A WORLD OF DECISIONS: HOW CHOICES THROUGHOUT THE ANNUAL CYCLE AFFECT SURVIVAL, CONDITION, AND PERFORMANCE OF A MIGRATORY SHOREBIRD A Dissertation Presented to the Faculty of the Graduate School of Cornell University In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Rose Jennetta Swift August 2018 © 2018 Rose Jennetta Swift A WORLD OF DECISIONS: HOW CHOICES THROUGHOUT THE ANNUAL CYCLE AFFECT SURVIVAL, CONDITION, AND PERFORMANCE OF A MIGRATORY SHOREBIRD Rose Jennetta Swift, Ph. D. Cornell University 2018 Migratory birds face a variety of threats and constraints throughout the annual cycle, and events that occur in one season can impact individuals not only within that period, but in subsequent seasons as well. I investigated the consequences of decisions about habitat use and species interactions across the full annual cycle on the survival, condition, and performance of Hudsonian Godwits (Limosa haemastica) in Beluga River, Alaska and Chiloé Island, Chile. On the breeding grounds, I examined how the benefits of a protective nesting association between godwits and the loud and aggressive Mew Gulls (Larus canus) varied across time and space. Hudsonian Godwits actively chose to nest within gull colonies, but the advantages were context-dependent. Although godwits experienced higher nest survival within colonies – presumably because gulls dissuaded nest predators from using the area – godwit chicks were more likely to be depredated within colonies. Godwits nesting within colonies were larger (females only) and less attentive (males attended the nest less and gave fewer alarm calls). Nest survival was best explained by individual condition improving with female size within colonies and male size outside of colonies. Turning to the non-breeding grounds in southern Chile, I assessed the degree to which patch quality, as indicated by density and condition of godwits, was affected by environmental attributes and disturbance from predators and/or human activities. Patch quality was primarily influenced by availability of foraging habitat, foraging success rates, and the responses of godwits to disturbance (i.e., vigilance and agitation). Lastly, I examined cross-seasonal interactions on individual survival and performance. Godwits had high survival throughout the annual cycle, with the lowest survival estimates during the breeding season and southbound migration. I also found evidence of carry-over, or reversible state, effects on future reproductive performance, with individuals in better condition or using higher quality patches on non- breeding grounds achieving higher reproductive success the following spring. Understanding the interactions among stages of the annual cycle, the relative influence of non-breeding and breeding season factors, and the consequences of individual decisions on survival, condition, and performance will help inform conservation for this rapidly declining species. BIOGRAPHICAL SKETCH Rose Swift was born in the Bay Area of California. Her parents, Brian and Karen, instilled a love of the outdoors in her from an early age. Camping when she was three months old and skiing when she was three years old, age was not a limitation to explore wilderness. She took that love for the outdoors with her as she began to explore universities. She went on to attend the University of California, Davis where she graduated with a B.S. in Wildlife, Fish, and Conservation Biology with an emphasis on Wildlife Biology. Her first field position was camping at a remote lake in Alaska for three months. There she not only fell in love with the spectacular landscape, but also became engaged with the bird communities she was seeing. Thanks to her field partner, Scarlett, an old pair of binoculars, and her trusty Sibley guide, she developed a passion for birds. At UC Davis, she engaged in many research projects and developed an honors thesis with Dr. John Wingfield investigating the phenological responses of arctic breeding birds to climate change. From these experiences launched a dream – to follow arctic breeding birds from the breeding grounds south and back north again. After graduating from UC Davis, Rose went on to work many field positions where she gained a variety of ornithological skills. She worked for several seasons at Hastings Natural History Reserve on Dr. Janis Dickinson’s long-term Western Bluebird (Sialia mexicana) behavioral ecology study. While there, she began to develop a project looking at the repeatability and heritability of egg size. From there, she flew to New Zealand to assist on a project looking at the breeding biology of North Island Brown Kiwi (Apteryx mantelli) where she had the opportunity to explore the biology of the endemic Kiwi Tick (Ixodes anatis) with Dr. Sarah Jamieson. From nest-searching in Montana to shorebird banding in California, she began to explore the topics she would choose to study for her graduate work. Soon she iii would begin to engage in conversations with professors, ultimately finding her love of birds would send her heading to the Lab of Ornithology at Cornell University. At Cornell, Rose has been involved in the Natural Resources and Lab of Ornithology communities, and she completed her Master’s thesis on nest site selection of Hudsonian Godwits (Limosa haemastica). Under the continued guidance of Dr. Amanda Rodewald, she has been able to fulfill her dream to work with a long-distance migratory bird and track a species throughout the year and across the hemisphere for her Ph.D. She will now head westward to the Northern Prairie Wildlife Research Center in North Dakota, where she’ll be working as a post-doctoral research ecologist on a joint project with USGS and Colorado State University. There she’ll work on building a population model for the threatened Piping Plover (Charadrius melodus), spreading her wings once again. iv For my family: Garrett – without you, none of this would have been possible. Your support and dedication are woven into every word. Mom and Dad – you told me to shoot for the stars as a kid and here I am. Your guidance, inspiration, and support have been instrumental along this journey. Lily and Holly – you inspired me my entire life to become a biologist. You are the two strongest, best role models a little sister could ask for. v ACKNOWLEDGMENTS First and foremost, I wish to thank Dr. Amanda Rodewald for her supervision, support, and expeditious editing. This project has not been a small one, and her attention to details, strategic thinking, and support have been essential in continuing this project and in my graduate career. She allowed me to chase my dreams and take on this challenging project with grace. She encouraged me and supported me when life drew me away from Ithaca. Thank you. Special thanks also go to Dr. Nathan Senner for supervision, encouragement, and assistance from the beginning. I am indebted to him for his expertise, establishing this project, his years of data, and continued support. I also appreciate the contributions of the rest of my M.S. and Ph.D. committees: Dr. John Fitzpatrick, Dr. Mike Webster, and Dr. Walter Koenig. Each of you has helped shape my research and thinking. I am also grateful for the statistical advice of Dr. Patrick Sullivan and the Cornell Statistical Consulting Unit. I am indebted to Brad Walker for his time and effort spent entering vegetation records and consulting with me on the floristics of the Churchill area. Additionally, I must thank Joe Barron and Katherine Hambury for their dedication to entering data. Adam Spaulding-Astudillo was integral in the ptilochronology study. Dr. Rodrigo Vasquez sponsored me for my non-breeding season work. And to all the scientists associated with the Bird Populations Studies and Conservation Science programs at the Lab of Ornithology for their expertise and advice. Lastly, Garrett MacDonald collaborated for chapter 4, and Dr. Jim Johnson and Dr. Brad Andres were collaborators for chapter 5. I send thanks to all my past and present lab mates, who contributed in many ways from moral support to statistical advice: Ruth Bennett, Bryant Dossman, Gemara Gifford, Darin McNeil, Zephyr Mohr-Felsen Züst, Steven Sevillano Ríos, Gerardo Soto, and Eric Wood. Their vi formal and informal discussions helped shape this project and my growth as a scientist. They’ve encouraged me, comforted me, and inspired me. This work would never have been possible without the many field assistants on the project throughout the years. To William Abbott, Amy Alstad, Hope Batcheller, Shawn Billerman, Jon DeCoste, Doug Gochfeld, Mike Harvey, Mike Hilchey, Tom Johnson, Andy Johnson, Julia Karagicheva, Jess Marion, Madi McConnell, Jay McGowan, Brittany Schultz, Glenn Seeholzer, Hannah Specht, Brad Walker, Ben Lagasse, Bret Davis, Kyle Parkinson, Justin Heseltine, James Klarevas-Irby, Garrett MacDonald, Reina Galvan, Lila Fried, Kayla Smith, and Mary Schvetz. Thank you. Your hours of work, companionship, and good humor were not overlooked. Your blood, sweat, and tears helped make all of this possible. To the administrative staff at the Cornell Lab of Ornithology, I owe you gratitude for your assistance. Thanks to Zhila Sadri, Cindy Marquis, and Micky Zifchock for always making my accounting easy. In Beluga, Judy and Larry Heilman continued to provide us a happy home complete with fresh eggs and produce from the garden. You made the days happier and easier. The staff at ConocoPhillips and Hilcorp has made our work functional, and Dan Ruthrauff, James Pearce, and the Alaska Science Center helped our work run smoothly. I am grateful for funding provided by the David and Lucile Packard Foundation, U.S. Fish and Wildlife Service, Faucett Family Foundation, National Science Foundation, Graduate Research Opportunities Worldwide program, CONICYT, the Athena Fund at the Cornell Lab of Ornithology, the Atkinson Sustainable Biodiversity Fund at Cornell University, the Einaudi Center at Cornell University, Cornell Graduate School Andrew Mellon Fund, Cornell Chapter of Sigma Xi, American Museum of Natural History, Ducks Unlimited Canada, Churchill Northern Studies Centre, American Ornithological Society, and Arctic Audubon Society. vii Finally, words are insufficient to thank my family for all that they have done for me over the last five years. I could not have accomplished this without Garrett’s patience and support as both a husband and a scientist. His constant and unwavering dedication and investment have improved every aspect of this dissertation; he has poured many hours into data collection, data entry, editing, and brainstorming with me. I would never have completed this dissertation without his constant love, encouragement, and eternal confidence that I could succeed. Thank you for keeping my eye on the bigger picture, for bringing me back to earth when things get crazy, and for your eternal love and support. And my family: Brian, Karen, Lily, and Holly, for their support and inspiration. My father deserves special thanks for reading through several edits, and my mother deserves them as well for making sure they made it to his hands and keeping me cheerful throughout. Holly has been inspiring me as a girl-scientist since I was in elementary school. Lily has always provided me with much needed perspective. You all have taken care of me in so many ways over the last five years (and the last thirty!) – Thank you! Lastly, thank you all for instilling in me the confidence and curiosity that has gotten me here. viii TABLE OF CONTENTS BIOGRAPHICAL SKETCH ......................................................................................................... iii DEDICATION .................................................................................................................................v ACKNOWLEDGEMENTS ........................................................................................................... vi LIST OF FIGURES ...................................................................................................................... xii LIST OF TABLES ....................................................................................................................... xiii CHAPTER ONE: INTRODUCTION ..............................................................................................1 Decisions during the breeding season ........................................................................................3 Decisions during the non-breeding season .................................................................................4 Seasonal interactions ..................................................................................................................5 Research questions ......................................................................................................................6 Study system ................................................................................................................................7 Thesis format ...............................................................................................................................8 References .................................................................................................................................10 CHAPTER TWO: CONTEXT-DEPENDENT COSTS AND BENEFITS OF A HETEROSPECIFIC NESTING ASSOCIATION .........................................................................16 Abstract .....................................................................................................................................17 Introduction ...............................................................................................................................18 Methods.....................................................................................................................................21 Study area and species ...........................................................................................................21 Nest distribution and fate .......................................................................................................22 Analyses of point patterns ......................................................................................................23 Vegetation parameters ...........................................................................................................26 Vegetation analyses ...............................................................................................................26 Godwit nest survival ..............................................................................................................27 Godwit chick survival ............................................................................................................29 Results .......................................................................................................................................30 Nest summary .........................................................................................................................30 Habitat ...................................................................................................................................30 Godwit nest survival ..............................................................................................................31 Godwit chick survival ............................................................................................................32 Discussion .................................................................................................................................33 Funding .....................................................................................................................................38 Acknowledgments .....................................................................................................................38 Data accessibility ......................................................................................................................39 References .................................................................................................................................40 Tables and Figures ....................................................................................................................45 Appendix A ...............................................................................................................................50 Appendix B ...............................................................................................................................64 ix CHAPTER THREE: NEST SURVIVAL WITHIN AND OUTSIDE OF A PROTECTIVE NESTING ASSOCIATION ...........................................................................................................72 Abstract .....................................................................................................................................73 Introduction ...............................................................................................................................74 Methods.....................................................................................................................................77 Study area and species ...........................................................................................................77 Nest distribution and fate .......................................................................................................78 Habitat metrics .......................................................................................................................78 Godwit body condition ...........................................................................................................79 Godwit defensive behaviors ...................................................................................................79 Godwit nest survival analyses ................................................................................................80 Results .......................................................................................................................................82 Discussion .................................................................................................................................83 Acknowledgments .....................................................................................................................86 References .................................................................................................................................87 Tables and Figures ....................................................................................................................92 Appendix C .............................................................................................................................100 CHAPTER FOUR: RISKS AND REWARDS OF FORAGING PATCHES FOR A NON-BREEDING SHOREBIRD ................................................................................................108 Abstract ...................................................................................................................................109 Introduction .............................................................................................................................110 Methods...................................................................................................................................113 Study species ........................................................................................................................113 Study area ............................................................................................................................114 Potential disturbances .........................................................................................................114 Field surveys and flock counts .............................................................................................115 Body condition .....................................................................................................................116 Foraging success and intertidal foraging habitat ...............................................................117 Human disturbances and predation risk ..............................................................................118 Landscape and bay characteristics ......................................................................................119 Data analysis .......................................................................................................................119 Results .....................................................................................................................................121 Discussion ...............................................................................................................................123 Acknowledgments ...................................................................................................................127 References ...............................................................................................................................129 Tables and Figures ..................................................................................................................136 Appendix D .............................................................................................................................160 Appendix E .............................................................................................................................176 x CHAPTER FIVE: SEASONAL SURVIVAL AND REVERSIBLE STATE EFFECTS IN A LONG-DISTANCE MIGRATORY SHOREBIRD ...........................................................183 Abstract ...................................................................................................................................184 Introduction .............................................................................................................................186 Methods...................................................................................................................................189 Study species ........................................................................................................................189 Seasonal survival .................................................................................................................191 Field methods ....................................................................................................................191 Data analysis ....................................................................................................................192 Reversible state effects .........................................................................................................194 Observational study ..........................................................................................................194 Field methods – non-breeding season ...........................................................................194 Field methods – breeding season ...................................................................................197 Data analysis .................................................................................................................198 Ptilochronology study .......................................................................................................200 Results .....................................................................................................................................202 Seasonal survival .................................................................................................................202 Reversible state effects .........................................................................................................202 Observational study ..........................................................................................................202 Ptilochronology study .......................................................................................................203 Discussion ...............................................................................................................................204 Acknowledgments ...................................................................................................................211 References ...............................................................................................................................213 Tables and Figures ..................................................................................................................220 Appendix F..............................................................................................................................241 Methods................................................................................................................................241 Feather mass .....................................................................................................................241 Data analysis ....................................................................................................................241 Results ..................................................................................................................................242 Discussion ............................................................................................................................242 References ............................................................................................................................244 Tables and Figures ...............................................................................................................245 Appendix G .............................................................................................................................246 Appendix H .............................................................................................................................256 xi LIST OF FIGURES Figure 2.1: Nest locations of Hudsonian Godwits and Mew Gulls ...............................................45 Figure 2.2: Ripley’s K test .............................................................................................................46 Figure 2.3: Daily survival rates of Hudsonian Godwit nests .........................................................48 Figure 2.A1: G function test from 2014 – 2016 .............................................................................59 Figure 2.A2: PCF function test from 2014 – 2016 ........................................................................60 Figure 2.A3: G function test in 2015 & 2016 ................................................................................61 Figure 2.A4: PCF function test in 2015 & 2016 ............................................................................62 Figure 2.A5: Ripley’s K test for successful and failed nests .........................................................63 Figure 3.1: Daily nest survival rates with female body condition .................................................98 Figure 3.2: Daily nest survival rates with male body condition ....................................................99 Figure 4.1: Locations of surveyed intertidal mudflats .................................................................155 Figure 4.2: Inner model ................................................................................................................157 Figure 4.3: Partial least squares path model ................................................................................158 Figure 4.4: Final partial least squares path model .......................................................................159 Figure 4.E1: Body molt scores with Julian date ..........................................................................177 Figure 4.E2: Abdominal profile index with Julian date ...............................................................178 Figure 4.E3: Probes per minute with Julian date .........................................................................179 Figure 4.E4: Swallows per minute with Julian date ....................................................................180 Figure 4.E5: Success rate with Julian date ...................................................................................181 Figure 4.E6: Success rate per minute with Julian date ................................................................182 Figure 5.1: Study area locations ...................................................................................................234 Figure 5.2: Inner model ................................................................................................................235 Figure 5.3: Partial least squares path model ................................................................................236 Figure 5.4: Annual survival estimates on the breeding grounds ..................................................237 Figure 5.5: Annual resighting probability on the breeding and non-breeding grounds ...............238 Figure 5.6: Weekly survival estimates throughout the annual cycle ...........................................239 Figure 5.7: Final partial least squares path model .......................................................................240 Figure 5.G1: Average growth bar width repeatability .................................................................246 Figure 5.G2: Feather mass repeatability ......................................................................................247 Figure 5.G3: Locations of breeding population individuals seen in Chile ..................................248 Figure 5.G4: Probes per minute of marked individuals compared to population ........................249 Figure 5.G5: Swallows per minute of marked individuals compared to population ...................250 Figure 5.G6: Success rate of marked individuals compared to population .................................251 Figure 5.G7: Success rate per minute of marked individuals compared to population ...............252 Figure 5.G8: Body molt scores of marked individuals compared to population .........................253 Figure 5.G9: Abdominal profile index of marked individuals compared to population .............254 Figure 5.G10: Movements of individuals among mudflats .........................................................255 xii LIST OF TABLES Table 2.AI: Principal components of microhabitat variables ........................................................50 Table 2.AII: Moran’s I test ............................................................................................................51 Table 2.AIII: AICC selection table nest survival from 2014 – 2016 ..............................................52 Table 2.AIV: AICC selection table nest survival from 2014 – 2016 by plot .................................53 Table 2.AV: AICC selection table nest survival from 2014 – 2016 by year ..................................54 Table 2.AVI: AICC selection table nest survival from 2009 – 2016 .............................................55 Table 2.AVII: AICC selection table nest survival from 2009 – 2016 by plot ................................56 Table 2.AVIII: AICC selection table nest survival from 2009 – 2016 by year ..............................57 Table 2.AIX: AICC selection table chick survival .........................................................................58 Table 2.BI: Nest survival, microhabitat, and proximity to gulls data ............................................64 Table 2.BII: Chick survival and proximity to gulls data ...............................................................70 Table 3.I: Model suites and variable names ...................................................................................92 Table 3.II: Mean and standard error of habitat and body condition ..............................................94 Table 3.III: Mean and standard error of defensive behaviors ........................................................95 Table 3.IV: AICC selection table nest survival within colonies .....................................................96 Table 3.V: AICC selection table nest survival outside of colonies ................................................97 Table 3.CI: Nest survival, microhabitat, proximity to gulls, and body condition data ................100 Table 3.CII: Defensive behaviors data .........................................................................................107 Table 4.I: Mean and standard deviation of number of visits, godwit densities, body condition, and foraging success ....................................................................................................................136 Table 4.II: Mean and standard deviation of alertness and agitation, human disturbances, and predation risk ...............................................................................................................................139 Table 4.III: Data for bay characteristics ......................................................................................142 Table 4.IV: Outer model fit .........................................................................................................145 Table 4.V: Outer model output ....................................................................................................146 Table 4.VI: Intertidal mudflat scores ...........................................................................................149 Table 4.VII: Pathway beta estimates and 95% CI .......................................................................152 Table 4.VIII: Direct and indirect effects ......................................................................................153 Table 4.DI: Patch quality, foraging success and habitat, and predation risk data .......................160 Table 4.DII: Human disturbances data ........................................................................................166 Table 4.DIII: Flock counts, survey times, and low tide times data .............................................172 Table 4.EI: Foraging regressions beta estimates and 95% CI .....................................................176 Table 5.I: Outer model fit ............................................................................................................220 Table 5.II: Outer model output ....................................................................................................221 Table 5.III: AICC selection table annual survival on breeding grounds ......................................222 Table 5.IV: AICC selection table annual survival on non-breeding grounds ...............................223 Table 5.V: Model averaged estimates of annual survival and resighting probabilities ...............224 Table 5.VI: AICC selection table within season survival on breeding grounds ...........................225 Table 5.VII: AICC selection table within season survival on non-breeding grounds in 2010 .....226 Table 5.VIII: AICC selection table within season survival on non-breeding grounds in 2011 ....227 Table 5.IX: Model averaged estimates of within season survival and resighting probabilities ..228 xiii Table 5.X: Estimates of seasonal and weekly survival ................................................................229 Table 5.XI: Pathway beta estimates and 95% CI .........................................................................230 Table 5.XII: Direct and indirect effects .......................................................................................231 Table 5.XIII: AICC selection table ptilochronology study ...........................................................232 Table 5.XIV: Pearson correlation coefficients .............................................................................233 Table 5.FI: AICC selection table of feather quality ......................................................................245 Table 5.HI: Non-breeding season data from observational study ................................................256 Table 5.HII: Breeding performance data from observational study ............................................258 Table 5.HIII: Feather data from ptilochronology study ...............................................................259 Table 5.HIV: Encounter histories annual non-breeding season ...................................................262 Table 5.HV: Encounter histories within 2009 – 2010 non-breeding season ...............................278 Table 5. HVI: Encounter histories within 2010 – 2011 non-breeding season .............................283 Table 5.HVII: Encounter histories annual breeding season .........................................................291 Table 5.HVIII: Encounter histories within breeding season ........................................................295 xiv CHAPTER ONE INTRODUCTION Every annual cycle consists of a series of stages or discrete periods that are defined by specific events, processes, and challenges, which collectively comprise an individual's life history (McNamara and Houston 2008, Wingfield 2008, Newton 2011). Examples of stages include the pre-alternate molt, spring migration, reproduction, pre-basic molt, and fall migration that make up the annual cycles of many birds (Sherry and Holmes 1995, Newton 2011). During each stage, birds are affected by a wide range of factors, including interactions with other species (e.g., Quinn and Ueta 2008), resource distributions (e.g., van Gils et al. 2006, Alves et al. 2013), predation risk (e.g., Martin 1993, Cresswell and Whitfield 1994), and habitat quality (e.g., Cody 1981, Piersma 2012), although the importance of each may vary across time and space. The responses of individuals to these factors can affect their body condition, survival, and performance within and across seasons. In this way, the annual cycle is a complicated process that is subject to potential seasonal interactions (Harrison et al. 2011, Senner et al. 2015). For most at-risk species, we have only a limited understanding of how the threats facing populations interact across their annual cycles in ways that affect their population dynamics. Traditionally, studies have focused on the breeding season, which can directly influence population dynamics via survival and reproduction, but populations also can be limited by factors occurring at non- breeding and stopover sites. Throughout the annual cycle, individuals must decide about which habitats to use and for what purpose, how to respond to predators or disturbances, and how to interact with other 1 species. These decisions are dynamic, highly variable throughout the full annual cycle, and affected by many biotic and abiotic factors throughout the year, as individuals encounter new and unfamiliar stopover sites, breeding habitat, nest sites, and foraging sites during the non- breeding season. Because individuals must balance the cost of current reproduction against survival and future reproduction (Williams 1966), they presumably consider, in some manner, the tradeoffs among risk of predation, body condition, and individual performance in habitat selection in each stage. For example, nest site selection in birds is driven by proximate and ultimate factors that maximize fitness (Hildén 1965). Favorable nest locations provide safety from predators, suitable microclimates, proximity to food resources, and enabling social conditions (Martin 1988, Smith et al. 2007a, Betts et al. 2008). On the non-breeding grounds, individuals prioritize survival and prey availability in habitat selection decisions. Distributions of shorebirds are often correlated with the availability and abundance of prey (e.g., Colwell and Landrum 1993), and predators can strongly influence habitat choices of individuals (Fernández and Lank 2006, Sprague et al. 2008). Individuals may also be excluded from high quality habitats or shift habitat use based on their condition or quality, further complicating our understanding of habitat selection (Ydenberg et al. 2002, Studds and Marra 2005). Our understanding of full annual cycle conservation is limited by our knowledge of decisions made by individuals throughout the year. Here, I investigated the consequences of decisions about habitat use and inter-specific interactions across the full annual cycle on the survival, condition, and performance of Hudsonian Godwits (Limosa haemastica) at Beluga River, Alaska and Chiloé Island, Chile. 2 Decisions during the breeding season: Nest survival can be influenced by a number of factors including nest age, weather, predator abundance, vegetation, microclimate, and human disturbance (Smith et al. 2007a, Smith and Wilson 2010). Birds can improve the likelihood of successful reproduction by selecting nest sites that minimize predation risk or that have vegetative features linked to nest success (Martin 1998). Across a wide range of species, studies have shown that nest microhabitats are selected non-randomly (Colwell and Oring 1990, Rodrigues 1994, Clark and Shutler 1999, Smith et al. 2007a). Although most studies of nest site selection examine the microhabitat surrounding nests (Martin 1998, Clark and Shutler 1999), nest site selection clearly is also shaped by interactions with other individuals (Tarof and Ratcliffe 2004, Gascoigne and Lipcius 2004), including interactions with conspecific or heterospecific neighbors (Hildén 1965, Fretwell and Lucas 1970, Pitelka et al. 1974, Betts et al. 2008), and the stage of the breeding cycle (e.g., incubation vs. brood-rearing; Blomqvist and Johansson 1995, Wiltermuth et al. 2015). Ultimately, the choice of a nest site must integrate many factors including habitat and community-level interactions that drive nest success. Predation, either perceived or real, can profoundly affect both the life history evolution (Ricklefs 1969) and more contemporary behaviors, including selection of nest sites (Martin et al. 2000). Birds employ several strategies to avoid predation, including egg or plumage crypsis, as well as the placement of nests in locations that are inaccessible to predators (e.g., islands or cliffs) or within protective associations (Bêty et al. 2002, Nguyen et al. 2003, Iverson 2014). In protective associations, the associate species benefits either from aggressive nest defense by the protector species or from information gleaned from the protector species about the whereabouts of predators (Nuechterlein 1981, Quinn and Ueta 2008). Protective associations thus reduce an 3 individual's risk of nest predation through community-level interactions. Many species in the Arctic have been found to nest near protector species such as owls, raptors, and gulls (Quinn et al. 2003, Nguyen et al. 2006, Kharitonov et al. 2013). However, the extent to which interactions between species are positive or negative can be a function of the biotic or abiotic context (e.g., 'context-dependent interactions'; Chamberlain et al. 2014), which is largely understudied in protective associations. Because protective associations lessen the risk of predation, they may alter selective pressures on nest survival, especially as related to anti-predator behaviors, nest site characteristics, and the quality of the individuals nesting within an area (Smith et al. 2007b). Therefore, identifying the strategies used to avoid predation and the drivers of nest survival is essential to understand reproductive performance. Decisions during the non-breeding season: During the non-breeding season, individuals must balance the risks and rewards associated with different habitats as they try to meet their two primary needs – survival and accumulation of sufficient reserves to fuel migration back to the breeding grounds. Individuals must choose among sites that vary widely in food quality, predation risk, and human disturbances (Hilton et al. 1999, Pettifor et al. 2000, Duijns et al. 2009), but individuals often face the dilemma of choosing between risky sites with high-quality resources or safe sites with low-quality resources (Piersma 2012, McArthur et al. 2014). These decisions determine the extent to which they are exposed to risks and rewards, with serious consequences for survival and condition. The degree to which the quality of foraging patches influences performance and condition may vary among individuals depending upon their vulnerability to different risks. 4 Individuals foraging in patches with high densities of food have higher intake rates, often spend less time foraging, accumulate mass more quickly, and have better overall body condition (Duijns et al. 2009). Risky environments not only increase risk of mortality but also can compromise body condition if individuals are repeatedly disturbed (Cresswell 2008, Cresswell et al. 2010). Human activity and predators can affect individual performance through changes in scanning behaviors, displacement flights, and opportunity costs of forgone foraging. These behavioral modifications may negatively impact body condition, especially when food is limited, by limiting foraging time (Goss-Custard et al. 2006) and can ultimately affect survival and fitness (Fernández and Lank 2006, Norris and Marra 2007, Cooper et al. 2015). Even when associated with risks, high reward patches can still be beneficial for short periods of time or for individuals in poor body condition (Cresswell 1994). Body condition of foraging individuals is influenced by a wide range of risks and rewards but generally improves with the quality of a patch, sometimes in ways that affect survival and fitness. Seasonal interactions: Stages of the annual cycle can interact and affect an individual’s probability of survival or reproductive performance at later stages. Seasonal "reversible state effects" are distinguished from direct impacts on survival or reproduction within a single season by affecting the state or condition of individuals transitioning to later seasons in ways that are both reversible and influence fitness (Senner et al. 2015). Reversible state effects have been described for a wide range of taxa, including mammals (Festa-Bianchet 1998, Perryman et al. 2002), reptiles (Broderick et al. 2001), and fish (Bunnell et al. 2007, Kennedy et al. 2008), but they have been most commonly studied in birds (reviewed in Harrison et al. 2011). Access to resources during 5 non-breeding months is especially well documented to have reversible state effects. One classic example is American Redstarts (Setophaga ruticulla), for which individuals that overwinter in high-quality mangrove habitats arrive at the breeding grounds earlier, in better condition, and fledge more young than individuals from poorer-quality scrub habitats (Norris 2005, Tonra et al. 2013, Cooper et al. 2015). Thus, the quality of habitats used throughout the non-breeding season is likely to have a number of reversible state effects for migratory birds, including consequences for individual condition (Battley et al. 2004, Hargitai et al. 2014), migratory timing (Marra et al. 1998, Prop et al. 2003), reproductive success (Norris et al. 2004, Paxton and Moore 2015), and survival rates (Norris and Marra 2007). As such, the conditions and quality of habitat on the non- breeding grounds may influence an individual's future survival probability, condition, and performance. Research Questions: • Do Hudsonian Godwits associate with a protector species, and if so, what are the costs and benefits throughout the different stages of the breeding season? • Do the characteristics of individuals or the drivers of nest survival vary within and outside of a protective nesting association? • What is the relative influence of foraging success, amount of foraging habitat, landscape and bay characteristics, predation risk, and human disturbances on habitat quality, flock density, and body condition of godwits? • When are the periods of highest mortality within the annual cycle, and how does non- breeding body condition, habitat quality, and foraging success influence breeding performance? 6 Study System: The Hudsonian Godwit (hereafter ‘godwit’), breeds in three disjunct regions across the Nearctic and overwinters in the Southern Cone of South America (Walker et al. 2011). An extreme long-distance migrant, Hudsonian Godwits fly ~16,000 km each year and exhibit a cyclical long-leap migration strategy. Here, I focus on a linked population which breeds in south- central Alaska (Beluga River) and over-winters in southern Chile (Senner 2012, Senner et al. 2014). Hudsonian Godwits are one of the fastest declining shorebird species breeding in North America (Smith et al. unpubl. data), and as such, understanding the threats on survival and reproductive performance throughout the annual cycle is prioritized (Senner 2010). The breeding season has clear implications for population dynamics. Godwits arrive to the breeding grounds synchronously and initiate breeding within a week of arrival. The breeding season is relatively short, spanning the months of May and June, and individuals show high pair and territory fidelity. Godwits breed in open bogs, tundra, and fens dominated by sedges, Carex spp., and dwarf birch, Betula glandulosa/nana (Swift et al. 2017a). Godwits typically rely on cryptic camouflage for nest protection, and both individuals of the breeding pair incubate and provide brood care (Walker et al. 2011). Adults divide incubation duties with females typically incubating during the day and males at night (Walker et al. 2011, Bulla et al. 2016). Our previous work showed that habitat heterogeneity did not explain spatial aggregations of godwit nests, but may instead be based on social cues (Swift et al. 2017b). Nest survival is high, with >80% of nests successfully hatching, but brood survival can be quite variable (Senner et al. 2017, Swift et al. 2017b). Chapter Two explores the consequences of community-level interactions across both breeding stages (incubation and brood care). Individuals may choose to nest in protective nesting associations, which occur when one or more species benefit directly from occupying nesting 7 areas defended from predators by a protector species (Quinn and Ueta 2008). Protective nesting associations may relax selection on nest survival within their boundaries or alter the individual characteristics that drive nest survival (Chapter Three). Individuals spend most of the year on the non-breeding grounds. During the long non- breeding season, godwits must recover from their southward migration, undergo two separate molts, and prepare for their northward migration and breeding season. Godwits typically are encountered when foraging in large flocks on intertidal mudflats along sheltered coastlines in the Chiloé Island region of southern Chile (García-Walther et al. 2017). Godwits usually arrive on the non-breeding grounds in October and are primarily stationary until northbound migration in April (Espinosa et al. 2005). Individuals must assess the potential risks and rewards of alternative patches when deciding on a foraging site (Chapter Four). Upon leaving the non-breeding grounds, godwits undertake an energetically-demanding 10,000 km non-stop flight to the Great Plains region of the United States in as little as 6-7 days before completing a second non-stop flight to reach the Alaskan breeding grounds. Chapter Five addresses how the condition of individuals, as well as the habitat quality where they forage during the non-breeding season, may influence their survival and future reproductive performance. Further, Chapter Five provides the first estimates of seasonal survival for this species. Thesis Format: I studied how decisions made by Hudsonian Godwits throughout the annual cycle impact survival, condition, and performance of individuals. The four subsequent chapters of my dissertation are separate manuscripts for publication. In Chapter Two, I examined the context- dependent relationship between Hudsonian Godwits and Mew Gulls (Larus canus) breeding in 8 Beluga River, Alaska. I investigated the costs and benefits of interspecific interactions, which can vary spatially and temporally, to see if the nature of their interactions varied with breeding stage. In Chapter Three, I examined the drivers of Hudsonian Godwit nest survival within and outside of a protective nesting association. In Chapter Four, I explored the relative influence of foraging success, foraging habitat, human disturbances, and predation risk on patch quality for foraging godwits during the non-breeding season. I specifically assessed the risks and rewards for 42 intertidal mudflats on the density and body condition of Hudsonian Godwits. In Chapter Five, I examined the relative influence of the non-breeding season on godwit future reproductive performance through reversible state effects. Additionally, we linked our measures of individual performance with the first seasonal survival analyses for this species. Godwits are declining rapidly throughout their range. 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For example, benefits associated with nesting near species that deter predators may give way to costs if the association increases the risk of predation during other stages of reproduction. We examined the extent to which the costs and benefits of heterospecific aggregations between a declining shorebird, the Hudsonian Godwit (Limosa haemastica), and a potential protector and predator, the Mew Gull (Larus canus), varied with breeding stage. Specifically, we assessed the spatial distribution and fate of 43 godwit and 262 gull nests in Beluga, Alaska, from 2014 – 2016. We then evaluated the effect of habitat and proximity to gulls on daily survival rates of 120 godwit nests from 2009 – 2016. We also examined the relationship between the proximity to gulls and survival of godwit chicks to five days old, the period when they are vulnerable to gull predation. Nests of godwits and gulls were significantly clustered across the landscape, a pattern that habitat heterogeneity failed to explain. Hatching success of godwit nests improved with proximity to the gull colony and increasing numbers of gull nests within 200m. In contrast, survival of godwit chicks to five days improved with increasing distance to the gull colony. The costs and benefits that godwits derived from associating with Mew Gulls were thus context-dependent, with benefits pre-hatch and costs post- hatch. Our results show how spatiotemporal variation in species interactions precludes simple generalizations about the nature of their outcomes. Keywords: Hudsonian Godwit, Limosa haemastica, Mew Gull, Larus canus, protective nesting association, predation 17 Introduction: Heterospecific associations generally arise when participants benefit from living in groups but avoid the costs of competition (Farine et al. 2014). Benefits from aggregations, such as improved access to food, detection of predators, and/or nest defense, derive not only from a group-size effect, but also from the unique or complementary characteristics of each species (Harrison and Whitehouse 2011, Sridhar et al. 2012). Heterospecific associations are widely documented across taxa, including fish (Lukoschek and Mccormick 2000), amphibians (Phelps et al. 2007), mammals (Querouil et al. 2008), and birds (Sridhar et al. 2009). However, studies of these associations are often restricted to specific periods of the year, such as mixed-species foraging flocks that form during the non-breeding season or protective associations occurring during nest incubation in the breeding season (Quinn and Ueta 2008, Sridhar et al. 2009). Understanding the costs and benefits to both the entire assemblage, as well as each species on its own, can inform how these interactions may shift throughout the duration of the association. One type of heterospecific association is a protective nesting association, which occurs when one or more species benefit directly from occupying nesting areas defended from predators by a protector species (Quinn and Ueta 2008). The protected species can derive a number of benefits from these associations including predator protection, information parasitism, reduced effort defending nests, and improved mate attraction. For example, Western Grebes (Aechmophorus occidentalis) react to the alarm calls of Forster’s Terns (Sterna forsteri) by covering eggs prior to departing the nest, and thereby increasing nest survival (Nuechterlein 1981). Of course, benefits for the protected species may vary among protector species. Yellow Warblers (Setophaga petechia) nesting near Gray Catbirds (Dumetella carolinensis), for instance, suffer less predation, while those nesting near Red-winged Blackbirds (Agelaius 18 phoeniceus) are parasitized less frequently by Brown-headed Cowbirds (Molothrus ater; Clark and Robertson 1979). At the same time, however, protective associations can incur costs that individuals must try to behaviorally mitigate. For example, Red-breasted Geese (Branta ruficollis), which suffer from direct predation and harassment when nesting near Peregrine Falcons (Falco peregrinus), are able to optimize their fitness by nesting at intermediate distances from falcon nests and thereby minimizing the amount of harassment suffered (Quinn and Kokorev 2002, Quinn and Ueta 2008). Alternatively, some protector species may fail to protect from certain predators. For example, Spotted Sandpipers (Actitis macularia) nest within Common Tern (Sterna hirundo) colonies for protection from minks (Mustela vison), but experience higher egg predation by migrating Ruddy Turnstones (Arenaria interpres), which are attracted to the high density of tern eggs (Alberico et al. 1991). That said, if risk varies predictably with distance to a protector species – even if risk differs across life stages (e.g., adult, egg, chick), individuals still may be able to optimize their decisions (Mönkkönen et al. 2007). The extent to which interactions between species are positive or negative can be a function of the biotic or abiotic context (e.g., 'context-dependent interactions'; Chamberlain et al. 2014). For instance, fluctuations in the population size of a predator’s primary prey can drive variation in the magnitude of the pressure predators place on alternative prey (McKinnon et al. 2014). Such scenarios have been reported for heterospecific breeding aggregations. The nesting association between Red Phalaropes (Phalaropus fulicarius) and Sabine’s Gulls (Xema sabini) improves nest success for phalaropes only in years when alternate prey are available for arctic foxes (Vulpes lagopus), one of the main predators of phalarope nests. Sabine’s Gulls are unable to defend against foxes; thus, nesting within the gull colony provides little protection for phalaropes when the abundance of foxes’ primary prey – collard lemmings (Dicrostonyx 19 torquatus) – is low (Smith et al. 2007). Likewise, artificial nests near Long-tailed Skuas (Stercorarius longicaudus) gained no survival advantage because skuas depredated clutches in spite of defending their own nests (Larsen and Grundetjern 1997). In this way, context- dependent interactions can have important consequences for population demography and dynamics. Elucidating how protective associations may change over time can therefore be especially important for uncommon or declining species. The long-distance migratory shorebird, the Hudsonian Godwit (Limosa haemastica, hereafter: ‘godwits’) is one such species for which its conservation is limited by a poor understanding of the cues used to select breeding habitat (Senner 2010). Godwits breed in sedge bogs that are dominated by muskeg interspersed with small ponds, spruce tree islands, and drier upland areas (Walker et al. 2011; Swift et al. 2017a). Though the occurrence and density of breeding godwits varies widely within and across bogs, godwits appear to form semi-permanent clusters within a subset of suitable breeding areas (Swift et al. 2017b). Interestingly, nesting clusters are likely a result of social cues rather than underlying heterogeneity in vegetation or predation risk (Swift et al. 2017b). Our initial observations suggested that godwits may preferentially nest near Mew Gulls (Larus canus, hereafter: ‘gulls’), a semi-colonial breeder that forms loud, aggressive defensive flocks whenever predators enter the colony (Moskoff and Bevier 2002; RJ Swift personal obs.). Because godwits seldom defend nests (Walker et al. 2011), they potentially have much to gain from nesting near larids, which are common protector species (Quinn and Ueta 2008). At the same time, godwits may have to balance an important cost – gulls are the main predator of godwit chicks (Senner et al. 2017). In this study, we investigated the degree to which the nests of godwits and gulls were 20 associated and the manner in which costs and benefits of this relationship might vary across different stages of the breeding season. Methods: Study area and species: From 2009 to 2011 and 2014 to 2016, we monitored breeding godwits within an ~8 km2 area at Beluga River, Alaska (61.21°N, 151.03°W). The study area was divided into two study plots of uninterrupted muskeg bog – North (550 ha) and South (120 ha) – that were separated by ~7 km of unmonitored boreal forest and muskeg bog. From 2014 – 2016, each plot was censused for both godwit and gull nests, although gulls were only partially censused in 2014. Spatial aggregations of godwit nests are not explained by habitat heterogeneity (Swift et al. 2017b). At Beluga River, godwits breed at a density of five breeding pairs per square kilometer. Godwits seldom defend nests against gulls or other predators during incubation, instead relying on cryptic camouflage (Walker et al. 2011). Mew Gulls are a common, facultatively colonial breeder in both marine and freshwater habitats (Moskoff and Bevier 2002), with nest densities of 10-40 nests per km2 in our Beluga River study area. Mew Gulls are aggressive toward potential predators, engaging in loud calls and active mobbing. Because they communally defend nests, gull reproductive success correlates with the aggression of a colony (Moskoff and Bevier 2002). Additionally, godwits and gulls nest highly synchronously (nest initiation within one day; RJ Swift unpublished data), despite gulls arriving on the breeding grounds several weeks prior to godwits (eBird 2017). The community of avian and mammalian predators active at Beluga River is diverse though only a small portion of godwit nests are depredated each year (Walker et al. 2011, Senner 21 et al. 2017). The main nest predators are red foxes (Vulpes vulpes), Common Ravens (Corvus corax), and Sandhill Cranes (Grus canadensis). Godwit adults are also prone to Northern Harrier (Circus cyaneus) predation while incubating. Based on anecdotal observations as well as remains of young godwit chicks (i.e., legs, USFWS metal band, and/or radio) found near active gull nests, we believe that gulls are the main predator of young godwit chicks (Senner et al. 2017), though they rarely depredate eggs (Moskoff and Bevier 2002). In addition to gulls, Great Horned Owls (Bubo virginianus), Common Ravens, and red foxes commonly depredate godwit chicks. Nest distribution and fate: Once found, nests were marked only with a GPS unit, as we did not physically mark nest locations to avoid associative learning of predator species (Reynolds 1985). For all godwit nests, we estimated hatch date using egg flotation and monitored nests every two to three days until signs of hatching, after which nests were monitored daily (Liebezeit et al. 2007). We typically checked nests by resighting incubating birds with binoculars from 20-30 m away. Adults were flushed weekly (at most) to minimize disturbances that might increase the probability of nest failure, and field teams did not visit nests directly when predators were observed nearby. Although we recorded the locations of all gull nests, only a subset of gull nests were monitored twice weekly. A nest was considered successful if ≥1 egg hatched and chicks successfully left the nest site. We used the presence of young at or near the nest as an indication of nest success. Nest failure was presumed when we found empty nests early in the incubation period and/or destroyed eggs. Due to low rates of nest abandonment and the strong influence of predators on nest survival in this system (Senner et al. 2017), we considered the failure rate of nests in our study to represent the depredation rate as well. 22 Analyses of point patterns: Point pattern analyses are the study of the spatial arrangements of points in space, where the datum of interest is the location of the point itself (Diggle 1979, 2003). Point pattern analyses assume a complete census of the study area, and most tests also assume that data are both stationary and isotropic (Fortin and Dale 2005). To comply with the assumption of a complete census, each plot was analyzed separately. Due to consistently small numbers of breeding godwits on South plot (n = 5 each year), spatial analyses are only reported for the North plot. Multi-type spatial patterns were analyzed only for 2015 and 2016, as not all nests were located in 2014. To test the null hypothesis that godwit and gull nests were distributed randomly within our study plots, we used a combination of first- and second-order multi-type point pattern tests. We imported godwit and gull nest data into program R v.3.4.0 (R Core Development Team 2017) and used the SPATSTAT package for point pattern analysis (Baddeley and Turner 2005). Multi-type tests examine patterns of nest locations between species. Significant associations in first-order nearest neighbor interactions suggest potential local interactions between species from individual nests, which may be indicative of territoriality between species. Significant associations in second-order analyses provide an assessment of potential interactions associated with the total abundance of nests. Evaluations of protective associations among nesting species are more likely to be influenced by the overall abundance of birds rather than the proximity of nearest neighbors, and it is thought that they may be better examined with second- order analyses (Andersen 1992, Diggle 2003). We considered a second-order aggregation of godwit and gull nests as evidence of clustering between species. For godwit and gull nests in each year, we conducted a first-order multi-type G function analysis as a preliminary tool to assess spatial patterns between the two species’ nests. For multi- 23 type point patterns, the G function estimated the distribution of the distance from a point of type i to the nearest point of type j, where i and j indicate the two species. The G function estimated the nearest neighbor distance distribution function G(r) from a point pattern within a defined window and compared it to the theoretical Poisson process. As our second-order test, we applied multi-type Ripley’s K (Ripley 1976, 1988) to detect spatial randomness at successively larger scales based upon the cumulative distribution function (i.e. the number of additional nests within a distance, r, of a random nest; Baddeley and Turner 2005). For a multi-type point pattern, the multi-type K function counted the expected numbers of points of type j within a given distance of a point of type i. We derived Ripley’s K from the multi-type nest dataset and compared it with the theoretical curve of the Poisson point pattern, which represented complete spatial randomness. We used the linearized form of K, L(r) = (K[r]) – πr2, to aid in interpretation and to stabilize the variance (Besag 1977, Haase 1995). Here, the expected number of nests in an area with radius r is subtracted from K[r], the observed number of nests in a circle with radius r. Under complete spatial randomness, the number of nests in a circle follows a Poisson distribution and L(r) = 0 for all distances. Though Ripley’s K-function is widely recognized as a useful tool for detecting spatial aggregations, the cumulative character of this statistic often hampers the detection of scale- dependent patterns (Condit et al. 2000, Schurr et al. 2004). If clumping occurs on a relatively small scale, the point density at larger scales will be above average as well because the increasing circular scales are cumulative. Consequently, we also performed the pair-correlation function (PCF; Ripley 1981, Stoyan and Stoyan 1994), which tests for interactions between points (i.e., nests) separated by a distance r. Unlike Ripley’s K function, which counts all nests contained within a circle, the PCF can be thought of as a circle centered at a given nest, where 24 the only nests counted are those that lie on the circle boundary (i.e., a ring). The PCF is the probability of observing a pair of nests separated by a distance r, divided by the corresponding probability for a Poisson process (Baddeley 2008). Interpretation of the PCF was similar to that of Ripley’s K in that values above the upper bounds of the confidence envelope indicate clustering and those below indicate inhibition. For a multi-type point pattern, the multi-type PCF function examines the probability of finding a point of type i at location x and a point of type j at location y. Lastly, we utilized multi-type Ripley’s K analyses to evaluate whether godwit nest fate was correlated with its spatial positioning relative to gull nests. For 2016 only, we evaluated successful and failed godwit nests separately relative to all gull nests found. We considered a second-order aggregation of successful godwit nests with all gull nests and second-order inhibition between failed godwit nests and all gull nests as evidence in support of the protective- association hypothesis. We compared the observed test statistic, Gij(r), Kij(r) or PCFij(r), against the distribution of Gij(r), Kij(r) or PCFij(r) from 199 permutations of point patterns based on a Poisson process model with the same density as the observed nests (Ripley 1976, Baddeley and Turner 2005). We graphed the confidence envelope to test for significant deviations from complete spatial randomness in each of our analyses. At each distance, observed Gij(r), Kij(r) or PCFij(r) below the confidence envelope indicated significant deviations from complete spatial randomness towards regularity or spatial inhibition. Observed Gij(r), Kij(r) or PCFij(r) above the confidence envelope indicated significant aggregation or clustering. Because variability in user-defined distances for this test can affect the outcome of Ripley’s K, we ran each test using the default range as prescribed by SPATSTAT. The recommended range for the distance lags (r) was 0 – 852 m for 25 the North plot. We initially performed these tests separately by year to verify that the spatial pattern and location of clusters were comparable among years but then pooled across all three years given that our sample sizes were relatively small. Vegetation parameters: After godwit nests were no longer active, we measured the habitat at each nest site and a suite of associated random points surrounding the nest. We defined the microhabitat (nest site) scale as the area within a 1-m diameter circle centered on the nest. In each godwit territory, we additionally placed 25 1-m diameter circular plots at randomly selected points. Points were selected from within a 200-m radius of the nest using a random number generator. All points were within the wet sedge dominated bog and study area boundaries. For each circular plot, we measured the distance to the nearest water body (≥ 2 cm deep) from the center of the circle, and within the plot itself, the percent cover for all species present. From this, we summarized the percentage of the circle covered by shrubs, sedges and grasses, and forbs, as well as the percentage of bare ground (water, mud, or rocks). We also summed the number of plant species present in the circular plot as a metric of species richness (see Swift et al. 2017a, b for more information). Vegetation analyses: We used Moran’s I test (Moran 1948) to examine if spatial patterning of godwit nest locations was correlated with an underlying spatial pattern in the habitat features used by godwits to choose their nest site. If certain vegetation characteristics drove settlement decisions, then clusters of nests should correspond to patches of especially favorable habitat. We selected focal 26 vegetation parameters based on previous work (Swift et al. 2017a) showing that godwits selected areas with greater numbers of plant species; more sedge/grass; forb; and tall shrubby cover between 30cm and 1m tall; less bare ground; and were closer to shallow water than random sites. To reduce the number of variables and tests performed, we used the distribution of the results of a principal component analysis (PCA) using these six variables for our Moran’s I tests. To explore spatial autocorrelation, the principal components were tested at three different scales using a different number of distance classes (20, 50, 100) in the freely available software SAM (Rangel et al. 2010), with greater numbers of distance classes representing a finer-scale analysis. Each distance class was defined such that an approximately equal number of pairs of points were considered in each distance class. We determined the significance of Moran’s I for each distance class using a randomization procedure with 999 simulations (Fortin and Dale 2005). Vegetation data for nest locations and randomly selected points were analyzed in both a combined dataset and a nests-only dataset. To account for non-independence among distance classes, the significance for each class was assessed using a Bonferroni correction. Moran’s I values were then plotted as a correlogram against k distance classes to aid in interpretation (Fortin and Dale 2005). A significant positive Moran’s I value indicated a patch of similarly structured vegetation; a significant negative value indicated dissimilar vegetation characteristics and was interpreted as a space between patches (Amico et al. 2008). Godwit nest survival: We examined the influence of the gull colony and habitat characteristics on godwit nest survival with mark-recapture analyses. Using all gull nests found from 2014 to 2016 combined, we created a minimum convex polygon for each plot that we defined as the gull colony. For each 27 godwit nest, we calculated the minimum distance to the gull colony boundary, the number of gull nests within 200 m, and the minimum distance to the nearest gull nest using ArcGIS (ESRI 2015). We also selected six habitat variables known to be used by godwits when choosing their nest sites (Swift et al. 2017a): distance to the closest water body (≥ 2 cm), % tall shrubby cover (between 30 cm and 1 m tall), % bare ground (water, mud, or rocks), % sedge and grass cover, % herbaceous forb cover, and the number of species within the 1-m circle plot. We used program MARK to estimate daily survival rates (DSRs) of godwit nests in six separate analyses (Dinsmore et al. 2002, Rotella et al. 2004). First, we examined the effects of gull proximity and habitat characteristics on nest DSR for 43 nests monitored from 2014 to 2016. We treated study plot and year as two subsets and initially modeled them separately. Within the subsets we modeled each variable alone as well as in combined habitat and proximity to gulls models. Distance to the gull colony and the nearest gull nest were highly correlated (r2=0.86) and were therefore not included together in models. We evaluated models using Akaike’s information criterion corrected for small sample sizes (AICC; Burnham and Anderson 2002), and present beta estimates with standard errors and confidence intervals (CIs). Second, we expanded our analysis to 120 godwit nests found from 2009 – 2011 and 2014 – 2016 and again examined the effects of gull proximity and habitat characteristics on nest DSR. However, because detailed data on gull nests was not collected from 2009 – 2011, our only gull-related metric was the distance to the gull colony boundary, which was presumed to be stable across years. We performed these tests on a combined dataset, by year, and by plot. 28 Godwit chick survival: To assess the influence of proximity to the gull colony on the survival of godwit chicks to five days-of-age, we radio-tracked a subset of godwit chicks from successfully hatching nests from 2014 to 2016. Generally, gulls are no longer predators of godwit chicks after day five when godwit chicks become too large a prey item for gulls and are highly mobile (Senner et al. 2017). We randomly selected one or two chicks from each brood to receive a small 0.62 g Holohill radio. We clipped the downy feathers from a small area on each chick’s back and attached radios above the uropygial gland with cyanoacrylate glue. We deployed up to 20 radios each year, but not all chicks were located alive within the first five days post-hatching. Each chick was located every two-to-three days until the chick had died or fledged. We randomly selected one location for each individual within the first five days post- hatch, leading to 29 observations from 25 broods over the three years. For each triangulated location, we calculated its distance to the gull colony, distance to the closest gull nest, number of gull nests within 200 m, and distance to the closest pond in ArcGIS (ESRI 2015). We also calculated the distance to the colony for the nest from which the chick hatched. We then used generalized linear mixed models with a logistic regression to examine the influence of gulls on chick survival to day five, with brood and year as random effects. We evaluated each variable in separate univariate models using AICC scores (Burnham and Anderson 2002) in program R (R Core Development Team 2017) with the ‘lme4’ and ‘bbmle’ packages (Bates et al. 2015, Bolker 2017). 29 Results: Nest Summary: We found 43 godwit nests from 2014 – 2016, and 120 godwit nests in total from 2009 – 2016. Of these, 83 godwit nests were found within the gull colony (Figure 1). Daily nest survival was high in each year (>97%) for godwits. Apparent nest success (successful nests/total number of nests) averaged 83% for gull nests (n = 151 nests monitored of 252 located; Figure 1). Godwit and gull nests were spatially clustered on the North plot based on second-order tests (Figure 2; Appendix A Figures A2, A4). Using a nearest neighbor G function, godwit and gull nests were randomly distributed in 2015, 2016, and the combined year dataset (Appendix A Figures A1, A3). However, our second-order analyses suggested a strong aggregation in both 2015 and 2016, as well as the combined years, based on comparison of Ripley’s K function with the Poisson point-process null model (Figure 2). Additionally, the PCF test showed similar clustering patterns for 2015, 2016, and the combined years (Appendix A Figures A2, A4). In 2016, successful godwit nests clustered with all gull nests based on the Ripley’s K test (Appendix A Figure A5a). However, failed godwit nests also were clustered with all gull nests (Appendix A Figure A5b). Habitat: We performed a principal components analysis on the microhabitat characteristics of godwit nests and associated random points from 2014 to 2016 to reduce habitat variables into a smaller set of principal components (PCs) and also to examine the combined effect of multiple habitat variables. At the microhabitat scale, the first two principal components were retained and explained about 55% of the variance. The first principal component (PC1; s.d. 1.45) described a 30 gradient of vegetation from the number of species (positive values) to habitats dominated by sedges and grasses (negative values; Appendix A Table AI); the second (PC2; s.d. 1.08) separated the distance to water (positive) from habitats characterized by forbs (negative). Vegetation attributes varied in the degree to which they were spatially autocorrelated (i.e., patchily distributed; Appendix A Table AII). Of the 24 tests of spatial autocorrelation conducted (2 PC variables × 3 distance classes × 2 point subsets (nests and nests + random points) x 2 study plots), 75% (n = 18) yielded no significant autocorrelation (Appendix A Table AII). No patchiness was detected within the nest-only dataset. Significant spatial autocorrelation was detected at distances ranging from 47 – 374 m for the North plot, depending on the number of classes and which PC variables were considered, and no spatial autocorrelation was detected for the South plot. Greater levels of patchiness were detected when all points were included than when restricted to nest locations, suggesting that areas surrounding godwit nests were similar in vegetation structure across the study area. Collectively, these results suggest that vegetation patchiness did not drive the spatial pattern of godwit nests. Godwit nest survival: Models that included measures of proximity to gull nests better explained godwit nest survival from 2014 – 2016 than did the constant survival model, while models with habitat measures had the least explanatory power (Appendix A Table AIII). Godwit nests were more likely to succeed as distance to the gull colony decreased (β = -0.008, CI -0.01, -0.003; Figure 3a), and the number of gull nests within 200 m increased (β = 0.29, CI -0.007, 0.59; Figure 3b) – a pattern that persisted whether nests were grouped by year or plot (Appendix A Tables AIV, AV). 31 We tested the influence of microhabitat variables as well as the distance to the gull colony on godwit nest survival with a linear trend for the 2009 – 2016 dataset. The distance to the gull colony again was the top model (wi = 0.47), with most habitat measures falling below the null model (Appendix A Table AVI). As the distance to the colony increased, godwit nests were more likely to fail (β = -0.003, CI -0.006, -0.0009; Figure 3c) regardless of whether nests were grouped by plot or year (Appendix A Tables AVII, AVIII). Godwit chick survival: Survival of godwit chicks to day five improved with increasing distance to the gull colony (β = 14.29, CI 3.72, 24.85; Appendix A Table AIX). Results were similar whether we used our entire sample or randomly selected one chick from each brood. Eight of fifteen godwit chicks that survived the five-day period moved out of the colony between locations. Seven of twenty-two (32%) godwit chicks born within the colony survived to day five compared to eight of thirteen (62%) godwit chicks born outside the colony. Only eight of nineteen chicks located within the gull colony at any point during the five-day period survived through this period. Whereas, seven of nine chicks located outside the gull colony survived. Nevertheless, godwit chicks moved similar distances per day regardless of whether the chick was located inside or outside the colony (within: 263.6 m, s.d. 201.82, n = 27; outside: 293.97 m, s.d. 177.78, n = 18). However, godwit chicks that were born within the gull colony that survived moved farther per day then predated chicks (survived: 286.0 m, s.d. 220.1, n = 15; predated: 235.6 m, s.d. 181.9, n = 12). 32 Discussion: Our results confirmed a heterospecific nesting association between Hudsonian Godwits and Mew Gulls in Beluga River, Alaska, but showed that benefits occurred only during the nesting stage when gulls played an indirect protective role. After hatch, the survival of godwit chicks was negatively associated with their proximity to gulls, which are an important chick predator. The association between gulls and godwits was thus context dependent, and godwits appear to optimize their fitness by adopting a strategy to nest within the gull colony but leave it after hatching. Thus, godwits seem to adaptively respond to a landscape where there is both spatial and temporal variation in suitable nesting habitat and predation risk throughout the breeding season (Mönkkönen et al. 2007, Seppänen et al. 2007). Although we found that nest survival of godwits was greater near gull colonies, a full demonstration that the nesting association is protective requires three conditions: (1) the ability to recognize potential protectors, (2) active selection of nest sites near protector species rather than simply in similar habitat, and (3) survival benefits exceed the effects of predator swamping (Quinn and Ueta 2008). The association between godwits and gulls meets each of these criteria. First, godwits nested near a species that exhibits loud, defensive behaviors that are easily detected by other species in the community. Protector species are chosen based on both quality and reliability (Larsen and Grundetjern 1997), and they must not affect resource availability for the protected species (Mönkkönen and Forsman 2002). Godwits seem to actively choose to nest near gulls, which are known to nest in association with shorebirds, waterfowl, and jaegers in Europe (Götmark and Andersson 1980, Moskoff and Bevier 2002). Combined, this suggests that godwits recognize gulls as a potential protector species. Second, the association occurs despite differences in microhabitat nesting preferences between the two species (Burger and Gochfeld 33 1988, Swift et al. 2017a). Mew Gull nests were most commonly found on islands in deep snowmelt ponds that had little vegetation (RJ Swift unpubl. data). While we did not test the spatial distribution or availability of gull breeding habitat, in general, habitat features, such as the pond complexes used by gulls, are randomly distributed across the bog (Swift et al. 2017b). Further, we found that habitat attributes of godwit nest sites (i.e., distance to water, tall shrubby cover) were not major determinants of godwit nest placement or survival; rather, the proximity to the gull colony and the density of nearby gull nests exhibited more influence on godwit nest survival. The aggregation of godwit and gull nests therefore likely has little to do with the spatial distribution of habitats for either gulls or godwits and, instead, is the result of social attraction. Hence, although vegetation characteristics explain some aspect of nesting associations, they do not fully account for the benefits derived from the association (Quinn et al. 2003, Kleindorfer et al. 2009). Third, the density of godwits nesting within gull colonies, though greater than that outside of the colony (average per year: 9.2 nests per km2 inside vs. 1.2 nests per km2 outside), was almost certainly too low to result in predator swamping given the abundance and diversity of predators in the system. However, given that other ground nesting birds (e.g., gulls, shorebirds, waterfowl, and passerines) also reach relatively high densities within gull colonies (average per year: 101.1 nests per km2 inside vs. 12.3 nests per km2 outside), predator swamping may still play a role. The extent to which these densities may result in swamping is thus unclear, as the higher densities may also attract predators to a prey-rich area. Several factors may promote a protective association between gulls and godwits. For protective associations to be effective, the breeding seasons of the two species must be synchronous, nest defense must continue throughout the active nesting period, and the protector species must be reliable (Quinn and Ueta 2008). Although we did not directly test whether 34 godwits can differentiate among potential protectors based on their quality, their nesting distribution suggests that they can. One supporting observation is that godwits do not nest near two other defensive larid species, Arctic Terns (Sterna paradisaea) and Bonaparte’s Gulls (Chroicocephalus philadelphia), that arrive later to the breeding grounds and thus do not breed in synchrony with godwits (RJ Swift unpubl. data, eBird 2017). In Beluga River, godwits and gulls initiate their nests at approximately the same time (RJ Swift unpubl. data), but gulls arrive to the breeding grounds several weeks earlier and have established territories prior to godwit arrival (RJ Swift unpubl. data; eBird 2017). Because the gull colony is also relatively stable in size and location, and because gulls have high nest site fidelity (Moskoff and Bevier 2002), godwits may be able to use information from previous breeding seasons when choosing nest locations. Furthermore, from 2014 – 2016, the average initiation date for gull nests was within one day of that of godwits (RJ Swift unpubl. data). Highly synchronous nesting and the slightly shorter incubation period of godwits (22 – 23 days) compared with gulls (23 – 27 days) translates into earlier hatch dates for godwits – potentially minimizing the threat of gull predation during the vulnerable chick period. Godwits therefore may be initiating nests as early as possible after arrival to minimize the risk of nesting within the gull colony, while still actively choosing to aggregate with gulls as a potential protector. Godwits nesting in association with Mew Gulls had 27% higher nest success than did those nesting outside of gull colonies. This benefit is likely the result of active protection from predators by gulls, whereby the defensive behaviors of gulls protect godwit nests when mutual predators such as red foxes (Vulpes vulpes) and Common Ravens (Corvus corax) are present in the gull colony. High nest survival is a common benefit of protective associations and has been documented in most known cases, but the mechanism for this protection is typically unknown 35 (Quinn and Ueta 2008). Alternatively, godwits may use the defensive behavior of gulls simply as an early warning system for approaching predators. In this scenario, the gull colony could serve as an ‘information center,’ with godwits acting as potential information parasites, gleaning information that alerts pairs to the presence of predators, and allowing them to engage in cryptic or defensive behaviors, similar to grebes nesting in tern colonies (Nuechterlein 1981, Burger 1984, Doligez et al. 2002). The effectiveness of protective aggregations, by way of deterring predators, may be more strongly driven by colony size than by species composition. Indeed, gulls experience greater nest success in larger colonies, presumably due to effective mobbing behaviors (Götmark and Andersson 1984). Colonies below a certain threshold density could attract predators, but not offer sufficient protection, and thus increase the likelihood of nest failure for both species, creating an ecological trap (Dwernychuk and Boag 1972, Schlaepfer et al. 2002). Accordingly, we detected the strongest effect on nest distributions with second-order tests, and larger number of gull nests increased godwit nest survival, suggesting that gull colony size was important for godwit nest survival. Thus, further study is needed to identify the mechanisms that drive the protective association between nesting godwits and gulls at Beluga River. During the chick stage, the benefits of nesting near gulls gave way to costs, with only 42% of chicks located within the colony surviving to day five compared to 70% outside the colony. While predation is a potential cost of nesting near a protector, few studies have shown that protectors can become predators during different breeding stages. For instance, Eurasian Kestrels (Falco tinnunculus) protect Eurasian Curlew (Numenius arquata) nests, but depredate a small percentage (5%) of curlew chicks each year (Norrdahl et al. 1995). However, curlew chicks are only an incidental prey item of kestrels. Furthermore, large colonies of gulls (over 500 36 pairs) have been known to completely eliminate cohorts of waterfowl chicks whose parents nested in association with the colony, creating an ecological trap for nesting waterfowl (Dwernychuk and Boag 1972). Therefore, the context-dependent relationship between godwits and gulls may not be unique. Despite the potential dual nature of heterospecific associations, the predictable spatial variation in predation risk from nesting and/or territorial predators can provide protected species with the opportunity to adaptively respond to the changing nature of these relationships (Thomson et al. 2006, Mönkkönen et al. 2007). For instance, individual godwits may be able to compensate for the trade-off between nest and chick predation risk by nesting at intermediate distances or on the edge of the gull colony (Mönkkönen et al. 2007). Alternatively, godwits could compensate for nesting within a stable, risky environment through brood movements, such as by leading their broods to safer habitats outside of dense gull breeding areas. Brood movements that avoid predator-rich or food-poor areas have been well-studied, including with Kentish plover (Charadrius alexandrinus) chicks that show increased survival and growth rates in a non-natal habitat that was food-rich and predator-poor (Kosztolányi et al. 2007). Accordingly, we relocated most godwit broods outside their natal territories and often in areas of the bog with few nesting gulls (RJ Swift personal obs.). Invertebrate prey biomass and habitat attributes vary little across the bog and are therefore unlikely to explain use of these areas (Senner et al. 2017; Swift et al. 2017b). In fact, individuals hatched from nests within and outside of the gull colony moved similar distances each day, suggesting biological constraints on the distances moved. Rather, godwits with surviving broods that survived to five days moved farther each day than those that were predated. Behavioral responses to nesting in risky environments 37 may thus allow godwits to compensate and increase chick survival despite nesting in close proximity to gulls. Our study thus provides evidence that Hudsonian Godwits benefit from nesting inside the Mew Gull colony through increased hatching success but bear a cost of lower chick survival due to gull depredation. Based on these findings, we suggest that the nature of interactions between godwits and gulls changes with breeding stage and is, therefore, context-dependent. Our study is among the first to examine the effects of protective associations beyond the nest stage and to document context-dependent interactions based on breeding stage. The costs and benefits of this association are clearly complex, and the lasting benefits (e.g., lifetime fitness) for nesting Hudsonian Godwits associating with Mew Gulls remain unclear and require further study. Funding: Thus work was supported by the National Science Foundation (1110444 to NRS and DGE-1144153 to RJS); U.S. Fish and Wildlife Service (4074 and 5147) to NRS; David and Lucile Packard Foundation to NRS; Faucett Family Foundation to NRS and RJS; Arctic Audubon Society to NRS; American Ornithological Society to NRS; Cornell Lab of Ornithology to NRS and RJS; Athena Fund at the Cornell Lab of Ornithology to NRS and RJS; Arctic Audubon Society to NRS, and Cornell University to NRS and RJS. Acknowledgments: Many thanks to numerous field assistants that assisted in data collection and the many colleagues that provided input along the way. All procedures performed in this study involving animals were in accordance with the ethical standards of Cornell University and as part of an approved animal use and care protocol. 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(D) Mew Gull nest locations on South plot from 2014 – 2016. The dashed line shows the Mew Gull colony boundary, and the solid line shows the study plot boundary. 45 Figure 2. Ripley’s K function (transformed to L(r)) for all Hudsonian Godwit (Limosa haemastica) and Mew Gull (Larus canus) nests found on North plot in 2015 (a), 2016 (b), and combined year dataset (c). The solid black line represents values for the point pattern (observed), dashed black line represent the expectation under complete spatial randomness (theoretical) of the Poisson null model, and the gray lines represent the confidence envelope based on 199 randomizations of a Poisson point process. Values above the upper bounds of the confidence envelope indicate clustering at distance r, and values below the lower bounds indicate inhibition. 46 47 Figure 3. Daily survival rates of Hudsonian Godwit (Limosa haemastica) nests at Beluga River, Alaska. From 2014 to 2016, survival declined with increasing distance to the Mew Gull (Larus canus) colony (a) and increased with increasing numbers of Mew Gull nests within 200 m (b). From 2009 to 2016, daily survival rates of Hudsonian Godwit nests declined with increasing distance to the Mew Gull colony (c). Ninety-five percent confidence intervals shown (gray lines). 48 49 APPENDIX A Table AI. Standard deviation, eigenvectors, and variance explained by principal components (PCs) of microhabitat variables measured at Hudsonian Godwit (Limosa haemastica) nests and randomly-selected sites near Beluga River, Alaska. Principal components PC1 PC2 Standard deviation 1.45 1.08 Proportion of variance 0.35 0.19 Cumulative proportion of variance 0.35 0.55 Variable loadings Distance to water (m) 0.35 0.48 % Bare -0.27 0.07 % Forb species 0.42 -0.53 % Sedges and grasses -0.51 -0.21 % Shrubs between 30cm and 1m tall 0.36 0.51 Number of plant species 0.49 -0.44 50 Table AII. Results of Moran’s I tests of spatial autocorrelation for principal components of vegetation features associated with Hudsonian Godwit (Limosa haemastica) nests for the North and South plots. Each variable was evaluated using 20, 50, and 100 distance classes and two subsets of point vegetation data: all points and nests only. The significance of Moran’s I coefficients for each distance class was evaluated using a Bonferroni correction. When significant spatial autocorrelation was detected at a given distance class, the median distance (m) of that class is reported; “NS” indicates that the result was not significant. When significant spatial autocorrelation was detected for multiple distance classes, the range of the median distance of the closest and farthest distance classes is reported, along with the p-value associated with those classes. The percentage of distance classes with a significant Moran’s I value is given as the % significant. The percentage of significant distance classes that had a positive Moran’s I value, indicating a cluster, is given as the % positive. All points Nests only Dist (m) % % p-value Dist (m) % % signif pos signif pos p-value North Plot PC1 20 355 5 100 0.001 NS NS NS NS 50 300-374 4 100 0.001 NS NS NS NS 100 369 1 100 0.001 NS NS NS NS PC2 20 84 5 100 0.001 NS NS NS NS 50 47 4 100 0.001 NS NS NS NS 100 108-359 2 100 0.001 NS NS NS NS South Plot PC1 20 NS NS NS NS NS NS NS NS 50 NS NS NS NS NS NS NS NS 100 NS NS NS NS NS NS NS NS PC2 20 NS NS NS NS NS NS NS NS 50 NS NS NS NS NS NS NS NS 100 NS NS NS NS NS NS NS NS 51 Table AIII. Summary of competing models evaluating relationships between Hudsonian Godwit (Limosa haemastica) nest survival, habitat variables, and proximity to Mew Gulls (Larus canus; MEGU). Models are ranked by ascending dAICC values with the number of parameters (k), and Akaike weights for all godwit nests found in 2014 – 2016 at Beluga River, Alaska. Model dAICC k Weight Distance to MEGU colony 0.00 2 0.46 Distance to MEGU colony + number of MEGU nests 1.23 3 0.18 Number of MEGU nests 1.57 2 0.16 Distance to MEGU nest 2.13 2 0.12 Distance to MEGU nest + number of MEGU nests 3.12 3 0.07 Intercept only 4.84 1 0.03 Distance to water + % sedge and grass cover + % tall shrubs + number of species + % forb cover + % bare ground 4.89 6 0.03 Distance to water 5.91 2 0.02 Number of species 6.19 2 0.02 % tall shrubby cover 6.32 2 0.01 % Forb cover 6.82 2 0.01 % Sedge and grass cover 6.85 2 0.01 52 Table AIV. Summary of competing models evaluating relationships between Hudsonian Godwit (Limosa haemastica) nest survival, habitat variables, and proximity to Mew Gulls (Larus canus; MEGU). Models are ranked by ascending dAICC values with the number of parameters (k), and Akaike weights for godwit nests grouped by plot (North and South) found in 2014 – 2016 at Beluga River, Alaska. Model dAICC k Weights Distance to MEGU colony 0.00 2 0.46 Number of MEGU nests 1.57 2 0.20 Distance to MEGU nest 2.13 2 0.16 Intercept only 4.84 1 0.04 Distance to water + % sedge and grass cover + % tall shrubs + number of species + % forb cover + % bare ground 4.89 6 0.04 Distance to water 5.91 2 0.02 Number of species 6.19 2 0.02 % tall shrubby cover 6.32 2 0.02 % Forb cover 6.82 2 0.02 % Sedge and grass cover 6.85 2 0.01 53 Table AV. Summary of competing models evaluating relationships between Hudsonian Godwit (Limosa haemastica) nest survival, habitat variables, and proximity to Mew Gulls (Larus canus; MEGU). Models are ranked by ascending dAICC values with the number of parameters (k), and Akaike weights for godwit nests grouped by year (2014, 2015, 2016). Model dAICC k Weights Distance to MEGU colony 0.00 2 0.46 Number of MEGU nests 1.63 2 0.21 Distance to MEGU nest 2.16 2 0.16 Intercept only 4.86 1 0.04 Distance to water + % sedge and grass cover + % tall shrubs + number of species + % forb cover + % bare ground 4.93 6 0.04 Distance to water 5.92 2 0.02 Number of species 6.20 2 0.02 % tall shrubs 6.34 2 0.02 % Forb cover 6.84 2 0.02 % Sedge and grass cover 6.86 2 0.01 54 Table AVI. Summary of competing models evaluating relationships between Hudsonian Godwit (Limosa haemastica) nest survival, habitat variables, and proximity to Mew Gulls (Larus canus). Models are ranked by ascending dAICC values with the number of parameters (k), and Akaike weights for all godwit nests found in 2009 – 2016. Model dAICC k Weights Distance to gull colony + T 0.00 2 0.47 Number of species + T 2.13 2 0.16 Intercept only + T 3.29 1 0.09 % Sedge and grass cover + T 3.54 2 0.08 % Forb cover + T 4.10 2 0.06 % tall shrubs + T 4.45 2 0.05 % Bare ground + T 4.50 2 0.05 Distance to water + T 5.01 2 0.04 * T, a linear time trend across the breeding season 55 Table AVII. Summary of competing models evaluating relationships between Hudsonian Godwit (Limosa haemastica) nest survival, habitat variables, and proximity to Mew Gulls (Larus canus). Models are ranked by ascending dAICC values with the number of parameters (k), and Akaike weights for godwit nests grouped by plot (North and South) found in 2009 – 2016. Model dAICC k Weights Distance to gull colony + T 0.00 2 0.47 Number of species + T 2.13 2 0.16 Intercept only + T 3.29 1 0.09 % Sedge and grass cover + T 3.54 2 0.08 % Forb cover + T 4.10 2 0.06 % tall shrubs + T 4.45 2 0.05 % Bare ground + T 4.50 2 0.05 Distance to water + T 5.01 2 0.04 * T, a linear time trend across the breeding season 56 Table AVIII. Summary of competing models evaluating relationships between Hudsonian Godwit (Limosa haemastica) nest survival, habitat variables, and proximity to Mew Gulls (Larus canus). Models are ranked by ascending dAICC values with the number of parameters (k), and Akaike weights for godwit nests grouped by year (2009, 2010, 2011, 2014, 2015, 2016). Model dAICC k Weights Distance to gull colony + T 0.00 2 0.47 Number of species + T 2.13 2 0.16 Intercept only + T 3.29 1 0.09 % Sedge and grass cover + T 3.54 2 0.08 % Forb cover + T 4.10 2 0.06 % tall shrubs + T 4.45 2 0.05 % Bare ground + T 4.50 2 0.05 Distance to water + T 5.01 2 0.04 * T, a linear time trend across the breeding season 57 Table AIX. Summary of competing GLMM models evaluating relationships between Hudsonian Godwit (Limosa haemastica) chick survival and proximity to Mew Gulls (Larus canus; MEGU). Models are ranked by ascending dAICC values with the number of parameters (k), and Akaike weights for godwit nests with random effects of brood and year. Model dAICC k Weight Distance to MEGU colony 0.00 4 0.95 Distance to MEGU nest 6.70 4 0.03 Null 9.90 3 0.01 Distance nest was to MEGU colony 10.90 4 0.00 Distance to closest pond 11.50 4 0.00 Number of MEGU nests 11.50 4 0.00 58 Figure A1. G function for all Hudsonian Godwit (Limosa haemastica) and Mew Gull (Larus canus) nests found on North plot between 2014 and 2016. The solid black line represents values for the point pattern (observed), dashed red line represent the expectation under complete spatial randomness (theoretical) of the Poisson null model, and the gray lines represent the confidence envelope based on 199 randomizations of a Poisson point process. Values above the upper bounds of the confidence envelope indicate clustering at distance r, and values below the lower bounds indicate inhibition. 59 Figure A2. PCF function for all Hudsonian Godwit (Limosa haemastica) and Mew Gull (Larus canus) nests found on North plot between 2014 and 2016. The solid black line represents values for the point pattern (observed), dashed red line represent the expectation under complete spatial randomness (theoretical) of the Poisson null model, and the gray lines represent the confidence envelope based on 199 randomizations of a Poisson point process. Values above the upper bounds of the confidence envelope indicate clustering at distance r, and values below the lower bounds indicate inhibition. 60 a b Figure A3. G function for Hudsonian Godwit (Limosa haemastica) and Mew Gull (Larus canus) nests found on North plot in 2015 (a) and 2016 (b). The solid black line represents values for the point pattern (observed), dashed red line represent the expectation under complete spatial randomness (theoretical) of the Poisson null model, and the gray lines represent the confidence envelope based on 199 randomizations of a Poisson point process. Values above the upper bounds of the confidence envelope indicate clustering at distance r, and values below the lower bounds indicate inhibition. 61 a b Figure A4. PCF function for Hudsonian Godwit (Limosa haemastica) and Mew Gull (Larus canus) nests found on North plot in 2015 (a) and 2016 (b). The solid black line represents values for the point pattern (observed), dashed red line represent the expectation under complete spatial randomness (theoretical) of the Poisson null model, and the gray lines represent the confidence envelope based on 199 randomizations of a Poisson point process. Values above the upper bounds of the confidence envelope indicate clustering at distance r, and values below the lower bounds indicate inhibition. 62 a b Figure A5. Ripley’s K function (transformed to L(r)) for (a) successful Hudsonian Godwit (Limosa haemastica) and all Mew Gull (Larus canus) nests and (b) failed Hudsonian Godwit and all Mew Gull nests found on North plot in 2016. The solid black line represents values for the point pattern (observed), dashed red line represent the expectation under complete spatial randomness (theoretical) of the Poisson null model, and the gray lines represent the confidence envelope based on 199 randomizations of a Poisson point process. Values above the upper bounds of the confidence envelope indicate clustering at distance r, and values below the lower bounds indicate inhibition. 63 APPENDIX B Table BI. Nest survival, nest site microhabitat characteristics, and proximity to Mew Gulls (Larus canus) data for all Hudsonian Godwit (Limosa haemastica) nests found at Beluga River, Alaska from 2009 to 2016. Distance to the closest gull nest and number of gull nests within 200 m were only collected between 2014 and 2016. 64 Day Last Last Nest Distance to Distance Number Distance % % Number Nest found day day % % active visited fate Year Plot gull colony to gull of gull to water 30cm (m) nest (m) nests (m) to 1m Bare Sedge Grass Forb of species GN1 10 31 31 Hatch 2009 North 0 NA NA 5.21 21 13 25 13 14 GN10 6 28 28 Hatch 2009 North 0 NA NA 10.88 73 5 31 16 12 GN12 10 31 31 Hatch 2009 North 0 NA NA 0.50 60 2 31 17 15 GN14 10 29 29 Hatch 2009 North 0 NA NA 17.31 50 0 31 15 14 GN17 8 17 19 Fail 2009 South 62.31 NA NA 0.36 33 35 19 7 9 GN18 10 12 15 Fail 2009 North 0 NA NA 16.50 36 0 30 16 18 GN2 6 30 30 Hatch 2009 North 0 NA NA 3.02 30 30 10 12 13 GN22 11 33 33 Hatch 2009 North 84.24 NA NA 9.14 57 6 26 24 15 GN27 15 33 33 Hatch 2009 North 0 NA NA 8.08 43 10 16 15 16 GN28 5 17 19 Fail 2009 South 0 NA NA 5.59 22 10 23 13 20 GN43 10 17 19 Fail 2009 South 0.89 NA NA 8.02 55 7 33 17 20 GN44 14 28 28 Hatch 2009 North 0 NA NA 12.92 40 0 38 16 17 GN45 19 32 32 Hatch 2009 North 450.32 NA NA 13.72 52 0 28 12 15 GN46 19 33 33 Hatch 2009 North 0 NA NA 0.30 33 10 32 13 16 GN47 21 27 27 Hatch 2009 North 0 NA NA 3.69 40 7 23 15 17 GN48 27 29 29 Hatch 2009 North 0 NA NA 0.50 18 2 30 15 13 GN49 28 33 33 Hatch 2009 North 0 NA NA 16.31 43 0 30 17 20 GN7 15 33 33 Hatch 2009 North 0 NA NA 12.80 39 0 34 13 18 GN8 27 28 28 Hatch 2009 North 0 NA NA 6.68 26 9 31 16 16 GN07 9 9 10 Fail 2010 North 98.14 NA NA 3.53 0 20 11 9 20 GN11 18 41 41 Hatch 2010 North 0 NA NA 2.67 4 7 9 10 18 GN41 7 7 8 Fail 2010 North 0 NA NA 0.50 12 26 12 12 24 GN43 9 9 10 Fail 2010 North 0 NA NA 30.28 15 1 10 8 17 GN47 10 34 34 Hatch 2010 South 106.36 NA NA 3.51 2 3 18 8 19 GN49 11 11 13 Fail 2010 North 0 NA NA 18.21 1 3 13 7 16 GN53 16 33 33 Hatch 2010 South 0 NA NA 5.03 5 25 18 10 20 GN55 20 20 21 Fail 2010 North 0 NA NA 1.86 3 4 11 10 20 GN56 22 22 23 Fail 2010 North 194.49 NA NA 4.52 8 10 13 7 14 65 TABLE BI (CONTINUED) Day Last Last Nest Distance to Distance Number Distance % % Number Nest found day day fate Year Plot gull colony to gull of gull to water 30cm % Sedge % of active visited (m) nest (m) nests (m) to 1m Bare Grass Forb species GN57 17 17 19 Fail 2010 North 176.65 NA NA 4.70 12 25 11 9 18 GN58 23 43 43 Hatch 2010 North 0 NA NA 18.59 5 2 8 13 25 GN61 30 31 31 Hatch 2010 South 82.99 NA NA 0.50 4 12 12 8 19 GNHUYU 1 29 29 Hatch 2010 South 87.92 NA NA 1.93 10 4 14 7 19 GNHX 13 38 38 Hatch 2010 North 0 NA NA 1.65 4 1 13 15 24 GNHXXC 4 6 9 Fail 2010 North 0 NA NA 11.07 13 1 10 8 17 GNKX 13 30 30 Hatch 2010 North 0 NA NA 16.00 15 1 10 12 23 GNPE 7 7 9 Fail 2010 North 0 NA NA 10.49 1 0 13 11 19 GNPEJA 14 41 41 Hatch 2010 North 0 NA NA 10.00 8 0 8 7 19 GNPK 20 46 46 Hatch 2010 North 0 NA NA 12.98 1 6 14 12 23 GNPM 43 44 44 Hatch 2010 North 0 NA NA 5.06 5 12 16 11 19 GNUL 2 30 30 Hatch 2010 North 0 NA NA 13.51 3 1 14 6 14 GNXKTV 14 26 27 Fail 2010 North 450.32 NA NA 10.14 5 2 13 7 17 GNYMXV 29 30 30 Hatch 2010 North 0 NA NA 17.12 22 1 8 7 19 GNYN 6 6 7 Fail 2010 North 38.20 NA NA 11.17 2 1 9 8 16 GNYN2 22 46 46 Hatch 2010 North 0 NA NA 1.60 2 12 41 5 9 GNYTXL 7 27 27 Hatch 2010 South 0 NA NA 4.19 18 7 33 11 19 GNYV 6 6 7 Fail 2010 North 0 NA NA 20.32 10 2 19 13 19 GNYV2 14 20 24 Fail 2010 North 0 NA NA 2.36 8 30 21 9 13 GN08 21 44 44 Hatch 2011 North 0 NA NA 8.50 15 0 47 7 10 GN10 27 37 37 Hatch 2011 North 0 NA NA 12.56 1 0 31 8 9 GN12 28 42 44 Fail 2011 North 10.11 NA NA 7.80 2 0 35 7 6 GN13 35 38 38 Hatch 2011 North 0 NA NA 6.13 25 0 32 15 12 GNAPAU 34 35 35 Hatch 2011 North 0 NA NA 7.47 30 0 16 8 8 GNC4MJ 6 33 33 Hatch 2011 North 92.80 NA NA 8.53 20 2 37 11 10 GNCC 7 14 16 Fail 2011 North 0 NA NA 16.57 5 0 66 7 10 66 TABLE BI (CONTINUED) Nest Day Last Last Distance to Distance Number Distance % day day Nest Year Plot gull colony to gull of gull to water 30cm % % Number found fate Bare Sedge % active visited (m) nest (m) nests (m) to 1m Grass Forb of species GNCT 32 33 33 Hatch 2011 North 0 NA NA 0.21 20 25 6 7 12 GNE5E9 26 41 41 Hatch 2011 North 0 NA NA 1.16 5 0 54 10 4 GNEAE7 6 32 32 Hatch 2011 North 0 NA NA 9.75 35 5 11 37 10 GNH7T2 19 40 40 Hatch 2011 North 0 NA NA 7.62 70 0 31 14 13 GNH8L0 8 29 29 Hatch 2011 South 0 NA NA 8.6 20 0 49 8 10 GNJMXH 13 17 19 Fail 2011 South 31.13 NA NA 4.66 65 5 46 9 10 GNJTMU 37 38 38 Hatch 2011 North 444.42 NA NA 9.63 30 1 48 11 12 GNK0PM 20 31 31 Hatch 2011 North 0 NA NA 13.17 60 3 45 10 9 GNK4T7 15 31 31 Hatch 2011 South 232.07 NA NA 25.82 40 4 41 7 6 GNK5L4 27 34 35 Fail 2011 North 69.03 NA NA 11.58 30 5 22 17 8 GNL5E6 20 31 33 Fail 2011 South 23.67 NA NA 24.94 65 5 43 6 8 GNM2P2 14 31 31 Hatch 2011 North 0 NA NA 8.02 20 12 44 17 5 GNM3U0 19 35 35 Hatch 2011 North 0 NA NA 16.67 4 0 28 17 14 GNN8X3 7 34 34 Hatch 2011 North 0 NA NA 8.08 20 5 55 9 12 GNTANA 30 31 31 Hatch 2011 North 0 NA NA 7.74 70 5 24 14 12 GNV9C0 22 37 37 Hatch 2011 North 0 NA NA 16.28 10 0 24 7 8 GNX50H 8 30 30 Hatch 2011 North 0 NA NA 6.71 75 0 30 13 13 GNX6H3 6 29 29 Hatch 2011 North 0 NA NA 5.43 10 0 47 9 10 GNXA 12 12 14 Fail 2011 North 127.49 NA NA 3.66 60 0 53 11 11 GNY0T4 13 22 24 Fail 2011 South 0 NA NA 29.26 55 2 43 16 12 GNY9L6 14 34 34 Hatch 2011 North 0 NA NA 12.01 20 5 18 19 15 BHD11 20 32 32 Hatch 2014 South 0 89.56 2 1.30 0 0 35 15 5 BHD17 33 47 47 Hatch 2014 South 0 107.06 1 0.25 0 75 14 2 3 BHD19 38 43 43 Hatch 2014 North 0 50.63 6 14.40 3 0 23 6 7 BJL17 14 31 31 Hatch 2014 North 155.91 343.99 0 3.50 8 0 30 8 7 BJL18 18 39 39 Hatch 2014 North 0 326.84 0 0.50 3 10 30 4 8 67 TABLE BI (CONTINUED) Day Last Last Nest Distance to Distance Number Distance % % Number Nest found day day fate Year Plot gull colony to gull of gull to water 30cm % % active visited (m) nest (m) nests (m) to 1m Bare Sedge Grass Forb of species BJL19 22 34 34 Hatch 2014 North 0 89.29 7 7.50 3 0 37 7 5 BJL23 30 31 31 Hatch 2014 North 1 240.74 0 11.25 5 0 10 13 8 BJL25 31 32 32 Hatch 2014 South 0 179.79 1 5.25 3 0 60 8 5 GJM06 16 35 35 Hatch 2014 South 61.82 99.48 4 0.25 15 20 31 11 7 GJM17 37 38 38 Hatch 2014 North 0 114.23 2 7.75 0 0 30 12 7 RJS27 35 35 37 Fail 2014 North 0 52.07 3 9.25 2 0 51 13 10 GJM05 8 31 31 Hatch 2015 North 0 168.67 4 8.25 30 0 53 7 6 GJM18 12 32 32 Hatch 2015 South 95.72 122.12 6 0.50 30 2 36 4 8 GJM35 16 31 31 Hatch 2015 North 0 132.57 2 25.5 15 0 34 10 8 GJM36 17 40 40 Hatch 2015 South 0 74.03 16 0.65 0 0 43 12 9 GJM56 23 30 30 Hatch 2015 North 0 35.88 9 13.60 30 0 31 14 11 JAK05 9 34 34 Hatch 2015 North 0 31.49 15 9.98 45 0 26 8 6 JAK21 13 34 34 Hatch 2015 North 0 36.79 5 1.45 40 0 25 7 6 JMH10 12 37 37 Hatch 2015 South 35.74 136.30 4 28.30 20 0 30 9 6 JMH120 37 38 38 Hatch 2015 South 83.54 126.89 3 12.50 10 0 31 9 5 JMH15 14 14 15 Fail 2015 North 352.56 414.19 0 7.75 5 0 41 13 11 JMH20 16 34 34 Hatch 2015 North 0 60.05 7 16.30 40 0 28 9 7 JMH28 20 32 32 Hatch 2015 North 47.46 162.91 1 7.25 40 0 25 15 11 KJP11 11 31 31 Hatch 2015 North 0 112.06 4 13.50 60 0 25 44 15 KJP18 12 31 31 Hatch 2015 South 0 65.62 5 8.00 15 0 55 9 5 KJP44 20 32 32 Hatch 2015 North 0 91.03 10 55.30 30 0 17 8 12 RJS05 27 40 40 Hatch 2015 North 0 120.49 7 8.40 20 0 36 9 8 KRS48 23 29 29 Hatch 2016 North 0 50.38 5 18.25 60 0 32 3 8 KRS63 29 29 30 Fail 2016 North 397.62 482.11 0 0.99 10 0 20 15 10 LKF04 8 31 31 Hatch 2016 South 4.28 92.35 3 4.25 12 0 38 12 12 LKF15 14 31 32 Fail 2016 North 0 140.29 4 21.3 50 0 9 11 8 68 TABLE BI (CONTINUED) Last Last Distance to Distance Number Distance % % Number Nest Day day day Nest gull colony to gull of gull to water 30cm % Sedge % of found active visited fate Year Plot (m) nest (m) nests (m) to 1m Bare Grass Forb species LKF22 19 37 37 Hatch 2016 South 181.24 182.20 1 27.50 0 0 66 9 10 LKF23 20 27 29 Fail 2016 North 4.88 76.03 4 7.65 20 0 33 7 7 MLS14 11 29 29 Hatch 2016 North 0 60.35 3 16.25 25 0 17 12 10 MLS37 19 37 37 Hatch 2016 South 295.37 386.06 0 4.30 8 0 36 9 8 RIG01 9 9 11 Fail 2016 North 105.40 127.82 1 5.50 8 0 31 5 10 RIG15 21 42 42 Hatch 2016 North 0 96.98 10 3.45 5 0 37 3 6 RJS01 6 9 12 Fail 2016 North 152.07 347.93 0 4.85 30 0 38 8 11 RJS02 9 15 18 Fail 2016 North 0 47.81 5 5.30 8 0 21 14 13 RJS04 12 32 32 Hatch 2016 North 0 89.39 5 38.50 35 0 13 7 10 RJS07 15 32 32 Hatch 2016 North 0 25.63 7 4.00 2 0 21 20 16 RJS10 17 32 32 Hatch 2016 South 211.02 240.26 0 20.50 18 0 14 9 13 RJS16 31 34 34 Hatch 2016 South 210.32 245.94 0 28.60 15 0 10 12 10 69 Table BII. Hudsonian Godwit (Limosa haemastica) chick survival to five-days-old and proximity to Mew Gull (Larus canus) data in Beluga River, Alaska from 2014 to 2016. 70 Chick Brood Year Chick Distance to Distance to Number of Distance to Nest’s distance Fate gull colony gull nest (m) (m) gull nests pond to gull colony (m) (m) H03 2014HUGOBHD17 2014 Died 0 78.07 1 56.10 0 J81 2014HUGOBJL17 2014 Survived 479.25 626.40 0 4.46 155.91 1AV 2014HUGOBJL18 2014 Died 0 169.19 1 1.85 0 H30 2014HUGOBJL19 2014 Died 0 107.25 8 29.88 0 1EP 2014HUGOBJL25 2014 Survived 157.63 383.23 0 71.29 0 C23 2014HUGOGJM06 2014 Survived 65.45 225.34 0 9.28 61.82 J03 2015HUGOFUV 2015 Died 0 293.75 0 5.26 NA 1KU 2015HUGOGJM18 2015 Died 148.36 172.55 5 27.47 95.72 E85 2015HUGOGJM35 2015 Survived 207.80 236.93 0 0 0 E53 2015HUGOJAK05 2015 Survived 0 125.63 7 20.32 0 1KJ 2015HUGOJAK21 2015 Survived 0 212.36 0 16.71 0 A83 2015HUGOJMH10 2015 Survived 191.29 227.43 0 11.17 35.74 H66 2015HUGOJMH28 2015 Died 0 114.03 2 18.22 47.46 H64 2015HUGOKJP18 2015 Survived 0 109.78 4 47.03 0 H96 2015HUGOKJP44 2015 Died 123.11 294.68 0 2.86 0 1LU 2015HUGORJS05 2015 Died 25.75 192.88 1 37.94 0 1KK 2016HUGOKRS48 2016 Died 0 68.20 6 7.99 0 1TU 2016HUGOLKF04 2016 Survived 0 60.35 3 4.83 4.28 1KA 2016HUGOLKF22 2016 Died 0 13.62 9 0 181.24 1CM 2016HUGOMLS14 2016 Died 0 35.44 5 8.46 0 C77 2016HUGOMLS37 2016 Died 0 43.12 19 3.55 295.37 1HH 2016HUGORIG15 2016 Died 0 70.16 8 0 0 H75 2016HUGORIG15 2016 Died 0 100.93 8 5.03 0 1LT 2016HUGORJS07 2016 Survived 0 116.02 8 30.22 0 H84 2016HUGORJS07 2016 Survived 0 100.95 8 30.58 0 1KV 2016HUGORJS10 2016 Survived 0 38.75 16 21.02 211.02 H11 2016HUGORJS10 2016 Survived 0 45.31 15 30.20 211.02 1MU 2016HUGORJS16 2016 Survived 146.10 198.77 1 21.91 210.32 C97 2016HUGORJS16 2016 Survived 152.36 204.26 0 11.44 210.32 71 CHAPTER THREE NEST SURVIVAL WITHIN AND OUTSIDE OF A PROTECTIVE NESTING ASSOCIATION 72 Abstract: A wide range of reproductive behaviors – including nest site selection, reproductive phenology, and defensive behaviors – can reflect selective pressures to reduce the risk of nest predation. However, such behaviors do not operate in isolation, and the interactions and feedbacks among them remain poorly understood. In this study, we tested the extent to which a protective nesting association mediated how nest site characteristics or individual traits of breeding Hudsonian Godwits (Limosa haemastica) influenced nest survival. From 2009 – 2016 at Beluga River, Alaska, we monitored 141 godwit nests located within and outside of breeding colonies of Mew Gulls (Larus canus), which aggressively defend their nests from predators and, thereby, may reduce predation risk for godwit nests located within their colonies. We examined how the characteristics of individuals and the drivers of nest survival may vary within and outside of Mew Gull colonies. Consistent with reduced predation risk, males were less often present at the nest during the day and gave fewer alarm calls within than outside of gull colonies, and females were also larger within gull colonies. Nest survival was best explained by a combination of individual attributes and nest site characteristics, though relationships differed within and outside of colonies. Specifically, survival of godwit nests outside of – but not within – colonies improved with male body condition, and survival within colonies improved with female body condition. Our study, thus, provides evidence that godwits nesting in association with Mew Gull colonies exhibit different drivers of nest survival within and outside of gull colonies. Keywords: protective nesting association, nest survival, individual quality, body condition, shorebird, microhabitat 73 Introduction: Nest predation is one of the most common causes of reproductive failure in birds and, thus, is expected to be an important evolutionary driver shaping nest site selection and behavior (Ricklefs 1969, Martin 1993). Adaptive behaviors to reduce nest loss by predation depend on a variety of ecological factors, including the environmental attributes of the nest site, as well as the behaviors of potential nest predators (Martin 1995, Martin et al. 2000, Jedlikowski and Brambilla 2017). Birds employ several strategies to avoid predation, including egg or plumage crypsis and the placement of nests in locations that are inaccessible to predators (e.g., islands or cliffs; Nguyen et al. 2003, McKinnon et al. 2010, Iverson 2014). Although high densities of nesting birds may attract predators in certain contexts, dense colonies can also provide protection. Individuals in synchronously breeding colonies are thought to benefit from predator swamping and improved detection of – and defense from – predators (Wiklund 1982, Wittenberger and Hunt 1985, Richardson and Bolen 1999). Another strategy that may be used to reduce predation risk is to nest within heterospecific aggregations known as “protective nesting associations”, that improve reproductive performance of at least one of the species (Burger 1984, Quinn and Ueta 2008). In protective associations, an associate species benefits from aggressive nest defense by a protector species or from information gleaned from the protector species about the whereabouts of predators (Nuechterlein 1981, Quinn and Ueta 2008). Protective associations can thus reduce an individual’s risk of nest predation through community-level interactions. Aggressive or defensive behaviors of protector species can dissuade individual predators from using particular areas and even affect entire predator communities. Nonetheless, protector species can also vary in both their effectiveness and reliability at deterring or excluding predators (Larsen and Grundetjern 1997, Quinn et al. 2003). For instance, the presence of predators, such 74 as owls and raptors, can alter both species composition and abundance of other predators near their nests. “Predator-exclusion zones” have been documented in the vicinity of breeding Snowy Owls (Nyctea scandiaca) and are associated with improved nest survival of the protected species (Bêty et al. 2001, van Kleef et al. 2007, Kharitonov et al. 2013). Even in cases where less aggressive protectors cannot exclude specific predators (Stenhouse et al. 2005), protected species may still benefit from a less complex or abundant predator community. However, the complexity of inter-specific interactions ultimately makes it difficult to generalize about the nature and extent of benefits derived from protective associations. For instance, Spotted Sandpipers (Actitis macularia) nest within Common Tern (Sterna hirundo) colonies for protection from minks (Mustela vison), but still experience high egg predation by Ruddy Turnstones (Arenaria interpres; Alberico et al. 1991). Thus, protective associations create complex heterogeneity in predation risk. Because protective associations lessen the risk of predation, they may alter selective pressures on nest survival more generally, especially as related to anti-predator behaviors, nest site characteristics, and the quality of the individuals nesting within an area (Smith et al. 2007). For example, Red Phalaropes (Phalaropus fulicarius) nesting within Sabine’s Gull (Xema sabini) colonies flush at greater distances in response to approaching predators and take more frequent and extended incubation recesses. By adjusting their anti-predator behaviors within the colony, individuals can minimize predation risk to both the nest and themselves (Smith et al. 2007). Individuals may also select for different nest site characteristics (e.g., concealment) within and outside of a nesting association (Smith et al. 2007). Lastly, higher quality individuals – in terms of age, experience, or physical condition – are likely to occupy better quality nest sites or obtain better quality mates (Lifjeld and Slagsvold 1988, Kim and Monaghan 2005, Johnson and Walters 75 2011), and may settle within protective associations to the exclusion of lower quality individuals. The predictable spatio-temporal variation in predation risk created by a protective association can thus alter the individual attributes and behaviors that influence nest survival. Our understanding of how nest site characteristics and individual attributes affect nest survival within and outside of a protective nesting association is limited. In this study, we focus on Hudsonian Godwits (Limosa haemastica; hereafter ‘godwits’) breeding in the sub-arctic, where they form stable non-habitat-based aggregations with Mew Gulls (Larus canus; hereafter ‘gulls’), which act as a protector species (Swift et al. 2017a, Swift et al. 2018). However, there is an important trade-off – whereas godwit nests within colonies are more likely to successfully hatch, the chicks hatched from those nests are more likely to be depredated by gulls (Swift et al. 2018). Godwits may therefore choose different nest locations across the landscape with regard to gull colonies to maximize either their nest or chick survival. Or, alternatively, they may adopt a strategy that increases both nest and chick survival through differences in behavior depending on the stage of the breeding season and their location relative to the gull colony. We investigated whether the characteristics of individual godwits, as well as the drivers of nest survival, differed within and outside of gull colonies. We hypothesized that for godwits nesting within gull colonies, selection pressure on nest survival may be relaxed due to the protection received from the nesting association. Specifically, we examined how individual body condition, microhabitat of nests, and defensive behaviors differed between birds nesting within and outside of colonies and the extent to which these factors influenced nest survival was context-dependent. Although accounts of heterospecific nesting colonies are relatively common in birds, few studies have compared drivers of nest survival of colonial and non-colonial individuals in the same area and year. Our study thus provides a unique perspective on the 76 influence of a protective nesting association on the drivers of nest survival of the protected species. Methods: Study area and species: We studied a population of Hudsonian Godwits breeding in Beluga River, Alaska (61.21°N, 151.03°W), within a study area of ~8 km2 from 2009 – 2012 and 2014 – 2016. The study area was divided into two study plots of uninterrupted muskeg bog of unequal sizes – North (550 ha) and South (120 ha) – that were separated by ~7 km of unmonitored boreal forest and muskeg bog. Godwits are monogamous with biparental care, where both the male and female defend the territory and incubate the nest. Adults divide incubation duties with females typically incubating during the day and males at night (Walker et al. 2011, Bulla et al. 2016). Godwits breed in open bogs, tundra, and fens dominated by sedges, Carex spp., and dwarf birch, Betula glandulosa/nana (Swift et al. 2017a). In Beluga River, the main predators of godwit nests include red foxes (Vulpes vulpes), Common Ravens (Corvus corax), and Sandhill Cranes (Grus canadensis). Adult godwits are also vulnerable to predation by Northern Harriers (Circus cyaneus) while incubating. Our previous work showed that habitat heterogeneity did not explain spatial aggregations of godwit nests (Swift et al. 2017b), but, instead, the presence of Mew Gull colonies did (Swift et al. 2018). Godwits nesting within a gull colony have increased nest survival compared with godwits nesting at increasing distances from a gull colony or with fewer numbers of nearby nesting gulls, thereby benefiting from a protective nesting association during incubation (Swift et al. 2018). 77 Nest distribution and fate: We systematically searched plots for nests every two-to-three days throughout the nesting season (May–July). We searched for nests (scrape containing ≥ one egg) using a combination of prior knowledge, systematic searching, and behavioral observations. Upon discovery of a nest, we recorded a GPS location and floated eggs to estimate the timing of nest initiation, and hence, the age of the nest (Liebezeit et al. 2007). We did not physically mark nest locations to minimize the chance of associative learning by predator species (Reynolds 1985). We revisited nests every two-to-three days until either one day prior to the expected hatch or until we observed starred or pipped eggs. We typically checked nests by resighting incubating birds with binoculars from 20- 30 m away. Adults were flushed weekly (at most) to minimize disturbances that might increase the probability of nest failure, and field teams did not approach nests directly when predators were observed nearby. A nest was considered successful if ≥1 egg hatched and chicks successfully left the nest site. Nest failure was presumed when we found empty nests early in the incubation period or destroyed eggs. Due to low rates of nest abandonment in this system (Senner et al. 2017), we considered the failure rate of nests in our study to represent the depredation rate as well. Habitat metrics: We measured the habitat at each nest site after the nest was no longer active. We defined the nest site as the area within a 1 m diameter circle centered on the nest. For each nest, we measured the distance to the nearest water body (≥ 2 cm deep) from the center of the circle, and, within the circle itself, the percent cover of all plant species present. We summarized the percentage of the circle covered by shrubs between 30 cm and 1 m tall as well as the percentage 78 of sedges, grasses, and forbs (see Swift et al. 2017a, b for more information). We also described the percent of the nest concealed by vegetation from 1 m directly above the nest scrape. Godwit body condition: Incubating individuals were captured using a mist net (n = 231) and marked with a U.S. Geological Survey metal band, a year-specific color band, and a uniquely coded alpha-numeric flag. For each individual, we measured its tarsus length (to a precision of 0.1 mm) and body mass (to 0.1 g). An individual’s size-adjusted mass (hereafter termed “condition”) was then calculated using the residuals of a regression between mass and tarsus. Because tarsus and size-adjusted mass were positively correlated (r = 0.63), we use the term “larger” to refer to greater size- adjusted mass throughout the manuscript. Measurements of mass were adjusted for the day of incubation because mass declines steadily in both males (-0.48, 95% CI -0.91, -0.05; p-value = 0.03) and females (-0.54, 95% CI -1.05, -0.02; p-value = 0.04) during the incubation period and captures occurred throughout incubation (mean: day 13; range: day 3 – 27). Only one individual from the breeding pair was captured for 14 nests, and neither individual was captured for 32 nests. Godwit defensive behaviors: For each godwit nest found in 2015 and 2016, we recorded the defensive behaviors exhibited by the parents during mid-incubation (day 11 – 13). Typically, two observers used a 2 – 5 minute observation period during which they recorded the number of calls and flights made by the individual(s) present at the nest. At the beginning of the observation period, one observer approached the nest and flushed the incubating adult while the other maintained a distance of 20- 79 30 m and recorded the ensuing period with an audio recording unit. Audio recordings were then transcribed using CowLog (Pastell 2016), and we calculated the number of calls and flights per minute from the transcription. Additionally, the minimum distance that an individual godwit approached the observer at the nest was recorded for each godwit present. Godwit nest survival analyses: We examined the influence of habitat characteristics and body condition on godwit nest survival with mark-recapture analyses (Table I). Using all gull nests found from 2014 – 2016, we created a minimum convex polygon for each plot that we defined as the gull colony (see Swift et al. 2018 for more information). For each godwit nest, we calculated the minimum distance to the gull colony boundary using ArcGIS (ESRI 2015), and any nest within 25 m of this boundary was considered effectively within the gull colony. We also selected habitat variables known to be used by godwits when choosing their nest sites (Swift et al. 2017a): distance to the closest water body (≥ 2 cm deep), percent tall shrubby cover (between 30 cm and 1 m tall), percent sedge, grass, and herbaceous forb cover, and percent overhead nest cover. Lastly, we included our adjusted measure of individual body condition for both members of the breeding pair. We used generalized linear mixed models (GLMM) with a random effect of ‘plot’ and a binomial link to test for significant differences between nests within and outside of gull colonies in program R (R Core Development Team 2017). Additionally, we compared the defensive behaviors of godwits nesting within or outside of gull colonies using a separate GLMM analysis. The significance of each model was assessed using a Bonferroni correction to account for non- independence among multiple tests. 80 We examined the effects of habitat characteristics and body condition on daily nest survival rates (DSR) using the nest-survival method in Program MARK (v. 8.2; White and Burnham 1999) for 141 nests monitored from 2009 – 2012 and 2014 – 2016 using parallel, separate analyses for nests found within (n = 103) and outside (n = 38) of gull colonies (Dinsmore et al. 2002, Rotella et al. 2004). Because our sample sizes were limited, especially outside of gull colonies, we could not perform a single comprehensive analysis and, instead, ran two separate analyses. Following Dinsmore et al. (2002), we compiled an encounter history for each nest by calculating its age when found, age when last known to be active, and age when last checked (i.e., age at hatch for successful nests). We incorporated covariates specific to individual nests and standardized them using the z transformation built into Program MARK (Dinsmore et al. 2002). We linked the response and explanatory variables of the linear model using the logit transformation; this forced parameter estimates to fall within the interval (0, 1) and encouraged model convergence (Dinsmore et al. 2002). Although they were not relevant to our hypotheses, we included both study plot and a linear time trend as covariates in all models as we predicted that differences between study plots and differences in nest age may be important for explaining variation in nest survival. We examined the null model, each variable individually, and all possible combinations between the variable sets both for nests within gull colonies and those outside of gull colonies (20 models; Table I). We ranked models using Akaike’s information criterion corrected for small sample size (AICC) and selected the most parsimonious model(s) based on AICC scores and model weights (wi; Burnham and Anderson 2002). We plotted and interpreted covariates for which the 95% confidence intervals (CI) of the ß estimate did not overlap zero. 81 Results: Between 2009 – 2012 and 2014 – 2016, 103 godwit nests were found inside or within 25 m of gull colonies and 38 outside of gull colonies. Daily nest survival was high each year (>96%), but, generally, nest success was 27% higher within gull colonies. Overall, neither nest site microhabitat attributes nor body condition of godwits differed within or outside of gull colonies (Wilk's λ = 0.97, p = 0.92; Table II). However, females were larger within gull colonies (Table II). Defensive behaviors of individuals nesting within and outside of gull colonies were marginally different (Wilk's λ = 0.37, p = 0.06): male godwits that nested outside of gull colonies called more times per minute than males within gull colonies and were present more often during diurnal nest visits (Table III). Due to small sample sizes (n = 32), especially outside of gull colonies (n = 12), we did not test the defensive behavior variables on nest DSR. For nests located within gull colonies, four competing models – each including female size-adjusted mass – best explained godwit nest DSR (Table IV). Nest survival most strongly improved with female condition (β = 0.10, 95% CI 0.02, 0.17; Figure 1), but also marginally increased with cover of tall shrubs between 30 cm and 1 m tall (β = 0.02, 95% CI -0.01, 0.05), herbaceous cover (β = 0.41, 95% CI -0.04, 0.85), and declining overhead nest cover (β = -0.31, 95% CI -0.62, 0.01). Outside of gull colonies, three competing models – each containing male size-adjusted mass – best explained godwit nest DSR (Table V). Nest survival improved with male condition (β = 0.15, 95% CI 0.02, 0.28; Figure 2) and was marginally explained by overhead cover (β = 0.06, 95% CI -0.01, 0.13) and female condition (β = -0.18, 95% CI -0.87, 0.5). 82 Discussion: Protective nesting associations created spatial variation in predation risk and altered drivers of nest success across the landscape in Beluga River, Alaska. Consistent with our previous work, habitat attributes surrounding nests were weak predictors of nest survival and did not differ inside and outside of colonies (Swift et al. 2017a, b, Swift et al. 2018). However, as expected, relationships between nest survival and individual traits differed when godwits associated with a protector species. Within colonies, female godwits were larger, male godwits were less often present at nests, and male godwits sounded fewer alarm calls than those nesting outside of colonies, which is consistent with reduced predation risk. Furthermore, some of these traits affected nest survival, with survival improving with the size-adjusted mass of the parents. However, this was only the case for males outside of colonies and females within colonies. In combination, our results suggest that the predictable spatio-temporal variation in predation risk created by a protective association can alter the drivers of nest survival for individuals. The lower rates of alarm calls and nest attendance by colony-nesting males were consistent with relaxed selective pressures within colonies due to the protective association with gulls. Other studies show that males with nests in high-risk areas must attend to their territories more often and vocalize more frequently to avoid depredation (Montgomerie and Weatherhead 1988, Martin 1992). Not only do gulls potentially dissuade predators from entering the colony, but also their vocalizations can communicate information about the presence and location of predators (Leger and Nelson 1982, Soard and Ritchison 2009, Shah et al. 2015). Accordingly, we found that males within colonies attended to their nests less during the daytime and called less frequently than did males outside of colonies. The strong positive association between male condition and nest survival outside of colonies is consistent with the idea that larger males are 83 more effective at protecting nests at night, when males typically incubate, because they require fewer incubation breaks (Montgomerie and Weatherhead 1988, Kleindorfer and Hoi 1997). Thus, outside of colonies alternate individual traits and behaviors appear to drive nest success as compared to within colonies, potentially creating alternate reproductive strategies based on an individual’s condition and size-adjusted mass. We also found that godwit females were larger within gull colonies, a pattern that could be attributed to several factors that are not mutually exclusive. For instance, the fact that females were larger inside the colony might reflect the fact that the colony provided higher quality nesting habitat and was possibly selected by the highest-quality females (Quinn and Ueta 2008). Or, alternatively, because the optimal body mass for a species should reflect a trade-off between the risks of starvation and predation (Lima 1986, Houston et al. 1993, Gosler et al. 1995), female godwits within colonies may be able to adaptively carry more mass without affecting their own survival. In support of this latter hypothesis, our study provides some evidence that the benefits of the “safe” zone extended beyond nest survival and included higher survival of incubating adults. Indeed, the only three adult mortality events we detected in seven years involved incubating females nesting outside of gull colonies. Irrespective of the cause, the larger size of females within colonies may promote nest survival if larger females better defend nests or chicks from predators (Larsen et al. 1996). Relaxed selection pressures within colonies may therefore allow individuals to maximize both nest and adult survival. In this context and in light of previously documented trade-offs between nest and chick survival across the breeding season (Swift et al. 2018), we suggest that female godwits adopt a bet-hedging approach, whereby they use reproductive strategies most appropriate for their individual body condition. The term “bet-hedging” can be used to refer to three different 84 strategies: (1) conservative bet-hedging, where individuals “play it safe” by adopting a less variable though less productive strategy; (2) diversified bet-hedging, which spreads risk and minimizes variance in long-term success; and (3) adaptive coin-flipping, where the strategy is selected each year based on the environment (Olofsson et al. 2009, Rees et al. 2010, Chalfoun and Schmidt 2012). These strategies need not be static, and individuals might assess risk and adjust reproductive strategies multiple times across the breeding cycle (Fontaine and Martin 2006, Chalfoun and Martin 2010). Predation risk in our study area was marked by substantial heterogeneity, in part because gulls are an important and spatially-constrained (i.e., to areas near nests) predator of godwit chicks. In our case, the 27% improvement in survival of colony nests was offset by the 28% lower chick survival within colonies (Swift et al. 2018). Conservative bet- hedging in our system may, therefore, occur if larger females opt to maximize adult and nest survival by nesting within gull colonies, despite the comparably greater risks during brood rearing. Females in better body condition may mitigate this risk if they more effectively deter predators and defend broods due to their large size (Hamer and Furness 1993) or can better move broods to safe areas. Alternatively, poor quality females may utilize a diversified bet-hedging approach if they are comparably more effective at defending nests than broods. In this way, individuals in poor condition could take advantage of higher chick survival outside of gull colonies despite the increased variance in nest survival. Ultimately, godwits may be able to improve their fitness by using strategies adjusted for predictable spatio-temporal patterns of risk across different stages of the breeding cycle. Hudsonian Godwits nesting in association with Mew Gull colonies thus exhibit different drivers of nest survival within and outside of gull colonies. Based on these findings, we suggest that godwits may place nests in the landscape to maximize nest, adult, and chick survival based 85 on individual size and relative body condition. Our study is among the first to examine the effects of protective associations on the drivers of nest survival, as well as potential differences between individuals choosing to nest within or outside of a protective association. More broadly, our findings suggest that within a heterospecific association, the drivers of nest selection and survival are complex, and further study is needed to disentangle the roles of both biotic and abiotic factors on nest survival. Acknowledgments: We thank W. Abbott, A. Alstad, H. Batcheller, S. Billerman, B. Davis, J. DeCoste, L. Fried, R. Galvan, D. Gochfeld, M. Harvey, J. Heseltine, M. Hilchey, A. Johnson, T. Johnson, J. Karagicheva, J. Klarevas-Irby, B. Lagasse, G. MacDonald, J. Marion, M. McConnell, J. McGowan, K. Parkinson, B. Schultz, M. Schvetz, G. Seeholzer, H. Specht, K. Smith, and B. Walker for their efforts in the field. Funding was provided by the David and Lucile Packard Foundation, U.S. Fish and Wildlife Service, Faucett Family Foundation, National Science Foundation (#1110444), Cornell Lab of Ornithology, Cornell University, the Athena Fund at the Cornell Lab of Ornithology, American Ornithologists’ Union, and Arctic Audubon Society. 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Fieldfare (Turdus pilaris) breeding success in relation to colony size, nest position and association with Merlins (Falco columbarius). Behavioral Ecology and Sociobiology 11:165–172. Wittenberger, J. F., and G. L. Hunt Jr (1985). The adaptive significance of coloniality in birds. Avian Biology 8:1–78. 91 TABLES AND FIGURES Table I. Model suites, variable names, and descriptions of potential variables affecting daily nest survival of Hudsonian Godwits (Limosa haemastica) at Beluga River, Alaska during 2009 – 2012 and 2014 – 2016. The defensive behaviors were only collected in 2015 and 2016 and were not included in the MARK models. 92 Suite Variable name Variable description BODY Male size-adjusted mass Residuals from mass, tarsus regression corrected for day of incubation CONDITION Female size-adjusted mass Residuals from mass, tarsus regression corrected for day of incubation HABITAT Distance to water (m) Distance to the closest body of water ≥ 2 cm deep % 30 cm1,000 individuals; Andres et al. 2009). The primary natural predator of godwits in the region is the Peregrine Falcon (Falco peregrinus), although foraging godwits occasionally also flush in response to Southern Caracaras 114 (Caracara plancus; RJ Swift unpubl. data). Peregrine Falcons are relatively uncommon, however, and occurred at only 5 of 42 bays we visited (RJ Swift unpubl. data). Additionally, only single individual falcons were ever seen at one time. Field Surveys and Flock Counts: We attempted to survey all known and accessible bays in the Chiloé Island region based on published accounts, eBird records, and prior knowledge of the distribution of godwits. In total, we surveyed 42 bays between 1 January and 9 March 2016 (Figure 1). We conducted surveys (n = 147) within three hours of the diurnal low tide on days with light winds (<20 km/hour) and little or no precipitation. The length of each scan varied (range 5 – 365 minutes, µ = 85 ± 86.9; all means presented ± SD), based on the presence and size of the godwit flock, the amount of available daylight, and the height of the tide. Each site was visited between one and nine times (µ = 3.5 ± 1.8; Table I), with two bays (Huildad and Chamiza-Norte) being visited only once due to poor weather conditions. The same two observers conducted all surveys using binoculars and spotting scopes equipped with a 20-60x eyepiece. We maximized detection of godwits by making observations from locations that provided complete visibility of each bay but which also prevented direct disturbance of the foraging flock. For each survey period, we recorded flock size and behavioral state (flying, foraging, or roosting). We classified godwits as foraging when they were probing and moving (e.g., with the advancing or receding tide), or as roosting if they remained stationary (typically with one leg up and heads tucked under their wings) and did not probe. Surveys in which most godwits were roosting rather than foraging were excluded from analyses. During the surveys, one observer carefully counted or estimated the godwit flock at least once each hour. 115 From these counts, we present a maximum density observed, adjusted by each bay’s perimeter at the high tide line (generated using Google Earth Pro v. 7.1.5) to account for size differences among bays. If no godwits were present at the start of a survey, we waited from 5 – 60 minutes for godwits to arrive (µ = 22 ± 21 minutes). Body Condition: We collected two measures of body condition: body molt scores (BMS) and abdominal profile indices (API). The BMS is an index between zero and four (with 0.5 increments) based on the amount of alternate plumage present on an individual (e.g., Piersma and Jukema 1993). As body feathers represent up to 75% of total feather mass (Battley and Piersma 1997, 2005) and replacement of these feathers implies a significant metabolic cost associated with feather production and thermoregulation (Klaassen 1995), body molt scores were used as an indicator of individual condition (e.g., Lourenço and Piersma 2015). API is a measure of condition based on the shape of the abdomen and is correlated with actual fat mass in shorebirds (Wiersma and Piersma 1995). Individual BMS and API were collected between one and four times per survey (depending on the flock size and the length of the survey) on a total of 1 – 76 individuals (µ = 24 ± 14.5). For large flocks (>20 individuals), BMS and API were taken for every fifth or tenth individual for which visibility and proximity allowed careful scoring. We assessed every individual in smaller flocks. The residuals from separate regressions of average BMS and API with Julian date were used in analyses to account for continuous molting and pre-migratory fattening. 116 Foraging Success and Intertidal Foraging Habitat: We conducted focal foraging observations at each bay with actively foraging godwits (n = 429). Using a voice recorder, we dictated our observations of godwit behaviors over a five- minute period and later transcribed recordings using CowLog (Pastell 2016). Because not all focal observations lasted for the full five minutes (e.g., an individual flew out of sight, reshuffled into the foraging flock such that we lost it, or began roosting), we converted all metrics to the number-per-minute-of-observation. We randomly selected individuals that were feeding within two meters of the tideline and followed them, dictating every behavior including: the number of probes made, the number of prey items captured and consumed (swallowed), and the number of alert or vigilant behaviors displayed. We attempted to follow at least five individuals per survey, although this varied due to the tidal conditions, the presence of foraging godwits, and the total number of godwits in a flock (range = 1 – 13 focal observations, µ = 5.17 ± 2.6 per survey). We defined a foraging probe as occurring when at least half of an individual’s bill was placed in the mud. Godwits frequently probe the mud in rapid succession without removing their bill; in these circumstances, we counted each movement as a separate probe if the bill was lifted one-third of the way out of the mud (Senner and Coddington 2011). We considered a bird to have obtained a prey item when we discerned a swallowing motion or saw an item in its bill. While godwits feed primarily on relatively large and easily observable polychaete worms (Ieno et al. 2000), godwits also feed on small items, such as fly larvae (Ribeiro et al. 2004, Senner and Coddington 2011, Walker et al. 2011). Such smaller food items can be consumed without removing the entirety of an individual’s bill from the mud and would not have been counted in our swallow or success rate estimates. Consequently, our estimates of foraging success represent a minimum level. 117 At the end of each focal observation, we recorded the primary foraging substrates used during the observation. From 289 focal observations where godwits foraged outside of standing water, godwits primarily used mud (n = 127) or algae (n = 149) as their foraging substrates. Combined, mud and algae account for ~96% of the known foraging substrates, and thus the combined amount of mud and algae was considered appropriate foraging habitat for analyses. Godwits occasionally foraged on both shellfish beds (n = 8) and in rocky areas (n = 5), but only at bays where more rock or shellfish were more available than average and, typically, more plentiful than mud or algae. From 1 February to 8 March, we estimated the approximate percentage of the tideline covered by each of the four intertidal substrates (mud, rock, algae, and shellfish) between one and twelve times depending on the length of survey (µ = 3.2 ± 2.3; collected every thirty minutes to an hour). The intertidal habitat of each bay was characterized on one to three different days (µ = 1.6 ± 0.7 days). Because intertidal habitat data were not collected at bays surveyed in January, we averaged the percent mud and algae throughout survey days, as the amount of non-foraging habitat (rock and shellfish) was presumed to be stable across this period. Human Disturbances and Predation Risk: During each survey, we recorded the presence of potential predators. We noted the species and number of individual predators as well as their behavior (e.g., flyover or perch) to derive the number of predator species and individuals present. In order to assess the type, amount, and length of human disturbances during each survey, we counted the number of humans, dogs, hoofed animals (typically oxen and horses), and boats that were present within the intertidal area, within 5 m of the tideline, and 100 m of foraging godwits once every hour. 118 Because the length of each survey varied, we assessed disturbances from 1 – 10 times (µ = 2.6 ± 1.8; Table II). During focal foraging observations, we also recorded the number of alert or vigilant behaviors exhibited by an individual, which we defined as non-foraging head movements (e.g., standing still, looking from side to side, or turning the head to scan the sky; see Nol et al. 2014). Finally, we recorded instances in which at least half of all godwits present in a bay flushed. We did not attempt to determine the cause (e.g., human or predator) for each displacement flight. All counts were divided by the length of the survey (minutes) for use in analyses. To derive a single estimation for each variable, we averaged each variable across individuals or measures for each survey. Landscape and Bay Characteristics: For each bay, we collected various land and water use metrics at the landscape scale (Table III). The distance to the nearest road was calculated using Google Earth Pro, and the substrate of the road was recorded in the field. We recorded the presence of and distances to aquaculture activities (both shellfish and salmon) in the bay, and estimated the percent of the visible bay that was covered by aquaculture activities. Lastly, we calculated three measures of bay size in Google Earth Pro: the approximate intertidal area, the length of the bay’s perimeter (highest high tideline), and the bay’s width (high tideline to water line). Data Analysis: Relationships among human disturbance, predation risk, foraging success, amount of intertidal habitat, landscape and bay characteristics, and the flock density and body condition of 119 godwits were modeled with partial least squares path model (PLS-PM). PLS-PM is a type of path analysis used to explore multiple relationships between blocks of variables and quantify their respective weights (Lleras 2005, Tenenhaus et al. 2005). This statistical method has only recently been applied to ecological datasets (e.g., Puech et al. 2015), but we selected it over covariance- based structural equation modeling approaches because it does not require a large dataset to perform optimally and because it produces values for each latent variable (Chin and Newsted 1999, Chin 2010). PLS-PM consists of two sub-models called the inner and outer models (Sanchez 2013). The outer model describes relationships between a set of observed variables (‘manifest variables’) and a synthetic ‘latent variable’ that is built from these manifest variables. A latent variable cannot be measured directly and is representative of a concept (e.g., habitat quality). For example, the manifest variables 1) ‘number of humans present’, 2) ‘number of humans within 5 m of tideline’, and 3) ‘number of humans within 100 m of godwits’ were used to approximate the latent variable ‘human disturbance’. The group formed by a latent variable and its associated manifest variable(s) is called a block. The inner model describes relationships between latent variables, and these relationships are treated as linear regressions. A fitted PLS-PM produces standardized path coefficients for all paths (i.e., direct and indirect effects) that normally range from -1 to 1. These path coefficients are equivalent to standardized regression coefficients, but have the advantage of specifying whether the relationship between latent variables has a positive or negative slope. Our PLS-PM contained thirteen latent variables (Figures 2, 3). In the preliminary PLS- PM, all potential manifest variables were included when constructing latent variables. However, before obtaining the final model, we made a set of verifications and transformations, as advised 120 by Sanchez (2013). First, we checked the unidimensionality of each reflective block with Cronbach’s alpha and Dillon–Goldstein’s rho (Table IV). We changed the sign of variables having negative weights to only integrate positively correlated variables in the same block. Then, we examined the loadings – i.e., the correlations between a latent variable and its manifest variables (Table V). A manifest variable was only retained if 50% of the variability in the manifest variable (i.e., factor loading > 0.7) was captured by the latent variable (Sanchez 2013). We retained some individual variables that met unidimensionality but had loadings < 0.7, which we acknowledged as an acceptable trade-off between model quality and meaningfulness. Cross- loadings allowed us to verify if the shared variance within a block was larger than with other blocks and were assessed similarly. Finally, the overall robustness of models was evaluated with coefficient-of-determination (R2) and Goodness of Fit (GoF) criterions and a bootstrapping procedure (n = 999). Ninety-five percent confidence intervals that did not encompass zero were considered statistically significant. For PLS-PM, R2 values for inner models are classified in three categories: low: R2 < 0.3, moderate: 0.3 < R2 < 0.6, and high: R2 > 0.6 (Sanchez 2013). The GoF measure assesses the overall predictive performance of both the inner and outer models (Sanchez 2013). Analyses were conducted using the R 3.4.3 software (R Core Development Team 2018) with the ‘plspm’ package (Sanchez et al. 2017). Results: Godwit densities ranged from 0 – 1,436 individuals per km, with a mean of 178 ± 266 individuals per km (Table I). Quetalmahue-Puente had the highest average godwit density (n = 633 ± 210 individuals per km; Table I). We failed to detect godwits at six bays, including two bays with historically high numbers (Putemún and Rilán) despite making repeated trips to both 121 (n = 4 trips each; Table I). Most bays varied considerably in both risks and rewards (Table VI). Levels of alertness and agitation for individuals varied among bays. Caulín had the highest levels of perceived disturbances, averaging 6.9 ± 6.3 flushes per survey and even reaching 18 flushes on one occasion (29 February 2016). Godwits scanned (alert/vigilant per minute) the most at Chacao (µ = 3.6 ± 4.2 times per minute; Table II). While individuals at Chamiza-Norte probed at higher rates (µ = 32.8 ± 0 times per minute; Table I), Astillero had the highest average foraging success rate (µ = 0.5 ± 0.2 items per minute; Table I). Our fitted PLS-PM (GoF = 0.37) identified three of our predictors – foraging success, amount of intertidal foraging habitat, and alertness and agitation – as directly affecting godwit density (Figure 4, Table VII). These three latent variables explained more than three-quarters of the total effects on godwit density (Table VIII). In turn, high godwit density was significantly associated with increased godwit body condition (Figure 4, Table VIII). Direct paths comprised 80% of the total effect on godwit density compared to 20% for indirect paths (Table VIII). However, the direct path comprised only 41% of the total effect on godwit body condition compared to 59% for indirect paths (Table VIII). Godwits aggregated into denser flocks that were in better body condition at bays where individuals had higher foraging success rates (Figure 4, Table VIII). However, disturbances resulting in displacement flights and higher levels of alertness reduced flocks densities and godwit body condition (Figure 4, Table VIII). The amount of intertidal foraging habitat also had a strong total effect on increasing both density and body condition of godwits (Table VIII). Overall, in bays with individuals in better than average condition, individuals were 35% less alert and agitated, had access to 77% more foraging habitat, experienced 61% more foraging success, and occurred in 17% larger flock 122 densities than in those bays in which individuals were in below average condition. Alertness and agitation had a larger effect on both density and body condition of godwits than did the combined total effect of foraging success and amount of intertidal habitat (godwit density: 0.49, body condition: 0.17; Table VIII). However, foraging habitat factors had a greater effect on both density and body condition than did the combined individual effects of our direct measurements of human disturbances and predation risk (godwit density: 0.30, body condition: 0.10). Discussion: Patch quality, as measured by density and body condition of Hudsonian Godwits in the Chiloé Island region of southern Chile, was primarily driven by increased availability of suitable foraging substrate, better foraging success, and less disturbance or perceived risk (i.e., as indicated by alertness and agitation). In general, flock density increased with foraging success and available substrate, and godwits in more dense flocks were in better body condition. Collectively these patterns suggest that density may be a suitable indicator of patch quality. Though we detected no strong signal from specific agents of disturbance (i.e., numbers of humans, dogs, hoofed animals, boats, and predators), responses of godwits to those disturbances (alertness and agitation) negatively impacted flock density and body condition. Human disturbances and predation risk may thus influence foraging patch decisions and reduce godwit densities through induced changes in foraging behaviors. Foraging shorebirds must cope with constantly changing resource availability inherent within the tidal cycle. Many species of shorebirds are highly mobile and move among many foraging patches in response to spatial or temporal changes in resources (Brown 1999, van Gils 123 et al. 2003). Habitat selection ultimately should reflect both the potential rewards and risks at a particular patch, though not all individuals may be able to optimize decisions and hence still occur at risky or low quality sites. For example, younger birds or those in poor condition are more likely to forage in risky areas (Cresswell 1994b, Duijns et al. 2009, Cresswell et al. 2010). We suspect that this was the case at sites like Chacao, which had high levels of disturbance and where godwits were in poor body condition. Shorebirds foraging at risky sites can use a variety of anti-predator behaviors to mitigate the riskiness of their chosen habitats, including increasing flock size to reduce danger and decreasing time spent feeding to increase vigilance rates (Cresswell 1994a, Whitfield 2003, Lind and Cresswell 2005). Alternatively, individuals can balance the benefits of foraging with the costs of predation by using less profitable sites if they avoid predators or human disturbance (Cresswell and Whitfield 1994, Ydenberg et al. 2002, Yasué et al. 2003). Accordingly, in our study, godwits were less numerous and in poorer condition in bays where they were more alert and agitated, and these behaviors predicted density better than did foraging success and amount of intertidal habitat. Alarmingly, we failed to detect any godwits using two historically important sites (Putemún and Rilán). Putemún previously hosted as many as 7,000 godwits (Andres et al. 2009), yet we detected zero godwits across four visits. Instead, Putemún was our second highest site in terms of human impact with as many as 24 people harvesting kelp and mussels at the tideline. Thus, foraging godwits may prioritize avoiding risky patches unless necessary due to individual condition. In addition to avoiding risky sites, we found that godwits aggregated at sites where individuals had higher foraging success rates and where more suitable foraging habitat was available. The distribution of foraging shorebirds is often directly correlated with the density of their main prey, and this relationship occurs spatially at both large (e.g., between-mudflats: 124 Goss-Custard 1970, Finn et al. 2008, Schlacher et al. 2014) and small scales (e.g., within- mudflat: Colwell and Landrum 1993, Ribeiro et al. 2004, Pomeroy 2006). While the distribution and availability of preferred prey for godwits in the Chiloé Island region are unknown, the strong effect of foraging success suggests heterogeneity in prey distribution or density among bays. However, foraging substrate may be as important as prey density for foraging godwits. For instance, Dunlin (Calidris alpina) foraging in wet substrates target different prey species, resulting in individuals obtaining 40% more energy intake than individuals foraging in dry substrates (Santos et al. 2010). Thus, foraging substrates may vary in terms of invertebrate communities, prey densities, and capture efficiencies (Senner and Coddington 2011). Because foraging success and foraging substrate were both related to godwit densities and body condition in our study, further exploration of the availability of prey communities at each bay is required to fully understand patch quality for godwits. Although not associated with direct effects, the perceived responses to human disturbances and predators (scanning behaviors and displacement flights) were negatively related to density and body condition of godwits. Peregrine Falcons are relatively uncommon in the Chiloé region (detected at only 5 of 42 bays), which may explain the lack of a direct influence predation risk on foraging patch decisions for godwits. However, the combined effect of predation risk and human activity may affect godwits through non-lethal effects. Many studies of human disturbance and predation focus only on the displacement of shorebirds from feeding areas or on lethal effects of predation rather than on less obvious and more difficult to measure behavioral changes (Burger 1981, Yasué 2005). In cases where there are few alternative foraging habitats nearby, shorebirds may change their behaviors in response to disturbances but may not be displaced. Non-lethal effects of disturbance or predation can reduce foraging rates, increase 125 scanning behaviors, and ultimately, affect an individual’s fitness (Goss-Custard et al. 2006, Cresswell 2008). Such reduced consumption may force individuals to use riskier sites, forage for longer periods of time, and, ultimately, impair their ability to accumulate fuel reserves for migration. Not only may the cumulative impacts from many small-scale disturbances equal or exceed that of large-scale disturbances, but even minimal reductions in foraging time may be meaningful when they accumulate over tidal cycles, weeks, or months (West et al. 2002, Goss- Custard et al. 2006). Moreover, disturbances that displace individuals from one site to another might compromise non-displaced individuals at the “new” site via density-dependent effects (Burton et al. 2006, Rutten et al. 2010). Thus, subtle behavioral changes may negatively affect godwits by reducing foraging time due to increased rates of alertness and agitation, which may alter pre-migratory fueling and condition of individuals. The association between patch quality and body condition can have implications for the ability of godwits to prepare for their migrations. Shorebirds undertake some of the most extreme migrations of any species (Gill et al. 2009, Senner et al. 2014, Conklin et al. 2017), and pre- migratory fueling plays a critical role in an individual’s ability to complete migration. In order to do so, some species double their weight either prior to migration or at key stopover sites (Kvist and Lindström 2003, Piersma et al. 2005), and many rely on high intake rates to rapidly increase their body mass and condition (Piersma et al. 2005, Duijns et al. 2009). Abrupt changes in migratory fueling rates have been linked to catastrophic population declines (e.g., rufa Red Knots using Delaware Bay, USA; Baker et al. 2004). In the Chiloé region, godwit body condition improved with foraging success and amounts of intertidal habitat and declined as alertness and agitation rose. Body condition on the non-breeding grounds has also been linked with reproductive success through reversible state effects (Harrison et al. 2011, Senner et al. 126 2015). In particular, individuals in better condition arrive at the breeding grounds earlier, which may promote survival and reproductive success (Marra et al. 1998, Duijns et al. 2017). Thus, the quality of foraging sites in the Chiloé region may have far-reaching consequences for godwit population dynamics. Our study provides evidence that the distribution and body condition of Hudsonian Godwits on the non-breeding grounds in the Chiloé Island region is strongly affected by levels of alertness and agitation of individuals and by the foraging potential of tidal mudflats. While foraging success and amount of foraging habitat were positively associated with godwit densities and body condition, behavioral responses (increased alertness and displacement flights) to perceived threats had a stronger negative influence on godwit densities and body condition. Based on these findings, we suggest that human activities that disturb shorebirds, such as humans or dogs at or near the tideline, should be minimized at bays with large flocks of foraging godwits. Easy and quick assessments of densities and relative body condition of the foraging flock may be able to aid conservation practitioners on selecting sites to implement management or to conserve. Furthermore, given the recent declines experienced by godwits, the impact of patch quality on the non-breeding grounds requires further in-depth study to assess the consequences for foraging godwits and any potential long-term effects reduced body condition may have. Acknowledgments: Many thanks to Rodrigo Vasquez who provided logistical support and the Conservation Science and Bird Population Studies lab groups, which provided input and advice on data collection. This work was supported by the National Science Foundation (DGE-1144153 to 127 RJS); Graduate Research Opportunities Worldwide program to RJS; CONICYT to RJS; Faucett Family Foundation to RJS; Cornell Lab of Ornithology to RJS; Athena Fund at the Cornell Lab of Ornithology to RJS, and Cornell University to RJS. All procedures performed in this study involving animals were in accordance with the ethical standards of Cornell University and as part of an approved animal use and care protocol. 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Journal of Avian Biology 33:47–55. 135 TABLES AND FIGURES Table I. Mean and standard deviation (sd) for the number of visits, body condition, godwit densities, foraging success, and amount of intertidal foraging habitat at each bay surveyed in the Chiloé Island, Chile region. Bays where no Hudsonian Godwits (Limosa haemastica) were ever seen have dashes (-) for body condition and foraging success variables. 136 Body Condition Godwit Density Foraging Success Intertidal Habitat Body Molt Abdominal Num Score Profile Flock size Probes per Success Swallows Success rate % Mud and Index per km minute Rate per minute per minute Algae of Visits Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Achao 4 2.2 0.6 3.0 0.0 117.8 95.4 14.4 10.0 0.1 0.1 1.7 1.1 0.0 0.0 85.8 7.8 Aldachildo 2 2.7 0.5 3.5 0.7 33.7 2.8 16.8 0.0 0.1 0.0 1.6 0.0 0.0 0.0 6.3 5.3 Ancud 4 0.7 0.5 3.1 0.3 18.3 10.2 25.6 6.6 0.3 0.1 6.4 2.4 0.1 0.0 10.0 0.0 Astillero 3 2.2 0.4 3.0 0.0 177.4 109.8 27.6 10.3 0.5 0.2 15.3 12.6 0.1 0.0 82.5 17.5 Aucar 4 0.7 0.4 3.0 0.0 25.3 34.4 14.1 9.7 0.1 0.1 2.4 2.7 0.0 0.0 15.0 0.0 Calén 3 1.4 0.0 3.0 0.0 29.8 51.5 6.4 11.0 0.0 0.1 0.7 1.3 0.0 0.0 26.3 26.3 Caulín 7 1.2 1.0 3.0 0.0 466.3 147.8 23.7 1.5 0.3 0.1 6.3 1.5 0.1 0.0 93.9 1.5 Chacao 2 0.8 1.1 3.0 0.0 30.9 33.9 16.9 17.3 0.2 0.2 5.7 7.8 0.0 0.0 13.3 0.0 Chamiza-Norte 1 3.3 0.0 4.0 0.0 104.2 0.0 32.8 0.0 0.2 0.0 7.4 0.0 0.1 0.0 100.0 0.0 Chamiza-Sur 4 2.0 1.5 3.5 0.6 491.4 552.6 24.1 5.7 0.2 0.0 3.5 0.4 0.0 0.0 82.5 8.2 Chúllec 7 0.6 0.4 3.0 0.0 345.9 463.9 8.8 12.1 0.1 0.1 2.6 3.6 0.0 0.0 89.3 1.9 Compu 3 2.5 0.6 3.5 0.7 80.5 70.1 17.2 15.0 0.2 0.2 6.4 5.8 0.0 0.0 37.5 10.0 Contuy 3 2.3 0.5 3.0 0.0 90.7 80.4 14.5 12.6 0.2 0.1 3.4 3.0 0.0 0.0 96.7 4.7 Contuy-Oeste 3 2.6 0.0 4.0 0.0 166.7 184.4 22.7 0.0 0.3 0.0 6.4 0.0 0.1 0.0 60.0 0.0 Curaco de Vélez 7 1.5 1.0 3.0 0.0 333.8 539.9 14.2 11.4 0.2 0.2 3.7 3.0 0.1 0.1 97.9 1.5 Huapilacuy 2 - - - - 0.0 - - - - - - - - - 55.0 0.0 Huelden 2 - - - - 0.0 - - - - - - - - - 5.0 0.0 Huildad 1 0.5 0.0 3.0 0.0 466.7 0.0 21.4 0.0 0.1 0.0 2.6 0.0 0.0 0.0 100.0 0.0 Ichuac 2 2.3 0.0 3.0 0.0 8.4 11.9 9.4 13.3 0.1 0.2 2.4 3.4 0.1 0.1 75.0 21.2 Lenca 2 - - - - 0.0 - - - - - - - - - 10.0 0.0 Linao 4 1.0 0.7 3.3 0.5 53.1 26.9 17.5 3.2 0.2 0.0 3.2 0.6 0.1 0.0 19.2 2.0 Llicaldad 3 1.1 1.4 3.0 0.0 30.3 31.7 21.4 0.1 0.2 0.0 3.3 0.5 0.0 0.0 85.0 0.0 Llicaldad-Sur 2 1.9 0.0 3.0 0.0 193.6 273.8 7.5 10.6 0.1 0.1 1.5 2.1 0.0 0.0 36.3 8.8 137 TABLE I (CONTINUED) Body Condition Godwit Density Foraging Success Intertidal Habitat Body Molt Abdominal Flock size Probes per Success Swallows Success rate % Mud and Num Score Profile Index per km minute Rate per minute per minute Algae of Visits Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Manao 3 1.3 1.0 3.0 0.0 141.0 142.7 21.4 1.3 0.1 0.1 2.2 1.7 0.0 0.0 69.3 3.3 Nercón 6 0.9 0.6 3.0 0.0 222.8 188.4 19.2 12.2 0.1 0.1 3.3 2.2 0.0 0.0 89.3 0.0 Nercón-Puente 4 1.3 0.7 3.0 0.0 323.2 345.4 26.7 2.4 0.3 0.3 8.6 7.5 0.1 0.1 88.3 0.0 Piluco 3 1.0 0.0 3.0 0.0 101.4 77.4 23.1 0.0 0.2 0.0 4.7 0.0 0.0 0.0 25.0 0.0 Pullao 5 1.6 1.0 3.0 0.0 318.4 248.3 20.9 2.4 0.3 0.1 5.7 3.1 0.1 0.0 91.5 10.7 Pullihue-Puente 2 0.0 0.0 3.0 0.0 0.3 0.4 9.5 13.4 0.2 0.3 3.5 4.9 0.0 0.0 90.0 0.0 Putemún 4 - - - - 0.0 - - - - - - - - - 68.1 8.8 Quellón 2 0.6 0.0 3.0 0.0 196.3 68.1 27.6 0.0 0.2 0.0 6.3 0.0 0.1 0.0 90.0 0.0 Quetalco 2 0.7 0.4 3.0 0.0 67.8 34.6 25.2 3.1 0.2 0.0 4.3 0.9 0.0 0.0 71.7 0.0 Quetalmahue-Este 3 0.6 0.6 3.0 0.0 37.3 27.3 6.5 1.0 0.1 0.0 0.4 0.2 0.0 0.0 45.0 0.0 Quetalmahue-Oeste 5 1.4 1.6 3.0 0.0 116.3 197.6 12.5 14.5 0.1 0.2 3.0 3.5 0.0 0.0 96.7 0.0 Quetalmahue-Puente 6 1.0 1.0 2.6 0.9 632.6 209.6 26.7 4.4 0.3 0.0 8.3 2.0 0.1 0.1 83.3 0.0 Quillaipe 2 2.5 0.0 3.0 0.0 37.8 53.5 9.9 14.1 0.1 0.1 1.3 1.8 0.0 0.0 78.3 0.0 Quinchao 2 - - - - 0.0 - - - - - - - - - 80.0 0.0 Rilán 4 - - - - 0.0 - - - - - - - - - 90.0 0.0 San Juan 5 1.3 0.8 3.0 0.0 231.8 222.4 20.4 4.2 0.1 0.0 2.9 0.7 0.0 0.0 74.4 30.4 Teguel 3 1.7 1.4 3.0 0.0 270.6 259.4 17.4 2.1 0.2 0.0 3.9 0.9 0.0 0.0 72.3 5.3 Ten Ten 9 1.2 1.2 3.0 0.0 28.7 45.2 7.7 10.8 0.1 0.1 1.9 3.5 0.0 0.0 95.0 2.5 Yaldad 2 2.3 0.0 3.0 0.0 128.2 67.4 12.9 0.0 0.1 0.0 0.7 0.0 0.0 0.0 85.0 0.0 Overall 3.5 1.4 1 3.1 0.3 178.2 266.1 15 11 0.1 0.1 3.5 3.7 0.04 0.04 70.7 30.3 138 Table II. Mean and standard deviation (sd) for alertness and agitation, human disturbances, and predation risk at each bay surveyed in the Chiloé Island, Chile region. Bays where no Hudsonian Godwits (Limosa haemastica) were ever seen have dashes (-) for alertness and agitation. 139 Alertness and Agitation Human Disturbances Predation Risk Number of Alert per Number of Number of Number of Number of Flushes minute Humans Dogs Hoofed Number of Number of Animals Boats Predators Predator Species Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Achao 5.0 5.6 0.3 0.4 11.3 8.1 1.9 0.1 0.0 0.0 0.4 0.4 1.0 1.4 0.5 0.6 Aldachildo 0.5 0.7 2.2 0.0 8.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 Ancud 1.0 0.8 1.2 1.2 2.8 1.8 1.3 0.9 0.0 0.0 3.5 4.0 0.0 0.0 0.0 0.0 Astillero 0.7 0.6 0.5 0.4 2.5 2.2 0.2 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Aucar 0.0 0.0 0.5 0.5 1.7 2.0 0.4 0.8 0.0 0.0 0.1 0.3 0.5 0.6 0.5 0.6 Calén 0.7 1.2 0.4 0.8 4.0 5.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Caulín 6.9 6.3 0.8 0.9 8.6 7.3 0.4 0.4 0.3 0.3 0.1 0.3 1.1 1.3 0.9 0.9 Chacao 1.0 1.4 3.6 4.2 9.3 3.9 0.3 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Chamiza-Norte 2.0 0.0 0.6 0.0 1.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Chamiza-Sur 2.3 1.5 0.5 0.5 16.8 9.3 3.4 1.4 2.0 1.9 0.0 0.0 0.0 0.0 0.0 0.0 Chúllec 2.4 4.4 0.1 0.2 1.3 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.1 2.2 1.0 1.8 Compu 1.3 2.3 0.1 0.1 0.6 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.7 1.2 0.3 0.6 Contuy 1.0 1.7 0.1 0.1 0.5 0.7 0.0 0.0 0.3 0.5 0.0 0.0 0.0 0.0 0.0 0.0 Contuy-Oeste 1.0 1.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Curaco de Vélez 3.1 3.7 0.3 0.4 3.4 2.4 0.5 0.6 0.6 1.3 0.2 0.4 0.3 0.5 0.3 0.5 Huapilacuy - - - - 6.0 0.0 1.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Huelden - - - - 10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Huildad 1.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Ichuac 0.5 0.7 0.3 0.4 0.3 0.4 0.3 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Lenca - - - - 2.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Linao 1.8 1.5 0.6 0.4 10.5 8.0 0.2 0.2 0.3 0.5 0.6 0.3 0.3 0.5 0.3 0.5 Llicaldad 1.7 1.5 2.8 3.4 9.3 1.1 1.8 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Llicaldad-Sur 0.0 0.0 0.4 0.6 13.0 5.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Manao 4.3 5.1 0.4 0.0 4.6 7.9 0.3 0.6 0.0 0.0 0.3 0.6 0.3 0.6 0.3 0.6 Nercón 1.3 1.8 0.2 0.3 7.3 6.1 1.6 1.2 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 140 TABLE II (CONTINUED) Alertness and Agitation Human Disturbances Predation Risk Number of Alert per Number of Number of Number of Number of Number of Number of Flushes minute Humans Dogs Hoofed Animals Boats Predators Predator Species Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Nercón-Puente 0.5 0.6 0.4 0.2 5.2 2.6 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Piluco 1.0 1.0 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Pullao 3.0 2.5 0.3 0.2 1.8 1.9 0.0 0.0 0.1 0.1 0.1 0.1 0.0 0.0 0.0 0.0 Pullihue-Puente 0.0 0.0 0.4 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.7 0.5 0.7 Putemún - - - - 15.3 21.7 0.3 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Quellón 1.0 1.4 0.3 0.0 2.5 0.7 0.8 1.1 0.0 0.0 0.3 0.4 0.5 0.7 0.5 0.7 Quetalco 0.5 0.7 0.3 0.4 4.0 2.1 0.0 0.0 0.5 0.7 0.8 0.4 0.0 0.0 0.0 0.0 Quetalmahue-Este 0.0 0.0 0.1 0.1 1.5 2.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Quetalmahue-Oeste 0.6 0.9 0.4 0.5 6.1 4.0 0.1 0.2 0.8 1.1 0.0 0.0 0.0 0.0 0.0 0.0 Quetalmahue-Puente 2.3 4.3 0.3 0.2 4.6 1.7 0.1 0.2 0.0 0.0 0.0 0.0 0.7 1.2 0.3 0.5 Quillaipe 3.0 4.2 0.6 0.8 5.0 0.0 1.5 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Quinchao - - - - 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Rilán - - - - 1.7 1.5 0.3 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 San Juan 1.4 1.5 0.3 0.3 4.3 3.4 0.8 1.0 0.0 0.0 0.1 0.2 0.2 0.4 0.2 0.4 Teguel 4.3 3.1 0.5 0.3 0.9 1.0 0.1 0.2 0.0 0.0 0.0 0.0 2.3 2.3 2.0 1.7 Ten Ten 0.3 0.5 0.2 0.3 4.3 3.1 0.9 1.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Yaldad 2.5 0.7 0.9 0.0 5.5 6.4 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 Overall 1.7 2.9 0.5 0.9 5 5.9 0.6 0.9 0.2 0.6 0.2 0.9 0.3 0.9 0.2 0.7 141 Table III. Values for bay characteristics, land use, and water at each bay surveyed in the Chiloé Island, Chile region. Each variable was collected once per bay. 142 Bay Characteristics Land Use Water Tidal Bay Bay Distance Area Width Perimeter Road to Road Distance to Shellfish Percent (km2) (m) (km) Substrate (m) Aquaculture Aquaculture Aquaculture Achao 0.4 69.8 2.0 Paved 60.0 None No 0.0 Aldachildo 0.3 154.4 1.0 Beach 0.0 Medium Yes 35.0 Ancud 1.0 72.4 1.7 Dirt 10.0 None No 0.0 Astillero 0.2 331.2 1.2 Beach 0.0 None No 0.0 Aucar 1.0 420.4 2.3 Paved 85.0 Distant Yes 75.0 Calén 0.3 113.6 3.0 Beach 0.0 Close Yes 30.0 Caulín 2.6 545.8 3.6 Beach 0.0 None No 0.0 Chacao 0.3 290.9 1.9 Paved 45.0 None No 0.0 Chamiza-Norte 1.9 635.9 5.8 Paved 200.0 None No 0.0 Chamiza-Sur 3.0 572.6 3.0 Paved 50.0 None No 0.0 Chúllec 0.5 299.5 2.0 Beach 0.0 Close Yes 70.0 Compu 1.2 638.8 4.0 Beach 0.0 Medium Yes 15.0 Contuy 2.2 1310.7 7.2 Beach 0.0 None No 0.0 Contuy-Oeste 0.7 1857.8 4.4 Dirt 50.0 None No 0.0 Curaco de Vélez 0.7 774.3 3.1 Paved 0.0 Distant Yes 10.0 Huapilacuy 0.2 25.7 4.6 Beach 0.0 Close Yes 10.0 Huelden 0.2 289.6 1.5 Dirt 20.0 Distant Yes 0.0 Huildad 2.0 1167.3 6.0 Dirt 75.0 Distant Yes 0.0 Ichuac 0.3 319.7 3.3 Beach 0.0 None No 0.0 Lenca 1.5 641.6 2.3 Beach 0.0 Distant No 1.0 Linao 0.2 204.5 5.8 Dirt 0.0 Medium Yes 40.0 Llicaldad 0.1 90.5 1.1 Paved 90.0 Distant Yes 10.0 Llicaldad-Sur 0.0 147.3 0.4 Paved 90.0 Distant Yes 5.0 Manao 1.1 476.9 2.7 Dirt 35.0 Distant Yes 5.0 Nercón 0.2 82.5 1.5 Beach 0.0 Distant Yes 25.0 143 TABLE III (CONTINUED) Bay Characteristics Land Use Water Tidal Bay Bay Distance Area Width Perimeter Road Distance to Shellfish Percent (km2) (m) (km) Substrate to Road (m) Aquaculture Aquaculture Aquaculture Nercón-Puente 0.1 183.7 0.8 Paved 0.0 Distant Yes 10.0 Piluco 0.1 38.9 1.5 Paved 0.0 None No 0.0 Pullao 2.1 233.7 4.6 Beach 0.0 Distant Yes 25.0 Pullihue-Puente 0.1 28.9 1.8 Paved 0.0 None No 0.0 Putemún 3.5 829.6 9.0 Dirt 10.0 None No 0.0 Quellón 0.3 65.6 2.7 Paved 30.0 None No 0.0 Quetalco 0.2 262.9 1.4 Beach 0.0 Close Yes 90.0 Quetalmahue-Este 0.1 29.6 1.1 Paved 35.0 None No 0.0 Quetalmahue-Oeste 0.3 137.1 1.3 Paved 100.0 None No 0.0 Quetalmahue-Puente 0.9 759.1 1.7 Beach 0.0 Close Yes 10.0 Quillaipe 1.3 1907.9 5.8 Dirt 25.0 Medium Yes 30.0 Quinchao 1.0 387.2 2.3 Beach 0.0 Close Yes 60.0 Rilán 0.4 657.7 2.5 Beach 0.0 Close Yes 40.0 San Juan 0.8 545.0 2.7 Beach 20.0 Close Yes 60.0 Teguel 0.3 445.9 1.5 Beach 0.0 Close Yes 50.0 Ten Ten 0.2 602.9 2.0 Paved 0.0 None No 0.0 Yaldad 1.4 382.1 3.9 Beach 0.0 Medium Yes 30.0 Overall 0.9 449.3 2.8 Dirt 18.9 Close to Medium Yes 18.4 144 Table IV. The outer model fit of the fitted partial least squares path model. The Cronbach’s alpha is a coefficient that is intended to evaluate how well a block of indicators measures their corresponding latent construct with values greater than 0.7 considered acceptable. The Dillon- Goldstein’s rho focuses on the variance of the sum of variables in the block of interest, with a block considered as unidimensional when the Dillon-Goldstein’s rho is larger than 0.7. Lastly, if a block is unidimensional, the first eigenvalue should be “much more” larger than 1, whereas the second eigenvalue should be smaller than 1. Cronbach’s Dillon- st nd alpha Goldstein’s 1 2 rho Eigenvalue Eigenvalue Foraging Success 0.93 0.95 3.32 0.41 Predation Risk 0.95 0.98 1.91 0.09 Humans 0.91 0.94 2.52 0.31 Dogs 0.81 0.89 2.21 0.67 Boats 0.99 0.98 2.96 0.03 Hoofed 0.81 0.89 2.21 0.68 Alertness and Agitation 0.84 0.92 1.72 0.28 Land Use 0.72 0.88 1.56 0.44 Water 0.80 0.89 2.17 0.71 Landscape and Bay Characteristics 0.77 0.87 2.05 0.63 Amount of Intertidal Habitat 1.00 1.00 1.00 0.00 Godwit Density 1.00 1.00 1.00 0.00 Body Condition 0.89 0.95 1.81 0.19 145 Table V. Outer model output of the fitted partial least squares path model. Weight indicates the weighting used in the outer model. Loadings are the correlations between a latent variable and its indicators. Communalities are the squared loading values and indicate the amount of variability explained by a latent variable (e.g., for probes per minute the communality value of 0.78 (0.88²) indicates that 78% of the variability for this variable is explained by the latent variable ‘Foraging Success’). 146 weight loading communality Foraging Success Probes per min 0.31 0.88 0.78 Success rate 0.28 0.97 0.94 Swallows per min 0.24 0.94 0.88 Success rate per min 0.28 0.85 0.71 Predation Risk Number of predator species 0.78 0.99 0.99 Number of predators 0.23 0.94 0.89 Humans Number of humans 0.42 0.95 0.90 Number of humans at tideline 0.44 0.94 0.89 Number of humans near godwits 0.22 0.85 0.71 Dogs Number of dogs 0.48 0.96 0.92 Number of dogs at tideline 0.41 0.93 0.65 Number of dogs near godwits 0.24 0.64 0.42 Boats Number of boats 0.32 0.99 0.99 Number of boats at tideline 0.37 0.99 0.98 Number of boats near godwits 0.36 0.99 0.99 Hoofed Animals Number of hoofed animals 0.29 0.91 0.83 Number of hoofed animals near tideline 1.00 0.97 0.95 Number of hoofed animals near godwits -0.36 0.68 0.47 Alertness and Agitation Number of flushes per min 0.64 0.95 0.91 Number of times alert/vigilant per min 0.43 0.89 0.81 Land Use Distance to road 0.20 0.69 0.47 Road substrate 0.88 0.98 0.97 147 TABLE V (CONTINUED) weight loading communality Water Distance to aquaculture 0.29 0.78 0.61 % aquaculture 0.39 0.79 0.63 Shellfish 0.47 0.96 0.92 Bay Characteristics Bay perimeter 0.21 0.82 0.67 Bay width 0.30 0.67 0.45 Tidal area 0.67 0.93 0.87 Amount of Intertidal Habitat % mud + % algae 1.00 1.00 1.00 Godwit Density Flock size per km 1.00 1.00 1.00 Body Condition Body molt score residuals 0.48 0.94 0.88 Abdominal profile index residuals 0.57 0.96 0.92 148 Table VI. Average standardized latent variable scores of each tidal mudflat surveyed. Only latent variables with significant relationships with density and body condition of Hudsonian Godwits (Limosa haemastica) are shown. 149 Body Godwit Alertness Amount of Condition Density and Intertidal Foraging Agitation Habitat Success Achao 0.40 -0.23 0.24 0.50 -0.37 Aldachildo 1.15 -0.55 0.53 -2.14 -0.13 Ancud 0.53 -0.60 1.16 -2.01 0.88 Astillero 0.86 0.00 0.52 0.39 1.58 Aucar 0.21 -0.58 0.24 -1.85 -0.23 Calén -0.75 -0.56 0.22 -1.48 -0.90 Caulín 0.41 1.09 -1.94 0.77 0.74 Chacao 0.62 -0.56 1.51 -1.90 0.37 Chamiza-Norte 1.59 -0.28 -0.04 0.97 1.12 Chamiza-Sur 1.08 1.18 -1.03 0.39 0.28 Chúllec -0.98 0.63 -0.16 0.62 -0.34 Compu 0.48 -0.37 0.10 -1.10 0.54 Contuy -0.01 -0.33 -0.05 0.86 0.07 Contuy-Oeste -0.64 -0.04 0.32 -0.36 0.33 Curaco de Vélez -0.16 0.59 -0.91 0.90 0.18 Huapilacuy -1.15 -0.67 0.12 -0.52 -1.31 Huelden -1.18 -0.67 0.12 -2.18 -1.31 Huildad 0.55 1.09 -1.63 0.97 -0.04 Ichuac -0.49 -0.64 0.34 0.14 -0.16 Lenca -1.43 -0.67 0.12 -2.01 -1.31 Linao 0.67 -0.47 0.20 -1.71 0.21 Llicaldad 0.73 -0.56 1.23 0.47 0.14 Llicaldad-Sur -0.37 0.06 0.21 -1.14 -0.74 Manao 0.72 -0.14 -0.08 -0.05 -0.05 Nercón 0.04 0.17 0.05 0.62 0.00 Nercón-Puente 0.23 0.55 0.07 0.58 0.70 Piluco -0.28 -0.29 0.71 -1.52 0.16 Pullao 0.33 0.53 -0.44 0.69 0.61 Pullihue-Puente -0.58 -0.67 0.24 0.64 -0.10 Putemún -1.00 -0.67 0.12 -0.09 -1.31 Quellón -0.44 0.07 0.02 0.64 0.45 Quetalco 0.44 -0.42 0.29 0.03 0.42 Quetalmahue-Este 0.60 -0.53 0.12 -0.85 -0.56 Quetalmahue-Oeste -0.35 -0.23 0.18 0.86 -0.20 Quetalmahue-Puente 0.19 1.71 -0.20 0.42 1.21 Quillaipe -0.13 -0.53 0.15 0.25 -0.56 Quinchao -1.14 -0.67 0.12 0.31 -1.31 Rilán -1.02 -0.67 0.12 0.64 -1.31 San Juan 0.17 0.20 -0.07 0.12 0.08 150 TABLE VI (CONTINUED) Body Godwit Alertness Amount of and Intertidal Foraging Condition Density Agitation Habitat Success Teguel 0.73 0.35 0.10 0.05 0.27 Ten Ten -0.18 -0.56 0.40 0.80 -0.34 Yaldad 0.04 -0.19 0.07 0.47 -0.34 151 Table VII. Results of bootstrapping procedure of the fitted partial least squares path model. Significant paths, where 95% confidence intervals (CI) did not cross 0, are in bold. Beta 95% CI Foraging Success -> Godwit Density 0.34 (0.23, 0.47) Predation Risk -> Alertness and Agitation 0.02 (-0.15, 0.06) Predation Risk -> Godwit Density 0.01 (-0.07, 0.16) Humans -> Alertness and Agitation 0.15 (0.04, 0.31) Dogs -> Alertness and Agitation 0.06 (-0.30, 0.45) Boats -> Alertness and Agitation 0.12 (-0.15, 0.39) Hoofed Animals -> Alertness and Agitation -0.06 (-0.24, 0.07) Humans -> Godwit Density -0.01 (-0.13, 0.14) Dogs -> Godwit Density -0.02 (-0.22, 0.21) Boats -> Godwit Density -0.01 (-0.15, 0.39) Hoofed Animals -> Godwit Density -0.03 (-0.12, 0.08) Alertness and Agitation -> Godwit Density -0.57 (-0.75, -0.35) Land Use -> Bay Characteristics 0.23 (-0.30, 0.44) Water -> Bay Characteristics -0.30 (-0.45, -0.14) Bay Characteristics -> Godwit Density -0.06 (-0.16, 0.04) Amount of Intertidal Habitat -> Foraging Success 0.11 (-0.07, 0.28) Amount of Intertidal Habitat -> Godwit Density 0.11 (0.01, 0.21) Godwit Density -> Body Condition 0.34 (0.23, 0.44) 152 Table VIII. The relative contribution of direct and indirect effects (calculated from standardized path coefficients), the total effect, and bootstrapped 95% confidence intervals for each path in the fitted partial least squares path model. Paths connect latent variables. Paths where the 95% confidence interval (CI) do not overlap zero are bolded. 153 direct indirect total 95% CI Land Use -> Bay Size 0.26 0.00 0.23 (-0.29, 0.43) Land Use -> Godwit Density 0.00 -0.01 -0.01 (-0.05, 0.03) Land Use -> Body Condition 0.00 0.00 0.00 (-0.02, 0.01) Water -> Bay Size -0.29 0.00 -0.29 (-0.44, -0.14) Water -> Godwit Density 0.00 0.02 0.02 (-0.01, 0.05) Water -> Body Condition 0.00 0.01 0.01 (0.00, 0.02) Bay Size -> Godwit Density -0.05 0.00 -0.06 (-0.16, 0.05) Bay Size -> Body Condition 0.00 -0.02 -0.02 (-0.06, 0.02) Amount of Intertidal Habitat -> 0.11 0.00 0.11 (-0.05, 0.25) Foraging Success Amount of Intertidal Habitat -> 0.10 0.04 0.14 (0.04, 0.24) Godwit Density Amount of Intertidal Habitat -> 0.00 0.04 0.05 (0.01, 0.09) Body Condition Foraging Success -> Godwit Density 0.35 0.00 0.35 (0.23, 0.48) Foraging Success -> Body Condition 0.00 0.11 0.12 (0.07, 0.19) Predation -> Alertness and Agitation 0.00 0.00 -0.03 (-0.17, 0.1) Predation -> Godwit Density 0.07 0.00 0.07 (-0.13, 0.25) Predation -> Body Condition 0.00 0.02 0.02 (-0.04, 0.09) Humans -> Alertness and Agitation 0.15 0.00 0.15 (0.02, 0.29) Humans -> Godwit Density -0.02 -0.09 -0.09 (-0.19, 0.04) Humans -> Body Condition 0.00 -0.04 -0.03 (-0.07, 0.01) Dogs -> Alertness and Agitation 0.03 0.00 0.06 (-0.34, 0.42) Dogs -> Godwit Density 0.00 -0.02 -0.05 (-0.29, 0.31) Dogs -> Body Condition 0.00 -0.01 -0.02 (-0.1, 0.11) Boats -> Alertness and Agitation 0.13 0.00 0.12 (-0.16, 0.42) Boats -> Godwit Density 0.01 -0.07 -0.08 (-0.32, 0.12) Boats -> Body Condition 0.00 -0.02 -0.03 (-0.11, 0.04) Hoofed Animals -> Alertness and -0.06 0.00 -0.06 (-0.25, 0.07) Agitation Hoofed Animals -> Godwit Density 0.00 0.04 0.01 (-0.13, 0.21) Hoofed Animals -> Body Condition 0.00 0.01 0.00 (-0.04, 0.07) Alertness and Agitation -> Godwit -0.59 0.00 -0.58 (-0.76, -0.36) Density Alertness and Agitation -> Body 0.00 -0.19 -0.19 (-0.28, -0.11) Condition Godwit Density -> Body Condition 0.33 0.00 0.33 (0.24, 0.43) 154 Figure 1. Map of Chile (gray) and South America (outlined, right panel). Locations of surveyed intertidal mudflats on Chiloé Island, mainland, and adjacent islands are indicated by black circles (left panel). Panel (A) shows mudflat locations in the northern region, (B) on the mainland, (C) in the central region, and (D) in the southern region of Chiloé Island. 155 156 Figure 2. The partial least squares inner path model. Ovals represent each of the ‘latent’ variables with the proposed relationships between each latent variable shown by the dark gray arrows. 157 Figure 3. The final partial least squares path model. ‘Manifest’ variables are shown in rectangles and ‘latent’ variables in ovals. The light gray arrows show the link between the manifest variables and each latent variable. The inner model describing the relationships between the latent variables is represented using dark gray arrows. 158 Water Land Use -0.30 0.23 ( -0.45, -0.14) (-0.30, 0.44) Predation Risk Bay Characteristics Humans 0.02 (-0.15, 0.06) 0.15 (0.04, 0.31) 0.01 -0.06(-0.07, 0.16) (-0.24, 0.07) -0.01 (-0.13, 0.14) Dogs -0.02 (-0.22, 0.21) 0.34 0.06 (0.23, 0.44) -0.06 (-0.30, 0.45) Alertness and Agitation Godwit Density Body Condition (-0.24, 0.07) -0.57(-0.75, -0.35) -0.03 (-0.12, 0.08) Hoofed Animals 0.12 (-0.15, 0.39) -0.01 (-0.15, 0.39) 0.34 0.11(0.23, 0.47) (0.01, 0.21) 0.11 (-0.07, 0.28) Boats Foraging Success Amount of Intertidal Foraging Habitat Figure 4. Partial least squares path diagram used to assess both direct and indirect effects on density and body condition of Hudsonian Godwits (Limosa haemastica) in the Chiloé Island region. Arrows point from predictor to response variables within the model and the thickness of the arrows is proportional to the respective path values (mean bootstrapped standardized path coefficients). Black lines represent significant relationships while gray lines represent non- significant relationships based on 999 bootstrapped iterations. For significant relationships, solid lines represent positive relationships, while dashed lines represent negative relationships. Coefficients of determination (R2) and 95% confidence intervals are reported for response variables within the model. 159 APPENDIX D Table DI. Survey data of non-breeding season patch quality, foraging success, intertidal foraging habitat, predation risk, and alertness and agitation of Hudsonian Godwits (Limosa haemastica) on Chiloé Island, Chile in 2016. When no godwits were seen body molt score, abdominal profile index, probes per min, success rate, success rate per min, number of flushes, and alertness per minute have a (-). 160 Flock Length size of Body Abdominal Probes Success % Number Number Number Alert Date Location Molt Profile per Success Swallows Mud of per Survey Score Index min Rate per min Rate and of Predator of per km (min) per min Algae Predators Species Flushes min Jan 02 Chacao 7 40 0.00 3.00 4.68 0.03 0.18 0.01 13.33 0 0 2.00 6.60 Jan 03 Aucar 0 65 0.00 0.00 0.00 0.00 0.00 0.00 15.00 1 1 0.00 0.00 Jan 03 Ancud 22 40 0.00 3.00 21.33 0.13 2.79 0.03 10.00 0 0 1.00 2.90 Jan 04 Caulín 429 350 0.30 3.00 24.63 0.30 7.60 0.06 93.94 3 2 10.00 1.07 Jan 05 Quetalmahue-Oeste 0 50 - - - - - - 96.67 0 0 - - Jan 05 Quetalmahue-Puente 636 50 0.30 3.00 NA NA NA NA 83.33 0 0 0.00 NA Jan 05 Quetalmahue-Harbor 65 10 0.20 3.00 7.14 0.08 0.58 0.02 45.00 0 0 0.00 0.00 Jan 05 Piluco 169 5 0.00 0.00 NA NA NA NA 25.00 0 0 1.00 NA Jan 06 Quetalmahue-Puente 311 120 0.20 3.00 30.01 0.33 10.02 0.08 83.33 0 0 1.00 0.00 Jan 06 Quetalmahue-Oeste 89 80 0.20 3.00 26.68 0.20 5.58 0.04 96.67 0 0 1.00 0.99 Jan 06 Quetalmahue-Este 35 7 0.20 3.00 NA NA NA NA 45.00 0 0 0.00 NA Jan 06 Piluco 17 5 0.00 0.00 NA NA NA NA 25.00 0 0 0.00 NA Jan 07 Linao 82 235 0.20 3.00 15.20 0.15 2.43 0.04 19.17 1 1 3.00 0.24 Jan 08 Manao 16 125 0.30 3.00 20.37 0.02 0.30 0.00 69.25 1 1 0.00 0.40 Jan 08 Caulín 425 180 0.30 3.00 22.89 0.25 6.34 0.06 93.94 0 0 1.00 0.03 Jan 09 Aucar 16 135 0.30 3.00 19.05 0.09 1.72 0.02 15.00 0 0 0.00 0.26 Jan 09 Ten Ten 37 15 0.30 3.00 NA NA NA NA 95.00 0 0 1.00 NA Jan 09 Putemún 0 15 - - - - - - 72.50 0 0 - - Jan 09 Rilán 0 60 - - - - - - 90.00 0 0 - - Jan 11 Putemún 0 60 - - - - - - 72.50 0 0 - - Jan 11 Ten Ten 0 10 - - - - - - 95.00 0 0 - - Jan 11 Nercón 400 45 0.40 3.00 18.07 0.20 5.77 0.05 89.29 0 0 1.00 0.20 Jan 11 Llicaldad 5 5 0.30 3.00 NA NA NA NA 85.00 0 0 0.00 NA Jan 11 Nercón 0 10 - - - - - - 89.29 0 0 - - Jan 11 Nercón-Puente 619 30 0.50 3.00 28.42 0.49 13.93 0.13 88.33 0 0 1.00 0.27 Jan 11 Nercón 196 30 0.40 3.00 26.46 0.09 2.68 0.02 89.29 0 0 0.00 0.00 Jan 11 Llicaldad 66 25 0.30 3.00 21.35 0.14 2.98 0.03 85.00 0 0 3.00 5.16 Jan 12 Pullao 571 285 0.60 3.00 21.35 0.14 2.98 0.03 96.25 0 0 3.00 0.10 Jan 13 Ten Ten 5 35 0.30 3.00 22.44 0.25 8.11 0.11 95.00 0 0 1.00 0.20 Jan 13 Rilán 0 65 - - - - - - 90.00 0 0 - - Jan 13 Putemún 0 5 - - - - - - 72.50 0 0 - - Jan 14 Ten Ten 89 15 0.50 3.00 NA NA NA NA 95.00 0 0 0.00 NA Jan 14 Curaco de Vélez 1435 335 0.60 3.00 21.70 0.20 4.36 0.02 97.92 0 0 7.00 1.08 161 TABLE DI (CONTINUED) Flock Length % Number size of Body Abdominal Probes Success Swallows Success Mud Number of Number Alert Date Location per Survey Molt Profile per Rate per min Rate and of of per km (min) Score Index min per min Predators Predator Algae Species Flushes min Jan 15 Chúllec 964 105 0.30 3.00 23.57 0.20 6.07 0.04 90.00 0 0 1.00 0.15 Jan 15 Quinchao 0 15 - - - - - - 80.00 0 0 - - Jan 15 Achao 0 5 - - - - - - 85.78 0 0 - - Jan 15 Curaco de Vélez 0 5 - - - - - - 97.92 0 0 - - Jan 16 Compu 0 10 - - - - - - 37.50 0 0 - - Jan 16 Huildad 467 60 0.50 3.00 21.40 0.12 2.64 0.02 100.00 0 0 1.00 0.26 Jan 16 Quellón 244 115 0.60 3.00 27.63 0.24 6.34 0.05 90.00 1 1 2.00 0.33 Jan 17 Yaldad 176 75 0.00 0.00 NA NA NA NA 85.00 1 1 3.00 NA Jan 18 Teguel 5 40 0.10 3.00 19.64 0.22 4.96 0.04 71.50 1 1 1.00 0.89 Jan 18 Quetalco 92 70 0.40 3.00 23.05 0.16 3.70 0.03 71.67 0 0 0.00 0.00 Jan 18 San Juan 572 100 0.70 3.00 14.63 0.14 2.56 0.04 88.00 0 0 1.00 0.07 Jan 19 Quetalmahue-Oeste 0 5 - - - - - - 96.67 0 0 - - Jan 19 Quetalmahue-Puente 485 145 0.80 3.00 28.99 0.32 9.47 0.07 83.33 1 1 2.00 0.34 Jan 20 Caulín 499 310 0.80 3.00 25.24 0.26 6.62 0.05 93.94 1 1 3.00 0.40 Jan 21 Chamiza-Sur 217 75 0.60 3.00 16.82 0.17 3.89 0.03 82.50 0 0 3.00 0.99 Jan 21 Quillaipe 0 5 - - - - - - 78.33 0 0 - - Jan 21 Lenca 0 10 - - - - - - 10.00 0 0 - - Jan 22 Chamiza-Sur 1320 180 0.90 3.00 29.99 0.18 3.44 0.04 82.50 0 0 3.00 0.80 Jan 23 Huapilacuy 0 25 - - - - - - 55.00 0 0 - - Jan 23 Pullihue-Puente 0 15 - - - - - - 90.00 1 1 - - Jan 24 Huelden 0 20 - - - - - - 5.00 0 0 - - Jan 24 Linao 42 90 0.60 3.00 22.08 0.18 3.89 0.08 19.17 0 0 1.00 0.23 Jan 24 Ancud 27 10 0.70 3.50 22.84 0.33 7.45 0.06 10.00 0 0 1.00 0.30 Jan 26 Calén 0 25 - - - - - - 26.25 0 0 - - Jan 26 San Juan 314 40 1.20 3.00 21.80 0.17 3.60 0.03 88.00 1 1 1.00 0.80 Jan 27 Nercón 331 185 1.00 3.00 18.95 0.18 3.66 0.04 89.29 0 0 3.00 0.71 Jan 27 Nercón-Puente 24 5 0.00 0.00 NA NA NA NA 88.33 0 0 0.00 NA Jan 27 Ten Ten 0 5 - - - - - - 95.00 0 0 - - Jan 28 Pullao 558 245 1.10 3.00 22.75 0.30 6.98 0.06 96.25 0 0 1.00 0.24 Jan 28 Rilán 0 5 - - - - - - 90.00 0 0 - - Jan 29 Curaco de Vélez 661 360 0.90 3.00 16.28 0.42 6.96 0.21 97.92 1 1 8.00 0.24 Jan 30 Chúllec 1055 360 0.80 3.00 20.29 0.27 6.97 0.07 90.00 6 5 12.00 0.35 162 TABLE DI (CONTINUED) Flock Length size of Body Abdominal Probes Success % Number Number Number Alert Date Location per Survey Molt Profile per Success Swallows Rate Mud of of of per km (min) Score Index min Rate per min per min and Predators Predator Algae Species Flushes min Jan 31 Aucar 9 85 0.80 3.00 21.43 0.31 6.23 0.06 15.00 1 1 0.00 0.90 Jan 31 Ancud 20 60 0.80 3.00 35.50 0.24 7.84 0.05 10.00 0 0 0.00 1.44 Feb 01 Quetalmahue-Puente 909 160 1.00 1.00 20.33 0.30 5.43 0.06 83.33 0 0 0.00 0.50 Feb 01 Piluco 118 90 1.00 3.00 23.11 0.20 4.68 0.04 25.00 0 0 2.00 1.10 Feb 02 Caulín 416 325 1.00 3.00 24.87 0.31 7.44 0.06 91.00 1 1 3.00 0.32 Feb 03 Linao 68 165 1.40 3.00 17.35 0.14 2.98 0.03 16.67 0 0 3.00 0.89 Feb 03 Caulín 222 20 0.00 0.00 NA NA NA NA 93.94 0 0 2.00 NA Feb 04 San Juan 83 180 0.80 3.00 24.62 0.09 2.15 0.03 88.00 0 0 1.00 0.36 Feb 04 Quetalco 43 70 0.90 3.00 27.43 0.18 4.95 0.04 71.67 0 0 1.00 0.59 Feb 05 Pullihue-Puente 1 35 0.00 3.00 18.92 0.37 6.97 0.07 90.00 0 0 0.00 0.80 Feb 05 Huapilacuy 0 45 - - - - - - 55.00 0 0 - - Feb 05 Quetalmahue-Este 11 25 1.30 3.00 5.79 0.03 0.30 0.01 45.00 0 0 0.00 0.16 Feb 07 Chacao 55 5 1.50 3.00 29.13 0.38 11.16 0.07 13.33 0 0 0.00 0.64 Feb 07 Huelden 0 35 - - - - - - 5.00 0 0 - - Feb 07 Manao 296 155 1.40 3.00 21.03 0.18 3.13 0.03 66.00 0 0 10.00 0.41 Feb 08 Ancud 4 30 1.10 3.00 22.84 0.33 7.45 0.06 10.00 0 0 2.00 0.30 Feb 08 Aucar 76 100 1.10 3.00 15.74 0.12 1.65 0.02 15.00 0 0 0.00 0.92 Feb 09 Calén 89 105 1.40 3.00 19.12 0.11 2.22 0.03 52.50 0 0 2.00 1.33 Feb 10 Pullao 310 210 2.00 3.00 17.45 0.19 3.35 0.05 95.00 0 0 6.00 0.50 Feb 10 Rilán 0 25 - - - - - - 90.00 0 0 - - Feb 11 Nercón 409 175 1.70 3.00 32.44 0.18 4.54 0.02 89.29 0 0 4.00 0.25 Feb 11 Llicaldad-Sur 387 50 1.90 3.00 14.92 0.20 3.00 0.00 42.50 0 0 0.00 0.89 Feb 11 Nercón-Puente 24 5 1.80 3.00 NA NA NA NA 88.33 0 0 0.00 NA Feb 11 Ten Ten 0 5 - - - - - - 95.00 0 0 - - Feb 12 Curaco de Vélez 7 5 0.00 0.00 NA NA NA NA 97.92 0 0 0.00 NA Feb 12 Chúllec 0 5 - - - - - - 90.00 0 0 - 0.00 Feb 12 Quinchao 0 15 - - - - - - 80.00 0 0 - 0.00 Feb 12 Achao 84 125 1.60 3.00 20.24 0.09 1.94 0.02 93.33 1 1 3.00 0.29 Feb 12 Chúllec 251 60 0.00 0.00 NA NA NA NA 85.00 1 1 3.00 NA Feb 13 Curaco de Vélez 72 140 1.60 3.00 25.49 0.17 4.28 0.03 100.00 1 1 1.00 0.05 Feb 13 Chúllec 0 10 - - - - - - 90.00 1 1 - - Feb 13 Astillero 247 60 1.80 3.00 NA NA NA NA 95.00 0 0 0.00 NA 163 TABLE DI (CONTINUED) Flock Length size of Body Abdominal Probes Success % Number Number Number Alert Date Location per Survey Molt Profile per Success Swallows Mud Rate per min Rate and of of Score Index min per min Predators Predator of per km (min) Algae Species Flushes min Feb 15 Contuy 119 105 1.90 3.00 20.61 0.29 5.64 0.09 91.25 0 0 3.00 0.27 Feb 15 Contuy-Oeste 368 10 0.00 0.00 NA NA NA NA 60.00 0 0 0.00 NA Feb 15 Compu 128 150 2.10 3.00 27.47 0.38 11.37 0.08 27.50 2 1 4.00 0.13 Feb 16 Yaldad 81 60 2.30 3.00 12.88 0.05 0.70 0.01 85.00 1 1 2.00 0.93 Feb 16 Quellón 148 25 0.00 0.00 NA NA NA NA 90.00 0 0 0.00 NA Feb 17 Nercón 1 5 0.00 0.00 NA NA NA NA 89.29 0 0 0.00 NA Feb 17 Nercón-Puente 625 100 1.60 3.00 25.03 0.13 3.34 0.03 88.33 0 0 1.00 0.53 Feb 18 Quetalmahue-Oeste 464 200 2.50 3.00 23.31 0.30 6.33 0.06 96.67 0 0 2.00 0.46 Feb 18 Quetalmahue-Puente 727 40 0.00 0.00 NA NA NA NA 83.33 0 0 0.00 NA Feb 19 Caulín 693 210 1.90 3.00 21.50 0.14 3.54 0.05 95.00 0 0 11.00 0.47 Feb 20 Manao 111 140 2.30 3.00 22.92 0.16 3.21 0.04 72.50 0 0 3.00 0.46 Feb 20 Linao 21 160 1.70 4.00 15.45 0.23 3.55 0.05 21.67 0 0 0.00 1.09 Feb 21 Teguel 284 135 2.30 3.00 15.38 0.19 3.30 0.04 78.00 5 4 7.00 0.28 Feb 22 Ten Ten 1 55 2.00 3.00 16.20 0.08 1.30 0.02 100.00 0 0 0.00 0.80 Feb 22 Putemún 0 10 - - - - - - 55.00 0 0 - - Feb 22 Astillero 234 60 2.50 3.00 34.94 0.62 24.18 0.13 62.50 0 0 1.00 0.20 Feb 22 Llicaldad-Sur 0 85 - - - - - - 30.00 0 0 - - Feb 22 Llicaldad 21 85 2.70 3.00 21.45 0.18 3.67 0.04 85.00 0 0 2.00 0.40 Feb 23 Pullao 12 10 0.00 0.00 NA NA NA NA 72.50 0 0 0.00 NA Feb 23 Ichuac 0 15 - - - - - - 60.00 0 0 - - Feb 23 Aldachildo 36 60 2.30 3.00 NA NA NA NA 2.50 0 0 1.00 NA Feb 24 Achao 213 110 2.40 3.00 15.59 0.13 2.38 0.06 75.00 0 0 4.00 0.10 Feb 25 Chúllec 0 20 - - - - - - 90.00 0 0 - - Feb 25 Curaco de Vélez 0 20 - - - - - - 95.00 0 0 - - Feb 25 Astillero 51 15 2.30 3.00 20.32 0.31 6.37 0.06 90.00 0 0 1.00 0.80 Feb 25 Ten Ten 7 15 2.80 3.00 NA NA NA NA 90.00 0 0 1.00 NA Feb 26 Contuy 153 170 2.60 3.00 22.94 0.19 4.46 0.05 100.00 0 0 0.00 0.14 Feb 26 Contuy-Oeste 6 20 0.00 0.00 NA NA NA NA 60.00 0 0 1.00 NA Feb 27 San Juan 185 120 2.50 3.00 20.62 0.16 3.31 0.04 88.00 0 0 4.00 0.16 Feb 27 Teguel 523 90 2.70 3.00 17.20 0.19 3.35 0.05 67.50 1 1 5.00 0.34 Feb 29 Caulín 582 365 2.70 3.00 22.85 0.29 6.41 0.06 95.83 3 2 18.00 2.48 Mar 01 Quetalmahue-Oeste 29 5 0.00 0.00 NA NA NA NA 96.67 0 0 0.00 NA 164 TABLE DI (CONTINUED) Flock Length size of Body Abdominal Probes Success Swallows Success % Number Number Number Alert Date Location per Survey Molt Profile per Mud of Score Index min Rate per min Rate and of of per km (min) per min Algae Predators Predator Species Flushes min Mar 01 Quetalmahue-Puente 727 280 2.80 3.00 27.31 0.37 8.37 0.34 83.33 3 1 11.00 0.46 Mar 02 Calén 0 10 - - - - - - 0.00 0 0 - - Mar 02 San Juan 6 15 0.00 0.00 NA NA NA NA 20.00 0 0 0.00 NA Mar 02 Pullao 141 155 2.80 3.00 22.01 0.45 9.44 0.09 97.50 0 0 5.00 0.23 Mar 03 Compu 114 180 2.90 4.00 24.08 0.32 7.96 0.06 47.50 0 0 0.00 0.23 Mar 03 Contuy 0 5 - - - - - - 98.75 0 0 - - Mar 03 Contuy-Oeste 126 105 2.60 4.00 22.67 0.28 6.37 0.07 60.00 0 0 2.00 0.11 Mar 03 Ten Ten 120 10 0.00 0.00 NA NA NA NA 95.00 0 0 0.00 NA Mar 04 Curaco de Vélez 160 120 2.90 3.00 21.67 0.30 6.48 0.09 98.75 0 0 6.00 0.24 Mar 04 Chúllec 151 7 0.00 0.00 NA NA NA NA 90.00 0 0 1.00 NA Mar 04 Achao 175 145 2.70 3.00 21.94 0.10 2.36 0.03 89.00 3 1 13.00 0.85 Mar 05 Aldachildo 32 85 3.00 4.00 16.82 0.12 1.64 0.02 10.00 0 0 0.00 2.23 Mar 05 Ichuac 17 50 2.30 3.00 18.84 0.27 4.77 0.11 90.00 0 0 1.00 0.61 Mar 06 Chamiza-Norte 104 100 3.30 4.00 32.77 0.23 7.42 0.05 100.00 0 0 2.00 0.60 Mar 06 Chamiza-Sur 231 85 3.00 4.00 22.84 0.13 3.00 0.03 92.50 0 0 3.00 0.21 Mar 07 Lenca 0 45 - - - - - - 10.00 0 0 - - Mar 07 Quillaipe 76 85 2.50 3.00 19.87 0.12 2.61 0.06 78.33 0 0 6.00 1.18 Mar 08 Chamiza-Sur 198 125 3.60 4.00 26.67 0.15 3.80 0.03 72.50 0 0 0.00 0.04 165 Table DII. Survey data of non-breeding season human disturbances on Chiloé Island, Chile in 2016 for patch quality analysis of non- breeding habitat for Hudsonian Godwits (Limosa haemastica). A five meter buffer was used to indicate disturbances at the tideline, and a hundred meter buffer was used to indicate disturbances near foraging godwits. 166 Number Number Number of of Number Number Number Number Number of Number of Dogs of Dogs of of Hoofed Hoofed Number Number Number Date Location of Humans Humans Animals Animals of of of Boats Humans at near of Dogs at near Hoofed at near Boats Boats at near Tideline Godwits Tideline Godwits Animals Tideline Godwits Tideline Godwits Jan 02 Chacao 12.00 3.00 2.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 03 Aucar 2.00 0.33 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 03 Ancud 1.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 04 Caulín 4.13 1.25 0.33 0.50 0.25 0.14 0.50 0.00 0.00 0.00 0.00 0.00 Jan 05 Quetalmahue-Oeste 2.00 1.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 05 Quetalmahue-Puente 4.00 4.00 4.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 05 Quetalmahue-Este NA NA NA NA NA NA NA NA NA NA NA NA Jan 05 Piluco NA NA NA NA NA NA NA NA NA NA NA NA Jan 06 Quetalmahue-Puente 5.00 1.25 3.75 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 06 Quetalmahue-Oeste 10.00 10.00 0.00 0.00 0.00 0.00 2.00 2.00 0.00 0.00 0.00 0.00 Jan 06 Quetalmahue-Este 3.00 3.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 06 Piluco NA NA NA NA NA NA NA NA NA NA NA NA Jan 07 Linao 6.10 4.60 5.80 0.20 0.20 0.20 0.00 0.00 0.00 0.50 0.30 0.30 Jan 08 Manao 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 08 Caulín 6.83 3.67 0.67 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 09 Aucar 0.40 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 09 Ten Ten NA NA NA NA NA NA NA NA NA NA NA NA Jan 09 Putemún NA NA NA NA NA NA NA NA NA NA NA NA Jan 09 Rilán NA NA NA NA NA NA NA NA NA NA NA NA Jan 11 Putemún 30.67 16.67 2.00 0.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 11 Ten Ten NA NA NA NA NA NA NA NA NA NA NA NA Jan 11 Nercón 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 11 Llicaldad NA NA NA NA NA NA NA NA NA NA NA NA Jan 11 Nercón 7.00 3.00 1.00 2.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 11 Nercón-Puente 7.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 11 Nercón 10.00 4.50 3.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 11 Llicaldad 10.00 5.00 0.00 2.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 12 Pullao 0.38 0.00 0.00 0.00 0.00 0.00 0.13 0.00 0.00 0.00 0.00 0.00 Jan 13 Ten Ten 6.00 2.00 1.50 3.50 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 13 Rilán 3.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 13 Putemún NA NA NA NA NA NA NA NA NA NA NA NA Jan 14 Ten Ten NA NA NA NA NA NA NA NA NA NA NA NA Jan 14 Curaco de Vélez 4.67 1.67 0.83 1.33 0.00 0.33 0.17 0.00 0.00 1.00 0.00 0.00 167 TABLE DII (CONTINUED) Number Number Number of of Number Number Number Number Number of Number Number Date Location of Humans Humans Number of Dogs of Dogs of of Hoofed Hoofed Number of of Boats Humans at near of Dogs at near Hoofed Animals Animals of Boats at near Tideline Godwits Tideline Godwits Animals at near Boats Tideline Godwits Tideline Godwits Jan 15 Chúllec 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 15 Quinchao NA NA NA NA NA NA NA NA NA NA NA NA Jan 15 Achao NA NA NA NA NA NA NA NA NA NA NA NA Jan 15 Curaco de Vélez NA NA NA NA NA NA NA NA NA NA NA NA Jan 16 Compu NA NA NA NA NA NA NA NA NA NA NA NA Jan 16 Huildad NA NA NA NA NA NA NA NA NA NA NA NA Jan 16 Quellón 2.00 0.00 0.00 1.50 0.00 0.00 0.00 0.00 0.00 0.50 0.00 0.50 Jan 17 Yaldad 10.00 0.00 3.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 18 Teguel 2.00 1.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 18 Quetalco 5.50 4.50 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.50 0.50 0.00 Jan 18 San Juan 4.00 2.00 0.50 0.50 0.00 0.00 0.00 0.00 0.00 0.50 0.00 0.00 Jan 19 Quetalmahue-Oeste NA NA NA NA NA NA NA NA NA NA NA NA Jan 19 Quetalmahue-Puente 7.67 2.33 4.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 20 Caulín 7.83 2.67 1.17 0.33 0.00 0.33 0.50 0.50 0.00 0.67 0.17 0.17 Jan 21 Chamiza-Sur 30.50 11.00 5.50 1.50 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 21 Quillaipe NA NA NA NA NA NA NA NA NA NA NA NA Jan 21 Lenca NA NA NA NA NA NA NA NA NA NA NA NA Jan 22 Chamiza-Sur 10.33 10.00 6.00 4.50 4.00 4.50 1.33 2.00 0.00 0.00 0.00 0.00 Jan 23 Huapilacuy NA NA NA NA NA NA NA NA NA NA NA NA Jan 23 Pullihue-Puente 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 24 Huelden NA NA NA NA NA NA NA NA NA NA NA NA Jan 24 Linao 20.00 8.50 7.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 Jan 24 Ancud 2.00 2.00 2.00 1.50 1.00 1.50 0.00 0.00 0.00 7.00 1.00 1.00 Jan 26 Calén NA NA NA NA NA NA NA NA NA NA NA NA Jan 26 San Juan 7.00 1.67 0.33 2.33 0.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 27 Nercón 3.60 2.00 0.00 0.80 0.40 0.40 0.00 0.00 0.00 0.20 0.00 0.00 Jan 27 Nercón-Puente NA NA NA NA NA NA NA NA NA NA NA NA Jan 27 Ten Ten NA NA NA NA NA NA NA NA NA NA NA NA Jan 28 Pullao 2.60 0.00 0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.00 0.00 0.00 Jan 28 Rilán 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 29 Curaco de Vélez 3.86 0.00 0.00 0.86 0.43 0.43 0.00 0.00 0.00 0.00 0.00 0.00 168 TABLE DII (CONTINUED) Number Number Number of of Number Number Number Number Number of Number of Dogs of Dogs of of Hoofed Hoofed Number Number Number Date Location of Humans Humans Animals Animals of of of Boats Humans at near of Dogs at near Hoofed at near Boats Boats at near Tideline Godwits Tideline Godwits Animals Tideline Godwits Tideline Godwits Jan 30 Chúllec 2.00 0.00 0.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 31 Aucar 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jan 31 Ancud 5.50 2.50 4.50 2.00 0.00 1.50 0.00 0.00 0.00 0.00 0.00 0.00 Feb 01 Quetalmahue-Puente 5.00 1.50 3.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 01 Piluco 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 02 Caulín 23.17 4.83 8.00 0.67 0.17 0.50 0.00 0.00 0.00 0.00 0.00 0.00 Feb 03 Linao 2.00 1.33 1.00 0.33 0.00 0.00 0.00 0.00 0.00 0.67 0.67 0.67 Feb 03 Caulín NA NA NA NA NA NA NA NA NA NA NA NA Feb 04 San Juan 8.33 2.33 1.67 1.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 04 Quetalco 2.50 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 Feb 05 Pullihue-Puente 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 05 Huapilacuy 6.00 3.00 1.00 1.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 05 Quetalmahue-Harbor 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 07 Chacao 6.50 3.00 3.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 07 Huelden 10.00 8.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 07 Manao 13.67 10.67 2.00 1.00 0.67 0.00 0.00 0.00 0.00 1.00 0.33 0.33 Feb 08 Ancud 2.00 2.00 2.00 1.50 1.00 1.50 0.00 0.00 0.00 7.00 1.00 1.00 Feb 08 Aucar 4.50 4.50 0.00 1.50 1.00 0.00 0.00 0.00 0.00 0.50 0.50 0.00 Feb 09 Calén 8.00 2.00 7.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 10 Pullao 4.67 1.33 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 10 Rilán 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 11 Nercón 16.00 7.67 9.00 3.00 2.67 1.67 0.00 0.00 0.00 0.00 0.00 0.00 Feb 11 Llicaldad-Sur 17.00 10.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 11 Nercón-Puente NA NA NA NA NA NA NA NA NA NA NA NA Feb 11 Ten Ten NA NA NA NA NA NA NA NA NA NA NA NA Feb 12 Curaco de Vélez 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 12 Chúllec NA NA NA NA NA NA NA NA NA NA NA NA Feb 12 Quinchao 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 12 Achao 3.60 0.25 0.75 1.80 0.25 0.25 0.00 0.00 0.00 0.60 0.00 0.25 Feb 12 Chúllec 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 13 Curaco de Vélez 5.00 0.67 1.33 0.00 0.00 0.00 3.33 3.33 3.33 0.00 0.00 0.00 169 TABLE DII (CONTINUED) Number Number Number Number of Number of of Number Number Number Number Number Date Location of Humans Humans Number of Dogs of Dogs of of Hoofed Hoofed Number of of Boats Humans at near of Dogs at near Hoofed Animals Animals of at near Boats Boats at near Tideline Godwits Tideline Godwits Animals Tideline Godwits Tideline Godwits Feb 13 Chúllec 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 13 Astillero 3.50 0.00 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 15 Contuy 1.00 0.00 0.00 0.00 0.00 0.00 0.67 0.00 0.00 0.00 0.00 0.00 Feb 15 Contuy-Oeste NA NA NA NA NA NA NA NA NA NA NA NA Feb 15 Compu 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 16 Yaldad 1.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 16 Quellón 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 17 Nercón NA NA NA NA NA NA NA NA NA NA NA NA Feb 17 Nercón-Puente 3.33 0.00 0.00 0.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 18 Quetalmahue-Oeste 6.20 3.40 3.20 0.40 0.20 0.20 0.40 0.40 0.40 0.00 0.00 0.00 Feb 18 Quetalmahue-Puente 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 19 Caulín 3.50 0.75 0.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 20 Manao 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 20 Linao 14.00 8.33 2.67 0.33 0.33 0.00 1.00 0.00 0.00 0.33 0.33 0.33 Feb 21 Teguel 0.67 0.00 0.00 0.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 22 Ten Ten 4.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 22 Putemún 0.00 4.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 22 Astillero 4.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 22 Llicaldad-Sur 9.00 7.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 22 Llicaldad 8.50 5.50 2.00 1.50 1.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00 Feb 23 Pullao 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 23 Ichuac 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 23 Aldachildo 8.00 8.00 0.00 1.00 1.00 0.00 0.00 0.00 0.00 1.00 1.00 0.00 Feb 24 Achao 10.50 3.00 2.00 2.00 1.80 1.80 0.00 0.00 0.00 0.67 0.00 0.67 Feb 25 Chúllec 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 25 Curaco de Vélez 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 25 Astillero 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 25 Ten Ten 7.00 7.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 26 Contuy 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 26 Contuy-Oeste 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 27 San Juan 2.33 2.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 170 TABLE DII (CONTINUED) Number Number Number Number of Number of of Number Number Number Number of Dogs of Dogs of of Hoofed Hoofed Number Number Number Date Location of Humans Humans of of Boats Humans at near of Dogs at near Hoofed Animals Animals of Tideline Godwits Animals at near Boats Boats at near Tideline Godwits Tideline Godwits Tideline Godwits Feb 27 Teguel 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Feb 29 Caulín 6.17 0.83 1.00 1.00 0.00 0.17 0.67 0.33 0.33 0.00 0.00 0.00 Mar 01 Quetalmahue-Oeste NA NA NA NA NA NA NA NA NA NA NA NA Mar 01 Quetalmahue-Puente 3.20 2.20 2.80 0.40 0.40 0.40 0.00 0.00 0.00 0.00 0.00 0.00 Mar 02 Calén 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mar 02 San Juan 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mar 02 Pullao 1.33 0.67 0.67 0.00 0.00 0.00 0.00 0.00 0.00 0.33 0.33 0.33 Mar 03 Compu 0.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mar 03 Contuy NA NA NA NA NA NA NA NA NA NA NA NA Mar 03 Contuy-Oeste 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mar 03 Ten Ten 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mar 04 Curaco de Vélez 6.00 2.67 0.00 0.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mar 04 Chúllec 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mar 04 Achao 19.67 6.33 14.67 2.00 1.00 1.33 0.00 0.00 0.00 0.00 0.00 0.00 Mar 05 Aldachildo 8.00 8.00 0.00 1.00 1.00 0.00 0.00 0.00 0.00 1.00 1.00 0.00 Mar 05 Ichuac 0.50 0.00 0.50 0.50 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00 Mar 06 Chamiza-Norte 1.00 1.00 1.00 2.00 2.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 Mar 06 Chamiza-Sur 15.00 8.00 9.00 3.00 3.00 3.00 2.00 2.00 2.00 0.00 0.00 0.00 Mar 07 Lenca 2.00 0.00 0.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mar 07 Quillaipe 5.00 3.50 5.00 1.50 1.50 1.50 1.00 1.00 1.00 0.00 0.00 0.00 Mar 08 Chamiza-Sur 11.50 3.50 2.50 4.50 1.00 2.50 4.50 2.00 2.00 0.00 0.00 0.00 171 Table DIII. Hudsonian Godwit (Limosa haemastica) flock counts, survey times, and low tide times at intertidal mudflats near Chiloé Island, Chile during patch quality surveys in 2016. 172 Date Location Time of Low Tide Start Time of Survey Maximum Flock Count Jan 02 2016 Chacao 14:00 14:55 13 Jan 03 2016 Aucar 16:00 13:40 0 Jan 03 2016 Ancud 15:50 16:10 37 Jan 04 2016 Caulín 15:00 12:35 1,500 Jan 05 2016 Quetalmahue-Oeste 16:30 13:50 0 Jan 05 2016 Quetalmahue-Puente 16:30 14:55 1,000 Jan 05 2016 Quetalmahue-Este 16:30 16:10 72 Jan 05 2016 Piluco 16:30 16:30 250 Jan 06 2016 Quetalmahue-Puente 17:30 15:30 500 Jan 06 2016 Quetalmahue-Oeste 17:30 17:55 100 Jan 06 2016 Quetalmahue-Este 17:30 19:18 39 Jan 06 2016 Piluco 17:30 19:40 25 Jan 07 2016 Linao 18:00 14:25 475 Jan 08 2016 Manao 07:00 07:00 42 Jan 08 2016 Caulín 19:00 16:00 1,500 Jan 09 2016 Aucar 08:00 08:15 40 Jan 09 2016 Ten Ten 08:00 12:15 73 Jan 09 2016 Putemún 20:00 17:20 0 Jan 09 2016 Rilán 20:00 18:00 0 Jan 11 2016 Putemún 09:35 09:45 0 Jan 11 2016 Ten Ten 09:35 11:20 0 Jan 11 2016 Nercón 09:35 11:50 616 Jan 11 2016 Llicaldad 21:30 18:42 5 Jan 11 2016 Nercón 21:30 18:54 0 Jan 11 2016 Nercón-Puente 21:30 19:15 514 Jan 11 2016 Nercón 21:30 20:00 302 Jan 11 2016 Llicaldad 21:30 20:50 71 Jan 12 2016 Pullao 10:15 8:30 2,000 Jan 13 2016 Ten Ten 10:50 9:30 10 Jan 13 2016 Rilán 10:50 10:55 0 Jan 13 2016 Putemún 10:50 12:50 0 Jan 14 2016 Ten Ten 11:35 07:45 178 Jan 14 2016 Curaco de Vélez 11:35 09:00 4,450 Jan 15 2016 Chúllec 12:23 09:45 221 Jan 15 2016 Quinchao 12:23 12:15 0 Jan 15 2016 Achao 12:23 13:00 0 Jan 15 2016 Curaco de Vélez 12:23 13:35 0 Jan 16 2016 Compu 13:00 10:25 0 Jan 16 2016 Huildad 13:00 11:50 2,800 Jan 16 2016 Quellón 13:30 14:05 660 Jan 17 2016 Yaldad 14:45 11:50 670 Jan 18 2016 Teguel 15:45 13:45 7 Jan 18 2016 Quetalco 15:45 14:55 132 Jan 18 2016 San Juan 15:45 16:40 1,550 Jan 19 2016 Quetalmahue-Oeste 16:30 13:50 0 Jan 19 2016 Quetalmahue-Puente 15:20 14:05 800 Jan 20 2016 Caulín 16:20 13:40 1,800 Jan 21 2016 Chamiza-Sur 19:00 18:00 650 Jan 21 2016 Quillaipe 19:00 16:32 0 Jan 21 2016 Lenca 19:00 16:55 0 Jan 22 2016 Chamiza-Sur 19:45 17:00 4,000 Jan 23 2016 Huapilacuy 19:30 17:20 0 Jan 23 2016 Pullihue-Puente 19:30 18:05 0 Jan 24 2016 Huelden 08:30 09:15 0 173 TABLE DIII (CONTINUED) Date Location Time of Low Tide Start Time of Survey Maximum Flock Count Jan 24 2016 Linao 08:30 10:15 245 Jan 24 2016 Ancud 20:30 18:28 45 Jan 26 2016 Calén 10:15 10:45 0 Jan 26 2016 San Juan 10:15 12:00 850 Jan 27 2016 Nercón 10:45 09:05 510 Jan 27 2016 Nercón-Puente 10:45 12:15 20 Jan 27 2016 Ten Ten 10:45 12:35 0 Jan 28 2016 Pullao 11:15 08:55 2,250 Jan 28 2016 Rilán 11:15 13:25 0 Jan 29 2016 Curaco de Vélez 11:45 09:20 2,050 Jan 30 2016 Chúllec 12:10 09:20 2,400 Jan 31 2016 Aucar 13:15 12:35 21 Jan 31 2016 Ancud 12:15 15:05 49 Feb 01 2016 Quetalmahue-Puente 14:00 11:30 1,500 Feb 01 2016 Piluco 14:00 14:35 175 Feb 02 2016 Caulín 15:00 12:05 1,600 Feb 03 2016 Linao 17:20 14:35 480 Feb 03 2016 Caulín 17:20 18:00 800 Feb 04 2016 San Juan 17:50 14:50 225 Feb 04 2016 Quetalco 17:50 18:20 62 Feb 05 2016 Pullihue-Puente 18:00 15:40 1 Feb 05 2016 Huapilacuy 18:00 16:30 0 Feb 05 2016 Quetalmahue-Harbor 18:00 17:55 12 Feb 07 2016 Chacao 08:45 09:55 103 Feb 07 2016 Huelden 20:30 17:05 0 Feb 07 2016 Manao 20:30 18:05 800 Feb 08 2016 Ancud 09:00 08:35 7 Feb 08 2016 Aucar 09:00 10:05 177 Feb 09 2016 Calén 21:30 18:50 266 Feb 10 2016 Pullao 10:00 08:00 1,250 Feb 10 2016 Rilán 10:00 11:45 0 Feb 11 2016 Nercón 10:40 09:10 630 Feb 11 2016 Llicaldad-Sur 10:40 12:15 151 Feb 11 2016 Nercón-Puente 10:40 13:10 20 Feb 11 2016 Ten Ten 10:40 13:30 0 Feb 12 2016 Curaco de Vélez 11:30 08:57 22 Feb 12 2016 Chúllec 11:30 09:15 0 Feb 12 2016 Quinchao 11:30 09:35 0 Feb 12 2016 Achao 11:30 10:10 230 Feb 12 2016 Chúllec 11:30 12:40 500 Feb 13 2016 Curaco de Vélez 12:00 09:40 224 Feb 13 2016 Chúllec 12:00 12:20 0 Feb 13 2016 Astillero 12:00 13:00 292 Feb 15 2016 Contuy 14:15 11:25 850 Feb 15 2016 Contuy-Oeste 14:15 13:47 1,600 Feb 15 2016 Compu 14:50 14:30 505 Feb 16 2016 Yaldad 15:40 12:20 320 Feb 16 2016 Quellón 15:40 13:50 400 Feb 17 2016 Nercón 17:00 16:00 1 Feb 17 2016 Nercón-Puente 17:00 16:00 520 Feb 18 2016 Quetalmahue-Oeste 17:15 15:40 510 Feb 18 2016 Quetalmahue-Puente 17:15 19:10 1,200 174 TABLE DIII (CONTINUED) Date Location Time of Low Tide Start Time of Survey Maximum Flock Count Feb 19 2016 Caulín 19:30 16:20 2,500 Feb 20 2016 Manao 08:00 08:05 300 Feb 20 2016 Linao 20:00 17:00 122 Feb 21 2016 Teguel 20:20 17:20 435 Feb 22 2016 Ten Ten 08:45 08:40 2 Feb 22 2016 Putemún 08:40 10:00 0 Feb 22 2016 Astillero 08:40 10:40 276 Feb 22 2016 Llicaldad-Sur 20:50 18:40 0 Feb 22 2016 Llicaldad 20:50 18:40 8 Feb 23 2016 Pullao 09:15 09:05 60 Feb 23 2016 Ichuac 09:15 10:30 0 Feb 23 2016 Aldachildo 09:15 11:10 36 Feb 24 2016 Achao 09:45 10:00 425 Feb 25 2016 Chúllec 10:10 09:40 0 Feb 25 2016 Curaco de Vélez 10:10 10:10 0 Feb 25 2016 Astillero 10:10 11:15 60 Feb 25 2016 Ten Ten 10:10 12:00 13 Feb 26 2016 Contuy 10:50 09:45 1,100 Feb 26 2016 Contuy-Oeste 10:50 12:50 25 Feb 27 2016 San Juan 10:55 09:50 500 Feb 27 2016 Teguel 10:55 12:25 800 Feb 29 2016 Caulín 11:45 08:45 2,100 Mar 01 2016 Quetalmahue-Oeste 12:30 09:45 60 Mar 01 2016 Quetalmahue-Puente 12:30 10:00 1,200 Mar 02 2016 Calén 14:00 11:30 0 Mar 02 2016 San Juan 14:00 12:30 16 Mar 02 2016 Pullao 14:00 13:25 850 Mar 03 2016 Compu 16:10 13:00 450 Mar 03 2016 Contuy 16:10 16:30 0 Mar 03 2016 Contuy-Oeste 16:10 16:45 550 Mar 03 2016 Ten Ten 16:10 19:55 240 Mar 04 2016 Curaco de Vélez 17:20 14:20 497 Mar 04 2016 Chúllec 17:20 16:28 300 Mar 04 2016 Achao 17:20 16:55 400 Mar 05 2016 Aldachildo 18:10 15:35 32 Mar 05 2016 Ichuac 18:10 17:20 55 Mar 06 2016 Chamiza-Norte 19:00 15:50 600 Mar 06 2016 Chamiza-Sur 19:00 17:55 700 Mar 07 2016 Lenca 19:45 16:45 0 Mar 07 2016 Quillaipe 19:45 17:55 435 Mar 08 2016 Chamiza-Sur 08:20 08:55 600 175 APPENDIX E Table EI. Beta estimates and 95% confidence intervals (CI) for generalized linear regressions against Julian date with a random effect of survey location. Beta 95% CI B ody Molt Score 0.05 (0.04, 0.05) Abdominal Profile Index 0.01 (0.008, 0.01) Probes per min 0.04 (-0.009, 0.08) Swallows per min 0.02 (-0.002, 0.05) Success Rate 0.001 (0.005, 0.002) Success Rate per min 0.001 (0.00, 0.001) 176 Body Molt Score 3 2 1 0 0 20 40 60 Julian Date Figure E1. Hudsonian Godwit (Limosa haemastica) body molt scores increased during the late non-breeding season on Chiloé Island, Chile from January to March 2016. Ninety-five percent confidence intervals shown (gray area). 177 Abdominal Profile Index 3.6 3.4 3.2 3.0 2.8 0 20 40 60 Julian Date Figure E2. Hudsonian Godwit (Limosa haemastica) abdominal profile index increased during the late non-breeding season on Chiloé Island, Chile from January to March 2016. Ninety-five percent confidence intervals shown (gray area). 178 Probes per min 23 21 19 17 0 20 40 60 Julian Date Figure E3. Hudsonian Godwit (Limosa haemastica) probes per minute increased during the late non-breeding season on Chiloé Island, Chile from January to March 2016. Ninety-five percent confidence intervals shown (gray area). 179 Swallows per min 6 5 4 3 0 20 40 60 Julian Date Figure E4. Hudsonian Godwit (Limosa haemastica) swallows per min increased during the late non-breeding season on Chiloé Island, Chile from January to March 2016. Ninety-five percent confidence intervals shown (gray area). 180 Success Rate 0.25 0.20 0.15 0.10 0 20 40 60 Julian Date Figure E5. Hudsonian Godwit (Limosa haemastica) success rate increased during the late non- breeding season on Chiloé Island, Chile from January to March 2016. Ninety-five percent confidence intervals shown (gray area). 181 Success Rate per min 0.075 0.050 0.025 0.000 0 20 40 60 Julian Date Figure E6. Hudsonian Godwit (Limosa haemastica) success rate per min increased during the late non-breeding season on Chiloé Island, Chile from January to March 2016. Ninety-five percent confidence intervals shown (gray area). 182 CHAPTER FIVE SEASONAL SURVIVAL AND REVERSIBLE STATE EFFECTS IN A LONG- DISTANCE MIGRATORY SHOREBIRD 183 Abstract: Events during any one part of the annual cycle can impact an individual’s condition and survival within a given season, as well as its performance and fitness in subsequent seasons. These reversible state effects can occur during any stage of the annual cycle and ultimately affect population dynamics. Gathering such information can be, in turn, critical for developing targeted conservation objectives for at-risk and declining species. To identify possible cross-seasonal interactions in a declining long-distance migratory shorebird, we estimated period-specific survival probabilities across the annual cycle using two distinct marked populations of Hudsonian Godwits (Limosa haemastica). We then examined the extent to which body condition, foraging success, and habitat quality during the non-breeding season impacted return rates and reproductive performance during the following breeding season of 25 marked individuals monitored throughout their annual cycle. Survival rates were high throughout the annual cycle, with daily survival reaching the lowest levels during migration and highest during the stationary non-breeding season; nonetheless, the breeding season and southbound migration accounted for the largest proportion of mortality events. Our results provide evidence of reversible state effects, such that overwintering godwits that used the highest-quality habitats and were in the best body condition prior to spring migration performed best on the breeding grounds, exhibiting higher nest and chick survival than their poorer-condition counterparts. This finding was corroborated by an analysis of feather growth bars collected repeatedly from individuals over three years, which indicated that nutritional status on the non-breeding grounds was positively related to chick survival. Therefore, reversible state effects were acting across non-breeding to breeding seasons and influenced variation in seasonal survival rates of Hudsonian Godwits. Our understanding of cross-seasonal interactions benefits from linking 184 observations of individual performance with demography to identify conservation actions that connect individual behaviors to survival. Keywords: cross-seasonal interactions, reproductive performance, body condition, foraging success, non-breeding season, ptilochronology, habitat quality 185 Introduction: Migratory species face a variety of risks as they use widely dispersed sites throughout their annual cycle (Newton 2008). Determining the places and periods associated with the greatest mortality risk is essential for understanding population dynamics. By quantifying survival during distinct periods of the annual cycle, we can begin to assess actionable conservation goals by identifying population bottlenecks. Most research has thus far focused on documenting whether local populations experience limiting factors during the breeding season and to a lesser degree during the non-breeding season or migration (Pasinelli et al. 2011, Marra et al. 2015, Studds et al. 2017). Theoretical and empirical studies have demonstrated that the breeding abundance of migratory birds can be limited by breeding, stopover, or non-breeding habitat (Sherry and Holmes 1995, Sutherland 1996, Piersma et al. 2016). For instance, reliance on the Yellow Sea is the best predictor of declines for 10 species of shorebirds using the East Asian Australasian Flyway (Studds et al. 2017). Further, processes operating across the annual cycle can interact in complex ways at both the individual and population levels (Harrison et al. 2011). To effectively conserve migratory species, we must therefore work to understand the factors that limit survival and population growth and how these may be impacted by events occurring during different parts of the annual cycle. Carry-over effects, which occur when events affecting an individual in one season alter the outcome of a subsequent season (although the concept can be broadened to other life stages and time-scales), have been demonstrated in many taxa including birds, mammals, fish, and invertebrates (Harrison et al. 2011, O’Connor et al. 2014, Senner et al. 2015). Although carry- over effects are generally considered to be non-lethal (Harrison et al. 2011, O’Connor et al. 2014), they can indirectly increase the risk of mortality resulting in lethal consequences (Norris 186 2005). Fitness consequences may not be readily apparent across short time scales and carry-over effects can even affect senescence or provoke maternal effects on offspring. Although some carry-over effects are irreversible (e.g., maternal effects), those experienced during adulthood may be reversible (e.g., ‘reversible state effects’; Senner et al. 2015). Reversible state effects can carry-over to affect individual fitness during subsequent life-history stages but can be at least partially compensated for over time and need not repeatedly influence an individual’s fitness (‘compensation hypothesis’; Conklin et al. 2013, Senner et al. 2014, Clausen et al. 2015). If individuals cannot compensate, however, they may experience reduced breeding performance. Assessing the magnitude and consequences of seasonal interactions is thus critical for determining the factors influencing individual fitness and population dynamics (Norris and Marra 2007, Harrison et al. 2011). For long-distance migrants, non-breeding season habitat quality, foraging success, and body condition are among those factors that can impact an individual’s future breeding performance. The importance of these factors derives from the fact that successful migration to breeding areas in these species hinges on (i) the accumulation of sufficient energy stores to cover the costs of flight and (ii) appropriate departure and arrival timing that optimize the chances of reproduction. The quality of winter habitat can influence a bird’s physiological stress levels (Marra and Holberton 1998), physical condition (Marra et al. 1998, Strong and Sherry 2001, Studds and Marra 2005), and departure timing for spring migration (Marra et al. 1998, Studds and Marra 2011), with cascading effects on the timing of arrival at breeding grounds and reproductive success (Marra et al. 1998, Norris et al. 2004). For example, Cassin’s Auklets (Ptychoramphus aleuticus) that have a higher proportion of energetically superior copepods (Neocalanus spp.) in their pre-breeding diets breed earlier and lay larger eggs than individuals 187 with high proportions of energetically poor juvenile rockfish (Sebastes spp.; Sorensen et al. 2009). Reversible state effects can thus occur during different stages of the annual cycle and affect an individual’s performance, and ultimately fitness, through a variety of mechanisms. Identifying the seasons, and thus regions, when mortality is comparably high or low is vital to understand population dynamics. Demographic rates are affected by intrinsic (e.g., age, sex, experience) and extrinsic (e.g., habitat quality, food availability, climate) factors, which can directly influence demographic parameters through mortality or indirectly through reversible state effects (Szostek and Becker 2015). Few previous studies, however, have been able to directly connect events occurring across seasons with both variation in individual performance and changes in population-level survival rates. For instance, severe weather on the non-breeding grounds reduced adult survival during both the winter and the following breeding season in Eurasian Oystercatchers (Haematopus ostralegus; Duriez et al. 2012). Yet we remain challenged to understand how selective pressures are acting on a population both within and across seasons. Connecting reversible state effects to demographic survival studies is a necessary step to identify when bottlenecks occur during the annual cycle, as well as their underlying causes, helping to inform conservation actions. To further explore the potential connections between reversible state effects and variation in survival rates across the annual cycle, we studied marked populations of Hudsonian Godwits (Limosa haemastica; hereafter ‘godwits’) during both the non-breeding and breeding seasons. This enables us to identify whether reversible state effects were acting across these time periods and, if so, determine how they influenced seasonal survival rates. Godwits breed in three geographically distinct populations across the Nearctic that each show high connectivity to disjunct non-breeding grounds in the Southern Cone of South America. Further, each of these 188 populations is thought to be declining (Andres et al. 2012, Smith et al. unpubl. data) largely for unknown reasons. We thus aimed to identify potential bottlenecks in the annual cycle of the population of godwits breeding in south-central Alaska and spending the non-breeding season on or near Chiloé Island, Chile. To do this, we first estimated seasonal survival probabilities across the annual cycle using two distinct, marked populations. Second, we examined how non- breeding body condition, habitat quality, and foraging success influenced breeding performance using a marked population that can be followed throughout their annual cycle. Third, we used ptilochronology to examine potential reversible state effects of an individual’s non-breeding season nutritional status on their future breeding performance. Our study used a unique approach to evaluate reversible state effects by corroborating direct observations of the same individuals during two stages of the annual cycle with indirect measures via ptilochronology. We then linked our measures of individual performance with the first seasonal survival analysis for this species to integrate direct and indirect effects of seasonal interactions on population dynamics. Our study improves our understanding of the full annual cycle of long-distance migrants, aiding our ability to conserve these rapidly declining species. Methods: Study Species: We studied a linked, marked population of Hudsonian Godwits that breed in south- central Alaska (Beluga River) and spend the non-breeding season on Chiloé Island in southern Chile (Senner et al. 2014). With annual declines of 3.45% over the last 30 years, the Hudsonian Godwit is among the fastest declining shorebird species breeding in North America (Smith et al. unpubl. data). 189 The non-breeding season is a critical period for godwits and one likely to be associated with reversible state effects. During the 192 days (~27 weeks) godwits spend on the non- breeding grounds (Espinosa et al. 2005, Senner et al. 2014), individuals must recover from their southward migration, undergo two separate molts, and prepare for their northward migration and breeding season (Conklin and Battley 2012). Godwits are typically found foraging in large flocks on tidal mudflats along sheltered coastlines (García-Walther et al. 2017). The connectivity and movements of individual godwits in the Chiloé region are poorly understood, but individuals are known to move among bays in response to disturbances, predators, tides, and weather (Andres et al. 2009). Indeed, color-marked individuals have been resighted at bays separated by as much as 40 km (NR Senner and RJ Swift unpubl. data). Godwits exhibit a cyclical, long-leap migration strategy. Upon leaving the non-breeding grounds, godwits undertake a 10,000 km non-stop flight to the Great Plains of the United States in as little as 6 – 7 days before completing a second non-stop flight to reach their Alaskan breeding grounds. Individuals are highly consistent in the timing of northbound migration across years, regardless of arrival date to the non-breeding grounds (Senner et al. 2014). During southbound migration, individuals stage in the pothole lakes of central Saskatchewan for approximately one month before undertaking several non-stop flights of 3-5 days during southbound migration. Most individuals fly over the Atlantic Ocean to stopover sites in the Amazon River Basin in Colómbia and Brazil and then on to the Buenos Aires Province, Argentina before arriving on the non-breeding grounds. Southward migration is more protracted than northward migration and typically lasts 11 – 12 weeks (Senner et al. 2014). The breeding season clearly impacts fitness and population dynamics for godwits. Godwits arrive to the breeding grounds synchronously and initiate breeding within a week of 190 arrival. Individuals show high fidelity to mates and territories and exhibit biparental care (Walker et al. 2011). Nest survival is high, with >80% of nests successfully hatching, but brood survival can be quite variable (Senner et al. 2017, Swift et al. 2018). Renesting propensity is high (~75%) if nests fail early in incubation (Walker et al. 2011). Godwits nest both within and outside of a protective nesting association with Mew Gulls (Larus canus) in non-habitat based clusters (Swift et al. 2017, Swift et al. 2018). The breeding season is relatively short, from May to early July, and individuals typically spend 10 – 11 weeks on the breeding grounds (Senner et al. 2014). Adults leave the breeding grounds immediately following the completion of the breeding season. Seasonal Survival: Field methods: We studied godwits during two time periods and at two locations during the annual cycle: (i) from 2007 – 2012 during the non-breeding season on Chiloé Island, Chile; and (ii) from 2009 – 2012 and 2014 – 2017 during the breeding season at Beluga River, Alaska (Figure 1). We visited the Chilean site (~42°30’S, 73°45’W) for a single two-week period annually in December or January. We visited one tidal mudflat, Pullao, daily to resight marked individuals. In addition, we surveyed several nearby tidal mudflats in the Castro region on Chiloé Island as time allowed and when flocks were present (e.g., Putemún, Rilán, Curaco de Vélez, Chúllec, Teguel, and Ten- Ten). During the breeding season, we monitored godwits within an ~8 km2 area at Beluga River, Alaska (61.21°N, 151.03°W) between 1 May and mid-July. We resighted godwits at their nest, on nearby tidal mudflats, or during the brood rearing period within the bogs. Physical recaptures of incubating individuals were also added to the resighting data. In 2017, we only conducted a shortened field season for resightings from 9 – 19 May. 191 Survival estimates were based on resightings of marked individuals. Returning birds were rarely missed in our surveys on the breeding grounds, but movements of individuals among tidal mudflats on the non-breeding grounds reduced resighting probabilities. Individually marked godwits breeding at our Alaska site are regularly resighted on Chiloé Island, but individuals marked at our Chile site are only rarely seen on the breeding grounds. Each year from 2007 – 2011, we made an effort to capture additional godwits in Chile using cannon nests to add to the marked population. Unmarked individuals on the breeding grounds were captured with a mist net while incubating their nests (see Chapter 3 for more information). In total, our Chilean dataset consisted of 773 marked birds, and the Alaskan dataset 118 marked adults. All individuals were marked with a uniquely coded alpha-numeric flag and metal band from the US Geological Survey (Alaska) or Chilean Bird Ringing Office (Chile). Data analysis: Survival (φ) and recapture (p) probabilities were modeled annually as well as within each stationary season. Sampling in Chile occurred during a two-week period in either December or January. To account for movements of individuals among mudflats and the imperfect detection of individuals within large flocks, we aggregated observations of individuals into three-to-four day windows across the study period. We then modeled encounters among these windows and with an additional resighting period in the following non-breeding season for the 2009 – 2010 and 2010 – 2011 non-breeding seasons. In Alaska, godwits were systematically resighted every 1 – 7 days from early-May to mid-July. We broke the breeding season down into one-week intervals from 1 May until the earliest recorded egg hatch date in our study (4 June; five weeks). 192 We combined all resightings post-incubation into one additional sampling period to account for individuals departing the breeding grounds immediately following nest or brood failure. Sets of candidate models were chosen prior to data analysis based on our knowledge of godwit biology and model goodness-of-fit (Burnham and Anderson 2002). For annual survival models, the base model for each model set included all time and group variables hypothesized to affect φ and p. Fit of global models was verified in program U-CARE (Choquet et al. 2009), as well as program RELEASE (Burnham et al. 1987) implemented in program RMark (Laake 2013). If models failed to meet goodness-of-fit criteria, we calculated the median c-hat value and adjusted our results table accordingly using QAICC values. Time and group variables used in candidate models are described below. Model notation follows Lebreton et al. (1992). In annual analyses, φ and p were modeled as either constant over time, as a function of year or sex, or as an interactive effect of year and sex. In seasonal analyses, φ and p were modeled as either constant over time, or as a function of time (e.g., different φ time for each encounter period) or sex. We did not model the interactive effect between sex and time within a season due to poor model fit. To determine each individual’s sex on the non-breeding grounds, we used a linear discriminant function analysis on the length of the tarsus (mm) and culmen (mm) of 228 captures of known-sex individuals from the breeding grounds. We conservatively placed 137 individuals captured in Chile (17.6%) into an ‘unknown sex’ category based on their measurements. Model selection methods based on Akaike’s information criterion corrected for small sample sizes (AICC; Burnham and Anderson 2002) were used to (i) provide the best estimates of annual and within season φ for godwits; and (ii) assess the statistical evidence for time- and sex- related differences in φ. Models in each candidate set were ranked by dAICC (or QAICC) 193 differences (Burnham and Anderson 2002). Program MARK’s model averaging procedure was used to compute the average estimates for φ from all models selected. Model averaging is based on model weights for each model and thus includes model selection uncertainty in the estimate of each parameter and its associated variance (Burnham and Anderson 2002). Annual survival probability is the product of survival probabilities during the stationary and migratory periods of the annual cycle, i.e., φannual = φnon-breeding* φbreeding * φmigration. We considered φannual to be survival from January to January as measured in Chile or from May to May as measured in Alaska, φbreeding from May to July in Alaska (11 weeks), and φnon-breeding from October to March in Chile (27 weeks). This equation allowed us to use within-season estimates of φ from Alaska and Chile to estimate survival during the migratory periods as φmigration: (φannual / φ(non-breeding * breeding) ). Because our annual survival estimates differed between our breeding and non-breeding season analyses (see Results), we present a range for our estimate of φmigration. Because our data were not amenable to calculating robust, year-specific estimates of φmigration, we did not compute survival probabilities separately for northbound and southbound migration. Survival probability during the migratory period was broken down to weekly estimates and then calculated for the length of the northbound and southbound migration (3 and 11 weeks, respectively) to enable a direct comparison of survivorship among periods. Reversible state effects: Observational study: Field Methods – non-breeding season: We attempted to survey all known and accessible tidal mudflats based on published distributions, eBird records, and prior knowledge of the occurrence of flocks of foraging godwits 194 for marked individuals from our breeding population (2015: n = 39 surveys at 21 tidal mudflats; 2016: n = 147 surveys at 42 tidal mudflats). Surveys occurred from 3 – 16 January 2015 and 1 January – 8 March 2016. During a survey, each individual present at a site was checked for leg flags from our marked breeding population and, if marked, identified to individual by their unique alpha-numeric flag code or color combination. We visually assessed two measures of body condition for each marked individual: body molt scores (BMS) and abdominal profile indices (API). The BMS is an index between zero and four (with 0.5 increments) based on the amount of alternate plumage present on an individual (e.g., Piersma and Jukema 1993). As body feathers represent up to 75% of total feather mass (Battley and Piersma 1997, 2005) and replacement of these feathers implies a significant metabolic cost associated with feather production and thermoregulation (Klaassen 1995), body molt scores were used as an indicator of individual condition (e.g., Lourenço and Piersma 2015). API is a measure of condition based on the shape of the abdomen and is correlated with actual fat mass in shorebirds (Wiersma and Piersma 1995). Overall flock BMS and API were collected between one and four times per survey (depending on the flock size and the length of the survey) on a total of 1 – 76 individuals (mean = 24 individuals, SD = 14.5) in the flock the marked individual was foraging with. All raw scores were converted to the difference between the average BMS and API of the flock and the marked individual. The residuals from separate regressions of average BMS and API with Julian date were used in analyses as an indicator of condition while controlling for continuous molting and pre-migratory fattening. Lastly, the residuals from a regression of BMS and API were also included as an indicator of condition. For each marked godwit, we conducted focal foraging observations (n = 87). Using a voice recorder, we dictated observations of godwit behaviors over a five-minute period and later 195 transcribed recordings using CowLog (Pastell 2016). Because not all focal observations lasted for the full five minutes (e.g., an individual flew out of sight, reshuffled into the foraging flock such that we lost it, or began roosting), we converted the metrics to the number-per-minute-of- observation and eliminated observations that included less than one minute of foraging behaviors. We recorded every behavior of the marked individual, including the number of probes made and the number of prey items captured and consumed (swallowed). We then calculated the number of swallows per minute, the success rate of the focal observation, and the success rate per minute. We modeled our methodology after the only other non-breeding foraging study of Hudsonian Godwits (Senner and Coddington 2011). We defined a foraging probe as occurring when at least half of an individual’s bill was placed in the mud. Godwits frequently probe the mud in rapid succession without removing their bill; in these circumstances, we counted each movement as a separate probe if the bill was lifted one-third of the way out of the mud (Senner and Coddington 2011). We considered a bird to have obtained a prey item when we discerned a swallowing motion or saw an item in its bill. While relatively large and conspicuous Polychaete worms are their primary prey (Ieno et al. 2000), godwits also feed on small items, such as fly larvae (Ribeiro et al. 2004, Senner and Coddington 2011, Walker et al. 2011). Such smaller food items can be consumed without removing the entirety of an individual’s bill from the mud and would not have been counted in our swallow or success rate estimates. Consequently, our estimates of foraging success are conservative. During each survey, we also collected data on predation risk, foraging success, alertness and agitation, human disturbances, land-use of the bay, and foraging substrate availability. These metrics were used to assess patch quality, indicated by flock body condition and godwit density, 196 in a separate path analysis (see Chapter 4). The scores for flock-averaged body condition and godwit density from the survey associated with our focal individual were used as indicators of patch quality. For each individual, we averaged each measure across multiple encounters (range: 1 to 7) to derive a single estimation for each variable. Field Methods – breeding season: We monitored breeding godwits in 2015 and 2016 in Beluga River, Alaska. Our study area was divided into two study plots of uninterrupted muskeg bog – North (550 ha) and South (120 ha) – that were separated by ~7 km of unmonitored boreal forest and muskeg bog. Both the adjacent tidal mudflats and study plots were surveyed daily for the first two weeks of May to resight returning individuals. Following this period, we systematically searched plots for nests every two-to-three days throughout the nesting season (May – July). We searched for nests using a combination of prior knowledge, systematic searching, and behavioral observations. Upon discovery of a nest, we recorded a GPS location and floated eggs to estimate the timing of nest initiation, and hence, age of the nest (Liebezeit et al. 2007). We did not physically mark nest locations to minimize the chance of associative learning by predator species (Reynolds 1985). We revisited nests every two-to-three days until either one day prior to the expected hatch or until we observed starred or pipped eggs. Adults were rarely flushed from nests, which were typically checked for incubating birds from 20-30 m away, in an effort to minimize disturbances that might increase the probability of nest failure. Field teams never approached nests directly when predators were observed nearby. A nest was considered successful if ≥1 egg hatched and chicks successfully left 197 the nest site. Nest failure was presumed when we found empty nests early in the incubation period or destroyed eggs. Due to low rates of nest abandonment in this system (Senner et al. 2017), we considered the failure rate of nests in our study to represent the depredation rate as well. We then radio-tracked a subset of godwit chicks from successfully hatching nests to assess brood survival. We randomly selected one or two chicks from each brood to receive a small 0.62 g Holohill radio. We clipped the downy feathers from a small area on each chick’s back and attached radios above the uropygial gland with cyanoacrylate glue. We deployed up to 20 radios each year, and each chick was located every two-to-three days until the chick had died or fledged. Additionally, we surveyed each plot every two-to-three days for any adult godwit exhibiting defensive behaviors (e.g., perched on a tree, alarm-calling, distraction displays). From this, we determined if at least one chick per brood survived to 20-days-old (yes/no; when radio batteries typically failed), the maximum number of days the brood survived, and the last date an adult was seen defending its brood. Data Analysis: Relationships among foraging success, body condition, patch quality, and the breeding performance of godwits were modeled with partial least squares path modeling (PLS-PM). PLS- PM is a type of path analysis, which is a multivariate technique used to explore multiple relationships between blocks of variables and to quantify their respective weights (Lleras 2005, Tenenhaus et al. 2005). This statistical method has only recently been applied to ecological datasets (e.g., Puech et al. 2015), but PLS-PM was selected over covariance-based structural equation modeling approaches primarily because it does not require a large dataset to perform 198 optimally and because it produces values for each latent variable (Chin and Newsted 1999, Chin 2010). PLS-PM consists of two sub-models called the inner and the outer model (Sanchez 2013). The outer model describes relationships between a set of observed variables (‘manifest variables’) and a synthetic ‘latent variable’ that is built from these manifest variables. A latent variable cannot be measured directly and is representative of a concept (e.g., habitat quality or microclimate). For example, the manifest variables 1) ‘swallows per minute’, 2) ‘success rate of focal observation’, and 3) ‘success rate per minute’ were used to approximate the latent variable ‘foraging success’. The group formed by a latent variable and its associated manifest variable(s) is called a block. The inner model describes relationships between latent variables, and relationships are treated as linear regressions. A fitted PLS-PM produces standardized path coefficients for all paths (i.e., direct and indirect effects) that usually range between 0 and ±1. These path coefficients are equivalent to standardized regression coefficients but have the advantage of specifying whether the relationship between latent variables has a positive or negative slope. Our PLS-PM contained four latent variables (Figures 2 and 3). In the preliminary PLS- PM, all potential manifest variables were included when constructing latent variables. However, before obtaining the final model, we made a set of verifications and transformations, as advised by Sanchez (2013). First, we checked the unidimensionality of each reflective block with Cronbach’s alpha and Dillon–Goldstein’s rho (Table I). We changed the sign of variables having negative weights to only integrate positively correlated variables in the same block. Then, we examined the loadings – the correlations between a latent variable and its manifest variables (Table II). A manifest variable was only retained if 50% of the variability in the manifest 199 variable (i.e., factor loading > 0.7) was captured by the latent variable (Sanchez 2013). We retained some individual variables that met unidimensionality but had loadings < 0.7, which we acknowledged as an acceptable trade-off between model quality and meaningfulness. Cross- loadings allowed us to verify if the shared variance within a block was larger than with other blocks and were assessed similarly. Finally, the overall robustness of models was evaluated with the coefficient-of-determination (R2) and goodness-of-fit (GoF) criterions with a bootstrapping procedure (n = 199). Ninety-five percent confidence intervals that did not encompass zero were considered to imply statistical significance. For PLS-PM, R2 values for inner models are classified in three categories: low: R2 < 0.3, moderate: 0.3 < R2 < 0.6, and high: R2 > 0.6 (Sanchez 2013). The GoF measure assesses the overall predictive performance of both the inner and outer model (Sanchez 2013). Analyses were conducted using the R 3.4.3 software (R Core Development Team 2018) with the ‘plspm’ package (Sanchez et al. 2017). Ptilochronology study: Ptilochronology, or the use of feather growth bars as an index of nutritional condition, has been used extensively since Grubb (1989) first introduced the concept. Feathers have a series of light and dark bands oriented obliquely to the rachis. Each light and dark band taken together (one growth bar) represents 24 hours of growth, and evidence suggests a direct relationship between width of these bars and nutritional condition (Grubb 1989, 1991, Grubb and Cimprich 1990, Grubb and Yosef 1994). When we captured an individual during the breeding season, we collected the two outermost rectrices to analyze their growth bars (n = 128 feathers from 64 individuals). Because godwit tail feathers are bicolored (black and white), we restricted our ptilochronological 200 measures to the black portion (farthest from the quill) where growth bars were more easily identified. However, on four feathers, no distinct growth bars could be distinguished. Each feather was scanned into Adobe Lightroom (CS6) and processed until growth bars were visible. Each scan was saved as a jpeg of a standardized size (5 x 4 inches) and number of pixels. The first five growth bars were marked and measured in Adobe Photoshop (CS6) using the ‘ruler tool’ in number of pixels. We then calculated the mean width of the first five growth bars. Each feather was marked and measured by two observers, and their average measure was used in analyses. We used generalized linear mixed models with a logistic or poisson regression to examine the influence of non-breeding nutritional condition on nest fate, chick fate, the maximum number of days a chick survived, nest initiation date, and the last date seen defending brood, with individual and year as random effects. We evaluated growth bars in a univariate model against a null model using AICC scores for each response variable separately (Burnham and Anderson 2002) with the ‘lme4’ and ‘bbmle’ R packages (Bates et al. 2015, Bolker 2017). Lastly, we ran a repeatability analysis on individual growth bar width among years using the rptR package (Stoffel et al. 2017) in program R with 1,000 bootstrap iterations. One unique aspect of our dataset is that we have direct observations of individuals foraging on the non-breeding grounds for which we also pulled rectrices and indirectly measured nutritional status through feather growth bars. For such linked observations of non-breeding condition, when we have foraging observations and growth bar measures for the same non- breeding season, we also ran a Pearson’s correlation matrix to understand the links between our focal foraging observations and growth bar measures. 201 Results: Seasonal Survival: Model selection (Tables III and IV) and estimates of annual survival and resighting probabilities (Table V) differed for the Alaskan and Chilean datasets. Males had a higher annual survival probability than females and model-averaged estimates indicated that survival probabilities were higher in Chile than Alaska. Survival estimates varied among years in the Alaskan dataset, with a noticeable reduction in survival between 2012 and 2015 (Figure 4). In general, resighting probabilities varied among years and were higher in Alaska for both sexes as compared to Chile (Figure 5). AICC ranking of within-season CJS models differed between the Alaskan and Chilean data sets (Tables VI, VII, and VIII). Both analyses, however, clearly indicated that weekly and period survival was high during the stationary breeding and non-breeding periods (Table IX). Males had higher estimated survivorship than females during both stationary periods (Table IX). Resighting probabilities varied across the stationary periods (Table IX). We estimated weekly survival probability during migration to be 0.986 – 0.993 depending on the annual survival estimate used. Thus, both estimated weekly and period survival rates during migration were lower than during the stationary non-breeding period, but may be higher than during the breeding period, particularly during the short northbound migration (Table X, Figure 6). Reversible state effects: Observational study: From our marked breeding population of ~20 – 30 breeding pairs per year (n = 118 202 banded adults; n = 496 total banded individuals including chicks), we saw 30 individuals on the non-breeding grounds in 2016 and six individuals in 2015. Eleven individuals were only seen in roosting flocks or were banded as hatch-year chicks and have not been seen on the breeding grounds in subsequent years. Individuals were resighted on anywhere from one to five days in 2016. The PLS-PM quantified the relative importance of each pathway on future breeding performance of godwits. Based on the fitted PLS-PM (GoF = 0.58), two of our predictors – patch quality and body condition – directly positively affected return rates and breeding performance (Figure 7, Table XI). Both of these latent variables explained ~90% of the total effects on breeding performance (Table XII). Godwits using higher quality tidal mudflats, as indicated by overall flock density and body condition, had improved foraging success and, ultimately, return rates and breeding performance (Figure 7, Table XII). Successful foragers on the non-breeding grounds were in better condition, and individuals in better condition had better breeding performance (Figure 7, Table XII). In fact, individuals using high quality patches had 47% higher foraging success scores, 57% higher condition scores, and had 77% higher scores for breeding performance. Interestingly, four of the five individuals that did not return to Alaska used lower than average patches, and all five individuals were in poorer than average condition. Ptilochronology study: Of the 64 individuals for which we measured growth bars (n = 12, 28, and 24 individuals in 2014, 2015, and 2016 respectively), only two individuals were captured in all three years, and 18 individuals were captured in two years. Growth bar widths within individuals varied among 203 years (R = 0.09, CI 0, 0.49; p-value = 0.344), indicating changes in nutritional condition among years for an individual. We found weak evidence that individuals with larger growth bars nested earlier in the year (β = -0.006, CI -0.02, 0.01; Table XIII), had chicks that were more likely to survive to 20- days-old (β = 0.56, CI -0.82, 2.09; Table XIII), hatched broods that were more likely to survive (β = 0.05, CI -0.02, 0.12; Table XIII), and defended broods for longer periods (β = 0.003, CI - 0.01, 0.02; Table XIII). We failed to detect a relationship between growth bar width and nest fate, perhaps in part because we had few nest failures represented in our dataset (i.e., 4 of 62 nests). Only 26% (16 of 62) of individuals had broods survive to 20-days-old. Of the 15 individuals for which we had foraging observations and growth bar measures for the same non-breeding season, we found a strong relationship between growth bar width and API corrected for date of observation (r2 = 0.76, Table XIV) but not BMS (r2 = 0.31, Table XIV). Growth bar widths were also related to foraging success: the success rate of the observation (r2 = 0.51, Table XIV), the success rate per minute (r2 = 0.50, Table XIV), and the number of swallows per minute (r2 = 0.50, Table XIV). Discussion: Our study is a rare example of how individual performance metrics and seasonal survival estimates can be integrated across the annual cycle for a long-distance migratory species. Adult survival was highest during the stationary non-breeding season but was slightly lower during migration and the breeding season, with the breeding season and southbound migration accounting for the largest proportion of mortality events because of their longer duration and lower survival rates. Furthermore, breeding performance of Hudsonian Godwits was positively 204 associated with non-breeding season factors, with individuals in better body condition or those that spent time at high quality mudflats during the non-breeding season siring chicks that survived longer. Finally, we found within-individual variation in feather growth bars among years, indicating that conditions on the non-breeding grounds may induce reversible state effects that influence an individual’s future breeding performance. The non-breeding season thus likely is a critical period that allows individuals to prepare for future stages of the annual cycle with potentially cascading effects. Body condition on the non-breeding grounds was positively related to an individual’s future breeding performance. Body condition during the non-breeding season may be influenced by habitat quality, prey availability, predation risk, or diet quality (West et al. 2002, Duijns et al. 2009, Sorensen et al. 2009) and is likely critical to the fitness of migratory birds (Bêty et al. 2003, Battley et al. 2004, Bearhop et al. 2004). Both our observational and ptilochronology data corroborated the relationship between body condition during the non-breeding season and future breeding performance. Consistent with previous findings (Chapter 4), our data suggest that foraging success rates largely drove non-breeding season body condition. Low rates of energy intake prior to migration and/or breeding can adversely affect reproduction (Ebbinge and Spaans 1995, Gill et al. 2001, Prop et al. 2003). Pre-migratory fueling substantially increases daily energy needs and may require extending periods of foraging and/or increasing the rate of energy intake (Blem 1980, Duijns et al. 2009). Moreover, patch quality affected foraging success directly and body condition indirectly. Intertidal mudflats may vary in prey availability or capture efficiencies, which then affects an individual’s foraging success and ultimately body condition. As such, the ability of birds to sufficiently refuel prior to migration may reflect differences in patch quality and foraging success among tidal mudflats. 205 We also found evidence for important links between habitat quality in the non-breeding season and an individual’s subsequent reproductive success. The quality of mudflats, as indicated by flock condition and density was a function of the availability of foraging habitat, foraging success (likely a proxy for prey availability), and number of disturbances. Mudflat quality was, in turn, positively associated with return rates to the breeding grounds and reproductive success. As such, our measure of patch quality supports the idea that the reversible state effects we documented were not driven exclusively by intrinsic variation among individuals, but also by habitat conditions that presumably affected entire flocks. As described in Chapter 4, mudflats in the Chiloé region vary widely in terms of both the type and intensity of human disturbances, which reduce foraging time and/or increase energy expenditure (through displacement flights) in ways that appear to reduce individual condition. An important caveat of our work is that we could not perfectly determine the quality of all habitats used by an individual during the non-breeding season because individuals likely used multiple bays and mudflats. Although the degree to which godwits move among tidal mudflats is unknown, our observations suggest that it is relatively common. Of the 20 individuals we saw multiple times in 2016, only five individuals were seen in a single location. The remaining 15 individuals were observed at two to three different sites that were separated by distances ranging from 3 – 40 km (µ = 14.9, sd = 14.4). That said, most individuals were restricted to sub-regions within the greater Chiloé Island area – restricting their movements to either the northern coastline (e.g., Quetalmahue and Caulín) or the Castro region (e.g., Pullao, Chúllec, Nercón). Whether our estimates of habitat quality are representative of the entire non-breeding season is unknown and requires further detailed study into the small-scale movements of individuals. 206 Although we determined that survival was highest during the non-breeding stationary period, we were unable to precisely differentiate survival rates between breeding and migratory periods. Migration is often considered to be the most taxing part of the migratory annual cycle due to the large distances moved, threats faced (e.g., severe weather), and unfamiliarity of stopover locations. For many species, migration is the period with lowest survival (Sillett and Holmes 2002, Klaassen et al. 2014, Lok et al. 2015, Rockwell et al. 2017). However, work with extreme long-distance birds has shown a pattern of higher survival during migration than other periods of the annual cycle (Leyrer et al. 2013, Rakhimberdiev et al. 2015, Senner et al. in review). For instance, Bar-tailed (Limosa lapponica) and Hudsonian Godwits both demonstrate high adult survival, little evidence for elevated migration mortality, no apparent minimization of non-stop flight distances, and low inter- and intra-individual variation in migratory performance (Conklin et al. 2017). Our results support this pattern, with high survival rates observed throughout the annual cycle and seasonal survival potentially being the lowest during the breeding season. High breeding season mortality is likely a combination of high predation risk compared to the rest of the annual cycle, as well as high energetic demands (Drent and Daan 1980). Interestingly, females had lower survival estimates in all of our analyses potentially due to the increased costs of egg-laying and diurnal incubation. In fact, all of the known adult mortalities documented in seven years of monitoring breeding godwits were incubating females. Although individuals may be able to compensate for this risk by choosing to nest within a protective nesting association (Swift et al. 2018, Chapter 3), the relatively low annual and breeding season survival estimates for females indicate that reproduction may be costly for godwits. 207 Our data suggests that individuals in better body condition during the previous non- breeding season had subsequently improved breeding performance. Individuals observed on the non-breeding grounds in better body condition were more likely to return to Alaska and had better nest and brood survival. Similarly, individuals in better nutritional condition during the previous non-breeding season had higher chick survival. However, on the breeding grounds godwits nest within and outside of a protective nesting association with Mew Gulls, where individuals within gull colonies have improved nest survival but reduced brood survival (Swift et al. 2018). Of the 25 individuals tracked in our observation based study, 16 nested within the gull colony (64%) which is lower than the average amount of the population that nest within the gull colony (73%). Therefore, our observational study is representative of the entire breeding population nesting within and outside of the Mew Gull colony. Contrary to our detection of reversible state effects on breeding performance mediated through body condition, Senner et al. (2014) found that returning godwits that migrated later than the population mean during one portion of their annual cycle did not continue to migrate later than the population mean for the entirety of their annual cycle, nor did individual’s suffer reduced breeding success or survival as a result of delayed arrivals at breeding sites. Our detection of reversible state effects in Hudsonian Godwits differs from previous work on this breeding population and may be explained by two non-mutually exclusive hypotheses. First, the current study focuses on body condition rather than the timing of migration, the latter of which may be more consistent within an individual than condition (Prop et al. 2003). As such, the two apparently contrasting results may be co-occurring. Alternatively, the detection of condition- mediated reversible state effects may indicate a shift in the resources or threats facing godwits between the previous study (2009 – 2012) and the current one (2015 – 2016). Therefore, though 208 timing of events during the annual cycle may show no effects on survival or future breeding performance of godwits, individuals in better body condition during the non-breeding season have improved return rates and subsequent breeding performance. Links between non-breeding ecology and subsequent breeding performance may reflect reversible state effects from the non-breeding to breeding season or individuals of differing quality. Costs of migration may disproportionately affect some individuals based on their relative condition or quality (Senner et al. 2015, Conklin et al. 2017). The apparent absence of a strong influence of timing delays on reproductive success suggests that strong selection has constrained the timing of migration and similar patterns are found in other extreme long distance migrants (Conklin and Battley 2012, Senner et al. 2014, Conklin et al. 2017). Individuals may continue to migrate at the same time as the rest of the population, especially on northbound migration, even if they are in poorer body condition (e.g., Ebbinge and Spaans 1995, Prop et al. 2003). As such, the positive reversible state effect we detected might reflect differences in individual quality with some individuals failing to meet a threshold to maintain their annual cycle strategy effectively. Though this possibility cannot be eliminated without repeat observations of individuals across years in our observational study, the annual variation in growth bars across our three-year dataset suggests that certain individuals may not be inherently of higher quality and that nutritional condition is in fact a reversible trait. Moreover, the apparent influence of habitat quality, as indicated by the qualities of the entire foraging flock, provides additional evidence that conditions on the non-breeding grounds may affect individual body condition beyond intrinsic or genetic effects. In long-distance migrants, variation in individual quality may result in differences that accrue across the annual cycle, but changes in individual condition among years 209 suggests that condition may indirectly influence future reproductive success through reversible state effects. Our data suggest that godwit annual survival has been declining across a period of several years in the Alaskan breeding population, suggesting a regime shift in either resource availability or the occurrence of the anthropogenic threats. Although the cause of this decline is unknown, our data corroborates steep declines observed in all three breeding populations at stopover sites during southward migration (Smith et al. unpubl. data). This decline in survival and population size coincides with the period during which we were able to simultaneously monitor godwits during both the non-breeding and breeding seasons and during which we found evidence of reversible state effects. Thus, one potential reason that previous studies in this system (e.g., Senner et al. 2014) had not detected reversible state effects, but our current study did, is that conditions have changed such that godwits now face a bottleneck either during the non-breeding season or northward migration. In this way, the correlation between non-breeding season body condition and future breeding performance may suggest that more recent within-season survival estimates on the non-breeding grounds may have declined from our estimates. Thus, the reversible state effects detected could instead indicate a continued narrowing of the individual quality spectrum that can continue to maintain such an extreme migration strategy or the degradation of resources necessary for individuals to do so. Our study provides evidence for positive reversible state effects of non-breeding season body condition on Hudsonian Godwits future breeding performance. The mudflats used throughout the non-breeding season also positively influenced breeding performance, and an individual’s foraging success improved their body condition. Further, body condition, measured both directly and indirectly, strongly influenced future chick survival. The reversible state effect 210 detected aligned with a period of low annual survival, as measured on the breeding grounds, and may indicate that godwits now face a bottleneck either during the non-breeding season or northward migration. Using this rare dataset of individually-linked observations of cross-season performance and demography enabled us to identify that reversible state effects were acting across non-breeding to breeding seasons and that they influenced variation in seasonal survival rates. Further studies of long-distance migratory species should continue to connect measures of individual performance (i.e., reversible state effects) to demographic survival studies so as to enable us to identify when bottlenecks occur during the annual cycle, as well as their underlying causes, and helping to inform conservation actions. Acknowledgments: We thank D. Barria, H. Batcheller, S.M. Billerman, R. Christensen, A.P. Contreras, B. Davis, L. DeCicco, A. Dey, F. Diaz, L.A. Espinosa, L. Fried, R. Galvan, S. Gates, J. Heseltine, W. Holman, G. Huenun, A.S. Johnson, T.B. Johnson, S.J. Kendall, J. Klarevas-Irby, B. Lagasse, M. Lambany, J. F. Lamarre, L. Niles, G. MacDonald, M. McConnell, K. Parkinson, M.K. Peck, M. Schvetz, H. Sitters, K. Smith, A. Spaulding-Astudillo, and A. Wells. Many thanks to Rodrigo Vasquez for logistical support as well as the Conservation Science and Bird Population Studies lab groups, which provided input and advice on data collection. Funding was provided by the David and Lucile Packard Foundation, U.S. Fish and Wildlife Service, Faucett Family Foundation, National Science Foundation (#1110444 and DGE-1144153), Graduate Research Opportunities Worldwide program, Cornell Lab of Ornithology, Cornell University, the Athena Fund at the Cornell Lab of Ornithology, American Ornithologists’ Union, and Arctic Audubon Society. All procedures performed in this study involving animals were in accordance with the 211 ethical standards of Cornell University and as part of an approved animal use and care protocol. The authors declare that they have no conflict of interest. 212 REFERENCES Andres, B. A., J. A. Johnson, J. Valenzuela, R. I. G. Morrison, L. A. Espinosa, and R. K. 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Lastly, if a block is unidimensional, the first eigenvalue should be “much more” larger than 1, whereas the second eigenvalue should be smaller than 1. Cronbach’s Dillon- st nd alpha Goldstein’s 1 2 rho Eigenvalue Eigenvalue Foraging Success 0.95 0.97 2.73 0.25 Body Condition 0.86 0.91 2.85 0.69 Patch Quality 0.77 0.90 1.62 0.38 Breeding Performance 0.82 0.88 2.60 0.92 220 Table II. Outer model output of the fitted partial least squares path model. Weight indicates the weighting used in the outer model. Loadings are the correlations between a latent variable and its indicators. Communalities are the squared loading values and indicate the amount of variability explained by a latent variable. weight loading communality Foraging Success Success rate 0.36 0.98 0.97 Swallows per min 0.32 0.91 0.82 Success rate per min 0.37 0.97 0.94 Body Condition Condition residuals 0.48 0.93 0.87 Body molt score 0.29 0.76 0.57 Abdominal profile index 0.38 0.86 0.73 Patch Quality Density scores 0.57 0.91 0.82 Condition scores 0.54 0.89 0.80 Breeding Performance Returned 0.55 0.89 0.79 Nest fate 0.32 0.88 0.77 Chick fate 0.08 0.53 0.28 Maximum chick days 0.25 0.76 0.58 221 Table III. Summary of competing models evaluating Hudsonian Godwit (Limosa haemastica) annual survival on the breeding grounds at Beluga River, Alaska. Models are ranked by ascending dAICC values with the number of parameters (k), and Akaike weights for all adult godwits banded and resighted from 2009 – 2017. Model dAICC k weight Phi(year) p(constant) 0.00 8 0.31 Phi(constant) p(year) 0.06 8 0.30 Phi(sex) p(year) 0.90 9 0.20 Phi(year) p(sex) 2.04 9 0.11 Phi(year) p(year) 4.04 14 0.04 Phi(year * sex) p(constant) 5.60 15 0.02 Phi(year * sex) p(sex) 7.64 16 0.01 Phi(constant) p(constant) 9.26 2 0.00 Phi(sex) p(constant) 9.67 3 0.00 Phi(year * sex) p(year) 10.27 21 0.00 Phi(constant) p(sex) 11.29 3 0.00 Phi(sex) p(sex) 11.65 4 0.00 Phi(constant) p(year * sex) 12.96 15 0.00 Phi(sex) p(year * sex) 14.27 16 0.00 Phi(year) p(year * sex) 17.23 21 0.00 Phi(year * sex) p(year * sex) 25.34 28 0.00 222 Table IV. Summary of competing models evaluating Hudsonian Godwit (Limosa haemastica) annual survival on the non-breeding grounds near Chiloé Island, Chile. Models are ranked by ascending adjusted dQAICC values with the number of parameters (k), and Akaike weights for all adult godwits banded and resighted from 2007 – 2012. Model dQAICC k weight Phi(constant) p(year) 0.00 6 0.66 Phi(sex) p(year) 1.56 7 0.30 Phi(year) p(year) 6.56 10 0.02 Phi(constant) p(year * sex) 9.16 11 0.01 Phi(sex) p(year * sex) 11.10 12 0.00 Phi(year * sex) p(year) 11.74 15 0.00 Phi(year) p(year * sex) 15.80 15 0.00 Phi(year * sex) p(year * sex) 21.09 20 0.00 Phi(year) p(constant) 74.43 6 0.00 Phi(year) p(sex) 76.26 7 0.00 Phi(year * sex) p(constant) 79.33 11 0.00 Phi(year * sex) p(sex) 81.35 12 0.00 Phi(constant) p(constant) 112.27 2 0.00 Phi(sex) p(constant) 114.24 3 0.00 Phi(constant) p(sex) 114.25 3 0.00 Phi(sex) p(sex) 116.24 4 0.00 223 Table V. Model averaged estimates of annual survival (φ) and resighting (p) probabilities and 95% confidence intervals (CI) for Hudsonian Godwits (Limosa haemastica) from Beluga River, Alaska, USA (2009 – 2017) and Chiloé Island, Chile (2007 – 2012). Location φ (95% CI) p (95% CI) Alaska 0.740 (0.582, 0.855) 0.953 (0.740, 0.995) Male 0.747 (0.587, 0.860) - - Female 0.734 (0.577, 0.849) - - C hile 0.8 21 (0.783, 0.853) 0.5 37 (0.450, 0.620) Male 0.824 (0.786, 0.856) - - Female 0.818 (0.780, 0.853) - - 224 Table VI. Summary of competing models evaluating Hudsonian Godwit (Limosa haemastica) within season survival on the breeding grounds in Beluga River, Alaska. Models are ranked by ascending dAICC values with the number of parameters (k), and Akaike weights for all adult godwits seen during each breeding season from 2009 – 2016. Model dAICC k weight Phi(sex) p(time) 0.00 7 0.62 Phi(constant) p(time) 1.36 6 0.31 Phi(time) p(time) 4.41 10 0.07 Phi(time) p(constant) 54.39 6 0.00 Phi(time) p(sex) 55.09 7 0.00 Phi(sex) p(sex) 66.42 4 0.00 Phi(sex) p(constant) 66.93 3 0.00 Phi(constant) p(constant) 70.36 2 0.00 Phi(constant) p(sex) 71.32 3 0.00 225 Table VII. Summary of competing models evaluating Hudsonian Godwit (Limosa haemastica) within season survival on the non-breeding grounds near Chiloé Island, Chile. Models are ranked by ascending adjusted dAICC values with the number of parameters (k), and Akaike weights for all adult godwits seen during the 2009 – 2010 non-breeding season. Model dAICC k weight Phi(sex) p(time) 0.00 5 0.52 Phi(constant) p(time) 0.35 4 0.43 Phi(time) p(time) 4.73 6 0.05 Phi(constant) p(sex) 27.81 3 0.00 Phi(constant) p(constant) 29.33 2 0.00 Phi(time) p(sex) 29.59 5 0.00 Phi(sex) p(sex) 29.87 4 0.00 Phi(sex) p(constant) 31.37 3 0.00 Phi(time) p(constant) 33.44 4 0.00 226 Table VIII. Summary of competing models evaluating Hudsonian Godwit (Limosa haemastica) within season survival on the non-breeding grounds near Chiloé Island, Chile. Models are ranked by ascending adjusted dQAICC values with the number of parameters (k), and Akaike weights for all adult godwits seen during the 2010 – 2011 non-breeding season. Model dQAICC k weight Phi(constant) p(sex) 0.00 3 0.48 Phi(sex) p(sex) 1.06 4 0.28 Phi(constant) p(constant) 3.09 2 0.10 Phi(time) p(sex) 4.08 5 0.06 Phi(sex) p(constant) 5.11 3 0.03 Phi(constant) p(time) 6.85 4 0.01 Phi(time) p(constant) 7.15 4 0.01 Phi(time) p(time) 9.01 6 0.00 Phi(sex) p(time) - - - 227 Table VIX: Model averaged estimates of within season survival (φ) and resighting (p) probabilities and 95% confidence intervals (CI) for Hudsonian Godwits (Limosa haemastica) from Beluga River, Alaska, USA (2009 – 2016) and Chiloé Island, Chile (2010 – 2011). Location φ (95% CI) p (95% CI) Alaska 0.960 (0.929, 0.978) 0.778 (0.678, 0.838) Male 0.969 (0.936, 0.986) - - Female 0.951 (0.921, 0.970) - - C hile 0.9 99 (0.998, 0.999) 0.7 02 (0.635, 0.764) Male 0.999 (0.998, 0.999) - - Female 0.999 (0.998, 0.999) - - 228 Table X. Estimates of seasonal survival, period length, and weekly survival estimates for Hudsonian Godwits (Limosa haemastica) from Beluga River, Alaska, USA and Chiloé Island, Chile. Southbound Northbound Migration Non-breeding Migration Breeding Weekly survival 0.986 – 0.993 0.999 0.986 – 0.993 0.992 Period length ~11 weeks 27 weeks ~3 weeks 11 weeks Period survival 0.855 – 0.928 0.987 0.958 – 0.979 0.915 229 Table XI. Results of bootstrapping procedure of the fitted partial least squares path model for Hudsonian Godwit (Limosa haemastica) reversible state effects. Significant paths, where 95% confidence intervals (CI) did not cross 0, are bolded. Beta 95% CI P atch Quality -> Foraging Success 0.59 (0.27, 0.77) Patch Quality -> Body Condition 0.09 (-0.17, 0.42) Patch Quality -> Breeding Performance 0.47 (0.06, 0.91) Foraging Success -> Body Condition 0.68 (0.38, 0.98) Foraging Success -> Breeding Performance -0.19 (-0.61, 0.27) Body Condition -> Breeding Performance 0.48 (0.14, 0.87) 230 Table XII. The relative contribution of direct and indirect effects (calculated from standardized path coefficients) and the total effect for each path in the fitted partial least squares path model for Hudsonian Godwit (Limosa haemastica) reversible state effeccts. Paths connect latent variables. direct indirect total Patch Quality -> Foraging Success 0.58 0.00 0.58 Patch Quality -> Body Condition 0.10 0.40 0.50 Patch Quality -> Breeding Performance 0.50 0.10 0.60 Foraging Success -> Body Condition 0.68 0.00 0.68 Foraging Success -> Breeding Performance -0.20 0.33 0.13 Body Condition -> Breeding Performance 0.48 0.00 0.48 231 Table XIII. Summary of competing models evaluating relationships between Hudsonian Godwit (Limosa haemastica) reproductive success and average growth bar width of outermost rectrices, indicating non-breeding season nutritional condition, of godwits captured on nests at Beluga River, Alaska in 2014 – 2016. Models are ranked by ascending dAICC values with the number of parameters (k), and Akaike weights. Model dAICC k Weight Nest Fate Null (Individual + Year random effect) 0.00 3 0.74 Average growth bar width 2.10 4 0.26 Chick Survival to 20-days-old Average growth bar width 0.00 4 0.93 Null (Individual + Year random effect) 5.10 3 0.07 Nest Initiation Date Average growth bar width 0.00 4 0.99 Null (Individual + Year random effect) 11.4 3 0.01 Maximum number of days a chick survived Average growth bar width 0.00 4 1.0 Null (Individual + Year random effect) 19.5 3 <0.001 Last date defending brood Average growth bar width 0.00 4 0.99 Null (Individual + Year random effect) 12.4 3 0.01 232 Table XIV. Pearson correlation coefficients for linked non-breeding season observations and feather data for Hudsonian Godwits (Limosa haemastica). Body molt score (BMS) and abdominal profile index (API) are corrected for date of observation. Non-breeding season observed Non-breeding season observed body condition foraging success Feather Data Average Condition BMS API Success Swallows Success Growth Feather Feather Residuals Rate per min Rate per min Bar Mass Length Width Average Growth Bar Width 0.26 0.31 0.76 0.51 0.49 0.50 1 Feather Mass 0.18 0.16 0.07 -0.06 0.09 -0.06 0.12 1 Feather Length 0.34 0.29 0.18 0.25 0.18 0.30 0.24 0.61 1 233 Figure 1. Locations of study areas in Beluga River, Alaska, USA and Chiloé Island, Chile and months in the annual cycle when Hudsonian Godwits (Limosa haemastica) are present at each location. 234 Figure 2. The partial least squares inner path model for Hudsonian Godwit (Limosa haemastica) reversible state effects. Ovals represent each of the ‘latent’ variables with the proposed relationships between each latent variable shown by the dark gray arrows. 235 Figure 3. The final partial least squares path model for Hudsonian Godwit (Limosa haemastica) reversible state effects. ‘Manifest’ variables are shown in rectangles and ‘latent’ variables in ovals. The light gray arrows show the link between the manifest variables and each latent variable. The inner model describing the relationships between the latent variables is represented using dark gray arrows. 236 Estimated Survival Annual Survival of Hudsonian Godwits breeding in Beluga River, Alaska Female Male Year Figure 4. Annual survival estimates of Hudsonian Godwits (Limosa haemastica) from the breeding grounds at Beluga River, Alaska, USA from 2009 to 2017 (minus 2013). Sex-specific estimates (male: yellow; female: black) and 95% confidence intervals shown (dashed lines and gray areas). 237 Figure 5. Annual resighting probability of Hudsonian Godwits (Limosa haemastica) on the breeding grounds at Beluga River, Alaska, USA (black) and non-breeding grounds on Chiloé Island, Chile (yellow). Estimates and 95% confidence intervals shown (dashed lines and gray areas). 238 Figure 6. Weekly survival estimates for the four seasonal intervals in the annual cycle of Hudsonian Godwits (Limosa haemastica). Breeding season survival May–July (11 weeks), non- breeding season survival October–March (27 weeks), and the two migration intervals represent survival during the 14-week northbound and southbound migration periods of unequal lengths. Within breeding season survival estimates are from 2009 – 2016 at Beluga River, Alaska, USA. Within non-breeding season survival estimates are from 2009 – 2010 and 2010 – 2011 on Chiloé Island, Chile. 239 Figure 7. Partial least squares path diagram of both direct and indirect effects on future breeding performance of Hudsonian Godwits (Limosa haemastica). Arrows point from predictor to response variables within the model and the thickness of the arrows is proportional to the respective path values (mean bootstrapped standardized path coefficients). Black lines represent significant relationships while gray lines represent non-significant relationships based on 199 bootstrapped iterations. Coefficients of determination (R2) and 95% confidence intervals are reported for response variables within the model. 240 APPENDIX F Methods: Feathers may also be shorter and lighter under nutritional stress conditions, when allocation of resources to plumage production may be limited (Murphy et al. 1988, Carbonell and Tellería 1999). Feather stiffness and hardness may be positively correlated with feather mass (Dawson et al. 2000), so feather mass could be an indirect measure of feather quality. One cost of having shorter and lighter feathers may be an impaired flight performance, which could affect foraging efficiency and increase predation risk (Slagsvold and Dale 1996). Also, lower-quality feathers might influence migration speed, prolonging the duration of migration (Marchetti et al. 1995, Hedenström and Alerstam 1998). Feather mass: For each outer rectrix pulled from incubating individuals (n = 128 feathers from 64 individuals), we weighed and measured the full length of the feather. Using a Mettler Toledo New Classic MF balance (MS802S), we weighed each feather (g) twice. The calibration of the scale was checked every tenth feather. Using calipers, we measured the full length of the barbed portion of the feather (mm) twice. The residuals from a regression of feather mass and length were used as an indicator of feather quality. Data analysis: We used generalized linear mixed models with a logistic or poisson regression to examine the influence of non-breeding feather quality on nest fate, chick fate, the maximum number of days a chick survived, nest initiation date, and the last date seen defending brood, 241 with individual and year as random effects. We evaluated feather quality in a univariate model to a null model using AICC scores for each response variable separately (Burnham and Anderson 2002) in program R (R Core Development Team 2018) with the ‘lme4’ and ‘bbmle’ packages (Bates et al. 2015, Bolker 2017). Lastly, we ran a repeatability analysis on individual feather mass and length among years using the rptR package (Stoffel et al. 2017) in program R with 1,000 bootstrap iterations. For linked observations of non-breeding condition, when we have foraging observations and growth bar measures for the same non-breeding season, we ran a Pearson’s correlation matrix to understand the links between our focal foraging observations and growth bar measures. Results: Breeding performance was not explained by feather quality (Table FI). Feather mass and length were both highly repeatable among years (Mass: R = 0.79, CI 0.57, 0.91, p-value < 0.001; Length: R = 0.95, CI 0.89, 0.98, p-value < 0.001). Of the 15 individuals for which we have foraging observations and feather measures for the same non-breeding season, we found no relationships between feather mass or length with either foraging success or body condition measures. Discussion: Tail feather mass and length were highly repeatable among years, but feather growth rate was not repeatable, which suggests that the latter trait mainly indicates environmental circumstances during molt, whereas feather mass and length may more strongly reflect structural or genetic effects. As godwits have an extended period to molt, feather quality may be highly 242 constrained. Similar patterns of constrained molt strategies are seen in other long distance migrants (Hargitai et al. 2014, Conklin and Battley 2012). 243 REFERENCES Bates, D., M. Maechler, B. Bolker, and S. Walker (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67:1–48. Bolker, B., and Team RDC (2017). bbmle: Tools for general maximum likelihood estimation. Burnham, K. P., and D. R. Anderson (2002). Model selection and multimodel inference: A practical information-theoretic approach. Springer Science and Business Media. Carbonell, R., and J. L. Tellería (1999). 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Marchetti, K., T. Price, and A. Richman (1995). Correlates of wing morphology with foraging behaviour and migration distance in the genus Phylloscopus. Journal of Avian Biology 26:177–181. Murphy, M. E., J. R. King, and J. Lu (1988). Malnutrition during the postnuptial molt of White- crowned Sparrows: Feather growth and quality. Canadian Journal of Zoology 66:1403– 1413. R Core Development Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. Slagsvold, T., and S. Dale (1996). Disappearance of female Pied Flycatchers in relation to breeding stage and experimentally induced molt. Ecology 77:461–471. Stoffel, M. A., S. Nakagawa, and H. Schielzeth (2017). rptR: Repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecology and Evolution 8:1639–1644. 244 TABLES AND FIGURES Table FI. Summary of competing models evaluating relationships between Hudsonian Godwit (Limosa haemastica) reproductive success and the residuals from a regression of feather mass and length, indicating feather quality, of godwits captured on nests at Beluga River, Alaska in 2014 – 2016. Models are ranked by ascending dAICC values with the number of parameters (k), and Akaike weights. Model dAICC k Weight Nest Fate Null (Individual + Year random effect) 0.00 3 0.74 Mass regression 2.10 4 0.26 Chick survival to 20-days-old Null (Individual + Year random effect) 0.00 3 0.63 Mass regression 1.10 4 0.37 Nest initiation date Null (Individual + Year random effect) 0.00 3 0.73 Mass regression 1.90 4 0.27 Maximum number of days brood survived Null (Individual + Year random effect) 0.00 3 0.73 Mass regression 2.00 4 0.27 Last date defending brood Null (Individual + Year random effect) 0.00 3 0.72 Mass regression 1.90 4 0.28 245 APPENDIX G IIndividual Figure G1. Average growth bar width varied among years for individual Hudsonian Godwits (Limosa haemastica) breeding at Beluga River, Alaska. 246 Individual Figure G2. Feather mass did not vary among years for individual Hudsonian Godwits (Limosa haemastica) breeding at Beluga River, Alaska. 247 Figure G3. Survey sites (black) including locations (yellow) where marked Hudsonian Godwits (Limosa haemastica) from the Beluga River, Alaska, USA breeding population were found on the non-breeding grounds in 2015 and 2016 on or near Chiloé Island, Chile. 248 Figure G4. Probes per minute from focal foraging observations of Hudsonian Godwits (Limosa haemastica) throughout the non-breeding season (January to March). Individuals from the Beluga River, Alaska, USA population (yellow) are shown compared to unmarked individuals (black). 249 Figure G5. Swallows per minute from focal foraging observations of Hudsonian Godwits (Limosa haemastica) throughout the non-breeding season (January to March). Individuals from the Beluga River, Alaska, USA population (yellow) are shown compared to unmarked individuals (black). 250 Figure G6. Success rate of individuals from focal foraging observations of Hudsonian Godwits (Limosa haemastica) throughout the non-breeding season (January to March). Individuals from the Beluga River, Alaska, USA population (yellow) are shown compared to unmarked individuals (black). 251 Figure G7. Success rate per minute from focal foraging observations of Hudsonian Godwits (Limosa haemastica) throughout the non-breeding season (January to March). Individuals from the Beluga River, Alaska, USA population (yellow) are shown compared to unmarked individuals (black). 252 Figure G8. Body molt scores from focal foraging observations of Hudsonian Godwits (Limosa haemastica) throughout the non-breeding season (January to March). Individuals from the Beluga River, Alaska, USA population (yellow) are shown compared to unmarked individuals (black). 253 Unmarked Marked Figure G9. Abdominal profile index from focal foraging observations of Hudsonian Godwits (Limosa haemastica) throughout the non-breeding season (January to March). Individuals from the Beluga River, Alaska, USA population (yellow) are shown compared to unmarked individuals (black). 254 Figure G10. Movements of individual Hudsonian Godwits (Limosa haemastica) from our marked breeding population at Beluga River, Alaska, USA among intertidal mudflats on or near Chiloé Island in 2016. 255 APPENDIX H Table HI. Non-breeding season data for linked observations of individual Hudsonian Godwits (Limosa haemastica) from the non-breeding grounds in southern Chile in 2015 and 2016. 256 Patch Quality Body Condition Foraging Success Flag Flock Flock Condition Probes Success Swallows Success Density Condition residuals BMS API per min Rate per min Rate per min 1AK 0.58 0.63 1.94 1.0 1.0 15.54 4.34 3.39 0.04 1KP 1.96 0.70 2.03 2.0 0.0 12.76 2.94 1.19 0.04 1LE 0.19 0.90 2.12 0.0 0.0 17.44 6.03 4.10 0.06 1MP -0.67 -0.79 0.63 0.0 0.0 15.35 2.69 2.00 0.03 1PE NA NA 0.67 0.0 -0.3 30.85 2.65 2.35 0.02 1PE 1.23 0.82 1.37 0.2 0.1 24.13 5.14 5.60 0.05 1PM 0.37 0.75 1.68 0.0 -0.4 24.54 2.69 3.32 0.03 A49 1.47 0.56 2.53 2.0 1.0 22.49 6.81 8.86 0.07 AE 1.80 0.52 2.81 0.3 0.5 22.14 7.75 6.79 0.08 NA 0.54 0.78 1.38 0.0 0.0 20.14 3.02 3.44 0.03 C01 1.48 0.69 0.01 0.0 0.0 24.64 3.65 1.90 0.04 C85 2.15 0.86 2.83 0.7 1.0 25.20 8.23 10.54 0.08 CJ -0.28 0.59 0.36 0.0 -1.0 27.13 2.34 2.94 0.02 CK -0.67 -0.79 -0.05 -1.0 0.0 33.73 1.20 1.05 0.01 CT 0.77 0.73 0.25 -1.5 -0.3 24.36 3.23 3.46 0.03 E40 2.32 0.81 1.47 -0.5 0.5 20.69 3.18 3.66 0.03 E95 3.28 1.59 2.60 0.5 0.8 27.19 6.13 7.96 0.06 EA NA NA 1.56 0.2 0.0 22.89 4.58 4.91 0.05 EA 0.81 0.83 1.37 1.0 0.2 16.88 5.24 5.70 0.06 H4 1.72 0.68 1.91 1.0 0.5 15.01 5.39 2.98 0.06 J12 1.30 0.70 1.36 0.4 0.0 27.71 4.61 5.90 0.05 LY 0.15 0.61 1.42 1.0 0.0 23.09 2.94 3.46 0.03 NX 0.16 0.88 2.46 0.5 1.0 24.98 4.76 6.27 0.05 UV NA NA 1.37 0.2 0.2 28.75 5.12 5.50 0.05 UV 2.29 0.55 0.79 0.0 0.0 21.75 6.03 3.82 0.06 257 Table HII. Breeding performance data for linked observations of individual Hudsonian Godwits (Limosa haemastica) from the breeding grounds at Beluga River, Alaska in 2015 and 2016. Breeding Performance Flag Nest Number of Return Initiation Nest Fate Chick Fate Days Last Day Date Brood Defending Survived 1AK Yes 128 Hatch Survived 17 158 1KP Yes 132 Hatch Died 14 176 1LE Yes 135 Hatch Died 4 169 1MP No NA NA NA NA NA 1PE No NA NA NA NA NA 1PE Yes 131 Hatch Died 13 167 1PM Yes 130 Hatch Survived 20 172 A49 Yes 128 Fail Died 0 NA AE Yes 128 Hatch Died 3 156 NA Yes 144 Hatch Died 3 169 C01 Yes 128 Hatch Survived 17 158 C85 Yes 130 Hatch Survived 20 173 CJ No NA NA NA NA NA CK No NA NA NA NA NA CT No NA NA NA NA NA E40 Yes 131 Hatch Survived 16 184 E95 Yes 132 Hatch Died 14 179 EA Yes NA Fail Died 0 NA EA Yes 129 Hatch Died 2 157 H4 Yes 144 Hatch Died 3 167 J12 Yes 131 Hatch Died 12 166 LY Yes 128 Hatch Died 3 156 NX Yes NA Fail Died 0 NA UV Yes NA Hatch Died NA NA UV Yes 129 Fail Died 0 NA 258 Table HIII. Data for Hudsonian Godwit (Limosa haemastica) feather growth bar width, mass, and length and linked reproductive performance measures from 2014 to 2016 in Beluga River, Alaska, USA. 259 Average Nest Number Flag Sex Year growth bar Mass Length Nest Chick Fate Initiation of days Last day width (g) (mm) Fate Date brood defending survived 1AK F 2014 55.05 0.05 89.35 Hatch Survived 133 30 188 C01 M 2014 48.60 0.03 81.55 Hatch Survived 133 30 181 C51 M 2014 50.45 0.03 82.00 Hatch Survived 131 35 186 C85 M 2014 58.55 0.04 87.25 Hatch Died 137 19 163 CK M 2014 53.60 0.03 84.95 Hatch Died 132 12 162 H18 M 2014 54.25 0.03 85.70 Hatch Died 133 19 177 H27 F 2014 56.90 0.05 91.95 Hatch Survived 141 23 189 H70 F 2014 49.90 0.05 91.60 Hatch Survived 131 35 182 J88 F 2014 56.80 0.05 87.90 Hatch Died 136 19 177 NA F 2014 49.75 0.05 84.50 Hatch Died 129 12 167 NX M 2014 51.87 0.04 82.05 Hatch Died 145 7 177 1KP F 2015 53.10 0.04 89.95 Hatch Survived 128 24 178 1LE M 2015 51.60 0.03 84.75 Hatch Died 138 11 174 1MN M 2015 58.60 0.03 82.70 Hatch Survived 129 20 176 1PE M 2015 51.50 0.04 87.25 Hatch Died 131 13 167 1PH M 2015 54.10 0.03 86.45 Hatch Died 129 7 162 1PJ F 2015 48.70 0.05 88.70 Hatch Died 128 4 158 A01 F 2015 56.60 0.05 92.15 Hatch Died 132 19 177 A49 M 2015 52.70 0.03 85.35 Hatch Died 138 14 177 A59 F 2015 55.00 0.05 87.95 Hatch Died 138 11 179 A92 M 2015 56.40 0.03 83.25 Hatch Died 130 21 177 AE F 2015 60.90 0.05 88.60 Hatch Died 128 21 175 AY F 2015 52.80 0.06 92.10 Hatch Died 129 2 157 C85 M 2015 57.95 0.04 86.90 Hatch Died 128 9 163 CX M 2015 52.55 0.03 85.50 Hatch Died 128 9 163 E00 F 2015 57.20 0.05 92.80 Hatch Died 130 5 161 E40 M 2015 50.70 0.03 84.40 Hatch Survived 131 21 179 E95 M 2015 60.90 0.04 84.75 Hatch Survived 128 26 180 EA M 2015 53.85 0.03 88.00 Hatch Died 129 2 157 H27 F 2015 57.00 0.06 91.45 Hatch Died 138 19 182 H42 M 2015 57.60 0.04 85.20 Hatch Died 132 19 177 J09 F 2015 49.70 0.04 83.80 Hatch Died 134 10 171 J12 F 2015 57.65 0.05 90.60 Hatch Survived 129 20 176 J25 F 2015 56.30 0.05 89.35 Hatch Died 129 11 166 J66 F 2015 56.55 0.05 89.00 Hatch Died 128 7 161 260 TABLE HIII (CONTINUED) Average Number Flag Sex Year growth bar Mass Length Nest Nest (g) (mm) Fate Chick Fate Initiation of days Last day width Date brood defending survived LY M 2015 53.35 0.03 83.45 Hatch Died 128 21 175 NX M 2015 54.45 0.02 81.60 Hatch Died 134 13 174 VY F 2015 55.60 0.06 92.95 Hatch Died 131 13 167 1AK F 2016 53.90 0.05 89.10 Hatch Survived 128 17 158 1EK M 2016 61.20 0.03 81.95 Hatch Died 136 5 166 1KL F 2016 59.80 0.04 86.35 Hatch Died 133 10 168 1KP F 2016 54.45 0.04 89.50 Hatch Died 132 14 176 1LE M 2016 49.50 0.03 84.35 Hatch Died 135 4 169 1MN M 2016 54.70 0.03 82.35 Hatch Died 131 12 166 1PJ F 2016 59.95 0.05 88.20 Fail Died 128 NA NA 1PM F 2016 48.85 0.05 85.85 Hatch Survived 130 20 172 1TH M 2016 60.65 0.04 82.30 Hatch Died 133 13 171 A59 F 2016 60.75 0.04 88.00 Hatch Died 135 4 169 A78 F 2016 59.10 0.04 87.35 Hatch Survived 131 16 184 AE F 2016 56.10 0.04 89.80 Hatch Died 128 3 156 C01 M 2016 60.50 0.03 82.75 Hatch Survived 128 17 158 C85 M 2016 59.10 0.04 88.20 Hatch Survived 130 20 173 E40 M 2016 60.70 0.03 84.90 Hatch Survived 131 16 184 E95 M 2016 56.90 0.04 84.20 Hatch Died 132 14 179 H4 M 2016 59.05 0.04 87.65 Hatch Died 144 3 167 H42 M 2016 56.60 0.03 84.20 Fail Died 137 NA NA J09 F 2016 49.85 0.04 82.30 Hatch Died 136 5 166 J12 F 2016 57.95 0.05 90.75 Hatch Died 131 12 166 J66 F 2016 54.25 0.05 88.75 Fail Died 137 NA NA LY M 2016 52.70 0.03 81.75 Hatch Died 128 3 156 M6 M 2016 56.25 0.03 85.10 Fail Died 137 NA NA NA F 2016 54.80 0.05 87.63 Hatch Died 144 3 169 261 Table HIV. Encounter histories of Hudsonian Godwit (Limosa haemastica) annual survival from the non-breeding grounds in Chiloé Island, Chile. Each non-breeding season spans from October to March (e.g., 2006 – 2007; October 2006 – March 2007). A “1” denotes the individuals was seen or captured during that non-breeding season, and a “0” specifies the individual was either not seen or had not yet been marked. 262 Flag 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 Male Female 00 1 1 0 1 0 0 Yes No 01 0 1 0 1 1 1 No Yes 04 1 0 0 0 1 1 No Yes 06 1 0 0 0 0 0 Yes No 07 1 0 0 0 0 0 Yes No 08 1 1 0 1 1 0 Yes No 10 1 1 0 0 1 0 No No 25 0 0 0 0 1 1 No No 50 0 1 0 1 0 0 No Yes 51 0 1 0 1 0 1 Yes No 52 0 1 0 1 1 1 No Yes 53 0 1 1 0 0 0 No Yes 54 0 1 0 0 0 0 Yes No 55 0 1 1 0 0 0 No Yes 56 0 1 0 0 0 0 No Yes 57 0 1 0 0 1 0 Yes No 58 0 1 0 1 1 1 Yes No 59 0 1 0 0 0 0 No Yes 60 0 1 0 0 1 0 Yes No 61 0 1 1 1 1 1 No Yes 62 0 1 0 0 1 1 No Yes 63 0 1 0 0 0 0 No Yes 64 0 1 1 0 0 0 Yes No 65 0 1 0 0 0 0 No No 66 0 1 0 0 0 0 No Yes 67 0 1 0 1 1 1 No Yes 68 0 1 0 1 1 0 Yes No 69 0 1 1 0 1 1 No Yes 70 0 1 0 0 0 0 No Yes 71 0 1 0 1 1 1 No Yes 72 0 1 1 1 0 0 Yes No 73 0 1 0 0 0 1 Yes No 74 0 1 0 0 0 1 No Yes 75 0 1 1 0 0 0 Yes No 76 0 1 1 0 0 1 Yes No 77 0 1 0 0 0 0 No Yes 78 0 1 0 0 1 1 Yes No 79 0 1 0 0 0 0 No No 80 0 1 0 0 0 0 No Yes 81 0 1 0 1 1 0 Yes No 82 0 1 1 1 1 1 No Yes 83 0 1 0 0 1 0 No Yes 84 0 1 0 0 0 0 Yes No 85 0 1 0 0 1 1 No Yes 86 0 1 0 0 0 0 No Yes 87 0 1 1 1 1 0 No No 88 0 1 0 1 1 1 No Yes 89 0 1 0 0 0 0 No Yes 90 0 1 0 1 1 1 Yes No 91 0 1 0 1 1 0 No Yes 92 0 1 1 1 1 0 Yes No 93 0 1 0 0 0 0 No Yes 94 0 1 0 0 0 0 Yes No 95 0 1 1 0 0 1 No Yes 263 TABLE HIV (CONTINUED) Flag 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 Male Female 96 0 1 0 0 0 0 Yes No 97 0 1 0 0 0 0 No Yes 98 0 1 0 0 0 0 No No 99 0 1 0 0 0 0 No Yes A0 0 1 0 0 0 0 No Yes A1 0 1 0 0 0 0 No Yes A2 0 1 0 0 1 0 No Yes A3 0 1 1 1 1 1 Yes No A4 0 1 1 1 1 1 No Yes A5 0 1 1 1 1 1 No Yes A6 0 1 0 0 0 0 No Yes A7 0 1 1 1 1 0 Yes No A8 0 1 0 0 0 1 No Yes A9 0 1 0 0 0 0 Yes No AAA 0 0 1 0 0 0 Yes No AAC 0 0 1 1 0 1 No Yes AAE 0 0 1 0 0 1 No No AAH 0 0 1 0 1 1 Yes No AAJ 0 0 1 0 0 0 No Yes AAK 0 0 1 0 0 0 No Yes AAL 0 0 1 1 1 1 No Yes AAM 0 0 1 0 0 0 No No AAN 0 0 1 0 1 1 No Yes AAP 0 0 1 1 1 0 No Yes AAT 0 0 1 0 0 0 No No AAU 0 0 1 1 1 1 No Yes AAX 0 0 1 0 0 0 No Yes AAY 0 0 1 0 1 1 No No ACA 0 0 1 1 1 1 No No ACC 0 0 1 1 1 1 No No ACE 0 0 1 0 0 0 No No ACH 0 0 1 1 1 0 Yes No ACJ 0 0 1 1 0 0 Yes No ACK 0 0 1 0 1 1 Yes No ACL 0 0 1 0 1 0 Yes No ACM 0 0 1 1 0 0 No No ACP 0 0 1 0 1 1 Yes No ACT 0 0 1 1 1 0 No Yes ACU 0 0 1 0 0 1 No No ACV 0 0 1 0 1 1 No No ACX 0 0 1 0 1 0 Yes No ACY 0 0 1 0 1 0 Yes No AEA 0 0 1 1 0 0 No Yes AEC 0 0 1 0 0 1 Yes No AEE 0 0 1 0 0 0 No No AEH 0 0 1 1 1 1 No Yes AEJ 0 0 1 0 1 1 No No AEK 0 0 1 1 1 1 No Yes AEL 0 0 1 0 0 0 No Yes AEM 0 0 1 0 0 0 Yes No AEN 0 0 1 1 0 0 No Yes AEP 0 0 1 1 1 1 Yes No 264 TABLE HIV (CONTINUED) Flag 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 Male Female AET 0 0 1 0 0 0 No No AEU 0 0 1 1 1 1 No Yes AEV 0 0 1 0 1 1 No No AEX 0 0 1 0 1 0 No No AEY 0 0 1 1 1 1 No No AHA 0 0 1 0 0 0 No Yes AHC 0 0 1 1 0 0 Yes No AHE 0 0 1 0 0 0 Yes No AHH 0 0 1 1 0 0 No No AHJ 0 0 1 0 1 0 No No AHK 0 0 1 0 0 0 Yes No AHL 0 0 1 0 1 1 Yes No AHM 0 0 1 1 0 0 Yes No AHN 0 0 1 0 0 0 No No AHP 0 0 1 1 1 1 Yes No AHT 0 0 1 0 1 1 Yes No AHU 0 0 1 0 1 1 Yes No AHV 0 0 1 0 1 0 No Yes AHX 0 0 1 0 1 0 No Yes AHY 0 0 1 1 0 0 No Yes AJA 0 0 1 1 1 1 No No AJC 0 0 1 1 1 1 Yes No AJE 0 0 1 0 0 0 No No AJH 0 0 1 1 1 0 No Yes AJJ 0 0 1 0 1 0 No Yes AJK 0 0 1 0 0 0 No Yes AJL 0 0 1 1 1 1 Yes No AJM 0 0 1 0 0 0 No No AJN 0 0 1 1 0 0 No Yes AJP 0 0 1 1 1 1 No No AJT 0 0 1 1 1 0 No Yes AJU 0 0 1 0 1 1 No No AJV 0 0 1 0 0 0 No No AJX 0 0 1 1 0 0 No Yes AJY 0 0 1 0 0 0 No Yes AKA 0 0 1 1 1 1 No No AKC 0 0 1 1 1 1 No No AKE 0 0 1 1 1 1 Yes No AKH 0 0 1 0 0 0 Yes No AKJ 0 0 1 0 0 1 Yes No AKK 0 0 0 1 1 0 No No AKL 0 0 0 1 1 1 Yes No AKM 0 0 0 1 1 1 Yes No AKN 0 0 0 1 1 1 No No AKP 0 0 0 1 0 0 Yes No AKT 0 0 0 1 1 1 No Yes AKU 0 0 0 1 1 1 No Yes AKV 0 0 0 1 1 0 No Yes AKX 0 0 0 1 1 1 No Yes AKY 0 0 0 1 1 0 No Yes ALA 0 0 1 1 1 1 No Yes ALE 0 0 1 0 0 0 Yes No 265 TABLE HIV (CONTINUED) Flag 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 Male Female ALH 0 0 1 1 1 1 Yes No ALJ 0 0 1 0 0 0 Yes No ALK 0 0 1 1 1 1 Yes No ALL 0 0 1 0 0 0 No Yes ALM 0 0 1 0 1 1 No Yes ALN 0 0 1 1 1 1 No Yes ALP 0 0 1 1 0 0 Yes No ALT 0 0 1 0 0 0 No Yes ALU 0 0 1 0 1 0 Yes No ALV 0 0 1 0 0 0 No Yes ALX 0 0 1 0 1 1 Yes No ALY 0 0 1 1 0 0 No Yes AMA 0 0 1 1 1 1 No No AMC 0 0 1 1 1 1 No No AME 0 0 1 1 1 1 No Yes AMH 0 0 1 1 1 1 No No AMJ 0 0 1 0 0 0 Yes No AMK 0 0 1 1 0 1 Yes No AML 0 0 1 1 1 1 Yes No AMM 0 0 1 1 1 1 Yes No AMN 0 0 1 0 1 1 No Yes AMP 0 0 1 0 1 1 Yes No AMT 0 0 1 1 1 1 Yes No AMU 0 0 1 1 1 0 No Yes AMV 0 0 1 1 1 1 Yes No AMX 0 0 1 0 0 0 No Yes AMY 0 0 1 1 1 0 No No ANA 0 0 1 1 0 0 No No ANC 0 0 1 1 1 1 Yes No ANE 0 0 1 0 0 1 No Yes ANH 0 0 1 0 0 0 No Yes ANJ 0 0 1 1 0 0 No Yes ANK 0 0 1 1 1 1 Yes No ANL 0 0 1 1 1 0 No Yes ANM 0 0 1 0 1 0 No No ANN 0 0 1 1 1 0 No Yes ANP 0 0 1 1 1 1 Yes No ANT 0 0 1 0 0 0 Yes No ANU 0 0 1 0 1 1 Yes No ANV 0 0 1 1 1 1 Yes No ANX 0 0 1 0 0 0 Yes No ANY 0 0 1 1 1 1 No No APA 0 0 1 1 1 0 No No APC 0 0 1 1 1 1 Yes No APE 0 0 0 1 0 1 No Yes APH 0 0 0 1 0 1 Yes No APJ 0 0 0 1 0 1 No Yes APK 0 0 0 1 1 0 No Yes APL 0 0 0 1 0 0 No Yes APM 0 0 0 1 0 1 No Yes APN 0 0 0 1 1 1 No Yes APP 0 0 0 1 0 0 No No 266 TABLE HIV (CONTINUED) Flag 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 Male Female APT 0 0 0 1 1 1 No No APU 0 0 0 1 1 0 No Yes APV 0 0 0 1 1 1 No Yes APX 0 0 0 1 0 0 No Yes APY 0 0 0 1 1 1 No Yes ATA 0 0 0 1 1 0 No No ATC 0 0 0 1 0 0 No Yes ATE 0 0 0 1 1 0 No Yes ATH 0 0 0 1 1 1 No Yes ATJ 0 0 0 1 1 1 No Yes ATK 0 0 0 1 0 1 Yes No ATL 0 0 0 1 1 1 No Yes ATM 0 0 0 1 1 1 No Yes ATN 0 0 0 1 1 1 No Yes ATP 0 0 0 1 1 1 No No ATT 0 0 0 1 1 1 No No ATU 0 0 0 1 1 0 Yes No ATV 0 0 0 1 1 1 No No ATX 0 0 0 1 1 1 Yes No ATY 0 0 0 1 1 1 No Yes AUA 0 0 0 1 0 0 No Yes AUC 0 0 0 1 0 0 No Yes AUE 0 0 0 1 0 0 Yes No AUH 0 0 0 1 0 0 No Yes AUJ 0 0 0 1 0 0 No Yes AUK 0 0 0 1 1 0 No No AUL 0 0 0 1 0 1 No Yes AUM 0 0 0 1 0 1 No Yes AUN 0 0 0 1 0 1 Yes No AUP 0 0 0 1 0 1 No Yes AUT 0 0 0 1 1 1 Yes No AUU 0 0 0 1 1 0 No Yes AUV 0 0 0 1 1 1 No Yes AUX 0 0 0 1 1 0 No No AUY 0 0 0 1 0 1 No Yes AVA 0 0 0 1 0 0 No Yes AVC 0 0 0 1 0 0 Yes No AVE 0 0 0 1 0 1 No Yes AVH 0 0 0 1 0 0 No Yes AVJ 0 0 0 1 0 1 Yes No AVK 0 0 0 1 1 1 No Yes AVL 0 0 0 1 1 1 No Yes AVM 0 0 0 1 0 1 Yes No AVN 0 0 0 1 0 0 No Yes AVP 0 0 0 1 0 0 No Yes AVT 0 0 0 1 0 0 No Yes AVU 0 0 0 1 1 0 No Yes AVV 0 0 0 1 0 0 No Yes AVX 0 0 0 1 0 0 Yes No AVY 0 0 0 1 0 0 Yes No AX 0 0 0 1 1 1 No No AXA 0 0 0 1 0 0 Yes No 267 TABLE HIV (CONTINUED) Flag 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 Male Female AXC 0 0 0 1 0 0 No Yes AXE 0 0 0 1 1 0 No Yes AXH 0 0 0 1 1 0 No Yes AXJ 0 0 0 1 0 0 No Yes AXK 0 0 0 1 0 0 No Yes AXL 0 0 0 1 1 1 No Yes AXM 0 0 0 1 0 0 No No AXN 0 0 0 1 0 0 No Yes AXP 0 0 0 1 0 1 No Yes AXT 0 0 0 1 0 1 No Yes AXU 0 0 0 1 0 0 No Yes AXV 0 0 0 1 0 1 No Yes AXX 0 0 0 1 1 1 Yes No AXY 0 0 0 1 1 1 Yes No AYA 0 0 0 1 1 0 No No AYC 0 0 0 1 1 1 No Yes AYE 0 0 0 1 1 1 No Yes AYH 0 0 0 1 1 0 Yes No AYJ 0 0 0 1 0 0 No Yes AYK 0 0 0 1 0 0 No Yes AYL 0 0 0 1 1 1 No Yes AYM 0 0 0 1 1 1 Yes No AYN 0 0 0 1 1 0 No Yes AYP 0 0 0 1 0 0 Yes No AYT 0 0 0 1 0 0 No Yes AYU 0 0 0 1 1 1 No Yes AYV 0 0 0 1 1 1 Yes No AYX 0 0 0 1 0 1 Yes No AYY 0 0 0 1 1 1 Yes No C0 0 1 0 0 0 1 Yes No C1 0 1 0 0 1 1 No Yes C2 0 1 0 1 1 1 Yes No C3 0 1 1 1 1 1 No No C4 0 1 1 0 0 0 No Yes C5 0 1 0 0 0 0 Yes No C6 0 1 0 0 0 1 No Yes C7 0 1 0 0 0 0 No Yes C8 0 1 0 0 0 1 Yes No C9 0 1 0 0 0 0 No Yes CA 0 0 0 0 1 1 Yes No CAA 0 0 0 1 1 1 No Yes CAC 0 0 0 1 1 0 No Yes CAE 0 0 0 0 1 1 No Yes CAH 0 0 0 1 0 0 No No CAJ 0 0 0 1 0 1 No Yes CAK 0 0 0 1 1 1 No Yes CAL 0 0 0 1 1 1 No Yes CAM 0 0 0 1 1 1 Yes No CAN 0 0 0 1 1 0 No Yes CAP 0 0 0 1 0 0 Yes No CAT 0 0 0 1 1 1 No No CAU 0 0 0 1 1 0 Yes No 268 TABLE HIV (CONTINUED) Flag 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 Male Female CAV 0 0 0 1 1 0 No Yes CAX 0 0 0 1 1 0 Yes No CAY 0 0 0 1 0 0 Yes No CC 1 1 0 0 0 0 Yes No CCA 0 0 0 1 1 1 No Yes CCC 0 0 0 1 1 1 Yes No CCE 0 0 0 1 1 0 No Yes CCH 0 0 0 1 1 0 Yes No CCJ 0 0 0 1 1 1 No No CCK 0 0 0 1 1 1 No Yes CCL 0 0 0 1 1 1 Yes No CCM 0 0 0 1 0 1 No Yes CCN 0 0 0 1 1 1 No Yes CCP 0 0 0 1 1 1 No Yes CCT 0 0 0 1 0 0 No No CCU 0 0 0 1 1 1 No No CCV 0 0 0 1 1 1 No Yes CCX 0 0 0 1 0 0 No No CCY 0 0 0 1 1 1 No Yes CE 1 0 1 0 0 0 No Yes CEA 0 0 0 0 1 0 Yes No CEC 0 0 0 0 1 1 No No CEE 0 0 0 0 1 0 No Yes CEH 0 0 0 0 1 1 No No CEJ 0 0 0 0 1 1 Yes No CEK 0 0 0 0 1 1 Yes No CEL 0 0 0 0 1 0 Yes No CEM 0 0 0 0 1 1 Yes No CEN 0 0 0 0 1 1 Yes No CEP 0 0 0 0 1 1 No No CET 0 0 0 0 1 1 No Yes CEU 0 0 0 0 1 1 No Yes CEV 0 0 0 0 1 1 No Yes CEX 0 0 0 0 1 0 Yes No CEY 0 0 0 0 1 1 Yes No CH 1 0 0 0 1 0 No No CHA 0 0 0 0 1 1 Yes No CHC 0 0 0 0 1 1 No No CHE 0 0 0 1 1 0 No Yes CHH 0 0 0 1 0 1 No Yes CHJ 0 0 0 1 1 1 No Yes CHK 0 0 0 1 1 0 Yes No CHL 0 0 0 1 0 1 No Yes CHM 0 0 0 1 0 1 No Yes CHN 0 0 0 1 0 0 No Yes CHP 0 0 0 1 1 1 No Yes CHT 0 0 0 1 0 0 Yes No CHU 0 0 0 1 1 1 No Yes CHV 0 0 0 1 1 0 No Yes CHX 0 0 0 1 0 0 No Yes CHY 0 0 0 1 1 1 No Yes CJ 1 0 0 1 1 1 No No 269 TABLE HIV (CONTINUED) Flag 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 Male Female CJA 0 0 0 0 1 0 No Yes CJC 0 0 0 0 1 1 No No CJE 0 0 0 0 1 1 No Yes CJH 0 0 0 0 1 0 No Yes CJJ 0 0 0 0 1 0 No No CJK 0 0 0 0 1 1 Yes No CJL 0 0 0 0 1 1 No Yes CJM 0 0 0 0 1 1 No Yes CJN 0 0 0 0 1 1 No No CJP 0 0 0 0 1 1 Yes No CJT 0 0 0 0 1 1 No Yes CJU 0 0 0 0 1 1 Yes No CJV 0 0 0 0 1 0 Yes No CJX 0 0 0 0 1 0 No Yes CJY 0 0 0 0 1 0 No No CK 1 0 0 0 0 0 No No CKA 0 0 0 0 1 1 No No CKC 0 0 0 0 1 1 Yes No CKE 0 0 0 0 1 1 Yes No CKH 0 0 0 0 1 1 No Yes CKJ 0 0 0 0 1 1 No Yes CKK 0 0 0 0 1 1 No Yes CKL 0 0 0 0 1 0 No Yes CKN 0 0 0 0 1 0 No Yes CKP 0 0 0 0 1 1 Yes No CKT 0 0 0 0 1 0 Yes No CKU 0 0 0 0 1 1 Yes No CKV 0 0 0 0 1 0 Yes No CKX 0 0 0 0 1 0 No Yes CKY 0 0 0 0 1 0 No Yes CL 1 0 0 0 0 0 No No CLA 0 0 0 0 1 0 Yes No CLC 0 0 0 0 1 1 No Yes CLE 0 0 0 0 1 1 No No CLH 0 0 0 0 1 0 No Yes CLJ 0 0 0 0 1 0 Yes No CLK 0 0 0 0 1 0 Yes No CLL 0 0 0 0 1 1 Yes No CLM 0 0 0 0 1 1 Yes No CLN 0 0 0 0 1 0 Yes No CLP 0 0 0 0 1 0 Yes No CLT 0 0 0 0 1 0 Yes No CLU 0 0 0 0 1 1 Yes No CLV 0 0 0 0 1 1 No Yes CLX 0 0 0 0 1 0 No Yes CLY 0 0 0 0 1 1 No Yes CM 1 1 0 1 1 1 No No CMA 0 0 0 0 1 0 No Yes CMC 0 0 0 0 1 1 Yes No CME 0 0 0 0 1 1 No Yes CMH 0 0 0 0 1 1 No Yes CMJ 0 0 0 0 1 0 No No 270 TABLE HIV (CONTINUED) Flag 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 Male Female CMK 0 0 0 0 1 1 No Yes CML 0 0 0 0 1 1 No No CMM 0 0 0 0 1 1 No Yes CMN 0 0 0 0 1 1 No No CMP 0 0 0 0 1 1 No No CMV 0 0 0 0 1 0 No Yes CMX 0 0 0 0 1 1 No Yes CMY 0 0 0 0 1 1 No Yes CN 1 0 0 0 0 1 No Yes CNE 0 0 0 0 1 0 No Yes CNH 0 0 0 0 1 0 No Yes CP 1 0 0 0 0 0 No No CPC 0 0 0 0 1 0 No Yes CPE 0 0 0 0 1 1 No Yes CPH 0 0 0 0 1 1 No Yes CPL 0 0 0 0 1 0 No Yes CPM 0 0 0 0 1 1 No Yes CPN 0 0 0 0 1 1 Yes No CPT 0 0 0 0 1 0 Yes No CPU 0 0 0 0 1 1 Yes No CPV 0 0 0 0 1 0 Yes No CPX 0 0 0 0 1 0 Yes No CPY 0 0 0 0 1 1 No Yes CT 1 0 0 0 1 0 No Yes CTA 0 0 0 0 1 1 Yes No CTE 0 0 0 0 1 1 No Yes CTH 0 0 0 0 1 0 No Yes CTJ 0 0 0 0 1 1 No Yes CTL 0 0 0 0 1 1 No Yes CTM 0 0 0 0 1 1 Yes No CTN 0 0 0 0 1 1 Yes No CTP 0 0 0 0 1 1 Yes No CTT 0 0 0 0 1 1 Yes No CTX 0 0 0 0 1 1 Yes No CTY 0 0 0 0 1 1 Yes No CU 1 0 0 1 1 0 No Yes CUA 0 0 0 0 1 0 No Yes CUC 0 0 0 0 1 1 Yes No CUE 0 0 0 0 1 0 Yes No CUJ 0 0 0 0 1 0 Yes No CUL 0 0 0 0 1 1 No Yes CUM 0 0 0 0 1 1 Yes No CUN 0 0 0 0 1 1 No Yes CUT 0 0 0 0 1 1 No Yes CUV 0 0 0 0 1 1 No Yes CV 1 0 0 0 1 1 Yes No CX 1 1 0 0 1 0 No No CY 1 1 0 0 0 1 Yes No E0 0 1 0 0 0 0 No Yes E1 0 1 0 1 0 0 Yes No E2 0 1 0 0 1 0 No Yes E3 0 1 1 0 0 0 No No 271 TABLE HIV (CONTINUED) Flag 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 Male Female E4 0 1 0 0 1 0 No Yes E5 0 1 0 0 0 0 Yes No E6 0 1 0 0 0 0 Yes No E7 0 1 0 0 0 0 No Yes E8 0 1 0 0 0 0 No Yes E9 0 1 0 1 1 0 No No EA 1 1 0 0 0 0 No Yes EC 1 0 0 0 0 0 No No EE 1 0 0 0 0 1 No Yes EH 1 1 1 1 1 1 Yes No EJ 1 1 0 0 0 0 No Yes EK 1 0 0 0 0 0 No Yes EL 1 1 0 1 1 1 No Yes EM 1 0 0 0 0 0 Yes No EN 1 0 0 0 0 0 No No EP 1 0 0 0 0 1 Yes No ET 1 0 0 1 0 0 Yes No EU 1 0 0 0 0 0 No No EV 1 0 0 0 0 1 Yes No EX 1 0 0 1 1 1 Yes No EY 1 0 0 0 0 0 No No H0 0 1 1 1 1 0 Yes No H1 0 1 0 0 1 0 Yes No H2 0 1 0 0 0 0 Yes No H3 0 1 0 0 0 0 Yes No H4 0 1 0 0 0 0 No Yes H5 0 1 0 0 0 0 No Yes H6 0 1 0 0 0 0 Yes No H7 0 1 0 0 0 0 No Yes H8 0 1 0 1 0 0 Yes No H9 0 1 0 1 1 1 Yes No HA 1 1 0 0 1 0 No Yes HC 1 1 0 0 0 0 No No HE 1 1 0 1 0 0 No Yes HH 1 0 0 0 1 0 Yes No HJ 1 0 0 0 0 0 Yes No HK 1 0 0 0 0 0 Yes No HL 1 1 0 1 1 0 Yes No HM 1 1 0 0 0 0 No Yes HN 1 1 0 0 1 0 Yes No HP 1 0 0 0 0 0 Yes No HT 1 0 0 0 0 0 Yes No HU 1 1 0 0 1 0 Yes No HV 1 1 0 0 0 0 Yes No HX 1 0 0 1 0 0 No Yes HY 1 1 0 1 0 0 No No J0 0 1 0 0 0 0 No No J1 0 1 0 0 0 0 Yes No J2 0 1 1 0 0 0 Yes No J3 0 1 1 0 1 1 No Yes J4 0 1 0 0 1 1 No No J5 0 1 0 0 0 0 No Yes 272 TABLE HIV (CONTINUED) Flag 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 Male Female J6 0 1 0 0 0 0 No Yes J7 0 1 0 0 0 0 No No J8 0 1 0 0 1 0 No Yes J9 0 1 0 0 0 0 Yes No JA 1 0 0 0 0 0 Yes No JC 1 1 0 0 0 0 Yes No JE 1 0 0 0 0 0 Yes No JH 1 0 1 0 0 0 No No JJ 1 0 0 1 1 1 No No JK 1 0 0 0 0 0 No No JL 1 1 0 0 0 0 No No JM 1 1 0 0 0 0 No No JP 1 0 0 1 1 1 Yes No JT 1 1 0 1 1 1 No Yes JU 1 1 0 1 0 0 No Yes JV 1 0 0 0 0 0 Yes No K0 0 1 1 0 1 0 No Yes K1 0 1 0 0 1 1 Yes No K2 0 1 0 0 0 0 No No K3 0 1 0 0 0 0 Yes No K4 0 1 0 0 0 0 Yes No K5 0 0 1 0 0 0 No Yes K6 0 1 0 1 1 0 No Yes K7 0 1 0 0 0 0 No Yes K8 0 1 0 0 1 1 No Yes K9 0 1 1 1 1 1 No Yes KA 1 1 0 0 0 0 Yes No KC 1 0 0 0 1 1 No Yes KE 1 0 0 0 0 0 Yes No KH 1 1 1 0 1 0 No Yes KJ 1 1 1 1 1 0 Yes No KK 1 0 0 0 0 0 No No KL 1 1 1 1 1 1 No Yes KN 1 0 0 1 0 0 No Yes KT 1 1 1 0 0 0 Yes No KU 1 0 0 0 0 0 No Yes KV 1 0 0 0 0 0 Yes No KX 1 0 0 0 0 0 Yes No KY 1 0 0 0 0 0 No Yes L0 0 1 0 0 1 1 Yes No L1 0 1 0 0 0 0 No Yes L2 0 1 0 0 0 0 Yes No L3 0 1 0 0 0 0 No Yes L4 0 1 0 0 1 0 Yes No L5 0 1 0 0 0 0 No Yes L6 0 1 0 0 0 0 Yes No L7 0 0 1 0 0 0 Yes No L8 0 0 1 0 0 0 No Yes L9 0 0 1 1 0 0 No Yes LA 1 0 0 0 0 0 Yes No LC 1 0 1 1 0 1 No Yes LE 1 1 0 0 0 0 Yes No 273 TABLE HIV (CONTINUED) Flag 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 Male Female LH 1 0 0 0 1 1 No No LJ 1 1 1 0 0 0 Yes No LK 1 1 0 0 0 0 Yes No LL 1 1 0 1 1 1 No Yes LM 1 1 0 0 0 0 No No LN 1 1 1 1 1 0 No No LP 1 1 0 0 1 0 No No LT 1 0 0 0 1 1 Yes No LU 1 1 0 0 1 1 Yes No LV 1 1 1 1 1 1 Yes No LX 1 0 0 0 0 1 Yes No M0 0 0 1 0 1 1 No Yes M1 0 0 1 1 1 0 No No M2 0 0 1 1 0 1 No Yes M3 0 0 1 0 1 1 Yes No M4 0 0 1 1 1 1 No Yes M5 0 0 1 0 1 0 No Yes M6 0 0 1 1 0 0 No Yes M7 0 0 1 0 0 0 No Yes M8 0 0 1 0 0 0 Yes No M9 0 0 1 1 1 1 Yes No MA 1 0 0 0 0 0 No No MC 1 0 0 0 0 0 Yes No ME 1 0 0 0 0 0 No No MH 1 0 0 0 1 0 Yes No MJ 1 0 0 0 0 0 No Yes MK 1 0 1 0 0 0 Yes No ML 1 0 0 1 0 0 No Yes MM 0 1 0 0 0 0 No Yes MN 1 0 0 0 0 0 Yes No MP 1 0 0 0 0 0 Yes No MT 1 0 0 0 0 0 Yes No MX 1 1 0 0 1 0 No Yes N0 0 0 1 1 1 0 Yes No N1 0 0 1 1 1 1 No Yes N2 0 0 1 1 1 1 Yes No N3 0 0 1 1 0 1 Yes No N5 0 0 1 0 0 0 No No N6 0 0 1 1 1 1 No Yes N7 0 0 1 0 0 0 Yes No N8 0 0 1 1 1 1 No No N9 0 0 1 1 1 1 No Yes NA 1 0 0 0 1 0 No Yes NC 1 1 0 1 1 1 Yes No NE 1 1 0 0 0 0 Yes No NH 1 0 0 0 1 0 No Yes NJ 1 0 0 0 0 0 Yes No NK 0 1 0 1 0 1 Yes No NL 0 1 0 0 0 1 No Yes NM 0 1 0 1 1 1 No Yes NN 0 1 0 0 0 0 Yes No NP 0 1 0 0 0 1 No Yes 274 TABLE HIV (CONTINUED) Flag 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 Male Female NT 0 1 1 1 1 1 No No NU 0 1 0 0 0 0 No Yes NV 0 1 1 0 0 1 Yes No NX 0 1 0 0 0 0 No Yes NY 0 1 1 0 1 0 No No P0 0 0 1 1 1 1 No Yes P1 0 0 1 0 0 0 No Yes PA 0 1 0 0 1 0 Yes No PC 0 1 0 0 1 0 No Yes PE 0 1 1 0 1 1 No No PH 0 1 0 0 0 0 No Yes PJ 0 1 1 0 0 0 No Yes PK 0 1 0 0 0 0 No Yes PL 0 1 0 0 0 1 No No PM 0 1 0 0 1 0 No Yes PN 0 1 1 1 1 0 No No PP 0 1 0 0 1 0 Yes No PT 0 1 0 1 1 1 No No PU 0 1 1 0 0 0 Yes No PV 0 1 0 1 0 0 Yes No PX 0 1 1 0 1 0 No Yes PY 0 1 0 0 0 0 No Yes T0 0 0 1 1 0 0 No Yes T1 0 0 1 1 1 1 Yes No T2 0 0 1 0 1 1 Yes No T3 0 0 1 1 1 1 No Yes T4 0 0 1 0 0 0 No Yes T5 0 0 1 0 0 0 Yes No T6 0 0 1 0 1 1 No No T7 0 0 1 0 0 0 Yes No T8 0 0 1 0 0 0 No No T9 0 0 1 0 0 0 No No TA 0 1 1 0 0 0 Yes No TC 0 1 0 0 0 0 No No TE 0 1 0 0 0 0 No Yes TH 0 1 0 0 1 0 No Yes TJ 0 1 0 0 0 0 Yes No TK 0 1 0 0 0 0 No Yes TL 0 1 0 0 0 0 No Yes TM 0 1 0 1 1 1 No Yes TN 0 1 0 0 0 0 No No TP 0 1 1 0 1 0 No No TT 0 1 0 0 1 1 Yes No TU 0 1 0 1 0 0 No Yes TV 0 1 0 0 0 0 No Yes TX 0 1 0 0 0 0 Yes No TY 0 1 0 0 1 0 Yes No U0 0 0 1 1 0 0 No Yes U1 0 0 1 0 1 1 Yes No U2 0 0 1 1 1 0 No Yes U3 0 0 1 0 1 1 No Yes U4 0 0 1 1 1 1 No No 275 TABLE HIV (CONTINUED) Flag 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 Male Female U5 0 0 1 0 0 0 Yes No U6 0 0 1 0 0 0 No Yes U7 0 0 1 0 0 1 Yes No U8 0 0 1 0 0 0 No Yes U9 0 0 1 0 0 0 No Yes UA 0 1 0 0 0 1 Yes No UC 0 1 0 0 0 0 No Yes UE 0 1 0 0 0 0 Yes No UH 0 1 0 0 0 0 No Yes UJ 0 1 0 0 0 0 No Yes UK 0 1 1 0 0 0 No Yes UL 0 1 0 0 0 1 No Yes UM 0 1 0 1 0 1 Yes No UN 0 1 0 0 0 0 Yes No UP 0 1 1 0 1 1 No Yes UT 0 1 0 0 0 0 No Yes UU 0 1 1 0 1 1 No No UV 0 1 0 0 1 0 No No UX 0 1 0 0 1 1 No Yes UY 0 1 1 0 0 1 No Yes V0 0 0 1 0 0 0 No Yes V1 0 0 1 1 1 1 Yes No V2 0 0 1 0 1 1 Yes No V3 0 0 1 0 0 0 Yes No V4 0 0 1 1 1 1 Yes No V5 0 0 1 0 0 0 No Yes V6 0 0 1 1 1 1 No Yes V7 0 0 1 0 0 0 No No V8 0 0 1 1 1 1 Yes No V9 0 0 1 0 0 0 Yes No VA 0 1 0 0 0 1 No No VC 0 1 0 0 0 1 No Yes VE 0 1 1 0 1 1 No Yes VH 0 1 0 1 0 0 Yes No VJ 0 1 0 0 0 0 No No VK 0 1 1 0 1 1 No Yes VL 0 1 1 1 0 1 No Yes VM 0 1 0 0 0 0 No Yes VN 0 1 0 0 0 0 Yes No VP 0 1 0 1 1 0 Yes No VT 0 1 1 1 1 1 No Yes VU 0 1 0 1 1 0 No Yes VV 0 1 0 0 0 0 No Yes VX 0 1 0 1 0 1 Yes No VY 0 1 0 0 0 0 Yes No X1 0 0 1 1 1 1 Yes No X2 0 0 1 0 0 1 No Yes X3 0 0 1 1 0 1 Yes No X4 0 0 1 0 0 0 No Yes X5 0 0 1 0 0 0 No No X6 0 0 1 1 1 0 No Yes X7 0 0 1 1 1 1 No No 276 TABLE HIV (CONTINUED) Flag 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 Male Female X8 0 0 1 1 1 1 No Yes X9 0 0 1 1 1 0 Yes No XA 0 1 0 0 0 0 No Yes XC 0 1 0 1 0 1 No Yes XE 0 1 0 0 0 0 No Yes XH 0 1 0 1 1 1 No Yes XJ 0 1 1 1 1 1 Yes No XK 0 1 0 0 0 0 Yes No XL 0 1 0 0 0 0 Yes No XM 0 1 0 1 0 1 Yes No XN 0 1 1 1 1 1 No No XP 0 1 0 1 0 0 No No XT 0 1 0 0 0 1 No Yes XU 0 1 0 0 0 1 Yes No XV 0 1 0 0 0 0 Yes No XX 0 1 0 1 0 1 No Yes XY 0 1 0 0 0 1 No No Y0 0 0 1 0 1 1 Yes No Y1 0 0 1 1 0 1 No No Y2 0 0 1 0 0 1 No No Y3 0 0 1 1 1 0 No Yes Y4 0 0 1 0 0 0 No Yes Y5 0 0 1 0 1 0 No Yes Y6 0 0 1 1 0 0 No Yes Y7 0 0 1 0 1 1 Yes No Y8 0 0 1 0 0 0 No Yes Y9 0 0 1 1 0 0 No Yes YA 0 1 0 0 0 1 Yes No YC 0 1 0 0 0 0 No Yes YE 0 1 0 0 0 1 No Yes YH 0 1 0 1 1 0 Yes No YJ 0 1 0 1 1 0 No Yes YK 0 1 0 0 0 0 No Yes YL 0 1 1 0 0 0 No Yes YM 0 1 0 0 0 0 No No YN 0 1 0 1 1 1 Yes No YP 0 1 0 1 1 0 Yes No YT 0 1 0 1 0 0 Yes No YU 0 1 0 0 1 1 No Yes YV 0 1 0 1 1 1 No Yes YX 0 1 0 0 0 0 No No YY 0 1 0 0 0 1 No No 277 Table HV. Encounter histories of Hudsonian Godwits (Limosa haemastica) for within season survival on the non-breeding grounds during the 2009 – 2010 season on Chiloé Island, Chile. Period 1 spanned three survey days (7 – 9 January) at Pullao (n = 126 observations). Period 2 spanned three survey days (10 – 12 January) at Ten-Ten (n = 16 observations) and Pullao (n = 152 observations). Period 3 spanned four survey days (13, 15, 16, and 19 January) at Pullao (n = 109 observation), Curaco de Vélez (n = 18 observations), Teguel (n = 6 observations), and Putemún (n = 3 observations). Period 4 was if the individual was seen at any time during the 2010 – 2011 non-breeding season. A “1” denotes the individuals was seen or re-captured during that non-breeding season, and a “0” specifies the individual was not seen. 278 Flag PER 1 PER 2 PER 3 PER 4 Male Female 0 0 1 0 0 Yes No 1 1 1 1 1 No Yes 8 0 1 0 1 Yes No 50 1 1 1 0 No Yes 51 0 1 0 1 Yes No 52 1 1 0 1 No Yes 58 0 1 1 1 Yes No 61 0 1 1 1 No Yes 67 1 0 1 1 No Yes 68 1 1 0 1 Yes No 71 1 0 0 1 No Yes 72 0 0 1 0 Yes No 81 1 1 1 1 Yes No 82 1 1 1 1 No Yes 87 1 0 1 1 No No 88 1 1 1 1 No Yes 90 1 0 1 1 Yes No 91 0 1 1 1 No Yes 92 1 1 1 1 Yes No A3 0 0 1 1 Yes No A4 1 1 1 1 No Yes A5 1 0 1 1 No Yes A7 1 1 1 1 Yes No AAC 1 0 1 1 No Yes AAL 1 0 0 1 No Yes AAP 1 1 0 1 No Yes AAU 1 1 1 1 No Yes ACA 1 1 1 1 No No ACC 0 1 0 1 No No ACH 0 0 1 1 Yes No ACJ 0 1 0 0 Yes No ACM 0 1 1 0 No No ACT 0 1 1 1 No Yes AEA 0 0 1 0 No Yes AEH 1 0 0 1 No Yes AEK 1 1 1 1 No Yes AEN 0 0 1 0 No Yes AEP 0 1 0 1 Yes No AEU 1 1 0 1 No Yes AEY 1 1 0 1 No No AHC 1 1 0 0 Yes No AHH 0 1 0 0 No No AHM 0 1 1 0 Yes No AHP 1 1 1 1 Yes No AHY 0 1 0 0 No Yes AJA 1 0 1 1 No No AJC 1 1 1 1 Yes No AJH 1 1 1 1 No Yes AJL 1 1 1 1 Yes No AJN 1 0 0 0 No Yes AJP 1 0 1 1 No No AJT 1 0 1 1 No Yes 279 TABLE HV (CONTINUED) Flag PER 1 PER 2 PER 3 PER 4 Male Female AJX 0 1 0 0 No Yes AKA 0 1 0 1 No No AKC 1 1 1 1 No No AKE 1 1 1 1 Yes No AL 1 0 0 0 No Yes ALA 1 0 1 1 No Yes ALH 0 0 1 1 Yes No ALK 0 1 0 1 Yes No ALN 1 1 1 1 No Yes ALP 0 0 1 0 Yes No ALY 0 1 0 0 No Yes AMA 1 1 1 1 No No AMC 0 0 1 1 No No AME 0 1 0 1 No Yes AMH 1 1 1 1 No No AMK 0 1 0 1 Yes No AML 1 0 0 1 Yes No AMM 1 0 1 1 Yes No AMT 0 1 0 1 Yes No AMU 0 1 0 1 No Yes AMV 0 1 1 1 Yes No AMY 0 1 0 0 No No ANA 0 1 0 0 No No ANC 1 0 0 1 Yes No ANJ 1 1 1 0 No Yes ANK 1 1 1 1 Yes No ANL 1 1 1 1 No Yes ANN 0 0 1 1 No Yes ANP 0 1 0 1 Yes No ANV 1 1 1 1 Yes No ANY 0 1 1 1 No No APA 0 1 1 1 No No APC 0 0 1 1 Yes No AX 1 1 1 1 No No C2 0 1 1 1 Yes No C3 1 0 1 1 No No CJ 1 1 1 1 No No CM 1 1 0 1 No No CU 0 0 1 1 No Yes E1 1 1 1 0 Yes No E9 1 0 0 1 No No EH 1 1 0 1 Yes No EL 0 1 0 1 No Yes ET 0 1 0 0 Yes No EX 0 1 0 1 Yes No H0 0 1 1 1 Yes No H8 0 0 1 0 Yes No H9 0 0 1 1 Yes No HE 1 1 1 0 No Yes HL 0 1 0 1 Yes No 280 TABLE HV (CONTINUED) Flag PER 1 PER 2 PER 3 PER 4 Male Female HX 1 1 0 0 No Yes HY 0 0 1 0 No No JJ 1 1 0 1 No No JP 0 0 1 1 Yes No JT 1 1 0 1 No Yes JU 0 1 0 0 No Yes K6 0 1 0 1 No Yes K9 1 1 0 1 No Yes KJ 0 1 1 1 Yes No KL 1 1 1 1 No Yes KN 0 1 0 0 No Yes L9 0 1 0 0 No Yes LC 1 1 0 1 No Yes LL 1 1 0 1 No Yes LN 0 0 1 1 No No LV 1 0 1 1 Yes No M1 0 0 1 1 No No M2 0 1 0 1 No Yes M4 0 1 0 1 No Yes M6 0 1 0 0 No Yes M9 1 1 1 1 Yes No ML 0 0 1 0 No Yes N0 0 1 1 1 Yes No N1 1 1 1 1 No Yes N2 0 1 1 1 Yes No N3 0 1 0 1 Yes No N6 0 1 0 1 No Yes N8 0 1 1 1 No No N9 0 0 1 1 No Yes NC 0 1 1 1 Yes No NK 1 1 0 1 Yes No NM 1 1 1 1 No Yes NT 1 0 0 1 No No P0 0 1 0 1 No Yes PN 0 0 1 1 No No PT 0 1 0 1 No No PV 0 1 0 0 Yes No T0 1 1 0 0 No Yes T1 1 0 1 1 Yes No T3 0 1 0 1 No Yes TM 1 1 0 1 No Yes TU 1 0 0 0 No Yes U0 1 0 0 0 No Yes U2 0 1 0 1 No Yes U4 0 1 1 1 No No UM 0 0 1 1 Yes No V1 1 1 1 1 Yes No V4 0 1 1 1 Yes No V6 0 0 1 1 No Yes V8 1 1 1 1 Yes No 281 TABLE HV (CONTINUED) Flag PER 1 PER 2 PER 3 PER 4 Male Female VH 1 0 0 0 Yes No VL 0 0 1 1 No Yes VP 0 1 1 1 Yes No VT 1 1 0 1 No Yes VU 1 1 0 1 No Yes VX 0 1 0 1 Yes No X0 1 0 0 0 No Yes X1 1 0 0 1 Yes No X3 1 1 0 1 Yes No X6 0 1 0 1 No Yes X7 1 1 1 1 No No X8 1 0 1 1 No Yes X9 0 0 1 1 Yes No XC 0 1 1 1 No Yes XH 0 1 0 1 No Yes XJ 0 1 0 1 Yes No XM 0 1 1 1 Yes No XN 0 1 0 1 No No XP 0 1 0 0 No No XX 0 1 0 1 No Yes Y1 0 1 0 1 No No Y3 0 1 1 1 No Yes Y6 0 1 0 0 No Yes Y9 0 1 1 0 No Yes YH 0 1 0 1 Yes No YJ 1 0 0 1 No Yes YN 1 1 0 1 Yes No YP 1 1 0 1 Yes No YT 1 0 0 0 Yes No YV 1 1 0 1 No Yes 282 Table HVI. Encounter histories of Hudsonian Godwits (Limosa haemastica) for within season survival on the non-breeding grounds during the 2010 – 2011 season on Chiloé Island, Chile. Period 1 spanned four survey days (5 – 8 January) at Pullao (n = 224 observations), at Putemún (n = 6 observations), and at Curaco de Vélez (n = 8 observations). Period 2 spanned four survey days (9 – 12 January) at Rilán (n = 68 observations), Pullao (n = 90 observations), Curaco de Vélez (n = 24 observations), and Putemún (n = 109 observations). Period 3 spanned four survey days (13, 15, 16, and 17 January) at Pullao (n = 235 observation), Curaco de Vélez (n = 10 observations), Chúllec (n = 2 observations), Rilán (n = 46 observations), and Putemún (n = 145 observations). Period 4 was if the individual was seen at any time during the 2011 – 2012 non- breeding season. A “1” denotes the individuals was seen or re-captured during that non-breeding season, and a “0” specifies the individual was not seen. 283 Flag PER 1 PER 2 PER 3 PER 4 Male Female 1 0 1 1 1 No Yes 4 0 1 1 1 No Yes 8 1 1 0 0 Yes No 10 1 1 0 0 No No 25 0 1 0 1 No No 52 0 1 0 1 No Yes 57 0 1 0 0 Yes No 58 0 0 1 1 Yes No 60 1 0 1 0 Yes No 61 1 1 1 1 No Yes 62 1 1 1 1 No Yes 67 1 1 1 1 No Yes 68 1 1 1 0 Yes No 69 1 0 1 1 No Yes 71 0 1 1 1 No Yes 78 1 1 1 1 Yes No 81 1 1 1 0 Yes No 82 0 1 1 1 No Yes 83 1 0 0 0 No Yes 85 1 0 0 1 No Yes 87 0 1 1 0 No No 88 1 1 1 1 No Yes 90 1 1 1 1 Yes No 91 1 1 1 0 No Yes 92 1 0 1 0 Yes No A2 1 0 0 0 No Yes A3 1 1 1 1 Yes No A4 1 1 1 1 No Yes A5 0 1 0 1 No Yes A7 1 1 1 0 Yes No AAH 0 1 1 1 Yes No AAL 0 1 1 1 No Yes AAN 0 1 1 1 No Yes AAP 0 0 1 0 No Yes AAU 1 1 1 1 No Yes AAY 0 1 0 1 No No ACA 1 1 1 1 No No ACC 1 1 1 1 No No ACH 1 1 1 0 Yes No ACK 0 1 1 1 Yes No ACL 0 1 1 0 Yes No ACP 0 1 0 1 Yes No ACT 1 1 1 0 No Yes ACV 1 1 1 1 No No ACX 0 1 1 0 Yes No ACY 0 1 1 0 Yes No AEH 1 0 0 1 No Yes AEJ 1 0 0 1 No No AEK 1 1 1 1 No Yes AEP 0 0 1 1 Yes No AEU 0 1 1 1 No Yes AEV 1 1 1 1 No No 284 TABLE HVI (CONTINUED) Flag PER 1 PER 2 PER 3 PER 4 Male Female AEX 0 0 1 0 No No AEY 1 1 1 1 No No AHJ 1 0 0 0 No No AHL 1 1 1 1 Yes No AHP 1 1 1 1 Yes No AHT 0 0 1 1 Yes No AHU 0 0 1 1 Yes No AHV 0 1 0 0 No Yes AHX 1 0 0 0 No Yes AJA 1 1 1 1 No No AJC 1 1 1 1 Yes No AJH 0 1 1 0 No Yes AJJ 0 1 0 0 No Yes AJL 1 0 1 1 Yes No AJP 0 1 1 1 No No AJT 1 1 0 0 No Yes AJU 0 1 1 1 No No AKA 0 1 1 1 No No AKC 1 1 0 1 No No AKE 1 1 1 1 Yes No AKK 1 0 0 0 No No AKL 1 0 1 1 Yes No AKM 1 1 1 1 Yes No AKN 1 1 1 1 No No AKT 1 1 0 1 No Yes AKU 1 0 0 1 No Yes AKV 1 1 1 0 No Yes AKX 0 0 1 1 No Yes AKY 1 1 1 0 No Yes ALA 1 1 1 1 No Yes ALH 0 0 1 1 Yes No ALK 1 1 1 1 Yes No ALM 1 1 1 1 No Yes ALN 1 1 1 1 No Yes ALU 1 0 1 0 Yes No ALX 1 1 1 1 Yes No AMA 0 1 1 1 No No AMC 1 1 1 1 No No AME 0 1 1 1 No Yes AMH 1 0 1 1 No No AML 1 1 1 1 Yes No AMM 1 1 1 1 Yes No AMN 1 0 0 1 No Yes AMP 1 1 0 1 Yes No AMT 1 1 1 1 Yes No AMU 0 0 1 0 No Yes AMV 1 1 0 0 Yes No AMY 0 1 0 0 No No ANC 1 1 0 1 Yes No ANK 1 1 1 1 Yes No 285 TABLE HVI (CONTINUED) Flag PER 1 PER 2 PER 3 PER 4 Male Female ANL 0 1 1 0 No Yes ANM 0 0 1 0 No No ANN 0 1 1 0 No Yes ANP 0 0 1 1 Yes No ANU 1 1 1 1 Yes No ANV 1 1 1 1 Yes No ANY 0 1 1 1 No No APA 0 1 1 0 No No APC 1 1 1 1 Yes No APK 1 0 0 0 No Yes APN 1 0 1 1 No Yes APT 0 1 1 1 No No APU 1 0 1 0 No Yes APV 1 0 0 1 No Yes APY 0 0 1 1 No Yes ATA 1 0 0 0 No No ATE 1 0 0 0 No Yes ATH 0 1 0 1 No Yes ATJ 1 1 1 1 No Yes ATL 1 1 1 1 No Yes ATM 1 1 1 1 No Yes ATN 1 1 1 1 No Yes ATP 1 1 1 1 No No ATT 1 0 1 1 No No ATU 1 1 0 0 Yes No ATV 1 1 1 1 No No ATX 1 1 0 1 Yes No ATY 1 1 1 1 No Yes AUK 1 0 0 0 No No AUT 1 0 0 1 Yes No AUU 0 1 0 0 No Yes AUV 1 1 1 1 No Yes AUX 1 0 0 0 No No AVK 1 0 0 1 No Yes AVL 1 0 1 1 No Yes AVU 0 1 1 0 No Yes AX 0 1 1 1 No No AXE 0 1 1 0 No Yes AXH 1 0 0 0 No Yes AXL 0 1 1 1 No Yes AXX 1 1 0 1 Yes No AXY 1 1 0 1 Yes No AYA 0 0 1 0 No No AYC 0 0 1 1 No Yes AYE 0 1 0 1 No Yes AYH 1 1 1 0 Yes No AYL 0 1 1 1 No Yes AYM 1 1 1 1 Yes No AYN 1 0 0 0 No Yes AYU 0 1 1 1 No Yes 286 TABLE HVI (CONTINUED) Flag PER 1 PER 2 PER 3 PER 4 Male Female AYV 0 0 1 1 Yes No AYY 0 1 1 1 Yes No C1 0 1 0 1 No Yes C2 0 1 1 1 Yes No C3 1 1 1 1 No No CA 1 0 0 1 Yes No CAA 1 0 0 1 No Yes CAC 1 1 0 0 No Yes CAK 1 1 1 1 No Yes CAL 1 0 0 1 No Yes CAM 1 0 1 1 Yes No CAN 1 0 0 0 No Yes CAT 1 0 0 1 No No CAU 1 1 1 0 Yes No CAV 1 1 0 0 No Yes CAX 1 1 1 0 Yes No CCA 1 1 1 1 No Yes CCC 1 1 1 1 Yes No CCE 1 1 1 0 No Yes CCH 0 1 1 0 Yes No CCJ 1 1 1 1 No No CCK 1 1 1 1 No Yes CCL 1 1 1 1 Yes No CCN 1 1 1 1 No Yes CCP 1 1 1 1 No Yes CCU 1 1 1 1 No No CCV 1 1 1 1 No Yes CCY 1 0 0 1 No Yes CH 1 0 0 0 No No CHE 1 1 1 0 No Yes CHJ 1 1 1 1 No Yes CHK 1 0 1 0 Yes No CHP 1 1 1 1 No Yes CHU 1 1 1 1 No Yes CHV 1 0 0 0 No Yes CHY 1 0 0 1 No Yes CJ 1 1 1 1 No No CM 1 0 1 1 No No CT 0 0 1 0 No Yes CU 0 1 0 0 No Yes CV 0 1 1 1 Yes No CX 0 1 0 0 No No E2 0 1 1 0 No Yes E4 0 0 1 0 No Yes E9 0 1 1 0 No No EH 1 1 1 1 Yes No EL 1 0 0 1 No Yes EX 1 1 1 1 Yes No H0 0 1 1 1 Yes No H1 1 1 0 0 Yes No 287 TABLE HVI (CONTINUED) Flag PER 1 PER 2 PER 3 PER 4 Male Female H9 0 1 1 0 Yes No HA 0 1 0 0 No Yes HH 1 0 0 0 Yes No HL 1 0 1 0 Yes No HN 1 1 1 0 Yes No HU 1 1 0 0 Yes No J3 1 1 0 1 No Yes J4 0 0 1 1 No No J8 1 0 0 0 No Yes JJ 0 1 1 1 No No JP 1 1 1 1 Yes No JT 1 0 0 1 No Yes K0 0 1 1 0 No Yes K1 0 1 0 1 Yes No K6 1 0 0 0 No Yes K8 0 1 1 1 No Yes K9 1 0 0 1 No Yes KC 0 1 0 1 No Yes KH 1 0 1 0 No Yes KJ 1 0 1 0 Yes No KL 1 1 1 1 No Yes L0 1 1 1 1 Yes No L4 0 0 1 0 Yes No LH 0 1 1 1 No No LL 0 1 0 1 No Yes LN 1 1 1 0 No No LP 0 1 1 0 No No LT 0 1 1 1 Yes No LU 0 1 1 1 Yes No LV 0 1 1 1 Yes No M0 1 1 0 1 No Yes M1 1 0 0 0 No No M3 0 0 1 1 Yes No M4 0 1 0 1 No Yes M5 0 1 1 0 No Yes M9 1 1 0 1 Yes No MH 0 1 0 0 Yes No MX 0 1 1 0 No Yes N0 0 0 1 0 Yes No N1 1 1 1 1 No Yes N2 1 1 1 1 Yes No N6 0 1 1 1 No Yes N8 1 1 1 1 No No N9 1 1 1 1 No Yes NA 0 0 1 0 No Yes NC 0 1 1 1 Yes No NH 1 0 1 0 No Yes NM 0 0 1 1 No Yes NT 1 0 1 1 No No NY 0 0 1 0 No No 288 TABLE HVI (CONTINUED) Flag PER 1 PER 2 PER 3 PER 4 Male Female P0 1 0 0 1 No Yes PA 0 1 0 0 Yes No PC 0 0 1 0 No Yes PE 1 1 1 1 No No PM 1 0 0 0 No Yes PN 1 0 0 0 No No PP 0 0 1 0 Yes No PT 0 1 1 1 No No PX 1 1 0 0 No Yes T1 0 0 1 1 Yes No T2 1 1 1 1 Yes No T3 1 0 0 1 No Yes T6 0 1 0 1 No No TH 1 0 0 0 No Yes TM 1 0 1 1 No Yes TP 0 1 0 0 No No TT 1 0 0 1 Yes No TY 0 1 0 0 Yes No U1 1 0 0 1 Yes No U2 1 0 0 0 No Yes U3 0 1 0 1 No Yes U4 1 1 1 1 No No UP 0 1 0 1 No Yes UU 1 0 1 1 No No UV 1 0 1 0 No No UX 1 0 0 1 No Yes V1 1 1 1 1 Yes No V2 1 1 1 1 Yes No V4 1 1 1 1 Yes No V6 0 1 1 1 No Yes V8 1 1 1 1 Yes No VE 0 0 1 1 No Yes VK 0 0 1 1 No Yes VP 0 0 1 0 Yes No VT 0 1 1 1 No Yes VU 0 1 1 0 No Yes X1 0 1 1 1 Yes No X6 1 0 0 0 No Yes X7 0 1 1 1 No No X8 1 1 1 1 No Yes X9 1 1 1 0 Yes No XH 1 0 0 1 No Yes XJ 0 0 1 1 Yes No XN 0 0 1 1 No No Y0 0 1 1 1 Yes No Y3 1 1 1 0 No Yes Y5 0 1 0 0 No Yes Y7 1 0 1 1 Yes No YH 1 0 0 0 Yes No YJ 0 1 0 0 No Yes 289 TABLE HVI (CONTINUED) Flag PER 1 PER 2 PER 3 PER 4 Male Female YN 1 0 1 1 Yes No YP 0 1 1 0 Yes No YU 1 0 1 1 No Yes 290 Table HVII. Encounter histories for annual survival of Hudsonian Godwits (Limosa haemastica) on the breeding grounds at Beluga River, Alaska from 2009 – 2017. A “1” denotes the individuals was seen or captured during that breeding season, and a “0” specifies the individual was either not seen or had not yet been marked. 291 ID 2009 2010 2011 2012 2014 2015 2016 2017 Male Female 1000 1 1 1 1 1 0 0 0 No Yes 1001 1 0 0 0 0 0 0 0 No Yes 1002 1 1 1 1 1 0 0 0 No Yes 1003 1 0 0 0 0 0 0 0 No Yes 1004 1 0 0 0 0 0 0 0 No Yes 1005 1 1 0 0 0 0 0 0 Yes No 1006 1 0 0 0 0 0 0 0 No Yes 1007 1 0 0 0 0 0 0 0 No Yes 1008 1 0 0 0 0 0 0 0 No Yes 1009 1 1 1 1 1 1 1 0 No Yes 1010 1 1 1 1 0 0 0 0 Yes No 1011 1 1 1 1 1 1 1 0 No Yes 1012 1 1 1 1 0 0 0 0 Yes No 1013 1 1 0 0 0 0 0 0 No Yes 1014 1 0 0 0 0 0 0 0 No Yes 1015 1 1 1 1 1 1 0 0 No Yes 1016 1 1 1 1 0 0 0 0 Yes No 1017 1 1 1 1 0 0 0 0 Yes No 1018 1 1 1 1 0 0 0 0 No Yes 1019 1 0 0 0 0 0 0 0 Yes No 1020 1 1 1 1 0 0 0 0 Yes No 1021 1 1 1 1 0 0 0 0 Yes No 1022 1 0 0 0 0 0 0 0 No Yes 1023 1 1 0 0 0 0 0 0 No Yes 1024 1 1 1 0 0 0 0 0 No Yes 1025 1 0 0 0 0 0 0 0 Yes No 1026 1 1 1 1 0 0 0 0 Yes No 1027 1 1 1 1 1 1 0 0 Yes No 1028 1 0 0 0 0 0 0 0 Yes No 1029 1 1 1 1 1 1 0 0 Yes No 1030 1 1 1 1 1 0 0 1 Yes No 1031 1 0 0 0 0 0 0 0 Yes No 1032 1 1 1 1 1 1 0 0 No Yes 1033 1 1 1 1 1 1 1 1 No Yes 1034 1 1 1 1 1 1 1 0 Yes No 1035 1 1 1 1 0 0 0 0 Yes No 1036 1 0 0 0 0 0 0 0 Yes No 1037 1 1 1 1 0 0 0 0 No Yes 1044 1 1 1 1 1 0 0 0 No Yes 1056 0 0 0 1 1 1 1 0 No Yes 1059 0 0 0 0 1 0 0 0 Yes No 1062 1 1 1 1 1 1 0 0 Yes No 1064 1 0 0 0 0 0 0 0 Yes No 1069 0 0 0 0 1 0 0 0 Yes No 1106 0 1 1 1 0 0 0 0 No Yes 1107 0 1 1 1 0 0 0 0 No Yes 1108 0 1 1 1 1 1 1 0 Yes No 1109 0 1 1 1 1 1 1 0 Yes No 1110 0 1 0 0 0 0 0 0 Yes No 1111 0 1 1 0 0 0 0 0 No Yes 1112 0 1 1 1 1 0 0 0 No Yes 1113 0 1 1 1 1 1 1 0 No Yes 292 TABLE HVII (CONTINUED) ID 2009 2010 2011 2012 2014 2015 2016 2017 Male Female 1114 0 1 0 0 0 0 0 0 Yes No 1115 0 1 0 0 0 0 0 0 No Yes 1116 0 1 1 1 1 0 0 0 No Yes 1136 0 1 0 0 0 0 0 0 No Yes 1137 0 1 1 1 1 1 1 1 Yes No 1146 0 1 1 0 0 0 0 0 No Yes 1147 0 1 1 1 0 0 0 0 Yes No 1166 0 1 1 1 1 1 0 0 Yes No 1180 0 0 1 1 0 0 0 0 No Yes 1181 0 0 1 1 1 0 0 0 Yes No 1182 0 0 1 1 0 0 0 0 No Yes 1183 0 0 1 1 0 0 0 0 Yes No 1184 0 0 1 1 0 0 0 0 Yes No 1185 0 0 1 1 0 0 0 0 Yes No 1186 0 0 1 0 0 0 0 0 No Yes 1187 0 0 1 1 0 0 0 0 Yes No 1188 0 0 1 0 0 0 0 0 Yes No 1189 0 0 1 0 0 0 0 0 No Yes 1190 0 0 1 1 1 0 0 0 No Yes 1191 0 0 1 1 1 1 1 1 Yes No 1192 0 0 1 0 0 0 0 0 Yes No 1193 0 0 1 0 0 0 0 0 No Yes 1194 0 0 1 1 1 1 1 1 Yes No 1195 0 0 1 1 0 0 0 0 No Yes 1196 0 0 1 1 0 0 0 0 Yes No 1212 0 0 0 0 1 0 0 0 No Yes 1229 0 0 1 0 0 0 0 0 No Yes 1246 0 0 0 0 1 1 1 0 Yes No 1277 0 0 0 1 0 0 0 0 No Yes 1278 0 0 0 1 1 0 0 0 Yes No 1279 0 0 0 1 1 1 1 0 Yes No 1280 0 0 0 1 1 1 1 0 No Yes 1281 0 0 0 1 1 0 0 0 No Yes 1282 0 0 0 1 1 0 0 0 Yes No 1283 0 0 0 1 0 0 0 0 No Yes 1284 0 0 0 1 1 0 0 0 No Yes 1285 0 0 0 1 0 0 0 0 No Yes 1286 0 0 0 1 0 0 0 0 Yes No 1287 0 0 0 1 1 1 0 0 Yes No 1288 0 0 0 1 1 1 1 0 No Yes 1289 0 0 0 1 1 0 0 0 No Yes 1299 0 0 0 0 1 0 0 0 No Yes 1300 0 0 0 0 1 1 1 0 No Yes 1301 0 0 0 0 1 1 1 0 No Yes 1302 0 0 0 0 1 1 1 0 Yes No 1303 0 0 0 0 1 0 0 0 Yes No 1304 0 0 0 0 1 1 1 1 Yes No 1305 0 0 0 0 1 1 1 0 No Yes 1314 0 0 0 0 1 1 1 1 Yes No 1323 0 0 0 0 1 1 0 0 No Yes 293 TABLE HVII (CONTINUED) ID 2009 2010 2011 2012 2014 2015 2016 2017 Male Female 1335 0 0 0 0 0 1 1 0 No Yes 1336 0 0 0 0 0 1 1 1 No Yes 1337 0 0 0 0 0 1 1 1 No Yes 1338 0 0 0 0 0 1 1 0 Yes No 1339 0 0 0 0 0 1 1 1 Yes No 1340 0 0 0 0 0 1 0 1 No Yes 1341 0 0 0 0 0 1 1 0 No Yes 1342 0 0 0 0 0 1 1 1 No Yes 1343 0 0 0 0 0 1 1 0 No Yes 1344 0 0 0 0 0 1 0 0 No Yes 1345 0 0 0 0 0 1 1 1 Yes No 1346 0 0 0 0 0 1 0 0 Yes No 1347 0 0 0 0 0 1 1 1 Yes No 1379 0 0 0 0 0 1 1 0 Yes No 1403 0 0 0 0 0 0 1 1 Yes No 1411 0 0 0 0 0 0 1 1 No Yes 294 Table HVIII. Encounter histories of Hudsonian Godwits (Limosa haemastica) for within season survival during the breeding season at Beluga River, Alaska from 2009 – 2016. Period 1 is from 1 – 7 May, Period 2 is from 8 – 14 May, Period 3 is from 15 – 21 May, Period 4 is from 22 – 28 May, Period 5 is from 29 May – 4 June, and Period 6 is from 5 June – 16 July. 295 ID PER 1 PER 2 PER 3 PER 4 PER 5 Male Female 1010 0 0 1 0 1 Yes No 1062 0 0 0 0 1 Yes No 1033 0 0 0 1 1 No Yes 1022 0 0 0 1 1 No Yes 1032 0 0 0 1 1 No Yes 1044 0 0 0 0 1 No Yes 1028 0 1 1 1 1 Yes No 1026 0 1 0 1 1 Yes No 1024 0 0 0 1 1 No Yes 1037 0 0 0 0 1 No Yes 1035 0 0 0 1 1 Yes No 1025 0 0 1 1 0 Yes No 1034 0 0 0 1 1 Yes No 1023 0 0 0 1 1 No Yes 1029 0 0 0 1 1 Yes No 1036 0 1 1 1 1 Yes No 1021 0 0 0 1 1 Yes No 1011 0 1 1 1 1 No Yes 1020 0 0 1 1 0 Yes No 1008 0 0 1 1 1 No Yes 1030 0 0 0 1 0 Yes No 1000 0 1 0 0 1 No Yes 1031 0 0 0 1 1 Yes No 1064 0 0 0 0 1 Yes No 1017 0 0 1 0 0 Yes No 1006 0 1 1 0 1 No Yes 1014 0 0 1 0 1 No Yes 1013 0 1 1 1 1 No Yes 1004 0 1 1 0 1 No Yes 1018 0 0 1 1 1 No Yes 1009 0 1 1 0 0 No Yes 1019 0 1 1 0 0 Yes No 1002 0 1 1 0 0 No Yes 1003 0 1 1 0 1 No Yes 1016 0 0 1 0 1 Yes No 1012 0 1 1 0 1 Yes No 1001 0 1 1 1 0 No Yes 1005 0 1 0 0 1 Yes No 1007 0 1 1 0 0 No Yes 1027 0 0 0 1 0 Yes No 1015 0 0 1 1 1 No Yes 1023 1 0 1 1 1 No Yes 1112 0 1 0 1 0 No Yes 1012 0 0 1 1 1 Yes No 1005 1 1 0 1 1 Yes No 1011 1 1 0 1 1 No Yes 1108 0 0 1 1 0 Yes No 1147 0 0 0 1 1 Yes No 1109 0 0 1 1 0 Yes No 1002 1 1 1 1 1 No Yes 1114 0 0 0 1 0 Yes No 1016 1 0 0 0 1 Yes No 296 TABLE HVIII (CONTINUED) ID PER 1 PER 2 PER 3 PER 4 PER 5 Male Female 1017 1 1 1 1 1 Yes No 1062 0 1 1 1 1 Yes No 1137 0 0 0 0 1 Yes No 1030 0 0 1 0 1 Yes No 1032 1 0 0 1 1 No Yes 1107 0 0 1 1 1 No Yes 1018 1 0 0 0 0 No Yes 1111 0 0 1 1 1 No Yes 1010 0 1 1 0 0 Yes No 1115 0 0 0 1 0 No Yes 1136 0 0 0 0 1 No Yes 1116 0 0 1 0 1 No Yes 1113 0 0 0 1 1 No Yes 1029 1 1 1 0 0 Yes No 1033 1 0 0 0 0 No Yes 1000 1 1 1 1 1 No Yes 1044 1 1 0 1 1 No Yes 1024 1 0 0 0 0 No Yes 1037 1 0 0 0 0 No Yes 1013 0 0 0 0 1 No Yes 1035 1 0 0 0 0 Yes No 1020 1 0 0 0 0 Yes No 1106 1 1 1 1 1 No Yes 1026 1 0 0 1 1 Yes No 1021 1 0 0 0 0 Yes No 1110 0 0 1 1 0 Yes No 1004 1 0 0 0 0 No Yes 1009 1 0 0 0 0 No Yes 1003 1 0 0 0 0 No Yes 1027 0 1 1 1 0 Yes No 1015 1 0 0 0 0 No Yes 1027 0 0 0 0 1 Yes No 1112 1 0 0 0 0 No Yes 1190 0 0 0 1 1 No Yes 1183 0 1 1 0 0 Yes No 1012 0 1 1 0 0 Yes No 1166 0 0 0 1 0 Yes No 1021 0 0 1 0 1 Yes No 1111 0 1 0 1 1 No Yes 1107 1 1 1 1 1 No Yes 1024 1 0 0 0 1 No Yes 1108 0 1 1 0 1 Yes No 1147 0 1 0 0 0 Yes No 1180 0 1 1 1 1 No Yes 1062 1 0 0 1 0 Yes No 1184 0 0 1 0 1 Yes No 1229 0 0 0 0 1 No Yes 1189 0 0 0 1 1 No Yes 1009 0 1 1 0 0 No Yes 1196 0 0 0 0 1 Yes No 297 TABLE HVIII (CONTINUED) ID PER 1 PER 2 PER 3 PER 4 PER 5 Male Female 1191 0 0 0 1 1 Yes No 1137 0 1 0 0 0 Yes No 1188 0 0 0 1 1 Yes No 1017 0 1 1 1 1 Yes No 1192 0 0 0 0 1 Yes No 1195 0 0 0 0 1 No Yes 1182 0 1 1 1 1 No Yes 1193 0 0 0 0 1 No Yes 1109 1 0 0 1 0 Yes No 1015 1 0 1 1 1 No Yes 1029 0 1 1 0 1 Yes No 1026 0 1 0 1 1 Yes No 1194 0 0 0 0 1 Yes No 1116 0 1 1 0 0 No Yes 1030 1 0 0 0 0 Yes No 1020 1 1 1 0 1 Yes No 1033 1 0 0 1 1 No Yes 1000 1 0 0 0 0 No Yes 1011 0 1 1 1 1 No Yes 1106 1 0 1 1 1 No Yes 1037 1 0 0 1 1 No Yes 1044 0 0 1 1 1 No Yes 1186 0 0 1 1 0 No Yes 1113 0 1 1 1 1 No Yes 1002 0 0 1 1 1 No Yes 1035 1 0 0 0 1 Yes No 1032 0 0 1 1 1 No Yes 1016 0 0 0 1 1 Yes No 1018 0 0 1 1 1 No Yes 1010 0 1 1 1 1 Yes No 1181 0 1 1 1 1 Yes No 1110 0 0 1 0 1 Yes No 1185 0 0 1 1 0 Yes No 1187 0 0 1 1 0 Yes No 1277 0 1 1 1 1 No Yes 1187 1 0 0 1 1 Yes No 1284 0 0 0 1 1 No Yes 1286 0 0 0 1 0 Yes No 1279 0 0 1 1 1 Yes No 1285 0 0 0 1 1 No Yes 1282 0 0 0 1 1 Yes No 1289 0 0 0 0 1 No Yes 1278 0 1 1 0 0 Yes No 1283 0 0 0 1 1 No Yes 1027 1 0 0 0 1 Yes No 1287 0 0 0 1 0 Yes No 1009 0 1 0 1 1 No Yes 1288 0 0 0 1 1 No Yes 1280 0 0 1 1 1 No Yes 1015 1 0 0 1 1 No Yes 298 TABLE HVIII (CONTINUED) ID PER 1 PER 2 PER 3 PER 4 PER 5 Male Female 1281 0 0 1 0 0 No Yes 1113 0 1 0 1 1 No Yes 1112 0 1 0 0 1 No Yes 1011 0 0 1 1 1 No Yes 1190 0 1 0 1 1 No Yes 1183 0 1 0 1 0 Yes No 1030 1 0 1 0 1 Yes No 1012 0 1 1 1 0 Yes No 1166 1 0 1 1 1 Yes No 1062 1 0 1 1 0 Yes No 1024 0 0 0 1 0 No Yes 1108 0 0 0 1 1 Yes No 1147 1 0 1 0 1 Yes No 1180 0 1 0 0 0 No Yes 1184 1 0 0 0 0 Yes No 1196 0 1 0 1 1 Yes No 1018 1 1 1 1 1 No Yes 1017 1 0 1 0 1 Yes No 1188 1 0 0 0 0 Yes No 1192 1 0 0 0 0 Yes No 1195 0 1 0 1 1 No Yes 1002 0 0 1 0 0 No Yes 1182 1 1 1 0 0 No Yes 1191 1 0 0 1 1 Yes No 1029 1 1 0 1 0 Yes No 1026 1 1 0 0 0 Yes No 1194 1 0 0 0 0 Yes No 1116 1 1 1 1 1 No Yes 1033 1 1 1 1 1 No Yes 1020 0 1 1 1 1 Yes No 1000 1 0 1 1 1 No Yes 1109 1 0 1 0 0 Yes No 1106 1 1 0 0 0 No Yes 1037 1 0 0 1 1 No Yes 1021 1 0 0 1 1 Yes No 1035 1 1 1 1 1 Yes No 1016 1 0 0 1 1 Yes No 1010 0 1 1 1 1 Yes No 1032 1 1 0 0 0 No Yes 1056 1 0 0 1 1 No Yes 1107 0 0 0 1 1 No Yes 1044 1 0 0 1 1 No Yes 1181 1 1 1 1 1 Yes No 1305 0 1 1 1 1 No Yes 1284 1 0 0 0 0 No Yes 1279 1 1 1 1 1 Yes No 1282 1 0 0 0 0 No Yes 1289 1 1 0 0 1 No Yes 1278 0 1 0 1 1 Yes No 1027 1 0 1 1 1 Yes No 299 TABLE HVIII (CONTINUED) ID PER 1 PER 2 PER 3 PER 4 PER 5 Male Female 1287 0 0 1 1 0 Yes No 1288 1 1 0 0 0 No Yes 1280 0 0 0 0 1 No Yes 1015 1 1 0 0 1 No Yes 1281 0 1 0 0 1 No Yes 1113 1 0 0 0 1 No Yes 1112 0 1 0 0 0 No Yes 1190 0 0 1 0 1 No Yes 1302 0 0 0 1 1 Yes No 1303 0 0 0 1 1 Yes No 1314 0 0 0 1 1 Yes No 1030 1 0 0 0 0 Yes No 1029 0 0 1 1 1 Yes No 1166 0 0 0 0 1 Yes No 1062 0 1 0 0 1 Yes No 1108 0 0 0 1 0 Yes No 1304 0 0 1 1 1 Yes No 1300 0 0 0 1 1 No Yes 1059 1 0 0 0 0 Yes No 1301 0 0 0 1 1 No Yes 1299 0 0 1 1 1 No Yes 1137 0 0 0 1 1 Yes No 1194 0 0 0 0 1 Yes No 1116 0 1 0 0 0 No Yes 1033 1 1 1 1 1 No Yes 1000 1 1 1 1 1 No Yes 1109 0 1 1 1 1 Yes No 1044 1 0 0 0 1 No Yes 1181 0 1 1 0 1 Yes No 1337 0 0 1 1 1 No Yes 1338 0 0 1 1 1 Yes No 1279 1 1 1 1 1 Yes No 1027 1 1 1 1 1 Yes No 1287 1 0 0 1 1 Yes No 1009 0 0 1 1 1 No Yes 1340 0 0 0 1 1 No Yes 1379 0 0 0 1 1 Yes No 1342 0 0 0 1 1 No Yes 1335 0 0 1 1 1 No Yes 1346 0 0 0 1 1 Yes No 1113 0 0 1 1 1 No Yes 1011 0 0 1 1 1 No Yes 1302 0 0 0 1 1 Yes No 1314 0 1 1 1 1 Yes No 1029 0 1 0 1 0 Yes No 1166 1 0 1 1 1 Yes No 1062 0 1 1 1 1 Yes No 1341 0 0 0 1 1 No Yes 1347 0 0 1 1 1 Yes No 1339 0 0 1 1 1 Yes No 300 TABLE HVIII (CONTINUED) ID PER 1 PER 2 PER 3 PER 4 PER 5 Male Female 1108 0 1 1 1 1 Yes No 1304 1 1 1 1 1 Yes No 1323 0 1 0 1 1 No Yes 1246 1 0 1 1 1 Yes No 1345 0 0 1 1 1 Yes No 1300 1 0 0 0 1 No Yes 1343 0 0 1 1 1 No Yes 1336 0 0 1 1 1 No Yes 1344 0 0 0 1 1 No Yes 1301 0 0 0 1 1 No Yes 1137 1 0 0 0 1 Yes No 1191 0 0 1 1 1 Yes No 1194 0 1 1 1 1 Yes No 1033 0 1 1 0 0 No Yes 1109 1 1 1 1 1 Yes No 1032 0 1 1 1 1 No Yes 1305 0 0 1 1 1 No Yes 1403 0 0 0 1 1 Yes No 1411 0 0 0 0 1 No Yes 1337 1 1 1 1 1 No Yes 1338 1 1 1 1 1 Yes No 1279 1 1 0 1 1 Yes No 1009 0 1 1 0 0 No Yes 1288 1 1 0 0 0 No Yes 1280 0 0 1 1 1 No Yes 1137 0 0 0 0 1 Yes No 1379 1 1 1 1 1 Yes No 1342 0 0 1 1 1 No Yes 1335 0 0 1 1 1 No Yes 1113 1 0 1 1 1 No Yes 1011 0 1 0 0 0 No Yes 1302 0 1 1 1 1 Yes No 1314 0 0 1 1 1 Yes No 1341 0 0 1 0 1 No Yes 1347 1 1 1 1 1 Yes No 1339 1 1 0 1 1 Yes No 1108 0 1 1 0 0 Yes No 1304 1 1 0 1 1 Yes No 1246 1 1 1 1 1 Yes No 1345 0 0 1 1 1 Yes No 1300 1 0 0 0 0 No Yes 1343 0 1 0 1 1 No Yes 1336 0 1 1 1 1 No Yes 1301 0 0 0 1 1 No Yes 1191 0 1 1 1 1 Yes No 1194 0 0 1 1 1 Yes No 1033 1 0 1 1 1 No Yes 1109 1 1 1 1 0 Yes No 1056 1 1 1 0 0 No Yes 301