherriff, Livingston Island, Antarctica. We

compared independent estimates of penguin diets based on isotopic values of eggshell organics

12 and shell membrane, two tissues that are produced during the egg-laying period, to validate the use of the tissue-specific discrimination factors derived in our captive study.

Materials and Methods

Captive Penguin Diet and Tissue Collection

We studied gentoo penguins from a captive breeding population maintained at the Henry

Doorly Zoo in Omaha, Nebraska. Penguins were kept on a consistent diet of Atlantic herring

(Clupea harengus ) for eight months prior to the start of tissue collection. To confirm the

isotopic consistency of penguin diets, five individual herring were randomly sampled per month

during the two months prior to the mean date of egg-laying (11 November 2007). We measured

the weight (g) and standard length (mm) of these 10 fish, which were then stored frozen prior to

isotopic analysis.

Eggs were marked as they were laid using a black permanent marker to track laying

order. Approximately seven days after laying eggs were candled to determine if they were fertile.

A sub-sample of five infertile eggs was collected and frozen for later isotopic analysis of egg

albumen and yolk. Following Hobson (1995), we assume that infertile eggs would be similar to

fertilized eggs in isotopic value, as fertilization is independent of egg formation. The remaining

eggs were allowed to be incubated by their parents until they either hatched or were determined

to be addled, at which time eggshell and membrane samples were collected. This provided us

with eggshell and shell membrane samples from nine two-egg clutches and two three-egg

clutches for a total of 20 eggs.

Wild Penguin Diet and Tissue Collection

13 During the austral summer of 2006-07, we collected penguin egg tissues from a colony of approximately 800 breeding pairs of gentoo penguins at Cape Sherriff, Livingston Island,

Antarctica (62°28’S, 60°47’W). In November and December 2006, 20 eggshells (including attached shell membranes) were opportunistically collected from hatched, depredated, addled or infertile eggs. Penguin prey items were collected during trawls conducted in the vicinity of the

South Shetland Islands by the U.S. Antarctic Marine Living Resource Program (US AMLR) during the austral summers of 2000-01 to 2006-07. Sampled prey species were representative of the two major components of gentoo penguin diets in this region: krill ( Euphausia superba ; n =

10) and fish ( Lepidonotothen squamifrons ; n = 7; Volkman et al., 1980; Karnovsky, 1997).

While whole krill were frozen prior to analysis, fish muscle was stored in 70% ethanol as

freezing of these samples was not possible in the field. We assume no effects of the different

storage methods as storage in ethanol does not significantly alter the isotopic composition of

tissues (Hobson et al., 1997).

Sample Preparation and Isotopic Analysis

Whole fish, fish muscle, and whole krill samples were homogenized and then dried for 48

hours in an oven at 60˚C. Eggshell, shell membrane, albumen and yolk were separated by hand

and sub-samples of yolk and albumen were collected and freeze-dried. After drying, fish, krill,

and penguin albumen and yolk were ground to a powder using an analytical mill. Lipids were

then extracted from whole fish and krill, fish muscle, and penguin yolk samples using a Soxhlet

apparatus with a 1:1 Petroleum-Ether : Ethyl-Ether solvent mixture for 6-8 hours (Seminoff et

al., 2007). The mean C/N ratio of lipid-free tissues ranged from 3.1±0.1 to 4.2±0.2; comparable

to values found in previous studies (Table 1, 2, 3; Sweeting et al., 2006; Cherel et al., 2007).

14 Table 1. The carbon to nitrogen ratio (C/N) and stable nitrogen and carbon isotope concentrations (mean ± SD) in food and egg components of captive gentoo penguins.

‰ Captive penguin food n C/N ratio and tissue δ15 N δ13 C

Herring: Whole fish 10 3.2±0.1 12.3±0.1 -19.4±0.3 Fish muscle 10 3.2±0.1 12.5±0.3 -19.5±0.3 Eggshell organics: Egg 1 9 3.5±0.1 13.9±0.3 -18.0±0.4 Egg 2 9 3.5±0.1 14.4±0.5 -18.0±0.3 Egg 3 2 3.5 14.1 -17.8 All Eggs 20 3.5±0.1 14.1±0.5 -18.0±0.4 Eggshell carbonates: Egg 1 9 - - -12.2±0.4 Egg 2 9 - - -12.1±0.8 Egg 3 2 - - -12.0 All Eggs 20 - - -12.1±0.7 Shell membrane: Egg 1 9 3.2±0.1 16.7±0.4 -16.6±0.5 Egg 2 9 3.2±0.1 16.6±0.5 -16.5±0.4 Egg 3 2 3.2 17.0 -16.6 All Eggs 20 3.2±0.1 16.7±0.5 -16.6±0.9 Albumen: 5 4.0±0.1 17.0±0.5 -18.6±0.7 Yolk: 5 4.2±0.2 16.0±0.5 -19.4±0.5

15 Table 2. Discrimination factors and two sample t-tests between captive penguin food and penguin tissues.

δ15 N δ13 C Tissue Food Discrimination Discrimination t (P) t (P) factor (‰) factor (‰) Eggshell organics Whole Fish 1.8 11.73 (<.0001) 1.4 10.82 (<.0001) Fish Muscle 1.6 10.17 (<.0001) 1.5 11.40 (<.0001) Eggshell carbonates Whole Fish - - 7.2 32.25 (<.0001) Fish Muscle - - 7.4 32.43 (<.0001) Shell membrane Whole Fish 4.4 27.54 (<.0001) 2.8 17.19 (<.0001) Fish Muscle 4.2 25.38 (<.0001) 2.9 17.58 (<.0001) Albumen Whole Fish 4.7 27.10 (<.0001) 0.8 3.00 (.010) Fish Muscle 4.5 22.83 (<.0001) 0.9 3.38 (.005) Yolk Whole Fish 3.7 23.50 (<.0001) 0.0 -0.19 (.855) Fish Muscle 3.5 19.07 (<.0001) 0.1 0.42 (.690)

16 Table 3. The carbon to nitrogen ratio (C/N) and stable nitrogen and carbon isotope concentrations (mean ± SD) in food and egg components of wild gentoo penguins at Cape Sheriff, Livingston Island, Antarctica, 2006.

‰ Wild penguin food n C/N ratio and tissue δ15 N δ13 C

Krill E. superba 10 3.7±0.2 3.3±0.7 -25.3±1.0 Fish L. squamifrons 7 3.1±0.1 12.1±0.5 -23.6±0.6 Eggshell organics 20 3.7±0.3 8.2±0.6 -23.4±0.3 Shell membrane 20 3.2±0.1 10.5±0.4 -22.4±1.0

17 Lipid extracted herring and wild penguin prey items were not acidified prior to isotopic analysis.

Approximately 0.5 mg of each of the above materials was loaded into tin cups for δ13 C and δ15 N analysis.

Isotope values of the organic matrix of penguin eggshells (hereafter called eggshell organics) were obtained after the removal of carbonate by dissolving ~10 mg of cleaned eggshell in a silver capsule through titration with five 20 µL aliquots of 6 N HCL. Acidified samples were stored at room temperature under a fume hood for 24 hours, and then dried for at least 48 hours in an oven at 60 °C. Acidified samples were not rinsed prior to drying so as to avoid biasing δ15 N values (Jacob et al., 2005). The above tissues were flash-combusted (Costech

ECS4010 elemental analyzer) and analyzed for carbon and nitrogen isotopes ( δ13 C and δ15 N)

through an interfaced Thermo Delta V Plus continuous flow stable isotope ratio mass

spectrometer (CFIRMS). Raw δ values were normalized on a two-point scale using depleted and enriched glutamic acid reference materials USGS-40 and USGS-41. Sample precision was 0.1‰ and 0.2‰, for δ13 C, and δ15 N, respectively.

Isotopic values of eggshell carbonates ( δ13 C) were determined by reacting 0.2 mg of

powdered eggshell with 0.1 ml of ultra-pure phosphoric acid at 70 °C. The resulting CO 2 was

introduced to a Thermo Delta V Plus CFIRMS through a Thermo Gas Bench II interface. Raw

δ13 C values were normalized to NBS-19 (calcite) and L-SVEC (lithium carbonate) referenced materials, with a sample precision of 0.05‰.

Stable isotope abundances are expressed in δ notation in per mill units (‰), according to

the following equation:

δX = [( Rsample / Rstandard ) - 1] · 1000

18 13 15 13 12 15 14 Where X is C or N and R is the corresponding ratio C / C or N / N. The Rstandard values

13 15 were based on the PeeDee Belemnite (VPDB) for C and atmospheric N 2 for N.

Model and Statistical Analysis

We quantified the contribution of krill ( E. superba ) and fish ( L. squamifrons ) to the diet

of gentoo penguins at Livingston Island by using a single-isotope, two-source linear mixing

model (Phillips and Gregg, 2001). Model results provide standard errors and confidence

intervals for source proportion estimates that account for the observed variability in the isotopic

signatures for the sources as well as the mixture. We used δ15 N values of the two common prey

items in this model because the difference in prey isotopic signatures was larger for δ15 N than for

δ13 C which showed some source/end-member overlap (Fig. 1). A previous study found that

using isotopic signatures of whole prey items with discrimination factors of prey muscle (or the

reverse) can lead to incorrect estimates of diet composition (Cherel et al., 2005). As our two

prey items differ in this regard (whole krill and fish muscle), we corrected whole krill and fish

muscle values independently (by adding whole prey and prey muscle discrimination factors,

respectively) prior to incorporating them into the model.

Data were examined for normality and equal variance and non-parametric methods were

employed when necessary. All tests were two-tailed and significance was assumed at the 0.05

level. Statistical calculations were preformed Number Cruncher Statistical Systems (NCSS;

Hintze, 2004).

Figure 1. Raw (A) and corrected (B) stable nitrogen and carbon isotope values for eggshell organics (EO; n = 20) and shell membrane (SM; n = 20) from gentoo penguin eggshells collected at Cape Sheriff, Livingston Island, Antarctica, 2006, relative to two common prey items, krill (Euphausia superba; n = 10 ) and fish ( Lepidonotothen squamifrons; n = 7). Penguin tissue values were corrected by subtracting the tissue-specific whole prey discrimination factors derived in this study. Error bars represent standard deviation.

Results

Captive Penguin Diet

We found no significant differences in the length and mass of herring collected during the two months prior to egg-laying ( Mann-Whitney U ; Z = 0.31, 0.32 and P = 0.754, 0.750, for body mass and standard length respectively). Furthermore, herring fed to penguins during these months did not differ significantly in isotopic values (Table 1; Z= 1.35, 1.88, 1.36, 0.94 and P =

0.175, 0.060, 0.175, 0.347 for whole fish δ15 N, fish muscle δ15 N, whole fish δ13 C and fish muscle

δ13 C, respectively). We therefore pooled these 10 herring for subsequent analysis.

The δ15 N values of herring muscle were slightly enriched (0.2‰ ± 0.3‰), on average, relative to whole fish. While this trend was statistically significant (Table 1; paired t-test : t =

2.29, P = 0.048) the difference between whole fish and fish muscle was within the magnitude of our δ15 N sample precision. In contrast, herring δ13 C did not differ significantly between whole

fish and fish muscle ( paired t-test : t = 0.55, P = 0.595).

Isotopic Values of Captive Penguin Tissues

The isotopic values of penguin eggs varied significantly among tissue types in both δ15 N

and δ13 C (Table 1). While shell membrane and albumen were isotopically similar in δ15 N values,

eggshell organics and yolk were significantly segregated and depleted compared to the δ15 N

values of all other egg tissues ( ANOVA : F3,50 = 109.52, P < 0.001). Egg tissues were also

13 significantly segregated by their δ C values (F 4,70 = 420.28, P < 0.001), with eggshell

carbonates, shell membrane, eggshell organics, albumen, and yolk increasingly depleted in 13 C,

respectively. Eggshell carbonates were highly enriched in 13 C relative to eggshell organics, by

5.8‰±0.5 on average ( paired t-test : t = 47.26, P < 0.001). Laying order affected the δ15 N values

21 of eggshell organics, with second eggs enriched in 15 N by 0.5‰±0.4, on average, relative to the

first egg of a clutch (Table 1; paired t-test : t = 3.79, P = 0.005). In contrast, we found no effect

of egg order on the δ15 N value of shell membrane ( paired t-test : t = 0.43, P = 0.680) or the δ13 C

values of eggshell organics, eggshell carbonates or shell membrane ( paired t-test : t = 0.50, 0.34,

1.11 and P = 0.631, 0.744, 0.288 for eggshell organics, eggshell carbonates and shell membrane, respectively).

Diet-Tissue Discrimination Factors

All penguin egg tissues examined were significantly enriched in 15 N relative to both

whole fish and fish muscle (Table 2). Furthermore, δ15 N discrimination factors varied by tissue type and whether values were based off of whole fish or fish muscle. δ15 N discrimination

factors were lowest in eggshell organics, highest in albumen and were slightly higher when

calculated from whole fish (1.8-4.7‰) than from fish muscle (1.6-4.5‰).

Tissues were also variably enriched in 13 C relative to diet with yolk as the only tissue not

enriched relative to both whole fish and fish muscle (Table 2). δ13 C discrimination factors were higher in inorganic (carbonate) egg tissues (7.2-7.4‰) relative to organic egg components (0.0-

2.9‰). There were no trends in δ13 C discrimination factors with age or when calculated from whole fish compared to when calculated from fish muscle (Table 2).

Stable Isotope Values and Diet Composition of Wild Penguins

We found significant differences among the raw isotopic values of wild penguin tissues and their common prey species (Table 3; ANOVA : F3,57 = 492.88, 30.24 and P < 0.001, 0.001, for

δ 15 N and δ 13 C, respectively). Post-hoc analysis determined that whole krill ( E. superba ),

22 eggshell organics, shell membrane and fish muscle (L. squamifrons ) all differed significantly in δ

15 N values from lowest to highest, respectively (Fig. 1A). The δ 13 C values of eggshell organics and fish muscle were isotopically similar, while these two groups were significantly enriched in

δ 13 C relative to whole krill and depleted in δ 13 C relative to shell membrane.

Pair-wise comparisons of wild gentoo penguin egg tissues also found that shell

membrane was significantly enriched in both δ 15 N (+2.4±0.8) and δ 13 C (+1.1±1.1) values compared to eggshell organics (Fig 1A; paired t-test : t = 13.4, 4.33 and P < 0.001, 0.001 for δ

15 N and δ 13 C respectively). The isotopic differences found between wild penguin shell membrane and eggshell organics were similar to those found in our captive penguins tissues for both δ 15 N (+2.5±0.5) and δ 13 C (+1.4±0.4) values ( t-test : t = 0.73, 1.30 and P = 0.468, 0.203 for δ

15 N and δ 13 C respectively). Furthermore, after correction with the tissue-specific discrimination factors derived in this study wild eggshell organics and shell membrane no longer differed in isotopic values (Fig 1B; paired t-test : t = 1.20, 1.35 and P = 0.243, 0.193 for δ 15 N and δ 13 C respectively).

Using corrected δ 15 N values, our isotopic model calculated similar diet compositions,

within 3.5%, during the egg-laying period whether based on eggshell organics or shell membrane

(Fig. 2). Our results suggest that female gentoo penguins at Cape Shirreff fed more heavily on

krill than fish (64.0-67.4% vs. 32.6-36.0%) during the egg-laying period of 2006.

Discussion

Our study highlights the importance of using tissue-specific isotopic discrimination

factors when reconstructing avian diets using isotopic mixing models. Similar to other studies,

we found that discrimination factors can differ substantially among specific tissues, likely

Figure 2. Predicted diet compositions of gentoo penguins at Cape Sherriff, Livingston Island, Antarctica, 2006-07, during the egg-laying period based on stable isotope analysis of eggshell organics and shell membrane. Estimates use the single isotope ( δ15 N), two-source (krill and fish) mixing model described by Phillips and Greg (2001). Error bars represent 95% confidence intervals.

24 reflecting differences in the biochemical and metabolic processes of tissue synthesis (Hobson and Clark, 1992; Hobson, 1995). Therefore evaluating the raw isotopic values of tissues, without incorporating tissue-specific discrimination factors, can lead to incorrect or conflicting assumptions about animal diets. Furthermore, these findings reaffirm the idea that in many cases generalized discrimination factors might not adequately represent isotopic discrimination in specific avian tissues and their use could lead to error in dietary reconstruction using isotopic mixing models.

Discrimination Factors in Avian Egg Components

Our study provides the first available data on isotopic discrimination factors in the egg components of penguins and, to our knowledge, the only data available from a captive seabird.

In general, the patterns of dietary isotopic fractionation we found in this study agree with data on egg components of other avian species (Von Schirnding et al., 1982; Shaffner and Swart, 1991;

Hobson, 1995; Johnson, 1995; Johnson et al., 1998). The δ13 C discrimination factors for gentoo

penguin eggshell organics, shell membrane, albumen and yolk were comparable to values found

in other groups of birds (Table 4). Gentoo penguin eggshell carbonates, which are derived from

metabolic carbon (Shaffner and Swart, 1991), are highly enriched in 13 C relative to their diets,

similar to other species. However, the magnitude of this enrichment is lowest in gentoo

penguins relative to other species with available data. The δ13 C discrimination factor for gentoo

penguin eggshell carbonates is 3.9-6.3‰ lower than carnivorous and other piscivorous species

and 7.1-9.0‰ lower than herbivorous species (Table 4). However, data for two other seabird

species, the white-tailed tropicbird (Phaethon lepturus ) and the elegant tern (Sterna elegans ),

come from a study in the wild where diets could not be controlled or completely qualified

25 Table 4. Estimates of δ15 N and δ13 C discrimination factors between food and avian egg components. Discrimination Factor (‰) Egg Tissue, Species Food Items Reference δ15 N δ13 C Eggshell organics: Gentoo penguin Pygoscelis papua Herring 1.8 1.4 This study Ostrich Struthio camelus C3 plants 2.1 Von Schirnding et al. 1982 Commercial diet 3.0 1.5 Johnson et al. 1998 Quail Coturnix japonica Commercial diet 1.0 2.0 Johnson 1995 Eggshell carbonate: Gentoo penguin Pygoscelis papua Herring 7.2 This study White-tailed tropicbird Phaethon lepturus Fish and squid a 13.5 Schaffner and Swart 1991

Elegent tern Sterna elegans Fish a 12.2 Schaffner and Swart 1991 Ostrich Struthio camelus C3 plants 16.2 Von Schirnding et al. 1982 Commercial diet 16.2 Johnson et al. 1998 Peregrine falcon Falco peregrinus Quail 11.1 Hobson 1995 Praire falcon Falco mexicanus Quail 11.6 Hobson 1995 Gyrfalcon Falco rusticolis Quail 11.2 Hobson 1995 Mallard Anas platyrhynchos Commercial diet 14.3 Hobson 1995 Quail Coturnix japonica Commercial diet 15.3 Johnson 1995 Commercial diet 15.6 Hobson 1995 Shell membrane: Gentoo penguin Pygoscelis papua Herring 4.4 2.9 This study Peregrine falcon Falco peregrinus Quail 3.5 2.6 Hobson 1995 Praire falcon Falco mexicanus Quail 3.2 3.0 Hobson 1995 Mallard Anas platyrhynchos Commercial diet 4.4 3.7 Hobson 1995 Quail Coturnix japonica Commercial diet 4.1 3.5 Hobson 1995 Albumen: Gentoo penguin Pygoscelis papua Herring 4.7 0.8 This study Peregrine falcon Falco peregrinus Quail 3.1 0.9 Hobson 1995 Praire falcon Falco mexicanus Quail 3.1 0.9 Hobson 1995 Gyrfalcon Falco rusticolis Quail 3.3 0.8 Hobson 1995 Mallard Anas platyrhynchos Commercial diet 3.0 1.4 Hobson 1995 Quail Coturnix japonica Commercial diet 2.4 1.6 Hobson 1995 Yolk (Lipid-free): Gentoo penguin Pygoscelis papua Herring 3.5 0.0 This study Peregrine falcon Falco peregrinus Quail 3.5 0.0 Hobson 1995 Praire falcon Falco mexicanus Quail 3.5 0.1 Hobson 1995 Gyrfalcon Falco rusticolis Quail 3.6 0.1 Hobson 1995 Mallard Anas platyrhynchos Commercial diet 3.1 -0.1 Hobson 1995 Quail Coturnix japonica Commercial diet 3.4 0.1 Hobson 1995

a Food items collected in the wild from adult regurgitates or from fish found near nesting sites.

26 (Shaffner and Swart, 1991). Thus our data may provide a more robust estimate of eggshell carbonate δ13 C discrimination factors in a piscivorous bird.

Our results agree with previous findings that the magnitude of 13 C enrichment in eggshell carbonate is higher in herbivores than in carnivores (Table 4). Comparable differences in the

δ13 C discrimination factors of herbivores and carnivores have been observed in the inorganic fraction of bone (Krueger and Sullivan, 1984), which suggests a similar mechanism might be responsible for patterns of enrichment in these two tissues. Hobson (1995) proposed that the greater proportion of lipids, which are depleted in 13 C, consumed by carnivores could lower the

δ13 C values of their eggshell relative to the eggshell of herbivores. This trend also could be due

to the export of isotopically light CO 2 via digestive gasification of carbohydrates, leading to more positive δ13 C values in herbivorous birds’ eggshell carbonates (Shaffner and Swart, 1991).

In contrast, the δ 15 N discrimination factors of egg components do not appear to differ between herbivores and carnivores. Gentoo penguins eggshell organics, shell membrane and yolk δ 15 N discrimination factors were similar to values that have been determined in other

species, while albumen was slightly more enriched (Table 4). The principle source of nitrogen in

egg tissues are the amino and R-groups of amino acids. The nitrogen isotope ratios of individual

amino acids exhibit consistent offsets relative to diet, with most amino acids enriched by 3.0‰

on average (Hare et al., 1991). While many of the tissue-specific δ 15 N discrimination factors

derived in this study significantly differed, the majority exhibited an offset from diet to tissue

within the 3.0-5.0‰ enrichment predicted per trophic level (DeNiro and Epstein, 1981;

Minagawa and Wada, 1984).

Mixing Models, Discrimination Factors, and Dietary Reconstruction

27 Isotopic mixing models are commonly used to determine the relative contribution of multiple food sources to an animal’s diet (Hobson, 1999; Post, 2002). These models are based on geometric procedures that reconstruct animal diets based on the mean δ15 N and/or δ13 C values

of each food source after correcting for the discrimination factor of the consumer tissue (Kline et

al., 1993; Phillips and Gregg, 2001; Phillips and Koch, 2002; Phillips and Gregg, 2003).

Accurate discrimination factors are important as models can be very sensitive to variations in

these values (Phillips and Koch, 2002; Post, 2002; Caut et al., 2008a). Even so, many studies

have used generalized discrimination factors without taking into account species, age, tissue, or

diet (Inger et al., 2006; Major et al., 2006; Reich and Worthy, 2006, Tierney et al., 2008). The

use of generalized discrimination factors across the tissues examined in our study would be

inappropriate as discrimination factors varied with tissue type and one tissue, eggshell organics,

deviated from the generalized discrimination factors of 3.0-5.0‰ for δ15 N (DeNiro and Epstein,

1981; Minagawa and Wada, 1984). For example, using a generalized δ15 N discrimination factor of 3.0‰ when reconstructing penguin diets based on shell membrane and eggshell organics would lead to conflicting over (+15.1%) and under estimates (-14.5%) of the actual abundance of krill in egg-laying diets, respectively. These results suggest that variation in discrimination factors among avian tissues can lead to erroneous estimates of diet composition and that caution is needed when using generalized discrimination factors.

In many cases the identification of exact discrimination factors (for a target species, tissue, and natural diet) is not possible in a controlled experiment. Many species are not available in captivity, some tissues must be sampled in an invasive or destructive manner, and, often, natural diets are impractical or impossible to reproduce in a laboratory setting. There is some evidence to suggest that differences in the elemental composition (C/N ratio) or isotopic

28 ratio can affect discrimination factors (see Caut et al., 2008b). Gentoo penguins naturally feed on variable amounts of crustaceans and fishes (Volkman et al., 1980; Karnovsky, 1997), but not on herring, the fish used in our captive study. While the lipid extracted C/N ratio of wild penguin food (krill and fish) and captive penguin food (herring) examined in our study were comparable (Table 1, 3), these species do differ in their isotopic composition. It is possible that isotopic differences between natural and captive diets, and their effects on discrimination factors, could affect the accuracy of dietary reconstructions using isotopic mixing models. While further laboratory experiments are needed to verify the effects of dietary isotopic ratios, a proposed linear relationship between dietary isotopic values and discrimination factors could help correct any confounding affects of differing dietary isotopic ratios (Caut et al., 2008b). Alternatively, mixing models that propagate uncertainty in discrimination factors can allow researchers to include these sources of error when calculating diet compositions (Moore and Semmens, 2008).

Cherel et al. (2005) found that using the δ15 N values of whole prey items with

discrimination factors of prey muscle (or the reverse) led to incorrect estimates of diet

composition. This finding was primarily due to an observed ~0.8‰ difference in the δ15 N values of fish muscle and whole fish. Disparity between the isotopic values of whole prey and prey muscle may be due to differences in protein turnover, metabolic routing and the macromolecule composition of whole fish and fish muscle (Cherel et al., 2005). When examining one of the same fish species (Atlantic herring) used in Cherel et al.’s study, we observe a much smaller difference between prey muscle and whole prey δ15 N values (0.2 vs. 0.8‰). These results

suggest that any isotopic differences between prey muscle and whole prey are not constant and

that variation from an assumed constant will directly impact the magnitude of isotopic

discrimination factors based on prey muscle. However, as the difference between whole

29 individuals and muscle tissue is unknown for gentoo penguin prey species, it is impossible for us to asses how this may have affected our model results. Due to these unknown effects, we suggest the preferential use of whole prey items when estimating the diets of wild penguins. In addition, isotopic discrimination factors based on prey muscle should only be used when the isotopic disparity between specific whole prey items and prey muscle tissue is well known.

In conclusion, the isotopic analysis of penguin tissues, including egg components, has great potential to provide information on the diets and foraging habitats of penguins throughout much of their annual cycles. Additionally, many tissues can be sampled non-invasively, with limited handling of live animals or through the collection of carcasses and depredated, infertile or addled eggs. However, as isotopic discrimination can be affected by species, tissue, and diet- specific variations, we recommend caution when comparing the isotopic values across tissues when specific isotopic discrimination factors are not available. Future work, especially controlled laboratory experiments, is needed to further explore sources of variation in isotopic discrimination factors and the precision with which they need to be estimated in order to yield accurate estimates of diet compositions.

References

Ainley, D.G., Ballard, G., Barton, K.J., Karl, B.J., Rau, G.H., Ribic, C.A., Wilson, P.R., 2003. Spatial and temporal variation of diet within a presumed metapopulation of Adélie penguins. Condor 105, 95–106.

Astheimer, L.B., Grau, C.R., 1985. The timing and energetic consequences of egg formation in the Adélie penguin. Condor 87, 256–268.

Caut, S., Angulo, E., Courchamp, F., 2008a. Caution on isotopic model use for analyses of consumer diet. Can. J. Zool. 86(5), 438–445.

Caut, S., Angulo, E., Courchamp, F., 2008b. Discrimination factors ( δ15 N and δ13 C) in an omnivorous consumer: effect of diet isotopic ratio. Funct. Ecol. 22, 255–263.

Cherel, Y., Hobson, K.A., Weimerskirch, H., 2000. Using stable-isotope analysis of feathers to distinguish moulting and breeding origins of seabirds. Oecologia 122, 155–162.

Cherel, Y., Hobson, K.A., Hassani, S., 2005. Isotopic discrimination factors between food and blood and feathers of captive penguins: Implications for dietary studies in the wild. Physiol. Biochem. Zool. 78(1), 106–115.

Cherel, Y., Hobson, K.A., Guinet, C., Vanpe, C., 2007. Stable isotopes document seasonal changes in trophic niches and winter foraging individual specialization in diving predators from the Southern Ocean. J Anim Ecol 76:826–836.

DeNiro, M.J., Epstein, S., 1978. Influence of diet on the distribution of carbon isotopes in animals. Geochim. Cosmochim. Acta. 42:495–506.

DeNiro, M.J., Epstein, S., 1981. Influence of diet on the distribution of nitrogen isotopes in animals. Geochim. Cosmochim. Acta. 45, 341–351.

Emslie, S.D., Patterson, W.P., 2007. Abrupt recent shift in δ13 C and δ15 N values in Adélie penguin eggshell in Antarctica. Proc. Nat. Acad. Sci. USA 104, 11666–11669.

Hare, P.E., Fogel, M.L., Stafford, T.W. Jr, Mitchell, A.D., Hoering, T.C., 1991. The isotopic composition of carbon and nitrogen in individual amino acids isolated from modern and fossil proteins. J. Archaeol. Sci. 18, 277–292.

Hintze, J., 2004. NCSS and PASS. Number cruncher statistical systems, Kaysville, Utah www.ncss.com

Hobson, K.A., 1995. Reconstructing avian diets using stable carbon and nitrogen isotope analysis of egg components: Patterns of isotopic fractionation and turnover. Condor 97, 752– 762.

Hobson, K.A., 1999. Tracing origins and migration of wildlife using stable isotopes: a review. Oecologia 120, 314–326.

Hobson, K.A., Clark, R.G., 1992. Assessing avian diets using stable isotopes. II. Factors influencing diet-tissue fractionation. Condor 94, 189–197.

Hobson, K.A., Gibbs, H.L., Gloutney, M.L., 1997. Preservation of blood and tissue samples for stable-carbon and stable-nitrogen isotope analysis. Can. J. Zool. 75, 1720–1723.

Inger, R., Ruxton, G.D., Newton, J., Colhoun, K., Mackie, K., Robinson, J.A., Bearhop, S., 2006. Using daily ration models and stable isotope analysis to predict biomass depletion by herbivores. J. Appl. Ecol. 43, 1022–1030.

31 Jacob, U., Mintenbeck, K., Brey, T., Knust, R., Beyer, K., 2005. Stable isotope food web studies: a case for standardized sample treatment. Mar. Ecol. Prog. Ser. 287, 251–253.

Johnson, B.J., 1995. The stable isotope biogeochemistry of ostrich eggshell and its application to late Quaternary paleoenvironmental reconstructions in South Africa. Ph.D. dissertation, University of Colorado.

Johnson, B.J., Fogel, M.L., Miller, G.H., 1998. Stable isotopes in modern ostrich eggshell: A calibration for paleoenvironmental applications in semi-arid regions of southern Africa. Geochim. Cosmochim. Acta. 62(14), 2451–2461.

Karnovsky, N.J., 1997. The fish component of Pygoscelis penguin diets. M.S. thesis, Montana State University Bozeman.

Kline, T.C. Jr, Goering, J.J., Mathisen, O.A., Poe, P.H., Parker, P.L., Scalan, R.S., 1993. Recycling of elements transported upstream by runs of Pacific salmon. II. δ15 N and δ13 C evidence in the Kvichak River watershed, Bristol Bay, southwestern Alaska. Can. J. Fish. Aquat. Sci. 50, 2350–2365.

Krueger, H.W., Sullivan, C.H., 1984. Models of carbon isotope fractionation between diets and bone. In: Turnland, J.R., Johnson, P.E. (Eds.) Stable isotopes in nutrition. ACS. Symp. Ser. 258., American Chemical Society, Washington, pp. 205–220.

Major, H.L., Jones, I.L., Charette, M.R., Diamond, A.W., 2006. Variations in the diet of introduced Norway rats ( Rattus norvegicus ) inferred using stable isotope analysis. J. Zool. 271(4), 463–468.

Minagawa, M., Wada, E., 1984. Stepwise enrichment of 15 N along food chains: further evidence and the relation between δ15 N and animal age. Geochim. Cosmochim. Acta. 48, 1135–1140.

Mizutani, H., Fukuda, M., Kabaya, Y., Wada, E., 1990. Carbon isotope ratio of feathers reveals feeding behavior of cormorants. Auk 107, 400–403.

Mizutani, H., Fukuda, M., Kabaya, Y., Wada, E., 1992. δ13 C and δ15 N enrichment factors of feathers of 11 species of adult birds. Ecology 73(4), 1391–1295.

Moore, J.W., Semmens, B.X., 2008. Incorporating uncertainty and prior information into stable isotope mixing models. Ecol. Lett. 11, 470–480.

Penny, R.L., 1967. Molt in the Adélie penguin. Auk 84, 61–71.

Phillips, D.L., Gregg, J.W., 2001. Uncertainty in source partitioning using stable isotopes. Oecologia 127, 171–179. (Erratum, 128, 304)

Phillips, D.L., Koch, P.L., 2002. Incorporating concentration dependence in stable isotope mixing models. Oecologia 130, 114–125.

Phillips, D.L., Gregg, J.W., 2003. Source partitioning using stable isotopes: coping with too many sources. Oecologia 136, 261–269.

Post, D.M., 2002. Using stable isotopes to estimate trophic position: models, methods, and assumptions. Ecology 83, 703–718.

Quillfeldt, P., McGill, R.A.R., Furness, R.W., 2005. Diet and foraging areas of Southern Ocean seabirds and their prey inferred from stable isotopes: review and case study of Wilson’s storm- petrel. Mar. Ecol. Prog. Ser. 296, 295–304.

Reich, K., Worthy, G.A.J., 2006. An isotopic assessment of the feeding habits of free-ranging manatees. Mar. Ecol. Prog. Ser. 322, 303–309.

Reich, K., Bjorndal, K., Martínez del Rio, C., 2008. Effects of growth and tissue type on the kinetics of 13 C and 15 N incorporation in a rapidly growing ectotherm. Oecologia 155(4), 651– 663.

Rubenstein, D.R., Hobson, K.A., 2004. From birds to butterflies: Animal movement patterns and stable isotopes. Trends. Ecol. Evol. 19, 256-263.

Schaffner, F.C., Swart, P.K., 1991. Influence of diet and environmental water on the carbon and oxygen isotopic signatures of seabird eggshell carbonate. Bull. Mar. Sci. 48, 23–38.

Schell. D.M., Saupe, S.M., Haubenstock, N., 1989. Bowhead whale ( Balaena mysticetus ) growth and feeding as estimated by δ13 C techniques. Mar. Biol. 103, 433–443.

Seminoff, J.A., Bjorndal, K.A., Bolten, A.B., 2007. Stable carbon and nitrogen isotope discrimination and turnover in pond sliders trachemys scripta : Insights for trophic study of freshwater turtles. Copeia 2007(3), 534–542.

Strickland, M.E., Polito, M., Emslie, S.D., 2008. Spatial and seasonal variation in Adélie penguin diet as inferred from stable isotope analysis of eggshell. J. N. C. Acad. Sci. 124(3), 65– 71.

Sweeting, C.J., Polunin, N.V.C, Jennings, S., 2006. Effects of chemical lipid extraction and arithmetic lipid correction on stable isotope ratios of fish tissues. Rapid Commun. Mass Spectrom. 20, 595–601.

Tierney, M., Southwell, C., Emmerson, L.M., Hindell, M.A., 2008. Evaluating and using stable- isotope analysis to infer diet composition and foraging ecology of Adélie penguins Pygoscelis adeliae . Mar. Ecol. Prog. Ser. 355, 297–307.

Vanderklift, M.A., Ponsard, S., 2003. Source of variation in consumer-diet δ15 N enrichment: a meta-analysis. Oecologia 136, 169–182.

33 Volkman, N.J., Presler, P., Trivelpiece, W., 1980. Diets of pygoscelid penguins at King George Island, Antarctica. Condor 82(4), 373–378.

Von Schirnding, Y., Vandermerwe, N.J., Vogel, J.C., 1982. Influence of diet and age on carbon isotope ratios in ostrich eggshell. Archaeometry 24, 3–20.

34 CHAPTER TWO: DIETARY ISOTOPIC DISCRIMINATION IN GENTOO PENGUIN

2 (PYGOSCELIS PAPUA ) FEATHERS

Introduction

Feathers are used commonly by ecologists for stable isotope analysis to assess foraging ecology and migration patterns of birds (Rubenstein and Hobson 2004). As feathers are metabolically inert after synthesis, they encapsulate information on a bird’s diet and foraging habitat during molt (Hobson 1999; Cherel et al. 2000; Phillips et al. 2007). However, to quantitatively assess avian diets and accurately track migratory patterns using the stable isotope values in feathers requires knowledge of tissue-specific isotopic discrimination factors (the differences in isotopic ratios between diet and consumer tissues). This information is necessary because the isotopic values of feathers and other tissues often exhibit a general enrichment in the heavier isotopes relative to their diet (Minagawa and Wada 1984). However, the amount of enrichment in feathers depends on the isotopes, species, tissue type and the physiological state of the bird at the time of molt (Hobson and Clark 1992; Hobson et al. 1993; Vanderklift and

Ponsard 2003; Cherel et al. 2005a; 2005b).

13 15 Carbon ( ∆ Cdiet−feather ) and nitrogen ( ∆ Ndiet−feather ) isotopic discrimination factors of

feathers have been determined for representatives of several major avian families using

controlled laboratory or zoo experiments (see review by Cherel et al. 2005b). Feather isotopic

discrimination has been quantified in three species of penguins: the King Penguin ( Aptenodytes

2 This chapter has been published as: Polito M.J., Abel S., Tobias C.R., and Emslie S.D. 2011. Dietary isotopic discrimination in gentoo penguin (Pygoscelis papua ) feathers. Polar Biology 34:1057–1063 patagonicus ), Humboldt Penguin ( Spheniscus humboldti ) and Rockhopper Penguin ( Eudyptes chrysocome ; Cherel et al. 2005b). Unlike many other species of birds, adult penguins undergo a catastrophic molt in which all of their feathers are replaced over a two to three week period while fasting (Stonehouse 1967). There is evidence that nutritional stress such as fasting can exaggerate 15 N enrichment and in some cases lead to 13 C depletion in feathers (Hobson et al.

1993; Cherel et al. 2005a; although see Kempster et al. 2007). This effect may be due to the mobilization of endogenous protein and lipid stores during fasting which tend to have higher

15 N/ 14 N ratios and lower 12C/ 13C than dietary protein sources (Cherel et al. 2005a). However, in

zoos penguins are generally offered food during the molt period (Crissey et al. 2005) and

previous studies of captive penguins have not quantified the extent of fasting and/or reduction in

dietary intake experienced by adults during molt or examined its effect on dietary isotopic

discrimination.

In this study we determined isotopic discrimination factors between adult Gentoo

Penguin ( Pygoscelis papua ) diet and feathers in a controlled study at the Henry Doorly Zoo,

Omaha, Nebraska. Furthermore we tested whether the length of the molt period and the

magnitude of voluntary dietary reduction during molt influence dietary isotopic discrimination

factors in this species. Based on the findings of Hobson et al. (1993) and Cherel et al. (2005a),

we hypothesize that penguins that have longer molt durations or reduce their food intake by a

15 13 higher magnitude will have higher ∆ Ndiet−feather and lower ∆ Cdiet−feather due to the increase use

of endogenous protein and lipid relative to recent dietary protein sources. We compare our

results with diet to feather isotopic discrimination factors in other penguin and fish eating bird

species from both captive and wild studies.

36 Materials and Methods

We maintained a captive population of 20 Gentoo Penguins (8 males, 12 females) on a consistent diet of Atlantic Herring ( Clupea harengus ) for ten months prior to the start of molt.

To confirm the isotopic consistency of penguin diets, we randomly sampled five individual herring per month over the three months leading up to and during the molt period (January to

March 2008). We measured the weight (g) and standard length (mm) of these 15 fish, and stored them frozen till later isotopic analysis. During the study we hand-fed penguins ad libitum allowing us to record the approximate mass of herring each individual consumed per day during the 30 days prior to molt and throughout the molt period. We calculated the length of the molt period as the number of days between when flippers swell in size and old feathers began to lift and stand out from the body to end of molt when new body feathers are fully grown. Prior to molt in January 2008, we measured the mass of penguins to the nearest 10 g. However, due to logistical constraints we could not measure the mass of penguins at the end of the molt period.

Lastly, we collected three newly grown breast feathers from each adult Gentoo Penguin following their annual molt in late March 2008.

We homogenized whole fish and fish muscle samples and dried them for 48 hours in an oven at 60˚C. We extracted lipids, which are depleted in 13 C, from dried fish tissues using a

Soxhlet apparatus with a 1:1 Petroleum-Ether : Ethyl-Ether solvent mixture for 8 hours

(Seminoff et al. 2007). Feathers were cleaned of surface contaminants using a 2:1 chloroform :

methanol rinse and cut into small fragments with stainless steel scissors. Approximately 0.5 mg

of each of the above materials was loaded into tin cups for δ13C and δ15 N analysis. Samples were flash-combusted (Costech ECS4010 elemental analyzer) and analyzed for carbon and nitrogen isotopes ( δ13 C and δ15 N) through an interfaced Thermo Delta V Plus continuous flow

37 stable isotope ratio mass spectrometer (CFIRMS). Raw δ values were normalized on a two-point

scale using depleted and enriched glutamic acid standard reference materials USGS-40 ( δ13 C: -

26.389±0.042; δ15N: -4.5±0.1) and USGS-41 ( δ13 C: 37.626±0.049; δ15N: 47.6±0.2). Sample precision based on internal repeats and duplicate standard reference materials was 0.1‰ and

0.2‰, for δ13 C ,and δ15 N, respectively.

Stable isotope ratios are expressed in δ notation in per mill units (‰), according to the

following equation:

δX = [( Rsample / Rstandard ) - 1] · 1000

13 15 13 12 15 14 Where X is C or N and R is the corresponding ratio C / C or N / N. The Rstandard values

13 15 were based on the PeeDee Belemnite (VPDB) for C and atmospheric N 2 for N.

13 15 We calculated ∆ Cdiet−feather and ∆ Ndiet−feather by subtracting the isotope values of each

individual’s feathers from the mean isotopic values of their diet (herring). Statistical calculations

were preformed using SAS (Version 9.1, SAS Institute 1999) and Number Cruncher Statistical

Systems (NCSS; Hintze, 2004). Prior to analysis we examined data using an Omnibus Normality

Test and Modified-Levene Equal-Variance Test and used non-parametric methods when

appropriate. We used Kruskall-Wallis tests and paired t-tests to test for consistency in the

length, mass and isotopic values of the herring fed to penguins during our study. We also used

two samples t-tests to test for differences in the molt characteristics and isotopic values of

feathers between sexes. We tested for a relationship between molt duration and the extent of

voluntary food reduction using simple linear regression. Lastly, we used general linear models

(GLM) and simple linear regressions when testing for an influence of molt duration, reduction in

dietary intake or their interaction on isotopic discrimination factors. All tests were two-tailed

and significance was assumed at the 0.05 level and means are presented ±SD.

Results

The size and mass of individual herring fed to penguins did not differ over the course of our study (H 3,15 = 1.94, 1.46 and P = 0.379, 0.482, for herring length and mass). In addition, the isotopic value of herring did not differ over the course of our study (H 4,25 = 2.78, 0.50, 2.22 ,

0.18 and P = 0.249, 0.779, 0.330, 0.914 for whole fish δ13 C, fish muscle δ13 C, whole fish δ15 N, and fish muscle δ15 N) and we found no differences in the isotopic signatures of herring muscle

relative to whole fish (Table 1). Therefore we grouped these 15 samples together and all

subsequent analyses are based solely on whole fish values. In addition, we found no differences

in the isotopic values of breast feathers between male and female Gentoo Penguins (Table 1).

The molt period ranged from 11 to 22 days and averaged 15.5 ± 2.5 days in length but did

not differ between males and females (Males: 16.1± 2.7; Females 14.5± 1.9; t = 1.41, P = 0.175).

Penguins in our study voluntarily reduced their dietary intake from 6.7 ± 1.7 % of their body

mass in herring day -1 prior to molt to 1.8 ± 1.2 % of body mass in herring day -1 during the molt

period. This represents an average reduction in food consumption of 71.7 ± 22.1 % (median:

78.9 %; range: 13.1 - 94.6 %). Voluntarily reduction in dietary intake did not differ between

males and females (Males: 75.0 ± 19.0; Females: 66.8 ± 26.7; t = 0.81, P = 0.429). We found no

relationship between molt duration and the extent of voluntary food reduction (R 2 = 0.01, P =

0.804).

Due to the lack of differences in the isotopic values of breast feathers, duration of molt, and reduction in dietary intake between sexes, they were pooled to conserve sample size when calculating isotopic discrimination factors and testing for an influence of molt duration and reduction in dietary intake. Breast feathers were significantly enriched in both δ13 C and δ15N

Table 1. The stable nitrogen and carbon isotope ratios (mean ± SD) in food and breast feathers of captive Gentoo Penguins.

Sample n C/N δ15 N (‰) t P δ13 C (‰) t P

Herring: 15 3.2±0.1 13.7±0.4 1.81 a 0.090 -17.2±1.0 0.73 a 0.472 Whole fish 15 3.2±0.1 13.8±0.3 -17.1±1.0 Fish muscle

Breast feathers: 8 3.1±0.1 17.2±0.6 0.11 b 0.911 -16.0±0.6 0.61 b 0.548 Male 12 3.1±0.1 17.2±0.2 -15.8±0.3 Female 20 3.1±0.1 17.2±0.4 -15.9±0.5 All a Paired t-test between whole fish and fish muscle. a Two sample t-test between male and female breast feathers.

40 values relative to their diets (Table 1; t = 5.23, 24.9 and P < 0.001, 0.001 for δ13 C and δ15N) and

13 15 calculated ∆ Cdiet−feather and ∆ Ndiet−feather averaged 1.3 ± 0.5 ‰ and 3.5 ± 0.4 ‰, respectively

13 15 (Table 2). ∆ Cdiet−feather and ∆ Ndiet−feather did not appear to be significantly influenced by molt

13 15 duration (F 1,20 = 3.51, 0.01 and P = 0.079, 0.933 for ∆ Cdiet−feather and ∆ Ndiet−feather ), reduction

13 15 in dietary intake (F 1,20 = 0.40, 0.35 and P = 0.534, 0.560 for ∆ Cdiet−feather and ∆ Ndiet−feather ) or

13 their interaction terms (F 1,20 = 0.00, 0.20 and P = 0.350, 0.663 for ∆ Cdiet−feather and

15 ∆ Ndiet−feather ). The lack of significant relationship between isotopic discrimination factors and molt characteristics was also apparent when testing each relationship separately using simple

13 linear regressions (Fig. 1). We also noted a single outlier in our study with both low ∆ Cdiet−feather

15 and ∆ Ndiet−feather (Fig. 1), however the results of both our GLM and simple linear regression

analyses did not vary when excluding this individual.

Discussion

While isotopic discrimination factors have been described in other penguin genera, our

15 data represent the first such values for Pygoscelis penguins. The Gentoo Penguin ∆ Ndiet−feather value calculated in our study is similar with those determined in other penguin species (Table 2) and consistent with the generalized 3.0 - 5.0 ‰ enrichment in δ15 N predicted per trophic level

(DeNiro and Epstein 1981; Minagawa and Wada 1984). Similarly, our Gentoo Penguin

13 ∆ Cdiet−feather value was within the range of discrimination factors determined for other penguin

15 13 species (Table 2). The Gentoo Penguin ∆ Ndiet−feather and ∆ Cdiet−feather values derived from our study will allow for greater application of the use of feathers to quantify Pygoscelis penguin diets using stable isotopes (Tierney et al. 2008). However, it is important to note that Gentoo penguins naturally feed on variable amounts of crustaceans and fishes and not herring as in our captive

Figure 1. The relationships between adult Gentoo Penguin feather dietary discrimination factors 13 15 (∆ Cdiet−feather and ∆ Ndiet−feather ), the length of the molt period and the magnitude of voluntary dietary reduction during molt. Open symbols represent males and closed symbols represent females. Linear regression equations, regression lines (dashed) and 95% confidence lines (dotted) are presented for each relationship.

Table 2. Estimates of δ15 N and δ13 C discrimination factors between food and penguin breast feathers. Diet and molt conditions are taken from references listed. Gentoo Penguin discriminations factors from this study are provided as mean±SD.

Discrimination Factor (‰) Species, age, tissue Diet, molt condition δ13 C δ15 N Reference a King penguin Aptenodytes patagonicus Adult, body feather Herring b, captive (reduced feeding) 0.1 3.5 1 Adult, body feather (distal region removed) Wild diet, fasting on land 1.6 3.6 2 Adult, body feather (distal region only) Wild diet, feeding at sea 0.4 2.1 2 Chick, down Wild diet, fed by parents 2.4 3.6 2 Chick, body feather Wild diet, fed by parents (under fed) 1.5 3.4 2 Gentoo penguin Pygoscelis papua Adult, body feather Herring b, captive (reduced feeding) 1.3±0.5 3.5±0.4 3 Humbolt penguin Spheniscus humboldti Adult, body feather Anchovy b, captive (unknown if feeding) - 4.8 4 Rockhopper penguin Eudyptes chrysocome Adult, body feather Capelin b, captive (reduced feeding) 0.1 4.4 1 a 1)Cherel et al. 2005a, 2) Cherel et al. 2005b, 3) This study, 4) Mitzutani et al. 1992 b Herring ( Clupea harengus ), anchovy ( Engraulis japonica ), capelin ( Mallotus villosus ) study (Miller et al. 2009). We therefore recommend the inclusion of a sensitivity analysis of the potential error involved with quantifying wild diets using isotopic discrimination factors derived from captive populations (Bond and Diamond 2010; Polito et al. 2011).

Contrary to our hypotheses, we found no significant relationships between molt duration or the magnitude of reduction in dietary intake with the isotopic discrimination factors calculated in our study. Our results differ with findings from previous studies on captive and wild birds, including penguins. Hobson et al. (1993) found that tissue δ15 N values of food-restricted

Japanese Quail ( Coturnix japonica ) chicks were significantly higher than in chicks fed ad

libitum. In the wild, fasting Ross’ Geese ( Chen rossii) have tissues enriched in 15 N (Hobson et al. 1993) and the proximal ends of adult King Penguin body feathers grown while fasting are enriched in 15 N and depleted in 13C relative to the distal tips and sheathes of body feathers which

15 are grown at sea (Cherel et al. 2005a). However, in our study Gentoo Penguin ∆ Ndiet−feather was not significantly affected by the duration molt or intensity of fasting. Furthermore, while we did

13 observe an apparent slight increase in ∆ Cdiet−feather values with molt duration; this trend was not

13 significant and likely influenced by a single outlier with a low ∆ Cdiet−feather (Fig. 1).

Unlike wild penguins, adults in our study had access to food while molting. It may be

that while captive adults were molting they did not attain a similar physiological state to wild

penguins and as such any fasting-induced variation in δ 15 N and δ 13C values was not apparent in

their feathers. In addition, not all studies have found evidence of 15 N enrichment and/or 13C

depletion in tissues produced while under nutritional stress (Kemspter et al. 2007; Williams et al.

2007; Sears et al. 2009). A recent review and case study using song sparrows suggest isotopic

values may not be linearly related to nutritional stress and that there may be a threshold level of

stress below which such isotopic changes are likely to be negligible (Kempster et al. 2007). Unfortunately, due to logistical constraints we could not quantify individuals’ mass loss over the molt period. It might be possible that if we had used this metric of fasting intensity any moderate effects of fasting on feather isotopic discrimination would have become apparent.

The feather discrimination factors we calculated in this study were similar to those previously estimated in both captive and fasting wild King Penguins (Table 2). Similar to our study, in a captive study of King Penguins, individuals were offered food during the molt period, but voluntarily reduced their food consumption dramatically (Cherel et al. 2005b, Y. Cherel,

15 pers. comm.). Even so, the ∆ Ndiet−feather derived from Cherel et al.’s (2005b) captive study was

15 nearly identical to the ∆ Ndiet−feather estimated in fasting, wild King Penguins (Table 2; Cherel et

al. 2005a). This result suggests that any food provided to adults in Cherel et al. (2005b) captive

study had little effect on feather δ15 N discrimination relative to naturally fasting wild King

Penguins. Most penguins gain a significant amount of weight prior to molt, including the preferential build up and storage of proteins in skeletal muscles (Stonehouse 1967; Cherel et al.

1993; Cherel 1995). Furthermore, pectoral muscles and integument protein metabolism provide approximately 77 % of the total protein needed for new feather synthesis in wild penguins

(Cherel et al. 1994). These findings suggest that adult penguin feathers are preferentially synthesized from a pool of body proteins and that any dietary proteins ingested during molt may

15 contribute little to feather synthesis. However, it is important to note that unlike ∆ Ndiet−feather ,

13 estimates of ∆ Cdiet−feather differed between captive and wild King Penguins by approximately

1.5 ‰ (Cherel et al. 2005a; 2005b). Seasonal variation in the diets of wild King Penguins, and thus isotopic differences in the δ13C signatures of their endogenous vs. dietary carbon pools, may account for differences in δ13C discrimination factors in comparison to captive penguins kept on

a constant diet. In addition, carbon and nitrogen used in feather synthesis may be sourced

45 13 independently from each other (Klasing 1998). ∆ Cdiet−feather values may not only be affected by

variation in the contribution of different carbon pools (endogenous vs. dietary) as in

15 ∆ Ndiet−feather values but possibly also by carbon sources within each pool (protein vs. lipid;

Bearhop et al. 2002). These results highlight the inherent complexity of the metabolic pathways involved in the synthesis of feather keratin.

15 When examining captive studies in general, the range of ∆ Ndiet−feather in adult penguins, which fast while they molt, and other fish eating birds, which do not, are similar (2.1 - 4.8 ‰ vs.

3.0 - 5.3 ‰; see review by Cherel et al. 2005a). As penguins are suggested to ‘‘feed on

15 themselves’’ during the molt one might expect penguins to have higher ∆ Ndiet−feather values than other seabirds on average. We suggest two possible explanations for this observation: (1) penguins in captive studies may not attain a similar fasting state such that their physiology is more similar to non-fasting fish eating birds, and/or (2) species-specific metabolic adaptations to limit nutritional stress while fasting and their relative reliance on endogenous vs. dietary pools during feather growth may confound comparisons across species. These two explanations are not exclusive and both may contribute to the lack of differences between captive studies of penguin and non-fasting, fish-eating birds. While captive penguins may not be under the same metabolic constraints during molt as wild penguins, small amounts of food provided to adults in captive

15 studies appear to have little to no effect on ∆ Ndiet−feather (Cherel 2005a; 2005b). Thus species- specific, and even-age specific differences in the relative importance of income and capital investment and variation in the biochemical pathways involved in feather synthesis are likely to confound such comparative examinations. For example, growing chicks have metabolic constraints that differ from adults and can often lead to differences in isotopic discrimination factors with age (Williams et al. 2007; Sears et al. 2009). While this may be true in some species,

46 15 there is little evidence to suggest such age affects in penguins as the ∆ Ndiet−feather values of King

Penguin chicks, which are fed during feather synthesis, are similar to those calculated for fasting

adult King Penguins (Table 2; Cherel et al. 2005a). It may be that King Penguin chicks and

some other fish eating birds that do not fast during molt also utilize variable amounts of amino

acids from endogenous pools for feather synthesis (Murphy 1996; Bearhop et al. 2002). A recent

study suggests an up to 60 % percent contribution of endogenous proteins to Greylag Geese

(Anser anser ) flight feather synthesis (Fox et al. 2009). In many avian species the utilization of

endogenous pools in addition to dietary pools during feather growth could be an adaptation to

molting during metabolically stressful periods such as migration or reproduction (Merilä 1997;

Yuri and Rohwer 1997; Bridge 2006; Ramos et al 2009).

In summary, the diet to feather isotopic discrimination factors calculated in our study are

similar to those estimated in wild penguins and as such will allow for a greater application of the

use of feathers to quantify Pygoscelis penguin diets using stable isotopes. However, further

work is needed to elucidate the biochemical pathways involved in feather synthesis, the relative

importance of income and capital investments and how these factors interact and affect isotopic

discrimination in captive and wild seabirds.

References

Bearhop S, Waldron S, Voiter SC, Furness RW (2002) Factors that influence assimilation rates and fractionation of nitrogen and carbon stable isotopes in avian blood and feathers. Phys Biochem Zool 75:451–458. doi: 10.1086/342800

Bond AL, Diamond AW (2010) Recent Bayesian stable isotope mixing models are highly sensitive to variation in discrimination factors. Ecol Appl In press. doi: 10.1890/09-2409.1

Bridge EL (2006) Influences of morphology and behaviour on wing-molt strategies in seabirds. Mar Ornithol 34:7–19.

47 Cherel Y (1995) Nutrient reserve storage, energetics, and food consumption during the prebreeding and premoulting foraging periods of King Penguins. Polar Biol 15: 209–214. doi: 10.1007/BF00239060

Cherel Y, Charrassin JB, Handrich Y (1993) Comparison of body reserve buildup in prefasting chicks and adults of King Penguins ( Aptenodytes patagonicus ). Physiol Zool 66:750–770.

Cherel Y, Charrassin JB, Challet E (1994) Energy and protein requirements for molt in the King Penguin Aptenodytes patagonicus. Am J Physiol 266:1182–1188.

Cherel Y, Hobson KA, Weimerskirch H (2000) Using stable-isotope analysis of feathers to distinguish moulting and breeding origins of seabirds. Oecologia 122, 155–162. doi: 10.1007/PL00008843

Cherel Y, Hobson KA, Bailleul F, Groscolas R (2005a) Nutrition, physiology, and stable isotopes: new information from fasting and molting penguins. Ecology 86(11)2881–2888. doi: 10.1890/05-0562

Cherel Y, Hobson KA, Hassani S (2005b) Isotopic discrimination factors between food and blood and feathers of captive penguins: Implications for dietary studies in the wild. Physiol Biochem Zool 78(1)106–115. doi: 10.1086/425202

Crissey S, Rice DF, Rice AL, McGill P, Slifka KS (2005) Diet and Nutrition. In: Penguin Husbandry Manual, 3rd edn. Penguin Taxon Advisory Group of the American Zoo and Aquarium Association, pp. 86–111 www.aviansag.org/standards.html

DeNiro MJ, Epstein S (1981) Influence of diet on the distribution of nitrogen isotopes in animals. Geochim Cosmochim Acta 45:341–351.

Fox AD, Hobson KA, Kahlert J (2009) Isotopic evidence for endogenous protein contributions to greylag goose Anser anser flight feathers. J Avian Biol 40:108–112. doi: 10.1111/j.1600- 048X.2009.04720.x

Hintze J (2004) NCSS and PASS. Number cruncher statistical systems, Kaysville, Utah www.ncss.com

Hobson KA (1999) Tracing origins and migration of wildlife using stable isotopes: a review. Oecologia 120:314–326. doi: 10.1007/s004420050865

Hobson KA, Clark RG (1992) Assessing avian diets using stable isotopes. II. Factors influencing diet-tissue fractionation. Condor 94:189–197.

Hobson KA, Alsaukas RT, Clark RG (1993) Stable nitrogen enrichment in avian tissues due to fasting and nutritional stress: implications for isotopic analysis of diet. Condor 95:388–394.

48 Kempster B, Zanette L, LongstaVe FJ, MacDougall-Shackleton SA, Wingfield JC, Clinchy M (2007) Do stable isotopes reflect nutritional stress? Results from a laboratory experiment on song sparrows. Oecologia 151:365–371. doi: 10.1007/s00442-006-0597-7

Klasing KC (1998) Comparative Avian Nutrition. CAB International, New York.

Merilä J (1997) Fat reserves and moult-migration overlap in goldcrests, Regulus regulus - a trade-off ? Ann Zool Fenn 34:229–234.

Miller AK, Karnovsky NJ, Trivelpiece WZ (2009) Flexible foraging strategies of gentoo penguins Pygoscelis papua over 5 years in the South Shetland Islands, Antarctica. Mar Biol 156(12)2527–2537. doi: 10.1007/s00227-009-1277-z

Minagawa M, Wada E (1984) Stepwise enrichment of 15 N along food chains: further evidence and the relation between δ15 N and animal age. Geochim Cosmochim Acta 48:1135–1140.

Mizutani H, Fukuda M, Kabaya Y, Wada E (1992) δ13 C and δ15 N enrichment factors of feathers of 11 species of adult birds. Ecology 73(4):1391–1295. doi: 10.2307/1940684

Murphy ME (1996) Energetics and nutrition of molt. In: Carey C (ed) Avian energetics and nutritional ecology. Chapman & Hall, New York, pp 158–198.

Phillips RA, Catry P, Silk JRD, Bearhop S, McGill R, Afanasyev V, Strange IJ (2007) Movements, winter distribution and activity patterns of Falkland and brown skuas: insights from loggers and isotopes. Mar Ecol Prog Ser 345:281–291. doi: 10.3354/meps06991

Polito MJ, Lynch HJ, Naveen R, Emslie SD (2011) Stable Isotopes Reveal Regional Heterogeneity in the Pre-Breeding Distribution and Diets of Sympatrically Breeding Pygoscelis Penguins. Mar Ecol Prog Ser. In press. doi: 10.3354/meps08863

Ramos R, Militã T, González-Solís J, Ruiz X (2009) Moulting strategies of a long-distance migratory seabird: the Mediterranean Cory’s shearwater. Ibis 151:151–159. doi: 10.1111/j.1474- 919X.2008.00877.x

Rubenstien DR, Hobson KA (2004) From birds to butterflies: Animal movement patterns and stable isotopes. Trends Ecol Evol 19:256–263. doi: 10.1016/j.tree.2004.03.017

Sears J, Hatch SA, O'Brien DM (2009) Disentangling effects of growth and nutritional stress on seabird stable isotope ratios. Oecologia 159:41–48

Seminoff JA, Bjorndal KA, Bolten AB (2007) Stable carbon and nitrogen isotope discrimination and turnover in pond sliders Trachemys scripta : Insights for trophic study of freshwater turtles. Copeia 2007(3):534–542. doi: 10.1643/0045-8511(2007)2007[534:SCANID]2.0.CO;2

Stonehouse B (1967) The general biology and thermal balance of penguins. Adv Ecol Res 4:131–196.

Tierney M, Southwell C, Emmerson LM, Hindell MA (2008) Evaluating and using stable- isotope analysis to infer diet composition and foraging ecology of Adélie penguins Pygoscelis adeliae . Mar Ecol Prog Ser 355:297–307. doi: 10.3354/meps07235

Vanderklift MA, Ponsard S (2003) Source of variation in consumer-diet δ15 N enrichment: a meta-analysis. Oecologia 136:169–182. doi: 10.1007/s00442-003-1270-z

Williams CT, Buck CL, Sears J, Kitaysky AS (2007) Effects of nutritional restriction on nitrogen and carbon stable isotopes in growing seabirds. Oecologia 153:11–18

Yuri T, Rohwer S (1997) Moult and migration in the northern rough-winged swallow. Auk 114:249–262.

50 CHAPTER THREE: ONTOGENETIC AND OCEANOGRAPHIC FACTORS INFLUENCE

THE STABLE ISOTOPE VALUES OF A KEYSTONE SPECIES , THE ANTARCTIC KRILL

3 (EUPHAUSIA SUPERBA ): IMPLICATIONS FOR DIETARY STUDIES OF KRILL PREDATORS

Introduction

Antarctic krill ( Euphausia superba ) is a keystone species in the Southern Ocean marine

food web with an estimated global biomass of 379 million metric tons (Law 1965, Atkinson et al.

2009). In the Southern Ocean, Antarctic krill act as a dominate primary consumer exploiting

large blooms of diatoms during the retreat of winter sea ice each year (Holm-Hansen et al. 2004,

Nicol 2006). In addition, their molt and fecal pellets contribute significantly to carbon flux and

the diets of other small particle feeders (Hopkins 1985, Pakhomov et al. 2002). As Antarctic krill

are also the principle prey item for many of the fish, squid, seabirds and mammals in the

Southern Ocean, krill occupy a key position in this ecosystem, linking primary production to a

diverse assemblage of secondary consumers (Everson 2000). While Antarctic krill is commonly

thought of as a grazer of phytoplankton, they are omnivorous and will readily consume

zooplankton and other heterotrophic prey (Atkinson & Snÿder 1997, Perissinotto et al. 2000).

Studies using both stomach content, fatty acid and stable isotope analyses suggest that carnivory

can be an important component of Antarctic krill diets, but further work is required to examine

spatial, temporal and ontogenetic variation in the use of heterotrophic sources (Atkinson et al.

2002, Ju & Harvey 2004, Schmidt et al. 2006).

3 This chapter is formatted to be submitted to the journal Marine Ecology Progress Series as : Polito M.J., Reiss, C.S., Trivelpiece W.Z., Patterson W.P., and Emslie S.D. Ontogenetic and oceanographic factors influence the stable isotope values of a keystone species, the Antarctic krill (Euphausia superba): implications for dietary studies of krill predators Stable isotope analysis is based on the principle that stable isotope ratios of elements, including carbon ( δ13 C) and nitrogen ( δ15 N), in animal tissues are largely determined by isotopic

abundances in an animal’s food web (DeNiro and Epstein 1978, 1981). Nitrogen stable isotope

values are commonly used to infer the trophic level of consumers as there is a general increase in

δ15 N values by 3-5‰ per trophic level (Minagawa & Wada 1984). Stable isotope values of

carbon often exhibit little to no change with trophic level and thus consumer tissues are most

reflective of differences in isotopic fractionation during primary production that propagate

throughout the food-web (DeNiro & Epstein 1978). For example, differences in δ13 C fractionation during photosynthesis between benthic macro algae and pelagic phytoplankton can be used to trace the use of nearshore, benthic habitats vs. offshore, pelagic habitats by marine predators (France 1995, Cherel & Hobson 2007). Water temperature can also affect the δ13 C

values at the base of pelagic food-webs, with lower temperatures increasing CO 2 (aq) fixation by

diatoms that more effectively discriminate against 13 C (Freeman & Hays 1992, Hinga et al.

1994). In addition, δ13 C fractionation in marine phytoplankton is an inverse function of cell

growth rate (Laws et al. 1995). Therefore, high levels of primary production, as measured by

surface chlorophyll a (Chl-a) concentration, can lead to an increase in the baseline δ13 C values of

pelagic food-webs (Schell 2000, Hilton et al. 2006, Jaeger & Cherel 2011).

Stable isotope mixing models have become a common tool in ecological studies of birds

and mammals (Crawford et al. 2008, Inger & Bearhop 2008). Isotopic mixing models use

geometric or Bayesian procedures to reconstruct animal diets based on δ13 C and δ15 N values of consumer tissues and isotopically distinct food sources (Phillips & Greg 2001, Parnell et al.

2010). However, estimating predator diets using these models is dependent on having an understanding of the isotope values of the potential prey items being consumed. It is often

52 assumed that temporal or spatial variation in the stable isotope values of predator tissues reflects changes in predator diet composition (i.e. dietary shifts between isotopically distinct prey species; Norris et al. 2007, Tierney et al. 2008, Polito & Goebel 2010). However, an alternate hypothesis is that this variation in predator stable isotope values could be due, at least in part, to intraspecific variation in the stable isotope values of key prey species that propagates throughout the food web. This is likely to be the case as studies have found that the stable isotope values of common marine prey species such as zooplankton and fish can vary over time and space, forced by oceanographic factors, ontogeny and other biological effects (Kline 1999, Park et al. 2011,

Stowasser et al. 2011). Unfortunately, due to logistical difficulties in sampling, studies of marine predator diets often incorporate prey source isotope values into isotopic mixing models that are derived from the literature and/or are based on small sample sizes that cover a limited temporal, spatial or ontogenetic range (Norris et al. 2007, Tierney et al. 2008, Hedd et al. 2010). Thus, any spatial, temporal or ontogenetic mismatch between the stable isotope values of prey sources used in a mixing model and the actual stable isotope values of prey consumed by predators have the potential to bias estimates of predator diet composition using stable isotope mixing models.

Furthermore, this bias is likely to be most significant when there is significant intraspecific variation in the stable isotope values of a key prey species that dominates a predator’s diet, such as Antarctic krill. While several reviews have noted the potential for prey source mismatches in dietary mixing models, no studies to our knowledge have examined or attempted to quantify this bias in detail (Crawford et al. 2008, Inger & Bearhop 2008, Bond & Jones 2010).

In this study we examine the magnitude and likely drivers of intraspecific variation in the stable isotope values of Antarctic krill. Stable isotope analyses allow us to evaluate trophic and habitat shifts in Antarctic krill relative to ontogeny, as well as quantify seasonal and spatial

53 variation in key oceanographic factors that influence stable isotope values independent of trophic or habitat shifts. Specifically, the objectives of this study are to (1) quantify the extent of intraspecific variation in the stable isotope values of Antarctic krill in relation to ontogenetic and oceanographic effects, (2) determine if inter-annual and/or species-specific variation in the size of krill consumed by predators is likely to influence stable isotope values of Southern Ocean krill predators, and (3) quantify potential intraspecific variation in the stable isotope values of

Antarctic krill that could bias estimates of krill predator diet composition in stable isotope mixing models.

Materials and Methods

Krill and oceanographic data collection. Sample collection for this study was conducted in

January of the austral summers of 2006/07 and 2008/09 onboard the R/V Yuzhmorgeologiya as

part of the U.S. Antarctic Marine Living Resource Program (AMLR). The AMLR program

conducts annual shipboard surveys aimed at collecting data on the oceanography, primary

productivity, zooplankton and fish communities around the Northern Antarctic Peninsula. The

survey is comprised of approximately 100 sampling stations distributed across three main

regions: near Elephant Island (EI), between the South Shetland Island and the Antarctic

Peninsula (South), and west of the South Shetland Islands in Drake’s Passage (West; Fig. 1).

We collected krill samples using a 6’ Isaacs-Kidd Midwater Trawl fitted with a 505 µm

mesh plankton net fished obliquely from a depth of 170 m or to approximately 10 m above

bottom in shallower waters. From these net tows, we collected a random sample of ten individual

Antarctic krill from nine random sampling stations in each year for a total of 180 individual krill

(90 krill in each year). These nine sampling stations were distributed equally across the three

Figure 1. The cruise tracks (solid lines) and sampling stations (circles) in the three regions (West, South and Elephant Islands) near the South Shetland Islands and northern Antarctic Peninsula where oceanographic data and Antarctic krill samples were collected in January 2006/07 and 2008/09. The star denotes the location of the U.S. AMLR program’s krill predator research field station at Admiralty Bay, King George Island.

55 regions of the AMLR survey: EI, South & West (Fig. 1). For each sampled krill, we measured the standard length to the nearest mm, from the anterior side of the eyeball to the tip of the telson. Krill ≤ 35 mm in length were considered juveniles as in Miller & Trivelpiece (2007).

Krill > 35 mm were visually sexed based on the presence or absence of a thelycom, however

specific maturity stages were not determined. Krill was kept frozen prior to isotopic analysis.

At these 18 sampling stations we also collected Chl-a, conductivity temperature-depth

(CTD), and bathymetric data. Water samples were obtained from a Sea-Bird SBE-32 carousel

water sampler with eight-liter sampling bottles. At each station Chl-a concentration (mg m -3)

were determined from a bottle triggered at a depth of 5 m by measurement of fluorescence (with

acidification) after extraction in absolute, acid-free methanol (Holm-Hansen & Riemann 1978).

A Sea-bird SBE-911plus CTD system (Sea-Bird Electronics, Inc) fitted with a Datasonics

altimeter (Teledyne Benthos) was used to measure the average depth and salinity and water

temperature within the upper mixed layer (approximately 0-40m depth) at each station.

Sample preparation and isotopic analyses . Whole krill were homogenized and then dried for 48 hours in an oven at 60˚C. Lipids were then extracted from krill samples using a Soxhlet apparatus with a 1:1, Petroleum-Ether: Ethyl-Ether solvent mixture for 8 hours (Seminoff et al.

2007). Lipid extracted krill were not acidified prior to isotopic analysis. Approximately 0.5 mg of each of the above materials was loaded into tin cups and flash-combusted (Thermo-Finnigan elemental analyzer) and analyzed for carbon and nitrogen isotopes ( δ13 C and δ15 N) through a

Con-Flo II interfaced with a Thermo-Fisher Delta Plus XL continuous flow stable isotope ratio

mass spectrometer (CFIRMS). Raw δ values were normalized on a two-point scale using

56 glutamic acid reference materials USGS-40 and USGS-41. Sample precision was 0.1‰ and

0.2‰, for δ13 C and δ15 N values respectively.

Stable isotope abundances are expressed in δ notation in per mill units (‰), according to

the following equation:

δX = [(R sample / R standard ) - 1] · 1000

13 15 13 12 15 14 Where X is C or N and R is the corresponding ratio C / C or N / N. The R standard values

13 15 were based on the PeeDee Belemnite (VPDB) for C and atmospheric N 2 for N.

Statistical analysis of krill samples. Statistical calculations were performed with SAS (Version

9.1, SAS Institute 1999) using generalized linear mixed models (GLM) fitted with the

GLIMMIX macro (Littell et al. 1996). This program allowed us to include sampling station location in our GLMs as a random effect, nested within region, and we collected krill from three unique sampling stations, within each region (EI, South and West) in each year. To evaluate differences in the relative abundance of juvenile, male and female krill between years and across sampling regions we used a multinomial GLM with a binomial error distribution and generalized logit link function. We then tested for differences in the standard length, δ13 C and

δ15 N values (as separate response variables) by year and region using GLM analyses with normal error distributions, identity link functions and Tukey-Kramer Multiple comparison tests. We used a similar GLM approach to assess differences in oceanographic factors (bathymetry, temperature, salinity and Chl-a concentration) by year and region.

We also used a GLM approach to identify ontogenetic and oceanographic factors that best predicted observed inter-annual or spatial variation in the stable isotope values of krill in our study. We first considered whether δ13 C and δ15 N values of krill were significantly influenced by

57 standard length and/or differed among sexes (juvenile, male or female). As we considered all krill ≤ 35 mm as juveniles, these two factors (standard length or sex) are likely confounded. We observed that δ13 C and δ15 N values increased with krill length, but were not significantly

influenced by sex (see results; Fig. 2) and therefore we did not include sex as a potential

predictor variable in subsequent analyses. To test for ontogenetic and oceanographic effects on

the stable isotope signatures of krill we compiled mean stable isotope, krill standard length, and

oceanographic variables (water temperature, salinity, depth at station and Chl-a concentration)

data for each of our 18 stations. We then parameterized full GLMs as follows: δ13 C or δ15 N as

response variables and krill standard length, temperature, salinity, depth at station (log-

transformed) and Chl-a concentration as covariates. We determined the most parsimonious

models for krill δ13 C or δ15 N values by removing non-significant covariate terms from each full

GLM model using a backwards, stepwise procedure based on minimizing the resulting model’s

Akaike’s Information Criterion value (AIC; Akaike 1973). Data were examined for normality

and equal variance and transformed as detailed when necessary. Significance was assumed at the

0.05 level and all means are presented ±SD.

Krill predator stable isotope values and dietary models . We next assessed the potential for inter- annual variation in oceanographic factors and the size of Antarctic krill consumed by predators to influence the stable isotope values of predators. Using an existing dataset, we examined inter- annual variation in the standard length of Antarctic krill consumed by Adélie Penguins

(Pygoscelis adeliae ) during chick provisioning (December – January) at Admiralty Bay, King

George Island (62°10’S, 58°30’W) from 1997/98 to 2006/07 (Hinke et al. 2007, W. Trivelpiece unpub. data). We extracted December to January surface Chl-a concentration measured by

58 satellite and provided by NASA (http://reason.gsfc.nasa.gov/Giovanni/ ) in each breeding season and averaged values within a 100 x 100 km box centered on the Admiralty Bay breeding colony.

This area describes the maximum foraging range of Adélie Penguins during chick provisioning at Admiralty Bay (Trivelpiece et al. 1987). We incorporated these predictor variables into the most parsimonious GLMs identified in our analyses above to predict the mean (±SD) δ13 C values

and δ15 N of Antarctic krill consumed by Adélie Penguins in each year. We used the resulting, predicted annual krill stable isotope values to calculate δ13 C and δ15 N values of feathers for

Adélie Penguin chicks consuming a diet of 80% Antarctic krill, and 20% fish. We chose these

proportions as krill is the dominant prey item of Adélie Penguins at Admiralty Bay and fish

comprise only a small portion of the diet during the breeding season (Volkman et al. 1980,

Jablonski 1985). For each year from 1997/98 to 2006/07, we calculated Adélie Penguin chick

feather values as the weighted mean and standard deviation of the predicted annual δ13 C or δ15 N value of Antarctic krill and the δ13 C or δ15 N value of a common prey fish, Pleuragramma antarcticum , kept constant in each year ( δ13 C: -27.7±0.4; δ15 N: 9.4±0.5; Polito et al. 2011a). We

then adjusted these values to represent feathers values using a diet to feather isotopic

discrimination factor derived from a controlled dietary study in Pygoscelis penguins ( δ13 C: +1.3;

δ15 N: +3.5; Polito et al. 2011b).

Furthermore, we modeled the potential for inter-annual variation in the isotope values of

Antarctic krill to bias estimates of predator diets using isotopic mixing models. To address this

issue we used the SIAR Bayesian multi-source isotopic mixing model in the R environment (R

Development Core Team 2007) that estimates the probability distributions of multiple prey

source contributions to a predators diet while accounting for the observed variability in predator

and prey isotope values and dietary isotopic fractionation (Parnell et al. 2010). To represent our

59 predator population, we generated a random sample of 40 Adélie Penguin chicks in each year

(1997/98 to 2006/07) with feather δ13 C or δ15 N values drawn from a normal distribution with the

mean and standard deviation based on a diet of 80% Antarctic krill, and 20% fish (as calculated

above). We used an annual sample size of 40 as it is representative of an upper sample size limit

based on previous isotopic studies of Pygoscelis penguins (Tierney et al. 2008, Polito et al.

2011c, Vasil et al. 2012). We then estimated the contribution of Antarctic krill and fish ( P.

antarcticum ) to the diet of these simulated Adélie Penguins using three different models. All

three models used consistent δ13 C or δ15 N values for fish ( P. antarcticum ) when estimating diet

composition in each year, but differed in the isotopic values they used for Antarctic krill. Our

first SIAR model acted as a control and used the predicted δ13 C or δ15 N values for Antarctic krill in each year derived above. In contrast, our two other SIAR models used predicted Antarctic krill values from the year with highest (2000/01) or lowest (1997/98) δ13 C or δ15 N values to estimate the diet composition across all years. For all three SIAR models we incorporated diet to feather discrimination factors (δ13 C: 1.3±0.5; δ15 N: 3.5±0.4; Polito et al. 2011b) and ran 1 million

iterations, thinned by 15, with an initial discard of the first 40,000 resulting in 64,000 posterior

draws.

Lastly, we assessed the potential for species-specific variation in the size of Antarctic

krill consumed by predators to lead to differences in the isotope value of the krill portion of their

diets. To address this issue we compiled the annual means and total range of krill sizes consumed

by 20 common Antarctic krill predators from published studies. We then predicted the size-

specific, Antarctic krill δ15 N values that would be consumed by each species if they were found

foraging in our study area using the most parsimonious GLM for δ15 N identified above. We did

60 not assess any potential variation in δ13C values of Antarctic krill consumed by each species due

to the likely relationships between δ13C and oceanographic variables as examined above.

Results

Variation in Antarctic krill and oceanographic factors

The standard length of Antarctic krill sampled in this study varied significantly across the three sampling regions (F 2,180 = 7.29, P = 0.0085). However, we did not find any differences in this standard length between years (F 1,180 = 0.07, P = 0.7948) or a significant year*region interaction (F 2,180 = 0.02, P = 0.9784). Krill from the EI and West sampling regions were larger than krill from the South sampling region in both years (Table 1). Similarly, there was a higher abundance of juvenile krill in the South sampling region relative to the other two regions (F 4,180 =

6.67, P < 0.0001), but no differences in the relative abundance of juvenile, male and female krill between years (F 2,180 = 0.18, P = 0.8371) or a significant year*region interaction (F 4,180 = 1.26, P

= 0.2841; Table 1). Krill δ15 N values were lower in the South sampling region relative to the

other two regions (F 2,180 = 4.78, P = 0.0297), but did not differ between years (F 1,180 = 2.53, P =

1.380) or have a significant year*region interaction (F 4,180 = 0.22, P = 0.8036). In contrast, krill

13 δ C values did not differ by region (F 2,180 = 3.30, P = 0.0723) or year (F 1,180 = 3.50, P = 0.0858), though there was a significant interaction due to the low krill δ13C values from the West region in 2008/09 (F 2,180 = 5.13, P = 0.0245).

Sampling station depth was shallower in the southern region (F 2,18 = 8.08, P = 0.0060) but did not differ between years (F 1,18 = 2.81, P = 0.1197) or have a significant year*region interaction (Table 1; F 2,18 = 1.62, P = 0.2385). Water temperature did not differ across years or regions (year: F 1,18 = 0.8, P = 0.3874; region: F 2,18 = 2.71, P = 0.1067; year*region: F 1,18 = 0.32, P

Table 1. Euphausia superba . Oceanographic variables at sampling sites and the sample size, standard length, sex ratio, elemental composition and stable isotope values of Antarctic krill collected from three regions in the northern Antarctic Peninsula in January of austral summers 2006/07 and 2008/09. See text for region descriptions.

Oceanographic variables Antarctic krill samples

Year Region Water Chl-a Max Salinity Length % % % Temperature conc. n C/N δ13 C (‰) δ15 N (‰) depth (m) (ppt) (mm) Juvenile Male Female (˚C) (mg m -3)

2006-07 EI 1456±1893 1.2±0.5 34.10±0.24 0.63±0.49 30 47.7±4.8 0.0 30.0 70.0 3.4±0.3 -26.4±0.9 3.7±0.5

South 425±317 0.8±0.8 34.32±0.12 1.14±0.27 30 36.5±8.7 46.7 23.3 30.0 3.5±0.1 -26.7±1.0 2.9±0.5

West 1950±1352 1.4±0.2 34.02±0.08 1.79±0.56 30 45.5±7.3 13.3 36.7 50.0 3.3±0.1 -26.1±0.7 3.6±0.8

All areas 1277±1353 1.1±0.5 34.15±0.19 1.18±0.64 90 43.2±8.5 20.0 30.0 50.0 3.4±0.2 -26.4±0.9 3.4±0.7

2008-09 EI 1875±1407 1.6±0.8 34.24±0.11 1.24±0.71 30 47.6±5.1 0.0 16.7 83.3 3.4±0.1 -26.1±1.2 3.2±0.6

South 455±164 0.7±1.1 34.27±0.18 1.17±0.52 30 37.4±5.3 46.7 33.3 20.0 3.5±0.1 -26.9±0.6 2.5±0.4

West 4186±433 1.9±0.1 34.04±0.16 0.66±0.59 30 46.6±5.6 3.3 56.7 40.0 3.6±0.2 -27.2±0.6 3.4±0.6

All areas 2172±1791 1.4±0.8 34.18±0.17 1.02±0.59 90 43.9±7.0 16.7 35.6 47.8 3.5±0.2 -26.7±1.0 3.0±0.6

62 = 0.7336). Salinity was lower in the West region (F 2,18 = 4.43, P = 0.0407) but did not differ

between years (F1,18 = 0.22, P = 0.6499) or have a significant year*region interaction (F 2,18 =

0.61, P = 0.5599). Chl-a concentration did not differ by region (F 2,18 = 0.48, P = 0.6273) or year

(F 1,180 = 0.41, P = 0.6273), though there was a significant interaction due to the low Chl-a concentrations found in the EI region in 2006/07 and West region in 2008/09 (Table 1; F 2,18 =

4.07, P = 0.0447).

Factors affecting the stable isotope values of Antarctic krill

13 15 13 Krill δ C and δ N values increased with standard length (δ C: F 2,180 = 7.65, P = 0.0063;

15 13 δ N: F 1,180 = 59.06, P < 0.0001) but were not significantly influenced by sex ( δ C: F 2,180 = 0.61,

15 P = 0.5443; δ N: F 2,180 = 1.04, P = 0.3567) after accounting for differences in size (Fig. 2).

When examining for ontogenetic and oceanographic effects on krill δ13 C values at the station

level, we found that the model with a significant effect of krill standard length (F 1,18 = 5.84, P =

0.0288) and Chl-a concentration (F 1,180 = 6.27, P = 0.0243) was the most parsimonious GLM based on AIC. Similarly, the most parsimonious model to explain variation in krill δ15N values included only a significant effect of krill standard length (F 1,18 = 27.5, P < 0.0001). Mean

Antarctic krill δ13 C and δ15N values at stations increased as the mean standard length of krill increased ( δ13 C: β = 0.0393, SE = 0.0162; δ15N: β = 0.0708, SE = 0.0135) and δ13 C values were higher at station with higher Chl-a concentration (β = 0.4401, SE = 0.1758).

Krill predator stable isotope values and dietary modeling

The mean standard length of Antarctic krill consumed by Adélie penguins at Admiralty

Bay varied over the ten breeding seasons examined, with the smallest mean lengths observed in

Figure 2. Euphausia superba . Relationship between standard length (SL) and the δ13C (a) and δ15 N (b) values of Antarctic krill collected near the South Shetland Islands and northern Antarctic Peninsula in January 2006/07 and 2008/09. Dashed lines are for reference only and details fitted linear regressions based on parameter estimates from separate generalized linear mixed models. When controlling for the effect of standard length there were no differences in δ13C and δ15 N values between juvenile (open points), male (grey filled points) and female (black filled points) krill.

64 1997/98 and the largest in 2000/01 (Fig. 3a). Satellite derived estimates of surface Chl-a concentration around the breeding colony varied to lesser degree with a minimum in 2002/03 and maximum in 2005/06 (Fig 3a). Using these factors to predict the δ13C and δ15N of krill consumed by Adélie Penguins from 1997/98 to 2006/07 suggests that the isotopic signatures of krill in

Adélie Penguin diets varied in concert with krill length by as much as 0.7‰ for δ13 C and 1.0‰

for δ15N between years (Fig. 3b). As such, the predicted δ13 C and δ15N values of Adélie Penguin

chick feathers consuming an inter-annually consistent diet of predominantly krill (80% krill,

20% fish) also fluctuated over a similar range from 1997/98 to 2006/07 (Fig. 4a). Dietary

predictions from our SIAR models suggest that using the highest predicted krill δ13 C and δ15N

values (2000/01) as the krill prey source over-estimated the proportion of krill in Adélie Penguin

diet by as much as 17.8% (14.2 percentage points) in some years (Fig. 4b). In contrast, using the

lowest predicted krill δ13 C and δ15N values (1997/98) under-estimated the proportion of krill in

Adélie Penguin diet by as much as 15.2% (12.2 percentage points) in some years (Fig. 4b).

Our literature review also suggests differences in the size and δ15N values of Antarctic

krill consumed by 20 common Southern Ocean krill predators (Table 2). Fish species consume

the smallest sized krill with the lowest predicted δ15N values. Squid, bird and seal species

overlapped greatly and whale species differ in the size and predicted δ15N values of krill that they consume. In addition, inter-annual variation in the mean length of krill consumed by

Chinstrap Penguins ( Pygoscelis antarctica ), Gentoo Penguins (P. papua) and Antarctic fur-seals

(Arctocephalus gazella ) suggest the potential for significant inter-annual fluctuations in the δ15N

values of krill in their diets, similar to what we observed in Adélie Penguins in this study.

Figure 3. Euphausia superba . The mean (±SD) standard length of Antarctic krill (a; black filled triangles) recovered from Adélie Penguin stomach contents and mean (±SD) surface Chl-a concentration (a; gray bars) measured by satellite within a 100 km by 1000 km box centered on the breeding colony, and the predicted mean (±SD) δ13C (b; open squares) and δ15 N values (b; grey filled squares) of Antarctic krill consumed by Adélie Penguins during chick provisioning at Admiralty Bay, King George Island from 1997/98 to 2006/07. Krill stable isotope values were predicted based on parameter estimates from separate generalized linear mixing models incorporating inter-annual variation in standard length ( δ13C and δ15 N) and surface Chl-a concentrations within the foraging range of Adélie Penguins ( δ13C).

Figure 4. Pygoscelis adeliae . The predicated mean (±SD) feather δ13 C (a; open points) and δ15 N (a; grey filled points) values of Adélie Penguin chicks consuming a consistent diet of 80% Antarctic krill and 20% fish ( Pleuragramma antarcticum ) and the mean proportion (± Bayesian 95% credibility intervals) of krill estimated in the diet of Adélie Penguin chicks using three variants of a two-source, Bayesian mixing model at Admiralty Bay, King George Island from 1997/98 to 2006/07. Chick feather values are calculated as the weighted mean (±SD) of year- specific predicted krill stable isotope values and constant fish stable isotope values in each year. The three mixing model variants differ in isotope values used for Antarctic krill in each year (predicted annual values, highest values (2000/01) or lowest (1997/98).

67 Table 2. Euphausia superba . The annual mean size (standard length) and size range of Antarctic krill consumed by 20 common Southern Ocean krill predators and the predicated mean and total range of the δ15 N values of Antarctic krill consumed by each predator species if they were found foraging in our study area during austral summers 2006/07 and 2008/09.

Size of krill consumed (mm) Predicted δ15 N value (‰) Krill predator species Source a Annual Mean Total range Annual Mean Total range Fish Antarctic silverfish ( Pleuragramma antarcticum ) 28 15 - 39 2.1±0.7 1.2±1.2 - 2.9±0.3 1 Antarctic jonasfish ( Notolepis coatsi ) 34 21 - 49 2.6±0.5 1.6±1.0 - 3.6±0.4 2 Electrona antarctica 25 17 - 39 1.9±0.8 1.4±1.2 - 2.9±0.3 1 Squid Glacial squid ( Psychroteuthis glacialis ) - 35 - 50 - 2.6±0.5 - 3.7±0.4 3 Antarctic neosquid ( Alluroteuthis antarcticus ) - 42 - 52 - 3.3±0.3 - 3.8±0.5 3 Birds Southern Fulmar ( Fulmarus glacialoides ) 40 - 48 26 - 65 3.0±0.3 - 3.5±0.3 2.0±0.8 - 4.8±1.0 4, 5 Antarctic petrel (Thalassoica antarctica) 40 - 48 27 - 53 3.0±0.3 - 3.5±0.3 2.1±0.8 - 3.9±0.5 4, 6, 7 Cape petrel ( Daption capense ) 40 - 48 26 - 56 3.0±0.3 - 3.5±0.3 2.0±0.8 - 4.1±0.6 8, 9 Antarctic prion ( Pachyptila desolata ) 19 14 - 23 1.5±1.1 1.1±1.3 - 1.8±0.9 4 Snow petrel ( Pagodroma nivea ) 37 26 - 50 2.8±0.4 2.0±0.8 - 3.7±0.4 4 Wilson's storm petrel ( Oceanites oceanicus ) 35 26 - 44 2.6±0.5 2.0±0.8 - 3.3±0.3 4 Adélie penguin ( Pygoscelis adeliae ) 35 - 48 14 - 67 2.6±0.5 - 3.6±0.1 1.1±1.3 - 4.9±1.0 10, 11 Chinstrap penguin ( Pygoscelis antarctica ) 38 - 52 18 - 64 2.8±0.4 - 3.8±0.5 1.4±1.1 - 4.7±0.9 11, 12 Gentoo penguin ( Pygoscelis papua ) 40 - 53 13 - 65 3.0±0.3 - 3.9±0.5 1.1±1.3 - 4.8±1.0 11, 12 Mammals Antarctic fur seal ( Arctocephalus gazella ) 41 - 52 33 - 59 3.1±0.3 - 3.8±0.5 2.5±0.5 - 4.3±0.7 13 Leopard seal ( Hydrurga leptonyx ) 48 38 - 56 3.5±0.3 2.8±0.4 - 4.1±0.6 14 Crabeater seal ( Lobodon carcinophagus ) 47 36 - 54 3.5±0.3 2.7±0.4 - 4.0±0.5 14 Antarctic minke whale ( Balaenoptera bonaerensis ) - 35 - 44 - 2.6±0.5 - 3.3±0.3 15 Fin whale ( Balaenoptera physalus ) - 45 - 65 - 3.3±0.3 - 4.8±1.0 15 Humpback whale ( Megaptera novaeangliae ) - 13 - 34 - 1.1±1.3 - 2.6±0.5 15 a 1) Lancraft et al. (2004), 2) Williams et al. (1985), 3) Kear 1992, 4) Ainley et al. (1984), 5) Norman & Ward (1992), 6) Montague (1984), 7) Lorentsen et al. (1998), 8) Soave et al. (1996), 9) Coria et al. 1997, 10) Hinke et al. 2007, 11) W. Trivelpeice unpub. data, 12) Miller & Trivelpiece (2007), 13) Osman et al. (2004), 14) Lowery et al. (1998), 15) Santora et al. (2010)

68 Discussion

Ontogenetic variation in the stable isotope values of krill

We found that stable isotope values of Antarctic krill varied with ontogeny. Krill δ15N,

and to a lesser extent δ13C, values increased with standard length. In addition, the spatial

variation in krill δ15N values we observed between regions was driven by the concurrent differences the size of krill found within each region. Krill δ15N values in our study ranged from

1.7 to 5.4‰ (total range 3.7‰), suggesting an influence of omnivory as there is a general

enrichment in δ15 N values by 3-5‰ per trophic level (Minagawa & Wada 1984). A likely explanation for the ontogenetic shift in δ15N, and to a lesser extent δ13C, values is the increasing

importance of carnivory by Antarctic krill as they grow larger. The relationship between δ13C values and standard length also suggest an increased utilization of benthic foraging habitats in larger krill where detritus and copepods are more commonly consumed (Schmidt et al. 2011).

These hypotheses are supported by previous studies. Using gut contents and fatty acid analyses,

Atkinson et al. (2002) found that juvenile Antarctic krill (28-38 mm) fed mainly on phytoplankton, while adults (48-58 mm) consumed small copepods in the Lazarev Sea in late autumn 1999. In addition, Stowasser et al. (2011) found a positive relationship between δ15N

values and body mass in Antarctic krill collected in the Scotia Sea in January and February 2008,

though the sample sizes in this study were limited. It is possible that while both large and small

krill utilize compression filter feeding to consume pelagic phytoplankton (Hamner 1988); larger

krill might be more selective or adept at raptorial capture of large heterotrophic particles (Granéli

et al. 1993).

Other factors may have also influenced the general increase in δ15N and δ13C values we observed with krill size. Rapid growth has been found to lower tissue δ15N values in fish,

69 reptiles, and birds (Trueman et al. 2005, Reich et al. 2008, Sears et al. 2009) as the ratio of nitrogen incorporation to loss is higher in growing animals than in non-growing animals

(Martínez del Rio and Wolf 2005). Field derived growth rates of Antarctic krill indicate that daily growth rates decrease with age (Siegel & Nicol 2000), suggesting that the low δ15N values

we observed in small krill was influenced to some degree by their relatively higher growth rate.

In addition, as we analyzed whole krill, differences in gut content, tissue and amino acid

composition over ontogeny may have influenced the stable isotope signatures of krill in our

study. Carbon isotope values in whole zooplankton can be influenced by gut contents with lower

δ13C values compared to body tissues (Hill & McQuaid 2011). The chitinous exoskeletons of

zooplankton also display low δ13C and δ15 N values relative to whole body isotope values

(Gorokhova & Hansson 1999). Furthermore, a previous study determined tissue-specific differences in δ15 N values between reproductively active males and female Antarctic krill driven

by variations in the relative proportions and δ15 N signatures of amino acids in these tissues

(Schmidt et al. 2004). While we did not observe differences in whole body δ15N and δ13C values between male and female Antarctic krill, we can not discount the possibility that size related isotopic trends may be due in part to ontogenetic differences in gut content, tissue and amino acid composition.

Oceanographic factors and krill stable isotope values

Our study substantiates increasing evidence that δ13C values of marine organisms can be influenced by temporal and spatial variation in primary production (Schell 2000, Hirons et al.

2001, Hilton et al. 2006). δ13 C values of krill in our study were positively related to Chl-a

concentration at their sampling site. Similarly, Schell et al. (1998) found spatial variation in δ13 C

70 values of zooplankton across the Bering, Chukchi, and Beaufort seas that were due to differences in oceanographic conditions, phytoplankton growth rates, thereby forcing isotopic discrimination at the base of each food-web. Furthermore, Jaeger & Cherel (2011) found that seasonal variation in feather δ13 C values of six southern ocean penguin species and Chl-a concentration were

positively correlated. Because many of the penguin species examined by Jaeger & Cherel (2011)

are krill predators, it suggests in combination our studies a casual link between the productivity

of the pelagic ecosystem and δ13 C values of Antarctic krill and their predators. In contrast, we

did not observe any significant relationships between krill δ13 C values and other oceanographic

factors. However, the range of temperatures observed in our study area is small relative to

previous experimental and field studies that have found temperature related effects on δ13 C values of pelagic phytoplankton (Rau et al. 1991, Hinga et al. 1994). In addition, while krill size and Chl-a concentration were the best predictors of krill δ13 C values in our study, it is important to note the temperature, salinity and bathymetry all interact to influence levels of primary production in our study region (Hewes et al. 2009). Krill δ15N values showed no relationships with any of the oceanographic factors examined in this study. However, there is evidence to suggest that at larger spatial scales, baseline δ15N values in the Southern Ocean can be influenced by factors we did not examine in our study. These include changes in phytoplankton communities and increases in the δ15N values of the nitrate pool by phytoplankton uptake (Lara et al. 2010).

Krill predator stable isotope values and dietary mixing models

Our study highlights the potential for variation in the size class of Antarctic krill consumed by predators and variations in local levels of primary production to influence the

71 isotope values of krill predators independent of shifts in diet composition. For example, our data suggest that feather δ13 C and δ15 N values of Adélie Penguin chicks consuming a consistent diet

of 80% krill and 20% fish over ten seasons at Admiralty Bay was likely to vary by as much as

0.7 to 1.0‰ between years. When not accounting for the effects of inter-annual variation in the

size of krill and levels of primary production on krill stable isotope values dietary mixing models

overestimated or underestimated the importance of krill in Adélie Penguin diets by as much as

15.2 to 17.8%, respectively. It is uncertain the extent to which this bias may have affected past

studies of krill predator diets using stable isotopes, as it is uncommon for these studies to report

the size of krill used as prey sources and if these samples are representative of the size class of

krill consumed by the predator in question (Hall-Aspland et al. 2005, Tierney et al. 2008, Polito

et al. 2011a).

A review of Antarctic krill predators suggests that predator size and feeding strategy

influences the size of krill they consumed. These differences translate into a predicted 2.4‰

range in the mean δ15 N values of Antarctic krill consumed across species if they were found feeding in our study area. Small schooling fish consume relatively small krill with the lowest predicted δ15 N values (Table 2). However, it is important to note the high error associated with

predicted δ15 N values of small krill due to the lack of krill < 24 mm in our study. Squid species

consume slightly larger krill than fish species, however previous studies suggests a positive

relationship between squid and fish body size and the size of krill that they consume (Nemoto et

al. 1998, Lancraft et al. 2005). In contrast, while the size of krill consumed by Southern Ocean

birds and pinnipeds is a function of availability; these species will preferentially select krill from

larger size classes that are predicted to have higher δ15 N values (Reid et al. 1996, Miller &

Trivelpiece 2007). One exception to this trend is found in the Antarctic prion ( Pachyptila

72 desolata ), that previous studies suggest may target small, juvenile krill (Prince 1980, Ainley et al. 1984). Unlike birds and pinnipeds, the feeding method of baleen whales does not allow for size specificity at the level of individual krill. However, there is evidence to suggest that baleen whales around the northern Antarctic Peninsula aggregate to krill hotspots that differed in krill size structure. Humpback whales ( Megaptera novaeangliae ) are associated with small (<35 mm) juvenile krill, Antarctic minke whales ( Balaenoptera bonaerensis ) with intermediate sized krill

(35–44 mm) and fin whales ( B. physalus ) with large krill (>45; Santora et al. 2010). Similar to our findings with Adélie Penguins, the predicted 2.4‰ range in the mean δ15 N values of

Antarctic krill consumed across the predators examined in our study is likely to translate to a similar maximal range in predator tissue δ15 N values when Antarctic krill is dominant component

of predator diets.

Our study provides the first quantification of the extent to which prey source mismatches

can bias estimates of krill predator diets using stable isotope mixing models in a biologically

relevant scenario. Even so, it is important to note that we used constant δ15N and δ13C values for

the “fish” source in our isotope mixing model. Unfortunately, isotopic variation in the “non-

krill” component of Adélie Penguin diets could not be assessed in this study, constituting another

potential source of bias when estimating krill predator diets using stable isotopes. However,

intraspecific variation in the stable isotope values of Antarctic krill is likely an important

influence on predator stable isotope values, relative to isotopic variation in non-krill dietary

items, due to the dominance of krill in diets. In contrast, the power for intraspecific variation in

krill stable isotope values to influence predator stable isotope values is likely to be diluted in

predators with more diverse diets. Nevertheless, a quantification of the extent of intraspecific

73 variation in the stable isotope values of non-krill prey in predator diet, such as fish species, and the possible influence on estimates of krill predator diets using stable isotopes is needed.

Conclusion

We found that stable isotope values of Antarctic krill are influenced by both ontogenetic

(size) and oceanographic factors (primary production). Our study highlights the increased importance of carnivory in Antarctic krill of larger size, and that high levels of primary production can increase ecosystem baselines and affect krill δ13 C values. Furthermore, our

modeling approaches detail how inter-annual and/or spatial variations in the stable isotope values

of Antarctic krill shift the isotope values of krill predators independent of changes in diet

composition. When not accounted for intraspecific variation in the stable isotope values of

Antarctic krill can lead to biased estimates of predator diets using stable isotope mixing models.

Therefore, future analyses of krill predator diets using stable isotopes require caution to avoid

spatial, temporal and ontogenetic mismatches between the stable isotope values of prey sources

utilized in mixing models and the actual stable isotope values of prey consumed by predators.

References

Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Csaki F (eds) Proceedings of the Second International Symposium on Information Theory. Akademiai Kiado, Budapest, p 267–281

Ainley DG, O’Conner EF, Boekelheide RJ (1984) The marine ecology of birds in the Ross Sea, Antarctica. Ornithol Mono 32:97

Atkinson A, Snÿder R (1997) Krill–copepod interactions at South Georgia, Antarctica, I. Omnivory by Euphausia superba . Mar Ecol Prog Ser 160:67–76

74 Atkinson A, Meyer B, Stübing D, Hagen W, Schmidt K, Bathmann UV (2002) Feeding and energy budgets of Antarctic krill Euphausia superba at the onset of winter. II. Juveniles and adults. Limnol Oceanogr 47:953–966

Atkinson A, Siegel V, Pakhomov EA, Jessopp MJ, Loeb V (2009) A re-appraisal of the total biomass and annual production of Antarctic krill. Deep-Sea Res I 56:727–740

Bond AL, Jones IL (2009) A practical introduction to stable-isotope analysis for seabird biologists: approaches, cautions and caveats. Mar Ornithol 37:183–188

Cherel Y, Hobson KA (2007) Geographical variation in carbon stable isotope signatures of marine predators: a tool to investigate their foraging areas in the Southern Ocean. Mar Ecol Prog Ser 329:281–287

Coria N, Soave G, Montalti D (1997) Diet of Cape petrel Daption capense during the post- hatching period at Laurie Island, South Orkney Islands, Antarctica. Polar Biol 18:236–239

Crawford K, Mcdonald RA, Bearhop S (2008) Applications of stable isotopes to the ecology of mammals. Mamm Rev 38:87–107

DeNiro MJ, Epstein S (1978) Influence of diet on the distribution of carbon isotopes in animals. Geochim Cosmochim Acta 42:495–506

DeNiro MJ, Epstein S (1981) Influence of diet on the distribution of nitrogen isotopes in animals. Geochim Cosmochim Acta 45:341–351

Everson I (2000) Role of krill in marine food webs: the Southern Ocean. In: Everson I (ed) Krill: biology, ecology and fisheries. Blackwell Science, Oxford, UK. p 194–201

France RL (1995) Carbon-13 enrichment in benthic compared to planktonic algae: foodweb implications. Mar Ecol Prog Ser 124:307–312

Freeman KH, Hayes JM (1992) Fractionation of carbon isotopes by phytoplankton and estimates of ancient CO 2 levels. Global Biogeochem Cy 6:185-198

Gorokhova E, Hansson S (1999) An experimental study on variations in stable carbon and nitrogen isotope fractionation during growth of Mysis mixta and Neomysis integer . Can J Fish Aquat Sci 56:2203–2210

Granéli E, Granéli W, Rabbani MM, Daugbjerg N, Fransz G, Cuzin-Roudy J, Alder VA (1993) The influence of copepod and krill grazing on the species composition of phytoplankton communities from the Scotia-Weddell Sea. Polar Biol 13: 201-213

Hall-Aspland SA, Rogers TL, CanWeld RB (2005) Stable carbon and nitrogen isotope analysis reveals seasonal variation in the diet of leopard seals. Mar Ecol Prog Ser 305:249–259

75 Hamner WM (1988) Biomechanics of filter feeding in the Antarctic krill Euphausia superba : review of past work and new observations. J Crustac Biol 8:149–163

Hedd A, Fifield DA, Burke CM, Montevecchi WA, Tranquilla LM, Regular PM, Buren AD, Robertson GJ (2010) Seasonal shift in the foraging niche of Atlantic puffins Fratercula arctica revealed by stable isotope ( δ15 N and δ 13 C) analyses. Aquat Biol 9:13–22

Hewes CD, Reiss CS, Holm-Hansen O (2009) A quantitative analysis of sources for summertime phytoplankton variability over 18 years in the South Shetland Islands (Antarctica) region. Deep- Sea Res I 56:1230–1241

Hill JM, CD McQuaid (2011) Stable isotope methods: The effect of gut contents on isotopic ratios of zooplankton. Estuar Coast Shelf Sci 92(3):480–485

Hinga KR, Arthur MA, Pilson MEO, Whitaker D (1994) Carbon isotope fractionation by marine phytoplankton in culture: the effects of CO 2 concentration, pH, temperature, and species. Global Biogeochem Cy 8:91-102

Hinke JT, Salwicka K, Trivelpiece SG, Watters GM, Trivelpiece WZ (2007) Divergent responses of Pygoscelis penguins reveal common environmental driver. Oecologia 153:845–855

Hilton GM, Thompson DR, Sagar PM, Cuthbert RJ, Cherel Y, Bury SJ (2006) A stable isotopic investigation into the causes of decline in a sub-Antarctic predator, the rockhopper penguin Eudyptes chrysocome . Global Change Biol 12:611–625

Hirons AC, Schell DM, Finney BP (2001) Temporal records of δ13C and δ15 N in North Pacific pinnipeds: inferences regarding environmental change and diet. Oecologia 129:591–601

Holm-Hansen O, Riemann B (1978) Chlorophyll a determination: improvements in methodology. Oikos 30:438–447

Holm-Hansen O, Naganobu M, Kawaguchi S, Kameda T, Krasovski I, Tchernyshkov P, Priddle J, Korb R, Brandon M, Demer D, Hewitt RP, Kahru M, Hewes CD (2004) Factors influencing the distribution, biomass, and productivity of phytoplankton in the Scotia Sea and adjoining waters. Deep Sea Res Part II 51:1333–1350

Hopkins TL (1985) Food web of an Antarctic midwater ecosystem. Mar Biol 89:197–212

Inger R, Bearhop S (2008) Applications of stable isotope analyses to avian ecology. Ibis 150:447–461

Jablonski B (1985) The diet of penguins on King George Island, South Shetland Islands. Acta Zool Cracov 29: 117–186

76 Jaeger A, Cherel Y (2011) Isotopic investigation of contemporary and historic changes in penguin trophic niches and carrying capacity of the Southern Indian Ocean. PLoS ONE 6(2): e16484

Ju SJ, Harvey HR (2004) Lipids as markers of nutritional condition and diet in the Antarctic krill Euphausia superba and Euphausia crystallorophias during austral winter. Deep-Sea Res II 51:2199–2214

Kear AJ (1992) The diet of Antarctic squid: comparison of conventional and serological gut contents analyses. J Exp Mar Biol Ecol 156:161–178

Kline TJ (1999) Temporal and spatial variability of 13 C/ 12 C and 15 N/ 14 N in pelagic biota of Prince William Sound, Alaska. Can J Fish Aquat Sci 56(1):94–117

Lancraft TM, Reisenbichler KR, Robison BH, Hopkins TL, Torres JJ (2004) A krill-dominated micronekton and macrozooplankton community in Croker Passage, Antarctica with an estimate of fish predation. Deep-Sea Res II 51:2247–2260

Lara RJ, Alder V, Franzosi CA, Kattner G (2010) Characteristics of suspended particulate organic matter in the southwestern Atlantic: Influence of temperature, nutrient and phytoplankton features on the stable isotope signature. J Marine Syst 79(1–2):199-209

Laws EA, Popp BN, Bidigare RR, Kennicutt MC, Macko SA (1995) Dependence of phytoplankton carbon isotopic composition on growth rate and [CO 2]aq: theoretical considerations and experimental results. Geochim Cosmochim Acta 59:1131–1138

Laws RM (1985) The ecology of the Southern Ocean. Am Sci 73:26–40

Littell RC, Milliken GA, Stroup WW, Wolfinger RD (1996) SAS Systems for Mixed Models. SAS Institute, Cary, NC, USA

Lorentsen S-H, Klages N, Røv N (1998) Diet and prey consumption of Antarctic petrels Thalassoica antarctica at Svarthamaren, Dronning Maud Land, and at sea outside the colony. Polar Biol 19(6):414–428

Lowry LF, Testa JW, Calvert W (1988) Notes on the winter feeding of crabeater and leopard seals near the Antarctic Peninsula. Polar Biol 8:475-478

Martínez del Rio C, Wolf BO (2005) Mass balance models for animal isotopic ecology. In: Starck MA, Wang T (eds) Physiological and ecological adaptations to feeding in vertebrates. Science Publishers, Enfield, pp 141–174

Miller AK, Trivelpiece WZ (2007) Cycles of Euphausia superba recruitment evident in the diet of Pygoscelid penguins and net trawls in the South Shetland Islands, Antarctica. Polar Biol 30:1615–1623

77 Minagawa M, Wada E, (1984) Stepwise enrichment of 15 N along food chains: further evidence and the relation between δ15 N and animal age. Geochim Cosmochim Acta 48:1135–1140

Montague TL (1983) The food of Antarctic petrels ( Thalassoica antarctica ). Emu 84:244–245

Nemoto T, Okiyama M, Iwasaki N, Kikuch T (1988) Squid as predators on krill ( Euphausia superba ) and prey for sperm whales in the Southern Ocean. In: Sahrhage D (ed) Antarctic Ocean and resources variability. Springer-Verlag, Berlin, p 292–296

Nicol S (2006) Krill, currents, and sea ice: Euphausia superba and its changing environment. Bioscience 56:111–120

Norris DR, Arcese P, Preikshot D, Bertram DF, Kyser TK (2007) Diet reconstruction and historic population dynamics in a threatened seabird. J Appl Ecol 44:875–884

Norman FI, Ward SJ (1992) Foods and aspects of growth in the Antarctic Petrel and Southern Fulmar breeding at Hop Island, Rauer Group, East Antarctica. Emu 92:207–222

Osman LP, Hucke-Gaete R, Moreno CA, Torres D (2004) Feeding ecology of Antarctic fur seals at Cape Shirreff, South Shetlands, Antarctica. Pol Biol 27:92–98

Pakhomov EA, Froneman PW, Perissinotto R (2002) Salp/krill interactions in the Southern Ocean: spatial segregation and implications for the carbon flux. Deep-Sea Res Part II 49:1881– 1907

Park JI, Kang CK, Suh HL (2011) Ontogenetic diet shift in the euphausiid Euphausia pacifica quantified using stable isotope analysis. Mar Ecol Prog Ser 429:103–109

Parnell AC, Inger R, Bearhop S, Jackson AL (2010) Source partitioning using stable isotopes: coping with too much variation. PLoS ONE 5: e9672

Perissinotto R, Gurney L, Pakhomov EA (2000) Contribution of heterotrophic material to diet and energy budget of Antarctic krill, Euphausia superba . Mar Biol 136:129–135

Phillips DL, Gregg JW (2001) Uncertainty in source partitioning using stable isotopes. Oecologia 127:171–179

Polito MJ, Goebel ME (2010) Investigating the use of stable isotope analysis of milk to infer seasonal trends in the diets and foraging habitats of female Antarctic fur seals. J Exp Mar Biol Ecol 395(1-2):1–9

Polito MJ, Lynch HJ, Naveen R, Emslie SD (2011a) Stable isotopes reveal regional heterogeneity in the pre-breeding distribution and diets of sympatrically breeding Pygoscelis penguins. Mar Ecol Prog Ser 421: 265–277

78 Polito MJ, Fisher S, Tobias CR, Emslie SD (2011b) Dietary isotopic discrimination in gentoo penguin ( Pygoscelis papua ) feathers. Polar Biol 34: 1057–1063

Polito MJ, Trivelpiece WZ, Karnovsky NJ, Ng E, Patterson WP, Emslie SD (2011c) Integrating stomach content and stable isotope analyses to quantify the diets of Pygoscelid penguins. PLoS ONE 6(10): e26642

Prince PA (1980) The food and feeding ecology of blue petrel (Halobaena caerulea ) and dove prion ( Pachyptila desolata ). J Zool 190:59–76

Rau GH, Takahashi T, Des Marais DJ, Sullivan CW (1991) Particulate organic matter δ13 C variations across the Drake Passage. J Geophys Res 96:15131-15135

Reich KJ, Bjorndal KA, Martínez del Rio C (2008) Effects of growth and tissue type on the kinetics of 13 C and 15 N incorporation in a rapidly growing ectotherm. Oecologia 155:651–663

Reid K, Trathan PN, Croxall JP, Hill HJ (1996) Krill caught by predators and nets: differences between species and techniques. Mar Ecol Prog Ser 140:13–20

Santora JA, Reiss CS, Loeb VJ, Veit RR (2010) Spatial association between hotspots of baleen whales and demographic patterns of Antarctic krill Euphausia superba suggests size-dependent predation. Mar Ecol Prog Ser 405: 255–269

Schell DM, Barnett BA, Vinette KA (1998) Carbon and nitrogen isotope ratios in zooplankton of the Bering, Chukchi and Beaufort seas. Mar Ecol Prog Ser 162:11–23.

Schell DM (2000) Declining carrying capacity in the Bering Sea: isotopic evidence from whale baleen. Limnol Oceanogr 45: 459–462

Schmidt K, McClelland JW, Mente E, Montoya JP, Atkinson A, Voss M (2004) Trophic-level interpretation based on δ15 N values: implications of tissue-specific fractionation and amino acid composition. Mar Ecol Prog Ser 266:43–58

Schmidt K, Atkinson A, Petzke KJ, Voss M, Pond DW (2006) Protozoans as a food source for Antarctic krill, Euphausia superba : Complementary insights from stomach content, fatty acids, and stable isotopes. Limnol Oceanogr 51:2409–2427

Schmidt K, Atkinson A, Steigenberger S, Fielding S, Lindsay MCM, Pond DW, Tarling GA, Klevjer TA, Allen CS, Nicol S, Achterberg EP (2011) Seabed foraging by Antarctic krill: implications for stock assessment, bentho-pelagic coupling and the vertical transfer of iron. Limnol Oceanogr 56:1411–1428

Sears J, Hatch SA, O’Brien DM (2009) Disentangling effects of growth and nutritional stress on seabird stable isotope ratios. Oecologia 159:41–48

79 Seminoff JA, Bjorndal KA, Bolten AB (2007) Stable carbon and nitrogen isotope discrimination and turnover in pond sliders Trachemys scripta : insights for trophic study of freshwater turtles. Copeia 534–542

Siegel V, Nicol S (2000) Population parameters. In: Everson I (ed) Krill biology, ecology and fisheries. Blackwell Science, London, p 103–149

Soave GE, Coria NR, Montalti D (1996) Diet of the Pintado Petrel Daption capense during the late incubation and chick rearing periods, at Laurie Island, South Orkney Islands, Antarctica, January–February1995. Mar Ornithol 24:35–37

Stowasser G, Atkinson A, McGill R.A.R, Phillips R.A., Collins MA, Pond DW (2011) Food web dynamics in the Scotia Sea in summer: A stable isotope study. Deep-Sea Res II 59-60:208–221

Tierney M, Southwell C, Emmerson LM, Hindell MA (2008) Evaluating and using stable isotope analysis to infer diet composition and foraging ecology of Adélie penguins Pygoscelis adeliae. Mar Ecol Prog Ser 355:297–307

Trivelpiece WZ, Trivelpiece SG, Volkman NJ (1987) Ecological segregation of Adélie, gentoo and chinstrap penguins at King George Island, Antarctica. Ecology 68:351–361

Trueman CN, McGill RAR, Guyard PH (2005) The effect of growth rate on tissue-diet isotope spacing in rapidly growing animal. An experimental study with Atlantic salmon ( Salmo salar ). Rapid Commun Mass Sp 29:3239–3247

Vasil CA, Polito MJ, Patterson WP, Emslie SD (2012) Wanted: dead or alive? Isotopic analysis (δ13 C and δ15 N) of Pygoscelis penguin chick tissues supports opportunistic sampling. Rapid Commun Mass Sp 26:487–493

Volkman NJ, Presler P, Trivelpiece W (1980) Diets of Pygoscelid penguins at King George Island, Antarctica. Condor 82:373–378

Williams R (1985) Trophic relationships between pelagic fish and Euphausiids in Antarctic waters. In Sigfried, WR, Condy PR, Laws RM (eds) Antarctic nutrient cycles and food webs. Springer-Verlag, Berlin, p 452–459

80 CHAPTER FOUR: INTEGRATING STOMACH CONTENT AND STABLE ISOTOPE

4 ANALYSES TO QUANTIFY THE DIETS OF PYGOSCELID PENGUINS

Introduction

Stomach content analysis (SCA) is one of the most common methods for dietary analysis and provides insight into the foraging ecology of seabirds and the distribution, abundance and demography of their prey [1, 2]. Early studies often involved sacrificing animals to examine stomach contents [3], while currently a non-destructive, but still invasive, "lavage" technique to force regurgitation is commonly applied [4, 5]. When recovered stomach contents are relatively undigested, it is possible to estimate the composition and frequency occurrence of prey species and often measure, weigh and sex individual prey [3]. In addition, identifying and measuring hard prey remains, such as squid beaks and otoliths, can provide information on the size and mass of prey species when prey has been partially or completely digested [6, 7, 8].

There are inherent drawbacks and biases when using SCA to quantify seabird diets. This technique has been most commonly used during chick rearing when adults bring food ashore for their chicks; thus, less is known about the diets of seabirds outside of the breeding season [2].

Stomach contents also reflect a “snapshot” of an individual’s recent diet (8-16 hours) and can be highly variable, requiring large sample sizes to statistically examine differences among species, regions and/or time [3, 9, 10]. In addition, SCA is biased towards recent dietary items and prey that does not readily digest, such as zooplankton, and can underestimate the amount of soft-

4 This chapter has been published as: Polito M.J., Trivelpiece W.Z., Karnovsky N.J., Ng E., Patterson W.P., and Emslie S.D. 2011. Integrating stomach content and stable isotope analyses to quantify the diets of pygoscelid penguins. PLoS ONE, 6(10):e26642 bodied prey, such as fish and squid [11, 12]. While hard prey remains from stomach contents or pellets provide information on prey species composition these data are often difficult to integrate into overall diet composition estimates [6, 8, 13].

Recent advances in stable isotope analysis (SIA) and isotopic mixing models have shown great promise in quantifying the dietary composition of seabirds [14, 15]. Isotopic analyses are based on the concept that animals “are what they eat” with tissue stable nitrogen ( δ15 N) and carbon ( δ13 C) ratios reflecting diet at the time of synthesis [16]. For example, feathers are

metabolically inert after synthesis, so feathers from fledgling-aged chicks integrate dietary

history during the chick-rearing period as feathers replace natal down [17, 18, 19]. Isotopic

mixing models use geometric or Bayesian procedures to reconstruct animal diets based on the

δ13 C and δ15 N values of consumer tissues and isotopically distinct food sources [20, 21]. SIA and

isotopic mixing models have the potential to provide relatively non-invasive and cost-effective

quantitative estimates of seabird diets throughout much of their annual cycle [22, 23, 24].

There are limitations to using SIA to quantify seabird diets. When the isotopic signatures

of prey species that occupy a similar trophic level overlap, such as in forage fish, it can be

difficult to estimate their relative contributions to consumer diets [25, 26]. Isotopic mixing

models are only as useful as the data that go into them, requiring a prior understanding of

possible prey sources and their distinctive isotopic values [15]. In many cases, prior information

is lacking and all possible prey sources cannot be readily identified [24]. When all prey isotopic

values are not available, “representative” species are often used or multiple sources are combined

a priori for each trophic or functional group [24, 27, 28]. Furthermore, while studies of seabird

diets using SIA are becoming commonplace, few studies have compared concurrent quantitative

estimates of diet composition between SCA and SIA [28, 29]. In addition, it is also common to

82 compare SIA data to SCA prey frequency of occurrence instead of more appropriate mass-based estimates of diet composition derived from SCA [30, 31, 32].

In this study we simultaneously quantify the chick-rearing diet composition of sympatrically breeding seabirds, the Chinstrap ( Pygoscelis antarctica ) and Gentoo penguin ( P. papua ) over two breeding seasons at Cape Shirreff, Livingston Island, Antarctica (62°28’S,

60°46’W) using both SIA and SCA. Similar to other Antarctic seabirds, Pygoscelis penguin diets

are generally composed of zooplankton, primarily Antarctic krill ( Euphausia superba ), and soft-

bodied, higher-trophic prey species, such as fish [33]. As chick-rearing diets have been well

studied using SCA at this site, it provides an excellent case study for comparison with SIA [34,

35, 36]. We seek to better understand the relative merits of both methods and highlight the use of

SCA to inform isotopic mixing models to better quantify the diets of seabirds using SIA.

Our primary objectives are to: 1) use simultaneous collection of SCA and SIA to compare

the ability of these two methods to detect inter-annual and inter-specific differences in diet

composition in Pygoscelis penguin chicks, 2) compare the predictive ability of a two-source

(krill vs. fish) linear mixing model among those using a representative fish species and those

using an a priori averaged species and year-specific fish values, and 3) evaluate a method of a

posteriori integrating SCA data to better elucidate the taxonomic composition of the fish portion

of diets using a multi-source Bayesian mixing model.

Materials and Methods

Ethics statement

Animal use in this study was conducted under approved animal use protocols from the

University of California San Diego Institutional Animal Care and Use Committee (S05480) and

83 in accordance to Antarctic Conservation Act permits provided by the U.S. National Science

Foundation to S. Emslie (2006-001) and R. Holt (2008-008).

Stomach contents, feather and prey samples

Fieldwork took place in January and February of 2008 and 2009 at a colony of approximately

4,500 breeding pairs of Chinstrap penguins and 800 breeding pairs of Gentoo penguins at Cape

Shirreff. We collected stomach content samples during the chick-rearing period after chicks had reached the crèche stage (>2.5 weeks of age). We sampled 2-5 unique breeding adults returning from foraging trips between 15:00-17:00 local time at 5 to 7-day intervals for a total of 10-14

Gentoo penguins and 30 Chinstrap penguins each year. We used the water-offloading technique following a modification of the CCAMLR Ecosystem Monitoring Program (CEMP) Standard

Methods [37]. Specifically, we did not analyze the complete contents of the stomach; rather we took approximately one-half (about 350 g). Most of the food beneath this upper portion is heavily digested and is difficult to objectively separate by prey species and its inclusion may bias both prey identification and diet composition estimates [10, 38]. We further justify this sampling method as parents ordinarily do not feed their entire food load to the chicks [39, 40]. Excess liquid was removed from each stomach sample by straining it through a fine sieve before weighing to obtain a sample mass (wet weight). From these samples, we determined the percentage of krill, fish, and other material by frequency occurrence and weight. We recovered fish otoliths from diet samples by swirling samples in a dark-bottomed pan and identified otoliths to the lowest possible taxonomic level using an internal reference collection and a published otolith guide [41]. We calculated the frequency occurrence and the minimum number of individuals (MNI) of each fish taxa following standard methods [42]. Specially, we estimated

84 MNI by summing the higher number of either right or left otoliths with half the number of eroded otoliths of unknown side to provide a conservative estimate of the total MNI represented in each stomach sample [42]. In addition, we used otolith measurements and published regression equations to calculate a total and percent of total reconstituted mass for each fish taxa identified (Appendix 1) [7, 13, 41, 43]. Due to the high number of small Pleuragramma

antarcticum otoliths recovered, we measured a random sub-sample of 20-75 P. antarcticum

otoliths per sample and used these values to estimate reconstituted mass for this species.

In February of each year, we collected three breast feathers from a random sample of 18-

20 fledgling chicks of each species while they were preparing to leave their natal colonies for the

sea at 7-10 weeks of age. From 2005 to 2009, we collected representative samples of penguin

prey species during trawls conducted along the South Shetland Islands and northern Antarctic

Peninsula and kept samples frozen prior to analysis. We further supplemented this prey library

with published isotopic values of two fish prey, Protomyctophum bolini and Champsocephalus

gunnari [44, 45].

Stable isotope analysis

We cleaned feathers using a 2:1 chloroform : methanol rinse, air-dried and cut them into small

fragments with stainless steel scissors. We homogenized whole prey samples, dried them for 48

hours in an oven at 60˚C and then extracted lipids from these samples using a Soxhlet apparatus

with a 1:1 Petroleum-Ether: Ethyl-Ether solvent mixture for 8 hours [46]. We flash-combusted

(Costech ECS4010 elemental analyzer) approximately 0.5 mg of each feather and prey sample

loaded into tin cups and analyzed for carbon and nitrogen isotopes ( δ13 C and δ15 N) through an

interfaced Thermo Delta V Plus continuous flow stable isotope ratio mass spectrometer

85 (CFIRMS). Raw δ values were normalized on a two-point scale using glutamic acid reference materials with low and high values (i.e. USGS-40 ( δ13 C = -26.4‰, δ15 N = -4.5‰) and USGS-41

(δ13 C = 37.6‰, δ15 N = 47.6‰)). Sample precision based on repeated sample and reference material was 0.1‰ and 0.2‰, for δ13 C, and δ15 N, respectively.

Stable isotope ratios are expressed in δ notation in per mil units (‰), according to the

following equation:

δX = [( Rsample / Rstandard ) - 1] · 1000

13 15 13 12 15 14 Where X is C or N and R is the corresponding ratio C / C or N / N. The Rstandard values

13 15 were based on the Vienna PeeDee Belemnite (VPDB) for δ C and atmospheric N 2 for δ N.

Isotopic mixing models

We used four model variants of the SIAR Bayesian mixing model [21] in the R environment (R Development Core Team 2007) to explore our ability to quantify chick diet composition (Appendix 2). The SIAR model estimates probability distributions of multiple source contributions to a mixture while accounting for the observed variability in source and mixture isotopic signatures, dietary isotopic fractionation, and elemental concentration. We used two SIAR model variants with two prey sources (Antarctic krill vs. “fish”) to estimate diet composition for each species/year combination using the δ13 C and δ15 N values of chick feathers.

Model 1 uses the δ13 C and δ15 N values of a representative fish species, P. antarcticum , which is commonly found in Pygoscelis penguin diets as the “fish” source [6]. Model 2 uses species and year specific "fish" δ13 C and δ15 N values calculated by averaging the δ13 C and δ15 N values of

multiple fish species weighted by their relative percent reconstituted fish mass (Appendix 1 and

2).

86 We used two additional variants of the SIAR mixing model with multiple prey sources

(6-7 depending on penguin species) to further evaluate methods of integrating stomach content data to better elucidate the taxonomic composition of the fish portion of penguin diets. For these models, we restricted our analyses to chick feather data from 2008 when the fish portion of chick diets was the most diverse. Model 3 is an initial multi-source model estimating the relative contribution of krill ( E. superba ) and all fish species in our prey library identified from otoliths in each species’ stomach contents (Appendix 1 and 2). Model 4 is an a posteriori informed model where we restricted the resulting posterior draws to those in which the relative importance of individual fish species was ranked in accordance to the abundance of each species identified through otolith analysis. For Model 4, we restricted posterior draws to only those where the estimated proportional contributions of the most abundant fish prey based on reconstituted fish mass was greater than the estimated proportional contributions of the second most abundant fish prey, and for the second most abundant fish prey greater than the third most abundant and so on for all fish species. For both the initial (Model 3) and informed (Model 4) multi-source models, we also summed results across fish prey and estimated the proportional contribution of each fish species to the fish portion (i.e. excluding krill) of penguin diets. For all SIAR models we incorporated Pygoscelis penguin feather δ15 N and δ13 C discrimination factors [47] and ran 1 million iterations, thinned by 15, with an initial discard of the first 40,000 resulting in 64,000 posterior draws.

Statistical analysis

Statistical calculations were performed using SAS (Version 9.1). We analyzed SCA data to test for differences between years and species using separate generalized linear models (Proc

87 Genmod). We used a binomial error distribution and logit link function for generalized linear models with the percent composition (by wet mass) or frequency occurrence of each of our three main prey group (krill, fish, and ‘other’ prey) as the response variables. For models that used

MNI of fish and reconstituted fish mass per sample as the response variables, we used a Poisson- error distribution with a logit link function. For all generalized linear models we conducted post- hoc analyses using a Bonferroni correction and reported chi-square and p-values from the likelihood ratio test statistics for type 3 tests.

To test for differences in the chick feather δ13 C and δ15 N values we used multivariate analysis of variance (MANOVA) along with Tukey-Kramer Multiple comparison values across species and years using PROC ANOVA. We used a similar MANOVA to examine the δ13 C and

δ15 N values of species in our prey library. We used model 95% credibility intervals to compare estimates of krill vs. fish among two-source SIAR model variants (Models 1 and 2) and SCA wet mass, and the percent contribution of individual fish species to fish portion of chick diets among multi-source SIAR model variants (Models 3 and 4) and SCA otolith-derived reconstituted fish mass. To facilitate direct comparison between SIAR models and SCA, we calculated Bayesian averages and 95% credibility intervals for each SCA dataset using Markov chain Monte Carlo

(MCMC) simulations via WinBUGS (Version 1.4). These MCMC simulations were implemented using the non-informative Dirichlet prior with an identical number of iterations, thins, and discards as our SIAR model analysis. Furthermore, we used Chi-Square goodness of fit tests to compare the distribution of mean estimates of the percent contribution of individual prey fish species to diets among multi-source SIAR models variants and SCA data.

Data were examined for normality and equal variance, all tests were two-tailed and significance was assumed at the 0.05 level. Stable isotope values of chick feathers and prey

88 species are presented ± standard deviation (SD), while diet composition estimates from stomach content analysis are presented ± standard error (SE) in tables and ±95% credibility intervals in figures.

Results

Stomach content analysis

Chinstrap penguin stomach samples had a higher percent contribution of krill relative to

2 Gentoo penguin samples (Table 1a; χ 1 = 10.91, p = 0.0010). However, we found no differences

2 2 by year ( χ 1 = 0.22, p = 0.6375) or a species*year interaction ( χ 1 = 0.00, p = 0.9805). Similarly,

Gentoo penguin samples contained a significantly higher percent contribution of fish relative to

2 Chinstrap penguin samples, ( χ 1 = 12.24, p = 0.0005), but we could not detect differences across

2 2 years ( χ 1 = 0.08, p = 0.7755) or a species*year interaction ( χ 1 = 0.26, p = 0.6078). The percent

contribution to stomach samples of other prey species, including cephalopods, amphipods and

2 2 other euphausiid species did not differ by penguin species ( χ 1 = 0.00, p = 0.9694), year ( χ 1 =

2 0.36, p = 0.5468) or a species*year interaction ( χ 1 = 0.00, p = 0.9694).

We found evidence of krill in all Chinstrap penguin samples and in all but one Gentoo

penguin sample (Table 1a). We found evidence of fish in all Gentoo penguin samples and in

36.7-50.0% of Chinstrap penguin samples, even when there was no detectable wet mass of fish

(Table 1a). However, the frequency occurrence of fish in Chinstrap penguin samples did not

2 differ across years ( χ 1 = 1.09, p = 0.2966). Similarly, the frequency occurrence of other prey

2 2 species did not differ by penguin species ( χ 1 = 1.57, p = 0.2107), year ( χ 1 = 0.00, p = 0.9481),

2 or a species*year interaction ( χ 1 = 0.00, p = 0.9481).

Table 1. The composition and occurrence of common prey groups and the minimum number of individual fish and reconstituted fish mass recovered from penguin stomach contents.

a) Percent composition of stomach b) Fish content per stomach

contents by wet mass (% FO) sample based on otoliths (total)

Krill - E. Reconstituted Species, year n Fish Other MNI superba mass (g) Chinstrap penguin a b a a a 2008 30 99.6±0.3 0.4±0.3 0.0±0.0 1.8±0.7 31.2±15.7 (100.0) (36.7) (10.0) (65) (936.3) a a a a b 2009 30 99.1±0.9 0.0±0.0 0.9±0.9 1.4±0.4 3.4±0.9 (100.0) (50.0) (10.0) (45) (103.0) Gentoo penguin a b a b c 2008 10 83.7±9.6 16.3±9.6 0.0±0.0 10.9±4.3 155.5±43.1 (90.0) (100.0) (20.0) (109) (1555.5) a b a c d 2009 14 68.2±10.8 30.8±10.4 0.9±0.7 211.9±70.5 294.3±80.4 (100.0) (100.0) (21.4) (2967) (4119.6) Other prey include cephalopods, Hyperiid amphipods, and small euphausiids (primarily Thysanoessa macrura ). Groups that do not share at least one superscript within a column are significantly different for the variable in question at the 0.05 level. Values are presented mean ± SE, with the frequency of occurrence (% FO) of common prey species and the total minimum number of individual (MNI) fish and reconstituted fish mass in grams presented in parentheses.

90 The MNI of fish and reconstituted fish mass per sample differed between species and years (Table 1b). Chinstrap penguin diets had lower MNI and reconstituted fish mass than

2 2 Gentoo penguins (MNI: χ 1= 959.14, p < 0.0001; reconstituted mass: χ 1= 959.14, p < 0.0001).

Across species and years Chinstrap penguins had higher reconstituted fish mass in diet samples in 2008 relative to 2009, while Gentoo penguins had both lower MNI and reconstituted fish

2 2 masses in 2008 relative to 2009 (MNI: χ 1 = 147.74, p < 0.0001; reconstituted mass: χ 1 =

1122.46, p < 0.0001). A total of 96.3% of all otoliths were identifiable to at least the genus level,

with six and five fish taxa represented in Chinstrap and Gentoo penguin diet samples,

respectively (Appendix 1).

Isotopic signatures of chick feathers and prey

We found δ15 N and δ13 C values of penguin chick feathers differed by species (Wilks’ λ, p

< 0.0001), year (Wilks’ λ, p = 0.0409) and had a significant species*year interaction (Wilks’ λ, p

< 0.0001). Gentoo penguin chicks had higher feather δ15 N values than Chinstrap penguin chicks in both years (Table 2, Fig.1). However, while Gentoo penguin chick feather δ15 N values were higher in 2009 relative to 2008, Chinstrap penguin chick feather δ15 N values did not differ

between years. Chinstrap and Gentoo penguin chicks had similar feather δ13 C values in 2008, but lower and higher values for Chinstrap and Gentoo penguins in 2009, respectively (Table 2). We found δ15 N and δ13 C values of species in our library of common penguin prey items also differed significantly (Wilks’ λ, p < 0.0001). The δ15 N and δ13 C values differed greatly between krill and fish species, while isotope values overlapped among many fish species (Table 2, Fig.1). Table 2. The carbon to nitrogen ratio and stable isotope signatures of penguin chick feathers and nine common krill and fish prey species.

Group, taxa or year n C/N δ15 N (‰) δ13 C (‰)

Chick feathers Chinstrap penguin, 2008 20 3.1±0.1 7.8±0.3 a -24.7±0.3 a Chinstrap penguin, 2009 20 3.1±0.1 7.5±0.3 a -25.2±0.3 b Gentoo penguin, 2008 20 3.1±0.1 8.9±0.6 b -24.6±0.3 a Gentoo penguin, 2009 21 3.1±0.1 9.8±0.8 c -24.3±0.3 c

Prey library Krill, Euphausia superba 40 3.7±0.2 3.3±0.6 a -26.4±1.4 a Fish, Protomyctophum bolini 13 3.2±0.1 9.2±0.5 -23.0±0.5 Fish, Electrona antarctica 41 3.3±0.1 8.8±0.7 b -25.5±0.7 b Fish, Gymnoscopelus nicholsi 6 3.4±0.1 9.4±0.3 bc -22.6±0.8 c Fish, Notolepis coatsi 3 3.2±0.1 7.2±0.8 d -25.7±0.4 abd Fish, Lepidonotothen squamifroms 10 3.3±0.1 9.6±0.8 c -24.2±0.7 d Fish, Pleuragramma antarcticum 30 3.4±0.2 9.4±0.5 c -24.7±0.4 d Fish, Trematomus newnesi 10 3.3±0.1 8.2±0.5 bd -24.8±0.5 bd Fish, Champsocephalus gunnari 5 3.3±0.1 8.5±0.3 -25.1±0.3 Carbon to nitrogen ratios (C/N) and stable isotope values ( δ15 N & δ13 C) are presented mean ± SD. Chick feathers and prey species that do not share at least one superscript within a column for each group (feathers or prey) are significantly different for the variable in question at the 0.05 level. P. bolini [45] and C. gunnari [44] were not included in prey species analyses.

Figure 1. Isotope signatures of penguin chick feathers in relation to nine common prey species. Values are presented ( δ13 C and δ15 N; mean ± SD). Chick feather values are presented after correction for dietary isotopic discrimination (Polito et al. 2011). Prey species abbreviation are Krill: Es ( Euphausia superba ), Fish: Ea ( Electrona antarctica ), Cg ( Champsocephalus gunnari) , Gn ( Gymnoscopelus nicholsi ), Ls ( Lepidonotothen squamifroms ), Nc ( Notolepis coatsi ), Pa (Pleuragramma antarcticum ), Pb ( Protomyctophum bolini ), and Tn ( Trematomus newnesi ). Two-source SIAR models

The two-source SIAR model variant that used P. antarcticum isotopic values as a representative ”fish” source (Model 1) and the variant that used a year and species-specific weighted “fish” isotopic values (Model 2) both predicted that Gentoo penguin chicks consumed relatively less krill and more fish than Chinstrap penguins in both years (Table 3). However, when examining model 95% credibility intervals these two model variants differed in their ability to detect species-specific, inter-annual differences in diet composition. While both two- source SIAR model variants predicted that Gentoo penguin chick diets contained a higher percentage of krill in 2008, only Model 2 detected a larger amount of fish in Chinstrap penguin chick diets during 2008 relative to 2009 (Table 3). Two-source SIAR model variants predicted a higher contribution of fish in the chick diets of both penguin species in comparison to diet composition estimates derived from SCA wet mass (Fig. 2). SCA estimates were also more variable than SIAR model predictions for Gentoo penguin chick diets. Furthermore, SCA derived estimates of the mean contribution of krill and fish in both species’ diets fell outside of our two-source SIAR models 95% upper and low credibility intervals, respectively (Fig. 2;

Tables 1 and 3).

Multi-source SIAR models

Both multi-source SIAR model variants (Models 3 and 4) predicted that Antarctic krill comprised the largest prey component of Chinstrap and Gentoo penguin chick diets in 2008

(Table 4). In addition both multi-source SIAR models broadly agreed with two-source SIAR model estimates of the relative proportion of krill vs. all fish species summed (Tables 3 and 4).

However, our initial multi-source SIAR model (Model 3) had difficulty estimating the relative Table 3. Predicted diet composition of penguin chicks at Cape Shirreff, Livingston Island derived from stable isotope analysis using two variants of the SIAR two-source Bayesian mixing model.

δ15 δ13 SIAR N & C two source models Model 1: P. antarcticum Model 2: weighted by % mass Species, year % Krill % Fish % Krill % Fish Chinstrap penguin 2008 83.8 (80.1-87.7) 16.2 (12.3-19.9) 79.2 (74.4-84.0) 20.8 (16.0-25.6) 2009 89.4 (85.2-93.5) 10.6 (6.5-14.8) 89.4 (85.2-93.5) 10.6 (6.5-14.8)

Gentoo penguin 2008 69.1 (64.9-73.2) 30.9 (26.8-35.1) 66.6 (62.1-71.1) 34.4 (28.9-37.9) 2009 53.1 (47.1-58.9) 46.9 (41.1-52.9) 52.3 (46.3-58.2) 47.7 (41.8-53.7) Diet compositions were estimated using SIAR [21] and are presented as mean estimates with 95% credibility intervals (in parentheses). Model 1 uses the δ15 N and δ13 C values of a representative fish species, Pleuragramma antarcticum , as the ‘fish’ source while Model 2 use yearly and species-specific weighted ‘fish’ δ15 N and δ13 C values (Appendices 1 and 2).

Figure 2. The estimated diet composition of penguin chicks based on stomach content and stable isotope analysis. Stomach content proportions are calculated as a percent of wet mass and proportion estimates of krill vs. fish using stable-isotope analysis are derived from a two-source Bayesian mixing model SIAR (Model 2) using annually weighted “fish” values listed in table S2 [21]. Proportions are presented mean ± Bayesian 95% credibility intervals.

96 Table 4. Predicted diet compositions of penguin chicks at Cape Shirreff, Livingston Island derived from stable isotope analysis using two variants of a multi-source Bayesian mixing model.

SIAR δ15 N & δ13 C multi source models

Chinstrap 2008 Gentoo 2008

Prey source Initial model Informed model Initial model Informed model

Krill Euphausia superba 79.4 (74.4-84.2) 78.1 (73.5-81.6) 65.2 (59.6-70.6) 65.2 (61-69.1)

Fish Protomyctophum bolini 2.6 (0.0-7.0) 0.6 (0.1-1.4) - -

Electrona antarctica 3.0 (0.0-8.0) 5.0 (2.7-8.3) - -

Gymnoscopelus nicholsi 2.2 (0.0-6.1) 2.9 (1.1-4.6) 3.5 (0.0-9.6) 7.8 (3.8-11.8)

Notolepis coatsi 6.7 (0.0-15.3) 10.3 (5.7-16.5) - -

Lepidonotothen squamifroms - - 5.5 (0.0-14.8) 1.4 (0.1-3.6)

Pleuragramma antarcticum 2.5 (0.0-6.9) 1.2 (0.3-2.4) 6.1 (0.0-16) 2.9 (0.8-5.4)

Trematomus newnesi 3.6 (0.0-9.5) 1.9 (0.7-3.3) 9.6 (0.0-22.8) 5.0 (1.9-8.3)

Champsocephalus gunnari - - 10 (0.0-23) 17.6 (10.2-27.7)

All Fish 20.6 (15.8-25.6) 21.9 (18.4-26.5) 34.8 (29.4-40.4) 34.8 (30.9-39)

Diet compositions were estimated using SIAR [21] and are presented as mean estimates with 95% credibility intervals (in parentheses). The initial model (SIAR Model 3) estimates the relative contribution of individual krill and fish species identified in stomach contents to overall penguin diets. The informed model (SIAR Model 4) restricts posterior draws of diet composition estimates to those agreeing with the relative abundance of each fish species based on reconstituted mass (Appendices1 and 2). All fish represents the sum of the predicted contribution of all fish species.

97 proportion of individual fish species to both penguin species chick diets in 2008. SIAR Model

3’s 95% credibility intervals broadly overlapped across fish species and the mean relative proportion of each fish species differed from estimates using otolith reconstituted mass (Table 4,

2 2 Fig. 3; Chinstrap: χ 5 = 62.65, p < 0.0001; Gentoo: χ 4 = 41.70, p < 0.0001).

In contrast, the a posteriori informed multi-source SIAR model (Model 4) performed better than the initial multi-source SIAR model (Model 3) at estimating the species composition of the fish portion of chick diets. While Model 4’s prediction of the mean relative proportion of each fish species in Chinstrap penguin chick diets differed slightly from estimates from otolith

2 reconstituted mass ( χ 5 = 14.55, p = 0.0125), the resulting 95 % credibility intervals were reduced

by 53.5±17.2 % in comparison to Model 3 (range: 33.2-82.2 %; Table 4, Fig. 3). Furthermore,

Model 4 prediction’s of the mean relative proportion of each fish species in Gentoo penguin

2 chicks’ diets was similar to estimates from otolith reconstituted mass ( χ 4 = 3.40, p = 0.4949). In addition, the resulting 95% credibility intervals were reduced by 52.0±27.7% in comparison to

Model 3 (range: 17.4-76.2%; Table 4, Fig. 3).

Discussion

Stomach content analysis

Our SCA analysis highlights several of the possible biases inherent when using this method. Similar to previous studies at Cape Shirreff, we observed evidence of fish such as otoliths, scales, and lenses in many Chinstrap penguin samples even when there was no measurable amount of fish tissue by wet mass [34, 36]. This evidence suggests that fish biomass consumed by adults digests completely prior to their return to the breeding colony or, more likely, is delivered to chicks in the heavily-digested component of adult stomach contents which

Figure 3. The fish species composition of penguin chick diets based on otolith and stable isotope analysis. Estimated dietary contributions exclude the krill portion of chick diets. Reconstituted mass derived from otolith measurements are compared with two variants (Models 3 and 4) of the SIAR multi-source Bayesian mixing model [21]. An initial model estimating the relative contribution of individual fish species identified from otoliths in stomach contents and an a posteriori informed model restricted to posterior draws agreeing with the relative abundance of each fish species by reconstituted mass (Appendices 1 and 2). Estimates are presented mean ± Bayesian 95% credibility intervals.

99 cannot be objectively quantified [6, 10]. Furthermore, because we collected stomach samples during the late afternoon, our sample does not include adults who foraged at night and tend to have a much higher percentage and occurrence of fish in their stomach samples [34, 48]. In addition, diet composition estimates derived from SCA in our study were often highly variable, making it difficult to detect differences among years and penguin species (Table 1). This finding does not appear to be unique in seabird dietary studies using SCA, which often requires high sample sizes and large differences between groups to detect inter-annual or species-specific differences in diet composition [3, 9]. However, our study suggests that the analysis of otoliths can still provide detailed information on species-specific and temporal variation in the consumption of fish prey species when overall diet composition estimates derived from stomach content wet mass are less informative.

Two-source, SIAR models

Two-source SIAR models predicted a relatively greater contribution of fish to chick- rearing diets in both species in comparison to SCA biomass estimates. This result is not unexpected as SCA is thought to underestimate the amount of fish in these species’ diets due to the digestion and diel biases described above [10, 12, 48]. In addition, two-source SIAR models also provided the least variable predictions of diet composition in comparison to SCA. The SIA of chick feathers provided an average value of each individual chick’s diet throughout the time of feather growth during the chick-rearing period [18, 19]. In contrast, SCA data represent a series of “snap-shots” (in this study every 5 to 7 days) of the food that one of two parents feed its chick [3]. Our study suggests that SIA of tissues that integrate diets over long time periods are innately less variable than SCA given a similar sample size and are more appropriate for examining inter-annual differences in chick diets. For example, the two-source SIAR models used in our study were able to identify inter-annual and species-specific differences in the relative abundance of fish and krill in diets not readily apparent using SCA.

When prior information on prey species composition is limited, such as outside the breeding season, using a representative prey source in isotopic mixing models can provide important information on seabird diets when little else is known [24]. However, our results also suggest that variation in prey species composition within trophic or functional groups can mask significant differences in diet composition that would not be apparent from isotopic values or mixing model predictions using only representative prey sources. This result was most apparent when examining the effect of fish prey δ15 N values on chick feather δ15 N values and the two-

source isotopic mixing models used in our study. For example, Chinstrap penguin chick feather

δ15 N values did not differ between years (Table 2). In addition, the 95% credibility intervals of

dietary estimate from the two-source SIAR model using P. antarcticum as the fish prey source

(Model 1) overlapped between years (Tables 3). In contrast, 95% credibility intervals of two-

source SIAR model using yearly and species-specific weighted “fish” values (Model 2) suggest a

greater abundance of fish in Chinstrap penguin chick diets in 2008 relative to 2009, which was

confirmed by otolith derived, average reconstituted fish mass. In 2008, the fish portion of

Chinstrap penguin chick diets was composed of six fish species with an estimated δ15 N value of

7.9±0.7 ‰, while P. antarcticum (δ15 N: 9.4±0.5 ‰) was the only fish species in 2009 diets

(Appendix 1 and 2). While this 1.5 ‰ difference is small relative to 4.6-6.1 ‰ differences

between fish and krill, it was enough to confound inter-annual comparisons of Chinstrap penguin

chick diets in our study.

101 Multiple-source, SIAR models

When parameterizing our two multi-source SIAR models we used otolith data to select the appropriate fish prey sources to include in each species model (Appendix 1 and 2). However, our initial multi-source SIAR model (Model 3) had difficulty precisely estimating the individual species composition of the fish portion of penguin diets due to the general similarities in δ13 C

and δ15 N values among many of the fish species included as prey sources (Fig. 3). Antarctic fish

species generally consume krill and other fish species and due to their similar tropic level, these

fish species tend to have similar δ15 N values [45]. While variation in the δ13 C values of Antarctic

fish species occupying different habitats can occur, overlap among the isotopic values of fish

within the prey-size range of penguins is common [45]. In addition, the δ13 C values of marine organisms can be affected by factors other than diet and habitat such as seasonal variations in primary production [49]. These issues can confound the use of isotopic models when estimating the relative contribution to predator diets of individual prey species occupying similar trophic levels such as fish.

We found that using SCA data to a posteriori refine multi-source SIAR model outputs

(Model 4) can provide greater resolution when estimating the contributions of isotopically similar prey species. When reducing our multi-source SIAR model’s posterior predictions to only those outcomes in which the importance of individual fish species were ranked similarly to estimates from otolith data, our informed multi-source SIAR model (Model 4) provided mean relative diet contributions that generally agreed with reconstituted fish masses and greatly reduced 95% credibility intervals relative to our initial SIAR multi-source model (Table 4, Fig.

3). Although not used in this study, the SIAR model package also allows users to input a priori estimates of the relative contribution of each prey species [21]. Informing our multi-source SIAR

102 models in this manner would have required us to provide accurate estimates of the contribution of Antarctic krill as well as each fish species to penguin diets. However, by using this method, any biases from both SCA biomass and otolith data would be incorporated into the model predictions. In contrast, we used a simple a posteriori ranking method that, while fitting fish prey

species to data derived from otoliths, provided no assumptions about the relative contribution of

krill to penguin diets. Therefore, unlike a priori estimates, our method put no constraints on the

relative abundance of krill vs. all fish species combined while still reducing the 95% credibility

intervals by approximately one-half relative to initial models.

Integrating SCA and SIA when estimating seabird diets

Our findings suggest that SIA can have greater accuracy than SCA to track inter-annual

and species-specific variations in diet composition at broad trophic levels (i.e. zooplankton vs.

fish). By focusing on tissues that integrate diets over long periods of time, SIA can avoid many

of the digestive and temporal biases of SCA and provide less variable estimates of seabird diets

in a less invasive manner. Therefore, when prey items identified from previous studies are

isotopically distinct or can be combined into biologically meaningful groups, SIA alone may be

sufficient to address a particular question without the need for additional SCA.

In contrast, it appears difficult to use SIA methods to estimate the fine scale taxonomic

composition of seabird diets to the same degree as is generally possible through SCA. However,

we found that when this level of accuracy is required, it is possible to integrate these two

methods to produce more refined estimates of diets. Simultaneously conducted SCA data can be

used to weight a priori combinations of isotopically similar prey in two-source mixing models to

better predict diets at broad trophic levels. In addition, when using multi-source models, SCA

103 can first inform which prey sources should be incorporated into models and second, a posteriori refined model predictions of prey contributions to better track inter-annual and species-specific differences in seabird diets using SIA. Moreover, as with all studies estimating diets using SIA, it is important to use taxonomically appropriate discrimination factors in isotopic mixing models, as they can be sensitive to these values [24, 50].

References

1. Boyd IL, Wanless S, Camphuysen CJ, editors. (2006) Top Predators in Marine Ecosystems: Their role in Monitoring and Management. Cambridge: Cambridge University Press. 392 p.

2. Barrett RT, Camphuysen K, Anker-Nilssen T, Chardine JW, Furness RW, et al. (2007) Diet studies of seabirds: a review and recommendations. ICES J Mar Sci 64: 1675-1691.

3. Duffy DC, Jackson S (1986) Diet studies of seabirds: a review of methods. Colon Waterbirds 9:1-17.

4. Wilson RP (1984) An improved stomach pump for penguins and other seabirds. J Field Ornithol 55: 109-112.

5. Ryan PG, Jackson S (1986) Stomach pumping: is killing seabirds necessary. Auk 103: 427- 428.

6. Karnovsky NJ (1997) The fish component of Pygoscelis penguin diets. MS thesis. Bozeman: Montana State University.76 p.

7. Olsson O, North AW (1997) Diet of the king penguin Aptenodytes patagonicus during three summers at South Georgia. Ibis 139: 504-512.

8. Votier SC, Bearhop S, MacCormick A, Ratcliffe NR, Furness RW (2003) Assessing the diet of great skuas, Catharacta skua , using five different techniques. Polar Biol 26: 20-26.

9. Tierney M (2009) Temporal variability and evaluation of methods used to infer diet of a Southern Ocean predator, the Adélie penguin Pygoscelis adeliae . PhD dissertation. Hobart: University of Tasmania. 183 p.

10. Gales RP (1987) Validation of the stomach-flushing technique for obtaining stomach contents of penguins. Ibis 129: 335-343.

104 11. Jackson S, Ryan PG (1986) Differential digestion rates of prey by white-chinned petrels Procellaria aequinoctialis . Auk 103: 617-619.

12. Jackson S, Duffy DC, Jenkins G (1987) Gastric digestion in marine vertebrate predators: in vitro standards. Funct Ecol 1: 287-291.

13. Casaux RJ, Favero M, Barrera-Oro ER, Silva P (1995) Feeding trial on an Imperial cormorant Phalacrocorax atriceps : preliminary results on fish intake and otolith digestion. Mar Ornithol 23: 101-106.

14. Hobson KA (2009) Trophic Interactions between Cormorants and Fisheries: Towards a More Quantitative Approach Using Stable Isotopes. Waterbirds 32(4): 481-490.

15. Bond AL, Jones IL (2009) A practical introduction to stable-isotope analysis for seabird biologists: approaches, cautions and caveats. Mar Ornithol 37: 183-188.

16. Inger R, Bearhop S (2008) Applications of stable isotope analyses to avian ecology. Ibis 150: 447-461.

17. Hobson KA, Clark RG (1992) Assessing avian diets using stable isotopes. I. Turnover of 13 C in tissues. Condor 94: 181-188.

18. Cherel Y, Hobson KA, Bailleul F, Groscolas R (2005) Nutrition, physiology, and stable isotopes: new information from fasting and molting penguins. Ecology 86: 2881-2888.

19. Jaeger A, Connan M, Richard P, Cherel Y (2010) Use of stable isotopes to quantify seasonal changes of trophic niche and levels of population and individual specialization in seabirds. Mar Ecol Prog Ser 401: 269-277.

20. Phillips DL, Gregg JW (2001) Uncertainty in source partitioning using stable isotopes. Oecologia 127: 171-179

21. Parnell AC, Inger R, Bearhop S, Jackson AL (2010) Source partitioning using stable isotopes: coping with too much variation. PLoS ONE 5:e9672.

22. Phillips RA, Bearhop S, McGill RAR, Dawson DA (2009) Stable isotopes reveal individual variation in migration strategies and habitat preferences in a suite of seabirds during the nonbreeding period. Oecologia 160: 795-806.

23. Ronconi RA, Koopman HN, McKinstry CAE, Wong SNP, Westgate AJ (2010) Inter-annual variability in diet of non-breeding pelagic seabirds Puffinus spp. at migratory staging evidence from stable isotopes and fatty acids. Mar Ecol Prog Ser 419: 267-282.

24. Polito MJ, Lynch HJ, Naveen R, Emslie SD (2011) Stable isotopes reveal regional heterogeneity in the pre-breeding distribution and diets of sympatrically breeding Pygoscelis penguins. Mar Ecol Prog Ser 421: 265-277.

105

25. Phillips DL, Newsome SD, Gregg JW (2005) Combining sources in stable isotope mixing models: alternative methods. Oecologia 144: 520-527.

26. Bugoni L, McGill RAR, Furness RW (2010) The importance of pelagic longline fishery discards for a seabird community determined through stable isotope analysis. J Exp Mar Biol Ecol 391(1-2): 190-200.

27. Norris DR, Arcese P, Preikshot D, Bertram DF, Kyser TK (2007) Diet reconstruction and historic population dynamics in a threatened seabird. J Appl Ecol 44: 875-884.

28. Tierney M, Southwell C, Emmerson LM, Hindell MA (2008) Evaluating and using stable isotope analysis to infer diet composition and foraging ecology of Adélie penguins Pygoscelis adeliae. Mar Ecol Prog Ser 355: 297-307.

29. Chiaradia A, Forero MG, Hobson KA, JM Cullen (2010) Changes in diet and trophic position of a top predator 10 years after a mass mortality of a key prey. ICES J Mar Sci 67(8): 1710-1720.

30. Knoff AJ, Macko SA, Edwin RM, Brown KM (2002) Stable isotope analysis of temporal variation in the diets of pre-fledged laughing gulls. Colon Waterbirds 25: 142-148.

31. Karnovsky NJ, Hobson KA, Iverson S, Hunt GL Jr (2008) Seasonal changes in diets of seabirds in the North Water Polynya: a multiple-indicator approach. Mar Ecol Prog Ser 357: 291-299.

32. Hedd A, Fifield DA, Burke CM, Montevecchi WA, Tranquilla LM, et al. (2010) Seasonal shift in the foraging niche of Atlantic puffins Fratercula arctica revealed by stable isotope (δ15 N and δ13 C) analyses. Aquat Biol 9: 13-22.

33. Volkman NJ, Presler P, Trivelpiece W (1980) Diets of Pygoscelid penguins at King George Island, Antarctica. Condor 82: 373–378.

34. Miller AK, Trivelpiece WZ (2008) Chinstrap Penguins alter foraging and diving behavior in response to the size of their principal prey, Antarctic krill. Mar Biol 154: 201-208.

35. Miller AK, Karnovsky NJ, Trivelpiece WZ (2009) Flexible foraging strategies of Gentoo Penguins provide a buffer against inter-annual changes in prey availability. Mar Biol 156: 2527-2537.

36. Miller AK, Kappes MA, Trivelpiece SG, Trivelpiece WZ (2010) Foraging-Niche Separation of Breeding Gentoo and Chinstrap Penguins, South Shetland Islands, Antarctica. Condor 112(4): 683-695.

37. CCAMLR (1997) CCAMLR Ecosystem Monitoring Program: standard methods for monitoring studies. Hobart: CCAMLR. 268 p.

106

38. Ainley DG, Ballard G, Barton KJ, Karl BJ, Rau GH, et al. (2003) Spatial and temporal variation of diet within a presumed metapopulation of Adélie penguins. Condor 105: 95-106. 39. Lishman GS (1985) The food and feeding ecology of Adélie Penguins ( Pygoscelis adeliae ) and Chinstrap Penguins ( P. antarctica ) at Signy Island, South Orkney Islands. J Zool Lon 205(A):245-263.

40. Ainley DG, Wilson PR, Barton KJ, Ballard G, Nur N, Karl B (1998) Diet and foraging effort of Adélie penguins in relation to pack-ice conditions in the southern Ross Sea. Polar Biol 20:311–319

41. Williams R, McEldowney A (1990) A guide to the fish otoliths from waters of the Australian Antarctic Territory, Heard and Macquarie Islands. ANARE Res. Notes 75: 1-169.

42. Polito M, Emslie SD, Walker W (2002) A 1,000-year record of Adélie penguin diets in the southern Ross Sea. Antarct Sci 14: 327-332.

43. Hecht T (1987) A guide to the otoliths of Southern Ocean fishes. S Afr J Antarct Res 17: 2- 87.

44. Nyssen F (2005) Role of benthic amphipods in Antarctic trophodynamics- a multidisciplinary study. PhD dissertation. Liège: Université de Liège. 271 p.

45. Cherel Y, Koubbi P, Giraldo G, Penot F, Tavernier E, et al. (2011) Isotopic niches of fishes in coastal, neritic and oceanic waters off Adélie Land, Antarctica. Polar Sci 5(2011):286-297.

46. Seminoff JA, Bjorndal KA, Bolten AB (2007) Stable carbon and nitrogen isotope discrimination and turnover in pond sliders Trachemys scripta : insights for trophic study of freshwater turtles. Copeia 3: 534-542.

47. Polito MJ, Fisher S, Tobias CR, Emslie SD (2011) Dietary Isotopic Discrimination in Gentoo Penguin ( Pygoscelis papua ) Feathers. Polar Biol 34:1057-1063.

48. Jansen JK, Boveng PL, Bengtson JL (1998) Foraging modes of chinstrap penguins: contrasts between day and night. Mar Ecol Prog Ser 165:161-172.

49. Jaeger A, Cherel Y (2011) Isotopic Investigation of Contemporary and Historic Changes in Penguin Trophic Niches and Carrying Capacity of the Southern Indian Ocean. PLoS ONE 6(2): e16484.

50. Bond AL, Diamond AW (2010) Recent Bayesian stable isotope mixing models are highly sensitive to variation in discrimination factors. Ecol Appl 21(4): 1017-1023.

107 CHAPTER FIVE: STABLE ISOTOPES REVEAL REGIONAL HETEROGENEITY IN THE

PRE -BREEDING DISTRIBUTION AND DIETS OF SYMPATRICALLY BREEDING

5 PYGOSCELIS PENGUINS

Introduction

Studies of the foraging ecology of penguins have been commonly used as a cost efficient method to monitor the Antarctic marine ecosystem (Trivelpiece et al. 1990, Reid & Croxall

2001). These studies are of increased relevance in the Antarctic Peninsula (AP) where recent changes in climate, sea ice conditions and the abundance of Antarctic krill ( Euphausia superba ) have been suggested to be responsible for contrasting population changes in Pygoscelis penguin

species (Fraser & Hoffmann 2003, Forcada et al. 2006, Hinke et al. 2007). For example, there is

evidence of a widespread and long-term decrease in Adélie penguin ( P. adeliae ) populations in

the AP, while Gentoo penguin populations ( P. papua ) are stable or increasing with recent

expansions in the southernmost part of their breeding range (Lynch et al. 2008).

While the foraging ecology of the Adélie and Gentoo penguins has been well studied,

research has been limited to a few breeding colonies throughout this large region. Adélie

penguins breed in the AP region from Marguerite Bay to Anvers Island along the western AP

and in the eastern AP, South Shetland and South Orkney Islands (Fig. 1a). Gentoo penguins

breed between the Yalour Islands to Trinity Island along the Western AP, in a few locations in

the eastern AP and throughout much of the South Shetland and South Orkney Islands (Fig.1a;

Woehler 1993, Lynch et al. 2008). During the breeding season Adélie penguins in the AP

5 This chapter has been published as: Polito M.J., Lynch H.J., Naveen R., and Emslie S.D. 2011. Stable isotopes reveal regional heterogeneity in the pre-breeding distribution and diets of sympatrically breeding Pygoscelis penguins. Marine Ecology Progress Series 421:265-277

Figure 1. Pygoscelis adeliae & Pygoscelis papua . The summer breeding distributions (a) of Adélie (light shading) and Gentoo (cross hatch) penguins and the location (b,c) of 23 breeding colonies sampled for eggshells in the South Orkney Islands, South Shetland Islands, eastern and western Antarctic Peninsula during austral summer of 2006-07. Breeding distributions adapted from data in Woehler (1993) and Lynch et al. (2008). Dashed lines delineate the location of the conspicuous gap in the breeding range of Adélie penguins along the western Antarctic Peninsula and South Shetlands Islands (the Adélie ‘gap’). Refer to Tables 2 and 3 for the corresponding site names for each of the 23 breeding colonies sampled in this study.

109 primarily feed on krill and small amounts of fish while Gentoo penguin diets are more flexible and contain variable amounts of krill and fish (Volkman et al. 1980, Karnovsky 1997, Miller et al. 2009). While these species’ distributions overlap, it is thought that during breeding Gentoo penguins forage preferentially inshore, in benthic habitats, while Adélie penguins often forage offshore, in pelagic waters (Trivelpiece et al. 1987, Kokubun et al. 2010, Wilson 2010).

In contrast, little is known about the diets and distributions of these species outside of the

breeding season. The few existing studies of winter diets of Adélie and Gentoo penguins in the

AP suggest that high trophic-level prey, such as fish and squid, are dominant prey items

(Jablonski 1985, Ainley et al. 1992, Coria et al. 2000). During winter, Adélie penguins are

concentrated in the pack ice while Gentoo penguins are generally non-migratory and remain near

their breeding colonies year-round (Fraser et al. 1992, Wilson et al. 1998, Tanton et al. 2004).

While the exact wintering distributions of Adélie penguin populations that breed in the AP are

unknown, high numbers have been observed in the winter pack ice east of the AP in the Weddell

Sea and west of the AP in the Bellingshausen Sea (Fraser et al. 1992, Ainley et al. 1992, Fraser

& Trivelpiece 1996, Chapman et al. 2004, Ribic et al. 2008). This finding, along with the

distinctive gap in the breeding range of Adélie penguins between Anvers Islands in the south and

King George Island and the Eastern AP in the north (the Adélie ‛gap’, Fig. 1a; Woehler 1993), has led some to hypothesize that Adélie penguins in the AP may comprise two distinct populations: one that breeds from Anvers Island to the south along the western AP and winters in the Bellingshausen Sea, and another that breeds in the South Orkney Islands, South Shetland

Islands, and eastern AP and winters in the Weddell Sea (Fraser et al. 1992, Fraser & Trivelpiece

1996, Trivelpiece & Fraser 1996). However, there is currently little evidence to support or reject this hypothesis and to our knowledge only one published study has tracked breeding Adélie penguins in this region from their colony to their winter habitats (Dunn et al. In Press).

The use of naturally occurring stable isotopes to describe the foraging ecology of penguins has become commonplace as the stable isotope ratios of carbon ( δ13 C) and nitrogen

(δ15 N) in animal tissues are largely determined by isotopic abundances in an animal’s food web

(Ainley et al. 2003, Cherel & Hobson 2007, Cherel 2008, Tierney et al. 2008, Polito et al. 2009).

Nitrogen isotope values are commonly used to infer trophic level and diets, while carbon values help trace vertical trends in habitat use (benthic vs. pelagic) as well as the geographic location of foraging habitats (Quillfeldt et al. 2005, Cherel & Hobson 2007, Quilfeldt et al. 2010).

Moreover, this technique shows great promise in elucidating the diets and foraging distributions of seabirds outside the breeding season when birds are away from their breeding colony

(Quillfeldt et al. 2005, Cherel et al. 2007, Polito et al. 2009, Phillips et al. 2009).

Previous studies on quail ( Coturnix japonica ) and ostrich ( Struthio camelus ) have found that the stable isotope analysis of eggshell provides information on diet during a brief period prior to breeding as eggshell components are derived from plasma through the metabolism of recently assimilated foods (Simkis & Tyler 1958, Hobson 1995, Johnson 1995, Johnson et al.

1998). However this is likely not the case in birds that utilize significant amounts of stored nutrient reserves during egg laying, such as penguins (Astheimer & Grau 1985, Hobson 1995).

Female Adélie penguins fast for up to two weeks prior to egg-laying and approximately 75% of the energy content of the clutch is deposited after females have begun their fast (Astheimer &

Grau 1985). Female Gentoo penguins also fast before egg laying, losing 12% of their total body mass at rate of 85 g day -1 prior to laying their first egg (Trivelpiece & Trivelpiece 1990).

Therefore, it is likely that the isotopic values of Adélie penguin and to a lesser extent Gentoo

111 penguin eggshells reflect an integration of dietary information during late winter foraging prior to their return to their breeding colonies and/or the onset of the egg-laying fast.

In this study we examine the pre-breeding diets and foraging habitats of female Adélie and Gentoo penguins using stable isotope analysis of eggshells collected from 23 breeding colonies along the eastern and western AP, South Shetland and South Orkney Islands.

Specifically, we test the test the hypothesis put forward by Fraser et al. (1992) and others that

Adélie penguin breeding populations, which are geographically separated by the Adélie ‛gap,’ inhabit geographically distinct habitats outside of the breeding season. A recent analysis of the

δ13 C signatures of pelagic primary producers highlight isotopic differences between the two proposed Adélie penguin wintering areas in Bellingshausen and Weddell seas (Quillfeldt et al.

2010). Due to these differences, the isotopic signatures of Adélie penguin eggshells should allow for discrimination between populations using these two winter habitats prior to breeding.

Secondly, we use an isotopic mixing model to quantify the diet composition of female Adélie and Gentoo penguins prior to breeding to confirm the few existing studies of winter diets which suggest that high trophic-level prey are important components of diets during this time period

(Jablonski 1985, Ainley et al. 1992, Coria et al. 2000). Lastly we examine how sensitive mixing model estimates of penguin diet compositions are to variations in isotopic discrimination factors between wild and captive penguin populations.

Materials and Methods

Study area and sample collection. We collected eggshell samples from 23 Adélie and Gentoo penguin breeding colonies along the AP (roughly 60°S to 66°S; Fig. 1b, c). This area defines the range of overlap for these two species and naturally clusters into four regions: the South Orkney

112 Islands, the South Shetland Islands, the western AP and the eastern AP (Fig. 1a). From

November 2006 to February 2007, eggshell samples were collected by researchers based on the tour ship National Geographic Endeavour and several land-based scientific stations. At each breeding colony we opportunistically collected 8 - 20 eggshells from hatched, depredated, addled or infertile eggs of each species. When sampling in mixed species colonies, we collected only eggshells that could be identified to species when they were found in close proximity to nests in non-mixed sections of colonies. We cleaned eggshells of organic debris in the field using a toothbrush and water; then dried and stored samples at room temperature prior to further preparation for isotopic analysis.

Penguin prey items were collected during trawls conducted in the vicinity of the AP,

South Shetland and South Orkney Islands by the U.S. Antarctic Marine Living Resource

Program during the austral summers of 2005-06 to 2008-09. Sampled prey species were representative of the two major components of Adélie and Gentoo penguin diets in the study area: krill ( Euphausia superba ; n = 20) and fish (Adélie penguin: Pleuragramma antarcticum, n

= 30; Gentoo penguin: Lepidonotothen squamifrons , n = 10; Volkman et al. 1980, Karnovsky

1997, Miller et al. 2009). Whole krill and fish samples were kept frozen prior to analysis.

Sample preparation and isotopic analysis. Eggshell membranes were removed using a Dremel

tool with a sanding attachment. Eggshells were then rinsed in distilled water, cleaned of any

remaining surface debris and then ground to a powder using an analytical mill. Isotope values of

the organic matrix of penguin eggshells were obtained after the removal of carbonate by

dissolving ~10 mg of cleaned eggshell in a silver capsule through titration with five 20 µL

aliquots of 6 N HCL. Acidified samples were stored at room temperature under a fume hood for

113 24 hours then dried for at least 48 hours in an oven at 60 °C. Acidified samples were not rinsed prior to drying and isotopic analysis to avoid biasing δ15 N values (Jacob et al. 2005). The mean

C/N value of acidified eggshells was 3.8 ± 0.2 which closely approached the assumed C/N value

for pure protein of 3.7 (Fry et al. 2003).

Whole krill and fish samples were homogenized and then dried for 48 hours in an oven at

60˚C. Lipids were then extracted from whole fish and krill samples using a Soxhlet apparatus

with a 1:1, Petroleum-Ether : Ethyl-Ether solvent mixture for 8 hours (Seminoff et al. 2007).

Lipid extracted prey items were not acidified prior to isotopic analysis. Approximately 0.5 mg

of each of the above materials was loaded into tin cups for δ13 C and δ15 N analysis.

The above tissues were flash-combusted (Costech ECS4010 elemental analyzer) and analyzed for carbon and nitrogen isotopes ( δ13 C and δ15 N) through an interfaced Thermo Delta V

Plus continuous flow stable isotope ratio mass spectrometer (CFIRMS). Raw δ values were

normalized on a two-point scale using depleted and enriched glutamic acid reference materials

USGS-40 and USGS-41. Sample precision was 0.1‰ and 0.2‰, for δ13 C and δ15 N respectively.

Stable isotope abundances are expressed in δ notation in per mill units (‰), according to

the following equation:

δX = [( Rsample / Rstandard ) - 1] · 1000

13 15 13 12 15 14 Where X is C or N and R is the corresponding ratio C / C or N / N. The Rstandard values

13 15 were based on the PeeDee Belemnite (VPDB) for C and atmospheric N 2 for N.

Statistical and dietary analysis. We grouped eggshell samples by species and region and used separate, univariate ANOVA with one factor and seven levels (one level for each species/region combination as no Gentoo penguin eggshells were collected from the South Orkney Islands).

114 We also applied Tukey-Kramer Multiple comparison tests to examine pair-wise differences in the isotopic values of eggshells ( δ13 C and δ15 N) across species and regions. In addition, we examined variation in the isotopic values of eggshells within and across regions in the AP for each species using separate, univariate ANOVAs with one factor (breeding colony) and Tukey-

Kramer Multiple comparison tests. To complement the above univariate analyses, we used multivariate discriminant analyses with a jackknife (leave-one-out) classification to examine our ability to differentiate between Adélie and Gentoo penguin samples and to assign Adélie and

Gentoo penguins from each region into discrete groups that share similar pre-breeding diets and foraging habitats based on the δ13 C and δ15 N values of their eggshells.

We examined variation in the isotopic values ( δ13 C and δ15 N) of common Adélie and

Gentoo penguin prey species using univariate ANOVAs. We quantified the contribution of krill

(E. superba ) and fish ( P. antarcticum for Adélie penguins and L. squamifrons for Gentoo

penguins) to the diets of pre-breeding female penguins in each region by using a single-isotope

(δ15 N), two-source linear mixing model (Phillips & Gregg 2001). Model results provide standard errors and confidence intervals for source proportion estimates that account for the observed variability in the isotopic signatures for the sources as well as the mixture. We corrected eggshell values for dietary isotopic fractionation using a δ15 N discrimination factor of +1.8‰ derived from a captive population of Gentoo penguins (Polito et al. 2009). The resulting mixing- model based diet composition estimates were compared across species/region combinations with t-tests calculated using the Satterthwaite (1946) approximation for the degrees of freedom. As discrimination factors can vary by species and/or the elemental and isotopic composition of an animal’s diet, we performed a sensitivity analysis (+0.8‰ to +2.8‰ by increments of 0.5‰) in order to examine how sensitive our estimates of female pre-breeding diets might be to possible

115 differences between δ15 N discrimination factors in the wild and those estimated in a captive

population (Post 2002, Cherel et al. 2005a, 2005b, Caut et al. 2008a, Robins et al. 2010).

Data were examined for normality and equal variance. All tests were two tailed and

significance was assumed at the 0.05 level. Statistical calculations were performed using SAS

(Version 9.1, SAS Institute 1999) and SPSS (Version 16.0, SPSS Inc. 1989-2007). All means

are presented ±SD, except diet composition estimates resulting from our linear mixing model,

which are presented ±SE.

Results

Inter-specific and spatial variation in eggshell isotopic values

We measured significant differences in eggshell δ15 N and δ13 C values across

15 13 species/region combinations ( δ N: F6,375 = 13.090, p < 0.001; δ C: F6,375 = 72.074, p < 0.001).

Post-hoc Tukey comparisons suggested that, in general, Adélie penguins have lower eggshell

δ15 N and δ13 C values than Gentoo penguins, except in the western AP where Adélie and Gentoo penguin eggshells have similar δ15 N values and in the eastern AP where these two species have similar δ13 C values (Table 1). Discriminant analysis found that isotopic values ( δ15 N and δ13 C) significantly differentiated eggshells of Adélie and Gentoo penguins (Wilks’ Lambda = 0.647, χ2

= 161.958, df = 2, p < 0.001). Overall correct assignment after jackknife validation was 79.2%

(77.2% of Adélie penguin and 81.3% of Gentoo penguin eggshells). The ability of the resulting discriminant function to correctly assign eggshell samples to species varied by region. Adélie penguin eggshells were correctly assigned to species with an accuracy of 100%, 100%, 87.8% and 46.0% for eggshells from the South Orkney Islands, South Shetland Islands, eastern AP and western AP, respectively. Gentoo penguin eggshells were correctly assigned to species with an

116 Table 1. Pygoscelis adeliae & Pygoscelis papua . The carbon to nitrogen ratio (C/N) and stable nitrogen and carbon isotope concentrations (mean ± SD) of Adélie and Gentoo penguin eggshells collected from breeding colonies in the South Orkney Islands, South Shetland Islands, eastern Antarctic Peninsula and western Antarctic Peninsula during austral summer of 2006-07.

Region, Species n C/N δ15 N (‰) 1 δ13 C (‰) 1 South Orkney Islands Adélie penguin 30 3.8±0.1 8.3±0.8 ab -24.8±0.5 a South Shetland Islands Adélie penguin 15 3.7±0.2 7.9±0.5 a -24.5±0.4 a Gentoo penguin 88 3.8±0.2 8.7±0.8 bc -23.5±0.5 b Eastern Antarctic Peninsula Adélie penguin 74 3.7±0.3 8.2±0.6 a -24.8±0.6 a Gentoo penguin 14 3.8±0.1 8.6±0.7 abc -24.5±0.5 a Western Antarctic Peninsula Adélie penguin 74 3.6±0.2 8.9±0.6 c -23.9±0.5 c Gentoo penguin 80 3.9±0.2 8.9±0.7 c -23.3±0.6 b 1Groups that do not share at least one superscript within a column are significantly different for the variable in question at the 0.05 level

117 accuracy of 84.1%, 21.4% and 88.7% for eggshells from the South Shetland Islands, eastern AP and western AP, respectively.

While we found few differences in Adélie penguin eggshell δ15 N and δ13 C values across breeding colonies in the South Orkney Islands, South Shetland Islands, and the eastern AP, differences between eggshells from these three regions and the western AP were relatively more

15 13 common (Table 2; δ N: F12,193 = 8.966, p < 0.001; δ C: F12,193 = 9.855, p < 0.001). Adélie

penguins breeding in the South Orkney Islands, South Shetland Islands, and eastern AP shared

similar eggshell δ15 N and δ13 C values and were lower than the isotopic values of eggshells from

Adélie penguins breeding in the western AP (Fig. 2a). While statistically significant, discriminant analysis based on δ15 N and δ13 C values did a poor job of differentiating between

eggshells of Adélie penguin breeding in the four sampled regions (Wilks’ Lambda = 0.574, χ2 =

104.756, df = 6, p < 0.001). Overall correct assignment after jackknife validation was 47.2%

(Fig. 3a; 26.7%, 66.7%, 20.3% and 78.4% for eggshells from the South Orkney Islands, South

Shetland Islands, eastern AP and western AP, respectively). However, eggshell isotopic values

were much better at discriminating between northern (South Orkney Islands, South Shetlands

and eastern AP) and southern (western AP) breeding colonies (Wilks’ Lambda = 0.596, χ2 =

98.44, df = 2, p < 0.001). Overall correct assignment after jackknife validation was 79.8% (Fig.

3c; 77.3% of the northern group and 81.8% of the southern group).

While there was some variation in the isotopic signatures of Gentoo penguin eggshells at the breeding colony level, differences within and across regions in δ15 N and δ13 C values were

15 less common relative to the differences observed in Adélie penguins (Table 3; δ N: F12,182 =

13 15 10.388, p < 0.001; δ C: F12,182 = 16.362, p < 0.001 ). Gentoo penguin eggshell δ N values did not differ across regions, while eggshell δ13 C values in Gentoo penguins breeding in the eastern Table 2. Pygoscelis adeliae . The location, carbon to nitrogen ratio (C/N) and stable nitrogen and carbon isotope concentrations (mean ± SD) of Adélie penguin eggshells collected from breeding colonies in the South Orkney Islands, South Shetland Islands, eastern Antarctic Peninsula and western Antarctic Peninsula during austral summer of 2006-07.

Region, Site (Number) Lat., Long. n C/N δ15 N (‰) 1 δ13 C (‰) 1 South Orkney Islands Shingle Cove, Coronation Is. (1) 60.65°S, 45.57°W 15 3.7±0.1 7.9±0.6 a -24.7±0.4 ab Gourlay Peninsula, Signy Is. (2) 60.72°S, 45.57°W 15 3.9±0.1 8.7±0.8 bcd -24.9±0.6 a South Shetland Islands Admiralty Bay, King George Is. (3) 62.17°S, 58.45°W 15 3.4±0.1 7.9±0.5 a -24.5±0.4 abc Eastern Antarctic Peninsula Tay Head, Joinville Is. (9) 63.53°S, 56.92°W 14 3.9±0.1 8.0±0.5 a 24.4±0.7 abc Brown Bluff, Tabarin Peninsula (10) 63.35°S, 55.55°W 15 3.8±0.2 8.5±0.5 abc 24.9±0.5 a Paulet Is. (11) 63.58°S, 55.78°W 15 3.5±0.1 7.9±0.4 a 24.9±0.5 a Devil Is. (12) 63.80°S, 57.28°W 15 3.8±0.2 8.4±0.8 abc 24.7±0.5 ab Penguin Point, Seymour Is. (13) 64.30°S, 56.68°W 15 3.6±0.1 8.0±0.7 ab 24.9±0.8 a Western Antarctic Peninsula Dream Island (15) 64.73°S, 64.23°W 14 3.7±0.1 8.9±0.3 cd 24.1±0.5 bcd Torgersen Island (16) 64.77°S, 64.08°W 15 3.6±0.1 8.9±0.5 cd 24.0±0.5 cd Bisco Point, Anvers Is. (17) 64.80°S, 63.79°W 15 3.6±0.1 8.7±0.5 bcd 24.1±0.4 bcd Yalour Is. (22) 65.23°S, 64.17°W 15 3.9±0.3 9.3±0.9 d 23.9±0.4 cd Berthelot Is. (23) 65.33°S, 64.15°W 15 3.6±0.1 8.7±0.5 bcd 23.7±0.4 d 1Groups that do not share at least one superscript within a column are significantly different for the variable in question at the 0.05 level

119

Figure 2. Pygoscelis adeliae & Pygoscelis papua . The stable nitrogen and carbon isotope concentrations of Adélie (a) and Gentoo (b) penguin eggshells collected from breeding colonies in the South Orkney Islands (SO), South Shetland Islands (SS), eastern Antarctic Peninsula (EAP) and western Antarctic Peninsula (WAP) during austral summer of 2006-07. Error bars represent standard deviation.

120

Figure 3. Pygoscelis adeliae & Pygoscelis papua . The results of discriminant analyses on the isotopic values ( δ15 N and δ 13 C) of Adélie and Gentoo penguin eggshells collected from breeding colonies in the South Orkney Islands (SO), South Shetland Islands (SS), eastern Antarctic Peninsula (EAP) and western Antarctic Peninsula (WAP) during austral summer of 2006-07. Discriminant analyses of Adélie and Gentoo penguin eggshells by region are presented as a scatter plot of the two resulting discriminant functions for each analysis (a,b), while analyses between Adélie penguin eggshells from northern (SO, SS & EAP) and southern regions and Gentoo penguins eggshells from eastern and western regions are presented as histograms of the single resulting discriminant function for each analysis (c,d).

121 Table 3. Pygoscelis papua . The location (latitude, longitude) carbon to nitrogen ratio (C/N) and stable nitrogen and carbon isotope concentrations (mean ± SD) of Gentoo penguin eggshells collected from breeding colonies in the South Shetland Islands, eastern Antarctic Peninsula and western Antarctic Peninsula during austral summer of 2006-07.

Region, Site (Number) Lat., Long. n C/N δ15 N (‰) 1 δ13 C (‰) 1 South Shetland Islands Admiralty Bay, King George Is. (3) 62.17°S, 58.45°W 15 3.8±0.1 8.4±0.5 ab -24.1±0.6 a Ardley Is., King George Is. (4) 62.22°S, 58.93°W 15 3.9±0.1 8.6±0.5 abc -23.4±0.4 bc Barton Peninsula, King George Is. (5) 62.24°S, 58.78°W 15 3.8±0.1 8.6±0.6 abc -23.2±0.4 cd Potter Peninsula, King George Is. (6) 62.25°S, 58.65°W 8 4.0±0.1 8.7±0.4 abc -23.1±0.5 cd Barrentos Is., Aitcho Is. (7) 62.41°S, 59.75°W 15 3.8±0.3 10.0±0.6 d -23.5±0.5 bc Cape Shirreff, Livingston Is.(8) 2 62.47°S, 60.78°W 20 3.7±0.3 8.2±0.6 a -23.4±0.3 bc Eastern Antarctic Peninsula Brown Bluff, Tabarin Peninsula (10) 63.35°S, 55.55°W 14 3.8±0.1 8.6±0.7 abc -24.5±0.5 a Western Antarctic Peninsula Cuverville Is. (14) 64.68°S, 62.63°W 15 3.9±0.1 8.5±0.4 abc -23.9±0.5 ab Bisco Point, Anvers Is. (17) 64.80°S, 63.79°W 9 4.0±0.1 9.2±0.5 bc -23.5±0.3 bc Jougla Point, Wiencke Is. (18) 64.82°S, 63.50°W 11 4.0±0.3 8.5±0.5 ab -23.4±0.5 bc Neko Harbour, Andvord Bay (19) 64.83°S, 62.55°W 15 3.9±0.2 9.1±0.8 bc -23.2±0.3 cd Pleneau Is. (20) 65.10°S, 64.07°W 15 3.8±0.1 8.8±0.6 abc -22.7±0.5 d Petermann Is. (21) 65.17°S, 64.17°W 15 3.8±0.1 9.3±0.6 cd -23.2±0.3 c 1Groups that do not share at least one superscript within a column are significantly different for the variable in question at the 0.05 level 2Data from Polito et al. 2009

122 AP were lower than those breeding in the South Shetland Islands or the eastern AP (Fig. 2b).

Similarly, while statistically significant, discriminant analysis based on δ15 N and δ13 C values did a poor job of differentiating between eggshells of Gentoo penguins breeding in the three sampled regions (Wilks’ Lambda = 0.763, χ2 = 48.206, df = 4, p < 0.001). Correct assignment after jackknife validation was 54.9% (Fig. 3b; 46.6%, 78.6% and 60.0% for eggshells from the South

Shetland Islands, eastern AP and western AP, respectively). However, eggshell isotopic values were much better at discriminating between Gentoo penguin colonies on the eastern and western

(including the South Shetland Islands) sides of the AP (Wilks’ Lambda = 0.792, χ2 = 41.693, df

= 2, p < 0.001). Overall correct assignment after jackknife validation was 84.6% (Fig. 3d; 78.6% of the eastern group and 85.1% of the western group).

Prey items and pre-breeding diet composition

We found significant differences across the isotopic values of common Adélie and

15 13 Gentoo penguin prey species (Table 4; δ N: F2,60 = 637.78, p < 0.001; δ C: F2,60 = 42.98, p <

0.001). Post-hoc Tukey comparisons determined that krill had significantly lower δ15 N and δ 13 C values relative to both fish species. The two fish species did not significantly differ in their δ15 N and δ 13 C values (Table 4).

Using eggshell δ 15 N values corrected with a discrimination factor derived from a captive

population of Gentoo penguins, our isotopic model estimated that fish comprised a significant

portion (46.8-62.9%) of female Adélie and Gentoo penguin diets prior to breeding (Table 5).

Mixing model estimates of the pre-breeding diets of female Gentoo penguins did not differed

significantly across regions (eastern AP vs. South Shetland Islands: t = 0.328, df = 45.888, p =

0.744; eastern AP vs. western AP: t = 0.981, df = 42.918, p = 0.332; South Shetland Islands vs. Table 4. The carbon to nitrogen ratio (C/N) and stable nitrogen and carbon isotope concentrations (mean ± SD) of common Adélie and Gentoo penguin prey species collected during trawls conducted in the vicinity of the Antarctic Peninsula, South Shetland and South Orkney Islands during the austral summers of 2005-06 to 2008-09.

Prey type, Species n C/N δ15 N (‰) 1 δ13 C (‰) 1 Krill E. superba 20 3.7±0.2 3.2±0.7 a -26.2±0.9 a Fish P. antarcticum 30 3.4±0.2 9.4±0.5 b -24.7±0.4 b L. squamifrons 10 3.3±0.1 9.6±0.8 b -24.2±0.7 b 1Groups that do not share at least one superscript within a column are significantly different for the variable in question at the 0.05 level

124 Table 5. Pygoscelis adeliae & Pygoscelis papua . Predicted diet compositions (mean ± SE) and 95% confidence intervals (95% CL) of female Adélie and Gentoo penguins during the pre- breeding period based on stable isotope analysis of eggshells collected from breeding colonies in the South Orkney Islands, South Shetland Islands, eastern Antarctic Peninsula and western Antarctic Peninsula during austral summer of 2006-07. Estimates use the single isotope ( δ15 N), two-source (krill and fish) mixing model described by Phillips and Greg (2001) with eggshell δ15 N values corrected for isotopic discrimination (+1.8‰; Polito et al. 2009).

Female pre-breeding diet composition (%) Region, Species n Krill 1 95% CL Fish 1 95% CL South Orkney Islands Adélie penguin 30 46.8±2.7 abc 41.2-52.3 53.2±2.7 abc 47.7-58.8 South Shetland Islands Adélie penguin 15 53.2±2.6 a 48.0-58.5 46.8±2.6 a 41.5-52.0 Gentoo penguin 88 42.2±2.8 bcd 36.3-48.1 57.8±2.8 bcd 51.9-63.7 Eastern Antarctic Peninsula Adélie penguin 74 48.4±1.8 ab 44.7-52.0 51.6±1.8 ab 48.0-55.3 Gentoo penguin 14 43.8±3.8 bcd 35.9-51.6 56.3±3.8 bcd 48.4-64.1 Western Antarctic Peninsula Adélie penguin 74 37.1±1.9 d 33.4-40.8 62.9±1.9 d 59.2-66.6 Gentoo penguin 80 39.1±2.9 cd 33.0-45.1 60.9±2.9 cd 54.9-67.0

1Groups that do not share at least one superscript within a column are significantly different for the variable in question at the 0.05 level

125 western AP: t = 0.775, df = 37.357, p = 0.443). In contrast, our results suggest that female

Adélie penguins breeding in the western AP consumed a larger proportion of fish during the pre- breeding period than Adélie penguins from the other three regions (Table 5; South Orkney

Islands vs. western AP: t = 2.916, df = 68.451, p = 0.005; South Shetland Islands vs. western AP:

t = 5.082, df = 36.8, p < 0.001; eastern AP vs. western AP: t = 4.331, df = 159.971, p < 0.001).

Furthermore, the results of our sensitivity analysis suggest that our estimates of the pre-breeding diets of female Adélie and Gentoo penguin would change by 15.6-16.2% per 1‰ deviation from our assumed δ 15 N discrimination factor of +1.8‰ (Fig. 4).

Discussion

Habitat use prior to breeding

During the breeding season, the foraging ranges of Adélie and Gentoo penguins are

restricted to areas near their colonies (<100 km) due to the need to return regularly for incubation

and chick-feeding duties (Trivelpiece et al. 1987, Clarke et al. 2006). During this time, Adélie

penguins are generally shallow divers and forage in offshore, pelagic habitats, while Gentoo

penguins are found closer inshore, diving relatively deeper to forage in benthic habitats

(Trivelpiece et al. 1987, Miller et al. 2009, Kokubun et al. 2010, Wilson 2010). We found that,

in general, Gentoo penguins had higher eggshell δ13 C values than Adélie penguins. Benthic and

inshore marine food webs are more enriched in 13 C than pelagic and offshore food webs due to differences in carbon sources between the two habitats (benthic macro-algae vs. pelagic phytoplankton; France 1995, Dunton 2001). Our results suggest that, similar to the breeding season, prior to breeding female Gentoo penguins feed more often in inshore, benthic habitats than Adélie penguins. These results agree with previous observations that Adélie penguins

Figure 4. Pygoscelis adeliae & Pygoscelis papua . The effect of variation in δ15 N discrimination factor on the estimated diet composition (% of krill in diet) of female Adélie and Gentoo penguins during the pre-breeding period based on stable isotope analysis of eggshells collected from breeding colonies in the South Orkney Islands (SO), South Shetland Islands (SS), eastern Antarctic Peninsula (EAP) and western Antarctic Peninsula (WAP) during austral summer of 2006-07. Estimates use the single isotope ( δ15 N), two-source (krill and fish) mixing model described by Phillips and Greg (2001). A δ15 N discrimination factor of +1.8‰ represents the value derived from a controlled dietary study of a captive population of Gentoo penguins (Polito et al. 2009).

127 inhabit the pack ice during winter while Gentoo penguins forage in open water, near-shore habitats and remain close (< 250 km) to their breeding colonies (Fraser et al. 1992, Wilson et al.

1998, Clausen & Putz 2003, Tanton et al. 2004).

A possible exception to this generalization can be seen in the eastern AP, where eggshell

δ13 C values did not differ between species. The single Gentoo penguin colony sampled in the

eastern AP (Brown Bluff, Tabarin Peninsula; Fig. 1) lies within Antarctic Sound where many

large tabular icebergs flowing westward from the Weddell Sea become grounded (Barker et al.

2007, M. Polito, pers. obs). Female Gentoo penguins at Brown Bluff may be foraging more in

pelagic habitats relative to Gentoo penguins in other regions and/or δ13 C values may be lower in this region due to a reduction in benthic foraging habitats due to iceberg scour (Barnes 1999,

Gerdes et al. 2003). While there may be regional variability in the types of foraging habitats used prior to breeding, it is likely that Gentoo penguins in the AP generally forage in near-shore habitats. Therefore variations in the stable isotope values of Gentoo penguin eggshells within and across regions probably reflect local scale variation in the environmental conditions of their near-shore foraging grounds.

Distribution prior to breeding and the Adélie ‛gap’

In addition to delineating vertical trends (benthic vs. pelagic), natural isotopic patterns in the marine environment can infer the geographic origin of animal diets (reviewed in Hobson

1999). In the Southern Ocean the δ13 C values of particulate organic matter track transitions between water mass sources defined by frontal zones and other oceanographic features with isotopic trends propagated up the food chain (Cherel & Hobson 2007, Phillips et al. 2009,

Quillfeldt et al. 2010). When examining Adélie penguins breeding in the AP, we found that the isotopic signatures of their eggshells divided female penguins into two discreet groups: one north of the Adélie ‛gap’ comprised of breeding colonies in the South Orkney Islands, South Shetland

Islands, and eastern AP, and the other south of the Adélie ‛gap’ comprised of breeding colonies

in the western AP. As shipboard survey data have shown that Adélie penguins are found almost

exclusively in the pack-ice outside of the breeding season, then the observed isotopic

differentiation between these two groups likely implies differences in their geographic location

prior to breeding, rather than habitat type per se (Fraser et al. 1992, Chapman et al. 2004, Ribic

et al. 2008). In addition, eggshells from breeding colonies to the south of the Adélie ‛gap’ had higher δ13 C values than those to the north. As the δ13 C signatures of pelagic primary producers

are higher in the Bellingshausen Sea relative to the Weddell Sea (Quillfeldt et al. 2010), our

isotopic analyses provide support for the hypothesis that Adélie penguins that breed to the south

of the Adélie ‛gap’ (Anvers Island to the south along the western AP) winter in the

Bellingshausen Sea and those that breed to the north of the Adélie ‛gap’(South Orkney Islands,

South Shetland Islands and eastern AP) winter in the Weddell Sea (Fraser et al. 1992, Fraser &

Trivelpiece 1996, Trivelpiece & Fraser 1996).

Studies of Adélie penguins using devices such as satellite-transmitters, Global

Positioning System (GPS), and other data loggers suggest that after breeding, adults can travel much as 17,000 km (though more commonly 1,000 to 2,000 km) away from their breeding colonies to reach the pack ice zone where they over-winter (Kerry et al. 1995, Davis et al. 1996,

2001, Clarke et al 2003, Ballard et al. 2010). In the AP region, recent tracking studies also provide support for our hypothesis of a common wintering range in the Weddell Sea for the three geographically disjunct Adélie penguin breeding groups found to the north of the Adélie ‘gap’.

Dunn et al. (In Press) tracked the winter movement of Adélie penguin breeding at Signy Island,

129 South Orkney Islands and found that adults travel as much as 1,300 km away from their breeding colony to winter in the pack ice of the Weddell Sea. In addition, adult Adélie penguins breeding on King George Island, South Shetland Islands, have been tracked to the pack ice in the eastern

Weddell Sea where they molt (W. Trivelpiece, unpubl. data). Unfortunately, to our knowledge no published studies have documented winter movements of Adélie penguins breeding in colonies along the eastern or western AP. While tracking studies can provide detailed distributional data, small sample sizes and limited spatial (number of breeding colonies) and temporal (duration of tracking) scales make it difficult to extrapolate penguin distributional data at the population level. Our study highlights how the stable isotope analysis of penguin tissues can be used to confirm distributional trends identified by tracking and shipboard studies on a regional scale.

Penguin diets prior to breeding

Most dietary studies of Adélie and Gentoo penguins use stomach content analysis to examine diet composition during the chick-rearing period when adults bring food ashore for their chicks. Studies in the AP suggest that during the breeding season, the diets of both species are often dominated by krill (by wet mass), but that Gentoo penguins also commonly consume fish

(Trivelpiece et al. 1987, Lynnes et al. 2004, Miller et al. 2009). In contrast, our results suggest that high trophic-level prey such as fish comprised a significant portion (46.8%-62.9%) of both female Adélie and Gentoo penguin diets prior to breeding. This result agrees with the few existing studies of winter diets of Adélie and Gentoo penguins in the AP region, which have found fish and squid to be important prey items for both species (Jablonski 1985, Ainley et al.

1992, Coria et al. 2000). While only the contributions of krill versus fish in penguin diets were

130 assessed in our mixing model analyses, it is important to note that as the fish and squid species consumed by Pygoscelis penguins often have similar δ15 N values, the estimated fish portion of

female penguins’ pre-breeding diets likely represent the dietary contribution of all high trophic-

level prey species such as fish and/or squid (Quillfeldt et al. 2005, Cherel 2008, M. Polito

unpubl. data).

During the breeding season Gentoo penguins in the AP generally consume a relatively

higher proportion of fish than Adélie penguins (Trivelpiece et al. 1987, Miller et al. 2009).

However, inter-specific comparisons of diets prior to breeding are less clear. Overall mean

Gentoo penguin eggshell δ15 N values were higher than Adélie penguin values and the range of

δ15 N values observed at the breeding colony level was also higher in Gentoo (8.4-10.0‰) than

Adélie penguins (7.9-9.3‰). This result implies a relatively larger contribution of high trophic- level prey such as fish in Gentoo penguin diets, similar to the breeding season. However when examining δ15 N values within regions, with the exception of the South Shetland Islands, δ15 N

values did not vary by species. Furthermore, while mixing model estimates of the proportion of

fish (relative to krill) was generally higher in Gentoo penguin diets in some cases 95%

confidence intervals overlapped between species (Table 5). These findings indicate that while

Gentoo penguins often consume a relatively higher proportion of fish than Adélie penguins prior

to breeding, local and regional scale factors also are important determinants of pre-breeding

diets.

Researchers have hypothesized that recent reductions in both krill recruitment and

abundance affect Adélie and Gentoo penguins differently, with the more krill-dependent Adélie

penguins expected to be at a relative disadvantage (Fraser et al. 1992, Smith et al. 1999, Forcada

et al. 2006, Hinke et al. 2007). In addition, due to the similar breeding success of these two

131 species, it has been hypothesized that their contrasting population trends are driven by factors operating outside the breeding season (Hinke et al. 2007, Carlini et al. 2009, Lynch et al. 2010).

We found that the pre-breeding diets of female Adélie penguins in the AP, in contrast to diets during the breeding season, commonly included a large percentage of high trophic-level prey such as fish and/or squid. It is possible that seasonal shifts in the consumption of krill and fish and/or squid by Pygoscelis penguins may be a reflection of the relative availability of these prey species in the marine environment. For example, a recent study examining Adélie penguin eggshells found compelling isotopic evidence of a shift from fish to krill in past penguin diets in conjunction with a proposed “krill surplus” following the extensive harvest of whales and seals in the Southern Ocean (Emslie & Patterson 2007). Therefore, the reduced amount of krill and higher consumption of fish and/or squid in pre-breeding diets relative to the breeding season may be a reflection of lower krill biomass available to penguins outside of the breeding season

(Lascara et al. 1998).

Isotopic mixing model and discrimination factors

Isotopic mixing models are commonly used to determine the relative contribution of multiple food sources to animal diets in marine systems (Hammill et al. 2005, Becker et al. 2007,

Navarro et al. 2009, Alves-Stanley et al. 2010). However, to our knowledge other than a preliminary analysis of a portion of our own dataset (Polito et al. 2009), only one other study has used isotopic mixing model procedures to estimate the diet composition of wild P ygoscelis penguins. Tierney et al. (2008) compared mixing model estimates of penguin diets based on the isotopic value of Adélie penguin chick blood to adult stomach contents collected during the chick-rearing period and found that these two methods provide similar estimates of penguin diets

132 at a broad taxonomic level (i.e. krill vs. fish). Unfortunately, as stomach content analysis is often logistically impossible during the pre-breeding period, we do not have comparable data against which to validate our estimates of female pre-breeding diet, as done by Tierney et al. (2008).

Isotopic mixing models are based on geometric procedures that reconstruct animal diets based on the isotopic value of each food source after correcting for isotopic discrimination (the differences in isotopic ratios between food items and consumer tissues; Phillips & Gregg 2001,

Post 2002, Phillips & Greg 2003, Caut et al. 2008b). Much of the uncertainty involved in the use of these methods derives from the selection of an appropriate discrimination factor as models can be sensitive to variations in these values (Post 2002, Caut et al. 2008b). When estimating the composition of female Adélie and Gentoo penguin diets prior to breeding, we corrected eggshell

δ15 N values for dietary isotopic fractionation using a discrimination factor of +1.8‰ derived from a captive population of Gentoo penguins (Polito et al. 2009). Eggshell δ15 N discrimination

factors have only been quantified in two other avian species, quail and ostrich, with

discrimination factors estimated at +1.0‰ to +3.0‰, respectively (Johnson 1995, Johnson et al.

1998). In addition to species-specific variation, other factors such as differences in physiological

condition and the protein quality of diets between captive and wild populations could also bias

discrimination factors and affect the accuracy of dietary reconstructions using isotopic mixing

models (Hobson et al. 1993, Cherel et al. 2005a, Robbins et al. 2010).

It is for these reasons that we performed a sensitivity analysis to examine how resilient

our estimates of female pre-breeding diets might be to variations in isotopic discrimination

factors. We found that a 1‰ deviation in δ 15 N discrimination factors resulted in a 15.6%-16.2% change in our dietary estimates. Using an eggshell δ15 N discrimination factor near the upper limit of what has been described in previous studies (+2.8‰), fish comprised between 30.6%-

133 46.8% of estimated diet composition (Fig. 4). Our model results suggest that it would take a

δ15 N discrimination factor >5.0‰ to result in an estimated diet composition that comprised solely of krill. As this figure is well outside the range of eggshell discrimination factors currently described (reviewed in Polito et al. 2009), these findings along with the few existing studies of winter diets help to support our conclusion that high trophic-level prey such as fish and/or squid are important components of female Adélie and Gentoo penguin diets prior to breeding.

Conclusions

This study highlights the use of stable isotope analysis to provide insight into the diets and foraging habitats of female Adélie and Gentoo penguins prior to breeding. Our results indicate that pre-breeding Gentoo penguins feed more in benthic, inshore habitats relative to

Adélie penguins, similar to differences observed during the chick-rearing period. However, unlike the breeding period when krill can dominate penguin diets, fish and/or other high trophic- level prey species comprise a significant portion of both female Adélie and Gentoo penguin pre- breeding diets. Finally, our findings confirm that Adélie penguin populations that are geographically separated during the breeding season by the Adélie ‘gap’ also inhabit geographically distinct habitats prior to breeding. While our study examined the pre-breeding foraging ecology of Adélie and Gentoo penguins over a large spatial scale, it is important to note the study’s temporal limitations as samples were collected during a single breeding season.

Further work examining the isotopic signatures of eggshell is warranted to investigate how inter- annual variation in the pre-breeding diets and foraging habitats of Adélie and Gentoo penguins may relate to breeding populations.

134

References

Alves-Stanley CD, Worthy GAJ, Bonde RK (2010) Feeding preferences of West Indian manatees in Florida, Belize, and Puerto Rico as indicated by stable isotope analysis. Mar Ecol Prog Ser 402:255–267

Ainley DG, Ribic CA, Fraser WR (1992) Does prey preference affect habitat choice in Antarctic seabirds? Mar Ecol Prog Ser 90:207–221

Ainley DG, Ballard G, Barton KJ, Karl BJ, Rau GH, Ribic CA, Wilson PR (2003) Spatial and temporal variation of diet within a presumed metapopulation of Adélie penguins. Condor 105:95–106

Astheimer LB, Grau CR (1985) The timing and energetic consequences of egg formation in the Adélie penguin. Condor 87:256-268

Ballard G, Toniolo V, Ainley DG, Parkinson CL, Arrigo KR, Trathan FN (2010) Responding to climate change: Adélie Penguins confront astronomical and ocean boundaries. Ecology 91(7)2056–2069

Barker PF, Filippelli GM, Florindo F, Martin EE, Scher HD (2007) Onset and role of the Antarctic circumpolar current. Deep-Sea Res Part II 54(21-22):2388–2398

Barnes DKA (1999) The influence of ice of polar near shore benthos. J Mar Biol Assoc UK 79:401–407

Becker BH, Peery MZ, Beissinger SR (2007) Ocean climate and prey availability affect the trophic level and reproductive success of the marbled murrelet, an endangered seabird. Mar Ecol Prog Ser 329:267–279

Carlini AR, Coria NR, Santos MM, Negrete J, Juares MA, Daneri GA (2009) Responses of Pygoscelis adeliae and P. papua populations to environmental changes at Isla 25 de Mayo (King George Island). Polar Biol 32(10):1427–1433

Caut S, Angulo E, Courchamp F (2008a) Discrimination factors ( δ15 N and δ13 C) in an omnivorous consumer: effect of diet isotopic ratio. Funct Ecol 22:255–263

Caut S, Angulo E, Courchamp F (2008b) Caution on isotopic model use for analyses of consumer diet. Can J Zool 86(5):438–445

Chapman EW, Ribic CA, Fraser WR (2004) The distribution of seabirds and pinnipeds in Marguerite Bay and their relationship to physical features during austral winter 2001. Deep-Sea Res Part II 51(17-18):2261–2278

135 Cherel Y (2008) Isotopic niches of emperor and Adélie penguins in Adélie Land, Antarctica. Marine Biology 154:813–821

Cherel Y, Hobson KA, Bailleul F, Groscolas R (2005a) Nutrition, physiology, and stable isotopes: new information from fasting and molting penguins. Ecology 86(11)2881-2888

Cherel Y, Hobson KA, Guinet C, Vanpé C (2007) Stable isotopes document seasonal changes in trophic niches and winter foraging individual specialization in diving predators from the Southern Ocean. J Anim Ecol 76:826–836

Clarke J, Kerry K, Fowler C, Lawless R, Eberhard S, Murphy R (2003) Post-fedging and winter migration of Adélie penguins Pygoscelis adeliae in the Mawson region of East Antarctica. Mar Ecol Prog Ser 248:267–278

Clarke J, Emmerson L, Otahal P (2006) Environmental conditions and life history constraints determine foraging range in breeding Adélie penguins. Mar Ecol Prog Ser 310:247–261

Clausen A, Pütz K (2003) Winter diet and foraging range of gentoo penguins ( Pygoscelis papua ) from Kidney Cove, Falkland Islands. Polar Biol 26:32–40

Coria N, Libertelli M, Casaux R, Darrieu C (2000) Inter-annual variation in the autumn diet of the gentoo penguin at Laurie Island, Antarctica. Waterbirds 23:511–517

Davis LS, Boersma PD, Court GS (1996) Satellite telemetry of the winter migration of Adélie penguins Pygoscelis adeliae. Polar Biol 16:221–225

Davis LS, Harcourt RG, Bradshaw CJA (2001) The winter migration of Adélie penguins breeding in the Ross Sea sector of Antarctica. Polar Biol 24:593–597

Dunn MJ, Silk JR, Trathan PN, (In Press) Post-breeding dispersal of Adélie penguins ( Pygoscelis adeliae ) nesting at Signy Island, South Orkney Islands . Polar Biol DOI 10.1007/s00300-010- 0870-4

Dunton KH (2001) δ15 N and δ13 C measurements of Antarctic Peninsula fauna: trophic relationships and assimilation of benthic seaweeds. Am Zool 41:99–112

Emslie SD, Patterson WP (2007) Abrupt recent shift in δ13 C and δ15 N values in Adélie penguin eggshell in Antarctica. Proc Natl Acad Sci USA 104:11666–11669

136

Forcada J, Trathan PN, Reid K, Murphy EJ, Croxall JP (2006) Contrasting population changes in sympatric penguin species in association with climate warming. Glob Change Biol, 12, 411–423

France RL (1995) Carbon-13 enrichment in benthic compared to planktonic algae: foodweb implications. Mar Ecol Prog Ser 124:307–312

Fraser W, Hofmann E (2003) A predator’s perspective on causal links between climate change, physical forcing and ecosystem response. Mar Ecol Prog Ser 265:1–15

Fraser WR, Trivelpiece WZ (1996) Factors controlling the distribution of seabirds: winter- summer heterogeneity in the distribution of Adélie penguin populations. In: Ross RM, Hofmann EE, Quetin LB (eds) Foundations for ecological research west of the Antarctic Peninsula. Antarctic Research Series, 70. American Geophysical Union, Washington, DC, p 257–272

Fraser WR, Trivelpiece WZ, Ainley DG, Trivelpiece SG. (1992) Increases in Antarctic penguin populations: reduced competition with whales or a loss of sea ice due to environmental warming? Polar Biol 11:525-531

Fry B, Baltz DN, Benfield MC, Fleeger JW, Gace A, Haas HL, Quinones-Rivera ZJ (2003) Stable isotope indicators of movement and residency for brown shrimp ( Farfantepenaeus aztecus ) in coastal Louisiana marshscapes. Estuaries 26:82–97

Gerdes D, Hilbig B, Montiel A (2003) Impact of iceberg scouring on macrobenthic communities in the high Antarctic Weddell Sea. Polar Biol 26:295–301

Hammill MO, Lesage V, Carter P (2005) What do harp seals eat? Comparing diet composition from different compartments of the digestive tract with diets estimated from stable isotope ratios. Can J Zool 83:1365–1372

Hinke JT, Salwicka K, Trivelpiece SG, Watters GM, Trivelpiece WZ (2007) Divergent responses in Pygoscelis penguins reveal a common environmental driver. Oecologia 153:845– 855

Hobson KA (1995) Reconstructing avian diets using stable carbon and nitrogen isotope analysis of egg components: Patterns of isotopic fractionation and turnover. Condor 97:752-762

Hobson KA (1999) Tracing origins and migration of wildlife using stable isotopes: a review. Oecologia 120:314-326

Hobson KA, Alisauskas RT, Clark RG (1993) Stable-nitrogen isotope enrichment in avian tissues due to fasting and nutritional stress: implications for isotopic analyses of diet. Condor 95:388–394

Jablonski B (1985) The diet of penguins on King George Island, South Shetland Islands. Acta Zool Cracov 29:117–186

137

Jacob U, Mintenbeck K, Brey T, Knust R, Beyer K (2005) Stable isotope food web studies: a case for standardized sample treatment. Mar Ecol Prog Ser 287:251–253

Johnson BJ (1995) The stable isotope biogeochemistry of ostrich eggshell and its application to late Quaternary paleoenvironmental reconstructions in South Africa. PhD dissertation, University of Colorado, Boulder, CO

Johnson BJ, Fogel ML, Miller GH, (1998) Stable isotopes in modern ostrich eggshell: A calibration for paleoenvironmental applications in semi-arid regions of southern Africa. Geochim Cosmochim Acta 62(14):2451–2461

Karnovsky NJ (1997) The fish component of Pygoscelis penguin diets. MS thesis, Montana State University, Bozeman, MN

Kerry K, Clarke J, Else G (1995) The foraging range of Adélie penguins at Béchervaise Island, Mac.Robertson Land, Antarctica as determined by satellite telemetry. In: Dann P, Norman I, Reilly P (eds) The penguins. Surrey Beatty & Sons, Sydney, p 216–243

Kokubun N, Takahashi A, Mori Y, Watanabe S, Shin HC (2010) Comparison of diving behavior and foraging habitat use between chinstrap and gentoo penguins breeding in the South Shetland Islands, Antarctica. Mar Biol 157(4)811-825

Lascara CM, Hofmann EE, Ross RR, Quetin LB (1999) Seasonal variability in the distribution of Antarctic krill, Euphausia superba , west of the Antarctic Peninsula. Deep-Sea Res I 46(6):925– 949

Lynch HJ, Naveen R, Fagan WF (2008) Censuses of penguins, blue-eyed shags, and southern giant petrel populations in the Antarctic Peninsula, 2001–2007. Mar Ornithol 36:83–97

Lynch HJ, Fagan WF, Naveen R (2010) Population trends and reproductive success at a frequently visited penguin colony on the western Antarctic Peninsula. Polar Biol 33:493–503

Lynnes AS, Reid K, Croxall JP (2004) Diet and reproductive success of Adélie and chinstrap penguins: linking response of predators to prey population dynamics. Polar Biol 27(9):544-554

Miller AK, Karnovsky NJ, Trivelpiece WZ (2009) Flexible foraging strategies of gentoo penguins Pygoscelis papua over 5 years in the South Shetland Islands, Antarctica. Mar Biol 156(12)2527–2537

Navarro J, Louzao M, Igual JM, Oro D, Delgado A, Arcos JM, Genovart M, Hobson KA, Forero MG (2009) Seasonal changes in the diet of a critically endangered seabird and the importance of trawling discards. Mar Biol 156:2571–2578

Phillips DL, Gregg JW (2001) Uncertainty in source partitioning using stable isotopes. Oecologia 127:171-179

138

Phillips DL, Gregg JW (2003) Source partitioning using stable isotopes: coping with too many sources. Oecologia 136:261–269

Phillips RA, Bearhop S, McGill RAR, Dawson DA (2009) Stable isotopes reveal individual variation in migration strategies and habitat preferences in a suite of seabirds during the nonbreeding period. Oecologia 160:795–806

Polito MJ, Fisher S, Tobias CR, Emslie SD (2009) Tissue-specific isotopic discrimination factors in gentoo penguin ( Pygoscelis papua ) egg components: Implications for dietary reconstructions using stable isotopes. J Exp Mar Biol Ecol 372:106–112

Post DM (2002) Using stable isotopes to estimate trophic position: models, methods, and assumptions. Ecology 83:703–718

Quillfeldt P, McGill RAR, Furness RW (2005) Diet and foraging areas of Southern Ocean seabirds and their prey inferred from stable isotopes: review and case study of Wilson’s storm- petrel. Mar Ecol Prog Ser 296:295-304

Reid K, Croxall JP (2001) Environmental response of upper trophic level predators reveals a system change in an Antarctic marine ecosystem. Proc R Soc Lond B 268:377-384

Ribic CA, Chapman E, Fraser WR, Lawson GL, Wiebe PH (2008) Top predators in relation to bathymetry, ice and krill during austral winter in Marguerite Bay, Antarctica. Deep-Sea Res Part II 55(3-4):485-499

Robbins CT, Felicetti LA, Florin ST (2010) The impact of protein quality on stable nitrogen isotope ratio discrimination and assimilated diet. Oecologia 162(3)571-579

Satterthwaite FE (1946) An approximate distribution of estimates of variance components. Biom Bull 2:110–114

Seminoff JA, Bjorndal KA, Bolten AB (2007) Stable carbon and nitrogen isotope discrimination and turnover in pond sliders trachemys scripta: Insights for trophic study of freshwater turtles. Copeia 2007(3):534–542

Simkiss K, Tyler C (1958) Reactions between eggshell matrix and metallic cations. Quart J Microscop Sci 99:5-13

Smith RC, Ainley D, Baker K, Domack E, Emslie S, Fraser B, Kennett J, Leventer A, Mosleythompson E, Stammerjohn S, Vernet M (1999) Marine ecosystem sensitivity to climate change. BioScience 49: 393–404

Tanton JL, Reid K, Croxall JP, Trathan PN (2004) Winter distribution and behaviour of gentoo penguins Pygoscelis papua at South Georgia. Polar Biol 27:299–303

139 Tierney M, Southwell C, Emmerson LM, Hindell MA (2008) Evaluating and using stable- isotope analysis to infer diet composition and foraging ecology of Adélie penguins Pygoscelis adeliae. Mar Ecol Prog Ser 355:297–307

Trivelpiece WZ, Fraser W (1996) The breeding biology and distribution of Adélie penguins: adaptations to environmental variability. In: Ross RM, Hofmann EE, Quetin LB (eds) Foundations for ecological research west of the Antarctic Peninsula. Antarctic Research Series, 70. American Geophysical Union, Washington, DC, p 273–285

Trivelpiece WZ, Trivelpiece SG (1990) Courtship period of Adélie, gentoo, and chinstrap penguins. In Davis LS, Darby JT (eds) Penguin Biology. Academic Press, San Diego, CA, p 113–127

Trivelpiece WZ, Trivelpiece SG, Volkman NJ (1987) Ecological segregation of Adélie, Gentoo and Chinstrap penguins at King George Island, Antarctica. Ecology 68:351–361

Trivelpiece WZ, Trivelpiece SG, Geupel GR, Kjelmyr J, Volkman NJ (1990) Adélie and chinstrap penguins: their potential as monitors of the Southern Ocean marine ecosystem. In: Kerry KR, Hempel G (eds) Antarctic ecosystems: ecological change and conservation. Springer, Berlin Heidelberg New York, p 191–202

Volkman NJ, Presler P, Trivelpiece W (1980) Diets of Pygoscelid penguins at King George Island, Antarctica. Condor 82:373–378

Wilson RP (2010) Resource partitioning and niche hyper-volume overlap in free-living Pygoscelid penguins. Functional Ecology 24(3):546-657

Wilson RP, Alvarrez B, Latorre L, Adelung D, Culik B, Bannasch R (1998) The movements of gentoo penguins Pygoscelis papua from Ardley Island, Antarctica. Polar Biol 19:407–413

Woehler EJ (1993) The distribution and abundance of Antarctic and subantarctic Penguins. Scientific Committee on Antarctic Research, Cambridge, UK

140 CHAPTER SIX: SYMPATRICALLY BREEDING PYGOSCELIS PENGUINS BALANCE

NICHE PLASTICITY AND SEGREGATION UNDER VARIABLE ENVIRONMENTAL

6 CONDITIONS

Introduction

Hutchinson (1957, 1978) formalized the concept of ecological niche as an n-dimensional hyper-volume with both scenopoetic and bionomic axes which represent the habitat and trophic components of niche space, respectively. Following Hutchinson’s principle studies, niche theory has provided an important framework for ecological investigations of resource use, geographic diversity, community composition and evolution (Chase & Leibold 2003). For example, while theory predicts that competition for limited resources is most likely to occur between species with similar ecological requirements (Ricklifs & Miller 1999), when species overlap they often exhibit morphological or behavioral characteristics which lead to segregated niche hyper- volumes (Hutchinson 1959, May & MacArthur 1972). While apparent niche segregation is not always a product of competition per se and may reflect phylogenetic niche conservatism (Brooks

& McLennan 1991, Donoghue 2008), specialization is a common mechanism of niche

differentiation which can lead to species co-existence (Wilson & Yoshimura 1994, Abrams

2006). However, specialization may come with a trade off as species with specialized habitat or

resource requirements are likely to be highly sensitive to environmental changes (Davies et al.

2004, Evans et al, 2005, Wilson et al, 2008). In contrast, generalist species that have broader and

6 This chapter is formatted to be submitted to the journal Oecologia as: Polito M.J.., Trivelpiece W.Z., Patterson W.P., Reiss, C.S., and Emslie S.D. Sympatrically breeding Pygoscelis penguins balance niche plasticity and segregation under variable environmental conditions. more plastic niches are expected to be more resilient to anthropogenic disturbances and/or changes in resource and habitat availability (Devictor et al. 2008).

The use of stable isotope analysis in theoretical and applied studies of ecological niches has developed rapidly in recent years (Bearhop et al. 2004, Newsome et al. 2007, Jackson et al.

2011). The “isotopic niche” approach (Newsome et al. 2007) is based on the principle that stable isotope ratios in animal tissues are largely determined by isotopic abundances in the animal’s food web (DeNiro and Epstein 1978, 1981). For example, nitrogen stable isotope ratios are commonly used to infer the trophic level of consumers as there is a general enrichment in δ15 N values by 3-5‰ per trophic level (Minagawa & Wada 1984). Carbon stable isotope ratios exhibit little to no trophic enrichment and are most reflective of differences in the source of primary production (DeNiro & Epstein 1978). In marine ecosystems, differences in δ13 C fractionation during photosynthesis between benthic macro-algae and pelagic phytoplankton can be used to trace the use of nearshore, benthic habitats vs. offshore, pelagic habitats (France 1995, Cherel &

Hobson 2007). When presented as bi-plots, δ15 N and δ13 C values delineate an animal’s isotopic niche (Newsome et al. 2007) which is comparable, although not identical, to the n-dimensional space defined by Hutchinson (1957, 1978) as it provides a representation of both an animal’s diet (bionomic) and habitat (scenopoetic) use. In addition, recently developed analytical and statistical approaches provide a framework with which to quantitatively compare isotopic niche position, width and overlap between community members and examine the plasticity of isotopic niches within species over time and space (Turner et al. 2010, Hammerschlag-Peyer et al. 2011,

Jackson et al. 2011).

We employ the isotopic niche approach to examine niche plasticity and segregation in sympatrically breeding Pygoscelis penguin species: the Chinstrap ( P. antarctica ), Gentoo ( P.

142 papua ) and Adélie penguin ( P. adeliae ) which co-occur in the Antarctic Peninsula region. These

species utilize similar nesting habitat, have similar breeding schedules and all consume primarily

Antarctic krill ( Euphausia superba; Trivelpiece et al. 1987, Miller et al. 2010). In addition, the

foraging ranges of these three species are locally constrained during the chick rearing period as

parents feed their chicks on a daily basis (Trivelpiece et al. 1987). Studies using stomach content

analysis and animal tracking methods suggest a general pattern of niche partitioning of these

species during the breeding season (Trivelpiece et al 1987, Lynnes et al. 2002), as they exploit

different niche hyper-volumes via slight differences in diet and differential area and depth

utilization (Kokubun et al. 2010, Miller et al. 2010, Wilson 2010). However, while these studies

are informative, logistical limitations and methodological biases make it difficult to integrate

bionomic and scenopoetic data and reduce the ability to track the consistency of ecological

niches over time and space. For example, stomach contents are often highly variable and biased

towards recent dietary items and prey that does not readily digest (Polito et al. 2011) and

tracking studies are often limited in sample size and temporal and/or spatial scale (Lynnes et al.

2002, Wilson 2010; although see Miller et al. 2010).

Stable isotope analysis provides a complementary approach to examine the ecological

niches of Pygoscelis penguins during the breeding season. The stable isotope values of fledgling-

aged chick feathers are metabolically inert after synthesis and provide an integrated measures of

trophic ( δ15 N) and habit ( δ13 C) niche history during the chick-rearing period (Polito et al. 2011).

Combining the isotope niche approach with more traditional methods such as stomach content

analysis allows for a greater ability to elucidate differences across and within species (Layman &

Allgeiers 2012). Furthermore, understanding niche plasticity and segregation in Pygoscelis

penguins is of increased importance due to recent climate-driven changes in the Antarctic

143 Peninsula marine environment (Ducklow et al. 2007). Reduction in the abundance of Antarctic krill have led to dramatic (>50%) declines in populations of Adélie and Chinstrap penguins in this region over the past 30 years (Trivelpiece et al. 2011, Lynch et al. 2012). In contrast, Gentoo penguin populations have been stable or expanding during this same time (Hinke et al. 2007,

Lynch et al. 2008, Lynch et al. 2012). A better understanding of the relative breadth and plasticity of Pygoscelis penguin niches will aid in interpreting these divergent population-level responses to recent declines in krill availability. In addition, due to prey mobility, continued declines in Antarctic krill are likely to lead to increased competition between sympatric

Pygoscelis penguins even in the presence of species-specific, segregated niche hyper-volumes

(Wilson 2010).

In this study we examine the stable isotope values of sympatrically breeding Pygoscelis

penguins across five breeding seasons that differ in the abundance and demography of their

principal prey, Antarctic krill. We compliment this approach by using stomach content analyses

to aid in interpreting species-specific, spatial and temporal variation in isotopic niches. The goal

of this study is not to directly measure competition interactions between sympatric species.

Instead, we seek to compare the degree of niche specialization, flexibility and overlap in

Pygoscelis species in an ecosystem where, due to recent declines, Antarctic krill may be an

increasingly limited resource for some Pygoscelis populations. Specifically, the objectives of this

study are to (1) quantify and interpret measures of isotopic niche position, width and plasticity

among Pygoscelis penguins, (2) examine relationships between the availability of Antarctic krill

and the trophic position ( δ15 N) and habitat use ( δ13 C) of Pygoscelis penguins, and (3) quantify

the type (trophic and/or habitat), consistency and degree of isotopic niche segregation between

Pygoscelis species that breed sympatrically.

144

Materials and methods

Study site and data collection

Because the austral summer crosses calendar years (October to February), we refer to each breeding season by split years (e.g., the 2006 breeding season is designated at 2006-07).

Field work was conducted during the austral summers of 2006-07 to 2010-11 at two locations in the South Shetlands, Antarctica (Fig. 1). Cape Shirreff, Livingston Island (62°28’S, 60°46’W) is a colony of approximately 4,500 and 800 pairs of sympatrically breeding Chinstrap penguins and

Gentoo penguins, respectively. While all three Pygoscelis species can be found around

Admiralty Bay, King George Island (62°10’W, 58°30’W), we focus this study on the colony of approximately 2,400 and 3,800 pairs of sympatrically breeding Adélie and Gentoo penguins at

Llano Point. Following the methods described in Polito et al. (2011), during the chick rearing period of each summer we collected and processed a total of 9–30 adult stomach contents from each species at each site using the water-offloading lavage technique (CCAMLR 1997). We then determined the percent contribution by wet mass of Antarctic krill, the euphausiid Thysanoessa macrura , fish, cephalopods and amphipods in each sample. From each sample containing

Antarctic krill, we measured the standard length (mm) and mass (g) of a random sample of 50 whole krill and used these data to subdivide the overall wet mass of Antarctic krill into adult

(>35 mm standard length) and juvenile (<35 mm standard length) components. Stomach content composition data were not collected at Admiralty Bay for Adélie penguins in 2010-11 and

Gentoo penguins in 2007-08. In late January to early February of each year, we collected three breast feathers from a random sample of 9–30 chicks of each species at each site while they were preparing to leave their natal colonies for the sea at 7–10 weeks of age.

145

Figure 1. Pygoscelis penguin breeding colonies and ship-board krill survey cruise tracks (solid lines) of sampling grids around Admiralty Bay, King George Island, and Cape Shirreff, Livingston Island, South Shetland Islands, Antarctic Peninsula.

146 To quantify inter-annual variation in environmental conditions we used a ship-based krill survey to assess the biomass and demography of Antarctic krill around the South Shetland

Islands during mid-January to early February of each year. Antarctic krill abundance (no. per

1,000 m3) and the percentage of adult Antarctic krill (> 35 mm standard length) was estimated using Isaacs-Kidd Midwater Trawl towed obliquely from the surface to a maximum depth of 200 m from sampling stations located in two grids (Fig. 1). The first is the west grid which is directly north of the western part of the island chain and includes the waters around Cape Shirreff.

Second, the south grid which encompasses the Bransfield Strait and includes the waters directly south of Admiralty Bay. In addition, from within each sampling grid we collected a random samples of 20 adult Antarctic krill (mean standard length = 44.0±4.5 to 49.4 ±2.3) in each year which were stored frozen prior to stable isotope analysis. These grids covered a larger and more offshore area than may be typically used by penguins foraging during the chick-rearing period but provide a relative index of the abundance, demography and the stable isotope composition of

Antarctic krill available to breeding penguins in each year (Miller & Trivelpiece 2007).

Stable isotope analysis

We cleaned feathers using a 2:1 chloroform : methanol rinse, air-dried and cut them into small fragments with stainless steel scissors. Whole krill were homogenized and then dried for

48 hours in an oven at 60˚C. Lipids were then extracted from krill samples using a Soxhlet apparatus with a 1:1, Petroleum-Ether : Ethyl-Ether solvent mixture for 8 hours (Seminoff et al.

2007). Lipid extracted krill were not acidified prior to isotopic analysis. We flash-combusted

(Thermo-Finnigan & Costech ECS4010 elemental analyzers) approximately 0.5 mg of each sample loaded into tin cups and analyzed for carbon and nitrogen isotopes ( δ13 C and δ15 N) through interfaced Thermo Finnigan Delta Plus XL and Delta V Plus continuous-flow stable isotope ratio mass spectrometers (CFIRMS). Raw δ values were normalized on a two-point scale

using glutamic acid reference materials with low and high values (i.e. USGS-40 ( δ13 C = -26.4‰,

δ15 N = -4.5‰) and USGS-41 ( δ13 C = 37.6‰, δ15 N = 47.6‰)). Sample precision based on

repeated sample and reference material was 0.1‰ and 0.2‰, for δ13 C, and δ15 N, respectively.

Stable isotope ratios are expressed in δ notation in per mil units (‰), according to the

following equation:

sample δX = [(R / R standard ) - 1] · 1000

Where X is 13 C or 15 N and R is the corresponding ratio 13 C / 12 C or 15 N / 14 N. The Rstandard

13 values were based on the Vienna PeeDee Belemnite (VPDB) for δ C and atmospheric N 2 for

δ15 N.

Data analysis

Similar to Jaworski & Ragnarsson (2006) we used a non-parametric multivariate approach based on Bray–Curtis similarity matrices to test for differences in penguin diet compositions during the chick-rearing period (Bray and Curtis 1957). We used analysis of similarity (ANOSIM; Clarke 1993) to detect significant differences in diet composition across species/sites combinations and years assessed via the global R-value. This test statistic becomes more significant as it gets farther from zero (range = 0 – 1). When significant differences were identified we then used a similarity percentage routine (SIMPER; Clarke 1993) to identify the dietary components which contributed most to overall differences in diet composition. We then used non-metric multi-dimensional scaling (MDS) analyses and hierarchical clustering

(CLUSTER) with 80% similarity groupings to visualize the degree of similarities in diets across

148 all species, site and year combinations. To increase clarity we graphically present MSD and

CLUSTER results as group centroids for each year species, site and year combinations due the large number of data points (371) resulting from this analyses. If the resulting stress value on the

MDS plot was less than 0.2 we assumed that relationships between samples were adequately represented (Clark and Warwick 2001).

Independent of dietary or habitat shifts, predator stable isotope values ( δ13 C and δ15 N) are

predicted to vary across years and sites due to variation in oceanographic conditions that

influence the baseline δ13 C and δ15N values of particulate organic matter in marine ecosystem

(Lara et al. 2010). This variation is especially true for δ13 C where spatial and temporal differences in the levels of primary production can significantly influence ecosystem baselines

(Schell 2000, Jeager et al. 2011). If uncorrected for these sources of variation, it could lead to spurious conclusions regarding the plastically of trophic and/or habitat niches of Pygoscelis penguins over time. However, as adult Antarctic krill are primary consumers and the dominate component of Pygoscelis penguin diets, they provide a proxy for ecosystem variability on chick feather stable isotope values. Therefore, prior to statistical analysis we facilitated direct comparisons across years by first correcting feather values using the year-specific stable isotope values of adult Antarctic krill collected from each sampling grid. Specifically, we standardize feather stable isotope values to reflect the ecosystem δ13 C and δ15 N baseline of the first year of our study using the differences in mean krill δ13 C and δ15 N values across sites and the mean

values in each subsequent year at each site as a correction factor.

Following Hammerschlag-Peyer et al. (2011), we examined chick feather stable isotope

values ( δ13 C and δ15 N) using both multivariate and univariate techniques to assess niche width, position, partitioning and overlap during the chick-rearing period. A consistent approach was

149 taken when examining intraspecific site combinations over time, Gentoo penguins at our two study sites (site comparisons), and sympatric species at each site (interspecific comparisons). We tested for plasticity in isotopic niche position by computing the Euclidean distance (ED) between group centroids ( δ13 C and δ15 N bivariate means) of groups (intraspecific and site comparisons) of isotope samples following the methods of Turner et al. (2010). We used this same approach to test for niche partitioning at each study site (interspecific comparisons). Isotopic niche positions were considered to be different if the ED between the two groups examined was significantly greater than zero after comparison to null distributions generated by a residual permutation procedure. If niche positions were found to differ using this approach, we examined the results of univariate general liner models and Tukey-Kramer multiple comparison tests. This procedure allowed us to determine which niche axis ( δ13 C and/or δ15 N) drove the observed shifts in niche

position or led to niche partitioning between species at the same site.

We examined plasticity in niche width (intraspecific and site comparisons) and the degree

of niche overlap (interspecific comparisons) using two stable isotope-based metrics. First we

calculated total niche area (TA) as the total area of the convex hull of the smallest convex

polygon that contains all individuals of a group in a δ13 C and δ15 N bi-plot (Layman et al. 2007).

While the TA metric is sensitive to sample size and the presence of outliers (Jackson et al. 2011), it can be thought of as a semi-quantitative measure of the total niche width of a population as it does not exclude particular individual niches from the characterization of the population niche

(Hammerschlag-Peyer et al. 2011). To complement this approach, we also calculated the niche areas of species, site and year combinations using a Bayesian ellipse-based metric (SEA B) calculated via the package Stable Isotope Analysis in R (SIAR; Parnell et al. 2008, Jackson et al.

2011). The SEA B metric can be thought of as a measure of the core population niche area as it is

150 unbiased with respect to sample size and the presence of outliers. The SEA B approach returns a

posterior distribution and 95% credibility intervals allowing for the quantitative comparisons of

core niche areas across species, sites and years. When pair-wise comparisons based on TA and

SEA B indicated significant differences in niche area we used Bartlett’s tests to examine the homogeneity of variance of δ13 C and δ15 N values between years (intraspecific comparisons),

species (interspecific comparisons) or sites to determine which isotopic niche axis drove the

observed differences in niche area. Last, we calculated the degree of total (TA overlap) and core

(SEA B overlap) niche overlap (i.e., the percent of species A’s niche area that overlaps with

species B) between sympatric species at each site in each year as a measure of inter-annual

variation in niche overlap.

As in other studies in our regions (Reiss et al. 2008, Trivelpiece et al. 2011), Antarctic

krill abundance was negatively correlated with the relative percentage of adult krill (Pearson’s r

= -0.69, P = 0.027), as differences in overall abundance across years and between study sites were driven primarily by the recruitment of small, juvenile Antarctic krill. Due to this confounding relationship, we used our index of krill demography (% adult Antarctic krill) to test for relationships between individual chick feather δ13 C and δ15 N values of each species/site

combination and krill availability across years using Pearson Product-Moment Correlation. Non

parametric statistics were conducted using the PRIMER 6 statistical package (Primer-E Ltd;

Clarke and Warwick 2001; Clarke and Gorley 2006). Isotope based niche-metric analyses and

parametric statistics were completed using the Program R (version 2.14.1) and the statistical

package SAS (Version 9.1). Data were examined for normality, all parametric tests were two-

tailed and significance was assumed at the 0.05 level. Data are presented ± standard deviation

(SD) unless otherwise noted.

151

Results

Environmental conditions

The abundance of Antarctic krill varied considerably between regions and across years

(Fig. 2). Near Admiralty Bay, Antarctic krill was most abundant in 2006-07, lowest in 2009-10 and intermediate in other years. Near Cape Shirreff, Antarctic krill was most abundant in 2006-

07 and 2007-08, relative to the other years. The very large abundance of Antarctic krill near

Admiralty Bay in 2006-08 was driven almost entirely by the large percentage of juvenile krill

(82.0%) occurring in that year. Adult krill were less common around Admiralty Bay (18.0-

58.3%) relative to the waters around Cape Shirreff (53.3-100.0%) in each year (Fig. 2). Annual mean δ15 N values of adult Antarctic krill ranged from 3.3 to 3.5‰ at both sampling areas with

δ15 N correction factors ranging from -0.2 to 0.0‰ (Appendix 3). Annual mean δ13C values of

adult Antarctic krill ranged from -25.6 to -27.0‰ in the south grid and -25.4 to -27.7‰ in the

west grid with δ13C correction factors ranging from -1.2 to 1.1‰.

Intraspecific comparisons

Adult and juvenile Antarctic krill dominated the diets of Adélie penguins in our study

(Fig. 3a). However, we found small but significant differences in Adélie penguin diets across the five years of our study (Fig. 3b; ANOSIM: global R = 0.107, P = 0.040). The occurrence of fish in Adélie penguin stomach contents was highest in 2006-07 (Appendix 4). Similarly, diets in

2009-10 differed from other years due to the higher occurrence and abundance of T. macura

present (Fig. 3b). The isotopic niche position of Adélie penguins differed across all years of our

study (ED = 0.3-0.9‰, All P < 0.001). Shifts in niche position were driven by shifts in both δ15 N

152

Figure 2. The mean abundance (no. per 1,000 m 3;bars) and demographic composition (% Adults; lines) of Antarctic krill (Euphausia superba ) assessed using ship-board trawls in two sampling grids (West and South) located near Pygoscelis penguin breeding colonies at Admiralty Bay, King George Island, and Cape Shirreff, Livingston Island, South Shetland Islands, Antarctic Peninsula. Antarctic krill > 35 mm standard length were considered adults.

153

Figure 3. The mean diet composition across five breeding seasons (a) and multi-dimensional scaling (MDS) ordination of annual diets composition of Pygoscelis penguins raising chicks at Admiralty Bay (AB), King George Island, and Cape Shirreff (CS), Livingston Island, South Shetland Islands, Antarctic Peninsula, from 2006-07 to 2010-11. MDS plot 2D stress = 0.09. Groupings of 80% similarity (black lines), based on cluster analysis, are superimposed to verify adequacy of ordination results.

154 13 (0.0-0.5‰; F 4,96 = 14.97, P < 0.001) and δ C (0.1-0.9‰; F 4,96 = 19.21, P < 0.001) values across years (Fig. 4; Appendix 3). TA of Adélie penguins varied by a maximum of 0.9‰ 2 over the

2 courses of our study (range: 0.2-1.1‰ ), with SEA B significantly larger in 2006-07 and 2009-10

relative to 2007-08 (Fig. 4; P = 0.021, 0.046). Variations in isotopic niche width across years

were driven by significant variation in δ13 C values (K2 =34.78, df = 4, P < 0.001) but not δ15 N values (K2 =5.51, df = 4, P < 0.239). Adélie penguin chick feather δ13 C and δ15 N values were

higher in years when the proportion of adult Antarctic krill available to them was lower ( δ13 C: r

= -0.29, P = 0.004; δ15 N: r = - 0.60, P < 0.001).

Chinstrap penguins also consumed predominantly adult and to a lesser extent juvenile

Antarctic krill (Fig. 3a). Chinstrap penguins stomach contents in 2008-09 and 2009-10 differed

from the other three years of our study (ANOSIM: global R = 0.223, P < 0.001) due primarily to

the low abundance of juvenile krill found in their diets in these two years (SIMPER; Appendix

4). The isotopic niche position of Chinstrap penguins differed across all five years of our study

15 (ED = 0.4-1.4‰, All P < 0.001) and was driven by shifts in both δ N (0.1-0.8‰; F 4,110 = 40.90,

13 P < 0.001) and δ C (0.0-1.3‰; F 4,100 = 65.65, P < 0.001) values across years (Fig. 5; Appendix

2 3). Chinstrap penguin TA varied inter-annually by as much as 0.5‰ , however SEA B not differ

across years (Fig. 3; All P > 0.061). Chinstrap penguin chick feather δ13 C and δ15 N values were higher in years when the proportion of adult Antarctic krill available to them was also low ( δ13 C:

r = - 0.46, P < 0.001; δ15 N: r = -0.21, P = 0.027).

Gentoo penguin stomach contents at Admiralty Bay did not differ over the course of our

study (ANOSIM: global R = 0.004, P = 0.510; Appendix 4). The isotopic niche position of

Gentoo penguins at Admiralty Bay differed by 0.2 to 0.7‰ across years (ED), with significant

differences between 2006-07 and 2007-08, 2008-09 and 2010-11 (All P < 0.039) as well as

Figure 4. The feather stable isotope values ( δ13C and δ15 N), isotopic niche position, and total and core isotopic niche area of Adélie and Gentoo penguin chicks at Admiralty Bay, King George Island, South Shetland Islands, Antarctic Peninsula, from 2006-07 to 2010-11. Statistics represent Bartlett’s tests of homogeneity of variance between species on feather stable isotope values.

156

Figure 5. The feather stable isotope values ( δ13C and δ15 N), isotopic niche position, and total and core isotopic niche area of Adélie and Gentoo penguin chicks at Cape Shirreff, Livingston Island, South Shetland Islands, Antarctic Peninsula, from 2006-07 to 2010-11. Statistics represent Bartlett’s tests of homogeneity of variance between species on feather stable isotope values.

157 between 2010-11 and 2008-09 and 2009-10 (All P < 0.024). Inter-annual differences in ED were

15 13 driven by shifts in δ N (0.1-0.7‰, F 4,84 = 4.11, P = 0.004) but not δ C (F 4,84 = 2.27, P = 0.069)

values (Fig. 4; Appendix 3). TA of Gentoo penguins at Admiralty Bay varied inter-annually by

2 as much as 3.1‰ , and SEA B was significantly larger in 2006-07 relative to 2007-08 at

Admiralty Bay (P < 0.001). This difference was a function of the greater variation in δ 13 C values observed in 2006-07 (K2 =23.8, df = 4, P < 0.001). Gentoo penguin chick feather δ15 N values at

Admiralty Bay were higher in years when the proportion of adult Antarctic krill available to

them was lower (r = -0.35, P < 0.001) while δ13 C values were not related to the availability of adult krill (r = 0.03, P = 0.768)

Diets at Cape Shirreff differed between 2008-09 and 2010-11 (ANOSIM: global R =

0.107, P = 0.034) due the relatively higher amount of fish and cephalopods in 2008-09 (Fig. 3b;

SIMPER; Appendix 4). At Cape Shirreff, the isotopic niche position of Gentoo penguins differed by 0.4 to 1.3‰ across years (ED), with significant differences between all years (P < 0.023) except for 2006-07 and 2010-11 (P = 0.112). Inter-annual differences in niche position were

15 13 driven by shifts in both δ N (0.0-0.9‰, F 4,111 = 6.97, P < 0.001) and δ C (0.1-1.2‰; F 4,111 =

67.56, P < 0.001) values (Fig. 5; Appendix 3). While TA of Gentoo penguins at Cape Shirreff

2 varied inter-annually by as much as 0.7‰ no inter-annual differences in the SEA B was observed

(Fig. 5). Gentoo penguin chick feather δ15 N values at Cape Shirreff were highest when the proportion of adult Antarctic krill available to penguins was also high (r = 0.25, P = 0.008). In contrast, feather δ13 C values were higher in years when the proportion of adult Antarctic krill available to penguins was lowest (r = -0.41, P < 0.001).

Site comparisons In general, fish was a more frequent and abundant component of Gentoo penguin diet at

Cape Shirreff relativity to Admiralty Bay (Figure 3a; ANOSIM: global R = 0.186, P < 0.001;

Appendix 4). In addition, the isotopic niche position of Gentoo penguins at the two sites differed in four out of five years (2007-08 to 2008-09: ED = 0.7-1.7‰; All P <0.001; 2006-07: ED =

0.3‰; P = 0.210) with pair-wise differences in both δ15 N and δ13 C values (Appendix 3). While

the direction of differences in δ13 C values between sites was variable, δ15 N values were 0.2 to

1.7‰ higher in Gentoo penguins at Cape Shirreff on average. TA of Gentoo penguins was generally larger at Admiralty Bay relative to Cape Shirreff with significantly larger SEA B in

2006-07 and 2010-11 (P < 0.001) due to the greater variation in δ13 C values found at Admiralty

Bay (K2 =27.77-28.08, df = 1, P < 0.001).

Interspecific comparisons

The composition of stomach contents during the chick-rearing period differed among

species/site combinations when averaged across the five years of our study (Fig. 3a; ANOSIM:

global R = 0.111, P < 0.001; Appendix 4). While overall stomach contents were dominated by

adult Antarctic krill, pair-wise ANOSIM suggests that only Gentoo penguins from Admiralty

Bay and Chinstrap penguins from Cape Shirreff had similar diets overall (all other comparisons

P < 0.003). Average dissimilarity (SIMPER) among species/sites ranged from 18.3% to 39.6%,

and were most influenced by differences in the amount of adult vs. juvenile Antarctic krill

(Adélie and Chinstrap vs. Gentoo penguins), fish and cephalopods (Cape Shirreff Gentoo

penguins), and T. macura (Adélie penguins). Pair-wise ANOSIM, MDS and CLUSTER analyses

all suggest that in each year stomach contents differed between sympatric species at each site

(Fig. 3b, Appendix 3).

159 The mean isotopic niche positions of Adélie and Gentoo penguins at Admiralty Bay differed significantly in all years (ED = 0.9-1.9‰, All P <0.001). Gentoo penguins had

13 15 significantly higher δ C values in all years (F 4,180 = 197.54, P < 0.001) and higher δ N values in all but 2008-09 (F 4,180 = 93.36, P < 0.001; Fig. 4; Appendix 3). The TA as well as SEA B of

Gentoo penguins was larger than Adélie penguins in all years (P = 0.046-0.001). This result was due to more variable δ13 C values in all years except 2009-10 and more variable δ15N values in all years except 2008-09 (Fig. 4). TA overlap ranged from 0.0 to 46.0% of Adélie and 0.0 to 9.3% of Gentoo penguin’s total niche area. TA overlap was highest for both species in 2006-07 which was also the only year in which SEA B overlapped.

The mean isotopic niche positions of Chinstrap and Gentoo penguins at Cape Shirreff differed significantly in all years (ED = 0.9-2.5‰, All P <0.001). Gentoo penguins had

15 13 significantly higher δ N values in all years (F 4,219 = 391.27, P < 0.001) and higher δ C values in

2006-07 and 2008-09 (F 4,180 = 68.91, P < 0.001; Fig. 5; Appendix 3). While TA of Gentoo

penguins was larger than Chinstrap penguins in all years, Gentoo penguin’s SEA B was only

greater in 2006-07, 2008-09 and 2010-11 (Fig. 5; P = 0.039-0.018). These differences were due

to the more variable δ15N values found in Gentoo penguins at Cape Shirreff in all years, as

variability in δ13 C values did not differ between species (Fig. 5). TA overlap was highest for both species in 2006-07, and ranged from 0.0 to 41.9% in Chinstrap penguins and 0.0 to 21.9% in

Gentoo penguins. SEA B did not overlap between Chinstrap and Gentoo penguins in any year at

Cape Shirreff.

Discussion

Niche position plasticity in Pygoscelis penguins

160 All three species of Pygoscelis penguins exhibited flexibility in their isotopic niche position across the five years of our study. Shifts in δ13 C and δ15N in Adélie penguins at

Admiralty Bay and Chinstrap and Gentoo penguins at Cape Shirreff were due to fluctuations in

both trophic position ( δ15N) and habit utilization ( δ13 C), while Gentoo penguins at Admiralty bay

appear to have only adjusted their trophic position over time. Our findings also suggest a

relationship between shifts in isotopic niche position and the availability and demography of

Antarctic krill. For Adélie and Gentoo penguins at Admiralty Bay and Chinstrap penguins at

Cape Shirreff, chick feather δ15 N values were highest in years when large amounts of juvenile

krill were available around their breeding colony. A similar trend was found in chick feather

δ13C values in Adélie penguins and Chinstrap and Gentoo penguins from Cape Shirreff. There is increasing evidence that relative to larger adult Antarctic krill, small juvenile Antarctic krill forage at a relatively lower trophic level and less in nearshore/benthic habitats and thus have lower tissue δ15 N and δ13C values (Stowasser et al. 2011, Schmidt et al. 2011, Polito et al. this

volume). Therefore, while the abundance of juvenile krill in stomach contents roughly mirrored

its availability in most years, it is very unlikely that an increased consumption of juvenile

Antarctic krill would lead to increases in chick feather δ15 N and δ13C values.

In contrast, a more fitting hypothesis is that these penguin species shifted their diets to include a greater percentage of higher trophic prey items, such as fish, in years where the bulk of

Antarctic krill available to them were composed of small juveniles. Central place foragers should maximize their net energy gain by balancing the energy content of their prey with the energy needed to find and handle the prey (MacArthur & Pianka 1966, Stephens & Krebs 1986). As penguins capture prey individually, foraging on smaller krill is likely less efficient and there is evidence to suggest that penguins preferentially select larger krill (Reid et al. 1996, Miller &

161 Trivelpiece 2007). It may be that in years when small Antarctic krill are abundant, fish and other higher trophic prey, which are more mobile but have higher energy content, become a relatively more profitable prey source (Clarke 1984, Hagen et al. 2000, Van de Putte et al. 2006). Evidence from both our stomach content analysis and a previous study at Cape Shirreff support this hypothesis. Chick feather δ15 N values, the frequency occurrence of fish in stomach contents and the availability of juvenile Antarctic krill were all highest in Adélie and Gentoo penguin’s diets at Admiralty Bay in 2006-07. Similarly, stomach contents and diving behaviors suggest that

Chinstrap penguins at Cape Shirreff increasingly target fish when large Antarctic krill are not available (Miller & Trivelpiece 2008). Shifts in penguin chick feather δ13C values may also be a

reflection of increases in the use of benthic habitats where fish prey and larger adult Antarctic

krill are more common in years when krill stocks offshore and along the shelf break are

composed primarily of juveniles (Takahashi et al. 2003, Miller et al. 2009, Schmidt et al. 2011).

Interestingly, Gentoo penguins at Cape Shirreff represent an exception to this trend as

their δ15 N values were highest in years when adult krill were more abundant. This pattern

appears to be driven primarily by the higher than average chick feather δ15 N values and amount

of fish found in stomach contents in 2008-09, a year in which juvenile krill were relativity absent

around Cape Shirreff. As fish prey were a consistent and relatively important component of

Gentoo penguin diets at Cape Shirreff, their reliance on non-krill prey likely make these

penguins less responsive to variation in the availability of Antarctic krill (Miller et al. 2009).

Niche width plasticity in Pygoscelis penguins

The isotopic niche width (i.e., variety of resources and/or habits types utilized) of Gentoo

penguins was generally broader and more flexible than the niche width of the Adélie and

162 Chinstrap penguins in our study. Our data agree with previous studies that suggest Gentoo penguins often have more diverse and flexible diets (Jablonksi 1985, Karnovsky 1997, Polito et al. 2011) and use a broader habitat range than other Pygoscelis penguins (Miller et al. 2010). The

overall broader and more flexible niche in Gentoo penguins is likely a product of increased

individual foraging specialization (Bearhop et al. 2006), which may help to reduce intraspecific

competition within the Gentoo penguin’s nearshore foraging range (Grémillet et al. 2004,

Masello et al. 2010). In contrast, Adélie penguins exhibited a small amount of plasticity in

habitat niche width, while the isotopic niche width of Chinstrap penguins was essentially static

over the course of our study. The small and relatively consistent isotopic niche widths observed

in Adélie and, to a larger extent, Chinstrap penguins suggests that individuals within these

populations generally respond to changes in environmental conditions in a similar manner. This

lower amount of individual variation in Adélie and Chinstrap penguins may be a reflection of

reduced intraspecific competition via their relatively larger foraging ranges and/or an adaptation

to foraging on generally abundant Antarctic krill swarms offshore and along the continental shelf

slope (Trivelpiece et. 1987). While intraspecific competition can also lead to the evolution of

sexual segregation of diet and/or foraging areas (Clark et al. 1998, Bearhop et al. 2006,

Weimerskirch et al. 2009), sex-specific differences in isotopic niches are likely to be obscured as

we analyzed chick feathers.

Site-specific trends in the isotopic niche of Gentoo penguins

There were conspicuous differences in the position, width and orientation of ecological

niches of Gentoo penguins at our two breeding sites. While the trophic position of Gentoo

penguins at both sites varied over time, penguins at Cape Shirreff consumed a higher amount of

163 fish prey and had higher δ15 N values in all years. Similarly, the width and orientation the isotopic

niche of Gentoo penguins at Cape Shirreff suggest that individuals use a similar habitat, but vary

considerably in their trophic position. This differs from Gentoo penguins at Admiralty Bay

which had higher plasticity in both the trophic and habitat niche widths. These trends may be a

reflection of differences in bathymetry and the diversity of marine habitats adjacent to these two

breeding sites. The waters surrounding Cape Shirreff have a wide shelf region and are shallow

enough for the benthos to be accessed during foraging dives (Miller et al. 2009), while deeper,

more pelagic waters are generally outside of the foraging range of Gentoo penguins (Miller et al.

2010). In contrast, the shelf region outside of Admiralty Bay is narrow and pelagic waters are

more accessible to foraging Gentoo penguins (Miller et al. 2010). The greater access to benthic

foraging habitats at Cape Shirreff likely facilitate the relatively higher proportion of fish in their

diets as Gentoo penguins most often consume benthic fish (Karnovsky 1997). Similarly, the

greater degree of variability in trophic positions and habitat use among Gentoo penguins at

Admiralty Bay is likely a reflection individual’s access to both nearshore, benthic and offshore,

pelagic foraging habitats. This access suggests that while Gentoo penguin appear to have quite

large theoretical niches, habitat availability and other environmental factors can act to restrain

their realized foraging and dietary niche (Lescroël & Bost 2005). Based on foraging studies from

other breeding sites around Antarctic it is also likely that static (bathymetry) and dynamic (sea

ice and prey availability) also act to modify the foraging niches of Adélie and Chinstrap

penguins in similar manners (Watanuki et al. 1997, Lynnes et al. 2002, Ainley et al. 2003,

Rombolá et al. 2010).

Interspecific niche partitioning and overlap

164 There were constant and significant differences in the mean isotopic niche positions sympatric penguin species at Admiralty Bay and Cape Shirreff during all five years of the study.

Niche partitioning between sympatric species was a function of the generally lower trophic positions and a greater utilization of offshore foraging habitats by Adélie and Chinstrap penguins relative to Gentoo penguins at both sites. As in previous studies, fish and other high-trophic prey were a less frequent and less abundant component of Adélie and Chinstrap penguins diets relative to Gentoo penguins (Volkman et al. 1980, Miller et al. 2010, Polito et al. 2011). Stomach contents also help validate differences in habitat use between sympatric species. The relatively higher occurrence of adult Antarctic krill and fish in Gentoo penguin diets at both sites are likely reflective of nearshore benthic foraging (Karnovsky 1997, Takahashi et al. 2003, Schmidt et al.

2011). In contrast, the higher proportion of juvenile Antarctic krill and the euphausiid T. macura in Adélie and Chinstrap penguin diets are reflective of foraging along the shelf slope and in more pelagic waters (Ichii et al. 1998, Miller et al. 2010). Interestingly, the relative importance of trophic and habit niche partitioning between sympatric species differed between our two study sites. Habitat differences between species were usually greater at Admiralty Bay, while differences in trophic positions were generally greater at Cape Shirreff. It may be that access to a diversity of foraging habitats close to Admiralty Bay facilitates a relatively higher degree of habitat niche partitioning. In contrast, while the wide shelf region around Cape Shirreff reduces the ability to partitioning habitat niches without increasing foraging distances, it may facilitate trophic partitioning as Gentoo penguins consume more benthic fish (Miller et al. 2009, 2010).

While mean isotopic niche positions of sympatric species in our study differed, our data also suggest that considerable overlap in isotopic niches can occur in some years. When partial niche overlap occurs, due to their generally smaller niche widths, the degree of overlap was

165 always higher in Adélie and Chinstrap penguins (0.0-46.0%) relative to Gentoo penguins (0.0-

21%). Similar to previous studies, krill availability also appears to influence the degree of niche overlap between sympatric species (Lynnes et al. 2002). For example, isotopic niche overlap was highest at both sites in 2006-07, a year when juvenile Antarctic krill were very abundant and

δ15 N values and stomach contents suggest that Adélie and Chinstrap penguins shifted their diets

to include a greater percentage of higher trophic prey items such as fish. While shifts in diets

contribute to partial niche overlap, it is likely that competitive overlap is also mediated by

differences in population sizes between species at each site and the breadth of each species’

niche. For example, the generalist strategy and broad niche found in Gentoo penguins may help

to mitigate competitive interactions when partial niche overlap occurs with a sympatric species

(Wilson & Yoshimura 1994), while the relatively smaller population of Gentoo penguins may

offset partial niche overlap with Chinstrap penguins at Cape Shirreff (Miller et al. 2010). Given

the above assumptions, partial niche overlap and competitive effects may be intensified in Adélie

penguins due to their narrower ecological niche and similar population size relative to Gentoo

penguins at Admiralty Bay. However, it is important to note that we do not quantify inter or

intraspecific competition directly in this study. Interactions with other community members,

temporal differences in foraging behaviors, and other factors that were not examined here also

influence niche partitioning and overlap in Pygoscelis penguins (Ainley et al. 2006, Wilson

2010).

Pygoscelis penguin ecological niches and recent declines in Antarctic krill

Our study indicates that Pygoscelis penguins breeding in the Antarctic Peninsula have the ability to adjust their trophic and habit niches in response to inter-annual variation in the marine

166 environment, which can lead to partial niche overlap among sympatric species in some years.

When large Antarctic krill is less abundant, Pygoscelis penguins appear to increase their consumption of higher trophic prey such as fish. However, these shifts in diets and δ15 N values, especially in Adélie and Chinstrap penguins, appear to be small (< 1.0‰) and not easily identified via stomach content analyses due to preservation biases (Miller & Trivelpiece 2008,

Polito et al. 2011). For example, a mixing model analysis of a subset of our data suggests a 1.0‰ shift in penguin δ15 N values reflects a ~15% shift in diet between fish and krill (Polito et al.

2011). Substantially larger shifts in penguin diets between fish and krill have been documented

due to a likely ‘krill surplus’ in the past century following the extensive harvest of whales and

seals in the Southern Ocean (Laws 1985, Emslie and Patterson 2007). The ability to shift diets

from krill to fish may help to buffer Pygoscelis penguins from recent declines in the biomass of

Antarctic krill (Atkinson et al. 2004) and the likely increased competition for krill as whale and

seal populations rebound (Ainley et al. 2006, Branch et al. 2011). However, it is uncertain if a

broadly fish-based diet would support populations of the three penguin species under current

conditions. For example, there is recent evidence of similarly dramatic declines in the abundance

of Antarctic silverfish ( Pleuragramma antarcticum ) along the western Antarctic Peninsula

(Torres et al. 2012) which is a common pelagic prey fish of Adélie and Chinstrap penguins in

both modern and historic studies (Karnovsky 1997, Polito et al. 2002, Polito et al. 2011).

Differences in the plasticity and breadth of dietary and habitat niches in Pygoscelis

penguins also provide insights into recent population trends in the Antarctic Peninsula over the

past 30 years. While populations of Adélie and Chinstrap penguins in this region have declined

dramatically, Gentoo penguin populations have been stable or expanding (Lynch et al. 2008,

Trivelpiece et al. 2011, Lynch et al. 2012). Of the three species, Gentoo penguins appear to have

167 the broadest and most plastic niche. This may provide them greater resilience to the effects of recent climate-driven changes in the Antarctic marine ecosystem (Vaughan et al. 2003,

Stammerjohn et al. 2008). Higher degrees of individual niche variation, proclivity for foraging on fish, and their use of nearshore, benthic habitats all allow Gentoo penguins to be less sensitive to the availability of Antarctic krill (Miller et al. 2009). Gentoo penguins may also benefit from competitive release as other Pygoscelis species decline (Miller et al. 2010, Trivelpiece et al.

2011) and as declining spring sea ice allows greater accessibility to ice-free coast lines for colony establishment near their southern range limit (Lynch et al. 2012).

In contrast, there are several features of the niches of Chinstrap and Adélie penguins that help address why their populations are declining in the Antarctic Peninsula. While it might not be appropriate to call these two species “krill specialists” (Ainley 2002), Adélie and Chinstrap penguins in our study consumed predominantly Antarctic krill, had narrower trophic niches with less individual niche variation, and foraged primarily in offshore habitats where Antarctic krill are a readily available prey source (Ichii et al. 1987). Together these factors predict that Adélie and Chinstrap penguins should be highly sensitive to environmental changes that impact the abundance of Antarctic krill (Fraser & Hoffmann 2003, Hinke et al. 2007), especially if these same environmental conditions have also reduced the availability of alternative prey such as pelagic fish (Torres et al. 2012). Furthermore, our study shows when Adélie and Chinstrap penguins forage less on krill, they risk greater niche overlap with sympatrically breeding Gentoo penguins. Therefore while all three Pygoscelis penguins appear to adjust their dietary and

habitat niches in response to environmental conditions to some degree, the flexible, generalist

niche exhibited by Gentoo penguin appears to be better suited to the rapidly changing climatic

and oceanographic conditions now occurring in the Antarctic Peninsula.

168

References

Abrams PA (2006) The prerequisites for and likelihood of generalist-specialist coexistence. Am Nat 167:329–342

Ainley DG (2002) The Adélie penguin: bellwether of climate change. Columbia University Press, New York

Ainley DG, Ballard G, Barton KJ, Karl BJ, Rau GH, Ribic CA, Wilson PR (2003) Spatial and temporal variation of diet within a presumed metapopulation of Adélie penguins. Condor 105:95–106

Ainley DG, Ballard G, Dugger KM (2006) Competition among penguins and cetaceans reveals trophic cascades in the western Ross Sea, Antarctica. Ecology 87:2080–2093

Atkinson A, Siegel V, Pakhomov E, Rothery P (2004) Long-term decline in krill stock and increase in salps within the Southern Ocean. Nature 432:100–103

Bearhop S, Adams CE, Waldron S, Fuller RA, Macleod H (2004) Determining trophic niche width: a novel approach using stable isotope analysis. J Anim Ecol 73:1007–1012

Bearhop S, Phillips RA, McGill R, Cherel Y, Dawson DA, Croxall JP (2006) Stable isotopes indicate sex-specific and long-term individual foraging specialization in diving seabirds. Mar Ecol Prog Ser 311:157–164

Branch TA (2011) Humpback whale abundance south of 60° S from three complete circumpolar sets of surveys. J Cetacean Res Manage 3:449 53–69

Bray JR, Curtis JT (1957) An ordination of the upland forest communities of southern Wisconsin. Ecol Monogr 27:325–349

Brooks DL, McLennan DA (1991) Phylogeny, ecology, and behavior: a research program in comparative biology. University of Chicago Press, Chicago

CCAMLR (1997) CCAMLR Ecosystem Monitoring Program: standard methods for monitoring studies. CCAMLR, Hobart

Chase JM, Leibold MA (2003) Ecological niches: linking classical and contemporary approaches. University of Chicago Press, Chicago

169 Clarke A (1984) Lipid content and composition of Antarctic krill, Euphausia superba Dana. J Crust Biol 4:285–294

Clarke KR (1993) Nonparametric multivariate analyses of changes in community structure. Aust J Ecol 18:117–143

Clarke KR, Warwick RM (2001) Changes in marine communities: an approach to statistical analysis and interpretation. PRIMER-E, Plymouth

Clarke KR, Gorley RN (2006) PRIMER, vers. 6: user manual/tutorial, PRIMER-E, Plymouth

Clarke JR, Manly B, Kerry K, Gardner H, Franchi E, Corsolini S, Focardi S (1998) Sex differences in Adélie penguin foraging strategies. Polar Biol 20:248–258

Davies KF, Margules CR, Lawrence JF (2004) A synergistic effect puts rare, specialized species at greater risk of extinction. Ecology 85:265–271

DeNiro MJ, Epstein S (1978) Influence of diet on the distribution of carbon isotopes in animals. Geochim Cosmochim Acta 42:495–506

DeNiro MJ, Epstein S (1981) Influence of diet on the distribution of nitrogen isotopes in animals. Geochim Cosmochim Acta 45:341–351

Devictor V, Julliard R, Clavel J, Jiguet F, Lee A, Couvet D (2008) Functional biotic homogenization of bird communities in disturbed landscapes. Global Ecol Biogeogr 17:252–261

Donoghue MJ (2008) A phylogenetic perspective on the distribution of plant diversity. Proc Natl Acad Sci USA 105(1):11549–11555

Ducklow HW, Baker K, Martinson DG, Quetin LB, Ross RM, Smith RC, Stammerjohn SE, Vernet M, Fraser W (2007) Marine pelagic ecosystems: the West Antarctic Peninsula. Philos T Roy Soc B 362:67–94

Emslie S.D., Patterson W.P. 2007. Abrupt recent shift in δ13 C and δ15 N values in Adélie penguin eggshell in Antarctica. Proc Natl Acad Sci USA 104:11666–11669

Evans KL, Greenwood JJD, Gaston KJ (2005) Dissecting the species–energy relationship. Proc Roy Soci B 272:2155–2163

France RL (1995) Carbon-13 enrichment in benthic compared to planktonic algae: foodweb implications. Mar Ecol Prog Ser 124:307–312

Fraser W, Hofmann E (2003) A predator’s perspective on causal links between climate change, physical forcing and ecosystem response. Mar Ecol Prog Ser 265:1–15

170 Grémillet D, Dell’Omo G, Ryan PG, Peters G, Ropert-Coudert Y, Weeks SJ (2004) Offshore diplomacy, or how seabirds mitigate intra-specific competition: a case study based on GPS tracking of Cape gannets from neighboring colonies. Mar Ecol Prog Ser 268:265–279.

Hagen W, Kattner G, Friedrich C (2000) The lipid compositions of high-Antarctic notothenioid fish species with different life strategies. Polar Biol 23:785–791

Hammerschlag-Peyer CM, Yeager LA, Araújo MS, Layman CA (2011) A hypothesis-testing framework for studies investigating ontogenetic niche shifts using stable isotope ratios. PLoS ONE 6(11):e27104

Hinke JT, Salwicka K, Trivelpiece SG, Watters GM, Trivelpiece WZ (2007) Divergent responses in Pygoscelis penguins reveal a common environmental driver. Oecologia 153:845– 855

Hutchinson GE (1957) Concluding remarks, Cold Spring Harbor Symposium. Quant. Biol 22:415-427

Hutchinson GE (1959) Homage to Santa Rosalia, or why are there so many kinds of animals? Am Nat 93:145-159

Hutchinson, GE (1978) An Introduction to Population Biology. Yale University Press, New Haven

Ichii T, Katayama K, Obitsu N, Ishii H, Naganobu M (1998) Occurrence of Antarctic krill (Euphausia superba ) concentration in the vicinity of the South Shetland Islands: relationship to environmental parameters. Deep Sea Res 45:1235–1262

Jablonski B (1985) The diet of penguins on King George Island, South Shetland Islands. Acta Zool Cracov 29:117–186

Jackson AL, Inger R, Parnell A, Bearhop S (2011) Comparing isotopic niche widths among and within communities: SIBER—Stable Isotope Bayesian Ellipses in R. J Anim Ecol 80:595−602

Jaeger A, Cherel Y (2011) Isotopic investigation of contemporary and historic changes in penguin trophic niches and carrying capacity of the Southern Indian Ocean. PLoS ONE 6(2): e16484

Jaworski A, Ragnarsson SA (2006) Feeding habits of demersal fish in Icelandic waters: a multivariate approach. ICES J Mar Sci 63:1682–1694

Karnovsky NJ (1997) The fish component of Pygoscelis penguin diets. MS thesis, Montana State University, Bozeman

171 Kokubun NA, Takahashi Y, Mori S, Watanabe, Shin H-C (2010) Comparison of diving behavior and foraging habitat use between Chinstrap and Gentoo Penguins breeding in the South Shetland Islands, Antarctica. Mar Biol 157:811–825

Laws RM (1985) The ecology of the Southern Ocean. American Scientist 73:26–40

Layman CA, Arrington DA, Montaña CG, Post DM (2007) Can stable isotope ratios provide quantitative measures of trophic diversity within food webs? Ecology 88: 42−48

Layman CA, Allgeier JE (2012) Characterizing trophic ecology of generalist consumers: a case study of the invasive lionfish in The Bahamas. Mar Eco Prog Ser 448:131–141

Lescroel A, Bost CA (2005) Foraging under contrasting oceanographic conditions: the gentoo penguin at Kerguelen Archipelago. Mar Ecol Prog Ser 302:245–261

Lynch HJ, Naveen R, Trathan PN, Fagan WF (2012) Spatially integrated assessment reveals widespread changes in penguin populations on the Antarctic Peninsula. Ecology In Press

Lynch HJ, Naveen R, Fagan WF (2008) Censuses of penguins, blue-eyed shags, and southern giant petrel populations in the Antarctic Peninsula, 2001–2007. Mar Ornithol 36: 83–97

Lynnes AS, Reid K, Croxall JP, Trathan P (2002) Conflict or co-existence? Foraging distribution and competition for prey between Adélie and chinstrap penguins. Mar Biol 141(6):1165-1174

MacArthur RH, Pianka ER (1966) On optimal use of a patchy environment. Am Nat 100:603– 609

Masello JF, Mundry R, Poisbleau M, Demongin L, Voigt CC, Wikelski M, Quillfeldt P (2010) Diving seabirds share foraging space within and among species. Ecosphere 1(6):19

May RM, MacArthur RH (1972) Niche overlap as a function of environmental variability. Proc Natl Acad Sci USA 69:1109-13

Miller AK, Trivelpiece WZ (2007) Cycles of Euphausia superba recruitment evident in the diet of pygoscelid penguins and net trawls. Polar Biol 30:1615–1623

Miller AK, Trivelpiece WZ (2008) Chinstrap Penguins alter foraging and diving behavior in response to the size of their principal prey, Antarctic krill. Mar Biol 154: 201-208

Miller AK, Karnovsky NJ, Trivelpiece WZ (2009) Flexible foraging strategies of gentoo penguins Pygoscelis papua over 5 years in the South Shetland Islands, Antarctica. Mar Biol 156:2527–2537

172

Miller AK, Kappes MA, Trivelpiece SG, Trivelpiece WZ (2010) Foraging-Niche Separation of Breeding Gentoo and Chinstrap Penguins, South Shetland Islands, Antarctica. Condor 112(4):683–695

Minagawa M, Wada E, (1984) Stepwise enrichment of 15 N along food chains: further evidence and the relation between δ15 N and animal age. Geochim Cosmochim Acta 48:1135–1140

Newsome SD, Martínez del Rio C, Bearhop S, Phillips DL (2007) A niche for isotopic ecology. Front Ecol Environ 5:429–436

Parnell A, Inger R, Bearhop S, Jackson AL (2008) SIAR: Stable isotope analysis in R.

Polito M, Emslie SD, Walker W (2002) A 1,000-year record of Adélie penguin diets in the southern Ross Sea. Antarct Sci 14: 327–332

Polito MJ, Trivelpiece WZ, Karnovsky NJ, Ng E, Patterson WP, Emslie SD (2011) Integrating stomach content and stable isotope analyses to quantify the diets of Pygoscelid penguins. PLoS ONE 6(10):e26642

Polito MJ, Reiss CS, Trivelpiece WZ, Patterson WP, Emslie SD (2012) Ontogenetic and oceanographic factors influence the stable isotope signatures of a keystone species, the Antarctic krill ( Euphausia superba ): Implications for dietary studies of krill predators using stable isotopes. - This volume -

Reid K, Trathan PN, Croxall JP, Hill HJ (1996) Krill caught by predators and nets: differences between species and techniques. Mar Ecol Prog Ser 140:13–20

Reiss CS, Cossio AM, Loeb V, Demer DA (2008) Variations in the biomass of Antarctic krill (Euphausia superba) around the South Shetland Islands, 1996–2006. ICES J Mar Sci 65:497– 508.

Ricklefs RE, Miller GL (1999) Ecology. W. H. Freeman and Company, New York

Rombola EF, Marschoff EM, Coria N (2010) Inter-annual variability in Chinstrap penguin diet at South Shetland and South Orkneys Islands. Polar Biol 33:799–806

Schell DM (2000) Declining carrying capacity in the Bering Sea: isotopic evidence from whale baleen. Limnol Oceanogr 45: 459–462

173 Seminoff JA, Bjorndal KA, Bolten AB (2007) Stable carbon and nitrogen isotope discrimination and turnover in pond sliders Trachemys scripta : insights for trophic study of freshwater turtles. Copeia 534–542

Stammerjohn SE, Martinson DG, Smith RC, Iannuzzi RA (2008) Sea ice in the western Antarctic Peninsula region: Spatio-temporal variability from ecological and climate change perspectives. Deep-Sea Res II 55:2041-2058

Stephens DW, Krebs JR (1986) Foraging theory. Princeton University Press, Princeton

Stowasser G, Atkinson A, McGill R.A.R, Phillips R.A., Collins MA, Pond DW (2011) Food web dynamics in the Scotia Sea in summer: A stable isotope study. Deep-Sea Res II 59-60:208–221

Takahashi A, Dunn MJ, Trathan PN, Sato K, Naito Y, Croxall JP (2003) Foraging strategies of chinstrap penguins at Signy Island, Antarctica: importance of benthic feeding on Antarctic krill. Mar Ecol Prog Ser 250:279–289

Trivelpiece WZ, Trivelpiece SG, Volkman NJ (1987) Ecological segregation of Adélie, gentoo and chinstrap penguins at King George Island, Antarctica. Ecology 68:351–361

Trivelpiece WZ, Hinke JT, Miller AK, Reiss CS, Trivelpiece SG, Watters GM (2011) Variability in krill biomass links harvesting and climate warming to penguin population changes in Antarctica. Proc Natl Acad Sci USA 108:7625–7628

Torres JJ, Fraser WR, Ashford JR, Ferguson J, Patarnello T, Agostini C, Parker M (2012) Disappearance of the Antarctic silverfish from the western peninsula shelf: a fish vulnerable to changing climate. TOS/ASLO/AGU 2012 Ocean Sciences Meeting, Salt Lake City, Utah

Turner TF, Collyer ML, Krabbenhoft TJ (2010) A general hypothesis-testing framework for stable isotope ratios in ecological studies. Ecology 91:2227−2233

Vaughan DG, Marshall G J, Connolley WM, Parkinson CL, Mulvaney R, Hodgson DA, King JC, Pudsey CJ, Turner J (2003) Recent rapid regional climate warming on the Antarctic Peninsula. Climate Change 60:243–274

Volkman NJ, Presler P, Trivelpiece W (1980) Diets of Pygoscelid penguins at King George Island, Antarctica. Condor 82:373–378

Watanuki Y, Kato A, Mori Y, Naito Y (1993) Diving performance of Adélie penguins in relation to food availability in fast sea-ice areas: comparison between years. J Anim Ecol 62:634-646

Weimerskirch H, Shaffer SA, Tremblay Y, Costa DP, Gadenne H, Kato A, Ropert-Coudert Y, Sato K, Aurioles D (2009) Species- and sex-specific differences in foraging behaviour and foraging zones in blue-footed and brown boobies in the Gulf of California. Mar Ecol Prog Ser 391:267–278

174 Wilson DS, Yoshimura J (1994) On the coexistence of generalists and specialists. Am Nat 144:692–707

Wilson SK, Burgess SC, Cheal AJ, Emslie M, Fisher R, Miller I, Polunin NVC, Sweatman HPA (2008) Habitat utilization by coral reef fish: implications for specialists vs. generalists in a changing environment. J Anim Ecol 77:220–228

Wilson RP (2010) Resource partitioning and niche hypervolume overlap in free-living Pygoscelid penguins. Funct Ecol 24:646–657

175 APPENDICES

176 Appendix 1. The frequency occurrence (%FO), minimum number of individuals (MNI), percent of MNI (%MNI), reconstituted mass (Mass), and percent of reconstituted mass (%Mass) of fish species identified from stomach contents collected from adult Chinstrap and Gentoo penguins during the crèche period at Cape Shirreff, Livingston Island in 2008 and 2009.

2008 2009 Penguin species, fish prey %FO MNI %MNI Mass (g) %Mass %FO MNI %MNI Mass (g) %Mass Chinstrap penguin Protomyctophum bolini 3.3 1 1.9 0.5 0.1 0.0 0 0.0 0.0 0.0 Electrona antarctica 23.3 28 52.8 161.1 17.2 0.0 0 0.0 0.0 0.0 Gymnoscopelus nicholsi 13.3 5 9.4 148.4 15.8 0.0 0 0.0 0.0 0.0 Notolepis coatsi 13.3 14 26.4 566.5 60.5 0.0 0 0.0 0.0 0.0 Pleuragramma antarcticum 3.3 2 3.8 1.9 0.2 43.3 42 100.0 103.0 100.0 Trematomus newnesi 10.0 3 5.7 58.0 6.2 0.0 0 0.0 0.0 0.0 Gentoo penguin Gymnoscopelus nicholsi 30.0 11 10.1 375.7 24.2 0.0 0 0.0 0.0 0.0 Lepidonotothen sp. 40.0 13 11.9 74.4 4.8 42.9 13 0.4 41.2 1.0 Pleuragramma antarcticum 40.0 61 56.0 124.8 8.0 92.9 2947 99.3 3701.3 89.8 Trematomus newnesi 40.0 4 3.7 312.2 20.1 7.1 1 0.0 179.6 4.4 Champsocephalus gunnari 50.0 14 12.8 668.4 43.0 21.4 4 0.1 197.6 4.8 Unknown fish sp. 50.0 6 5.5 - - 7.1 2 0.1 - -

177 Appendix 2. Discrimination factors, sample sizes, isotopic signatures and elemental concentrations of prey source inputs into SIAR mixing models.

Model, Inputs n δ15 N (‰) δ13 C (‰) % N % C Source SIAR Model 1 Discrimination factor - 3.5±0.4 1.3±0.5 - - [47] Source 1: Krill ( E. superba ) 40 3.3±0.6 -26.4±1.4 10.0±1.0 37.0±4.2 - Source 2: Fish ( P. antarcticum ) 30 9.4±0.5 -24.7±0.4 12.0±1.0 40.0±2.0 [24]

SIAR Model 2 Discrimination factor - 3.5±0.4 1.3±0.5 - - [47] Source 1: Krill ( E. superba ) 40 3.3±0.6 -26.4±1.4 10.0±1.0 37.0±4.2 - Source 2: Fish (Weighted by %mass) Chinstrap 2008 11 7.9±0.7 -25.1±0.5 12.8±1.0 41.0±1.2 - Chinstrap 2009 30 9.4±0.5 -24.7±0.4 12.0±1.0 40.0±2.0 - Gentoo 2008 8 8.8±0.4 -24.4±0.5 12.4±1.0 40.8±1.8 - Gentoo 2009 28 9.3±0.5 -24.7±0.4 12.0±1.0 40.0±2.0 -

SIAR Models 3 & 4 (2008 only) Chinstrap Penguins: Discrimination factor - 3.5±0.4 1.3±0.5 - - [47] Source 1: Krill ( E. superba ) 40 3.3±0.6 -26.4±1.4 10.0±1.0 37.0±4.2 - Source 2: Fish ( P. bolini ) 13 9.2±0.5 -23.0±0.5 12.0±1.0 1 40.0±2.0 1 [45] Source 3: Fish ( E. antarctica ) 41 8.8±0.7 -25.5±0.7 12.0±1.0 39.0±2.0 - Source 4: Fish (G. nicholsi ) 6 9.4±0.3 -22.6±0.8 13.0±1.0 43.0±1.0 - Source 5: Fish ( N. coatsi ) 3 7.2±0.8 -25.7±0.4 13.0±1.0 41.0±1.0 - Source 6: Fish ( P. antarcticum ) 30 9.4±0.5 -24.7±0.4 12.0±1.0 40.0±2.0 [24] Source 7: Fish ( T. newnesi) 10 8.2±0.5 -24.8±0.5 13.0±1.0 41.0±2.0 - Gentoo Penguins: Discrimination factor - 3.5±0.4 1.3±0.5 - - [47] Source 1: Krill ( E. superba ) 40 3.3±0.6 -26.4±1.4 10.0±1.0 37.0±4.2 - Source 2: Fish ( G. nicholsi ) 6 9.4±0.3 -22.6±0.8 13.0±1.0 43.0±1.0 - Source 3: Fish ( L. squamifroms ) 10 9.6±0.8 -24.2±0.7 11.0±1.0 37.0±2.0 [24] Source 4: Fish ( P. antarcticum ) 30 9.4±0.5 -24.7±0.4 12.0±1.0 40.0±2.0 [24] Source 5: Fish ( T. newnesi ) 10 8.2±0.5 -24.8±0.5 13.0±1.0 41.0±2.0 - Source 6: Fish ( C. gunnari ) 5 8.5±0.3 -25.1±0.3 12.0±1.0 40.0±2.0 [44] %N and %C values for P. bolini and C. gunnari were not available so averaged values from the six other fish species examined in this study were used. See respective numbers in reference section of the text for source information.

178 Appendix 3. The mean stable isotope composition (±SD) of Antarctic krill and Pygoscelis penguin chick feathers collected from two breeding colonies in the South Shetlands Islands, Antarctica from 2006-07 to 2010-11. Sample sizes for each group are in parentheses. Chick feathers that do not share at least one superscript number within a row, or superscript letter within a column are significantly different for the variable in question at the 0.05 level

Breeding season ( n) Tissue Isotope Group 2006-07 2007-08 2008-09 2009-10 2010-11

Antarctic krill δ13 C (‰) South grid (20) -26.6±0.5 (20) -27.0±0.9 (20) -26.8±0.7 (20) -26.4±0.8 (20) -25.6±1.0 (E. superba ) North grid (20) -26.6±0.8 (20) -27.7±0.5 (20) -27.3±0.6 (20) -26.4±0.8 (20) -25.4±0.7

δ15 N (‰) South grid (20) 3.3±0.5 (20) 3.5±0.4 (20) 3.3±0.4 (20) 3.5±0.3 (20) 3.5±0.4

North grid (20) 3.4±0.5 (20) 3.5±0.4 (20) 3.5±0.4 (20) 3.3±0.5 (20) 3.5±0.2

Chick δ13 C (‰) Admiralty Bay, Adélie (15) -24.3±0.5 a,1,3 (20) -24.4±0.2 a,1,3 (21) -24.1±0.3 a,1 (20) -25.0±0.5 a,2 (20) -24.7±0.2 a,3 feathers (Pygoscelis Admiralty Bay, Gentoo (15) -23.3±0.9 b,1 (20) -23.6±0.3 b,1 (9) -23.2±0.6 b,1 (20) -23.3±0.7 b,1 (20) -23.7±0.7 b,1 spp.) Cape Shirreff, Gentoo (30) -23.2±0.3 b,1 (20) -23.5±0.3 b,2,3 (21) -23.6±0.3 c,3 (20) -24.4±0.3 c,4 (20) -23.3±0.2 c,1,2

Cape Shirreff, Chinstrap (30) -23.8±0.3 c,1 (20) -23.7±0.3 b,2 (18) -24.5±0.3 d,3 (20) -24.5±0.5 c,2 (20) -23.2±0.2 c,4

δ15 N (‰) Admiralty Bay, Adélie (15) 8.0±0.2 a,1 (20) 7.6±0.1 a,2 (21) 7.7±0.2 a,c,2 (20) 7.5±0.2 a,2 (20) 7.7±0.2 a,1,2

Admiralty Bay, Gentoo (15) 8.7±0.6 b,1 (20) 8.1±0.4 b,2 (9) 8.0±0.2 a,2 (20) 8.2±0.5 b,1,2 (20) 8.4±0.6 b,1,2

Cape Shirreff, Gentoo (30) 8.9±0.7 b,1 (20) 8.8±0.6 c,1 (21) 9.7±0.8 b,2 (20) 8.8±0.5 c,1 (20) 9.2±0.6 c,1,2

Cape Shirreff, Chinstrap (30) 8.2±0.3 a,1 (20) 7.7±0.3 a,2 (18) 7.4±0.3 c,3 (20) 8.0±0.2 b,2 (20) 7.5±0.2 a,3

179 Appendix 4. The percent contribution (±SE) and frequency occurrence of six prey groups to the chick rearing diets of Pygoscelis penguins from two sites in the South Shetlands Islands, Antarctica from 2006-07 to 2010-11. Groups that do not share at least one letter within a column have significantly different mean diet compositions based on non-parametric multivariate analysis of similarity (ANOSIM, Clarke 1993)

Percent composition of stomach contents by wet mass (% FO)

Site, penguin species Year n Krill ANOSIM Fish Cephalopod Amphipod Adult E. Juvenile E. comparisons T. macura superba superba 78.6±5.1 21.0±5.1 0.3±0.2 0.1±0.1 0.0±0.0 0.1±0.3 Admiralty Bay, Adélie 2006-07 20 a (100.0) (85.0) (5.0) (40.0) (0.0) (5.0) 79.7±4.5 18.8±4.6 1.4±1.3 0.0±0.0 0.0±0.0 0.0±0.1 2007-08 20 a (100.0) (80.0) (30.0) (35.0) (0.0) (10.0) 83.9±3.5 15.5±3.5 0.4±0.3 0.1±0.1 0.0±0.0 0.0±0.0 2008-09 20 a (100.0) (95.0) (10.0) (25.0) (0.0) (0.0) 82.9±4.7 10.7±4.2 6.3±3.2 0.1±0.1 0.0±0.0 0.0±0.1 2009-10 19 b (100.0) (78.9) (36.8) (10.5) (0.0) (10.5) 87.5±7.0 8.6±6.6 0.0±0.0 3.9±3.0 0.0±0.0 0.0±0.0 Admiralty Bay, Gentoo 2006-07 15 c (100.0) (46.7) (0.0) (73.3) (0.0) (6.7) 92.9±4.9 1.6±0.7 0.0±0.0 5.5±5.0 0.0±0.0 0.0±0.0 2008-09 20 c (95.0) (30.0) (0.0) (40.0) (0.0) (0.0) 96.2±2.8 0.4±0.2 0.0±0.0 3.4±2.8 0.0±0.0 0.0±0.0 2009-10 15 c (100.0) (20.0) (0.0) (46.7.0) (0.0) (0.0) 98.8±0.5 1.2±0.5 0.0±0.0 0.1±0.0 0.0±0.0 0.0±0.0 2010-11 15 c (100.0) (40.0) (0.0) (20.0) (0.0) (0.0)

180 Appendix 4 .Continued..

Percent composition of stomach contents by wet mass (% FO)

Site, penguin species Year n Krill ANOSIM Fish Cephalopod Amphipod Adult E. Juvenile E. comparisons T. macura superba superba 74.0±8.6 5.9±4.0 0.0±0.0 20.1±8.6 0.0±0.0 0.0±0.1 Cape Shirreff, Gentoo 2006-07 15 d (100.0) (26.7) (0.0) (100.0) (0.0) (6.7) 75.0±10.0 8.7±3.8 0.0±0.0 16.3±9.6 0.0±0.0 0.0±0.1 2007-08 10 d (90.0) (70.0) (0.0) (100.0) (0.0) (20.0) 68.2±10.8 0.0±0.0 0.0±0.0 30.8±10.4 0.9±0.7 0.0±0.0 2008-09 14 e (100.0) (0.0) (0.0) (100.0) (14.3) (7.1) 75.1±11.0 0.0±0.0 0.0±0.0 24.8±11.0 0.1±0.1 0.0±0.0 2009-10 10 d (100.0) (0.0) (0.0) (80.0) (10.0) (0.0) 72.8±6.0 14.8±4.4 0.0±0.0 11.9±6.6 0.4±0.4 0.0±0.1 2010-11 13 d (100.0) (69.2) (0.0) (84.6) (15.4) (15.4) 79.2±3.7 20.8±3.7 0.0±0.0 0.0±0.0 0.0±0.0 0.0±0.0 Cape Shirreff, Chinstrap 2006-07 30 a (100.0) (90.0) (0.0) (43.3) (0.0) (10.0) 77.9±3.3 21.7±3.3 0.0±0.0 0.4±0.3 0.0±0.0 0.0±0.0 2007-08 30 a (100.0) (100.0) (0.0) (36.7) (3.3) (6.7) 94.0±1.0 5.0±0.8 0.9±0.9 0.0±0.0 0.0±0.0 0.0±0.0 2008-09 30 c (100.0) (80.0) (6.7) (50.0) (0.0) (10.0) 98.8±1.0 0.2±0.2 0.0±0.0 1.0±1.0 0.0±0.0 0.0±0.0 2009-10 30 c (100.0) (3.3) (0.0) (16.7) (0.0) (0.0) 81.5±2.7 18.4±2.8 0.0±0.0 0.1±0.0 0.0±0.0 0.0±0.0 2010-11 29 a (100.0) (100.0) (0.0) (34.5) (0.0) (0.0)

181