Social and Behavioral Responses to Environmental Stressors in Bottlenose Dolphins in Shark Bay, Australia

SOCIAL AND BEHAVIORAL RESPONSES TO ENVIRONMENTAL STRESSORS IN BOTTLENOSE DOLPHINS IN SHARK BAY, AUSTRALIA

A Dissertation submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biology

Madison Layne Miketa, Sc.B.

Washington, DC August 10, 2018

ii SOCIAL AND BEHAVIORAL RESPONSES TO ENVIRONMENTAL STRESSORS IN BOTTLENOSE DOLPHINS IN SHARK BAY, AUSTRALIA

Madison Layne Miketa, Sc.B.

Thesis Advisor: Janet Mann, Ph.D.

ABSTRACT

Animals experience a variety of natural and anthropogenic environmental stressors, and responses to such stressors can impact fitness. Bottlenose dolphins are long-lived, socially complex, and behaviorally flexible. I examined how individuals behaviorally adjust in response to such stressors and whether long-term bonds might be a mitigating factor.

One such stressor for dolphins and whales is maternal care, where the mother must dive to find food, leaving her less able calf at the surface. Chapter 1 investigates mother-calf diving behavior in wild bottlenose dolphins, showing that while calves increased their dive durations with age, mothers modified their behavior according to calf sex and distance from their calves in a way that favored female calves. This is consistent with sex-specific socio-ecological strategies, where vertical learning is more important for female than male calves. Mothers are affording daughters more direct experience of her foraging behavior than sons.

Another stressor is human impacts, especially fishing gear entanglement. In a rare observation, Chapter 2 shows how a dolphin behaved before, during entanglement and after she was free from the fishing line. During entanglement, the dolphin decreased association and social behavior with others, decreased foraging and increased in traveling and erratic behavior.

Chapter 3 quantifies the paradoxical impact of a major seagrass die-off on dolphin behavior, where dolphins increased their use of seagrass habitat following the die-off. Seagrass specialists shifted habitat use and foraging from their preferred seagrass species to the less

iii preferred, but less damaged species. Calf survival and birthrate did not decrease post-die-off, but there may longer-term impacts if prey abundance decreases.

In the first detailed examination of female-female bonds, Chapter 4 explores the temporal dynamics and factors driving such bonds. Most females maintained strong long-term bonds for

4-18 years, and was associated with calving success. Partner similarity, degree of alter, and bond strength were important for the maintenance of ties, while friends-of-friends and age difference were important for the creation of ties.

Long-term datasets on long-lived, socially complex mammals are rare, but invaluable for understanding how individuals respond to a changing environment and stressful events.

iv My dissertation is dedicated to my friends and family for all of their love and support. And specifically, this dissertation is in loving memory of my Papa.

My sincere gratitude, Madison

v ACKNOWLEDGEMENTS

I have been extremely fortunate to work with so many amazing people and animals and in such a beautiful part of the world over the last 5 years. It’s hard to put into words what a special experience this has been. There are many people who have supported me along the way and helped me get to this point. I am forever grateful for all of them.

I would like to thank my thesis committee. Dr. Gina Wimp, Dr. Shweta Bansal, and Dr.

Lisa Singh were incredibly helpful throughout my PhD and this work would not have been possible without them. All of you made my chapters better by asking thoughtful and challenging questions. You also helped me put my work into perspective and see the bigger picture. I cannot tell you how much I appreciate all of your support and positivity during this process.

This would work not have been possible without all of the hard work and dedication from present and past members of the Shark Bay Dolphin Research Project. Not only are all of you my colleagues that helped push me to be better, but you are also great friends and my life in DC would not be the same without you. Dr. Ewa Krzyszczyk, you hosted me on my very first visit to

Georgetown and introduced me to the project. You taught me so much about cetaceans, photo-

ID, fieldwork, and you were always keen to have thoughtful discussions when I was stuck on problem. You’re such a loving and thoughtful friend; you always bring the lab together and celebrate all of our little victories (and of course, birthdays). I’m so glad that we finally got to share a boat this last field season! Dr. Eric Patterson, your enthusiasm and open door policy made you my sounding board for all things science (especially statistics). You taught me how to use R and QGIS, you taught me about statistics, and you trained me in the field – I always viewed you as my secondary advisor. I cannot thank you enough for your kindness, your support,

vi and everything you taught me during my time at Georgetown. Dr. Maggie Stanton, you’re a social networking whiz and I thank you so much for your assistance with my social bonds chapter. You were always so patient during our conversations and would fix up my code at lightening speeds. You also provide fantastic insights and draft edits, I’m so grateful that you agreed to be part of my project. That chapter would not have been possible without all of your assistance. Dr. Meg Wallen, it was so fun to watch you grow as a person and a scientist and I’m so glad we developed such a strong friendship. I appreciate all of the times that you were willing to stop what you were doing and talk things through with me (which was often towards the end!).

Thank you for all of your advice and sharing your experiences with me throughout the process.

Caitlin Karniski, we started in the lab at nearly the same time (although in different roles) and it’s been a lifesaver to have you by my side these last 5 years. I know that I can always come to you with anything and that we can always have a good laugh. You’ve been such a great friend during this process and I’m so excited to watch you defend next year. Vivienne Foroughirad, you’re such a badass and an inspiration. Your coding skills and knowledge of basically everything are insanely impressive. Thank you for always being open to have work conversations and provide coding assistance over the last five years – and of course all of the laughs. You’re going to be amazing at your defense and I hope I get to welcome you into the family one day ;). Anni Jacoby, you’ve been so supportive and fun during this process. You’ve grown so much and it’s been awesome to see you thrive on the Potomac Project. Jillian Wisse, I remember the days that we were recording lemur copulations together…and I’d like to think that

I brought you into the marine mammal world. It’s been so fun to watch you blossom and forge your own path. Lex Levengood, we only had one field season together, but it was a fun one! I’m glad we had that time together and I hope we get to work together again in the future. Taylor

vii Evans, it’s been so fun having you in the lab and I love that you encourage more lab gatherings. I think your project is really fascinating and I’ve had so much fun learning about your work since you joined the lab. Molly McEntee, I’m glad we got some time together in the field this past season and bonded over iced beverages. I’m excited to see where your PhD takes you! Desirae

Cambrelen, you make me laugh harder than almost anyone I know. You are such a joy to have around and I can’t wait to see you kick ass in your Masters program. I sincerely hope we get to work together on animal welfare issues one day. Margaret Stebbins and Lena Bichell, you were the best field assistants I could have asked for, especially for my first solo field season. Thank you both so much for your unwavering support and all of the amazing memories.

Thank you also to the entire Georgetown Biology Department for all of your support these last 5 years. It’s been a wonderful environment to work in and I appreciate all of your friendly faces. I would like to especially thank Zach Batz for his friendship – you were always there to commiserate and provide endless laughs. I would also like to thank the Bansal Lab for their support and friendship.

I’d like to thank everyone in Monkey Mia who has befriended us and helped us without hesitation. That includes, but is not limited to, the DBCA rangers, Harvey and the Shotover crew,

Grant Brown, and Monkey Mia Resort staff. I would like to especially thank Emily Ward for her love and friendship. You are so loving and kind and we all love you so much.

There are also a number of people from my undergraduate career who I need to thank.

First, Dr. Ruth Colwill, you really cemented this path for me in your Animal Minds seminar during my first semester at Brown. I was absolutely enthralled by the research on animal cognition and behavior and I knew that was what I wanted to study. You then continued to mentor me, introduced me to so many fascinating topics, and taught me how to quantitatively

viii measure animal behavior. Thank you for believing in me and setting me on this path that I’ve continued ever since. Dr. Dov Sax, you helped me learn how to do research and taught me all about conservation biology. You were such a kind and supportive mentor, and have continued to be one even after I graduated. Thank you for all of your help getting me where I am today.

Finally, Dr. Jamie Seymour, you probably wish I didn’t show up at your office door that fateful day 9 years ago because I’ve been pestering you ever since. Seriously though, you are such a great person and I feel so fortunate that you took me in and have continued to advise me over the years. Thank you for everything you’ve done for me.

To all of my friends outside of this field who have supported me through this process, thank you. Emily Bolt, you’ve been my best friend for the last 20+ years and at this point, you’re more like family. Thank you for your constant love and friendship and for always reminding me what is important in life. Hayley Faba-Sack, Jackie Booker, and Lindsey Ronis, you three keep me going with your love and thoughtfulness. Somehow we’ve made long distance friendship work and I’m so grateful we all meet 9+ years ago in Cairns. We’ve shared so many important life events together, but you guys are also there daily via group text. Thanks for being such great friends to me. Michaela Seigo, you’ve gone from my college teammate to my grad school friend and it’s been really fun to make that transition. I love that we’re finally living near one another – it’s always great when we get to stop in and see each other. I’ve also loved going through this process with you as you’ve become a Doctor. I’m so proud of you!

Anyone who knows me knows how important my family is to me. You all mean the world to me and I cannot thank you enough for everything you’ve given me. You’ve always supported my dreams and encouraged me to go after everything I’ve ever wanted. You’re always there for me for the ups and the downs and you ALL make me laugh so hard. I’ve learned so

ix much from each and every one of you -- you’ve all made me a better person. Nana, how could I not succeed with such a strong and smart role model; plus your undying love, support, and encouragement. Mom and Dad, you’re also my best friends. You’ve given me the world and made sure that I had every opportunity to chase my dreams. You love me no matter what and you’re always there to support me. Pam, Debbie, Thurmond, Grantham, and Hadley, all of you mean so much to me. And all of you have given me so much love and enthusiasm throughout my life. I’m so lucky to have a family like you all. And finally thank you my Papa who was always my biggest fan. He taught me my first word and encouraged me to be anything I wanted (which was always a marine biologist). I know he was so proud of my when I started this process and I wish more than anything that he could be here now.

And finally, thank you to my advisor, Dr. Janet Mann. Thank you for giving me this incredible opportunity. It has been such an honor to be part of the Shark Bay Dolphin Research project. Thank you for all of your guidance and support during this process. You’ve shown me what it means to be a scientist and your devotion to the project is inspiring. I feel so fortunate to have had this opportunity and learn from you.

x TABLE OF CONTENTS

INTRODUCTION ...... 1

CHAPTER I. CALF AGE AND SEX AFFECT MATERNAL DIVING BEHAVIOR IN

SHARK BAY BOTTLENOSE DOLPHINS ...... 6

Introduction ...... 6

Methods...... 10

Study Site and Population ...... 10

Data Collection and Subjects ...... 11

Statistical Analysis ...... 13

Ethical Note ...... 15

Results ...... 15

Calf Diving Development ...... 15

Mother–Calf Proximity and Calf Dives ...... 16

Mother–Calf Proximity and Calf Dives during Maternal Foraging...... 16

Mother–Calf Proximity and Maternal Dives ...... 17

Mother–Calf Proximity and Maternal Foraging Dives ...... 17

Discussion ...... 18

Figures and Tables ...... 25

CHAPTER II. BEHAVIORAL RESPONSES TO FISHING LINE ENTANGLEMENT OF A

JUVENILE BOTTLENOSE DOLPHIN IN SHARK BAY, AUSTRALIA ...... 33

Introduction ...... 33

xi Methods...... 35

Study Site ...... 35

Study Subject ...... 35

Data Collection ...... 36

Results ...... 36

Group Composition ...... 36

Activity Budget ...... 37

Discussion ...... 37

Figures and Tables ...... 41

CHAPTER III. THE BEHAVIORAL AND REPRODUCTIVE IMPACTS OF A SEAGRASS

DIE-OFF ON AN APEX PREDATOR, THE BOTTLENOSE DOLPHIN ...... 45

Introduction ...... 45

Materials and Methods ...... 49

Study Site and Population ...... 49

Study Subjects and Data Collection ...... 50

Quantifying Die-off Impact ...... 52

Data Processing ...... 53

Statistical Analyses ...... 57

Results ...... 58

Habitat Use and Foraging Behavior ...... 58

Foraging Tactics...... 61

Calving Rate and Calf Survival ...... 61

xii Discussion ...... 62

Habitat Use and Foraging Behavior ...... 63

Foraging Tactics...... 66

Reproduction and Fitness ...... 66

Ecological Implications ...... 67

Further Implications and Consequences ...... 69

Figures and Tables ...... 71

CHAPTER IV. STABLE FEMALE SOCIAL BONDS ARE ASSOCIATED WITH FITNESS IN

WILD BOTTLENOSE DOLPHINS...... 77

Introduction ...... 77

Methods...... 80

Data Collection and Study Subjects ...... 80

Association Index...... 82

Top Associates ...... 83

Reproductive Success ...... 85

Stochastic Actor-Oriented Model (SAOM) ...... 86

Results ...... 88

Top Associates ...... 88

Reproductive Success ...... 89

Stochastic Actor-Oriented Model (SAOM) ...... 90

Discussion ...... 91

Figures and Tables ...... 100

xiii

CONCLUSION ...... 107

APPENDIX A: Chapter I Supplemental Material ...... 110

APPENDIX B: Chapter III Supplemental Material ...... 111

APPENDIX C: Acknowledgements ...... 115

Chapter I...... 115

Chapter II ...... 115

Chapter III ...... 115

Chapter IV ...... 116

REFERENCES ...... 117

Introduction ...... 117

Chapter I...... 123

Chapter II ...... 134

Chapter III ...... 139

Chapter IV ...... 150

xiv LIST OF FIGURES

Figure I.2. Partial effects of age on calf dive duration during maternal foraging bouts for (a) female calves in contact with mothers, (b) male calves in contact with mothers, (c) female calves near mothers, (d) male calves near mothers and (e) calves far from mothers ...... 26

Figure II.1. EDE’s dorsal fin during the entanglement period (June 15, 2015) and after freed from entanglement (July 1, 2015) ...... 41

Figure II.2. Group composition for EDE before, during, and after the entanglement period ...... 42

Figure II.3. Activity Budget for EDE before, during, and after the entanglement period ...... 43

Figure III.1. Monkey Mia study area ...... 71

Figure III.2. Proportional number of sightings from survey data ‘pre’ and ‘post’ the 2011 marine heat wave controlling for search effort ...... 72

Figure III.3. Proportional seagrass use from focal follow data pre and post the 2011 marine heat wave for seagrass specialists (n=11): a) Proportion of time spent in seagrass, b) Proportion of foraging time spent in seagrass, c) Proportion of seagrass time spent foraging, d) Proportion of time spent bottom-grubbing in seagrass ...... 73

Figure III.4. General die-off measure for seagrass specialists: a) Proportion of time spent in seagrass, b) Proportion of time spent foraging in seagrass, c) Proportion of time spent bottom- grubbing in seagrass ...... 74

Figure III.5. Species-specific die-off measure for seagrass specialists: a) Proportion of time spent in seagrass, b) Proportion of time spent foraging in seagrass, c) Proportion time spent bottom- grubbing in seagrass ...... 75

Figure III.6. Species-specific selection ratio: a) Proportion of time spent in seagrass, b) Proportion of time spent foraging in seagrass, d) Proportion time spent bottom-grubbing in seagrass ...... 76

Figure IV.1. Networks for the four two-year periods used in the stochastic actor-oriented model ...... 100

Figure IV.2. Frequency of bond duration ...... 101

Figure IV.3. Distribution of top associates ...... 102

Figure IV.4. Half-weight index by minimum bond duration (due to censored data) ...... 103

Figure B1. Foraging tactics from survey data ...... 112

xvi LIST OF TABLES

Table I.1. Mother and calf diving parameter estimates from the Markov-chain Monte Carlo GLMMs for dive duration ...... 29

Table I.2. Mother and calf diving parameter estimates from the Markov-chain Monte Carlo GLMMs for dive duration during maternal foraging bouts ...... 31

Table II.1. Ethogram for a subset of common behavioral events ...... 44

Table IV.1. Description of stochastic actor-oriented model parameters ...... 104

Table IV.2. Summary data for four two-year periods used in the stochastic actor-oriented model...... 105

Table IV.3. Stochastic actor-oriented model results ...... 106

Table A1. Breathing rates of bottlenose dolphin mothers (N = 4) and calves (N = 4) collected in relatively shallow water (<6 m) in Monkey Mia ...... 110

Table B1. Common foraging tactics used by dolphins in Shark Bay ...... 113

xvii INTRODUCTION

Animals make decisions and adjust their behavior in response to various natural and anthropogenic stressors on a daily basis. Some stressors are associated with everyday life, such as finding a mate or caring for offspring, while others are more rare, such as habitat loss due to a disturbance or becoming entangled in human pollution. In response to these different stressors, individuals must adjust their behavior accordingly. However, these behavioral responses may result in a number of associated costs. Ultimately, behavioral changes can result in reduced fitness due to a number of factors, for example, decreased foraging or mating opportunities, or increased predation risk (e.g. Møller, 2012; Ozgul et al. 2010; Sinervo et al. 2010; Tuomainen &

Candolin, 2011). In response to these stressors, social animals may form strong and stable social bonds to help mitigate stress and lessen costs on fitness.

Dolphins are long-lived, large-brained, highly social mammals that exhibit great behavioral plasticity. They are widely distributed and found in a variety of habitats, which should require a greater ability to change behavior accordingly. Dolphins are also capable of social learning (Sargeant & Mann, 2009), flexible foraging (Mann & Sargeant, 2003; McFadden, 2003;

Nowacek, 2002), dynamic grouping patterns (Galezo et al. 2018; Gowans et al. 2007; Smolker et al. 1992), and innovation (Patterson & Mann, 2011, 2015), which suggest the ability to succeed in a changing environment. However, despite this flexibility, behavioral responses are likely associated with costs. Because dolphins are extremely social animals, they may rely on their social relationships to mitigate some of those costs.

First, I examined the behavioral responses to a stressor associated with maternal care.

Cetaceans face a dilemma in that they are born in a marine environment, where they must be able to swim to the surface in order to breathe air, but forage at depth. Although dolphin calves are

1 physically precocious at birth, they have limited swimming and diving capabilities. As a result, young calves are unable to dive as deep or as long as their mothers. This produces a challenge for mothers who need to remain young calves, but must also rapidly accelerate and dive in order to catch prey. Balancing these two needs results in a maternal tradeoff between diving and foraging, and remaining near calves.

In Chapter 1, I investigated the development of diving abilities throughout the calf period, and how calf diving development correlated with maternal diving behavior. Based on studies of captive dolphins, I predicted that calves would increase their dive durations with age, as their swimming and diving capabilities improved. In response to calf diving ability, mothers were predicted to shorten their dive durations accordingly, but only when calves were in close proximity. When mothers and calves were far from each other, mothers would not shorten their dive durations as a way to maximize their foraging and minimize costs.

Next, I examined the behavioral responses to a stressor associated with anthropogenic pollution. Marine debris and pollution, and specifically entanglement in discarded fishing gear, are major problems facing marine life worldwide. Negative effects of marine entanglement range from short-term physical injury and behavioral changes to prolonged impacts that may result in death. Most entanglement studies are based on necropsies or scarring (e.g. Neilson et al. 2009;

Wells et al. 2008). In contrast, very few have examined changes in cetacean behavior in response to entanglement (e.g. Mann et al. 1995; Wells et al. 1998; van der Hoop et al. 2014).

Chapter 2 follows a juvenile female dolphin before, during, and after a period of entanglement in fishing line. This study is unique in that not only did I observe an individual before entanglement, while entangled in fishing line, and after freeing herself from the fishing line, but this individual has also been closely monitored since birth. Thus, I used our long-term

2 dataset to provide a unique opportunity to examine the effects of entanglement on dolphin behavior in order to understand the behavioral impacts of anthropogenic pollution on a marine mammal.

Finally, I examined the behavioral responses to a stressor associated with a rare environmental disturbance. Climate is increasing the frequency and severity of extreme climatic events (IPCC, 2014). These disturbances can result in habitat loss and degradation due to negative impacts on habitat-forming species. Among these foundational species, seagrasses form some of the richest ecosystems on earth (Orth et al. 2006), however approximately 25% of seagrass species are classified as either ‘Threatened’ or ‘Near-Threatened’ (Short et al. 2011).

Seagrasses are threatened by climate change, among other things, and have a very slow recovery time (Meehan & West, 2000; Orth et al. 2006). Loss of seagrass habitat can result in bottom-up impacts that affect the entire ecosystem. Large, long-lived species that exhibit slow life histories such that they take many years to reach reproductive age and give birth to relatively few offspring, may be especially sensitive to environmental changes due to climate change (Cardillo,

2005; Foden et al. 2013; Jenouvrier, 2009; Visser, 2008).

Chapter 3 provided a unique opportunity to observe the impacts of a major disturbance on dolphin behavior and reproduction. Shark Bay, a nearly pristine environment with extensive seagrass beds, experienced a major seagrass die-off as the result of a marine heat wave. Because the Shark Bay Dolphin Research Project has been monitoring the residential population of bottlenose dolphins in Shark Bay for over 30 years, I was able to quantify the impacts of the heat wave on habitat use, foraging, and reproduction. I examined the 5 years pre and post die-off and expected to find a decrease in seagrass habitat use. I further examined a subset of dolphins termed “seagrass specialists,” due to their preferred use of seagrass beds prior to the heat wave,

3 and predicted that those individuals would be especially negatively impacted by the die-off. I hypothesized that seagrass specialists would decrease their use of seagrass habitat, have decreased foraging success in seagrass, use a seagrass-specific less, and switch to non-seagrass- specific foraging tactics. Additionally, I predicted that, due to resource stress, were would be a decrease in birth rate and an increase in calf mortality after the die-off.

In my last chapter, I examined the presence of strong, long-term social bonds as a potential mitigating factor to environmental stressors, such as those studied in Chapters 1-3. In many mammalian species, social relationships are associated with better health outcomes (Holt-

Lunstad et al. 2010; House et al. 1988; Sayal et al. 2002; Umberson & Karas Montez, 2010).

Specifically, in non-human animals, the quality and duration of female-female bonds have been correlated with lower stress (Engh et al. 2006; Wittig et al. 2008, 2016), increased lifespan

(Archie et al. 2014; Silk et al. 2010b; Thompson & Cords, 2018), and reproductive success

(Cameron et al. 2009; Silk et al. 2003). Shark Bay bottlenose dolphins (Tursiops aduncus), my study species, are bisexually philopatric, long lived, and live in a fission-fusion social structure.

Although studies have described long-term associations between males that are characterized as alliances (Connor & Krützen, 2015), much less is known about the stability of female bonds.

Stable female-female bonds may act to mitigate environmental stressors and provide numerous fitness benefits.

In Chapter 4, I examine the presence and variation and fitness consequences of long-term female-female social bonds, as well as the factors driving the formation and maintenance of bonds. Previous studies in Shark Bay found that females exhibit social variability and loosely associate with other females based on various preferences (Smolker et al. 1992; Gibson & Mann,

2008; Mann et al. 2000; Frère et al. 2010a). However, until this study, the duration of female-

4 female bonds had not been quantified. Here, I identified a female’s top female associates within a given 2-year time period using the Half-Weight Index (HWI). Then, I examined whether females had the same top associate(s) across time periods. I defined long-term bonds as those lasting across at least two time periods (4 years). I predicted that females maintained long-term bonds, likely with female kin and age-mates. I also predicted that females that maintained at least one long-term bond would have higher reproductive success than females that did not.

This work contributes to our understanding of how individuals, specifically of a long-lived, large brained, socially complex species, alter their behavior in response to environmental stressors.

Additionally, this dissertation examines the presence of strong long-term social bonds as a mitigating factor to various stressors. I predicted that Shark Bay dolphins would exhibit behavioral plasticity in their ability to adjust in response to environmental stressors, however, I still expected there to be costs associated with those behavioral changes. All of the chapters in this dissertation require long-term, in depth behavioral data and without the use of our unique longitudinal dataset, none of this work would have been possible.

5 CHAPTER I

Calf age and sex affect maternal diving behavior in Shark Bay bottlenose dolphins*

INTRODUCTION

In some iteroparous taxa, females provide extensive care to offspring lasting from months to years (e.g. marsupials, ungulates: Fisher et al. 2002; delphinids: Whitehead & Mann, 2000; primates: Campbell et al. 2011). While direct care increases offspring survival, such care can compromise resource acquisition (e.g. Thometz et al. 2016) resulting in trade-offs between maternal care and foraging (Royle et al. 2012). Females engage in multiple strategies of care designed to guard, hide, assist and protect offspring. Females that den or cache their offspring, such as birds (Royle et al. 2012), carnivores (spotted hyaenas, Crocuta crocuta: Boydston et al.

2006; African wild dogs, Lycaon pictus: van der Meet et al. 2013; Pallas’s cats, Otocolobus manul: Ross et al. 2010) and pinnipeds (Australian sea lion, Neophoca cinerea: Fowler et al.

2007; Steller sea lions, Eumetopias jubatu: Milette & Trites, 2003; harp seals, Pagophilus groenlandicus: Van Opzeeland et al. 2009), leave their young unguarded in relatively safe locations. In other species, females adjust their foraging patterns to remain in close proximity to their offspring. For example, Sardinian mouflon, Ovis orientalis musimon, shift to lower-quality foraging areas to reduce the costs of separation and predation (Ciuti et al. 2009). In the marine environment, female southern sea otters, Enhydra lutris nereis, with young pups exhibit

* Published: Miketa, M.L., Patterson, E.M., Krzyszczyk, E., Foroughirad, V., Mann, J. (2018). Mother-Calf Diving Behavior in Shark Bay Bottlenose Dolphins. Animal Behaviour, 137, 107- 117.

6 suboptimal diving behavior in comparison to adult females without pups, likely due to trade-offs between optimal foraging and pup care, as they spend more time at the surface attending to their pups (Thometz et al. 2016). Primates (Altmann & Samuels, 1992; Caperos et al. 2012), bats

(Ross, 2001) and marsupials (Fisher et al. 2002) typically carry offspring. Lastly, some females use a following strategy, such as ungulates (Fisher et al. 2002) and cetaceans (e.g. humpback whales, Megaptera novaeangliae: Szabo & Duffus, 2008; Tyson et al. 2012), in which offspring travel in close proximity to their mother. Each of these strategies imposes energetic costs because they impact maternal time budgets and foraging efficiency.

Female cetaceans combine following and carrying strategies in response to limited calf abilities in a marine environment. Immediately after birth, cetacean calves must be precocious enough to swim and reach the surface to breathe and to avoid predators as there are few places to safely hide. However, young calves have limited swimming and diving abilities. Thus, delphinid females ‘carry’ offspring in the form of echelon or infant position (Noren, 2008; Noren et al.

2008) in which the calf is parallel to or underneath its mother (Mann & Smuts, 1999; Mann &

Watson-Capps, 2005), allowing calves to gain the hydrodynamic benefits of drafting off their mothers, but exerting an energetic cost in terms of increased drag on the mother (Noren, 2008;

Noren & Edwards, 2011; Weihs, 2004). Cetaceans also use the following strategy since once offspring can travel independently (e.g. humpback whales: Szabo & Duffus, 2008; Tyson et al.

2012). In several species, adult cetaceans have been observed carrying calves on their backs

(Smultea et al. 2017). The most common example of this is during predatory attacks by killer whales, Orcinus orca, where humpback whale females physically carry their vulnerable calves on their heads or backs (Pitman et al. 2015).

7 For species such as cetaceans that use the following and carrying strategies, maternal foraging still adds an additional challenge, as mothers must typically separate from calves for brief periods in order to find and catch prey. Calves also begin to separate from their mothers well before weaning to develop foraging skills and meet their nutritional needs (bottlenose dolphins, Tursiops aduncus: Mann & Smuts, 1999; killer whales: Guinet & Bouvier, 1995; long- finned pilot whales, Globicephala melas: Gannon et al. 1997). Mysticete mothers such as humpback and grey whales, Eschrichtius robustus, also incur high travel costs during migration from breeding to the feeding grounds (Braithwaite et al. 2015; Christiansen et al. 2016;

Rodriguez de la Gala-Hernandez et al. 2008).

Like most cetaceans, bottlenose dolphin (Tursiops truncatus and T. aduncus) mothers face a number of costs by adopting the carrying or following strategy. Young calves have limited swimming, diving and breath-holding abilities. Bradycardia, an important reflex that slows the heart rate when the animal dives to conserve oxygen is not fully developed in bottlenose dolphins until approximately 3.5 years of age (Noren et al. 2004), close to the average weaning age (Mann et al. 2000). Breath-holding capability also develops with age as it is positively correlated with body size, with blood oxygen storage reaching adult capacity at age 3 (Noren et al. 2002; Noren & Williams, 2000). Calf swimming efficiency, measured as distance per stroke, also increases with age, tapering off around age 2 (Noren et al. 2006), enabling the calf to begin to travel, socialize, dive and hunt more independently (Mann & Sargeant, 2003; Sargeant &

Mann, 2009; Stanton et al. 2011). As a result, young dolphin calves are unable to dive as long, deep or often as their mothers. Thus females may face costs because their offspring are not as physically capable as adults and may hinder their mothers’ mobility, especially during maternal foraging bouts.

8 Some studies show that delphinid mothers spend more time at the surface to accommodate their young calves (e.g. bottlenose dolphins: Mann & Smuts, 1999; beluga whales,

Delphinapterus leucas: Heide-Jørgensen et al. 2001). Increased surface time imposes two major costs: decrease in swimming efficiency due to drag at the surface (Fish, 2000) and decrease in time spent foraging, especially if preferred prey species are found at depth. For example, lactating spotted dolphins, Stenella attenuata, shift from deep-water squid to less nutritional, surface-swimming flying fish presumably so they do not have to dive for long periods and leave their calves at the surface (Bernard & Hohn, 1989). Sperm whales balance foraging dives with calf care by staggering their deep foraging dives, presumably so at least one group member remains near the calf at the surface (Gero et al. 2008; Whitehead, 1996).

Despite extensive research on cetacean diving, no study has investigated how mothers and calves simultaneously change their diving behavior over the entire period of dependency and the implications for trade-offs between calf care and maternal foraging requirements. To investigate this trade-off, we examined the diving behavior of females and their calves from birth to weaning in bottlenose dolphins in Shark Bay, Australia, where females have an extended lactation period of 3–8 years per calf (Mann et al. 2000). We hypothesized that females shorten dives most when calves are young and mortality risk is highest (Mann et al. 2000), but as calves matured, both calf and maternal dive durations would increase accordingly. In addition, we predicted that mother–calf proximity would be an important factor; mothers were predicted to shorten dives most when calves were close and thereby able to nurse and benefit from their mother’s protection. When mothers and calves were separated, mothers were not expected to shorten their dives as this would not benefit the mother or the calf. These hypotheses assume that

9 mothers are balancing calf care when near offspring with maximizing foraging opportunities when far from offspring.

METHODS

Study Site and Population

The Shark Bay Dolphin Research Project has been collecting demographic, behavioral, genetic, ecological and life history data on Indo-Pacific bottlenose dolphins (T. aduncus) in

Shark Bay, Western Australia since 1984. The population is residential and bisexually philopatric (Tsai & Mann, 2013), such that individuals can be monitored from birth to death. The study site is a 300 km2 area in the eastern gulf of Shark Bay off Monkey Mia (25°47’S,

113°43’E). To date over 1600 individual dolphins have been monitored using photo- identification of unique dorsal fin markings and shape, and other distinctive features (Mann,

2000; Bichell et al. 2017). Sexes for dolphins in this study were determined by views of the genital area or by association with a calf (Smolker et al. 1992). We use the term calves here to refer to dependent offspring that are still nursing, or not yet weaned (Mann et al. 2000). Weaning age was determined by taking the midpoint between the last sighting of a calf in infant position or spending at least 80% of time with the mother, and when mother–calf association decreased to less than 50% (Mann et al. 2000). All calves in this study were of known age and sex. Birth dates were estimated from the last sighting of a mother before the birth of her calf to first sighting of a mother with the calf, as well as physical features of the calf such as fetal folds, behavior and body size (Mann et al. 2000). All birth dates were accurate to within a single birth season, and more than half of birth dates could be pinpointed to within a month. Ages used in models were to the estimated day of birth (in years). Calves ranged in age from less than 1 month to 3.8 years

10 old. We excluded calves older than 4 years of age but still nursing because of limited sample size.

Data Collection and Subjects

For this study, we used data from individual focal follows collected from 1989 to 2014.

Focal follows are detailed quantitative behavioral observations in which we observed individual animals and recorded activity, group composition and associated environmental data (location, habitat, depth, temperature, etc.) (Karniski et al. 2015). Prior to 1997, maternal and calf predominant activity was noted for each dive bout. After 1997, activity state was recorded using

1 min point sampling, but continuous dive data were still collected. Despite the different sampling methods, activity budgets are virtually identical (Mann, 1999). During mother–calf focal follows, mother–calf distance and dive bouts were recorded using continuous sampling. A dive bout indicates the length of a dive (in seconds) recorded as the time the dolphin dives under the surface to the time that the dolphin resurfaces. For each dive, we assigned the depth (range

2.00–12.50 m, mean ± SD = 7.28 ± 1.82 m) as the closest depth recording in time from the boat’s depth-sounder within 5 min of the dive onset (mean ± SD = 75.02 ± 48.36 s). Because we were interested in discrete diving rather than near-surface underwater behavior, we excluded dives by dolphins that were in water less than 2 m deep, the average body length of Shark Bay dolphins, or surfacing by dolphins within 20 s of their previous breath (N = 16). This 20 s cutoff point was determined based on breathing rates from calves and adults in relatively shallow water (<6 m depth) that were exhibiting surface behavior rather than deliberate dives (Appendix, Table A1).

At each surface interval or point sample, we also assigned mother–calf distance and activity state as the distance and activity observed prior to the onset of the dive. Mother–calf distance was estimated by distance codes that refer to ranges (≤30 cm, 30 cm–2 m, 2–5 m, 5–10 m, 10–20 m,

11 20–50 m, 50–100 m, >100 m, >200 m). Dives in infant or echelon position were included in the

≤30 cm distance category. Activity states of foraging, resting, socializing and travelling were assigned as in Karniski et al. (2015).

Because we were interested in mother–calf behavior, specifically the influence of calf proximity and age on maternal diving behavior, we analyzed dive bouts with regard to three mother–calf distance categories: contact, near, and far. We defined ‘contact’ as when mother and calf were in echelon position, infant position or ≤30 cm, ‘near’ as when mother and calf were

>30 cm and ≤10 m, and ‘far’ as when mother and calf were >10 m apart. We chose these categories because we define mother–calf separations as being >10 m (Mann & Smuts, 1999;

Smolker et al. 1993), which is consistent with dolphin spatial patterns in Shark Bay and our definition of association (in the same group) based on a 10 m chain rule (Smolker et al. 1992).

The 10 m chain rule tends to describe the general spatial grouping of dolphins in Shark Bay: individuals that are within 10 m of one another are often engaged in the same behavior and stay close for at least a short duration, whereas individuals farther than 10 m do not appear to be engaged in similar behaviors nor do they remain within any predictable distance of one another.

Thus, calves that are >10 m from their mothers are typically not in the same group as their mothers unless they are connected by other individuals. Although mothers and calves can likely hear each other even when they are hundreds of meters apart, visual contact is likely only in the contact and near categories (Smolker et al. 1993). There were more dives when mothers and calves were in contact (15,803) and near (8,097) compared to when they were far (3,488). When mothers and calves separated, researchers continued to record observations on both individuals until one of the pair was out of visual range. At this point, researchers continued to follow either the mother or the calf depending on a number of factors, such as whether the mother or the infant

12 was the predetermined focal animal, the visibility of the individual, or other data collection priorities. However, calves spend most of their time near their mothers and are only more than 10 m away on average 17% of the time (Gibson & Mann, 2008a).

We further specifically isolated foraging dives in order to investigate how mothers balance the trade-offs between calf care and foraging effort. Maternal foraging dives were characterized by frequent changes in direction, accelerations, irregular movement and intermittent prey catches (Karniski et al. 2015). For this subset of dives, we examined both the mothers’ dive durations and calves’ dive durations during maternal foraging bouts.

Statistical Analysis

We explored the effects of multiple predictors on calf and mother dive durations, which were treated as response variables in generalized linear mixed models (GLMM). For all models, dive durations were measured in seconds and age was a continuous variable measured in decimal years. In total, we ran 13 models under multiple conditions with two different response variables, mother dive duration and calf dive duration (detailed below). To construct these models, we used

R v.3.4.1 (R Core Team, 2017) and the package MCMCglmm (Hadfield, 2010), which employs a Bayesian approach to GLMMs using Markov-chain Monte Carlo simulations. We used a

Gaussian error distribution since the response variable was near normal and the Gaussian model fit our data most closely. We used the default weak priors (nu = 0, V = 1, alpha.mu = 0, alpha.V

= 0), 1,000,000 iterations, a burn-in of 3000 and a thinning interval of 10. Convergence and mixing were assessed after each model run using trace and posterior distribution plots. For all analyses, significance was set at α = 0.05.

Our first model examined how calf dive durations changed with age, regardless of the calf’s distance from its mother. Fixed factors included calf sex, calf age, their interaction, and

13 depth, and random factors included mother and calf identity. Based on results from this model

(further detailed in Results below) we included calf age in the maternal model as a proxy for calf diving abilities. We included calf age rather than actual calf dive duration for two reasons. First, if dives are paired, for example when mother and calf are in infant or echelon position, their dive durations will be the same and thus no longer independent from one another. Second, since we were interested in how mothers adjust their diving behavior in response to overall calf development, including swimming ability and independence, not just diving ability, calf age is perhaps more informative. As calf age and dive duration were highly correlated, we only used calf age, rather than calf dive duration, when modelling maternal dive bouts.

Next, we ran 12 models to examine the mother–calf diving relationship under multiple conditions. For each, the response variable was either calf dive duration or maternal dive duration, fixed factors were calf sex, calf age, their interaction, depth, and random factors were mother identity and calf identity. We ran three models (contact, near, far) with calf dive duration as the response and three models (contact, near, far) with the mother dive duration as the response. We also ran the same six models (calf dive duration: contact, near, far; mother dive duration: contact, near, far), for maternal foraging bouts only. Since social factors may influence mother and or calf diving behavior, we included group size as a fixed factor in all of our models; however, effects of group size on maternal dive duration were nonsignificant (P > 0.1) and there was no evidence of mothers adjusting dive duration based on group size, so we removed this variable from our analyses. Furthermore, foraging is a relatively solitary activity. Out of our data set, 60% of dives and 87% of foraging dives occurred when mothers and calves were alone. For all models, if there was a significant calf age*sex interaction, we ran separate models for each sex. We examined variance inflation factors, which indicated that collinearity between calf age

14 and depth was not a major concern (VIFs <2). That is, calves did not necessarily spend more time in deeper water with age.

Ethical Note

This work was approved by the Georgetown University Animal Care and Use Committee

(permits 07-041, 10-023, 13-069), the West Australian Department of Biodiversity, Conservation and Attractions (DBCA) (permits SF009311, SF008076, SF009876) and the University of

Western Australia (animal ethics permit 600-37). The study was entirely observational and care was taken when approaching and following animals (e.g. to not approach the dolphins erratically or too closely (50 m) and to drive alongside rather than behind the dolphins).

RESULTS

Our data set included 27,388 dive bouts, 13,586 from mothers (N = 26; 754 focal hours) and 13 802 from calves (N = 41; 782 focal hours). Of the 13,802 dives from calves, 7,231 dives were from female calves (N = 20) and 6,571 dives were from male calves (N = 21). The number of dives per calf ranged from 7 to 1,761 (mean ± SD = 336.6 ± 346.2). The number of dives per mother ranged from 7 to 2,206 (522.5 ± 484.9). The subset of maternal foraging dives included

5,274 calf dives and 5,255 maternal foraging dives for 37 calves and 26 mothers.

Calf Diving Development

First, we analyzed all calf diving data to identify the relationship between calf age and dive duration. We found a significant interaction between calf age and calf sex on calf dive duration (coefficient β = -1.76, 95% confidence interval, CI (-3.10, -0.43), P < 0.001). Both

15 female calves (β = 4.28, CI (3.25, 5.29), P < 0.001) and male calves (β = 12.67, CI (1.78, 3.55),

P < 0.001) increased dive duration with age, but the significant interaction indicated that they did so at different rates, with females increasing dive duration faster with age. These results indicate that calf age is an adequate proxy for dive duration, allowing us to use calf age in mother dive duration models as previously mentioned. Additionally, depth was positively correlated with calf dive duration for both sexes (females: β = 6.36, CI (5.88, 6.85), P < 0.001; males: β = 5.74, CI

(5.16, 6.35), P < 0.001), regardless of mother–calf distance (Tables 1.1, 1.2).

Mother–Calf Proximity and Calf Dives

When calves were in contact with their mothers, we found an interaction between calf age and calf sex on calf dive duration. Both female and male calves significantly increased their dive duration with age (Figure 1.1a, b, Table 1.1). However, the estimate for female calves was higher, indicating a steeper slope for the increase in dive duration. When calves were near their mothers (<10 m), the interaction between calf age and calf sex on calf dive duration was not significant. Both female and male calves significantly increased their dive duration with age

(Figure 1.1c, Table 1.1). When calves were far from their mothers (>10 m), the calf sex*age interaction on dive durations was not significant (Table 1.1). Both female and male calves significantly increased their dive duration with age (Figure 1.1d, Table 1.1).

Mother–Calf Proximity and Calf Dives during Maternal Foraging

When calves were in contact with their foraging mothers, we found an interaction between calf age and calf sex on calf dive duration (Table 1.2). In contrast to the full data set, during maternal foraging, female calves significantly increased dive duration with age (Figure

16 1.2a, Table 1.2), but male calves did not (Figure 1.2b, Table 1.2). When calves were near their foraging mothers (<10 m), the interaction between calf sex*age on calf dive duration was not significant (Table 1.2). However, we also examined the sexes separately, given the interaction in previous models, and found that female calves near foraging mothers significantly increased dive duration with age (Figure 1.2c) while male calves did not (Figure 1.2d, Table 1.2). Similar to all dive bouts, the interaction between calf sex*age on calf dive duration when calves were far from their foraging mothers (>10 m) was not significant (Table 1.2). Both female and male calves significantly increased their dive duration with age (Figure 1.2e, Table 1.2).

Mother–Calf Proximity and Maternal Dives

When mothers were in contact with their calves, the interaction between calf sex and age on mother dive duration was significant (Table 1.1). We found an increase in the mother’s dive duration with female calf age (Figure 1.3a, Table 1.1), but not with male calf age (Figure 1.3b,

Table 1.1). The analysis for mothers when calves were near (<10 m) did not show a significant interaction between calf age and calf sex on mother dive duration (Table 1.1). We found a marginally nonsignificant increase in the mother’s dive duration with calf age (Figure 1.3c,

Table 1.1). When mothers were far from their calves (>10 m), the interaction for calf sex*age on mother’s dive duration was not significant, so we analyzed the sexes together (Table 1.1).

Mothers did not significantly change their dive duration with calf age (Figure 1.3d, Table 1.1).

Mother–Calf Proximity and Maternal Foraging Dives

While mothers were foraging in contact with their calves, the calf age*sex interaction on mother dive duration was marginally nonsignificant, so we analyzed the sexes separately (Table

17 1.2). Mothers significantly increased their dive duration with calf age when foraging in contact with female calves (Figure 1.4a), but not when foraging in contact with male calves (Figure 1.4b,

Table 1.2). While mothers were foraging near their calves (<10 m), the calf age*sex interaction on mother dive duration was not significant (Figure 1.4c, Table 1.2). Mothers did not significantly adjust their dive duration in response to calf age (Table 1.2). While mothers were foraging far from their calves (>10 m), the calf age*sex interaction on mother dive duration was not significant (Table 1.2), so we analyzed the sexes together. Mothers did not significantly change their dive duration with calf age (Table 1.2, Figure 1.4d).

DISCUSSION

To our knowledge, this is the first study to examine longitudinal changes in mother–calf diving behavior in a wild cetacean from birth to weaning. Consistent with research on captive dolphins (Noren et al. 2006; Noren et al. 2004), we found that dolphin calves of both sexes increased dive duration with age. This is similar to diving development in other marine mammals

(e.g. New Zealand fur seals, Arctocephalus forsteri: Baylis et al. 2005; harbour seals, Phoca vitulina: Bowen et al. 1999; southern sea otters: Payne & Jameson, 1984; humpback whales:

Cartwright & Sullivan, 2009), and may suggest that longer dive durations coincide with the acquisition of foraging skills. As early as 3 months of age, bottlenose dolphin calves in Shark

Bay engage in shallow-water foraging tactics that do not require long dives (e.g. ‘snacking’ sensu Mann & Sargeant, 2003; Mann & Smuts, 1999). While such tactics are important for young calves, older calves decrease time spent engaged in such foraging behavior and begin to adopt deeper-water tactics (Mann & Sargeant, 2003; Mann & Smuts, 1999). With improving diving capability, calves increase both their rate of foraging and proportion of time spent

18 foraging with age (Foroughirad & Mann, 2013; Mann & Sargeant, 2003). Tactics requiring deeper dives begin to occur regularly at 7–8 months. One deep-diving foraging tactic, sponge tool use, does not occur until at least 2 years of age (Mann & Sargeant, 2003). In our study, calves increased their dive duration until weaning; however, average dives were still shorter than average dive duration for adult females without calves (131.1 ± 8.90 s) and adult males (132.9 ±

11.50 s), whereas average dive duration for calves age 3–4 years was 84.30 ± 5.23 s.

While calves increased their dive duration with age, sex differences were notable. Female calves increased their dive durations at a faster rate than males. There are several possible explanations for these sex differences. First, sexual dimorphism helps explain sex differences in diving development in some species (Noren & Williams, 2000), but Shark Bay bottlenose dolphins are monomorphic (Connor et al. 2000; Smolker et al. 1992). Second, sex differences in mother–calf separation rate could impact diving behavior. However, no such sex differences have been found in previous work (Gibson & Mann, 2008a; Mann & Watson-Capps, 2005), and we controlled for distance in our analyses, rendering sex differences in mother-calf separation rate an unlikely explanation. Third, and most plausible, the sex differences observed in this study could be due to sex-specific socioecological strategies that are well documented in Shark Bay dolphins, with females spending more time foraging and exhibiting greater foraging specialization than males at all stages of development (Gibson & Mann, 2008a; Krzyszczyk et al.

2017; Mann & Patterson, 2013; Mann & Sargeant, 2003; Mann et al. 2008; Sargeant et al. 2005;

Smolker et al. 1992). Finally, females may mature more quickly than males, allowing them to dive longer at a younger age. Bimaturism, differential timing of maturity between sexes due to different durations of growth, occurs in some species, especially those with a polygynous mating system. For example, female pinnipeds generally reach sexual maturity faster than males

19 (Riedman, 1990) and female great apes reach adult body weight faster than males (Watts &

Pusey, 2002). Read et al. (1993) found that female T. truncatus grew at a faster rate than males during the first 6 years of life, even though this species is sexually dimorphic, with males reaching larger adult size. Bimaturism is consistent with the socioecological strategy hypothesis, for which we have most support.

As mothers did not adjust their dive durations when at a distance from their calves, we suggest that mothers change their diving behavior as a function of their immediate caregiving responsibilities, at least for daughters. Remarkably, mothers only adjusted their dive durations when in contact with female, but not male, calves. This suggests that mothers are biasing their diving and foraging behavior to benefit female calves more than male calves, possibly because foraging skill development is more critical for daughters than for sons, consistent with the socioecological hypothesis. Males and females in Shark Bay exhibit different reproductive strategies, which are apparent during development. Calves of both sexes are physically precocious and begin to separate for brief periods at a young age (Mann & Watson-Capps,

2005). During these separations, calves, especially males, begin to form their own social bonds

(Stanton et al. 2011). In comparison to females, male calves have stronger ties to other males during separations (Stanton et al. 2011), socialize more preweaning (Krzyszczyk et al. 2017) and spend more time in groups on longer separations. Female calves emulate their mothers (usually by foraging) more during separations; males prioritize finding associates, particularly when their mother is relatively solitary (Gibson & Mann, 2008b). These social bonds are particularly significant for male calves (1) because it predicts survival into the juvenile period (Stanton &

Mann, 2012) and (2) because male alliance formation is critical to reproductive success (Krützen et al. 2004). In contrast to males, Shark Bay females tend to adopt and specialize in their

20 mothers’ foraging tactics (Mann & Sargeant, 2003; Sargeant & Mann, 2009; Sargeant et al.

2005) and associate more with their mothers than sons do postweaning even though both sexes remain in their natal area (Tsai & Mann, 2013).

Given these different sex-specific socioecological strategies, we suggest that dolphin mothers adjust their dive behavior when in contact with female calves to preferentially allow females to observe maternal foraging tactics while physically entraining with her movement, breathing and diving. Average dive duration for all maternal dives ranged from 72.85 ± 3.64 s to

82.09 ± 5.18 s. In contrast, average dive duration for females without calves was 131.05 ± 8.98 s.

This suggests that mothers are adjusting to some degree for all calves, however, mothers with female calves adjust to a greater degree. Observational learning has been identified in both captive and wild dolphins (for review, see Yeater & Kuczaj, 2010) and there is evidence from our study population of vertical social learning (mother to offspring) for multiple specialized foraging tactics (Sargeant & Mann, 2009). Thus, in order for vertical transmission of foraging tactics to occur, calves must be close enough to their mothers to observe their mothers’ foraging behavior. While in contact, entrainment may be occurring, which can facilitate observational learning (Fuhrman et al. 2014). Calves in contact position can visually and acoustically observe their mothers during foraging bouts, receiving acoustic feedback from the mother’s echolocation during foraging. Furthermore, many species, including dolphins, are capable of vocal mimicry

(Reiss & McCowan, 1993; Schachner et al. 2009). In another population, wild Atlantic spotted dolphin, Stenella frontalis, mothers appeared to alter their foraging behavior in the presence of calves, and calves assumed an observation position, in which they faced their mothers during foraging (Bender et al. 2009). In our study, calves increased dive duration when far from their mothers, which may be evidence that they are practicing and developing their foraging tactics

21 while on separations. Gibson and Mann (2008a) found that during separations greater than 50 m, female calves spent a greater proportion of time foraging than male calves. Thus, we suggest that females are observing their mothers foraging and then practicing those techniques during separations more than males. Such observation- and practice-based learning may not be surprising given that in order to reach peak performance in some foraging tactics, such as sponging, individuals must learn the behavior at a young age, specialize in it, and then continue to improve well into adulthood (Patterson et al. 2016).

When we analyzed maternal foraging dive bouts separately, we found that mothers again only adjusted their dive durations when in contact with female calves. This is consistent with our explanation that foraging behavior likely explains the observed sex differences. To illustrate this point further, we return to an example with sponge foraging, a highly female-biased, habitat- specific foraging tactic in which individuals tend to specialize (Mann & Sargeant, 2003; Mann et al. 2008). To become a sponger, one’s mother must be a sponger, and even then, typically more females become spongers, as 91% of daughters and only 50% of sons adopted sponging from their mothers (Mann & Patterson, 2013; Mann et al. 2008). Females adopt sponging earlier

(Mann et al. 2008) and sponge more often as adults than males (Mann & Patterson, 2013). Thus, dependency, when calves have ample opportunities to observe their mothers, is a critical period of time for females to develop this tactic. Although sponging is only used by a small fraction of the females in Shark Bay (Mann & Sargeant, 2003), and in this study, 29% of calves and 39% of mothers were spongers, Shark Bay dolphins have been observed using over 13 different foraging tactics, a number of which appear to be vertically transmitted (Mann & Sargeant, 2003; Sargeant

& Mann, 2009; Sargeant et al. 2005; Sargeant et al. 2007). Thus, the same principles for sponging likely apply to other sex-biased foraging tactics.

22 There are several costs and benefits to mothers and calves associated with adjusting diving behavior. Maternal foraging bouts may require mothers to increase distance from calves as they must accelerate rapidly in order to catch prey. These separations may leave calves vulnerable to predators, such as tiger sharks, Galeocerdo cuvier, which are seasonally abundant in Shark Bay (Heithaus, 2001; Heithaus & Dill, 2002) and pose a threat, with approximately one- third of nursing calves having shark bite scars (Mann & Barnett, 1999). Therefore, mothers and calves likely benefit by adjusting in terms of decreased predation risk. However, such behavioral adjustments may increase drag at the surface, decrease foraging opportunities and lead to a loss of successful foraging events. Nevertheless, maternal foraging rate and percentage of time foraging does not significantly change as a function of calf age (Mann & Sargeant, 2003), which indicates that mothers forage just as frequently with young, inexperienced calves as they do with older calves, regardless of the demands of their calves. Adult females appear to become more efficient foragers with age, but likely suffer foraging costs with a young calf regardless. For example, female spongers increase their efficiency in this tactic as they increase in age, but peak in their mid-20s, when they are most likely to be experiencing the high costs of motherhood

(Patterson et al. 2016).

Since our data suggest that mothers only adjust their diving behavior in response to daughters, it raises an important question: do mothers invest more in daughters? In species with extensive maternal care, and no paternal care, mothers have greater influence on the reproductive value of female offspring than of male offspring (Leimar, 1996). A mother might influence her daughter’s reproductive success more than her son’s by vertical transmission of foraging skills.

Males are less likely to adopt maternal foraging tactics and show less specialization (e.g. Mann

& Patterson, 2013). In this way mothers can behaviorally influence the quality of grandoffspring

23 through daughters but not through sons. This logic follows Zefferman (2016), who proposed support for uniparental teaching hypothesis in which Shark Bay mothers preferentially teach daughters to sponge over sons due to benefits gained from future generations.

Our results confirm that dolphin calves increase their dive durations with age, similar to other species. Because young dolphins are incapable of repeated long, deep dives, mothers appear to adjust their behavior in order balance their own needs to forage while maintaining close proximity to calves, but do so more for female calves than for male calves. Most work examining sex-biased investment has examined polygynous follower species, namely ungulates

(e.g. feral horses, Equus caballus: Cameron et al. 1999; red deer, Cervus elaphus: Clutton-Brock et al. 1984; bighorn sheep, Ovis canadensis: Bérubé et al. 1996). In contrast, dolphins are a promiscuous species that use a combination of following and carrying strategies. Studies on promiscuous primate species provide inconsistent evidence regarding sex-biased maternal investment (reviewed in Brown, 2001). We found that mothers favor their female offspring in terms of adjusting maternal behavior, here, diving. Evidence to support sex-biased maternal care has also been observed in a few species outside ungulates (e.g. pinnipeds: Trillmich, 1996; primates: Brown, 2001); however, examining such biases in natural settings, especially in long- lived marine mammals, is rare. Here we provide new insights into calf diving development and the associated behavioral trade-offs made by mothers. Our results contribute to a better understanding of the intricacies of the mother–calf relationship and how females mitigate the demands of motherhood by selectively altering behavior. Future studies investigating the fitness costs of such behavioral adjustments would allow for an important analysis of how mothers balance resource acquisition with calf care and learning.

24 FIGURES AND TABLES

Figure I.1. Partial effects of age on calf dive duration for (a) female calves in contact with mothers, (b) male calves in contact with mothers, (c) calves near mothers and (d) calves far from mothers. Points are predictions from the full model and lines are the partial effects of age after marginalizing all other factors.

Figure I.2. Partial effects of age on calf dive duration during maternal foraging bouts for

(a) female calves in contact with mothers, (b) male calves in contact with mothers, (c) female calves near mothers, (d) male calves near mothers and (e) calves far from mothers.

Points are predictions from the full model and lines are the partial effects of age after marginalizing all other factors.

Figure I.3. Partial effects of calf age on mother dive duration for (a) mothers in contact with female calves, (b) mothers in contact with male calves, (c) mothers near calves and (d) mothers far from calves. Points are predictions from the full model and lines are the partial effects of age after marginalizing all other factors.

Figure I.4. Partial effects of calf age on mother dive duration during maternal foraging bouts for (a) mothers in contact with female calves, (b) mothers in contact with male calves, (c) mothers near calves and (d) mothers far from calves. Points are predictions from the full model and lines are the partial effects of age after marginalizing all other factors.

28 Table I.1. Mother and calf diving parameter estimates from the Markov-chain Monte

Carlo GLMMs for dive duration

Fixed effect Estimate 95% CI Ne P

Lower Upper

Maternal dives

Contact

Female calves Calf age 4.171 2.921 5.409 99700 <0.001

Depth 6.950 6.332 7.572 99700 <0.001

Male calves Calf age 0.4276 -0.8541 1.7028 99700 0.513

Depth 7.0152 6.1521 7.8696 99700 <0.001

Near

Calf age 1.6678 -0.1487 3.4130 99436 0.0658

Calf sex 1.1825 -6.1784 8.6402 99700 0.7570

Depth 6.8335 6.1992 7.5083 99700 <0.001

Calf age*sex -0.8024 -3.2213 1.6195 99700 0.5160

Far Calf age 0.4382 -2.5480 3.3433 99700 0.765

Calf sex 5.9532 -5.6582 17.4311 86654 0.301

Depth 5.0273 3.5935 6.4319 37826 <0.001

Calf age*sex 0.3919 -4.3180 5.0494 13454 0.842

Calf dives

Contact

Female Calf age 5.257 4.017 6.501 99700 <0.001

Depth 6.981 6.367 7.582 100727 <0.001

Male Calf age 2.205 1.021 3.375 99700 <0.001

29 Table I.1. (cont.)

Fixed effect Estimate 95% CI Ne P

Lower Upper

Depth 5.725 4.892 6.524 86057 <0.001

Near

Calf age 3.605 1.810 5.425 99700 <0.001

Calf sex -2.326 -9.650 4.865 99700 0.5247

Depth 5.870 5.222 6.511 99700 <0.001

Calf age*sex -0.591 -2.955 1.871 99700 0.6299

Far

Calf age 3.9584 0.4059 7.5864 99700 0.0318

Calf sex 0.9811 -12.9031 15.1565 98177 0.8978

Depth 3.3920 2.2336 4.5824 99700 <0.001

Calf age*sex -0.6042 -5.2470 4.1157 99700 0.8019

CI: confidence interval; Ne: effective sample size. Statistically significant P values are shown in bold.

30 Table I.2. Mother and calf diving parameter estimates from the Markov-chain Monte

Carlo GLMMs for dive duration during maternal foraging bouts

Fixed effect Estimate 95% CI Ne P

Lower Upper

Maternal dives

Contact

Female calves Calf age 5.753 3.541 8.062 98804 <0.001

Depth 7.200 6.028 8.372 34626 <0.001

Male calves Calf age 1.506 -1.330 4.352 99265 0.299

Depth 10.295 8.342 12.266 56498 <0.001

Near

Calf age 1.5625 -0.8801 4.0530 28625 0.20869

Calf sex -0.4808 -8.4329 7.6871 17570 0.87637

Depth 8.0046 6.9346 9.0710 20977 <0.001

Calf age*sex -1.3898 -5.2190 2.3221 6218 0.47661

Far

Calf age 0.3187 -2.6268 3.2065 95901 0.823

Calf sex 8.6396 -3.1984 20.8082 98704 0.154

Depth 5.6732 4.1697 7.1606 45699 <0.001

Calf age*sex 0.5516 -4.1747 5.2529 68926 0.809

Calf dives

Contact

Female Calf age 7.111 4.826 9.427 99700 <0.001

Depth 7.429 6.263 8.611 97600 <0.001

31 Table I.2. (cont.)

Fixed effect Estimate 95% CI Ne P

Lower Upper

Male Calf age 2.2392 -0.3209 4.8722 99700 0.0909

Depth 7.7355 5.9875 9.4747 100707 <0.001

Near

Calf age 3.9392 0.9538 6.9426 99700 0.011

Calf sex 0.4852 -10.1960 10.7509 99700 0.927

Depth 6.1181 5.0291 7.2024 99700 <0.001

Calf age*sex -2.2504 -6.2548 1.8070 101611 0.271

Near

Female Calf age 3.9340 0.8292 7.1369 99700 0.0159

Depth 5.8887 4.4586 7.3083 99700 <0.001

Male Calf age 1.861 -0.930 4.591 26149 0.1857

Depth 6.558 4.923 8.227 82889 <0.001

Far

Calf age 4.6875 0.5332 8.7178 2030 0.0285

Calf sex -0.9591 -16.8916 14.6101 6057 0.9057

Depth 3.4684 2.0435 4.8644 99700 <0.001

Calf age*sex 0.3669 -5.6709 6.4131 3839 0.9121

CI: confidence interval; Ne: effective sample size. Statistically significant P values are shown in bold.

32 CHAPTER II

Behavioral responses to fishing line entanglement of a juvenile bottlenose dolphin in Shark

Bay, Australia*

INTRODUCTION

Marine pollution and debris has become an issue of epic proportions. Plastic makes up the majority of marine litter with over 250,000 tons floating in the sea (Derraik, 2002; Eriksen et al. 2014). In 2010 alone it has been estimated that 4-12 million metric tons of plastic entered the ocean (Jambeck et al. 2015). The impacts of debris, such as ingestion, entanglement, and habitat degradation are occurring in every ocean worldwide. Entanglement in marine debris is a major problem for marine fauna, including marine mammals, sea turtles, sea birds, fish, sharks, and crustaceans (Laist, 1997; Colmenero et al. 2017; Moore et al. 2009). Marine debris, such as fishing gear, is particularly problematic for marine animals that breathe air, travel large distances and dive for food. Synthetic fishing gear, which do not breakdown naturally, remain in the oceans for decades and entangle large numbers of cetaceans, pinnipeds, and sea turtles well after active use (Stelfox et al. 2016). The International Whaling Commission estimates that 308,000 whales and dolphins die each year due to entanglement in fishing gear (IWC 2016). The impacts of entanglement may be short-term and non-lethal, or prolonged and result in death from infection, loss of appendages or drowning due to constricted body parts. Other possible outcomes are starvation due to impaired foraging ability, and exhaustion due to hydrodynamic drag.

* Published: Miketa, M.L., Krzyszczyk, E., Mann, J. (2017). Behavioral Responses to Fishing Line Entanglement of a Juvenile Bottlenose Dolphin in Shark Bay, Australia. Matters, 3(12), e201711000011.

33 Gear from commercial fisheries are responsible for the majority of serious entanglements recorded. Drift nets are estimated to kill thousands of dolphins (Delphinus delphis, Stenella coeruleoalba) in the Mediterranean each year (Tudela et al. 2005). The majority of humpback whales (Megaptera novaeangliae) in near-shore waters of northern Southeast Alaska bear scars of non-lethal entanglement (Neilson et al. 2009). Fishing gear entanglement is the leading cause of death for large whales in the Northwest Atlantic (van der Hoop et al. 2013), and is one of the greatest threats to the critically endangered Mediterranean monk seal (Karamanlidis et al. 2008).

While bycatch have caused the vaquita population to diminish to 30 individuals, making it the most endangered cetacean in the world (Jaramillo-Legorreta et al. 2007; CIRVA-8, 2016).

Coastal cetaceans are also affected by recreational fishing practices. For example, in

Sarasota Bay, Florida, veterinary records from 1988-2006 show over 600 deceased bottlenose dolphins (Tursiops truncatus) have ingested fishing gear (Wells et al. 2008). Similarly, observations of individuals entangled in fishing line also subsequently died from their injuries

(Wells et al. 2008). Studies of entanglement are usually based on necropsies or scarring (e.g.

Neilson et al. 2009; Wells et al. 2008), but there are a few that have examined the impact of entanglement on cetacean behavior (e.g. Mann et al. 1995; Wells et al. 1998; van der Hoop et al.

2014). Virtually no studies have detailed behavioral changes based on observations of individuals before, during, and after entanglement. Here, we describe observations of a female bottlenose dolphin (Tursiops aduncus), EDE, before, during and after entanglement in fishing line. The entanglement was non-lethal and it took EDE one week to free herself from the line.

34 METHODS

Study Site

The study site is a 300 km2 area in the eastern gulf of Shark Bay, Western Australia off of a tourist site called Monkey Mia (25° 47’ S, 113° 43’ E). Shark Bay is a UNESCO World

Heritage Site where human impacts are minimal. Since 1984, the Shark Bay Dolphin Research

Project has collected demographic, behavioral, genetic, ecological, and life history data on over

1,600 individual dolphins using photo-identification methods (Mann, 2000). Sexes are assigned based on views of the genital area or association with a calf (Smolker et al. 1992). Birthdates are estimated based on last sighting date of the mother without a calf and first sighting date with a calf, in addition to physical features of the calf (Mann et al. 2000).

Monkey Mia, the launch site for our study, is also home to a long-term provisioning program, attracting more than 100,000 visitors to Shark Bay per year (Stoeckl et al. 2005). Five adult dolphins, from three matrilines, are offered up to three handouts of fish per day through a program managed by the Department of Parks and Wildlife in a small protected shallow water area of the shoreline (Foroughirad & Mann 2013). This provisioning area is in close proximity to two jetties, two boat ramps, and a mooring area for two commercial catamarans and a number of recreational boats. Fishing commonly occurs from the Monkey Mia shoreline and near shore from small boats.

Study Subject

EDE, the focal individual in this study is a well-known and frequently sighted female dolphin born 19-Nov-2003, who has been followed for 53.9 hours since weaning in 2007. She

35 has 8 living maternal kin. EDE’s mother and grandmother are provisioned, and while EDE regularly visits the provisioning area, she does not receive fish handouts.

Data Collection

Data for this study were collected during focal follows (Karniski et al. 2015) conducted

2010-2015. Focal follows are systematic boat-based observations on an individual’s behavior and group composition using one-minute point sampling. Behavioral states include foraging, resting, socializing, traveling, and other (for definitions see Karniski et al. 2015). Some behavioral events were continuously recorded (frequency and/or duration, see Table II.1). Group composition was determined using the 10 m chain rule, such that individuals within 10 m of another are considered to be in the same group. Each follow had to be greater than 30 mins to be included in this analysis.

Three different time periods were compared: ‘before’ EDE became entangled (1,854 mins; September 2010-May 2015), ‘during’ the entanglement (276 mins; June 16-18, 2015), and

‘after’ she freed herself from the fishing line (749 mins; June 27, 2015-October 2015). In order to equalize sample size across periods, we randomly subsampled 276 mins from each the

‘before’ period and the ‘after’ period for 1,000 iterations. Subsamples produced the same results as the full dataset. EDE’s entanglement occurred on June 15. Summary statistics are presented since the sample size is one.

RESULTS

Group Composition

Prior to entanglement, EDE was in a group 89% of the time (61% of that with at least one maternal kin); however, during her entanglement, she was with others only 10% of the time

36 (22% of that time with at least one maternal kin) (Figure II.2). After she freed herself from entanglement, she spent 98% of her time in groups (71% of that time with at least one of her maternal kin) (Figure II.2).

Activity Budget

Figure II.3 shows EDE’s behavior before, during and after entanglement. Notable changes in response to entanglement were no socializing, little foraging, in which we did not observe any successful foraging events such as fish catches or ‘with fish’, and an increase in traveling, particularly traveling more at faster speeds (5-6 kph) (5% before, 30% during, 3% after) and less at slower speeds (2-3 kph) (63% before, 20% during, 54% after). She also engaged in other behaviors, such as leaps, fast accelerations (fast swims), and rapid surfacings.

Fast swims occurred at a rate of 5.68/hour compared to 1.62/hour before and 0.71/hour after entanglement. Her rapid surfacing rate increased to 3.79/hour, compared to 0.60/hour before and

0.32/hour after entanglement.

DISCUSSION

Entanglement in fishing gear is a major threat to nearly all marine mammals. Detailed observations on individuals throughout an entanglement event are rare, but provide important insight into behavioral impacts of such events. Our longitudinal dataset on EDE enabled us to compare her behavior and social interactions before, during, and after entanglement. Several marked changes stand out. First, she spent 30-50% less time foraging and 45-60% more time traveling during entanglement than before or after. In addition to traveling more, she increased her travel speed and engaged in other fast and erratic behaviors such as fast swims, rapid

37 surfacings and leaps. She did not socialize at all when entangled. Post-entanglement, her activity budget was similar to pre-entanglement.

Some of our observations are similar to those observed in Mann et al. (1995), where costly behaviors such as fast travel and leaping increased during entanglement. Further, the additional drag from trailing fishing gear can exacerbate energy expenditure and prevent efficient foraging. Van der Hoop et al. (2014) placed a Dtag on an entangled Northern right whale and found slower diving ascents and descents, as well as shallower dives during entanglement than for whales that were not entangled. Modeling the drag force of gear attached to the entangled right whale also showed that whales entangled in fishing gear experience a greater energetic demand (van der Hoop et al. 2014). Further, there appears to be a critical duration period in which the duration of additional drag due to entanglement can be a predictor of survival (van der

Hoop et al. 2017). Thus, the combination of increased energetic behaviors and drag due to fishing line may further decrease the chances of survival, especially with long durations of entanglement (van der Hoop et al. 2016).

In addition to physical and behavioral impacts, there was a drastic decrease in EDE’s social interactions. She was alone during the vast majority of the entanglement period, the inverse of time in groups before and after, respectively. Similarly, Wells et al. (1998) observed an entangled female dolphin in Sarasota Florida who was alone during each sighting of her entanglement, in comparison to only 24% of sightings alone when not entangled. Weinrich

(1996) observed 30-40 Atlantic white-sided dolphins (Lagenorhynchus acutus) abandon a conspecific immediately after the individual became entangled in a gillnet. They noted that not a single conspecific remained or returned to investigate or assist the entangled individual. While this observation isn’t surprising, one might expect that close kin may remain with a distressed

38 mother, calf, or sibling. When a young calf was entangled in Shark Bay, the mother remained nearby and others joined briefly, but none were close to the calf except the mother (Mann et al.

1995).

There are multiple examples of dolphins pushing a dead conspecific at the surface, in what appears to be an attempt to assist the deceased (Cockcroft & Sauer, 1990; Dudzinski et al.

2003; Mann & Barnett, 1999). Mann and Barnett (1999) observed dolphins attempting to intervene and defend a calf from a tiger shark attack. Gibson (2006) observed a non-lethal shark attack on a calf in which there was very little response to the presence of several small to medium-sized sharks or the attack from the calf, mother or group members. Therefore, dolphins do not always flee or abandon an individual in a potentially dangerous situation, particularly when it is between a mother and calf.

This begs the question then, why have we observed a pattern of seemingly social avoidance by conspecifics following entanglement? One hypothesis is that social avoidance is a strategy to avoid danger or an unfamiliar object. Furthermore, avoidance of diseased conspecifics has been observed in social lobsters (Behringer et al. 2006). Even EDE’s brief encounters with others were simply in passing.

Previous studies of entanglement focused on large whale species, such as right and humpback whales, which are classified as solitary (Dines et al. 2015). Here, we use longitudinal data to quantify the impact of entanglement in a highly social species. In addition to marked behavioral changes, we identified an additional stressor, isolation, which has received little attention with respect to entanglement. As such, the costs of entanglement (e.g., infection, injury, energetic costs, inability to forage), are likely exacerbated by social isolation. Bottlenose

39 dolphins have highly differentiated social relationships that last for years (Connor et al. 2000).

Thus, the effect of even short-term isolation could be substantial.

These observations highlight the need for regulating the disposal of fishing gear and decreasing pollution in marine habitats. Marine debris is an increasing global threat that impacts a wide array of species. The number of individuals entangled in marine debris is three times higher than that of the 1990s (Baulch & Perry, 2014) and nearly 50% more species have been reported in marine debris encounters since 1997 (Gall & Thompson, 2015). Shark Bay is a relatively pristine habitat, listed as a UNESCO World Heritage Site in 1991, which experiences very low anthropogenic pressures. Despite that, in addition to EDE, at least five provisioned dolphins and their calves have been entangled in fishing gear at Monkey Mia (Mann et al. 1995; unpublished records). Many other sites around the world experience much greater fishing pressure (both recreational and commercial) and human impacts, thus even stricter regulations should exist in these regions. While such detailed observations of entanglement are rare, they offer a glimpse into the costs of entanglement for dolphins and whales.

40 FIGURES AND TABLES

Figure II.1. EDE’s dorsal fin during the entanglement period (June 15, 2015) and after freed from entanglement (July 1, 2015).

0 6

T Before

n During e

0 After

2 0 Alone Group

Figure II.2. Group composition for EDE before, during, and after the entanglement period.

0 7 Before

0 During 6

After

1 0 Foraging Resting Socializing Traveling Other Activity

Figure II.3. Activity Budget for EDE before, during, and after the entanglement period.

43 Table II.1. Ethogram for a subset of common behavioral events.

Behavioral Description event Fast swim When a dolphin rapidly accelerates at or near the surface of the water Rapid surface When a dolphin quickly surfaces but maintains a horizontal position and the ventral side does not clear the surface of the water Leap When a dolphin surfaces rapidly in the horizontal position and the entire body cleared the surface of the water Synch When two (or more) dolphins breathe in close proximity at exactly the same time, generally <5 m apart With fish When a dolphin is observed with a fish in its mouth, but the actual catch was not observed Fish catch When a dolphin is observed catching a fish with its mouth

44 CHAPTER III

The behavioral and reproductive impacts of seagrass die-off on an apex predator, the bottlenose dolphin*

INTRODUCTION

Major disturbances and severe climatic events such as extreme heat, flooding, fire, and drought are becoming more frequent with climate change (IPCC, 2014), with widespread and long-term impacts on ecosystems. Species-level responses include phenological shifts, range shifts, or major mortality events (Moritz & Agudo, 2013; Poloczanska et al. 2013; Valladares et al. 2014). While the impact of disturbances on top predators is not well understood, information on their responses and the temporal and spatial scale of those responses are critical for effective management and conservation of ecosystem dynamics in a changing environment. Further, long- term datasets examining the effects of climate change on extant marine life are rare, despite the fact that marine systems worldwide are undergoing major changes (Poloczanska et al. 2013;

Hoegh-Guldberg & Bruno, 2010). In this study, we examine the bottom-up effects of an extreme climatic event, a marine heat wave that caused a catastrophic seagrass decline, on reproduction and foraging ecology of a top predator, the Indo-Pacific bottlenose dolphin (Tursiops aduncus).

Disturbances and temperature increases can severely impact marine habitat-forming species, including coral, mangroves, salt marsh grasses, and seagrass beds. Loss of these foundational species can result in bottom-up impacts affecting top predators. For example, coral reef bleaching, caused by increased temperatures and acidity (Hughes et al. 2017), affects

* Authorship for paper: Madison L. Miketa, Eric M. Patterson, Ewa Krzyszczyk, Megan M. Wallen, Kathy Murray, Michael Rule, Bart Huntley, Cindy Bessey, and Janet Mann

45 ecosystem dynamics across trophic levels, including elevating the risk of extinction for top predators (Alonso et al. 2015). While human impacts such as overfishing can be detrimental, bottom-up effects such as loss of habitat complexity may ultimately drive ecological shifts

(Wilson et al. 2008). Marine food web models predict that climate change will have drastic negative impacts on primary producers; however, we cannot fully predict such impacts without a better understanding of ecosystem complexity, including predator-prey interactions (Brown et al.

2010).

Seagrasses are foundational species that are especially sensitive to disturbances and climatic events. These sentinel species also provide critical ecosystem services (Orth et al. 2006), including those that directly benefit human health (Lamb et al. 2017). Seagrass habitats are among the richest ecosystems in the world and support many coastal ocean habitats (Orth et al.

2006). These meadows are highly productive (Duarte, 2000), provide shelter (Heck et al. 2003), food and nutrients (Short & Wyllie-Echeverria, 1996), carbon sequestration (Duarte et al. 2005), and act as natural biocides (Lamb et al. 2017). Approximately 25% of all seagrass species are either ‘Threatened’ or ‘Near-Threatened’ according to the IUCN Red List (Short et al. 2011), with the major threats being climate change, nutrient increase, decline in water quality, and human development (Orth et al. 2006). Recovery of seagrasses after a disturbance is often slow, taking decades to centuries (Meehan & West, 2000). For example, seagrass beds in Cockburn

Sound, Australia have remained in low densities after a major die-off occurred between 1967 and

1972 as a result of increased nutrient inputs (Walker et al. 2006). Because of seagrass habitat’s key, foundational role in ecosystems, any disturbance in seagrass habitat is likely to have bottom-up effects that impact the entire ecosystem, including top predators.

46 Shark Bay, Western Australia, is a UNESCO World Heritage Site, and one of the most pristine, richest, and extensive seagrass communities in the world (Kendrick et al. 2012; Walker et al. 1988). Approximately 85-90% of the shallows in Shark Bay have historically been covered by two of the least resilient and slow growing seagrass species, Amphibolis antarctica and

Posidonia australis (Duarte, 1991; Heithaus & Dill 2002; Kendrick et al. 2000). The most abundant species, A. antarctica is structurally complex and provides habitat, refuge, and foraging grounds to a variety of species including fish, crustaceans, mollusks, birds, sea snakes, sea turtles, dugongs, and dolphins.

In 2011, the otherwise pristine seagrass meadows of Shark Bay experienced a significant die-off following a sustained marine heat wave during which sea surface temperatures (SSTs) rose 2-3°C above average for three months (January-March) and reached a record high of approximately 4°C above normal (Hetzel, 2013; Pearce & Feng, 2013; Thomson et al. 2015). In

2012, researchers and The Western Australia Department of Biodiversity, Conservation and

Attractions (DBCA) began quantifying the resulting massive seagrass die off (Caputi et al. 2014;

DBCA, unpublished data). During 2012, seagrass coverage decreased by 65-80% in Shark Bay

(Burkholder et al. 2013; Thomson et al. 2015). Furthermore, the median percent coverage of A. antarctica decreased from 83.4% before the heat wave to 1.7% after the heat wave (Thomson et al. 2015), and as of 2014, it showed no signs of recovery (Nowicki et al. 2017). In addition, many banks previously covered by A. antarctica now consist of bare sand (Nowicki et al. 2017).

As a result of the die-off, between 2 and 9 million metric tons of CO2 were released into the atmosphere (Arias-Ortiz et al. 2018).

Since 1984, the eastern gulf of Shark Bay has been the site of a longitudinal study of a residential population of Indo-Pacific bottlenose dolphins (T. aduncus). Until the 2011 heat

47 wave, no known major biotic or abiotic events have impacted this ecosystem. Thus, the 2011 heat wave provides a natural experiment to examine how dramatic climatic events such as a marine heat wave may alter seagrass density and subsequently impact delphinid ecology and reproduction. Shark Bay dolphins primarily consume fish and cephalopods; seagrass beds constitute primary hunting grounds for many bottlenose dolphins in Shark Bay (Heithaus, 2004;

Sargeant et al. 2007), especially those that specialize in foraging tactics specific to seagrass

(Sargeant et al. 2007). In fact, it is these seagrass ‘specialists’ that are most likely to suffer with seagrass habitat loss, given they may not readily move elsewhere or change their behavior

(Clavel et al. 2011).

In this study, we examined the effects of a seagrass decline, following a marine heat wave, on dolphin foraging ecology and reproduction. We hypothesized that dolphin habitat use would shift in response to the die-off and this impact would be more pronounced among seagrass specialists. Because dolphins may exhibit a compensatory response by shifting to other foraging tactics or engaging in novel foraging types, we also examined foraging behavior diversity by documenting changes in the use of different foraging tactics. Specifically, we predicted that compared to prior to the heat wave dolphins would: (1) use areas previously covered with seagrass less, reasoning that damaged areas would have lower prey density; (2) have lower foraging success (lower catch rate) in these areas; (3) forage less in these areas; (4) use a seagrass-specific foraging tactic, bottom-grubbing, less; and, (5) switch to other (non-seagrass specific) foraging tactics. As a consequence of changes in habitat-use and foraging, we anticipated reproductive costs since females would have fewer or lower quality resources to sustain pregnancy and lactation. We predicted that the calving rate and calf survival would be lower following the seagrass die-off compared to earlier years.

48 Using a unique, longitudinal dataset spanning decades, this study provides a rare, in- depth look at the impact of habitat loss on individual behavior and reproduction in a top predator.

Although seagrass degradation is common, without extensive, longitudinal datasets like the one used here, we are rarely able to examine the bottom-up impacts of the loss of foundational species on top predators.

MATERIALS AND METHODS

Study Site and Population

The study site was a 300 km2 area in the eastern gulf of Shark Bay off Monkey Mia

(Figure III.1., 25° 47’ S, 113° 43’ E). This area consists of six major habitats: seagrass beds, channel, deep open, sand flats, deep ecotone, and shallow ecotone (Patterson 2012; Wallen et al.

2016). We refer to seagrass habitat in this study as habitat previously classified as seagrass prior to the die-off (Figure III.1). Therefore we are comparing the same geographic area ‘pre’ and

‘post’ heat wave, although substantial areas previously classified as ‘seagrass’ before 2011 were no longer covered with seagrass or were only sparsely covered.

The Shark Bay Dolphin Research Project has been studying a resident population of

Indo-Pacific bottlenose dolphins (T. aduncus) since 1984. Since then, the project has amassed extensive behavioral, genetic, ecological, and life history data. Shark Bay dolphins are bisexually philopatric such that individuals of both sexes remain in the area throughout their lifetime (Tsai

& Mann, 2013). To date, over 1700 individual dolphins have been identified through photo- identification of unique marks and scars on the dorsal fin and other distinctive features such as pigment patterns (Mann, 2000; Bichell et al. 2017). All individuals in this study were of known sex and had an estimated age. Sexes were determined by views of the genital area or by

49 association with a calf (Smolker et al. 1992), and when available, confirmed with genetic data

(Frère et al. 2010). Ages were determined from known or estimated birthdates, physical features and behavioral characteristics (Mann & Smuts, 1999; Mann et al. 2000), and/or the presence and degree of ventral speckling (Krzyszczyk & Mann, 2012).

Study Subjects and Data Collection

Behavioral and demographic data came from two primary sampling methods. The first, surveys, consisted of dolphin sightings of at least 5 minutes in duration, in which we recorded group composition and dolphin identity, predominant group behavior, GPS location and associated ecological data (habitat, depth, temperature, etc.) using scan sampling techniques.

Basic demographic data on the population were extracted primarily from surveys. Survey data were also used to quantify dolphin sightings in seagrass.

For this study, adult dolphins (≥ 10 years) were the primary subjects because they were of reproductive age with established foraging tactics (Sargeant & Mann, 2009; Patterson et al.

2016). We also only included dolphins that were sighted at least 22 times in each period (‘pre’ and ‘post’). This cut-off was chosen because a minimum of 22 sightings from surveys was required to be sighted in each habitat at least once according to habitat availability. Although all adult individuals with sufficient data were included in the analysis of sighting data, we also restricted our dataset to individuals that used seagrass more than expected based on habitat availability, hereafter referred to as “seagrass specialists.” Relative habitat availability was calculated as the proportion of our study area covered by a particular habitat type, which is 19% for seagrass (Patterson, 2012). After controlling for search effort (described further below in

Data Processing), selection ratios (Manly et al. 2002) were calculated for all adults using the

50 ratio of proportional seagrass use to proportional seagrass availability per individual. Those dolphins with a selection ratio greater than 1.0 (i.e., used seagrass more than expected based on availability) were considered to be ‘seagrass specialists’ (n = 17). These dolphins were not all seagrass foraging specialists in the sense that seagrass foraging dominated their foraging type

(Mann et al. 2008), but it was clearly their preferred foraging habitat. We were particularly interested in these specialists as we anticipated that they would be impacted more than dolphins that were less reliant on seagrass. Thus, we analyzed data for 43 dolphins (21 female, 22 male), with a subset of 17 being seagrass specialists (12 female, 5 male). The total survey dataset included 10,512 surveys (mean ± SD = 123.86 ± 50.28 per dolphin).

The second data sampling method used was focal follows. During individual focal follows detailed behavior, group composition, and similar ecological data were collected using continuous and point sampling methods (Karniski et al. 2015). Foraging events were instantaneous occurrences of behavior recorded opportunistically (Mann, 1999) (Table S1). GPS points, which allowed us to identify habitat post data collection, were taken either every 30 seconds or every 100 meters depending on the vessel used. GPS data from focal follows were used to quantify time spent in seagrass, and focal follow observation data were used to quantify behavior in seagrass. Activity state was recorded using one-minute point sampling, but behavioral events such as fast-swims and fish catches were collected continuously as observed.

Four activity states: forage, rest, social and travel, were assigned as in Karniski et al. (2015). For focal follow data, we focused on seagrass specialists, as defined by survey data above, with a minimum of three observation hours (Gibson & Mann, 2009) pre (2006-2010) and post (2011-

2015) die-off, which resulted in a sample size of 11 females with 429.65 hours of focal follow data (mean ± SD = 39.05 ± 12.01 per dolphin).

51 To examine relevant shifts in dolphin foraging behavior, we compared data collected five years prior to the heat wave (April 2006-December 2010) to data collected five years after the heat wave (June 2011-December 2015). The heat wave took place from January to March 2011, and by 2012 the seagrass die-off was catastrophic (Thomson et al. 2015). While the exact rate of the seagrass die-off is not known, other regions of Western Australia experienced immediate impacts of the heat wave, including large-scale fish deaths (Pearce et al. 2011). Therefore, beginning the ‘post’ period approximately 3 months after the heat wave is likely sufficient to measure impact.

Quantifying Die-off Impact

To understand the impacts of the seagrass die-off in more detail, we examined a subset of data collected in a 34.9 km2 area of the Monkey Mia banks (Figure III.1). This area was chosen as it encompassed a time series shallow water habitat classification, quantified and mapped by

West Australian Department of Biodiversity, Conservation, and Attractions (DBCA)

(unpublished data) which represented the various degrees of seagrass die-off ‘pre’ and ‘post’ the heat wave. The habitat classifications were derived using high resolution (1.5 and 2.0 meters)

WorldView-2 satellite images captured on 7 May 2010 and 3 June 2013 by Digital Globe Inc.

Using field sampled training data, supervised classifications were produced for both dates as part of DBCA’s Marine Monitoring Program. This dataset featured seagrass species (P. australis and

A. antarctica) and density classes (sparse, medium and dense). In ArcGIS 10.3 (ERSI, 2014) these habitat classifications were then spatially intersected with GPS points for focal follow, survey, and track data in order to identify and attribute each GPS point location with the year, habitat type, and species density. The ‘pre’ and ‘post’ observations were intersected with the

2010 and 2013 habitat classifications. The foraging budget metrics described above were then re-

52 examined according to degree of seagrass damage. These data provided us with two levels of detail on the same area of seagrass (1) general: areas of seagrass that were ‘more damaged or

‘less damaged’ by the heat wave regardless of species, (2) species-specific: seagrass species and density. The general die-off measures were defined as a decrease in seagrass density regardless of species. We were specifically interested in nine seagrass habitats: sparse A. antarctica, medium A. antarctica, dense A. antarctica, sparse P. australis, medium P. australis, dense P. australis, medium mixed, dense mixed, and sand. Mixed habitats were either a mixture of A. antarctica and P. australis or the habitat was unable to be classified as one species due to image quality. Sparse was classified as <40% coverage, medium was classified as >40% to 70% coverage, and dense was classified as >70% coverage. Dense mixed was only present in the ‘pre’ period, whereas medium A. antarctica, sparse A. antarctica, sparse P. australis, and medium mixed were only present in the ‘post’ period. Finally, in order to control for different availabilities of the habitats and to understand whether dolphins were shifting to use different habitats more or less than they were available, we added a third measure, (3) species-specific selection ratios.

Data Processing

Field data were processed and summarized to produce the metrics for use in our analyses.

To extract major habitat type from the previously derived habitat layers (Patterson, 2012; Wallen et al. 2016), we used R statistical software (R Core Team, 2017) to intersect our habitat layers with GPS location data collected during surveys and follows. For survey data, we used the GPS point taken at the start of the survey. For focal follow data, we assigned habitat to each one- minute behavioral point sample and/or event by assigning the nearest GPS point recorded within

5 minutes of the behavior. Surveys and focal follow point samples or events without an

53 associated habitat were excluded from the analysis. Following this, we calculated five metrics to be used in our analyses on the effect of the heat wave on dolphin foraging behavior: sighting frequency, proportion of time spent in seagrass, proportion of foraging time spent in seagrass, proportion of seagrass time spent foraging, and proportion of time spent bottom-grubbing in seagrass.

For the first metric, sighting frequency, we used survey data to determine the number of sightings per dolphin in seagrass in the two periods. We calculated the proportion of sightings in seagrass as the number of sightings in seagrass divided the number of total sightings per period per individual. We controlled for search effort by calculating how many minutes researchers spent searching for dolphins in each habitat based on research vessel tracks. We included all vessel tracks from 2006-2015, excluding those that occurred during focal follows, or those in which the boat was moving faster than survey speed (>15 km/h), as researchers were not dedicated to searching for dolphins during those periods. We then calculated sighting frequency as:

푆푠 (푆 ) 푡 퐻 ( 푠) 퐻푡 where Ss is the number of sightings of a dolphin in seagrass, St is the total number of sightings of that dolphin across all habitat types, Hs is the number of hours researchers spent searching for dolphins in seagrass, and Ht is the total number of hours researchers spent searching for dolphins across all habitat types.

For the remaining four metrics, we used focal follow data. Here, correcting for search effort was not necessary since, by definition, we were with the dolphin of interest and not searching for them. Further, for these metrics we did not specifically examine seagrass type, only

54 areas historically classified by seagrass. Thus, calculating a selection ratio would be unnecessary at the denominator would be the same in both periods. The proportion of time spent in seagrass was calculated as simply the number of minutes observed in seagrass divided by the total number of minutes observed in each period. The proportion of foraging time spent in seagrass was calculated as the number of minutes actively foraging in seagrass divided by the number of minutes spent foraging in any habitat. The proportion of seagrass time spent foraging was calculated as the number of minutes actively foraging in seagrass divided by the total number of minutes the animal was observed in seagrass. Foraging bouts were also defined by type (Mann &

Sargeant, 2003), with specific focus on bottom-grubbing because it occurs primarily in seagrass

(Sargeant et al. 2007). During bottom-grubbing, dolphins swim nearly vertical in the water column and use their beaks to ferret prey from the seafloor or seagrass (Table S1). The behavior is obvious in shallow water, but might also occur out of view in deeper water. We calculated the proportion of time spent bottom-grubbing in seagrass as the number of minutes spent bottom- grubbing in seagrass divided by the total number of minutes spent foraging in seagrass in each period.

For the fine-scale habitat data (DBCA unpublished), we measured each of the following for both general and species-specific measures using focal follow data. First, the proportion of time spent in seagrass was calculated as the number of minutes observed in the seagrass habitat of interest divided by the total number of minutes observed in seagrass in each period. The proportion of foraging time spent in seagrass and the proportion of seagrass time spent foraging were combined into a single metric, the proportion of time spent foraging in seagrass, because the dataset was limited to observations within seagrass in order to examine behavior in specific seagrass habitats. The proportion of time spent foraging in seagrass was calculated as the

55 number of minutes actively foraging in a specific seagrass habitat divided by the number of minutes spent foraging in seagrass habitat. Sand was included as part of these analyses because there were large sand patches within the seagrass beds. Additionally some seagrass habitats became sand following the heat wave, so we wanted to see how dolphins used those habitats across the two periods.

We also calculated selection ratios based on the fine-scale habitat data to account for changes in species-specific density habitat availability in each period. Proportions for the

‘species-specific selection ratio’ were calculated as:

푀ℎ (푀 ) 푡 퐴 ( ℎ) 퐴푡 where Mh is the number of minutes a dolphin was observed in a species-specific habitat, Mt is the total number of minutes a dolphin was observed in seagrass, Ah is the area of the species-specific habitat (in km), and At is the total seagrass area (in km).

In addition to the above metrics, we used focal follow events to examine the foraging rate in seagrass. We calculated a single foraging rate as a total measure of four foraging events: 1) fish catches, 2) seen with fish – when the catch was not seen, 3) fast swims – accelerations common when chasing fish, and 4) bottom-grubbing – instantaneous bottom-grubbing events.

The foraging event rate was calculated as the number of times these events occurred divided by the number of focal follow minutes spent in seagrass during each period.

We examined 16 foraging tactics observed during surveys in the ‘pre’ period compared to the ‘post’ period to see if there were any shifts or use of novel tactics (Table S1). We only used survey data where foraging was the predominant group activity, which is the activity performed by at least 50% of the individuals for at least 50% of the first 5 minutes of a survey (Mann, 1999;

56 Karniski et al. 2015). We calculated the proportion of surveys where each foraging tactic was observed out of the total number of foraging surveys in each period (Supplement 1).

In order to examine impacts of the heat wave on reproduction and calf survival we analyzed calving rate and calf mortality for the population in the ‘pre’ and ‘post’ periods. To calculate calving rate, we took the number of calves born in each period and divided that by the number of sighted females that were of reproductive age in that period (i.e. all females over the age of 11 (Mann et al. 2000)). For calving rate (births per female) we used the years 2005-2009 for ‘pre’ (N = 230 females) and 2010-2014 for ‘post’ (N = 234 females), since most calves are born by December given the austral spring-summer birth season (Mann et al. 2000). For calf mortality, we used any calf that survived to age 3 (as in Mann et al. 2000) divided by all calves born in that period. We used a different ‘pre’ time period, 2002-2006, because calves born in

2008 or 2009 would still be dependent on their mothers when the heat wave occurred (N = 156 calves). The ‘post’ period included calves born 2010-2014 (N = 148 calves). We confirmed that there was similar effort between the two periods in terms of observing calves based on the number of months of observation.

Statistical Analyses

Data were not normally distributed so we used non-parametric tests for all analyses. We used paired permutation tests blocked by individual for sighting frequency, all four metrics of seagrass habitat use measured on general seagrass die-off (proportion of time spent in seagrass, proportion of foraging time spent in seagrass, proportion of seagrass time spent foraging, proportion of time spent bottom-grubbing in seagrass), and foraging rate. We also used paired permutation tests blocked by individual for the metrics associated with the two species-specific measures (proportion of time spent in seagrass, proportion of time spent foraging in seagrass,

57 proportion of time spent bottom-grubbing in seagrass). We used a permutation Chi-squared test to test for the analyses of reproductive output and foraging tactics. All analyses were performed in R v.3.4.1 (R Core Team, 2017) using the coin package (Hothorn et al. 2006).

RESULTS

Habitat Use and Foraging Behavior

According to sighting (survey) data, adult dolphins were sighted more frequently in seagrass habitat following the 2011 heat wave than before the heat wave (Z = -2.9779, P =

0.0029) (Figure III.2). In contrast, seagrass specialists were not sighted at significantly different rates in seagrass habitat pre and post heat wave (Z = -0.11491, P = 0.9085) (Figure III.2).

Similarly, based on focal follow data, seagrass specialists increased the proportion of time spent in seagrass (Z = -2.0121, P = 0.044, Figure III.3a). However, they did not significantly change their proportion of time spent in damaged beds after the heat wave (Z =

1.5507, P = 0.121, Figure III.4a). Specifically, seagrass specialists significantly increased their use of medium P. australis (Z = -2.095, P = 0.0362, Figure III.5a), and sand (Z = -2.4398, P =

0.0147, Figure III.5a), and there was a trend of using dense P. australis more post heat wave (Z

= -1.906, P = 0.0567, Figure III.5a). Specialists significantly decreased their use of dense A. antarctica (Z = 3.0393, P = 0.002371, Figure III.5a) after the heat wave. Even when controlling for habitat availability using selection ratios, seagrass specialists significantly increased their use of medium P. australis (Z = -2.0251, P = 0.04286, Figure III.6a) and sand (Z = -2.3918, P =

0.01677, Figure III.6a) and decreased their use of dense A. antarctica (Z = 2.6266, P = 0.008624,

Figure III.6a) after the heat wave. Habitat use between periods did not change significantly for any other habitat. In the ‘pre’ period, seagrass specialists used medium P. australis nearly as

58 much as it was available and in the ‘post’ period they used it one and a half times its availability

(Figure III.6a). Pre die-off, dense A. antarctica was clearly the preferred habitat; dolphins used it nearly twice as much as it was available. In the ‘post’ period, specialists used A. antarctica only slightly more than half of its availability (Figure III.6a). Post die-off, they also spent time in newly created habitats, including sparse A. antarctica, medium A. antarctica, sparse P. australis, and medium mixed.

Seagrass specialists did not significantly change the proportion of foraging time spent in seagrass (Z = -1.0533, P = 0.2922, Figure III.3b) or the proportion of seagrass time spent foraging (Z = -0.0127, P = 0.9898, Figure III.3c). However, seagrass specialists decreased their proportion of time spent foraging in seagrass (the combined metric for only seagrass habitat) that was more damaged after the heat wave, and increased their proportion of time spent foraging in seagrass in less damaged beds (Z = 2.3338, P = 0.0196, Figure III.4b). Specifically, seagrass specialists increased their time spent foraging in dense P. australis (Z = -2.0758, P = 0.0379,

Figure III.5b) and sand (Z = -2.1602, P = 0.0308, Figure III.5b) after the heatwave. They significantly decreased their time spent foraging in dense A. antarctica (Z = 2.9341, P = 0.0033,

Figure III.5b) after the heat wave. When controlling for habitat availability using selection ratios, seagrass specialists significantly decreased their time spent foraging in dense A. antarctica (Z =

2.117, P = 0.0343, Figure III.6b), increased their time spent foraging in sand (Z = -2.1149, P =

0.0344, Figure III.6b) and there was a non-significant increase in dense P. australis after the heat wave (Z = -1.8179, P = 0.0691, Figure III.6b). In contrast to habitat use, there were no significant changes in the use of medium P. australis for foraging between periods (Z = -0.0710,

0.9434). In the ‘pre’ period, seagrass specialists used dense P. australis for foraging approximately a third as much as it was available and in the ‘post’ period they used it for

59 foraging more than half as much as it was available (Figure III.6b). Prior to the die-off, dense A. antarctica was their preferred habitat and they used it twice as much as it was available. Post die- off it was used nearly equal to availability (Figure III.6b). Post die-off, they also utilized new habitats for foraging, including sparse A. antarctica, medium A. antarctica, sparse P. australis, and medium mixed.

The proportion of time spent bottom-grubbing in seagrass did not change between the two periods (Z = -1.0613, P = 0.2886, Figure III.3d), although there was a near significant decrease in proportion of time spent bottom-grubbing in seagrass in damaged seagrass habitats

(Z = 1.8428, P = 0.0654, Figure III.4c). Specifically, seagrass specialists decreased their time spent bottom-grubbing in dense A. antarctica (Z = 2.4805, P = 0. 0.0131, Figure III.5c) after the heat wave. In contrast to the previous metrics, there was no significant change in the use of medium P. australis, dense P. australis, or sand for bottom-grubbing after the heatwave (Fig

III.5c). After controlling for habitat availability using selection ratios, seagrass specialists did not significantly change their use of any habitat for bottom-grubbing; however, there was a nonsignificant decrease in use of dense A. antarctica after the heat wave (Z = 1.7085, P = 00.0875,

Figure III.6c). Furthermore, seagrass specialists bottom-grubbed in new habitats, sparse A. antarctica, medium A. antarctica, and medium mixed more than half of their availability in the

‘post’ period (Fig III.6c).

The rate of foraging in seagrass, indicated by fish catches, with fish, fast swims, and bottom-grubbing, increased following the heat wave from 0.42 to 1.41 instances per hour (Z =

2.5169, P = 0.0118).

60 Foraging Tactics

The proportion of foraging tactics observed in survey data changed from the ‘pre’ to

‘post’ period (X2 = 152.82, df = 19, P ≤ 0.001; Figure S1). Although bottom-grubbing did not change between time periods, some of the more common foraging tactics changed frequency.

Two deep-water foraging tactics decreased, tail-out peduncle diving foraging and sponge foraging, and two shallow-water foraging tactics increased, snacking and mill foraging, across time periods (Supplement 1). Rare tactics remained infrequent in both time periods and no novel foraging tactics were observed in either period.

Calving Rate and Calf Survival

The calving rate was not significantly different between the two periods (X2 = 0.8830, df

= 1, P = 0.3474). In the ‘pre’ period (2006-2010), 155 calves were born to 230 reproductive-age females for a rate of 0.68 calves/female over 5 years (0.14 annual rate). In the ‘post’ period

(2011-2015), 148 calves were born to 234 reproductive-age females for a rate of 0.63 calves/female over 5 years (0.13 annual rate). Seagrass specialists had a significantly higher reproductive rate than non-seagrass specialists in both periods. In the ‘pre’ period the rate for specialists (N = 19) was 0.95 calves/female over 5 years (0.19 annual rate) and non-specialists

(N = 211) was 0.65 calves/female over 5 years (0.13 annual rate) (X2 = 5.7563, df = 1, P =

0.01643). In the ‘post’ period the rate for specialists (N = 21) was 0.95 calves/female over 5 years (0.19 annual rate) and non-specialists (N = 213) was 0.60 calves/female over 5 years (0.12 annual rate) (X2 = 8.9115, df = 1, P = 0.0028).

Calf mortality was not significantly different between the ‘pre’ and ‘post’ period (X2 =

1.5712, df = 1, P = 0.21). In the ‘pre’ period (2002-2006), 98/156 calves (63%) survived to age

61 3. In the ‘post’ period (2010-2014), 104/148 calves (70%) survived to age 3. Calves born to seagrass specialists did not have significantly different mortality than those born to non- specialists in either period. In the ‘pre’ period, 67% (12/18) of seagrass specialists’ and 63%

(86/138) of non-specialists’ calves survived to age 3 (X2 = 0.0099, df = 1, P = 0.9206). In the

‘post’ period, 58% (11/19) of seagrass specialists’ and 72% (93/129) of non-specialists’ calves survived to age 3 (X2 = 0.9907, df = 1, P = 0.3196).

DISCUSSION

Extreme weather events and temperate anomalies, due to climate change, are affecting a variety of ecosystems worldwide. Impacts on foundational species, such as seagrass, can lead to widespread changes throughout an entire ecosystem, all the way up to apex predators. We demonstrate that following catastrophic environmental damage, dolphins adjusted multiple aspects of habitat use and foraging behavior, but in ways that were somewhat unexpected.

Counter to our hypotheses and predictions, after the heat wave dolphins spent more time in seagrass habitats compared to the time spent in these same habitats before the heat wave.

Dolphins also increased the proportion of their foraging and bottom-grubbing time in healthier seagrass habitats that were less damaged by the heat wave. Surprisingly, the foraging rate increased in seagrass habitat after the heat wave. At close examination of the type and density of seagrass habitat, we found that seagrass specialists preferred dense A. antarctica to all other habitat types as indicated by measures of habitat use, foraging, and bottom-grubbing prior to the die-off. Despite this preference for dense A. antarctica, after the die-off seagrass specialists switched to using some P. australis habitats and lower density A. antarctica habitats, due to loss of their preferred foraging habitat. However, for bottom-grubbing, they continued to utilize

62 lower density A. antarctica as much as it was available and some P. australis habitats, rather than completely switching to a new habitat. Despite massive ecological change, calf survival and calving rates were not impacted by the heat wave during the 5-year post heat wave time period we examined. However, consistent with previous work (Mann et al. 2000), seagrass specialists had a higher birthrate than other females, demonstrating the importance of this habitat.

Habitat Use and Foraging Behavior

Possible explanations for the increase in seagrass use are primarily related to dolphin’s responses to changes in their prey and/or predators: (I) prey may have shifted to healthier seagrass beds following the heat wave; (II) prey abundance may have declined following the heat wave; (III) prey may not have changed abundance or habitat use following the heat wave; (IV) prey type may have shifted following the heat wave, with smaller fish remaining in seagrass beds while larger prey move out; (V) changes in predator (e.g., tiger shark) habitat use related to the heat wave may influence dolphins habitat use following the heat wave.

I. First, if prey shifted to areas damaged less by the die-off, dolphins might follow suit.

We found that dolphins did shift to seagrass beds that were less damaged by the heat wave. This result may indicate that some prey moved to the less damaged beds, which would explain why seagrass specialists shifted from more damaged beds to less damaged beds while foraging and bottom-grubbing. Specifically, seagrass specialists utilized lower density seagrass habitats that became available in the ‘post’ period. Thus, it appears that dolphins both increased their use of seagrass habitat after the heat wave and shifted to the healthier beds, specifically when foraging.

II. If prey declined following the die-off, we might expect increased time searching for prey. However, we did not find an increase in the proportion of time spent foraging or bottom-

63 grubbing in seagrass. Yet, we did find an increase in foraging rate, measured by instantaneous foraging events, but no increase in proportion of time foraging, which may indicate that even if there was a decrease in abundance, prey are easier to find without needing to dedicate more time to foraging. Following the heat wave, prey may be easier to find in damaged beds due to decreased density, and dolphins may spend the same amount of time foraging, catching fewer, but easier to find fish. Interestingly, seagrass specialists increased their use of dense P. australis and sand when foraging in general, but did not shift to other habitat types when bottom- grubbing. This indicates that seagrass specialists may be able to shift to other habitat types when foraging more generally, but were unable to find suitable bottom-grubbing habitat.

III. If there was no change in prey abundance or habitat use, and prey remained in damaged beds, prey may be more accessible to dolphins given the loss of seagrass density.

Among dolphins that specialized in seagrass, the proportion of time spent foraging in seagrass did not increase, nor did the proportion of time spent bottom-grubbing. If prey were easier to find following the heat wave-related seagrass die-off, dolphins may not need to be actively foraging or bottom-grubbing to find prey, but simply spending time in seagrass, quickly sighting and catching prey. This is supported by an increased rate of foraging events. While there was not an increase in proportion of time spent foraging, this may be due to the loss of seagrass density, which may lead to dolphins encountering fish more frequently and fish chases being brief.

Therefore dolphins may be dedicating less time to searching for fish while quickly consuming prey. This hypothesis is also supported by evidence that dolphins increased foraging in sparse and medium densities of P. australis and A. antarctica, as well as sand, which should provide fewer hiding place for fish than dense A. antarctica, dolphins’ preferred habitat prior to the die- off.

64 IV. Additionally, prey types in seagrass habitat may have shifted in that smaller fish may have remained in seagrass beds while larger prey moved out. This could be a consequence of decreased seagrass coverage, or due to a shift to sparser seagrass habitats that provide less refuge for larger fish, leaving only smaller fish in the damaged banks. A shift to smaller prey would result in an increase in foraging effort since dolphins would need to catch a higher number of individual small prey to meet their nutritional requirements. In addition, smaller fish may be more difficult to find, resulting in an increased amount of time search for prey. Therefore, the presence of predominately smaller fish in damaged seagrass beds could also explain why there was an increase in time spent in seagrass following the heat wave. Although we did not find an increase in the proportion of time spent foraging, we did find an increase in the foraging event rate in seagrass, a finding consistent with this hypothesis.

V. Finally, changes in predation pressure could contribute to the observed changes in habitat use. Tiger sharks are seasonally abundant in the seagrass meadows of Shark Bay

(Heithaus, 2001; Heithaus & Dill, 2002). Nearly 75% of adult dolphins bear shark bite scars

(Heithaus, 2001) indicating that attacks are common, although generally non-lethal as dolphins are not a main prey item of tiger sharks. However, shark presence may still alter dolphin habitat use. Heithaus and Dill (2006) found that dolphins decreased the use of shallow microhabitats when tiger sharks were present, despite the increased fish biomass relative to deeper habitats.

Tiger sharks may also be altering habitat use in response to seagrass die-off. Thus, if the die-off has led to a decrease in predation pressure due to fewer sharks or a sparser habitat in which dolphins can more easily detect sharks, dolphins may have increased the amount of time spent in seagrass beds.

65 Foraging Tactics

It was surprising to find a decrease in deep water (>7m) foraging tactics and an increase in tactics more common in shallower water. This might be in response to shifts in prey abundance and density. If prey have moved out of shallow seagrass beds, other shallow to mid- water foraging tactics, such as mill foraging and snacking, may have increased, consistent with our findings. However we did not observe an increase in deep water foraging tactics, indicating that dolphins did not shift from shallow water habitats to deeper habitats for foraging. Finally, there were no increases in rare foraging tactics following the die-off. These results indicate that shallow-water habitats remained productive and that specialists did not appear to shift tactics readily, although some population-level changes were evident.

Reproduction and Fitness

We did not find any impacts of the seagrass die-off on fitness in terms of calving rate or calf survival. Dolphins are considered generalists and flexible foragers. Thus, dolphins are likely driven by prey and are capable of adjusting their behavior in order to take advantage of changes in prey distribution or location, so that environmental changes have minimal impacts on fitness.

However, some individuals specialize in specific foraging tactics or habitats. Further, since calves tend to adopt the foraging tactics of their mothers (Mann & Sargeant, 2003; Sargeant &

Mann, 2009), there might be longer-term consequences for calves of seagrass specialists that were not documented here. Another finding is that seagrass specialists had a higher calving rate in both periods. Seagrass habitat has high prey abundance (Heithaus, 2004) and is likely one of the most productive habitats in the bay. Therefore, females that specialize in seagrass habitat

66 may benefit from higher growth or reproductive rates. As of five years post die-off reproductive rates appear to remain high, however there may be a lag in reproductive impacts.

Ecological Implications

Prior to the 2011 marine heat wave, the seagrass meadows of Shark Bay were considered one of the most pristine in the world, supporting a complex ecosystem with many marine megafauna. While the loss of seagrass biomass may be detrimental to the ecosystem, it may also result in increased foraging opportunities for dolphins, at least initially. Seagrass acts as a refuge for fish by proving both visual and acoustic protection from predators (Wilson et al. 2013).

Dolphins use both vision and echolocation to find their prey (Au, 1993, Thomas et al. 2004); however, seagrass attenuates sound and interferes with echolocation (Wilson et al. 2013).

Further, increased biomass is correlated with decreased sound propagation and mature meadows were found to provide better acoustic refuge than meadows dominated by quick-growing pioneer species (Wilson et al. 2013). Therefore, such drastic decreases in seagrass biomass in a slow- growing species, A. antarctica, (Thomson et al. 2015; Nowicki et al. 2017) likely resulted in a completely different soundscape. The decrease in seagrass cover means very little visual or acoustic hiding places for prey species. Thus, seagrass beds that are sparse or less complex in coverage may be easier hunting grounds for dolphins. This could explain the increase in sightings in seagrass habitat, increased foraging rate in seagrass, and the increase in using P. australis and lower density A. antarctica for foraging following the heat wave.

Since the heat wave, once densely covered seagrass beds have become patchy and barren, resembling the transition zones between seagrass and sand in an edge habitat. While seagrass beds are ideal foraging grounds due to their prey abundance (Heithaus, 2004), shallow habitats in

67 general also contain greater densities of prey (Heithaus & Dill, 2002). There is evidence that edge and edge-like habitats can have high species richness and diversity (Odum, 1971; Reis et al.

2004; Saunders et al. 1991). Therefore, seagrass habitat that resembles edge habitat may be a productive foraging ground. Eierman & Connor (2014) found that dolphins in Belize foraged more in boundary microhabitats, where seagrass met open sand flats, due to higher prey density.

However, fish density and richness are unpredictable in patchy seagrass habitats and results vary by study (Connolly & Hindell ,2006; Gillanders, 2006; Horinouchi et al. 2009; Hughes et al.

2002; Macreadie et al. 2009; Salita et al. 2003; Smith et al. 2008). Nevertheless, sudden complete loss of seagrass beds appears to result in major declines in most seagrass dwelling fish species (Hughes et al. 2002; Nakamura, 2010; Pihl et al. 2006).

Previous work in Shark Bay has shown that more fish species are found in seagrass in comparison to unvegetated areas (Heithaus, 2004). The most common fish in seagrass, Pelates octolineatus, is a prey item of Shark Bay dolphins (Dill et al. 2003). Although, not all fish species prefer vegetated seagrass beds. While five species were found only in seagrass, three fish species were found only in unvegetated areas, with Pentapodus vitta being the most common

(Heithaus, 2004). However, fish disturbance is dependent on multiple factors, as there are complex interactions observed between vegetation cover, depth, and season. Other regions of

Western Australia experienced fish mortality, decrease in fish stocks, range expansions, range constrictions, and change in community composition following this 2011 heat wave (Caputi et al.

2014; Kangas et al. 2013; Pearce et al. 2011; Wernberg et al. 2013). Following the same 2011 heat wave, in Jurien Bay, an area 613 km south of Shark Bay, temperate fish species did not decline, but there were changes in fish composition driven by an influx of warm water fish species (Wernberg et al. 2013). Other sites have also found gradual range shifts of warm water

68 fish with temperature increases (Fodrie et al. 2010). It is possible that compositional changes in fish species may also have occurred in Shark Bay. In this case, although the species of prey may have changed, prey may still be available. Thus, without detailed fish surveys in the seagrass beds of Shark Bay, it is impossible to know how dolphin prey density and distribution have changed since the die-off.

Further Implications and Consequences

Understanding the bottom-up impacts of habitat loss on higher trophic level species is critical as many foundational habitats, such as seagrass beds, are threatened by anthropogenic impacts and climate change. In addition to habitat formation, seagrass beds provide many vital ecosystem services, which are also threatened by impacts of climate change, and while some seagrass dependent species will be able to adapt to environmental changes, others will not.

Species that will be affected the most are those that are residential or geographically constrained, occupy a narrow niche, are physiologically limited, long-lived, and/or specialized in terms of habitat or foraging tactics. Loss of habitat may result in decreased foraging abilities, which could lead to decline in health and reproduction, and ultimately negative consequences at the population level. Shark Bay dolphins live in a relatively pristine habitat with very few anthropogenic pressures. However, they are residential and many rely on specific habitats for which they use specialized foraging tactics. Here, we found a change in habitat use following an extreme heat wave, which resulted in a major seagrass die-off. It appears that in response, dolphins are both increasing their use of seagrass and shifting to areas that were less damaged by the die-off, likely following prey distribution. We also found that they shifted to a seagrass species that was more available after the die-off, even though it was not their preferred habitat

69 type, and they shifted to using sand more frequently, which may be costly as sand habitats are generally less productive than seagrass habitats.

In our study, we did not identify any negative impacts of the heat wave on dolphin fitness; however, the scale of our study is relatively short-term for such a long-lived species living in a complex ecosystem, where seagrass recovery will likely take decades. If Shark Bay dolphins are continuing to consume prey that inhabit seagrass at a similar or increased rate following the die-off, this resource may not be sustainable as the seagrass beds have not shown evidence of recovery. Consequently, understanding the full impacts of the Shark Bay seagrass die-off on dolphins will require future monitoring of both the dolphins and their ecosystem.

70 FIGURES AND TABLES

Figure III.1. Monkey Mia study area. Location of study area (a) within Australia and (b) the eastern gulf, off Monkey Mia. The study area extent in purple with seagrass visual reference zones in grey shown over Landsat imagery captured in September 2002 (c) and August 2013 (d).

Seagrass appears as dark, well-defined areas in the water of the eastern gulf and loss is apparent to the north and east of Monkey Mia in the 2013 image. Classifications for the ‘pre’ period were based on 2010 seagrass coverage, however images from 2002 better visually illustrate seagrass coverage due to image quality, and seagrass coverage did not change from 2002 to 2010.

71 2.0

1.5

t r

o Period p

o Pre

r 1.0 P

Post

e M

0.5

0.0

All Dolphins Seagrass Specialists Dolphin Group Figure III.2. Proportional number of sightings from survey data ‘pre’ and ‘post’ the 2011 marine heat wave controlling for search effort. NB: Seagrass habitats were the same habitats before and after, even though some beds were no longer covered by seagrass in the ‘post’ period.

Bars are standard error. Significance level (**) is p<0.05.

72 a) Proportion of Time Spent in Seagrass b) Proportion of Foraging Time Spent in Seagrass

1.00 1.00

0.75 0.75

o r

0.50 r 0.50

e M 0.25 M 0.25

0.00 0.00 Pre Post Pre Post Period Period c) Proportion of Seagrass Time Spent Foraging d) Proportion of Time Spent Bottom−Grubbing in Seagrass

1.00 1.00

0.75 0.75

o r

0.50 r 0.50

e M 0.25 M 0.25

0.00 0.00 Pre Post Pre Post Period Period factor(Period) Pre Post Figure III.3. Proportional seagrass use from focal follow data pre and post the 2011 marine heat wave for seagrass specialists (n=11): a) Proportion of time spent in seagrass, b)

Proportion of foraging time spent in seagrass, c) Proportion of seagrass time spent foraging, d) Proportion of time spent bottom-grubbing in seagrass. Bars are standard error.

Significance level (**) is p<0.05.

73 a) Proportion of Time Spent in Seagrass b) Proportion of Time Spent Foraging in Seagrass c) Proportion of Time Spent Bottom−Grubbing in Seagrass

1.00 ** * 1.00 1.00

0.75 0.75 0.75

n **

t r

o * Period

p o

r 0.50 0.50 0.50 Pre

n Post

e M 0.25 0.25 0.25

0.00 0.00 0.00

Less More Less More Less More Seagrass Damage Seagrass Damage Seagrass Damage Figure III.4. General die-off measure for seagrass specialists: a) Proportion of time spent in seagrass, b) Proportion of time spent foraging in seagrass, c) Proportion of time spent bottom-grubbing in seagrass. Seagrass classified as ‘more’ damaged decreased in density from the ‘pre’ to ‘post’ periods and ‘less’ damage either remained the same or increased density. Bars are standard error. Significance level (**) is p<0.05, (*) is p<0.10.

74 a) Proportion of Time Spent in Seagrass b) Proportion of Time Spent Foraging in Seagrass

1.00 1.00

0.75 0.75 ** ** **

o r

0.50 r 0.50

M M

* **

0.25 0.25

0.00 NA NA NA NA NA 0.00 NA NA NA NA NA

Sparse P.A. Medium P.A. Dense P.A. Sparse A.A. Medium A.A. Dense A.A. Medium Mixed Dense Mixed Sand Sparse P.A. Medium P.A. Dense P.A. Sparse A.A. Medium A.A. Dense A.A. Medium Mixed Dense Mixed Sand Period Habitat Habitat Before c) Proportion of Time Spent Bottom−Grubbing in Seagrass After

1.00

0.75

p o

r 0.50

e M

0.25

0.00 NA NA NA NA NA

Sparse P.A. Medium P.A. Dense P.A. Sparse A.A. Medium A.A. Dense A.A. Medium Mixed Dense Mixed Sand Habitat Figure III.5. Species-specific die-off measure for seagrass specialists: a) Proportion of time spent in seagrass, b) Proportion of time spent foraging in seagrass, c) Proportion time spent bottom-grubbing in seagrass. Bars are standard error. Significance level (**) is p<0.05.

75 a) Proportion of Time Spent in Seagrass b) Proportion of Time Spent Foraging in Seagrass

3 3

** ** ** ** **

2 2

n *

M M 1 1

0 NA NA NA NA NA 0 NA NA NA NA NA

e M 1

0 NA NA NA NA NA

Sparse P.A. Medium P.A. Dense P.A. Sparse A.A. Medium A.A. Dense A.A. Medium Mixed Dense Mixed Sand Habitat Figure III.6. Species-specific selection ratio: a) Proportion of time spent in seagrass, b)

Proportion of time spent foraging in seagrass, d) Proportion time spent bottom-grubbing in seagrass. Dotted grey line is a Selection Ratio of 1, indicating that dolphins are using the habitat as expected from availability. Bars are standard error. Significance level (**) is p<0.05 and (*) is p<0.10.

76 CHAPTER IV

Stable female social bonds are associated with fitness in wild bottlenose dolphins*

INTRODUCTION

Group living can provide numerous generalized benefits such as information transfer, cooperative care of young, cooperative foraging, and anti-predator behavior. However, among societies with differentiated bonds, recent work has focused on the importance of bond quality, such as strength and duration. Specifically, strong female-female social bonds are linked to individually-specific benefits over and above those associated with group living. These benefits include a longer life span (Archie et al. 2014; Silk et al. 2010b; Thompson & Cords, 2018), higher reproductive success (Cameron et al. 2009; Silk et al. 2003), and an attenuated stress response (Wittig et al. 2008). Indeed, one possible mechanism underlying the relationship between social bonds and fitness is stress buffering. Prolonged exposure to stress, as measured by glucocorticoids, can have detrimental effects on immune function and health (Marketon &

Glaser, 2008). However, the presence of strongly bonded associates can mitigate the stress response (Engh et al. 2006; Wittig et al. 2008, 2016). Notably, while bond strength and duration are typically correlated (Silk et al. 2012; Silk et al. 2010b; Thompson & Cords, 2018), the stability of strong relationships is associated with increased fitness over and above the benefits of group living. For example, female blue monkeys (Cercopithecus mitis) that had strong bonds but changed partners had the lowest survival probabilities over time; whereas females that maintained strong bonds with the same partners had the highest survival (Thompson & Cords,

* Authorship for paper: Madison L. Miketa, Margaret A. Stanton, Vivienne Foroughirad, Céline H. Frère, Alexis Levengood, Janet Mann

77 2018). Therefore, we are specifically interested in the persistence of strong bonds, even though long-term bonds of weaker strength (i.e., casual acquaintances) are also likely to be important.

Long-term differentiated bonds have been well documented in cohesive social groups

(sperm whales: Physeter macrocephalus, Gero et al. 2008, 2015; Whitehead et al. 2012; resident killer whales: Orcinus orca, Baird, 2000; African elephants: Loxodota africana, Moss & Poole,

1983; baboons: Papio spp., Silk et al. 2006b, 2010a) where individuals necessarily spend extensive amounts of time, even a lifetime, in close proximity. Much less is known about the maintenance of social bonds in societies with high fission-fusion dynamics, where individuals

‘choose’ whom to associate with, in what context, and for how long. Because individuals in these types of societies are frequently joining and splitting groups, they help elucidate the social factors that are important for forming and maintaining close social bonds between individuals.

A fission-fusion social system is characterized by subgroup (or party) membership that is changing both temporally and spatially. Such dynamics fall on a continuum from a high degree of fission-fusion, in which there is high variation in party composition, party size, and spatial cohesion, to a lower degree of fission-fusion, in which there is lower variation in one or more of those three dimensions (Aureli et al. 2008). Shark Bay bottlenose dolphins (Tursiops aduncus), along with chimpanzees and spider monkeys, exhibit a high degree of fission-fusion dynamics, as there is great heterogeneity among all three dimensions (Galezo et al. 2018). Nevertheless, while social subgroups or parties are constantly changing, there may still be underlying structure driven by consistent individual choices. Frequently relationships in these high fission-fusion societies are categorized into rapid dissociations, casual acquaintances, which are short-term relationships that associate and disassociate repeatedly, or constant companions that associate permanently (Whitehead, 1995). A growing number of studies have investigated the presence of

78 long-term bonds in societies with high fission-fusion dynamics (e.g. Bechstein’s bats, Myotis bechsteinii: Kerth et al. 2011; giraffes, Giraffa Camelopardalis: Bercovitch & Berry, 2012;

Carter et al. 2013a,b; dolphins: Tursiops spp., Lusseau et al. 2006, Wiszniewski et al. 2010,

Connor & Krützen, 2015; chimpanzees, Pan troglodytes: Mitani, 2009). However, many studies are limited by data availability or dispersal, thus it is rare to find studies that persist across individual lifetimes or include both sexes. Most studies are on the order of weeks, months or a few years. Consequently, the definition of long-term bonds depends on practical considerations such as study duration and life history characteristics of the species under investigation.

Our study focuses of the presence of long-term bonds in adult female Indo-Pacific bottlenose dolphins (T. aduncus) in Shark Bay, Australia. Shark Bay bottlenose dolphins live in a highly complex fission-fusion society with overlapping social networks in an open community

(Mann et al. 2012; Galezo et al. 2018). The population is bisexually philopatric, so that males and females remain in the area for their entire lives (Tsai & Mann, 2013). Males form long-term stable alliances with other males that enable them to compete for and sequester individual females for mating access (Connor & Krützen, 2015). In contrast, females exhibit more social variability, forming variable social networks that involve preferential associations with other females (Smolker et al. 1992; Gibson & Mann, 2008; Mann et al. 2000; Frère et al. 2010a). In contrast to males, females are socially tolerant and exhibit very little aggressive behavior (Scott et al. 2005). While male alliances have been extensively studied and show stability over time, up to 20 years (Connor & Krützen, 2015), the durations of female bonds are not known. Females change group composition on average about 6 times per hour (Galezo et al. 2018), demonstrating very high fission-fusion dynamics based on activity state and other factors. Known benefits of female sociality in Shark Bay include increased calving success (Frère et al. 2010b; Mann et al.

79 2000) and protection from aggressive males (Galezo et al. 2018), but the benefits of long-term bonds per se are not known. Female social partners may assist in defending each other from aggressive males during consortship attempts or events, alleviate stress, assist with calf care, provide protection from predators, and share socioecological information. In fact, in a recent study, the only time adult females did not show avoidance of adult males is when multiple females were present (Galezo et al. 2018). Here, we have the opportunity to quantify bond duration throughout the adulthood of individual Indo-Pacific bottlenose dolphins and examine multiple factors driving the formation and maintenance of these bonds over a 30-year period, including fitness correlates. Specifically, we asked (1) How can long-term bonds be characterized using an association index? (2) What is the stability of top associates and what is the variation in bond length between females? (3) Do females with stable top partners have higher reproductive success than females that do not maintain a top partner, (4) What factors are important for the formation and maintenance of strong bonds?

METHODS

Data Collection and Study Subjects

The Shark Bay Dolphin Research Project has been collecting data on Indo-Pacific bottlenose dolphins in Shark Bay, Western Australia since 1984. The database repository includes demographic, behavioral, genetic, ecological, and life history data on over 1700 individuals to date. The study site is a 300 km2 area in the eastern gulf of Shark Bay off of

Monkey Mia (25° 47’ S, 113° 43’ E). Individual dolphins are identified through photo- identification of unique marks and scars on the dorsal fin and other distinctive features such as pigment patterns (Mann, 2000; Bichell et al. 2017).

80 Data for this study came from surveys, which are sighting records of one or more individual dolphins. Each sighting record is at least five minutes in duration, in which we record dolphin identity, group composition, behavioral, and ecological data using scan sampling

(Altmann, 1974; Mann, 1999). Group composition is determined using the 10-meter chain rule in which one dolphin is considered to be in a group with another dolphin if they are within 10 meters of one another, or connected by other dolphins (Karniski et al. 2015; Smolker et al. 1992).

For this study, we were interested in adult ( 10 year-old) female dolphins. Sexes were determined by views of the genital area or by the repeated association with a dependent calf

(Mann & Smuts, 1998), and when available, confirmed with genetic data (Frère et al. 2010a).

Ages were determined from known or estimated birthdates, physical features and behavioral characteristics (Mann & Smuts, 1999; Mann et al. 2000), and/or the presence and degree of ventral speckling (Krzyszczyk & Mann, 2012). Relatedness was determined from either the maternal partial pedigree from 30 years of observations, or genetic relatedness obtained from a panel of >4000 single nucleotide markers (SNPs) where it was available (Foroughirad et al. in prep). The observed maternal pedigree data were converted into predicted relatedness coefficients (e.g. mother and calf coefficient of relatedness= 0.50, half-sisters = 0.25), assuming unique and unrelated fathers where those data were not available. The missing paternal data may downwardly biases our estimates of relatedness, and makes our inference regarding the effect of relatedness more conservative. Pedigree and genetic relatedness data were combined as they were shown to be highly correlated for our population (Foroughirad et al. in prep).

81 Association Index

For this study we used two different analyses to examine long-term bonds among adult females. First, we examined the bond duration of top associates; second we investigated the factors that predicted the creation and maintenance of social bonds. For both analyses, we measured dyadic association using group composition from survey data. Two dolphins were considered to associate if they were observed in the same group. Females had to meet a minimum sighting requirement in order to be included in each analysis. We calculated the number of sighting days in two-year periods starting with their 10th birthday. For both the Top

Associates analysis and the Stochastic-Actor Oriented Model analysis, each female in the dyad were required to have at least 11 sighting days in each time period in order to be considered

(Mann et al. 2012). Stanton & Mann (2012) determined that a 15 sighting minimum was required for a 4 year period using the Cormack-Jolly-Seber model for mark-recapture data. That number was then adjusted to 11 sightings for a 3-year time period in Mann et al. 2012. To be conservative, we also used an 11-sighting minimum for a 2-year time period.

For each dyad, the strength of the association was measured by the half-weight coefficient. The Half-Weight Index (HWI) measures the proportion of sampling periods (in this case days) during which two individuals were sighted together divided by the total number of independent sampling periods of each (in association or alone), discounting sampling periods where only one member of the dyad was observed (Cairns & Schwager, 1987; Whitehead, 2008).

풙 푯푾푰 = , where x = number sampling periods where dolphins A and B were {풙+풚풂풃+ퟎ.ퟓ(풚풂+풚풃)} observed in the same group, y = number of sampling periods where dolphin A and dolphin B ab are both present but in different groups, y = number of sampling periods where dolphin A was a observed without dolphin B, y = number of sampling periods where dolphin B was observed b

82 without dolphin A. Values range from zero (never seen together) to one (always seen together).

The sampling period for half-weight calculation was one day. This measure is most appropriate when each dyad is more likely to be observed separate than together (Cairns & Schwager, 1987).

Further, it is the most commonly used measure of association in cetaceans (e.g. bottlenose dolphins, Tursiops spp.: Frère et al. 2010a,b; Lusseau et al. 2006; Möller et al. 2006; Stanton &

Mann, 2012; Guana dolphins, Sotalia guianensis: Cantor et al. 2012; Sperm whales, Physeter macrocephal: Gero et al. 2008, 2015; short-finned pilot whale, Globicephala macrorhynchus:

Mahaffy et al. 2015; long-finned pilot whales Globicephala melas: Augusto et al. 2017) and other species (giraffes, Giraffa camelopardalis: Carter et al. 2013a,b; eastern grey kangaroos,

Macropus giganteus: Best et al. 2013; Carter et al. 2009; Grant's gazelles, Nanger granti:

Williams et al. 2017; Gould’s wattled bats, Chalinolobus gouldii: Godinho et al. 2015; blacktip reef sharks, Carcharhinus melanopterus: Mourier et al. 2012) and therefore render our results more comparable to previous studies.

Top Associates

To examine bond duration we focused on top associates (see de Silva et al. 2011; Silk et al. 2006b; Silk et al. 2010a, b; Silk et al. 2012; Thompson & Cords, 2018). We first identified each adult female’s top associates during every two-year period between 1988 and 2016.

Because half-weight coefficients were calculated for two-year periods for each individual female starting when they turned 10 years of age, time periods of different females did not necessarily overlap and top partner status and association rates were not always symmetrical.

Top associates were defined as those in the top 2.5 percentile of a female’s associations

(Frère et al. 2010a,b). We used a percentile rather than an absolute value threshold because the

83 distribution and range of association indices varied widely among females. Adult females can have vastly different number of associates (0-139) and can spend anywhere from zero to 100 percent of their time in groups (Gibson & Mann, 2008a; Mann et al. 2008). This conservative cut-off is very high, but increases the probability of selecting true top associations. This resulted in most females having one to two top associates (maximum of three) per time period. Short- term bonds were defined as top associations that did not persist across multiple time periods ( two years). Long-term bonds were defined as top associations that continued across two or more time periods ( four years), though these were not necessarily consecutive due to observation gaps, e.g., a female was not sighted 11 times during a 2-year interval. “Long-term” bonds have not been clearly defined in previous studies and terms describing long-term relationships vary.

Generally, “long-term” is relative to the length of the study and/or co-residency of conspecifics

(e.g. Cantor et al. 2012; Carter et al. 2013a; Kerth et al. 2011; Langergraber et al. 2009; Mahaffy et al 2015; Mitani, 2009; Ortega-Ortiz et al. 2012; Silk et al. 2006b; Smith et al. 2016;

Wiszniewski et al. 2010), rather than specific to the life history of the species. Frequently, long- term bonds are described by the “stability” of association trough time (e.g. Mason & Sterck,

2013; Silk et al. 2006b; 2009; 2012). Here, we use a definition similar to Silk et al. (2006a,b;

2010a), which also only looked at top partners, where enduring bonds were defined at those that were consistent from year to year (here, 2-year time period to 2-year time period). In terms of life history, four years is typical for both weaning age and interbirth intervals in Shark Bay

(Mann et al. 2000; Karniski et al. 2018). Adulthood can last 30 years or more, in our study females were observed for an average of 9.92 years (min= 4, max= 32). Using a longer time period cut-off would not allow us to measure long-term bonds in younger females, thus using four-year periods allows the inclusion of young adult females. To test if there was a significant

84 difference in bond strength between short-term and long-term bonds, we performed a permutation test using the R package coin (Hothorn et al. 2006). All analyses were performed in

R v.3.5.0 (R Core Team, 2018).

Reproductive Success

Next, using the same adult females from the Top Associates analysis, we examined whether maintaining a long-term bond correlated with reproductive output. Out of the 127 adult females from the previous analysis, 3 were excluded because they did not have enough reproductive years to analyze (minimum of 4 years with known calf survival). Long-term bond maintenance was binary, either the female maintained at least one long-term bond during adulthood (1), or she did not (0). Reproductive success was binary, either the female had at least one calf that survived to age 3 (1), or she did not (0) ( 11 years old, age of first birth) (see Mann et al. 2000; Karniski et al. 2018). In our study, 22% of females were considered ‘failed females’, as they did not have a surviving calf.

We used a generalized linear model with a binomial distribution to test whether the presence of a long-term bond correlated with reproductive success using the R base package

Stats. All analyses were performed in R v.3.5.0 (R Core Team, 2018). The response variable was calving success (1 for yes, 0 for no), the fixed factor was long-term bond presence (1 for yes, 0 for no). To equalize between females that had more years of observation, and thus had more opportunities to maintain a long-term bond, we included number of years observed as a random factor in the model. We chose a binary measure because calving rate declines with age, such that younger females have much higher calving success than older females (Karniski et al. 2018).

Consequently, if we used a rate, this might bias our results toward younger females having a

85 higher calving success. Additionally, we were interested in if there was a correlation between being a failed female and maintaining a long-term bond.

Stochastic Actor-Oriented Model (SAOM)

Finally, we used network analyses with a less stringent bond strength cut-off to determine the factors most important for the formation and maintenance of social bonds. Stochastic Actor-

Oriented Models (SAOMs) are actor-based models that examine longitudinal network changes by modeling the influence of network processes and covariates on an actor’s decision to form, maintain, or terminate a tie (Snijders et al. 2010; Ripley et al. 2018). The underlying assumption is that the data are continuous, however the data are modeled through consecutive discrete time periods. Although widely used in human literature, they a great option for animal social networks

(Fisher et al. 2017), and have recently been applied to a variety of species (e.g. vervet monkeys:

Chlorocebus pygerythrus, Borgeaud et al. 2016; rooks: Corvus frugilegus, Boucherie et al. 2016; spotted hyenas: Crocuta crocuta, Illany et al. 2015; fruit flies: Drosophila melanogaster:

Pasquaretta et al. 2016; Barbary macaque: Macaca sylvanus, Sosa et al. 2017).

We used a subset of sighting data collected from 1998 to 2005 in order to create four undirected weighted female networks using HWIs for consecutive two-year time periods (Figure

IV.1). These years were chosen because they contained the most sighting data for adult females based on consistent observational effort, with the least sampling heterogeneity. Unlike the top associates analysis (above), all two-year time periods used in the SAOM were aligned, as they started with 1998 and were not female specific.

In order to make sure that the two-year time period was appropriate we calculated the

Jaccard Index for transition between each of the 4 networks, which is the recommended measure

86 of time period similarity for this model (Ripley et al. 2018). For this study, the range of Jaccard values were 0.55-0.61 (mean = 0.59), which is well above the acceptable cut-off of 0.3 (Ripley et al. 2018).

Networks were created using weighted edges, however weighted networks were filtered and dichotomized in order to fit the binary framework of the SAOM. All edges greater than twice the mean HWI were included in the binary networks (Whitehead, 2008), a measure of association preference that is commonly used in cetacean literature (e.g. Elliser & Herzing, 2014;

Gero et al. 2005, 2008; Mahaffy et al. 2015). The mean HWI for all 4 networks was 0.029, so ties with a HWI of 0.058 or greater were coded as present (1), and ties with a HWI less than

0.058 were coded as absent (0). This cut-off is less strict than the first analysis, which only allowed us to examine temporal stability between top associates; here we examine the temporal dynamics of a broader range of associates. Although bond strength is likely correlated with bond duration, individuals may maintain long-term relationships with individuals that are not their top associates. Thus, this different association cut-off allowed us to examine the factors that contribute to maintenance of those weaker ties as well.

We used RSiena (1.2-12) (Snijders et al. 2010) to run the model over four two-year networks. Individuals that were not available in a given time period, due to not meeting the minimum sighting requirement, age, or death were coded as structural zeros, indicating that ties could not exist with those actors. We included density, GWESP, balance, and degree of alter as structural effects (Table IV.1). Age difference was included as a constant dyadic covariate and strong partner (defined as bonds with a HWI of 0.25 or greater, the average bond strength for top partners in the top partner analysis) as a time varying dyadic covariate. We chose to employ the creation and endowment function because we were interested in factors that contributed to the

87 maintenance of existing ties versus those that contributed to the creation of new ties. The creation function models the contribution of the parameter for tie formation. The endowment function models the contribution of the parameter for tie maintenance. While there is no formal model selection established for the SAOM, we used a forward selection procedure as suggested by Fisher et al. (2017). Parameters that were not significant and lowered the goodness-of-fit were removed from the model (see Results) (Fisher et al. 2017).

RESULTS

Top Associates

This dataset included 127 adult females with 630 ID-time periods after filtering for the 11 sightings per period minimum. 686 of 7,506 possible dyads were identified as top partners. There were an average of 4.96 time periods per female (min= 2, max= 16). The majority of females,

82/127 (65%), had at least one long-term top associate that spanned 4 years or more (Figure

IV.2). However across all 686 top partner dyads, only 174 (25%) lasted for at least 4 years. Of these 686 dyads, 529 were unique due to top associations being calculated for each individual.

Relationships were censored due to age, minimum data requirements, or death of an associate.

Average bond duration was nearly the same for dyads that had more time periods available than the maximum duration of their relationship (n=506, mean duration= 2.75 years) and dyads whose relationship lasted the entirety of available time periods for the dyad (n=180, mean duration=

2.73 years) (Figure IV.2). Twelve females maintained a top associate for at least 10 years; 3 were mother-daughter pairs, and 3 had a relatedness coefficient below 0.125, 2 were unrelated, and 4 were of unknown relatedness. Of the 12 bonds ranging from 10-18 years, 7 ended due to death of a partner. The longest bonds observed were 16 years and 18 years (Figure IV.2). Relatedness

88 data (either genetic or matrilineal) were available for 267/686 (39%) dyads. Of the 267 dyads with relatedness data, 93% came from genetic relatedness and 7% exclusively from maternal pedigree. Although maternal pedigree data were known for 27% of the 267 dyads with relatedness data, we used genetic relatedness where available and only supplemented unknown genetic relatedness with maternal pedigree. Out of the subset of dyads with relatedness data,

88/267 (33%) of the dyads that maintained a bond for at least 4 years had a coefficient of relatedness of at least 0.125. Also out of that subset, the average bond duration was 3.662.44 years (meanSD) for females with a coefficient of relatedness of at least 0.125 and 2.781.98 years (meanSD) years for females with a coefficient of relatedness less than 0.125. The percentage of top associate dyads that had a relatedness coefficient of at least 0.125 was 31% out of the subset of dyads with relatedness data. 150/686 pairs (22%) were age-mates, defined as born within 3 years of each other.

The average half-weight coefficient observed across all top partner dyads was 0.250.13

(meanSD), the minimum was 0.03, and the maximum was 0.77 (Figure IV.3). The average half- weight coefficient for kin with a relatedness coefficient of at least 0.125 was 0.340.13

(meanSD) with a minimum of 0.07 and maximum of 0.62, and for non-kin 0.230.13

(meanSD) with a minimum of 0.03 and maximum of 0.60. Long-term bonds were significantly stronger than short-term bonds (Z = 7.6131, P<0.001; Figure IV.4).

Reproductive Success

Females that maintained at least one long-term bond with a top associate during adulthood (n = 81) were more likely to have a surviving calf than females that did not maintain any long-term bonds with top associates (n = 43) (=0.9263, SE=0.4445, Z=2.084, P=0.0371).

89 Only 33% of females that did not maintain a long-term bond had a surviving calf, while 84% of females that did maintain at least one long-term bond had a surviving calf.

Stochastic Actor-Oriented Model (SAOM)

We used the structural zeros method with 10,909 iteration steps (Ripley et al. 2018). The model properly converged, the overall maximum convergence ratio was 0.0597 (excellent convergence is considered <0.20) and all t statistics for deviations from targets were less than the absolute value of 0.1 (Table IV.3). The model met the time heterogeneity assumption indicating that each time period was similar enough for modeling (chi-squared = 6.31, df = 12, p = 0.8996) and had proper goodness-of-fit indicating that the model properly fit the observed data. The goodness-of-fit was measured by sienaGOF for which the joint Monte Carlo Mahalanobis distance was 18.39 with a p-value of 0.908 for indegree distribution and distance 18.29 with a p- value of 0.15 for geodesic distribution (non-significant values indicate proper fit).

This dataset included 65 adult females present during the four two-year time periods

(Table IV.3). The creation effect for transitive closure (GWESP) had a significant positive influence on network structure (Table IV.3) indicating that sharing a common associate is important for the creation of ties. Age difference between females had a significant negative effect on the creation of ties, demonstrating that females closer in age were more likely to form new ties (Table IV.3). For the maintenance of ties, balance had a significant positive effect signifying that the greater number of the same associates a pair had, the more likely they were to maintain a tie (Table IV.3). Bond strength also had a significant positive effect, meaning that females were more likely to maintain ties with preferred females (HWI  0.25) (Table IV.3).

Finally, degree of alter had a significant negative effect, indicating that a female was less likely

90 to maintain a tie with another female that was highly connected (Table IV.3). Parameters that were not significant and lowered the goodness-of-fit were removed from the model. Those parameters were relatedness between ties (available for 58.9% of ties) (creation and endowment), balance (creation), degree of alter (creation), strong partner (creation), GEWSP (endowment), and age difference (endowment).

DISCUSSION

In this study we examined the temporal dynamics of female relationships by investigating the persistence of top partners as well as examining the factors important for tie existence and maintenance in a longitudinal network. First, we found that more than half of female dolphins in

Shark Bay maintained long-term bonds with their top partners, who may be kin or non-kin. Some relationships lasted nearly two decades and were censored only by the death of a partner.

Secondly, females that maintained at least one long-term bond with a top associate were more likely to have a surviving calf than females that did not maintain any long-term bonds. Finally, we found factors that predicted the durability of social bonds that differed from those that predict the creation of new bonds. Namely balance, partner strength, and degree of alter were important for bond maintenance, while ‘friends of friends’ and age difference were important for bond creation.

Our study combined two analyses, one at the dyadic level and the other at the network, in order to better understand the existence and persistence of female bonds. In animal study systems, the most common analysis to measure the temporal dynamics of relationships at the population level is Lagged Association Rates (e.g. Best et al. 2013; Carter et al. 2013a; Lusseau et al. 2003; Smith et al. 2016; Whitehead, 2009; Wiszniewski et al. 2010). However, we were

91 interested in the persistence of top partners at the dyadic level, so we used a method more similar to those used in primate and elephant studies (de Silva et al. 2011; Silk et al. 2006a,b; Silk et al.

2010a; Silk et al. 2010b; Silk et al. 2012; Thompson & Cords, 2018). In order to examine the underlying factors influencing the maintenance of existing ties, in addition to the creation of new ties, we employed a social network analysis. Specially, we chose a stochastic-actor oriented model because it models longitudinal changes in networks and assumes that ties are choices made by the actor with knowledge of the surrounding network. Another great aspect of this model is that it models parameters that influence the creation and maintenance of ties separately.

The existence and persistence of top partners likely confers a number of fitness benefits for Shark Bay females, as observed in other species. First, social groups in general are important for protection against predators. In Shark Bay tiger sharks, Galeocerdo cuvier, are seasonally abundant (Heithaus & Dill, 2002; Heithaus, 2001) and nearly three-quarters of adults and approximately one-third of nursing calves bear shark bite scars (Heithaus, 2001; Mann &

Barnett, 1999). Because females are generally found in multi-generational family groups, their calves often receive extra protection provided by other adult females (Frère et al. 2010b), which may be especially important during mother-calf separations (Mann & Watson-Capps, 2005).

Mothers may be able to be less vigilant when trusted top partners are present, allowing them to spend more time on calf care, resting, or foraging. Carter et al. (2009) found that female eastern grey kangaroos (Macropus giganteus) were found to be less vigilant and graze longer when preferred partners were present. Another benefit of long-term associations with a top partner is cooperation against herding and aggression from male alliances. Male consortship events are very stressful as females are ‘captured’ and receive threats and aggression (Connor & Krützen,

2015; Wallen et al. 2016). Thus, the presence of top partners may help alleviate physiological

92 stress from male harassment (Galezo et al. 2018). Shark Bay females were found to be more likely to engage in contact-swimming with one another when in male-biased groups (Connor et al. 2006). This behavior may be an effort for females to reduce male harassment and alleviate stress during consortships. In feral female horses stronger social bonds were correlated with lower male harassment and increased reproductive success (Cameron et al. 2009). Enduring strong bonds may be even more beneficial. In female primates, strong (Silk et al. 2006b, 2010a) and enduring relationships (Silk et al. 2006b) were the most equitable. Therefore there is the potential that top partners might be more likely to assist one another in such situations, especially since stable and strong relationships tend to be more reciprocal. If a female helps her close associate now, her associate might then help her in a future situation. Lastly, beyond just having strong bonds, consistent relationships with top partners have been associated with lower levels of stress hormones (Wittig et al. 2008, 2016), increased life span (Silk et al. 2010b), and higher reproductive success (Silk et al. 2003) in female baboons. More specifically, in female blue monkeys, the consistency of strong bonds was the main predictor of survival. Strong bonds that were inconsistent were correlated with a higher mortality risk than bonds that were consistently weak or consistently strong (Thompson & Cords, 2018). However, it is important to note that directionality of this relationship is unknown. Similarly the loss of a preferred partner in baboons resulted in increased stress levels (Engh et al. 2006). While the underlying mechanisms are not known, it is clear that there are numerous benefits to having a consistent top partner, many of those associated with stress and fitness.

Shark Bay females have the unusual challenge of countering with extremely complex allied male coercion. Male alliances in Shark Bay are multilevel and among the most complex in cetaceans. In addition, alliance partners may be stable for up to 20 years (Connor & Krützen,

93 2015). These males work together both within and between alliances to herd females for mating purposes. Given the various stressors of this coercive mating system, we expected females to maintain long-term bonds with one another. Some female bond strengths were on par with half- weight coefficients observed in male alliances (Figure IV.3). Half-weight coefficients between males in first-order alliances are generally 0.70 or greater and those between males in second- order alliances 0.30 or greater (Connor et al. 1999). Nearly half the top partner female dyads we examined had a HWI greater 0.30 and two top partner dyads had a HWI greater than 0.70.

However, because females must spend a great deal of time foraging and caring for calves, females may be unable to associate as frequently or consistently as males. Further, during male consortships, females are taken away from their core home ranges (Wallen et al. 2016), which may disrupt female-female bonds. Nevertheless, their relationships may be just as important/persistent even though association rates are lower than those of males.

Both the Top Associates analysis and the network analysis identified bond strength as a predictor of bond duration. First, in the Top Associates analysis, there was a trend of increasing bond duration with bond strength among the subset of dyads identified as top partners. Bond strength was significantly higher in long-term bonds than short-term bonds (Figure IV.4). In the

SAOM, ‘strong partners’, those with a HWI equal to the average HWI from the Top Associates analysis, were more like to maintain existing ties. As previously mentioned, strong relationships require substantial investment, so it is understandable that females would sustain those investments through time. Additionally, studies have shown that strong bonds tend to be more equitable (Silk et al. 2004; 2006a, b). Equitable relationships may be longer-lasting because investment is higher and benefits are delayed. Indeed, equitability is also predictor of bond

94 length (Silk et al. 2006b). Additionally, the benefits associated with strong relationships specifically, such as stress buffering, may be more important for long-term fitness.

Age difference between adult females was an important predictor of the creation of new bonds, meaning that females closer in age were more likely to form new bonds. We expected this covariate to be more important for the maintenance of bonds, as females close in age likely begin forming a relationship during earlier life history stages. In chacma baboons females closer in age were more likely to form longer-lasting bonds (Silk et al. 2010a). However, this is likely due to the fact that age proximity is an indicator of paternal kinship, which is not the case for Shark Bay dolphins. Instead, young adult females may be drawn to other females that are similar in age and familiar from the calf or juvenile period. Additionally, females close in age are entering adulthood at similar times and may be forming new bonds with other adult females in early adulthood. These relationships may be especially important as females start becoming attractive to males and becoming first time mothers. During this time strong bonds may help with buffering the stress of male harassment as well as provide social support when raising a calf for the first time. And because juveniles form bonds that may last into adulthood, some females may have preferred female partnerships that formed in the calf or juvenile period. Age difference may be less important for the maintenance of ties because adulthood can last over 30 years and over that timespan females frequently associate with multiple generations, not just close age mates.

Transitive closure was an important predictor of both creation and maintenance of bonds, but in slightly different ways. Females were more likely to create new bonds with ‘friends of friends.’ Balance, calculated as the number of same ties, predicted tie maintenance. Structural balance has also been found to be important to the social structure of rock hyraxes and may help stabilize social structures (Ilany et al. 2013). Maintaining social bonds in triads of positively

95 associated females may increase stability in the social structure and facilitate cooperation between groups of females. Cheney et al. (2016) suggested that clustering might be especially important for facilitating cooperation in fission-fusion dynamics, where individuals interact intermittently. Because Shark Bay females are natally philopatric (Tsai and Mann 2013), there is an opportunity for multigenerational associations to form between kin, increasing the likelihood of maternal inheritance of associates. Both modeling and observational data have shown that social networks can be vertically inherited (Goldenberg et al. 2016; Ilany and Akcay, 2016). The average weaning age in Shark Bay is 4 years old (Mann et al. 2000; Karniski et al. 2018), during this time calves are associating with their mothers’ associates. Further, female calves adopt some maternal social patterns (Gibson & Mann, 2008a; b) and likely continue to emulate their mother’s social preferences throughout the calf and juvenile periods. Inheritance of maternal social networks would lead to an increase in clustering and triadic closure. Additionally, our definition of association, co-membership in the same group, could result in the gambit of the group effect (Whitehead & Dufault 1999). Using group membership for dyadic association rates may result in some loss of detail since every individual in the group is assumed to associate with each other equally. For example, dyads may be interacting at different rates in a group, however all will be assigned the same association measure. However, adult female groups change, on average 6 times per hour (Galezo et al. 2018), such that individuals can still choose with whom to, and whom not to associate. Although constant group changes are characteristic of fission- fusion dynamics, dyads that interact consistently will be observed together repeatedly in various groups. Cheney et al. (2016) suggested that clustering might be more important for individuals exhibiting fission-fusion dynamics to check-in with one another, in contrast to those living stable groups with predictable association frequencies.

96 Degree of alter, or the number of ties a partner has, was also important for the maintenance of bonds. Females tended to maintain ties with associates that had a lower degree, or fewer ties. Relationships, especially strong relationships, can require many resources and substantial time investment. Partners with too many ties may be unable to invest the require resources to maintain a consistent association. This may explain why many populations, regardless of social structure, are mostly categorized by short-term relationships with a small number of long-term partnerships (de Silva et al. 2011; Lusseau et al. 2006; Silk et al. 2010a,

2018; Whitehead, 1995).

Although dyadic relatedness was not an important predictor of bond creation or maintenance, female bonds between kin are likely still important. Because we don’t have relatedness data, and especially paternal relatedness data, for nearly half of the females in the

SAOM analysis, the importance of female kin may be underestimated. Shark Bay dolphins are bisexually philopatric and multiple generations of a matriline frequently associate. Repeated interactions with related females from infancy to adulthood provide the opportunity to form strong, consistent relationships. Further, mothers and their daughters continue to associate closely from birth through to adulthood (Tsai and Mann 2013). Important socioecological information is passed down through the matriline, such as foraging tactics (Sargeant & Mann,

2009; Mann et al. 2008). Kin selection may also explain both the investment required and benefits obtained from female-female relationships. Thus, our study may not have captured the true importance of female relatedness in maintenance of long-term bonds.

Ultimately, we were interested in examining the potential fitness impacts of long-term bonds with top partners. As predicted, we found that maintaining at least one long-term bond with a top associate was correlated with higher likelihood of having a surviving calf. This is

97 similar to other studies that found that females that were more socially integrated had higher offspring survival (Cameron et al. 2009; Silk et al. 2003). Failed females, those that did not have a surviving calf, may experience higher stress levels or less female assistance during her lifetime, leading to lower calf survival. This was found in mares, where unrelated females that were more socially integrated not only had higher offspring survival, but also lower rates of harassment

(Cameron et al. 2009). However, it is important to note that the relationship between long-term bond presence and calf survival observed in our study is not necessarily directional and that there could be other confounding factors contributing to reproductive failure. For example, failed females may be unable to maintain a long-term bond because they are continually harassed by males, or because other females choose not to associate with them. In this case, low reproductive success would be driving the absence of long-term bonds, rather than the opposite. We would also like to explore the correlation between strong long-term bonds and lifespan, as observed in in other systems.

Our study focuses on the stability on strong bonds. However, this is not meant to take away from the importance of weak bonds and short-term relationships, which typically make up the majority of social bonds in a community. Weak bonds confer substantial fitness benefits.

They may be important for social integration and information transfer (Brent, 2015; Granovetter,

1973), and network cohesion (Goulas et al. 2015). Additionally the number of weak bonds has been correlated with infant survival and longevity in baboons (McFarland et al. 2017). Weak bonds may be more related to ecological conditions, such as importance for thermoregulation in cold stress (McFarland, 2017), or reflect resource availability (Henzi et al. 2009). Weak ties may be especially important in a society with high fission-fusion dynamics where individuals frequently change group composition. Further, long-term bonds may also exist between dyads

98 with weak bonds. The strict cut-off used in the Top Associates analysis included only the top 1-2

(maximum 3) partners per female. This excludes the majority of a female’s associates. Thus, there are very likely dyads with lower half-weight coefficients that also maintain stable relationships through time that likely provide a number of important benefits. For example,

Thompson & Cords (2018) found that consistent weak bonds were less beneficial than consistent strong bonds, but more beneficial than inconsistent strong bonds. This suggests that regardless of strength, the consistency of a relationship is also related to fitness.

We provided a novel look into the temporal dynamics of female relationships in Shark

Bay. We used two different methods to understand the duration of female-female bonds and the factors that are important for the maintenance of female bonds. Previously the literature emphasized long-term bonds in males only (through complex alliances), although the longevity of these has not been explicitly quantified. We have shown that females maintain long-term relationships, and some are as strong as those found in male alliances, as measured by half- weight coefficients. Shark Bay females face a number of challenges such as allied sexual coercion, long-term calf care, predation pressures, and patchy and ephemeral prey, the stressors of these may be minimized by maintaining consistent relationships with preferred partners.

Female-female bonds confer a number of benefits in a variety of species. Understanding the underlying drivers of female relationships provides better insight into those benefits and the evolution of bonds. Previous work has mostly described female relationships in stable matrilineal groups, however, here we show the importance of long-term bonds among top partners in a long- lived species that exhibits fission-fusion dynamics.

99 FIGURES AND TABLES

1998−1999 2000−2001 2002−2003 2004−2005

Figure IV.1. Networks for the four two-year periods used in the stochastic actor-oriented model. Nodes are individual female dolphins and edges are weighted by half-weight coefficient.

Edges were filtered by HWI = 0.058.

100 135

120

105

s r

i 75

a P

MaxAvailability

f o

Max r

e NotMax

b 60

u N

4 6 8 10 12 14 16 18 Bond Length (minimum years)

Figure IV.2. Frequency of bond duration. Long-term bonds are present in Shark Bay females.

Blue bars indicate that the bond duration observed was the maximum bond duration possible based on availability of the partner. Grey bars indicate that the bond duration had potential to last longer based on additional time periods that the partner was available, but that partner was not in the top 2.5% of associations.

101

Figure IV.3. Distribution of top associates. The average half-weight coefficient observed was

0.25, the median was 0.24, the minimum was 0.03, and the maximum was 0.77.

102 0.8

0.6

h g

i 0.4

−

a H

0.2

0.0

2 4 6 8 10 12 14 16 18 Bond Length (minimum years)

Figure IV.4. Half-weight index by minimum bond duration (due to censored data). Long- term bonds were significantly stronger than short-term bonds (Z = 7.6131, P<0.001). NB: 15 years and 18 years each consist of a single pair.

103

Table IV.1. Description of stochastic actor-oriented model parameters.

Parameter Description Density The baseline tendency to form a tie Balance The tendency to have the same outgoing ties as an associate. Partner who makes the same choices in ties (has same “attitude” towards ties) Degree of alter The tendency to form a tie with an individual based on their number of ties Strong partner Ties with a HWI of 0.25 or greater (the average bond strength for top partners in the top partner analysis) Geometrically Weighted The tendency to form a tie with an associate whom is connected Edgewise Shared to an existing associate; transitive closure; ‘friend of friend’ Partners (GWESP) Age difference The absolute difference in age between ego and alter

104 Table IV.2. Summary data for four two-year periods used in the stochastic actor-oriented model.

Years N Mean degree Mean ± SE HWI 1998-1999 40 4.923 0.041 ± 0.002 2000-2001 34 3.292 0.034 ± 0.002 2002-2003 57 6.277 0.022 ± 0.001 2004-2005 52 4.585 0.021 ± 0.001

105 Table IV.3. Stochastic actor-oriented model results. Creation of ties refers to the creations of new ties. Endowment (endow) refers to the maintenance of existing ties.

Parameter Function Estimate Std Convergence P-value Description Error t-ratio Density -2.0832 0.2549 -0.0165 <0.001 Balance Endow 0.1382 0.0303 -0.0262 <0.001 Maintain ties with partners who make the same choices in ties Degree of Endow -0.1285 0.0652 -0.0368 0.0486 Maintain ties with alter partners with a lower degree Strong Endow 1.6768 0.5190 -0.0005 0.0012 Maintain ties with partner partners with strong bonds GWESP Creation 2.7104 0.4485 0.0009 <0.001 Create ties with ‘friends of friends’ Age Creation -0.0669 0.0261 -0.0311 0.0105 Create ties with difference partners similar in age

106 CONCLUSION

I investigated the behavioral responses to various environmental stressors in Shark Bay bottlenose dolphins and the presence of long-term social bonds as a mitigating factor. I examined the behavioral impacts of stressors associated with maternal care, anthropogenic pollution, and habitat loss, and found that dolphins were able to adjust their behavior in response. However, these behavioral responses came with trade-offs. I also quantified the duration of strong social bonds, which have been found to attenuate stress response, and their impacts on fitness.

In examining trade-offs in maternal diving behavior and foraging, I found that mothers adjust their diving behavior in response to female, but not male calves in close proximity.

Previous work has shown that female calves are more likely to adopt their mothers foraging tactics, many of which are vertically transmitted through the matriline. Thus, due to different socio-ecological strategies between males and females, it is most beneficial for mothers to stay in close proximity to female calves, rather than male calves, when diving. This study provides evidence that mothers selectively alter their behavior, perhaps to maximize benefits to offspring while minimizing costs (Miketa et al. 2018).

Utilizing data from a rare fishing line entanglement event in Shark Bay, I was able to examine the impacts of entanglement on the behavior of a juvenile female. In response to the entanglement, EDE, the study subject, drastically altered her behavior. She decreased her time spent foraging and socializing, and increased time spent traveling. EDE also engaged in a number of erratic behaviors, such as leaps and fast accelerations. During her entanglement, EDE was largely alone, the inverse of time spent in groups before the entanglement period. After freeing herself from the fishing line, EDE’s behavior returned to the baseline observed pre- entanglement (Miketa et al. 2017). This chapter provided a rare opportunity to for the in-depth

107 study of the impacts of entanglement on an individual level. Further, few studies provide insight on the impacts of entanglement on sociality, which may have detrimental long-term impacts on a social species, such as the bottlenose dolphin. We showed that short-term behavioral responses to a human induced stressor result in a number of costly changes in behavior.

I investigated the impacts of habitat loss after a major seagrass die-off occurred at our study site. I found that those individuals shifted to utilize seagrass beds that were less damaged by the die-off. Specifically, dolphins shifted from their preferred species Amphibolis antarctica, to their less preferred species Posidonia australis. They also increased their use of sand habitats.

However, I found no impact on calf survival or calving rate. Here, we see that dolphins are flexible in their habitat-use and foraging abilities, even those that exhibit strong habitat presences. As disturbances and extreme temperature fluctuations become more common, habitat degradation of foundational species, such as seagrass, will also become more frequent.

Understanding the bottom-up impacts of such habitat loss on top predators and the costs associated with individual behavioral responses will be critical for predicting the future survival of species in changing environments.

Finally, I examined a potential mitigating factor to environmental stressors. Long-term, strong social bonds have been found to confer a number of fitness benefits in a variety of mammalian species. Specifically, stable relationships between preferred patterns can also act as stress buffers, improving response to environmental stressors. Therefore, I investigated the presence and fitness impacts of strong long-term social bonds between adult females. I found that long-term bonds do exist between females, with some bonds lasting up to 18 years. Further, females that maintained at least one long-term were more likely to have a surviving calf than females that did not maintain any long-term bonds. To our knowledge, this was the first study to

108 show that female-female bonds can last throughout adulthood in a wild cetacean. Additionally, this was the first study to show that strong, stable bonds are correlated with fitness benefits in a cetacean.

Unquestionably, bottlenose dolphins are capable of adjusting to changing environments.

However, those behavioral changes may incur short-term and long-term costs. It is important that we understand how individuals will respond to environmental stressors, such as disturbances or anthropogenic pollution, which are likely to become more frequent and more severe. Further, strong, long-term bonds are present between adult female Shark Bay dolphins. These social bonds may help mitigate stressors and provide numerous fitness benefits. Our ability to predict behavioral responses to environmental stressors and the role of social bonds to buffer such stressors will aid in future conservation efforts as we identify populations at the greatest risk.

109 APPENDIX A

Chapter I Supplemental Material

Table A1. Breathing rates of bottlenose dolphin mothers (N = 4) and calves (N = 4) collected in relatively shallow water (<6 m) in Monkey Mia. Average breathing rates were 2.86 breaths/min for calves (2–4 years old) and 2.90 breaths/min for mothers (13–31 years old). Average breathing rates presented here indicate approximately 20 s between breaths.

Subject ID No. of minutes observed Mean breathing rate

Calves

BUD 85 2.58

EDE 120 3.03

INI 130 3.12

YAD 105 2.69

Mothers

SUR 150 2.40

PIC 120 3.10

PUC 115 2.71

NIC 140 3.34

110 APPENDIX B

Chapter III Supplemental Material

RESULTS

Bottom-grubbing, the seagrass specific foraging tactic, did not significantly change between periods when compared to all other foraging tactics combined (X2 = 1.4379, df = 1, P = 0.2305), but mill foraging (X2 = 112.09, df = 1, P ≤ 0.001) and snacking (X2 = 6.0654, df = 1, P =

0.01379) increased from the ‘pre’ to the ‘post’ period. Two deep-water tactics, tail-out and peduncle dive foraging (X2 = 12.888, df = 1, P ≤ 0.001) and sponge foraging (X2 = 35.372, df =

1, P ≤ 0.001) significantly decreased from the ‘pre’ to the ‘post’ period in comparison to all other foraging tactics combined. No novel foraging tactics were observed in the ‘post’ period. There were 6 tactics that were infrequent both pre and post heat wave.

111 FIGURES AND TABLES

TDPDFOR *

SPF *

SNK *

SHELL

SHAGROB

SFFOR c

i RTFOR

c a

T Period MLLFOR *

g Pre n i LPF

g Post

a r

o KPLFOR F

CST

BUPFOR

BTG

BRDMLL

BEG

BCHFOR

0.0 0.2 0.4 0.6 Proportion

Figure B1. Foraging tactics from survey data. Abbreviations are TDPDFOR is tail-out peduncle dive forging, SPF is sponge-foraging, SNK is snacking, SHELL is shelling,

SHAGROB is shag robbing, SFFOR is shallow sand flat foraging, RTFOR is rooster-tail foraging, MILLFOR is mill foraging, LPF is leap and porpoise feeding, KPLFOR is kerplunking,

CST is coastal foraging, BUPFOR is belly-up foraging, BTG is bottom-grubbing, BRDMLL is bird milling, BEG is begging from boat or beach, BCHFOR is beaching.

112 Table B1. Common foraging tactics used by dolphins in Shark Bay.

Foraging Type Description Reference Beaching - BCHFOR Dolphin chases fish close to the shoreline Mann & Sargeant, such that the ventrum is on the seafloor or 2003 beach; fish are trapped onto the shore with the dolphin launching partially or fully out of the water onto the beach; occurs in shallow water. Begging - BEG Dolphin approaches boats or people within Mann & Sargeant, 1-2 m and opens jaw or brings head out the 2003 water. Bird milling - Dolphins are surfacing within or around a Mann & Sargeant, BRDMLL feeding group of cormorants and pelicans. 2003 Typically occurs in shallow water (<4 m). Bottom-grubbing - Dolphin orients towards and pokes rostrum Mann & Sargeant, BTG into seagrass or the seafloor, with the body 2003 positioned vertically. Belly-up foraging - Dolphin chases fish belly-up in the final Unpublished BUPFOR chase of a moderately sized fish (not the small fish observed in snacking). Chase is usually short and in one direction. Coastal foraging - Dolphins are chasing fish within 10 m of Unpublished CSTFOR shoreline-often mullet. Characterized by fast-swims and occasional hydroplaning but not beaching. Kerplunking - Dolphins are foraging in shallow-water and Connor et al. 2000 KPLFOR perform a fluke-slap on the surface of the water that produces a 1-3.5 m high splash and an audible “kerplunk” in air. This may be repeated in rapid succession or occur once. Leap and porpoise Dolphins are multidirectional (milling) and Mann & Sargeant, feeding - LPF leaping continuously within an area, may be 2003 relatively small or spread out. Mill foraging - Dolphin surfaces irregularly and changes Mann & Sargeant, MILLFOR directions on each surface, often with rapid 2003 surfaces. Rooster-tail foraging Dolphin swims rapidly near the surface so Mann & Sargeant, - RTFOR that a sheet of water trails off the dorsal fin. 2003 Typical in moderate water depth (3-6 m). Shallow sand-flat Sand-flat foraging (often after whiting and Unpublished foraging - SFFOR garfish)-foraging in very shallow sand banks with little seagrass and little to no bottom grubbing.

113 Table B1 (cont.)

Foraging Type Description Reference Shag robbing - Dolphin steals a fish from a cormorant Mann & Patterson, SHAGROB (often called a shag); observed after a 2013 dolphin goosed the underbelly of a shag, causing the shag to drop the fish and fly away. Shelling - SHELL Dolphins carry shells of large dead Allen et al. 2011; mollusks, typically trumpeter or bailer Mann & Patterson, shells, from the seafloor to the waters 2013 surface in their beaks and wave them around to extract prey that is hiding inside. Snacking - SNK Dolphin chases fish belly-up and traps fish Mann & Smuts, 1999 at the surface of the water. Usually longer than BUPFOR and dolphin is changing direction while belly-up. Sponge foraging - Dolphin carries a sponge on its rostrum Smolker et al. 1997; SPF during stereotyped tail-out dive/peduncle Mann & Sargeant, dive foraging; dolphin remains submerged 2003 for 2-3 min; dolphin tends to change directions often; occurs almost exclusively in the channels (8-12 m). Tail-out peduncle Dolphin surfaces in discrete bouts with tail- Mann & Sargeant, dive foraging – out and/or peduncle dives at a rate of 2003 TDPDFOR 0.3/min; dolphin remains submerged 1–3 min.

114 APPENDIX C

Acknowledgements

CHAPTER I

We thank all members of the Shark Bay Dolphin Research Project and are grateful to the

West Australian Department of Biodiversity, Conservation and Attractions (DBCA), the

University of Western Australia and the Monkey Mia Dolphin Resort (Royal Automobile Club) for logistical field support. The study was funded by the National Science Foundation (grants

0941487, 0918308, 0316800), DBCA and Georgetown University.

CHAPTER II

We thank all members of the Shark Bay Dolphin Research Project and are grateful to the

Department of Parks and Wildlife (DPaW) of Western Australia and the Monkey Mia Dolphin

Resort for logistical field support. This work was approved by the Georgetown University

Animal Care and Use Committee (permits 07-041, 10-023, 13-069), DPaW (permits SF009311,

SF008076, SF009876) and The University of Western Australia (animal ethics permit 600-37).

The study was funded by the National Science Foundation (grants 0941487, 0918308, 0316800),

DPaW, and Georgetown University.

CHAPTER III

We thank all members of the Shark Bay Dolphin Research Project, past and present.

MLM would especially like to thank field assistants Margaret Stebbins and Lena Bichell for their support while collecting these data. We are grateful to the West Australian Department of

115 Biodiversity, Conservation and Attractions (DBCA), the University of Western Australia and the

Monkey Mia Dolphin Resort (Royal Automobile Club) for logistical field support. The study was funded by the National Science Foundation (grants 0941487, 0918308, 0316800, 1559380),

DBCA, Georgetown Environment Initiative, and Georgetown University. This work was approved by the Georgetown University Animal Care and Use Committee (permits 07-041, 10-

023, 13-069), DBCA (permits SF009311, SF008076, SF009876) and the University of Western

Australia (animal ethics permit 600-37). The scientific results and conclusions, as well as any views or opinions expressed herein, are those of the author(s) and do not necessarily reflect those of NOAA or the Department of Commerce.

CHAPTER IV

We thank all members of the Shark Bay Dolphin Research Project. We are grateful to the

West Australian Department of Biodiversity, Conservation and Attractions (DBCA), the

University of Western Australia and the Monkey Mia Dolphin Resort (Royal Automobile Club) for logistical field support. The study was funded by the National Science Foundation (grants

0941487, 0918308, 0316800, 1559380), DBCA, Georgetown Environment Initiative, and

Georgetown University. This work was approved by the Georgetown University Animal Care and Use Committee (permits 07-041, 10-023, 13-069), DBCA (permits SF009311, SF008076,

SF009876) and the University of Western Australia (animal ethics permit 600-37).

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