Population Structure and Mating System of the Australian Sea Lion (Neophoca Cinerea) Heidi Ahonen

Home , Australian sea lion, Grey seal, Harp seal, Hooded seal, Liguanea Island, Neophoca

Population structure and mating system of the Australian sea lion (Neophoca cinerea) Heidi Ahonen

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Heidi Ahonen. Population structure and mating system of the Australian sea lion (Neophoca cinerea). Neurons and Cognition [q-bio.NC]. Université Paris Sud - Paris XI; Macquarie university (Sydney, Australie), 2013. English. NNT : 2013PA11T059. tel-01070276

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ÉCOLE DOCTORALE : ED 419 BioSigne Laboratoire Centre de Neurosciences Paris Sud (CNPS)

DISCIPLINE Neurosciences

THÈSE DE DOCTORAT

soutenue le 30/09/2013

par

Heidi AHONEN

Population Structure and Mating System of the Australian sea lion ( Neophoca cinerea )

Directeur de thèse : Thierry AUBIN DR (CNRS, UMR 8195, Univ. Paris Sud XI) Co-directeur de thèse : Adam STOW PR (Macquarie University, Sydney, Australia)

Composition du jury :

Président du jury : Sylvie GRANON PR (CNRS, UMR 8195, Univ. Paris Sud XI) Rapporteurs : Fritz TRILLMICH PR (Univ Bielefeld, Bielefeld, Allemagne ) Martin WHITING PR (Macquarie University, Sydney, Australia) Examinateurs : Isabelle CHARRIER CR (CNRS, UMR 8195, Univ. Paris Sud XI) TABLE OF CONTENTS

STATEMENT OF CANDIDATE ……………………………………………………………………………………… ..3 STATEMENT OF CONTRIBUTION ……………………………………………………………………………...... 4 ACKNOWLEDGEMENTS ………………………………………………………………………………………………..5 EXTENDED SUMMARY ………………………………………………………………………………………………… 9 RÉSUMÉ ÉTENDU ………………………………………………………………………………………………………. 12 CHAPTER 1: Introduction ……………………………………………………………………………………………16 Genetic structure and dispersal capacity in highly mobile organisms …………………………17 Land-breeding marine animals and possible processes driving genetic structure ………19 Vocalisation and social structure -alternative way to look differentiation …………………. 21 Australian sea lion as study species ……………………………………………………………………………24 Acoustic vocalisation in male Australian sea lion ………………………………………………………. 29 Thesis rationale and aims ………………………………………………………………………………………… ..31 Additional data as Appendices ………………………………………………………………………………… ..32 CHAPTER 2: Materials and Methods …………………………………………………………………………. 34 Study sites …………………………………………………………………………………………………………………35 Animal identification and marking …………………………………………………………………………… .38 Genetic sample collection …………………………………………………………………………………………42 Laboratory procedures and genetic data analyses …………………………………………………… .45 Acoustic sample collection ……………………………………………………………………………………… ..47 Analysis of acoustic parameters ……………………………………………………………………………… ..49 CHAPTER 3: Characterization of 12 novel microsatellite loci and cross-amplification of four loci in the endangered Australian sea lion (Neophoca cinerea) ………………………… .52 Abstract …………………………………………………………………………………………………………………… .53 Introduction ………………………………………………………………………………………………………………53 Materials and Methods ……………………………………………………………………………………………. .54 Results and Discussion ………………………………………………………………………………………………. 55 CHAPTER 4: Local Lads: Spatial genetic structure in male Australian sea lions (Neophoca Cinerea) …………………………………………………………………………………………………………………… .57 Abstract …………………………………………………………………………………………………………………… ..58 Introduction ……………………………………………………………………………………………………………….58 Materials and Methods ………………………………………………………………………………………………62 Results ……………………………………………………………………………………………………………………… .67 Discussion ………………………………………………………………………………………………………………… .77 1

CHAPTER 5: The sound remains the same? Adult male Australian sea lion barking calls reveal clear geographic variations that do not reflect known genetic structure ………… 84 Abstract ……………………………………………………………………………………………………………………..85 Introduction ……………………………………………………………………………………………………………….85 Materials and Methods ………………………………………………………………………………………………89 Results ……………………………………………………………………………………………………………………….95 Discussion ………………………………………………………………………………………………………………..105 CHAPTER 6: Sharing the love: paternity analysis within and between three Australian sea lion colonies …………………………………………………………………………………………………….. .113 Abstract ……………………………………………………………………………………………………………………114 Introduction ……………………………………………………………………………………………………………..115 Materials and Methods …………………………………………………………………………………………...120 Results ……………………………………………………………………………………………………………………..129 Discussion ………………………………………………………………………………………………………………..142 CHAPTER 7: General discussion ………………………………………………………………………………..148 REFERENCES …………………………………………………………………………………………………………….168 APPENDIX 1 …………………………………………………………………………………………………………… ..184 APPENDIX 2 ……………………………………………………………………………………………………………..185 APPENDIX 3 ……………………………………………………………………………………………………………..186 APPENDIX 4……………………………………………………………………………………………………………..187 APPENDIX 5……………………………………………………………………………………………………………..188 APPENDIX 6 ……………………………………………………………………………………………………………..196

STATEMENT OF CANDIDATE

I certify that the work in this thesis has not previously been submitted for a degree nor has it been submitted as part of requirements for a degree to any other university or institution other than Macquarie University.

I also certify that the thesis is an original piece of research and it has been written by me. Any help and assistance that I have received in my research work and the preparation of the thesis itself have been appropriately acknowledged.

In addition, I certify that all information sources and literature used are indicated in the thesis.

The research presented in this thesis was approved by DEH SA Wildlife Ethics Committee (44/2008) and conducted under permission obtained from Department for Environment and Heritage, South Australia (permit number Z25675) and Department of Environment and Conservation, Western Australia (DEC WA License SF007255 and SF008193).

Heidi Ahonen Date: 16.7.2013

STATEMENT OF CONTRIBUTION

General Introduction, General Materials and Methods and General discussion are written by me with feedback from my principle supervisors.

The majority of the lab work was carried out by me. I want to acknowledge the contributions of following people who have been working under my supervision to help complete parts of this study: Jessica Boomer, Heather Baldwin, Siobhan Dennison, Anne- Sophie Lafuite, Lara Paranhos and Simon Giuliano.

Chapter 3 : Characterization of 12 novel microsatellite loci and cross-amplification of four loci in the endangered Australian sea lion ( Neophoca cinerea )

Ahonen, H., Lowther, AD., Goldsworthy SD., Harcourt RG., Stow AJ. 2012 Characterization of 12 novel microsatellite loci and cross-amplification of four loci in the endangered Australian sea lion (Neophoca cinerea). Conservation Genetic Resources 5:283-285

Conception: 95%, Data Collection: 100%, Analysis: 90%, Writing: 95%

Chapter 4: Local Lads: Spatial genetic structure in male Australian sea lions (Neophoca Cinerea)

Conception: 95%, Data Collection: 100%, Analysis: 90%, Writing: 95%

Chapter 5 : The sound remains the same? Adult male Australian sea lion barking calls reveal clear geographic variations that do not reflect known genetic structure

Conception: 95%, Data Collection: 100%, Analysis: 100%, Writing: 95%

Chapter 6: Sharing the love: paternity analysis within and among three Australian sea lion colonies

Conception: 95%, Data Collection: 100%, Analysis: 95%, Writing: 95%

ACKNOWLEDGMENTS

There are many people that helped and supported me during this journey and to all of them I owe much gratitude.

Firstly I would like to thank my supervisors Adam Stow and Isabelle Charrier whose expertise, guidance, understanding and patience have been invaluable during this PhD.

Adam, eight years ago you welcomed me with open arms into your group, gave me the opportunity to explore the world of research and encouraged me to go forward. Years have passed, but I will never forget how it all started. Isa, you introduced me to the world of acoustic communication and offered friendship and support in every situation. You also showed me the French way of living and made sure my time in Paris was wonderful.

I am also grateful for Rob Harcourt and Simon Goldsworthy for their guidance and support in different stages throughout my candidature. You have introduced the world of

Australian sea lions to me and given me an adventure of a lifetime. Thank you also Simon for welcoming me to SARDI, giving me a place to work and showing me the joys of

Adelaide.

I want to especially thank Andy Lowther and Ben Pitcher, my fellow Australia sea lion researchers. You showed me the way and shared many of these adventures with me. I am grateful of the advice and guidance, but also all the fun time we had in the field.

During this PhD I have been lucky to be able to be part of three excellent research teams: the Marine Mammal Research Group and the Conservation Genetics Group at Macquarie

University and the Bioacoustics Team at l'Université Paris Sud. Thank you for making me 5 feel welcomed and as a part of each of the teams. I have met so many wonderful people and hope our paths will still cross in the future. I would like to express my special gratitude to The Bioacoustics Team who has supported my work, made me feel at home during my short visits and gave me this amazing opportunity to live in Paris.

A big thank you goes to all the people that has provided help during my fieldwork and made those trips unforgettable; Andrew Dawson, Dave Slip, Francisco Viddi, Kerstin

Bilgmann, Lyall Grieve, Phil Allen, Ben Arthur, Sam Blight and Clair Holland. I could not have done this without you! I would also like to thank Sue Gibbs, who always opened her home for tired travellers and offered incredible hospitability.

I am grateful for all the people that have helped me in lab; Jessica Boomer, Heather

Baldwin, Siobhan Dennison, Anne-Sophie Lafuite, Lara Paranhos, Simon Giuliano and

Shannon Smith. During my PhD I also had the opportunity to work in two excellent labs in

Adelaide. I thank Kathy Saint from University of Adelaide/ South Australia Museum and

Luciano Beheregaray from Flinders University that I was able to finish my lab work in these two top class facilities and meet group of fantastic people.

Funding for this project was provided by Macquarie University, CNRS and Université Paris

Sud, Australia Marine Mammal Centre and Sea World Research and Rescue Foundation. I thank the Ceduna State Emergency Services for providing their time and vessel to transport to Lilliput and Blefuscu Islands. I am also grateful to the staff of the Department of Environment, Water and Natural Resources all along the Spencer Gulf and western

Eyre Peninsula; in particular Brett Dalzell and Robbie Sleep. I thank Mathew Dasey, Susie

Glac, and Richard Koch from Jurien Bay Marine Park for their generosity and assistance

6 during my fieldtrips to Western Australia. I would also like to thank the staff at

Department of Biological Sciences and especially thank Teresa Potalivo who always answered my questions with a smile and gave invaluable advice.

There are few special people that made the time at Macquarie University fun and enjoyable: Gill and Mark Ellis, Colm Halbert and Tony Morrison. You will always have a special place in my heart. Special thanks to Kerstin Bilgmann and Niina Tujula for friendship, support and always being there. I would also like to warmly thank Siobhan

Dennison and her family for offering me a friendly and warm place to stay, making me feel at home and welcomed.

I would like to send special thanks and love to my close friends who have always supported me: Tanja, Kari, Antto, Nikla and Tulle Laine, Eeva Ylinen and Roope Groot,

Jenni Saarto, Sini Kivilaakso and Jaana Asriel. You are the true Finnish support group! No matter where in the world I have been, how far the distance or time is since we have last seen, you have always been there keeping things in perspective.

I am deeply thankful to my mum Heli and my dad Erkki, you have both made this all possible. I have always had your love and support, your guidance and wisdom, whatever has happened I have never been alone. You have always encouraged me to pursue my dreams, taught me never to give up and reminded me of the good things in my life. I can never thank you enough, I love you.

And last but not least, to my two hairy boys, Andy and Monty. You came in my life during my PhD and have brought so much happiness, laughter and love to this journey. Thank

7 you for being there. Andy, you have taught me so many things from life in the field to proper use of a gun to the fascinating world of rugby, but also about strength, self- confidence and determination. Throughout the many challenges you have been there encouraging, supporting and pushing me forward. Monty, you kept me always smiling and took me out of house so that I would not become a hermit. Now boys it is time to start a new adventure, tails up!

EXTENDED SUMMARY

The aquatic and terrestrial partitioning in ecology of otariids makes them an ideal group to study the range of their behavioural and physical factors that influence connectivity.

The marine environment where they forage typically allows broad dispersal and enhances homogeneity, whereas terrestrial breeding can restrict the movement ability of one or both sexes. The application of both molecular and non-molecular techniques appears to be useful in outlining population boundaries but also in describing causal processes of species discontinuity.

The endangered Australian sea lion ( Neophoca cinerea ) is endemic to the southern and western coastline of Australia, distributed across 76 breeding colonies with 86% of all breeding occurring in South Australia. Over 60% of the breeding colonies are small producing less than 30 pups per breeding episode. The unique breeding biology with non- annual, asynchronous reproductive cycle and prolonged breeding season has been proposed to influence on range of ecological and behaviour traits of Australian sea lion.

The level of female population structure in Australian sea lion is unparalleled in marine vertebrates with little or no interchange of females among breeding colonies. Considering the high number of small and isolated colonies and low recovery and re-colonisation rates the knowledge of male reproductive movement is essential. The unique asynchrony in timing of breeding among geographically close colonies provides males with the possibility of breeding at more than one colony during any single reproductive cycle. Male mating success across different colonies could counteract the high degree of structure driven by extreme site fidelity in female sea lions. In this study I explored the population

9 structure and breeding system of Australian sea lions and determined how male strategies influence dispersal patterns and variance in male reproductive success. At the same time I investigated the extent of geographical variation in male barking call, a potential alternative method to measure differentiation across species range.

Genetic analyses based on microsatellite DNA markers demonstrated that Australian sea lion exhibits male-biased dispersal. However the scale over which substantial structure forms among breeding colonies implies that male dispersal is limited to a remarkably small scale. It appears that such a restricted dispersal in males might be explained by fine- scale foraging specialisation combined with reproductive benefits from site-specific knowledge during their prolonged breeding season. The Australian sea lion exemplifies how ecological and behavioural factors might be the drivers of a high degree of fine scale population structure as opposed to more traditional geographic explanations such as spatial distance and topographic barriers.

In general the key element in high levels of polygyny is the ability of males to monopolise several females. In this study I showed that level of polygyny in Australian sea lion falls at the lower end of continuum generally described for pinniped species. The prolonged breeding season where receptive females are spread out in time and space appears to be the main factor explaining observed low variance in reproductive success. The genetic structure I detected suggests that male movements for breeding at least, only occur over a small scale and the results from paternity analysis support this by showing the presence of alternative mating strategies (roaming vs staying) with some males actively moving and breeding between close colonies.

Comparisons of male Australian sea lion barking calls revealed small though significant geographical variation across species range. A combination of acoustic parameters contributed to the discrimination between colonies and indicated that variation in male vocalisation may be driven by site-specific selection for changes in certain vocal parameters. Neither genetic nor geographical distances between colonies were strongly associated with this observed acoustic variation. The general low level of exchange among colonies could point to drift as explaining differences in vocalisation and hence strong concordance between genetic data and acoustic data would not be expected. I hypothesise that alternatively acoustic variations may be driven by ecological pressures such as sound transmission properties or differences in male body size. However to confirm the source of variation in this species further investigations are need.

This study adds considerable knowledge to our current understanding of Australian sea lion dispersal behaviour and genetic colony structuring. It also characterises the mating system and demonstrates how unique breeding biology of this species determines the level of variance in male reproductive success and enables the presence of alternative mating strategies. Use of alternative non-molecular methods such as acoustic differences to infer population structure emphasize the complexity associated with the processes involved in the establishment of within-species divergence.

RÉSUMÉ ÉTENDU

Le mode de vie à la fois aquatique et terrestre des otariidés fait de cette famille un modèle idéal pour étudier les facteurs comportementaux et physiques influençant sur la connectivité. Le milieu marin où ils s’alimentent permet généralement une large dispersion et augmente l'homogénéité, alors que la reproduction en milieu terrestre peut restreindre la capacité de mouvement de l'un ou des deux sexes d’une espèce.

L'utilisation de techniques moléculaires et non moléculaires peut permettre de définir les limites des différentes populations, mais aussi de décrire les causes de la discontinuité de population chez une espèce.

Le lion de mer Australien ( Neophoca cinerea ), listé comme une espèce en danger par l’IUCN, est une espèce endémique des côtes Sud et Ouest de l'Australie. Elle est répartie en 76 colonies de repro duction avec 86% de la reproduction de l’espèce s’effectuant en

Australie du Sud. Plus de 60% des colonies de reproduction sont relativement petites produisant moins de 30 petits par saison. Ce système de reproduction unique de par ce cycle non-annuel, asynchrone avec une saison de reproduction prolongée a suggéré une forte influence sur le façonnement des traits écologiques et comportementaux du lion de mer Australien.

Le niveau de la structure de population génétique chez les femelles lion de mer Australien est inégalé chez les vertébrés marins avec peu ou pas d'échanges de femelles entre colonies de reproduction. Compte tenu du nombre important de petites colonies isolées, ainsi que du faible taux de rétablissement de l’espèce, une meilleure connaissance des mouvements des mâles lors de la reproduction est donc essentielle. L'asynchronisme

12 unique de la reproduction entre colonies proches géographiquement est une bonne opportunité pour les mâles de se reproduire dans plusieurs colonies lors d’une même saiso n de reproduction. L’accès des mâles à plusieurs sites de reproduction pourrait donc contrecarrer le fort degré de structure génétique de population chez les femelles dû à une fidélité extrême au site de reproduction. Dans cette étude, j'ai exploré la structure génétique de la population et le système de reproduction des lions de mer Australiens, et j’ai aussi déterminé comment les stratégies des mâles influencent les patrons de dispersion et la variance de leur succès reproducteur. En parallèle, j'ai étudié les variations géographiques des cris de mâles, ce qui représente une méthode alternative et originale permettant de mesurer la différenciation de populations de l’espèce.

Les analyses génétiques basées sur des marqueurs microsatellites ont montré que le lion de mer Australien présente une dispersion mâle-spécifique. Cependant l'échelle sur laquelle se forme la structure entre les différentes colonies implique que la dispersion des mâles se limite à une faible échelle. Cette dispersion restreinte chez les mâles pourrait s’expliquer par la forte spécialisation de leur prospection alimentaire à un site donné. La connaissance parfaite de ces sites d’alimentation est un avantage important, particulièrement lors de la saison de reproduction prolongée. Le lion de mer Australien illustre de façon remarquable comment les facteurs écologiques et comportementaux peuvent moduler la structure de la population à une fine échelle, par opposition aux explications géographiques classiques tels que la distance spatiale et les obstacles topographiques.

En général, l'élément clé dans les systèmes à forte polygynie est la capacité des mâles à monopoliser plusieurs femelles. Dans cette étude, j'ai montré que le niveau de polygynie

13 chez le lion de mer Australien est à l’extrémit é inférieure du continuum généralement décrit pour les pinnipèdes. La saison de reproduction étendue, où le nombre de femelles réceptives s’étale dans le temps et l'espace, semble être le facteur principal expliquant la faible variance du succès reproducte ur des mâles. La structure génétique, que j’ai pu détecter, suggère que les mouvements des mâles, au moins pendant la reproduction, ne se font que sur une petite échelle. Ces résultats, en combinaison avec l'analyse de paternité, suggèrent la présence de stratégies de reproduction alternatives (males résidents vs males nomades) avec certains mâles se déplaçant activement entre différentes colonies de reproduction proches.

Les comparaisons des cris de mâles lion de mer Australien révèlent une variation géographique faible mais significative sur la distribution générale de l'espèce. Une combinaison de paramètres acoustiques permet une bonne discrimination entre les colonies. De plus, la variation des vocalisations des mâles semble spécifique à chaque site, favorisant des changements de certains paramètres vocaux. Cette variation acoustique n’est aucunement liée aux distances génétiques ou géographiques entre les colonies. Le faible niveau général des échanges d’individus entre colonies pourrait impliquer une dérive et donc expliquer les différences des vocalisations. Une forte concordance entre les données génétiques et les données acoustiques ne serait donc pas attendue. J’ai donc

émis une hypothèse alternative où les variations acoustiques s’expliquent par des différences de contraintes écologiques telles que les propriétés de transmission du son et/ou par des facteurs biologiques comme des différences de taille corporelle des mâles.

Cependant, pour confirmer la source de variation acoustique dans cette espèce, des

études supplémentaires sont nécessaires.

Cette étude est une contribution importante car elle a permis d’augmenter considérablement notre connaissance sur le lion de mer Australien, et notamment de mieux comprendre son comportement de dispersion et la structure génétique de sa population. J’ai pu définir son système de reproduction, et démontrer comment son unique système de reproduction détermine le niveau de variance du succès reproducteur des mâles mais offre aussi des alternatives dans la stratégie de reproduction. L'utilisation de méthodes non-moléculaires alternatives, comme l’analyse des différences acoustiques pour déterminer la structure de la population, révèle la complexité des différents processus impliqués dans la mise en place de divergence au sein de l'espèce.

CHAPTER 1: General Introduction

Beagle Island, Western Australia 2010

The primary selective forces responsible for shaping species life-history traits come from the physical and biological environment in which a species resides. Thus from both applied and esoteric standpoints, revealing those aspects of the environment which are important in either facilitating or hindering connectivity between groups of conspecifics is central to understanding key biological processes such as metapopulation dynamics and the distribution of species. The field of molecular ecology has applied increasingly more complex and powerful genetic molecular techniques to attempt to test the relative influence of environmental features on genetic discontinuities, thus highlighting the physical and biological aspects of the environment in which a species resides features are important to species distributions. A crucial first step in identifying these features is comprehensively describing the molecular structure across the distribution of a species. However, by itself knowledge of gene flow among groups of individuals does little to reveal the range of behavioral and physical factors that influence connectivity. Thus the application of alternative techniques of delineating population boundaries, such as the vocal distinctiveness of demes, may prove useful in describing other causal processes of species discontinuity.

Genetic structure and dispersal capacity in highly mobile organisms

The maximal capacity of organisms to disperse might be expected to determine the geographical limits beyond which genetic differentiation become evident. However this is not always the case, and the absolute movement capabilities of individuals may not be indicative of dispersal patterns and genetic partitioning within species (Milot et al. 2008).

Several large terrestrial predators are excellent examples of highly mobile and widely distributed animals where distinct genetic structure can be observed (e.g. coyote Canis latrans ; (Sacks et al. 2004), grey wolf ( Canis lupus );(Geffen et al. 2004), (Pilot et al. 2006),

17 lynx Lynx lynx and Lynx Canadensis ; (Rueness et al. 2003a, Rueness et al. 2003b) and puma Puma concolor ; (McRae et al. 2005)). In many of these species differentiation appears to be explained by ecological processes rather than by geographical barriers or historical processes such as past colonization, range expansion or isolation in glacial refugia. For example in grey wolf ( Canis lupus ) genetic differentiation was correlated with climate, habitat types, and diet composition (Pilot et al. 2006).

The marine environment in general is an environment that promotes genetic homogeneity, as it lacks obvious barriers to dispersal such as rivers and mountains that might drive genetic differentiation in terrestrial environments. Accordingly, it is reasonable to expect that various highly mobile marine organisms should show panmixia across large geographic areas. Indeed patterns like this have been observed in mobile marine vertebrates such as teleost fishes (see review by Graves, 1998). Yet, an increasing number of studies on various marine taxa (e.g. turtles, some predatory marine fishes, sharks, cetaceans and pinnipeds) have reported that genetic differentiation can occur over relatively small geographic distances (e.g. Bowen et al. 1992, Goodman 1998,

Knutsen et al. 2003, Natoli et al. 2004, Bilgmann et al. 2007, Graves et al. 2009,

Rosenbaum et al. 2009, Blower et al. 2012, Tillett et al. 2012). Observed fine-scale structures have evoked explanations including the existence of biogeographic and environmental barriers such as salinity, temperature, oceanographic currents and productivity levels, niche partitioning through habitat preferences or resource speciation and effect of social structure and site fidelity to species dispersal (Lyrholm et al. 1999,

Hoelzel et al. 2002, Natoli et al. 2005, Mendez et al. 2010, Mendez et al. 2011, Lowther et al. 2012). Thus, this seemingly homogenous environment has proven to be more complex

18 than previously expected offering opportunities to study a variety of processes driving genetic structure in highly mobile animals.

Land-breeding marine animals and possible processes driving genetic structure

One group of marine organisms that are stimulating much research are those that are secondarily evolved to exist in the marine environment yet remain tied to terrestrial environments for breeding purposes (e.g. seabirds and pinnipeds). For animals such as albatrosses, penguins and seals, landmasses often in the form of offshore islands are necessary for breeding and raising offspring. Fidelity to breeding sites (i.e. return to the same site to breed) and philopatry (i.e. return to the natal colony to breed) are common in many of these animals. In these circumstances, strong breeding site fidelity might be considered as a mechanism to create genetic structure. In seabirds surprisingly many studies have not found strong genetic differentiation between sites, but in pinnipeds population subdivision has been observed over different scales (for seabirds Avise et al.

2000 and references within this; for pinnipeds e.g. Maldonado et al. 1995; Hoffman et al.

2006; Gonzalez-Suarez et al. 2009; Campbell et al. 2008; Lowther et al. 2012). However the pattern of fidelity and the extent of dispersal vary, depending on the sex of the individual and the reproductive system of the species (Greenwood 1980).

In species where parental care is required, proximity to adequate and predictable food source becomes an important consideration when selecting the location of breeding sites.

Central place foragers (Orians and Pearson 1979) such as penguins, ﬂying seabirds and pinnipeds are constricted to breeding sites situated close enough to foraging sites to be able to acquire sufficient food to meet the energetic demands of themselves and their

19 offspring and return to the breeding location within the time offspring are able to fast.

Intuitively, animals should be free to move between breeding sites that are within range of suitable foraging habitats and subsequent gene flow should result in panmixia amongst these sites (Lowther et al. 2012). In penguins and flying seabirds, generally both sexes provide parental care, however in pinnipeds, like many mammals, only females provide care to offspring (Boness 1991). As such one could reasonably expect males to be able to forage and disperse across a wider range. Indeed many pinniped species display this typical mammalian patter of male-biased dispersal (Fabiani et al. 2003, Hoffman et al.

2006, Gonzalez-Suarez et al. 2009).

Male biased dispersal in mammals is also associated with polygyny and female defence by male (Greenwood 1980). When resources or other environmental factors force females to aggregate, males compete intensively for females leading to a polygynous mating system (Clutton-Brock 1989). Central-place foraging otariid females must optimise between suitable breeding sites and good quality foraging habitat. Intuitively then, only a subset of available habitats will be suitable, leading to aggregations of females at sites that provide access to foraging grounds. Furthermore, the breeding cycle of most centrally-foraging otariids is cued to the seasonal predictability of food resources. Thus, the spatial and temporal aggregation of receptive females gives males the opportunity to obtain multiple matings by competing for terrestrial habitat and / or harems of females.

The mating system of otariids has been generally described as highly polygynous. (Stirling and Kleiman 1983, Le Boeuf 1991, Lindenfors et al. 2002). However, this is not the case in all otariids; the marine environment at lower latitudes is less predictable and seasonally variable (Gentry and Kooyman 1986). Consequently, otariids such as the Galapagos sea lions ( Zalophus wollebaeki ) have a much longer breeding season (5-6 months, Trillmich

1986 ), and thus males are likely to find it harder to monopolise females. As such, polygyny in these lower latitude species is less extreme than those observed in higher latitude species. Highly polygynous breeding systems, where only a handful of unrelated males father offspring, are likely to enhance structure, as many offspring receive paternal genes from the same source and local co-ancestry will be increased (Chesser 1991). On the other hand even a rare dispersal event in highly polygynous mating systems can have homogenizing effect. For example in Southern elephants seals a single male has been recorded successfully father 19 offspring 8000km from its natal colony (Fabiani et al.

2003).

Vocalisation and social structure -alternative way to look differentiation

Genetic methods are widespread and frequently used to assess connectivity and differences between groups of organisms. However non-molecular assessments are often used as alternative methods to investigate differences and boundaries between populations. The vocal call structure of species can vary widely among and within populations and vocalisations can be sensitive indicators of speciation and population divergence (Mundinger 1982, Claridge and Morgan 1993, Martens 1996, Price and Lanyon

2002, Miller and Baker 2009, Thinh et al. 2011, Meyer et al. 2012) Hence characterising geographical variation in vocalisations can be a useful tool for exploring inter-population interactions and may reflect genetic, behavioural, or ecological differences between populations (Slabbekoorn and Smith 2002a, b). Variation in vocal signals has been well documented in various organisms including insects, frogs, birds and mammals (Kroodsma and Canady 1985, Catchpole et al. 1995, Cocroft and Ryan 1995, Wilczynski and Ryan

1999, Zuk et al. 2008, Yu et al. 2011). The occurrence of these differences has been linked

21 with influences that are cultural (e.g. vocal learning; Slater 1986, Deecke et al. 2000) and environmental (adaptations to acoustic environments or ecological niches; Nicholls et al.

2006) and is also seen in co-occuring with genetic partitioning (Kroodsma and Canady

1985, McCracken and Sheldon 1997, MacDougall-Shackleton and MacDougall-Shackleton

2001, Van Parijs et al. 2003). It is well documented that geographical separation can lead to reproductive isolation. This process may promote genetic drift and thus also divergences in vocalisation (Grant and Grant 1996, Martens 1996, Payne 1996). Under this vicariant scenario congruence between genetic and vocal variation may be detected.

Alternatively, variation in vocalisations may be maintained without concordant differentiation in selectively-neutral genetic markers if there is selection irrespective of genetic isolation (e.g. individuals have to learn a novel call or song after dispersal) or selection towards vocalisations with suitable sound-transmission qualities within different habitats (acoustic adaptation hypothesis; Morton 1975, Slabbekoorn and Smith 2002a,

Nicholls et al. 2006). Differences in vocalisation can also maintain and reinforce genetic divergence (Ellers and Slabbekoorn 2003). For instance geographical variation in song or call may affect gene flow through its influence on sexual and social interactions e.g. the ability of males to attract females or to establish and maintain a territory (Kroodsma et al.

1984, Baker and Cunningham 1985, Slabbekoorn and Smith 2000, Ellers and Slabbekoorn

2003). For example in white-crowned sparrows, Zonotrichia leucophrys, females show stronger responses to local dialects (Baker et al. 1981, Baker 1982, 1983, Lampe and

Baker 1994) and in birds generally multiple studies have shown that territorial males respond most strongly to familiar dialects (e.g. Baker et al. 1981, Tomback et al. 1983,

Searcy et al. 1997). Males with deviant song characteristics may be less efficient in using songs to deter competitors, which may then result in reduced success in establishing a territory (Jenkins 1978, Baker 1982). In both of these scenarios males with deviant song 22 characteristics are likely to be less successful and song divergence could reduce dispersal of successful males between populations.

In contrast to the avian literature, the extent of geographic variation in vocalisation and the relative contribution of selection and genetic drift to this intraspecific variation are largely unexploited in mammals (Davidson and Wilkinson 2002, Campbell et al. 2010).

Vocal communication is important in many mammalian social interactions, e.g., in mother-offspring recognition, male-male competition and mate attraction (Krebs and

Davies 1993, Andersson 1994, Insley et al. 2003). Geographic vocal variation has been found in both marine mammals (Payne and Guinee 1983, Weilgart and Whitehead 1997,

Stafford et al. 2001) and terrestrial mammals (Maeda and Masataka 1987, Slobodchikoff et al. 1998, Mitani et al. 1999, Delgado 2007, Kershenbaum et al. 2012). One mammalian group in which there is clear evidence of geographic variation in male vocalisations is the order Pinnipedia. However, these studies have focused only on phocid species (Le Boeuf and Petersen 1969, Thomas and Stirling 1983, Cleator et al. 1989 Charrier et al. in press,

Thomas and Golladay 1995, Perry and Terhune 1999, Sanvito and Galimberti 2000, Van

Parijs et al. 2000, Van Parijs et al. 2003, Risch et al. 2007, Terhune et al. 2008).

Furthermore only a few studies have been able to conclusively demonstrate which mechanisms are responsible for these differences and directly tested the relationship between vocal and genetic divergence. Thus, there is a considerable gap in the scientific literature pertaining to vocal distinctiveness in otariid species, and the possible role of such vocal variation may play in mating success and genetic structure.

Australian sea lion as study species

Th e present range of the Australia’s only endemic otariid, Australian sea lion ( Neophoca cinerea ), extends from the Houtman Abrolhos on the west coast of Western Australia

(WA) to the Pages Islands in South Australia (SA) (Figure 1.1). In the late 18th and early

19th century the Australian sea lion was subjected to heavy harvest, resulting in a reduction of numbers and extirpation of colonies in Bass Strait and within the current range (Ling 1999, Shaughnessy et al. 2005, Robinson et al. 2008). Despite the rapid recovery observed in two sympatric fur seal species, the Australian fur seal

(Arctocephalus pusillus doriferus ) and New Zealand fur seal ( Arctocephalus forsteri ), the

Australia sea lion has not shown similar sustained recovery rates (Shaughnessy et al.

2011). A recent species census estimate of ~14,700 animals makes this species one of the rarest otariids in the world and listed as endangered in IU NC ‘Red List’ (Goldsworthy et al.

2009, Shaughnessy et al. 2011). Animals are distributed across 76 breeding colonies (18 of these are classified as haul-out sites with occasional pupping) and approximately 86% of pups are born in South Australia, with the remaining 14% in Western Australian colonies

(Goldsworthy et al. 2009). Most (over 60%) Australian sea lion colonies are small producing less than 30 pups per breeding episode (Goldsworthy et al. 2009). Only eight colonies produce more than 100 pups per breeding season. A slow recovery rate and small colony sizes mean that the Australian sea lion is highly susceptible to increases in mortality rates above the normal range experienced through natural mortality. The principal source for additional mortality is bycatch in dermesal gillnet fisheries

(Goldsworthy and Page 2007).

Figure 1.1 The present range of Australian sea lion (blue) along the south and south western coast of Australia

The Australian sea lion has a prolonged, aseasonal breeding pattern (~17-18 months reproductive cycle) that is temporally asynchronous across its range (Higgins and Gass

1993, Gales et al. 1994). This contrasts with all other pinnipeds where annual synchronous breeding is the norm (Gales et al. 1994). Furthermore in Australian sea lions breeding seasons extends over several months varying from five months in smaller colonies up to seven or eight months in large colonies (McIntosh et al. 2006, Goldsworthy et al. 2007, Shaughnessy 2008). Mature males guard females themselves as opposed to a defined territory (female defence polygyny; Boness 1991). Although males have been observed to guard up to four females in any one time, in some colonies the breeding density is so low that males will only guard one female at a time (Marlow 1975, Higgins and Gass 1993). Australian sea lion females stay with their pup for up two weeks post parturition (perinatal attendance period; Marlow 1975; Higgins and Gass 1993). During this time females exhibit a post partum oestrus of 4-10 days (Higgins and Gass 1993).

After perinatal attendance period, females alternate foraging trips with ashore nursing periods (~2 days each) throughout 15- to 18- month lactation (Higgins and Gass 1993).

Australian sea lion females reach sexual maturity at 4 – 6 years of age and males at 8 – 9 years (Shaughnessy 1999)(Shaughnessy 1999). The oldest wild male was identified as 21.5 years and wild female as 26 years although breeding longevity appears to be approximately 24 years in females (McIntosh 2007). Sexual dimorphism is notable in

Australian sea lions: adult males reach a maximum mass of 200 –300 kg (Walker and Ling

1981) and adult females average 84 kg (McIntosh 2007). Furthermore pelage coloration makes identification of adult males and females straightforward. Mature male Australian sea lions are dark brown with a blond/white area of fur extending from the crown of the

26 head down the nape of the neck and adult females are silver-grey to fawn dorsally with a creamy venter (Figure 1.2).

While the ecological determinants of the extended breeding cycle in Australian sea lions remain unclear, it has been suggested that this unusual system has evolved in response to an aseasonal, nutrient-poor environment in which Australian sea lions forage (Gales et al.

1994). Long maternal care and lactation in situation like this would allow increased development of the pups foraging skills before weaning and a decreased rate of energy transfer between the mother and the pup. However a recent study has shown that sea lion foraging areas both in South Australia and Western Australia are subjected to seasonal upwelling events (Bonney Upwelling in SA and Capes Current in Western

Australia; Lowther et al. 2013). In contrast to being a low productivity and aseasonal environment, at least part of the region seem to be replete with primary productivity and seasonality annulling the poor nutrient hypothesis. Lowther and Goldsworthy (2012) took this one step forward and proposed that area where Australian sea lions forage includes pockets of better foraging grounds and that extended period of maternal investment would provide dependent pups the opportunity to gain considerable experience (>6 months) exploring suitable, high productivity foraging grounds before the onset of nutritional independence.

Figure 1.2 Adult male, adult female and approximately two month of Australia sea lion pup, Olive Island 2009.

Similar to the extended reproductive cycle, the cause of unique asynchronous breeding is also unclear. Gales et al. (1994) hypothesised that the asynchrony of the pupping seasons between colonies may avoid seasonal peaks of resource exploitation that might otherwise be seen if all colonies pupped at the same time. However this theory is again based on nutrient-poor environment. At this stage, we can only conclude that the mechanism by which this divergence of pupping might be driven by selection is unclear. Nevertheless this system of asynchronous breeding may be maintained by fine-scale female philopatry to the point where females return exclusively to their natal colony (Gales et al. 1994).

Dispersal from the natal colony would result in entering a breeding system possibly out of synchrony with conspecifics, and limit the chances of finding a mate. Indeed two recent mitochondrial DNA studies have shown that females exhibit strong natal site philopatry

(Campbell et al. 2008, Lowther et al. 2012). Significant matrilineal clustering of breeding colonies was detected in extremely fine spatial scale (<40km) and authors proposed that foraging specialization within discrete fine-scale foraging areas and habitats at the individual level limits the dispersive capacity of adult female Australian sea lions driving the population structure (Lowther et al. 2012). Despite a small preliminary study with limited samples and few cross-amplified microsatellite loci, no work has been done to look male mediated gene-flow in this species (Campbell 2003).

Acoustic vocalisation in male Australian sea lion

A preliminary study on male barking call has suggested that there might be alternative structure apparent in Australian sea lions (Attard et al. 2010). Acoustic analyses revealed differentiation in the overall acoustic structure of male calls between two South

Australian colonies, Kangaroo Island and Lewis Island. Furthermore this study showed

29 that males are capable of discriminating the barking calls of males from their own colony to those from a distant colony. Australian sea lion males have the most depauperate vocal repertoire of all otariid males (Stirling and Warneke 1971, Stirling 1972, Fernandez-

Juricic et al. 1999, Phillips and Stirling 2001, Tripovich et al. 2005). Male Australian sea lions have been observed producing different call types: a barking call, a bleating call and a female-like call plus a guttural threat and growl (Stirling 1972; Gwilliam et al. 2008).

Despite the occurrence of other call types, the predominant vocalisation produced by male Australian sea lion is the barking call which is used in almost all social interactions.

This simple repertoire may reflect the ecological circumstances in which these animals breed, with very low density colonies and asynchronous breeding, and thus limited types of social interactions. Indeed it has been suggested that social complexity can drive vocal complexity (Blumstein et al. 1997, Freeberg et al. 2012, Pollard and Blumstein 2012). Yet males are able to discriminate between males and females of their own species, and distinguish the calls of conspecifics from other species (Gwilliam et al. 2008). Up to now, acoustic studies on Australian sea lion have mainly focused on mother-pup recognition

(Charrier and Harcourt 2006, Charrier et al. 2009, Pitcher et al. 2009, Pitcher et al. 2010a, b). However, as in many vertebrate species, Australian sea lion males use vocalizations in social contexts such as female attraction and male competition (Krebs and Davies 1993;

Andersson 1994). Considering the potential of male barking call to reflect inter-colony differences vocalisation might offer an alternative method to investigate differentiation in

Australian sea lions.

Thesis rationale and aims

The main driver for this work was to substantially increase our understanding of the population structure and breeding system of the endangered Australian sea lion, and determine how male strategies influence dispersal patterns and variance in male reproductive success. To answer these questions I used two independent but complementary methods, molecular genetic and acoustic analyses. First I developed and optimised species specific microsatellite library ( Chapter 3 ). These biparentally inherited genetic markers allowed me to determine the extent of male-mediated gene flow and furthermore they are a powerful tool for high-resolution analysis such as parentage assignment. In chapter 4, I determined if genetic partitioning is evident with microsatellite markers and evaluated whether male movement pattern in Australian sea lions mirrors known female philopatry, or follows a more typical mammalian model of male-biased dispersal. Chapter 5 takes an alternative approach to investigate differentiation in Australian sea lions. In this chapter, I quantified the extent of geographical variation in male barking call across breeding colonies spanning the entire species range. These colonies were separated by ~5 to 2700 km enabling me to investigate both micro- and macro-geographical scales. Furthermore, I was able to assess whether vocal structure reflects genetic structure in this species. My final data chapter

(Chapter 6 ) concentrates on defining the mating system in Australian sea lions and estimating the variance in male reproductive success. Characterizing the degree of reproductive skew is fundamental to identifying processes that influence effective population size, inbreeding, gene ﬂow rates and dispersal patterns. This is particularly important for species showing a long lifespan (up to 30 years), living in philopatry and where several successive generations of related animals occur in the same colony. Given

31 the unique reproductive ecology of this species, it may be expected that the mating system of Australian sea lions is less polygynous than those exhibited by other otariid species, including those that live sympatrically with Australian sea lions. Moreover, the well-documented asynchronous breeding pattern among colonies of Australian sea lions may enable males to increase their overall reproductive success. In this chapter, I also evaluated the level and the effect of cross-colony paternity to overall male reproductive success. Finally, I investigated the possibility of presence of dissortative mating on the basis of relatedness.

Additional data as Appendices

In addition to my main thesis work, I have included two supplementary studies as

Appendices. Appendix 5 details a playback experiment performed on different colonies to assess if Australian sea lion males are able to distinguish vocal geographical differences.

From our preliminary study, we found that males are capable of discriminating between barking calls of males from their own colony and those from a distance colony (Attard et al. 2010). I conducted playback experiments on four colonies to further investigate whether acoustic differences are meaningful and if these differences will impact their breeding behaviour. This work was a big part of my PhD. I have analysed the behavioural responses to all playback experiments and conducted most statistical tests. However due to time limitation of the PhD and the extensiveness of this project, I have not been able to include these results as full chapter. Nevertheless I feel these results can offer invaluable biological information about vocal differentiation in this species and how it is perceived by males. This also allows me to use these results in my general discussion and enlighten

32 the reader regards on my extensive work done on Australian sea lions these last four years.

I have also attached one published article for which the work was carried out during my

PhD and I am co-authoring both with my supervisor Dr. Isabelle Charrier and my colleague

Prof. Robert Harcourt ( Appendix 6). This work is not directly relevant to the common theme of my thesis, but gives important and interesting information on acoustic features that Australian sea lion males rely on to assess the threat level of potential rivals. Animal vocal signals are produced in many social contents such as territorial defence, mate selection, and parent – offspring interaction and thus can convey diverse information about the emitter (e.g. social rank, motivation, individual identity, body size). To understand the biological function of vocal signals it is not only important to estimate the information about the signaller encoded in the signal, but also to determine if and how this information is perceived by the receiver. We directly investigated what information males use to evaluate potential threat posed by rival males by modifying either spectral profile or bark rhythmicity in male vocalisation and assessing how they adjust their behaviour in response to these changes. These results and further analyses on information encoded in male Australian sea lion barking call can be used in the assessment of what makes a male successful.

CHAPTER 2: General Materials and Methods

This section will provide a broad overview of approaches taken to conduct this study.

Greater details for both genetic and acoustic data analyses have been provided within each chapter.

Study sites

This study was carried out on nine Australian sea lion colonies during 2009-2010 and

2010-2011 breeding seasons. Six of these colonies are located along southern coast of

Australia and three along west coast of Western Australia (Figure 2.1). Colonies in South

Australia were: Lewis Island, Liguanea Island, Olive Island, Lilliput Island and Blefuscu

Island. Sampled colonies in Western Australia were: Beagle Island, North Fisherman Island and Buller Island. Physical environment varied among sampled breeding colonies. Six

South Australian colonies have open rocky areas with boulder fields and the coverage of vegetation (mainly box thorn bushes) varies between all colonies. All colonies have some level of elevation from the sea except Blefuscu Island. In Western Australia both Beagle

Island and North Fisherman Island are small islands with sandy beaches and thick boxthorn bushes in middle of the islands. Buller Island is best described as small rocky colony with limited shore line and vegetation. Additional colony characteristics are presented in Table 2.1.

Research on these islands was conducted under permission obtained from Department for Environment and Heritage, South Australia (permit number Z25675) and Department of Environment and Conservation, Western Australia (DEC WA License SF007255 and

SF008193).

Figure 2.1 The locations of Australian sea lion colonies used in this study.

Table 2.1. Colony characteristics and breeding density of the nine studied Australian sea lion colonies.

Breeding season Location Size No. of pups No. of pups duration Source Colony (State) (Latitude, Longitude) (Km 2) born/season born per day (months)

Lewis Is (SA) 34°57'20''S , 136°1'53''E 0.13 131 6 0.73 1 Liguanea Is (SA) 34°59'6''S , 135°37'20''E 1.46 43 6 0.24 2 West Waldegrave Is (SA) 33°35'37''S , 134°45'32''E 0.40 157 7 0.74 3 Olive Is (SA) 32°43'18''S , 133°58'5''E 0.22 196 7 0.93 4 Lilliput Is (SA) 32°26'4''S , 133°41'34''E 0.10 66 7 0.31 4 Blefuscu Is (SA) 32°28'1''S , 133°38'40''E 0.06 104 7 0.50 4 Buller Is (WA) 30°39'24''S , 115°6'54''E 0.007 42 5 0.28 5 North Fisherman Is (WA) 30°7'47''S , 114°56'38''E 0.03 48 5 0.32 5 Beagle Is (WA) 29°48'23''S , 114°52'39''E 0.01 58 5 0.39 5

Sources for pup count data: (1) Goldsworthy et al. (2008), (2) Shaughnessy et al. (2009), (3) Shaughnessy et al. (2005), (4) S. Goldsworthy unpublished, based on recent calculations, (5) Campbell and Gales unpublished data in: Goldsworthy et al. 2009 Source for length of breeding season: Goldsworthy et al. 2009 and Gales et al. 1992 for WA

Animal identification and marking

Photo identification and paint marking of adult males

Adult males were identified mainly through natural markings (e.g. fresh and old scarring, flipper tears and marks). Every sampled male was photographed. For each individual several photos of back and front flippers, muzzle, both sides of the animal and of any obvious natural markings were taken (see figure 2.2). These photos were carefully looked through and an identification chart detailing unique markings was created for each male.

Every time a supposedly new male was found, he was carefully checked against list of sampled males and thus identified either as already sampled or unsampled male.

Paint marking was used in few colonies, but we quickly noticed that in many occasions paint did not stay very long on the fur of the animals. Hence identification from natural markings was proven to be the most reliable, although time consuming method. All male identities were confirmed through microsatellite genotyping and less than seven individuals per colony were resampled. In most of these cases, double sampling was already expected before genetic confirmation.

Figure 2.2 Examples of natural markings that were used to identify males

Hair clipping and flipper tagging of pups

All pups used in this study were marked individually by either using hair clippings or plastic flipper tags. Marking occurred while animal was restrained for tissue sampling.

Figure 2.3 shows unique symbols that were cut into the fur of the rump. These symbols were used in different combinations to be able to create individual number series

(method developed originally for work of Andrew Lowther). Hair clipping was temporary, regrowing when the animal moulted.

Some colonies were part of multi-year monitoring program and in these colonies all sampled pups were double-tagged in the rear web of the fore flippers with an individually numbered and coloured two-part plastic tag (Gallagher Supertag, Gallagher Animal

Management Systems, Melbourne, Australia). Only pups with sufficiently developed flipper tissue were tagged (approximately 12kg).

Figure 2.3 Unique marking symbols used to identify pups. These symbols were cut into the fur of the rump of each sampled pup and used in different combinations to be able to create individual number series.

Identification of mothers

To remove the need to catch and restrain adult females, most mothers were identified through their marked pups. While resting and nursing on land, mothers stay close proximity to their own pup laying often in close bodily contact (Stirling 1972; Ahonen personal observation). Females were sampled after pup’s identity was confirmed from either clip marking or flipper tags. In few occasions females where either tagged or clipped while animal was anesthetised as a part of another project. Afterwards sampled mother-pups pairs were genetically verified.

Genetic sample collection

Remote biopsy sampling of adult animals

A small tissue sample was obtained for adult males and females using Paxarms biopsy system (Paxarms New Zealand Ltd, http://paxarms.co.nz ; see Figure 2.4). The system consists of a modified 0.22 calibre rifle and biopsy dart made out of aluminium body and stainless steel tip. For sample retention, three evenly distributed small triangular shaped barbs are located ~2 mm from the leading edge of the tip. Diameter and the length of the tips used for female samples were smaller than those used for males. Additional weigh was added to the dart when females were sampled to make sure sample was retrieved.

Small plug consisting of hair, tissue and blubber was obtained and stored inside individually labelled tubes that were filled with 90% ethanol.

All males (mate-guarding or not) present at colony were sampled, and the colony was screened several times per day for new males. A great care was taken to not only sample 42 mate-guarding males but also males resting in outer skirts of the colony. These males were normally more skittish and ran away rather than defended the area around them.

Limitations in the number of mothers sampled were due to fact that most were not individually marked and could be located and sampled only when interacting with their own pup.

Tissue sampling of pups

Pups were captured with the aid of a hoop net, manually restrained, sexed and measured for standard length (straight-line length from tip-of-nose to tip-of-tail, to nearest 0.5 cm) and mass (25 x 0.1 kg spring balance, Salter Weigh-Tronix, Blackburn, Victoria, Australia)

(Figure 2.5). A small piece of skin was cut with scalpel from the trailing edge of the hind flipper. All tissue samples were stored inside individually labelled tubes and filled with

95% ethanol.

Figure 2.4 Remote biopsy sampling of adult animals, Beagle Island 2009

Figure 2.5 Capture and tissue sampling of pups

Laboratory procedures and genetic data analyses

For each tissue sample the total genomic DNA extracted using a standard salting out protocol (Sunnucks and Hales 1996).

Microsatellite development and optimisation

A species-specific microsatellite panel of 12 loci was developed and optimised to be able to examine genetic connectivity between colonies and characterise the mating system of the Australian sea lion. Furthermore four microsatellite loci developed for Galapagos sea lions (Wolf et al. 2006, Hoffman et al. 2007b)were successfully cross-amplified and used in the analyses. Microsatellite development and optimisation are further detailed in

Chapter 3 “Characterization of 12 novel microsatellite loci and cross -amplification of four loci in the endangered Australian sea lion (Neophoca cinerea)” .

Genotyping

A total of 1,500 individuals were genotyped for 16 loci. Not all these individuals were used in the chapters presented in this thesis, but samples were collected and processed during the PhD and will be used for future work coming out from this project. PCR reactions contained ~10 –50 ng genomic DNA, 200 µM each dNTP, 1x Reaction Buffer, 2 mM MgCl 2, 0.5 µM of each primer, 1 unit of Taq polymerase in a total volume of 10-µl.

Each of the forward primers was directly labelled using fluorescent dyes FAM, NED, VIC and PET (ABI). PCR conditions were as follows: Initial denaturation for 3 min at 94 °C followed by five cycles of denaturation (94°C, 30 s), primer annealing (60, 58, 56, 54, and

52°C, 30 s), and polymerase extension (72°C, 45 s). Following this series 35 cycles of 45 denaturation (94°C, 30 s), primer annealing (50°C, 30 s), and polymerase extension (72°C,

45 s) were performed before ending with a final extension (72°C, 10 min). PCR amplicons were then electrophoresed on an AB 3730xl DNA analyser. Allele sizes were determined using an internal size standard LIZ and scored manually using Peak ScannerTM Software v.1.0 (Applied Biosystems). Over ten precent of the samples were re-genotyped to ensure data integrity and scoring consistency.

Data analyses

Summary statistics - MICROCHECKER version 2.2.3 (van Oosterhout et al. 2004) was used to assess for scoring errors and null alleles. Conformation to Hardy-Weinberg equilibrium was evaluated using Genepop version 4.0.10 (Raymond and Rousset 1995) and locus independence tested using FSTAT version 2.9.3.2 (Goudet 1995). Genetic diversity measurements including number of alleles (Na), observed (H O) and expected (H E) heterozygosity and inbreeding coefficient (F IS ) were calculated for each colony using the program FSTAT version 2.9.3.2 (Goudet 1995). Furthermore polymorphic information content (PIC) was calculated for each colony using CERVUS. The probability of genotypic identity (PID) amongst unrelated and full sibling pairs was calculated using the software

GenAlEx 6.5 (Peakall and Smouse 2012). Statistical significance levels were corrected for multiple comparisons using sequential Bonferroni adjustments (Rice 1989).

Genetic differentiation – Two different measurements were used to estimate level of genetic variation between colonies: the pairwise FST- values were estimated using FSTAT

(Goudet 1995) and the pairwise Jost’s D values using the R packag e DEMEtics (Gerlach et al. 2010). Spatial genetic structuring among colonies was examined using Spatial

Autocorrelation analysis implemented in the software GenAlEx 6.5 (Peakall and Smouse

2012). Mantel tests were performed using the R package Vegan (Oksanen et al. 2013).

Furthermore two additional procedures were used to characterise patterns of genetic divergence across sampling sites: SAMOVA 1.0 (spatial analysis of molecular variance;

Dupanloup et al. 2002) and “Genetic Landscape Surfaces” (GLS) implemented in AIS

(Miller 2005). GLS is a method to visualise genetic distance patterns across the full analysed landscape.

Recent migration rates - Contemporary migration rates were estimated using BayesAss

3.0 (Wilson and Rannala 2003).

Paternity analysis - Paternity analyses were performed using the software CERVUS ver.3.0.3 (Kalinowski et al. 2007). Pairwise relatedness estimates were calculated for mother-father pairs identified from paternity analysis using the software GenAlEx 6.5

(Peakall and Smouse 2012; Queller & Goodnight (1989) estimator).

Acoustic sample collection

Adult male vocalisations were recorded using a Sennheiser ME 67 shotgun microphone

(Sennheiser, Wedemark, Germany; Frequency Response: 50 Hz –20 kHz 2.5 dB) connected to a Marantz PMD 660 digital recorder (Eindhoven, the Netherlands; Frequency

Response: 20 Hz –20 kHz 0.5 dB; Sampling rate 22.05 kHz). During recording the microphone was held between 3 and 8 meters from the animal to maximize the signal quality (Figure 2.6). Recordings were made for each colony at similar periods during breeding season and only mate-guarding males were recorded. Barking calls are always

47 produced in series from 4-5 units to several tens, and several calling bouts were obtained for each individual. To avoid any significant differences in calling rate (duration between barking units), recordings were done in similar behavioural circumstances (e.g. when mate-guarding males had been disturbed by another animal or were interacting with the female they were guarding).

Figure 2.6 Recording of adult male Australian sea lion

Analysis of acoustic parameters

Australian sea lion colonies are noisy environments and recordings were often hindered by abiotic noise (wind and sea waves), but also noise from seabirds colonies on the islands and vocalisations from conspecifics such as other mate-guarding males and mother- pup pairs. To obtain accurate measurements, only individuals with calls exhibiting a good signal-to-noise ratio were used for the further analysis. For each male I selected two different series of 10 consecutive barks (following Gwilliam et al. 2008,

Attard et al. 2010).

To assess acoustic variation in male Australian sea lion barking calls, I measured two temporal and eight spectral variables for each barking callusing Avisoft SAS Lab Pro

(R.Specht, Avisoft Bioacoustics, Berlin Germany; Figure 2.7). Two temporal variables were the total duration of the barking call (Dur; in ms) and the duration between barking calls

(InterDur; in ms). These were measured on the oscillogram (i.e. graphical representation of the sound wave, amplitude as a function of time). Owing to the noisy nature of the male Australian sea lion barking call, the spectral parameters that could be measured were limited. For instance, it was not possible to measure the exact frequency value of the fundamental frequency and its relative harmonics since unlike other male otariids

(Fernandez-Juricic et al. 1999, Page et al. 2002, Tripovich et al. 2005) the Australian sea lion barking call did not present a clear harmonic structure. However I was still able to measure eight spectral parameters: frequency values of the first, second and third maximum peak in amplitude (Peak 1, Peak 2 and Peak 3, respectively; in Hz); the ratio of the amplitude values of Peak 1 to Peak 2 and Peak 2 to Peak 3 (RAMP1 and RAPM2 respectively) and three quartile measurements which characterize the distribution of

49 energy across the spectrum (Q25, Q50 and Q75; in Hz). The spectral parameters were measured from the averaged amplitude spectrum (Hamming window, frequency resolution: 10.7 Hz) calculated on the total length of the barking call. For the three quartile measurements whole length of the barking call was also used, but the frequency and amplitude measurements were performed on a 0-4 kHz frequency scale since most of the energy is found below 4 kHz and calls sampled at 22.05 kHz.

Statistical analyses for acoustic variable were performed with program STATISTICA version 8 (StatSoft, Tulsa, OK, USA ) and R.

Figure 2.7 Barking call of male Australia sea lion displaying parameters measures in acoustic analysis. A: oscillogram on which duration parameters were measured (Dur and InterDuration). B: spectrograma C: average energy spectrum on which the first three highest energy peaks, Peak 1, Peak 2 and Peak3 were measure. A and B are showing a serie of 10 consecutive barks, whereas C is a presentation of one bark.

CHAPTER 3: Characterization of 12 novel microsatellite loci and

cross-amplification of four loci in the endangered Australian sea

lion (Neophoca cinerea)

Inquisitive youngster –Beagle Island, Western Australia

Conservation Genetic Resources (2012)5:283-285

Abstract

We describe a microsatellite panel of 12 newly developed and four cross-amplification loci for the endangered Australian sea lion, Neophoca cinerea . Next-generation 454 sequencing was used to obtain species-specific partial genomic library. We genotyped 28 individuals sampled from a single breeding colony to characterize these 16 polymorphic loci. Number of alleles per locus ranged from three to seven, and observed and expected heterozygosites from 0.333 to 0.852 and from 0.377 to 0.787, respectively. These markers will be used to examine genetic diversity, genetic connectivity among colonies and the mating system of

Australian sea lions.

Introduction

The Australian sea lion is an endemic Australian otariid, occurring from the Pages Islands in South Australia to the Houtman Abrolhos Islands in Western Australia. The current species census estimate of ~14700 individuals makes the species one of the rarest otariids in the world and listed as endangered in IUNC ‘Red List’ (Shaughnessy et al. 2011).

Recent mitochondrial DNA studies have shown that females exhibit strong natal site philopatry (Campb ell et al. 2008, Lowther et al. 2012). Given this species’ vulnerability to anthropogenic impacts and the high degree of female genetic structure, determining the extent of male-mediated gene flow in this species is important (Goldsworthy et al. 2009,

Lowther et al. 2012). Furthermore biparentally inherited genetic markers like microsatellite loci are a powerful tool for high-resolution analysis such as parentage and assignment testing. Due to a unique pattern of asynchronous breeding between geographically close colonies, Australian sea lion males have the potential to sire pups throughout much of the year and at different colonies. Male-mediated gene flow will result in genetic panmixia amongst colonies where there are no barriers to male reproductive movements. Our 53 objective was to develop a microsatellite panel to address genetic differentiation within and among Australian sea lion colonies. We characterize 12 new microsatellite loci for

Australian sea lion and describe the successful cross-amplification of four microsatellite loci developed from Galapagos sea lion ( Zalophus californianus wollebaeki ).

Materials and Methods

For microsatellite development, a total of 5 µg of genomic DNA was extracted from tissue belonging to one specimen of N. cinerea and sent to AGRF (www.agrf.com.au). High throughput DNA sequencing was performed using Titanium GS-FLX System (454

LifeScience/Roche FLX) following Gardner et al. (2011). A total of 93,738 sequences were obtained with average fragment size of 342bp. MSATCOMMANDER 0.8.2

(Faircloth 2008) was used to extract microsatellite motifs of between 2bp and 6bp and with greater than eight repeats in length. This resulted in 2,778 putative microsatellite motifs.

Potential target loci were examined by eye to identify loci with appropriate flanking regions for primer design. We found that due to the relatively short lengths of sequence reads containing dinucleotide repeats, most of the dinucleotide loci were generally unsuitable for primer design owing to lack of a suitable flanking region. PRIMER 3

(Untergasser et al. 2012) was used to design 28 primer pairs flanking tetranucleotides with

10-18 repeat motifs for amplification trial. Twelve primer pairs amplified consistently and were selected for further analysis. In addition, we tested amplification success of 14 published microsatellite loci developed for Galapagos sea lion (Wolf et al. 2006, Hoffman et al. 2007). From these, four loci were selected for further analysis. For each of the 16 microsatellite loci, the forward primers were directly labelled using fluorescent dyes FAM,

NED, VIC and PET (ABI). A total of 28 individuals sampled from Olive Island, South

Australia were used for characterization of these loci.

A touchdown PCR profile was carried out with a MJ Research PTC100 thermocycler in

10-µl volumes containing 10 –50 ng genomic DNA, 200 µM each dNTP, 1 Î Reaction

Buffer, 2 mM MgCl 2, 0.5 µM of each primer, 1 units of Taq polymerase; and with cycling conditions: Initial denaturation for 3 min at 94 °C followed by five cycles of denaturation

(94°C, 30 s), primer annealing (60, 58, 56, 54, and 52°C, 30 s), and polymerase extension

(72°C, 45 s). Following this series 35 cycles of denaturation (94°C, 30 s), primer annealing

(50°C, 30 s), and polymerase extension (72°C, 45 s) were performed before ending with a final extension (72°C, 10 min). PCR amplicons were then electrophoresed on an AB

3730xl DNA analyser. Allele sizes were determined using an internal size standard LIZ and scored manually using Peak Scanner TM Software v.1.0 (Applied Biosystems). Twenty precent of the samples were re-analysed at each locus to ensure data integrity and scoring consistency.

MICROCHECKER version 2.2.3 (van Oosterhout et al. 2004) was used to assess for scoring errors and null alleles. Summary data including number of alleles (N a), observed

(H O) and expected (H E) heterozygosity and inbreeding coefficient (F IS ) were calculated using the program FSTAT version 2.9.3.2 (Goudet 1995). Conformation to Hardy-

Weinberg was evaluated using Genepop version 4.0.10 (Raymond & Rousset 1995). Tests for linkage disequilibrium between pairs of loci (with Bonferroni corrections for multiple tests) were conducted using FSTAT version 2.9.3.2 (Goudet 1995).

Results and Discussion

Each of the 16 primer pairs successfully amplified polymorphic loci with unambiguous alleles. None of the loci showed evidence for null alleles or scoring errors. Number of alleles per locus ranged from three to seven, and observed and expected heterozygosites from 0.333 to 0.852 and from 0.377 to 0.787, respectively. There was no evidence of 55 significant linkage disequilibrium among loci and none of the loci showed significant departure from HWE (P>0.05) (Appendix1). The combination of 12 newly developed and

4 previously published microsatellite markers will enable us to examine genetic connectivity between colonies and mating system of endangered and unique Australia sea lion.

Chapter 4: Local Lads: Spatial genetic structure in male Australian

sea lions ( Neophoca Cinerea )

In preparation for publication

Abstract

Understanding dispersal patterns and spatial structure throughout a species range is critical for effective assessment and management. In common with many mammals, pinnipeds typically display male-biased dispersal. Studies using mitochondrial DNA have shown that endangered Australian sea lion (Neophoca cinerea) exhibits extreme matrilineal substructuring. If male dispersal was as restricted as female movement, most of the breeding colonies would be considered closed populations and further exposed to increased risks associated with inbreeding and environmental change. To address these concerns we used 16 microsatellites loci to investigate the extent and the rate of male mediated gene flow in this species. We found that Australian sea lion exhibits male- biased dispersal, however, the relatively strong differentiation among breeding colonies implies that male dispersal is limited to a remarkably small scale (>100km). This is intriguing considering the species dispersal potential and the unique asynchronous breeding that might facilitate male reproductive movements across geographically close colonies.

Introduction

Knowledge of dispersal behaviour is fundamental to explaining the ecological requirements of an organism. Dispersal results in redistribution of organisms and their genes, thus influencing a wide range of processes including population regulation and dynamics, spatial distribution and genetic structure (Lidicker 1975, Taylor and Taylor

1977, Greenwood 1980, Bowler and Benton 2005). Restrictions in dispersal may promote inbreeding and genetic differentiation among neighbouring groups, whereas widespread

58 dispersal may prevent localised adaptation (Wright 1943, 1946). Individuals that disperse are often predominantly of one sex- or age- class and can be moving voluntarily or enforced. Quantifying if and which sex or age class disperses offers information that can be further used to design strategies for conservation management.

Dispersal in many species is sex biased (Greenwood 1980). Female philopatry and male dispersal are considered to be typical in mammals, while female biased dispersal is pervasive in birds (Greenwood 1980, Wolff 1994, Clarke et al. 1997). It is widely accepted that the gender that disperses is reliant on the mating system (Greenwood 1980, Dobson

1982, Perrin and Goudet 2001). Greenwood (1980) proposed that male-biased dispersal in mammals is associated with polygyny and female defence by males, whereas avian female-biased dispersal is associated with monogamy and resource defence strategy of males. Along with the social competition, inbreeding avoidance can also account for sex- biased dispersal (Greenwood 1980, Dobson 1982, Pusey 1987, Clutton-Brock 1989, Perrin and Mazalov 1999, 2000). More recently the influence of kin competition and cooperation on dispersal patterns has been raised (Perrin and Goudet 2001, Perrin and

Lehmann 2001, Le Galliard et al. 2006, Lawson Handley and Perrin 2007, Dobson 2013).

Pinnipeds, like many marine mammals are capable of dispersing many hundreds even thousands of kilometres. Despite this potential mobility, many pinniped species display typical mammalian male-biased dispersal. Females are likely to remain within their natal group and show sufficiently strong philopatry to create significant matrilineal substructure detectable with mitochondrial DNA (mtDNA) markers (Andersen et al. 1998,

Burg et al. 1999, Hoelzel et al. 2001, Baker et al. 2005, Gonzalez-Suarez et al. 2009).

However, when biparentally inherited nuclear markers have been used, structuring can

59 be found in some situations, but overall the patterns are weaker, indicating that females are usually more philopatric than males (Andersen et al. 1998, Burg et al. 1999, Hoelzel et al. 2001; Hoffman et al. 2006, Gonzalez-Suarez 2009).

Australia’s only endemic otariid, the Australian sea lion ( Neophoca cinerea ), ranges from the Houtman Abrolhos Islands in Western Australia (WA) to the Pages Islands in South

Australia (SA). Approximately 80% of this species breeds in South Australian waters

(Goldsworthy et al. 2009). The current species census estimate of ~14700 individuals makes it one of the rarest otariids in the world and listed as endangered in IUCN ‘Red List’

(Shaughnessy et al. 2011). In the late 18th and early 19th century the Australian sea lion was subjected to heavy harvest, resulting in a reduction of numbers and extirpation of colonies in Bass Strait and within the current range (Ling 1999, Shaughnessy et al. 2005,

Robinson et al. 2008). Unlike for the two sympatric fur seal species, the Australian fur seal

(Arctocephalus pusillus doriferus ) and New Zealand fur seal ( Arctocephalus forsteri ), the recovery for Australian sea lion hasn’t been as rapid and sustained (Shaughnessy et al.

2011). Most Australian sea lion colonies are small (over 60%), producing less than 30 pups per breeding episode (Goldsworthy et al. 2009). Reduced recovery rate and small colony sizes mean that the Australian sea lion is highly susceptible to increases in mortality rates above the normal range of natural mortality. The principal source for additional mortality is bycatch in dermesal gillnet fisheries (Goldsworthy and Page 2007).

Two recent mitochondrial DNA studies have shown that females exhibit strong natal site philopatry (Campbell et al. 2008, Lowther et al. 2012). Campbell et al. (2008) suggested almost no effective migration of adult females even between colonies separated by short

(20 km) distances. However this study concentrated mainly on Western Australian

60 colonies and covered only a small part of the Australian sea lion range in South Australia.

Lowther et al. (2012) extended mtDNA study across South Australia including 17 of the largest colonies. Their findings showed that the degree of population structure is more complex than previously described, yet still supported strong matrilineal structuring among Australian sea lion colonies. Pronounced female genetic structure combined with the species vulnerability to anthropogenic impacts emphasises the importance of determining the extent of male movement and male-mediated gene flow.

The unusual breeding biology of Australian sea lion makes the dispersal behaviour of this species difficult to predict. Typically in pinnipeds, synchronized breeding across colonies means males can only sire pups from a single colony in any one season and long term

(multi-year) site fidelity imparts a higher degree of success for individual males (Harcourt et al. 2007a). Thus selection may drive male fidelity to a single colony, enhancing genetic structure. The Australian sea lion has a non-annual breeding cycle that is asynchronous between geographically close colonies. Accordingly, Australian sea lion males have the potential to sire pups throughout much of the year by visiting different colonies. With this scenario, male-mediated gene flow will result in panmixia amongst colonies within the extent of male reproductive movements. A preliminary study by Campbell (2003) suggested typical male dispersal of the order of 250-300 km, but was based on limited samples and few microsatellite loci.

To investigate male dispersal and gene flow we used 16 polymorphic microsatellite loci optimised for Australian sea lion (Ahonen et al. 2013). Our aims were (i) to determine whether genetic partitioning is evident with biparentally inherited microsatellite markers;

(ii) to evaluate whether male movement pattern reflects known female philopatry or

61 follows typical mammalian male-biased dispersal; and (iii) to discuss the causal factors behind such a restricted dispersal in a potentially far ranging marine predator.

Materials and Methods

Sample collection and DNA extraction

Biopsy samples were collected from adult male Australian sea lions throughout their range (Figure 4.1). Samples were obtained using Paxarms biopsy system (Paxarms New

Zealand Ltd, http://paxarms.co.nz ). A total of 198 samples were collected during 2009-

2010 breeding season from nine different breeding colonies. Six of these colonies are situated along southern coast of South Australia and three along west coast of Western

Australia. Samples were preserved in 95% ethanol and total genomic DNA extracted using a standard salting out protocol (Sunnucks & Hales 1996).

Figure 4.1 Location of sampling sites. The numbers in bracket indicated the number of sampled males for each breeding colony. Highlighted area represents the species range.

Microsatellite genotyping

We used 12 species specific microsatellite loci (Ahonen et al. 2013) and cross-amplified four loci developed for Galapagos sea lions (Wolf et al. 2006, Hoffman et al. 2007). Each of the forward primers was directly labelled using fluorescent dyes FAM, NED, VIC and

PET (ABI). PCR reactions contained ~10 –50 ng genomic DNA, 200 µM each dNTP, 1 Î

Reaction Buffer, 2 mM MgCl 2, 0.5 µM of each primer, 1 unit of Taq polymerase in a total volume of 10-µl. PCR conditions were as follows: Initial denaturation for 3 min at 94 °C followed by five cycles of denaturation (94°C, 30 s), primer annealing (60, 58, 56, 54, and

52°C, 30 s), and polymerase extension (72°C, 45 s). Following this series 35 cycles of denaturation (94°C, 30 s), primer annealing (50°C, 30 s), and polymerase extension (72°C,

45 s) were performed before ending with a final extension (72°C, 10 min). PCR amplicons were then electrophoresed on an AB 3730xl DNA analyser. Allele sizes were determined using an internal size standard LIZ and scored manually using Peak Scanner TM Software v.1.0 (Applied Biosystems). Ten precent of the samples were re-genotyped to ensure data integrity and scoring consistency.

Data analysis

Genetic diversity

MICROCHECKER version 2.2.3 (van Oosterhout et al. 2004) was used to assess for scoring errors and null alleles. Conformation to Hardy-Weinberg equilibrium was evaluated using

Genepop version 4.0.10 (Raymond & Rousset 1995). Tests for linkage disequilibrium between pairs of loci (with Bonferroni corrections for multiple tests) were conducted

64 using FSTAT version 2.9.3.2 (Goudet 1995). Genetic diversity measurements including number of alleles (Na), observed (H O) and expected (H E) heterozygosity and inbreeding coefficient (F IS ) were calculated for each colony using the program FSTAT version 2.9.3.2

(Goudet 1995). Colony differences in levels of allelic richness were tested for significance by using non parametric Wilcoxon sign rank test.

Genetic differentiation and spatial genetic structure

To estimate the level of genetic variation between sampled colonies, two different measures were used: the pairwise F ST - values were estimated using FSTAT (Goudet 1995) and the pairwise Jost’s D values using the R package DEMEtics (Gerlach et al. 2010). Jost’s

D has been shown to provide more accurate measure of differentiation when using highly polymorphic microsatellite loci (Jost 2008, Gerlach et al. 2010).

Spatial genetic structuring among colonies was examined using Spatial Autocorrelation analysis implemented in the software GenAlEx 6.5 (Peakall and Smouse 2012). This analysis allows inferences to be made about spatial scale over which the genetic structure occurs. The variable distance class option was used to manually enter ecologically meaningful distance classes with 0 km representing individuals sampled at the same colony. Each of the other distance bins contain pairwise relatedness values between individuals sampled at different colonies. The 95% confidence intervals (CIs) around a random distribution were used to assess the significance of any spatial autocorrelation.

Mantel testing with Jost D as a measure of genetic divergence among colonies was used to further assess the relationship between genetic distance and geographical distance.

Mantel testing was conducted with Fst values for comparison. These calculations were

65 performed using the R package Vegan (Oksanen et al. 2013). Geographical distance for the Mantel test was calculated as the shortest swimming distance (km) between colonies

(Gonzalez-Suarez et al. 2009). Both spatial autocorrelation and the Mantel test were conducted with samples across both regions and with samples collected within South

Australia.

We used two additional procedures to characterise patterns of genetic divergence across sampling sites and just within SA colonies. First, SAMOVA 1.0 (spatial analysis of molecular variance; Dupanloup et al. 2002) was employed to test hypotheses of population number (K) ranging from K = 2 to K = 8 for across SA-WA range and K = 2 to K

=5 within SA colonies. Each hypothesis was then further tested using AMOVA in Arlequin

(Excoffier and Lischer 2010). Optimal cluster selection was based on maximized between- cluster significant differences (Fct) as recommended by Dupanloup et al. (2002). Second, in order to visualise genetic distance patterns across the full landscape analysed in this study we employed procedure referred as “Genetic Landscape Surfaces” (GLS) implemented in AIS (Miller 2005). This procedure produces a 3-dimensional surface plot where X and Y axes correspond to geographical locations and surface heights (Z-axes) represent genetic distances. GLS interpolation was parameterized at multiple scales with surfaces based on the midpoints of edges derived from Delaunay triangulation and residual genetic distances. Surface topology did not vary with different distance weighting values or grid sizes and we display the topology based on X and Y coordinates in bins of

50 with a residual genetic distance weighting of one.

Recent migration rates

We estimated contemporary migration rates among the nine sea lion colonies using

BayesAss 3.0 (Wilson and Rannala 2003). This program applies a Bayesian method to multilocus genotypes and determines recent migration rates between populations over the last several generations by using MCMC simulations. The MCMC was run for total of

10 x 10 6 iterations, discarding the first 2 x 10 6 iterations as burn-in. Samples were collected every 1000 iterations to infer the posterior probability distribution of migration rates. Acceptance rates for continuous parameters (migration rate, allele frequencies and inbreeding coefficients) were relatively high so different delta values were run to find the best fitting run parameters. Convergence was confirmed with conducting multiple runs initialized with different seeds and comparing the posterior mean parameter estimates.

Convergence was also visualised in Tracer program v.1.5 (Rambaut and Drummond 2012).

Results

Genetic diversity

Levels of genetic diversity measured by heterozygosity, allele richness and number of alleles are given in Table 4.1 (Appendix 2 has detailed results for each colony and locus).

None of the loci significantly deviated from HWE and there was no evidence of linkage disequilibrium between loci (at nominal level of P < 0.05 after Bonferroni correction for multiple tests). Mean allelic richness, observed heterozygosity and expected heterozygosity were highest for Olive Island (3.637, 0.638 and 0.640 respectively). Allelic richness was lowest for North Fisherman Island (2.275), whereas observed heterozygosity

67 and expected heterozygosity were lowest for Beagle Island (0.345 and 0.365 respectively). Allelic richness was significantly lower in all Western Australian colonies when compared to each South Australian colony (p-values for each comparison varied from 0.003 to 0.0005). Two out of 16 loci were monomorphic among Western Australian colonies. No significant differences were observed between within South Australian colonies comparisons or within Western Australian colonies comparisons (all p-values

>0.05).

Genetic differentiation and spatial genetic structure

To measure the level of genetic differentiation among colonies of male Australia sea lions,

Jost’s D and Fst between each colony was computed (Table 4.2). Both measures showed significant levels of diffe rentiation among most colonies. The pairwise Jost’s D estimates ranged from -0.005 (Blefuscu Is- Lilliput Is) to 0.479 (West Waldegrave Is-Beagle Is). Fst values tended to be slightly lower than Jost’s D values ranging from -0.005 (Blefuscu Is-

Lilliput Is) to 0.364 (Blefuscu Is-Beagle Is). Overall, the fixation indices showed high levels of differentiation between South Australian and Western Australian colonies (Jost’s D

0.369-0.479 and Fst 0.246-0.364). Within South Australia pairwise estimates ranged from

-0.005 to 0.164 for Jost’s D and from -0.005 to 0.099 for Fst. Low and non-significant values between Olive, Blefuscu and Lilliput Islands suggest that individuals are probably moving between these three colonies. Similarly both pairwise measures of genetic distance did not identify significant differences between the three Western Australian colonies.

Table 4.1 Genetic diversity and heterozygosity in male Australian sea lions. Parameter

Colony N Na AR HO HE FIS

Lewis Is 28 4.2 3.297 0.566 0.556 -0.019

Liguanea Is 25 4.0 3.377 0.591 0.606 0.024

West Waldegrave Is 21 4.1 3.460 0.604 0.607 0.004

Olive Is 28 4.3 3.637 0.638 0.640 0.002

Blefuscu Is 20 4.1 3.441 0.593 0.603 0.018

Lilliput Is 12 3.6 3.341 0.582 0.606 0.039

Buller Is 8 2.6 2.538 0.420 0.417 -0.007

North Fisherman Is 22 2.6 2.275 0.393 0.382 -0.027

Beagle Is 34 2.7 2.300 0.345 0.365 0.056 Parameters are as follows: number of individuals analysed for each colony (N), mean number of alleles (Na), allelic richness (AR) observed heterozygosity (HO) and expected heterozygosity (HE), inbreeding coefficient (FIS). Each locus conformed to expectations under Hardy-Weinberg equilibrium and pairwise comparisons between loci revealed no linkage disequilibrium.

Table 4.2 Pairwise Jost’s D and Fst estimates between nine Australian sea lion colonies

a) Jost's D Lewis Liguanea WW Olive Blefuscu Lilliput Buller NF Lewis Liguanea 0.059

WW 0.080 0.055 Olive 0.109 0.121 0.080

Blefuscu 0.094 0.126 0.080 0.001 Lilliput 0.157 0.164 0.134 0.008 -0.005

Buller 0.392 0.369 0.475 0.401 0.429 0.379 NF 0.394 0.385 0.471 0.417 0.442 0.372 0.006 Beagle 0.419 0.418 0.479 0.444 0.457 0.399 0.022 0.017

b) Fst Lewis Liguanea WW Olive Blefuscu Lilliput Buller NF Lewis Liguanea 0.037

WW 0.050 0.032 Olive 0.065 0.064 0.038

Blefuscu 0.060 0.068 0.040 0.003 Lilliput 0.099 0.083 0.064 0.007 -0.005

Buller 0.290 0.246 0.299 0.257 0.292 0.272 NF 0.322 0.290 0.337 0.299 0.336 0.316 0.008 Beagle 0.348 0.322 0.360 0.328 0.364 0.347 0.028 0.020 Values in bold are significant at P < 0.05 after correction for multiple tests.

There were significant associations between genetic differences and geographic distances across nine sampling sites and also within SA colonies (Figure 4.2, 4.3 and Table 4.3). The outcome of spatial autocorrelation analysis across WA and SA sampling sites is illustrated in Figure 4.2. Permutated 95% confidence intervals are close and p-values were significant for classes 0; >0-50; >50-150; >150-250 and >250-400 where positive spatial autocorrelation was observed. The correlogram shows that relatedness gets lower as geographic distance increases with an x-intercept of 686 km. When spatial autocorrelation analysis was done for only SA samples, finer scale and informative spatial genetic structure could be detected. In Figure 5.3 the correlation values are positive and significant for classes 0; >0-10 and >10-50 with an intercept of 121 km. Mantel tests results support results from spatial autocorrelation analyses with isolation by distance apparent both across both regions and across South Australian colonies (Table 4.3).

Table 4.3 Results for Isolation by Distance tests conducted for colonies across species range and for colonies only in South Australia (SA).

2 p R Across species range Jost's D 0.001 0.973 Fst 0.002 0.969 Only SA colonies

Jost's D 0.008 0.834 Fst 0.002 0.886

Figure 4.2 Spatial genetic structure across all nine sampling sites (n = 198). The genetic correlation, r, denotes pairwise relatedness (±95% confidence error bars). The dashed lines represent the permuted 95% confidence limits.

Figure 4.3 Spatial genetic structure across SA sampling sites (n = 134). The genetic correlation, r, denotes pairwise relatedness (±95% confidence error bars). The dashed lines represent the permuted 95% confidence limits.

Our SAMOVA results indicated clear division between SA and WA colonies. In the analysis including all nine colonies F CT was greatest for the K = 2 (AMOVA in Arlequin F CT = 0.27, p- value 0.01). When the analysis was performed on SA colonies only, SAMOVA clustered colonies into three putative groups: Olive, Lilliput and Blefuscu Islands formed one cluster, Liguanea and West Waldegrave Islands forming a second group and Lewis Island being characterized as an isolated colony (AMOVA in Arlequin F CT = 0.05, p-value 0.02).

Genetic landscape surface (GLS) plots generated in AIS broadly supported these clusters, with peaks (individuals that were spatially close together were more genetically similar to each other than to individuals spatially further away) and troughs (individuals being as similar to each other irrespective of the geographical distances separating them) being clearly identified (Figure 4.4). GLS peaks around Blefuscu, Lilliput and Olive Islands indicate that individuals in this area are more similar to each other than they are for animals that are geographically further away (Figure 4.4A). The degree of genetic isolation between WA and SA was highlighted by the discontinuity of the plot. When only SA colonies were included Blefuscu and Lilliput stood out as genetically similar to each other

(high peak) but dissimilar to surrounding colonies (troughs) (Figure 4.4B).

Figure 4.4 Genetic landscape surface (GLS) of individual pairwise genetic distances. Peaks representing samples whose pairwise genetic distances exhibit maximal differences to other samples.

A) Across all nine sampled colonies

B) Within SA colonies

Recent migration rates

Overall, recent migration rates among colonies were relatively low such that each colony was characterized by a high proportion (more than 68%) of local individuals to migrant individuals (less than 30%), suggesting that these breeding sites are generally isolated

(Table 4.4). Results show that Lewis and Beagle Islands have not received significant proportions of migrants over the most recent generations. The other seven colonies were characterised by moderate contemporary migration rates between colonies. The migration direction was predominantly unidirectional and occurred from Lewis Island to

Liguanea and West Waldegrave Islands, from Blefuscu Island to Olive and Lilliput Islands, and from Beagle Island to North Fisherman and Buller Islands.

Table 4.4 Estimates of migration rates between sampled colonies calculated with the program BAYESASS. Standard deviation for each estimate is given in brackets. Numbers in bold highlight the informative data points.

Migrated into Migrated from Lewis Island Liguanea Is West Waldegrave IsOlive Is Blefuscu Is Lilliput Is Buller Is North Fis herman Is Beagle Is Lewis Island 0.9046(±0.0280) 0.2372(±0.0279) 0.2357(±0.0330) 0.0119(±0.0111) 0.0472(±0.0321) 0.0155(±0.0149) 0.0161(±0.0153) 0.0091(±0.0089) 0.0081(±0.0078) Liguanea Is 0.0151(±0.0136) 0.6982(±0.0185) 0.0119(±0.0140) 0.0088(±0.0085) 0.0157(±0.0145) 0.0134(±0.0128) 0.0159(±0.0150) 0.0093(±0.0090) 0.0078(±0.0077) West Waldegrave Is 0.0126(±0.0119) 0.0098(±0.0096) 0.6828(±0.0151) 0.0089(±0.0085) 0.0200(±0.0175) 0.0132(±0.0125) 0.0161(±0.0153) 0.0092(±0.0089) 0.0077(±0.0074) Olive Is 0.0103(±0.0099) 0.0090(±0.0088) 0.0103(±0.0099) 0.6800(±0.0124) 0.0147(±0.0141) 0.0132(±0.0125) 0.0159(±0.0153) 0.0093(±0.0088) 0.0077(±0.0076) Blefuscu Is 0.0193(±0.0172) 0.0109(±0.0108) 0.0208(±0.0200) 0.2585(±0.0240) 0.8562(±0.0408) 0.2208(±0.0330) 0.0162(±0.0154) 0.0092(±0.0090) 0.0079(±0.0077) Lilliput Is 0.0094(±0.0092) 0.0088(±0.0087) 0.0097(±0.0094) 0.0079(±0.0076) 0.0114(±0.0109) 0.6845(±0.0163) 0.0161(±0.0153) 0.0094(±0.0092) 0.0077(±0.0075) Buller Is 0.0096(±0.0092) 0.0088(±0.0088) 0.0096(±0.0094) 0.0081(±0.0080) 0.0114(±0.0113) 0.0131(±0.0124) 0.6891(±0.0198) 0.0091(±0.0089) 0.0077(±0.0075) North Fisherman Is 0.0103(±0.0099) 0.0087(±0.0086) 0.0097(±0.0093) 0.0079(±0.0076) 0.0119(±0.0113) 0.0132(±0.0125) 0.0163(±0.0153) 0.6778(±0.0106) 0.0077(±0.0076) Beagle Is 0.0089(±0.0088) 0.0086(±0.0084) 0.0095(±0.0092) 0.0079(±0.0077) 0.0116(±0.0112) 0.0132(±0.0127) 0.1983(±0.0387) 0.2575(±0.0233) 0.9377(±0.0196)

Discussion

We show that Australian sea lion exhibits male-biased dispersal, however, the relatively strong differentiation among breeding colonies implies that male dispersal is limited to a remarkably small scale, given the species dispersal potential. These animals have been documented moving up to ~350km (Gales et al. 1992; Lowther et al. in press) and, due to asynchronous breeding times across colonies, they are presented with the unique potential to breed in different colonies throughout much of the year. These breeding opportunities appear not to have been fully exploited, which is surprising. Our findings provide important information on the extent of male driven gene flow and increase our current knowledge of the ecology of this endangered species.

Genetic diversity

Even though we focus on adult males, these results, especially our estimates of genetic diversity, reflect the whole colony as microsatellites are biparentally inherited. The level of genetic diversity measured as observed and expected heterozygosity was slightly lower than reported for a number of other pinnipeds (Allen et al. 1995, Hoelzel et al. 2001, Palo et al. 2001, Davis et al. 2002, Hoffman et al. 2006, Coltman et al. 2007, Lancaster et al.

2007, Hoffman et al. 2009). Overall, genetic diversity measures were higher in South

Australian colonies than in Western Australian colonies. Long term isolation from other colonies, genetic drift, and possibly founder effects could explain why these colonies have lower genetic diversity. No significant deviation from the HWE as a result of either

77 heterozygosity excess (Fis < 0) or deficiency (Fis > 0) was observed at any of the loci, indicating that no contemporary inbreeding occurs in any of the sampled colonies.

Genetic differentiation and male-mediated gene flow

Significant genetic differentiation between colonies was observed both between and within regions. Differentiation between South Australian and Western Australian colonies is not surprising considering the distance (>2200km) between these two regions.

However the degree of differentiation is high in comparison to other pinniped studies where samples were separated by similar or longer distances (Hoelzel et al. 2001, Palo et al. 2001, Coltman et al. 2007, Graves et al. 2009, Andersen et al. 2011, Feijoo et al. 2011).

There is no physical barriers separating these two regions, and isolation by distance appears to provide the best explanation for this differentiation. Strong differentiation suggests that animals within these two regions have been isolated for a long time and no significant migration occurs between them. Genetic partitioning between regions has also been detected with mtDNA data (Campbell et al. 2008).

Relatively strong genetic differentiation was also observed on a smaller geographical scale. Distances among the six South Australian colonies included in this study varied from only few kilometres to close to 400km. Clear differences in the genetic composition were observed between eastern and western South Australian colonies (separated by

300-400km). These distances are close to the limit of expected male dispersal capabilities

(Gales et al. 1992; Lowther et al. in press). Significant structuring was also detected between West Waldegrave Island and Olive, Blefuscu and Lilliput Islands, where distances vary between 120 and 160 km. These ranges are within realistic swimming distances for

78 male Australian sea lion. Interestingly, West Waldegrave Island is geographically closer to colonies in east (Olive, Blefuscu and Lilliput Islands) than colonies is west (Liguanea and

Lewis Island), but SAMOVA and GSL results indicated that animals from this colony are more closely grouped with animals from Liguanea and Lewis Islands. Recent migration rates estimated with BAYESASS supported the pattern of genetic connectivity between these three colonies. Consequently, while a general pattern of isolation-by-distance exists, there are factors other than geographic distance influencing genetic connectivity.

Within observed genetic structure Australian sea lion shows typical mammalian male- biased dispersal. Overall, the levels of genetic partitioning observed were weaker than those obtained using mtDNA, indicating that females of this species are more philopatric than males. Significant matrilineal clustering of breeding colonies was previously detected at extremely fine spatial scales (<40km) (Lowther et al. 2012). Here our results indicate that the scale over which substantial structure forms is approximately 120 km.

Nonetheless, the geographic extent of male mediated gene flow is intriguing considering the species potential for long-distance movements. Genetic differentiation at these relatively short spatial scales has not been detected for many other pinnipeds (e.g. grey seal ( Halichoerus grypus ) Allen et al. 1995, Graves et al. 2009; Harbour seal ( Phoca vitulina ) Goodman et al 1998; South American sea lion ( Otaria flavescens ) Feijoo et al.

2010; Californian sea lion ( Zalophus californianus ) González-Suárez et al. 2009; Australian fur seal ( Arctocephalus pusillus doriferus ) Lancaster et al. 2010).

Blefuscu, Lilliput and Olive Islands are genetically similar implying that males are moving among these three colonies separated by less than 40km. This is a contrast to earlier mtDNA results, which suggested restricted female migration between Olive Island and

79 two Nuyts colonies (Lowther et al. 2010). A similar pattern of regular male-mediated gene flow was also observed among three Western Australian colonies (separated by less than

100km). Campbell et al. (2008) reported genetic subdivision at mtDNA between Buller

Island and neighbouring Beagle and North Fisherman Island, where as a lack of differentiation among these three colonies was observed in this study. Recent migration rate estimates are consistent with our estimates of genetic distance showing that both

Lilliput and Olive Island receive migrants from Blefuscu Island. Also Beagle Island seemed to supply migrants to both North Fisherman and Buller Island, but not receive them itself.

A recent mtDNA study identified a genetic barrier between colonies within the Spencer

Gulf and those outside (Lowther et al. 2012) which is concordant with a similar barrier for sympatric marine predators such as bottlenose dolphins (Bilgmann et al. 2007). This was especially obvious with data from Liguanea Island that is situated just outside Spencer

Gulf, but is genetically very distinctive from colonies within the gulf. The level of genetic partitioning at microsatellite loci is less pronounced, small but significant pairwise Fst and

Jost’s D values were obtained between Lewis Island and Liguanea Island, and SAMOVA characterised Lewis Island as an isolated own colony. However GLS couldn’t detect a clear genetic peak around this colony. Migration rate estimates indicated that Lewis Island has not received recent migration, but instead has been a source for migrants to Liguanea

Island and probably also to West Waldegrave Island. These results indicate some level of restriction to gene flow between colonies within and outside Spencer Gulf, though some male dispersal is still likely.

Our results raise an intriguing question of why males would restrict their dispersal to distances much shorter than their swimming ability. In Western Australia males migrate

80 up to 280 km each breeding season between male haul-out sites and breeding sites

(Gales et al. 1992). A recent tracking study has shown similar travelling distances (up to

368km) between feeding grounds and haul-outs at neighbouring colonies in South

Australia (Lowther et al. in press). Hence Australian sea lion males could easily migrate among many of the breeding sites. Furthermore asynchrony in breeding timing between colonies offers males a unique opportunity to increase their overall reproductive success.

Despite such mobility and potential for increased reproductive success, our results indicate that males do not take such opportunity. Instead relatively strong genetic structure is apparent between colonies and regular movement only between colonies that are less than 100km from each other. The tracking study also revealed that after each foraging trip, males almost exclusively returned back to the colony where they were originally tagged (Lowther et al. in press). This might suggest that male Australian sea lions maintain one colony as their haul-out site year around. Similar behavioural patterns have been observed in Galapagos sea lions (Zalophus wollebaeki; Wolf et al. 2008,

Pörschmann et al. 2010). Reproductive success in such a situation is proposed to be affected by long-term interactions with others (Wolf et al. 2005). Indeed this has been hypothesised for both male Australian and New Zealand fur seals, where lack of post breeding dispersal allows males to gain knowledge of breeding areas throughout the year and establish themselves among the hierarchy of the males at each colony (Page et al.

2005, Kirkwood et al. 2006).

Adult Australian sea lion males could acquire similar benefits by staying in a familiar colony, while movement to unfamiliar colonies might increase the risk of lost mating opportunities rather than increase the number of available mates. Familiarity and experience has been shown to confer significant mating advantages in other pinnipeds,

81 such as Weddell seals (Harcourt et al. 2007a, Harcourt et al. 2007b). Charrier et al (2011) have pointed to a mechanism by which this might act in Australian sea lions by experimentally demonstrating that males can assess each other by their vocalisations, and thus adjust their behaviour in regards to the potential threat of the vocalising male.

Furthermore Lowther et al. (in press) recently raised the hypothesis that lack of seasonal dispersal might be driven by the unique reproductive cycle of females and the need to predict the onset of breeding at colonies. Breeding is prolonged over several months in

Australian sea lion colonies and plasticity in start of breeding event has also been observed (the breeding cycle can vary between 15 and 18 months; Ahonen, personal observation). This makes predicting the commencement of the breeding period difficult.

In polygynous mating systems such as those of otariid seals, males must increase their body mass in order to compete successfully against other males and to achieve mating success (Arnould and Duck 1997). However, the accumulation of fat is likely to have energetic costs both in terms of storage and transportation (Page et al. 2005). To limit these costs, the onset of the breeding cycle must be accurately known in order to delay the accumulation of sufficient extra mass as late as possible. Our results show that if males move and breed between colonies the scale is relatively small. This suggests that males choose to stay in area where they can regularly monitor the breeding state of each colony, primarily minimising the risk of losing out on reproductive opportunities but also streamlining the energetic costs associated with fat accumulation.

Conclusion and future directions

Knowledge of extent and rate of male mediated gene flow is essential for such a unique species as the Australian sea lion which exhibits extreme matrilineal substructuring, several unusual life history traits and low recovery and decolonisation rates. If male dispersal was as restricted as female movement, most of the breeding colonies would be considered closed populations and exposed to increased risks associated with inbreeding and environmental change. First, we have shown that genetic divergence among west coast of Western Australia and South Australian colonies is relatively high. Determining the genetic status of colonies on south coast of Western Australia will complete the picture of genetic differentiation across the full geographical range of the species. Even though males are inferred to disperse further than females, the observed genetic structuring is still notably stronger than in many other pinnipeds. The relatively limited gene flow highlights the question of what proportion of males sire pups, data that is relevant to predicting the effect of demographic changes.

CHAPTER 5: The sound remains the same? Adult male Australian

sea lion barking calls reveal clear geographic variations that do

not reflect known genetic structure

Liguanea Island 2009

In preparation for submission to Animal Behaviour

Abstract

Male vocalisation plays an important role in reproductive activities of many species, and thus significant variations between populations might alter social interactions.

Geographic variation in the structure of males’ vocalisations is well studied in birds, but largely ignored in mammals. This study quantifies the extent of geographical variation in male Australia sea lion ( Neophoca cinerea ) barking calls and discusses which factors drive vocal differences in this species. We recorded male barking calls from seven breeding colonies separated by ~5 to 2700 km enabling us to investigate acoustic differences on both micro- and macrogeographical scales. Our results revealed significant geographical variation across sample sites however vocal differentiation was not uniform. Neither genetic nor geographical distances between colonies were strongly associated with acoustic variation. It is likely that combination of factors, either ecological, environmental or both, explain regional differences in vocal repertoire.

Introduction

Geographical variation in vocalisations has been documented in a wide range of organisms including birds, mammals, frogs, lizards and insects (Catchpole and Slater 1995;

Cocroft and Ryan 1995; Wilczynski and Ryan 1999; Zuk et al. 2008; Yu et al 2011).

Variation can occur on different spatial scales and may be demonstrated through the production of unique call types, variations in call usage, and intra-individual-call differences between locations. Microgeographical variability, which is the basis for true vocal dialects, may occur in interbreeding populations of conspecifics (Mundinger 1982).

Conversely, geographically isolated populations may show macrogeographical variations

85 in their vocalisations. In species that are capable of vocal learning, cultural drift can influence acoustic variation (Ficken and Popp 1995, Deecke et al. 2000). Vocal learning has been described in birds and mammals, however evidence for vocal learning in mammals is often difficult to obtain and patchy (Janik and Slater 1997). Ecological factors such as acoustic properties of the habitat, the sound environment of local biota or anatomical differences between individuals can also explain variation in vocalisations

(Mitani et al. 1999 and references within this article, Pitcher et al 2012,). Such factors are often suggested as potential sources of macrogeographical variation. Furthermore, a number of studies have shown correlations between genetic partitioning and vocal distinctiveness (Kroodsma and Canady 1985; McCracken and Sheldon 1997; MacDougall-

Shackleton and MacDougall-Shackleton 2001; Van Parijs et al. 2003).

In many mammals males use vocalizations in social contexts either to attract females or in male-male competition (Krebs and Davies 1993; Andersson 1994). Male calls and the ability to correctly interpret them is therefore subject to strong selection in the context of mating success. Accordingly, variation in calls likely reflects successful attributes in the face of recent local selection (Janik and Slater 1997). One mammalian group in which there is clear evidence of geographic variation in male vocalisations is the pinnipeds (fur seals, sea lions, walruses and true seals). However, studies of geographical variation in male vocalisations have focused mainly on phocid species (true seals), with differences in vocal repertoire and vocalisations characteristics reported in northern elephant seals,

Mirounga angustirostris (Le Boeuf and Peterson 1969; Le Boeuf and Petrinovich 1974),

Weddell seals, Leptonychotes weddellii (Thomas and Stirling 1983; Abgrall et al. 2003;

Terhune et al. 2008), bearded seals, Erignathus barbatu s (Cleator et al. 1989, Risch et al.

2007), harp seals, Pagophilus groenlandicus (Perry and Terhune 1999), leopard seals,

Hydruga leptonyx (Thomas and Golladay 1995), and harbour seals, Phoca vitulina (Van

Parijs et al. 2000; Van Parijs et al. 2003; Bjørgesæter and Ugland 2004). The majority of these studies have measured larger-scale geographical variations in male mating signals

(Risch et al. 2007). Differences in male phocid vocalisations have been attributed to isolation of populations by geographical distance, strong site fidelity to specific breeding site, differences in acoustic transmission properties between sites and possible cultural effects with vocal learning and founder effect. However, over wide geographical ranges the factors influencing vocal variation are not always clear and combinations of factors can be behind the observed differences (Van Parijs et al. 2003).

This study investigates the extent of geographical variation in the male barking call in

Australia’s only endemic otariid, the Australian sea lion (Neophoca cinerea ) and its causation. Australian sea lions range across a geographic expanse of 2,200 km, from the

Houtman Abrolhos, (28°44'34.90"S, 113°49'7.08"E) Western Australia (WA), to The Pages

Island (35°47'5.37"S, 138°17'15.29"E), east of Kangaroo Island, South Australia (SA) with approximately 80% of the species breeding in SA waters (Goldsworthy et al. 2009). Most

(over 60%) Australian sea lion colonies are small, i.e. producing less than 30 pups per breeding episode and a recent census estimate of ~14.700 individuals makes this species one of the rarest otariids in the world (Goldsworthy et al. 2009; Shaughnessy et al. 2011).

The predominant call type produced by male Australian sea lion is the barking call (Stirling

1972; Gwilliam et al. 2008). Despite the occurrence of other call types in their vocal repertoire males use the barking call in almost all social interactions. This makes their vocal repertoire the most depauperate of all otariid males (Stirling, 1971; Stirling and

Warneke, 1971; Fernandez-Juricic et al., 1999; Phillips and Stirling, 2001; Tripovich et al.,

2005). Gwilliam et al. (2008) showed that despite its simple structure, male sea lions are

87 able to distinguish the calls of conspecifics from those of other species and can discriminate between males and females of their own species. Moreover, the barking call of male Australian sea lions has sufficient embedded information to provide the potential for individual discrimination (Gwilliam et al. 2008). Despite this simple vocal repertoire,

Australian sea lion males’ barks were found to significantly vary between two geographically distant colonies and, critically, mature, reproductively active males were able to perceive these geographical variations (Attard et al. 2010). The main source for these differences was hypothesized to be reproductive isolation induced by strong site fidelity and large geographic distance. Female site fidelity is well documented in

Australian sea lions and matrilineal structuring is higher than in any other marine mammal (Campbell et al. 2008; Lowther et al. 2012). However, Attard et al. (2010) were not able to test whether a genetic barrier is associated with vocal variation. This study provides the opportunity to compare acoustic and genetic differences and to assess whether reproductive isolation or other factors like cultural and environmental drive vocal differences in male Australian sea lion barks.

The aims of this study are twofold. First, this study quantifies the extent of geographical variation in male barking calls across the species range. Second, it assesses whether vocal structure reflects genetic structure. Specifically, we hypothesise that if vocal similarity correlates with genetic relatedness then acoustic differences should be apparent between colonies and vocal variation should increase as genetic differentiation increases.

Materials and Methods

Study locations and animals

This study was carried out on seven Australian sea lion colonies during 2009-2010 and

2010-2011 breeding seasons (Figure 5.1). Five of these colonies are situated along southern coast of South Australia and two along west coast of Western Australia. As the pupping season lasts for several months and the cumulative number of pups increases sigmoidally (Shaughnessy et al. 2011), we timed our visit for each colony when the pupping season was well started, and thus males were still actively mate-guarding female s (“peak of the breeding cycle”). Mean pup production in each colony varies from ~

43 on Liguanea Island to ~206 on Olive Island (Shaughnessy et al. 2011). Compared to other otariid species (fur seals and sea lions), the breeding colonies of Australian sea lion occur in very low densities. Table 5.1 presents characteristics and breeding density for each studied colony. Of these seven colonies, Blefuscu and Lilliput Islands bred synchronously but were asynchronous from all other sampled colonies. Similarly North

Fisherman (NF) and Beagle Islands bred synchronously but asynchronously from all other colonies. The shortest distance between studied colonies was ~5km between Lilliput and

Blefuscu and the longest was ~ 2749km between Beagle (WA) and Lewis Island

(SA)(calculated as the shortest swimming distance between colonies).

Figure 5.1 Location of sampling sites. Numbers in bracket indicated the number of sampled males for each breeding colony. Highlighted area represents the species range.

Table 5.1 Colony characteristics and breeding density of the seven studied Australian sea lion colonies.

Breeding cycle Size No. of pups No. of pups Colony duration Source (Km 2) born/season born per day (months) Lewis Is (SA) 0.13 131 6 0.73 1 Liguanea Is (SA) 1.46 43 6 0.24 2 Olive Is (SA) 0.22 196 7 0.93 3 Lilliput Is (SA) 0.10 66 7 0.31 3 Blefuscu Is (SA) 0.06 104 7 0.50 3 North Fisherman Is 0.03 48 5 0.32 (WA) 4 Beagle Is (WA) 0.01 58 5 0.39 4 Sources for pup count data: (1) Goldsworthy et al. (2008), (2) Shaughnessy et al. (2009), (3) S. Goldsworthy unpublished, based on recent calculations, (5) Campbell and Gales unpublished data in: Goldsworthy et al. 2009

Recording procedures and equipment

Male barking calls were recorder at close distance (3-8 meters) by using a Sennheiser ME

67 shotgun microphone (Sennheiser, Wedemark, Germany; Frequency Response: 50 Hz –

20 kHz 2.5 dB) connected to a Marantz PMD 660 digital recorder (Eindhoven, the

Netherlands; Frequency Response: 20 Hz –20 kHz 0.5 dB; Sampling rate 22.05 kHz). Adult

Australian sea lion males use the barking call in almost all social interactions, but are most commonly and intensively used in female defence and competitive interactions among males (Gwilliam et al. 2008). Barking calls are always produced in series from 4-5 units to several tens. For data integrity only mate-guarding males were recorded. To avoid any significant differences in calling rate (duration between barking calls), recordings were done in similar circumstances (e.g. when mate-guarding males had been disturbed by another animal or were interacting with the female they were guarding). A total of 110 males were recorded and several calling bouts were obtained for each individual. Males were identified through natural markings (e.g. scarring, flipper tears and marks) in order to avoid the possibility of replicated sampling. Furthermore most of the males were also genetically sampled and identification was confirmed through microsatellite genotyping.

Analysis of acoustic parameters

High quality recordings are essential to obtain accurate measurements. Australian sea lion colonies are noisy environments and good recordings were often hindered by abiotic noise (wind and sea waves), noise from seabirds colonies on the islands and vocalisations from other conspecifics such as other mate-guarding males and mother- pup pairs. As a result, we were able to use calls from 62 of 110 individuals exhibiting good signal to noise

92 ratio for the further analysis. Sample numbers for each colony are presented in Figure 5.1.

For each male two different series of 10 consecutive barks were selected (following

Gwilliam et al. 2008, Attard et al. 2010).

To assess acoustic variation in male Australian sea lion barking calls, 10 variables were measured using Avisoft SAS Lab Pro (R.Specht, Avisoft Bioacoustics, Berlin Germany). Two temporal features were measured: the total duration of the barking call (Dur; in ms) and the duration between barking calls (InterDur; in ms). Nine spectral features were also measured: frequency values of the first, second and third maximum peak in amplitude

(Peak 1, Peak 2 and Peak 3, respectively; in Hz) and the ratio of the amplitude values of

Peak 1 to Peak 2 and Peak 2 to Peak 3 (RAMP1 and RAPM2 respectively).The last three spectral measurements were the 3 quartiles which characterize the distribution of energy across the spectrum (Q25, Q50 and Q75; in Hz). The temporal variables were measured on the oscillogram (i.e. graphical representation of the sound wave, amplitude as a function of time) and the spectral parameters from the averaged amplitude spectrum

(Hamming window, frequency resolution: 10.7 Hz) calculated on the total length of the barking call. For the three quartile measurements whole length of the barking call was also used, but the frequency and amplitude measurements were performed on a 0-4 kHz frequency scale since most of the energy is found below 4 kHz and calls sampled at 22.05 kHz.

Statistical analyses

A discriminant function analysis (DFA) was carried out on the 10 acoustic variables to assess differences in barking calls between seven Australian sea lion colonies. The

93 assignment of barking calls to the different colonies was cross-validated by the leave-one- out method, which involves leaving out each of the cases in turn, calculating the functions based on the remaining n-1 cases and then classifying the left-out case . The squared

Mahalanobis distances between the group (i.e., sampling locality) centroids were derived from DFA and used as measures of call divergence among colonies. For each acoustic variable a Kruskal-Wallis ANOVA was also performed to assess differences between colonies. To identify which of the pairwise colony comparisons were significantly different a non-parametric equivalent of Tukeys HSD for multiple comparisons was employed

(implemented in the R package 'nparcomp'; Konietschke et al. 2012). Furthermore interindividual variation was also measured for each sampled male to investigate the occurrence of the previously observed individual discrimination (Gwilliam et al. 2008). To do this, we performed a second DFA including the 10 acoustic variables but with male identity as the grouping variable.

Last we aimed to examine the correlations between geographic origin, vocal similarity and genetic similarity. To test the statistical relationship between acoustic structure, genetic structure and geographical distance, a Partial Mantel test was performed. Three distance matrixes included geographic distance (calculated as the shortest swimming distance (km) between colonies), vocal distance (the squared Mahalanobis distances as measure of similarity in vocal profile among colonies) and genetic distance (generated using Slatkin’s linearised Fst values estimated in program FSTAT (Goudet 1995) using 16 microsatellite markers and male samples reported in Chapter 4).

Results

Acoustic differences between colonies

A total of 1240 male barks from 62 males were analysed to investigate geographic variation in male Australian sea lion barking calls. The DFA performed on 10 acoustic parameters revealed significant differences in barking calls between the seven colonies

(Wilks' Lambda = 0.510, F 60,6417 = 14.651, p < 0.00001). The first and second discriminant roots accounted for 85.5% of the total variance (74.3% and 11.2% respectively; Table 5.2).

Interdur and Q75 were mostly correlated with the first root and RAMP1, Q25 and Q50 were correlated with the second root. Overall the percentage of barking calls correctly assigned to the right colony was higher than expected by chance, with an average classification rate of 36% (by chance 1/7 = 14%). The correct assignment accuracy ranged from 14% to 61% (see Table 5.3), with 14% for Lilliput Island, 24% for Liguanea Island,

33% for Blefuscu Island, 35% for Lewis Island, 39% for North Fisherman Island, 43% for

Olive Island and 61% for Beagle Island. The cross-validation achieved similar classification results, overall being 34% and assignment accuracy ranging from 13% to 60%.

Interestingly most of the misclassified calls were assigned to geographically distant colonies and not to the nearest colony (see Table 5.3 and Figure 5.1).

Table 5.2 The standardised canonical coefficients for the first two roots extracted from DFA performed on 10 acoustic parameters.

Acoustic Root1 Root2 variable Dur (ms) -0.077 -0.064 Interdur (ms) -1.094 -0.032 Peak1 (Hz) 0.003 -0.178 Peak2 (Hz) 0.111 -0.294 Peak3 (Hz) -0.007 -0.144 RAMP1 0.012 0.462 RAMP2 0.124 0.379 Q25 (Hz) 0.083 -0.516 Q50 (Hz) -0.045 0.761 Q75 (Hz) -0.474 0.389 Eigenval 0.602 0.091 Cumulative total 74.3 85.5 variation %

Table 5.3 Assignments of male Australia sea lion barking calls to the seven sampled colonies. Numbers in brackets correspond to cross-validated classification rates.

Call assignments Colony Lewis Liguanea Olive Blefuscu Lilliput NF Beagle N calls Lewis 70 21 24 38 10 16 21 200 Liguanea 27 39 23 39 1 18 13 160 Olive 23 22 86 28 7 14 20 200 Blefuscu 53 44 18 65 11 7 2 200 Lilliput 29 25 33 25 22 16 10 160 NF 16 8 12 7 8 54 35 140 Beagle 40 2 8 1 1 17 111 180 % correct 35(32) 24(22) 43(42) 33(28) 14(13) 39(37) 62(60) % by 16 13 16 16 13 11 15 chance

Canonical scores of the first and second roots were plotted for all seven colonies (Figure

5.2). The 50% ellipses are presented to better depict the central groupings of the canonical scores of calls from each of the sampled location. All the 50% ellipses overlap but the degree of overlapping varies between colonies. The clearest division can be observed between South Australian colonies (Lewis Island, Liguanea Island, Olive Island,

Blefuscu Island and Lilliput Island) and Western Australian colonies (North Fisherman

Island and Beagle Island).Overlapping between the two Western Australian colonies is much less than within some of the South Australian colonies. Within South Australian colonies Liguanea and Lilliput overlapped the least and Lilliput and Olive the most. Vocal differentiation between South and Western Australian colonies can also be detected with squared Mahalanobis distance results (table 5.5B).

A DFA was also performed on a regional scale where all seven colonies were assigned to either the South Australian or the Western Australian region. The mean classification rate for correctly assigning calls to the right region was 85% with 95% for South Australia and

54% for Western Australia. The cross-validation achieved similar classification results, overall being 84% with 95% for SA and 51% for WA.

Figure 5.2 Ellipses enclosing 50% of the canonical scores obtained from a DFA of 10 acoustic variables measured from barking calls of Australia sea lion males. (NF=North Fisherman colony).

From the 10 tested variables, all except RAMP2 (the ratio of the amplitude values of Peak

2 to Peak 3), showed statistically significant differences between seven colonies. Mean values of all variables for each colony are shown in Table 5.4, and p-values for pair- comparisons of colonies are presented in Appendix 3.

Kruskal-Wallis ANOVA identified significant differences in mean duration of male bark between colonies (H (6, N= 1240) =168.1897 p =0.0001). Mean duration varied from the shortest at Blefuscu Island (51 ± 14 ms) to the longest at Beagle Island (77 ± 33). The non- parametric equivalent of Tukeys HSD for multiple comparisons indicated that the duration of barking calls at Beagle Island, North Fisherman Island and Olive Island were longer than those at the other colonies.

Similarly the mean inter-call duration differed significantly between colonies (H (6, N=

1240) =346.7633 p =0.0001) ranging from the shortest at Beagle Island 114 ± 40 (ms) to the longest at Blefuscu Island (179 ± 24). Inter-call duration was significantly shorter at

Beagle Island and North Fisherman Island than at the other colonies. The barking call from

Blefuscu Island had significantly longer inter-bark durations when compared to other locations.

There were significant differences in the mean frequency values of Peak1 between locations H (6, N= 1240) =80.4897 p =.00001. Peak1 was the highest at Blefuscu (861 ±

314 Hz) and the lowest at North Fisherman Island (687 ± 314). Barking calls produced by males from North Fisherman were significantly lower in frequency than those from all other colonies. Similarly, Peak 2 and Peak3 showed significant differences between seven colonies, H (6, N= 1240) =30.8425 p =.00001 and H (6, N= 1240) =18.7629 p =.0046,

99 respectively. Peak2 was found the highest at Beagle Island (1240 ± 475 Hz) and the lowest at Lilliput Island (979 ± 585 Hz). The frequency value for Peak 3 was the highest at

Liguanea Island (1589 ± 944 Hz) and the lowest at Olive Island (1214 ± 618 Hz).

The ratio of the amplitude values of Peak 1 to Peak 2 (RAMP1) showed significant differences between locations H (6, N= 1240) =27.0629 p =.0001 ranging from the highest value at North Fisherman Island (2.60 ± 1.58) to the lowest value at Liguanea Island (1.97

± 0.91).

Finally, Kruskal-Wallis ANOVA found significant differences between colonies in all

Quartile measurements (Q25: H (6, N= 1240) =45.6444 p =.00001; Q50: H (6, N= 1240)

=32.8890 p =.00001; and Q75: H (6, N= 1240) =64.0025 p =.00001). Mean Q25, Q50 and

Q75 were found the highest at Liguanea Island (1130 ± 371, 2566 ± 1140 and 5916 ± 1782

Hz, respectively). Mean Q25 was lowest at North Fisherman Island (954 ± 386 Hz) and mean Q50 and Q75 were lowest at Beagle Island (1990 ± 732 and4525 ± 1698 Hz, respectively).

100

Table 5.4 Mean values (±SD) for the 10 acoustic variables at each sampled colony and Kruskal-Wallis ANOVA results

interdur Colony dur (ms) Peak1 (Hz) Peak2 (Hz) Peak3 (Hz) Ramp1 Ramp2 Q25% (Hz) Q50% (Hz) Q75% (Hz) (ms) Lewis Is 55 ± 19 163 ± 24 837 ± 388 1216 ± 624 1425 ± 646 2.13 ± 1.56 1.44 ± 0.45 1025 ± 357 2145 ± 876 4747 ± 1695 Liguanea Is 62 ± 30 170 ± 45 811 ± 320 1181 ± 609 1589 ± 944 1.97 ± 0.91 1.45 ± 0.46 1130 ± 371 2566 ± 1140 5916 ± 1782 Olive Is 69 ± 17 166 ± 30 778 ± 271 1084 ± 645 1214 ± 618 2.21 ± 1.06 1.48 ± 0.51 983 ± 369 2130 ± 1029 4894 ± 1934 Blefuscu Is 51 ± 14 179 ± 24 861 ± 314 1157 ± 627 1453 ± 922 2.15 ± 1.27 1.47 ± 0.55 1048 ± 345 2277 ± 1038 5105 ± 2011 Lilliput Is 61 ± 44 164 ± 25 779 ± 306 979 ± 585 1372 ± 729 2.25 ± 1.08 1.54 ± 0.59 962 ± 379 2015 ± 912 4749 ± 1727 NF Is 73 ± 25 122 ± 41 687 ± 314 1063 ± 500 1390 ± 756 2.60 ± 1.58 1.64 ± 0.76 954 ± 386 2430 ± 1337 5505 ± 2008 Beagle Is 77 ± 33 114 ± 40 796 ± 294 1240 ± 475 1394 ± 634 2.03 ± 1.14 1.49 ± 0.64 1013 ± 297 1990 ± 732 4525 ± 1698 Kruskal- H = 168.1897 H = 346.7633 H = 80.4897 H = 30.8425p H = 18.7629 H = 27.0629 H = 7.9931 p H = 45.6444 H = 32.8890 H = 64.0025 Wallis p = 0.0001 p = 0.0001 p = 0.0001 = 0.0001 p = 0.0001 p = 0.0001 = 0.2386 p = 0.0001 p = 0.0001 p = 0.0001 ANOVA

101

Discrimination among individuals

The second DFA revealed significant differences among the 62 sampled males (Wilks'

Lambda: 0.018 F 610,11575 = 9.44 p < 0.00001). First and second discriminant roots accounted for 72.4% of the total variance (59.8% and 12.6% respectively). Inter-call duration was mostly correlated with the first root and Q50 and Q75 with the second root.

The classification matrix indicated that 34% of barks on average were assigned to the correct individuals (range: 0-80%, only 1 male showed a 0% classification rate), which is significantly above the chance level of 1.6%. The results of the cross-validated DFA were slightly lower, giving a mean classification rate of 27% (range: 0-75%, only 5 males showed a 0% classification rate).

Correlation between vocal structure, genetic structure and geographic distance

To generate genetic distance matrices and to measure the level of genetic differentiation among seven colonies of male Australian sea lions, Slatkin’s linearised Fst between each colony was computed. The pairwise Fst estimates ranged from -0.005 (Blefuscu Is- Lilliput

Is) to 0.530 (Olive Is-Beagle Is) showing significant levels of genetic differentiation among most of the colonies (Table 5.5A). Results from Mantel indicated that significant genetic isolation by distance is apparent between seven sampling sites (r = 0.979, p = 0.002).

Similarly, vocal distance (based on squared Mahalanobis distance, table 5.5B) and geographic distance were significantly correlated (r = 0.857, p = 0.027). However, a partial

Mantel test showed that, when controlled for the effect of genetic differences, the 102 significant correlation disappears between acoustic and geographic distances (r =-0.046, p

= 0.536). Likewise, we found that vocal distance was significantly correlated with genetic distance when all seven colonies were included in the analysis (r = 0.879, p = 0.013).

However, when controlled for effect of geographic distance, the correlation between vocal distance and genetic distance, was no longer significant (r = 0.383, p = 0.086). As the distances between South Australian and Western Australian colonies are quite long (over

2200km), and are likely to strongly influence these results, Mantel tests were also performed just for colonies within South Australia (n=5, geographic range ~ 400 km).

Significant genetic isolation by distance was also observed within SA colonies (r = 0.9202 p

= 0.008). In contrast to the previous results, vocal distance and geographic distance were not significantly correlated for the SA colonies (r = -0.121, p = 0.671). Genetic distance did not correlate with vocal distance within SA whether controlling for geographic distance or not (r = 0.421, p = 0.196, r = 0.050, p = 0.402 respectively).

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Table 5.5 Genetic and acoustic distance matrices for seven Australian sea lion colonies: (A) Pairwise Slatkin ’s Fst estimates (Fst/1 -Fst); (B) Squared Mahalanobis distances. Values in bold are significant at P < 0.05 after correction for multiple test.

Lewis Liguanea Olive Blefuscu Lilliput NF Lewis - 0.039 - Liguanea Olive 0.070 0.068 - 0.063 0.072 0.003 - Blefuscu Lilliput 0.110 0.091 0.007 -0.005 - NF 0.475 0.408 0.426 0.507 0.463 - Beagle 0.534 0.475 0.489 0.573 0.530 0.021

Lewis Liguanea Olive Blefuscu Lilliput NF Lewis - 0.927 - Liguanea Olive 0.603 0.729 - 0.415 0.458 0.627 - Blefuscu Lilliput 0.318 0.816 0.270 0.505 - NF 2.103 3.455 2.538 3.482 2.026 - Beagle 2.780 5.098 3.730 5.154 3.225 1.558

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Discussion

Very few studies have investigated how male vocalisations vary geographically in mammals. Comparisons of male Australian sea lion barking calls revealed small though significant geographical variation across seven breeding colonies. The barking call produced by Australian sea lion males is a vocalisation showing a simple structure, so a priori one would expect considerable overlap in the acoustic features of barking calls among colonies. In this paper, we have shown that vocalisations encode important information essential for the species such as the colony identity and the individual identity. Classification rates of calls between colonies were higher (or equal) than chance for all colonies. Furthermore, vocal discrimination among individuals was observed for 62 sampled males with an average classification rate of 34% versus 1.6% by chance. Yet this observed acoustic variation does not reflect either genetic structure or clear geographic patterns. Below we discuss our findings in regards to factors that could explain such variation.

Pattern of acoustic variation in male barking calls

A combination of acoustic parameters contributed to the discrimination between colonies. Discriminant function analysis showed that temporal parameter such as bark rhythmicity (inter-bark duration) and spectral features such as Quartiles are important in discriminating between colonies. To a lesser extent, an amplitude variable such as RAMP1 is also important. The differences in mean values for each parameter varied between colonies and were not uniformly significantly different among all colonies (Table 5.4 &

Appendix 3). This suggests that Australian sea lion males’ vocalisations show different

105 characteristics between geographical areas. Variation in male barks may be driven by site- specific selection for changes in certain vocal parameters. Similar explanation for observed variation has been found in male harbour seals (Van Parijs et al. 2003) and many bird species (Tubaro and Segura 1995, Appleby and Redpath 1997, Slabbekoorn and

Smith 2002b, Tack et al. 2005)

The differences observed in male barking call between Australian sea lion colonies were smaller and at first reading not as striking as other pinnipeds (Terhune 1994, Thomas and

Golladay 1995, Abgrall et al. 2003, Van Parijs et al. 2003, Bjørgesæter et al. 2004, Risch et al. 2007, Terhune et al. 2008). However care must be taken in making direct comparisons with other species, particularly in light of the richer and more diverse vocal repertoire of phocid species. In many of these studies authors have been able to compare differences on three levels: differences in call repertoire, usage and acoustic features. Functionally the existence of significant differentiation in such a simple call structure as the male

Australian sea lion barking call, suggest that this distinction has at least as much biological import as that seen among colonies of more florid species. Experimentally, there are also important differences between terrestrially recorded seals and those recorded underwater. In many species where underwater vocalisations have been measured, the exact number of individuals is almost invariably unknown. This can lead to biases whereby the majority of calls used in analysis are actually from a small number of individuals, leading to higher classification rates. In our study we analysed calls from 62 known individuals with 7 to 10 males per colony and 20 calls from each male, so we are confident that our sample is well balanced.

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While we have clearly found a high level of distinctiveness in male barking calls across the range, many acoustic features did overlap between areas. Barking calls exhibit a simple structure, they are short in duration and with a narrow frequency bandwidth. This means the combination of acoustic features between individuals is very limited and we can predict overlap in acoustic features among colonies. However, despite this physical limitation we found significant acoustic differences at both colony and individual levels.

This highlights that the structure of the vocalisation is sufficient to allow diversity, and that acoustic differences observed among colonies are not due to a lack of interindividual differences (DFA classification rates above chance, see table 5.3).

The barking call is the prevailing call type for male Australian sea lion and is used in all social interactions irrespective of whether breeding is occurring. Attard et al. (2010) showed that males discriminate between calls from two colonies separated by 180 km.

Our results confirm that there are differences in male bark characteristics, but suggest they may not be as unequivocal as first thought. When all seven colonies were included the overall classification rate was 34 % (ranging from 14% to 62%), compared to 64 % in

Attard et al. (2010). However if we compare any two colonies, we find correct classification rates ranging from 63% to 88%. It seems that when including multiple colonies for comparisons, we are able to see the real magnitude of differentiation. In harbour seals Van Parijs et al. (2003) also reported that between-site variation reduced considerably when multiple sites were compared. Thus, comparing only two colonies may over-estimate population structure and over-simplify the factors influencing vocal variations.

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Processes driving acoustic differences in male Australian sea lions

In many cases, differences in vocalisations within species have been simply attributed to drift due to reproductive isolation between populations that are separated by long geographical distances or physical barriers (Thomas et al. 1988; Cleator et al. 1989;

Thomas and Golladay 1995; Perry and Terhune 1999; Risch et al. 2007). In the present study, the seven sampled colonies were separated by ca. 5 to 2700 km enabling us to investigate both macro- and micro-geographical scales. At macro (regional) scale,

Western Australian and South Australian colonies showed one of the clearest differentiations in male barking calls (DFA mean classification rate = 85%). The mean inter-bark duration was significantly shorter in Western Australian colonies when compared to those in South Australian colonies. Significant differences in bark duration were also observed, duration being significantly shorter in South Australian colonies

(except in Olive Island) compared to those in Western Australia. These regional differences could be attributed to geographic isolation as the distance between all WA and all SA colonies is >2200km. Males are not expected to move between these two regions, and this geographic isolation can be also seen in genetic differentiation (Fst- results table 5.5; Campbell et al. 2008; Chapter 4).

Across smaller geographical scales (5-400km) differences in male barks were more subtle.

Within South Australian colonies small differences in bark characteristics were observed however these differences did not follow the distance pattern where nearby colonies would be more similar. Only two studies in pinnipeds have investigated vocal variation across micro-geographical distances of the same scale we describe (Pahl et al. 1997, Van

Parijs et al. 2003). In terms of molecular genetic structure, mtDNA studies have shown

108 that females exhibit strong natal site philopatry, with genetic structure occurring at scales of up to 40km (Campbell et al. 2008, Lowther et al. 2012). We also detected significant levels of genetic differentiation among most of the sampled colonies using microsatellite markers, suggesting that males follow a similar pattern of fine-scale genetic structure.

However our results clearly show that acoustic variation does not follow this observed genetic structure and hence these vocal differences cannot be explained by genetic relatedness. Furthermore many of the misclassified barks were assigned to geographically distant colonies and not to the nearest colony. Interestingly, differences between two

Western Australian colonies were greater than differences within South Australian colonies. When all seven colonies were included, the Mantel test indicated that genetic distance was significantly correlated with measured vocal distance, however when the effect of geographical distance was removed this correlation disappeared. It is more likely that this observed correlation is rather due to physical isolation between Western

Australian and South Australian colonies, which is also expected to drive genetic differences between these two regions.

Differences in the physical environment can be a powerful driver for differentiation of acoustic traits (Van Parijs et al. 2003). It is likely that divergence in vocalisations between colonies may in part have arisen through selection for elements that enhance signal transmission in their local environment (Morton 1975, Wiley & Richards 1982). Five of the sampled colonies in South Australia have open rocky areas with boulder fields, and the coverage of vegetation varies between all colonies. In Western Australia both Beagle

Island and North Fisherman Island are small islands with sandy beaches and thick boxthorn bushes in middle of the islands. Thus in WA most mate-guarding is concentrated in a small area near the waterline. Older pups with their mums can move deeper into the

109 bushes, away from the mating action but most of the males are concentrated in a small area, making density of mate-guarding males high. This is in stark contrast to Olive,

Lilliput, Blefuscu, Liguanea and Lewis Islands in South Australia where mate-guarding is spread over a much larger area. Consequently, differences in bark rhythmicity (call and inter-call durations) might be explained by this difference in male density and thus by differences in background noise (more individuals vocalising at the same time in addition to the noise from the ocean). Males in the denser mate-guarding environments produce faster rate calls and longer calls than those in lower-density environments. The difference observed between South Australian and Western Australian colonies could conceivably be driven by these ecological factors and not simply by geographical isolation. Divergence in calls reflects the difference in sound propagation properties of the different colonies and thus is consistent with the acoustic adaptation hypothesis (Morton 1975). Similar findings have been shown in Satin Bowerbird where vocalisations characteristics from genetically related populations diverge with habitat and those from genetically unrelated populations but similar habitat converge (Nicholls et al. 2006). Similar results have also been found in other bird species (in oscines: Slabbekorn and Smith 2002; in suboscines:

Seddon 2005). This clearly shows the importance of habitat selection on the origin and the maintenance of vocal variation. In our case, we can hypothesise that by producing faster and longer calls, males enhance the opportunity to be detected by conspecifics.

This follows the theory of information: increasing redundancy in signals improves the probability of receiving a message in a noisy channel (Shannon and Weaver 1949).

If some differences can be explained by habitat selection, neither genetic nor geographical differences between SA colonies fully explain the observed vocal variation.

Some other aspects such as morphological traits (size, weight) linked to food

110 resources/availability for instance have not been investigated and thus could be a potential source of vocal variability. Indeed, phenotypic traits related to sound production can affect vocalisation structure. Body size in vertebrates has been shown to affect call features such as spectral characteristics (for instance resonant frequencies in mammals:

Reby and McComb 2003; in birds: Nowicki 1987), the fundamental frequency (in birds:

Ryan and Brenowitz 1985, Nowicki and Marler 1988).

Finally, we can raise the question on the perception of these micro- and macrogeographical variations and how this can affect the breeding behaviour. In a preliminary study, we have shown that males are capable of discriminating between the barking calls of males from their own colony and those from distant colony (Attard et al. 2010). The next step of this work is to understand if males perceive and use this acoustic variation for competitive advantage during the breeding season and how this might further drive the evolution of calls. Playback experiments on males from several colonies are thus needed to investigate whether these acoustic differences are biologically meaningful and if they will affect their breeding behaviour. Studies on birds have shown that territorial owners respond more aggressively to vocalisations of local individuals than those from non-local individuals (Nelson and Soha 2004, Brunton et al. 2008) suggesting that locals represent a bigger threat for mate access and thus breeding success. In regards to female’s choice, it has also been demonstrated using playback exper iments that females prefer local dialects (Baker 1982, Baker et al. 1987, Searcy et al. 1997). In contrast to birds, there is an evident lack of exhaustive data on geographical vocal variation in mammals and the origin of such variation (genetic, vocal learning, sexual selection, habitat selection.). In the past, geographical vocal variation found in some mammals

(chimpanzee, northern elephant seal) were thought to be an evidence for vocal learning

111 but recent findings showed this is not the case (see for review Tyack 2008). Further investigations are thus needed to better understand the source of variation in vocal production within and between populations in mammals.

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CHAPTER 6: Sharing the love: paternity analysis within and among three Australian sea lion colonies

In preparation for publication

113

Abstract

A key element in high levels of polygyny is the ability of males to monopolise several females. In general the environmental potential for polygyny is high in terrestrially breeding pinnipeds because most species display female clustering during a synchronous breeding season. This provides males with the opportunity to control access to many females over a short period of time. Australia’s on ly endemic otariid, the Australian sea lion ( Neophoca cinerea ) differs from this general pattern with the breeding season spread over several months leading to low number of receptive females present at any one time.

Uniquely, breeding at different colonies is also asynchronous and non-annual. Given this, we predict that monopolization of females by a small number of males is unlikely and that instead paternities will be relatively evenly distributed across numerous males. We estimated variance in reproductive success for individual male Australian sea lions in three colonies using 16 microsatellite markers. We also assessed the potential for males to increase their reproductive success by siring pups at neighbouring colonies either synchronous or asynchronous in their breeding seasonality. Australian sea lions display relatively modest rates of polygyny with the majority of successful males siring only one or two pups per breeding cycle. Within all three colonies, over half of the sampled males were assigned paternity over the course of two seasons. The presence of alternative mating strategies (roaming vs staying) is apparent in this species with some males actively moving and breeding between the sampled colonies. Relatedness does not seem to be a driving force behind mate-choice and dispersal in this species.

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Introduction

In mating systems where only maternal care is provided, males maximize their fitness by fertilising as many females as possible, whereas females are selected to choose high- quality partners and thereby improve the prospects of their offspring surviving and later breeding (Trivers 1972, Clutton-Brock 1989). For males the number of offspring sired is often the sole measure of male reproductive success. When resources or other environmental factors force females to aggregate, males compete intensively for females leading to a polygynous mating system (Clutton-Brock 1989). Males may either control access to females (female defence polygyny; Davies 1991) or defend territories with critical resources (territoriality or resource defence polygyny; Emlen and Orling 1977). In these circumstances male reproductive success can vary greatly among males, and selection is thought to act on traits that increase an advantage in reproduction (e.g. body size, dominance, and fasting ability). Characterizing the degree of reproductive skew is fundamental, especially in long-lived and philopatric species, to the understanding of population-level processes such as inbreeding, gene ﬂow rates a nd dispersal patterns

(Keller and Reeve 1994, Clutton-Brock 1998, Frankham 2002).

In polygynous mammals, male mating success was traditionally measured as the time during which a male has exclusive access to an oestrous female, harem size or from the number of observed copulations (Coltman et al. 1999a). In these cases males have been assigned mating success based solely on behavioural observation. However, this methodology often overestimates expected reproductive success of dominant males and overlooks the possible advantage of alternative mating tactics and postcopulatory selection (Boness 1993). Assessing true reproductive success from observations is

115 problematic especially when mating behaviour is covert (extra-pair copulation or “sneaky mating”), nocturnal or aquatic. Assig ning paternity with molecular markers has revealed that the skew in male mating success is often less pronounced than expected from theory, or predicted from observations (Westneat 2000).There is now a growing list of species in which the agreement between observed mating success and the number of genetically assigned paternities is poor (e.g. Birkhead et al. 1990, Harris et al. 1991,

Coltman et al. 1999a, Pemberton et al. 1999).

Pinniped mating systems

Pinnipeds have proven to be one mammalian group where molecular methods have changed our understanding of their mating system and male reproductive success. The mating system in pinnipeds is generally described as highly polygynous (Stirling and

Kleiman 1983; Le Boeuf 1991;Lindenfors et. al. 2002). Separation between aquatic feeding and terrestrial parturition and postnatal pup care reduces competition for resources within breeding colonies and for those species that breed on land this allows females to aggregate at high densities. Furthermore relatively high synchrony of oestrus in most pinniped species offers males excellent conditions for monopolizing mates

(Boness 1991). In polygynous species paternities are expected to be strongly skewed towards large and dominant males (Clutton-Brock 1989). However, molecular marker- based studies have revealed the important role of alternative male tactics in pinniped mating systems (Amos et al. 1993, Coltman et al. 1999b, Worthington Wilmer 1999,

Harcourt et al. 2007b, Pörschmann et al. 2010). Aquatic mating is thought to reduce polygyny because females are more mobile in water and territories more difficult for males to defend (Bartholomew 1970). Pörschmann et al. (2010) found that in Galapagos

116 sea lions ( Zalophus wollebaeki ) skew towards large and dominant males was weak. In contrast to the relatively short reproductive season for most pinnipeds species, the reproductive season in Galapagos sea lions extends over a period of several months (5-6 months). Monopolising of females by any male is difficult and selection in Galapagos sea lions seems to favour males that are present in colony for a long time rather than dominance per se. Furthermore the presence of alternative mating tactics in Galapagos sea lions appears likely as several males secured relatively high reproductive success despite being present in the colony for only short periods. (Pörschmann et al. 2010)

Australian sea lions’ unique reproductive system

The Australian sea lion ( Neophoca cinerea ) has a breeding biology that is unique among pinnipeds and other mammals. A protracted 17-18 months reproductive cycle is asynchronous across the range of breeding colonies (Higgins 1993, Gales et al. 1994). This is in contrast to an annual, synchronised breeding of nearly all pinnipeds (Gales et al.

1994). Furthermore the breeding season extends over a 5-8 months period varying between small and large colonies (Goldsworthy et al. 2007; Shaughnessy 2008). The annual pregnancy rate of mature females is ~71 % (Higgins and Gas 1993). The gestation period consists of 3.5-5 months embryonic diapause and a prolonged placental gestation of up to 14 months, which is the longest of any seal species (Gales et al. 1997). Females display a post partum oestrus of 4-10 days followed by a 15- to 18- month lactation period (Higgins and Gass 1993). Australian sea lion females reach sexual maturity at 4 – 6 years of age and males at 8 – 9 years (Shaughnessy 1999).

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In Australian sea lions, males guard the females themselves as opposed to a defined territory (female defence polygyny). Males remain close to females preventing other animals from approaching and herd females in an effort to keep them from departing until the onset of oestrus and successful mating. Mature males can successfully guard up to four females at any one time (Marlow 1975), however at some colonies breeding density is so low that males can attend only one female at a time (serial polygyny; Higgins and Gass 1993). This pattern is repeated until the male is compelled to go to sea and forage (up to 4 weeks; Higgins 1990). As the breeding season is prolonged males have the opportunity to return after replenishing reserves and then compete for the right to repeatedly guard new females over the course of a season. As a result aggressive encounters do take place and males defend females with warnings such as guttural clicking, growling and barking vocalizations, posturing, and by fighting with rivals (Stirling

1972, Marlow 1975, Higgins and Tedman 1990, Charrier et al. 2011).

Male mating success, the roaming strategy

Considerable uncertainty surrounds the levels of male reproductive success over the whole tenure of breeding season. Current assumptions are based on behavioural observations done at a single colony, Seal Bay, South Australia (Higgins 1990). Mating is mainly terrestrial and the high level of sexual dimorphism (adult males reaching a maximum mass of 200 –300 kg (Walker and Ling 1981) and adult females averaging 84 kg

(McIntosh 2007) suggests a high level of polygyny. However, a protracted pupping season ensures that there is a high turnover of males and limits the development of any harem system (Ling and Walker 1978, Higgins 1993, Gales 1994, Gales and Costa 1997). Given the above, we predict that monopolization of females by a small number of males is

118 unlikely and that instead paternities will be relatively evenly distributed across numerous males. Nevertheless, asynchrony in timing of breeding among colonies may offer

Australian sea lion males a unique opportunity to increase their overall reproductive success. The results presented in chapter 3 show that despite the potential for long distance movements (up to ~350km; Gales et al. 1992; Lowther et al. in press), genetic structure across breeding sites is relatively strong and regular male gene flow apparent only between colonies that are less than 100km from each other. In this study we assess the potential for males to increase their mating success by siring pups at different neighbouring colonies. The three Australian sea lion colonies that were selected for this study are separated by less than 40km. Two of the colonies breed synchronously, whereas breeding on the third one starts at least three months earlier (Goldsworthy et al.

2007). Based on low levels of differentiation at microsatellite loci (Chapter 4), male movement and reproduction across these colonies is expected.

Male mating success, inbreeding avoidance

Considering the strong female philopatry, high number of small and isolated colonies, short distance male movements and breeding longevity of Australian sea lion there is also a high potential for mating with close relatives (Shaughnessy 1999; McIntosh 2007;

Goldsworthy et al. 2009; Chapter 4). Inbreeding can erode genetic variation and reduce fitness of individuals, contribute to population size reduction, increase vulnerability to rapid environmental changes and likelihood of extinction (Pusey and Wolf 1996, Saccheri et al. 1998, Keller and Waller 2002). Sex biased dispersal and multiple mating including extra-pair or extra group paternity are common examples of mechanisms evolved to avoid inbreeding within group living species (Pusey and Wolf 1996). Another well

119 documented mechanism is kin recognition and avoidance of close kin mating partners.

For example Amos et al. (2001) found a significant negative relationship between paternal similarity and genetic estimates of reproductive success in two marine mammals and three species of albatross thereby revealing selection towards mating maximally dissimilar mates. In a related study, Hoffman reported that in Antarctic fur seals

(Arctocephalus gazelle ) females select males that are heterozygous and less related to them than other geographically closer, potential mates (Hoffman et al. 2007a). If disassortative mating based on relatedness is used as an inbreeding avoidance mechanism in Australian sea lions, we would expect that mother-father pairs identified through paternity analysis would be significantly less related to each other than adult male and females (potential mates) in general.

The objective of this study was to estimate variance in mating success for male Australian sea lions in three colonies using genetic methods. We used paternity analysis to infer (i) the mating system and variance in male reproductive success of the Australian sea lion,

(ii) to evaluate the level and effect of cross-colony paternity to overall male reproductive success (iii) to investigate the possibility of disassortative mating on the basis of relatedness.

Materials and Methods

Study sites and sample collection

The study was conducted at three Australia sea lion colonies situated along the west coast of South Australia (Figure 6.1). Blefuscu and Lilliput Islands are separated only by

120 few kilometres and breed synchronously whereas Olive Island is ~38km away and the breeding period is asynchronous with the other two colonies. There is restricted female migration between Olive Island and two Nuyts colonies (Lowther et al. 2012), however a recent microsatellite study suggests male mediated gene flow does occur among these three colonies (Chapter 4).

Data was collected during 2009 and 2010 breeding seasons for all colonies. Predicted season commencement and duration for Olive Island in 2009 was November 2008 to June

2009 and in 2010 was May 2010 to October 2010. For Lilliput and Blefuscu Islands the breeding season in 2009 was February 2009 to August 2009 and in 2010 it was August

2010 to February 2011 (Goldsworthy et al. 2007). Given the protracted pupping season

(5-7 months) staying through whole breeding episode was not logistically feasible and would have caused significant disturbance. The cumulative number of pups increases sigmoidally (Shaughnessy et al. 2011) and the sampling trip for each colony was timed so that the pupping season was well underway enabling us to sample approximately 60% of pups born in each season. During this “peak of the breeding cycle” males are still actively mate-guarding females. Breeding on Olive Island in both 2009 and 2010 was finished by the time we visited Lilliput and Blefuscu Islands, confirming the asynchrony between these colonies during the study period.

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Figure 6.1 Location of sampling sites. Highlighted area represents the species range.

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A small tissue sample was obtained from adult males using a Paxarms biopsy system

(Paxarms New Zealand Ltd, http://paxarms.co.nz). Every male present at a colony (mate- guarding or not) was sampled, and males were identified through natural markings (e.g. scarring, flipper tears and marks) in order to reduce the possibility of replicated sampling.

Genetic identity was confirmed through microsatellite genotyping (see below). Less than seven males were sampled twice in each colony (Table 6.1) and in most of the cases double sampling was expected prior to genetic confirmation. Pups were captured with a hoop net, manually restrained and a small piece of skin was taken from the trailing edge of the hind flipper. Pups were either tagged with individually numbered and coloured two-part plastic tags or marked with individual identifying patterns clipped into the hair across the back of each pup. For mothers the Paxarms biopsy system was used to obtain tissue samples. On a few occasions females were captured by hand with the hoop net and a sample taken from the trailing edge of the hind flipper. Limitations in the number of mothers sampled were due to fact that most were not individually marked and could be located and sampled only when interacting with their pup. Adoption and allosuckling in

Australian sea lions is a relatively rare occurrence (Marlow 1972, Pitcher et al. 2011) and we could expect that most females attending pups were in fact their mothers. The number of adult females sampled from Blefuscu Island in the 2010 season is notably lower than the previous season. We had problems with our biopsy system during this fieldtrip lowering our sample rate of females. Details of the total sample numbers are presented in Table 6.1. Samples were preserved in 95% ethanol and total genomic DNA extracted using a standard salting out protocol (Sunnucks & Hales 1996).

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Table 6.1 The number of Australian sea lions sampled from three breeding colonies in South Australia. (These numbers included only animals that we used for the paternity analysis)

Breeding season 2009- 2010- Colony 2010 2011 Olive Island adult males 27 31 a adult females 43 40 pups 102 100

Lilliput Island

adult males 13 15 b adult females 32 24 pups 44 58

Blefuscu Island

adult males 22 15 c adult females 35 5 pups 60 59 a includes seven resighted males from season 2009-2010 b includes five resighted males from season 2009-2010 c includes 4 resighted males from season 2009-2010 These males were removed from the dataset when males were pooled together within colony for the paternity analysis.

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Microsatellite genotyping

Individuals were genotyped at 12 species specific microsatellite loci (Ahonen et al. 2013) and four cross-amplified loci developed for Galapagos sea lions (Wolf et al. 2006,

Hoffman et al. 2007). Each of the forward primers was directly labelled using fluorescent dyes FAM, NED, VIC and PET (ABI). PCR reactions contained ~10 –50 ng genomic DNA, 200

µM each dNTP, 1 Î Reaction Buffer, 2 mM MgCl 2, 0.5 µM of each primer, 1 unit of Taq polymerase in a total volume of 10-µl. PCR conditions were as follows: Initial denaturation for 3 min at 94 °C followed by five cycles of denaturation (94°C, 30 s), primer annealing

(60, 58, 56, 54, and 52°C, 30 s), and polymerase extension (72°C, 45 s). Following this series 35 cycles of denaturation (94°C, 30 s), primer annealing (50°C, 30 s), and polymerase extension (72°C, 45 s) were performed before ending with a final extension

(72°C, 10 min). PCR amplicons were then electrophoresed on an AB 3730xl DNA analyser.

Allele sizes were determined using an internal size standard LIZ and scored manually using Peak Scanner TM Software v.1.0 (Applied Biosystems). Ten percent of the samples were re-genotyped to ensure data integrity and scoring consistency.

MICROCHECKER version 2.2.3 (van Oosterhout et al. 2004) was used to assess for scoring errors and null alleles. The data set was checked for duplicate genotypes using the

Identity function of program CERVUS ver.3.0.3 (Kalinowski et al. 2007). Conformation to

Hardy-Weinberg equilibrium was evaluated using Genepop version 4.0.10 (Raymond &

Rousset 1995) and locus independence tested using FSTAT version 2.9.3.2 (Goudet 1995).

Statistical significance levels were corrected for multiple comparisons using sequential

Bonferroni adjustments (Rice 1989). Genetic diversity measurements including number of alleles (Na), expected (H E) heterozygosity and polymorphic information content (PIC)

125 were calculated for each colony using CERVUS. The probability of genotypic identity (P ID ) amongst unrelated and full sibling pairs was calculated using the software GenAlEx 6.5

(Peakall and Smouse 2012).

Paternity assignment

Paternity analyses were performed using the software CERVUS ver.3.0.3 (Kalinowski et al.

2007). CERVUS assigns paternity using a likelihood-based approach, taking into account user-defined scoring errors and the proportion of candidate parents sampled. The program calculates a logarithm-of-the-odds score for all candidate parents and carries out simulation to estimate the critical difference in LOD score between the most likely and second most likely candidate parent, at 80% and 95% confidence levels. In the case of mother-offspring pairs that were confidently identified based on the field observation,

CERVUS was first used to confirm that the pairs had genotypes compatible with a parent- offspring relationship.

Paternity analysis was conducted separately for each site and each breeding season. All adult males sampled during two consecutive breeding seasons were considered as possible candidate fathers for each one colony. All sampled males had features typical of sexually mature males (blonde mane, large body size) and the possibility of them being present at some stage of both breeding seasons was high. As we were not able to sample males through the whole breeding season, pooling males together increased the number of sampled candidate males for each colony.

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All simulations were run for 10 000 cycles and the proportion of genotyped loci was >98% for each colony (calculated by CERVUS). A genotyping error rate of 1% was used for all simulations, this estimate was based on the average observed mother/offspring genetic mismatches. The number of candidate males and proportion of sampled candidates varied for each colony. We estimated that approximately 52%, 70% and 63% of candidates were sampled at Olive Island, Lilliput Island and Blefuscu Island, respectively

(based on estimates of total number of adult males per each colony, Simon Goldsworthy unpublished data). Paternity assignments were accepted if the candidate father was not genetically incompatible at more than one locus and could be the father with 80% or 90% confidence.

In addition to within-colony analyses, pups from one colony were also screened against males from the two other breeding sites in order to examine possible cross-colony paternities. In this case the number of sampled candidate males was 104 individuals for each tested colony and season. The total number of candidate males was then estimated to be all males available for these three colonies and the percentage of sampled candidate males then approximated to be 58%.

The mean and variance in male mating success was calculated for each colony and across three colonies (for seasons separately and seasons combined). Also we calculated two measures that have been used to compare mating success between studies: standardised variance and Boness variance. The standardised variance (variance divided by mean) in mating success is a measure used to compare levels of polygyny between studies

(Coltman et al. 1998), whereas the Boness variance is a measure that has been used previously to compare the variance in mating success between seal species based on

127 observed copulations (Boness et al. 1993). As a data on individuals having zero copulations is lacking for many species, this measure is standardised across species by calculating the standardised variance based on the variance among successful males, including only one male with no matings per study. In monogamous mating systems the standardised variance or Boness variance equals zero, whereas in mating systems with moderate to strong levels of polygyny these estimates range from five to 50 (Boness et al.

1993).

Pairwise relatedness within mother-father - pairs

Pairwise relatedness estimates were calculated for mother-father pairs identified from paternity analysis using the software GenAlEx 6.5 (Peakall and Smouse 2012; Queller &

Goodnight (1989) estimator).

To test for significant differences in the distribution of pairwise relatedness between breeding pairs and the distribution of pairwise relatedness between each adult, a

Kolmogorov-Smirnov test was used. Furthermore, we used the Welch two sample t-test to examine if the mean of mother-father relatedness estimate differs from mean overall relatedness between adult males and females. All statistical analyses were done using software package R.

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Results

Characterisation of microsatellite loci

All loci were polymorphic, with the total number of alleles per locus ranging from three to seven in each of the three colonies. The average per locus gene diversity (H E) estimates varied between 0.457 and 0.784 for Olive Island, between 0.349 and 0.734 for Lilliput

Island and between 0.377 and 0.778 for Blefuscu Island. For each colony the characteristics of the 16 microsatellite loci used are given in Table 6.2. None of the loci showed significant deviation from Hardy-Weinberg equilibrium nor was there evidence of linkage disequilibrium between any pairs of loci (at nominal level of P < 0.05 after

Bonferroni correction for multiple tests).

Paternity assignment

Overview of the analyses. In total 423 pups from three colonies and two consecutive breeding seasons were used in the paternity analyses. Analyses for each colony were first conducted including only males sampled from the colony in which pups were born. With this approach, paternity was assigned to 182 pups (43%) with 80% or 95% confidence.

When pups were screened against all 104 available male genotypes additional cross- colony paternities were detected, but also a few original assignments were no longer significant resulting in overall assignment rate of 42% (179 pups). In each colony the majority of pups were assigned to males sampled within the pups’ natal colony.

For 178 (42%) of the pups we had the mother’s genotype to assist in paternity assignments, however not every pup with a known mother genotype was assigned a 129 father. For Blefuscu Island in 2010 the number of mother-pup pairs was considerably lower than for other sample cohorts. This is also reflected in the lower percentage at which paternity was assigned (31%).

CERVUS also assigned “most likely” fathers for a further 45 pups when all 104 sampled males were used. In all cases delta LOC was under the acceptance for 80% confidence level, however no mismatches were observed between father and pup genotypes and the proposed father had the highest LOC score. Despite the relatively high probability of these being real paternity assignments we conservatively decided to not include these.

Our rates of assignment were consistently lower than those expected from the simulation. This may indicate the presence of a greater number of relatives, a higher error rate or a lower proportion of candidates sampled than simulated. In our case the most probable situation is that there are more (unsampled) candidate fathers than we estimated.

From here onwards we present results for each colony separately, first showing the results where only males that were sampled at the colony being analysed are included as candidate fathers, and then using all males collectively from the three colonies included as candidate fathers. All assigned paternities are detailed in Table 6.3. Appendix 4 details paternity assignments across colonies when all 104 sampled males are used in the analysis. Australian sea lion males are highly mobile and the distance between these colonies is less than 40 km. The potential for an individual male to visit each of these three colonies is therefore high.

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Olive Island. 39 (38%) and 52 (52%) paternities could be assigned to 102 pups sampled in

2009 and 100 pups sampled in 2010 respectively. A total of 51 male genotypes were included in assignment analysis for both seasons. We estimate this to be approximately

52% of possible candidate males encountered within this colony (mature males, over 8 years old). The number of successful males in 2009 was 27 (53%) and 28 (55%) for 2010.

The highest number of paternities assigned to a single male was three (8% of males) and four (2% of males) in 2009 and 2010 respectively.

Including all 104 available male genotypes in the paternity assignments changed the results very slightly for both breeding seasons. A total of 44 (43%) and 48 (48%) paternities were assigned for 2009 and 2010, respectively. With cross-colony males, ten new paternities were assigned for 2009 pups, which had not previously been assigned fathers. Among 2010 pups 3 new paternity assignments were added from cross-colony paternity. For both seasons a few assignments became non-significant when additional males were added (5 for 2009 and 7 for 2010). This is mainly due to two candidate males having similar high LOC scores reducing the delta value. 77% (2009) and 94% (2010) of the paternities were assigned to males sampled at Olive Island (Appendix 4).

Lilliput Island. At Lilliput Island, a total of 19 (43%) and 26 (45%) paternities were assigned for 44 and 58 pups sampled in 2009 and 2010 respectively. From a pool of 23 sampled father candidates, 11 were assigned paternities for breeding season 2009 and 15 for the following breeding season. The highest number of paternities assigned to a single male was four (4% of the males) and five (4% of the males) in breeding season 2009 and 2010 respectively.

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When males from two other colonies were added to paternity analysis the total number of paternities increased to 22 (50%) for the 2009 breeding season but reduced to 23

(40%) for the 2010 breeding season. For both breeding seasons the majority of the pups were assigned to males within natal colony (77% 2009 and 66% for 2010, Appendix 4).

Among the 2009 breeding season five pups, which had not been previously assigned a father, could be allocated to males sampled outside of Lilliput Island. For 2010 eight new paternities were added from cross colony paternity. Again for both seasons adding males to the analysis caused a few paternities to drop off (2 for 2009 and 11 for 2010).

Blefuscu Island. Of the 60 pups sampled for 2009 and 59 sampled for 2010, 17 (28%) and

29 (49%) were assigned a father. Out of 33 genotype males 11 were successful during

2009 and 17 for 2010. The highest number of paternities assigned to a single male was three (3% of the males) and four (3% of the males) in 2009 and 2010 respectively.

As with Olive and Lilliput Islands, the paternity assignments changed when males from other colonies were included. A total of 24 (40%) and 18 (31%) paternities were assigned for 2009 and 2010, respectively. Only half of the paternities were assigned to males from

Blefuscu Island (58% for 2009 and 50% for 2010; Appendix 4). For both seasons, six new paternities were added with cross-colony fathers and on two occasions for 2009 and three for 2010 a male from another island got a higher assignment than original male. A notable drop in paternities assigned in 2010 was observed (three for 2009 and 20 for

2010). This is probably due to a low number of known mother genotypes available for pups. In all cases two males now had high LOD scores, reducing the acceptable delta

132 value for confident assignment. Actually five of these dropped off assignments were still classified as “most likely” f athers.

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Table 6.2 Number of alleles (Na), expected heterozygosity (HE), polymorphic information content (PIC), cumulative probability of identity between two unrelated individuals (PID) and cumulative probability of identity between two full siblings (PID(Sib)) of 16 microsatellite loci used for paternity analysis.

Total exclusion probability for first and second parent Olive Island = 0.989133 and 0.999754, respectively Total exclusion probability for first and second parent Lilliput Island = 0.973280 and 0.998950, respectively Total exclusion probability for first and second parent Blefuscu Island = 0.983386 and 0.999483, respectively Original citations of the Galápagos sea lion microsatellite loci: 1 Hoffman et al. 2007, 2 Wolf et al. 2006

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Table 6.3 Paternity assignments within colonies, seasons and with different number of sampled candidate males

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Overall male reproductive success

For this section we have included only males that were assigned with both analysis methods and left out the paternities that were excluded due to adding of cross-colony males. This was the most conservative method with which to present the overall male reproductive success among three Australian sea lion colonies. From 104 sampled candidate males 76 (73%) were assigned a paternity or multiple paternities in at least one colony over the two seasons (Appendix 4). Although over half of the males from each colony failed to sire pups within one season, the majority gained paternity when seasons and colonies were combined. A total of 76%, 83% and 55% of males sampled at Olive

Island, Lilliput Island and Blefuscu Island were assigned at least one paternity with the mean number of pups sired per male being 0.85 over two seasons. Among these successful males paternal skew was relatively low (Figure 6.2, Appendix 4). The highest number of paternities assigned to a single male in any one season was five and this was achieved by only one male. When seasons were combined and cross-colony paternities taken into account the maximum sired by any individual was eight pups. This male was sighted and sampled only on Lilliput Island, however he achieved paternities at all 3 colonies and during both breeding seasons. Thirty-eight of the 76 successful males sired pups only in the colony where they were sampled. Twenty-two had paternity assignments in two colonies whereas only two males successfully sired pups in all 3 colonies. A few males from each island did not have any paternity assignments in the colony where they were sampled but had a least one paternity in one of the other colonies. In every colony the highest number of paternities was assigned to males sampled within that same colony. In Olive Island 49% of the males that were successful within their own colony

136 achieved paternities in both seasons. For both Lilliput and Blefuscu Islands this number was 43%.

The mean, variance, standardised variance and Boness variance were calculated for each colony and across three colonies (both with separate and combined seasons) and are shown in Table 6.5. The mean number of pups assigned per male per season varied from

0.74 to 1.13 when only males from a single colony were used. When the pool of possible males was raised to all 104 sampled males the mean assignments per male dropped and varied from 0.17 to 0.46. Interestingly when colonies were pooled the mean number of pups assigned per male did not change much from single colony assignments.

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Figure 6.2 Distribution of paternities for all 104 sampled males (combined from three colonies)

A) Season 2009

B) Season 2010

C) Seasons combined

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Table 6.5 Summary statistics for male mating success. Results are presented for each colony and across three colonies with seasons separately and seasons combined.

No. of Mean per succesful Colony and breeding season, males paternities Max per Mean per Standardised male (Boness tested (own/all) assigned male male Variance variance variance) Olive Island (N 1 =102, N 2=100*) 2009 -own 39 (38%) 3 0.76 0.81 1.06 1.39 (0.43) 2009 -all 44 (43%) 3 0.42 0.51 1.21 1.29 (0.34) 2010 -own 52 (52%) 4 1.02 1.23 1.21 1.79 (0.44) 2010 -all 48(48%) 4 0.46 0.73 1.59 1.60 (0.44) combined -own 91 (45%) 5 1.78 2.56 1.44 2.46 (0.76) combined -all 92 (46%) 5 0.88 1.62 1.84 2.00 (0.72)

Lilliput Island (N 1 =44, N 2=58*) 2009 -own 19 (43%) 4 0.83 1.19 1.43 1.58 (0.68) 2009 -all 22 (50%) 4 0.21 0.38 1.80 1.38 (0.62) 2010 -own 26 (45%) 5 1.13 1.68 1.49 1.86 (0.76) 2010 -all 23 (40%) 2 0.22 0.27 1.23 1.28 (0.21) combined -own 45 (44%) 9 1.96 4.65 2.37 2.81 (1.52) combined -all 45 (44%) 6 0.43 0.94 2.19 1.61 (0.99)

Blefuscu Island N 1 =60, N 2=59*) 2009 -own 17 (28%) 3 0.74 0.67 0.91 1.42 (0.41) 2009 -all 24 (40%) 2 0.23 0.25 1.09 1.20 (0.18) 2010 -own 29 (49%) 4 0.88 1.20 1.36 1.61 (0.63) 2010 -all 18 (31%) 2 0.17 0.16 0.94 1.00 (0.11) combined -own 46 (39%) 5 1.39 2.18 1.57 2.19 (0.77) combined -all 42 (35%) 3 0.40 0.49 1.23 1.35 (0.27)

Across 3 colonies (N 1 =206, N 2=217*) 2009 90 (44%) 5 0.87 0.96 1.10 1.48 (0.50) 2010 89 (41%) 4 0.86 1.03 1.20 1.65 (0.41) combined 179 (42%) 8 1.70 2.51 1.48 2.32 (0.86) * N1 = number of pups sampled 2009; N2 = number of pups sampled 2010

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Mother-Father relatedness

The distributions of pairwise relatedness for mother-father-pairs (91 pairs altogether) and overall between adult males and females (a total of 104 males and 168 females, altogether 17472 male-female pair possibilities) was calculated and compared (Figure

6.3). Pairwise relatedness was normally distributed around a mean relatedness value of zero in both histograms with no significant differences detected in the shape of the distributions (Kolmogorov-Smirnov test, p = 0.808). Mean pairwise relatedness for mother-father-pairs was 0.009 and overall between adult males and females -0.001

(Welch Two Sample t-test p = 0.615)

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Figure 6.3 Distribution of pairwise relatedness: A) overall between adult females and males B) for mother-father pairs obtained from paternity analysis

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Discussion

Most studies on mating systems and male reproductive success have of necessity focussed on a single population or colony (Clutton-Brock 1988, Fabiani 2004, Hayes et al.

2006, Twiss et al. 2006, Harcourt et al. 2007a, Pörschmann et al. 2010). In this study we investigated variance in reproductive success within and across three colonies of

Australian sea lions using genetic methods. Our results reveal that Australian sea lions display relatively modest rates of polygyny with the majority of successful males siring one or two pups per breeding cycle. Within all the colonies over half of the sampled males were assigned paternity over the course of two seasons. Only a few males within each colony gained higher than average reproductive success, with the highest number of paternities achieved by a single male within any one season being five. However,

Australian sea lion males can increase their mating success by siring pups at more than one colony and a significant proportion of successful males (15-42% of successful males per season/colony) display this strategy. Neither mate selection nor dispersal appear to be driven by relatedness.

Variance in reproductive success among Australian sea lion s

Polygyny in Australian sea lions falls at the lower end of continuum generally described for pinniped species (Boness et al. 1993). Furthermore, we can conclude that the reproductive skew in Australian sea lions is not as pronounced as reported in other otariid species (e.g. Goldsworthy S. D. 1999, Hoffman et al. 2003, Kiyota et al. 2008, Caudron et al. 2010). In general reproductive behaviour of polygynous male is most influenced by the temporal and spatial distribution of females (Emlen and Oring 1977; Clutton-Brock 1989).

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When females are dispersed spatially and temporary or are highly mobile monopolisation of females becomes difficult and energetically costly lowering the level of polygyny.

Similar, low, levels of polygyny and skew have been reported in two aquatically mating phocids, harbour seals ( Phoca vitulina ; Coltman et al. 1998; Hayes et al. 2006) and

Weddell seals ( Leptonychotes weddellii ; Harcourt et al. 2007). In both of those species, females enter the water before the end of lactation and start of oestrus and have been recorded spending long periods underwater (Reijnders et al. 1990, Boness et al. 2006).

Monopolizing females in the three-dimensional environment appears difficult and hence the variance in reproductive success is expected to be reduced in aquatically mating species (Bartholomew 1970, Riedman 1990, Le Boeuf 1991). Aquatic mating is not observed in Australian sea lions and given their limited diving ability compared to phocidae, it is considered unlikely that this species would employ such mating tactic.

However the prolonged breeding season (5-8 months) appears to make monopolization of females difficult and this might explain the rates of reproductive success being similar to those observed in aquatically mating species. For each of the three study colonies the maximum duration of the breeding season is approximately 210 days and recent pup production estimates are 196, 66 and 104 pups for Olive, Lilliput and Blefuscu Islands, respectively. Given this we estimate that the number of receptive females on any one day at each colony is less than one (0.93, 0.31, 0.50 for Olive, Lilliput and Blefuscu Islands, respectively). Even during “the peak of breeding activities” the number of receptive females should not be much higher. Hence it is almost impossible for male to monopolise many females at the same time. While rare in otariids, this is similar to that seen in the

Galapagos sea lion (Pörschman et al. 2010). In Australian sea lions the low number of receptive females is exacerbated by the long breeding season precluding males from mate-guarding females for the whole period. At Seal Bay, one of the most intensively 143 studied Australian sea lion colonies, males spend up to four weeks ashore before leaving the colony, presumably to feed (Higgings 1990). We observed similar male attendance periods during our stay at other colonies. However, we do not know how many males return and resume mate-guarding. This behaviour increases the high turn-over of the males and may explain the relative equity of successful siring observed in these three

Australian sea lion colonies.

Cross-colony paternity – prevalence and effect for male’s reproductive success

Reproductive asynchrony combined with the prolonged breeding season offers males the opportunity to increase their overall breeding success by siring pups in more than one colony. The three colonies under investigation in this study offered the opportunity to find out if males do sire pups in other colonies, if so, how common, cross-colony paternities are, and the extent to which this strategy influences a male’s overall mating success. We found that the majority of paternities were assigned to local males but that in each colony and each season at least three pups were assigned a father from a different colony with the highest being 10 pups fathered by non-local male. We could not detect directionality or a pattern between colonies and cross-breeding, indicating that males from all three colonies move to other colonies to breed. Moreover, the degree of synchrony in breeding time did not seem to influence the rate of cross colony paternity assignments. Olive Island where breeding starts at least three to four months earlier than the two synchronously breeding colonies, Lilliput and Blefuscu Island supplied an equivalent number of paternities to Lilliput and Blefuscu colonies as they did to each other. Most of the males that bred across colonies were observed to breed at no more

144 than two colonies. Only two males achieved paternities in all three colonies but not within the same breeding cycle.

Overall our data suggest that moving to other colonies does not confer an increase in a male’s reproductive success com pared to staying in the same colony, all things being equal. The mean and variance in reproductive success were similar when paternities from all colonies were combined, compared with estimates from a single colony. However, siring at multiple colonies did ensure that more than one pup was sired by that male, and it is entirely possible that without moving that individuals reproductive success might have been lower. In Weddell seals, site familiarity (experience at a site) confers a competitive advantage to individual males (Harcourt et al 2007a). Given the proximity of these sites, Australian sea lion males that roam may sacrifice individual site familiarity for the gain of finding a female in oestrous elsewhere, whilst at the same time replenishing energy reserves depleted during mate guarding. We do not have information on preferred foraging areas for the individual males in this study, but in a parallel study,

Lowther et al (in press) have shown that male Australian sea lions do use site specific foraging areas. Supplementary mating’s by animals roaming further afield may be as much a function of proximity to preferred foraging areas as to actively seeking out available females.

Presence of disassortative mating on the basis of relatedness

We could not detect any significant differences between pairwise relatedness values of mother-father pairs and between adult male and female genotypes in general. Based on this we can deduce that relatedness is not a driving force behind mate-choice and

145 dispersal. All pairwise relatedness values were actually normally distributed close to a relatedness value of zero (unrelated) indicating that in general these colonies don’t see m to have “relatedness issue”. Furthermore our results in chapter 4 indicate that no contemporary inbreeding occurs in any of the sampled colonies. Still the risk of inbreeding has been elevated due to strong female philopatry and the high number of small and isolated colonies (Lowther et al 2012). It could be that the modest polygyny with low reproductive skew as reported here is enough to keep the genetic variation above the waterline or there may be some other genetic cue for example MHC acting as an inbreeding avoidance mechanism in this species.

Conclusions

Our findings are in agreement with behavioural observations and confirm low polygyny in this species. We hypothesise that the prolonged breeding season where receptive females are spread out in time and space is the main factor explaining observed the low variance in reproductive success. The presence of alternative mating strategies (roaming vs staying) is apparent in this species with some males actively moving and breeding between these colonies supporting the earlier genetic structure obtained with microsatellite markers. Despite the relative equity observed in males successfully siring pups, some males still achieved higher than average number of paternities. Further investigation is needed to understand processes and traits determining individual reproductive success. The implications of these findings for conservation of an endangered endemic species are profound. The modest polygyny observed in Australia sea lions is in effect “a good news story” for this species. Variance in reproductive success is an important determinant of genetically effective population size (Ne; Wright 1938,

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Nunney 1993, Frankham 1995). Increased polygyny associated with reproductive skew will reduce Ne through increased variance in reproductive success (Frankham 1995,

Anthony and Blumstein 2000). If reproductive skew was notably higher in Australian sea lion colonies and same males overly successful over multiple seasons, the effective population size would probably be smaller than that seen given these findings of low polygny. Higher levels of polygyny would erode genetic variation, particularly under isolation (Frankham, Ballou &Briscoe, 2002). Thus, assuming that the low levels of polygyny we observed reflects the prevailing mating system across the entire range, we suspect that genetic variability at the species level will stay robust, despite the number of small and isolated colonies inhabited by these animals.

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CHAPTER 7: General discussion

Sunset at Spencer Gulf, Lewis Island 2010

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The endangered, endemic Australian sea lion is unique among pinnipeds in having a non- annual, non-seasonal, asynchronous reproductive cycle. It is characterised by the highest level of genetic structure of any marine mammal and its range consists for the most part of multiple small colonies dispersed across the south and west of the Australian continent. This geographic range concords with the marine environment of the only southern continent not to have a highly productive upwelling system, instead being characterised by both a western and an eastern warm southerly flowing boundary current. The mechanism by which this somewhat eccentric breeding system might have evolved is uncertain, but the implications give rise to a number of interesting questions pertaining not only to the evolution of mating systems, but to questions of immense importance for conservation and management of this unique endemic.

In this thesis I have characterised male-mediated gene flow and genetic partitioning among Australian sea lion colonies across much of its distribution. I assessed the degree of polygyny in this species, I characterised individual reproductive success at three colonies and identified the existence of alternative mating strategies most probably arising from their unique reproductive ecology. At the same time I built on some earlier work that had unexpectedly shown that mate-guarding males differentiate barks from other males of their own colony with those from another breeding colony (Attard et al

2010). Vocal communication plays an essential role in social interactions especially during the breeding season. Males produce barking calls while guarding females and to advertise other males of their presence. Barks, while fairly simple in structure, carry significant information that allows males to perceive barks from different localities, differently. This leads directly to the possibility that divergence in vocalisation structure has arisen from long-term separation of subpopulations. Therefore I determined the degree and extent of

149 vocal distinctiveness both within and among colonies throughout the range. Finally, I was able to determine whether both measures of differentiation provide concordant information.

Studies of male otariids are generally under-represented in the scientific literature when compared to their conspe cific females. Presumably this is a function of ‘conservation triage’ whereby funding is often limited and focussed on applied outcomes. When faced with needing to maximize research outcomes with limited resources, focus may be given to the sex that appears most instrumental to maintaining census size. In systems where only females provide parental care, research is often focused on females. In case of otariids females give birth to pups and solely provide maternal care to maximise offspring survival. Moreover, the mating system in this group is generally considered polygynous and presumably only a proportion of males contribute genetically to the next generation.

Thus research concentrating on females might be, at first glance, justifiable to maximize the outcome of limited conservation funds.

However in light of available information on Australian sea lion population structure, lack of information on adult male reproductive movements was until now a considerable knowledge gap in our understanding of their ecology, their life history strategies and, critically, how genetic diversity may be maintained. This omission is particularly relevant given the unprecedented levels of natal site philopatry of conspecific adult females. If male dispersal in this species was as restricted as female movement, most breeding colonies would be considered closed populations and exposed to increased risks associated with inbreeding. Erosion of genetic variation and reduced fitness of individuals would likely contribute to population size reduction, increased vulnerability to stochastic

150 environmental events and lead to an increase in the probability of extinction (Pusey and

Wolf 1996, Saccheri et al. 1998, Keller and Waller 2002). Sex-biased dispersal is considered one of the main mechanisms to avoid inbreeding within group living animals

(Pusey and Wolf 1996). Hence knowledge of extent and rate of male mediated gene flow was an important starting point for my thesis and is a critical component of good conservation outcomes.

Spatial genetic structure in male Australian sea lion

In Chapter 4 I showed that the Australian sea-lion exhibits male-biased dispersal, however the relatively strong differentiation among breeding colonies implies that male dispersal is limited to a remarkably small scale. The scale over which substantial structure forms

(~100km) is not detected in many pinnipeds, nor in other marine vertebrates (e.g. grey seal (Halichoerus grypus) Allen et al. 1995, Graves et al. 2009; Harbour seal (Phoca vitulina) Goodman 1998; Lemon shark (Negaprion brevirostris) Duncan et al. 2006;

Californian sea lion (Zalophus californianus) González-Suárez et al. 2009; South American sea lion (Otaria flavescens) Feijoo et al. 2010; White shark ( Carcharodon carcharias )

Blower et al. 2013 . Indeed this is in distinct contrast to the other otariid species living in

South Australia, the Australian fur seal ( Arctocephalus pusillus doriferus ), which exhibits complete panmixia across breeding colonies and whose population is growing exponentially (Lancaster et al. 2010).

Although isolation by distance explains the large scale divergence observed among South

Australian and Western Australian colonies, other factors must play a role in this finer scale differentiation. Satellite tracking data from South Australia has shown that genetic

151 divergence lies within the geographic range of regular male foraging trips (in average 170 km per trip, range up to 368 km; Lowther et al. in press). Although in that study tracking was done only for small number of individuals it does cover several colonies and highlights the mobility of this species at least with respect to foraging. Similar distances have been measured in Western Australia for male movements between male haul-out sites and breeding sites (Gales et al. 1992). One of the important findings of the recent tracking work that might provide insight into the male dispersal revealed in this thesis, is that over several months individual males returned back to the same colony where they were tagged, and moreover utilised same at sea-region for foraging, irrespective of oceanographic conditions or time of the year (Lowther et al in press). It seems that male

Australian sea lions might be displaying a degree of fine-scale foraging specialisation similar to that observed in conspecific females (Lowther et al. 2011). That male Australian sea lions movements were invariant regardless of distinct changes in food availability and oceanographic conditions, suggests that they are limited in the degree of plasticity that they can display in the face of change, and the implications of this in light of the genetic findings in this thesis are profound. They appear constrained to dispersing only within range of colonies that are close enough to provide a familiar and reliable food source, and possibly mating possibilities. Asynchrony may allow males to increase their fitness by mating at more than one colony. At the same time this prevents males from far-flung dispersal outside the breeding season, a common strategy in other otariids. Ultimately this might limit population recovery and genetic mixing. For example, the panmictic

Australian fur seals forage in central Bass Strait, and while the breeding colonies are dispersed, most are within travelling distance of common foraging grounds, keeping cost- effectiveness of foraging similar despite which colony the animal forages from (Arnould and Hindell 2001). Both females and males are therefore free to move and breed among 152 all these colonies, explaining the observed high gene flow (Lancaster et al. 2010). For male Australian sea lions, movements between colonies that are close enough to preferred foraging habitat might be feasible, and this has been observed with colonies within 100km from each other. However long-distance relocation to another breeding colony would necessitate identifying and specialising in a new foraging environment, which seems beyond the behavioural repertoire of male Australian sea lions given they do not disperse beyond the range that females also utilise. Male sea lions do not fast over the whole breeding season, the breeding seasons are prolonged and overlapping so access to food resources is needed year round.

Strong inter-annual site fidelity has been observed in other otariids where males return to same breeding areas and even to exactly same spot where they were breeding during the previous season (e.g. Northern fur seal, Callorhinus ursinus; Gentry 1998; Antarctic fur seal, Arctocephalus gazelle , Hoffman et al. 2006). Species that display such a high degree of philopatry between breeding seasons would be expected to exhibit significant genetic differentiation. However, relatively few studies have been able to test the consequences of strong site fidelity to genetic structure. This is partly due to the fact that many otariids live in colonies that are not easily and regularly accessed to collect sufficient samples across the species range. For Northern fur seals where effects of site fidelity to population structure have been examined, the genetic analysis revealed only weak mtDNA structure in part of the distribution and no structure was apparent using microsatellite markers

(Dickerson et al. 2010). Authors could not exclusively distinguish between contemporary gene flow and historical re-colonisation, but hypothesised that lack of structure would be at least partly be due to insufficient time since rapid re-colonization. Either way, it is obvious that despite this strong site fidelity, both males and females still move between

153 colonies and movement has been rapid and effective after intense harvesting (Loughlin and Miller 1989, Ream et al. 1999, Dickerson et al. 2010). This is in distinct contrast to

Australian sea lions where post-harvest recovery has been limited and re-colonisation of extinct colonies not observed (Goldsworthy et al. 2009; Shaughnessy et al. 2011). Fine- scale genetic structure combined with small colony sizes, low density and low reproductive rate confounded by limited foraging plasticity may help explain low recovery rate of this species.

Given their unique breeding system, how might males maximise their reproductive opportunities? If males haul out year round in the same colony they might also acquire important information about the breeding areas and perhaps establish themselves among the hierarchy of the males at the colony. Similar benefits have been hypothesised for both male Australian and New Zealand fur seals where post breeding dispersal is at least partly limited for some individuals (Page et al. 2005, Kirkwood et al. 2006). Movement to unfamiliar colonies might increase the risk of lost mating opportunities rather than increase the number of available mates in Australian sea lions. I have experimentally demonstrated that Australian sea lion males can assess each other by their vocalisations and adjust their behaviour in regards to the potential threat of the vocalising males

(Appendix 6). However it is not known whether such discrimination also occurs outside the breeding cycle or this mechanism is only effective during mating activities. Perhaps more likely the information that males might gain by staying close to familiar colony is the knowledge of the status of reproductive cycle. Generally in otariids the breeding period is seasonal, short and highly predictable. This means that males can disperse widely to feed and return with a high degree of certainty to the breeding activities. In Australian sea lions, the protracted reproductive cycle and the high degree of variability in the start of

154 breeding makes dispersal a risky strategy. Misjudging when breeding occurs could be disadvantageous in two ways: i) risk of losing out on reproductive opportunities and ii) mistiming of fat accumulation and the energetic cost associated with it. The latter is linked to polygynous mating system and male-male competition of otariid species.

Increased body mass is considered important to successfully compete against rival males

(Arnould and Duck 1997) but is critical to enhancing fasting ability and longer attendance in breeding colony (Pörschmann et al. 2010). For serial mate guarding males fasting ability is very likely to play an important role in male success.

In Australian sea lions it appears that the gain in local knowledge as inferred by fine-scale foraging specialisation, combined with a limited ability disperse due to protracted breeding opportunities may explain the restricted dispersal and the conception of males as “Local Lads”. The Australian sea lion exemplifies the new insi ghts gained from a deeper understanding of mechanisms and shows that ecological and behavioural factors might be the drivers of a high degree of fine scale population structure as opposed to more traditional geographic explanations such as spatial distance and topographic barriers.

Mating system and variance in reproductive success

Characterisation of a species mating system is especially important for species such as the

Australian sea lion which exhibits extreme matrilineal substructuring, fine-scale restrictions in male dispersal and unique breeding biology. Knowledge of the proportion of males siring pups within each colony can elucidate our understanding of risks for inbreeding and helps us to determine the effective population size (Frankham, Ballou &

Briscoe 2003). Levels of polygyny can influence genetic structure in two ways. Increased

155 polygyny associated with reproductive skew will reduce Ne through increased variance in reproductive success and increase differentiation through drift (Chesser 1991; Frankham

1995, Anthony & Blumstein 2000).On the other hand in polygynous mating systems even a rare dispersal event between colonies can have potential for a homogenizing effect

(Fabiani et al. 2003).

Although the degree of sexual dimorphism present in Australian sea lions might lead to the prediction of a highly polygynous breeding system, the results I have presented here show different. The measured level of polygyny in Australian sea lion falls at the lower end of continuum generally described for pinniped species (Boness et al. 1993). This confirms the predictions based on direct observations of copulations (Higgins 1990). A key element to high levels of polygyny is the potential for males to monopolise females

(Clutton-Brock 1989). In general the environmental potential for polygyny is high in pinnipeds because most species display female clustering during synchronous breeding season, giving the males the opportunity to control access to multiple females over a short period of time. As I have outlined in detail the Australian sea lion differs strikingly from this general pattern with breeding occurring in small colonies over several months leading to low number of receptive females at any one time. With such a low OSR, monopolising of multiple females simultaneously becomes extremely difficult.

However, due to asynchronous breeding time across colonies, male Australian sea lions are presented with the unique potential to breed in different colonies throughout much of the year. Results from chapter 4 show that these breeding opportunities are not being exploited on the geographical scale of over 100km. However male dispersal was observed among colonies on smaller scale. In Chapter 6 I was able to further investigate this at the

156 individual level and identify males that move among colonies to breed. To understand the influence of these movements to genetic structure the level of polygyny and population size is relevant. Even rare dispersal in highly polygynous mating systems can have homogenizing effect, for example in elephants seals a single male has been recorded successfully fathering 19 offspring 8000km from its natal colony (Fabiani et al. 2003).

However, because an increase in polygyny is likely to be associated with a decrease in effective population size, in small colonies, random genetic drift may result in genetic divergence, and thus counter this homogenising effect.

In my thesis I have shown that a significant proportion of successful males (15-42% of successful males per season/colony) also sire pups across colonies (<100km). No directionality or a pattern between colonies and cross-breeding was observed indicating that males from all three islands (Olive, Lilliput and Blefuscu Islands) can move to other islands and breed. Moreover, asynchrony or synchrony in breeding time doesn’t seem to influence the rate of cross-colony paternity assignments. However, most of the males that do breed across colonies were observed to breed in no more than two colonies.

Overall the results do not confer an incr ease in a male’s reproductive success compared to staying in the same colony. However as reproductive success in general is low (only 1-2 pups per male), acquiring even one extra paternity from a non-local colony could be considered beneficial, especially if this behaviour was repeated over a lifetime. The three colonies under investigation in this study are separated only by >40km. Accessing foraging sites from all these colonies should be simple for any male sea lion. Each male would have to assess if it is better to stay in one single colony or to go and explore opportunities in nearby colonies. This could be the case between two synchronous colonies as Lilliput and

Blefuscu Islands separated by few kilometres only. If a male is not able to find and secure

157 a receptive female, it might be advantageous to swim to a neighbouring colony and thus assess other opportunities there. Between asynchronous colonies breeding would be already finished so it would be simply a case of moving to other colony when breeding starts there. Energetically this would not make a big difference as males will have the opportunity to forage during prolonged breeding season.

Vocal structure

Non-molecular assessments are often used as alternative methods to investigate differences and boundaries between populations. In Chapter 5 I showed that small though significant differences are found in male Australian sea lion barking call across seven colonies. A combination of acoustic parameters contributed to the discrimination between colonies and indicated that variation in male vocalisation may be driven by site- specific selection for changes in certain vocal parameters. In contrast to phocid species where geographical variation in male vocalisation is fairly extensively shown (Le Boeuf and Peterson 1969; Thomas and Stirling 1983; Cleator et al. 1989; Thomas and Golladay

1995; Perry and Terhune 1999; Sanvito and Galimberti 2000; Van Parijs et al 2000; Risch et al. 2007; Terhune et al. 2008; Van Parijs et al. 2003; Charrier et al. in press) little is known about otariids. This study is the first investigating acoustic differences on both micro- and macro-geographical scale in otariids. The differences measured in male barking call between different Australian sea lion colonies were smaller and, at first reading, not as striking as in phocids (Terhune et al. 2008; Risch et al. 2007; Abgrall et al.

2003; Bjørgesæter et al. 2004; Van Parijs et al. 2003; Terhune 1994; Thomas and Golladay

1995). Vocal repertoire in phocid species is often richer and vocalisations present a more complex structure. In Australian sea lion, male present a depauperate vocal repertoire

158 with their main vocalisation, the barking call exhibiting a rather simple structure (see figure 2.7 in General materials and methods). In comparison to phocids, a call type like trills produced by Weddell and bearded seals, shows a much more complex structure

(long down-sweep call with different frequency modulation patterns) enabling a great variability between males. Furthermore acoustic differences within these species can be compared on three different levels: differences in call repertoire, usage and acoustic features. The existence of significant vocal differentiation in Australian sea lions suggests that this distinction has at least as much biological import as that seen among colonies of more florid species.

Pattern of vocal differentiation did not hold for all geographic scales. A clear difference in male barking calls was observed at a regional scale, between Western Australian and

South Australian colonies (separated by >2200km). Males are not expected to move between these two regions, and similar disparity has also been detected with genetic markers (Campbell et al. 2008; Chapter 4). Differences in the male bark were more subtle across smaller geographical scales (5-400km). However these differences did not follow the distance pattern where nearby colonies would be more similar, and many of the misclassified barks were assigned to geographically distant colonies and not to the nearest colony.

Only a few studies in pinnipeds have been able to conclusively demonstrate which mechanisms explain the vocal differences detected (Van Parijs et al. 2003). Generally variation in vocal signals can arise from cultural (e.g. vocal learning; Slater 1986; Deecke et al. 2000), environmental (adaptations to acoustic environments or ecological niches;

Nicholls et al. 2006) or genetic factors (reproductive isolation leading to genetic drift;

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McCracken and Sheldon 1997; MacDougall-Shackleton and MacDougall-Shackleton 2001;

Perry and Terhune 1999). In chapter 4, I was able to establish that male dispersal is limited to a remarkably small scale with substantial genetic structure forming over distances greater than 100km. When acoustic structure was compared with genetic structure, in general there was no observed correlation between these two traits. The general low levels of exchange among colonies could point to drift as explaining differences in vocalisation and hence strong concordance between genetic data and acoustic data would not be expected. Alternatively acoustic variations may be driven by ecological pressures such as sound transmission properties and thus, the expectation of concordance between acoustic and genetic data would be even less likely. The maintenance of these acoustic variations may act as reproductive barriers and thus reinforce the genetic differences between colonies. However this has yet to be tested in

Australian sea lions.

Acoustic differences in the barking call of the male Australian sea lion could be instead explained by environmental factors, as they have in mother pup calls (Charrier et al. 2009,

Pitcher et al. 2012). Differences in the physical environment can be a powerful driver for differentiation of acoustic traits and divergence in vocalisations between colonies, and this may partially have arisen through selection for elements that enhance signal transmission in their local environment (Morton 1975, Wiley and Richards 1982). Physical attributes of colonies in Western Australia forces males to concentrate in a small and noisy area, making the density of mate-guarding males very high compared to the situation in the South Australian colonies. Observed differences in bark rhythmicity

(longer calls and shorter inter-call durations in Western Australia) might be explained by this difference in male density as well as differences in background noise (more

160 individuals vocalising at the same time in addition to the noise from the ocean). Males in the denser mate-guarding environments produce faster rate calls and longer calls than those in lower-density colonies, enhancing their chance to be detected by conspecifics.

Divergence in barking calls may reflect the difference in sound propagation properties of the different colonies and thus is consistent with the acoustic adaptation hypothesis

(Morton 1975). Similar findings have been shown in Satin Bowerbird where advertisement call characteristics from genetically related populations diverge with habitat and those from genetically unrelated populations but similar habitat, converge

(Nicholls et al. 2006).

Neither genetic differences, geographic distance nor habitat selection fully explain the vocal variation among South Australian colonies. Alternative hypothesis and further testing is needed to resolve this. Body size in vertebrates has been shown to affect call features such as spectral characteristics (for instance resonant frequencies in mammals:

Reby and McComb 2003; in birds: Nowicki 1987), the fundamental frequency (in birds:

Ryan & Brenovitz 1985; Nowicki and Marler 1988). Furthermore, in primates, pant hoots from two chimpanzee populations differed in quantitative acoustic measures and partly this variation can be explained by differences in body size (Mitani et al. 1999).

Morphological traits such as size and weight could be tested as a potential source for vocal variability in Australian sea lion. The distribution of female body size is skewed in some Australian sea lion colonies (based on multiple measurements of female mass across colonies; pers. com. A. Lowther). If females are larger in some colonies than in others why couldn’t male body size also differ across colonies? For instance, the larger body size of males from one colony could be linked to male foraging behaviour and ability

161 to dive deeper and catch bigger prey (Weise et al. 2010) . However we currently don’t know enough about male foraging behaviour across colonies to draw such conclusions.

Can males perceive acoustic differences?

I showed that acoustic structure was apparent across the Australian sea lion range, but that the pattern of variation might not have been as straightforward as was first expected. Based on our preliminary study (Attard et al 2010), we were expecting that acoustically, colonies close by would be more similar than colonies further away. Here I have showed that this pattern is not so straightforward. An intriguing question now arises: can males perceive these small but significant vocal differences? Part of my PhD work was to answer this question and conduct playback experiments in some colonies I visited. My results for this experimental work are almost completed and I have included them in Appendix 5 (with both a short description of the study design and an interpretation of the results). From these playback experiments, male responses to local calls were, in general, much stronger than responses to foreign calls indicating that

Australian sea lion males can perceive observed acoustic differences. In colonies where this was noticed, the acoustic differences between local and foreign calls were apparent and correct classification rate was generally high without too much misclassifications to calls from other tested colonies (Appendix 5, Chapter 5 table 5.3). However differences in responses to playbacks were not as clear in all colonies. For two colonies males reacted in a similar way to local calls as to calls from foreign colonies and the distance between colonies did not make any difference to responses. In both situations some vocal overlapping was detected between tested colonies and local calls were misclassified to other tested colonies. These similarities between some local and foreign calls might thus

162 induce confusion to males, and this can potentially explain the observed non- discrimination.

Clearly I need to conduct further analysis, but from these findings I can conclude that these acoustic differences seem to be biologically meaningful for Australian sea lion males. As I have now a better picture of the acoustic differences across colonies, it would be interesting to validate these results by conducting further playback experiments. For instance, I could use calls from colonies that have clearly different call structure and calls that have similar structure). In addition, I could also conduct playback experiments between colonies that have clearly different habitat properties and hence differences in sound propagation properties.

Future directions

This thesis provides important information from Australian sea lion male perspective.

However, there are still points that need to be addressed and work that can be done in the future:

This study and two recent studies on matrilineal substructuring (Campbell et al. 2008,

Lowther et al. 2012) cover the majority of Australian sea lion range and form an informative picture of colony structuring and dispersal pattern in this species. However we are missing data from the south coast of Western Australia. This area comprises 20 breeding colonies (5 classified as haul-out sites with occasional pupping) with pup estimates ranging from only 1-5 pups to a maximum of 66 pups. These colonies have not been properly surveyed for many years and we don’t have knowledge of th e current

163 status of these colonies. Campbell et al. (2008) included four of these colonies in their mtDNA study as the preliminary study on microsatellites markers (Campbell 2003). But this study was hindered with small sample size and usage of only few cross-amplified loci.

Genetically, this area is interesting as it resides between the colonies in South Australia and west coast of Western Australia. I have shown here that the genetic divergence is high between these two regions. Based on our current knowledge of dispersal patterns in this species we might expect that this area has a fine-scale structure, but it is unclear if it would be as distinct from these two regions or if they would act as a slow gradient from one end of species range to the other. As both demographic and genetic knowledge is limited for this region, future work is needed for appropriate management purposes.

In Chapter 6 I characterised the mating system for the Australian sea lion and confirmed a prior hypothesis that in general polygyny is low in this species. However this conclusion is based on three studied colonies. During my PhD I was able to collect similar data for paternity analysis from five additional colonies. With this data I could conduct a large scale paternity analysis and assess if there is plasticity in mating system or does the low level of polygyny we observed here reflect the prevailing mating system. Some of these additional colonies are more isolated (>100km to closest colony) which will allow me to investigate how variance in male reproductive success can change (if changes) when cross-colony paternity is not possible. Additionally it would be interesting to expand both the mating system and acoustic studies to high density and larger colonies, such as The

Pages Islands and Dangerous reef. Dangerous reef produces >500 pups (Goldsworthy et al. 2009) on a relatively small breeding area (~0.6-0.2 km2 including outer reefs).

Compared to other Australian sea lion colonies this is drastically different environment and resembles more colonies of other otariids. It would be interesting to test if this

164 colony shows a higher level of polygyny or a skew in male reproductive success or if the long breeding season still dilutes the effect of higher density colony. For acoustic study, such colony would offer an excellent environment to test how the high density of individuals can modulate the structure of male barking calls.

Despite the overall low variance in reproductive success few males gained higher than average number of pa ternities. The next question would be “What makes a male successful in this system?” Traditionally in polygynous species reproductive skew is expected to be strong towards large and dominant males (Clutton-Brock 1989). However reproductive success is likely to be function of interplay of number of factors rather than just one (Haley et al. 1994, Harcourt et al. 2007a). In pinnipeds, many studies have tested how male’s body size, age, dominance status, experience or tenure to hold territory are correlated with reproductive success (Hoffman et al. 2006; Harcourt et al. 2007a;

Pörschman et al. 2010). During my PhD, I was not able to evaluate if any behavioural traits (like dominance status or body size) influence males’ reproductive success.

However I can calculate different body measurements (i.e., body length, head size, body volume, and animal perimeter) from standardised high-resolution photographs that were taken for each male. By using basic photogrammetry (Baker 1960; Haley et al.

1991).body-size features could be compared and correlated with male reproductive success. In addition I could also investigate if any vocal trait reflects male’s quality and male’s reproductive success.

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Conclusions

This project was instigated in order to address an important knowledge gap, i.e. what is the influence of male mating behaviour and all of its antecedents, on the reproductive biology, population viability and population structure of the endangered endemic

Australian sea lion. Over the course of this study I have determined sex-biased dispersal patterns, identified the degree of polygyny, linked that to effective population size and connectivity across a large part of their range and provided critical baseline data for a range of interesting behavioural investigations. Specifically I have demonstrated that the

Australian sea lion does exhibit the typical mammalian pattern of male-biased dispersal.

However male dispersal is limited to a remarkably small scale considering the foraging habits of these animals, and so contributes critically to the most well-defined genetic structure of any marine mammal. I have developed hypotheses about why this pattern might occur. I suggest that such a restricted dispersal pattern may be explained by the reproductive benefits gained from site-specific knowledge and experience and/or by fine- scale foraging habitat preferences. Considering the high number of small and isolated colonies this low level of dispersal raises the question of whether inbreeding is a possible threat for the species. I did not test directly for inbreeding but the consistency in the values for allelic richness and expected heterozygosity I found suggest that none of the sampled colonies exhibit relatively high levels of inbreeding and therefore they are not at higher risk of inbreeding depression.

In a new and exciting approach I used male vocalisation as an alternative method to investigate differences and structuring across colonies. The results show a mismatch between genetic and vocal structure. Male Australian sea lion barking calls do show vocal

166 differentiation across colonies but this is not associated with genetic structure. Instead the generally low levels of exchange among colonies point to drift as explaining differences in vocalisation and hence strong concordance between genetic data and acoustic data is not expected. I have suggested that environmental or morphological factors may further contribute to the differences I have detected.

Several key behavioural and reproductive traits of Australian sea lion place them at odds with the almost universal patterns seen in other otariids. The prolonged breeding season where receptive females are spread out in time and space is almost certainly a major driving factor for the modest polygyny observed in this species. Microsatellite markers indicate that male movements for breeding only occur over a small scale. My paternity analyses support the low level of dispersal and have identified alterative mating strategies

(roaming vs staying) that are equally successful and in combination with the prolonged breeding season and asynchronous breeding likely contribute to the limited dispersal.

In sum, the Australian sea lion exhibits a unique reproductive pattern that is at odds with that found in every other member of this sub-order. The attributes discovered over the course of this project provide further insights into the evolution of, and consequences for future viability, of this unique, remarkable endemic Australian.

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Appendix 1 Characterisation of 12 new microsatellite loci for the Australian sea lion (Neophoca cinerea )and 4cross Ͳspecies amplified microsatellite loci from the Galápagos sea lion (Zalophus californianus wollebaeki) Allele size Locus Primer Sequence (5'-3') Repeat motif N N H H F P-value ran ge (bp) a O E IS

NCJKW F: TAGCCCCAAAGTAGAAGAGG (ATTT) 11 191-203 28 4 0.500 0.585 0.146 0.136 R: CTGGGGATCTAACCTATGG

NCW1W F: TACCAGCAATTGTAGGATGC (ATTT) 13 204-216 28 4 0.714 0.604 -0.183 0.473 R: TCATAATCTCAGGGTTGTGG

NCW1R F: GAGCAACATGCTCAGAGG (AAAT) 11 188-200 26 4 0.731 0.740 0.012 0.356 R: GGCAATGCAGAACTCTACC

NCQDM F: CCTTCAAGCTGAATTCTTCC (ATGT) 11 240-248 28 3 0.464 0.481 0.034 1.000 R: GAGCACTGGGTGTTATACG

NCTUC F: GACTGGGAGACAGAAGATAGG (AGAT) 11 158-170 28 4 0.714 0.675 -0.058 0.873 R: ACCTTGTGATATGGTTGTAGC

NCUYM F: TTCCATCCATGTTGTTGC (ATCT) 12 201-221 28 6 0.714 0.684 -0.044 0.228 R: ATACCTCAGCACCTCTCTCC

NCLH2 F: GGGAGGTAATTAGGTCATGG (AGAT) 11 188-196 28 3 0.429 0.535 0.199 0.605 R: CAGGTTGCTTCTGTGTGC

NCUVP F: GGTCCTTAGGCTAGTGTTCC (CTTT) 13 193-213 27 6 0.852 0.781 -0.091 0.808 R: GGAACTTGAACAGACATTTCC

NC61H F: CAATGGGAAGAATAACTTGG (ATCT) 12 218-234 28 5 0.679 0.787 0.138 0.287 R: GCTTTGATTTCCTCATCTCC

NCAH9 F: CATTATCCCCACATCTAAGC (AAAT) 11 141-153 28 4 0.571 0.738 0.226 0.323 R: CAACTGTTACTCCTGTGAAGG

NCMZ1 F: CATATTGGATTAGGGCTTACC (CTTT) 15 272-292 28 6 0.750 0.765 0.020 0.727 R: GCTTAAGATTCTCCCTCTCC

NCE06 F:GATGCTTAATCCACTGAACC (AAAT) 12 178-190 27 4 0.333 0.377 0.117 0.464 R:CAAATTGGTGATTTCTGGTG 1 ZcwF07 F: TATTCCTAGAGGGGCAAGTCAAG (GT) 2TT(GT) 20 139-145 28 3 0.679 0.590 -0.150 0.832 R: CATTGACTCTCTGAAATGGTGTC 2 ZcwB09 F: AGCCTCAGATAAGCCCCACATA (AC) 20 (TC)(AC) 7(CA) 2 (TA) 2CCTACAA(AC) 3 180-188 28 3 0.607 0.589 -0.030 0.250 R: AGGCTTCTGACCTGTGCTTCTT 1 ZcwE04 F: GCTGCTGTTACCACCTTTGTT (GT) 20 (ATCT) 3 118-134 27 7 0.630 0.608 -0.036 0.249 R: TAAGAAGACCCAGGATAGAGACCAG 1 ZcwG04 F: TTGCTGGTGAGTTGGATGAC (AC)GC(AC) 25 164-170 28 4 0.786 0.718 -0.095 0.543 R: AGAAGAGGGTCCTGTTCACTTG

Nnumber of individuals, Nanumber of alleles, HO observed heterozygosity, HE expected heterozygosity, FIS inbreeding coefficient. None of the loci showed significant departure from Hardy–Weinberg equilibrium (P>0.05). Original citations of the Galápagos sea lion microsatellite loci: 1Hoffman et al. 2007, 2Wolf et al. 2006 .

184 Appendix 2Summary statistics for each colony and locus. Parameters are as follows: mean number of alleles (Na), observed heterozygosity (HO) and expected heterozygosity (HE), allelic richness (AR), inbreeding coefficient (FIS) and number of individuals analysed for each colony (N). Microsatellite loci Colony Parameter NCJKW NCW1W NCW1R NCQDM NCTUC NCUYM NCLH2 NCUVP NC61H ZcwE04 1 NCAH9 NCMZ1 ZcwG04 1 ZcwB09 2 ZcwF07 1 NCE06 Average Lewis Is Na 44234436654664334.2 HO 0.615 0.538 0.346 0.240 0.750 0.679 0.643 0.821 0.500 0.571 0.571 0.464 0.643 0.679 0.643 0.357 0.566

HE 0.534 0.598 0.383 0.288 0.689 0.653 0.573 0.736 0.437 0.515 0.624 0.625 0.720 0.602 0.573 0.341 0.556

AR 3.068 2.997 1.991 2.488 3.712 3.647 2.695 4.552 3.150 3.667 3.742 3.884 4.477 3.136 2.932 2.616 3.297 FIS Ͳ0.153 0.099 0.096 0.168 Ͳ0.088 Ͳ0.038 Ͳ0.122 Ͳ0.116 Ͳ0.145 Ͳ0.109 0.084 0.257 0.107 Ͳ0.127 Ͳ0.121 Ͳ0.049 N 26 26 26 25 24 28 28 28 28 28 28 28 28 28 28 28 27.2 Liguanea Is Na 4 3 4 3 4 5 4 6 4 5 5 3 4 4 3 34.0 HO 0.320 0.560 0.760 0.417 0.640 0.739 0.640 0.708 0.500 0.625 0.708 0.500 0.583 0.400 0.800 0.560 0.591

HE 0.349 0.638 0.658 0.350 0.643 0.659 0.689 0.784 0.601 0.641 0.636 0.554 0.614 0.585 0.653 0.641 0.606 AR 2.793 2.980 3.566 2.455 3.457 3.821 3.713 5.112 3.434 3.986 3.828 2.761 3.264 2.916 2.989 2.962 3.377 FIS 0.084 0.122 Ͳ0.154 Ͳ0.192 0.005 Ͳ0.121 0.071 0.097 0.169 0.025 Ͳ0.114 0.098 0.050 0.316 Ͳ0.226 0.126 N 25 25 25 24 25 23 25 24 24 24 24 24 24 25 25 25 24.4 West Waldegrave IsNa 4 3 4 3 4 4 3 4 5 5 4 7 5 4 3 44.1 HO 0.476 0.600 0.810 0.381 0.619 0.700 0.571 0.550 0.619 0.667 0.714 0.524 0.600 0.667 0.750 0.421 0.604 HE 0.401 0.554 0.710 0.408 0.737 0.700 0.567 0.629 0.729 0.649 0.625 0.788 0.692 0.565 0.591 0.367 0.607

AR 2.976 2.583 3.789 2.324 3.899 3.799 2.816 3.250 4.183 3.817 3.594 5.274 4.083 3.148 2.836 2.985 3.460 FIS Ͳ0.187 Ͳ0.083 Ͳ0.141 0.067 0.160 0.000 Ͳ0.008 0.126 0.150 Ͳ0.028 Ͳ0.143 0.335 0.133 Ͳ0.179 Ͳ0.269 Ͳ0.147 N 21 20 21 21 21 20 21 20 21 21 21 21 20 21 20 19 20.6 Olive Is Na 44434636564643344.3 HO 0.500 0.714 0.731 0.464 0.714 0.714 0.429 0.852 0.679 0.692 0.571 0.750 0.786 0.607 0.679 0.333 0.638

HE 0.585 0.604 0.740 0.481 0.675 0.684 0.535 0.781 0.787 0.585 0.744 0.765 0.718 0.589 0.590 0.377 0.640 AR 3.315 3.383 3.903 2.686 3.560 4.171 2.441 5.273 4.656 3.819 3.872 4.569 3.893 2.950 2.914 2.787 3.637 FIS 0.146 Ͳ0.183 0.012 0.034 Ͳ0.058 Ͳ0.044 0.199 Ͳ0.091 0.138 Ͳ0.184 0.232 0.020 Ͳ0.095 Ͳ0.030 Ͳ0.150 0.117 N 28 28 26 28 28 28 28 27 28 26 28 28 28 28 28 27 27.6 Blefuscu Is Na 4 3 5 4 4 4 3 6 5 6 4 5 4 3 2 34.1 HO 0.600 0.350 0.737 0.421 0.526 0.647 0.550 0.550 0.889 0.611 0.474 0.778 0.800 0.450 0.600 0.500 0.593 HE 0.528 0.516 0.724 0.367 0.604 0.757 0.463 0.729 0.791 0.564 0.766 0.743 0.676 0.432 0.505 0.488 0.603

AR 2.932 2.350 4.256 2.985 3.366 3.953 2.348 4.852 4.788 4.004 3.943 4.278 3.698 2.572 2.000 2.731 3.441 FIS Ͳ0.137 0.321 Ͳ0.018 Ͳ0.147 0.128 0.146 Ͳ0.188 0.245 Ͳ0.124 Ͳ0.084 0.382 Ͳ0.046 Ͳ0.183 Ͳ0.043 Ͳ0.188 Ͳ0.024 N 20 20 19 19 19 17 20 20 18 18 19 18 20 20 20 20 19.2 Lilliput Is Na 33432526534452333.6 HO 0.727 0.500 0.667 0.250 0.667 0.800 0.583 0.333 0.818 0.500 0.833 0.750 0.636 0.583 0.167 0.500 0.582

HE 0.559 0.656 0.750 0.235 0.500 0.717 0.515 0.731 0.800 0.519 0.761 0.750 0.727 0.424 0.504 0.542 0.606

AR 2.879 2.982 3.934 2.420 2.000 4.807 2.000 5.088 4.832 2.935 3.974 3.960 4.478 2.000 2.583 2.583 3.341 FIS Ͳ0.301 0.237 0.111 Ͳ0.065 Ͳ0.333 Ͳ0.116 Ͳ0.132 0.544 Ͳ0.023 0.036 Ͳ0.095 0.000 0.125 Ͳ0.375 0.669 0.077 N 11 10 12 12 12 10 12 12 11 12 12 12 11 12 12 12 11.6 Buller Is Na 22323324332232232.6 HO 0.286 0.125 0.625 0.625 0.286 0.571 0.143 0.857 0.375 0.500 0.500 0.250 0.500 0.375 0.125 0.571 0.420

HE 0.262 0.125 0.500 0.446 0.607 0.536 0.143 0.702 0.643 0.429 0.393 0.411 0.420 0.464 0.125 0.464 0.417 AR 2.000 1.875 2.992 2.000 3.000 3.000 2.000 4.000 3.000 2.983 2.000 2.000 2.875 2.000 1.875 3.000 2.538 FIS Ͳ0.091 0.000 Ͳ0.250 Ͳ0.400 0.529 Ͳ0.067 0.000 Ͳ0.220 0.417 Ͳ0.167 Ͳ0.273 0.391 Ͳ0.191 0.192 0.000 Ͳ0.231 N 78887777888888877.6 North Fisherman IsNa 2 1 3 2 3 3 2 3 4 2 2 3 3 2 2 42.6 HO 0.091 0.000 0.409 0.318 0.500 0.500 0.273 0.500 0.864 0.545 0.591 0.238 0.545 0.273 0.136 0.500 0.393 HE 0.089 0.000 0.403 0.489 0.554 0.598 0.240 0.513 0.619 0.472 0.509 0.219 0.424 0.409 0.130 0.448 0.382

AR 1.540 1.000 2.520 2.000 2.693 2.915 1.916 2.857 3.408 2.000 2.000 2.150 2.528 1.996 1.693 3.180 2.275 FIS Ͳ0.024 NA Ͳ0.016 0.350 0.098 0.165 Ͳ0.135 0.025 Ͳ0.395 Ͳ0.156 Ͳ0.162 Ͳ0.087 Ͳ0.286 0.333 Ͳ0.050 Ͳ0.116 N 22 22 22 22 22 22 22 22 22 22 22 21 22 22 22 22 21.9 Beagle Is Na 31323413432332242.7 HO 0.091 0.000 0.333 0.406 0.500 0.735 0.000 0.412 0.441 0.618 0.324 0.147 0.469 0.438 0.152 0.455 0.345

HE 0.145 0.000 0.340 0.396 0.523 0.724 0.000 0.438 0.550 0.534 0.488 0.140 0.517 0.501 0.142 0.409 0.365 AR 1.836 1.000 2.578 1.993 2.873 3.753 1.000 2.364 3.374 2.878 2.000 1.818 2.528 2.000 1.709 3.095 2.300 FIS 0.373 Ͳ 0.019 Ͳ0.025 0.044 Ͳ0.016 Ͳ 0.059 0.198 Ͳ0.157 0.338 Ͳ0.051 0.094 0.127 Ͳ0.067 Ͳ0.111 N 33 33 33 32 34 34 34 34 34 34 34 34 32 32 33 33 33.3

185 Appendix 3 : P-values for pairwise colony comparisons calculated with a non-parametric equivalent of Tukeys HSD for multiple comparisons.

Variable Comparison Dur Interdur Peak1 Peak2 Peak3 RAMP1 RAMP2 Q25 Q50 Q75

Lewis-Liguanea 0.1214 0.9666 0.9509 0.9849 0.9969 0.7206 0.9999 0.0260 0.0171 0.0001 Lewis-Olive 0.0001 0.7150 1.0000 0.2243 0.0101 0.8384 0.9567 0.6698 0.8741 0.9889 Lewis-Blefuscu 0.5956 0.0001 0.0130 0.8458 0.8735 0.9965 1.0000 0.9618 0.9973 0.3123 Lewis-Lilliput 0.3647 0.9996 0.9708 0.0065 0.9245 0.8566 0.9550 0.2786 0.4426 1.0000 Lewis-NF 0.0001 0.0001 0.0001 0.1154 0.8895 0.1023 0.3558 0.1398 0.9997 0.0100 Lewis-Beagle 0.0001 0.0001 0.9626 0.9941 0.9988 0.7814 1.0000 0.9996 0.4141 0.9819 Liguanea-Olive 0.0001 0.9999 0.9304 0.5943 0.0020 0.0681 0.9958 0.0001 0.0007 0.0001 Liguanea- 0.5738 0.9708 0.9997 0.2130 0.1411 0.0013 Blefuscu 0.0006 0.0140 0.4765 0.9966 Liguanea-Lilliput 0.9926 0.9971 0.5665 0.0316 0.6774 0.1065 0.9937 0.0001 0.0001 0.0001 Liguanea-NF 0.0007 0.0001 0.0001 0.3786 0.6179 0.0017 0.5865 0.0001 0.2545 0.1776 Liguanea-Beagle 0.0005 0.0001 0.5331 0.6587 0.9375 1.0000 0.9992 0.0546 0.0001 0.0001 Olive-Blefuscu 0.0001 0.0009 0.0061 0.9403 0.3433 0.4744 0.9302 0.1217 0.6269 0.8170 Olive-Lilliput 0.0001 0.9444 0.9747 0.7350 0.3631 1.0000 1.0000 0.9913 0.9942 0.9860 Olive-NF 0.9999 0.0001 0.0001 0.9999 0.4520 0.6934 0.8648 0.8864 0.8220 0.1105 Olive-Beagle 0.9987 0.0001 0.9670 0.0213 0.0487 0.0948 0.9134 0.3596 0.9954 0.7381 Blefuscu-Lilliput 0.0001 0.0001 0.0009 0.1756 1.0000 0.5314 0.9308 0.0264 0.2333 0.3309 Blefuscu-NF 0.0001 0.0001 0.0001 0.8242 1.0000 0.0267 0.3066 0.0136 1.0000 0.8320 Blefuscu-Beagle 0.0001 0.0001 0.0005 0.3428 0.9882 0.9827 1.0000 0.9972 0.2115 0.0648 Lilliput-NF 0.0001 0.0001 0.0001 0.8738 1.0000 0.8021 0.9365 0.9980 0.4723 0.0134 Lilliput-Beagle 0.0001 0.0001 1.0000 0.0002 0.9951 0.1376 0.9142 0.1045 1.0000 0.9917 NF-Beagle 0.9999 0.6863 0.0001 0.0067 0.9890 0.0026 0.2953 0.0516 0.4647 0.0010

186 Appendix 4Paternity assignments and reproductive success across colonies and seasons for 76 males sampled from Olive, Lilliput and Blefuscu Islands

Paternity at Olive Paternity at Lilliput Paternity at Blefuscu Total No. of Male Sample site Sample year 2009 2010 2009 2010 2009 2010 paternities OLM1 Olive Is 2009 3 2 5 OLM3 Olive Is 2009* 1 1 2 OLM4 Olive Is 2009 1 1 2 OLM5 Olive Is 2009 2 2 1 5 OLM6 Olive Is 2009 1 1 1 3 OLM8 Olive Is 2009 1 1 OLM9 Olive Is 2009 2 2 OLM10 Olive Is20091 3 1 1 6 OLM11 Olive Is 2009 4 4 OLM12 Olive Is 2009 2 2 OLM14 Olive Is 2009 2 2 1 5 OLM15 Olive Is 2009* 1 1 2 OLM16 Olive Is 2009* 1 2 3 OLM17 Olive Is 2009* 1 1 OLM18 Olive Is 2009 1 1 1 3 OLM21 Olive Is 2009* 1 1 OLM22 Olive Is 2009 1 1 OLM23 Olive Is 2009 2 2 4 OLM25 Olive Is 2009* 1 1 OLM26 Olive Is 2009* 2 2 OLM27 Olive Is 2009 2 2 OLM28 Olive Is 2009 1 1 OLM29 Olive Is 2009 1 1 1 3 OLM2 Ͳ10 Olive Is 2010 1 3 4 OLM3 Ͳ10 Olive Is 2010 1 1 OLM4 Ͳ10 Olive Is 2010 3 3 OLM5 Ͳ10 Olive Is 2010 1 1 2 OLM6 Ͳ10 Olive Is 2010 2 2 OLM7 Ͳ10 Olive Is 2010 2 1 3 OLM8 Ͳ10 Olive Is 2010 1 3 4 OLM9 Ͳ10 Olive Is 2010 1 1 OLM10 Ͳ10 Olive Is 2010 1 1 OLM15 Ͳ10 Olive Is 2010 1 1 2 OLM18 Ͳ10 Olive Is 2010 1 2 1 4 OLM19 Ͳ10 Olive Is 2010 1 1 OLM25 Ͳ10 Olive Is 2010 1 1 OLM26 Ͳ10 Olive Is 2010 1 1 2 OLM28 Ͳ10 Olive Is 2010 1 1 1 3 OLM30 Ͳ10 Olive Is 2010 1 1 2 LILM1 Lilliput Is2009 1 4 2 1 8 LILM5 Lilliput Is 2009 1 1 LILM6 Lilliput Is 2009 1 1 LILM8 Lilliput Is 2009 2 2 LILM11 Lilliput Is 2009 1 1 LILM12 Lilliput Is 2009 3 2 5 LILM14 Lilliput Is 2009 1 2 3 LILM15 Lilliput Is 2009* 1 1 2 LILM16 Lilliput Is 2009* 2 1 3 LILM18 Lilliput Is 2009 1 1 2 LILM2 Ͳ10 Lilliput Is 2010 2 2 LILM3 Ͳ10 Lilliput Is 2010 1 1 1 3 LILM5 Ͳ10 Lilliput Is 2010 1 1 LILM6 Ͳ10 Lilliput Is 2010 1 1 LILM7 Ͳ10 Lilliput Is 2010 1 2 1 4 LILM9 Ͳ10 Lilliput Is 2010 1 1 LILM12 Ͳ10 Lilliput Is 2010 1 1 LILM13 Ͳ10 Lilliput Is 2010 1 1 LILM15 Ͳ10 Lilliput Is 2010 1 1 BLEM2 Blefuscu Is 2009 1 1 2 BLEM3 Blefuscu Is 2009 2 2 BLEM4 Blefuscu Is 2009 1 1 1 3 BLEM5 Blefuscu Is 2009 3 1 4 BLEM8 Blefuscu Is 2009 1 1 BLEM13 Blefuscu Is 2009 2 2 BLEM14 Blefuscu Is 2009 1 1 BLEM16 Blefuscu Is2009* 1 2 1 4 BLEM17 Blefuscu Is 2009 2 2 BLEM19 Blefuscu Is 2009 1 1 1 3 BLEM20 Blefuscu Is 2009 1 1 BLEM24 Blefuscu Is 2009 2 2 BLEM4 Ͳ10 Blefuscu Is 2010** 1 1 1 3 BLEM7 Ͳ10 Blefuscu Is 2010 1 1 2 BLEM8 Ͳ10 Blefuscu Is 2010 1 1 2 BLEM9 Ͳ10 Blefuscu Is 2010 1 1 2 BLEM11 Ͳ10 Blefuscu Is 2010 1 1 2 BLEM16 Ͳ10 Blefuscu Is 2010 1 1 TOTAL 44 48 22 23 24 18 179 *Sighted and sampled also in 2010, but was removed from the pool of males to avoid repeated genotypes ** Sighted and sampled also in 2009, but was removed from the pool of the males to avoid repeated genotypes

187 APPENDIX 5: Playback experiments on four Australian sea lion

colonies – Can males perceive geographic vocal variations?

188 Design of the playback experiments

Based on our previous study (Attard et al. 2010), we decided to perform playback experiments on four different colonies of Australian sea lions to assess if males can perceive observed geographic acoustic differences: Olive Island (SA), Liguanea Island (SA),

Lewis Island (SA) and Beagle Island (WA). We hypothesized at the beginning of my PhD that acoustic variations in males’ calls would be higher between distant colonies than within close colonies.

For each colony, we tested local males with three series of males’ calls: one series composed of calls from local males, one series with calls from a distant colony, and one series with calls from the closest colony. To avoid pseudo-replication (McGregor 1992), each stimulus was used only once and the order of presentation for local and foreign call series were randomised. The loudspeaker was placed 7 –8 m from the tested male, and the broadcast level adjusted to approximately natural amplitude to simulate a natural vocal behaviour. Playbacks were initiated when the target male was in a relaxed state

(i.e., laying down and not interacting with other conspecifics). Males were given a minimum 5 min gap between stimuli. A playback session was excluded from analysis if the subject was disturbed by another animal during the playback.

For each playback series, the behavioural responses of tested males were observed for 60 s from the beginning of the playback series: latencies (look, vocalize, for posture change, approach) and distance to approach. A principal components analysis (PCA) was then performed on these five behavioural measures (McGregor 1992). The PCA scores of the

189 first principal component (PC1) were then compared using Wilcoxon matched-pair tests to determine whether responses differed between the playback stimuli.

Tested colonies

Test colony Distant colony Closer colony Olive Is Kangaroo Is (KI) Lewis Is Liguanea Is Olive Is Lewis Is Lewis Is Olive Is Liguanea Is Beagle Is Olive Is none * Beagle Is was only tested with one distant colony (SA) as we did not have calls from any other Western Australia colonies at this stage of the experiment.

Assignments of male Australia sea lion barking calls - based on a discriminant function analysis (DFA) performed on 10 acoustic parameters (Chapter 5)

Bark Allocations (n) Colony Lewis Liguanea Olive Blefuscu Lilliput NF Beagle N calls Lewis 70 21 24 38 10 16 21 200 Liguanea 27 39 23 39 1 18 13 160 Olive 23 22 86 28 7 14 20 200 Blefuscu 53 44 18 65 11 7 2 200 Lilliput 29 25 33 25 22 16 10 160 NF 16 8 12 7 8 54 35 140 Beagle 40 2 8 1 1 17 111 180 % correct 35 24 43 33 14 39 62 Tested colonies in yellow, stimuli colonies in red.

190 Results

OLIVE ISLAND

Wilcoxon Matched Pairs Test

TEST Valid N T Z p-level Olive & Lewis 14 17 2.229 0.026 Olive & KI 14 9 2.731 0.007 Lewis & KI 14 30 1.412 0.16 Highlighted results are significant at p <0.05

In summary:

Olive Males reacted more strongly to local males in comparison to calls from the two other colonies.

The percentage of calls correctly assigned to Olive Island was high 43% and calls from Olive Island do not mix much with Lewis Island (23/200). Kangaroo Island (KI) calls were not included in DFA performed in Chapter 5.

The high variation between calls clearly explains the high discrimination between colonies’ calls.

191 BEAGLE ISLAND

Wilcoxon Matched Pairs Test

TEST Valid N T Z p-level Beagle & Olive 15 22 2.158 0.031 Highlighted result is significant at p <0.05

In summary:

Beagle Is males reacted strongly to local males and reactions to Olive males calls (foreign) were significantly lower.

The percentage of calls correctly assigned to Beagle Is was very high 62% and Beagle calls were misclassified to Olive males with a very low proportion (8/180).

Such acoustic differences can clearly explain the discrimination between calls.

192 LIGUANEA ISLAND (LIGGY)

Wilcoxon Matched Pairs Test

TEST Valid N T Z p-level Liguanea & Lewis 9 13 1.125 0.260 Liguanea & Olive 9 11 1.362 0.173 Lewis & Olive 9 18 0.533 0.594 No significant differences p >0.05

In summary:

Liguanea males reacted more strongly to local males but their responses to calls from the other two colonies were also high.

Misclassification rates of Liguanea calls to other colonies were higher in comparison to e.g. Olive Island and Beagle Island (bark allocation table). The male barking call might not have enough differences (vocal overlapping with other colonies) and this might explain why Liguanea males responded in the same way to local calls and Lewis Is and Olive Is calls. In spite of the different geographic range, calls seem to sound “similar”. Another point to consider is that only 9 males were tested, and some additional males would have given more significant trends.

193 LEWIS ISLAND

Wilcoxon Matched Pairs Test

TEST Valid N T Z p-level Lewis & Liguanea 12 13 1.478 0.139 Lewis & Olive 12 31 0.178 0.859 Liguanea & Olive 12 15 1.600 0.110 No significant differences p >0.05

In summary:

Lewis males reacted in the same way to local calls than to calls from the two other foreign colonies.

The percentage of calls correctly assigned to Lewis Is was relatively high (35%) and calls were not misclassified to Liguanea and Olive with a high rate (21/200 and 24/200 respectively). However, Liguanea calls can be misclassified with a high proportion to both Lewis and Olive calls, so there are some vocal overlaps inducing confusion that can potentially explain the observed non-discrimination.

194 Furthermore our preliminary study was done on Lewis Island and in this occasion Lewis males clearly differentiated between local and foreign call (Kangaroo Island). This indicates that when foreign calls have clear differences, the perception of vocal variation is possible for Lewis Island males.

Conclusion: In a general manner, Australian sea lion males do perceive the acoustic differences of calls from different colonies, and such discrimination is clearly explained by a vocal differentiation between colonies. Some colonies are more and less acoustically similar than others, and in these situations, males respond similarly to calls from local and non- local colony. Further research is needed to investigate why some colonies sound more similar and how these differences can impact on the Australian sea lion male breeding behaviour?

195 APPENDIX 6: What Makes an Australian Sea Lion (Neophoca

cinerea) Male's Bark Threatening?

Journal of Comparative Psychology

196

197 198 199 200 201 202 203 204

205

Time to thank, salute and sign off……..

206 Titre Structure de population et système de reproduction chez le lion de mer Australien ( Neophoca cinerea )

Résumé Le lion de mer Australien a un cycle de reproduction non-annuel et asynchrone entre les colonies. Contrairement aux autres pinnipèdes, ce système unique offre l’opportunité aux mâles de se reproduire dans plusieurs sites lors d’une saison de reproduction. L’accès des m âles à plusieurs sites de reproduction pourrait contrecarrer le fort degré de structure génétique de population chez les femelles dûe à une fidélité extrême au site de reproduction. J'ai utilisé deux méthodes indépendantes mais complémentaires, moléculaire et acoustique , afin d’ étudier la structure de la population et le système de reproduction. Pour l'analyse moléculaire, j'ai développé une banque de microsatellites spécifiques à l'espèce. Ces marqueurs ont été utilisés pour examiner le flux génétique des mâles dans les différentes colonies de reproduction, le succès reproducteur, et les taux de paternité inter- et intra-colonies. De plus, j'ai mesuré la variation géographique dans les cris des mâles, ce qui représente une approche alternative pour mesurer les convergences/divergences entre colonies. J'ai montré que si les mâles présentent une certaine dispersion entre colonies, elle est limitée à une courte échelle en dépit de l’opportunité des mâles de se reproduire dans différents sites. Les analyses acoustiques des cris de mâles ont révélé des variations géographiques significatives entre colonies, qui ne reflètent pas la structure génétique. Les analyses de paternité indiquent un faible taux de polygynie, la majorité des mâles ne produisant qu’un ou deux jeunes par saison. Des stratégies alternatives de reproduction (nomade ou sédentaire) sont présentes dans cette espèce, certains mâles se déplaçant activement entre différentes colonies proches. Le système de reproduction unique du lion de mer Australien semble fortement influer à la fois les modalités de dispersion, mais aussi la structure de population et le système de reproduction.

Mots clés Structure génétique, flux de gènes, paternité, succès reproducteur, comportement vocal, structure acoustique, Neophoca cinerea

Title Population structure and mating system of the Australian sea lion ( Neophoca cinerea )

Abstract The Australian sea lion has a non-annual and asynchronous breeding cycle across geographically close colonies. In contrast to other pinnipeds, this unique reproductive system provides the opportunity for males to breed in different colonies during one breeding cycle. Male mating success across different colonies could counteract the high degree of structure driven by extreme site fidelity in females. I used two, independent but complementary methods, molecular and acoustic to investigate their population structure and mating system. For molecular analysis I developed a species-specific microsatellite library. These markers were used to examine the extent and rate of male mediated gene-flow across breeding colonies but also to determine the breeding success and paternity both within and across spatially close colonies. Also, I investigated the geographical variation in male barking call. This represents an alternative approach to measure boundaries and relationships between colonies. Males exhibit dispersal; however, this is limited to remarkably small scale in regards to the high potential for dispersal and opportunity to breed in different colonies. Acoustic analyses of the male barking calls revealed significant geographical variation across sites; however this observed acoustic variation did not reflect the genetic structure. Paternity analyses revealed that males display relatively modest rates of polygyny with the majority of successful males siring only one or two pups per breeding cycle. The presence of alternative mating strategies (roaming vs staying) is apparent in this species with some males actively moving and breeding between close colonies. It appears that the unique breeding biology of Australian sea lion influences dispersal patterns, population structure and mating system.

Keywords Genetic structure, gene flow, paternity assignment, reproductive success, vocal behaviour, acoustic structure, Neophoca cinerea

Intitulé et Adresse du laboratoire Centre de Neurosciences Paris Sud, CNPS - UMR 8195, Université Paris Sud, Bâtiment 446, 91405 Orsay, France