Factors Influencing Foraging Behaviour in the Australasian Gannet and for Seabirds in General, and Discusses Future Directions for Research

Home , Australasian gannet, Lawrence Rocks, Northern gannet

Factors influencing foraging behaviour

in the Australasian gannet

Lauren Patricia Angel

B.Sc (Marine Biology & Zoology) (Hons)

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

Deakin University

October, 2015

Abstract

The foraging decisions made by an individual are reflected in their foraging success (i.e. prey capture), resulting in enough nutrition to support self-maintenance and chick growth. As such, foraging success is directly linked to reproductive success in breeding seabirds. However, only a small proportion of individuals contribute to future generations. Hence, foraging behaviour can greatly influence an individuals’ fitness and species demography. The Australasian gannet (Morus serrator) is a large pelagic seabird breeding in Australia and New Zealand. However, despite the accelerated warming of sea surface temperatures in south-eastern

Australia, little is known about whether this species can buffer the effects of climate change. Furthermore, an understanding of natural variability in foraging behaviour is needed to estimate species’ adaptability.

Two colonies, Point Danger (ca. 300 breeding pairs) and Pope’s Eye (ca. 180 breeding pairs), of opposing oceanographic regimes were studied to identify factors which may cause variation in foraging metrics, fine-scale behaviour, foraging effort and diet. These variables were obtained using GPS, tri-axial accelerometry and stable isotope analysis. The factors investigated as potential causes of variation were sex, age, colony size/habitat, year, breeding stage, inter- and intra-individual preferences.

The present study found Australasian gannets are sexually dimorphic with a degree of sexual segregation in their foraging area use. Colony size/location influenced foraging trip distance and duration. The foraging ranges of the two colonies did not overlap indicating colony specific foraging ranges. Inter-annual variation was compared in years of differing prey availability at Pope’s Eye.

However, birds appeared to be constrained, not increasing trip distance or duration in

iii poor years. Rather, energy expenditure and the proportion of a foraging trip spent foraging increased. Furthermore, females were found to be more flexible in their foraging behaviour and strategies than males in both breeding stages. At Pope’s Eye males typically foraged closer to the colony with a high site fidelity. The sex difference in site fidelity may be due to females searching for high quality prey, reflecting the greater initial reproductive investment.

The findings of the present study highlight potential drivers of variability in foraging behaviour. However, sexual segregation and colony-specific foraging ranges present potential limitations to foraging behaviour flexibility in the face of climate change.

Preface

The ethical guidelines of Deakin University Animal Ethics Committee and

Animal Welfare Committee were followed during the present studies. The protocol was approved by Deakin University Animal Ethics Committee and Animal Welfare

Committee (Permit No. A86/2010). The project was conducted in accordance with the regulations of the Department of Sustainability and Environment Victoria (Permit

No. 0005745). Birds captured for the duration of the study were banded under

ABBBS R-Class Banding Permit No. 3046. This project was supported financially from research grants provided by Holsworth Wildlife Research Endowment,

Victorian Marine Science Consortium, Point Danger Management Committee and the Centre for Integrative Ecology, Deakin University.

The core chapters of this thesis (Chapters 2-6) have been published or submitted for publication in peer-reviewed journals. I am the principal contributor to all chapters of this thesis and the primary author on all publications arising from this thesis. Associate Professor John Arnould is co-author on all publications for his contribution to the study design, fieldwork, and editorial advice throughout the project. Maud Berlincourt is co-author on Chapters 4-6 due to her assistance with initial analysis of GPS data. John Speakman is co-author on Chapter 2 and 3 for his contribution to processing blood samples. Emma Tew (Chapter 2 and 4), Melanie

Wells (Chapter 2), Marlenne Rodríguez-Malagón (Chapter 2), and Sophie Barker

(Chapter4) are co-authors for their contributions of additional data to the project.

Tiphaine Jeanniard-du-Dot (Chapter 2) and Victoria Warwick Evans (Chapter 4) are co-authors for their contribution to the conception of data analysis methods used in these chapters.

Publications arising from thesis

Chapter 2

Angel L.P., Wells M. R., Rodríguez-Malagón M.A., Tew E., Speakman J.R.,

Arnould J.P.Y (in review) Sexual size dimorphism and body condition in the

Australasian gannet. PLoS ONE.

Chapter 3

Angel L.P., Jeanniard-du-Dot, T., Speakman J.R., Arnould J.P.Y. (in review)

Determining activity-specific energy expenditure in a large seabird using accelerometry. Royal Society Open Science.

Chapter 4

Angel L.P., Berlincourt M., Barker S., Tew E., Warwick-Evans V., Arnould J.P.Y.

(2015) Eating locally: Australasian gannets increase their effort in a restricted foraging range. Biology Open 4:1298-1305 (doi:10.1242/bio.013250).

Chapter 5

Angel L.P., Berlincourt M., Arnould J.P.Y. (in review) Foraging ecology of

Australasian gannets: The influence of oceanographic regimes. Marine Ecology

Progress Series.

Chapter 6

Angel L.P., Berlincourt M., Arnould J.P.Y. (in review) Inter- and intra-individual variation in foraging behaviour in Australasian gannets. Marine Biology.

Acknowledgements

First and foremost, I would like to thank my supervisor, Associate Professor

John Arnould. Without your encouragement and support in all aspects of my candidature I would never have been able to see the light. Thank-you for the opportunity to work on such a graceful, yet aggressive species, it has truly been an amazing experience.

The amount of field work this project required could not have been done without my field team. Thank-you for making the days waiting for gannets to come home enjoyable. In particular, thank-you Marion Fourquez and Emma Tew for the great times out in the brutal wind. I would like to thank my Point Danger family: Andy and Tilly Govanstone for the warm welcome into their home each field season,

Ewen Lovell for his insight and guidance, and the Point Danger Management

Committee who completely understand the beauty of gannets and the need to protect them. Thank-you also to my Pope’s Eye family: Robbie and Jenny Main for providing a home to stay in, Sea All Dolphin Swims for the trips out to the colony on weekends (and getting to swim with seals and dolphins each time), the Victorian

Marine Science Consortium and Liz McGrath. In particular, I’d like to thank Rod

Watson, you made every trip out to Pope’s Eye an absolute joy. I’ll see you at

Napona!

Thank-you to the Arnould lab group for making my time in the BA building enjoyable – in particular, Andrew Hoskins, Maud Berlincourt, Laëtitia

Kernaléguen, Travis Knox, Emma Tew, Melanie Wells and Grace Sutton. Thank- you also to Evelyn Chia, Michelle Bassett, Kasun Ekanayake, John Patykowsi, and

Carolyn Ewers for being instrumental in my procrastination attempts.

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Thank-you to my all my friends for putting up with me complaining about data analysis and stress about writing up. Andrew Hoskins and Holly Lourie, thank-you for providing me with almost daily updates on Benson and Nelson. Maud

Berlincourt and Melanie Wells, you have truly ignited a spark of obsession in me with your passion for seabirds.

To my ever loving family (dad, mum, Karl, Emma, Hannah, Lily, Mia, Kieran,

Elise, Greg, and Elizabeth!), thank-you so much for coming to see me when I was too stressed to visit you. Things are always easier when you know family has your back. Thank-you to my meower, Basil, for being a constant source of cuddles and an anti-depressant to this experience.

I met Tom at the very beginning. Sometimes you don’t know what you’re missing until it walks in. Who knew you were exactly what I needed to get through this stage of my life.

“I’m a seagull. I like to fly”

“Don’t you forget that the reason you fly is to eat”

– Jonathan Livingstone Seagull

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Table of Contents

Abstract ...... iii

Preface ...... v

Publications arising from thesis ...... vi

Acknowledgements...... vii

General overview...... 13

Factors influencing the foraging behaviour of seabirds ...... 14 Changes in the environment and the Australasian gannet...... 16 Research aims and thesis overview ...... 20

Sexual dimorphism and body condition in the Australasian gannet...... 23

Abstract...... 24 Introduction...... 25 Methods ...... 28 Study site and animal handling...... 28 Body composition and condition index...... 29 Results ...... 31 Body mass and morphometrics ...... 31 Body condition index...... 35 Discussion...... 38

Determining activity-specific energy expenditure in a large seabird using accelerometry...... 46

Abstract...... 47 Introduction...... 48 Methods ...... 51 Study site and animal handling...... 51 Data processing and statistical analyses...... 52 Results ...... 56 Discussion...... 67

Eating locally: Australasian gannets increase their effort in a restricted foraging range...... 73

Abstract...... 74 Introduction...... 75 Methods ...... 78 Study site and animal handling...... 78 Data processing and statistical analyses...... 79 Results ...... 83 Discussion...... 89

Foraging ecology of Australasian gannets: The influence of oceanographic regimes...... 95

Abstract...... 96 Introduction...... 97 Methods ...... 100 Study site and animal handling...... 100 Data processing, environmental variables and statistical analyses...... 102 Results ...... 104 Discussion...... 111

Inter- and intra-individual variation in foraging behaviour in Australasian gannet ...... 119

Abstract...... 120 Introduction...... 121 Methods ...... 124 Study site and animal handling...... 124 Data processing and statistical analyses...... 126 Results ...... 129 Discussion...... 139

General discussion...... 146

Intrinsic factors influencing foraging behaviour ...... 147 Extrinsic factors influencing foraging behaviour ...... 152 Implications and future directions ...... 155

References ...... 158

List of tables

Table 2.1. Mean body mass (kg) and morphometrics (mm) ± standard error for male and female Australasian Gannets measured at Pope’s Eye and Point Danger colonies, south-eastern Australia...... 32 Table 2.2. Comparison of mean body mass (kg) and morphometric (mm) differences ± standard error within breeding pairs (n) of Australasian Gannets from Pope’s Eye and Point Danger colonies...... 35

Table 2.3. Top-ranked AICc models for explaining total body fat (%) from morphometric measurements in adult Australasian ganneWV ǻ$,& Body mass (kg), wing ulna and tarsus (mm) were selected as important variables by the model...... 37 Table 3.1. Concurrent measures of energy expenditure from doubly-labelled water and accelerometer (VeDBA) for each individual (n = 21)...... 58 Table 3.2. Parameters incorporated into models predicting total energy expenditure

(DLWEE) and VeDBASUM for each individual...... 63 Table 3.3. Linear mixed effects model outputs predicting total energy expended

(DLWEE; model 1) and total energy expended at-sea (DLWAT-SEA; model 2)...... 64 Table 3.4. Parameter estimates and activity-specific energy estimates...... 66 Table 4.1. Seasonal averages from weekly sea surface temperature (SST, °C) and chlorophyll-a (Chl-a, mg·m-3) concentration in surrounding regions of Pope’s Eye gannet colony during and prior to the breeding season...... 84 Table 4.2. Foraging trip parameters of Australasian gannets from Pope’s Eye...... 87 Table 5.1. At-sea movements and behaviour of Australasian gannets foraging from two colonies in Bass Strait, Australia...... 107 Table 6.1. Foraging parameters for 21 Australasian gannets with multiple trips, from Pope’s Eye gannet colony, south-eastern Australia...... 133 Table 6.2. Stable isotopes for each foraging strategy...... 138

List of figures

Figure 1.1. Location of Australasian gannet breeding colonies in Bass Strait, south- eastern Australia ...... 19 Figure 2.1. Location of Australasian gannet colonies used in present study...... 28 Figure 2.2. Distribution of discriminant scores for male and female Australasian gannets at two breeding colonies...... 34 Figure 2.3. Estimated total body fat (%) reliably predicts actual total body fat (%) in Australasian gannets based on a body condition index...... 37 Figure 2.4. Mean body condition (%) ± standard error for males and females from Pope’s Eye and Point Danger gannet colonies...... 38 Figure 3.1. Variation between individuals in the proportion of the study period spent performing each behaviour...... 59 Figure 3.2. Correlations between estimated energy expended derived from the doubly-

labelled water method (DLWEE)...... 60 Figure 3.3. Estimates of activity-specific energy expenditure ...... 65 Figure 4.1. Location of Pope’s Eye gannet colony ...... 80 Figure 4.2. Comparison of mean ± standard error foraging behaviour and effort for Australasian gannets across three years (2011 – blue; 2012 – green; 2013 – purple) in incubation and chick rearing breeding stages...... 88 Figure 5.1. Location of Australasian gannet colonies in Bass Strait, south-eastern Australia ...... 101 Figure 5.2. Comparison of male and female foraging ranges using kernel density based utilization distributions...... 109 Figure 5.3. :KROHEORRGį&DQGį1YDOXHV...... 111 Figure 6.1. Pope’s Eye gannet colony (red circle), situated on the boundary of Port Phillip Bay (average depth 14m) and Bass Strait (average depth 50-70m), in south-eastern Australia ...... 125 Figure 6.2. Number of new cells (1 km2) entered for each consecutive foraging trip per individual (n = 21). Different foraging strategies defined in Figure 5.3 are indicated by colour (Group 1: green; Group 2: purple; Group 3: navy; Group 4: light blue)...... 134 Figure 6.3. Identification of foraging strategies...... 137

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Chapter 1

General overview

Chapter 1: General overview

Factors influencing the foraging behaviour of seabirds

While 60% of the earth’s surface is ocean, only 2-3% of bird species are able to utilise the marine ecosystem, highlighting the difficulty of exploiting such an environment (Croxall 1987). Seabirds must forage for food that is both temporally and spatially stochastic, however, seabirds are highly dynamic, thus able to adapt to survive (Schreiber and Burger 2001). As top-order predators, seabirds optimise the three-dimensional environment available, foraging across marine habitats (i.e. coastal, pelagic and open seas) and at various depths, i.e. from surface feeders up to diving 500 m deep (Kooyman and Kooyman 1995).

Seabirds are considered to be K-selected species (Stearns 1976), with long life spans (20-60 years), delayed maturity (i.e. breeding age), and small clutch sizes

(Schreiber and Burger 2001). During the breeding season, 95% of seabirds become colonial; nesting in trees, on the ground, on cliffs or in burrows (Schreiber and

Burger 2001). As Central Place Foragers, breeding seabirds must return to the colony to provision and protect the egg/young. The effort involved with raising young can be particularly extensive, as seen in the frigatebird (Fregata andrewsi) where young are fed at the nest for 5-6 months (Schreiber and Ashmole 1970) or in king penguins

(Aptenodytes patagonicus) where young require approximately 11 months of parental provisioning to become large enough to forage alone (Olsson 1996). As thermoregulation and guarding chicks is required for a large proportion of the chick’s development, bi-parental care is often employed, with one adult remaining at the nest with the egg/chick. However, time allocated to foraging is greatly restricted by the fasting ability of the partner and/or chick. Although some seabirds can forage up to

33 days and cover upwards of 15,000 km in a single trip (e.g. wandering albatross,

Diomedea exulans; Jouventin and Weimerskirch 1990), others are greatly limited by

Chapter 1: General overview their body size and mode of foraging, remaining within 30 km of the colony (e.g. little penguins, Eudyptula minor; Collins et al. 1999). Hence, not all seabirds have access to profitable prey within their potential foraging range and must balance the demands of self-provisioning and parental care within these constraints (Orians and

Pearson 1979).

Depending on the foraging range of the species, individuals can be faced with various biotic and abiotic factors. This variability can alter the availability of prey and result in populations developing various phenotypical, behavioural and dietary adaptations to maximise foraging success (Lack 1968). As seabirds can breed in high densities (tens to hundreds of thousands), competition from conspecifics can quickly deplete prey resources around the colony (Ashmole 1963). Adaptations to foraging trip length can occur to compensate for competition, such as longer foraging trips when breeding in large colonies (Lewis et al. 2001, Ballance et al. 2009) or increased trip duration as the breeding season progresses (Rishworth et al. 2014b).

Alternatively, birds are faced with inter-annual variation, whereby changes in prey abundance can be compensated for with a shift in foraging behaviour (Burke and

Montevecchi 2009, Hennicke and Weimerskirch 2014) or dietary plasticity, such as switching to a less desirable prey to survive (Wanless et al. 2005).

Within populations, an individual’s foraging behaviour may vary due to competition or niche specialisation (Pianka 1981). Sex-specific segregation can occur due to differences in morphology, reproductive roles, or nutritional requirements. For example, in diving species, males may dive to deeper depths than females due to their larger body size, i.e. Japanese cormorants, Phalacrocorax capillatus (Watanuki et al. 1996) and common guillemots, Uria aalge (Thaxter et al.

2009). Alternatively, segregation can occur due to age, with experience needed to

Chapter 1: General overview acquire prey or capabilities reduced with old age (Clutton-Brock 1988). For example, in the great grebe (Podicephorus major), adults are able to capture prey more successfully and spend less time handling prey than juveniles (Gomes et al. 2009).

Hence, extrinsic (i.e. competition, geographic location, breeding stage, and temporal variation) and intrinsic factors (i.e. sex, age and body condition) can influence the foraging behaviour of seabirds.

A breeding seabirds’ foraging behaviour can influence foraging success (i.e. prey capture), resulting in chick growth and an individual’s survival (Pyke 1984). As such, foraging success can be directly linked to breeding success and have implications for the population projection due to adult survival and recruitment.

However, with climate change widely regarded as having an effect of marine ecosystems (Stenseth et al. 2002) the current understanding of foraging variability in seabirds needs to be improved to estimate population trajectories.

Changes in the environment and the Australasian gannet

Seabirds rely on both terrestrial habitats for breeding and marine habitats for foraging. As a consequence, seabirds will be influenced by many of the predicted climate change induced perturbations, such as habitat loss, invasive species, spatial shifts in distribution, and changes in food web dynamics (Sydeman et al. 2012).

Indeed, there is evidence that the effects of climate change have already begun to influence breeding populations (Thompson and Ollason 2001, Cullen et al. 2009).

In south-eastern Australia, the Bonney Upwelling is the largest and most predictable upwelling in the region (Butler et al. 2002). The Bonney Upwelling is a northern boundary current located to the west of Bass Strait with the upwelled water

Chapter 1: General overview being transported into Bass Strait by the South Australian Current (Middleton and

Bye 2007). Many marine fauna benefit from the nutrient cascade effects of the upwelling (i.e. whales, sharks and seabirds) (Butler et al. 2002, Berlincourt and

Arnould 2015b). Additionally, Bass Strait is also influenced by the cold, nutrient rich

Subantarctic Surface Water brought up from the south (Gibbs et al. 1986). However, on the eastern side of Bass Strait, the increasing strength of the East Australian

Current (EAC), a warm water current, has influenced sea surface temperatures in the region (Poloczanska et al. 2007, Ridgway and Hill 2009, Hobday and Lough 2011).

The EAC has extended further south in recent decades with shifts in species distribution already observed as a result of the increased sea surface temperatures

(Ridgway and Hill 2009, Hobday et al. 2014).

The effects this warming will have on the seabirds breeding in the region has gained some attention in recent years (Cullen et al. 2009). However, for Australasian gannets (Morus serrator), a species with 16,600 breeding pairs in Bass Strait (Bunce et al. 2002), a basal understanding of what factors influence their behaviour is lacking. Hence, the information needed to make predictions for how the species will respond to shifts in prey distribution and availability is unavailable.

The Australasian gannet is a large pelagic seabird (1.9-3.3kg, Chapter 2) and the second rarest Sulid in the world (Nelson 1978). The species breeds on coastal islands in New Zealand and southern Australia (Figure 1.1) (Nelson 1978). Recent trends indicate the gannet population is increasing in New Zealand (2.3% increase per year;

Wodzicki et al. 1984) and Australia (6% increase per year; Bunce et al. 2002, Pyk et al. 2013). However currently established colonies are considered to be at full capacity due to limited nesting space.

Chapter 1: General overview

In northern Bass Strait, two colonies have grown at a faster rate than any other

(average increase of 24% per year). Lawrence Rocks (3,100 pairs) was established in

1873, while a sub-colony (Point Danger; ca. 660 breeding pairs) was recently established (1996) to accommodate the growing population (Norman et al. 1998,

Bunce et al. 2002). This colony is located in western Bass Strait, within reach of the profitable Bonney Upwelling and close to the continental shelf edge. On the other hand, in Port Phillip Bay, there are seven small colonies (510 breeding pairs), the largest being Pope’s Eye (ca. 180 breeding pairs) established in 1984 (Bunce et al.

2002, Pyk et al. 2013). The location of this colony provides birds with the opportunity to forage inside a shallow embayment, or on a wide area of continental shelf, i.e. Bass Strait. The present study focuses on Point Danger (38°23’36”S,

141°38’54”E) and Pope’s Eye (38°16’42”S, 144°41’48”E), as these two colonies provide a unique opportunity to study the effects of contrasting environmental conditions on the foraging behaviour of Australasian gannets (Figure 1.1).

Gannets have delayed maturity (breeding from 3-5years old) and a strong philopatry to their natal colony (Nelson 1978). Breeding is asynchronous and begins in August/September when nest construction begins and pairs form. The egg is incubated for 37-50 days (Wingham 1984a) during which time birds perform foraging trips of 20-40 h (Chapter 4 and 6) while the breeding partner remains on the nest for protection and thermoregulation of the egg. Once the chick hatches, it is fed at the nest for 95-108 days before being capable of foraging for itself around late

January/February (Wingham 1984b). During chick rearing, adults continue to alternate remaining at the nest, with shorter foraging trips (10-20 h; Chapter 4 and 6) to meet the demands of the chick.

Chapter 1: General overview

Figure 1.1. Location of Australasian gannet breeding colonies in Bass Strait, south- eastern Australia: Lawrence Rocks/Point Danger (PD), Port Phillip Bay/Pope’s Eye

(PE), Black Pyramid (BP), Eddystone Rock (ER), and Pedra Branca (PB).

Australasian gannets feed on inshore pelagic schooling fish (Bunce 2001), foraging by vertical plunge-diving, a highly specialised technique whereby prey is detected visually from the air before a dive is made. Plunge diving provides the advantage of surprise and is considered to be one of the most accurate foraging methods

(Machovsky-Capuska et al. 2011b, Machovsky Capuska et al. 2011). Furthermore, gannets take advantage of heterospecifics (such as other seabirds, sharks, seals and dolphins) which concentrate prey close to the surface within diving depth of the gannets (Machovsky-Capuska et al. 2011b).

To date, research on the foraging behaviour of Australasian gannets has been limited to treating the population as a whole (Bunce 2001, 2004, Machovsky-Capuska

Chapter 1: General overview et al. 2014a). However, factors such as sex, age, and body condition can influence their foraging behaviour. Preliminary research has shown sexual segregation in habitat use

(Barker 2012) and diet (Guggenheimer 2012), indicative of sexual size dimorphism.

Additionally, Pyk (2012) found that individuals with a history of high reproductive success remained closer to the breeding colony when foraging throughout the breeding season. Hence, intrinsic factors could be influencing the foraging behaviour of

Australasian gannets. Furthermore, little information is available on the effects of habitat (Garthe et al. 2007) or annual variation on gannet foraging behaviour (Hamer et al. 2007, Garthe et al. 2011). Hence, the influence of extrinsic factors on foraging behaviour are also largely unknown.

Research aims and thesis overview

The major aims of the current thesis were to identify factors attributing to variation in the foraging behaviour of Australasian gannets. More specifically, intrinsic factors, such as sex, age and morphology, and extrinsic factors, such as temporal variation, geographic location, and competition, were investigated as potential causes of inter- individual variation. Additionally, intra-individual variation was explored as a potential variable when assessing the foraging behaviour of a population. To achieve these aims, behavioural data were collected using bio-logging technologies.

Furthermore, new techniques for assessing sex and body condition from morphometric measurements were developed, as well as a method to measure energy expenditure from tri-axial accelerometer data loggers to infer the energetic costs of different foraging strategies.

Chapter 1: General overview

The thesis is structured with the central data chapters (Chapters 2 - 6) reporting on specific studies that have been published or submitted for publication in peer-reviewed journals. The current chapter provides an overview of factors influencing the foraging behaviour of seabirds and current knowledge of the Australasian gannet, while the following data chapters address more specific questions.

Body size can greatly affect how an individual interacts with its environment while foraging. Although typically considered monomorphic (males and females being of the same size), preliminary studies of the Australasian gannet have indicated sex- specific foraging behaviour with differences between males and females in their foraging range (Barker 2012) and diet (Guggenheimer 2012). This behaviour is typical of dimorphic species and, hence, the question of the degree of dimorphism between measures of body size for the Australasian gannet are addressed in Chapter 2 at Point

Danger and Pope’s Eye breeding colonies. Additionally, a technique was developed to estimate total body fat (%) based on morphometric measurements easily attainable in the field.

The trade-off between energy expended and acquired while foraging can have implications on whether an individual has surplus energy to invest into reproduction, growth or survival (Goldstein 1988). Hence, energy expenditure can greatly affect an individual’s fitness. The recent advancement of a relatively non-invasive method to measure energy expenditure in free-ranging animals (tri-axial accelerometer data loggers) is proving promising, however validation of this technique is still in the preliminary stages. In Chapter 3, acceleration data is validated as a viable method to estimate energy expenditure for Australasian gannets.

As Central Place Foragers, seabirds are restricted in their foraging range due to the fasting ability of either their partner and/or the offspring. As such, seabirds tend to

Chapter 1: General overview forage in profitable prey patches in close proximity to their nest. However, in periods of reduced prey availability adults may increase their foraging effort and/or extend their range in search of resources to ensure chick growth and personal maintenance

(Cairns 1987, Abrams 1991). In Chapter 4 variation in the foraging behaviour of

Australasian gannets across three years of apparent differing prey availability is investigated at the Pope’s Eye colony. Furthermore, the foraging range of breeding adults can be influenced by conspecifics, either from nearby colonies (Wakefield et al.

2013) or by individuals from the same colony (Stauss et al. 2012), as seen in the closely related northern gannet. As such, the influence of other breeding colonies and sex on the foraging behaviour of Australasian gannets is addressed in Chapter 5 at both Point

Danger and Pope’s Eye colonies. Additionally, this chapter discusses how differences in colony size and oceanographic regimes may influence foraging behaviour between two colonies.

A major limitation for many seabird studies is measuring foraging behaviour from the first foraging trip recorded from a few individuals as a representation of the population (Soanes et al. 2013). However, inter- and intra-individual variation can occur in foraging behaviour. Chapter 6 looks at causes of variation between and within individuals such as sex, age, breeding stage, or year at Pope’s Eye. Foraging strategies are identified from multiple foraging trips for each individual and related to the predictability of the habitat in which birds forage.

Lastly, Chapter 7 summarises the key findings of Chapters 2-6, and discusses these in a broader conceptual context. This chapter examines the factors influencing foraging behaviour in the Australasian gannet and for seabirds in general, and discusses future directions for research.

Chapter 2

Sexual dimorphism and body condition in the Australasian

gannet

A version of this chapter has been submitted as:

Angel L.P., Wells M. R., Rodríguez-Malagón M.A., Tew E., Speakman J.R., Arnould J.P.Y (in review) Sexual size dimorphism and body condition in the Australasian gannet. PLoS ONE.

Chapter 2: Sexual dimorphism and body condition

Abstract

Sexual size dimorphism is widespread throughout seabird taxa and several drivers leading to its evolution have been hypothesised. While the Australasian Gannet

(Morus serrator) has previously been considered nominally monomorphic, recent studies have documented sexual segregation in diet and foraging areas, traits often associated with size dimorphism. The present study investigated the sex differences in body mass and structural size of this species at two colonies (Pope’s Eye, PE;

Point Danger, PD) in northern Bass Strait, south-eastern Australia. Females were found to be 3.1% and 7.3% heavier (2.74 ± 0.03, n = 92; 2.67 ± 0.03 kg, n = 43) than males (2.66 ± 0.03, n = 92; 2.48 ± 0.03 kg, n =43) at PE and PD, respectively.

Females were also larger in wing ulna length (0.8% both colonies) but smaller in bill depth (PE: 2.2%; PD: 1.7%) than males. Despite this dimorphism, a discriminant function provided only mild accuracy in determining sex. A similar degree of dimorphism was also found within breeding pairs. Using hydrogen isotope dilution, a body condition index was developed from morphometrics to estimate total body fat

(TBF) stores, where TBF (%) = 24.43+1.94*(body mass/wing ulna length)–

0.58*tarsus length (r2 = 0.84, n = 15). This index was used to estimate body composition in all sampled individuals. There was no significant difference in TBF

(%) between the sexes for any stage of breeding or in any year of the study at either colony suggesting that, despite a greater body mass, females were not in a better condition than males. While the driving mechanism for sexual dimorphism in this species is currently unknown, studies of other Sulids indicate segregation in foraging behaviour, habitat and diet may be a contributing factor.

Chapter 2: Sexual dimorphism and body condition

Introduction

Dimorphism has evolved in response to selection pressures favouring particular phenotypes. A widespread occurrence across animal taxa, sexual size dimorphism is the morphological difference between males and females of the same species (Shine

1989). While some debate surrounds the evolutionary process, three principle hypotheses have been proposed to explain the cause of sexual size dimorphism. The division of labour hypothesis relates to males and females within a breeding pair investing their effort into different roles. The sexual selection hypothesis focuses on male-male competition for mating opportunities and territorial defence, ultimately favouring larger body size in males (Hedrick and Temeles 1989). The food competition hypothesis relates to ecological causation, whereby differences in body size reduce competition for resources through segregation of prey and habitat use

(Shine 1989). Many studies have proposed sexual selection as a primary cause, while niche segregation is a consequence and maintainer, of dimorphism (Navarro et al.

2009).

In species where differences in dimorphism between the sexes is not immediately apparent, many studies determine sex based on morphometrics using discriminant function analyses (Amat et al. 1993, Bosch 1996, Coulson 2009). These functions incorporate body mass and other structural measurements to accurately predict sex from calibrations of known sex individuals (Dechaume-Moncharmont et al. 2011).

While these functions can differ between species and populations (Evans et al. 1993), they also can be reliable, inexpensive and non-invasive.

Variation in body mass between the sexes may reflect differences in structural size and/or differences in body composition due to relative contribution of fat and lean mass (Schulte-Hostedde et al. 2001). Body condition can represent energy

Chapter 2: Sexual dimorphism and body condition reserves and, hence, is an indication of an individual’s health and nutritional state

(Viblanc et al. 2012). Furthermore, determining body condition is a valuable tool as it can be used to indicate how animals are managing natural environmental variation and stressors (Mata et al. 2006).

Seabirds are typically referred to as sexually monomorphic (Fairbairn and Shine

1993). However, there are exceptions, such as the dimorphic Procellariiformes (i.e. petrels and albatross) and penguins (Croxall 1995). Fairbairn and Shine (1993) proposed male-biased dimorphism occurs in species with a large average body mass and in conditions where primary productivity is high. Additionally, selection pressures from competition and flight performance may have caused proportionally larger males (Shaffer et al. 2001b, González-Solís and Croxall 2005), with the degree of dimorphism varying between colonies (Barbraud and Jouventin 1998, Renner et al. 1998, Arnould et al. 2004). Conversely, some seabird species display reversed sexual size dimorphism with females larger in body mass than males, e.g. frigatebirds and tropic birds (Serrano-Meneses and Székely 2006). Furthermore, the degree of dimorphism can vary greatly between closely related species (Fairbairn

1997) as seen in the Family Sulidae, comprised of seven species of boobies (Sula spp. and Papasula spp.) and three species of gannet (Morus spp.) (Nelson 1978).

The boobies display a high degree of reversed sexual dimorphism (10-38 % difference), with females being larger in body mass, wing ulna length and culmen length than males of the same species (Guerra and Drummond 1995, Lormee et al.

2005, Weimerskirch et al. 2009a). Studies have documented habitat segregation in the foraging behaviour of Sulids (Guerra and Drummond 1995, Weimerskirch et al.

2006), indicating food competition as possibly resulting in reversed sexual dimorphism in these species. Differences in body condition between the sexes has

Chapter 2: Sexual dimorphism and body condition also been related to differences in foraging effort due to a smaller body mass in males (Lormee et al. 2003, Velando and Alonso-Alvarez 2003).

In contrast, while subtle differences in plumage have been described (Nelson

1978, Ropert-Coudert et al. 2005, Ismar et al. 2014), gannets have conventionally been considered monomorphic. While Cape gannets (Morus capensis) display distinct dimorphism in the length of the gular stripe, there is strong evidence indicating they are monomorphic in size (Rishworth et al. 2014a). In contrast, a recent study has shown northern gannets (M. bassanus) to be reverse dimorphic with females significantly heavier than males during chick rearing and displaying sexual segregation in foraging behaviour and diet composition (Stauss et al. 2012). These findings highlight the potential for different selection pressures faced by the sexes, such as their response to climate change.

Populations of Australasian gannet (M. serrator) in south-eastern Australia, forage in one of the fastest warming regions in the world (Lough and Hobday 2011).

However, given the varied degree of dimorphism within gannet species, it is unknown if the species is size dimorphic. If dimorphism is present in this species, differences in the foraging ecology between sexes would be expected as sexual segregation is commonly observed in dimorphic species (Ruckstuhl and Clutton-

Brock 2005). Indeed, preliminary studies have found segregation in the diet and foraging range of Australasian gannets (Barker 2012, Guggenheimer 2012). Hence, a greater knowledge about the Australasian gannet’s morphology could have implications for adaptive management (Hobday et al. 2014, Hobday et al. 2015).

Therefore, the aims of the present study were to investigate the degree of sexual dimorphism and body condition of the Australasian Gannet at two breeding colonies.

Chapter 2: Sexual dimorphism and body condition

Methods

Study site and animal handling

The study was conducted over three breeding seasons (2012 - 2014) at the Pope’s

Eye (38°16’42”S 144°41’48”E) and Point Danger (38°23’36”S 141°38’54”E) gannet colonies in northern Bass Strait, south-eastern Australia (Figure 2.1). A total of 276

Australasian Gannets were weighed and measured at Pope’s Eye (94 pairs) and Point

Danger (44 pairs). Sampling was conducted across the breeding season, during incubation and chick rearing. Only pairs were used in the study, with both adults being sampled in the same stage of breeding (sampled either same day or 8 days maximum of each other).

Figure 2.1. Location of Australasian gannet colonies used in present study. Point

Danger (indicated by red square) and Pope’s Eye (indicated by red circle).

Individuals were captured at the nest (the egg/chick were covered for protection) and weighed using a suspension balance (± 25 g, Salter Australia Pty Ltd, Australia).

Incubation and brooding time throughout the breeding season varies from 2 h - 5 d

(Chapter 4, 5 and 6) and, while the effect of fasting duration can influence body

Chapter 2: Sexual dimorphism and body condition mass, individuals were randomly captured with respect to how long they had been at the nest. Hence, fasting duration is unlikely to have caused a consistent bias in the body mass measurements.

Exposed culmen (bill length), bill depth, and tarsus length were measured using

Vernier callipers (± 0.1 mm). Wing chord length could not be measured due to feather deterioration potentially biasing results (Coulson 2009) and, hence, the length of the ulna bone (hereafter, referred to as wing ulna) was measured (± 1 mm) using a slide ruler (Rising and Somers 1989). Due to logistical constraints, not all measurements were possible on all birds. A blood sample was then collected by venipuncture of the tarsus vein for genetic sexing (DNA Solutions, Australia).

Handling time was less than 10 minutes and birds were returned directly onto their nest. Nests were monitored for the remainder of the season.

For each morphometric variable, a size dimorphism index (SDI) was calculated from the mean measurements of males and females, following the methods of Lovich and Gibbons (1992), where the extent of dimorphism (percent difference) was calculated as:

݈݉ܽ݁ ܵܦܫ = ቤെ ቆ ቇ +1ቤ × 100 ݂݈݁݉ܽ݁

Body composition and condition index

The gross body composition of a sub-sample of individuals was determined in order to develop a body condition index from morphometric measurements.

Following measurements of mass and morphometrics, a 0.5 mL background blood sample was collected into a heparinised syringed by venipuncture of the tarsus vein

(to determine the background levels of 2H) before individuals were administered an

Chapter 2: Sexual dimorphism and body condition

2 intraperitoneal injection of 1.74 ± 0.03 mL H2O (34.1% AP). They were returned to the nest for 3.4 ± 0.1 h as an isotope equilibration period, before another blood sample was collected to determine the size of the total body water pool (Flint and

Nagy 1984). Previous studies have found labelled hydrogen to equilibrate with the body water pool within 2 h (Gales 1989, Shaffer et al. 2001a). Samples were stored

(4°C) for several hours before the plasma and red cell fractions were separated by centrifugation and aliquots (50-75 μL) of plasma stored in flame sealed capillary tubes until analysis.

In the laboratory, capillaries that contained the plasma samples were vacuum distilled, and the water from the resulting distillate was used to produce H2. The isotope ratio 2H:1H was analysed using gas source isotope ratio mass spectrometry

(Isoprime IRMS and Isochrom mG; Micromass, Manchester, United Kingdom). The isotope dilution space was calculated using the plateau method (Halliday and Miller

1977). Previous studies have found that hydrogen isotope dilution overestimates the total body water (TBW) pool (Shaffer et al. 2006), as such, the isotope dilution space was corrected by 3% to take this into account. Using previously published relationships of gross chemical body composition (Layton et al. 2000), the following calculations were used to determine lean body mass (LBM) and total body fat (TBF) from body mass (BM) and TBW:

ܮܯܤ (݇݃) = ܶܤܹ (݇݃)/0.73

ܶܤܨ (݇݃) = ܤܯ (݇݃) െܮܤܯ (݇݃)

ܶܤܨ(%) = ܶܤܨ (݇݃)/ܤܯ (݇݃) × 100

The initial calculation for LBM takes into account the hydration constant typical for birds (0.73) described by Mata et al. (2006). A general linear model (GLM) was

Chapter 2: Sexual dimorphism and body condition then constructed using a priori knowledge of actual body condition to develop a condition index for all individuals where linear measurements were complete, as well as body mass divided by each measurement. Model selection was based on second- order Akaike information criteria and model averaging. The resulting best fit model describing TBF (%) content from mass and morphometric measurements was then used to assess sex, stage and year effects on body condition.

The assumptions of independence and normal distribution were tested with a Chi-

Square test and Shapiro–Wilk’s test, respectively. Differences between the sexes were analysed with a three-way ANOVA with sex, stage and year as interaction terms. Differences within breeding pairs were analysed with a paired t-test. Analysis was carried out using the R statistical environment 3.1.3. Results are presented as mean ± standard error.

Results

Body mass and morphometrics

Body mass did not differ between stages (Three-way ANOVA, F1,249 = 0.76, P =

0.38) or years (F1,249 = 2.34, P = 0.10) but was found to differ significantly between the two colonies (F1,249 = 14.38, P < 0.001). Additionally, body mass significantly differed between the sexes, with females being consistently heavier across stages and years than males, at both Pope’s Eye (2.74 ± 0.03 vs 2.66 ± 0.03, 3.1% larger, F1,183 =

4.81, P = 0.03) and Point Danger (2.67 ± 0.03 kg vs 2.48 ± 0.03 kg, 7.3% larger, F1,84

= 24.75, P < 0.001; Table 2.1). While tarsus length (Pope’s Eye: F1,144 = 0.22, P =

0.64; Point Danger: F1,49 = 0.06, P = 0.81) and bill length (Pope’s Eye: F1,146 = 0.47,

P = 0.50; Point Danger: F1,72 = 0.01, P = 0.91) were not significantly different

Chapter 2: Sexual dimorphism and body condition between the sexes, females were found to have smaller bill depth (2.2% smaller,

F1,145 = 10.42, P = 0.002) but larger wing ulna (0.8% larger, F1,147 = 45.01, P = 0.03) at Pope’s Eye. While the degrees in variation between the sexes in bill depth and wing ulna were of similar magnitude and direction at Point Danger (1.7% and 0.8%, respectively), the differences were not significant (F1,72 = 2.25, P = 0.14; F1,72 =

1.89, P = 0.17). However, this could be due to the smaller sample size at this colony as a power analysis revealed that, for a 1-ȕ VDPSOHVL]HVRIDQGSDLUV would be sufficient to find a significant difference in bill depth and wing ulna length, respectively, at Point Danger.

Male n Female n SDI Body mass* 2.66±0.03 93 2.74±0.03 92 3.1 Bill depth* 33.3±0.2 85 32.6±0.2 71 -2.2 Pope’s Eye Bill length 92.2±0.3 85 91.7±0.3 73 -0.6 Wing ulna* 202.6±0.4 85 204.2±0.6 74 0.8 Tarsus 67.9±0.3 82 67.8±0.3 74 -0.7 Body mass* 2.48±0.03 43 2.67±0.03 43 7.3 Bill depth 32.8±0.2 41 32.3±0.3 43 -1.7 Point Danger Bill length 92.3±0.4 41 90.86±1.28 43 -1.5 Wing ulna 202.8±0.7 41 204.3±0.9 43 0.8 Tarsus 69.0±0.5 29 68.8±0.5 31 -0.3 *Significant difference between sexes (P < 0.05)

Chapter 2: Sexual dimorphism and body condition

To assess whether the degree of sexual size dimorphism was sufficient to

accurately determine sex from morphometric measurements, a discriminant function

was developed. A discriminant score (DS) was calculated for each study site as the

morphometric variables and the degree of sexual dimorphism can differ between

colonies in seabirds (Evans et al. 1993, Arnould et al. 2004). Significant discriminant

functions were developed for Pope’s Eye (:LONV¶Ȝ = 0.88, F6,151 = 13.31, P < 0.01):

ܦܵ = (െ1.83 × ܤܯ) + (0.57 × ܤܦ) + (െ0.13 × ܹܷ) +(0.15×ܶ)

and Point Danger (WilNV¶Ȝ = 0.70, F6,59 = 19.52, P < 0.01):

ܦܵ = (െ4.71 × ܤܯ) + (0.24 × ܤܦ) + (െ0.07 × ܤܮ) + (െ0.05 × ܹܷ) + (0.08 × ܶ)

where BM is body mass (kg), BD is bill depth (mm), BL is bill length, WU is wing

ulna (mm) and T is tarsus (mm). The predictive accuracy of the discriminant function

was 81.4% for Point Danger but 68.2% for Pope’s Eye due to high overlap of scores

between sexes (Figure 2.2).

The degree of sexual dimorphism was also assessed within breeding pairs at both

colonies. Individuals within pairs at Pope’s Eye differed significantly in body mass

(Paired-samples t-test, t90 = 2.30, P = 0.02) with females heavier than their partner in

62% of cases. A significant difference in bill depth (t67 = -3.24, P = 0.002) and wing

ulna length (t70 = 2.04, P = 0.05; Table 2.2) were also found. In contrast, individuals

in pairs at Point Danger only differed significantly in body mass (t41 = 5.62, P <

0.001), with females heavier than males in 83% of pairs. On average, females were

2.2% heavier than their partner at Pope’s Eye (82 ± 3 g; range: -24.0-33.6 g

difference) and 7.3% heavier at Point Danger (203 ± 3 g; range: -12.9-27.5 g

difference). The sexual dimorphism index suggests intra-colony and intra-pair

dimorphism was similar at Pope’s Eye (SDI values given in Table 2.1 and Table 2.2).

Chapter 2: Sexual dimorphism and body condition

Figure 2.2. Distribution of discriminant scores for male and female Australasian gannets at two breeding colonies. Scores given for Pope’s Eye (A) and Point Danger colonies (B), where males are blue, and females are red. Scores are based on the discriminant functions provided in text.

At Point Danger, bill depth and bill length was more dimorphic within breeding pairs than within the colony, however assortative mating was not apparent for these

2 2 variables (bill depth: r = 0.01, F1,38 = 0.55, P = 0.46; bill length: r < 0.001, F1,38 =

0.04, P = 0.85). Furthermore, there was no evidence for assortative mating in relation to body mass at either colony as no correlation was found between female and male

2 2 mass within pairs (Pope’s Eye: r = 0.04, F1,89 = 3.44, P = 0.07; Point Danger: r =

Chapter 2: Sexual dimorphism and body condition

0.03, F1,40 = 1.37, P = 0.25). Additionally, no relationship was found in body

2 condition within pairs (Pope’s Eye: r < 0.001, F1,58 = 0.02, P = 0.89; Point Danger:

2 r = 0.03, F1,24 = 0.61, P = 0.44).

Table 2.2. Comparison of mean body mass (kg) and morphometric (mm) differences

± standard error within breeding pairs (n) of Australasian Gannets from Pope’s Eye and Point Danger colonies.

Mean difference n SDI Mass* 0.08±0.04 91 2.2 Bill depth* -0.8±0.2 68 -2.6 Pope's Eye Bill length -0.6±0.5 70 -0.7 Wing ulna* 1.6±0.2 71 0.7 Tarsus -0.1±0.5 61 -0.4 Mass* 0.20±0.04 42 7.3 Bill depth -0.6±0.4 41 -2.1 Point Danger Bill length -1.5±1.4 41 -3.5 Wing ulna 1.4±0.1 41 0.8 Tarsus -0.6±0.7 26 -1.1 *Significant difference within pairs (P < 0.05)

Body condition index

Gross body composition data were obtained from a total of 15 individuals (4 females, 11 males) (Tew 2014). Total body fat (TBF; %) ranged from 5.6 - 18.5%

(10.5 ± 1.0) and was not significantly different between the sexes (F1,13 = 1.58, P =

0.23). The top-ranked statistical models explaining TBF from linear morphometrics were determined (Table 2.3). However, as the combined weight of the models was low (Ȧi > 0.9), the coefficients and standard errors were calculated using model averaging. Tarsus length and wing ulna length were selected as the most important variables. While the top two models included both these variables, the most

Chapter 2: Sexual dimorphism and body condition parsimonious model describing the relationship between TBF (%) and morphometric measurements was chosen as:

ܤܯ ܶܤܨ(%) =24.43+1.94×൬ ൰െ0.58 × ܶ ܹܷ where BM is body mass (kg), WU is wing ulna (mm) and T is tarsus (mm). The predicted TBF (%) was highly correlated to the measured TBF (r2 = 0.84; Figure 2.3) indicating the model can be used as a valid body condition index (BCI). This BCI was then used to determine the condition of individuals in which the predictor variables were measured from Pope’s Eye (range: 10.1-17.7%; n = 153) and Point

Danger (range: 12.8-21.0%; n = 59). At both colonies, BCI did not differ between years (Pope’s Eye: F2,143 = 0.13, P = 0.88; Point Danger: F2,49 = 1.11, P = 0.34) or breeding stages (Pope’s Eye: F1,143 = 0.08, P = 0.78; Point Danger: F1,49 = 2.14, P =

0.15) and, thus, data were combined. The BCI was not significantly different between males (14.6 ± 0.1 %) and females (14.5 ± 0.2 %) at Pope’s Eye (F1,143 =

0.28, P = 0.60; Figure 2.4). Similarly, males (15.2 ± 0.3 %) and females (15.1 ± 0.3

%) at Point Danger were of a similar BCI (F1,49 = 0.06, P = 0.80). Additionally, no difference in BCI was found within breeding pairs (Pope’s Eye: t59 = 0.31, P = 0.76;

Point Danger: t25 = 0.87, P = 0.39).

Chapter 2: Sexual dimorphism and body condition

Table 2.3. Top-ranked AICc models for explaining total body fat (%) from

PRUSKRPHWULFPHDVXUHPHQWVLQDGXOW$XVWUDODVLDQJDQQHWV ǻ$,& %RG\PDVV

(kg), wing ulna and tarsus (mm) were selected as important variables by the model.

Model AICc ǻ$,& AIC Weight 0.256 0.00 65.0 ݏݑݏݎݓ݅݊݃ ݑ݈݊ܽ) െݐܽ ÷ ݏݏܽ݉ ݋݀ݕܾ) െݓ݅݊݃ ݑ݈݊ܽ 65.1 0.10 0.243 (ݏݑݏݎݐܽ ÷ ݏݏܽ݉ ݋݀ݕܾ) 0.137 1.30 66.3 ݏݑݏݎݐܽ ÷ ݏݏܽ݉ ݋݀ݕܾ 0.061 2.88 67.9 ݏݑݏݎെ ݐܽ ݏݏܽ݉ ݋݀ݕܾ 0.056 3.03 68.1 ݏݑݏݎെݐܽ (ݏݑݏݎݐܽ ÷ ݏݏܽ݉ ݋݀ݕܾ)

Figure 2.3. Estimated total body fat (%) reliably predicts actual total body fat (%) in

Australasian gannets based on a body condition index. Measurements of body mass

(kg), wing ulna and tarsus length (mm) were selected. Plot shows the predicted model (solid line) and the 95% confidence interval (dashed line).

Chapter 2: Sexual dimorphism and body condition

Figure 2.4. Mean body condition (%) ± standard error for males and females from

Pope’s Eye and Point Danger gannet colonies. Where males are blue and females are red.

Discussion

Body mass and morphometrics

Within the Sulids, boobies display reverse sexual dimorphism with females larger than males by 10-38% (depending on species) (Nelson 1978); whereas gannets have nominally been considered monomorphic. Recently, however, the northern gannet has been shown to also display reversed dimorphism, with females 7-8% heavier than males (Stauss et al. 2012, Cleasby et al. 2015). In addition, females of this species travel further from the colony and target different prey items (Lewis et al.

2002, Stauss et al. 2012) suggesting ecological effects resulting from this

Chapter 2: Sexual dimorphism and body condition dimorphism. As Sulids are a closely related group (Patterson et al. 2011), the presence of reverse sexual dimorphism might, therefore, be expected to some degree in all species. However, there is strong evidence indicating they while the Cape gannet displays distinct dimorphism in the length of the gular stripe, it has been shown to be size monomorphic, with no significant difference in the average body mass of males and females (Rishworth et al. 2014a). Hence, the degree of dimorphism across the gannet species, and the influence this has on their behaviour is of interest. The results of the present study, with females at both study colonies being heavier than males consistently across years and breeding stages, confirm the nominally monomorphic Australasian gannet also exhibits reversed sexual dimorphism.

Interestingly, the degree of body mass dimorphism was much greater at Point

Danger (7.3%) than at Pope’s Eye (3.1%). The degree of sexual dimorphism can vary geographically within species, for example, in little penguins (Eudyptula minor) the dimorphism in bill depth varies considerably between colonies in both Australia and New Zealand (Renner and Davis 1999, Arnould et al. 2004). Such differences in the degree of dimorphism may be due to food availability or inter-population growth patterns (Lovich and Gibbons 1992). Alternatively, sexual selection may be driving the greater divergence in dimorphism (Einoder et al. 2008).

In the present study, females were found to be structurally larger in wing ulna length (0.8% larger at both colonies), indicative of a slightly greater overall body size

(Rising and Somers 1989). This is consistent with findings in brown (Sula leucogaster), red-footed (S. sula) and Peruvian (S. variegata) boobies where females have a greater wing cord length (2.8-3.1% larger) than males (Lewis et al. 2005,

Zavalaga et al. 2009). Wing size and shape must be optimal to provide enough lift to

Chapter 2: Sexual dimorphism and body condition support body mass in flight and, consequently, variations in wing morphology can alter flight behaviours (Warham 1977). Thus, a difference in the wing morphology between sexes, however slight, may have energetic consequences for foraging strategies (Spear and Ainley 1997). As a plunge diving species, take-off from the sea surface is considered the most energetically expensive activity while foraging (Green et al. 2010). With a greater body mass, females may require greater effort to take-off compared to males and a different wing morphology may assist in this.

Consistent with the findings in Australasian gannets in New Zealand (Ismar

2010), males in the present study had a greater bill depth at Pope’s Eye (2.2% larger) and Point Danger (1.7%). The development of larger bills in males may have evolved for nest defence against conspecifics, courtship ritual (bill fencing in Sulids) (Nelson

1978), or to reduce intra-specific competition by enabling them to target prey of a larger size (Forero et al. 2001). If the larger bill depth observed in males allows them to target different prey, they may consume a greater proportion of larger prey items/species (Forero et al. 2002). Indeed, female blue-footed boobies (S. nebouxii) possess a larger bill length/depth than males and consume similar prey species but target individuals of a larger size (Castillo-Guerrero and Mellink 2011). Prey size and species consumed by Australasian gannets have previously been reported (Bunce

2001), however differences between the sexes are currently unknown.

In a species with no obvious differences in their plumage or body size between sexes, the use of behavioural observations (e.g. copulation, egg-laying by females, or nape-biting by males) have been used to determine the sex of individuals (Daniel et al. 2007). While effective, this method is usually only applicable at the start of the breeding season. Alternatively, discriminant functions have been widely used in seabird species to determine the sex of individuals in the field from morphometric

Chapter 2: Sexual dimorphism and body condition measurements with an accuracy > 90% (Amat et al. 1993, Bosch 1996, Hull 1996).

However, the applicability of a discriminant function across a species is not always possible and can depend on the degree of variation in morphometrics between colonies (Arnould et al. 2004). In the present study, a broad range of body sizes were found within the sexes at both colonies. With no clear bimodal distribution of morphometrics by sex, the accuracy of the function was weak at Pope’s Eye (68.2%) and, although it was more accurate at Point Danger (81.4%), it appears DNA analysis is still the most reliable method for determining sex in the Australasian gannet.

In the present study, the degree of dimorphism in Australasian gannets was also investigated within pairs with similar results to that found when comparing intra- colony differences. Females were significantly heavier than their partner at both colonies, with bill depth and wing ulna only significantly different at Pope’s Eye. As body size and structure can evolve due to the need to exploit different niches, dimorphism within pairs can result in a greater exploitation of a diverse array of resources (Serrano-Meneses and Székely 2006). Previous studies on Sulids have found females forage further from the colony than males, depending on breeding stage (Weimerskirch et al. 2006, Stauss et al. 2012). This indicates a niche segregation between the sexes, with males possibly remaining closer to the colony to maintain territory and females foraging further but contributing a greater proportion to the chicks’ diet (Gilardi 1992). Interestingly, no assortative mating was found in the present study, suggesting females are not selecting mates based on body size or condition. However, as gannets are long lived and monogamous (Nelson 1978), size convergence after pairing could occur, with the condition of an individual influencing the traits of their partner over time (Choudhury et al. 1992).

Chapter 2: Sexual dimorphism and body condition

Body condition

Body condition, pertaining to an animals’ fat reserves, can be used as a proxy for individual investment in offspring (Weimerskirch et al. 1994) or reflective of environmental fluctuations in foraging conditions (Monaghan et al. 1989, Chastel et al. 1995). Body condition is often presented as a body condition index (BCI) based on an individual’s body mass relative to its structural size (Schulte-Hostedde et al.

2005), although the accuracy of the BCI varies between species (Labocha and Hayes

2012). Additionally, the linear morphometric measurements used to calculate BCI can strongly affect the relationship. As presented in Schamber et al. (2009), if an unverified measurement is used, despite being verified in another species, false conclusions can be made about body condition. While widely used in seabirds

(Lormee et al. 2003, O'Dwyer et al. 2007, Kato et al. 2008), body condition indices are very rarely validated (Golet and Irons 1999, Shaffer et al. 2006).

In Sulids, no validation of body condition as a measure of total body fat exists.

Studies relating to body condition report either body mass (Ewing et al. 2005,

Bijleveld and Mullers 2009) or an index with body mass regressed against wing length, tarsus or culmen length (Lormee et al. 2003, Lewis et al. 2006, Moseley et al.

2012, Hennicke and Weimerskirch 2014). Furthermore, across studies of the same species these indices have not been consistent and, hence, comparison is not possible.

In the present study, a BCI derived from body mass and two structural measurements

(wing ulna and tarsus length) was found to be highly correlated to empirical measures of total body fat (%). Unfortunately, the sample size for the isotope dilution study was limited. While sex did not influence the model, it is possible that with a larger sample size this could have been evident. However, the development of the body condition index relates to how body fat content is reflected in morphometric

Chapter 2: Sexual dimorphism and body condition variables and, as such, physiologically it would likely not be impacted by sex. This validated BCI, presents a quick monitoring assessment tool with potential applicability to other gannet and Sulid species.

The TBF (%) of Australasian gannets ranged between 10.1-17.7% at Pope’s Eye and 12.8-21.0% at Point Danger. As no fat content data is available in Sulids these results were compared to other seabirds. Body fat in the black-legged kittiwake

(Rissa tridactyla) has been reported as 4-13 % of the total body mass (Golet and

Irons 1999), while Shaffer et al. (2006) found glaucous gulls (Larus hyperboreus) had an average fat content of 3.6 ± 2.6%. The higher body fat of gannets may be reflective of their high calorific diet (Bunce 2001).

In the present study, female gannets were in a similar condition to males, both across the population and within breeding pairs. This may indicate that there is equal effort invested in rearing offspring between the sexes (Weimerskirch et al. 2006).

Lormee et al. (2003) found the BCI of red-footed boobies remained stable throughout the breeding season, yet Weimerskirch et al. (2006) found males lost condition faster than females, probably due to the smaller body size of males. Cape gannets, nominally monomorphic in body size, have been reported to lose condition throughout the breeding season in both sexes, although females remain in a better condition than males (Mullers and Tinbergen 2009). As body condition varied in dimorphic and monomorphic species, body size may not be the sole reason for variation in body fat between the sexes. Schultner et al. (2013) suggested birds may choose to maintain lower energy/fat stores than they can physiologically possess in an effort to reduce flight costs, a factor of equal importance to both sexes. Similarly, body fat stores act as buoyancy for diving birds and, can thus reduce the depths and durations to which they can attain (Elliott et al. 2008). Attaining the necessary depths

Chapter 2: Sexual dimorphism and body condition to predate with minimal effort would be equally beneficial to both sexes and may explain the lack of difference in body condition observed in Australasian gannets.

During the course of the present study, breeding success (% chicks fledged) (Pyk et al. 2013) measured for the entire colony varied greatly, being lower in 2013 (8.6 and 0.0%) than in 2012 (30.1 and 10.0%) and 2014 (24.7 and 48%), at Pope’s Eye and Point Danger, respectively. The lower breeding success is suggestive of poor prey conditions in 2013 (Stenseth et al. 2002) yet no difference in body condition was found for any stages between years throughout the study. Cape gannets have also been found to maintain a stable body condition across years of differing environmental conditions (Mullers and Tinbergen 2009, Moseley et al. 2012). This is consistent with gannets, being long-lived, prioritising their own survival (i.e. maintain body condition) when food availability drops below a critical threshold, at the expense of the current breeding attempt (Erikstad et al. 1998).

As females are larger in body mass and wing ulna length, yet possess proportionally similar body fat content to males, this suggests females are simply proportionally larger than males. The sexual selection hypothesis suggest that in reverse dimorphic species a larger body size in females may be indicative of an individual’s ability to produce larger eggs (Selander 1972). However, gannet egg size and mass shows very little variation (Nelson 1966, Wingham 1984a). The division of labour hypothesis has been proposed as a potential factor in dimorphism of booby species (Guerra and Drummond 1995, Lormee et al. 2005, Weimerskirch et al.

2009a, Castillo-Guerrero and Mellink 2011). However, male and female gannets spend similar time away from the nest foraging (Ismar 2010). Sexual dimorphism in

Australasian gannets may, therefore, be due to food competition. As with other Sulids

(Guerra and Drummond 1995, Lewis et al. 2005, Weimerskirch et al. 2006, Stauss et

Chapter 2: Sexual dimorphism and body condition al. 2012, Cleasby et al. 2015), preliminary studies of Australasian gannets have found sexual segregation in foraging range, habitat and diet (Barker 2012,

Guggenheimer 2012). Consequently, future studies of foraging behaviour in gannets should assess males and females separately as dimorphism can have ecological consequences.

Chapter 3

Determining activity-specific energy expenditure in a large

seabird using accelerometry

A version of this chapter has been submitted as:

Angel L.P., Jeanniard-du-Dot T., Speakman J.R., Arnould J.P.Y. (in review)

Determining activity-specific energy expenditure in a large seabird using

accelerometry. Royal Society Open Science.

Chapter 3: Determining activity-specific energy expenditure

Abstract

Accelerometry is increasingly being used to estimate energy expenditure in animals, based on the concept that most behaviours require movement and, hence, have an energetic cost. However, validation of accelerometry as an accurate proxy for field metabolic rate and activity-specific energy expenditure in free-ranging species over ecologically meaningful time periods is limited. In the present study, 21 Australasian gannets (Morus serrator) from the Pope’s Eye colony (38°16’42”S 144°41’48”E), south-eastern Australia, were equipped with GPS and tri-axial accelerometers and dosed with doubly-labelled water (DLW) to measure energy expenditure during normal behaviour for 3-5 days. The predictive power of the correlation between total energy expenditure from the DLW and Vectorial dynamic body acceleration

(VeDBA) was good (r2 = 0.63) but improved when models included total distance travelled and body mass (r2 = 0.84). The removal of time spent on land, comparing the two methods while the birds were at sea, did not improve the relationship.

Activity-specific energy estimates were most accurate from the VeDBA of flapping and gliding flight, while resting on the sea surface, on land and foraging were poorly correlated. These results indicate that accelerometry can be used to measure energy expenditure in free-ranging gannets, but accuracy depends on the inclusion of behavioural and morphometric information.

Chapter 3: Determining activity-specific energy expenditure

Introduction

As energy is potentially a limited resource, or there are limits in the rate at which it can be used (Speakman and Król 2010), its allocation to specific activities can have important implications for an individual’s fitness (McNamara and Houston 1996).

The rate at which energy is expended and acquired influences how an individual invests in reproduction, growth and survival (Goldstein 1988). Measuring energy expenditure, therefore, is of fundamental importance for addressing basic biological and ecological questions.

Due to logistical constraints, direct measurement of energy expenditure in free- ranging animals (i.e. field metabolic rate, FMR) is problematic. To date, the most widely used methods have been the doubly-labelled water (DLW) method (Lifson and McClintock 1966, Speakman 1998) and the heart rate (fH) technique (Butler and

Jones 1997). In addition to the financial costs involved restricting sample size, both techniques have limitations with the DLW method providing only a single estimate value averaged over the duration of the study (limited to 24 hours - 28 days due to isotope washout rate) and the heart rate (ƒH) method requiring calibration with the rate of oxygen consumption for each new species studied (Butler et al. 2004).

Furthermore, it is possible that cardio-vascular processes unrelated to energy expenditure (e.g. stress) may influence the relationship between heart rate and oxygen consumption (Gleiss et al. 2011).

A recent development in estimating the energy expenditure of animals is accelerometry, based on the concept that most behaviours require movement and that energy must be expended to achieve this movement (Wilson et al. 2006).

Accelerometers measure movement in gravitational force on one (Sato et al. 2008), two (Yoda et al. 1999, Yoda et al. 2001), or three dimensional axes (Wilson et al.

Chapter 3: Determining activity-specific energy expenditure

2006). Overall dynamic body acceleration (ODBA) can be used as a proxy for energy expenditure, obtained from the absolute values of dynamic acceleration (total acceleration minus static acceleration) summed for all three axes (Shepard et al.

2008, Wilson et al. 2008). The accelerometer is typically placed in the centre of the animals’ torso as movement in the limbs and extremities can still be detected (Wilson et al. 2006). However, in cases where the device cannot be consistently aligned centrally on the animals’ torso, vectorial dynamic body acceleration (VeDBA) is a better proxy for energy expenditure (Gleiss et al. 2011, Qasem et al. 2012), calculated as the square root of the dynamic acceleration summed for all three axes.

An added benefit of accelerometry is that fine-scale behaviours can also be identified (Shepard et al. 2008, Sakamoto et al. 2009). Originally used on Adélie penguins (Pygoscelis adeliae) to identify porpoising behaviour (Yoda et al. 1999), accelerometers are now being deployed on a wide range of species for collecting a variety of behavioural data (Sakamoto et al. 2009, Campbell et al. 2013). For example, changes in the acceleration amplitude can identify walking from running

(McClune et al. 2014) or device position can been used to identify dive angle

(Tanaka et al. 2001, Cook et al. 2010). Hence, estimates of energy expenditure can be obtained for individual behaviours. Furthermore, accelerometer data loggers can be attached externally, reducing animal handling time and stress (Wilson and

McMahon 2006). Therefore, with increasing miniaturistion and improved battery life of data loggers (Rutz and Hays 2009), accelerometry has the potential to enable large scale data collection of behavioural energetics in free-ranging animals over biologically meaningful durations in ecological studies (Ropert-Coudert and Wilson

2005, Brown et al. 2012).

This potential is supported by the results of several captive studies. A strong

Chapter 3: Determining activity-specific energy expenditure correlation between ODBA and oxygen consumption or heart rate has been demonstrated for a range of species (mammals, birds, sea turtles) under laboratory conditions (Wilson et al. 2006, Halsey et al. 2007, Fahlman et al. 2008, Green et al.

2009, Halsey et al. 2009, Halsey et al. 2011a), indicating accelerometry can be a good proxy for metabolic rate. However, few studies have validated these relationships in free-ranging individuals undertaking their natural range of behaviours over ecologically meaningful periods. Consequently, the accuracy and usefulness of accelerometry for measuring energy expenditure in free-ranging animals remains largely unknown (Elliott et al. 2013, Miwa et al. 2015).

Throughout the world, seabirds are major consumers of marine biomass and play an important role in determining ecosystem structure and function (Furness and

Monaghan 1987, Boyd et al. 2006). Anticipated alterations to ocean currents and environmental variability due to climate change are likely to have major impacts on the distribution and abundance of seabird prey in many parts of the world (Barbraud et al. 2012). Knowledge of how seabirds apportion energy to various behaviours, and the factors affecting this, is crucial to understanding how they may respond to such changes. Consequently, there is a need for developing and validating widely applicable techniques capable of determining activity-specific energy expenditures in these species (Elliott et al. 2013). Gannets are large, wide-ranging pelagic seabirds

(Nelson 1978) and ideal candidates for determining activity-specific energy expenditure as they have a limited suite of at-sea behaviours which are reflected in distinct accelerometry signals (Ropert-Coudert et al. 2004a). The aims of the present study, therefore, were to determine whether tri-axial accelerometry can be used to: 1) accurately predict overall energy expenditure; and 2) develop activity-specific energy estimates in the Australasian gannet (Morus serrator).

Chapter 3: Determining activity-specific energy expenditure

Methods

Study site and animal handling

The study was conducted 4-10 December 2014 at the Pope’s Eye Australasian gannet colony (38°16’42”S 144°41’48”E) in south-eastern Australia. Adult birds raising young, downy chicks (20-50 days old; Wingham 1984b) were randomly selected throughout the colony. Adults were captured by hand at the nest and the chick covered for protection from conspecifics. Individuals were weighed in a cloth bag by suspension scale (± 25 g, Salter Australia Pty Ltd, Australia) before exposed culmen (bill length), bill depth, and tarsus length were measured using Vernier callipers (± 0.1 mm), and total head length was measured using a slide ruler (± 1 mm). Wing chord length could not be measured due to feather deterioration potentially biasing results (Coulson 2009) and, hence, the length of the ulna bone

(hereafter, referred to as wing ulna) was measured with the slide ruler (Rising and

Somers 1989).

To determine the at-sea movements of breeding birds, individuals were equipped with a GPS (IgotU GT-600, Mobile Action Technologies Inc., Taiwan; 26.50 g) recording location (± 10 m) every 2 min. To obtain information on activity patterns and foraging behaviour (Shepard et al. 2010), all individuals were also instrumented with a tri-axial accelerometer data logger (X8 500mAph, Gulf Coast Data Concepts

LLC, USA; 14.12 g) sampling at 25 Hz. The devices were encapsulated in heat shrink plastic (whole package 52.6 g, <3% body mass; Phillips et al. 2003) and attached with water-proof tape (Tesa® 4651, Beiersdorf AG, Germany) to the central tail feathers following the methods of Wilson et al. (1997). Device positioning ensured it was covered by the wings during a plunge dive in an attempt reduce drag

(Hamer et al. 2000).

Chapter 3: Determining activity-specific energy expenditure

An initial blood sample (0.1 mL) to establish background levels of 2H and 18O

(method D; Speakman and Racey 1987)was collected by venipuncture of a tarsus vein before individuals were administered an intraperitoneal injection (1.85 ± 0.03 mL) of doubly-labelled water (DLW, with 64.3% 18O and 34.1% 2H). Syringes were weighed before and after injection to calculate the mass of DLW injected into each bird (± 0.001 g, FX300i milligram balance, A&D Company Ltd). Birds were returned to their nest for 2-6 hours (3.36 ± 0.09 h), allowing for equilibration of the isotopes with the body water pool, before a second blood sample collected.

Individuals were then left to resume normal behaviour, including foraging at sea, for

3-5 days before being recaptured at the colony, weighed, their devices removed and a final blood sample collected. Whole blood samples were stored in flame-sealed glass capillary tubes at room temperature until isotopic analysis 2 months later.

Data processing and statistical analyses

The downloaded GPS locations were processed using a speed filter (McConnell et al. 1992) and summary statistics calculated using the adehabitatHR package

(Calenge 2006) in the R statistical environment (R Core Team 2015). Trip parameters indicative of foraging effort (total distance travelled and trip duration) were calculated for each foraging trip and summed for the duration of the study (i.e. from the time of equilibration blood sample to the final blood sample).

Data obtained from the tri-axial accelerometers were used to visually assess behaviour in IGOR Pro (Version 6.34, WaveMetrics, USA) based on previous studies of plunge diving species (Ropert-Coudert et al. 2004b, Weimerskirch et al.

2005b, Ropert-Coudert et al. 2009a). Five key behaviours were identified: flapping flight, gliding flight, resting on sea surface, resting on land, and foraging (i.e.

Chapter 3: Determining activity-specific energy expenditure diving). The Ethographer package was used to perform a k-means algorithm clustering analysis (see Sakamoto et al. 2009) and identify behaviours using an unsupervised continuous wavelet transformation (1 second window). Each cluster was assigned a behaviour based on the previous identification. Additional conditions were applied in R statistical environment (R Core Team 2015) to identify miss- classification of behaviours produced by similar movement. For example, travelling speed was used to separate gliding from resting on the sea surface, while GPS location was used to discern resting on land from resting at sea. The time spent performing each behaviour during the whole study period was then calculated.

Additionally, gannets perform both plunge dives and duck dives, known as surface foraging events (Machovsky-Capuska et al. 2011b, Warwick-Evans et al. 2015). As such, the total number of plunge dives and surface foraging events, and the dive rate

(dives·h-1) for each was determined. Dive type was determined by whether a gannet was in flight prior to a dive (plunge) or resting on the sea surface (surface foraging event).

From the raw tri-axial data, the static (reflecting animal positioning with respect to gravity) and dynamic acceleration were separated (Shepard et al. 2008). The dynamic acceleration for each axis, X (surge), Y (sway), and Z (heave), was then used to calculate dynamic body acceleration. As devices placed on the back of the bird have a high likelihood of the device being lost (Vandenabeele et al. 2014), the accelerometer could not be placed in the animals centre of gravity. Thus, vectorial dynamic body acceleration (VeDBA) was used as an estimate of energy expenditure

(Gleiss et al. 2011, Qasem et al. 2012). VeDBA (g) was calculated as:

ܸ݁ܦܤܣ = ඥܺଶ + ܻଶ+ ܼଶ

Chapter 3: Determining activity-specific energy expenditure

From this, sum (VeDBASUM) and mean (VeDBAMEAN) of all values were calculated.

Isotope enrichment of blood samples was determined by isotope ratio mass

VSHFWURPHWU\ 2SWLPD0LFURPDVV,506DQG,VRFKURPȝ*0DQFKHVWHU8.

Specifically, 2H enrichment was determined by online chromium reduction and 18O enrichment was determined from the small-sample equilibration technique

(Speakman and Król 2005). The injectate enrichment was estimated from the average of five subsample solutions (consisting of the original injectate diluted with tap water), processed through mass spectrometry (Speakman 1997). The initial total body water pool was determined from the 18O dilution space using the plateau method (Speakman 1997). Final total body water pool was calculated from body mass assuming the pool contributed a constant proportion of the total mass of the animal throughout the study (Lifson and McClintock 1966). Isotope enrichment levels were converted into total energy expenditure (DLWEE; kJ) based on the single pool model and assuming 25% evaporative water loss (Equation 7.17; Speakman

-1 1997). Mean daily energy expenditure (DLWDEE; kJ·day ) was calculated by dividing DLWEE with the study duration.

To assess if accelerometry on its own can reliably predict energy expenditure in breeding gannets, the relationship between DLWEE and VeDBASUM was determined.

Linear mixed effects models were then used to incorporate parameters likely to influence energy expenditure (i.e. total distance travelled, total number of dives, dive rate, body mass, morphometrics, and sex) to establish if the relationship could be improved, based off methods developed by Jeanniard-du-Dot (2015).

For each individual, VeDBA values were also summed within each behaviour to determine activity-specific energy expenditure. To estimate activity-specific energy expenditure, a general linear model was fitted to the data, the parameter estimates

Chapter 3: Determining activity-specific energy expenditure were then added into the following equation:

Eq. 1. ܦܮܹாா = ܥ௅஺ே஽ × ܶ௅஺ே஽ + ܥோாௌ்ூேீ × ܶோாௌ்ூேீ + ܥி௅஺௉௉ூேீ ×

ܶி௅஺௉௉ூேீ + ܥீ௅ூ஽ூேீ × ܶீ௅ூ஽ூேீ + ܥிைோ஺ீூேீ × ܶிைோ஺ீூேீ where DLWEE is total energy expended (kJ), Ci is the parameter estimates for the rate

-1 of energy expenditure for each activity (kJ·day ) and Ti is the time spent per activity

(days) as determined from the accelerometry data. Using this equation, the energy expended while at sea (i.e. away from the colony) was calculated as:

Eq. 2. ܦܮܹ஺்ିௌா஺ = ܦܮܹாா െ (ܥ௅஺ே஽ × ܶ௅஺ே஽)

The relationship between DLWAT-SEA and VeDBAAT-SEA SUM (the summation of all behaviours bar VeDBALand) was then determined using linear mixed effects models as previously described, to see if the relationship was more predictive for periods when birds were more active. Activity-specific VeDBA values were then fitted to the corresponding energy expenditures estimated from equation 1 (i.e. CBEHAVIOUR x

TBEHAVIOUR). The resulting linear equations were then used to predict energy expenditure for each behaviour.

All the statistical analyses were performed using R (version 3.1.3; R Core Team

2015). To assess which foraging parameters influence effort, linear mixed effects models were used with sex as a fixed effect. Model selection was conducted using

Akaike’s Information Criterion (AICc) and Akaike weight (wi) (AICcmodavg and

MuMIn packages). In cases where there was no clearly most parsimonious model, the averaged estimates of coefficients (c) and standard errors were calculated by model averaging (Symonds and Moussalli 2011). Results are presented as mean ± standard error.

Chapter 3: Determining activity-specific energy expenditure

Results

Concurrent accelerometer, GPS and DLW data were obtained from a total of 21 birds (11 males, 10 females; Table 3.1). The birds spent 50.6 ± 4.8% of the study period (45.8 ± 6.5 h) at the colony (i.e. resting on land; Figure 3.1). On average 44.3

± 4.7 h (range: 7.8-114.7) was spent at-sea, during which time 1-5 foraging trips were made covering a total distance of 383.3 ± 36.3 km (range: 99.3-879.8). At sea, the highest proportion of time was spent resting on the sea surface (33.5 ± 4.2%) followed by time spent in flight (9.7 ± 1.0% flapping flight; 3.6 ± 0.4% gliding flight). Individuals spent only 2.7 ± 0.3% of the study period foraging (i.e. diving), performing on average 350.9 ± 34.4 plunge dives (2.0 ± 0.2 dives·h-1) and 417.7 ±

41.3 surface foraging events (2.3 ± 0.2 dives·h-1).

Total energy expenditure over the study period (DLWEE) was estimated as 11,180

-1 ± 800 kJ, expended at a rate (DLWDEE) of 3,098 ± 174 kJ·day (Table 3.1).

Comparison between the sexes revealed no differences in body mass (One-way

ANOVA: F1,19 = 0.23, P = 0.64), foraging trip distance (F1,19 = 0.24, P = 0.63), duration (F1,19 = 0.54, P = 0.47), DLWDEE (F1,19 = 0.03, P = 0.86) or DLWEE (F1,19 =

0.1, P = 0.72), thus, data were combined. Total vectorial dynamic body acceleration

6 6 (VeDBASUM) was 1.230 × 10 ± 0.146 × 10 g and found to be positively correlated

2 with DLWEE (r = 0.63, F1,19 = 31.90, P < 0.001; Figure 3.2A), giving the relationship of:

ିଷ ଷ ܦܮܹாா(݇ܬ) = 4.351 × 10 × ܸ݁ܦܤܣௌ௎ெ(݃) + 5.826 × 10

-1 The relationship between the rate of energy expenditure (DLWDEE; kJ·day ) and

2 mean acceleration (VeDBAMEAN; g) was poor (r = 0.24, F1,19 = 5.83, P = 0.03), indicating time as an important factor determining daily energy expenditure. Hence,

Chapter 3: Determining activity-specific energy expenditure total estimates of energy expenditure and acceleration, rather than rates, were used in all further analyses.

Chapter 3: Determining activity-specific energy expenditure

Table 3.1. Concurrent measures of energy expenditure from doubly-labelled water and accelerometry (VeDBA) for each individual (n = 21).

Bird ID Sex Body mass Duration of study Duration at sea Trips DLWDEE DLWEE VeDBAMEAN VeDBASUM (kg) (h) (h) (n)(kJ·day-1)(kJ)(g)(g) 1 M 2.675 74.35 29.9 3 2,991.1 8,800.6 0.15 0.910 × 106 2 M 2.475 95.83 28.5 3 2,518.9 9,708.6 0.18 1.142 × 106 3 F 2.450 76.37 114.7 2 3,931.2 12,508.2 0.35 2.514 × 106 4 M 2.425 72.02 46.8 1 1,982.4 5,767.1 0.30 1.000 × 106 5 F 2.475 120.88 49.9 3 3,077.3 14,893.5 0.12 1.064 × 106 6 F 2.650 72.67 36.6 2 3,667.9 10,739.1 0.21 0.970 × 106 7 M 2.425 77.27 67.7 3 4,093.1 12,500.7 0.23 1.489 × 106 8 M 2.450 97.53 33.2 2 2,131.5 8,223.0 0.20 0.936 × 106 9 M 2.625 124.6 46.0 4 2,276.5 11,621.8 0.12 0.812 × 106 10 F 2.775 76.05 32.0 2 3,915.0 11,677.0 0.17 1.037 × 106 11 M 3.025 123.33 52.9 5 2,965.2 14,763.2 0.16 1.660 × 106 12 F 2.600 74.72 43.8 1 2,109.3 6,170.3 0.05 0.035 × 106 13 F 2.525 72.18 52.2 2 3,735.3 10,637.0 0.16 0.878 × 106 14 M 2.350 71.48 7.8 2 3,603.6 10,236.4 0.18 1.113 × 106 15 F 2.725 101.42 42.4 3 2,619.5 10,647.0 0.12 0.955 × 106 16 F 2.475 71.25 40.3 3 1,907.1 5,363.4 0.14 0.826 × 106 17 M 2.500 72.02 42.1 2 2,347.0 6,615.6 0.16 0.330 × 106 18 F 2.975 99.67 33.6 3 2,729.6 10,889.7 0.13 1.044 × 106 19 M 2.800 117.83 21.2 2 4,023.5 19,267.7 0.31 3.081 × 106 20 F 2.425 102.58 38.6 3 3,634.9 14,959.0 0.24 2.062 × 106 21 M 2.500 97.9 70.5 2 4,798.5 18,795.5 0.24 1.982 × 106 Mean ± SE 2.59 ± 0.04 90.4 ± 4.0 47.9 ± 5.0 2.5 ± 0.2 3,098 ± 174 11,180 ± 800 0.19 ± 0.02 1.230 ± 0.146 ×106

Chapter 3: Determining activity-specific energy expenditure

Figure 3.1. Variation between individuals in the proportion of the study period spent performing each behaviour: resting on land (green), resting at sea (blue), gliding flight (yellow), flapping flight (red), and foraging (purple).

Chapter 3: Determining activity-specific energy expenditure

Figure 3.2. Correlations between estimated energy expended derived from the doubly-labelled water method (DLWEE) against: (a) total vectorial dynamic body

2 acceleration (VeDBASUM; r = 0.63); (b) model estimate incorporating VeDBASUM, distance travelled, and body mass (r2 = 0.84). Removal of time on land gave correlations between total energy expended at-sea (DLWAT-SEA) against: (c)

2 VeDBAAT-SEA SUM (r = 0.45); (d) model estimate incorporating distance travelled, and total head length (r2 = 0.76). Plots show the linear regression (solid line) and 95% confidence interval (dashed line).

Chapter 3: Determining activity-specific energy expenditure

The VeDBASUM values were then modelled with additional morphometric and

foraging parameters which could potentially influence the relationship (body mass,

sex, and parameters listed in Table 3.2). The most parsimonious model for DLWEE

included total distance travelled, body mass and VeDBASUM (wi = 0.37; see model

2 1.1 in Table 3.3) and which provided a higher correlation (r = 0.84, F1,19 = 102.6, P

< 0.001; Figure 3.2B) and, thus, more predictive relationship:

ି଺ ܦܮܹாா(݇ܬ) =12.20×ܦܶ + 6385 × ܤܯ + 0.237 × 10 × ܸ݁ܦܤܣௌ௎ெ(݃) െ 12920

where DT is distance travelled (km) and BM is body mass (kg).

To investigate whether the relationship between energy expenditure and

accelerometry data was more predictive for periods when birds were more active,

analyses were repeated for when animals were just at sea (equation 2). However,

although the resulting relationship between DLWAT-SEA (6,968 ± 1,033 kJ) and

2 VeDBAAT-SEA (9,021 ± 946 g) was significant, it was of lower predictive power (r =

0.45, F1,19 = 15.24, P < 0.001; Figure 3.2C). Furthermore, model selection after

incorporating the parameters in table 2 did not find VeDBAAT-SEA to be an important

variable (model averaging, c = 0.001±0.001, z = 1.10, P = 0.27) with the most

parsimonious model including only total distance travelled and total head length (r2 =

0.75, F1,19 = 56.45, P < 0.001; model 2.1 in Table 3.3; Figure 3.2D).

The activity-specific VeDBA values were compared with the activity estimates

determined from DLWEE in equation 1 to develop activity-specific energy

expenditure estimates (Table 3.4). The best correlations were found for flapping

2 2 flight (r = 0.83, F1,19 = 93.2, P < 0.001) and gliding flight (r = 0.87, F1,19 = 121.2, P

< 0.001). While the relationships for activity-specific VeDBA and energy

2 expenditure were also significant for resting on the sea surface (r = 0.59, F1,19 =

Chapter 3: Determining activity-specific energy expenditure

2 27.4, P < 0.001), foraging behaviour (r = 0.42, F1,19 = 13.8, P < 0.001) and resting

2 on land (r = 0.25, F1,19 = 6.4, P = 0.02; Figure 3.3), these were of low predictive accuracy.

Chapter 3: Determining activity-specific energy expenditure

Table 3.2. Parameters incorporated into models predicting total energy expenditure (DLWEE) and VeDBASUM for each individual.

Bill Bill Total Bird ID Wing ulna Tarsus Distance Plunge dives Surface foraging depth length head (mm) (mm) (mm) (mm) (mm) (km) n dives·h-1 n dives·h-1 1 32.7 88.2 190 207 70.1 217.5 417 2.5 331 2.0 2 32.7 93.2 190 202 69.1 224.3 195 1.2 629 3.8 3 34.0 93.3 187 211 68.0 536.8 429 2.3 254 1.4 4 33.1 90.7 187 198 67.3 228.7 218 3.4 119 1.9 5 35.0 96.0 197 208 61.9 587.6 255 1.3 599 2.9 6 31.1 90.2 193 206 70.4 373.4 324 2.5 292 2.3 7 31.0 91.2 189 207 26.5 370.2 641 3.6 422 2.4 8 31.9 90.4 197 203 65.4 293.3 515 3.9 566 4.3 9 34.0 90.8 186 204 70.4 306.8 122 0.8 316 2.0 10 32.3 94.5 192 217 70.3 416.5 521 3.1 457 2.8 11 32.4 91.6 193 207 70.5 334.7 489 1.9 666 2.6 12 30.6 90.0 185 206 67.4 99.3 253 0.5 154 0.3 13 34.2 90.4 192 200 67.6 358.9 301 2.0 298 2.0 14 32.1 89.1 184 207 68.4 499.3 372 2.2 706 4.2 15 32.6 94.0 191 208 65.6 444.2 217 1.1 347 1.7 16 32.5 93.7 192 204 65.6 264.5 559 3.5 414 2.6 17 32.0 92.4 190 202 66.0 221.3 150 0.1 205 0.2 18 33.0 88.5 191 201 68.1 336.2 206 1.0 641 3.0 19 34.4 89.3 176 198 50.2 424.5 261 1.0 110 0.4 20 31.3 88.5 188 208 68.1 631.1 232 1.0 513 2.2 21 32.2 89.3 183 204 24.7 879.8 692 3.0 733 3.2 Mean ± SE 32.6 ± 0.3 91.2 ± 0.5 189.2 ± 1.0 205.1 ± 0.9 62.9 ± 2.7 383.3 ± 36.3 350.9 ± 34.4 2.0 ± 0.2 417.7 ± 41.3 2.3 ± 0.2

Chapter 3: Determining activity-specific energy expenditure

Table 3.3. Linear mixed effects model outputs predicting total energy expended

(DLWEE; model 1) and total energy expended at-sea (DLWAT-SEA; model 2). model 1 Df AICc Delta Weight

1.1 Distance + Mass + VeDBASUM 5 345.8 0.00 0.37

1.2 Distance + Mass + VeDBASUM + Tarsus 6 346.7 0.88 0.24

1.3 Distance + Mass + VeDBASUM + Sex 6 348.4 2.60 0.10

1.4 Distance + Mass + VeDBASUM + Wing 6 349.8 4.00 0.05

1.5 Distance + Mass + VeDBASUM + Dive Rate 6 349.9 4.10 0.05 model 2 Df AICc Delta Weight 2.1 Distance + Total Head 4 359.1 0.00 0.35 2.2 Distance + Total Head + Mass 5 359.2 0.07 0.33 2.3 Distance 3 361.2 2.13 0.12

2.4 Distance + Total Head + VeDBAAT-SEA SUM 5 361.4 2.29 0.11 2.5 Distance + Mass 4 361.8 2.73 0.09

Chapter 3: Determining activity-specific energy expenditure

Figure 3.3. Estimates of activity-specific energy expenditure calculated from equation 1 (Energy expended; kJ) and equation 2 (VeDBASUM; g). Plot shows the predicted model linear regression (solid line) and 95% confidence interval (dashed line). Regression statistics provided in Table 3.4.

Chapter 3: Determining activity-specific energy expenditure

Table 3.4. Parameter estimates and activity-specific energy estimates. Parameter estimates were calculated from DLWEE and incorporated into equation 1 with proportion of time providing a total energy expenditure for each behaviour (tEE). The linear regression equations were derived to estimate activity-specific energy expenditure (tEE) from the accelerometry ethograms. Pearson correlation coefficient derived from the

2 relationship between the linear equation and tEE provided as r , indicating the accuracy of energy estimates from accelerometry for each behaviour.

Parameter estimates Average t Behaviour EE Linear equation r2 (CBEHAVIOUR) (CBEHAVOURxTBEHAVIOUR) -1 Resting on land 2,283 kJ·day 4,213 ± 481 kJ 0.0050xVeDBALAND + 2704 0.25 -1 Resting at-sea 4,543 kJ·day 5,483 ± 737 kJ 0.0066xVeDBARESTING + 610 0.59 -1 Flapping flight 15,128 kJ·day 5,076 ± 495 kJ 0.0022xVeDBAFLAPPING + 1272 0.83 -1 Gliding flight 2,183 kJ·day 270 ± 25 kJ 0.0004xVeDBAGLIDING + 81.23 0.87 -1 Foraging 1,994 kJ·day 192 ± 27 kJ 0.0014xVeDBAFORAGING + 36.67 0.42

Chapter 3: Determining activity-specific energy expenditure

Discussion

The use of accelerometry to determine behavioural patterns has become widespread, for birds, mammals and fish (Watanabe et al. 2005, Tsuda et al. 2006,

Holland et al. 2009, Martiskainen et al. 2009, Whitney et al. 2010, Bouten et al.

2013) and is increasingly being used for estimating energy expenditure (Wilson et al.

2006, Halsey et al. 2008, Green et al. 2009, Halsey et al. 2009, Halsey et al. 2011a).

However, validation studies have so far been mainly limited to captive situations or brief periods on free-ranging animals (Elliott et al. 2013) such that the accuracy of accelerometry for estimating energy expenditure over ecologically meaningful durations is not well established. In the present study, individuals were sampled for

3.6 ± 0.2 days, encompassing the full range of natural behaviours. While total

VeDBA was significantly correlated to DLW derived measures of total energy expenditure, a model incorporating distance travelled and body mass was found to provide a better correlation. These results indicate that estimating energy expenditure from accelerometry may not be entirely straightforward, with species-specific calibrations and incorporation of other variables needed to improve the predictive relationships.

Validation of VeDBA as a measure of energy expenditure

Using the DLW method, the present study estimated the FMR of chick-rearing

Australasian gannets to be 3,098±182 kJ·day-1, 28% more than that previously estimated from heart rate at the same study site (Green et al. 2013). While some studies have suggested that the DLW method may over-estimate FMR (Gales 1989,

Boyd et al. 1995, Visser and Schekkerman 1999), Green et al. (2013) found that

Chapter 3: Determining activity-specific energy expenditure

metabolic rate in Australasian gannets was temporally variable throughout the breeding season, presumably in response to changing foraging conditions, and this may explain the higher values observed in the present study. Indeed, similar FMR values of 3,380 kJ·day-1 have been reported for similar sized gannets Cape gannets

(Morus capensis) (Adams et al. 1991). Data for males and females were combined as no sex differences were evident due to the small sample size.

In contrast to the strong correlations (r2 = 0.81-0.91) observed beween energy expenditure and ODBA/VeDBA in laboratory studies running species on treadmills

(Wilson et al. 2006, Halsey et al. 2009), the lower correlations between accelerometry and FMR obtained using the DLW method in the present study indicates VeDBA on its own is not a good proxy for mean or total energy expenditure and metabolic rate in the Australasian gannets. This could be due to several factors. For example, VeDBA during gliding flight can be influenced by wind speed and direction independently of energy expenditure (Amélineau et al.

2014) and, while plunge-diving involves high accelerations, is unlikely to be energetically costly. Therefore, variations in these behaviours could alter the relationships between VeDBA and energy expenditure. Similarly, birds resting on the sea surface may experience a lot of movement due to wave action. In addition, laboratory studies of diving species have found a low accuracy for the relationship between energy expenditure and ODBA/VeDBA (r2 = 0.46-0.74) (Fahlman et al.

2008, Enstipp et al. 2011, Halsey et al. 2011a, Halsey et al. 2011b, Dalton et al.

2014), potentially due to the dampening of movements and loss of body heat to the water (Halsey et al. 2011b). Despite such potential factors, Elliott et al. (2013) found dynamic body acceleration provided a good proxy for energy expenditure in another diving seabird, thick-billed murres (Uria lomvia). However, in that study the

Chapter 3: Determining activity-specific energy expenditure

contribution of aquatic behaviours to total activity and energy expenditure was low whereas, in the present study, individuals spent on average 36% of their time at sea diving or resting on the surface and could have been major sources of bias in the relationships between VeDBA and energy expenditure.

Perhaps most importantly, energy expenditure is not only comprised of movement, but also encompasses physiological processes associated with basal metabolic rate, digestion, reproduction and thermoregulation (Dalton et al. 2014).

The low correlation between VeDBASUM and DLWEE may be due to individual variation in successful prey capture and, thus, digestive costs. Alternatively, the amount of time resting on land could expose individuals to different ambient temperatures and, hence, thermoregulatory costs. Stronger relationships between energy expenditure and behaviour are expected to occur when the movement involved exceeds the energy expenditure of these other variables by a factor > 10

(Darveau et al. 2002, Wilson et al. 2006). However, for gannets, although plunge diving involves a high acceleration rate (up to 7 g) (Yang et al. 2012), it is of low energetic cost (Green et al. 2010). Hence, the relationship between VeDBA and overall energy expenditure may have been weakened by inter-individual variation in such behaviour.

In the present study, the relationship between DLW and VeDBA was improved with the addition of total distance travelled and body mass. Foraging trip distance varied greatly between individuals which may influence energy expenditure. Indeed, previous studies on seabirds have found the distance and duration of a foraging trip to be positively correlated with FMR (Ballance et al. 2009, Masden et al. 2010). In addition, body mass is also correlated to FMR in both seabirds and other vertebrate species, with a greater body mass resulting in a greater overall energy expended

Chapter 3: Determining activity-specific energy expenditure

(Nagy 1987, Shaffer et al. 2004, Nagy 2005). Given that accelerometry does not incorporate basal metabolic rate and thermoregulation, the inclusion of body mass into the model may account for variation between individuals due to these factors.

Hence, it is not surprising that incorporation of these variables provided a better relationship between VeDBA and overall energy expenditure.

It should be noted that DLW itself is an estimate of energy expenditure, giving a single value from the total duration an individual is sampled. Hence, the variation between DLWEE and VeDBASUM could also be caused by errors arising within the

DLW estimate.

Activity-specific energy expenditure

Variation in the allocation of time for each behaviour can influence the trade-off between energy expended and acquired. As such decisions made while foraging may have potential repercussions for an individual’s reproductive success and overall fitness. Thus, understanding the cost of specific behaviours allows for comparison between individuals and could potentially be used to predict breeding success. The present study found the proportion of time spent performing each behaviour varied greatly between individuals with some spending a high proportion of time at sea resting on the sea surface while others spent more time in flight (Figure 3.3).

In the present study, the proportion of time spent performing each behaviour was used to divide the overall energy expenditure (obtained from the DLW) and compare with the accelerometer data for each behaviour. VeDBA from flapping flight and gliding flight were the most tightly correlated with energy expenditure, most likely due to the cost of these behaviours being directly related to their movement. In

Chapter 3: Determining activity-specific energy expenditure

contrast, resting on the sea surface which can incur movement from external factors

(i.e. wave motion) and energetic costs from internal factors (i.e. digestion and thermoregulation), did not correlate with the degree of movement recorded.

Furthermore, birds resting on land are likely to be preening, tail wagging, and defending their nests from conspecifics, behaviours which involve bursts of movement with low costs. Indeed, energy expenditure estimated from accelerometry for both resting at sea and on land higher than estimates determined from heart rate

(fH) estimates (at sea: 2,278 kJ·day-1; on land: 783 kJ·day-1; Green et al. 2013).

The low correlation between foraging VeDBA and energy expenditure is possibly due to this behaviour category encompassing plunge diving and surface foraging events, with the smaller behaviours these entail (i.e. free falling, underwater flapping etc.). Variation in the effort expended within and between these behaviours could be a contributing factor to the weak relationship. Furthermore, effort may be overestimated from plunge diving as it involves high acceleration with little cost, while flapping underwater in pursuit of prey, either as an extension of a plunge dive or a surface duck-dive (Ropert-Coudert et al. 2009a) may underestimate effort due to movement potentially dampened by the viscosity of water, giving a poor correlation with energy expenditure (Halsey et al. 2011b). Indeed, a previous estimate of effort found the cost of foraging to be twice the value estimated in the present study (4,778 kJ·day-1; Green et al. 2013).

In summary, the results of the present study have shown that accelerometry provides a simple method for measuring energy expenditure in Australasian gannets but that relationships with VeDBA require information on distance travelled and mass to have high predictive power. In addition, expanding on previous studies of short duration (Elliot et al 2014), the results show that the method is accurate over

Chapter 3: Determining activity-specific energy expenditure

extended periods encompassing all behaviours. Hence, accelerometers provide a relatively inexpensive tool for enabling large numbers of individuals to be sampled over ecologically meaningful time-scales in this and other large seabirds.

Activity-specific energy expenditures could only be reliably predicted for the two flight modes (flapping and gliding). However, as such behaviours have been shown to vary in relation to local prey availability in Australasian gannets (Chapter 4), the results of the present study indicate accelerometry can also be used to investigate how individuals alter their the energy budgets in response to environmental variability. The degree to which accelerometry can be widely used as a reliable tool to determine energy expenditure in other seabirds will depend on the predictive power of the relationships with various behaviours as well as how variable animals are in the other traits, such as basal metabolic rate and thermoregulation expenditure, that affect energy expenditure but accelerometry cannot detect. Further studies are required to assess this in species with different flying and foraging modes.

Chapter 4

Eating locally: Australasian gannets increase their effort in a

restricted foraging range

A version of this chapter has been published as:

Angel L.P., Berlincourt M., Barker S., Tew E., Warwick-Evans V., Arnould J.P.Y.

(2015) Eating locally: Australasian gannets increase their effort in a restricted foraging

range. Biology Open 4:1298-1305

Chapter 4: Gannets increase their effort

Abstract

During the breeding season, seabirds adopt a Central Place Foraging strategy and are restricted in their foraging range by the fasting ability of their partner/chick and the cost of commuting between the prey resources and the nest. Because of the spatial and temporal variability of marine ecosystems, individuals must adapt their behaviour to increase foraging success within these constraints. The at-sea movements, foraging behaviour and effort of the Australasian gannet (Morus serrator) was determined over three sequential breeding seasons of apparent differing prey abundance to investigate how the species adapts to inter-annual fluctuations in food availability. GPS and tri-axial accelerometer data loggers were used to compare the degree of annual variation within two stages of breeding

(incubation and chick rearing) at a small gannet colony situated between two larger, nearby colonies. Interestingly, neither males nor females increased the total distance travelled or duration of foraging trip in any breeding stage (P > 0.05 in all cases) despite apparent low prey availability. However, consistently within each breeding stage, mean vectorial dynamic body acceleration (an index of energy expenditure) was greater in years of poorer breeding success (increased by a factor of three to eight), suggesting birds were working harder within their range. Additionally, both males and females increased the proportion of a foraging trip spent foraging in a poorer year across both breeding stages. Individuals from this colony may be limited in their ability to extend their range in years of low prey availability due to competition from conspecifics in nearby colonies and, consequently, increase foraging effort within this restricted foraging area.

Chapter 4: Gannets increase their effort

Introduction

Individuals provisioning young at a natal site are limited in their foraging range by the cost of transport and/or the fasting ability of, and predation risk to, the unattended offspring (Orians and Pearson 1979). Consequently, profitable prey patches which occur close to the central location will be targeted first as they provide the highest rate of net energy intake versus effort expended and minimise the time away from the offspring (Pyke et al. 1977). However, in periods of reduced prey availability, Central Place Foragers will have to increase effort and/or extend their range in search of resources to meet the nutritional needs of the offspring and their own maintenance (Cairns 1987, Abrams 1991).

Marine Central Place Foragers, such as breeding seabirds, are subject to high spatial and temporal variability in their environment (Weimerskirch 2007). Changes in sea-surface temperature, wind stress and ocean circulation can all alter prey distribution at multiple spatial and temporal scales (Montevecchi 1993), and individuals must adapt their behaviour in order to maximise foraging success and, consequently, breeding performance (Inchausti et al. 2003, Sandvik et al. 2012,

Watanuki and Ito 2012). For example, as seen in common murres (Uria aalge) in

Canada, individuals may increase their foraging range in response to environmental variability, flying further from the colony in years when preferred prey is low (Burke and Montevecchi 2009). Alternatively, common murres in Scotland switched to more predictable but less energy-dense prey (Wanless et al. 2005) while black-browed albatross (Thalassarche melanophris) have been shown to increase the frequency of chick-provisioning to compensate for smaller, less available prey (Weimerskirch et al. 1997).

Chapter 4: Gannets increase their effort

In addition, it has long been hypothesised that neighbouring colonies of conspecifics will spatially segregate in foraging areas to reduce intra-specific competition and that colony size may be limited by available habitat within the maximum foraging range (Adams 2001). Indeed, Wakefield et al. (2013) recently documented how northern gannet (Morus bassanus) populations around the United

Kingdom forage in largely mutually exclusive areas despite their potential home ranges overlapping. However, while density-dependent competition at each colony will influence the extent of an individuals’ foraging range, geographic boundaries

(e.g. continental shelf-edge, protruding coastlines), in conjunction with proximity to other colonies, may restrict the ability of individuals to extend their range in times of reduced prey availability. Such restrictions could lead to differential reproductive responses to environmental change between colonies and, ultimately, influence population trajectories.

The Australasian gannet (Morus serrator) is a large marine predator breeding in colonies of 10-12,300 nests in New Zealand and Australia (Nelson 1978, Bunce et al.

2002). As with other Sulids (gannets and boobies), the species forages mainly on pelagic schooling fish, primarily by plunge diving, often feeding in association with conspecifics and heterospecific competitors (Bunce and Norman 2000). In south- eastern Australia, Australasian gannets forage on the shallow (and in places, narrow) continental shelf region of Bass Strait, located between mainland Australia and

Tasmania, playing an important role in the ecosystem (Bunce 2001). Bass Strait is influenced by warm surface waters and cool, deep Antarctic and sub-Antarctic waters (Gibbs 1992), as well as the seasonally strong Bonney Upwelling (Nieblas et al. 2009). Inter-annual variability in the environmental conditions of Bass Strait has been shown to influence the diet, foraging behaviour and breeding performance of

Chapter 4: Gannets increase their effort marine predators in the region (Mickelson et al. 1992, Gibbens and Arnould 2009,

Ropert-Coudert et al. 2009b, Hoskins and Arnould 2014, Knox et al. 2014).

While other Sulids have been shown to increase their foraging range (Hamer et al.

2007, Hennicke and Weimerskirch 2014), alter their diet (Pichegru et al. 2007) and fly towards previously successful areas (Weimerskirch et al. 2005a) when local conditions are poor, little is known about the behavioural response of Australasian gannets to environmental variability. Whereas Bunce and Norman (2000) found that

Australasian gannets altered their diet during a large scale mortality of their preferred prey, the impact on the colony was only short-term (Pyk et al. 2013). As south- eastern Australia is predicted to experience substantial oceanographic changes in coming decades (Poloczanska et al. 2007, Ridgway and Hill 2009), information regarding behavioural adaptation to persisting environmental change is necessary to predict the response of the population. This is especially so in view of the precarious nature of some colonies (i.e. small colony size; Norman et al. 1998), the added impact of fisheries interactions and other detrimental anthropogenic effects (Bunce

2001), and the important ecological role (and economic significance in ecotourism) some colonies may have in unique localised habitats (Edmunds 2003). Therefore, the aims of the present study were to determine whether Australasian gannets alter their:

(1) at-sea movements; and/or (2) foraging effort in response to variability in a proxy for local prey conditions.

Chapter 4: Gannets increase their effort

Methods

Study site and animal handling

The study was conducted between October and February, over three consecutive breeding seasons (2011-2013) at the Pope’s Eye gannet colony (38°16’42”S

144°41’48”E; Figure 4.1), in south-eastern Australia. Sampling was separated into two stages, incubation and chick rearing. During incubation, gannets take turns to guard the egg while their partner forages. Whereas during chick rearing, adults continue to alternate remaining at the nest but obtain enough food for the chick and self-provisioning. Hence, the two stages cover the different time restrictions faced by a foraging adult due to the fasting capabilities of its chick and breeding partner.

Study nests were selected at random throughout the colony in each of these stages and, where possible, both partners were sampled to ensure an equal sex ratio. All nests in the colony were monitored fortnightly for the duration of the breeding season. Breeding success was determined by the percentage of laid eggs which became fledged chicks. Chicks sighted for 90 consecutive days since hatching and displaying juvenile plumage were presumed to have fledged (Pyk et al. 2007).

Adults were captured on the nest and the egg/chick covered for protection from aggressive conspecifics on neighbouring nests. In order to determine the at-sea movements of breeding birds, individuals were equipped with a GPS data logger

(IgotU GT-600, Mobile Action Technologies Inc., Taiwan; 26.5 g) recording location (±10 m) every 2 min. In addition, to obtain information about at-sea activity patterns and foraging effort (Shepard et al. 2010), all individuals were also instrumented with a tri-axial accelerometer data logger (X8 500mAh, Gulf Coast

Data Concepts LLC, USA; 14.1 g) sampling at 25 Hz. The devices were encapsulated in heat shrink (whole package 52.6 g, < 3% body mass; Phillips et al.

Chapter 4: Gannets increase their effort

2003) and attached with water-proof tape (Tesa, Beiersdorf AG, Germany) to the central tail feathers following the methods of Wilson et al. (1997). Device positioning ensured it was covered by the wings during a plunge dive in an attempt to reduce drag (Hamer et al. 2000). Individuals were then weighed in a cloth bag using a suspension scale (± 25 g, Salter Australia Pty Ltd, Australia) and morphometric measurements (i.e. bill length and depth, total head, wing and tarsus length) were taken before individuals were returned to the nest with the whole procedure lasting less than 10 minutes. Individuals were recaptured after 1-12 days, weighed as previously described and the devices removed. Due to logistical constraints, devices were recovered from most individuals beyond the battery life. As such, mass gain by the adult and chick on recovery could not be matched up to the GPS and accelerometry data to infer foraging success. A single blood sample (0.1 mL) was then collected by venepuncture of a tarsus vein for genetic sexing (DNASolutions,

Australia) before the individual was released. Individuals were sampled only once in each breeding season.

Data processing and statistical analyses

To determine if environmental conditions which may influence prey availability varied during the study, weekly sea surface temperatures (SST, °C) and sea-surface chlorophyll-a concentrations (chl-a, mg·m-3) were extracted from areas known to be frequented by foraging gannets (Pyk 2012; this study), i.e. Port Phillip Bay (38°23’S

- 37°47’S; 144°18’E - 145°11’E) and Bass Strait (40°12’S - 38°30’; 144°5’E -

146°48’E; Figure 4.1). The influence of environmental conditions in the months prior to breeding have previously been found to influence breeding success in

Chapter 4: Gannets increase their effort

Figure 4.1. Location of Pope’s Eye gannet colony (indicated by the red dot) in south-eastern Australia (dark rectangle on inset map). Environmental data was extracted from Port Phillip Bay (A) and Bass Strait (B). In addition, environmental data were obtained in the region of the seasonally active Bonney Upwelling (C; shown in inset map). Bathymetry and edge of the continental shelf are indicated by light grey lines. Other gannet colonies in the region are indicated by a black dot

(proportional to their size, as detailed in the legend). Colony size for Pope’s Eye includes all birds nesting in Port Phillip Bay due to their close proximity. As birds typically remain on the continental shelf to forage, arrows indicate potential foraging areas and direction for each gannet colony, based on colony size and location

(Wakefield et al. 2013).

Chapter 4: Gannets increase their effort

seabirds (Thompson and Ollason 2001, Barbraud and Weimerskirch 2003).

Therefore, conditions prior to breeding commencing (June-August) as well as during the breeding months (October-January) were considered. Additionally, SST and chl- a for the nearby Bonney Upwelling (-39°S – 38°S; 136°E - 142°E)(strongest between January and March; Butler et al. 2002, Hobday and Hartog 2014), an oceanographic feature of ecological importance to Bass Strait (Butler et al. 2002), were obtained. The Bonney Upwelling has been shown to influence the breeding success of fur seals in the subsequent summer (i.e. cascade effect on nutrients;

Gibbens and Arnould 2009) and, thus, may influence prey availability for the

Australasian gannet. Therefore, environmental variables from the Bonney Upwelling region in the January-March period prior to the breeding season were investigated.

Weekly means for environmental variables were obtained from the AVHRR sensors

(SST; resolution 4 km; courtesy of CSIRO Oceans and Atmosphere Remote Sensing) and MODIS satellites (chl-a; 4 km; courtesy of NASA. http://oceandata.sci.gsfc.nasa.gov).

GPS locations were processed through a speed filter (McConnell et al. 1992) and summary statistics calculated with the adehabitatHR package (Calenge 2006) in the

R statistical environment (R Core Team 2015). While the number of foraging trips per individual ranged from 1-16 trips, most individuals in the first year of the study only conducted one trip before the batteries failed. Hence, stages were compared between years using only the first foraging trip for each individual. Foraging trip metrics were calculated (i.e. maximum distance from colony, total distance and duration, and average speed) for the foraging trip.

Data obtained from the tri-axial accelerometer were used to visually assess behaviour in IGOR Pro (Version 6.34, WaveMetrics, USA) based on previous

Chapter 4: Gannets increase their effort

studies of plunge diving species (Ropert-Coudert et al. 2004b, Weimerskirch et al.

2005b, Ropert-Coudert et al. 2009a). Four key behaviours were identified: resting at the sea surface, flapping flight, gliding flight, and foraging (including plunge diving and surface foraging; Warwick-Evans et al. 2015). The Ethographer package was used to perform a k-means algorithm clustering analysis (see Sakamoto et al. 2009) and identify behaviour using an unsupervised continuous wavelet transformation (1 second window). Each cluster was assigned a behaviour based on the visual identification. From this, the proportion of time spent performing each behaviour, within a foraging trip, was calculated. Additionally, the total number of dives were used to calculate dive rate (dives·h-1) averaged over the foraging trip. Overall dynamic body acceleration (ODBA) is typically used as an indication of energetic expenditure obtained from the accelerometry data (Wilson et al. 2006, Gomez Laich et al. 2011, Elliott et al. 2013). However, as the accelerometer could not be placed in the centre of gravity of the bird due to likely removal (Vandenabeele et al. 2014), vectorial dynamic body acceleration (VeDBA) was determined to be more appropriate than ODBA (Gleiss et al. 2011). VeDBA was calculated following the methods outlined by Qasem et al. (2012), the sum of all values (total VeDBA) and the mean VeDBA were then calculated. Total VeDBA was considered as it incorporates the trip duration into the values, whereas mean VeDBA allows comparison of the rate of energy expended across years.

As behavioural data were proportional, an arcsine transformation was performed.

The assumptions of independent and normally distributed data were tested with a

Chi-Square test and Shapiro–Wilk’s test, respectively. Where these assumptions were not met, a log10 transformation was performed. Data was analysed using one-

Chapter 4: Gannets increase their effort

way ANOVAs followed by Tukey’s post hoc tests to assess inter-annual variation.

Results are presented as mean ± standard error.

Results

There were significant differences in the fledging success of gannets between the three years of the study (Table 4.1), decreasing from 64.7% in 2011 to 30.1% in 2012 and 8.62% in 2013 (Chi-squared test, Ȥð2 = 46.45, P < 0.001). These findings coincided with variations in environmental conditions in the region. Although sea surface temperature measured within Bass Strait and Port Phillip Bay, was not significantly different across the three years during the breeding months (One-way

ANOVA, F2,28 = 0.71, P = 0.50; F2,28 = 0.67, P = 0.52; Table 4.1). During the winter months preceding breeding, both SST and chl-a in Bass Strait (F2,39 = 8.89, P <

0.001; F2,39 = 7.08, P = 0.002) and Port Phillip Bay (F2,39 = 4.33, P = 0.02; F2,38 =

16.31, P < 0.001) were significantly different, with colder temperatures and higher chl-a concentrations occurring in years when fledging success was highest (64.7% breeding success; Table 4.1). Furthermore, in the Bonney Upwelling during January-

March prior to the breeding season, average SST significantly increased (from 17.4 ±

0.2°C in 2011 to 19.1 ± 0.2°C in 2013; F2,36 = 12.39, P < 0.001) and chl-a significantly reduced (from 0.29 ± 0.02 mg·m-3 in 2011 to 0.19 ± 0.01 mg·m-3 in

2013; F2,36 = 12.51, P < 0.001), corresponding with years when breeding success was lower (Table 4.1). Consequently, these observations suggest that environmental conditions across the three years may have resulted in a decrease in food availability during the breeding seasons investigated which negatively impacted the birds’ ability to raise offspring.

Chapter 4: Gannets increase their effort

Table 4.1. Seasonal averages from weekly sea surface temperature (SST, °C) and chlorophyll-a (Chl-a, mg·m-3) concentration in surrounding regions of Pope’s Eye gannet colony during and prior to the breeding season. Breeding success was calculated as the percentage of chicks fledged from total eggs laid. Results are presented as mean ± standard error.

2011 2012 2013 F Statistic df P Eggs laid 119 156 116 Eggs hatched 110 108 32 Chicks fledged 77 47 10 Breeding success 64.7% 30.1% 8.6% Bass Strait SST during 15.7 ± 0.4 16.2 ± 0.4 16.3 ± 0.4 0.71 2,28 0.5 SST prior 13.4 ± 0.2a 13.4 ± 0.2a 14.2 ± 0.1b 8.89 2,39 <0.001 Chl-a during 0.25 ± 0.2 0.23 ± 0.02 0.24 ± 0.01 0.28 2,28 0.8 Chl-a prior 0.70 ± 0.03a 0.60 ± 0.03b 0.56 ± 0.02b 7.08 2,39 0.002 Port Phillip Bay SST during 17.5 ± 0.6 18.4 ± 0.5 18.30 ± 0.7 0.67 2,28 0.5 SST prior 12.1 ± 0.2a 11.6 ± 0.2a 12.4 ± 0.2b 4.33 2,39 0.02 Chl-a during 4.15 ± 0.38 a 1.94 ± 0.35b 2.21 ± 0.28b 14.52 2,28 <0.001 Chl-a prior 11.62 ± 1.52a 5.13 ± 0.88b 3.41 ± 0.50b 16.31 2,38 <0.001 Bonney Upwelling SST prior 17.4 ± 0.2a 18.2 ± 0.2b 19.1 ± 0.2c 12.39 2,36 <0.001 Chl-a prior 0.29 ± 0.02a 0.20 ± 0.01b 0.19 ± 0.01b 12.51 2,36 <0.001 Significant differences and homogenous subsets (P > 0.05) are indicated by superscripts.

Chapter 4: Gannets increase their effort

A total of 103 Australasian gannets were tracked from Pope’s Eye across the three consecutive years (2011, n = 32; 2012, n = 46; 2013, n = 25). Because so few nests successfully hatched chicks in 2013, no data were obtained during chick rearing of that year. Furthermore, due to device failure in incubation 2011, of the 15 devices deployed, GPS and acceleration data were only available for 2 males and 4 females.

Hence, results incorporating incubation 2011 should be interpreted cautiously. The total distance travelled during a foraging trip had a significant difference between sex

(F1,99 = 8.26, P = 0.005) and stage (F1,99 = 6.74, P = 0.01). Hence, inter-annual variability was analysed separately for each sex. While a high percentage of birds sampled were from pairs (44% in 2011, 74% in 2012, and 72% in 2013), no correlations were found between breeding pairs in foraging behaviour (Pearson correlation: foraging range: r2 = 0.03; distance travelled: r2 = 0.003; trip duration: r2 =

0.02, P > 0.05 in all cases) or effort (total VeDBA: r2 = 0.05; mean VeDBA: r2 = 0.4,

P > 0.05 in all cases). Thus, for the purposes of this study individuals within pairs were considered independent from each other.

There were no significant differences between years in the total distance travelled, maximum distance from the colony or duration of the foraging trip in both breeding stages (P > 0.05 in all cases; Table 4.2) for either sex. While males travelled at a greater average speed during incubation in 2011 (14.2 ± 3.5 km·h-1) compared to

-1 -1 2012 (6.9 ± 1.0 km·h ) and 2013 (7.0 ± 0.6 km·h ; F1,21 = 3.85, P = 0.04), average speed did not increase in any other stage of breeding in years of lower apparent food availability. During this stage, males spent a significantly greater proportion of the foraging trip flapping in 2013 (28.4 ± 5.9%) compared to 2011 (11.6 ± 2.7%) and

2013 (14.8 ± 1.7%; F2,16 = 8.27, P = 0.02; Figure 4.2C). However, males did not significantly differ in the proportion of foraging trip spent gliding (F2,16 = 0.01, F1,16

Chapter 4: Gannets increase their effort

= 1.69; Figure 4.2E) or resting (F2,16 = 3.35, F1,16 = 0.10; Figure 4.2A) between years, in either stage (P > 0.05 all cases). Females spent a greater proportion of their foraging trip gliding in 2011 (25.8 ± 2.0%) compared to 2012 (17.3 ± 1.9%; F1,23=

11.98, P < 0.01; Figure 4.2F), during chick rearing. However, they did not significantly increase the proportion of the foraging trip spent flapping (F2,16 = 0.47,

F1,23 = 0.02, Figure 4.2D) or resting (F2,16 = 0.32, F1,23 = 0.65; Figure 4.2B) across years, in incubation or chick rearing, respectively (P > 0.05 all cases).

In contrast, the proportion of time at sea spent foraging increased in 2012, a year of lower prey availability, for males during incubation (0.3 ± 0.3% in 2011 compared to 6.6 ± 0.9% in 2012; F2,16 = 14.58, P < 0.001) and chick rearing (1.4 ± 0.4% in

2011 compared to 8.3 ± 1.4% in 2012; F1,16 = 25.73, P < 0.001). However, the time spent foraging did not significantly differ between 2012 and 2013 for incubation (P =

0.74, Figure 4.2G). Females also increased the proportion of time spent foraging between from 2011 to 2012, in incubation (2.3 ± 2.0% to 7.1 ± 1.2%; F2,16 = 7.83, P

= 0.006) and chick rearing (1.1 ± 0.2% to 5.8 ± 0.9%; F1,23 = 13.45, P < 0.001), but not in incubation between 2012 and 2013 (P = 0.66, Figure 4.2H). Dive rate was only significantly greater for males in 2012 during chick rearing (0.1 ± 0.03 dives·h-1 in 2011 to 0.5 ± 0.1 in 2012 to; F1,16 = 12.90; Figure 4.2I). In no other stages did males or females increase their dive rate.

Chapter 4: Gannets increase their effort

Table 4.2. Foraging trip parameters of Australasian gannets from Pope’s Eye. Results are presented as mean ± standard error.

Incubation Chick rearing Males 2011 (n=2) 2012 (n=8) 2013 (n=14) 2011 (n=11) 2012 (n=17) Maximum distance (km) 49.6 ± 31.9 38.5 ± 7.4 30.0 ± 7.2 40.7 ± 11.1 41.1 ± 4.8 Total distance (km) 157.1 ± 114.7 193.9 ± 31.9 161.9 ± 28.8 155.1 ± 35.1 144.7 ± 18.0 Trip duration (h) 14.5 ± 12.1 27.3 ± 3.4 22.0 ± 2.4 11.6 ± 2.5 11.1 ± 1.4 Average speed (km·h-1) 14.2 ± 3.5a 6.9 ± 1.0b 7.0 ± 0.6b 12.5 ± 1.1 13.3 ± 1.4

Females 2011 (n=4) 2012 (n=9) 2013 (n=11) 2011 (n=15) 2012 (n=14) Maximum distance (km) 81.7 ± 27.9 54.4 ± 18.6 99.4 ± 3.6 54.7 ± 8.5 50.2 ± 6.9 Total distance (km) 370.1 ± 79.3 214.5 ± 52.7 514.8 ± 157.9 220.4 ± 30.2 164.7 ± 22.5 Trip duration (h) 41.8 ± 16.9 25.7 ± 3.2 43.5 ± 11.0 18.5 ± 2.7 13.6 ± 1.8 Average speed (km·h-1) 11.5 ± 1.8 7.6 ± 1.2 9.6 ± 1.1 10.6 ± 0.8 10.0 ± 0.6 Significant differences and homogenous subsets (P > 0.05) are indicated by superscripts

Chapter 4: Gannets increase their effort

Figure 4.2. Comparison of mean ± standard error foraging behaviour and effort for

Australasian gannets across three years (2011 – blue; 2012 – green; 2013 – purple) in incubation (circles) and chick rearing (squares) breeding stages. Behaviours include: proportion of foraging trip spent resting on the sea surface (A,B); flapping flight

(C,D); gliding flight (E,F); foraging (G,H); and dive rate (dives·h-1; I,J). While energy expenditure is represented by: mean Vectorial Dynamic Body Acceleration

(g; K,L). In variables with significant results homogenous subsets (P > 0.05) are indicated by superscripts. 88

Chapter 4: Gannets increase their effort

Mean VeDBA (an index of energy expenditure) increased by a factor of three to eight (depending on stage) for both sexes, suggesting a higher rate of foraging effort, during years of presumably lower prey availability (P < 0.001 in all cases; Figure

4.2K-L). Total energy expended (total VeDBA) was significantly greater in poorer years. Total VeDBA during incubation increased from 1.7 × 105 ± 1.6 × 105g and 1.9

×105 ± 0.7 × 105g in 2011, to 7.9 × 105 ± 0.9 × 105g and 6.6 × 105 ± 1.4 × 105g in

5 5 5 5 2012, to 10.0 × 10 ± 1.3 × 10 g and 28.0 × 10 ± 6.1 × 10 g in 2013 for males (F2,16

= 5.67, P = 0.02) and females (F2,16 = 17.64, P < 0.001), respectively. During chick rearing total VeDBA increased from 1.5 × 105 ± 0.6 × 105g and 3.3 × 105 ± 1.1 ×

105g in 2011 to 3.3 × 105 ± 1.1 × 105g and 6.1 × 105 ± 1.0 × 105g in 2012 for males

(F1,16 = 10.10, P = 0.006) and females (F1,23 = 5.29, P = 0.02), respectively.

Discussion

In years of reduced local food availability, numerous seabirds have been shown to extend their foraging range to acquire sufficient resources for chick provisioning and self-maintenance (Cairns 1987). As Central Place Foragers, however, breeding seabirds are restricted in their foraging range due to the fasting capability of either their partner, during incubation, or their offspring, during chick rearing (Ricklefs et al. 1985, Clutton-Brock 1991). Should environmental conditions or prey availability drop below the critical threshold, long-lived birds typically prioritise their own survival resulting in a decrease in breeding success (Erikstad et al. 1998). In the present study, the breeding success in 2011 (64.7%) was similar to the historical average (63.4±7.2% between 1988-2006; Pyk et al. 2013) however it dropped substantially in the two subsequent years which coincided with poor environmental conditions consistent with reduced prey availability (Stenseth et al. 2002). Analysis

Chapter 4: Gannets increase their effort

of the foraging behaviour by Australasian gannets from this colony across the three breeding seasons suggests that, unlike observations in most seabirds, individuals did not increase their foraging range in response to apparent reduced prey availability but instead increased effort within their restricted range.

Consistent with that observed in other species (Thompson and Ollason 2001,

Monticelli et al. 2007), the results of the present study found indices of marine primary productivity in regions adjacent to the colony in the months preceding the breeding season were higher in years of greater reproductive success. As has been proposed for species elsewhere (Frederiksen et al. 2004, Cullen et al. 2009), such relationships could be used to broadly predict fledging success in Australasian gannets at this and other colonies. In the winter months prior to the breeding season sea surface temperature was lower and chlorophyll-a was higher in the regions where the gannets forage (Bass Strait and Port Phillip Bay). Furthermore, fluctuation in indices of primary productivity in the Bonney Upwelling (500 km to the west) 8-10 months prior to the breeding season were found to correspond to differences in fledging success. A similar time lag of the influence of the Bonney Upwelling has previously been observed in Australian fur seal breeding in central northern Bass

Strait (Gibbens and Arnould 2009). These results highlight the importance of this seasonally active upwelling in influencing the nutrient cascade, numerous trophic levels and top predators of the region. Indeed, in 2013 low breeding success was also observed in other Bass Strait marine predators such as short-tailed shearwaters

Puffinus tenuirostris (Berlincourt and Arnould 2015a), little penguins Eudyptula minor (Berlincourt and Arnould 2015b), and Australian fur seals Arctocephalus pusillus doriferus (Arnould unpublished data), indicative of poor foraging conditions.

Chapter 4: Gannets increase their effort

Throughout periods of reduced food availability increased foraging range and duration has been observed in numerous seabirds (Hamer et al. 1993, Monaghan et al. 1994, Ronconi and Burger 2008). In the Sulidae, Abbott’s booby (Papasula abbotti) and northern gannets have been found to perform longer foraging trips in response to variable environmental conditions (Hamer et al. 2007, Hennicke and

Weimerskirch 2014). Pichegru et al. (2010), however, found that while Cape gannets

(Morus capensis) did not travel further from the colony when local resources decreased, individuals increased their foraging trip duration and total distance travelled in search of prey within their foraging range and also switched to less energy dense prey, provided by fisheries discards (Pichegru et al. 2007). A similar reliance on fisheries discards has been observed in northern gannets in times of poor food conditions (Votier et al. 2013), an option less available to Australasian gannets in Bass Strait due to the smaller scale of commercial fisheries operating there (Bunce

2001). In the present study, in years of apparent reduced food availability neither male nor female Australasian gannets increased trip duration, maximum range or total distance travelled. Indeed, 97% of birds remained within 150 km of the colony, similar to previous observations for individuals at the study colony (Pyk 2012), with only 6% of birds foraging for longer than 48 h in the poor years. While, Cape and northern gannets, from colonies of similar size to Pope’s Eye, have been found to forage 114-160 km from the colony (Grecian et al. 2012, Ludynia et al. 2012), it is expected that in years of presumably poor prey availability, gannets would forage to their maximum potential. Australasian gannets are capable of foraging up to 550 km from the colony (Machovsky-Capuska et al. 2014b). Hence, the results of the present study suggest a constraint may be limiting the foraging range of gannets from Pope’s

Eye.

Chapter 4: Gannets increase their effort

Intra-specific competition may also influence the distance and duration a marine predator forages (Lewis et al. 2001). Indeed, density-dependent competition has been shown in Cape and northern gannets to create mutually exclusive foraging areas between adjacent colonies (Grémillet et al. 2004, Wakefield et al. 2013). Pope’s Eye colony is comprised of up to 180 breeding pairs, with an additional 330 nests scattered throughout Port Phillip Bay (Pyk et al. 2013). Two other major

Australasian gannet colonies occur in relatively close proximity: Lawrence Rocks

(3,100 pairs) and its sub-colony Point Danger (660 pairs) in western Bass Strait; and

Black Pyramid (12,300 pairs) in southern Bass Strait (Bunce et al. 2002). As gannets typically remain on the continental shelf to forage (location of preferred prey;

Fletcher and Tregonning 1992), these much larger colonies are likely to have established mutually exclusive foraging zones stretching into Bass Strait, potentially restricting the foraging range of individuals from the Pope’s Eye colony (Figure 4.1).

Consequently, in years of reduced local prey availability, individuals from Pope’s

Eye may not be able to extend their foraging range due to intra-specific competition

(Wakefield et al. 2013). Such competition could potentially lead to a reduced foraging range if individuals from other larger colonies extend their movements in search of prey in times of reduce availability.

As gannets forage in three-dimensions, the effort involved in resource acquisition may not be accurately reflected from horizontal movements alone (Shepard et al.

2008). In the present study, the proportion of foraging trips spent resting on the sea surface were similar between years and consistent with previous reports from the same colony (Green et al. 2010). However, mean VeDBA and total VeDBA (an index of energy expenditure; Gleiss et al. 2011, Qasem et al. 2012) was significantly greater in years of lower apparent food availability suggesting the birds were

Chapter 4: Gannets increase their effort

working harder searching for prey (Shepard et al. 2008). Indeed, the proportion of time spent foraging in a trip across both sexes and stages was found to be higher in

2012 compared to 2011, increasing their foraging when prey availability was apparently low. Foraging time included both plunge diving (used to calculate dive rate) and surface foraging, a foraging event consuming a high percent of the foraging time (Garthe et al. 2000, Warwick-Evans et al. 2015). While the energetic cost of plunge diving is considered relatively low, the effort involved in taking-off from the water surface may be substantial (Green et al. 2010). Indeed, the combination of increased foraging and flapping flight for take-off may be causal to the increase in mean VeDBA across years. As the proportion of time spent foraging did not significantly increase from 2012 to 2013, birds may have reached their limit for energy expended.

To cover a range of temporal restrictions on the foraging adult, two stages of breeding were investigated; incubation and chick rearing. During incubation adults take turns guarding the nest with only self-maintenance and the fasting ability of the partner driving foraging decisions. During chick rearing, the duration of foraging trips reduces due to the chicks’ fasting ability, and chick-maintenance becomes important for foraging decisions. Interestingly, regardless of breeding stage, both males and females increased the proportion of the trip foraging and both proxies of energy expenditure (total and mean VeDBA) from 2011 to 2012, presumably a year of poor prey availability. As a long lived species, increased effort in poor conditions will only occur until a certain threshold, which appears to have been encountered in

2013 when only 10 chicks were raised to fledging age from the 116 nests which attempted to breed.

Chapter 4: Gannets increase their effort

The results of the present study, therefore, suggest that Australasian gannets from the Pope’s Eye colony did not extend their foraging range in response to reduced prey availability but rather increased foraging effort within their normal range, potentially restricted by conspecifics at nearby colonies. However, as evidenced by the reduced fledging success, an increased foraging effort was not sufficient to compensate for the supposed reduction in prey availability. It should be noted that these findings stem from analysis of the first trip of each individual and a low sample size in incubation of 2011. Hence, these results should be interpreted with caution.

However, these findings highlight the potential for intra-specific density-dependent competition within meta-populations to potentially limit the ability of component sub-populations to adjust behaviour in response to environmental variability. With predictions of increased sea surface temperatures and other physical changes to the ocean (IPCC 2013), knowledge of how seabirds can adapt (Sydeman et al. 2012) and the factors limiting those adaptations are essential for effective management of seabird populations in the future.

Chapter 5

Foraging ecology of Australasian gannets: The influence of

oceanographic regimes

A version of this chapter has been submitted as:

Angel L.P., Berlincourt M., Arnould J.P.Y. (in review) Foraging ecology of

Australasian gannets: The influence of oceanographic regimes. Marine Ecology

Progress Series.

Chapter 5: Geographic variation in foraging behaviour

Abstract

The predictability of prey due to oceanographic features can result in large aggregations of apex predators. Central Place Foragers, such as seabirds, are limited in their foraging duration and range during the breeding period which can restrict their ability to reach such locations. Segregation by colony and sex can further restrict foraging range and may have direct implications for the foraging ecology of a species. In parts of its range, the Australasian gannet (Morus serrator) breeds in colonies of relatively close proximity and has recently been found to display sexual dimorphism. Two neighbouring breeding colonies in Bass Strait experiencing divergent environmental conditions were investigated to determine whether these conditions or sex are important variables influencing the foraging behaviour of this species. GPS tracking and accelerometry were paired with stable isotope analysis to compare differences in foraging effort, habitat use and diet. Birds from Point Danger, a large colony located near a seasonally strong upwelling, travelled considerably further (77%) than birds from Pope’s Eye, a small colony in a nutrient poor embayment. However, within colonies no sexual differences in foraging effort were found. While the colonies did not overlap in foraging areas, a degree of sexual segregation was apparent within both colonies (Point Danger 46.3% overlap and

Pope's Eye 73.7% overlap in home range). Furthermore, the stable isotopes indicate the colonies fed at different trophic niches. As such, it is likely Australasian gannets breeding in Bass Strait will be affected differently by climate change due to their foraging ecology, with differences between colonies and sex.

Chapter 5: Geographic variation in foraging behaviour

Introduction

While the marine environment is both temporally and spatially variable, ocean currents, eddies, bathymetry and other physical and biological processes can concentrate primary productivity into specific regions (Haury et al. 1978). This results in locations of predictable prey for marine apex predators by influencing the cascade of secondary and tertiary consumers (Reid and Hindell 2000, Weimerskirch

2007). The presence of such oceanographic features have been found to correlate with large aggregations of marine mammals and seabirds (Tynan 1998, Becker and

Beissinger 2003).

Seabirds cover large areas when foraging and are capable of exploiting small- and large-scale prey patches (Fauchald et al. 2000). However, during the breeding season seabirds become Central Place Foragers, restricted in the duration of foraging trips by the fasting ability of their partner (in incubation) or their offspring (during chick rearing) (Orians and Pearson 1979). Hence, a knowledge of profitable and predictable food patches would reduce time searching for prey during this restrictive period. However, inter- and intra-specific factors can influence the foraging range of individuals, therefore affecting their access to the most profitable areas.

Mutually exclusive foraging areas between nearby colonies have been observed in many seabird species and is thought to be driven by density-dependent competition

(Ainley et al. 2004, Wakefield et al. 2011, Chiaradia et al. 2012). As such, colonies may experience different environmental conditions and, consequently, comparison between and within populations may provide insights into the different adaptive capabilities within a species (Tremblay and Cherel 2003). Spatial segregation is also known to occur between the sexes in seabirds. Sexual segregation most commonly occurs in species with size dimorphism (Ruckstuhl and Clutton-Brock 2005), thought

Chapter 5: Geographic variation in foraging behaviour to be driven by sexual selection or nutritional requirements (Shine 1989). Thus, within and between colonies of seabirds, access to profitable food patches may be disproportionate due to colony-specific and sexual segregation. Few studies have compared the foraging range and behaviour of seabirds between colonies located in variable environments (Garthe et al. 2007). Hence, there is a lack of understanding how species may respond to environmental fluctuations.

Gannets (Morus spp.) are large, pelagic seabirds found in temperate regions

(Nelson 1978). Numerous studies have investigated their foraging ecology and, while these have demonstrated colony-specific home ranges (Lewis et al. 2001, Wakefield et al. 2013), foraging behaviour appears generally consistent with individuals performing similar dives across colonies (Grémillet et al. 2004). However, comparisons between colonies have been largely limited to regions of similar oceanographic conditions (Hamer et al. 2001, Grémillet et al. 2004, Pettex et al.

2012). The exception to this is Garthe et al. (2007) who found that two colonies of northern gannets (Morus bassanus), located in differing oceanographic regions, varied in prey consumption, foraging distance, duration, and dive type.

The Australasian gannet (Morus serrator) breeds on coastal islands in Australia and New Zealand (Nelson 1978). To date, studies on the foraging behaviour of the species have been conducted at colonies with similar foraging habitats, finding little variation in foraging behaviour (Machovsky-Capuska et al. 2014a). However, in south-eastern Australia, some colonies occur within 40-60 km of the continental shelf edge in a region influenced by a seasonally predictable, nutrient-rich upwelling

(Cai and Lennon 1993), while less than 300 km away colonies established on artificial structures inside Port Phillip Bay (Pyk et al. 2013) have led to a unique situation where birds may forage in the shallow embayment (average depth of 14 m;

Chapter 5: Geographic variation in foraging behaviour

Berelson et al. 1998) or in the open waters of Bass Strait (average depth of 50-70 m;

Sandery and Kämpf 2007), a region with a limited nutrient regime (Gibbs et al.

1986) and poorly mixed waters influenced by multiple currents in the summer months (Figure 5.1) (Sandery and Kämpf 2005). The influence of these divergent oceanographic regimes on the diet, foraging range, habitat use and behaviour of

Australasian gannets is not known. Such knowledge is important for predicting how their populations may respond to environmental variability (Hobday et al. 2014).

This is especially relevant as the waters of south-eastern Australia are some of the fasting warming in the world and the predicted oceanographic changes will have consequences for the abundance and distribution of species and the structure of marine ecosystems (Hobday and Lough 2011).

In addition, as the Australasian gannets was considered nominally monomorphic

(Nelson 1978, Ismar et al. 2014), previous studies on their foraging ecology have not assessed males and females separately (Green et al. 2010, Machovsky-Capuska et al.

2011b, Green et al. 2013, Machovsky-Capuska et al. 2014a). Northern gannets have been shown to be sexually dimorphic, with larger females foraging further offshore and on different prey to the smaller males (Stauss et al. 2012, Cleasby et al. 2015). A recent study has also documented the Australasian gannet to be sexually dimorphic, with females larger than males (Chapter 2). However, it is not known whether the species displays similar patterns of sexual segregation in diet or foraging habitat to that observed in the northern gannet and how environmental conditions may influence this.

The aims of the present study, therefore, were to determine in two neighbouring colonies experiencing divergent oceanographic regimes whether differences exist in:

Chapter 5: Geographic variation in foraging behaviour

1) foraging behaviour; 2) habitat use; and 3) dietary niche at the colony and/or sex level.

Methods

Study site and animal handling

The study was conducted between October 2012 and January 2013, at Point

Danger (38°23’36”S 141°38’54”E, 3,7600 breeding pairs including Lawrence

Rocks) and Pope’s Eye (38°16’42”S 144°41’48”E, 510 breeding pairs including other colonies in Port Phillip Bay) colonies in northern Bass Strait, south-eastern

Australia (Figure 5.1). These two adjacent colonies are <300 km apart and a third larger colony (12,300 individuals) is situated 250 km south of Pope’s Eye in southern

Bass Strait (Figure 5.1). Individuals were captured at the nest during the incubation period and the egg was covered for protection. Where possible, both adults of a breeding pair were sampled to ensure equal sex ratios. Individuals were weighed using a suspension balance (± 25 g, Salter Australia Pty Ltd, Australia). Tarsus length was measured using Vernier callipers (± 0.1 mm) and wing ulna length was measured (± 1 mm) using a slide ruler.

A total of 29 birds were instrumented at Point Danger (5 males and 7 females) and

Pope’s Eye (8 males and 9 females). Individuals were equipped with a GPS data logger (IgotU GT-600, Mobile Action Technologies Inc., Taiwan; 26.50 g) recording location (± 10 m) every 2 min and a tri-axial accelerometer data logger (X8, Gulf

Coast Data Concepts LLC, USA; 14.12 g) measuring at 25 Hz to determine at-sea movements and foraging behaviours, respectively. Devices were encapsulated in heat shrink plastic (whole package 52.6 g, <3% body mass; Phillips et al. 2003) and

100

Chapter 5: Geographic variation in foraging behaviour attached with water-proof tape (Tesa® 4651, Beiersdorf AG, Germany) to the central tail feathers following the methods of Wilson et al. (1997). Device positioning ensured it was covered by the wings during a plunge dive in an attempt reduce drag

(Hamer et al. 2000). In most cases, individuals were recaptured after a single foraging trip. A single blood sample (0.1 mL) was then collected by venipuncture of a tarsus vein for genetic sexing (DNASolutions, Australia) and stable isotope analysis before the bird was released.

Figure 5.1. Location of Australasian gannet colonies in Bass Strait, south-eastern

Australia. Lawrence Rocks and Point Danger (PD), Port Philip Bay colonies and

Pope’s Eye (PE), Black Pyramid (BP), Eddystone Rock (ER), and Pedra Branca

(PB). Direction of currents (South Australian Current, SAC; East Australian Current,

EAC; Subantarctic Surface Water, SASW), which may enter Bass Strait, are indicated by arrows. Bathymetry and edge of continental shelf is shown in light grey.

101

Chapter 5: Geographic variation in foraging behaviour

Data processing, environmental variables and statistical analyses

Body mass (BM; kg), tarsus (T; mm) and wing ulna (WU; mm) length were used to estimate body condition (i.e. total body fat %) based on the equation presented in

Chapter 2:

ܶܤܨ% = 24.43 + 1.94 × (ܤܯ ÷ ܹܷ) െ 0.58 × ܶ

GPS locations were processed using a speed filter (McConnell et al. 1992) and summary statistics calculated using the adehabitatHR package (Calenge 2006) in the

R statistical environment (R Core Team 2015). Trip parameters indicative of effort were calculated (i.e. maximum distance from colony, total distance and duration, and average flight speed) for the first foraging trip for each individual. Foraging area overlap was quantified by calculating the colony home-range (95% contour) and core foraging area (50% contour) for each sex. A kernel density estimation, with an ad- hoc smoothing parameter and cell size of 250 m2, was calculated for each contour and a utilization distribution overlap index used to estimate overlap (Fieberg and

Kochanny 2005).

Data from the tri-axial accelerometer was visually assessed for behaviour in

IGOR Pro (Version 6.34, WaveMetrics, USA) based on previous studies of plunge diving species (Ropert-Coudert et al. 2004b, Weimerskirch et al. 2005b, Ropert-

Coudert et al. 2009a). These behaviours were then assigned to the results of a k- means algorithm clustering analysis following the methods of Sakamoto et al.

(2009). The proportion of time spent performing each behaviour during a foraging trip was calculated: resting at sea surface; flapping flight; gliding flight; and foraging

(including plunge diving and surface foraging; Warwick-Evans et al. 2015).

Additionally, the total number of plunge dives and surface foraging events performed were used to calculate plunge dive rate and surface dive rate (dives·h-1) averaged

102

Chapter 5: Geographic variation in foraging behaviour over the entire foraging trip. As the accelerometers could not be positioned near the animal’s centre of the gravity (Vandenabeele et al. 2014), mean vectorial body dynamic acceleration (VeDBA; g) and total VeDBA were used as indices of energy expenditure following the methods outlined in Qasem et al. (2012).

To determine if wind conditions influence foraging behaviour differently at each colony, average monthly wind speed and direction were extracted from areas where individuals were observed to frequent during the study (i.e. along the Bonney

Upwelling coast, northern central Bass Strait, and Port Phillip Bay). Data was obtained from the National Centre for Environmental Prediction/National Centre for

Atmospheric Research (NCEP/NCAR) Reanalysis Project (Kalnay et al. 1996).

NCEP values were extracted for daily wind speed and direction and averaged for the duration of the breeding season for the three respective areas (Bonney Upwelling: -

39°S – 38°S; 136°E - 142°E; Bass Strait: 40°12’S - 38°30’; 144°5’E - 146°48’E;

Port Phillip Bay: 38°23’S - 37°47’S; 144°18’E - 145°11’E).

In order to investigate whether differences in the diet of Australasian gannets

RFFXUVEHWZHHQFRORQLHVDQGVH[HVQLWURJHQ į151 DQGFDUERQ į13C) isotopes in whole blood samples were analysed. Nitrogen has an average enrichment of 3-4‰ at each trophic step and is used to determine a consumer’s trophic position (Deniro and

Epstein 1981). While carbon also increases slightly with trophic level (enrichment of

1-2‰; Hobson and Welch 1992), vaULDWLRQLQį13C gradients can be caused by foraging inshore/offshore or in benthic/pelagic zones (Hobson et al. 1994). As such,

į151DQGį13C can be used to infer the diet of a consumer. Additionally, the turnover rate of protein varies between body tissues, reflecting different temporal scales

(Kelly 2000). In the present study, whole blood was used as it is indicative of the

103

Chapter 5: Geographic variation in foraging behaviour past two weeks (Hobson and Clark 1992) and, thus, provides a longer time-scale to compare between the colonies than the tracking data.

Blood samples were ground up and lipids extracted following the methods of

Ehrich et al. (2011). Samples were processed through a CHN elemental analyser

(CE1110, Carlo Erba) and analysed with an interfaced Fisons isochrom continuous- flow isotope ratio mass spectrometer (GV instruments). Replicate measurements for

LQWHUQDOODERUDWRU\VWDQGDUGVIRXQGDSUHFLVLRQRIÅDQGÅIRUį13&DQGį15N, respectively. The C:N mass ratio for males (3.24 ± 0.02) and females (3.26 ± 0.02) was less than 3.5 indicating lipids in the samples were sufficiently extracted as to not influence the results (Post et al. 2007).

As the behavioural variables were proportional, they were arcsine transformed for statistical analysis. Assumptions for independence and normality of data were tested using a Chi-Square and Shapiro–Wilk’s test, respectively. For all foraging metrics, where the assumptions were not met, a log10 transformation was performed. Data was analysed using two-way ANOVAs with sex and colony as factors, followed by

Tukey’s post hoc test. Results are presented as mean ± standard error, unless otherwise stated.

Results

Body mass was signifcantly lower for both males and females at Point Danger

(2.30 ± 0.08 kg and 2.46 ± 0.07 kg) than at Pope’s Eye (2.50 ± 0.10 kg and 2.67 ±

0.08 kg; Two-way ANOVA, F1,25 = 5.84, P = 0.02). However, wing length at Point

Danger (males: 206.0 ± 2.4mm; females: 208.4 ± 2.2 mm) was longer than at Pope’s

Eye (males: 201.8 ± 0.9mm; females: 202.4mm; F1,25 = 6.00, P = 0.014). Tarsus

104

Chapter 5: Geographic variation in foraging behaviour length did not signficiantly differ between Point Danger (males: 71.9 ± 1.5 mm, females: 68.9 ± 1.2mm) and Pope’s Eye (males: 68.3 ± 1.1mm; females: 67.4 ±

1.3mm; F1,25 = 3.48, P = 0.07). Furthermore, body condition (TBF%) did not differ between Point Danger (males: 17.2 ± 0.9 %; females: 15.5 ± 0.7%) and Pope’s Eye

(males: 15.2 ± 0.6 %; females: 14.6 ± 0.8 %; F1,25 = 3.49, P = 0.07). No sex effect was found in body mass (F1,25 = 3.29, P = 0.818), wing length (F1,25 = 1.16, P =

0.28), tarsus length (F1,25 = 1.59, P = 0.22) or body condition (F1,25 = 1.60, P = 0.22).

Foraging effort

During the study period, birds from Point Danger travelled 77% further from the colony than those breeding at Pope’s Eye (F1,25 = 27.01, P < 0.01; Table 5.1). A signficant difference between the colonies was also observed in the total trip distance

(F1,25 = 25.64) and duration (F1,25 = 14.50, P < 0.001 in both cases, Table 5.1).

Hence, males and females from Point Danger had a larger foraging home range (41.1

×103 km2 and 70.7 × 103 km2, respectively) than birds from Pope’s Eye (2.6 × 103 km2 and 1.2 × 103 km2, respectively). Comparison between the sexes found that females and males had similar foraging ranges (F1,25 = 2.11, P = 0.16), total distance

(F1,25 = 0.57, P = 0.46), and total duration (F1,25 = 0.21, P = 0.66) during a foraging trip.

Furthermore, the average flight speed for males and females was much greater at

Point Danger (27.3 ± 1.1 km·h-1 and 28.9 ± 1.8 km·h-1) compared to Pope’s Eye

-1 -1 (21.0 ± 2.1 km·h and 22.2 ± 2.1 km·h ; F1,25 =7.93, P = 0.01). No sex differences in travelling speed were detected at either colony (F1,25 = 0.39, P = 0.54). To determine whether the different travelling speed were related to flying conditions around the

105

Chapter 5: Geographic variation in foraging behaviour colonies, wind speed and direction was assessed in areas used by the birds. No signficant difference was found for average wind speed (F2,8 = 0.50, P = 0.62) between colonies, with birds from Point Danger foraging along the Bonney Coast

(8.2 ± 1.3 km·h-1) facing similar wind conditions to birds from Pope’s Eye foraging in Port Phillip Bay (9.5 ± 2.2 km·h-1) and Bass Strait (10.8 ± 2.3 km·h-1). Similarly, the predominant wind direction experienced by birds at both colonies was not significantly different (Bonney Coast: 256.2 ± 3.3°, Port Phillip Bay: 242.0 ± 11.8°,

Bass Strait: 258.6 ± 7.9°; F2,8 = 1.30, P = 0.32).

Point Danger individuals spent signficantly more time in gliding flight (males

24.4 ± 1.7%; females 28.4 ± 3.2%) than Pope’s Eye birds (males 14.2 ± 2.8%; females 18.3 ± 2.8%; F1,22 = 10.56, P = 0.004), although not in flapping flight (F1,22

= 3.23, P = 0.09; Table 5.1). The proportion of time spent resting on the sea surface was 10-15% less at Point Danger than at Pope’s Eye (F1,22 = 6.43, P = 0.02; Table

5.1). While no difference was detected in the proportion of time spent foraging (F1,22

= 3.24, P = 0.09) or in the rate of surface foraging events (F1,22 = 0.43, P = 0.52;

Table 5.1), birds from Point Danger were found to have a higher rate of plunge dives

(males: 4.8 ± 0.5 dives·h; females: 3.5 ± 0.8 dives·h) than Pope’s Eye birds (males:

2.5 ± 0.3 dives·h; females: 2.2 ± 0.1 dives·h; F1,22 = 10.33, P = 0.004). For all behaviours and foraging rates, no signficant difference was found between the sexes

(P > 0.05 in all cases).

Mean VeDBA was signficantly greater at Point Danger (males: 0.45 ± 0.03 g; females: 0.43 ± 0.03 g) than Pope’s Eye (males: 0.32 ± 0.03g; females: 0.40 ± 0.03 g; F1,22 = 5.07, P = 0.04. Total VeDBA was also greater at Point Danger (males: 25.6

×105 ± 7.4 × 105 g; females: 21.6 × 105 ± 4.3 × 105 g) than Pope’s Eye (males: 7.9 ×

5 5 5 5 10 ± 0.9 × 10 g; females: 6.6 × 10 ± 1.4 × 10 g; F1,22 = 28.20, P < 0.001),

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Chapter 5: Geographic variation in foraging behaviour

Table 5.1. At-sea movements and behaviour of Australasian gannets foraging from two colonies in Bass Strait, Australia.

Point Danger Pope's Eye Male Female Male Female n = 5 n = 7 n = 8 n = 9 Distance from colony (km)* 172.5 ± 58.3 238.2 ± 42.0 38.5 ± 7.4 54.4 ± 18.6 Total distance (km)* 679.7 ± 232.2 772.5 ± 115.4 193.9 ± 31.9 214.5 ± 52.7 Trip duration (h)* 48.7 ± 15.7 55.2 ± 6.6 27.3 ± 3.4 25.7 ± 3.2 Average speed (km·h-1)* 27.3 ± 1.1 28.9 ± 1.8 21.0 ± 2.1 22.2 ± 2.1 Time resting (%)* 49.7 ± 1.9 46.9 ± 2.6 64.4 ± 4.7 56.6 ± 4.8 Time flapping (%) 20.9 ± 2.2 19.9 ± 2.0 14.8 ± 1.7 18.0 ± 1.9 Time gliding (%)* 24.4 ± 1.7 28.4 ± 3.2 14.2 ± 2.8 18.3 ± 2.8 Time foraging (%) 5.0 ± 0.6 4.8 ± 0.7 6.6 ± 0.9 7.1 ± 1.2 Plunge dives·h-1* 4.8 ± 0.5 3.5 ± 0.8 2.0 ± 0.6 1.5 ± 0.7 Surface dives·h-1 2.5 ± 0.3 2.2 ± 0.1 2.4 ± 0.8 1.4 ± 0.6 *Significant difference between colonies (P < 0.05)

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Chapter 5: Geographic variation in foraging behaviour

suggesting both a greater rate and total amount of energy expenditure at Point

Danger. There was no strong evidence to suggest a difference in the rate of mean

VeDBA (F1,25 = 2.23; P = 0.15) and total VeDBA (F1,25 = 0.97, P = 0.36) between males and females, regardless of colony.

Foraging segregation

Calculations of the core foraging area (50% contour) and home range (95% contour) were conducted to assess the overlap between the colonies and between the sexes within each colony. Point Danger birds covered a home range of 8,619.9 × 103 km2 and a core area of 1,705.8 × 103 km2, a much greater area compared to Pope’s

Eye birds who covered a home range of 261.6 × 103 km2 and a core area of 33.1 ×

103 km2. The core foraging areas of the two colonies did not overlap, and minimal overlap (0.005%) in the home range occurred (Figure 5.2).

Although no sex differences were detected in any metric of foraging effort, sexual segregation in foraging areas was clearly evident at each colony. At Point Danger, home range overlapped 46.3%, with males foraging predominantly to the west of the colony and females foraging both east and west of the colony. Furthermore, core foraging areas overlapped only 4.2%, occurring in the waters immediately around the colony (Figure 5.2a). A greater overlap in foraging area was found at Pope’s Eye where home range overlapped 73.7% and core foraging area overlapped 18.4%. The overlap of the core and home range foraging areas was concentrated around the entrance to Port Phillip Bay, south of Pope’s Eye, an area where birds leaving the colony need to traverse to access open waters (Figure 5.2b). Only males were observed to forage inside Port Phillip Bay (north of Pope’s Eye).

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Chapter 5: Geographic variation in foraging behaviour

Figure 5.2. Comparison of male (blue) and female (red) foraging ranges using kernel density based utilization distributions at 95%

(dotted lines) and 50% (solid lines) contours during the incubation stage of breeding at Point Danger (A) and Pope’s Eye (B) gannet colonies. Breeding colony locations are indicated by black circle.

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Chapter 5: Geographic variation in foraging behaviour

Dietary segregation

Stable isotope analysis of whole blood indicated males and females at Point

'DQJHUZHUHVLJQLILFDQWO\OHVVHQULFKHGLQį15N (12.5 ± 0.1‰ and 12.6 ± 0.2‰, respectively) than males and females from Pope’s Eye (14.3 ± 0.3‰ and 14.2 ±

0.1‰, respectively; F1,51 =34.33, P < 0.001). These results indicate birds at Pope’s

Eye foraged at a higher trophic niche. Furthermore, males and females at Point

'DQJHUKDGORZHUį13C values (-20.3 ± 0.1‰ and -20.0 ± 0.2‰, respectively) than birds at Pope’s Eye (-18.3 ± 0.3‰ and -19.6 ± 0.3‰, respectively; F1,51 = 22.05, P <

0.001), indicating a different geographic origin of prey.

15 6LJQLILFDQWGLIIHUHQFHVZHUHIRXQGEHWZHHQWKHVH[HVLQį N (F1,51 = 4.421, P =

13 0.04) and į C (F1,51 = 4.73, P = 0.03). However, within colonies, a Tukey’s post hoc

FRPSDULVRQIRXQGQRVLJQLILFDQWGLIIHUHQFHEHWZHHQPDOHVDQGIHPDOHVLQį15N

(Point Danger males vs. females: P = 0.95; Pope's Eye males vs. females: P = 0.20) indicating the prey items they consumed were of a similar trophic level. While no

GLIIHUHQFHLQį13C values were found between sexes at Point Danger (P = 0.99) and

Pope's Eye (P = 0.06), an enrichment difference of 1.3‰ between the sexes, in the case of Pope's Eye, could be considered biologically significant. In addition, the

VWDQGDUGGHYLDWLRQLQį13C values was greater at Pope’s Eye for both sexes in comparison to Point Danger, indicating a greater degree of variation in the geographic origin of prey at Pope’s Eye than at Point Danger. Furthermore, there was

DVLJQLILFDQWGLIIHUHQFHLQį13C between males from Pope's Eye and males (P <

0.001) and females (P = 0.001) from Point Danger, but not for females from Pope's

Eye (P = 0.12; P = 0.19). These results suggest geographic variation in the origin of prey for males at Pope’s Eye when compared to females at Pope’s Eye and both sexes at Point Danger (Figure 5.3).

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Chapter 5: Geographic variation in foraging behaviour

Figure 5.3. :KROHEORRGį&DQGį1YDOXHV (presented as mean ± standard deviation) for breeding male (blue) and female (red) gannets at Point Danger

(females, n = 6; males, n = 6) and Pope’s Eye (females, n = 9; males, n = 7).

Discussion

As colonial breeders, gannets can spend up to 4 months rearing a chick from incubation (40-44 days) to fledging (89-108 days; Courtenay-Latimer 1954, Nelson

1966, Wingham 1984a, b). During this extended period, either the fasting ability of the partner incubating the egg, or both the partner and the chick, restrict the available time to forage (Orians and Pearson 1979). Thus, individuals are limited in their foraging range and rely on prey occurring in relatively close proximity to the colony. 111

Chapter 5: Geographic variation in foraging behaviour

Furthermore, the presence of different oceanic regimes surrounding colonies may influence the foraging behaviour and effort of birds breeding in the region. In the present study, geographic segregation and variation in foraging behaviour were observed between two adjacent colonies of Australasian gannets during incubation.

Foraging effort

Birds from Point Danger were found to weigh less than Pope’s Eye. However, geographical variation in a species’s body size is common and can reflect differences in food availability or inter-population growth patterns (Lovich and Gibbons 1992).

Although no sex differences were detected in the present study, this is most likely due to a small sample size as a power analysis revealed a sample size of only 45 birds at Pope’s Eye and 22 birds at Point Danger was needed to detect a significant difference (1-ȕ ). Indeed, females have been found to be on average 180 g heavier across the breeding season and between years at both colonies (Chapter 2).

Interestingly, there were no differences in body condition between the colonies for either sex despite individuals from Point Danger travelling faster, further and having a greater home range than those from Pope’s Eye. This suggests that Point Danger individuals had a greater abundance of food and/or lower flights costs.

As Australasian gannets typically prefer foraging on the continental shelf

(location of preferred prey; Fletcher and Tregonning 1992), the greater foraging range of the Point Danger colony could reflect the narrowness of the continental shelf requiring individuals to fly further to locate sufficient resources. In addition,

Point Danger (660 pairs) and the nearby (2 km) Lawrence Rocks (3,100 pairs) colonies are considerably larger than Pope’s Eye (180 pairs) and its nearby colonies

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Chapter 5: Geographic variation in foraging behaviour

in Port Phillip Bay (330 pairs; Bunce et al. 2002). Consequently, local prey resources might be depleted much faster at Point Danger due to intra-colony competition, requiring individuals to travel further and cover a greater area in search of prey, as has been observed in northern gannets where foraging trip duration and distance travelled is influenced by colony size (Lewis et al. 2001).

Birds from Point Danger were found to have greater average travelling speeds during a foraging trip. As gannets use a combination of flapping and gliding flight, they are sensitive to wind speed and direction (Amélineau et al. 2014). There was little difference in the mean direction (west-southwest, 242-258°) and speed of the wind between the two colonies. However, due to the location of the colonies relative to their respective foraging areas, at Point Danger the wind direction was perpendicular to the coast and the foraging routes taken by individuals feeding along the narrow continental shelf. Consequently, these birds are likey to have tacked with the wind (Weimerskirch et al. 2000) which may explain the higher proportion of their trip spent gliding. In contrast, birds at Pope’s Eye commuted to and from feeding zones with a head or tail wind, respectively, similar to that found for northern gannets (Amélineau et al. 2014). The difference in wind direction relative to foraging areas may also have consequences for the energy expended to reach profitable prey patches.

Point Danger birds spent 10-15% less time during a foraging trip resting on the sea surface than Pope’s Eye birds. Furthermore, birds from Point Danger had twice as many plunge dives per hour compared to Pope’s Eye. However, the total time spent foraging did not differ between the colonies. This could be due to Pope’s Eye birds performing more U-shaped plunge dives which are known to be more frequent in coastal areas and of a longer duration (Machovsky-Capuska et al. 2011b, Cleasby

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Chapter 5: Geographic variation in foraging behaviour

et al. 2015). Furthermore, the greater density of birds breeding at Point Danger and

Lawrence Rocks could also be a contributing factor to the greater plunge dive rate observed from Point Danger birds, with more dives required to successfully outcompete conspecifics (Thiebault et al. 2014b).

Mean and total VeDBA have been found to be positively correlated to the rate and total amount of energy expenditure, respectively, in Australasian gannets (Chapter 3) and were, therefore, used to compare foraging energy expenditure between the colonies. The results suggest a higher energy expenditure rate in Point Danger birds which is likely due to their higher plunge dive rate. While the cost of a plunge dive is minimal due to its short duration, the take-off from the sea surface incurs a high energetic cost (Green et al. 2010). Point Danger birds also had a higher total VeDBA suggesting a greater total energy expenditure which is likely due to their signficantly longer foraging trips. Despite the higher energy expenditure, body condition was similar between the two colonies suggesting that birds from Point Danger forage with greater efficiency or experience a greater food availability. Interestingly, no signficant differences were detected in any foraging parameter or metric of foraging effort between the sexes, for either colony. This is consistent with there being no difference in body condition between the sexes (current chapter and Chapter 2).

Foraging segregation

Gannets have been recorded to travel anywhere from 185-520 km in a foraging trip (Grémillet et al. 2004, Stauss et al. 2012, Machovsky-Capuska et al. 2014a). In the present study, the adjacent colonies are located < 300 km apart and, thus, a small amount of overlap could be expected (Lewis et al. 2001, Grémillet et al. 2004,

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Chapter 5: Geographic variation in foraging behaviour

Wakefield et al. 2013). However, the establishment of mutually exclusive foraging areas observed in the present study may result in a restriction in the type of habitat available to each colony. At Point Danger, the narrowness of the continental shelf requires birds to travel a greater distance from the colony to cover the same area as

Pope’s Eye. However, at Pope’s Eye birds have two habitats to forage in, an embayment (Port Phillip Bay) or in the open waters of Bass Strait.

Interestingly, the maximum distance Pope’s Eye birds ranged into Bass Strait was only 20% of the distance from the colony covered by birds from Point Danger. This seemingly restricted foraging range for Pope’s Eye birds may be due to the smaller size of their colony resulting in less intra-colony competition. However, in years of poor prey availability and, hence, increased competition, Pope’s Eye birds did not increase this range (Chapter 4). Therefore, it is more likely that this restriction is due to competitve exclusion caused by indiviudals from Point Danger and another much larger gannet colony (Black Pyramid) in southern Bass Strait (12,300 breeding pairs;

Bunce et al. 2002) (Figure 5.1). Hence, individuals from Pope’s Eye may have limited capacity to adapt their behaviour to buffer the anticipated effects of the rapidly warming oceanic region of south-eastern Australia (Hobday and Pecl 2014,

Hobday et al. 2015).

Similar to what has been observed in northern gannets (Stauss et al. 2012), the findings in the present study clearly reveal a degree of sexual segregation at both colonies investigated. At Point Danger, females foraged both east and west of the colony whereas males exclusively travelled west in search of prey. Furthermore, females foraging extended beyond the continental shelf whereas males appeared to remain closer inshore. At Pope’s Eye, while the degree of segregation was smaller than observed at Point Danger, females and males diverged in their focal foraging

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Chapter 5: Geographic variation in foraging behaviour

habitats. The majority of females foraged in northern Bass Strait, concentrating their feeding near the entrance to Port Philip Bay (see Figure 5.2). In contrast, half the males foraged within the shallow waters of Port Phillip Bay while the other half foraged in northern Bass Strait in the same area as the females.

Foraging in mainly inshore areas has previously been observed in northern gannets (Lewis et al. 2002, Stauss et al. 2012, Cleasby et al. 2015), and is consistent with males of other Sulid species making shorter foraging trips (Gilardi 1992,

Weimerskirch et al. 2006, Weimerskirch et al. 2009b). This behaviour may be residual territorial defence in males due to the need to establish nesting sites at the start of the breeding season (Nelson 1978, Matthews et al. 2008). Alternatively, sexual segregation may be due to different nutritional needs (Lewis et al. 2002) or competitive displacement between the sexes (Stauss et al. 2012, Cleasby et al. 2015), factors commonly related to the presence of sexual dimorphism (Ruckstuhl and

Clutton-Brock 2005). Similar to other Sulid species, Australasian gannets display reversed sexual dimorphism with females heavier than males (3.1% at Pope’s Eye and 7.3% at Point Danger; Chapter 2). The sexual segregation in habitat use observed at both colonies in the present study, therefore, may be due to sexual dimorphism.

Dietary segregation

The degree of segregation in the diet of the two colonies was inferred from stable isotope analysis of whole blood samples. Differences between the two colonies were

IRXQGLQį 15N values, with Point Danger birds feeding at an enrichment 1.6-1.8‰ lower than Pope’s Eye. The higher trophic position of Pope’s Eye birds may reflect feeding at higher trophic levels (Kelly 2000). Alternatively, as inshore water tends to

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Chapter 5: Geographic variation in foraging behaviour

have longer food-chains and, hence, greater nitrogen absorption through the trophic levels (Hobson and Clark 1992, Hobson et al. 1994, Cherel and Hobson 2007), the results could indicate that they are feeding on more inshore species than those at

Point Danger. Indeed, Pope’s Eye is located within a shallow embayment which is also ringed by a large human population centre (city of Melbourne) resulting in a high nitrogen influx into Port Phillip Bay (Figure 5.1) (Axelrad et al. 1981, Berelson et al. 1998).

Intra-FRORQ\FRPSDULVRQVIRUPDOHVDQGIHPDOHVIRXQGQRVHJUHJDWLRQLQį15N, despite the niche segregation in foraging areas. As Australasian gannets forage by plunge diving and surface foraging, they are often limited to feeding on schooling fish concentrated by other predators (i.e. tuna, dolphins and fur seals) brought near the sea surface (Machovsky-Capuska et al. 2011b). Hence, the similarity of the trophic position between the sexes is possibly due to the limited number of such prey species available within their range.

7LVVXHį13C values are influenced by spatial gradients in marine phytoplankton, whereby coastal/inshorHFRPPXQLWLHVDUHPRUHHQULFKHGLQį13C than pelagic/offshore communities (Hobson et al. 1994, Kelly 2000, Cherel and Hobson

2007). As Pope’s Eye is situated in a semi-enclosed embayment, individuals feeding

RQSUH\RULJLQDWLQJIURPWKHUHJLRQZRXOGEHH[SHFWHGWRKDYHKLJKHUį13C values.

Indeed, Pope’s Eye males were observed to forage inside the bay. The enriched carbon from inshore waters tends not to transport more than a few kilometres offshore (Gearing 1988). Females from Pope’s Eye feed almost exclusively in Bass

6WUDLWDQGWKHLUORZHUį13C values were reflective of the more offshore waters than males. In contrast, Point Danger is located near the continental shelf and is greatly influenced by deep oceanic waters (Cai and Lennon 1993). The observed carbon

117

Chapter 5: Geographic variation in foraging behaviour

values for Point Danger males and females match those reported for offshore environments in previous studies (Hobson et al. 1994, Cherel and Hobson 2007).

+HQFHDGHFUHDVLQJį13C gradient can be seen from the inshore males at Pope’s Eye, to the offshore females at Pope’s Eye, to the open water prey consumed by birds from Point Danger.

In summary, Australasian gannets foraging in Bass Strait were found to differ in foraging effort, habitat use and diet between two colonies of divergent oceanographic regimes. Point Danger birds performed longer foraging trips and a higher rate of plunge dives than birds at Pope’s Eye. However, no difference in foraging behaviour or effort was found between the sexes. Predicting how Australasian gannet populations may respond to the anticipated effects of climatic variariablity (Hobday et al. 2014) is greatly complicated by differences between the colonies and sexes.

Although foraging behaviours appear to be consistent between the sexes within colonies, segregation in habitat use further limits the potential to adapt. While gannets have been shown to adapt to short-term perturbation in their prefered prey

(Bunce and Norman 2000), less is known about their long-term adaptability.

118

Chapter 6

Inter- and intra-individual variation in foraging behaviour in

Australasian gannet

A version of this chapter has been submitted as:

Angel L.P., Berlincourt M., Arnould J.P.Y. (in review) Inter- and intra-individual

variation in foraging behaviour in Australasian gannets. Marine Biology.

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Chapter 6: Foraging site fidelity

Abstract

The foraging behaviour of seabirds is largely influenced by prey availability and distribution. However, within a population, individuals can exploit prey resources differently due to sex, age, body size or stage of breeding. Furthermore, variation in habitat use can occur at the individual level due to varying degrees of site fidelity.

However, the degree of fidelity may be due to the available habitat rather than individual preference. The present study investigated causes of inter- and intra- variability in Australasian gannets (Morus serrator) at a colony with two habitats types available for foraging (a shallow embayment or offshore waters). Inter- individual variability was influenced by sex and breeding stage, with males foraging closer to the colony in both breeding stages and the restrictions of chick fasting reducing foraging trip duration. Intra-individual variability was attributed to sex, with males being more consistent than females. As a higher proportion of males foraged inside the bay (85%), it is likely the difference in consistency observed between sexes is due to the predictability of the habitats in which they foraged.

Correspondingly, the results suggest males foraging in shallow habitats may be using private knowledge of previously successful foraging areas, while females foraging in the more heterogeneous environment, use local enhancement (conspecifics and heterospecifics) to locate prey.

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Chapter 6: Foraging site fidelity

Introduction

The marine environment is temporally and spatially stochastic and, as such, the location of prey for marine predators is considered unpredictable (Weimerskirch

2007). However, recent evidence suggests that the marine environment may not be as unpredictable as previously thought. Indeed, rather than foraging at random, seabirds typically have quite direct foraging paths when leaving the colony (Weimerskirch

2007). Oceanic features such as upwellings, eddies and frontal zones concentrate primary productivity leading to an increase in these locations of secondary and tertiary consumers (Haury et al. 1978). As such, high concentrations of seabirds have been found foraging at these locations due to the concentrated prey available (Tynan

1998, Becker and Beissinger 2003).

However, within a population, individuals can exploit prey resources differently.

Segregation in habitat use can occur between individuals due to differences in morphometrics, sex, age, or breeding stage (depending on partner/chick fasting abilities) (Selander 1966). Particularly for seabirds, variation in body size, can influence flight performance (Shaffer et al. 2001b) and diving depth (Kato et al.

2000, Cook et al. 2007). Sexual segregation, commonly observed in dimorphic species, may be attributed to not only body size but different parental roles or nutritional requirements (Wearmouth and Sims 2008). Additionally, due to the specialised foraging techniques of some species, seabirds have been found to become more efficient foragers with age (Daunt et al. 2007, Yoda et al. 2011) and, hence, profitable foraging habitats may be more accessible to older individuals.

Variation in habitat use may also occur at the individual level. Individuals can be consistent in their foraging habitat, returning to previously successful locations or be highly variable, continually seeking out new locations (Wanless et al. 1990). As

121

Chapter 6: Foraging site fidelity long-lived species with delayed maturity, it is possible seabirds use the juvenile period before breeding to develop a knowledge of profitable and predictable food locations around the colony (Lack 1968). If this hypothesis is correct, the foraging strategies observed in breeding seabirds are likely to have been developed over many years.

The foraging decisions made by an individual are reflected in their foraging success (i.e. prey capture), with self-maintenance and chick growth reliant on success

(Pyke 1984). Therefore, foraging decisions may be directly linked to reproductive success in breeding seabirds. However, many breeding adults fail to produce offspring which survive to reproduce themselves, with only a small proportion of individuals in a population contributing to future generations (Newton 1995).

Therefore, causes of variation in foraging behaviour can influence individual fitness and population demography.

In gannets (Morus spp.), comparisons of variation in the use of foraging habitat between individuals has been largely limited to the dimorphic northern gannet

(Morus bassanus). Males have been found to remain in more inshore waters with higher primary productivity than females (Stauss et al. 2012, Cleasby et al. 2015). To date, most studies on gannets have focused on chick rearing adults of unknown age.

Hence, individual variation has only been attributed to body size and sex. Garthe et al. (2007) found variation in foraging behaviour is potentially attributed to habitat and prey availability. Indeed, intra-individual comparisons have found gannets vary in their foraging area fidelity, potentially due to the predictability of prey (Hamer et al. 2001). However, it is unknown if this is a colony-specific strategy.

Pope’s Eye is a small breeding colony (ca. 180 breeding pairs) of Australasian gannets (Morus serrator) established on an artificial structure in Port Phillip Bay,

122

Chapter 6: Foraging site fidelity south-eastern Australia (Pyk et al. 2013). This has led to an unusual situation in terms of the foraging habitats available to individuals. The colony is located near the boundary of Port Phillip Bay and Bass Strait. Port Phillip Bay is connected by a small entrance (3 km wide) to Bass Strait resulting in a slow turnover of water in the bay (average residence of 1 year; Harris et al. 1996). Fish abundance is relatively stable between years (Parry et al. 1995), with favourable conditions for larval recruitment (Jenkins et al. 1998) potentially due to the consistently high chlorophyll- a concentrations around the coastal regions of the bay throughout the year (Beardall et al. 1997). Therefore, the shallow embayment (average depth 14 m; Berelson et al.

1998) offers gannets an area to feed on prey that would otherwise be unattainable

(Wells 2015) in stable and predictable abundance.

Gannets from the Pope’s Eye colony may also forage in the open waters of Bass

Strait (average depth 50-70 m; Sandery and Kämpf 2007), a broad continental shelf area where at least 18 species of seabirds breed in high numbers (Schumann et al.

2014). Furthermore, biomass consumption estimates for just four of these species suggest 1,270,200 tonnes of prey are consumed each year (Schumann et al. 2014), highlighting abundance of fish in the region. Unlike benthic, shallow habitats where prey are of lower density and more evenly distributed, open water habitats are highly patchy being influenced by oceanographic perturbations (Lalli and Parsons 1993).

Hence, there is a trade-off between foraging in a more predictable environment with fewer prey, such as Port Phillip Bay, or in a less predictable environment with greater prey availability, such as Bass Strait.

Studies to date on the foraging behaviour of Australasian gannets have focused on populations as a whole (Bunce 2001, Machovsky-Capuska et al. 2014a). Little is known about the degree of variation in foraging behaviour between and within

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Chapter 6: Foraging site fidelity individuals. Body size, sex, age or breeding stage may all influence the foraging behaviour of individuals, with different habitats being exploited or prey consumed as a result (Selander 1966, Polis 1984). Whether such factors influence the foraging strategies of individuals is largely unknown. Therefore, the aims of the present study were to investigate causes of inter- and intra-variability in gannets foraging from

Pope’s Eye and the foraging strategies employed by individuals.

Methods

Study site and animal handling

The study was conducted at the Pope’s Eye gannet colony (38°16’42”S

144°41’48”E; Figure 6.1), in south-eastern Australia. Adult Australasian gannets were captured from November to mid-December during incubation in 2012 (8 males,

9 females) and 2013 (13 males, 11 females), and in chick rearing from mid-

December to January in 2012 (15 males and 14 females). Study nests were selected at random throughout the colony and all nests in the colony were monitored fortnightly for the duration of the breeding season. Breeding success was determined as the proportion of laid eggs which became fledged chicks. Chicks sighted for 90 consecutive days since hatching and displaying juvenile plumage were presumed to have fledged (Pyk et al. 2007). In 2013, due to poor environmental conditions (Angel et al. 2015), only 10 chicks successfully fledged (8.6% colony breeding success).

Consequently, chick-rearing adults were only sampled in 2012.

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Chapter 6: Foraging site fidelity

Figure 6.1. Pope’s Eye gannet colony (red circle), situated on the boundary of Port

Phillip Bay (average depth 14m) and Bass Strait (average depth 50-70m), in south- eastern Australia

In order to determine the at-sea movements of the adults, individuals were equipped with a GPS data logger (IgotU GT-600, Mobile Action Technologies Inc.,

Taiwan; 26.5 g) recording location (±10 m) every 2 min. In addition, to obtain information about at-sea activity patterns and foraging effort (Shepard et al., 2010), all individuals were also instrumented with a tri-axial accelerometer data logger (X8

500mAh, Gulf Coast Data Concepts LLC, USA; 14.1 g) sampling at 25 Hz. The devices were encapsulated in heat shrink (whole package 52.6 g,< 3% body mass;

Phillips et al. 2003) and attached with water-proof tape (Tesa, Beiersdorf AG,

Germany) to the central tail feathers following the methods of Wilson et al. (1997).

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Chapter 6: Foraging site fidelity

Device positioning ensured it was covered by the wings during a plunge dive in an attempt to reduce drag (Hamer et al. 2000).

Individuals were weighed in a cloth bag using a suspension scale (± 25 g, Salter

Australia Pty Ltd, Australia) and morphometric measurements (i.e. bill length and depth, total head, wing and tarsus length) were taken before returning individuals to the nest with the whole procedure lasting less than 10 minutes. Individuals were recaptured after 1-21 days, weighed as previously described and the devices removed. A single blood sample (0.1 mL) was then collected by venepuncture of a tarsus vein for genetic sexing (DNASolutions, Australia) and stable isotope analysis, before the bird was released. Due to an ongoing banding scheme of Australasian gannets in Port Phillip Bay (since 1966; Norman and Menkhorst 1995), almost all birds in the study were of known age. However, although a high proportion of birds were banded as fledglings some were banded as adults and, as such, only minimum age can be established.

Data processing and statistical analyses

To estimate body condition (i.e. total body fat; TBF%), body mass (BM; kg), tarsus (T; mm) and wing ulna (WU; mm) length were used based on the equation presented in Chapter 2:

ܶܤܨ% = 24.43 + 1.94 × (ܤܯ ÷ ܹܷ) െ 0.58 × ܶ

GPS locations for each foraging trip were processed through a speed filter

(McConnell et al. 1992) to remove erroneous locations and summary statistics were calculated with the adehabitatHR package (Calenge 2006) in the R statistical environment (R Core Team 2015). Foraging trip metrics (i.e. maximum distance

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Chapter 6: Foraging site fidelity from colony, total distance and duration, average speed, and bearing direction on colony departure and return) were calculated where complete trips were available.

To investigate intra-individual variation, birds with 5 or more consecutive foraging trips were identified: in incubation 2013 (6 males, 2 females) and chick rearing 2012 (9 males, 4 females). To estimate area use consistency, the area used by foraging gannets during the study was divided into a 1 km2 grid using the raster package in R, and cells entered for each trip calculated. A cumulative total of unique cells for each consecutive foraging trip was determined and the asymptote of the curve calculated with the nls function in R. The asymptote was considered to be a representative measure for the area use consistency index for each individual.

Data obtained from the tri-axial accelerometer were used to visually assess behaviour in IGOR Pro (Version 6.34, WaveMetrics, USA) based on previous studies of plunge diving species (Ropert-Coudert et al. 2004b, Weimerskirch et al.

Ethographer package was used to perform a k-means algorithm clustering analysis

(see Sakamoto et al. 2009) and identify behaviour using an unsupervised continuous wavelet transformation (1 second window). Each cluster was assigned a behaviour based on the visual identification. From this, the proportion of time spent performing each behaviour, within a foraging trip, was calculated. Additionally, the total number of dives were used to calculate dive rate (dives·h-1) averaged over the foraging trip for both plunge and surface foraging events.

Vectorial dynamic body acceleration (VeDBA) was calculated from the accelerometry data, following the methods outlined by Qasem et al. (2012), as a

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Chapter 6: Foraging site fidelity proxy for energy expenditure. As the accelerometer could not be placed in the centre of gravity of the bird due to likely removal (Vandenabeele et al. 2014), VeDBA was determined to be more appropriate than ODBA (Gleiss et al. 2011). Total VeDBA

(the sum of all values) and mean VeDBA were then calculated. Total VeDBA was considered as it incorporates the trip duration into the values, whereas mean VeDBA allows comparison of the rate of energy expended.

In order to investigate inter-individual variability in the diet of Australasian

JDQQHWVQLWURJHQ į151 DQGFDUERQ į13C) isotopes in plasma were analysed. Lipids were extracted from the plasma samples following the methods of Ehrich et al.

(2011). Samples were then processed through a CHN elemental analyser (CE1110,

Carlo Erba) and analysed with an interfaced Fisons isochrom continuous-flow isotope ratio mass spectrometer (GV instruments). Replicate measurements for internal laboratory standards found a precision of ÅDQGÅIRUį13&DQGį15N, respectively. The C:N mass ratio (3.57 ± 0.01) indicates lipids in the samples were sufficiently extracted as to not influence the results (Post et al. 2007). Additionally, to assess variability in diet between foraging strategies over different time-scales, plasma and red blood cells were compared for a sub-set of individuals (birds with 5 or more consecutive foraging trips). The turnover rate of isotopes varies between body tissues, reflecting different temporal scales (Kelly 2000), with plasma being indicative of short-term (1 week) and red blood cells indicative of long-term (3-4 weeks) diet of avian species (Hobson and Clark 1993). Hence, plasma values reflect diet during the bio-logging period and red blood cells reflect long-term behaviour.

Statistical analyses were performed using R 3.1.3 (R Core Team 2015). The assumptions of independent and normally distributed data were tested with a Chi-

Square test and Shapiro–Wilk’s test, respectively. To identify causes of inter- and

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Chapter 6: Foraging site fidelity intra-individual variability, the R package nlme (Pinheiro et al. 2015) was used to fit linear mixed-effect models to the data, with sex, age, and breeding stage as fixed effects, and individual as a random effect to account for pseudo-replication. To investigate the presence of different foraging strategies, an agglomerative hierarchical clustering analysis with Euclidian distance and Ward’s linkage criterion was used incorporating variables found to influence the area use consistency index.

Results are presented as mean ± standard error.

Results

Inter-individual variation

A total of 70 Australasian gannets were instrumented for the present study.

Foraging trip metrics were available for all individuals but, due to equipment malfunction and logistical difficulties, behavioural data and stable isotope values were available for only 38 individuals. Body mass was significantly different between the sexes (Mixed-effects ANOVA: F1,26 = 7.49, P = 0.011) and years (F1,26 =

5.65, P = 0.03), with females significantly heavier than males in 2012 (males: 2.64 ±

0.07 kg; females: 2.88 ± 0.07 kg) and 2013 (males: 2.80 ± 0.9 kg; females: 2.97 ±

0.09 kg). No differences in body mass were found between breeding stages (F1,26 =

0.58, P = 0.45). The total body fat of males (15.25 ± 0.36 %) and females (14.79 ±

0.60 %) was similar (F1,26 = 0.45, P = 0.51), with no significant variation between stages (F1,26 = 0.96, P = 0.34) and years (F1,26 = 0.76, P = 0.39). Morphometric measurements suggest males and females were of a similar structural size (wing:

F1,26 = 0.00, P = 0.99; tarsus: F1,26 = 0.45, P = 0.51). Age was not significantly different for body mass (F1,26 = 0.60, P = 0.45), total body fat (F1,26 = 0.71, P =

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Chapter 6: Foraging site fidelity

0.41), or morphometric measurements (wing: F1,26 = 0.96, P = 0.34; tarsus: F1,26 =

0.71, P = 0.41).

During the study, birds performed 4.7 ± 0.2 foraging trips with a total 314 trips recorded (range: 1-23 trips). The maximum distance travelled from the colony was influenced by sex (F1,65 = 9.00, P = 0.004), but not stage (F1,65 = 0.09, P = 0.77), with males remaining closer to the colony (25.8 ± 2.7 km and 41.3 ± 3.1 km) than females (61.5 ± 8.3 km and 50.8 ± 3.3 km), in incubation and chick rearing respectively. The total distance travelled was significantly different between sex

(F1,65 = 5.57, P = 0.02) and breeding stage (F1,65 = 7.39, P = 0.008). Males travelled shorter distances (157.5 ± 16.3 km and 136.2 ± 10.3 km) than females (294.0 ± 38.7 km and 183.9 ± 16.9 km), in incubation and chick rearing respectively, with both sexes performing longer trips in incubation. Trip duration was only significantly different between breeding stages (F1,65 = 34.33, P < 0.001) with no influence of sex

(F1,65 = 1.76, P = 0.19), with birds foraging for twice as long in incubation (males:

21.9 ± 2.0 h; females: 29.6 ± 3.0 h) than in chick rearing (males: 11.3 ± 0.9 h; females: 16.0 ± 1.6 h). The year and minimum age of the individuals (14.1 ± 0.8 years old) did not influence inter-individual differences for any of the foraging metrics investigated (F1,65 < 2.46, P > 0.12 in all cases).

Sex had little influence on foraging behaviour, with males and females spending similar proportions of their trips in each behavioural state (F1,33 < 3.73, P > 0.06 in all cases). However, incubating adults were found to spend a lower proportion of their trip in flapping flight in 2012 (17.7 ± 1.4%) and 2013 (17.0 ± 1.0%) compared to chick rearing adults in 2012 (24.4 ± 1.4%; F1,33 = 11.83, P = 0.002). There may also be a difference in the time spent resting at-sea between breeding stages, with birds in incubation in 2012 (60.9 ± 2.6%) and 2013 (62.1 ± 2.0%) spending a higher

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Chapter 6: Foraging site fidelity proportion of their trip on the sea surface than those in chick rearing (53.1 ± 2.1%), with this difference approaching significance (F1,33 = 3.91, P = 0.057). Time spent in gliding flight (F1,33 = 0.01, P = 0.93) and foraging (F1,33 = 0.42, P = 0.52) was similar between incubation in 2012 (14.7 ± 1.4%; 6.8 ± 1.0%) and 2013 (16.6 ± 2.2;

5.6 ± 0.5%) and chick rearing in 2012 (16.8 ± 1.0%; 4.4 ± 5.0). No effect of year or age was found for any behaviour (F1,33 < 2.44, P > 0.13 in all cases).

Comparison of foraging effort found birds in incubation performed a higher rate of dives in both 2012 (1.0 ± 0.3 dives·h-1) and 2013 (1.9 ± 0.3 dives·h-1) than in

-1 chick rearing in 2012 (0.4 ± 0.1 dives·h ; F1,33 = 17.14, P < 0.001) with a significant year effect as birds in 2013 performed 2-4 times as many dives (F1,33 = 4.16, P =

0.05). The rate of surface foraging events was also influenced by breeding stage, with a higher rate during incubation in 2012 (1.2 ± 0.4 dives·h-1) and 2013 (1.4 ± 0.2

-1 -1 dives·h ) compared to chick rearing (0.4 ± 0.1 dives·h ; F1,33 = 13.80, P < 0.001), although there was no effect of year (F1,33 = 0.53, P = 0.47). Total energy expenditure (VeDBA) during a foraging trip was influenced by breeding stage, with birds in incubation in 2012 (0.74 × 106 ± 0.11 × 106 g) and 2013 (1.73 × 106 ± 0.17 ×

106 g) expending more effort than birds in chick rearing (0.67 × 106 ± 0.06 × 106 g;

F1,33 = 39.9, P < 0.001), with a year effect as birds in 2013 worked harder than in

2012 (F1,33 = 19.5, P < 0.001). However, mean VeDBA was not influenced by stage

(F1,33 = 12.13, P = 0.15) or year (F1,33 = 1.71, P = 0.20), as birds in incubation (2012:

0.30 ± 0.03 g; 2013: 0.36 ± 0.02 g) and chick rearing (0.39 ± 0.02 g) expended energy at a similar rate. Sex and age had no effect on foraging effort (F1,33 < 1.19, P

> 0.28 in all cases).

15 'LHWDU\DQDO\VLVIRXQGį N values varied between sexes (F1,34 = 12.11, P =

0.001) and stages (F1,34 = 21.98, P < 0.001), with a shift from a high trophic level in

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Chapter 6: Foraging site fidelity incubation (males: 17.6 ± 0.4 ‰, females: 16.3 ± 0.6 ‰) to a lower trophic level in chick rearing (males: 15.9 ± 0.2 ‰, females: 15.5 ± 0.2 ‰), with males feeding at a higher trophic level than females in both stages. Birds also fed at a lower trophic level in 2012 (15.9 ± 0.2 ‰) than in 2013 (17.0 ± 0.4 ‰; F1,34 = 5.77, P = 0.02). The

15 DJHRIDQLQGLYLGXDODOVRLQIOXHQFHGį N (F1,34 = 13.11, P = 0.001), with a slight positive correlation between trophic level and age (r2 = 0.16), as younger birds tend to feed at a lower trophic level. Carbon values were influenced by stage (F1,34 = 4.37,

P = 0.04) but not by sex (F1,34 = 3.72, P = 0.06), with birds in incubation feeding at a

KLJKHUį13C value (males: -18.5 ± 0.4 ‰; females: -19.2 ± 0.1 ‰) than birds in chick rearing (males: -19.2 ± 0.1 ‰; females: -19.3 ± 0.2 ‰). No effect of year (F1,34 =

13 3.23, P = 0.08) or age (F1,34 = 0.78, P ZDVGHWHFWHGIRUį C values.

Intra-individual variation

Five or more trips were recorded for 21 birds (Table 6.1) and used to identify causes of intra-individual variation. Only one individual was of an unknown age, with the minimum age of birds sampled 13.3 ± 1.2 years old (range: 7-23 years old).

The asymptotic cumulative number of unique grid cells used by individuals was used as an index of area use consistency (Figure 6.2), with a low number of new cells used per trip indicating high foraging site fidelity.

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Table 6.1. Foraging parameters for 21 Australasian gannets with multiple trips, from Pope’s Eye gannet colony, south-eastern Australia. Results are presented as average foraging metrics ± standard error across all trips for each individual.

No. Breeding Distance from Total Distance Total Duration Asymptote (new Foraging Bird ID Sex Age trips stage Colony (km) (km) (h) cells used per trip) strategy 1 5 M 20 EGG 33.7 ± 2.4 154.6 ± 21.2 22.38 ± 1.63 583 Group1 2 10 M 19 EGG 11.6 ± 3.3 68.3 ± 16.3 7.73 ± 1.87 197 Group2 3 8 M 14 EGG 12.3 ± 0.8 135.5 ± 34.7 25.81 ± 6.25 251 Group2 4 17 M 14 EGG 6.0 ± 0.7 36.1 ± 10.3 4.80 ± 2.11 50 Group2 5 5 M 8 EGG 20.1 ± 3.9 187.9 ± 30.1 40.15 ± 8.03 400 Group2 6 5 M - EGG 24.7 ± 5.1 150.9 ± 25.8 23.42 ± 1.50 620 Group1 7 6 F 23 EGG 29.3 ± 5.7 158.3 ± 41.9 19.33 ± 5.70 693 Group1 8 6 F 7 EGG 19.9 ± 3.6 159.8 ± 25.7 22.22 ± 2.84 311 Group2 9 6 M 23 CHICK 25.2 ± 6.4 102.5 ± 20.2 10.48 ± 2.18 543 Group1 10 6 M 20 CHICK 23.3 ± 8.6 99.5 ± 35.6 8.82 ± 3.13 507 Group1 11 6 M 13 CHICK 31.1 ± 4.1 103.6 ± 17.8 10.05 ± 1.76 610 Group1 12 13 M 16 CHICK 41.6 ± 13.7 128.8 ± 39.5 10.89 ± 3.46 2165 Group3 13 8 M 16 CHICK 52.1 ± 16.2 188.7 ± 57.9 19.56 ± 4.70 1304 Group4 14 5 M 14 CHICK 62.9 ± 16.9 191.5 ± 41.2 9.39 ± 1.97 1535 Group4 15 23 M 11 CHICK 24.0 ± 3.3 74.5 ± 15.3 7.27 ± 1.86 724 Group1 16 5 M 11 CHICK 30.2 ± 7.2 119.8 ± 30.7 8.33 ± 2.60 676 Group1 17 11 M 11 CHICK 50.4 ± 11.8 156.3 ± 40.8 13.12 ± 3.90 1309 Group4 18 12 F 11 CHICK 54.7 ± 11.9 218.3 ± 71.6 19.71 ± 7.18 724 Group4 19 9 F 11 CHICK 55.8 ± 7.9 260.6 ± 66.4 27.33 ± 6.69 1206 Group4 20 12 F 10 CHICK 55.9 ± 10.3 213.4 ± 58.3 18.62 ± 4.62 2215 Group3 21 13 F 7 CHICK 56.3 ± 9.1 173.4 ± 38.9 12.52 ± 3.41 1860 Group4

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Chapter 6: Foraging site fidelity

Figure 6.2. Number of new cells (1 km2) entered for each consecutive foraging trip per individual (n = 21). Different foraging strategies defined in Figure 5.3 are indicated by colour (Group 1: green; Group 2: purple; Group 3: navy; Group 4: light blue).

Area use consistency index (area use CI) was influenced by sex (F1,17 = 9.13, P =

0.008), stage (F1,17 = 24.11, P < 0.001) and inter-trip variance in maximum distance from the colony between trips (F1,17 = 10.11, P = 0.006), with the most parsimonious model with overwhelming support (wi > 0.9; Symonds and Moussalli 2011) incorporating these three predictor variables (wi = 0.967, AICc = 288.0):

௏௔௥ܦܯ×ݐܽ݃݁ +9.3ݏ + ݔ݁ݏ + 844.6 = ܫܥ ݁ݏݑ ܽ݁ݎܣ

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Chapter 6: Foraging site fidelity

where MDVar is inter-trip variance in maximum distance from the colony (km).

Males were found to be more consistent between foraging trips (350.2 ± 91.8 new

cells used per trip and 1041.4 ± 190.1 new cells used per trip) than females (502.0 ±

191.0 new cells used per trip and 1740 ± 209.9 new cells used per trip) in incubation

and chick rearing, respectively. Birds in incubation were comparatively more

consistent than in chick rearing. There was a positive relationship between the area

use consistency index and inter-trip variance in maximum distance from the colony

(Pearson correlation: r2 = 0.60, P < 0.001) suggesting individuals travelling similar

distances between trips were consistent in their foraging areas. However, a higher

consistency in area use was not strongly correlated with lower inter-trip variance in

duration of foraging trips (r2 = 0.07, P = 0.25), indicating individuals with strong

foraging area fidelity still vary their trip durations.

Factors influencing total VeDBA, the foraging effort consistency index (foraging

effort CI), were also assessed. Variation in total VeDBA was influenced by sex (F1,17

= 12.22, P = 0.007), inter-trip variance in maximum distance from the colony (F1,17 =

12.15, P = 0.007), and inter-trip variance in the proportion of time spent foraging

(F1,17 = 7.36, P = 0.02; wi = 0.996, AICc = 317.1):

ସ ହ ସ ௏௔௥ܨܶ × ௏௔௥ +1.35×10ܦܯ × ݔ +1.68×10݁ݏ + 10×1.85= ܫܥ ݐݎ݋݂݂݁ ݃݊݅݃ܽݎ݋ܨ

where MDVar is inter-trip variance in maximum distance from the colony (km) and

TFVar is inter-trip variance in the proportion of the time spent foraging (%). Females

had a greater inter-trip variance in their foraging effort (variance in foraging effort:

1.91 × 106 ± 0.55 × 106 g) compared to males (variance in foraging effort: 0.89 × 106

± 0.19 × 106 g). Breeding stage and morphometrics did not appear to be important

variables. The positive relationship between variation in total VeDBA and inter-trip

variance in maximum distance (r2 = 0.51, P = 0.006) and inter-trip variance in

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Chapter 6: Foraging site fidelity

proportion of time spent foraging (r2 = 0.33, P = 0.04) suggests higher variation in foraging behaviour has consequences for foraging effort consistency.

Foraging strategies

Incorporating the area use consistency index and the three variables influencing consistency (sex, stage, and inter-trip variance in maximum distance from the colony), a hierarchical clustering analysis identified four main foraging strategies

(Figure 6.3A; Table 6.1). Group 1 (87.5% males) consisted of birds with moderate area fidelity (507-724 new cells used per trip) foraging both around the entrance to and within Port Phillip Bay with some intra-individual variability in distance travelled from the colony (range of inter-trip variance: 17.4-62.8 km; e.g. Figure

6.3B). Group 2 (80% males) was composed of birds with a high area fidelity (49.5-

400 new cells used per trip) foraging within the bay for the majority of foraging trips

(e.g. Figure 6.3C). These birds showed very little variation in how far they travelled from the colony between trips (range of inter-trip variance: 5.0-26.8 km). Group 3

(one male, one female) had the lowest area fidelity (2165-2215 new cells per trip) and performed short and long foraging trips with high variability in distance from the colony (range of inter-trip variance: 88.5-91 km; e.g. Figure 6.3D). Group 4 (50% males) had a low area fidelity (1206-1860 new cells used per trip) foraging in coastal areas outside the bay with a high variation in distance from the colony between foraging trips (range of inter-trip variance: 53-136 km; e.g. Figure 6.3E).

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Chapter 6: Foraging site fidelity

Figure 6.3. Identification of foraging strategies. Hierarchical clustering of consistency in area use (A). Leaves of the tree represent each individual

Australasian gannet, while the 4 groups are differentiated by colour (Group 1: green; Group 2: purple; Group 3: navy; Group 4: light blue). Examples of each foraging strategy (B-E) adopted by birds from Pope’s Eye gannet colony (indicated by white circle).

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Chapter 6: Foraging site fidelity

Comparison of the plasma isotope values between three of the four foraging strategies (Group 3 had only one individual with isotope values) found significant

15 13 GLIIHUHQFHVLQERWKį N (ANOVA: F2,14 = 19.44, P DQGį C (F2,14 = 27.47,

P < 0.001; Table 6.2.). A Tukey’s post-hoc test found that only Group 2 was significantly different from the other tZRJURXSVZLWKKLJKHUį15N (Group 1 and 2: P

< 0.001; Group 1 and 4: P = 0.53; Group 2 and 4: P DQGORZHUį13C (Group

1 and 2: P < 0.001; Group 1 and 4: P = 0.91; Group 2 and 4: P < 0.001), indicating these individuals potentially fed on prey at a higher trophic level and more inshore species than the other two strategies. Comparison of the red blood cell isotope values, representing a longer time interval, IRXQGVLJQLILFDQWGLIIHUHQFHVLQį15N

13 (ANOVA: F2,8 = 17.85, P DQGį C (F2,8 = 11.54, P = 0.004; Table 6.2.).

Tukey’s post-hoc WHVWUHYHDOHGWKDW*URXSZDVVLJQLILFDQWO\GLIIHUHQWLQį15N

(Group 1 and 2: P = 0.10; Group 1 and 4: P < 0.001; Group 2 and 4: P = 0.007), but

*URXSUHPDLQHGVLJQLILFDQWO\GLIIHUHQWLQį13C (Group 1 and 2: P = 0.03; Group 1 and 4: P = 0.22; Group 2 and 4: P = 0.005).

Table 6.2. Stable isotopes for each foraging strategy, results are presented as mean ± standard deviation.

Plasma Red cells n į13C (‰) į15N (‰) į13C (‰) į15N (‰) Group 1 8 -19.0 ± 0.1 16.4 ± 0.4 -18.3 ± 0.3 17.6 ± 0.2 Group 2 5 -17.4 ± 0.4 18.9 ± 0.2 -17.0 ± 0.4 16.7 ± 0.8 Group 3 1 -19.2 15.2 - - Group 4 4 -19.2 ± 0.2 15.9 ± 0.4 -19.2 ± 0.1 14.8 ± 0.2

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Chapter 6: Foraging site fidelity

Discussion

The foraging behaviour of seabirds is largely influenced by the location and availability of prey. In the present study, an Australasian gannet colony on the edge of an embayment and offshore habitat, in south-eastern Australia, was investigated to determine causes inter- and intra-individual variation in foraging behaviour and habitat use. Inter-individual differences were found to be driven by sex and breeding stage, while intra-individual variation suggests sex is also a determining factor for foraging consistency, with males foraging more frequently in the shallow, and presumably more predictable, embayment.

Inter-individual variation

In the present study, females travelled further from the colony than males. Males foraging inshore or making shorter trips is common in other Sulid species (Gilardi

1992, Lewis et al. 2002, Weimerskirch et al. 2006, Weimerskirch et al. 2009b). This behaviour may be due to territorial defence, as males establish nests at the start of the breeding season (Nelson 1978, Matthews et al. 2008), sexual segregation driven by different nutritional needs (Lewis et al. 2002) or competitive displacement (Stauss et al. 2012, Cleasby et al. 2015). Furthermore, division of labour has been suggested as a possible driver for other Sulid species (Guerra and Drummond 1995, Lormee et al.

2005, Weimerskirch et al. 2009a), although males and females spent equal time attending the nest in the present study (based on foraging trip duration), a division may still be present in the quantity or quality of food provided to offspring during the chick rearing stage, as seen in other Sulids in years of poor conditions (Anderson and

Ricklefs 1992, Guerra and Drummond 1995, Tershy and Croll 2000).

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Chapter 6: Foraging site fidelity

Variation in trip duration was accounted for by breeding stage, with birds in incubation foraging for longer durations than in chick rearing. This is likely due to the fasting ability of the partner incubating the egg compared to a chick, as birds were able to forage for twice as long in incubation. Interestingly, despite the longer trip duration, maximum distance from the colony was similar between the two stages. It could be assumed that prey are abundant within this foraging range and, as such, there isn’t a need to travel further. However, this behaviour is also observed in relatively poor years when prey availability is low (Chapter 4) with established colony-specific foraging ranges most likely causing the restricted movement

(Wakefield et al. 2013).

Ashmole (1963) predicted that the colonial nature of breeding seabirds would cause prey depletion in the habitat surrounding natal colonies resulting in increasing competition as the season progressed. In addition to the Australasian gannets breeding in Port Phillip Bay, many other seabird species (i.e. little penguins,

Eudyptula minor, and short-tailed shearwaters, Puffinus tenuirostris) and Australian fur seals (Arctocephalus pusillus doriferus) breed in the region, contributing to the seasonal depletion. In the present study, although the proportion of a foraging trip spent foraging (i.e. plunge diving and surface foraging events) was similar across breeding stages, birds in chick rearing were found to spend more time in flapping flight, indicative of greater time spent searching for food. Furthermore, birds in chick rearing were found to perform less dives than those in incubation, perhaps indicative of a greater abundance of small prey items during incubation. Birds in chick rearing had a lower total energy expenditure over a foraging trip than birds in incubation.

Hence, the longer distance and duration of foraging trips made in incubation appears to be more costly than the increased time in flight searching for food over a shorter

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Chapter 6: Foraging site fidelity

time-scale, seen in chick rearing. This was further evident in the similarity in rate of energy expenditure between the breeding stages. The higher dive rate and total energy expenditure observed in 2013 is likely due to the poor prey availability that year (Chapter 4).

The results of the stable isotope analyses suggests birds fed at a higher trophic level and on more inshore species during incubation than chick rearing. The diet of

Australasian gannets at the study colony has previously been documented as being comprised of high quantities of pilchard (Sardinops sagax) and barracouta (Thyrsites atun) (Bunce 2001), located at different trophic positions (pilchard: ca. 13 ‰; barracouta: ca. 16 ‰; Chiaradia et al. 2010). Although the energy content of pilchard

(8.6 kJ·g-1) and barracouta (7.1 kJ·g-1) are similar, barracouta are typically much larger prey (Bunce 2001). Gannets consume greater proportions of pilchards than other prey, with breeding success declining in years when pilchard are not abundant

(Bunce and Norman 2000, Pyk et al. 2013). Hence, the change in trophic level may represent a shift in prey species due to natural depletion of preferred prey throughout the season, i.e. shifting from pilchard to barracouta, or perhaps an influx of higher trophic species later in the season. A shift in prey species could potentially explain

WKHYDULDWLRQLQį13C across breeding stages, as carbon values can differ due to the geographic origin of prey (i.e. basal isotopic signature can vary between oceanographic waters) (Kelly 2000, Cherel and Hobson 2007).

Intra-individual variation

In the present study, birds in incubation were more consistent in area use than those in chick rearing. This could be due to competition, with a higher consistency

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indicating a low degree of competition as preferred resources are not yet limited

(O'Connor and Brown 1977, Ceia et al. 2012). Alternatively, the lower consistency in chick rearing may be due to the nutritional requirements of the chick. Low-quality prey, while sufficient for maintaining adult body condition, can have negative effects on the growth and survival of offspring (Batchelor and Ross 1984, Wanless et al.

2005). Both northern and Cape gannets have been found to shift prey species as they changeover from non-breeding to breeding (Hamer et al. 2000, Grémillet et al.

2008). Hence, switching to high quality prey, or prey of a different nutritional content, in new foraging habitats may ensure sustained chick growth.

In the present study, males were more consistent in their area use and foraging effort than females in both breeding stages. Of the birds foraging in Port Phillip Bay, a high proportion were males (85%), compared to birds foraging in Bass Strait where the sex ratio was equal. As a shallow embayment, Port Phillip Bay is potentially a more stable environment than Bass Strait (Lalli and Parsons 1993). As females typically remained in Bass Strait, they would not have as many predictable locations of prey available, requiring variation in the areas they forage and the effort to reach those areas.

The difference in habitat preference between the sexes may be due to the nutritional requirements of each sex, with females potentially requiring a greater variety or prefer less predictable prey species (Tait et al. 2014). Additionally, females may simply be more efficient foragers in offshore waters as their greater body mass allows for deeper dives to be made (Cleasby et al. 2015), thus males are restricted to foraging in shallow habitats.

Foraging strategies

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Chapter 6: Foraging site fidelity

In the present study, 38% of birds with multiple trips foraged in Bass Strait

(Group 3 and 4), where prey is influenced by oceanographic processes with multiple surface water masses entering the region (Gibbs 1992). As such, these birds had very little consistency in their area use. In heterogeneous environments, seabirds may enhance their likelihood of finding food in a shorter time scale by using public knowledge, either by travelling towards congregations of conspecifics (local enhancement hypothesis; Thorpe 1956), or shadow the direction of departing and arriving birds to the breeding colony (information center hypothesis; Ward and

Zahavi 1973), both of which may denote a profitable food patch. In the present study, birds in Group 3 and 4 travelled along the coastline at the start of a foraging trip before extending out into Bass Strait. The point at which birds altered their course was inconsistent and it is possible these birds used foraging conspecifics to make decisions. Although these two groups were similar, Group 3 were much less consistent than Group 4, with very few cells being revisited despite 12 and 13 trips recorded for each of the birds in Group 3. These birds may have potentially encountered fewer conspecifics while foraging resulting in the lower consistency in area use.

Alternatively, birds in Group 2 remained in Port Phillip Bay for all foraging trips with a high area use consistency. Seabirds are thought to possess private knowledge, with memory-based foraging decisions (Milinski 1994). As some habitats can be more predictable than others, seabirds may display foraging site fidelity, repeatedly travelling to an area previously known to be successful (Becker et al. 1993, Irons

1998). Given the high fidelity to particular locations, it is possible these birds rely solely on private knowledge to locate prey. However, local enhancement cannot be ruled out as birds foraged relatively close to the colony and may have seen

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Chapter 6: Foraging site fidelity

congregations of conspecifics at such locations when departing the colony (Thiebault et al. 2014b).

Interestingly, Group 1 also foraged inside the bay. However, these birds had a higher variability in the distance travelled from the colony and lower area use consistency than birds in Group 2. It is possible that Group 1 used private knowledge but altered their path with additional information from foraging conspecifics. This strategy is similar to that observed in northern gannets where individuals head out on a similar bearing to previous foraging trips but subsequently alter their foraging location, potentially due to information gathered from conspecifics (Hamer et al.

2001, Pettex et al. 2012). Other seabird species have been found to return to previous foraging areas but also explore new areas (Wanless et al. 1990, 1991, Weimerskirch et al. 1993). These birds may be rotationally harvesting known prey patches and seeking out new locations for future foraging trips.

Interestingly, within the two strategies of birds foraging in Port Phillip Bay there were varying degrees of consistency. As these birds were sampled in the same week, it is unlikely prey movements caused this disparity. Furthermore, sex, morphometrics, and age did not account for variation in area use consistency. Hence, birds foraging inside Port Phillip Bay may have varying degrees of individual specialisation.

Carbon isotope values are influenced by spatial gradients in marine phytoplankton, whereby coastal/inshore communities are mRUHHQULFKHGLQį13C than pelagic/offshore communities (Hobson et al. 1994, Kelly 2000, Cherel and Hobson

2007). In the present study, stable isotopes from plasma overlapped temporally with the tracking data (1 week), confirming birds in Group 2 fed on more inshore prey than birds in Group 1 and 4. These results are not surprising, given Group 2 birds

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Chapter 6: Foraging site fidelity

foraged exclusively within Port Phillip Bay. The carbon values for the red blood cells

(3-4 weeks) suggest these differences between strategies are consistent in the long- term. Nitrogen values for plasma and red blood cells did vary between strategies.

However, this shift in nitrogen is likely to reflect temporal variability in prey availability DQGDEXQGDQFHDVWKHį15N value can vary between prey species due to their trophic position (Kelly 2000).

In summary, sex and breeding stage were the most important variables influencing inter-individual variation. Although these variables were important for intra- individual variation, the habitat an individual forages in appears to be more important for determining foraging consistency. Gannets are thought to forage using both local enhancement and memory (Hamer et al. 2001, Wakefield et al. 2013). However, gannets do not have uniformity in their foraging behaviour across habitats (Garthe et al. 2007). In the present study, the unique opportunity to compare individual foraging strategies across opposing habitats revealed foraging site fidelity varies between individuals within a colony, with more consistent individuals potentially relying solely on private knowledge.

145

Chapter 7

General discussion

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Chapter 7: General discussion

It is essential to understand how a species adapts to environmental variability, particularly with the current rate of climate change (IPCC 2013). Species which display natural plasticity are more likely to buffer the effects of climate change

(Chambers et al. 2011). The overall objective of this study was to understand the factors, both intrinsic and extrinsic, attributing to variation in the foraging behaviour of Australasian gannets (Morus serrator). In the preceding chapters, I have found the foraging behaviour of the Australasian gannet is influenced by sex (Chapter 5 and 6) potentially driven by sexual size dimorphism (Chapter 2), breeding stage (Chapter 6), inter-annual variation (Chapter 4) and the habitat surrounding the colony (Chapter 5 and 6). The development of techniques to assess total body fat (Chapter 2) and indices of effort (Chapter 3) have been used to assess the consequences of these factors. The aim of the current chapter is to synthesise the major findings and discuss the implications of the combined studies in the broader context of seabird ecology.

Intrinsic factors influencing foraging behaviour Seabird populations are thought to be regulated by competition (Furness and

Birkhead 1984). As resources are limited (i.e. prey availability), niche segregation can reduce competition within the population (Van Valen 1965). The causes of segregation can be due to morphological or physiological variation within a population (Polis 1984). Sex-specific foraging behaviours and segregation are often attributed to sexual size dimorphism, resulting in the larger bodied sex able to travel further from the colony (Lewis et al. 2005) or dive to deeper depths (Watanuki et al.

1996). Additionally, sex-based nutritional requirements can result in different prey consumption (Clarke et al. 1998). Furthermore, the age of individuals can result in

147

Chapter 7: General discussion behavioural differences, either as the result of morphology, experience, or competition from older individuals (Forslund and Pärt 1995).

Seabirds provide a unique opportunity to investigate the causes of sexual size dimorphism. Although considered to be primarily monomorphic (Fairbairn and Shine

1993, Weimerskirch et al. 2006), some families of seabirds display pronounced male-biased dimorphism (males larger than females; i.e. albatross and petrels) and others are strongly female-biased (i.e. Sulids and frigatebirds) (Serrano-Meneses and

Székely 2006). The presence of size dimorphism is varied in the Sulids (gannets and boobies), ranging from monomorphism in the Cape gannet (Morus capensis;

Rishworth et al. 2014a) to female brown boobies being 38% heavier (Sula leucogaster; Lewis et al. 2005). Hence, Australasian gannets sit at the lower end of the scale with females 3.1-7.3% heavier than males, depending on colony (Chapter

2).

The division of labour hypothesis has been proposed as a potential driver of dimorphism, with males and females performing different roles in parental care

(Hedrick and Temeles 1989). Sulid males have been found to attend the nest more frequently (Weimerskirch et al. 2009a) or defend it more aggressively (Guerra and

Drummond 1995). Males may be inferior foragers due to their small body size limiting the amount of prey they can carry to the nest (Anderson and Ricklefs 1992,

Guerra and Drummond 1995). A larger body size in females may allow greater food carrying capacity, with females able to forage further from the colony (Weimerskirch et al. 2006, Castillo-Guerrero and Mellink 2011). Indeed, females provide larger and more frequent meals to young, depending on the breeding stage or environmental conditions (Anderson and Ricklefs 1992, Gilardi 1992, Weimerskirch et al. 2009a,

Castillo-Guerrero and Mellink 2011). However, in the present study, Australasian

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Chapter 7: General discussion gannets did not differ in the time spent at the nest according to sex, derived from foraging trip duration (Chapter 2). However, females did perform longer foraging trips in both maximum distance from the colony and total distance travelled (Chapter

6).

An alternative cause of size dimorphism postulated for Sulids is the food competition hypothesis (Shine 1989). Sex differences have been observed in foraging behaviour, with Sulid males typically foraging shorter distances and remaining in coastal waters (Chapter 5 and 6; Gilardi 1992, Lewis et al. 2005, Weimerskirch et al.

2006, Weimerskirch et al. 2009a, Zavalaga et al. 2010, Stauss et al. 2012). However, in the present study, there appears to be no physiological benefit between the strategies, with males and females being of a similar body condition (Chapter 2 and

5).

A critical review by Serrano-Meneses and Székely (2006) suggests sexual selection most likely influences dimorphism in seabirds. Assortative mating has previously been described in seabirds, with preferences for structural size (Devlin et al. 2004, Helfenstein et al. 2004), body condition (Wendeln 1997), and age (Nisbet et al. 1984). Assortative mating was not evident for body mass or condition in the

Australasian gannet, suggesting birds do not have a preference for these traits when selecting a partner (Chapter 2). Although, size convergence can occur after pairing, resulting in breeding partners influencing each other’s traits over time (Choudhury et al. 1992). However, despite being considered to be socially monogamous (Nelson

1978), Ismar (2010) found the divorce rate of Australasian gannets at Cape

Kidnappers, New Zealand, to be 40-43%. Hence, size convergence is unlikely to influence body size and condition in this species. The average age of birds breeding at Pope’s Eye (121 birds handled in the present study) was 10.1 ± 0.7 years old for

149

Chapter 7: General discussion males and 10.3 ± 0.7 years old for females (range: 5-27 years old), with a 5.1 ± 0.6 year difference in age between breeding partners (n = 40 pairs, range: 0-13 years difference). Analysis of breeding pairs indicated for both males and females there was no preference for a particular age (older or younger) or for similar aged partners

2 (Pearson correlation: r = 0.01, F1,38 = 0.36, P = 0.55) in Australasian gannets at

Pope’s Eye. As such, sexual selection based on age is also unlikely in this species.

For long-lived species, reproductive success and survival can increase with age

(Newton 1989, Forslund and Pärt 1995). Delayed maturity often observed in seabirds may allow young birds to develop the necessary foraging skills to sustain self- maintenance and growth (Lack 1968). However, young birds breeding for the first time may lack the ability to meet the needs of offspring resulting in reduced breeding success (Forslund and Pärt 1995). For example, in pigeon guillemots (Cepphus

Columba), individuals which are able to obtain large, lipid rich prey for their offspring are more likely to successfully reproduce than those that do not switch prey from their non-breeding diet (Golet et al. 2000). A young or inexperienced bird may not yet know to switch prey for offspring provisioning (Forero et al. 2002, Limmer and Becker 2009). Hence, an individual’s fitness can be reliant on age and experience. However, Pyk et al. (2007) found age does not influence breeding success or determine the species of prey consumed in Australasian gannets.

Furthermore, foraging capabilities are also thought to improve with age due to experience finding, capturing, and handling food (Daunt et al. 2007). Zimmer et al.

(2011) found middle-aged little penguins (5-10 years old; Eudyptula minor) performed shallower dives with a reduced foraging effort compared to younger and older birds. Gannets typically feed by plunge diving, a highly specialized foraging technique often engaged when feeding with conspecifics and heterospecifics in dense

150

Chapter 7: General discussion aggregations and inexperience may lead to fatal collisions (Machovsky-Capuska et al. 2011a). Interestingly, for Australasian gannets, age was not found to influence any foraging effort metric (i.e. total and mean VeDBA) or behaviour (Chapter 6).

However, natural selection may lead to only the most efficient foragers surviving to breeding age (Lack 1954, Ashmole 1963). Hence, the results of the present study may not be representative of the influence of age, but rather, surviving individuals.

With the exception of penguins, foraging seabirds are constrained by the need to detect underwater prey from the air (Irons 1998). Individual strategies may evolve to increase the likelihood of detecting food. Seabirds can identify and follow indicators of prey, such as odour cues (i.e. Procellariiformes; Bang 1965), oceanographic features (Thiebot et al. 2013), or conspecifics (Yoda et al. 2011). Previous studies on

Cape gannets have suggested that by following other gannets, foraging time is reduced and success is increased (Lett et al. 2014, Thiebault et al. 2014a). Indeed, the presence of large aggregations of gannets appears to represent the quality of a food patch (i.e. abundance of prey) to searching gannets (Thiebault et al. 2014b).

Therefore, associating with conspecifics at sea could improve foraging efficiency.

For example, juvenile brown boobies following adults reduced time spent searching but increased flight duration (Yoda et al. 2011). However, feeding in aggregations can result in exploitation competition between foragers and faster resource depletion

(Hoffman et al. 1981, Maniscalco et al. 2001). Silverman et al. (2004) suggest cooperation between foragers occurs through information transfer (i.e. local enhancement hypothesis; Thorpe 1956) across species, with the benefits of associating with other foragers outweighing the costs of competition.

However, foraging in groups is not necessarily the better strategy. In great cormorants (Phalacrocorax carbo sinensis), solitary foragers are more successful

151

Chapter 7: General discussion than group foragers (Voslamber et al. 1995). In predictable environments, the use of memory-based decisions can lead to individual specialisation and reduced competition (Cook et al. 2006). Black-legged kittiwakes (Rissa tridactyla) return to previously used areas over multiple foraging trips, indicating private knowledge about locations of prey (Irons 1998). Wanless et al. (1990) found common murres

(Uria aalge), razorbills (Alca torda) and Atlantic puffins (Fratercula arctica) return to previous areas but also target new areas, perhaps due to low foraging success or depletion of prey. A similar behaviour has been observed in northern gannets (Morus bassanus), where birds leave the colony along a previous bearing but deviate their path to new foraging areas, potentially due to encountering conspecifics (Hamer et al. 2001). However, this behaviour is not consistent across the species, as Garthe et al. (2007) found colonies adopted different foraging strategies due to oceanic regimes. As a consequence of different foraging strategies, the consistency of area use and foraging effort can vary (Chapter 6). However, the benefits of different foraging strategies within a colony could not be determined in the present study.

Future studies should relate prey capture rate, chick growth and, subsequently, breeding success to these strategies.

Extrinsic factors influencing foraging behaviour As Central Place Foragers, breeding seabirds alternate between foraging to provision their young and spending time at the nest protecting their young. Seabirds must adjust their foraging behaviour within each breeding stage as the energetic demands of incubating an egg or provisioning young differ (Ricklefs 1983). Foraging trip duration can be restricted by the fasting ability of either the breeding partner, during incubation, or the young, during chick rearing. Thus, foraging trips can be up

152

Chapter 7: General discussion to 50% longer in the incubation stage (Chapter 4 and 6; Charrassin et al. 1998). The time allocated to searching for prey can determine the foraging range and, hence, suitable habitat and resource availability within this range is crucial for foraging and reproductive success.

Seabird colonies can be up to hundreds of thousands of breeding pairs, resulting in high local competition for food resources and increased foraging trips. As birds typically benefit from foraging as close to the breeding colony as possible, local depletion of prey resources can result in birds travelling further to find prey (Birt et al. 1987). Indeed, trip duration and distance has been correlated with colony size in gannets (Chapter 5; Lewis et al. 2001), penguins (Ainley et al. 2000), kittiwakes and alcids (Furness and Birkhead 1984).

Ashmole (1963) proposed seabird populations are regulated by food availability surrounding the breeding colony. Indeed, the larger colony in the present study, Point

Danger, is located close to the highly productive Bonney Upwelling (Cai and Lennon

1993, Butler et al. 2002). Whereas, the smaller colony, Pope’s Eye, is located in a region of low primary productivity and a limited nutrient regime (Gibbs et al. 1986).

As a consequence of habitat structure and prey abundance, the foraging ecology of a species can vary between colonies (Chapter 5). For example, Garthe et al. (2007) found that an inshore and offshore colony of northern gannets fed on different prey.

Additionally, the habitat structure can influence the dive strategy (Garthe et al. 2007) and, subsequently, prey capture success (Machovsky-Capuska et al. 2011b). As such, comparisons between colonies provides insights into the species’ adaptability

(Tremblay and Cherel 2003).

Competition for food can increase between and within colonies in years of low prey availability, resulting in low breeding success and high adult mortality (Lack

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Chapter 7: General discussion

1954, Cairns 1987). As seabirds are long-lived, adult survival is more important for long-term individual fitness than current breeding attempts (Erikstad et al. 1998). In the present study, breeding success at Pope’s Eye decreased from 64.7% (typical for this colony; Pyk et al. 2013) in 2011 to 30.1% in 2012 and 8.6% in 2013 (Chapter 4).

Additionally, breeding success at Point Danger decreased from 50.5% in 2011

(Barker 2012) to 10% in 2012 and 0% in 2013 (Angel unpublished data). Similar patterns of reduced breeding success were observed in other seabirds breeding in the region (Berlincourt and Arnould 2015a, b), suggesting poor prey availability in the latter years of the study.

In poor years, gannets from Pope’s Eye were found to remain within their typical foraging range and maintain foraging trip duration and distance (Chapter 4). This conflicts with previous studies on Cape (Pichegru et al. 2010) and northern gannets

(Hamer et al. 2007, Garthe et al. 2011), as well as many other seabird species

(Monaghan et al. 1994, Ronconi and Burger 2008, Burke and Montevecchi 2009) where birds typically travelled greater distances and for longer durations in poor years, resulting in a greater foraging range, inevitably increasing the potential of encountering prey. Due to the presence of larger, nearby gannet colonies in the present study, it is possible birds from Pope’s Eye were unable to extend their foraging range without impinging on neighbouring colony home ranges. Essentially, birds at Pope’s Eye may be cornered by these two larger colonies. These findings raise concern for the population trajectory of gannets from this colony with the predicted climate change effects.

While numerous factors influence variability in foraging behaviour of

Australasian gannets, the establishment of mutually exclusive foraging ranges between gannet colonies and sexual segregation within colonies, in addition to

154

Chapter 7: General discussion geographical barriers, present a potential problem for the flexibility of foraging behaviour in the Australasian gannet with the increasing climatic variability.

Australasian, Cape and northern gannets have all shown great dietary adaptability to reduced prey availability in short-term studies (Bunce and Norman 2000, Grémillet et al. 2008, Votier et al. 2010). However, information regarding long-term adaptability in foraging behaviour is still limited (Hamer et al. 2007, Pichegru et al.

2010, Garthe et al. 2011).

Implications and future directions Seabirds, the most threatened marine taxonomic group (Croxall et al. 2012), are particularly sensitive to the effects of climate change (Barbraud et al. 2012, Sandvik et al. 2012). However, in the case of Bass Strait, little is known about the responses of seabirds to the current rate of climate change (Chambers et al. 2011, Berlincourt and Arnould 2015a, b). South-eastern Australia waters are warming at a rate four times the global average (Lough and Hobday 2011). Changes in the distribution and abundance of marine species have already been reported, including pelagic fish which are the main prey item of Australasian gannets (Hobday et al. 2007,

Poloczanska et al. 2007). Determining seabird population trajectories requires information, not only about the expected environmental conditions (Hobday and

Lough 2011), but the current status of marine predators and their potential adaptability (Hobday et al. 2014).

Demography (i.e. reproduction and recruitment) and adult survival can be influenced by climate change (Thompson and Ollason 2001, Sandvik et al. 2012).

Australasian gannet populations have increased in recent decades (Greene 1999,

Bunce et al. 2002) due to reduced harassment and depredation by humans (Norman

155

Chapter 7: General discussion and Menkhorst 1995) and establishment of new nesting sites (Alderman et al. 2011,

Pyk et al. 2013). However, as a long-lived species, gannets typically prioritise their survival over reproductive success in poor years (Erikstad et al. 1998). If reproduction does suffer as the result of climatic variability, the effects may be delayed 3-19 years due to the maturation period of gannets (Pyk et al. 2013). Thus, the effect of current climatic conditions on Australasian gannet populations may take decades to become evident.

The foraging behaviour of Australasian gannets suggests these birds are unable to buffer the effects of poor prey availability, resulting in reduced reproduction

(Chapter 5). Sexual segregation and colony-specific foraging ranges present potential limitations to foraging behaviour flexibility. If the main prey of gannets (i.e. pilchard

Sardinops sagax, and barracouta Thyrsites atun) shift in distribution, as has already been reported for other species (Hobday et al. 2007, Poloczanska et al. 2007), the adaptability of gannets from Pope’s Eye is questionable. However, birds from Point

Danger should be able to forage further afield due to no other colonies nearby. The present study was only able to assess Pope’s Eye over multiple years. However, these findings highlight the importance of colony variability on assessing species adaptation. Seabird species may not be able to adapt to climatic variation if colonies are situated in geographically limiting environments (e.g. pack ice encroaching on ice-free habitats; Ainley et al. 2004) or limited by increased competition outside the home range (Ainley et al. 2003). Hence, assessing a species’ adaptability to climate change cannot be limited to a single colony (Sandvik et al. 2012).

Seabirds have been long considered accurate indicators of ecosystem health with poor conditions reflected in their foraging ecology and breeding success (Einoder et al. 2008). Climatic change may result in seabird populations modifying their foraging

156

Chapter 7: General discussion ecology, foraging distribution, or going extinct (Grémillet and Boulinier 2009). Both adult survival and juvenile recruitment influence population dynamics (Sandvik et al.

2012). However, breeding success is often sacrificed for adult survival in years of poor prey availability (Frederiksen et al. 2004). Therefore, future studies should focus on long-term monitoring of breeding success to assess the effects of climate change on population demographics. Furthermore, developing an understanding of the foraging behaviour of a species requires monitoring individuals in both the breeding and non-breeding seasons, as well as throughout their life cycle (i.e. juveniles and adults). Additionally, future studies need to monitor multiple colonies to assess a population’s ability to adapt to environmental variability and potential shifts in foraging distribution. Globally, seabirds are capable of adapting to environmental change, however, accurate predictions of seabird population trajectories and adaptations are needed to ensure adequate protection is provided for less adaptable species (Sydeman et al. 2012).

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Gannets no doubt, when man is redundant, will nest on the wreckage of the world he leaves;

Spread their wings to the measure of man,

and continue in calm, their immaculate diving

- Simon Baynes